From 9da34a9e315765adcf07a47bb046714a57274822 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 10 Sep 2019 16:31:21 -0600 Subject: [PATCH 001/136] Build MARBL This commit uncomments out code so that MARBL is built out of pkg/MARBL/src --- cime_config/buildlib | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/cime_config/buildlib b/cime_config/buildlib index 98306805..19cb53dd 100755 --- a/cime_config/buildlib +++ b/cime_config/buildlib @@ -43,14 +43,12 @@ def buildlib(caseroot, libroot, bldroot): if not os.path.exists(cvmix_srcdir): expect(False, "CVMix external not found") - # Current code base does not need to build MARBL yet - if False: - # MARBL source code is brought in as a git submodule (or my manage_externals) - logger.info("Making sure MARBL code is available...") - marbl_srcdir = os.path.join(srcroot,"components","mom","MOM6","pkg","MARBL","src") - # If MARBL is not found, abort - if not os.path.exists(marbl_srcdir): - expect(False, "MARBL external not found") + # MARBL source code is brought in as a git submodule (or my manage_externals) + logger.info("Making sure MARBL code is available...") + marbl_srcdir = os.path.join(srcroot,"components","mom","MOM6","pkg","MARBL","src") + # If MARBL is not found, abort + if not os.path.exists(marbl_srcdir): + expect(False, "MARBL external not found") casetools = case.get_value("CASETOOLS") gmake_j = case.get_value("GMAKE_J") @@ -86,7 +84,7 @@ def buildlib(caseroot, libroot, bldroot): os.path.join(srcroot,"components","mom","MOM6","src","ocean_data_assim", "core"), os.path.join(srcroot,"components","mom","MOM6","src","ocean_data_assim", "geoKdTree"), cvmix_srcdir, -# marbl_srcdir, + marbl_srcdir, os.path.join(srcroot,"components","mom","MOM6","src","parameterizations","lateral"), os.path.join(srcroot,"components","mom","MOM6","src","parameterizations","vertical"), os.path.join(srcroot,"components","mom","MOM6","src","tracer"), From daee02aceda7af896e1d587eb2d1cd5f6978b0f0 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 11 Sep 2019 10:13:08 -0600 Subject: [PATCH 002/136] Add a C1850MOMECO compset This creates OCN_BGC_CONFIG in env_run.xml; as long as OCN_BGC_CONFIG != none (the default value), USE_MARBL_TRACERS = True. --- cime_config/config_component.xml | 19 +++++++++++++++++-- cime_config/config_compsets.xml | 5 +++++ param_templates/MOM_input.yaml | 8 ++++++++ param_templates/json/MOM_input.json | 10 +++++++++- 4 files changed, 39 insertions(+), 3 deletions(-) diff --git a/cime_config/config_component.xml b/cime_config/config_component.xml index 7fc501f5..c169150f 100644 --- a/cime_config/config_component.xml +++ b/cime_config/config_component.xml @@ -5,7 +5,8 @@ - MOM6 OCEAN + MOM6 OCEAN + Ecosystem @@ -21,7 +22,7 @@ char spinup,production,development production - case_comp + run_component_mom env_run.xml Predefined ocean diagnostics configurations for various purposes. This variable controls the frequency, precision, and field lists of diagnostics to be outputted. @@ -31,6 +32,20 @@ + + char + none,latest + none + + latest + + run_component_mom + env_run.xml + Version of ocean BGC tunings to use (default values can be overwritten by user_nl_mom and user_nl_marbl). + "none" => run without MARBL tracers. + + + ========================================= MOM6 naming conventions diff --git a/cime_config/config_compsets.xml b/cime_config/config_compsets.xml index 1198296d..42d2d62f 100644 --- a/cime_config/config_compsets.xml +++ b/cime_config/config_compsets.xml @@ -43,6 +43,11 @@ 2000_DATM%NYF_SLND_DICE%SSMI_MOM6_DROF%NYF_SGLC_SWAV + + C1850MOMECO + 1850_DATM%NYF_SLND_DICE%SSMI_MOM6%ECO_DROF%NYF_SGLC_SWAV + + CMOM_IAF 2000_DATM%IAF_SLND_DICE%IAF_MOM6_DROF%IAF_SGLC_SWAV diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index 5cb9f6e3..a9bb2f04 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -296,6 +296,14 @@ Global: value: $OCN_GRID == "gx1v6": True $OCN_GRID == "tx0.66v1": True + USE_MARBL_TRACERS: + description: | + "[Boolean] default = False + If true, use the MARBL tracer package." + datatype: logical + units: Boolean + value: + $OCN_BGC_CONFIG != "none": True COORD_CONFIG: description: | "This specifies how layers are to be defined: diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index 0d76e49b..89be04a8 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -203,6 +203,14 @@ "$OCN_GRID == \"tx0.66v1\"": true } }, + "USE_MARBL_TRACERS": { + "description": "\"[Boolean] default = False\nIf true, use the MARBL tracer package.\"\n", + "datatype": "logical", + "units": "Boolean", + "value": { + "$OCN_BGC_CONFIG != \"none\"": true + } + }, "COORD_CONFIG": { "description": "\"This specifies how layers are to be defined:\nfile - read coordinate information from the file\nspecified by (COORD_FILE).\nlinear - linear based on interfaces not layesrs.\nts_ref - use reference temperature and salinity\nts_range - use range of temperature and salinity\n(T_REF and S_REF) to determine surface density\nand GINT calculate internal densities.\ngprime - use reference density (RHO_0) for surface\ndensity and GINT calculate internal densities.\nts_profile - use temperature and salinity profiles\n(read from COORD_FILE) to set layer densities.\nUSER - call a user modified routine.\nThe file from which the coordinate densities are read.\"\n", "datatype": "string", @@ -1810,4 +1818,4 @@ } } } -} +} \ No newline at end of file From ed0391d00ab74e2fbfacb8b418711103d855ca9c Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 11 Sep 2019 10:19:24 -0600 Subject: [PATCH 003/136] Add G1850MOMECO compset --- cime_config/config_compsets.xml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/cime_config/config_compsets.xml b/cime_config/config_compsets.xml index 42d2d62f..0e3121fb 100644 --- a/cime_config/config_compsets.xml +++ b/cime_config/config_compsets.xml @@ -65,6 +65,11 @@ 2000_DATM%NYF_SLND_CICE_MOM6_DROF%NYF_SGLC_SWAV + + G1850MOMECO + 1850_DATM%NYF_SLND_CICE_MOM6%ECO_DROF%NYF_SGLC_SWAV + + GMOM_IAF 2000_DATM%IAF_SLND_CICE_MOM6_DROF%IAF_SGLC_SWAV From ac8c8c27dc052e9f3cf0527df52944a47c81c29b Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 11 Sep 2019 10:54:03 -0600 Subject: [PATCH 004/136] Add MARBL tracers to diagnostic output list Hardcodes the names of 32 tracers to include in diag_table if running with MARBL enabled. Note that eventually this will have to be replaced with something more robust that relies on MARBL to report the names of the active tracers... as it stands, enabling CISO will not increase output at all, and running with cocco will not work (spCaCO3 is not a valid tracer name in that configuration) --- param_templates/diag_table.yaml | 20 +++- param_templates/json/diag_table.json | 156 +++++++++++++++++++++++++++ 2 files changed, 175 insertions(+), 1 deletion(-) diff --git a/param_templates/diag_table.yaml b/param_templates/diag_table.yaml index 35cb1336..271f30a7 100644 --- a/param_templates/diag_table.yaml +++ b/param_templates/diag_table.yaml @@ -11,6 +11,13 @@ FieldLists: - &tracers ["agessc", "T_ady_2d", "T_adx_2d", "T_diffy_2d", "T_diffx_2d"] + # NOTE: for now I'm hard-coding the MARBL tracer names. Eventually we will rely on + # MARBL to tell us these names + - &MARBL ["PO4", "NO3", "SiO3", "NH4", "Fe", "Lig", "O2", "DIC", "DIC_ALT_CO2", + "ALK", "ALK_ALT_CO2", "DOC", "DON", "DOP", "DOPr", "DONr", "DOCr", + "zooC", "spChl", "spC", "spP", "spFe", "spCaCO3", "diatChl", "diatC", + "diatP", "diatFe", "diatSi", "diazChl", "diazC", "diazP", "diazFe"] + - &surface_flds ["SSH", "tos", "sos", "SSU", "SSV", "mass_wt", "opottempmint", "somint", "Rd_dx", "speed", "mlotst"] @@ -46,7 +53,7 @@ FieldLists: Files: # native grid hist: - suffix: + suffix: $OCN_DIAG_MODE == "spinup": "hm%4yr" else: "hm%4yr-%2mo" output_freq: 1 @@ -67,6 +74,17 @@ Files: - module: "ocean_model" packing: 2 # single precision lists: [ *prognostic ] + $OCN_BGC_CONFIG != "none": + - module: "ocean_model" + packing: 1 # double precision + lists: [ *prognostic, + *hist_additional, + *kpp_diags, + *forcing_flds, + *surface_flds, + *visc_flds, + *tracers, + *MARBL ] else: - module: "ocean_model" packing: 1 # double precision diff --git a/param_templates/json/diag_table.json b/param_templates/json/diag_table.json index c4e3c56a..328d4ca3 100644 --- a/param_templates/json/diag_table.json +++ b/param_templates/json/diag_table.json @@ -30,6 +30,40 @@ "T_diffy_2d", "T_diffx_2d" ], + [ + "PO4", + "NO3", + "SiO3", + "NH4", + "Fe", + "Lig", + "O2", + "DIC", + "DIC_ALT_CO2", + "ALK", + "ALK_ALT_CO2", + "DOC", + "DON", + "DOP", + "DOPr", + "DONr", + "DOCr", + "zooC", + "spChl", + "spC", + "spP", + "spFe", + "spCaCO3", + "diatChl", + "diatC", + "diatP", + "diatFe", + "diatSi", + "diazChl", + "diazC", + "diazP", + "diazFe" + ], [ "SSH", "tos", @@ -173,6 +207,128 @@ ] } ], + "$OCN_BGC_CONFIG != \"none\"": [ + { + "module": "ocean_model", + "packing": 1, + "lists": [ + [ + "uo", + "vo", + "h", + "e", + "thetao", + "so", + "rhoinsitu", + "KE", + "rhopot0" + ], + [ + "soga", + "thetaoga", + "umo", + "vmo", + "uh", + "vh", + "vhbt", + "uhbt", + "uhml", + "vhml" + ], + [ + "KPP_OBLdepth:oml" + ], + [ + "tauuo", + "tauvo", + "friver", + "prsn", + "prlq", + "evs", + "hfsso", + "rlntds", + "hfsnthermds", + "sfdsi", + "rsntds", + "hfds", + "ustar", + "hfsifrazil", + "wfo", + "vprec", + "ficeberg", + "fsitherm", + "hflso", + "pso", + "seaice_melt_heat", + "Heat_PmE", + "salt_flux_added" + ], + [ + "SSH", + "tos", + "sos", + "SSU", + "SSV", + "mass_wt", + "opottempmint", + "somint", + "Rd_dx", + "speed", + "mlotst" + ], + [ + "volcello", + "diftrelo", + "diftrblo", + "difmxybo", + "difmxylo", + "vhGM", + "uhGM" + ], + [ + "agessc", + "T_ady_2d", + "T_adx_2d", + "T_diffy_2d", + "T_diffx_2d" + ], + [ + "PO4", + "NO3", + "SiO3", + "NH4", + "Fe", + "Lig", + "O2", + "DIC", + "DIC_ALT_CO2", + "ALK", + "ALK_ALT_CO2", + "DOC", + "DON", + "DOP", + "DOPr", + "DONr", + "DOCr", + "zooC", + "spChl", + "spC", + "spP", + "spFe", + "spCaCO3", + "diatChl", + "diatC", + "diatP", + "diatFe", + "diatSi", + "diazChl", + "diazC", + "diazP", + "diazFe" + ] + ] + } + ], "else": [ { "module": "ocean_model", From d611a88a61b2adeb578a6faddaa3df7db4db4857 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 11 Sep 2019 15:54:26 -0600 Subject: [PATCH 005/136] Update PE layouts for compsets with MARBL Default to 432 tasks on cheyenne instead of 144 when running with MARBL's tracers. --- cime_config/config_pes.xml | 108 +++++++++++++++++++++++++++++-------- 1 file changed, 87 insertions(+), 21 deletions(-) diff --git a/cime_config/config_pes.xml b/cime_config/config_pes.xml index b1744315..6b2b2be8 100644 --- a/cime_config/config_pes.xml +++ b/cime_config/config_pes.xml @@ -76,7 +76,7 @@ 0 - + none 1 @@ -111,7 +111,7 @@ - + none 36 @@ -144,7 +144,7 @@ 0 - + none 36 @@ -179,7 +179,7 @@ - + none 48 @@ -212,7 +212,7 @@ 0 - + none 48 @@ -247,7 +247,7 @@ - + none 48 @@ -280,7 +280,7 @@ 0 - + none 48 @@ -420,7 +420,7 @@ 0 - + none 36 @@ -455,7 +455,40 @@ - + + none + + 36 + 36 + 36 + 72 + 432 + 1 + 1 + 1 + + + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + + + 0 + 0 + 0 + 0 + 72 + 0 + 0 + 0 + + + none 36 @@ -488,7 +521,40 @@ 0 - + + none + + 36 + 36 + 36 + 36 + 432 + 1 + 1 + 1 + + + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + + + 0 + 0 + 0 + 0 + 36 + 0 + 0 + 0 + + + none 36 @@ -522,8 +588,8 @@ - - + + none 48 @@ -556,7 +622,7 @@ 0 - + none 48 @@ -590,8 +656,8 @@ - - + + none 48 @@ -624,7 +690,7 @@ 0 - + none 48 @@ -661,7 +727,7 @@ - + none 1 @@ -698,7 +764,7 @@ - + none 48 @@ -731,7 +797,7 @@ 0 - + none 48 @@ -766,7 +832,7 @@ - + none 36 @@ -799,7 +865,7 @@ 0 - + none 36 From afcf6d4792ec65b9949335facb7673f087e7f9ca Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 12 Sep 2019 16:10:23 -0600 Subject: [PATCH 006/136] Generate marbl_in in RUNDIR Uses the CESM_x1 default values, allows override via user_nl_marbl --- cime_config/MARBL_scripts/MARBL_settings.py | 80 +++++++++++++++++++++ cime_config/buildnml | 15 ++++ 2 files changed, 95 insertions(+) create mode 100644 cime_config/MARBL_scripts/MARBL_settings.py diff --git a/cime_config/MARBL_scripts/MARBL_settings.py b/cime_config/MARBL_scripts/MARBL_settings.py new file mode 100644 index 00000000..57726324 --- /dev/null +++ b/cime_config/MARBL_scripts/MARBL_settings.py @@ -0,0 +1,80 @@ +""" Interface to the marbl_settings_class object +""" + +class MARBL_settings_for_MOM(object): + def __init__(self, MARBL_dir, caseroot, ocn_grid, run_type, continue_run, ocn_bgc_config): + + import sys, os + + # Set up arguments for marbl_settings_class constructor + # Note that this is a dictionary that will be used to pass named variables to class constructor + # Arguments to be passed are + # 1. default_settings_file: full path to default_settings.json; can be in SourceMods, otherwise comes from MARBL + # 2. grid: CESM_x1 if running tx0.66v1 (until we come up with better tunings) + # 3. saved_state_vars_source: "settings_file" for startup run, otherwise GCM + MARBL_args = dict() + + settings_file = "settings_"+ocn_bgc_config+".json" + + # User can put settings_file in SourceMods, otherwise use file provided by MARBL + MARBL_args["default_settings_file"] = os.path.join(caseroot,"SourceMods","src.mom", settings_file) + if not os.path.isfile(MARBL_args["default_settings_file"]): + MARBL_args["default_settings_file"] = os.path.join(MARBL_dir, "defaults", "json", settings_file) + + # User can modify user_nl_marbl (or user_nl_marbl_####) in caseroot + MARBL_args["input_file"] = os.path.join(caseroot, "user_nl_marbl") + if not os.path.isfile(MARBL_args["input_file"]): + MARBL_args["input_file"] = None + + # Specify grid + if ocn_grid.startswith("tx0.6"): + MARBL_args["grid"] = "CESM_x1" + else: + MARBL_args["grid"] = "CESM_other" + + # If not a startup run, MARBL may want initial bury coefficient from restart file + if run_type == "startup" and not continue_run: + MARBL_args["saved_state_vars_source"] = "settings_file" + else: + MARBL_args["saved_state_vars_source"] = "GCM" + + # Import MARBL_settings_file_class, which may come from MARBL_tools or SourceMods/src.mom + # (i) need MARBL_dir in path for both branches of this if statement because even if + # MARBL_settings_file_class.py is in SourceMods, it needs to import MARBL_tools itself + sys.path.append(MARBL_dir) + # (ii) Here's where we import from either MARBL_tools or SourceMods + settings_class_dir = os.path.join(caseroot, "SourceMods", "src.mom") + if not os.path.isfile(os.path.join(settings_class_dir, "MARBL_settings_file_class.py")): + from MARBL_tools import MARBL_settings_file_class + else: + import imp + import logging + logger = logging.getLogger(__name__) + logging.info('Importing MARBL_settings_file_class.py from %s' % settings_class_dir) + settings_class_module = settings_class_dir+'/MARBL_settings_file_class.py' + if os.path.isfile(settings_class_module): + MARBL_settings_file_class = imp.load_source('MARBL_settings_file_class', settings_class_module) + else: + logger.error('Can not find %s' % settings_class_module) + sys.exit(1) + + # Generate settings object + self._MARBL_settings = MARBL_settings_file_class.MARBL_settings_class(**MARBL_args) + + ################################################################################ + # PUBLIC CLASS METHODS # + ################################################################################ + + def get_tracer_names(self): + """ Returns a list of all tracers in current configuration + """ + return self._MARBL_settings.get_tracer_names() + + ####################################### + + def write_settings_file(self, settings_file_out): + """ Write a settings file containing all MARBL settings + """ + from MARBL_tools import generate_settings_file + generate_settings_file(self._MARBL_settings, settings_file_out) + diff --git a/cime_config/buildnml b/cime_config/buildnml index 1b908425..a2bc514c 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -30,10 +30,17 @@ def prep_input(case): Buildconf = case.get_value("CASEBUILD") rundir = case.get_value("RUNDIR") srcroot = case.get_value("SRCROOT") + ocn_bgc_config = case.get_value("OCN_BGC_CONFIG") + caseroot = case.get_value("CASEROOT") + ocn_grid = case.get_value("OCN_GRID") + run_type = case.get_value("RUN_TYPE") + continue_run = case.get_value("CONTINUE_RUN") + # The scope of the following path expansion is limited to this script only, # and is needed to import MOM6 input file classes: sys.path.append(os.path.join(srcroot,"components","mom","cime_config","MOM_RPS")) + sys.path.append(os.path.join(srcroot,"components","mom","cime_config","MARBL_scripts")) from FType_MOM_params import FType_MOM_params from FType_input_nml import FType_input_nml from FType_input_data_list import FType_input_data_list @@ -52,6 +59,14 @@ def prep_input(case): MOM_input = FType_MOM_params(MOM_input_src) MOM_input.write(MOM_input_dest, case) + # Create marbl_in + if ocn_bgc_config != "none": + MARBL_dir = os.path.join(srcroot, "components", "mom", "MOM6", "pkg", "MARBL") + from MARBL_settings import MARBL_settings_for_MOM + MARBL_settings = MARBL_settings_for_MOM(MARBL_dir, caseroot, ocn_grid, run_type, + continue_run, ocn_bgc_config) + MARBL_settings.write_settings_file(os.path.join(rundir, "marbl_in")) + # Create input.nml: input_nml_src = os.path.join(json_templates_dir, "input_nml.json") input_nml_dest = os.path.join(rundir,"input.nml") From 2cb827b5c2231d3987d42a54d0c8b53d4ca3e595 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 13 Feb 2020 11:19:11 -0700 Subject: [PATCH 007/136] Re-run yaml_to_json.py Updates MOM_input.json and diag_table.json --- param_templates/json/MOM_input.json | 2 +- param_templates/json/diag_table.json | 11 ++--------- 2 files changed, 3 insertions(+), 10 deletions(-) diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index 4c972930..d3d224dc 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -2411,4 +2411,4 @@ } } } -} +} \ No newline at end of file diff --git a/param_templates/json/diag_table.json b/param_templates/json/diag_table.json index 063379fe..b66051b7 100644 --- a/param_templates/json/diag_table.json +++ b/param_templates/json/diag_table.json @@ -285,14 +285,10 @@ [ "soga", "thetaoga", - "umo", - "vmo", "uh", "vh", "vhbt", - "uhbt", - "uhml", - "vhml" + "uhbt" ], [ "KPP_OBLdepth:oml" @@ -336,13 +332,10 @@ "mlotst" ], [ - "volcello", "diftrelo", "diftrblo", "difmxybo", - "difmxylo", - "vhGM", - "uhGM" + "difmxylo" ], [ "agessc", From 3c39bf586dfceb82eb524d0bbc131fe4cedee627 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 13 Feb 2020 11:19:32 -0700 Subject: [PATCH 008/136] Update Externals.cfg and Externals_MOM.cfg --- Externals.cfg | 4 ++-- Externals_MOM.cfg | 5 +++++ 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/Externals.cfg b/Externals.cfg index 1802e62a..98125a99 100644 --- a/Externals.cfg +++ b/Externals.cfg @@ -1,8 +1,8 @@ [mom] -tag = dev/ncar_20190927 +branch = mnlevy1981/add_MARBL protocol = git local_path = MOM6 -repo_url = https://github.com/NCAR/MOM6.git +repo_url = git@github.com:mnlevy1981/MOM6.git required = True externals = ../Externals_MOM.cfg diff --git a/Externals_MOM.cfg b/Externals_MOM.cfg index 5a9e9011..fe15c474 100644 --- a/Externals_MOM.cfg +++ b/Externals_MOM.cfg @@ -13,5 +13,10 @@ protocol = git from_submodule=True required = True +[pkg/MARBL] +protocol = git +from_submodule=True +required = True + [externals_description] schema_version = 1.0.0 From 9e057ca152e447a2f12fb7860b13721111f12598 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 13 Feb 2020 11:37:33 -0700 Subject: [PATCH 009/136] Add transports to diag_table I missed a change in the default diag_table --- param_templates/diag_table.yaml | 1 + param_templates/json/diag_table.json | 9 +++++++++ 2 files changed, 10 insertions(+) diff --git a/param_templates/diag_table.yaml b/param_templates/diag_table.yaml index b47ac163..b8d0ac66 100644 --- a/param_templates/diag_table.yaml +++ b/param_templates/diag_table.yaml @@ -110,6 +110,7 @@ Files: *forcing_flds, *surface_flds, *visc_flds, + *transports, *tracers, *MARBL ] else: diff --git a/param_templates/json/diag_table.json b/param_templates/json/diag_table.json index b66051b7..4268b310 100644 --- a/param_templates/json/diag_table.json +++ b/param_templates/json/diag_table.json @@ -337,6 +337,15 @@ "difmxybo", "difmxylo" ], + [ + "volcello", + "vmo", + "vhGM", + "vhml", + "umo", + "uhGM", + "uhml" + ], [ "agessc", "T_ady_2d", From 9c8824e37ddb81f9259ad140a1e0756cf7abc814 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 1 May 2020 15:05:47 -0600 Subject: [PATCH 010/136] MARBL is no longer a submodule of MOM Bring it in as a typical external instead --- Externals_MOM.cfg | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/Externals_MOM.cfg b/Externals_MOM.cfg index fe15c474..b750a306 100644 --- a/Externals_MOM.cfg +++ b/Externals_MOM.cfg @@ -13,9 +13,11 @@ protocol = git from_submodule=True required = True -[pkg/MARBL] +[MARBL] protocol = git -from_submodule=True +tag = marbl0.36.0 +repo_url = https://github.com/marbl-ecosys/MARBL +local_path = pkg/MARBL required = True [externals_description] From e8838328ce194e0f597f0f53e709a0792f3b7bae Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 4 May 2020 13:46:48 -0600 Subject: [PATCH 011/136] Group calls needed to add MARBL Eventually MARBL will only be built if a BGC-enabled compset is specified, so it makes sense to only look for pkg/MARBL and add it to paths if it will be needed. At this point it is in an "if True:" block, but down the line the condition will change. --- cime_config/buildlib | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/cime_config/buildlib b/cime_config/buildlib index 19cb53dd..0f28101e 100755 --- a/cime_config/buildlib +++ b/cime_config/buildlib @@ -46,9 +46,6 @@ def buildlib(caseroot, libroot, bldroot): # MARBL source code is brought in as a git submodule (or my manage_externals) logger.info("Making sure MARBL code is available...") marbl_srcdir = os.path.join(srcroot,"components","mom","MOM6","pkg","MARBL","src") - # If MARBL is not found, abort - if not os.path.exists(marbl_srcdir): - expect(False, "MARBL external not found") casetools = case.get_value("CASETOOLS") gmake_j = case.get_value("GMAKE_J") @@ -84,11 +81,15 @@ def buildlib(caseroot, libroot, bldroot): os.path.join(srcroot,"components","mom","MOM6","src","ocean_data_assim", "core"), os.path.join(srcroot,"components","mom","MOM6","src","ocean_data_assim", "geoKdTree"), cvmix_srcdir, - marbl_srcdir, os.path.join(srcroot,"components","mom","MOM6","src","parameterizations","lateral"), os.path.join(srcroot,"components","mom","MOM6","src","parameterizations","vertical"), os.path.join(srcroot,"components","mom","MOM6","src","tracer"), os.path.join(srcroot,"components","mom","MOM6","src","user")] + # Eventually if statement should boil down to "if we want MARBL enabled" + if True: + # If MARBL is not found, abort + expect(os.path.exists(marbl_srcdir), "MARBL external not found") + paths.append(marbl_srcdir) with open(filepath_file, "w") as filepath: filepath.write("\n".join(paths)) From 26e30356117166c9286253de2f8f67509d53e772 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 4 May 2020 13:51:07 -0600 Subject: [PATCH 012/136] Update comment to refer to cime mods Adding -D_USE_MARBL_TRACERS to the build requires a CIME mod: $ git diff diff --git a/config/cesm/machines/config_compilers.xml b/config/cesm/machines/config_compilers.xml index fca45de02..c3cb51a2c 100644 --- a/config/cesm/machines/config_compilers.xml +++ b/config/cesm/machines/config_compilers.xml @@ -80,7 +80,7 @@ using a fortran linker. $(FC_AUTO_R8) - $(FC_AUTO_R8) -Duse_LARGEFILE + $(FC_AUTO_R8) -Duse_LARGEFILE -D_USE_MARBL_TRACERS FALSE This is likely where changes to only enable the build CPP in some compsets will need to go, and it will be handy to have a comment pointing to the file. --- cime_config/buildlib | 3 +++ 1 file changed, 3 insertions(+) diff --git a/cime_config/buildlib b/cime_config/buildlib index 0f28101e..bdf83c81 100755 --- a/cime_config/buildlib +++ b/cime_config/buildlib @@ -86,6 +86,9 @@ def buildlib(caseroot, libroot, bldroot): os.path.join(srcroot,"components","mom","MOM6","src","tracer"), os.path.join(srcroot,"components","mom","MOM6","src","user")] # Eventually if statement should boil down to "if we want MARBL enabled" + # Note that the -D_USE_MARBL_TRACERS flag is currently hard-coded into + # $CIMEROOT/config/cesm/machines/config_compilers.xml + # Not sure how to restrict when it is enabled if True: # If MARBL is not found, abort expect(os.path.exists(marbl_srcdir), "MARBL external not found") From 31cd020d62c711f0631f72c5e4010a723bd30918 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 10 Jun 2020 21:49:41 -0600 Subject: [PATCH 013/136] Split BGC fields into separate monthly stream --- param_templates/diag_table.yaml | 33 ++-- param_templates/json/diag_table.json | 222 +-------------------------- 2 files changed, 27 insertions(+), 228 deletions(-) diff --git a/param_templates/diag_table.yaml b/param_templates/diag_table.yaml index 7747541b..874badab 100644 --- a/param_templates/diag_table.yaml +++ b/param_templates/diag_table.yaml @@ -15,7 +15,8 @@ FieldLists: - &MARBL ["PO4", "NO3", "SiO3", "NH4", "Fe", "Lig", "O2", "DIC", "DIC_ALT_CO2", "ALK", "ALK_ALT_CO2", "DOC", "DON", "DOP", "DOPr", "DONr", "DOCr", "zooC", "spChl", "spC", "spP", "spFe", "spCaCO3", "diatChl", "diatC", - "diatP", "diatFe", "diatSi", "diazChl", "diazC", "diazP", "diazFe"] + "diatP", "diatFe", "diatSi", "diazChl", "diazC", "diazP", "diazFe", + "PV_CO2"] - &surface_flds ["SSH", "tos", "sos", "SSU", "SSV", "mass_wt", "opottempmint", "somint", "Rd_dx", "speed", "mlotst"] @@ -101,18 +102,6 @@ Files: *tracers, *visc_flds, *transports ] - $OCN_BGC_CONFIG != "none": - - module: "ocean_model" - packing: 1 # double precision - lists: [ *prognostic, - *hist_additional, - *kpp_diags, - *forcing_flds, - *surface_flds, - *visc_flds, - *transports, - *tracers, - *MARBL ] else: - module: "ocean_model" packing: 1 # double precision @@ -125,6 +114,24 @@ Files: *transports, *tracers ] + # BGC output + hist: + suffix: + else: "hm_bgc%4yr-%2mo" + output_freq: 1 + output_freq_units: "months" + time_axis_units: "days" + new_file_freq: 1 + new_file_freq_units: "months" + reduction_method: "mean" # time average + regional_section: "none" # global + fields: + $OCN_BGC_CONFIG != "none": + - module: "ocean_model" + packing: 1 # double precision + lists: [ *MARBL, + *transports ] + # essential variable mapped to z_space hist_z_space: suffix: diff --git a/param_templates/json/diag_table.json b/param_templates/json/diag_table.json index 52ff27c6..e348717e 100644 --- a/param_templates/json/diag_table.json +++ b/param_templates/json/diag_table.json @@ -58,7 +58,8 @@ "diazChl", "diazC", "diazP", - "diazFe" + "diazFe", + "PV_CO2" ], [ "SSH", @@ -209,150 +210,21 @@ }, "hist": { "suffix": { - "$OCN_DIAG_MODE == \"spinup\"": "hm%4yr", - "else": "hm%4yr-%2mo" + "else": "hm_bgc%4yr-%2mo" }, "output_freq": 1, - "output_freq_units": { - "$OCN_DIAG_MODE == \"spinup\"": "years", - "$TEST == True": "days", - "else": "months" - }, + "output_freq_units": "months", "time_axis_units": "days", "new_file_freq": 1, - "new_file_freq_units": { - "$OCN_DIAG_MODE == \"spinup\"": "years", - "$TEST == True": "days", - "else": "months" - }, + "new_file_freq_units": "months", "reduction_method": "mean", "regional_section": "none", "fields": { - "$OCN_DIAG_MODE == \"spinup\"": [ - { - "module": "ocean_model", - "packing": 2, - "lists": [ - [ - "soga", - "thetaoga", - "uh", - "vh", - "vhbt", - "uhbt" - ], - [ - "agessc", - "T_ady_2d", - "T_adx_2d", - "T_diffy_2d", - "T_diffx_2d" - ], - [ - "diftrelo", - "diftrblo", - "difmxybo", - "difmxylo" - ], - [ - "volcello", - "vmo", - "vhGM", - "vhml", - "umo", - "uhGM", - "uhml" - ] - ] - } - ], "$OCN_BGC_CONFIG != \"none\"": [ { "module": "ocean_model", "packing": 1, "lists": [ - [ - "uo", - "vo", - "h", - "e", - "thetao", - "so", - "rhoinsitu", - "KE", - "rhopot0" - ], - [ - "soga", - "thetaoga", - "uh", - "vh", - "vhbt", - "uhbt" - ], - [ - "KPP_OBLdepth:oml" - ], - [ - "tauuo", - "tauvo", - "friver", - "prsn", - "prlq", - "evs", - "hfsso", - "rlntds", - "hfsnthermds", - "sfdsi", - "rsntds", - "hfds", - "ustar", - "hfsifrazil", - "wfo", - "vprec", - "ficeberg", - "fsitherm", - "hflso", - "pso", - "seaice_melt_heat", - "Heat_PmE", - "salt_flux_added" - ], - [ - "SSH", - "tos", - "sos", - "SSU", - "SSV", - "mass_wt", - "opottempmint", - "somint", - "Rd_dx", - "speed", - "mlotst" - ], - [ - "diftrelo", - "diftrblo", - "difmxybo", - "difmxylo" - ], - [ - "volcello", - "vmo", - "vhGM", - "vhml", - "umo", - "uhGM", - "uhml" - ], - [ - "agessc", - "T_ady_2d", - "T_adx_2d", - "T_diffy_2d", - "T_diffx_2d" - ], [ "PO4", "NO3", @@ -385,81 +257,8 @@ "diazChl", "diazC", "diazP", - "diazFe" - ] - ] - } - ], - "else": [ - { - "module": "ocean_model", - "packing": 1, - "lists": [ - [ - "uo", - "vo", - "h", - "e", - "thetao", - "so", - "rhoinsitu", - "KE", - "rhopot0" - ], - [ - "soga", - "thetaoga", - "uh", - "vh", - "vhbt", - "uhbt" - ], - [ - "KPP_OBLdepth:oml" - ], - [ - "tauuo", - "tauvo", - "friver", - "prsn", - "prlq", - "evs", - "hfsso", - "rlntds", - "hfsnthermds", - "sfdsi", - "rsntds", - "hfds", - "ustar", - "hfsifrazil", - "wfo", - "vprec", - "ficeberg", - "fsitherm", - "hflso", - "pso", - "seaice_melt_heat", - "Heat_PmE", - "salt_flux_added" - ], - [ - "SSH", - "tos", - "sos", - "SSU", - "SSV", - "mass_wt", - "opottempmint", - "somint", - "Rd_dx", - "speed", - "mlotst" - ], - [ - "diftrelo", - "diftrblo", - "difmxybo", - "difmxylo" + "diazFe", + "PV_CO2" ], [ "volcello", @@ -469,13 +268,6 @@ "umo", "uhGM", "uhml" - ], - [ - "agessc", - "T_ady_2d", - "T_adx_2d", - "T_diffy_2d", - "T_diffx_2d" ] ] } From 57a7731b5157222a40b072e40333e89add72aa4a Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 10 Jun 2020 22:17:32 -0600 Subject: [PATCH 014/136] Clean up additional stream definition --- param_templates/diag_table.yaml | 5 +- param_templates/json/diag_table.json | 151 ++++++++++++++++++++++++++- 2 files changed, 152 insertions(+), 4 deletions(-) diff --git a/param_templates/diag_table.yaml b/param_templates/diag_table.yaml index 874badab..79838ede 100644 --- a/param_templates/diag_table.yaml +++ b/param_templates/diag_table.yaml @@ -115,9 +115,8 @@ Files: *tracers ] # BGC output - hist: - suffix: - else: "hm_bgc%4yr-%2mo" + hist_bgc: + suffix: "hm_bgc%4yr-%2mo" output_freq: 1 output_freq_units: "months" time_axis_units: "days" diff --git a/param_templates/json/diag_table.json b/param_templates/json/diag_table.json index e348717e..58e2f46a 100644 --- a/param_templates/json/diag_table.json +++ b/param_templates/json/diag_table.json @@ -210,9 +210,158 @@ }, "hist": { "suffix": { - "else": "hm_bgc%4yr-%2mo" + "$OCN_DIAG_MODE == \"spinup\"": "hm%4yr", + "else": "hm%4yr-%2mo" }, "output_freq": 1, + "output_freq_units": { + "$OCN_DIAG_MODE == \"spinup\"": "years", + "$TEST == True": "days", + "else": "months" + }, + "time_axis_units": "days", + "new_file_freq": 1, + "new_file_freq_units": { + "$OCN_DIAG_MODE == \"spinup\"": "years", + "$TEST == True": "days", + "else": "months" + }, + "reduction_method": "mean", + "regional_section": "none", + "fields": { + "$OCN_DIAG_MODE == \"spinup\"": [ + { + "module": "ocean_model", + "packing": 2, + "lists": [ + [ + "soga", + "thetaoga", + "uh", + "vh", + "vhbt", + "uhbt" + ], + [ + "agessc", + "T_ady_2d", + "T_adx_2d", + "T_diffy_2d", + "T_diffx_2d" + ], + [ + "diftrelo", + "diftrblo", + "difmxybo", + "difmxylo" + ], + [ + "volcello", + "vmo", + "vhGM", + "vhml", + "umo", + "uhGM", + "uhml" + ] + ] + } + ], + "else": [ + { + "module": "ocean_model", + "packing": 1, + "lists": [ + [ + "uo", + "vo", + "h", + "e", + "thetao", + "so", + "rhoinsitu", + "KE", + "rhopot0" + ], + [ + "soga", + "thetaoga", + "uh", + "vh", + "vhbt", + "uhbt" + ], + [ + "KPP_OBLdepth:oml" + ], + [ + "tauuo", + "tauvo", + "friver", + "prsn", + "prlq", + "evs", + "hfsso", + "rlntds", + "hfsnthermds", + "sfdsi", + "rsntds", + "hfds", + "ustar", + "hfsifrazil", + "wfo", + "vprec", + "ficeberg", + "fsitherm", + "hflso", + "pso", + "seaice_melt_heat", + "Heat_PmE", + "salt_flux_added" + ], + [ + "SSH", + "tos", + "sos", + "SSU", + "SSV", + "mass_wt", + "opottempmint", + "somint", + "Rd_dx", + "speed", + "mlotst" + ], + [ + "diftrelo", + "diftrblo", + "difmxybo", + "difmxylo" + ], + [ + "volcello", + "vmo", + "vhGM", + "vhml", + "umo", + "uhGM", + "uhml" + ], + [ + "agessc", + "T_ady_2d", + "T_adx_2d", + "T_diffy_2d", + "T_diffx_2d" + ] + ] + } + ] + } + }, + "hist_bgc": { + "suffix": "hm_bgc%4yr-%2mo", + "output_freq": 1, "output_freq_units": "months", "time_axis_units": "days", "new_file_freq": 1, From 30385bfc23e62a4eb90902c384aa0d9afea0739b Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Sun, 26 Jul 2020 13:47:31 -0600 Subject: [PATCH 015/136] Update to latest MARBL --- Externals_MOM.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Externals_MOM.cfg b/Externals_MOM.cfg index b750a306..3da73f6e 100644 --- a/Externals_MOM.cfg +++ b/Externals_MOM.cfg @@ -15,7 +15,7 @@ required = True [MARBL] protocol = git -tag = marbl0.36.0 +tag = marbl0.39.1 repo_url = https://github.com/marbl-ecosys/MARBL local_path = pkg/MARBL required = True From a49ff6893df5cbef2d4391843a210331cc01da20 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 28 Aug 2020 15:09:13 -0600 Subject: [PATCH 016/136] Update PE layouts and output list Added ECOSYS_ATMPRESS and ECOSYS_IFRAC to hm_bgc history file to make sure the correct values were coming in from the coupler. Also increased NTASKS_OCN to 864 and set up pe layout for G1850MOMECO compset. --- cime_config/buildnml | 1 + cime_config/config_pes.xml | 32 ++++++++++++++-------------- param_templates/diag_table.yaml | 2 +- param_templates/json/diag_table.json | 8 +++++-- 4 files changed, 24 insertions(+), 19 deletions(-) diff --git a/cime_config/buildnml b/cime_config/buildnml index 8c18ccd0..111cdcc6 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -19,6 +19,7 @@ sys.path.append(os.path.join(CIMEROOT, "scripts", "Tools")) # The scope of the following path expansion is limited to this script only, # and is needed to import MOM6 input file classes: sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),"MOM_RPS")) +sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),"MARBL_scripts")) from standard_script_setup import * from CIME.case import Case diff --git a/cime_config/config_pes.xml b/cime_config/config_pes.xml index 59d662f8..7219799e 100644 --- a/cime_config/config_pes.xml +++ b/cime_config/config_pes.xml @@ -455,14 +455,14 @@ - + none - 36 - 36 - 36 - 72 - 432 + 108 + 108 + 108 + 108 + 864 1 1 1 @@ -482,13 +482,13 @@ 0 0 0 - 72 + 108 0 0 0 - + none 108 @@ -521,14 +521,14 @@ 0 - + none - 36 - 36 - 36 - 36 - 432 + 72 + 72 + 72 + 72 + 864 1 1 1 @@ -548,13 +548,13 @@ 0 0 0 - 36 + 72 0 0 0 - + none 72 diff --git a/param_templates/diag_table.yaml b/param_templates/diag_table.yaml index eb4d7455..0f3a35e5 100644 --- a/param_templates/diag_table.yaml +++ b/param_templates/diag_table.yaml @@ -16,7 +16,7 @@ FieldLists: "ALK", "ALK_ALT_CO2", "DOC", "DON", "DOP", "DOPr", "DONr", "DOCr", "zooC", "spChl", "spC", "spP", "spFe", "spCaCO3", "diatChl", "diatC", "diatP", "diatFe", "diatSi", "diazChl", "diazC", "diazP", "diazFe", - "PV_CO2"] + "PV_CO2", "ECOSYS_ATM_PRESS", "ECOSYS_IFRAC"] - &surface_flds ["SSH", "tos", "sos", "SSU", "SSV", "mass_wt", "opottempmint", "somint", "Rd_dx", "speed", "mlotst"] diff --git a/param_templates/json/diag_table.json b/param_templates/json/diag_table.json index c5bec91d..3bb29f74 100644 --- a/param_templates/json/diag_table.json +++ b/param_templates/json/diag_table.json @@ -59,7 +59,9 @@ "diazC", "diazP", "diazFe", - "PV_CO2" + "PV_CO2", + "ECOSYS_ATM_PRESS", + "ECOSYS_IFRAC" ], [ "SSH", @@ -409,7 +411,9 @@ "diazC", "diazP", "diazFe", - "PV_CO2" + "PV_CO2", + "ECOSYS_ATM_PRESS", + "ECOSYS_IFRAC" ], [ "volcello", From 144a414725e64785f5f10cba1f0449f1ae74bc98 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 21 Sep 2020 17:36:34 -0600 Subject: [PATCH 017/136] Add hm_bgc stream to testing Stream is daily through test suite. Also added PH to the stream (regardless of whether run is a test) --- cime_config/config_archive.xml | 1 + param_templates/diag_table.yaml | 12 +++++++++--- param_templates/json/diag_table.json | 18 ++++++++++++++---- 3 files changed, 24 insertions(+), 7 deletions(-) diff --git a/cime_config/config_archive.xml b/cime_config/config_archive.xml index 711569b0..1a2b889f 100644 --- a/cime_config/config_archive.xml +++ b/cime_config/config_archive.xml @@ -3,6 +3,7 @@ r h.?[_\d+]+.nc$ hm.?[_\d+]+.nc$ + hm_bgc.?[_\d+]+.nc$ frc.?[_\d+]+.nc$ sfc.?[_\d+]+.nc$ rho2.?[_\d+]+.nc$ diff --git a/param_templates/diag_table.yaml b/param_templates/diag_table.yaml index 0f3a35e5..f1f3dabe 100644 --- a/param_templates/diag_table.yaml +++ b/param_templates/diag_table.yaml @@ -16,7 +16,7 @@ FieldLists: "ALK", "ALK_ALT_CO2", "DOC", "DON", "DOP", "DOPr", "DONr", "DOCr", "zooC", "spChl", "spC", "spP", "spFe", "spCaCO3", "diatChl", "diatC", "diatP", "diatFe", "diatSi", "diazChl", "diazC", "diazP", "diazFe", - "PV_CO2", "ECOSYS_ATM_PRESS", "ECOSYS_IFRAC"] + "PV_CO2", "ECOSYS_ATM_PRESS", "ECOSYS_IFRAC", "PH"] - &surface_flds ["SSH", "tos", "sos", "SSU", "SSV", "mass_wt", "opottempmint", "somint", "Rd_dx", "speed", "mlotst"] @@ -119,10 +119,16 @@ Files: hist_bgc: suffix: "hm_bgc%4yr-%2mo" output_freq: 1 - output_freq_units: "months" + output_freq_units: + $OCN_DIAG_MODE == "spinup": "years" + $TEST == True : "days" + else: "months" time_axis_units: "days" new_file_freq: 1 - new_file_freq_units: "months" + new_file_freq_units: + $OCN_DIAG_MODE == "spinup": "years" + $TEST == True: "days" + else: "months" reduction_method: "mean" # time average regional_section: "none" # global fields: diff --git a/param_templates/json/diag_table.json b/param_templates/json/diag_table.json index 3bb29f74..8807cd0b 100644 --- a/param_templates/json/diag_table.json +++ b/param_templates/json/diag_table.json @@ -61,7 +61,8 @@ "diazFe", "PV_CO2", "ECOSYS_ATM_PRESS", - "ECOSYS_IFRAC" + "ECOSYS_IFRAC", + "PH" ], [ "SSH", @@ -366,10 +367,18 @@ "hist_bgc": { "suffix": "hm_bgc%4yr-%2mo", "output_freq": 1, - "output_freq_units": "months", + "output_freq_units": { + "$OCN_DIAG_MODE == \"spinup\"": "years", + "$TEST == True": "days", + "else": "months" + }, "time_axis_units": "days", "new_file_freq": 1, - "new_file_freq_units": "months", + "new_file_freq_units": { + "$OCN_DIAG_MODE == \"spinup\"": "years", + "$TEST == True": "days", + "else": "months" + }, "reduction_method": "mean", "regional_section": "none", "fields": { @@ -413,7 +422,8 @@ "diazFe", "PV_CO2", "ECOSYS_ATM_PRESS", - "ECOSYS_IFRAC" + "ECOSYS_IFRAC", + "PH" ], [ "volcello", From 0533a9991b94f4a71644dabda1cdffd8c6e6da2f Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 29 Sep 2020 16:45:54 -0600 Subject: [PATCH 018/136] Add DUST_FLUX and IRON_FLUX to output stream --- param_templates/diag_table.yaml | 3 ++- param_templates/json/diag_table.json | 8 ++++++-- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/param_templates/diag_table.yaml b/param_templates/diag_table.yaml index f1f3dabe..1aae3ad2 100644 --- a/param_templates/diag_table.yaml +++ b/param_templates/diag_table.yaml @@ -16,7 +16,8 @@ FieldLists: "ALK", "ALK_ALT_CO2", "DOC", "DON", "DOP", "DOPr", "DONr", "DOCr", "zooC", "spChl", "spC", "spP", "spFe", "spCaCO3", "diatChl", "diatC", "diatP", "diatFe", "diatSi", "diazChl", "diazC", "diazP", "diazFe", - "PV_CO2", "ECOSYS_ATM_PRESS", "ECOSYS_IFRAC", "PH"] + "PV_CO2", "ECOSYS_ATM_PRESS", "ECOSYS_IFRAC", "PH", "DUST_FLUX", + "IRON_FLUX"] - &surface_flds ["SSH", "tos", "sos", "SSU", "SSV", "mass_wt", "opottempmint", "somint", "Rd_dx", "speed", "mlotst"] diff --git a/param_templates/json/diag_table.json b/param_templates/json/diag_table.json index 8807cd0b..0fed182c 100644 --- a/param_templates/json/diag_table.json +++ b/param_templates/json/diag_table.json @@ -62,7 +62,9 @@ "PV_CO2", "ECOSYS_ATM_PRESS", "ECOSYS_IFRAC", - "PH" + "PH", + "DUST_FLUX", + "IRON_FLUX" ], [ "SSH", @@ -423,7 +425,9 @@ "PV_CO2", "ECOSYS_ATM_PRESS", "ECOSYS_IFRAC", - "PH" + "PH", + "DUST_FLUX", + "IRON_FLUX" ], [ "volcello", From 70f5330e2c106e338ad26c103f4a644aac800ad4 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 2 Oct 2020 11:58:07 -0600 Subject: [PATCH 019/136] Add NOx_FLUX and NHy_FLUX to hm_bgc --- param_templates/diag_table.yaml | 2 +- param_templates/json/diag_table.json | 8 ++++++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/param_templates/diag_table.yaml b/param_templates/diag_table.yaml index 1aae3ad2..6d0cebea 100644 --- a/param_templates/diag_table.yaml +++ b/param_templates/diag_table.yaml @@ -17,7 +17,7 @@ FieldLists: "zooC", "spChl", "spC", "spP", "spFe", "spCaCO3", "diatChl", "diatC", "diatP", "diatFe", "diatSi", "diazChl", "diazC", "diazP", "diazFe", "PV_CO2", "ECOSYS_ATM_PRESS", "ECOSYS_IFRAC", "PH", "DUST_FLUX", - "IRON_FLUX"] + "IRON_FLUX", "NOx_FLUX", "NHy_FLUX"] - &surface_flds ["SSH", "tos", "sos", "SSU", "SSV", "mass_wt", "opottempmint", "somint", "Rd_dx", "speed", "mlotst"] diff --git a/param_templates/json/diag_table.json b/param_templates/json/diag_table.json index 0fed182c..b0a7e127 100644 --- a/param_templates/json/diag_table.json +++ b/param_templates/json/diag_table.json @@ -64,7 +64,9 @@ "ECOSYS_IFRAC", "PH", "DUST_FLUX", - "IRON_FLUX" + "IRON_FLUX", + "NOx_FLUX", + "NHy_FLUX" ], [ "SSH", @@ -427,7 +429,9 @@ "ECOSYS_IFRAC", "PH", "DUST_FLUX", - "IRON_FLUX" + "IRON_FLUX", + "NOx_FLUX", + "NHy_FLUX" ], [ "volcello", From 427913f5d4770212cd6bafb3dde22c2f38647c34 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 12 Nov 2020 11:56:42 -0700 Subject: [PATCH 020/136] Add FESEDFLUX to diag table --- param_templates/diag_table.yaml | 2 +- param_templates/json/diag_table.json | 6 ++++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/param_templates/diag_table.yaml b/param_templates/diag_table.yaml index 6d0cebea..5c5608c1 100644 --- a/param_templates/diag_table.yaml +++ b/param_templates/diag_table.yaml @@ -17,7 +17,7 @@ FieldLists: "zooC", "spChl", "spC", "spP", "spFe", "spCaCO3", "diatChl", "diatC", "diatP", "diatFe", "diatSi", "diazChl", "diazC", "diazP", "diazFe", "PV_CO2", "ECOSYS_ATM_PRESS", "ECOSYS_IFRAC", "PH", "DUST_FLUX", - "IRON_FLUX", "NOx_FLUX", "NHy_FLUX"] + "IRON_FLUX", "NOx_FLUX", "NHy_FLUX", "FESEDFLUX"] - &surface_flds ["SSH", "tos", "sos", "SSU", "SSV", "mass_wt", "opottempmint", "somint", "Rd_dx", "speed", "mlotst"] diff --git a/param_templates/json/diag_table.json b/param_templates/json/diag_table.json index b0a7e127..3aebb519 100644 --- a/param_templates/json/diag_table.json +++ b/param_templates/json/diag_table.json @@ -66,7 +66,8 @@ "DUST_FLUX", "IRON_FLUX", "NOx_FLUX", - "NHy_FLUX" + "NHy_FLUX", + "FESEDFLUX" ], [ "SSH", @@ -431,7 +432,8 @@ "DUST_FLUX", "IRON_FLUX", "NOx_FLUX", - "NHy_FLUX" + "NHy_FLUX", + "FESEDFLUX" ], [ "volcello", From dbf45492b4568843270cba9ad273ddb481d695ee Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 22 Jan 2021 16:19:43 -0700 Subject: [PATCH 021/136] Multiple json files to generate diag_table If MARBL is enabled (OCN_BGC_CONFIG anything but "none"), then diag_table comes from both diag_table.json and diag_table_MARBL.json; the latter is copied to Buildconf/momconf and then read in, but future commits will generate the json file in place in Buildconf/ --- cime_config/buildnml | 33 ++--- param_templates/diag_table.yaml | 32 ----- param_templates/json/diag_table.json | 123 ------------------ param_templates/json/diag_table_MARBL.json | 138 +++++++++++++++++++++ 4 files changed, 155 insertions(+), 171 deletions(-) create mode 100644 param_templates/json/diag_table_MARBL.json diff --git a/cime_config/buildnml b/cime_config/buildnml index 37185394..2288454a 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -66,20 +66,6 @@ def prep_input(case): MOM_input = FType_MOM_params.from_json(MOM_input_src) MOM_input.write(output_path=MOM_input_dest, output_format="MOM_input", case=case) - # Create marbl_in - if ocn_bgc_config != "none": - MARBL_dir = os.path.join(srcroot, "components", "mom", "MOM6", "pkg", "MARBL") - from MARBL_settings import MARBL_settings_for_MOM - MARBL_settings = MARBL_settings_for_MOM(MARBL_dir, caseroot, ocn_grid, run_type, - continue_run, ocn_bgc_config) - MARBL_settings.write_settings_file(os.path.join(rundir, "marbl_in")) - - # Create input.nml: - input_nml_src = os.path.join(json_templates_dir, "input_nml.json") - input_nml_dest = os.path.join(rundir,"input.nml") - input_nml = FType_input_nml.from_json(input_nml_src) - input_nml.write(input_nml_dest, case) - # 2. Create MOM_override: user_nl_mom = FType_MOM_params.from_MOM_input(os.path.join(caseroot,"user_nl_mom")) if "MOM_override" in os.listdir(SourceMods_dir): @@ -104,19 +90,34 @@ def prep_input(case): input_nml = FType_input_nml.from_json(input_nml_src) input_nml.write(input_nml_dest, case) - # 5. Create mom.input_data_list: + # 5. Create marbl_in (if case is set up with ocean BGC) + if ocn_bgc_config != "none": + MARBL_dir = os.path.join(srcroot, "components", "mom", "MOM6", "pkg", "MARBL") + from MARBL_settings import MARBL_settings_for_MOM + MARBL_settings = MARBL_settings_for_MOM(MARBL_dir, caseroot, ocn_grid, run_type, + continue_run, ocn_bgc_config) + MARBL_settings.write_settings_file(os.path.join(rundir, "marbl_in")) + + # 6. Create mom.input_data_list: input_data_list_src = os.path.join(json_templates_dir, "input_data_list.json") input_data_list_dest = os.path.join(Buildconf,"mom.input_data_list") input_data_list = FType_input_data_list.from_json(input_data_list_src) input_data_list.write(input_data_list_dest, case, MOM_input_final) - # 6. Create diag_table: + # 7. Create diag_table: if "diag_table" in os.listdir(SourceMods_dir): run_cmd("cp "+os.path.join(SourceMods_dir,"diag_table")+" "+rundir) else: diag_table_src = os.path.join(json_templates_dir, "diag_table.json") diag_table_dest = os.path.join(rundir,"diag_table") diag_table = FType_diag_table.from_json(diag_table_src) + if ocn_bgc_config != "none": + # 7b. add ocean BGC diagnostics to diag_table + # TODO: instead of copying json file here, python script should generate it based on ecosys_diagnostics + diag_table_src = os.path.join(json_templates_dir, "diag_table_MARBL.json") + diag_table_buildconf = os.path.join(Buildconf, "momconf", "diag_table_MARBL.json") + shutil.copy(diag_table_src, diag_table_buildconf) + diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) diag_table.write(diag_table_dest, case) def init_MOM_override(rundir): diff --git a/param_templates/diag_table.yaml b/param_templates/diag_table.yaml index 5c5608c1..4a62fdf4 100644 --- a/param_templates/diag_table.yaml +++ b/param_templates/diag_table.yaml @@ -10,15 +10,6 @@ FieldLists: - &tracers ["agessc", "T_ady_2d", "T_adx_2d", "T_diffy_2d", "T_diffx_2d"] - # NOTE: for now I'm hard-coding the MARBL tracer names. Eventually we will rely on - # MARBL to tell us these names - - &MARBL ["PO4", "NO3", "SiO3", "NH4", "Fe", "Lig", "O2", "DIC", "DIC_ALT_CO2", - "ALK", "ALK_ALT_CO2", "DOC", "DON", "DOP", "DOPr", "DONr", "DOCr", - "zooC", "spChl", "spC", "spP", "spFe", "spCaCO3", "diatChl", "diatC", - "diatP", "diatFe", "diatSi", "diazChl", "diazC", "diazP", "diazFe", - "PV_CO2", "ECOSYS_ATM_PRESS", "ECOSYS_IFRAC", "PH", "DUST_FLUX", - "IRON_FLUX", "NOx_FLUX", "NHy_FLUX", "FESEDFLUX"] - - &surface_flds ["SSH", "tos", "sos", "SSU", "SSV", "mass_wt", "opottempmint", "somint", "Rd_dx", "speed", "mlotst"] @@ -116,29 +107,6 @@ Files: *transports, *tracers ] - # BGC output - hist_bgc: - suffix: "hm_bgc%4yr-%2mo" - output_freq: 1 - output_freq_units: - $OCN_DIAG_MODE == "spinup": "years" - $TEST == True : "days" - else: "months" - time_axis_units: "days" - new_file_freq: 1 - new_file_freq_units: - $OCN_DIAG_MODE == "spinup": "years" - $TEST == True: "days" - else: "months" - reduction_method: "mean" # time average - regional_section: "none" # global - fields: - $OCN_BGC_CONFIG != "none": - - module: "ocean_model" - packing: 1 # double precision - lists: [ *MARBL, - *transports ] - # essential variable mapped to z_space hist_z_space: suffix: diff --git a/param_templates/json/diag_table.json b/param_templates/json/diag_table.json index 3aebb519..a580eb22 100644 --- a/param_templates/json/diag_table.json +++ b/param_templates/json/diag_table.json @@ -26,49 +26,6 @@ "T_diffy_2d", "T_diffx_2d" ], - [ - "PO4", - "NO3", - "SiO3", - "NH4", - "Fe", - "Lig", - "O2", - "DIC", - "DIC_ALT_CO2", - "ALK", - "ALK_ALT_CO2", - "DOC", - "DON", - "DOP", - "DOPr", - "DONr", - "DOCr", - "zooC", - "spChl", - "spC", - "spP", - "spFe", - "spCaCO3", - "diatChl", - "diatC", - "diatP", - "diatFe", - "diatSi", - "diazChl", - "diazC", - "diazP", - "diazFe", - "PV_CO2", - "ECOSYS_ATM_PRESS", - "ECOSYS_IFRAC", - "PH", - "DUST_FLUX", - "IRON_FLUX", - "NOx_FLUX", - "NHy_FLUX", - "FESEDFLUX" - ], [ "SSH", "tos", @@ -369,86 +326,6 @@ ] } }, - "hist_bgc": { - "suffix": "hm_bgc%4yr-%2mo", - "output_freq": 1, - "output_freq_units": { - "$OCN_DIAG_MODE == \"spinup\"": "years", - "$TEST == True": "days", - "else": "months" - }, - "time_axis_units": "days", - "new_file_freq": 1, - "new_file_freq_units": { - "$OCN_DIAG_MODE == \"spinup\"": "years", - "$TEST == True": "days", - "else": "months" - }, - "reduction_method": "mean", - "regional_section": "none", - "fields": { - "$OCN_BGC_CONFIG != \"none\"": [ - { - "module": "ocean_model", - "packing": 1, - "lists": [ - [ - "PO4", - "NO3", - "SiO3", - "NH4", - "Fe", - "Lig", - "O2", - "DIC", - "DIC_ALT_CO2", - "ALK", - "ALK_ALT_CO2", - "DOC", - "DON", - "DOP", - "DOPr", - "DONr", - "DOCr", - "zooC", - "spChl", - "spC", - "spP", - "spFe", - "spCaCO3", - "diatChl", - "diatC", - "diatP", - "diatFe", - "diatSi", - "diazChl", - "diazC", - "diazP", - "diazFe", - "PV_CO2", - "ECOSYS_ATM_PRESS", - "ECOSYS_IFRAC", - "PH", - "DUST_FLUX", - "IRON_FLUX", - "NOx_FLUX", - "NHy_FLUX", - "FESEDFLUX" - ], - [ - "volcello", - "vmo", - "vhGM", - "vhml", - "umo", - "uhGM", - "uhml" - ] - ] - } - ] - } - }, "hist_z_space": { "suffix": { "$OCN_DIAG_MODE == \"spinup\"": "h%4yr", diff --git a/param_templates/json/diag_table_MARBL.json b/param_templates/json/diag_table_MARBL.json new file mode 100644 index 00000000..27afd6b4 --- /dev/null +++ b/param_templates/json/diag_table_MARBL.json @@ -0,0 +1,138 @@ +{ + "FieldLists": [ + [ + "PO4", + "NO3", + "SiO3", + "NH4", + "Fe", + "Lig", + "O2", + "DIC", + "DIC_ALT_CO2", + "ALK", + "ALK_ALT_CO2", + "DOC", + "DON", + "DOP", + "DOPr", + "DONr", + "DOCr", + "zooC", + "spChl", + "spC", + "spP", + "spFe", + "spCaCO3", + "diatChl", + "diatC", + "diatP", + "diatFe", + "diatSi", + "diazChl", + "diazC", + "diazP", + "diazFe", + "PV_CO2", + "ECOSYS_ATM_PRESS", + "ECOSYS_IFRAC", + "PH", + "DUST_FLUX", + "IRON_FLUX", + "NOx_FLUX", + "NHy_FLUX", + "FESEDFLUX" + ], + [ + "volcello", + "vmo", + "vhGM", + "vhml", + "umo", + "uhGM", + "uhml" + ] + ], + "Files": { + "hist_bgc": { + "suffix": "hm_bgc%4yr-%2mo", + "output_freq": 1, + "output_freq_units": { + "$OCN_DIAG_MODE == \"spinup\"": "years", + "$TEST == True": "days", + "else": "months" + }, + "time_axis_units": "days", + "new_file_freq": 1, + "new_file_freq_units": { + "$OCN_DIAG_MODE == \"spinup\"": "years", + "$TEST == True": "days", + "else": "months" + }, + "reduction_method": "mean", + "regional_section": "none", + "fields": { + "$OCN_BGC_CONFIG != \"none\"": [ + { + "module": "ocean_model", + "packing": 1, + "lists": [ + [ + "PO4", + "NO3", + "SiO3", + "NH4", + "Fe", + "Lig", + "O2", + "DIC", + "DIC_ALT_CO2", + "ALK", + "ALK_ALT_CO2", + "DOC", + "DON", + "DOP", + "DOPr", + "DONr", + "DOCr", + "zooC", + "spChl", + "spC", + "spP", + "spFe", + "spCaCO3", + "diatChl", + "diatC", + "diatP", + "diatFe", + "diatSi", + "diazChl", + "diazC", + "diazP", + "diazFe", + "PV_CO2", + "ECOSYS_ATM_PRESS", + "ECOSYS_IFRAC", + "PH", + "DUST_FLUX", + "IRON_FLUX", + "NOx_FLUX", + "NHy_FLUX", + "FESEDFLUX" + ], + [ + "volcello", + "vmo", + "vhGM", + "vhml", + "umo", + "uhGM", + "uhml" + ] + ] + } + ] + } + } + } +} \ No newline at end of file From 1103a56e12f998b10c19eebac5b10dd22e757634 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 22 Jan 2021 16:32:39 -0700 Subject: [PATCH 022/136] Previous commit assumed momconf/ existed With a new case, it turned out buildnml was trying to copy a file to Buildconf/momconf before that directory was created --- cime_config/buildnml | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/cime_config/buildnml b/cime_config/buildnml index 2288454a..9fb1dace 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -48,12 +48,18 @@ def prep_input(case): ocn_grid = case.get_value("OCN_GRID") run_type = case.get_value("RUN_TYPE") continue_run = case.get_value("CONTINUE_RUN") + momconfdir = os.path.join(caseroot, "Buildconf", "momconf") SourceMods_dir = os.path.join(caseroot,"SourceMods","src.mom") + use_MARBL = (ocn_bgc_config != "none") # Make sure that rundir exists. If not, make it: if not os.path.exists(rundir): os.makedirs(rundir) + # Make sure that momconfdir exists. If not, make it: + if not os.path.exists(momconfdir) and use_MARBL: + os.makedirs(momconfdir) + # Parse json files and create MOM6 input files in rundir json_templates_dir = os.path.join(comp_root_dir_ocn,"param_templates","json") @@ -91,7 +97,7 @@ def prep_input(case): input_nml.write(input_nml_dest, case) # 5. Create marbl_in (if case is set up with ocean BGC) - if ocn_bgc_config != "none": + if use_MARBL: MARBL_dir = os.path.join(srcroot, "components", "mom", "MOM6", "pkg", "MARBL") from MARBL_settings import MARBL_settings_for_MOM MARBL_settings = MARBL_settings_for_MOM(MARBL_dir, caseroot, ocn_grid, run_type, @@ -111,11 +117,11 @@ def prep_input(case): diag_table_src = os.path.join(json_templates_dir, "diag_table.json") diag_table_dest = os.path.join(rundir,"diag_table") diag_table = FType_diag_table.from_json(diag_table_src) - if ocn_bgc_config != "none": + if use_MARBL: # 7b. add ocean BGC diagnostics to diag_table # TODO: instead of copying json file here, python script should generate it based on ecosys_diagnostics diag_table_src = os.path.join(json_templates_dir, "diag_table_MARBL.json") - diag_table_buildconf = os.path.join(Buildconf, "momconf", "diag_table_MARBL.json") + diag_table_buildconf = os.path.join(momconfdir, "diag_table_MARBL.json") shutil.copy(diag_table_src, diag_table_buildconf) diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) diag_table.write(diag_table_dest, case) From 0a850b7bc95b66b72f1489f371efa68b51ce74b6 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 4 Feb 2021 12:11:43 -0700 Subject: [PATCH 023/136] generate Buildconf/momconf/ecosys_diagnostics File is created by buildnml; next step is to turn it into a JSON file --- .../MARBL_scripts/MARBL_diagnostics.py | 61 ++++ cime_config/MARBL_scripts/MARBL_settings.py | 33 +- .../MARBL_scripts/MOM_ecosys_diagnostics.py | 299 ++++++++++++++++++ cime_config/buildnml | 36 ++- 4 files changed, 424 insertions(+), 5 deletions(-) create mode 100644 cime_config/MARBL_scripts/MARBL_diagnostics.py create mode 100644 cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py diff --git a/cime_config/MARBL_scripts/MARBL_diagnostics.py b/cime_config/MARBL_scripts/MARBL_diagnostics.py new file mode 100644 index 00000000..404a35f5 --- /dev/null +++ b/cime_config/MARBL_scripts/MARBL_diagnostics.py @@ -0,0 +1,61 @@ +""" Interface to the marbl_diagnostics_class object +""" + +class MARBL_diagnostics_for_MOM(object): + def __init__(self, MARBL_dir, caseroot, MARBL_settings): + + import sys, os + + # Set up arguments for marbl_diagnostics_class constructor + # Note that this is a dictionary that will be used to pass named variables to class constructor + # Arguments to be passed are + # 1. default_diagnostics_file: full path to default_diagnostics.json + # (can be in SourceMods, otherwise comes from MARBL) + # 2. MARBL_settings: a MARBL_settings_for_MOM object + MARBL_args = dict() + + # User can put default_values.json in SourceMods, otherwise use file provided by MARBL + MARBL_args["default_diagnostics_file"] = os.path.join(caseroot,"SourceMods","src.mom","diagnostics_latest.json") + if not os.path.isfile(MARBL_args["default_diagnostics_file"]): + MARBL_args["default_diagnostics_file"] = os.path.join(MARBL_dir, "defaults", "json", "diagnostics_latest.json") + + MARBL_args["MARBL_settings"] = MARBL_settings._MARBL_settings + + # Import MARBL_diagnostics_file_class, which may come from MARBL_tools or SourceMods/src.mom + # (i) need MARBL_dir in path for both branches of this if statement because even if + # MARBL_diagnostics_file_class.py is in SourceMods, it needs to import MARBL_tools itself + sys.path.append(MARBL_dir) + # (ii) Here's where we import from either MARBL_tools or SourceMods + diagnostics_class_dir = os.path.join(caseroot, "SourceMods", "src.mom") + if not os.path.isfile(os.path.join(diagnostics_class_dir, "MARBL_diagnostics_file_class.py")): + from MARBL_tools import MARBL_diagnostics_file_class + else: + import imp + import logging + logger = logging.getLogger(__name__) + logging.info('Importing MARBL_diagnostics_file_class.py from %s' % diagnostics_class_dir) + diagnostics_class_module = diagnostics_class_dir+'/MARBL_diagnostics_file_class.py' + if os.path.isfile(diagnostics_class_module): + MARBL_diagnostics_file_class = imp.load_source('MARBL_diagnostics_file_class', diagnostics_class_module) + else: + logger.error('Can not find %s' % diagnostics_class_module) + sys.exit(1) + + # Generate diagnostics object + self._MARBL_diagnostics = MARBL_diagnostics_file_class.MARBL_diagnostics_class(**MARBL_args) + + ################################################################################ + # PUBLIC CLASS METHODS # + ################################################################################ + + def write_diagnostics_file(self, diagnostics_file_out, diagnostics_list_out, append): + """ Add all MARBL diagnostics to file containing MOM diagnostics + Also create a list of diagnostics generated by MOM + """ + from MARBL_tools import generate_diagnostics_file + generate_diagnostics_file(self._MARBL_diagnostics, diagnostics_file_out, append) + fout = open(diagnostics_list_out,"w") + for key in self._MARBL_diagnostics.diagnostics_dict.keys(): + fout.write(key+"\n") + + diff --git a/cime_config/MARBL_scripts/MARBL_settings.py b/cime_config/MARBL_scripts/MARBL_settings.py index 57726324..b44cfcb8 100644 --- a/cime_config/MARBL_scripts/MARBL_settings.py +++ b/cime_config/MARBL_scripts/MARBL_settings.py @@ -72,9 +72,40 @@ def get_tracer_names(self): ####################################### + def get_autotroph_names(self, calcifier_only=False): + """ Returns a list of all autotrophs in current configuration + """ + autotroph_list = [] + for n in range(1, self._MARBL_settings.settings_dict['autotroph_cnt']+1): + autotroph_name = self._MARBL_settings.settings_dict['autotroph_settings(%d)%%sname' % n].strip('"') + imp_calcifier = (self._MARBL_settings.settings_dict['autotroph_settings(%d)%%imp_calcifier' % n].strip('"') == '.true.') + exp_calcifier = (self._MARBL_settings.settings_dict['autotroph_settings(%d)%%exp_calcifier' % n].strip('"') == '.true.') + if imp_calcifier or exp_calcifier or (not calcifier_only): + autotroph_list.append(autotroph_name) + return autotroph_list + + ####################################### + + def get_zooplankton_names(self): + """ Returns a list of all zooplankton in current configuration + """ + zooplankton_list = [] + for n in range(1, self._MARBL_settings.settings_dict['zooplankton_cnt']+1): + zooplankton_name = self._MARBL_settings.settings_dict['zooplankton_settings(%d)%%sname' % n].strip('"') + zooplankton_list.append(zooplankton_name) + return zooplankton_list + + ####################################### + + def ladjust_bury_coeff(self): + """ Returns True if ladjust_bury_coeff = .true. + """ + return (self._MARBL_settings.settings_dict['ladjust_bury_coeff'].strip('"') == '.true.') + + ####################################### + def write_settings_file(self, settings_file_out): """ Write a settings file containing all MARBL settings """ from MARBL_tools import generate_settings_file generate_settings_file(self._MARBL_settings, settings_file_out) - diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py new file mode 100644 index 00000000..bc7581bb --- /dev/null +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -0,0 +1,299 @@ +""" Scripts to generate MOM-specific diagnostic list in generic MARBL format + (e.g. MARBL tracer state) +""" + +def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_list, calcifier_list, ladjust_bury_coeff, ecosys_diag_filename): + """ Subroutine to write a file in the same format as marbl_diagnostics containing + a list of POP-generated diagnostics that should be included based on the + MARBL configuration + """ + + with open(ecosys_diag_filename,"w") as fout: + # File header with information on how to use generated file + fout.write("# This file contains a list of all ecosystem-related diagnostics MOM output for a given MARBL configuration,\n") + fout.write("# as well as the recommended frequency and operator for outputting each diagnostic.\n") + fout.write("# Some diagnostics are computed in MOM, while others are provided by MARBL.\n") + fout.write("# The format of this file is:\n") + fout.write("#\n") + fout.write("# DIAGNOSTIC_NAME : frequency_operator\n") + fout.write("#\n") + fout.write("# And fields that should be output at multiple different frequencies will be comma-separated:\n") + fout.write("#\n") + fout.write("# DIAGNOSTIC_NAME : frequency1_operator1, frequency2_operator2, ..., frequencyN_operatorN\n") + fout.write("#\n") + fout.write("# Frequencies are never, low, medium, and high.\n") + fout.write("# Operators are instantaneous, average, minimum, and maximum.\n") + fout.write("#\n") + fout.write("# To change BGC-related diagnostic output, copy this file to SourceMods/src.mom/\n") + fout.write("# and edit as desired.\n") + + # File will contain MOM and MARBL diagnostics, so we provide header to make + # the provenance of each diagnostic clear + fout.write("#\n########################################\n") + fout.write("# MOM-generated diagnostics #\n") + fout.write("########################################\n#\n") + + # TODO: get these forcing fields into history buffer + # # Add tracer-agnostic forcing fields to requested diagnostics + # fout.write("# Dust and Carbon Fluxes from the Coupler\n#\n") + # fout.write("ATM_FINE_DUST_FLUX_CPL : medium_average\n") + # fout.write("ATM_COARSE_DUST_FLUX_CPL : medium_average\n") + # fout.write("SEAICE_DUST_FLUX_CPL : medium_average\n") + # fout.write("ATM_BLACK_CARBON_FLUX_CPL : medium_average\n") + # fout.write("SEAICE_BLACK_CARBON_FLUX_CPL : medium_average\n") + + # TODO: add running means, and then define these diagnostics + # If adjusting bury coefficients, add running means to requested diagnostics + if ladjust_bury_coeff: + fout.write("#\n# Running means computed for MARBL\n#\n") + fout.write("MARBL_rmean_glo_scalar_POC_bury_coeff : medium_average\n") + fout.write("MARBL_rmean_glo_scalar_POP_bury_coeff : medium_average\n") + fout.write("MARBL_rmean_glo_scalar_bSi_bury_coeff : medium_average\n") + + # 1. Create dictionary with default tracer output for all tracers + # - This dictionary also stores some tracer properties (currently just for budget-specific diagnostics) + # NOTE: using OrderedDict to maintain alphabetical listing of tracers in keys() + from collections import OrderedDict + full_diag_dict = OrderedDict() + for tracer_short_name in sorted(active_tracers): + per_tracer_dict = dict() + + # Properties used to determine frequency of budget terms + per_tracer_dict['properties'] = dict() + per_tracer_dict['properties']['include budget terms'] = False + per_tracer_dict['properties']['has surface flux'] = False + + # Default frequencies for per-tracer diagnostics + # - tracer state should be output monthly + # - everything else is off by default + # TODO: get right name for surface tracer flux (POP used STF_{tracer}) and tendency (POP used J_{tracer}) + # TODO: add vertical integral for J_{tracer} and {tracer} (full column and 100m) + # also, what is difference between Jint_100m_{tracer} and tend_zint_100m_{tracer}? + per_tracer_dict['diags'] = OrderedDict() + per_tracer_dict['diags'][tracer_short_name] = 'medium_average' + per_tracer_dict['diags']['STF_%s' % tracer_short_name] = 'never_average' + per_tracer_dict['diags']['J_%s' % tracer_short_name] = 'never_average' + per_tracer_dict['diags']['Jint_100m_%s' % tracer_short_name] = 'never_average' + per_tracer_dict['diags']['Jint_%s' % tracer_short_name] = 'never_average' + per_tracer_dict['diags']['%s_zint_100m' % tracer_short_name] = 'never_average' + per_tracer_dict['diags']['tend_zint_100m_%s' % tracer_short_name] = 'never_average' + + # Some diagnostics are not defined for all tracers; diagnostics with 'none' + # are not added to diagnostics file and will not show up in tavg_contents + # TODO: can MOM6 compute these values? + per_tracer_dict['diags']['%s_RIV_FLUX' % tracer_short_name] = 'none' + per_tracer_dict['diags']['FvPER_%s' % tracer_short_name] = 'none' + per_tracer_dict['diags']['FvICE_%s' % tracer_short_name] = 'none' + full_diag_dict[tracer_short_name] = dict(per_tracer_dict) + + # TODO: turn on some of these diagnostics once they are properly defined + if False: + # 2. Update dictionary for tracers that don't just rely on default diagnostic output + # This is organized per-tracer, and specific blocks are ignored if MARBL is not + # configured to run with that particular tracer + # PO4 + if 'PO4' in full_diag_dict.keys(): + full_diag_dict['PO4']['diags']['PO4_RIV_FLUX'] = 'medium_average' + full_diag_dict['PO4']['diags']['J_PO4'] = 'low_average' + full_diag_dict['PO4']['diags']['Jint_100m_PO4'] = 'medium_average' + full_diag_dict['PO4']['diags']['tend_zint_100m_PO4'] = 'medium_average' + full_diag_dict['PO4']['properties']['has surface flux'] = True + # NO3 + if 'NO3' in full_diag_dict.keys(): + full_diag_dict['NO3']['diags']['NO3_RIV_FLUX'] = 'medium_average' + full_diag_dict['NO3']['diags']['J_NO3'] = 'low_average' + full_diag_dict['NO3']['diags']['Jint_100m_NO3'] = 'medium_average' + full_diag_dict['NO3']['diags']['tend_zint_100m_NO3'] = 'medium_average' + full_diag_dict['NO3']['properties']['has surface flux'] = True + # SiO3 + if 'SiO3' in full_diag_dict.keys(): + full_diag_dict['SiO3']['diags']['SiO3_RIV_FLUX'] = 'medium_average' + full_diag_dict['SiO3']['diags']['J_SiO3'] = 'low_average' + full_diag_dict['SiO3']['diags']['Jint_100m_SiO3'] = 'medium_average' + full_diag_dict['SiO3']['diags']['tend_zint_100m_SiO3'] = 'medium_average' + full_diag_dict['SiO3']['properties']['has surface flux'] = True + # NH4 + if 'NH4' in full_diag_dict.keys(): + full_diag_dict['NH4']['diags']['J_NH4'] = 'low_average' + full_diag_dict['NH4']['diags']['Jint_100m_NH4'] = 'medium_average' + full_diag_dict['NH4']['diags']['tend_zint_100m_NH4'] = 'medium_average' + full_diag_dict['NH4']['properties']['has surface flux'] = True + # Fe + if 'Fe' in full_diag_dict.keys(): + full_diag_dict['Fe']['diags']['Fe_RIV_FLUX'] = 'medium_average' + full_diag_dict['Fe']['diags']['J_Fe'] = 'low_average' + full_diag_dict['Fe']['diags']['Jint_100m_Fe'] = 'medium_average' + full_diag_dict['Fe']['diags']['tend_zint_100m_Fe'] = 'medium_average' + full_diag_dict['Fe']['properties']['include budget terms'] = True + full_diag_dict['Fe']['properties']['has surface flux'] = True + # Lig + if 'Lig' in full_diag_dict.keys(): + pass # Lig just uses default settings + # O2 + if 'O2' in full_diag_dict.keys(): + full_diag_dict['O2']['diags']['STF_O2'] = 'medium_average, high_average' + full_diag_dict['O2']['diags']['Jint_100m_O2'] = 'medium_average' + full_diag_dict['O2']['diags']['tend_zint_100m_O2'] = 'medium_average' + full_diag_dict['O2']['properties']['include budget terms'] = True + full_diag_dict['O2']['properties']['has surface flux'] = True + # DIC + if 'DIC' in full_diag_dict.keys(): + full_diag_dict['DIC']['diags']['DIC_RIV_FLUX'] = 'medium_average' + full_diag_dict['DIC']['diags']['J_DIC'] = 'medium_average' + full_diag_dict['DIC']['diags']['Jint_100m_DIC'] = 'medium_average' + full_diag_dict['DIC']['diags']['tend_zint_100m_DIC'] = 'medium_average' + full_diag_dict['DIC']['diags']['FvPER_DIC'] = 'medium_average' + full_diag_dict['DIC']['diags']['FvICE_DIC'] = 'medium_average' + full_diag_dict['DIC']['properties']['include budget terms'] = True + full_diag_dict['DIC']['properties']['has surface flux'] = True + # DIC_ALT_CO2 + if 'DIC_ALT_CO2' in full_diag_dict.keys(): + full_diag_dict['DIC_ALT_CO2']['diags']['DIC_ALT_CO2_RIV_FLUX'] = 'medium_average' + full_diag_dict['DIC_ALT_CO2']['diags']['J_DIC_ALT_CO2'] = 'medium_average' + full_diag_dict['DIC_ALT_CO2']['diags']['Jint_100m_DIC_ALT_CO2'] = 'medium_average' + full_diag_dict['DIC_ALT_CO2']['diags']['tend_zint_100m_DIC_ALT_CO2'] = 'medium_average' + full_diag_dict['DIC_ALT_CO2']['diags']['FvPER_DIC_ALT_CO2'] = 'medium_average' + full_diag_dict['DIC_ALT_CO2']['diags']['FvICE_DIC_ALT_CO2'] = 'medium_average' + full_diag_dict['DIC_ALT_CO2']['properties']['include budget terms'] = True + full_diag_dict['DIC_ALT_CO2']['properties']['has surface flux'] = True + # ALK + if 'ALK' in full_diag_dict.keys(): + full_diag_dict['ALK']['diags']['ALK_RIV_FLUX'] = 'medium_average' + full_diag_dict['ALK']['diags']['STF_ALK'] = 'medium_average' + full_diag_dict['ALK']['diags']['J_ALK'] = 'low_average' + full_diag_dict['ALK']['diags']['Jint_100m_ALK'] = 'medium_average' + full_diag_dict['ALK']['diags']['tend_zint_100m_ALK'] = 'medium_average' + full_diag_dict['ALK']['diags']['FvPER_ALK'] = 'medium_average' + full_diag_dict['ALK']['diags']['FvICE_ALK'] = 'medium_average' + full_diag_dict['ALK']['properties']['has surface flux'] = True + # ALK_ALT_CO2 + if 'ALK_ALT_CO2' in full_diag_dict.keys(): + full_diag_dict['ALK_ALT_CO2']['diags']['ALK_ALT_CO2_RIV_FLUX'] = 'medium_average' + full_diag_dict['ALK_ALT_CO2']['diags']['STF_ALK_ALT_CO2'] = 'medium_average' + full_diag_dict['ALK_ALT_CO2']['diags']['J_ALK_ALT_CO2'] = 'low_average' + full_diag_dict['ALK_ALT_CO2']['diags']['Jint_100m_ALK_ALT_CO2'] = 'medium_average' + full_diag_dict['ALK_ALT_CO2']['diags']['tend_zint_100m_ALK_ALT_CO2'] = 'medium_average' + full_diag_dict['ALK_ALT_CO2']['diags']['FvPER_ALK_ALT_CO2'] = 'medium_average' + full_diag_dict['ALK_ALT_CO2']['diags']['FvICE_ALK_ALT_CO2'] = 'medium_average' + full_diag_dict['ALK_ALT_CO2']['properties']['has surface flux'] = True + # DOC + if 'DOC' in full_diag_dict.keys(): + full_diag_dict['DOC']['diags']['DOC_RIV_FLUX'] = 'medium_average' + full_diag_dict['DOC']['diags']['Jint_100m_DOC'] = 'medium_average' + full_diag_dict['DOC']['diags']['tend_zint_100m_DOC'] = 'medium_average' + full_diag_dict['DOC']['properties']['include budget terms'] = True + full_diag_dict['DOC']['properties']['has surface flux'] = False # this should be True if EBM is off + # DON + if 'DON' in full_diag_dict.keys(): + full_diag_dict['DON']['diags']['DON_RIV_FLUX'] = 'medium_average' + full_diag_dict['DON']['properties']['has surface flux'] = True + # DOP + if 'DOP' in full_diag_dict.keys(): + full_diag_dict['DOP']['diags']['DOP_RIV_FLUX'] = 'medium_average' + full_diag_dict['DOP']['properties']['has surface flux'] = True + # DOPr + if 'DOPr' in full_diag_dict.keys(): + full_diag_dict['DOPr']['diags']['DOPr_RIV_FLUX'] = 'medium_average' + full_diag_dict['DOPr']['properties']['has surface flux'] = True + # DONr + if 'DONr' in full_diag_dict.keys(): + full_diag_dict['DONr']['diags']['DONr_RIV_FLUX'] = 'medium_average' + full_diag_dict['DONr']['properties']['has surface flux'] = True + # DOCr + if 'DOCr' in full_diag_dict.keys(): + full_diag_dict['DOCr']['diags']['DOCr_RIV_FLUX'] = 'medium_average' + full_diag_dict['DOCr']['diags']['Jint_100m_DOCr'] = 'medium_average' + full_diag_dict['DOCr']['diags']['tend_zint_100m_DOCr'] = 'medium_average' + full_diag_dict['DOCr']['properties']['include budget terms'] = True + full_diag_dict['DOCr']['properties']['has surface flux'] = False # this should be True if EBM is off + # DI13C + if 'DI13C' in full_diag_dict.keys(): + full_diag_dict['DI13C']['diags']['DI13C_RIV_FLUX'] = 'medium_average' + full_diag_dict['DI13C']['diags']['J_DI13C'] = 'medium_average' + full_diag_dict['DI13C']['diags']['Jint_100m_DI13C'] = 'medium_average' + full_diag_dict['DI13C']['diags']['tend_zint_100m_DI13C'] = 'medium_average' + full_diag_dict['DI13C']['diags']['FvPER_DI13C'] = 'medium_average' + full_diag_dict['DI13C']['diags']['FvICE_DI13C'] = 'medium_average' + full_diag_dict['DI13C']['properties']['has surface flux'] = True + # DO13Ctot + if 'DO13Ctot' in full_diag_dict.keys(): + full_diag_dict['DO13Ctot']['diags']['DO13Ctot_RIV_FLUX'] = 'medium_average' + full_diag_dict['DO13Ctot']['diags']['Jint_100m_DO13Ctot'] = 'medium_average' + full_diag_dict['DO13Ctot']['diags']['tend_zint_100m_DO13Ctot'] = 'medium_average' + full_diag_dict['DO13Ctot']['properties']['has surface flux'] = True + # DI14C + if 'DI14C' in full_diag_dict.keys(): + full_diag_dict['DI14C']['diags']['DI14C_RIV_FLUX'] = 'medium_average' + full_diag_dict['DI14C']['diags']['J_DI14C'] = 'medium_average' + full_diag_dict['DI14C']['diags']['Jint_100m_DI14C'] = 'medium_average' + full_diag_dict['DI14C']['diags']['tend_zint_100m_DI14C'] = 'medium_average' + full_diag_dict['DI14C']['diags']['FvPER_DI14C'] = 'medium_average' + full_diag_dict['DI14C']['diags']['FvICE_DI14C'] = 'medium_average' + full_diag_dict['DI14C']['properties']['has surface flux'] = True + # DO14Ctot + if 'DO14Ctot' in full_diag_dict.keys(): + full_diag_dict['DO14Ctot']['diags']['DO14Ctot_RIV_FLUX'] = 'medium_average' + full_diag_dict['DO14Ctot']['diags']['Jint_100m_DO14Ctot'] = 'medium_average' + full_diag_dict['DO14Ctot']['diags']['tend_zint_100m_DO14Ctot'] = 'medium_average' + full_diag_dict['DO14Ctot']['properties']['has surface flux'] = True + + # 3. Per-autotroph diagnostics + for autotroph_name in autotroph_list: + tracer_short_name = autotroph_name+'C' + if tracer_short_name in full_diag_dict.keys(): + full_diag_dict[tracer_short_name]['diags']['%s_zint_100m' % tracer_short_name] = 'high_average' + tracer_short_name = autotroph_name+'CaCO3' + if tracer_short_name in full_diag_dict.keys(): + full_diag_dict[tracer_short_name]['diags']['%s_zint_100m' % tracer_short_name] = 'high_average' + tracer_short_name = autotroph_name+'Chl' + if tracer_short_name in full_diag_dict.keys(): + full_diag_dict[tracer_short_name]['diags']['%s_SURF' % tracer_short_name] = 'high_average' + + # 4. Per-zooplankton diagnostics + for zooplankton_name in zooplankton_list: + tracer_short_name = zooplankton_name+'C' + if tracer_short_name in full_diag_dict.keys(): + full_diag_dict[tracer_short_name]['diags']['%s_zint_100m' % tracer_short_name] = 'high_average' + + # 5. Write tracer-specific diagnostics to file + for tracer_short_name in full_diag_dict.keys(): + # (a) Process ['properties'] dictionary for budget terms + # Note that this step will add to the ['diags'] dictionary but will not change previously set values + per_tracer_properties = full_diag_dict[tracer_short_name]['properties'] + if per_tracer_properties['include budget terms']: + value = "low_average" + else: + value = "never_average" + + # TODO: more variables to figure out MOM6 equivalent of + if False: + # These diagnostics should be included by default for tracers requested budget terms + for key in ['UE', 'VN', 'WT', 'DIA_IMPVF', 'HDIFE', 'HDIFN', 'HDIFB', 'TEND', 'RF_TEND']: + specific_key = '%s_%s' % (key, tracer_short_name) + if specific_key not in full_diag_dict[tracer_short_name]['diags'].keys(): + full_diag_dict[tracer_short_name]['diags'][specific_key] = value + + if per_tracer_properties['include budget terms'] and per_tracer_properties['has surface flux']: + value = "low_average" + else: + value = "never_average" + + # These diagnostics should be included by default for tracers requested budget terms + # ONLY for tracers that have non-zero surface fluxes + for key in ['KPP_SRC']: + specific_key = '%s_%s' % (key, tracer_short_name) + if specific_key not in full_diag_dict[tracer_short_name]['diags'].keys(): + full_diag_dict[tracer_short_name]['diags'][specific_key] = value + + # (b) Loop through ['diags'] dictionary and write all diagnostics to file + fout.write("#\n# Diagnostics for tracer %s\n#\n" % tracer_short_name) + for diag in full_diag_dict[tracer_short_name]['diags'].keys(): + per_tracer_dict = full_diag_dict[tracer_short_name]['diags'] + if per_tracer_dict[diag] != 'none': + fout.write("%s : %s\n" % (diag, per_tracer_dict[diag])) + + # 6. Add section header for MARBL diagnostics + # (Another tool appends MARBL diagnostics to this file) + fout.write("#\n########################################\n") + fout.write("# MARBL-generated diagnostics #\n") + fout.write("########################################\n#\n") diff --git a/cime_config/buildnml b/cime_config/buildnml index 9fb1dace..865059b3 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -119,11 +119,39 @@ def prep_input(case): diag_table = FType_diag_table.from_json(diag_table_src) if use_MARBL: # 7b. add ocean BGC diagnostics to diag_table - # TODO: instead of copying json file here, python script should generate it based on ecosys_diagnostics - diag_table_src = os.path.join(json_templates_dir, "diag_table_MARBL.json") + srcmods_version = os.path.join(SourceMods_dir, "diag_table_MARBL.json") diag_table_buildconf = os.path.join(momconfdir, "diag_table_MARBL.json") - shutil.copy(diag_table_src, diag_table_buildconf) - diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) + if os.path.isfile(srcmods_version): + shutil.copy(srcmods_version, diag_table_buildconf) + else: + srcmods_version = os.path.join(SourceMods_dir, "ecosys_diagnostics") + ecosys_diags_buildconf = os.path.join(momconfdir, "ecosys_diagnostics") + if os.path.isfile(srcmods_version): + shutil.copy(srcmods_version, ecosys_diags_buildconf) + else: + # Create ecosys_diagnostics with MOM-generated diagnostics + from MOM_ecosys_diagnostics import write_ecosys_diagnostics_file + from MARBL_diagnostics import MARBL_diagnostics_for_MOM + write_ecosys_diagnostics_file(MARBL_settings.get_tracer_names(), + MARBL_settings.get_autotroph_names(), + MARBL_settings.get_zooplankton_names(), + MARBL_settings.get_autotroph_names(calcifier_only=True), + MARBL_settings.ladjust_bury_coeff(), + ecosys_diags_buildconf, + ) + # (iii) Generate MARBL_diagnostics_for_MOM object + MARBL_diagnostics = MARBL_diagnostics_for_MOM(MARBL_dir, caseroot, MARBL_settings) + + # (iv) append MARBL diagnostics to ecosys_diagnostics + # This also creates marbl_diagnostics_list to help track which + # diagnostics came from MARBL + MARBL_diagnostics.write_diagnostics_file(ecosys_diags_buildconf, + os.path.join(momconfdir, "marbl_diagnostics_list"), + append=True) + # TODO: instead of copying json file here, python script should generate it based on ecosys_diagnostics + diag_table_src = os.path.join(json_templates_dir, "diag_table_MARBL.json") + shutil.copy(diag_table_src, diag_table_buildconf) + diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) diag_table.write(diag_table_dest, case) def init_MOM_override(rundir): From 722e10e3afed8212bfe728f3552fc9b8eccfc0d4 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 4 Feb 2021 12:23:46 -0700 Subject: [PATCH 024/136] Refactor buildnml Create generate_diag_table_MARBL() function since there are a few steps in the process --- cime_config/buildnml | 85 ++++++++++++++++++++++++++------------------ 1 file changed, 51 insertions(+), 34 deletions(-) diff --git a/cime_config/buildnml b/cime_config/buildnml index 865059b3..55c3c114 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -117,42 +117,14 @@ def prep_input(case): diag_table_src = os.path.join(json_templates_dir, "diag_table.json") diag_table_dest = os.path.join(rundir,"diag_table") diag_table = FType_diag_table.from_json(diag_table_src) + diag_table.write(diag_table_dest, case) if use_MARBL: - # 7b. add ocean BGC diagnostics to diag_table - srcmods_version = os.path.join(SourceMods_dir, "diag_table_MARBL.json") + generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir) + # TODO: instead of copying json file here, python script should generate it based on ecosys_diagnostics + diag_table_src = os.path.join(json_templates_dir, "diag_table_MARBL.json") diag_table_buildconf = os.path.join(momconfdir, "diag_table_MARBL.json") - if os.path.isfile(srcmods_version): - shutil.copy(srcmods_version, diag_table_buildconf) - else: - srcmods_version = os.path.join(SourceMods_dir, "ecosys_diagnostics") - ecosys_diags_buildconf = os.path.join(momconfdir, "ecosys_diagnostics") - if os.path.isfile(srcmods_version): - shutil.copy(srcmods_version, ecosys_diags_buildconf) - else: - # Create ecosys_diagnostics with MOM-generated diagnostics - from MOM_ecosys_diagnostics import write_ecosys_diagnostics_file - from MARBL_diagnostics import MARBL_diagnostics_for_MOM - write_ecosys_diagnostics_file(MARBL_settings.get_tracer_names(), - MARBL_settings.get_autotroph_names(), - MARBL_settings.get_zooplankton_names(), - MARBL_settings.get_autotroph_names(calcifier_only=True), - MARBL_settings.ladjust_bury_coeff(), - ecosys_diags_buildconf, - ) - # (iii) Generate MARBL_diagnostics_for_MOM object - MARBL_diagnostics = MARBL_diagnostics_for_MOM(MARBL_dir, caseroot, MARBL_settings) - - # (iv) append MARBL diagnostics to ecosys_diagnostics - # This also creates marbl_diagnostics_list to help track which - # diagnostics came from MARBL - MARBL_diagnostics.write_diagnostics_file(ecosys_diags_buildconf, - os.path.join(momconfdir, "marbl_diagnostics_list"), - append=True) - # TODO: instead of copying json file here, python script should generate it based on ecosys_diagnostics - diag_table_src = os.path.join(json_templates_dir, "diag_table_MARBL.json") - shutil.copy(diag_table_src, diag_table_buildconf) - diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) - diag_table.write(diag_table_dest, case) + shutil.copy(diag_table_src, diag_table_buildconf) + diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) def init_MOM_override(rundir): # Create an empty MOM_override: @@ -197,6 +169,50 @@ def _copy_files_to_momconf(case): for filename in ["MOM_input", "MOM_override", "diag_table", "input.nml"]: shutil.copy(os.path.join(rundir,filename), momconfdir) +def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir): + """ + Create Buildconf/momconf/diag_table_MARBL.json + + If diag_table_MARBL.json is in SourceMods, use that version. + Otherwise, generate it from Buildconf/momconf/ecosys_diagnostics + (If ecosys_diagnostics is in SourceMods, use that version instead.) + """ + # 1. Check for diag_table_MARBL.json in SourceMods + srcmods_version = os.path.join(SourceMods_dir, "diag_table_MARBL.json") + diag_table_buildconf = os.path.join(momconfdir, "diag_table_MARBL.json") + if os.path.isfile(srcmods_version): + shutil.copy(srcmods_version, diag_table_buildconf) + return + + # 2. Create ecosys_diagnostics + # (a) Check for file in SourceMods, otherwise generate it + srcmods_version = os.path.join(SourceMods_dir, "ecosys_diagnostics") + ecosys_diags_buildconf = os.path.join(momconfdir, "ecosys_diagnostics") + if os.path.isfile(srcmods_version): + shutil.copy(srcmods_version, ecosys_diags_buildconf) + else: + # Create ecosys_diagnostics with MOM-generated diagnostics + from MOM_ecosys_diagnostics import write_ecosys_diagnostics_file + from MARBL_diagnostics import MARBL_diagnostics_for_MOM + write_ecosys_diagnostics_file(MARBL_settings.get_tracer_names(), + MARBL_settings.get_autotroph_names(), + MARBL_settings.get_zooplankton_names(), + MARBL_settings.get_autotroph_names(calcifier_only=True), + MARBL_settings.ladjust_bury_coeff(), + ecosys_diags_buildconf, + ) + # (iii) Generate MARBL_diagnostics_for_MOM object + MARBL_diagnostics = MARBL_diagnostics_for_MOM(MARBL_dir, caseroot, MARBL_settings) + + # (iv) append MARBL diagnostics to ecosys_diagnostics + # This also creates marbl_diagnostics_list to help track which + # diagnostics came from MARBL + MARBL_diagnostics.write_diagnostics_file(ecosys_diags_buildconf, + os.path.join(momconfdir, "marbl_diagnostics_list"), + append=True) + + # 3. Generate diag_table based on contents of ecosys_diagnostics + # pylint: disable=too-many-arguments,too-many-locals,too-many-branches,too-many-statements ############################################################################### @@ -217,6 +233,7 @@ def buildnml(case, caseroot, compname): return ############################################################################### + def _main_func(): caseroot = parse_input(sys.argv) From f961ffebe7a0d00b702d748d88a04ee451fb7ab8 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 5 Feb 2021 16:19:45 -0700 Subject: [PATCH 025/136] Updates to ecosys_diagnostics generation buildnml still relies on param_templates/json/diag_table_MARBL.json but the switch to ecosys_diagnostics should provide as many useful parameters as possible when it is made. --- .../MARBL_scripts/MOM_ecosys_diagnostics.py | 342 +++++++++--------- cime_config/buildnml | 2 +- 2 files changed, 174 insertions(+), 170 deletions(-) diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py index bc7581bb..8c3aa3c5 100644 --- a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -50,11 +50,11 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li fout.write("MARBL_rmean_glo_scalar_POP_bury_coeff : medium_average\n") fout.write("MARBL_rmean_glo_scalar_bSi_bury_coeff : medium_average\n") - # 1. Create dictionary with default tracer output for all tracers - # - This dictionary also stores some tracer properties (currently just for budget-specific diagnostics) # NOTE: using OrderedDict to maintain alphabetical listing of tracers in keys() from collections import OrderedDict full_diag_dict = OrderedDict() + # 1. Create dictionary with default tracer output for all tracers + # - This dictionary also stores some tracer properties (currently just for budget-specific diagnostics) for tracer_short_name in sorted(active_tracers): per_tracer_dict = dict() @@ -73,170 +73,172 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li per_tracer_dict['diags'][tracer_short_name] = 'medium_average' per_tracer_dict['diags']['STF_%s' % tracer_short_name] = 'never_average' per_tracer_dict['diags']['J_%s' % tracer_short_name] = 'never_average' - per_tracer_dict['diags']['Jint_100m_%s' % tracer_short_name] = 'never_average' - per_tracer_dict['diags']['Jint_%s' % tracer_short_name] = 'never_average' - per_tracer_dict['diags']['%s_zint_100m' % tracer_short_name] = 'never_average' - per_tracer_dict['diags']['tend_zint_100m_%s' % tracer_short_name] = 'never_average' + # per_tracer_dict['diags']['Jint_100m_%s' % tracer_short_name] = 'never_average' + # per_tracer_dict['diags']['Jint_%s' % tracer_short_name] = 'never_average' + # per_tracer_dict['diags']['%s_zint_100m' % tracer_short_name] = 'never_average' + # per_tracer_dict['diags']['tend_zint_100m_%s' % tracer_short_name] = 'never_average' # Some diagnostics are not defined for all tracers; diagnostics with 'none' # are not added to diagnostics file and will not show up in tavg_contents # TODO: can MOM6 compute these values? - per_tracer_dict['diags']['%s_RIV_FLUX' % tracer_short_name] = 'none' - per_tracer_dict['diags']['FvPER_%s' % tracer_short_name] = 'none' - per_tracer_dict['diags']['FvICE_%s' % tracer_short_name] = 'none' + if False: + per_tracer_dict['diags']['%s_RIV_FLUX' % tracer_short_name] = 'none' + per_tracer_dict['diags']['FvPER_%s' % tracer_short_name] = 'none' + per_tracer_dict['diags']['FvICE_%s' % tracer_short_name] = 'none' full_diag_dict[tracer_short_name] = dict(per_tracer_dict) # TODO: turn on some of these diagnostics once they are properly defined - if False: - # 2. Update dictionary for tracers that don't just rely on default diagnostic output - # This is organized per-tracer, and specific blocks are ignored if MARBL is not - # configured to run with that particular tracer - # PO4 - if 'PO4' in full_diag_dict.keys(): - full_diag_dict['PO4']['diags']['PO4_RIV_FLUX'] = 'medium_average' - full_diag_dict['PO4']['diags']['J_PO4'] = 'low_average' - full_diag_dict['PO4']['diags']['Jint_100m_PO4'] = 'medium_average' - full_diag_dict['PO4']['diags']['tend_zint_100m_PO4'] = 'medium_average' - full_diag_dict['PO4']['properties']['has surface flux'] = True - # NO3 - if 'NO3' in full_diag_dict.keys(): - full_diag_dict['NO3']['diags']['NO3_RIV_FLUX'] = 'medium_average' - full_diag_dict['NO3']['diags']['J_NO3'] = 'low_average' - full_diag_dict['NO3']['diags']['Jint_100m_NO3'] = 'medium_average' - full_diag_dict['NO3']['diags']['tend_zint_100m_NO3'] = 'medium_average' - full_diag_dict['NO3']['properties']['has surface flux'] = True - # SiO3 - if 'SiO3' in full_diag_dict.keys(): - full_diag_dict['SiO3']['diags']['SiO3_RIV_FLUX'] = 'medium_average' - full_diag_dict['SiO3']['diags']['J_SiO3'] = 'low_average' - full_diag_dict['SiO3']['diags']['Jint_100m_SiO3'] = 'medium_average' - full_diag_dict['SiO3']['diags']['tend_zint_100m_SiO3'] = 'medium_average' - full_diag_dict['SiO3']['properties']['has surface flux'] = True - # NH4 - if 'NH4' in full_diag_dict.keys(): - full_diag_dict['NH4']['diags']['J_NH4'] = 'low_average' - full_diag_dict['NH4']['diags']['Jint_100m_NH4'] = 'medium_average' - full_diag_dict['NH4']['diags']['tend_zint_100m_NH4'] = 'medium_average' - full_diag_dict['NH4']['properties']['has surface flux'] = True - # Fe - if 'Fe' in full_diag_dict.keys(): - full_diag_dict['Fe']['diags']['Fe_RIV_FLUX'] = 'medium_average' - full_diag_dict['Fe']['diags']['J_Fe'] = 'low_average' - full_diag_dict['Fe']['diags']['Jint_100m_Fe'] = 'medium_average' - full_diag_dict['Fe']['diags']['tend_zint_100m_Fe'] = 'medium_average' - full_diag_dict['Fe']['properties']['include budget terms'] = True - full_diag_dict['Fe']['properties']['has surface flux'] = True - # Lig - if 'Lig' in full_diag_dict.keys(): - pass # Lig just uses default settings - # O2 - if 'O2' in full_diag_dict.keys(): - full_diag_dict['O2']['diags']['STF_O2'] = 'medium_average, high_average' - full_diag_dict['O2']['diags']['Jint_100m_O2'] = 'medium_average' - full_diag_dict['O2']['diags']['tend_zint_100m_O2'] = 'medium_average' - full_diag_dict['O2']['properties']['include budget terms'] = True - full_diag_dict['O2']['properties']['has surface flux'] = True - # DIC - if 'DIC' in full_diag_dict.keys(): - full_diag_dict['DIC']['diags']['DIC_RIV_FLUX'] = 'medium_average' - full_diag_dict['DIC']['diags']['J_DIC'] = 'medium_average' - full_diag_dict['DIC']['diags']['Jint_100m_DIC'] = 'medium_average' - full_diag_dict['DIC']['diags']['tend_zint_100m_DIC'] = 'medium_average' - full_diag_dict['DIC']['diags']['FvPER_DIC'] = 'medium_average' - full_diag_dict['DIC']['diags']['FvICE_DIC'] = 'medium_average' - full_diag_dict['DIC']['properties']['include budget terms'] = True - full_diag_dict['DIC']['properties']['has surface flux'] = True - # DIC_ALT_CO2 - if 'DIC_ALT_CO2' in full_diag_dict.keys(): - full_diag_dict['DIC_ALT_CO2']['diags']['DIC_ALT_CO2_RIV_FLUX'] = 'medium_average' - full_diag_dict['DIC_ALT_CO2']['diags']['J_DIC_ALT_CO2'] = 'medium_average' - full_diag_dict['DIC_ALT_CO2']['diags']['Jint_100m_DIC_ALT_CO2'] = 'medium_average' - full_diag_dict['DIC_ALT_CO2']['diags']['tend_zint_100m_DIC_ALT_CO2'] = 'medium_average' - full_diag_dict['DIC_ALT_CO2']['diags']['FvPER_DIC_ALT_CO2'] = 'medium_average' - full_diag_dict['DIC_ALT_CO2']['diags']['FvICE_DIC_ALT_CO2'] = 'medium_average' - full_diag_dict['DIC_ALT_CO2']['properties']['include budget terms'] = True - full_diag_dict['DIC_ALT_CO2']['properties']['has surface flux'] = True - # ALK - if 'ALK' in full_diag_dict.keys(): - full_diag_dict['ALK']['diags']['ALK_RIV_FLUX'] = 'medium_average' - full_diag_dict['ALK']['diags']['STF_ALK'] = 'medium_average' - full_diag_dict['ALK']['diags']['J_ALK'] = 'low_average' - full_diag_dict['ALK']['diags']['Jint_100m_ALK'] = 'medium_average' - full_diag_dict['ALK']['diags']['tend_zint_100m_ALK'] = 'medium_average' - full_diag_dict['ALK']['diags']['FvPER_ALK'] = 'medium_average' - full_diag_dict['ALK']['diags']['FvICE_ALK'] = 'medium_average' - full_diag_dict['ALK']['properties']['has surface flux'] = True - # ALK_ALT_CO2 - if 'ALK_ALT_CO2' in full_diag_dict.keys(): - full_diag_dict['ALK_ALT_CO2']['diags']['ALK_ALT_CO2_RIV_FLUX'] = 'medium_average' - full_diag_dict['ALK_ALT_CO2']['diags']['STF_ALK_ALT_CO2'] = 'medium_average' - full_diag_dict['ALK_ALT_CO2']['diags']['J_ALK_ALT_CO2'] = 'low_average' - full_diag_dict['ALK_ALT_CO2']['diags']['Jint_100m_ALK_ALT_CO2'] = 'medium_average' - full_diag_dict['ALK_ALT_CO2']['diags']['tend_zint_100m_ALK_ALT_CO2'] = 'medium_average' - full_diag_dict['ALK_ALT_CO2']['diags']['FvPER_ALK_ALT_CO2'] = 'medium_average' - full_diag_dict['ALK_ALT_CO2']['diags']['FvICE_ALK_ALT_CO2'] = 'medium_average' - full_diag_dict['ALK_ALT_CO2']['properties']['has surface flux'] = True - # DOC - if 'DOC' in full_diag_dict.keys(): - full_diag_dict['DOC']['diags']['DOC_RIV_FLUX'] = 'medium_average' - full_diag_dict['DOC']['diags']['Jint_100m_DOC'] = 'medium_average' - full_diag_dict['DOC']['diags']['tend_zint_100m_DOC'] = 'medium_average' - full_diag_dict['DOC']['properties']['include budget terms'] = True - full_diag_dict['DOC']['properties']['has surface flux'] = False # this should be True if EBM is off - # DON - if 'DON' in full_diag_dict.keys(): - full_diag_dict['DON']['diags']['DON_RIV_FLUX'] = 'medium_average' - full_diag_dict['DON']['properties']['has surface flux'] = True - # DOP - if 'DOP' in full_diag_dict.keys(): - full_diag_dict['DOP']['diags']['DOP_RIV_FLUX'] = 'medium_average' - full_diag_dict['DOP']['properties']['has surface flux'] = True - # DOPr - if 'DOPr' in full_diag_dict.keys(): - full_diag_dict['DOPr']['diags']['DOPr_RIV_FLUX'] = 'medium_average' - full_diag_dict['DOPr']['properties']['has surface flux'] = True - # DONr - if 'DONr' in full_diag_dict.keys(): - full_diag_dict['DONr']['diags']['DONr_RIV_FLUX'] = 'medium_average' - full_diag_dict['DONr']['properties']['has surface flux'] = True - # DOCr - if 'DOCr' in full_diag_dict.keys(): - full_diag_dict['DOCr']['diags']['DOCr_RIV_FLUX'] = 'medium_average' - full_diag_dict['DOCr']['diags']['Jint_100m_DOCr'] = 'medium_average' - full_diag_dict['DOCr']['diags']['tend_zint_100m_DOCr'] = 'medium_average' - full_diag_dict['DOCr']['properties']['include budget terms'] = True - full_diag_dict['DOCr']['properties']['has surface flux'] = False # this should be True if EBM is off - # DI13C - if 'DI13C' in full_diag_dict.keys(): - full_diag_dict['DI13C']['diags']['DI13C_RIV_FLUX'] = 'medium_average' - full_diag_dict['DI13C']['diags']['J_DI13C'] = 'medium_average' - full_diag_dict['DI13C']['diags']['Jint_100m_DI13C'] = 'medium_average' - full_diag_dict['DI13C']['diags']['tend_zint_100m_DI13C'] = 'medium_average' - full_diag_dict['DI13C']['diags']['FvPER_DI13C'] = 'medium_average' - full_diag_dict['DI13C']['diags']['FvICE_DI13C'] = 'medium_average' - full_diag_dict['DI13C']['properties']['has surface flux'] = True - # DO13Ctot - if 'DO13Ctot' in full_diag_dict.keys(): - full_diag_dict['DO13Ctot']['diags']['DO13Ctot_RIV_FLUX'] = 'medium_average' - full_diag_dict['DO13Ctot']['diags']['Jint_100m_DO13Ctot'] = 'medium_average' - full_diag_dict['DO13Ctot']['diags']['tend_zint_100m_DO13Ctot'] = 'medium_average' - full_diag_dict['DO13Ctot']['properties']['has surface flux'] = True - # DI14C - if 'DI14C' in full_diag_dict.keys(): - full_diag_dict['DI14C']['diags']['DI14C_RIV_FLUX'] = 'medium_average' - full_diag_dict['DI14C']['diags']['J_DI14C'] = 'medium_average' - full_diag_dict['DI14C']['diags']['Jint_100m_DI14C'] = 'medium_average' - full_diag_dict['DI14C']['diags']['tend_zint_100m_DI14C'] = 'medium_average' - full_diag_dict['DI14C']['diags']['FvPER_DI14C'] = 'medium_average' - full_diag_dict['DI14C']['diags']['FvICE_DI14C'] = 'medium_average' - full_diag_dict['DI14C']['properties']['has surface flux'] = True - # DO14Ctot - if 'DO14Ctot' in full_diag_dict.keys(): - full_diag_dict['DO14Ctot']['diags']['DO14Ctot_RIV_FLUX'] = 'medium_average' - full_diag_dict['DO14Ctot']['diags']['Jint_100m_DO14Ctot'] = 'medium_average' - full_diag_dict['DO14Ctot']['diags']['tend_zint_100m_DO14Ctot'] = 'medium_average' - full_diag_dict['DO14Ctot']['properties']['has surface flux'] = True + # 2. Update dictionary for tracers that don't just rely on default diagnostic output + # This is organized per-tracer, and specific blocks are ignored if MARBL is not + # configured to run with that particular tracer + # PO4 + if 'PO4' in full_diag_dict.keys(): + # full_diag_dict['PO4']['diags']['PO4_RIV_FLUX'] = 'medium_average' + full_diag_dict['PO4']['diags']['J_PO4'] = 'low_average' + # full_diag_dict['PO4']['diags']['Jint_100m_PO4'] = 'medium_average' + # full_diag_dict['PO4']['diags']['tend_zint_100m_PO4'] = 'medium_average' + full_diag_dict['PO4']['properties']['has surface flux'] = True + # NO3 + if 'NO3' in full_diag_dict.keys(): + # full_diag_dict['NO3']['diags']['NO3_RIV_FLUX'] = 'medium_average' + full_diag_dict['NO3']['diags']['J_NO3'] = 'low_average' + # full_diag_dict['NO3']['diags']['Jint_100m_NO3'] = 'medium_average' + # full_diag_dict['NO3']['diags']['tend_zint_100m_NO3'] = 'medium_average' + full_diag_dict['NO3']['properties']['has surface flux'] = True + # SiO3 + if 'SiO3' in full_diag_dict.keys(): + # full_diag_dict['SiO3']['diags']['SiO3_RIV_FLUX'] = 'medium_average' + full_diag_dict['SiO3']['diags']['J_SiO3'] = 'low_average' + # full_diag_dict['SiO3']['diags']['Jint_100m_SiO3'] = 'medium_average' + # full_diag_dict['SiO3']['diags']['tend_zint_100m_SiO3'] = 'medium_average' + full_diag_dict['SiO3']['properties']['has surface flux'] = True + # NH4 + if 'NH4' in full_diag_dict.keys(): + full_diag_dict['NH4']['diags']['J_NH4'] = 'low_average' + # full_diag_dict['NH4']['diags']['Jint_100m_NH4'] = 'medium_average' + # full_diag_dict['NH4']['diags']['tend_zint_100m_NH4'] = 'medium_average' + full_diag_dict['NH4']['properties']['has surface flux'] = True + # Fe + if 'Fe' in full_diag_dict.keys(): + # full_diag_dict['Fe']['diags']['Fe_RIV_FLUX'] = 'medium_average' + full_diag_dict['Fe']['diags']['J_Fe'] = 'low_average' + # full_diag_dict['Fe']['diags']['Jint_100m_Fe'] = 'medium_average' + # full_diag_dict['Fe']['diags']['tend_zint_100m_Fe'] = 'medium_average' + full_diag_dict['Fe']['properties']['include budget terms'] = True + full_diag_dict['Fe']['properties']['has surface flux'] = True + # Lig + if 'Lig' in full_diag_dict.keys(): + pass # Lig just uses default settings + # O2 + if 'O2' in full_diag_dict.keys(): + full_diag_dict['O2']['diags']['STF_O2'] = 'medium_average, high_average' + # full_diag_dict['O2']['diags']['Jint_100m_O2'] = 'medium_average' + # full_diag_dict['O2']['diags']['tend_zint_100m_O2'] = 'medium_average' + full_diag_dict['O2']['properties']['include budget terms'] = True + full_diag_dict['O2']['properties']['has surface flux'] = True + # DIC + if 'DIC' in full_diag_dict.keys(): + # full_diag_dict['DIC']['diags']['DIC_RIV_FLUX'] = 'medium_average' + full_diag_dict['DIC']['diags']['J_DIC'] = 'medium_average' + # full_diag_dict['DIC']['diags']['Jint_100m_DIC'] = 'medium_average' + # full_diag_dict['DIC']['diags']['tend_zint_100m_DIC'] = 'medium_average' + # full_diag_dict['DIC']['diags']['FvPER_DIC'] = 'medium_average' + # full_diag_dict['DIC']['diags']['FvICE_DIC'] = 'medium_average' + full_diag_dict['DIC']['properties']['include budget terms'] = True + full_diag_dict['DIC']['properties']['has surface flux'] = True + # DIC_ALT_CO2 + if 'DIC_ALT_CO2' in full_diag_dict.keys(): + # full_diag_dict['DIC_ALT_CO2']['diags']['DIC_ALT_CO2_RIV_FLUX'] = 'medium_average' + full_diag_dict['DIC_ALT_CO2']['diags']['J_DIC_ALT_CO2'] = 'medium_average' + # full_diag_dict['DIC_ALT_CO2']['diags']['Jint_100m_DIC_ALT_CO2'] = 'medium_average' + # full_diag_dict['DIC_ALT_CO2']['diags']['tend_zint_100m_DIC_ALT_CO2'] = 'medium_average' + # full_diag_dict['DIC_ALT_CO2']['diags']['FvPER_DIC_ALT_CO2'] = 'medium_average' + # full_diag_dict['DIC_ALT_CO2']['diags']['FvICE_DIC_ALT_CO2'] = 'medium_average' + full_diag_dict['DIC_ALT_CO2']['properties']['include budget terms'] = True + full_diag_dict['DIC_ALT_CO2']['properties']['has surface flux'] = True + # ALK + if 'ALK' in full_diag_dict.keys(): + # full_diag_dict['ALK']['diags']['ALK_RIV_FLUX'] = 'medium_average' + full_diag_dict['ALK']['diags']['STF_ALK'] = 'medium_average' + full_diag_dict['ALK']['diags']['J_ALK'] = 'low_average' + # full_diag_dict['ALK']['diags']['Jint_100m_ALK'] = 'medium_average' + # full_diag_dict['ALK']['diags']['tend_zint_100m_ALK'] = 'medium_average' + # full_diag_dict['ALK']['diags']['FvPER_ALK'] = 'medium_average' + # full_diag_dict['ALK']['diags']['FvICE_ALK'] = 'medium_average' + full_diag_dict['ALK']['properties']['has surface flux'] = True + # ALK_ALT_CO2 + if 'ALK_ALT_CO2' in full_diag_dict.keys(): + # full_diag_dict['ALK_ALT_CO2']['diags']['ALK_ALT_CO2_RIV_FLUX'] = 'medium_average' + full_diag_dict['ALK_ALT_CO2']['diags']['STF_ALK_ALT_CO2'] = 'medium_average' + full_diag_dict['ALK_ALT_CO2']['diags']['J_ALK_ALT_CO2'] = 'low_average' + # full_diag_dict['ALK_ALT_CO2']['diags']['Jint_100m_ALK_ALT_CO2'] = 'medium_average' + # full_diag_dict['ALK_ALT_CO2']['diags']['tend_zint_100m_ALK_ALT_CO2'] = 'medium_average' + # full_diag_dict['ALK_ALT_CO2']['diags']['FvPER_ALK_ALT_CO2'] = 'medium_average' + # full_diag_dict['ALK_ALT_CO2']['diags']['FvICE_ALK_ALT_CO2'] = 'medium_average' + full_diag_dict['ALK_ALT_CO2']['properties']['has surface flux'] = True + # DOC + if 'DOC' in full_diag_dict.keys(): + # full_diag_dict['DOC']['diags']['DOC_RIV_FLUX'] = 'medium_average' + # full_diag_dict['DOC']['diags']['Jint_100m_DOC'] = 'medium_average' + # full_diag_dict['DOC']['diags']['tend_zint_100m_DOC'] = 'medium_average' + full_diag_dict['DOC']['properties']['include budget terms'] = True + full_diag_dict['DOC']['properties']['has surface flux'] = False # this should be True if EBM is off + # DON + if 'DON' in full_diag_dict.keys(): + # full_diag_dict['DON']['diags']['DON_RIV_FLUX'] = 'medium_average' + full_diag_dict['DON']['properties']['has surface flux'] = True + # DOP + if 'DOP' in full_diag_dict.keys(): + # full_diag_dict['DOP']['diags']['DOP_RIV_FLUX'] = 'medium_average' + full_diag_dict['DOP']['properties']['has surface flux'] = True + # DOPr + if 'DOPr' in full_diag_dict.keys(): + # full_diag_dict['DOPr']['diags']['DOPr_RIV_FLUX'] = 'medium_average' + full_diag_dict['DOPr']['properties']['has surface flux'] = True + # DONr + if 'DONr' in full_diag_dict.keys(): + # full_diag_dict['DONr']['diags']['DONr_RIV_FLUX'] = 'medium_average' + full_diag_dict['DONr']['properties']['has surface flux'] = True + # DOCr + if 'DOCr' in full_diag_dict.keys(): + # full_diag_dict['DOCr']['diags']['DOCr_RIV_FLUX'] = 'medium_average' + # full_diag_dict['DOCr']['diags']['Jint_100m_DOCr'] = 'medium_average' + # full_diag_dict['DOCr']['diags']['tend_zint_100m_DOCr'] = 'medium_average' + full_diag_dict['DOCr']['properties']['include budget terms'] = True + full_diag_dict['DOCr']['properties']['has surface flux'] = False # this should be True if EBM is off + # DI13C + if 'DI13C' in full_diag_dict.keys(): + # full_diag_dict['DI13C']['diags']['DI13C_RIV_FLUX'] = 'medium_average' + full_diag_dict['DI13C']['diags']['J_DI13C'] = 'medium_average' + # full_diag_dict['DI13C']['diags']['Jint_100m_DI13C'] = 'medium_average' + # full_diag_dict['DI13C']['diags']['tend_zint_100m_DI13C'] = 'medium_average' + # full_diag_dict['DI13C']['diags']['FvPER_DI13C'] = 'medium_average' + # full_diag_dict['DI13C']['diags']['FvICE_DI13C'] = 'medium_average' + full_diag_dict['DI13C']['properties']['has surface flux'] = True + # DO13Ctot + if 'DO13Ctot' in full_diag_dict.keys(): + # full_diag_dict['DO13Ctot']['diags']['DO13Ctot_RIV_FLUX'] = 'medium_average' + # full_diag_dict['DO13Ctot']['diags']['Jint_100m_DO13Ctot'] = 'medium_average' + # full_diag_dict['DO13Ctot']['diags']['tend_zint_100m_DO13Ctot'] = 'medium_average' + full_diag_dict['DO13Ctot']['properties']['has surface flux'] = True + # DI14C + if 'DI14C' in full_diag_dict.keys(): + # full_diag_dict['DI14C']['diags']['DI14C_RIV_FLUX'] = 'medium_average' + full_diag_dict['DI14C']['diags']['J_DI14C'] = 'medium_average' + # full_diag_dict['DI14C']['diags']['Jint_100m_DI14C'] = 'medium_average' + # full_diag_dict['DI14C']['diags']['tend_zint_100m_DI14C'] = 'medium_average' + # full_diag_dict['DI14C']['diags']['FvPER_DI14C'] = 'medium_average' + # full_diag_dict['DI14C']['diags']['FvICE_DI14C'] = 'medium_average' + full_diag_dict['DI14C']['properties']['has surface flux'] = True + # DO14Ctot + if 'DO14Ctot' in full_diag_dict.keys(): + # full_diag_dict['DO14Ctot']['diags']['DO14Ctot_RIV_FLUX'] = 'medium_average' + # full_diag_dict['DO14Ctot']['diags']['Jint_100m_DO14Ctot'] = 'medium_average' + # full_diag_dict['DO14Ctot']['diags']['tend_zint_100m_DO14Ctot'] = 'medium_average' + full_diag_dict['DO14Ctot']['properties']['has surface flux'] = True + # TODO: reinstate these once we have vertical integral function + if False: # 3. Per-autotroph diagnostics for autotroph_name in autotroph_list: tracer_short_name = autotroph_name+'C' @@ -265,19 +267,21 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li else: value = "never_average" - # TODO: more variables to figure out MOM6 equivalent of - if False: - # These diagnostics should be included by default for tracers requested budget terms - for key in ['UE', 'VN', 'WT', 'DIA_IMPVF', 'HDIFE', 'HDIFN', 'HDIFB', 'TEND', 'RF_TEND']: - specific_key = '%s_%s' % (key, tracer_short_name) - if specific_key not in full_diag_dict[tracer_short_name]['diags'].keys(): - full_diag_dict[tracer_short_name]['diags'][specific_key] = value + # These diagnostics should be included by default for tracers requested budget terms + # TODO: need MOM6 equivalent of the following POP variables: + # ['DIA_IMPVF', 'HDIFE', 'HDIFN', 'HDIFB', 'RF_TEND'] + for key in ['adx', 'ady', 'tendency_vert_remap', 'tendency']: + specific_key = '%s_%s' % (tracer_short_name, key) + if specific_key not in full_diag_dict[tracer_short_name]['diags'].keys(): + full_diag_dict[tracer_short_name]['diags'][specific_key] = value - if per_tracer_properties['include budget terms'] and per_tracer_properties['has surface flux']: - value = "low_average" - else: - value = "never_average" + if per_tracer_properties['include budget terms'] and per_tracer_properties['has surface flux']: + value = "low_average" + else: + value = "never_average" + # TODO: more variables to figure out MOM6 equivalent of + if False: # These diagnostics should be included by default for tracers requested budget terms # ONLY for tracers that have non-zero surface fluxes for key in ['KPP_SRC']: diff --git a/cime_config/buildnml b/cime_config/buildnml index 55c3c114..2a7a57d9 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -117,7 +117,6 @@ def prep_input(case): diag_table_src = os.path.join(json_templates_dir, "diag_table.json") diag_table_dest = os.path.join(rundir,"diag_table") diag_table = FType_diag_table.from_json(diag_table_src) - diag_table.write(diag_table_dest, case) if use_MARBL: generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir) # TODO: instead of copying json file here, python script should generate it based on ecosys_diagnostics @@ -125,6 +124,7 @@ def prep_input(case): diag_table_buildconf = os.path.join(momconfdir, "diag_table_MARBL.json") shutil.copy(diag_table_src, diag_table_buildconf) diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) + diag_table.write(diag_table_dest, case) def init_MOM_override(rundir): # Create an empty MOM_override: From 877688cda2839588156769bf03609d2c5ac687ad Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 5 Feb 2021 16:20:55 -0700 Subject: [PATCH 026/136] Add STF_O2 and J_DIC to output lists Adding these two fields to test the STF_ and J_ fields in the MOM6 source code; if they look okay, next step will be to convert ecosys_diagnostics to json --- param_templates/json/diag_table_MARBL.json | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/param_templates/json/diag_table_MARBL.json b/param_templates/json/diag_table_MARBL.json index 27afd6b4..147ef148 100644 --- a/param_templates/json/diag_table_MARBL.json +++ b/param_templates/json/diag_table_MARBL.json @@ -41,7 +41,9 @@ "IRON_FLUX", "NOx_FLUX", "NHy_FLUX", - "FESEDFLUX" + "FESEDFLUX", + "STF_O2", + "J_DIC" ], [ "volcello", @@ -118,7 +120,9 @@ "IRON_FLUX", "NOx_FLUX", "NHy_FLUX", - "FESEDFLUX" + "FESEDFLUX", + "STF_O2", + "J_DIC" ], [ "volcello", From 304797aceb31afc885f04156d27238e44b51fe0d Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 9 Feb 2021 12:34:10 -0700 Subject: [PATCH 027/136] diag_table_MARBL.json from ecosys_diagnostics First pass at turning the ecosys_diagnostics file MARBL generates into something MOM can turn into diag_table entries. It hard codes the following: 1. double precision output (should be easy to make this an option) 2. variables on native grid (not sure about interpolating to z-space) 3. Names for streams (all have hm_bgc_...) Also, while buildnml supports the idea of logical variables to control ALT_CO2 output and to say "all MARBL variables should be output". The former is useful for avoiding duplicate output when the ALT_CO2 tracers are identical to the regular tracers, and the latter is useful for testing, so I do plan on incorporating them soon. NOTE: this update requires modifying FMS: * increase max_num_axis_sets by 3 (from 25 to 28) * increase max_output_fields by 300 (from 300 to 600) the latter will need to be even bigger once lMARBL_output_all option exists --- .../MARBL_scripts/MARBL_diagnostics.py | 6 +- .../MARBL_diags_to_diag_table.py | 291 ++++++++++++++++++ cime_config/buildnml | 16 +- param_templates/json/diag_table_MARBL.json | 142 --------- 4 files changed, 298 insertions(+), 157 deletions(-) create mode 100755 cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py delete mode 100644 param_templates/json/diag_table_MARBL.json diff --git a/cime_config/MARBL_scripts/MARBL_diagnostics.py b/cime_config/MARBL_scripts/MARBL_diagnostics.py index 404a35f5..e5285348 100644 --- a/cime_config/MARBL_scripts/MARBL_diagnostics.py +++ b/cime_config/MARBL_scripts/MARBL_diagnostics.py @@ -48,14 +48,10 @@ def __init__(self, MARBL_dir, caseroot, MARBL_settings): # PUBLIC CLASS METHODS # ################################################################################ - def write_diagnostics_file(self, diagnostics_file_out, diagnostics_list_out, append): + def write_diagnostics_file(self, diagnostics_file_out, append): """ Add all MARBL diagnostics to file containing MOM diagnostics Also create a list of diagnostics generated by MOM """ from MARBL_tools import generate_diagnostics_file generate_diagnostics_file(self._MARBL_diagnostics, diagnostics_file_out, append) - fout = open(diagnostics_list_out,"w") - for key in self._MARBL_diagnostics.diagnostics_dict.keys(): - fout.write(key+"\n") - diff --git a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py new file mode 100755 index 00000000..f8cb5440 --- /dev/null +++ b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py @@ -0,0 +1,291 @@ +#!/usr/bin/env python + +""" Convert MARBL diagnostics file to diag_table_MARBL.json + +MARBL diagnostics file is a file containing a list of + + DIAGNOSTIC_NAME : frequency_operator[,frequency2frequency2_operator2, ..., frequencyN_operatorN] + +MOM uses this same format for defining its ecosystem-based diagnostics to allow +users to change the requested MOM and MARBL diagnostics in the same place. + +usage: MARBL_diags_to_diag_table.py [-h] -i ECOSYS_DIAGNOSTICS_IN -t + DIAG_TABLE_OUT [-l LOW_FREQUENCY_STREAM] + [-m MEDIUM_FREQUENCY_STREAM] + [-g HIGH_FREQUENCY_STREAM] + [--lMARBL_output_all LMARBL_OUTPUT_ALL] + [--lMARBL_output_alt_co2 LMARBL_OUTPUT_ALT_CO2] + +Generate MOM diag table from MARBL diagnostics + +optional arguments: + -h, --help show this help message and exit + -i ECOSYS_DIAGNOSTICS_IN, --ecosys_diagnostics_in ECOSYS_DIAGNOSTICS_IN + File generated by MARBL_generate_diagnostics_file + (default: None) + -t DIAG_TABLE_OUT, --diag_table_out DIAG_TABLE_OUT + Location of diag table (JSON) file to create (default: + None) + -l LOW_FREQUENCY_STREAM, --low_frequency_stream LOW_FREQUENCY_STREAM + Stream to put low frequency output into (required if + not lMARBL_output_all) (default: 0) + -m MEDIUM_FREQUENCY_STREAM, --medium_frequency_stream MEDIUM_FREQUENCY_STREAM + Stream to put medium frequency output into (required + if not lMARBL_output_all) (default: 0) + -g HIGH_FREQUENCY_STREAM, --high_frequency_stream HIGH_FREQUENCY_STREAM + Stream to put high frequency output into (required if + not lMARBL_output_all) (default: 0) + --lMARBL_output_all LMARBL_OUTPUT_ALL + Put all MARBL diagnostics in hm_bgc stream (default: + False) + --lMARBL_output_alt_co2 LMARBL_OUTPUT_ALT_CO2 + Include ALT_CO2 diagnostics in streams (default: + False) +""" + +####################################### + +class DiagTableClass(object): + """ + Class that is used to generate JSON file to extend diag_table from ecosys_diagnostics file + """ + def __init__(self): + """ + Constructor: creates a dictionary object to eventually dump to JSON + """ + # TODO: other streams change names in spinup mode, so I kept that practice here. However, + # I don't like how I handle the names... namely, hm_bgc_annual in spinup mode has + # completely different variables from hm_bgc_annual in "regular" mode + self._diag_table_dict = dict() + + # "medium" frequency should be treated like "hm" stream -- annual in spinup runs, monthly otherwise + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual%4yr", "else": "hm_bgc_monthly%4yr-%2mo"} + output_freq_units_dict = {'$OCN_DIAG_MODE == "spinup"': "years", "$TEST == True": "days", "else": "months"} + self._diag_table_dict["medium"] = self._dict_template(suffix_dict, output_freq_units_dict) + + # "high" frequency should be treated like "sfc" stream -- 5-day averages in spinup, daily otherwise + # unlike "sfc", this stream will write one file per month instead of per year (except in spinup) + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_daily5%4yr", "else": "hm_bgc_daily%4yr-%2mo"} + output_freq_dict = {'$OCN_DIAG_MODE == "spinup"': 5, "else": 1} + new_file_freq_units_dict = {'$OCN_DIAG_MODE == "spinup"': "years", "else": "months"} + self._diag_table_dict["high"] = self._dict_template(suffix_dict, "days", new_file_freq_units_dict, output_freq_dict) + + # "low" frequency should be treated as annual averages + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual2%4yr", "else": "hm_bgc_annual%4yr"} + self._diag_table_dict["low"] = self._dict_template(suffix_dict, "years") + + + def update(self, varname, frequency, lMARBL_output_all): + if lMARBL_output_all: + use_freq = ['medium'] + else: + use_freq = [] + for freq in frequency: + use_freq.append(freq) + + # iv. Update dictionary + for freq in use_freq: + if freq == "never": + continue + self._diag_table_dict[freq]["fields"][0]["lists"][0].append(varname) + + + def dump_to_json(self, filename): + import json + + out_dict = dict() + out_dict["Files"] = dict() + transports = ["volcello", "vmo", "vhGM", "vhml", "umo", "uhGM", "uhml"] + for freq in self._diag_table_dict: + if len(self._diag_table_dict[freq]["fields"][0]["lists"][0]) > 0: + out_dict["Files"][freq] = self._diag_table_dict[freq].copy() + out_dict["Files"][freq]["fields"][0]["lists"].append(transports) + if out_dict["Files"]: + with open(filename, "w") as fp: + json.dump(out_dict, fp, separators=(',', ': '), sort_keys=False, indent=3) + else: + print("WARNING: no JSON file written as no variables were requested") + + + def _dict_template(self, suffix, output_freq_units, new_file_freq_units=None, output_freq=1, new_file_freq=1, packing=1): + """ + Return the basic template for MOM6 diag_table dictionary. + Variables will be added to output file by appending to template["fields"][0]["lists"][0] + + Parameters: + * suffix: string used to identify output file; could also be a dictionary + where keys are logical evaluations + * output_freq_units: units used to determine how often to output; similar + to suffix, this can also be a dictionary + * new_file_freq_units: units used to determine how often to generate new stream + files; if None, will use output_freq_units (default: None) + * output_freq: how frequently to output (default: 1) + * new_file_freq: how frequently to create new files (default: 1) + * packing: integer that is used to determine precision when writing output + (default: 1 => double precision output) + """ + template = dict() + template["suffix"] = suffix + template["output_freq"] = output_freq + template["new_file_freq"] = new_file_freq + template["output_freq_units"] = output_freq_units + if new_file_freq_units: + template["new_file_freq_units"] = new_file_freq_units + else: + template["new_file_freq_units"] = output_freq_units + template["time_axis_units"] = "days" + template["reduction_method"] = "mean" + template["regional_section"] = "none" + template["fields"] = [{"module": "ocean_model", "packing": packing, "lists" : [[]]}] + return template + + +####################################### + +def _parse_args(): + """ Parse command line arguments + """ + + import argparse + + parser = argparse.ArgumentParser(description="Generate MOM diag table from MARBL diagnostics", + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + + # Command line argument to point to MARBL diagnostics input file (required!) + parser.add_argument('-i', '--ecosys_diagnostics_in', action='store', dest='ecosys_diagnostics_in', + required=True, help='File generated by MARBL_generate_diagnostics_file') + + # Command line argument to point to diag table output file (required!) + parser.add_argument('-t', '--diag_table_out', action='store', dest='diag_table_out', + required=True, help='Location of diag table (JSON) file to create') + + # Command line arguments for the different streams to use (low, medium, high) + parser.add_argument('-l', '--low_frequency_stream', action='store', dest='low_frequency_stream', + type=int, default= 0, help='Stream to put low frequency output into (required if not lMARBL_output_all)') + + parser.add_argument('-m', '--medium_frequency_stream', action='store', dest='medium_frequency_stream', + type=int, default= 0, help='Stream to put medium frequency output into (required if not lMARBL_output_all)') + + parser.add_argument('-g', '--high_frequency_stream', action='store', dest='high_frequency_stream', + type=int, default= 0, help='Stream to put high frequency output into (required if not lMARBL_output_all)') + + # Should all MARBL diagnostics be included in the hm_bgc stream? + parser.add_argument('--lMARBL_output_all', action='store', dest='lMARBL_output_all', + type=bool, default=False, help="Put all MARBL diagnostics in hm_bgc stream") + + # Should MARBL's ALT_CO2 diagnostics be included in the diag table? + parser.add_argument('--lMARBL_output_alt_co2', action='store', dest='lMARBL_output_alt_co2', + type=bool, default=False, help="Include ALT_CO2 diagnostics in streams") + + return parser.parse_args() + +####################################### + +def _parse_line(line_in): + """ Take a line of input from the MARBL diagnostic output and return the variable + name, frequency, and operator. Lines that are commented out or empty should + return None for all three; non-empty lines that are not in the proper format + should trigger errors. + + If they are not None, frequency and operator are always returned as lists + (although they often have just one element). + """ + import logging + import sys + + line_loc = line_in.split('#')[0].strip() + # Return None, None if line is empty + if len(line_loc) == 0: + return None, None, None + + logger = logging.getLogger("__name__") + line_split = line_loc.split(':') + if len(line_split) != 2: + logger.error("Can not determine variable name from following line: '%s'" % line_in) + sys.exit(1) + + freq = [] + op = [] + for freq_op in line_split[1].split(','): + freq_op_split = freq_op.strip().split('_') + if len(freq_op_split) != 2: + logger.error("Can not determine frequency and operator from following entry: '%s'" % line_split[1]) + sys.exit(1) + freq.append(freq_op_split[0]) + op.append(freq_op_split[1]) + + return line_split[0].strip(), freq, op + +####################################### + + +def diagnostics_to_diag_table(ecosys_diagnostics_in, + diag_table_out, + lMARBL_output_all, + lMARBL_output_alt_co2): + """ + Build a diag_table dictionary to dump to JSON format + """ + + import os, sys, logging + logger = logging.getLogger("__name__") + labort = False + processed_vars = dict() + + # 1. Check arguments: + # ecosys_diagnostics_in can not be None and must be path of an existing file + if ecosys_diagnostics_in == None: + logger.error("Must specific ecosys_diagnostics_in") + labort = True + elif not os.path.isfile(ecosys_diagnostics_in): + logger.error("File not found %s" % ecosys_diagnostics_in) + labort = True + if labort: + sys.exit(1) + + # 2. Set up diag_table object + diag_table = DiagTableClass() + + # 3. Read ecosys_diagnostics_in line by line, convert each line to diag table entry + with open(ecosys_diagnostics_in, 'r') as file_in: + all_lines = file_in.readlines() + + for line in all_lines: + varname, frequency, operator = _parse_line(line.strip()) + # i. Continue to next line in the following circumstances + # * varname = None + if varname == None: + continue + # * Skip ALT_CO2 vars unless explicitly requested + if (not lMARBL_output_alt_co2) and ("ALT_CO2" in varname): + continue + + # ii. Abort if varname has already appeared in file at given frequency + for freq in frequency: + if freq not in processed_vars: + processed_vars[freq] = [] + if varname in processed_vars[freq]: + logger.error(f"{varname} appears in {ecosys_diagnostics_in} with frequency %{freq} multiple times") + sys.exit(1) + processed_vars[freq].append(varname) + + # iii. Update diag table + diag_table.update(varname, frequency, lMARBL_output_all) + + # File footer + diag_table.dump_to_json(diag_table_out) + +####################################### + +if __name__ == "__main__": + # Parse command line arguments + import logging + args = _parse_args() + + logging.basicConfig(format='%(levelname)s (%(funcName)s): %(message)s', level=logging.DEBUG) + + # call diagnostics_to_diag_table() + diagnostics_to_diag_table(args.ecosys_diagnostics_in, + args.diag_table_out, + args.lMARBL_output_all, + args.lMARBL_output_alt_co2) diff --git a/cime_config/buildnml b/cime_config/buildnml index 2a7a57d9..55d8f670 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -118,12 +118,10 @@ def prep_input(case): diag_table_dest = os.path.join(rundir,"diag_table") diag_table = FType_diag_table.from_json(diag_table_src) if use_MARBL: - generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir) - # TODO: instead of copying json file here, python script should generate it based on ecosys_diagnostics - diag_table_src = os.path.join(json_templates_dir, "diag_table_MARBL.json") diag_table_buildconf = os.path.join(momconfdir, "diag_table_MARBL.json") - shutil.copy(diag_table_src, diag_table_buildconf) - diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) + generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf) + if os.path.isfile(diag_table_buildconf): + diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) diag_table.write(diag_table_dest, case) def init_MOM_override(rundir): @@ -169,7 +167,7 @@ def _copy_files_to_momconf(case): for filename in ["MOM_input", "MOM_override", "diag_table", "input.nml"]: shutil.copy(os.path.join(rundir,filename), momconfdir) -def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir): +def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf): """ Create Buildconf/momconf/diag_table_MARBL.json @@ -179,7 +177,6 @@ def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfd """ # 1. Check for diag_table_MARBL.json in SourceMods srcmods_version = os.path.join(SourceMods_dir, "diag_table_MARBL.json") - diag_table_buildconf = os.path.join(momconfdir, "diag_table_MARBL.json") if os.path.isfile(srcmods_version): shutil.copy(srcmods_version, diag_table_buildconf) return @@ -205,13 +202,12 @@ def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfd MARBL_diagnostics = MARBL_diagnostics_for_MOM(MARBL_dir, caseroot, MARBL_settings) # (iv) append MARBL diagnostics to ecosys_diagnostics - # This also creates marbl_diagnostics_list to help track which - # diagnostics came from MARBL MARBL_diagnostics.write_diagnostics_file(ecosys_diags_buildconf, - os.path.join(momconfdir, "marbl_diagnostics_list"), append=True) # 3. Generate diag_table based on contents of ecosys_diagnostics + from MARBL_diags_to_diag_table import diagnostics_to_diag_table + diagnostics_to_diag_table(ecosys_diags_buildconf, diag_table_buildconf, False, False) # pylint: disable=too-many-arguments,too-many-locals,too-many-branches,too-many-statements diff --git a/param_templates/json/diag_table_MARBL.json b/param_templates/json/diag_table_MARBL.json deleted file mode 100644 index 147ef148..00000000 --- a/param_templates/json/diag_table_MARBL.json +++ /dev/null @@ -1,142 +0,0 @@ -{ - "FieldLists": [ - [ - "PO4", - "NO3", - "SiO3", - "NH4", - "Fe", - "Lig", - "O2", - "DIC", - "DIC_ALT_CO2", - "ALK", - "ALK_ALT_CO2", - "DOC", - "DON", - "DOP", - "DOPr", - "DONr", - "DOCr", - "zooC", - "spChl", - "spC", - "spP", - "spFe", - "spCaCO3", - "diatChl", - "diatC", - "diatP", - "diatFe", - "diatSi", - "diazChl", - "diazC", - "diazP", - "diazFe", - "PV_CO2", - "ECOSYS_ATM_PRESS", - "ECOSYS_IFRAC", - "PH", - "DUST_FLUX", - "IRON_FLUX", - "NOx_FLUX", - "NHy_FLUX", - "FESEDFLUX", - "STF_O2", - "J_DIC" - ], - [ - "volcello", - "vmo", - "vhGM", - "vhml", - "umo", - "uhGM", - "uhml" - ] - ], - "Files": { - "hist_bgc": { - "suffix": "hm_bgc%4yr-%2mo", - "output_freq": 1, - "output_freq_units": { - "$OCN_DIAG_MODE == \"spinup\"": "years", - "$TEST == True": "days", - "else": "months" - }, - "time_axis_units": "days", - "new_file_freq": 1, - "new_file_freq_units": { - "$OCN_DIAG_MODE == \"spinup\"": "years", - "$TEST == True": "days", - "else": "months" - }, - "reduction_method": "mean", - "regional_section": "none", - "fields": { - "$OCN_BGC_CONFIG != \"none\"": [ - { - "module": "ocean_model", - "packing": 1, - "lists": [ - [ - "PO4", - "NO3", - "SiO3", - "NH4", - "Fe", - "Lig", - "O2", - "DIC", - "DIC_ALT_CO2", - "ALK", - "ALK_ALT_CO2", - "DOC", - "DON", - "DOP", - "DOPr", - "DONr", - "DOCr", - "zooC", - "spChl", - "spC", - "spP", - "spFe", - "spCaCO3", - "diatChl", - "diatC", - "diatP", - "diatFe", - "diatSi", - "diazChl", - "diazC", - "diazP", - "diazFe", - "PV_CO2", - "ECOSYS_ATM_PRESS", - "ECOSYS_IFRAC", - "PH", - "DUST_FLUX", - "IRON_FLUX", - "NOx_FLUX", - "NHy_FLUX", - "FESEDFLUX", - "STF_O2", - "J_DIC" - ], - [ - "volcello", - "vmo", - "vhGM", - "vhml", - "umo", - "uhGM", - "uhml" - ] - ] - } - ] - } - } - } -} \ No newline at end of file From 8947002d06d2241bc95f22dbff150742f3251a4e Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 10 Feb 2021 12:52:24 -0700 Subject: [PATCH 028/136] Update some diag_manager namelist defaults MARBL needs more output fields and more output files, so those variables are now set in input.nml depending on value of TEST and OCN_BGC_CONFIG --- param_templates/input_nml.yaml | 10 ++++++++++ param_templates/json/input_nml.json | 14 ++++++++++++++ 2 files changed, 24 insertions(+) diff --git a/param_templates/input_nml.yaml b/param_templates/input_nml.yaml index 697c6cfe..c6dafb18 100644 --- a/param_templates/input_nml.yaml +++ b/param_templates/input_nml.yaml @@ -23,4 +23,14 @@ fms_nml: diag_manager_nml: flush_nc_files: value: .true. + max_output_fields: + value: + $OCN_BGC_CONFIG != "none" and $TEST != True: 600 + $OCN_BGC_CONFIG != "none" and $TEST == True: 1200 + else: 300 + max_num_axis_sets: + value: + $OCN_BGC_CONFIG != "none" and $TEST != True: 27 + $OCN_BGC_CONFIG != "none" and $TEST == True: 25 + else: 24 ... diff --git a/param_templates/json/input_nml.json b/param_templates/json/input_nml.json index ea844127..625d35c8 100644 --- a/param_templates/json/input_nml.json +++ b/param_templates/json/input_nml.json @@ -30,6 +30,20 @@ "diag_manager_nml": { "flush_nc_files": { "value": ".true." + }, + "max_output_fields": { + "value": { + "$OCN_BGC_CONFIG != \"none\" and $TEST != True": 600, + "$OCN_BGC_CONFIG != \"none\" and $TEST == True": 1200, + "else": 300 + } + }, + "max_num_axis_sets": { + "value": { + "$OCN_BGC_CONFIG != \"none\" and $TEST != True": 27, + "$OCN_BGC_CONFIG != \"none\" and $TEST == True": 25, + "else": 24 + } } } } \ No newline at end of file From bfe7f62a85ea30fac343e95eb24b1b5bd205d7dc Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 11 Feb 2021 16:17:31 -0700 Subject: [PATCH 029/136] 3D tracers should be on z levels This means splitting all hm_bgc streams into hm_bgc_{freq} and hm_bgc_{freq}_z, with the 2D fields in the former and 3D fields in the latter. It also means generate_diag_table_MARBL() needs to know which fields are 2D and which are 3D; for MARBL fields it can look at the vertical_grid key in the diagnostics JSON file but for the MOM6 fields we assume STF_{tracer} are the only 2D fields for now. --- .../MARBL_scripts/MARBL_diagnostics.py | 4 +- .../MARBL_diags_to_diag_table.py | 32 ++++++++---- .../MARBL_scripts/MOM_ecosys_diagnostics.py | 40 ++++++++++++++- cime_config/buildnml | 50 +++++++++++++------ param_templates/input_nml.yaml | 4 +- param_templates/json/input_nml.json | 4 +- 6 files changed, 102 insertions(+), 32 deletions(-) diff --git a/cime_config/MARBL_scripts/MARBL_diagnostics.py b/cime_config/MARBL_scripts/MARBL_diagnostics.py index e5285348..5bd69325 100644 --- a/cime_config/MARBL_scripts/MARBL_diagnostics.py +++ b/cime_config/MARBL_scripts/MARBL_diagnostics.py @@ -42,7 +42,7 @@ def __init__(self, MARBL_dir, caseroot, MARBL_settings): sys.exit(1) # Generate diagnostics object - self._MARBL_diagnostics = MARBL_diagnostics_file_class.MARBL_diagnostics_class(**MARBL_args) + self.MARBL_diagnostic_dict = MARBL_diagnostics_file_class.MARBL_diagnostics_class(**MARBL_args) ################################################################################ # PUBLIC CLASS METHODS # @@ -53,5 +53,5 @@ def write_diagnostics_file(self, diagnostics_file_out, append): Also create a list of diagnostics generated by MOM """ from MARBL_tools import generate_diagnostics_file - generate_diagnostics_file(self._MARBL_diagnostics, diagnostics_file_out, append) + generate_diagnostics_file(self.MARBL_diagnostic_dict, diagnostics_file_out, append) diff --git a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py index f8cb5440..c5c9be02 100755 --- a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py +++ b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py @@ -58,10 +58,14 @@ def __init__(self): # completely different variables from hm_bgc_annual in "regular" mode self._diag_table_dict = dict() + # NOTE: the "_z" in frequency => convert to z-space rather than output on native grid + # "medium" frequency should be treated like "hm" stream -- annual in spinup runs, monthly otherwise suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual%4yr", "else": "hm_bgc_monthly%4yr-%2mo"} output_freq_units_dict = {'$OCN_DIAG_MODE == "spinup"': "years", "$TEST == True": "days", "else": "months"} self._diag_table_dict["medium"] = self._dict_template(suffix_dict, output_freq_units_dict) + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual_z%4yr", "else": "hm_bgc_monthly_z%4yr-%2mo"} + self._diag_table_dict["medium_z"] = self._dict_template(suffix_dict, output_freq_units_dict, module="ocean_model_z") # "high" frequency should be treated like "sfc" stream -- 5-day averages in spinup, daily otherwise # unlike "sfc", this stream will write one file per month instead of per year (except in spinup) @@ -69,13 +73,17 @@ def __init__(self): output_freq_dict = {'$OCN_DIAG_MODE == "spinup"': 5, "else": 1} new_file_freq_units_dict = {'$OCN_DIAG_MODE == "spinup"': "years", "else": "months"} self._diag_table_dict["high"] = self._dict_template(suffix_dict, "days", new_file_freq_units_dict, output_freq_dict) + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_daily5_z%4yr", "else": "hm_bgc_daily_z%4yr-%2mo"} + self._diag_table_dict["high_z"] = self._dict_template(suffix_dict, "days", new_file_freq_units_dict, output_freq_dict, module="ocean_model_z") # "low" frequency should be treated as annual averages suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual2%4yr", "else": "hm_bgc_annual%4yr"} self._diag_table_dict["low"] = self._dict_template(suffix_dict, "years") + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual2_z%4yr", "else": "hm_bgc_annual_z%4yr"} + self._diag_table_dict["low_z"] = self._dict_template(suffix_dict, "years", module="ocean_model_z") - def update(self, varname, frequency, lMARBL_output_all): + def update(self, varname, frequency, is2D, lMARBL_output_all): if lMARBL_output_all: use_freq = ['medium'] else: @@ -87,7 +95,9 @@ def update(self, varname, frequency, lMARBL_output_all): for freq in use_freq: if freq == "never": continue - self._diag_table_dict[freq]["fields"][0]["lists"][0].append(varname) + # append _z to frequency for 3D vars + suffix="" if is2D else "_z" + self._diag_table_dict[f"{freq}{suffix}"]["fields"][0]["lists"][0].append(varname) def dump_to_json(self, filename): @@ -95,11 +105,12 @@ def dump_to_json(self, filename): out_dict = dict() out_dict["Files"] = dict() - transports = ["volcello", "vmo", "vhGM", "vhml", "umo", "uhGM", "uhml"] for freq in self._diag_table_dict: if len(self._diag_table_dict[freq]["fields"][0]["lists"][0]) > 0: out_dict["Files"][freq] = self._diag_table_dict[freq].copy() - out_dict["Files"][freq]["fields"][0]["lists"].append(transports) + if out_dict["Files"][freq]["fields"][0]["module"] == "ocean_model": + transports = ["volcello", "vmo", "vhGM", "vhml", "umo", "uhGM", "uhml"] + out_dict["Files"][freq]["fields"][0]["lists"].append(transports) if out_dict["Files"]: with open(filename, "w") as fp: json.dump(out_dict, fp, separators=(',', ': '), sort_keys=False, indent=3) @@ -107,7 +118,7 @@ def dump_to_json(self, filename): print("WARNING: no JSON file written as no variables were requested") - def _dict_template(self, suffix, output_freq_units, new_file_freq_units=None, output_freq=1, new_file_freq=1, packing=1): + def _dict_template(self, suffix, output_freq_units, new_file_freq_units=None, output_freq=1, new_file_freq=1, module="ocean_model", packing=1): """ Return the basic template for MOM6 diag_table dictionary. Variables will be added to output file by appending to template["fields"][0]["lists"][0] @@ -121,8 +132,9 @@ def _dict_template(self, suffix, output_freq_units, new_file_freq_units=None, ou files; if None, will use output_freq_units (default: None) * output_freq: how frequently to output (default: 1) * new_file_freq: how frequently to create new files (default: 1) - * packing: integer that is used to determine precision when writing output - (default: 1 => double precision output) + * module: string that determines vertical grid; "ocean_model_z" maps to Z space, "ocean_model" stays on native grid, "ocean_model_rho2" is sigma2 + * packing: integer that is used to determine precision when writing output; 1 => double precision, 2 => single + (default: 1) """ template = dict() template["suffix"] = suffix @@ -136,7 +148,7 @@ def _dict_template(self, suffix, output_freq_units, new_file_freq_units=None, ou template["time_axis_units"] = "days" template["reduction_method"] = "mean" template["regional_section"] = "none" - template["fields"] = [{"module": "ocean_model", "packing": packing, "lists" : [[]]}] + template["fields"] = [{"module": module, "packing": packing, "lists" : [[]]}] return template @@ -221,6 +233,7 @@ def _parse_line(line_in): def diagnostics_to_diag_table(ecosys_diagnostics_in, diag_table_out, + diag2D_list, lMARBL_output_all, lMARBL_output_alt_co2): """ @@ -270,7 +283,8 @@ def diagnostics_to_diag_table(ecosys_diagnostics_in, processed_vars[freq].append(varname) # iii. Update diag table - diag_table.update(varname, frequency, lMARBL_output_all) + is2D = varname in diag2D_list + diag_table.update(varname, frequency, is2D, lMARBL_output_all) # File footer diag_table.dump_to_json(diag_table_out) diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py index 8c3aa3c5..e7821163 100644 --- a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -4,7 +4,7 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_list, calcifier_list, ladjust_bury_coeff, ecosys_diag_filename): """ Subroutine to write a file in the same format as marbl_diagnostics containing - a list of POP-generated diagnostics that should be included based on the + a list of MOM-generated diagnostics that should be included based on the MARBL configuration """ @@ -301,3 +301,41 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li fout.write("#\n########################################\n") fout.write("# MARBL-generated diagnostics #\n") fout.write("########################################\n#\n") + + +def get_2D_vars_from_ecosys_diagnostics(ecosys_diag_filename): + """ + Read in ecosys_diagnostics, return a list of all variables that are 2D + whether they are written to a stream or not + """ + varlist = [] + with open(ecosys_diag_filename,"r") as fin: + lines = fin.readlines() + + for line in lines: + lstr = line.strip() + # if user has modified ecosys_diagnostics and put it in SourceMods, + # then file may include MARBL diagnostics. We can stop looking at + # variable names when we reach that section + if "MARBL-generated diagnostics" in lstr: + break + + # if line starts with "#" it is a comment and we can continue to next line + if lstr[0] == "#": + continue + + # Pull varname out of line, append it to varlist if it is 2D + varname = lstr.split(":")[0].strip() + if _2D_varcheck(varname): + varlist.append(varname) + + return varlist + + +def _2D_varcheck(varname): + """ + Return true if variable is 2D + """ + if varname.startswith("STF_"): + return True + return False \ No newline at end of file diff --git a/cime_config/buildnml b/cime_config/buildnml index 55d8f670..91d1fce5 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -182,32 +182,50 @@ def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfd return # 2. Create ecosys_diagnostics - # (a) Check for file in SourceMods, otherwise generate it + # (a) We need to generate a MARBL_diagnostics_for_MOM object + # It's necessary for generating ecosys_diagnostics, but + # even if we copy ecosys_diagnostics from SourceMods the + # object contains important information about the vertical + # dimension of MARBL's diagnostics + from MARBL_diagnostics import MARBL_diagnostics_for_MOM + MARBL_diagnostics = MARBL_diagnostics_for_MOM(MARBL_dir, caseroot, MARBL_settings) + + # (b) Check for file in SourceMods, otherwise generate it srcmods_version = os.path.join(SourceMods_dir, "ecosys_diagnostics") ecosys_diags_buildconf = os.path.join(momconfdir, "ecosys_diagnostics") if os.path.isfile(srcmods_version): shutil.copy(srcmods_version, ecosys_diags_buildconf) else: - # Create ecosys_diagnostics with MOM-generated diagnostics + # (i) Create ecosys_diagnostics with MOM-generated diagnostics from MOM_ecosys_diagnostics import write_ecosys_diagnostics_file - from MARBL_diagnostics import MARBL_diagnostics_for_MOM - write_ecosys_diagnostics_file(MARBL_settings.get_tracer_names(), - MARBL_settings.get_autotroph_names(), - MARBL_settings.get_zooplankton_names(), - MARBL_settings.get_autotroph_names(calcifier_only=True), - MARBL_settings.ladjust_bury_coeff(), - ecosys_diags_buildconf, - ) - # (iii) Generate MARBL_diagnostics_for_MOM object - MARBL_diagnostics = MARBL_diagnostics_for_MOM(MARBL_dir, caseroot, MARBL_settings) - - # (iv) append MARBL diagnostics to ecosys_diagnostics + MOM6_diags = write_ecosys_diagnostics_file(MARBL_settings.get_tracer_names(), + MARBL_settings.get_autotroph_names(), + MARBL_settings.get_zooplankton_names(), + MARBL_settings.get_autotroph_names(calcifier_only=True), + MARBL_settings.ladjust_bury_coeff(), + ecosys_diags_buildconf, + ) + # (ii) append MARBL diagnostics to ecosys_diagnostics MARBL_diagnostics.write_diagnostics_file(ecosys_diags_buildconf, append=True) - # 3. Generate diag_table based on contents of ecosys_diagnostics + # 3. We need a list of all the 2D fields in ecosys_diagnostics + # because those fields use the ocean_model module rather than + # ocean_model_z + + # (a) get list of 2D diagnostics computed in MOM6 + from MOM_ecosys_diagnostics import get_2D_vars_from_ecosys_diagnostics + diag2D_list = get_2D_vars_from_ecosys_diagnostics(ecosys_diags_buildconf) + + # (b) append list of 2D diagnostics computed in MARBL + for varname in MARBL_diagnostics.MARBL_diagnostic_dict.diagnostics_dict: + if MARBL_diagnostics.MARBL_diagnostic_dict.diagnostics_dict[varname]["vertical_grid"] == "none": + diag2D_list.append(varname) + + # 4. Generate diag_table based on contents of ecosys_diagnostics + # and knowledge of which diags are 2D from MARBL_diags_to_diag_table import diagnostics_to_diag_table - diagnostics_to_diag_table(ecosys_diags_buildconf, diag_table_buildconf, False, False) + diagnostics_to_diag_table(ecosys_diags_buildconf, diag_table_buildconf, diag2D_list, False, False) # pylint: disable=too-many-arguments,too-many-locals,too-many-branches,too-many-statements diff --git a/param_templates/input_nml.yaml b/param_templates/input_nml.yaml index c6dafb18..18823a61 100644 --- a/param_templates/input_nml.yaml +++ b/param_templates/input_nml.yaml @@ -30,7 +30,7 @@ diag_manager_nml: else: 300 max_num_axis_sets: value: - $OCN_BGC_CONFIG != "none" and $TEST != True: 27 - $OCN_BGC_CONFIG != "none" and $TEST == True: 25 + $OCN_BGC_CONFIG != "none" and $TEST != True: 30 + $OCN_BGC_CONFIG != "none" and $TEST == True: 26 else: 24 ... diff --git a/param_templates/json/input_nml.json b/param_templates/json/input_nml.json index 625d35c8..99c75a3e 100644 --- a/param_templates/json/input_nml.json +++ b/param_templates/json/input_nml.json @@ -40,8 +40,8 @@ }, "max_num_axis_sets": { "value": { - "$OCN_BGC_CONFIG != \"none\" and $TEST != True": 27, - "$OCN_BGC_CONFIG != \"none\" and $TEST == True": 25, + "$OCN_BGC_CONFIG != \"none\" and $TEST != True": 30, + "$OCN_BGC_CONFIG != \"none\" and $TEST == True": 26, "else": 24 } } From 1d225db39c30cdfe39eaa34b49d0c38eb1f32414 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 18 Feb 2021 11:46:10 -0700 Subject: [PATCH 030/136] Add env_build.xml flag for building MARBL We want CESM to build MARBL by default, but having this flag will let us test building with the stubs in config_src/externals/MARBL instead of the full library. --- cime_config/buildlib | 8 +++++--- cime_config/config_component.xml | 13 +++++++++++++ 2 files changed, 18 insertions(+), 3 deletions(-) diff --git a/cime_config/buildlib b/cime_config/buildlib index a098b1a2..02875f6c 100755 --- a/cime_config/buildlib +++ b/cime_config/buildlib @@ -46,10 +46,12 @@ def buildlib(caseroot, libroot, bldroot): # MARBL source code is brought in as a git submodule (or my manage_externals) logger.info("Making sure MARBL code is available...") - marbl_srcdir = os.path.join(comp_root_dir_ocn,"MOM6","pkg","MARBL","src") - # If MARBL is not found, abort + if case.get_value("MOM6_BUILD_MARBL") == "TRUE": + marbl_srcdir = os.path.join(comp_root_dir_ocn,"MOM6","pkg","MARBL","src") + else: + marbl_srcdir = os.path.join(comp_root_dir_ocn,"MOM6","config_src","external","MARBL") if not os.path.exists(marbl_srcdir): - expect(False, "MARBL external not found") + expect(False, f"MARBL external not found in {marbl_srcdir}") casetools = case.get_value("CASETOOLS") gmake_j = case.get_value("GMAKE_J") diff --git a/cime_config/config_component.xml b/cime_config/config_component.xml index d83601bf..45407b85 100644 --- a/cime_config/config_component.xml +++ b/cime_config/config_component.xml @@ -31,6 +31,19 @@ + + char + TRUE,FALSE + TRUE + build_component_mom + env_build.xml + This variable controls whether MOM6 builds with the full MARBL library (default) or just + with stubs. If set to FALSE then setting USE_MARBL_TRACERS = True in the Parameters file + will result in a runtime error. It is recommended to leave this variable set to TRUE, it + exists to allow testing of the MARBL stub in the CESM test suite. + + + char spinup,production,development,none From fdec97e4a5a727c3d3d019c47f958cf58367cb1a Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 18 Feb 2021 14:57:25 -0700 Subject: [PATCH 031/136] Improvements for testing 1. Created aux_mom_MARBL testlist -- tests CMOM compset with the MARBL stubs and C1850MOMECO 2. Got lmarbl_output_all flag set based on env_build::TEST -- needed to increase max_fields_per_file in FMS:diag_data.F90 (via src_override) from 300 to 600 -- increased max_input_fields from default of 600 to 900 when running a test with MARBL tracers -- update diag_table to create daily output files from test suite (currently creating one file per day, may want to save disk space by dropping the -DD portion of the name and overwriting YYYY-MM with the latest daily output) --- .../MARBL_diags_to_diag_table.py | 4 ++-- cime_config/buildnml | 9 ++++--- cime_config/testdefs/testlist_mom.xml | 24 +++++++++++++++++++ .../mom/no_ecosys/shell_commands | 1 + param_templates/input_nml.yaml | 4 ++++ param_templates/json/input_nml.json | 6 +++++ 6 files changed, 43 insertions(+), 5 deletions(-) create mode 100644 cime_config/testdefs/testmods_dirs/mom/no_ecosys/shell_commands diff --git a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py index c5c9be02..fc0853a3 100755 --- a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py +++ b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py @@ -61,10 +61,10 @@ def __init__(self): # NOTE: the "_z" in frequency => convert to z-space rather than output on native grid # "medium" frequency should be treated like "hm" stream -- annual in spinup runs, monthly otherwise - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual%4yr", "else": "hm_bgc_monthly%4yr-%2mo"} + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual%4yr", "$TEST == True": "hm_bgc_daily%4yr-%2mo-%2dy", "else": "hm_bgc_monthly%4yr-%2mo"} output_freq_units_dict = {'$OCN_DIAG_MODE == "spinup"': "years", "$TEST == True": "days", "else": "months"} self._diag_table_dict["medium"] = self._dict_template(suffix_dict, output_freq_units_dict) - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual_z%4yr", "else": "hm_bgc_monthly_z%4yr-%2mo"} + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual_z%4yr", "$TEST == True": "hm_bgc_daily_z%4yr-%2mo-%2dy", "else": "hm_bgc_monthly_z%4yr-%2mo"} self._diag_table_dict["medium_z"] = self._dict_template(suffix_dict, output_freq_units_dict, module="ocean_model_z") # "high" frequency should be treated like "sfc" stream -- 5-day averages in spinup, daily otherwise diff --git a/cime_config/buildnml b/cime_config/buildnml index 91d1fce5..0f62aad1 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -119,7 +119,10 @@ def prep_input(case): diag_table = FType_diag_table.from_json(diag_table_src) if use_MARBL: diag_table_buildconf = os.path.join(momconfdir, "diag_table_MARBL.json") - generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf) + # For now, user has no control over lMARBL_output_all + # TODO: mimic what is done in POP and allow it via user_nl_mom? + lMARBL_output_all = case.get_value("TEST") + generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, lMARBL_output_all) if os.path.isfile(diag_table_buildconf): diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) diag_table.write(diag_table_dest, case) @@ -167,7 +170,7 @@ def _copy_files_to_momconf(case): for filename in ["MOM_input", "MOM_override", "diag_table", "input.nml"]: shutil.copy(os.path.join(rundir,filename), momconfdir) -def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf): +def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, lMARBL_output_all): """ Create Buildconf/momconf/diag_table_MARBL.json @@ -225,7 +228,7 @@ def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfd # 4. Generate diag_table based on contents of ecosys_diagnostics # and knowledge of which diags are 2D from MARBL_diags_to_diag_table import diagnostics_to_diag_table - diagnostics_to_diag_table(ecosys_diags_buildconf, diag_table_buildconf, diag2D_list, False, False) + diagnostics_to_diag_table(ecosys_diags_buildconf, diag_table_buildconf, diag2D_list, lMARBL_output_all, False) # pylint: disable=too-many-arguments,too-many-locals,too-many-branches,too-many-statements diff --git a/cime_config/testdefs/testlist_mom.xml b/cime_config/testdefs/testlist_mom.xml index 32b8aaa1..3711d81c 100644 --- a/cime_config/testdefs/testlist_mom.xml +++ b/cime_config/testdefs/testlist_mom.xml @@ -98,4 +98,28 @@ + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/cime_config/testdefs/testmods_dirs/mom/no_ecosys/shell_commands b/cime_config/testdefs/testmods_dirs/mom/no_ecosys/shell_commands new file mode 100644 index 00000000..74ee6913 --- /dev/null +++ b/cime_config/testdefs/testmods_dirs/mom/no_ecosys/shell_commands @@ -0,0 +1 @@ +./xmlchange MOM6_BUILD_MARBL=FALSE diff --git a/param_templates/input_nml.yaml b/param_templates/input_nml.yaml index 18823a61..c9b03b7f 100644 --- a/param_templates/input_nml.yaml +++ b/param_templates/input_nml.yaml @@ -23,6 +23,10 @@ fms_nml: diag_manager_nml: flush_nc_files: value: .true. + max_input_fields: + value: + $OCN_BGC_CONFIG != "none" and $TEST == True: 900 + else: 600 max_output_fields: value: $OCN_BGC_CONFIG != "none" and $TEST != True: 600 diff --git a/param_templates/json/input_nml.json b/param_templates/json/input_nml.json index 99c75a3e..ebe9b7de 100644 --- a/param_templates/json/input_nml.json +++ b/param_templates/json/input_nml.json @@ -31,6 +31,12 @@ "flush_nc_files": { "value": ".true." }, + "max_input_fields": { + "value": { + "$OCN_BGC_CONFIG != \"none\" and $TEST == True": 900, + "else": 600 + } + }, "max_output_fields": { "value": { "$OCN_BGC_CONFIG != \"none\" and $TEST != True": 600, From dd9cb02cfdb63eb45483bcd5726355ca0a338d26 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 2 Mar 2021 15:50:05 -0700 Subject: [PATCH 032/136] Add nuopc tests to test list --- cime_config/testdefs/testlist_mom.xml | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/cime_config/testdefs/testlist_mom.xml b/cime_config/testdefs/testlist_mom.xml index 3711d81c..502a6e93 100644 --- a/cime_config/testdefs/testlist_mom.xml +++ b/cime_config/testdefs/testlist_mom.xml @@ -106,6 +106,22 @@ + + + + + + + + + + + + + + + + From 0eadbf0de9fe72078cb2efd10b858a830f711ef8 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 18 Mar 2021 14:57:32 -0600 Subject: [PATCH 033/136] Add more MARBL output to default diag_table --- .../MARBL_scripts/MOM_ecosys_diagnostics.py | 76 +++++++++---------- 1 file changed, 36 insertions(+), 40 deletions(-) diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py index e7821163..73f2c2b2 100644 --- a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -66,16 +66,14 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li # Default frequencies for per-tracer diagnostics # - tracer state should be output monthly # - everything else is off by default - # TODO: get right name for surface tracer flux (POP used STF_{tracer}) and tendency (POP used J_{tracer}) - # TODO: add vertical integral for J_{tracer} and {tracer} (full column and 100m) - # also, what is difference between Jint_100m_{tracer} and tend_zint_100m_{tracer}? + # TODO: add vertical integral for tend_zint_100m_{tracer} per_tracer_dict['diags'] = OrderedDict() per_tracer_dict['diags'][tracer_short_name] = 'medium_average' per_tracer_dict['diags']['STF_%s' % tracer_short_name] = 'never_average' per_tracer_dict['diags']['J_%s' % tracer_short_name] = 'never_average' - # per_tracer_dict['diags']['Jint_100m_%s' % tracer_short_name] = 'never_average' - # per_tracer_dict['diags']['Jint_%s' % tracer_short_name] = 'never_average' - # per_tracer_dict['diags']['%s_zint_100m' % tracer_short_name] = 'never_average' + per_tracer_dict['diags']['Jint_100m_%s' % tracer_short_name] = 'never_average' + per_tracer_dict['diags']['Jint_%s' % tracer_short_name] = 'never_average' + per_tracer_dict['diags']['%s_zint_100m' % tracer_short_name] = 'never_average' # per_tracer_dict['diags']['tend_zint_100m_%s' % tracer_short_name] = 'never_average' # Some diagnostics are not defined for all tracers; diagnostics with 'none' @@ -95,34 +93,34 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li if 'PO4' in full_diag_dict.keys(): # full_diag_dict['PO4']['diags']['PO4_RIV_FLUX'] = 'medium_average' full_diag_dict['PO4']['diags']['J_PO4'] = 'low_average' - # full_diag_dict['PO4']['diags']['Jint_100m_PO4'] = 'medium_average' + full_diag_dict['PO4']['diags']['Jint_100m_PO4'] = 'medium_average' # full_diag_dict['PO4']['diags']['tend_zint_100m_PO4'] = 'medium_average' full_diag_dict['PO4']['properties']['has surface flux'] = True # NO3 if 'NO3' in full_diag_dict.keys(): # full_diag_dict['NO3']['diags']['NO3_RIV_FLUX'] = 'medium_average' full_diag_dict['NO3']['diags']['J_NO3'] = 'low_average' - # full_diag_dict['NO3']['diags']['Jint_100m_NO3'] = 'medium_average' + full_diag_dict['NO3']['diags']['Jint_100m_NO3'] = 'medium_average' # full_diag_dict['NO3']['diags']['tend_zint_100m_NO3'] = 'medium_average' full_diag_dict['NO3']['properties']['has surface flux'] = True # SiO3 if 'SiO3' in full_diag_dict.keys(): # full_diag_dict['SiO3']['diags']['SiO3_RIV_FLUX'] = 'medium_average' full_diag_dict['SiO3']['diags']['J_SiO3'] = 'low_average' - # full_diag_dict['SiO3']['diags']['Jint_100m_SiO3'] = 'medium_average' + full_diag_dict['SiO3']['diags']['Jint_100m_SiO3'] = 'medium_average' # full_diag_dict['SiO3']['diags']['tend_zint_100m_SiO3'] = 'medium_average' full_diag_dict['SiO3']['properties']['has surface flux'] = True # NH4 if 'NH4' in full_diag_dict.keys(): full_diag_dict['NH4']['diags']['J_NH4'] = 'low_average' - # full_diag_dict['NH4']['diags']['Jint_100m_NH4'] = 'medium_average' + full_diag_dict['NH4']['diags']['Jint_100m_NH4'] = 'medium_average' # full_diag_dict['NH4']['diags']['tend_zint_100m_NH4'] = 'medium_average' full_diag_dict['NH4']['properties']['has surface flux'] = True # Fe if 'Fe' in full_diag_dict.keys(): # full_diag_dict['Fe']['diags']['Fe_RIV_FLUX'] = 'medium_average' full_diag_dict['Fe']['diags']['J_Fe'] = 'low_average' - # full_diag_dict['Fe']['diags']['Jint_100m_Fe'] = 'medium_average' + full_diag_dict['Fe']['diags']['Jint_100m_Fe'] = 'medium_average' # full_diag_dict['Fe']['diags']['tend_zint_100m_Fe'] = 'medium_average' full_diag_dict['Fe']['properties']['include budget terms'] = True full_diag_dict['Fe']['properties']['has surface flux'] = True @@ -132,7 +130,7 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li # O2 if 'O2' in full_diag_dict.keys(): full_diag_dict['O2']['diags']['STF_O2'] = 'medium_average, high_average' - # full_diag_dict['O2']['diags']['Jint_100m_O2'] = 'medium_average' + full_diag_dict['O2']['diags']['Jint_100m_O2'] = 'medium_average' # full_diag_dict['O2']['diags']['tend_zint_100m_O2'] = 'medium_average' full_diag_dict['O2']['properties']['include budget terms'] = True full_diag_dict['O2']['properties']['has surface flux'] = True @@ -140,7 +138,7 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li if 'DIC' in full_diag_dict.keys(): # full_diag_dict['DIC']['diags']['DIC_RIV_FLUX'] = 'medium_average' full_diag_dict['DIC']['diags']['J_DIC'] = 'medium_average' - # full_diag_dict['DIC']['diags']['Jint_100m_DIC'] = 'medium_average' + full_diag_dict['DIC']['diags']['Jint_100m_DIC'] = 'medium_average' # full_diag_dict['DIC']['diags']['tend_zint_100m_DIC'] = 'medium_average' # full_diag_dict['DIC']['diags']['FvPER_DIC'] = 'medium_average' # full_diag_dict['DIC']['diags']['FvICE_DIC'] = 'medium_average' @@ -150,7 +148,7 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li if 'DIC_ALT_CO2' in full_diag_dict.keys(): # full_diag_dict['DIC_ALT_CO2']['diags']['DIC_ALT_CO2_RIV_FLUX'] = 'medium_average' full_diag_dict['DIC_ALT_CO2']['diags']['J_DIC_ALT_CO2'] = 'medium_average' - # full_diag_dict['DIC_ALT_CO2']['diags']['Jint_100m_DIC_ALT_CO2'] = 'medium_average' + full_diag_dict['DIC_ALT_CO2']['diags']['Jint_100m_DIC_ALT_CO2'] = 'medium_average' # full_diag_dict['DIC_ALT_CO2']['diags']['tend_zint_100m_DIC_ALT_CO2'] = 'medium_average' # full_diag_dict['DIC_ALT_CO2']['diags']['FvPER_DIC_ALT_CO2'] = 'medium_average' # full_diag_dict['DIC_ALT_CO2']['diags']['FvICE_DIC_ALT_CO2'] = 'medium_average' @@ -161,7 +159,7 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li # full_diag_dict['ALK']['diags']['ALK_RIV_FLUX'] = 'medium_average' full_diag_dict['ALK']['diags']['STF_ALK'] = 'medium_average' full_diag_dict['ALK']['diags']['J_ALK'] = 'low_average' - # full_diag_dict['ALK']['diags']['Jint_100m_ALK'] = 'medium_average' + full_diag_dict['ALK']['diags']['Jint_100m_ALK'] = 'medium_average' # full_diag_dict['ALK']['diags']['tend_zint_100m_ALK'] = 'medium_average' # full_diag_dict['ALK']['diags']['FvPER_ALK'] = 'medium_average' # full_diag_dict['ALK']['diags']['FvICE_ALK'] = 'medium_average' @@ -171,7 +169,7 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li # full_diag_dict['ALK_ALT_CO2']['diags']['ALK_ALT_CO2_RIV_FLUX'] = 'medium_average' full_diag_dict['ALK_ALT_CO2']['diags']['STF_ALK_ALT_CO2'] = 'medium_average' full_diag_dict['ALK_ALT_CO2']['diags']['J_ALK_ALT_CO2'] = 'low_average' - # full_diag_dict['ALK_ALT_CO2']['diags']['Jint_100m_ALK_ALT_CO2'] = 'medium_average' + full_diag_dict['ALK_ALT_CO2']['diags']['Jint_100m_ALK_ALT_CO2'] = 'medium_average' # full_diag_dict['ALK_ALT_CO2']['diags']['tend_zint_100m_ALK_ALT_CO2'] = 'medium_average' # full_diag_dict['ALK_ALT_CO2']['diags']['FvPER_ALK_ALT_CO2'] = 'medium_average' # full_diag_dict['ALK_ALT_CO2']['diags']['FvICE_ALK_ALT_CO2'] = 'medium_average' @@ -179,7 +177,7 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li # DOC if 'DOC' in full_diag_dict.keys(): # full_diag_dict['DOC']['diags']['DOC_RIV_FLUX'] = 'medium_average' - # full_diag_dict['DOC']['diags']['Jint_100m_DOC'] = 'medium_average' + full_diag_dict['DOC']['diags']['Jint_100m_DOC'] = 'medium_average' # full_diag_dict['DOC']['diags']['tend_zint_100m_DOC'] = 'medium_average' full_diag_dict['DOC']['properties']['include budget terms'] = True full_diag_dict['DOC']['properties']['has surface flux'] = False # this should be True if EBM is off @@ -202,7 +200,7 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li # DOCr if 'DOCr' in full_diag_dict.keys(): # full_diag_dict['DOCr']['diags']['DOCr_RIV_FLUX'] = 'medium_average' - # full_diag_dict['DOCr']['diags']['Jint_100m_DOCr'] = 'medium_average' + full_diag_dict['DOCr']['diags']['Jint_100m_DOCr'] = 'medium_average' # full_diag_dict['DOCr']['diags']['tend_zint_100m_DOCr'] = 'medium_average' full_diag_dict['DOCr']['properties']['include budget terms'] = True full_diag_dict['DOCr']['properties']['has surface flux'] = False # this should be True if EBM is off @@ -210,7 +208,7 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li if 'DI13C' in full_diag_dict.keys(): # full_diag_dict['DI13C']['diags']['DI13C_RIV_FLUX'] = 'medium_average' full_diag_dict['DI13C']['diags']['J_DI13C'] = 'medium_average' - # full_diag_dict['DI13C']['diags']['Jint_100m_DI13C'] = 'medium_average' + full_diag_dict['DI13C']['diags']['Jint_100m_DI13C'] = 'medium_average' # full_diag_dict['DI13C']['diags']['tend_zint_100m_DI13C'] = 'medium_average' # full_diag_dict['DI13C']['diags']['FvPER_DI13C'] = 'medium_average' # full_diag_dict['DI13C']['diags']['FvICE_DI13C'] = 'medium_average' @@ -218,14 +216,14 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li # DO13Ctot if 'DO13Ctot' in full_diag_dict.keys(): # full_diag_dict['DO13Ctot']['diags']['DO13Ctot_RIV_FLUX'] = 'medium_average' - # full_diag_dict['DO13Ctot']['diags']['Jint_100m_DO13Ctot'] = 'medium_average' + full_diag_dict['DO13Ctot']['diags']['Jint_100m_DO13Ctot'] = 'medium_average' # full_diag_dict['DO13Ctot']['diags']['tend_zint_100m_DO13Ctot'] = 'medium_average' full_diag_dict['DO13Ctot']['properties']['has surface flux'] = True # DI14C if 'DI14C' in full_diag_dict.keys(): # full_diag_dict['DI14C']['diags']['DI14C_RIV_FLUX'] = 'medium_average' full_diag_dict['DI14C']['diags']['J_DI14C'] = 'medium_average' - # full_diag_dict['DI14C']['diags']['Jint_100m_DI14C'] = 'medium_average' + full_diag_dict['DI14C']['diags']['Jint_100m_DI14C'] = 'medium_average' # full_diag_dict['DI14C']['diags']['tend_zint_100m_DI14C'] = 'medium_average' # full_diag_dict['DI14C']['diags']['FvPER_DI14C'] = 'medium_average' # full_diag_dict['DI14C']['diags']['FvICE_DI14C'] = 'medium_average' @@ -233,29 +231,27 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li # DO14Ctot if 'DO14Ctot' in full_diag_dict.keys(): # full_diag_dict['DO14Ctot']['diags']['DO14Ctot_RIV_FLUX'] = 'medium_average' - # full_diag_dict['DO14Ctot']['diags']['Jint_100m_DO14Ctot'] = 'medium_average' + full_diag_dict['DO14Ctot']['diags']['Jint_100m_DO14Ctot'] = 'medium_average' # full_diag_dict['DO14Ctot']['diags']['tend_zint_100m_DO14Ctot'] = 'medium_average' full_diag_dict['DO14Ctot']['properties']['has surface flux'] = True - # TODO: reinstate these once we have vertical integral function - if False: - # 3. Per-autotroph diagnostics - for autotroph_name in autotroph_list: - tracer_short_name = autotroph_name+'C' - if tracer_short_name in full_diag_dict.keys(): - full_diag_dict[tracer_short_name]['diags']['%s_zint_100m' % tracer_short_name] = 'high_average' - tracer_short_name = autotroph_name+'CaCO3' - if tracer_short_name in full_diag_dict.keys(): - full_diag_dict[tracer_short_name]['diags']['%s_zint_100m' % tracer_short_name] = 'high_average' - tracer_short_name = autotroph_name+'Chl' - if tracer_short_name in full_diag_dict.keys(): - full_diag_dict[tracer_short_name]['diags']['%s_SURF' % tracer_short_name] = 'high_average' + # 3. Per-autotroph diagnostics + for autotroph_name in autotroph_list: + tracer_short_name = autotroph_name+'C' + if tracer_short_name in full_diag_dict.keys(): + full_diag_dict[tracer_short_name]['diags']['%s_zint_100m' % tracer_short_name] = 'high_average' + tracer_short_name = autotroph_name+'CaCO3' + if tracer_short_name in full_diag_dict.keys(): + full_diag_dict[tracer_short_name]['diags']['%s_zint_100m' % tracer_short_name] = 'high_average' + tracer_short_name = autotroph_name+'Chl' + if tracer_short_name in full_diag_dict.keys(): + full_diag_dict[tracer_short_name]['diags']['%s_SURF' % tracer_short_name] = 'high_average' - # 4. Per-zooplankton diagnostics - for zooplankton_name in zooplankton_list: - tracer_short_name = zooplankton_name+'C' - if tracer_short_name in full_diag_dict.keys(): - full_diag_dict[tracer_short_name]['diags']['%s_zint_100m' % tracer_short_name] = 'high_average' + # 4. Per-zooplankton diagnostics + for zooplankton_name in zooplankton_list: + tracer_short_name = zooplankton_name+'C' + if tracer_short_name in full_diag_dict.keys(): + full_diag_dict[tracer_short_name]['diags']['%s_zint_100m' % tracer_short_name] = 'high_average' # 5. Write tracer-specific diagnostics to file for tracer_short_name in full_diag_dict.keys(): From 8ff28098f7c0c73bd251f48f5ac99451ca2e043c Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 18 Mar 2021 14:57:52 -0600 Subject: [PATCH 034/136] New compsets Add C1850MOMECO_IAF and G1850MOMECO_IAF compsets --- cime_config/config_compsets.xml | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/cime_config/config_compsets.xml b/cime_config/config_compsets.xml index f4d25dd1..7cc1680a 100644 --- a/cime_config/config_compsets.xml +++ b/cime_config/config_compsets.xml @@ -53,6 +53,11 @@ 2000_DATM%IAF_SLND_DICE%IAF_MOM6_DROF%IAF_SGLC_SWAV + + C1850MOMECO_IAF + 1850_DATM%IAF_SLND_DICE%IAF_MOM6%ECO_DROF%IAF_SGLC_SWAV + + CMOM_JRA 2000_DATM%JRA_SLND_DICE%SSMI_MOM6_DROF%JRA_SGLC_SWAV @@ -75,6 +80,11 @@ 2000_DATM%IAF_SLND_CICE_MOM6_DROF%IAF_SGLC_SWAV + + G1850MOMECO_IAF + 1850_DATM%IAF_SLND_CICE_MOM6%ECO_DROF%IAF_SGLC_SWAV + + GMOM_JRA 2000_DATM%JRA_SLND_CICE_MOM6_DROF%JRA_SGLC_SWAV From a6d2dc32fd51b2d615e98ce265ca22c9332c35f3 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 18 Mar 2021 15:41:46 -0600 Subject: [PATCH 035/136] Add JRA compsets with MOM6%ECO I realized that the IAF compset still relies on the CORE data, and we need different compsets to run with JRA forcing. This commit adds both C and G support with [CG]1850MOMECO_JRA compsets --- cime_config/config_compsets.xml | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/cime_config/config_compsets.xml b/cime_config/config_compsets.xml index 7cc1680a..22be3f07 100644 --- a/cime_config/config_compsets.xml +++ b/cime_config/config_compsets.xml @@ -63,6 +63,11 @@ 2000_DATM%JRA_SLND_DICE%SSMI_MOM6_DROF%JRA_SGLC_SWAV + + C1850MOMECO_JRA + 1850_DATM%JRA_SLND_DICE%SSMI_MOM6%ECO_DROF%JRA_SGLC_SWAV + + @@ -90,6 +95,11 @@ 2000_DATM%JRA_SLND_CICE_MOM6_DROF%JRA_SGLC_SWAV + + G1850MOMECO_JRA + 1850_DATM%JRA_SLND_CICE_MOM6%ECO_DROF%JRA_SGLC_SWAV + + From a4a6065dce4289c24cd936de853d44ac53448e1f Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 1 Apr 2021 17:20:48 -0600 Subject: [PATCH 036/136] Some general clean-up 1. do not need 3D grid variables for the 2D output (greatly reduces size of history file) 2. Everything but MOM should run on a single node on cheyenne for C / G compsets with MARBL enabled --- .../MARBL_scripts/MARBL_diags_to_diag_table.py | 3 --- .../MARBL_scripts/MOM_ecosys_diagnostics.py | 14 +++++--------- cime_config/config_pes.xml | 16 ++++++++-------- 3 files changed, 13 insertions(+), 20 deletions(-) diff --git a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py index fc0853a3..b873f824 100755 --- a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py +++ b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py @@ -108,9 +108,6 @@ def dump_to_json(self, filename): for freq in self._diag_table_dict: if len(self._diag_table_dict[freq]["fields"][0]["lists"][0]) > 0: out_dict["Files"][freq] = self._diag_table_dict[freq].copy() - if out_dict["Files"][freq]["fields"][0]["module"] == "ocean_model": - transports = ["volcello", "vmo", "vhGM", "vhml", "umo", "uhGM", "uhml"] - out_dict["Files"][freq]["fields"][0]["lists"].append(transports) if out_dict["Files"]: with open(filename, "w") as fp: json.dump(out_dict, fp, separators=(',', ': '), sort_keys=False, indent=3) diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py index 73f2c2b2..6fbaba6c 100644 --- a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -78,11 +78,9 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li # Some diagnostics are not defined for all tracers; diagnostics with 'none' # are not added to diagnostics file and will not show up in tavg_contents - # TODO: can MOM6 compute these values? + # TODO: once MOM has river flux forcing, include it in diagnostic output if False: per_tracer_dict['diags']['%s_RIV_FLUX' % tracer_short_name] = 'none' - per_tracer_dict['diags']['FvPER_%s' % tracer_short_name] = 'none' - per_tracer_dict['diags']['FvICE_%s' % tracer_short_name] = 'none' full_diag_dict[tracer_short_name] = dict(per_tracer_dict) # TODO: turn on some of these diagnostics once they are properly defined @@ -264,9 +262,7 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li value = "never_average" # These diagnostics should be included by default for tracers requested budget terms - # TODO: need MOM6 equivalent of the following POP variables: - # ['DIA_IMPVF', 'HDIFE', 'HDIFN', 'HDIFB', 'RF_TEND'] - for key in ['adx', 'ady', 'tendency_vert_remap', 'tendency']: + for key in ['adx', 'ady', 'dfx', 'dfy', 'tendency_vert_remap', 'tendency']: specific_key = '%s_%s' % (tracer_short_name, key) if specific_key not in full_diag_dict[tracer_short_name]['diags'].keys(): full_diag_dict[tracer_short_name]['diags'][specific_key] = value @@ -276,11 +272,11 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li else: value = "never_average" - # TODO: more variables to figure out MOM6 equivalent of + # TODO: one more variable to figure out MOM6 equivalent of if False: - # These diagnostics should be included by default for tracers requested budget terms + # This diagnostic should be included by default for tracers requested budget terms # ONLY for tracers that have non-zero surface fluxes - for key in ['KPP_SRC']: + for key in ['KPP_NLtransport_']: specific_key = '%s_%s' % (key, tracer_short_name) if specific_key not in full_diag_dict[tracer_short_name]['diags'].keys(): full_diag_dict[tracer_short_name]['diags'][specific_key] = value diff --git a/cime_config/config_pes.xml b/cime_config/config_pes.xml index 7219799e..4e22ebeb 100644 --- a/cime_config/config_pes.xml +++ b/cime_config/config_pes.xml @@ -458,10 +458,10 @@ none - 108 - 108 - 108 - 108 + 36 + 36 + 36 + 36 864 1 1 @@ -524,10 +524,10 @@ none - 72 - 72 - 72 - 72 + 36 + 36 + 36 + 36 864 1 1 From 637a6239223685aa73914733bde128b68832c20a Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 2 Apr 2021 08:46:29 -0600 Subject: [PATCH 037/136] Add FLUX_CPL diagnostics to history output --- .../MARBL_scripts/MOM_ecosys_diagnostics.py | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py index 6fbaba6c..6d69ee29 100644 --- a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -33,14 +33,12 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li fout.write("# MOM-generated diagnostics #\n") fout.write("########################################\n#\n") - # TODO: get these forcing fields into history buffer - # # Add tracer-agnostic forcing fields to requested diagnostics - # fout.write("# Dust and Carbon Fluxes from the Coupler\n#\n") - # fout.write("ATM_FINE_DUST_FLUX_CPL : medium_average\n") - # fout.write("ATM_COARSE_DUST_FLUX_CPL : medium_average\n") - # fout.write("SEAICE_DUST_FLUX_CPL : medium_average\n") - # fout.write("ATM_BLACK_CARBON_FLUX_CPL : medium_average\n") - # fout.write("SEAICE_BLACK_CARBON_FLUX_CPL : medium_average\n") + fout.write("# Dust and Carbon Fluxes from the Coupler\n#\n") + fout.write("ATM_FINE_DUST_FLUX_CPL : medium_average\n") + fout.write("ATM_COARSE_DUST_FLUX_CPL : medium_average\n") + fout.write("SEAICE_DUST_FLUX_CPL : medium_average\n") + fout.write("ATM_BLACK_CARBON_FLUX_CPL : medium_average\n") + fout.write("SEAICE_BLACK_CARBON_FLUX_CPL : medium_average\n") # TODO: add running means, and then define these diagnostics # If adjusting bury coefficients, add running means to requested diagnostics @@ -328,6 +326,4 @@ def _2D_varcheck(varname): """ Return true if variable is 2D """ - if varname.startswith("STF_"): - return True - return False \ No newline at end of file + return varname.startswith("STF_") or varname.endswith("_FLUX_CPL") From 0cc72ab9c0ad27ad7360a66569dd974fe39fb173 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Sat, 3 Apr 2021 10:54:19 -0600 Subject: [PATCH 038/136] Add RIV_FLUX output to history files --- .../MARBL_scripts/MOM_ecosys_diagnostics.py | 49 +++++++------------ 1 file changed, 19 insertions(+), 30 deletions(-) diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py index 6d69ee29..7df1d01e 100644 --- a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -76,9 +76,7 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li # Some diagnostics are not defined for all tracers; diagnostics with 'none' # are not added to diagnostics file and will not show up in tavg_contents - # TODO: once MOM has river flux forcing, include it in diagnostic output - if False: - per_tracer_dict['diags']['%s_RIV_FLUX' % tracer_short_name] = 'none' + per_tracer_dict['diags']['%s_RIV_FLUX' % tracer_short_name] = 'none' full_diag_dict[tracer_short_name] = dict(per_tracer_dict) # TODO: turn on some of these diagnostics once they are properly defined @@ -87,21 +85,21 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li # configured to run with that particular tracer # PO4 if 'PO4' in full_diag_dict.keys(): - # full_diag_dict['PO4']['diags']['PO4_RIV_FLUX'] = 'medium_average' + full_diag_dict['PO4']['diags']['PO4_RIV_FLUX'] = 'medium_average' full_diag_dict['PO4']['diags']['J_PO4'] = 'low_average' full_diag_dict['PO4']['diags']['Jint_100m_PO4'] = 'medium_average' # full_diag_dict['PO4']['diags']['tend_zint_100m_PO4'] = 'medium_average' full_diag_dict['PO4']['properties']['has surface flux'] = True # NO3 if 'NO3' in full_diag_dict.keys(): - # full_diag_dict['NO3']['diags']['NO3_RIV_FLUX'] = 'medium_average' + full_diag_dict['NO3']['diags']['NO3_RIV_FLUX'] = 'medium_average' full_diag_dict['NO3']['diags']['J_NO3'] = 'low_average' full_diag_dict['NO3']['diags']['Jint_100m_NO3'] = 'medium_average' # full_diag_dict['NO3']['diags']['tend_zint_100m_NO3'] = 'medium_average' full_diag_dict['NO3']['properties']['has surface flux'] = True # SiO3 if 'SiO3' in full_diag_dict.keys(): - # full_diag_dict['SiO3']['diags']['SiO3_RIV_FLUX'] = 'medium_average' + full_diag_dict['SiO3']['diags']['SiO3_RIV_FLUX'] = 'medium_average' full_diag_dict['SiO3']['diags']['J_SiO3'] = 'low_average' full_diag_dict['SiO3']['diags']['Jint_100m_SiO3'] = 'medium_average' # full_diag_dict['SiO3']['diags']['tend_zint_100m_SiO3'] = 'medium_average' @@ -114,7 +112,7 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li full_diag_dict['NH4']['properties']['has surface flux'] = True # Fe if 'Fe' in full_diag_dict.keys(): - # full_diag_dict['Fe']['diags']['Fe_RIV_FLUX'] = 'medium_average' + full_diag_dict['Fe']['diags']['Fe_RIV_FLUX'] = 'medium_average' full_diag_dict['Fe']['diags']['J_Fe'] = 'low_average' full_diag_dict['Fe']['diags']['Jint_100m_Fe'] = 'medium_average' # full_diag_dict['Fe']['diags']['tend_zint_100m_Fe'] = 'medium_average' @@ -132,70 +130,62 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li full_diag_dict['O2']['properties']['has surface flux'] = True # DIC if 'DIC' in full_diag_dict.keys(): - # full_diag_dict['DIC']['diags']['DIC_RIV_FLUX'] = 'medium_average' + full_diag_dict['DIC']['diags']['DIC_RIV_FLUX'] = 'medium_average' full_diag_dict['DIC']['diags']['J_DIC'] = 'medium_average' full_diag_dict['DIC']['diags']['Jint_100m_DIC'] = 'medium_average' # full_diag_dict['DIC']['diags']['tend_zint_100m_DIC'] = 'medium_average' - # full_diag_dict['DIC']['diags']['FvPER_DIC'] = 'medium_average' - # full_diag_dict['DIC']['diags']['FvICE_DIC'] = 'medium_average' full_diag_dict['DIC']['properties']['include budget terms'] = True full_diag_dict['DIC']['properties']['has surface flux'] = True # DIC_ALT_CO2 if 'DIC_ALT_CO2' in full_diag_dict.keys(): - # full_diag_dict['DIC_ALT_CO2']['diags']['DIC_ALT_CO2_RIV_FLUX'] = 'medium_average' + full_diag_dict['DIC_ALT_CO2']['diags']['DIC_ALT_CO2_RIV_FLUX'] = 'medium_average' full_diag_dict['DIC_ALT_CO2']['diags']['J_DIC_ALT_CO2'] = 'medium_average' full_diag_dict['DIC_ALT_CO2']['diags']['Jint_100m_DIC_ALT_CO2'] = 'medium_average' # full_diag_dict['DIC_ALT_CO2']['diags']['tend_zint_100m_DIC_ALT_CO2'] = 'medium_average' - # full_diag_dict['DIC_ALT_CO2']['diags']['FvPER_DIC_ALT_CO2'] = 'medium_average' - # full_diag_dict['DIC_ALT_CO2']['diags']['FvICE_DIC_ALT_CO2'] = 'medium_average' full_diag_dict['DIC_ALT_CO2']['properties']['include budget terms'] = True full_diag_dict['DIC_ALT_CO2']['properties']['has surface flux'] = True # ALK if 'ALK' in full_diag_dict.keys(): - # full_diag_dict['ALK']['diags']['ALK_RIV_FLUX'] = 'medium_average' + full_diag_dict['ALK']['diags']['ALK_RIV_FLUX'] = 'medium_average' full_diag_dict['ALK']['diags']['STF_ALK'] = 'medium_average' full_diag_dict['ALK']['diags']['J_ALK'] = 'low_average' full_diag_dict['ALK']['diags']['Jint_100m_ALK'] = 'medium_average' # full_diag_dict['ALK']['diags']['tend_zint_100m_ALK'] = 'medium_average' - # full_diag_dict['ALK']['diags']['FvPER_ALK'] = 'medium_average' - # full_diag_dict['ALK']['diags']['FvICE_ALK'] = 'medium_average' full_diag_dict['ALK']['properties']['has surface flux'] = True # ALK_ALT_CO2 if 'ALK_ALT_CO2' in full_diag_dict.keys(): - # full_diag_dict['ALK_ALT_CO2']['diags']['ALK_ALT_CO2_RIV_FLUX'] = 'medium_average' + full_diag_dict['ALK_ALT_CO2']['diags']['ALK_ALT_CO2_RIV_FLUX'] = 'medium_average' full_diag_dict['ALK_ALT_CO2']['diags']['STF_ALK_ALT_CO2'] = 'medium_average' full_diag_dict['ALK_ALT_CO2']['diags']['J_ALK_ALT_CO2'] = 'low_average' full_diag_dict['ALK_ALT_CO2']['diags']['Jint_100m_ALK_ALT_CO2'] = 'medium_average' # full_diag_dict['ALK_ALT_CO2']['diags']['tend_zint_100m_ALK_ALT_CO2'] = 'medium_average' - # full_diag_dict['ALK_ALT_CO2']['diags']['FvPER_ALK_ALT_CO2'] = 'medium_average' - # full_diag_dict['ALK_ALT_CO2']['diags']['FvICE_ALK_ALT_CO2'] = 'medium_average' full_diag_dict['ALK_ALT_CO2']['properties']['has surface flux'] = True # DOC if 'DOC' in full_diag_dict.keys(): - # full_diag_dict['DOC']['diags']['DOC_RIV_FLUX'] = 'medium_average' + full_diag_dict['DOC']['diags']['DOC_RIV_FLUX'] = 'medium_average' full_diag_dict['DOC']['diags']['Jint_100m_DOC'] = 'medium_average' # full_diag_dict['DOC']['diags']['tend_zint_100m_DOC'] = 'medium_average' full_diag_dict['DOC']['properties']['include budget terms'] = True full_diag_dict['DOC']['properties']['has surface flux'] = False # this should be True if EBM is off # DON if 'DON' in full_diag_dict.keys(): - # full_diag_dict['DON']['diags']['DON_RIV_FLUX'] = 'medium_average' + full_diag_dict['DON']['diags']['DON_RIV_FLUX'] = 'medium_average' full_diag_dict['DON']['properties']['has surface flux'] = True # DOP if 'DOP' in full_diag_dict.keys(): - # full_diag_dict['DOP']['diags']['DOP_RIV_FLUX'] = 'medium_average' + full_diag_dict['DOP']['diags']['DOP_RIV_FLUX'] = 'medium_average' full_diag_dict['DOP']['properties']['has surface flux'] = True # DOPr if 'DOPr' in full_diag_dict.keys(): - # full_diag_dict['DOPr']['diags']['DOPr_RIV_FLUX'] = 'medium_average' + full_diag_dict['DOPr']['diags']['DOPr_RIV_FLUX'] = 'medium_average' full_diag_dict['DOPr']['properties']['has surface flux'] = True # DONr if 'DONr' in full_diag_dict.keys(): - # full_diag_dict['DONr']['diags']['DONr_RIV_FLUX'] = 'medium_average' + full_diag_dict['DONr']['diags']['DONr_RIV_FLUX'] = 'medium_average' full_diag_dict['DONr']['properties']['has surface flux'] = True # DOCr if 'DOCr' in full_diag_dict.keys(): - # full_diag_dict['DOCr']['diags']['DOCr_RIV_FLUX'] = 'medium_average' + full_diag_dict['DOCr']['diags']['DOCr_RIV_FLUX'] = 'medium_average' full_diag_dict['DOCr']['diags']['Jint_100m_DOCr'] = 'medium_average' # full_diag_dict['DOCr']['diags']['tend_zint_100m_DOCr'] = 'medium_average' full_diag_dict['DOCr']['properties']['include budget terms'] = True @@ -206,8 +196,6 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li full_diag_dict['DI13C']['diags']['J_DI13C'] = 'medium_average' full_diag_dict['DI13C']['diags']['Jint_100m_DI13C'] = 'medium_average' # full_diag_dict['DI13C']['diags']['tend_zint_100m_DI13C'] = 'medium_average' - # full_diag_dict['DI13C']['diags']['FvPER_DI13C'] = 'medium_average' - # full_diag_dict['DI13C']['diags']['FvICE_DI13C'] = 'medium_average' full_diag_dict['DI13C']['properties']['has surface flux'] = True # DO13Ctot if 'DO13Ctot' in full_diag_dict.keys(): @@ -221,8 +209,6 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li full_diag_dict['DI14C']['diags']['J_DI14C'] = 'medium_average' full_diag_dict['DI14C']['diags']['Jint_100m_DI14C'] = 'medium_average' # full_diag_dict['DI14C']['diags']['tend_zint_100m_DI14C'] = 'medium_average' - # full_diag_dict['DI14C']['diags']['FvPER_DI14C'] = 'medium_average' - # full_diag_dict['DI14C']['diags']['FvICE_DI14C'] = 'medium_average' full_diag_dict['DI14C']['properties']['has surface flux'] = True # DO14Ctot if 'DO14Ctot' in full_diag_dict.keys(): @@ -326,4 +312,7 @@ def _2D_varcheck(varname): """ Return true if variable is 2D """ - return varname.startswith("STF_") or varname.endswith("_FLUX_CPL") + return (varname.startswith("STF_") or + varname.endswith("_FLUX_CPL") or + varname.endswith("_RIV_FLUX") + ) From 7fee3692844683c2f5fed88155cafca89bf9e587 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 8 Jul 2021 09:57:50 -0600 Subject: [PATCH 039/136] Update to latest MARBL release MARBL updated the interface for some of its python code, which is reflected in MARBL_settings.py in this commit. --- Externals_MOM.cfg | 2 +- cime_config/MARBL_scripts/MARBL_settings.py | 14 +++++++------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/Externals_MOM.cfg b/Externals_MOM.cfg index 52655350..93a600d7 100644 --- a/Externals_MOM.cfg +++ b/Externals_MOM.cfg @@ -10,7 +10,7 @@ required = True [MARBL] protocol = git -tag = marbl0.39.1 +tag = marbl0.39.3 repo_url = https://github.com/marbl-ecosys/MARBL local_path = pkg/MARBL required = True diff --git a/cime_config/MARBL_scripts/MARBL_settings.py b/cime_config/MARBL_scripts/MARBL_settings.py index b44cfcb8..5188f72c 100644 --- a/cime_config/MARBL_scripts/MARBL_settings.py +++ b/cime_config/MARBL_scripts/MARBL_settings.py @@ -76,10 +76,10 @@ def get_autotroph_names(self, calcifier_only=False): """ Returns a list of all autotrophs in current configuration """ autotroph_list = [] - for n in range(1, self._MARBL_settings.settings_dict['autotroph_cnt']+1): - autotroph_name = self._MARBL_settings.settings_dict['autotroph_settings(%d)%%sname' % n].strip('"') - imp_calcifier = (self._MARBL_settings.settings_dict['autotroph_settings(%d)%%imp_calcifier' % n].strip('"') == '.true.') - exp_calcifier = (self._MARBL_settings.settings_dict['autotroph_settings(%d)%%exp_calcifier' % n].strip('"') == '.true.') + for n in range(1, self._MARBL_settings.settings_dict['autotroph_cnt']['value']+1): + autotroph_name = self._MARBL_settings.settings_dict['autotroph_settings(%d)%%sname' % n]['value'].strip('"') + imp_calcifier = (self._MARBL_settings.settings_dict['autotroph_settings(%d)%%imp_calcifier' % n]['value'].strip('"') == '.true.') + exp_calcifier = (self._MARBL_settings.settings_dict['autotroph_settings(%d)%%exp_calcifier' % n]['value'].strip('"') == '.true.') if imp_calcifier or exp_calcifier or (not calcifier_only): autotroph_list.append(autotroph_name) return autotroph_list @@ -90,8 +90,8 @@ def get_zooplankton_names(self): """ Returns a list of all zooplankton in current configuration """ zooplankton_list = [] - for n in range(1, self._MARBL_settings.settings_dict['zooplankton_cnt']+1): - zooplankton_name = self._MARBL_settings.settings_dict['zooplankton_settings(%d)%%sname' % n].strip('"') + for n in range(1, self._MARBL_settings.settings_dict['zooplankton_cnt']['value']+1): + zooplankton_name = self._MARBL_settings.settings_dict['zooplankton_settings(%d)%%sname' % n]['value'].strip('"') zooplankton_list.append(zooplankton_name) return zooplankton_list @@ -100,7 +100,7 @@ def get_zooplankton_names(self): def ladjust_bury_coeff(self): """ Returns True if ladjust_bury_coeff = .true. """ - return (self._MARBL_settings.settings_dict['ladjust_bury_coeff'].strip('"') == '.true.') + return (self._MARBL_settings.settings_dict['ladjust_bury_coeff']['value'].strip('"') == '.true.') ####################################### From 9dbb3ae6b846006d8f873c567ea40d95e7a0a9e6 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 13 Jul 2021 13:51:27 -0600 Subject: [PATCH 040/136] Improved diagnostic handling 1. Use hm_ for output on model grid, h_ for interpolated 3d output 2. MARBL_diags_to_diag_table.py should be smart enough to know if output field contains 3D variables (frequency ends in _z) and is being output on the model grid (ocean_model instead of ocean_model_z); when both of those conditions are met, the tranport variables such as volcello are added 3. Added BOT_FLUX_TO_TEND to output; this gets posted before the unit conversion from mks to cgs, so units are 1/m --- .../MARBL_scripts/MARBL_diags_to_diag_table.py | 16 ++++++++++------ .../MARBL_scripts/MOM_ecosys_diagnostics.py | 3 +++ 2 files changed, 13 insertions(+), 6 deletions(-) diff --git a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py index b873f824..1bcdf981 100755 --- a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py +++ b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py @@ -59,27 +59,28 @@ def __init__(self): self._diag_table_dict = dict() # NOTE: the "_z" in frequency => convert to z-space rather than output on native grid + # NOTE: "hm" => 3D vars on model grid, "h" => interpolated # "medium" frequency should be treated like "hm" stream -- annual in spinup runs, monthly otherwise - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual%4yr", "$TEST == True": "hm_bgc_daily%4yr-%2mo-%2dy", "else": "hm_bgc_monthly%4yr-%2mo"} + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual%4yr", "$TEST == True": "h_bgc_daily%4yr-%2mo-%2dy", "else": "h_bgc_monthly%4yr-%2mo"} output_freq_units_dict = {'$OCN_DIAG_MODE == "spinup"': "years", "$TEST == True": "days", "else": "months"} self._diag_table_dict["medium"] = self._dict_template(suffix_dict, output_freq_units_dict) - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual_z%4yr", "$TEST == True": "hm_bgc_daily_z%4yr-%2mo-%2dy", "else": "hm_bgc_monthly_z%4yr-%2mo"} + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual_z%4yr", "$TEST == True": "h_bgc_daily_z%4yr-%2mo-%2dy", "else": "h_bgc_monthly_z%4yr-%2mo"} self._diag_table_dict["medium_z"] = self._dict_template(suffix_dict, output_freq_units_dict, module="ocean_model_z") # "high" frequency should be treated like "sfc" stream -- 5-day averages in spinup, daily otherwise # unlike "sfc", this stream will write one file per month instead of per year (except in spinup) - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_daily5%4yr", "else": "hm_bgc_daily%4yr-%2mo"} + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_daily5%4yr", "else": "h_bgc_daily%4yr-%2mo"} output_freq_dict = {'$OCN_DIAG_MODE == "spinup"': 5, "else": 1} new_file_freq_units_dict = {'$OCN_DIAG_MODE == "spinup"': "years", "else": "months"} self._diag_table_dict["high"] = self._dict_template(suffix_dict, "days", new_file_freq_units_dict, output_freq_dict) - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_daily5_z%4yr", "else": "hm_bgc_daily_z%4yr-%2mo"} + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_daily5_z%4yr", "else": "h_bgc_daily_z%4yr-%2mo"} self._diag_table_dict["high_z"] = self._dict_template(suffix_dict, "days", new_file_freq_units_dict, output_freq_dict, module="ocean_model_z") # "low" frequency should be treated as annual averages - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual2%4yr", "else": "hm_bgc_annual%4yr"} + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual2%4yr", "else": "h_bgc_annual%4yr"} self._diag_table_dict["low"] = self._dict_template(suffix_dict, "years") - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual2_z%4yr", "else": "hm_bgc_annual_z%4yr"} + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual2_z%4yr", "else": "h_bgc_annual_z%4yr"} self._diag_table_dict["low_z"] = self._dict_template(suffix_dict, "years", module="ocean_model_z") @@ -108,6 +109,9 @@ def dump_to_json(self, filename): for freq in self._diag_table_dict: if len(self._diag_table_dict[freq]["fields"][0]["lists"][0]) > 0: out_dict["Files"][freq] = self._diag_table_dict[freq].copy() + if out_dict["Files"][freq]["fields"][0]["module"] == "ocean_model" and freq[-2:] == "_z": + transports = ["volcello", "vmo", "vhGM", "vhml", "umo", "uhGM", "uhml"] + out_dict["Files"][freq]["fields"][0]["lists"].append(transports) if out_dict["Files"]: with open(filename, "w") as fp: json.dump(out_dict, fp, separators=(',', ': '), sort_keys=False, indent=3) diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py index 7df1d01e..7ffcb2be 100644 --- a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -40,6 +40,9 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li fout.write("ATM_BLACK_CARBON_FLUX_CPL : medium_average\n") fout.write("SEAICE_BLACK_CARBON_FLUX_CPL : medium_average\n") + fout.write("# Bottom Flux to Tendency Conversion\n#\n") + fout.write("BOT_FLUX_TO_TEND : medium_average\n") + # TODO: add running means, and then define these diagnostics # If adjusting bury coefficients, add running means to requested diagnostics if ladjust_bury_coeff: From 017cb8d39aa731625f794958cb33902cbe15e233 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Sun, 25 Jul 2021 13:18:20 -0600 Subject: [PATCH 041/136] Set MIX_BOUNDARY_TRACER_ALE=True for MARBL currently triggers based on OCN_BGC_TRACER != None, but maybe we want this variable true at all times? --- param_templates/MOM_input.yaml | 9 +++++++++ param_templates/json/MOM_input.json | 8 ++++++++ 2 files changed, 17 insertions(+) diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index 4f04da2a..e304a51b 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -3282,6 +3282,15 @@ Global: datatype: string value: $OCN_GRID == "tx0.25v1": "geothermal_hf" + MIX_BOUNDARY_TRACER_ALE: + description: | + "[Boolean] default = True + If true and in ALE mode, mix the passive tracers in massless layers at the + bottom into the interior as though a diffusivity of KD_MIN_TR were operating." + datatype: logical + units: Boolean + value: + $OCN_BGC_CONFIG != "none": True KPP: N_SMOOTH: diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index 56d55412..a0fa5d05 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -2626,6 +2626,14 @@ "value": { "$OCN_GRID == \"tx0.25v1\"": "geothermal_hf" } + }, + "MIX_BOUNDARY_TRACER_ALE": { + "description": "\"[Boolean] default = True\nIf true and in ALE mode, mix the passive tracers in massless layers at the\nbottom into the interior as though a diffusivity of KD_MIN_TR were operating.\"\n", + "datatype": "logical", + "units": "Boolean", + "value": { + "$OCN_BGC_CONFIG != \"none\"": true + } } }, "KPP": { From c769c1d28d330620ecc11fa8a9baa8fda0bd64ca Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 26 Jul 2021 13:21:26 -0600 Subject: [PATCH 042/136] Updates from testing on izumi 1. Added to input_data_list so that check_input_data reports missing files correctly. Note that when this update gets back to cheyenne, it will start looking for files in $INPUTDATAROOT rather than my glade work directory 2. Updated the izumi PE layout for compsets with MARBL enabled There seems to be an issue with running CESM+MOM on izumi, so even if I had the right input files the runs would still crash... but it'll be nice to have these updates in place for future testing with the CGD machine. --- cime_config/config_pes.xml | 37 +++++++++++++++++++++-- param_templates/input_data_list.yaml | 10 +++++- param_templates/json/input_data_list.json | 15 +++++++++ 3 files changed, 59 insertions(+), 3 deletions(-) diff --git a/cime_config/config_pes.xml b/cime_config/config_pes.xml index 4e22ebeb..6c459d91 100644 --- a/cime_config/config_pes.xml +++ b/cime_config/config_pes.xml @@ -589,7 +589,7 @@ - + none 48 @@ -622,7 +622,40 @@ 0 - + + none + + 48 + 48 + 48 + 48 + 336 + 1 + 1 + 1 + + + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + + + 0 + 0 + 0 + 0 + 48 + 0 + 0 + 0 + + + none 48 diff --git a/param_templates/input_data_list.yaml b/param_templates/input_data_list.yaml index dda50468..d4d326e7 100644 --- a/param_templates/input_data_list.yaml +++ b/param_templates/input_data_list.yaml @@ -35,4 +35,12 @@ mom.input_data_list: $OCN_GRID == "tx0.25v1": "${INPUTDIR}/geothermal_davies2013_v1.nc" ocean_seaw: $OCN_GRID == "tx0.25v1": "${INPUTDIR}/seawifs-clim-1997-2010.1440x1080.v20180328.nc" -... + marbl_tracers_ic_file: + $OCN_BGC_CONFIG != "None": + $OCN_GRID == "tx0.66v1": "${INPUTDIR}/ecosys_jan_IC_omip_MOM_tx0.66v1_c190925.nc" + marbl_fesedflux_file: + $OCN_BGC_CONFIG != "None": + $OCN_GRID == "tx0.66v1": "${INPUTDIR}/fesedflux_total_reduce_oxic_tx0.66v1.c201204.nc" + marbl_feventflux_file: + $OCN_BGC_CONFIG != "None": + $OCN_GRID == "tx0.66v1": "${INPUTDIR}/feventflux_5gmol_tx0.66v1.c201204.nc" diff --git a/param_templates/json/input_data_list.json b/param_templates/json/input_data_list.json index 02093c7d..3684ebea 100644 --- a/param_templates/json/input_data_list.json +++ b/param_templates/json/input_data_list.json @@ -45,6 +45,21 @@ }, "ocean_seaw": { "$OCN_GRID == \"tx0.25v1\"": "${INPUTDIR}/seawifs-clim-1997-2010.1440x1080.v20180328.nc" + }, + "marbl_tracers_ic_file": { + "$OCN_BGC_CONFIG != \"None\"": { + "$OCN_GRID == \"tx0.66v1\"": "${INPUTDIR}/ecosys_jan_IC_omip_MOM_tx0.66v1_c190925.nc" + } + }, + "marbl_fesedflux_file": { + "$OCN_BGC_CONFIG != \"None\"": { + "$OCN_GRID == \"tx0.66v1\"": "${INPUTDIR}/fesedflux_total_reduce_oxic_tx0.66v1.c201204.nc" + } + }, + "marbl_feventflux_file": { + "$OCN_BGC_CONFIG != \"None\"": { + "$OCN_GRID == \"tx0.66v1\"": "${INPUTDIR}/feventflux_5gmol_tx0.66v1.c201204.nc" + } } } } \ No newline at end of file From 8b037396727541e322aca9f21afb25d2c1118097 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 25 Aug 2021 16:30:46 -0600 Subject: [PATCH 043/136] Update to latest MARBL tag This tag includes bot_flux_to_tend --- Externals_MOM.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Externals_MOM.cfg b/Externals_MOM.cfg index 93a600d7..1fd0e9ab 100644 --- a/Externals_MOM.cfg +++ b/Externals_MOM.cfg @@ -10,7 +10,7 @@ required = True [MARBL] protocol = git -tag = marbl0.39.3 +tag = marbl0.40.0 repo_url = https://github.com/marbl-ecosys/MARBL local_path = pkg/MARBL required = True From 77a749ac57bd1163a20f73254dd397537d3b80b0 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 2 Sep 2021 14:13:20 -0600 Subject: [PATCH 044/136] Easier to set vertical grid for BGC history files Introduced OCN_BGC_HIST_VERT_GRID into env_run.xml, with three possible values: 1. interpolated [default] -- will write output on a set z-grid (mom6.h_bgc_*) 2. native -- will write output on the native MOM6 vertical grid (mom6.hm_bgc_*) 3. both -- will write both of the above files Currently the default is "interpolated", though I think we want the test suite to run on native (and my current testing is using xmlchange to get native output as well) --- .../MARBL_diags_to_diag_table.py | 63 ++++++++++++++----- .../MARBL_scripts/MOM_ecosys_diagnostics.py | 12 ++++ cime_config/buildnml | 7 ++- cime_config/config_component.xml | 14 +++++ 4 files changed, 78 insertions(+), 18 deletions(-) diff --git a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py index 1bcdf981..b8532124 100755 --- a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py +++ b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py @@ -49,7 +49,7 @@ class DiagTableClass(object): """ Class that is used to generate JSON file to extend diag_table from ecosys_diagnostics file """ - def __init__(self): + def __init__(self, vert_grid): """ Constructor: creates a dictionary object to eventually dump to JSON """ @@ -61,30 +61,51 @@ def __init__(self): # NOTE: the "_z" in frequency => convert to z-space rather than output on native grid # NOTE: "hm" => 3D vars on model grid, "h" => interpolated - # "medium" frequency should be treated like "hm" stream -- annual in spinup runs, monthly otherwise + # "medium" frequency should be treated like "mom6.hm" stream -- annual in spinup runs, monthly otherwise + # i. 2D vars suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual%4yr", "$TEST == True": "h_bgc_daily%4yr-%2mo-%2dy", "else": "h_bgc_monthly%4yr-%2mo"} output_freq_units_dict = {'$OCN_DIAG_MODE == "spinup"': "years", "$TEST == True": "days", "else": "months"} self._diag_table_dict["medium"] = self._dict_template(suffix_dict, output_freq_units_dict) - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual_z%4yr", "$TEST == True": "h_bgc_daily_z%4yr-%2mo-%2dy", "else": "h_bgc_monthly_z%4yr-%2mo"} - self._diag_table_dict["medium_z"] = self._dict_template(suffix_dict, output_freq_units_dict, module="ocean_model_z") - - # "high" frequency should be treated like "sfc" stream -- 5-day averages in spinup, daily otherwise + # ii. 3D vars on interpolated grid + if vert_grid in ["interpolated", "both"]: + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual_z%4yr", "$TEST == True": "h_bgc_daily_z%4yr-%2mo-%2dy", "else": "h_bgc_monthly_z%4yr-%2mo"} + self._diag_table_dict["medium_z"] = self._dict_template(suffix_dict, output_freq_units_dict, module="ocean_model_z") + # iii. 3D vars on native grid + if vert_grid in ["native", "both"]: + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual_z%4yr", "$TEST == True": "hm_bgc_daily_z%4yr-%2mo-%2dy", "else": "hm_bgc_monthly_z%4yr-%2mo"} + self._diag_table_dict["medium_native_z"] = self._dict_template(suffix_dict, output_freq_units_dict, module="ocean_model") + + # "high" frequency should be treated like "mom6.sfc" stream -- 5-day averages in spinup, daily otherwise # unlike "sfc", this stream will write one file per month instead of per year (except in spinup) + # i. 2D vars suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_daily5%4yr", "else": "h_bgc_daily%4yr-%2mo"} output_freq_dict = {'$OCN_DIAG_MODE == "spinup"': 5, "else": 1} new_file_freq_units_dict = {'$OCN_DIAG_MODE == "spinup"': "years", "else": "months"} self._diag_table_dict["high"] = self._dict_template(suffix_dict, "days", new_file_freq_units_dict, output_freq_dict) - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_daily5_z%4yr", "else": "h_bgc_daily_z%4yr-%2mo"} - self._diag_table_dict["high_z"] = self._dict_template(suffix_dict, "days", new_file_freq_units_dict, output_freq_dict, module="ocean_model_z") + # ii. 3D vars on interpolated grid + if vert_grid in ["interpolated", "both"]: + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_daily5_z%4yr", "else": "h_bgc_daily_z%4yr-%2mo"} + self._diag_table_dict["high_z"] = self._dict_template(suffix_dict, "days", new_file_freq_units_dict, output_freq_dict, module="ocean_model_z") + # iii. 3D vars on native grid + if vert_grid in ["native", "both"]: + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_daily5_z%4yr", "else": "hm_bgc_daily_z%4yr-%2mo"} + self._diag_table_dict["high_native_z"] = self._dict_template(suffix_dict, "days", new_file_freq_units_dict, output_freq_dict, module="ocean_model") # "low" frequency should be treated as annual averages + # i. 2D vars suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual2%4yr", "else": "h_bgc_annual%4yr"} self._diag_table_dict["low"] = self._dict_template(suffix_dict, "years") - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual2_z%4yr", "else": "h_bgc_annual_z%4yr"} - self._diag_table_dict["low_z"] = self._dict_template(suffix_dict, "years", module="ocean_model_z") + # ii. 3D vars on interpolated grid + if vert_grid in ["interpolated", "both"]: + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual2_z%4yr", "else": "h_bgc_annual_z%4yr"} + self._diag_table_dict["low_z"] = self._dict_template(suffix_dict, "years", module="ocean_model_z") + # iii. 3D vars on native grid + if vert_grid in ["native", "both"]: + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual2_z%4yr", "else": "hm_bgc_annual_z%4yr"} + self._diag_table_dict["low_native_z"] = self._dict_template(suffix_dict, "years", module="ocean_model") - def update(self, varname, frequency, is2D, lMARBL_output_all): + def update(self, varname, frequency, is2D, lMARBL_output_all, vert_grid): if lMARBL_output_all: use_freq = ['medium'] else: @@ -97,8 +118,13 @@ def update(self, varname, frequency, is2D, lMARBL_output_all): if freq == "never": continue # append _z to frequency for 3D vars - suffix="" if is2D else "_z" - self._diag_table_dict[f"{freq}{suffix}"]["fields"][0]["lists"][0].append(varname) + if is2D: + self._diag_table_dict[f"{freq}"]["fields"][0]["lists"][0].append(varname) + else: + if vert_grid in ["interpolated", "both"]: + self._diag_table_dict[f"{freq}_z"]["fields"][0]["lists"][0].append(varname) + if vert_grid in ["native", "both"]: + self._diag_table_dict[f"{freq}_native_z"]["fields"][0]["lists"][0].append(varname) def dump_to_json(self, filename): @@ -182,6 +208,10 @@ def _parse_args(): parser.add_argument('-g', '--high_frequency_stream', action='store', dest='high_frequency_stream', type=int, default= 0, help='Stream to put high frequency output into (required if not lMARBL_output_all)') + parser.add_argument('-v', '--vert_grid', action='store', dest='vert_grid', + default= 'native', choices=['native', 'interpolated', 'both'], + help='BGC history output grid') + # Should all MARBL diagnostics be included in the hm_bgc stream? parser.add_argument('--lMARBL_output_all', action='store', dest='lMARBL_output_all', type=bool, default=False, help="Put all MARBL diagnostics in hm_bgc stream") @@ -235,6 +265,7 @@ def _parse_line(line_in): def diagnostics_to_diag_table(ecosys_diagnostics_in, diag_table_out, diag2D_list, + vert_grid, lMARBL_output_all, lMARBL_output_alt_co2): """ @@ -258,7 +289,7 @@ def diagnostics_to_diag_table(ecosys_diagnostics_in, sys.exit(1) # 2. Set up diag_table object - diag_table = DiagTableClass() + diag_table = DiagTableClass(vert_grid) # 3. Read ecosys_diagnostics_in line by line, convert each line to diag table entry with open(ecosys_diagnostics_in, 'r') as file_in: @@ -285,7 +316,7 @@ def diagnostics_to_diag_table(ecosys_diagnostics_in, # iii. Update diag table is2D = varname in diag2D_list - diag_table.update(varname, frequency, is2D, lMARBL_output_all) + diag_table.update(varname, frequency, is2D, lMARBL_output_all, vert_grid) # File footer diag_table.dump_to_json(diag_table_out) @@ -302,5 +333,7 @@ def diagnostics_to_diag_table(ecosys_diagnostics_in, # call diagnostics_to_diag_table() diagnostics_to_diag_table(args.ecosys_diagnostics_in, args.diag_table_out, + args.diag2D_list, + args.vert_grid, args.lMARBL_output_all, args.lMARBL_output_alt_co2) diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py index 7ffcb2be..dd15f22c 100644 --- a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -43,6 +43,18 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li fout.write("# Bottom Flux to Tendency Conversion\n#\n") fout.write("BOT_FLUX_TO_TEND : medium_average\n") + fout.write("# Other physics terms\n#\n") + fout.write("h: medium_average\n") + fout.write("DICh_tendency: medium_average\n") + fout.write("DIC_advection_xy: medium_average\n") + fout.write("DIC_tendency_vert_remap: medium_average\n") + fout.write("DIC_dfxy_cont_tendency: medium_average\n") + fout.write("DIC_lbdxy_cont_tendency: medium_average\n") + fout.write("DIC_dfxy_conc_tendency: medium_average\n") + fout.write("DIC_lbdxy_conc_tendency: medium_average\n") + fout.write("vertdiff_tend_DIC: medium_average\n") + fout.write("vertdiff_tend_O2: medium_average\n") + # TODO: add running means, and then define these diagnostics # If adjusting bury coefficients, add running means to requested diagnostics if ladjust_bury_coeff: diff --git a/cime_config/buildnml b/cime_config/buildnml index d1c28d6f..61d00abe 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -42,6 +42,7 @@ def prep_input(case): comp_root_dir_ocn = case.get_value("COMP_ROOT_DIR_OCN") srcroot = case.get_value("SRCROOT") ocn_bgc_config = case.get_value("OCN_BGC_CONFIG") + vert_grid = case.get_value("OCN_BGC_HIST_VERT_GRID") caseroot = case.get_value("CASEROOT") ocn_grid = case.get_value("OCN_GRID") run_type = case.get_value("RUN_TYPE") @@ -120,7 +121,7 @@ def prep_input(case): # For now, user has no control over lMARBL_output_all # TODO: mimic what is done in POP and allow it via user_nl_mom? lMARBL_output_all = case.get_value("TEST") - generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, lMARBL_output_all) + generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, vert_grid, lMARBL_output_all) if os.path.isfile(diag_table_buildconf): diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) diag_table.write(diag_table_dest, case) @@ -168,7 +169,7 @@ def _copy_files_to_momconf(case): for filename in ["MOM_input", "MOM_override", "diag_table", "input.nml"]: shutil.copy(os.path.join(rundir,filename), momconfdir) -def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, lMARBL_output_all): +def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, vert_grid, lMARBL_output_all): """ Create Buildconf/momconf/diag_table_MARBL.json @@ -226,7 +227,7 @@ def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfd # 4. Generate diag_table based on contents of ecosys_diagnostics # and knowledge of which diags are 2D from MARBL_diags_to_diag_table import diagnostics_to_diag_table - diagnostics_to_diag_table(ecosys_diags_buildconf, diag_table_buildconf, diag2D_list, lMARBL_output_all, False) + diagnostics_to_diag_table(ecosys_diags_buildconf, diag_table_buildconf, diag2D_list, vert_grid, lMARBL_output_all, False) # pylint: disable=unused-argument diff --git a/cime_config/config_component.xml b/cime_config/config_component.xml index 3c509bf0..c76440f8 100644 --- a/cime_config/config_component.xml +++ b/cime_config/config_component.xml @@ -81,6 +81,20 @@ "none" => run without MARBL tracers. + + + char + interpolated,native,both + interpolated + run_component_mom + env_run.xml + Vertical grid to use in BGC-specific history files that contain 3D variables + "native" will output on whatever levels MOM is using (e.g. z*); stream will be mom6.h_bgc* + "interpolated" will output will be interpolated to static z levels; stream will be mom6.hm_bgc* + "both" will write both mom6.h_bgc and mom6.hm_bgc files + + + ========================================= MOM6 naming conventions From a4b9ba82f9faeedfcfc7a1ae6b37d4b77668df42 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 2 Sep 2021 15:55:49 -0600 Subject: [PATCH 045/136] Revert changes to MOM_ecosys_diagnostics.py Accidentally committed changes that added some diagnostic output that is useful for me in testing but not necessary for the model as a whole --- cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py index dd15f22c..7ffcb2be 100644 --- a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -43,18 +43,6 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li fout.write("# Bottom Flux to Tendency Conversion\n#\n") fout.write("BOT_FLUX_TO_TEND : medium_average\n") - fout.write("# Other physics terms\n#\n") - fout.write("h: medium_average\n") - fout.write("DICh_tendency: medium_average\n") - fout.write("DIC_advection_xy: medium_average\n") - fout.write("DIC_tendency_vert_remap: medium_average\n") - fout.write("DIC_dfxy_cont_tendency: medium_average\n") - fout.write("DIC_lbdxy_cont_tendency: medium_average\n") - fout.write("DIC_dfxy_conc_tendency: medium_average\n") - fout.write("DIC_lbdxy_conc_tendency: medium_average\n") - fout.write("vertdiff_tend_DIC: medium_average\n") - fout.write("vertdiff_tend_O2: medium_average\n") - # TODO: add running means, and then define these diagnostics # If adjusting bury coefficients, add running means to requested diagnostics if ladjust_bury_coeff: From 285a1cddc6c820c58a7d128623ddb11d5c5c7ccd Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 10 Sep 2021 13:12:00 -0600 Subject: [PATCH 046/136] Update to latest MARBL tag --- Externals_MOM.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Externals_MOM.cfg b/Externals_MOM.cfg index 1fd0e9ab..58e33681 100644 --- a/Externals_MOM.cfg +++ b/Externals_MOM.cfg @@ -10,7 +10,7 @@ required = True [MARBL] protocol = git -tag = marbl0.40.0 +tag = marbl0.40.3 repo_url = https://github.com/marbl-ecosys/MARBL local_path = pkg/MARBL required = True From 8d57ecd6fbc4b9bfd824af9671646b5ee83bc431 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 15 Sep 2021 12:15:17 -0600 Subject: [PATCH 047/136] Make sure MARBL streams are archived / tested Previously, ERS tests weren't comparing the hm_bgc streams that MOM was producing - this let a bug slip in where river fluxes weren't set up properly on restart runs. I updated the logic in config_archive.xml to make sure h_bgc and hm_bgc streams are tested, and then I updated the diag_table so that the test writes hm_bgc_YYYY_MM (so a 5-day test will create this file when writing output on day one and then over-write the data each subsequent day). This is necessary because otherwise a 5-day test will generate a tremendous amount of output for cprnc to check. --- cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py | 6 +++--- cime_config/config_archive.xml | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py index b8532124..f714a814 100755 --- a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py +++ b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py @@ -63,16 +63,16 @@ def __init__(self, vert_grid): # "medium" frequency should be treated like "mom6.hm" stream -- annual in spinup runs, monthly otherwise # i. 2D vars - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual%4yr", "$TEST == True": "h_bgc_daily%4yr-%2mo-%2dy", "else": "h_bgc_monthly%4yr-%2mo"} + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual%4yr", "$TEST == True": "h_bgc%4yr-%2mo", "else": "h_bgc_monthly%4yr-%2mo"} output_freq_units_dict = {'$OCN_DIAG_MODE == "spinup"': "years", "$TEST == True": "days", "else": "months"} self._diag_table_dict["medium"] = self._dict_template(suffix_dict, output_freq_units_dict) # ii. 3D vars on interpolated grid if vert_grid in ["interpolated", "both"]: - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual_z%4yr", "$TEST == True": "h_bgc_daily_z%4yr-%2mo-%2dy", "else": "h_bgc_monthly_z%4yr-%2mo"} + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual_z%4yr", "$TEST == True": "h_bgc_z%4yr-%2mo", "else": "h_bgc_monthly_z%4yr-%2mo"} self._diag_table_dict["medium_z"] = self._dict_template(suffix_dict, output_freq_units_dict, module="ocean_model_z") # iii. 3D vars on native grid if vert_grid in ["native", "both"]: - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual_z%4yr", "$TEST == True": "hm_bgc_daily_z%4yr-%2mo-%2dy", "else": "hm_bgc_monthly_z%4yr-%2mo"} + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual_z%4yr", "$TEST == True": "hm_bgc_z%4yr-%2mo", "else": "hm_bgc_monthly_z%4yr-%2mo"} self._diag_table_dict["medium_native_z"] = self._dict_template(suffix_dict, output_freq_units_dict, module="ocean_model") # "high" frequency should be treated like "mom6.sfc" stream -- 5-day averages in spinup, daily otherwise diff --git a/cime_config/config_archive.xml b/cime_config/config_archive.xml index 1a2b889f..9e7b80a1 100644 --- a/cime_config/config_archive.xml +++ b/cime_config/config_archive.xml @@ -3,7 +3,7 @@ r h.?[_\d+]+.nc$ hm.?[_\d+]+.nc$ - hm_bgc.?[_\d+]+.nc$ + h(m)?_bgc(_monthly|_daily|_annual)?(_z)?.?[_\d+]+.nc$ frc.?[_\d+]+.nc$ sfc.?[_\d+]+.nc$ rho2.?[_\d+]+.nc$ From 3fbb5dc41e583ef8978344fe27a3a0f85e46681d Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 23 Sep 2021 13:41:28 -0600 Subject: [PATCH 048/136] Easier to switch between monthly and nstep output For the h[m]_bgc (and _z) streams, there's now a single logical flag controlling whether this output is monthly (default) or nstep (useful for debugging). If this is a permanent feature then it needs to be implemented in a cleaner manner, perhaps with an env_run.xml flag, but it could be that this gets pulled out once initial testing is complete. --- .../MARBL_diags_to_diag_table.py | 31 ++++++++++++++----- 1 file changed, 24 insertions(+), 7 deletions(-) diff --git a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py index f714a814..ca85ff20 100755 --- a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py +++ b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py @@ -57,23 +57,40 @@ def __init__(self, vert_grid): # I don't like how I handle the names... namely, hm_bgc_annual in spinup mode has # completely different variables from hm_bgc_annual in "regular" mode self._diag_table_dict = dict() + # TODO: need a cleaner way to implement this; basically it's a flag that changes the monthly bgc + # history file to nstep output (also requires a change in MOM_ecosys_diags to drop dust / iron + # fluxes and RIV_FLUX diags... for some reason they can't be written at first time step? + # One possible solution: OCN_BGC_DIAG_MODE in env_run? + self._nstep_output = False # NOTE: the "_z" in frequency => convert to z-space rather than output on native grid # NOTE: "hm" => 3D vars on model grid, "h" => interpolated # "medium" frequency should be treated like "mom6.hm" stream -- annual in spinup runs, monthly otherwise # i. 2D vars - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual%4yr", "$TEST == True": "h_bgc%4yr-%2mo", "else": "h_bgc_monthly%4yr-%2mo"} - output_freq_units_dict = {'$OCN_DIAG_MODE == "spinup"': "years", "$TEST == True": "days", "else": "months"} - self._diag_table_dict["medium"] = self._dict_template(suffix_dict, output_freq_units_dict) + new_file_freq_units = "days" if self._nstep_output else None + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual%4yr", + "$TEST == True": "h_bgc%4yr-%2mo", + "else": "h_bgc_monthly%4yr-%2mo"} + output_freq_units_dict = {'$OCN_DIAG_MODE == "spinup"': "years", + "$TEST == True": "days", + f"{self._nstep_output} == True": "hours", + "else": "months"} + self._diag_table_dict["medium"] = self._dict_template(suffix_dict, output_freq_units_dict, new_file_freq_units=new_file_freq_units) # ii. 3D vars on interpolated grid if vert_grid in ["interpolated", "both"]: - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual_z%4yr", "$TEST == True": "h_bgc_z%4yr-%2mo", "else": "h_bgc_monthly_z%4yr-%2mo"} - self._diag_table_dict["medium_z"] = self._dict_template(suffix_dict, output_freq_units_dict, module="ocean_model_z") + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "h_bgc_annual_z%4yr", + "$TEST == True": "h_bgc_z%4yr-%2mo", + f"{self._nstep_output} == True": "h_bgc_nstep_z%4yr-%2mo-%2dy", + "else": "h_bgc_monthly_z%4yr-%2mo"} + self._diag_table_dict["medium_z"] = self._dict_template(suffix_dict, output_freq_units_dict, new_file_freq_units=new_file_freq_units, module="ocean_model_z") # iii. 3D vars on native grid if vert_grid in ["native", "both"]: - suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual_z%4yr", "$TEST == True": "hm_bgc_z%4yr-%2mo", "else": "hm_bgc_monthly_z%4yr-%2mo"} - self._diag_table_dict["medium_native_z"] = self._dict_template(suffix_dict, output_freq_units_dict, module="ocean_model") + suffix_dict = {'$OCN_DIAG_MODE == "spinup"': "hm_bgc_annual_z%4yr", + "$TEST == True": "hm_bgc_z%4yr-%2mo", + f"{self._nstep_output} == True": "hm_bgc_nstep_z%4yr-%2mo-%2dy", + "else": "hm_bgc_monthly_z%4yr-%2mo"} + self._diag_table_dict["medium_native_z"] = self._dict_template(suffix_dict, output_freq_units_dict, new_file_freq_units=new_file_freq_units, module="ocean_model") # "high" frequency should be treated like "mom6.sfc" stream -- 5-day averages in spinup, daily otherwise # unlike "sfc", this stream will write one file per month instead of per year (except in spinup) From 1037abafb635238cc5bf1863ce1249453d7a1c49 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 24 Sep 2021 10:21:24 -0600 Subject: [PATCH 049/136] Remove "none" as valid OCN_BGC_CONFIG option Determine default value of USE_MARBL_TRACERS from compset rather than checking to see if OCN_BGC_CONFIG != none. Also, in buildnml, use_MARBL comes directly from MOM_input rather than that same logic around OCN_BGC_CONFIG --- cime_config/buildnml | 42 ++++++++++++++++++++--------- cime_config/config_component.xml | 8 ++---- param_templates/MOM_input.yaml | 3 ++- param_templates/json/MOM_input.json | 3 ++- 4 files changed, 36 insertions(+), 20 deletions(-) diff --git a/cime_config/buildnml b/cime_config/buildnml index 61d00abe..7e1494f3 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -49,27 +49,42 @@ def prep_input(case): continue_run = case.get_value("CONTINUE_RUN") momconfdir = os.path.join(caseroot, "Buildconf", "momconf") SourceMods_dir = os.path.join(caseroot,"SourceMods","src.mom") - use_MARBL = (ocn_bgc_config != "none") # Make sure that rundir exists. If not, make it: if not os.path.exists(rundir): os.makedirs(rundir) - # Make sure that momconfdir exists. If not, make it: - if not os.path.exists(momconfdir) and use_MARBL: - os.makedirs(momconfdir) - # Parse json files and create MOM6 input files in rundir json_templates_dir = os.path.join(comp_root_dir_ocn,"param_templates","json") # 1. Create MOM_input: + # TODO: should we always read from rundir/MOM_input, or only read from + # disk when pulling MOM_input from SourceMods? if "MOM_input" in os.listdir(SourceMods_dir): run_cmd("cp "+os.path.join(SourceMods_dir,"MOM_input")+" "+rundir) + # use_MARBL = False + # with open(os.path.join(SourceMods_dir,"MOM_input")) as input_file: + # for line in input_file: + # if line.split('=')[0].strip() == "USE_MARBL_TRACERS": + # use_MARBL = bool(line.split('=')[1]) + # break else: MOM_input_src = os.path.join(json_templates_dir, "MOM_input.json") MOM_input_dest = os.path.join(rundir,"MOM_input") MOM_input = FType_MOM_params.from_json(MOM_input_src) MOM_input.write(output_path=MOM_input_dest, output_format="MOM_input", case=case) + # try: + # use_MARBL = MOM_input._data["Global"]["USE_MARBL_TRACERS"]["value"] + # except: + # use_MARBL = False + + # Need to know value of USE_MARBL_TRACERS from MOM_input + use_MARBL = False + with open(os.path.join(rundir,"MOM_input")) as input_file: + for line in input_file: + if line.split('=')[0].strip() == "USE_MARBL_TRACERS": + use_MARBL = bool(line.split('=')[1]) + break # 2. Create MOM_override: user_nl_mom = FType_MOM_params.from_MOM_input(os.path.join(caseroot,"user_nl_mom")) @@ -95,7 +110,13 @@ def prep_input(case): input_nml = FType_input_nml.from_json(input_nml_src) input_nml.write(input_nml_dest, case) - # 5. Create marbl_in (if case is set up with ocean BGC) + # 5. Create mom.input_data_list: + input_data_list_src = os.path.join(json_templates_dir, "input_data_list.json") + input_data_list_dest = os.path.join(Buildconf,"mom.input_data_list") + input_data_list = FType_input_data_list.from_json(input_data_list_src) + input_data_list.write(input_data_list_dest, case, MOM_input_final) + + # 6. Create marbl_in (if case is set up with ocean BGC) if use_MARBL: MARBL_dir = os.path.join(srcroot, "components", "mom", "MOM6", "pkg", "MARBL") from MARBL_settings import MARBL_settings_for_MOM @@ -103,12 +124,6 @@ def prep_input(case): continue_run, ocn_bgc_config) MARBL_settings.write_settings_file(os.path.join(rundir, "marbl_in")) - # 6. Create mom.input_data_list: - input_data_list_src = os.path.join(json_templates_dir, "input_data_list.json") - input_data_list_dest = os.path.join(Buildconf,"mom.input_data_list") - input_data_list = FType_input_data_list.from_json(input_data_list_src) - input_data_list.write(input_data_list_dest, case, MOM_input_final) - # 7. Create diag_table: if "diag_table" in os.listdir(SourceMods_dir): run_cmd("cp "+os.path.join(SourceMods_dir,"diag_table")+" "+rundir) @@ -117,6 +132,9 @@ def prep_input(case): diag_table_dest = os.path.join(rundir,"diag_table") diag_table = FType_diag_table.from_json(diag_table_src) if use_MARBL: + # Make sure that momconfdir exists. If not, make it: + if not os.path.exists(momconfdir): + os.makedirs(momconfdir) diag_table_buildconf = os.path.join(momconfdir, "diag_table_MARBL.json") # For now, user has no control over lMARBL_output_all # TODO: mimic what is done in POP and allow it via user_nl_mom? diff --git a/cime_config/config_component.xml b/cime_config/config_component.xml index c76440f8..f5f5359a 100644 --- a/cime_config/config_component.xml +++ b/cime_config/config_component.xml @@ -70,15 +70,11 @@ char - none,latest - none - - latest - + latest + latest run_component_mom env_run.xml Version of ocean BGC tunings to use (default values can be overwritten by user_nl_mom and user_nl_marbl). - "none" => run without MARBL tracers. diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index e304a51b..353d20fb 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -331,7 +331,8 @@ Global: datatype: logical units: Boolean value: - $OCN_BGC_CONFIG != "none": True + bool(re.search("MOM6%[^_]*ECO", $COMPSET ))== True: True + else: False COORD_CONFIG: description: | "This specifies how layers are to be defined: diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index a0fa5d05..970b5ad4 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -232,7 +232,8 @@ "datatype": "logical", "units": "Boolean", "value": { - "$OCN_BGC_CONFIG != \"none\"": true + "bool(re.search(\"MOM6%[^_]*ECO\", $COMPSET ))== True": true, + "else": false } }, "COORD_CONFIG": { From 0a633373248b9c269895a60431c33965ffd3c9d1 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 24 Sep 2021 10:29:48 -0600 Subject: [PATCH 050/136] Get use_MARBL from MOM_input_final This gives buildnml a chance to reconcile MOM_input and MOM_override --- cime_config/buildnml | 26 ++++++-------------------- 1 file changed, 6 insertions(+), 20 deletions(-) diff --git a/cime_config/buildnml b/cime_config/buildnml index 7e1494f3..f94771e8 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -58,33 +58,13 @@ def prep_input(case): json_templates_dir = os.path.join(comp_root_dir_ocn,"param_templates","json") # 1. Create MOM_input: - # TODO: should we always read from rundir/MOM_input, or only read from - # disk when pulling MOM_input from SourceMods? if "MOM_input" in os.listdir(SourceMods_dir): run_cmd("cp "+os.path.join(SourceMods_dir,"MOM_input")+" "+rundir) - # use_MARBL = False - # with open(os.path.join(SourceMods_dir,"MOM_input")) as input_file: - # for line in input_file: - # if line.split('=')[0].strip() == "USE_MARBL_TRACERS": - # use_MARBL = bool(line.split('=')[1]) - # break else: MOM_input_src = os.path.join(json_templates_dir, "MOM_input.json") MOM_input_dest = os.path.join(rundir,"MOM_input") MOM_input = FType_MOM_params.from_json(MOM_input_src) MOM_input.write(output_path=MOM_input_dest, output_format="MOM_input", case=case) - # try: - # use_MARBL = MOM_input._data["Global"]["USE_MARBL_TRACERS"]["value"] - # except: - # use_MARBL = False - - # Need to know value of USE_MARBL_TRACERS from MOM_input - use_MARBL = False - with open(os.path.join(rundir,"MOM_input")) as input_file: - for line in input_file: - if line.split('=')[0].strip() == "USE_MARBL_TRACERS": - use_MARBL = bool(line.split('=')[1]) - break # 2. Create MOM_override: user_nl_mom = FType_MOM_params.from_MOM_input(os.path.join(caseroot,"user_nl_mom")) @@ -100,6 +80,12 @@ def prep_input(case): MOM_input_final = FType_MOM_params.from_MOM_input(os.path.join(rundir,"MOM_input")) MOM_override_final = FType_MOM_params.from_MOM_input(os.path.join(rundir,"MOM_override")) MOM_input_final.append(MOM_override_final) + # Need to know value of USE_MARBL_TRACERS from MOM_input + try: + use_MARBL = MOM_input_final._data["Global"]["USE_MARBL_TRACERS"]["value"] + except: + print("NOTE: could not find USE_MARBL_TRACERS in MOM_input_final") + use_MARBL = False # 4. Create input.nml: if "input.nml" in os.listdir(SourceMods_dir): From 825c08b9fa14a2478ed45d7c9dae7d6a5edcae19 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 27 Sep 2021 15:02:15 -0600 Subject: [PATCH 051/136] ECO -> MARBL in compset names MOM6%MARBL instead of MOM6%ECO, and C1850MOMMARBL instead of C1850MOMECO; also cleaned up a few bugs (python thought use_MARBL was always true because it was coming in as a string instead of logical) --- cime_config/buildnml | 2 +- cime_config/config_component.xml | 4 ++-- cime_config/config_compsets.xml | 24 ++++++++++++------------ cime_config/config_pes.xml | 6 +++--- param_templates/MOM_input.yaml | 2 +- param_templates/json/MOM_input.json | 2 +- 6 files changed, 20 insertions(+), 20 deletions(-) diff --git a/cime_config/buildnml b/cime_config/buildnml index f94771e8..64b6dbb4 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -82,7 +82,7 @@ def prep_input(case): MOM_input_final.append(MOM_override_final) # Need to know value of USE_MARBL_TRACERS from MOM_input try: - use_MARBL = MOM_input_final._data["Global"]["USE_MARBL_TRACERS"]["value"] + use_MARBL = (MOM_input_final._data["Global"]["USE_MARBL_TRACERS"]["value"].strip().lower() == "true") except: print("NOTE: could not find USE_MARBL_TRACERS in MOM_input_final") use_MARBL = False diff --git a/cime_config/config_component.xml b/cime_config/config_component.xml index f5f5359a..6e10de29 100644 --- a/cime_config/config_component.xml +++ b/cime_config/config_component.xml @@ -5,8 +5,8 @@ - MOM6 OCEAN - Ecosystem + MOM6 OCEAN + MARBL Biogeochemistry Tracer Library diff --git a/cime_config/config_compsets.xml b/cime_config/config_compsets.xml index 22be3f07..76f909f4 100644 --- a/cime_config/config_compsets.xml +++ b/cime_config/config_compsets.xml @@ -44,8 +44,8 @@ - C1850MOMECO - 1850_DATM%NYF_SLND_DICE%SSMI_MOM6%ECO_DROF%NYF_SGLC_SWAV + C1850MOMMARBL + 1850_DATM%NYF_SLND_DICE%SSMI_MOM6%MARBL_DROF%NYF_SGLC_SWAV @@ -54,8 +54,8 @@ - C1850MOMECO_IAF - 1850_DATM%IAF_SLND_DICE%IAF_MOM6%ECO_DROF%IAF_SGLC_SWAV + C1850MOMMARBL_IAF + 1850_DATM%IAF_SLND_DICE%IAF_MOM6%MARBL_DROF%IAF_SGLC_SWAV @@ -64,8 +64,8 @@ - C1850MOMECO_JRA - 1850_DATM%JRA_SLND_DICE%SSMI_MOM6%ECO_DROF%JRA_SGLC_SWAV + C1850MOMMARBL_JRA + 1850_DATM%JRA_SLND_DICE%SSMI_MOM6%MARBL_DROF%JRA_SGLC_SWAV @@ -76,8 +76,8 @@ - G1850MOMECO - 1850_DATM%NYF_SLND_CICE_MOM6%ECO_DROF%NYF_SGLC_SWAV + G1850MOMMARBL + 1850_DATM%NYF_SLND_CICE_MOM6%MARBL_DROF%NYF_SGLC_SWAV @@ -86,8 +86,8 @@ - G1850MOMECO_IAF - 1850_DATM%IAF_SLND_CICE_MOM6%ECO_DROF%IAF_SGLC_SWAV + G1850MOMMARBL_IAF + 1850_DATM%IAF_SLND_CICE_MOM6%MARBL_DROF%IAF_SGLC_SWAV @@ -96,8 +96,8 @@ - G1850MOMECO_JRA - 1850_DATM%JRA_SLND_CICE_MOM6%ECO_DROF%JRA_SGLC_SWAV + G1850MOMMARBL_JRA + 1850_DATM%JRA_SLND_CICE_MOM6%MARBL_DROF%JRA_SGLC_SWAV diff --git a/cime_config/config_pes.xml b/cime_config/config_pes.xml index 6c459d91..24debbbe 100644 --- a/cime_config/config_pes.xml +++ b/cime_config/config_pes.xml @@ -455,7 +455,7 @@ - + none 36 @@ -521,7 +521,7 @@ 0 - + none 36 @@ -622,7 +622,7 @@ 0 - + none 48 diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index 353d20fb..4323d6eb 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -331,7 +331,7 @@ Global: datatype: logical units: Boolean value: - bool(re.search("MOM6%[^_]*ECO", $COMPSET ))== True: True + bool(re.search("MOM6%[^_]*MARBL", $COMPSET ))== True: True else: False COORD_CONFIG: description: | diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index 970b5ad4..85c89880 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -232,7 +232,7 @@ "datatype": "logical", "units": "Boolean", "value": { - "bool(re.search(\"MOM6%[^_]*ECO\", $COMPSET ))== True": true, + "bool(re.search(\"MOM6%[^_]*MARBL\", $COMPSET ))== True": true, "else": false } }, From 8d055d95907386b7a229bea64aca6b17ac49563f Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 6 Oct 2021 14:30:08 -0600 Subject: [PATCH 052/136] Update default PE layouts Also re-ordered the compsets so that MOM6%MARBL compsets would use the pe layouts meant for MARBL runs --- cime_config/config_pes.xml | 54 +++++++++++++++++++------------------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/cime_config/config_pes.xml b/cime_config/config_pes.xml index 24debbbe..9386de65 100644 --- a/cime_config/config_pes.xml +++ b/cime_config/config_pes.xml @@ -455,13 +455,13 @@ - + none - 36 - 36 - 36 - 36 + 108 + 108 + 108 + 108 864 1 1 @@ -488,13 +488,13 @@ 0 - + none - 108 - 108 - 108 - 108 + 36 + 36 + 36 + 36 864 1 1 @@ -514,20 +514,20 @@ 0 0 0 - 0 - 108 + 36 + 72 0 0 0 - + none - 36 - 36 - 36 - 36 + 72 + 72 + 72 + 72 864 1 1 @@ -554,13 +554,13 @@ 0 - + none - 72 - 72 - 72 - 72 + 36 + 36 + 36 + 36 864 1 1 @@ -581,7 +581,7 @@ 0 0 0 - 72 + 36 0 0 0 @@ -622,14 +622,14 @@ 0 - + none 48 48 48 48 - 336 + 144 1 1 1 @@ -655,14 +655,14 @@ 0 - + none 48 48 48 48 - 144 + 336 1 1 1 From fcd40888adf6d72fbe8afa3324f864247fd5c1f4 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 6 Oct 2021 14:38:47 -0600 Subject: [PATCH 053/136] Avoid duplicate PE match I think this regular expression will allow things like MOM6%CFC or MOM6%CFC%MARBL and figure out the proper PE layout --- cime_config/config_pes.xml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/cime_config/config_pes.xml b/cime_config/config_pes.xml index 9386de65..73f4b107 100644 --- a/cime_config/config_pes.xml +++ b/cime_config/config_pes.xml @@ -455,7 +455,7 @@ - + none 108 @@ -521,7 +521,7 @@ 0 - + none 72 @@ -622,7 +622,7 @@ 0 - + none 48 From 8571b4e9572a9ab7c653f380297899b44b5e2435 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 6 Oct 2021 14:42:33 -0600 Subject: [PATCH 054/136] _SURF and _zint_100m diagnostics are 2D These fields were causing MOM to generate a daily_z stream despite being 2D --- cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py index 7ffcb2be..710fcaf8 100644 --- a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -317,5 +317,7 @@ def _2D_varcheck(varname): """ return (varname.startswith("STF_") or varname.endswith("_FLUX_CPL") or - varname.endswith("_RIV_FLUX") + varname.endswith("_RIV_FLUX") or + varname.endswith("_SURF") or + varname.endswith("_zint_100m") ) From 05e3fd3ee7a40e849c80a8e2c30f43fa3f047406 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 7 Oct 2021 09:52:17 -0600 Subject: [PATCH 055/136] Pare down variables in 3D native output Don't need the full transport list, just volcello (and I added h as well). Also added geolat and geolon to every (bgc) stream file --- cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py index ca85ff20..939d927d 100755 --- a/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py +++ b/cime_config/MARBL_scripts/MARBL_diags_to_diag_table.py @@ -152,9 +152,9 @@ def dump_to_json(self, filename): for freq in self._diag_table_dict: if len(self._diag_table_dict[freq]["fields"][0]["lists"][0]) > 0: out_dict["Files"][freq] = self._diag_table_dict[freq].copy() + out_dict["Files"][freq]["fields"][0]["lists"].append(["geolat", "geolon"]) if out_dict["Files"][freq]["fields"][0]["module"] == "ocean_model" and freq[-2:] == "_z": - transports = ["volcello", "vmo", "vhGM", "vhml", "umo", "uhGM", "uhml"] - out_dict["Files"][freq]["fields"][0]["lists"].append(transports) + out_dict["Files"][freq]["fields"][0]["lists"].append(["volcello", "h"]) if out_dict["Files"]: with open(filename, "w") as fp: json.dump(out_dict, fp, separators=(',', ': '), sort_keys=False, indent=3) From cba3546f1592ec454592af7ab9452199c298958b Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 14 Apr 2022 18:13:57 -0600 Subject: [PATCH 056/136] More PEs when running with MARBL --- cime_config/config_pes.xml | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/cime_config/config_pes.xml b/cime_config/config_pes.xml index 358b917b..f034a6ce 100644 --- a/cime_config/config_pes.xml +++ b/cime_config/config_pes.xml @@ -76,7 +76,7 @@ 0 - + none 1 @@ -111,7 +111,7 @@ - + none 72 @@ -144,7 +144,7 @@ 0 - + none 72 @@ -212,7 +212,7 @@ - + none 48 @@ -280,7 +280,7 @@ - + none 48 @@ -453,7 +453,7 @@ 0 - + none 36 @@ -528,7 +528,7 @@ 36 36 36 - 864 + 2844 1 1 1 @@ -627,7 +627,7 @@ 36 36 36 - 864 + 2844 1 1 1 @@ -655,7 +655,7 @@ - + none 48 @@ -756,7 +756,7 @@ - + none 48 @@ -826,7 +826,7 @@ - + none 1 @@ -896,7 +896,7 @@ 0 - + none 96 @@ -964,7 +964,7 @@ 0 - + none 108 From f2d0c5a467e7757163266cebdc1d2525538dd5e4 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 25 Apr 2022 15:57:33 -0600 Subject: [PATCH 057/136] Three different updates rolled into one 1. Move Jint_ output out of the _z stream (those are 2D fields) 2. Some improved PE layouts were only applied to the CORE-forced grids; now they are applied to JRA-forced grids too 3. Use full JRA string in compset long-name --- .../MARBL_scripts/MOM_ecosys_diagnostics.py | 1 + cime_config/config_compsets.xml | 2 +- cime_config/config_pes.xml | 68 ++++++++++++++++++- 3 files changed, 69 insertions(+), 2 deletions(-) diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py index 710fcaf8..e07f2041 100644 --- a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -316,6 +316,7 @@ def _2D_varcheck(varname): Return true if variable is 2D """ return (varname.startswith("STF_") or + varname.startswith("Jint") or varname.endswith("_FLUX_CPL") or varname.endswith("_RIV_FLUX") or varname.endswith("_SURF") or diff --git a/cime_config/config_compsets.xml b/cime_config/config_compsets.xml index 4645ed3b..c1da79bd 100644 --- a/cime_config/config_compsets.xml +++ b/cime_config/config_compsets.xml @@ -102,7 +102,7 @@ G1850MOMMARBL_JRA - 1850_DATM%JRA_SLND_CICE_MOM6%MARBL_DROF%JRA_SGLC_SWAV + 1850_DATM%JRA-1p4-2018_SLND_CICE_MOM6%MARBL_DROF%JRA-1p4-2018_SGLC_SWAV diff --git a/cime_config/config_pes.xml b/cime_config/config_pes.xml index f034a6ce..39ebbdcf 100644 --- a/cime_config/config_pes.xml +++ b/cime_config/config_pes.xml @@ -931,7 +931,7 @@ - + none 72 @@ -964,6 +964,39 @@ 0 + + none + + 36 + 36 + 36 + 36 + 2844 + 1 + 1 + 1 + + + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + + + 0 + 0 + 0 + 0 + 36 + 0 + 0 + 0 + + none @@ -997,6 +1030,39 @@ 0 + + none + + 36 + 36 + 36 + 36 + 2844 + 1 + 1 + 1 + + + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + + + 0 + 0 + 0 + 36 + 72 + 0 + 0 + 0 + + none From e53f1f5662680d98e397b96afebd317ea1d17326 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 20 Sep 2022 15:40:24 -0600 Subject: [PATCH 058/136] set CPL_I2O_PER_CAT to match use_MARBL call ./xmlchange CPL_I2O_PER_CAT if it is different from use_MARBL --- cime_config/buildnml | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/cime_config/buildnml b/cime_config/buildnml index 40c2e8f4..307490d9 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -87,6 +87,15 @@ def prep_input(case): print("NOTE: could not find USE_MARBL_TRACERS in MOM_input_final") use_MARBL = False + # coupler should pass per-category ice fraction and shortwave if running with MARBL + cmd = f"cd {caseroot} ; ./xmlquery --value CPL_I2O_PER_CAT" + CPL_I2O_PER_CAT_curr = run_cmd(cmd)[1] + CPL_I2O_PER_CAT = "TRUE" if use_MARBL else "FALSE" + if (CPL_I2O_PER_CAT != CPL_I2O_PER_CAT_curr): + cmd = f"cd {caseroot} ; ./xmlchange CPL_I2O_PER_CAT={CPL_I2O_PER_CAT}" + run_cmd(cmd) + + # 4. Create input.nml: if "input.nml" in os.listdir(SourceMods_dir): run_cmd("cp "+os.path.join(SourceMods_dir,"input.nml")+" "+rundir) From c0e3ccd9399ea322311c1d6d911f93749f12c2e6 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 21 Sep 2022 14:58:42 -0600 Subject: [PATCH 059/136] Add additional comment buildnml Just a note about how we only change CPL_I2O_PER_CAT if the current value does not match the correct value (where "correct value" is "whatever USE_MARBL is") --- cime_config/buildnml | 1 + 1 file changed, 1 insertion(+) diff --git a/cime_config/buildnml b/cime_config/buildnml index 307490d9..50903d51 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -88,6 +88,7 @@ def prep_input(case): use_MARBL = False # coupler should pass per-category ice fraction and shortwave if running with MARBL + # (only need to call xmlchange when we are actually changing the value) cmd = f"cd {caseroot} ; ./xmlquery --value CPL_I2O_PER_CAT" CPL_I2O_PER_CAT_curr = run_cmd(cmd)[1] CPL_I2O_PER_CAT = "TRUE" if use_MARBL else "FALSE" From 3fcc0244ba46d2d4cbd3f80874252c40cf1a10d7 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Mon, 3 Oct 2022 09:47:14 -0600 Subject: [PATCH 060/136] Add ICE_NCAT and USE_ICE_CATEGORIES to MOM_input USE_ICE_CATEGORIES should be true when compset includes %MARBL; ICE_NCAT should be set according to what is in env_build.xml --- param_templates/MOM_input.yaml | 16 ++++++++++++++++ param_templates/json/MOM_input.json | 14 ++++++++++++++ 2 files changed, 30 insertions(+) diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index 34fbde8c..d847ce2a 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -348,6 +348,22 @@ Global: value: bool(re.search("MOM6%[^_]*MARBL", $COMPSET ))== True: True else: False + USE_ICE_CATEGORIES: + description: | + "[Boolean] default = False + If true, send multiple ice categories from the coupler to MARBL." + datatype: logical + units: Boolean + value: + bool(re.search("MOM6%[^_]*MARBL", $COMPSET ))== True: True + else: False + ICE_NCAT: + description: | + "[Integer] default = value of ICE_NCAT in env_build.xml + If USE_ICE_CATEGORIES is true, this is the number of categories + to send from the coupler to MARBL." + datatype: integer + value: $ICE_NCAT COORD_CONFIG: description: | "This specifies how layers are to be defined: diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index 75eb8dee..ba6198d2 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -249,6 +249,20 @@ "else": false } }, + "USE_ICE_CATEGORIES": { + "description": "\"[Boolean] default = False\nIf true, send multiple ice categories from the coupler to MARBL.\"\n", + "datatype": "logical", + "units": "Boolean", + "value": { + "bool(re.search(\"MOM6%[^_]*MARBL\", $COMPSET ))== True": true, + "else": false + } + }, + "ICE_NCAT": { + "description": "\"[Integer] default = value of ICE_NCAT in env_build.xml\nIf USE_ICE_CATEGORIES is true, this is the number of categories\nto send from the coupler to MARBL.\"\n", + "datatype": "integer", + "value": "$ICE_NCAT" + }, "COORD_CONFIG": { "description": "\"This specifies how layers are to be defined:\nfile - read coordinate information from the file\nspecified by (COORD_FILE).\nlinear - linear based on interfaces not layesrs.\nts_ref - use reference temperature and salinity\nts_range - use range of temperature and salinity\n(T_REF and S_REF) to determine surface density\nand GINT calculate internal densities.\ngprime - use reference density (RHO_0) for surface\ndensity and GINT calculate internal densities.\nts_profile - use temperature and salinity profiles\n(read from COORD_FILE) to set layer densities.\nUSER - call a user modified routine.\nThe file from which the coordinate densities are read.\"\n", "datatype": "string", From b2a567874ffd9191fdaa644a712d0e3493793a32 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 4 Oct 2022 19:00:35 -0600 Subject: [PATCH 061/136] add per-category fraction / sw diags The new diagnostics in MOM6 are FRACR_CAT_N and QSW_CAT_N, where N ranges from 1 to ice_ncat+1 --- .../MARBL_scripts/MOM_ecosys_diagnostics.py | 17 ++++++++++++----- cime_config/buildnml | 6 ++++-- 2 files changed, 16 insertions(+), 7 deletions(-) diff --git a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py index e07f2041..8e6bc51f 100644 --- a/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py +++ b/cime_config/MARBL_scripts/MOM_ecosys_diagnostics.py @@ -2,7 +2,7 @@ (e.g. MARBL tracer state) """ -def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_list, calcifier_list, ladjust_bury_coeff, ecosys_diag_filename): +def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_list, calcifier_list, ladjust_bury_coeff, ecosys_diag_filename, ice_ncat): """ Subroutine to write a file in the same format as marbl_diagnostics containing a list of MOM-generated diagnostics that should be included based on the MARBL configuration @@ -31,18 +31,24 @@ def write_ecosys_diagnostics_file(active_tracers, autotroph_list, zooplankton_li # the provenance of each diagnostic clear fout.write("#\n########################################\n") fout.write("# MOM-generated diagnostics #\n") - fout.write("########################################\n#\n") + fout.write("########################################\n") - fout.write("# Dust and Carbon Fluxes from the Coupler\n#\n") + fout.write("#\n# Dust and Carbon Fluxes from the Coupler\n#\n") fout.write("ATM_FINE_DUST_FLUX_CPL : medium_average\n") fout.write("ATM_COARSE_DUST_FLUX_CPL : medium_average\n") fout.write("SEAICE_DUST_FLUX_CPL : medium_average\n") fout.write("ATM_BLACK_CARBON_FLUX_CPL : medium_average\n") fout.write("SEAICE_BLACK_CARBON_FLUX_CPL : medium_average\n") - fout.write("# Bottom Flux to Tendency Conversion\n#\n") + fout.write("#\n# Bottom Flux to Tendency Conversion\n#\n") fout.write("BOT_FLUX_TO_TEND : medium_average\n") + fout.write("#\n# per-category forcings\n#\n") + if ice_ncat > 0: + for m in range(ice_ncat+1): + fout.write(f"FRACR_CAT_{m+1}: medium_average\n") + fout.write(f"QSW_CAT_{m+1}: medium_average\n") + # TODO: add running means, and then define these diagnostics # If adjusting bury coefficients, add running means to requested diagnostics if ladjust_bury_coeff: @@ -320,5 +326,6 @@ def _2D_varcheck(varname): varname.endswith("_FLUX_CPL") or varname.endswith("_RIV_FLUX") or varname.endswith("_SURF") or - varname.endswith("_zint_100m") + varname.endswith("_zint_100m") or + "_CAT_" in varname ) diff --git a/cime_config/buildnml b/cime_config/buildnml index 50903d51..97e009ad 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -47,6 +47,7 @@ def prep_input(case): ocn_grid = case.get_value("OCN_GRID") run_type = case.get_value("RUN_TYPE") continue_run = case.get_value("CONTINUE_RUN") + ice_ncat = case.get_value("ICE_NCAT") momconfdir = os.path.join(caseroot, "Buildconf", "momconf") SourceMods_dir = os.path.join(caseroot,"SourceMods","src.mom") @@ -135,7 +136,7 @@ def prep_input(case): # For now, user has no control over lMARBL_output_all # TODO: mimic what is done in POP and allow it via user_nl_mom? lMARBL_output_all = case.get_value("TEST") - generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, vert_grid, lMARBL_output_all) + generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, vert_grid, lMARBL_output_all, ice_ncat) if os.path.isfile(diag_table_buildconf): diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) diag_table.write(diag_table_dest, case, MOM_input_final) @@ -183,7 +184,7 @@ def _copy_files_to_momconf(case): for filename in ["MOM_input", "MOM_override", "diag_table", "input.nml"]: shutil.copy(os.path.join(rundir,filename), momconfdir) -def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, vert_grid, lMARBL_output_all): +def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, vert_grid, lMARBL_output_all, ice_ncat): """ Create Buildconf/momconf/diag_table_MARBL.json @@ -220,6 +221,7 @@ def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfd MARBL_settings.get_autotroph_names(calcifier_only=True), MARBL_settings.ladjust_bury_coeff(), ecosys_diags_buildconf, + ice_ncat ) # (ii) append MARBL diagnostics to ecosys_diagnostics MARBL_diagnostics.write_diagnostics_file(ecosys_diags_buildconf, From 1640efe842a8c4932004070104ad3c607ac38122 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 13 Oct 2022 09:27:36 -0600 Subject: [PATCH 062/136] Clean up CPL_I2O_PER_CAT and add some defaults Using case.set_value() istead of calling xmlchange for CPL_I2O_PER_CAT. Also, changing the default values in the Fortran for several dust / iron parameters to match what we want in C / G compsets and now MOM_input will provide different values for B compsets. --- cime_config/buildnml | 16 ++++++++-------- param_templates/MOM_input.yaml | 21 +++++++++++++++++++++ param_templates/json/MOM_input.json | 21 +++++++++++++++++++++ 3 files changed, 50 insertions(+), 8 deletions(-) diff --git a/cime_config/buildnml b/cime_config/buildnml index 97e009ad..1f0dad50 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -24,7 +24,7 @@ sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),"MARBL_s from standard_script_setup import * from CIME.case import Case from CIME.buildnml import parse_input -from CIME.utils import run_cmd +from CIME.utils import run_cmd, append_status from FType_MOM_params import FType_MOM_params from FType_input_nml import FType_input_nml from FType_input_data_list import FType_input_data_list @@ -89,13 +89,13 @@ def prep_input(case): use_MARBL = False # coupler should pass per-category ice fraction and shortwave if running with MARBL - # (only need to call xmlchange when we are actually changing the value) - cmd = f"cd {caseroot} ; ./xmlquery --value CPL_I2O_PER_CAT" - CPL_I2O_PER_CAT_curr = run_cmd(cmd)[1] - CPL_I2O_PER_CAT = "TRUE" if use_MARBL else "FALSE" - if (CPL_I2O_PER_CAT != CPL_I2O_PER_CAT_curr): - cmd = f"cd {caseroot} ; ./xmlchange CPL_I2O_PER_CAT={CPL_I2O_PER_CAT}" - run_cmd(cmd) + # calling set_value will() also change the value in env_run.xml; we only do that + # if the value needs to be updated + CPL_I2O_PER_CAT_curr = case.get_value("CPL_I2O_PER_CAT") # returns python logical, not string + if (CPL_I2O_PER_CAT_curr != use_MARBL): + case.set_value("CPL_I2O_PER_CAT", use_MARBL) + append_status(f"xmlchange via MOM buildnml: CPL_I2O_PER_CAT={use_MARBL}", + "CaseStatus") # 4. Create input.nml: diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index d847ce2a..b7a920f4 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -348,6 +348,27 @@ Global: value: bool(re.search("MOM6%[^_]*MARBL", $COMPSET ))== True: True else: False + DUST_RATIO_THRES: + description: | + "TODO: Add description + " + datatype: real + value: + $OCN_BGC_CONFIG != "none" and $COMP_ATM == "cam": 60.0 + DUST_RATIO_TO_FE_BIOAVAIL_FRAC: + description: | + "TODO: Add description + " + datatype: real + value: + $OCN_BGC_CONFIG != "none" and $COMP_ATM == "cam": 1.0 / 170.0 + FE_BIOAVAIL_FRAC_OFFSET: + description: | + "TODO: Add description + " + datatype: real + value: + $OCN_BGC_CONFIG != "none" and $COMP_ATM == "cam": 0.01 USE_ICE_CATEGORIES: description: | "[Boolean] default = False diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index ba6198d2..8e317879 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -249,6 +249,27 @@ "else": false } }, + "DUST_RATIO_THRES": { + "description": "\"TODO: Add description\n\"\n", + "datatype": "real", + "value": { + "$OCN_BGC_CONFIG != \"none\" and $COMP_ATM == \"cam\"": 60.0 + } + }, + "DUST_RATIO_TO_FE_BIOAVAIL_FRAC": { + "description": "\"TODO: Add description\n\"\n", + "datatype": "real", + "value": { + "$OCN_BGC_CONFIG != \"none\" and $COMP_ATM == \"cam\"": "1.0 / 170.0" + } + }, + "FE_BIOAVAIL_FRAC_OFFSET": { + "description": "\"TODO: Add description\n\"\n", + "datatype": "real", + "value": { + "$OCN_BGC_CONFIG != \"none\" and $COMP_ATM == \"cam\"": 0.01 + } + }, "USE_ICE_CATEGORIES": { "description": "\"[Boolean] default = False\nIf true, send multiple ice categories from the coupler to MARBL.\"\n", "datatype": "logical", From 05d11fc61993a3a957b2c1dca82f7a03864587e3 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 27 Jan 2023 17:02:21 -0700 Subject: [PATCH 063/136] Update standalone to include KPP_MARBL I'm moving this test from my branch of MOM6-examples into MOM_interface now that we support some solo_driver examples here --- standalone/build/build_examples.sh | 2 +- .../INPUT/BATS/IC_FILE_COLUMN.nc | Bin 0 -> 164131 bytes .../INPUT/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 0 -> 22254 bytes .../BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 0 -> 13284 bytes .../single_column/INPUT/BATS/MOM_override | 3 + .../examples/single_column/KPP_MARBL/INPUT | 1 + .../single_column/KPP_MARBL/MOM_input | 394 ++++ .../single_column/KPP_MARBL/MOM_override | 1 + .../single_column/KPP_MARBL/MOM_override2 | 4 + .../KPP_MARBL/MOM_parameter_doc.all | 1671 +++++++++++++++++ .../KPP_MARBL/MOM_parameter_doc.debugging | 83 + .../KPP_MARBL/MOM_parameter_doc.layout | 56 + .../KPP_MARBL/MOM_parameter_doc.short | 381 ++++ .../KPP_MARBL/RESTART/.gitignore | 2 + .../single_column/KPP_MARBL/data_table | 11 + .../single_column/KPP_MARBL/diag_table | 670 +++++++ .../single_column/KPP_MARBL/input.nml | 21 + .../examples/single_column/KPP_MARBL/marbl_in | 0 .../KPP_MARBL/single_column_z.ipynb | 581 ++++++ .../single_column/KPP_MARBL/vgrid_cm4.cdl | 38 + .../single_column/KPP_MARBL/vgrid_cm4_10.cdl | 38 + .../single_column/KPP_MARBL/vgrid_cm4_4.cdl | 38 + 22 files changed, 3994 insertions(+), 1 deletion(-) create mode 100644 standalone/examples/single_column/INPUT/BATS/IC_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column/INPUT/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column/INPUT/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc create mode 120000 standalone/examples/single_column/KPP_MARBL/INPUT create mode 100644 standalone/examples/single_column/KPP_MARBL/MOM_input create mode 120000 standalone/examples/single_column/KPP_MARBL/MOM_override create mode 100644 standalone/examples/single_column/KPP_MARBL/MOM_override2 create mode 100644 standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all create mode 100644 standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.debugging create mode 100644 standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.layout create mode 100644 standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short create mode 100644 standalone/examples/single_column/KPP_MARBL/RESTART/.gitignore create mode 100644 standalone/examples/single_column/KPP_MARBL/data_table create mode 100644 standalone/examples/single_column/KPP_MARBL/diag_table create mode 100644 standalone/examples/single_column/KPP_MARBL/input.nml create mode 100644 standalone/examples/single_column/KPP_MARBL/marbl_in create mode 100644 standalone/examples/single_column/KPP_MARBL/single_column_z.ipynb create mode 100644 standalone/examples/single_column/KPP_MARBL/vgrid_cm4.cdl create mode 100644 standalone/examples/single_column/KPP_MARBL/vgrid_cm4_10.cdl create mode 100644 standalone/examples/single_column/KPP_MARBL/vgrid_cm4_4.cdl diff --git a/standalone/build/build_examples.sh b/standalone/build/build_examples.sh index 2b1a76ff..8fb38d3b 100755 --- a/standalone/build/build_examples.sh +++ b/standalone/build/build_examples.sh @@ -29,6 +29,6 @@ make -j36 NETCDF=3 REPRO=1 libfms.a cd ${INTERFACE_ROOT}/standalone/build mkdir -p ${COMPILER}/MOM6 cd ${COMPILER}/MOM6 -${MKMF_ROOT}/list_paths -l ${MOM_ROOT}/{config_src/infra/FMS2,config_src/memory/dynamic_symmetric,config_src/drivers/solo_driver,config_src/external,src/{*,*/*}}/ +${MKMF_ROOT}/list_paths -l ${MOM_ROOT}/{config_src/infra/FMS2,config_src/memory/dynamic_symmetric,config_src/drivers/solo_driver,pkg/MARBL/src,config_src/external,src/{*,*/*}}/ ${MKMF_ROOT}/mkmf -t ${TEMPLATE_DIR}/cheyenne-${COMPILER}.mk -o '-I../FMS' -p MOM6 -l '-L../FMS -lfms' -c '-Duse_libMPI -Duse_netCDF -DSPMD' path_names make -j36 NETCDF=3 REPRO=1 MOM6 diff --git a/standalone/examples/single_column/INPUT/BATS/IC_FILE_COLUMN.nc b/standalone/examples/single_column/INPUT/BATS/IC_FILE_COLUMN.nc new 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zfKpCFk9K4@-85JXK{yJOQhT^YK}U6Ah%4dY9R*z!TOuNq)Grd35O}B%n5xgq5vL 0, melt potential will be computed. The actual depth over which + ! melt potential is computed will be min(HFREEZE, OBLD), where OBLD is the + ! boundary layer depth. If HFREEZE <= 0 (default), melt potential will not be + ! computed. +INTERPOLATE_P_SURF = False ! [Boolean] default = False + ! If true, linearly interpolate the surface pressure over the coupling time + ! step, using the specified value at the end of the step. +FRAZIL = True ! [Boolean] default = False + ! If true, water freezes if it gets too cold, and the accumulated heat deficit + ! is returned in the surface state. FRAZIL is only used if + ! ENABLE_THERMODYNAMICS is true. +DO_GEOTHERMAL = False ! [Boolean] default = False + ! If true, apply geothermal heating. +BOUND_SALINITY = False ! [Boolean] default = False + ! If true, limit salinity to being positive. (The sea-ice model may ask for more + ! salt than is available and drive the salinity negative otherwise.) +SALINITY_UNDERFLOW = 0.0 ! [PPT] default = 0.0 + ! A tiny value of salinity below which the it is set to 0. For reference, one + ! molecule of salt per square meter of ocean is of order 1e-29 ppt. +TEMPERATURE_UNDERFLOW = 0.0 ! [degC] default = 0.0 + ! A tiny magnitude of temperatures below which they are set to 0. +C_P = 3925.0 ! [J kg-1 K-1] default = 3991.86795711963 + ! The heat capacity of sea water, approximated as a constant. This is only used + ! if ENABLE_THERMODYNAMICS is true. The default value is from the TEOS-10 + ! definition of conservative temperature. +USE_PSURF_IN_EOS = True ! [Boolean] default = True + ! If true, always include the surface pressure contributions in equation of + ! state calculations. +P_REF = 2.0E+07 ! [Pa] default = 2.0E+07 + ! The pressure that is used for calculating the coordinate density. (1 Pa = 1e4 + ! dbar, so 2e7 is commonly used.) This is only used if USE_EOS and + ! ENABLE_THERMODYNAMICS are true. +FIRST_DIRECTION = 0 ! default = 0 + ! An integer that indicates which direction goes first in parts of the code that + ! use directionally split updates, with even numbers (or 0) used for x- first + ! and odd numbers used for y-first. +ALTERNATE_FIRST_DIRECTION = False ! [Boolean] default = False + ! If true, after every dynamic timestep alternate whether the x- or y- direction + ! updates occur first in directionally split parts of the calculation. If this + ! is true, FIRST_DIRECTION applies at the start of a new run or if the next + ! first direction can not be found in the restart file. +CHECK_BAD_SURFACE_VALS = False ! [Boolean] default = False + ! If true, check the surface state for ridiculous values. +DEFAULT_ANSWER_DATE = 99991231 ! default = 99991231 + ! This sets the default value for the various _ANSWER_DATE parameters. +DEFAULT_2018_ANSWERS = False ! [Boolean] default = False + ! This sets the default value for the various _2018_ANSWERS parameters. +SURFACE_2018_ANSWERS = False ! [Boolean] default = False + ! If true, use expressions for the surface properties that recover the answers + ! from the end of 2018. Otherwise, use more appropriate expressions that differ + ! at roundoff for non-Boussinesq cases. +SURFACE_ANSWER_DATE = 99991231 ! default = 99991231 + ! The vintage of the expressions for the surface properties. Values below + ! 20190101 recover the answers from the end of 2018, while higher values use + ! updated and more robust forms of the same expressions. If both + ! SURFACE_2018_ANSWERS and SURFACE_ANSWER_DATE are specified, the latter takes + ! precedence. +USE_DIABATIC_TIME_BUG = False ! [Boolean] default = False + ! If true, uses the wrong calendar time for diabatic processes, as was done in + ! MOM6 versions prior to February 2018. This is not recommended. +SAVE_INITIAL_CONDS = True ! [Boolean] default = False + ! If true, write the initial conditions to a file given by IC_OUTPUT_FILE. +IC_OUTPUT_FILE = "MOM_IC" ! default = "MOM_IC" + ! The file into which to write the initial conditions. +WRITE_GEOM = 1 ! default = 1 + ! If =0, never write the geometry and vertical grid files. If =1, write the + ! geometry and vertical grid files only for a new simulation. If =2, always + ! write the geometry and vertical grid files. Other values are invalid. +USE_DBCLIENT = False ! [Boolean] default = False + ! If true, initialize a client to a remote database that can be used for online + ! analysis and machine-learning inference. +ICE_SHELF = False ! [Boolean] default = False + ! If true, enables the ice shelf model. +USE_PARTICLES = False ! [Boolean] default = False + ! If true, use the particles package. +ENSEMBLE_OCEAN = False ! [Boolean] default = False + ! If False, The model is being run in serial mode as a single realization. If + ! True, The current model realization is part of a larger ensemble and at the + ! end of step MOM, we will perform a gather of the ensemble members for + ! statistical evaluation and/or data assimilation. +HOMOGENIZE_FORCINGS = False ! [Boolean] default = False + ! If True, homogenize the forces and fluxes. + +! === module MOM_domains === +REENTRANT_X = True ! [Boolean] default = True + ! If true, the domain is zonally reentrant. +REENTRANT_Y = True ! [Boolean] default = False + ! If true, the domain is meridionally reentrant. +TRIPOLAR_N = False ! [Boolean] default = False + ! Use tripolar connectivity at the northern edge of the domain. With + ! TRIPOLAR_N, NIGLOBAL must be even. +NIGLOBAL = 2 ! + ! The total number of thickness grid points in the x-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NJGLOBAL = 2 ! + ! The total number of thickness grid points in the y-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NIHALO = 2 ! default = 4 + ! The number of halo points on each side in the x-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +NJHALO = 2 ! default = 4 + ! The number of halo points on each side in the y-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. + +! === module MOM_hor_index === +! Sets the horizontal array index types. + +! === module MOM_grid === +! Parameters providing information about the lateral grid. +REFERENCE_HEIGHT = 0.0 ! [m] default = 0.0 + ! A reference value for geometric height fields, such as bathyT. + +! === module MOM_fixed_initialization === +INPUTDIR = "INPUT/BATS" ! default = "." + ! The directory in which input files are found. + +! === module MOM_grid_init === +GRID_CONFIG = "cartesian" ! + ! A character string that determines the method for defining the horizontal + ! grid. Current options are: + ! mosaic - read the grid from a mosaic (supergrid) + ! file set by GRID_FILE. + ! cartesian - use a (flat) Cartesian grid. + ! spherical - use a simple spherical grid. + ! mercator - use a Mercator spherical grid. +AXIS_UNITS = "degrees" ! default = "degrees" + ! The units for the Cartesian axes. Valid entries are: + ! degrees - degrees of latitude and longitude + ! m - meters + ! k - kilometers +SOUTHLAT = 31.0 ! [degrees] + ! The southern latitude of the domain or the equivalent starting value for the + ! y-axis. +LENLAT = 1.0 ! [degrees] + ! The latitudinal or y-direction length of the domain. +WESTLON = -30.0 ! [degrees] default = 0.0 + ! The western longitude of the domain or the equivalent starting value for the + ! x-axis. +LENLON = 1.0 ! [degrees] + ! The longitudinal or x-direction length of the domain. +RAD_EARTH = 6.378E+06 ! [m] default = 6.378E+06 + ! The radius of the Earth. +TOPO_CONFIG = "flat" ! + ! This specifies how bathymetry is specified: + ! file - read bathymetric information from the file + ! specified by (TOPO_FILE). + ! flat - flat bottom set to MAXIMUM_DEPTH. + ! bowl - an analytically specified bowl-shaped basin + ! ranging between MAXIMUM_DEPTH and MINIMUM_DEPTH. + ! spoon - a similar shape to 'bowl', but with an vertical + ! wall at the southern face. + ! halfpipe - a zonally uniform channel with a half-sine + ! profile in the meridional direction. + ! bbuilder - build topography from list of functions. + ! benchmark - use the benchmark test case topography. + ! Neverworld - use the Neverworld test case topography. + ! DOME - use a slope and channel configuration for the + ! DOME sill-overflow test case. + ! ISOMIP - use a slope and channel configuration for the + ! ISOMIP test case. + ! DOME2D - use a shelf and slope configuration for the + ! DOME2D gravity current/overflow test case. + ! Kelvin - flat but with rotated land mask. + ! seamount - Gaussian bump for spontaneous motion test case. + ! dumbbell - Sloshing channel with reservoirs on both ends. + ! shelfwave - exponential slope for shelfwave test case. + ! Phillips - ACC-like idealized topography used in the Phillips config. + ! dense - Denmark Strait-like dense water formation and overflow. + ! USER - call a user modified routine. +MINIMUM_DEPTH = 0.5 ! [m] default = 0.0 + ! The minimum depth of the ocean. +MAXIMUM_DEPTH = 6000.0 ! [m] + ! The maximum depth of the ocean. + +! === module MOM_open_boundary === +! Controls where open boundaries are located, what kind of boundary condition to impose, and what data to apply, +! if any. +OBC_NUMBER_OF_SEGMENTS = 0 ! default = 0 + ! The number of open boundary segments. +MASKING_DEPTH = -9999.0 ! [m] default = -9999.0 + ! The depth below which to mask points as land points, for which all fluxes are + ! zeroed out. MASKING_DEPTH is ignored if it has the special default value. +CHANNEL_CONFIG = "none" ! default = "none" + ! A parameter that determines which set of channels are + ! restricted to specific widths. Options are: + ! none - All channels have the grid width. + ! global_1deg - Sets 16 specific channels appropriate + ! for a 1-degree model, as used in CM2G. + ! list - Read the channel locations and widths from a + ! text file, like MOM_channel_list in the MOM_SIS + ! test case. + ! file - Read open face widths everywhere from a + ! NetCDF file on the model grid. +ROTATION = "betaplane" ! default = "2omegasinlat" + ! This specifies how the Coriolis parameter is specified: + ! 2omegasinlat - Use twice the planetary rotation rate + ! times the sine of latitude. + ! betaplane - Use a beta-plane or f-plane. + ! USER - call a user modified routine. +F_0 = 7.59943E-05 ! [s-1] default = 0.0 + ! The reference value of the Coriolis parameter with the betaplane option. +BETA = 0.0 ! [m-1 s-1] default = 0.0 + ! The northward gradient of the Coriolis parameter with the betaplane option. +BETA_LAT_REF = 0.0 ! [degrees] default = 0.0 + ! The reference latitude (origin) of the beta-plane +GRID_ROTATION_ANGLE_BUGS = False ! [Boolean] default = False + ! If true, use an older algorithm to calculate the sine and cosines needed + ! rotate between grid-oriented directions and true north and east. Differences + ! arise at the tripolar fold. + +! === module MOM_verticalGrid === +! Parameters providing information about the vertical grid. +G_EARTH = 9.8 ! [m s-2] default = 9.8 + ! The gravitational acceleration of the Earth. +RHO_0 = 1035.0 ! [kg m-3] default = 1035.0 + ! The mean ocean density used with BOUSSINESQ true to calculate accelerations + ! and the mass for conservation properties, or with BOUSSINSEQ false to convert + ! some parameters from vertical units of m to kg m-2. +BOUSSINESQ = True ! [Boolean] default = True + ! If true, make the Boussinesq approximation. +ANGSTROM = 1.0E-10 ! [m] default = 1.0E-10 + ! The minimum layer thickness, usually one-Angstrom. +H_TO_M = 1.0 ! [m H-1] default = 1.0 + ! A constant that translates the model's internal units of thickness into m. +NK = 75 ! [nondim] + ! The number of model layers. + +! === module MOM_tracer_registry === + +! === module MOM_EOS === +EQN_OF_STATE = "WRIGHT" ! default = "WRIGHT" + ! EQN_OF_STATE determines which ocean equation of state should be used. + ! Currently, the valid choices are "LINEAR", "UNESCO", "WRIGHT", "NEMO" and + ! "TEOS10". This is only used if USE_EOS is true. +EOS_QUADRATURE = False ! [Boolean] default = False + ! If true, always use the generic (quadrature) code code for the integrals of + ! density. +TFREEZE_FORM = "LINEAR" ! default = "LINEAR" + ! TFREEZE_FORM determines which expression should be used for the freezing + ! point. Currently, the valid choices are "LINEAR", "MILLERO_78", "TEOS10" +TFREEZE_S0_P0 = 0.0 ! [deg C] default = 0.0 + ! When TFREEZE_FORM=LINEAR, this is the freezing potential temperature at S=0, + ! P=0. +DTFREEZE_DS = -0.054 ! [deg C PSU-1] default = -0.054 + ! When TFREEZE_FORM=LINEAR, this is the derivative of the freezing potential + ! temperature with salinity. +DTFREEZE_DP = 0.0 ! [deg C Pa-1] default = 0.0 + ! When TFREEZE_FORM=LINEAR, this is the derivative of the freezing potential + ! temperature with pressure. + +! === module MOM_restart === +PARALLEL_RESTARTFILES = False ! [Boolean] default = False + ! If true, the IO layout is used to group processors that write to the same + ! restart file or each processor writes its own (numbered) restart file. If + ! false, a single restart file is generated combining output from all PEs. +RESTARTFILE = "MOM.res" ! default = "MOM.res" + ! The name-root of the restart file. +MAX_FIELDS = 100 ! default = 100 + ! The maximum number of restart fields that can be used. +RESTART_CHECKSUMS_REQUIRED = True ! [Boolean] default = True + ! If true, require the restart checksums to match and error out otherwise. Users + ! may want to avoid this comparison if for example the restarts are made from a + ! run with a different mask_table than the current run, in which case the + ! checksums will not match and cause crash. + +! === module MOM_tracer_flow_control === +USE_USER_TRACER_EXAMPLE = False ! [Boolean] default = False + ! If true, use the USER_tracer_example tracer package. +USE_DOME_TRACER = False ! [Boolean] default = False + ! If true, use the DOME_tracer tracer package. +USE_ISOMIP_TRACER = False ! [Boolean] default = False + ! If true, use the ISOMIP_tracer tracer package. +USE_RGC_TRACER = False ! [Boolean] default = False + ! If true, use the RGC_tracer tracer package. +USE_IDEAL_AGE_TRACER = False ! [Boolean] default = False + ! If true, use the ideal_age_example tracer package. +USE_MARBL_TRACERS = True ! [Boolean] default = False + ! If true, use the MARBL tracer package. +USE_REGIONAL_DYES = False ! [Boolean] default = False + ! If true, use the regional_dyes tracer package. +USE_OIL_TRACER = False ! [Boolean] default = False + ! If true, use the oil_tracer tracer package. +USE_ADVECTION_TEST_TRACER = False ! [Boolean] default = False + ! If true, use the advection_test_tracer tracer package. +USE_OCMIP2_CFC = False ! [Boolean] default = False + ! If true, use the MOM_OCMIP2_CFC tracer package. +USE_CFC_CAP = False ! [Boolean] default = False + ! If true, use the MOM_CFC_cap tracer package. +USE_generic_tracer = False ! [Boolean] default = False + ! If true and _USE_GENERIC_TRACER is defined as a preprocessor macro, use the + ! MOM_generic_tracer packages. +USE_PSEUDO_SALT_TRACER = False ! [Boolean] default = False + ! If true, use the pseudo salt tracer, typically run as a diagnostic. +USE_BOUNDARY_IMPULSE_TRACER = False ! [Boolean] default = False + ! If true, use the boundary impulse tracer. +USE_DYED_OBC_TRACER = False ! [Boolean] default = False + ! If true, use the dyed_obc_tracer tracer package. +USE_NW2_TRACERS = False ! [Boolean] default = False + ! If true, use the NeverWorld2 tracers. + +! === module MARBL_tracers === +MARBL_SETTINGS_FILE = "marbl_in" ! default = "marbl_in" + ! The name of a file from which to read the run-time settings for MARBL. +ATM_CO2_CONST = 284.317 ! [ppm] default = 284.317 + ! Value to send to MARBL as xco2 +ATM_ALT_CO2_CONST = 284.317 ! [ppm] default = 284.317 + ! Value to send to MARBL as xco2_alt_co2 +NDEP_SCALE_FACTOR = 1.0E+05 ! [not defined] default = 1.0E+05 + ! Scale factor applied to nitrogen deposition terms +BOT_FLUX_MIX_THICKNESS = 1.0 ! [m] default = 1.0 + ! Bottom fluxes are uniformly mixed over layer of this thickness +USE_ICE_CATEGORIES = False ! [Boolean] default = False + ! If true, allocate memory for shortwave and ice fraction split by ice thickness + ! category. +ICE_NCAT = 0 ! default = 0 + ! Number of ice thickness categories in shortwave and ice fraction forcings. + +! === module MARBL_tracers === +MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c221027.nc" + ! The file in which the MARBL tracers initial values can be found. +MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc" + ! The file in which the iron sediment flux forcing field can be found. +MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211109.nc" + ! The file in which the iron vent flux forcing field can be found. +MARBL_FESEDFLUX_SCALE_FACTOR = 1.157407407407407E-06 ! [not defined] default = 1.157407407407407E-06 + ! Conversion factor between FESEDFLUX file and MARBL units (umol / m^2 / d -> + ! nmol / cm^2 / s) + +! === module MOM_coord_initialization === +COORD_CONFIG = "none" ! default = "none" + ! This specifies how layers are to be defined: + ! ALE or none - used to avoid defining layers in ALE mode + ! file - read coordinate information from the file + ! specified by (COORD_FILE). + ! BFB - Custom coords for buoyancy-forced basin case + ! based on SST_S, T_BOT and DRHO_DT. + ! linear - linear based on interfaces not layers + ! layer_ref - linear based on layer densities + ! ts_ref - use reference temperature and salinity + ! ts_range - use range of temperature and salinity + ! (T_REF and S_REF) to determine surface density + ! and GINT calculate internal densities. + ! gprime - use reference density (RHO_0) for surface + ! density and GINT calculate internal densities. + ! ts_profile - use temperature and salinity profiles + ! (read from COORD_FILE) to set layer densities. + ! USER - call a user modified routine. +GFS = 9.8 ! [m s-2] default = 9.8 + ! The reduced gravity at the free surface. +REMAP_UV_USING_OLD_ALG = False ! [Boolean] default = False + ! If true, uses the old remapping-via-a-delta-z method for remapping u and v. If + ! false, uses the new method that remaps between grids described by an old and + ! new thickness. +REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" + ! Coordinate mode for vertical regridding. Choose among the following + ! possibilities: LAYER - Isopycnal or stacked shallow water layers + ! ZSTAR, Z* - stretched geopotential z* + ! SIGMA_SHELF_ZSTAR - stretched geopotential z* ignoring shelf + ! SIGMA - terrain following coordinates + ! RHO - continuous isopycnal + ! HYCOM1 - HyCOM-like hybrid coordinate + ! HYBGEN - Hybrid coordinate from the Hycom hybgen code + ! SLIGHT - stretched coordinates above continuous isopycnal + ! ADAPTIVE - optimize for smooth neutral density surfaces +REGRIDDING_COORDINATE_UNITS = "m" ! default = "m" + ! Units of the regridding coordinate. +ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_cm4.nc,dz" ! default = "UNIFORM" + ! Determines how to specify the coordinate resolution. Valid options are: + ! PARAM - use the vector-parameter ALE_RESOLUTION + ! UNIFORM[:N] - uniformly distributed + ! FILE:string - read from a file. The string specifies + ! the filename and variable name, separated + ! by a comma or space, e.g. FILE:lev.nc,dz + ! or FILE:lev.nc,interfaces=zw + ! WOA09[:N] - the WOA09 vertical grid (approximately) + ! FNC1:string - FNC1:dz_min,H_total,power,precision + ! HYBRID:string - read from a file. The string specifies + ! the filename and two variable names, separated + ! by a comma or space, for sigma-2 and dz. e.g. + ! HYBRID:vgrid.nc,sigma2,dz +!ALE_RESOLUTION = 4*2.0, 2*2.01, 2*2.02, 2.05, 2.07, 2.09, 2.13, 2.18, 2.24, 2.3, 2.4, 2.5, 2.62, 2.78, 2.95, 3.17, 3.42, 3.71, 4.07, 4.48, 4.97, 5.55, 6.23, 7.04, 7.99, 9.11, 10.43, 11.98, 13.8, 15.94, 18.42, 21.32, 24.66, 28.51, 32.91, 37.92, 43.56, 49.87, 56.88, 64.55, 72.91, 81.87, 91.4, 101.39, 111.73, 122.29, 132.93, 143.47, 153.78, 163.7, 173.07, 181.78, 189.76, 196.89, 203.17, 208.58, 213.14, 216.87, 219.87, 222.19, 223.94, 225.18, 226.04, 226.59, 226.9, 227.07, 227.14, 2*227.16, 227.17 ! [m] + ! The distribution of vertical resolution for the target + ! grid used for Eulerian-like coordinates. For example, + ! in z-coordinate mode, the parameter is a list of level + ! thicknesses (in m). In sigma-coordinate mode, the list + ! is of non-dimensional fractions of the water column. +MIN_THICKNESS = 0.001 ! [m] default = 0.001 + ! When regridding, this is the minimum layer thickness allowed. +REMAPPING_SCHEME = "PLM" ! default = "PLM" + ! This sets the reconstruction scheme used for vertical remapping for all + ! variables. It can be one of the following schemes: PCM (1st-order + ! accurate) + ! PLM (2nd-order accurate) + ! PLM_HYBGEN (2nd-order accurate) + ! PPM_H4 (3rd-order accurate) + ! PPM_IH4 (3rd-order accurate) + ! PPM_HYBGEN (3rd-order accurate) + ! WENO_HYBGEN (3rd-order accurate) + ! PQM_IH4IH3 (4th-order accurate) + ! PQM_IH6IH5 (5th-order accurate) +VELOCITY_REMAPPING_SCHEME = "PLM" ! default = "PLM" + ! This sets the reconstruction scheme used for vertical remapping of velocities. + ! By default it is the same as REMAPPING_SCHEME. It can be one of the following + ! schemes: PCM (1st-order accurate) + ! PLM (2nd-order accurate) + ! PLM_HYBGEN (2nd-order accurate) + ! PPM_H4 (3rd-order accurate) + ! PPM_IH4 (3rd-order accurate) + ! PPM_HYBGEN (3rd-order accurate) + ! WENO_HYBGEN (3rd-order accurate) + ! PQM_IH4IH3 (4th-order accurate) + ! PQM_IH6IH5 (5th-order accurate) +FATAL_CHECK_RECONSTRUCTIONS = False ! [Boolean] default = False + ! If true, cell-by-cell reconstructions are checked for consistency and if + ! non-monotonicity or an inconsistency is detected then a FATAL error is issued. +FATAL_CHECK_REMAPPING = False ! [Boolean] default = False + ! If true, the results of remapping are checked for conservation and new extrema + ! and if an inconsistency is detected then a FATAL error is issued. +REMAP_BOUND_INTERMEDIATE_VALUES = False ! [Boolean] default = False + ! If true, the values on the intermediate grid used for remapping are forced to + ! be bounded, which might not be the case due to round off. +REMAP_BOUNDARY_EXTRAP = False ! [Boolean] default = False + ! If true, values at the interfaces of boundary cells are extrapolated instead + ! of piecewise constant +REMAPPING_2018_ANSWERS = False ! [Boolean] default = False + ! If true, use the order of arithmetic and expressions that recover the answers + ! from the end of 2018. Otherwise, use updated and more robust forms of the + ! same expressions. +REMAPPING_ANSWER_DATE = 99991231 ! default = 99991231 + ! The vintage of the expressions and order of arithmetic to use for remapping. + ! Values below 20190101 result in the use of older, less accurate expressions + ! that were in use at the end of 2018. Higher values result in the use of more + ! robust and accurate forms of mathematically equivalent expressions. If both + ! REMAPPING_2018_ANSWERS and REMAPPING_ANSWER_DATE are specified, the latter + ! takes precedence. +PARTIAL_CELL_VELOCITY_REMAP = False ! [Boolean] default = False + ! If true, use partial cell thicknesses at velocity points that are masked out + ! where they extend below the shallower of the neighboring bathymetry for + ! remapping velocity. +REMAP_AFTER_INITIALIZATION = True ! [Boolean] default = True + ! If true, applies regridding and remapping immediately after initialization so + ! that the state is ALE consistent. This is a legacy step and should not be + ! needed if the initialization is consistent with the coordinate mode. +REGRID_TIME_SCALE = 0.0 ! [s] default = 0.0 + ! The time-scale used in blending between the current (old) grid and the target + ! (new) grid. A short time-scale favors the target grid (0. or anything less + ! than DT_THERM) has no memory of the old grid. A very long time-scale makes the + ! model more Lagrangian. +REGRID_FILTER_SHALLOW_DEPTH = 0.0 ! [m] default = 0.0 + ! The depth above which no time-filtering is applied. Above this depth final + ! grid exactly matches the target (new) grid. +REGRID_FILTER_DEEP_DEPTH = 0.0 ! [m] default = 0.0 + ! The depth below which full time-filtering is applied with time-scale + ! REGRID_TIME_SCALE. Between depths REGRID_FILTER_SHALLOW_DEPTH and + ! REGRID_FILTER_SHALLOW_DEPTH the filter weights adopt a cubic profile. +REMAP_VEL_MASK_BBL_THICK = -0.001 ! [m] default = -0.001 + ! A thickness of a bottom boundary layer below which velocities in thin layers + ! are zeroed out after remapping, following practice with Hybgen remapping, or a + ! negative value to avoid such filtering altogether. + +! === module MOM_state_initialization === +FATAL_INCONSISTENT_RESTART_TIME = False ! [Boolean] default = False + ! If true and a time_in value is provided to MOM_initialize_state, verify that + ! the time read from a restart file is the same as time_in, and issue a fatal + ! error if it is not. Otherwise, simply set the time to time_in if present. +INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False + ! If true, initialize the layer thicknesses, temperatures, and salinities from a + ! Z-space file on a latitude-longitude grid. + +! === module MOM_initialize_layers_from_Z === +TEMP_SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "temp_salt_z.nc" + ! The name of the z-space input file used to initialize temperatures (T) and + ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and + ! SALT_Z_INIT_FILE must be set. +TEMP_Z_INIT_FILE = "WOA_column.nc" ! default = "WOA_column.nc" + ! The name of the z-space input file used to initialize temperatures, only. +SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "WOA_column.nc" + ! The name of the z-space input file used to initialize temperatures, only. +Z_INIT_FILE_PTEMP_VAR = "PTEMP" ! default = "ptemp" + ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. +Z_INIT_FILE_SALT_VAR = "SALT" ! default = "salt" + ! The name of the salinity variable in SALT_Z_INIT_FILE. +Z_INIT_HOMOGENIZE = True ! [Boolean] default = False + ! If True, then horizontally homogenize the interpolated initial conditions. +Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False + ! If True, then remap straight to model coordinate from file. +Z_INIT_REMAPPING_SCHEME = "PPM_IH4" ! default = "PPM_IH4" + ! The remapping scheme to use if using Z_INIT_ALE_REMAPPING is True. +Z_INIT_REMAP_GENERAL = False ! [Boolean] default = False + ! If false, only initializes to z* coordinates. If true, allows initialization + ! directly to general coordinates. +Z_INIT_REMAP_FULL_COLUMN = False ! [Boolean] default = False + ! If false, only reconstructs profiles for valid data points. If true, inserts + ! vanished layers below the valid data. +Z_INIT_REMAP_OLD_ALG = False ! [Boolean] default = False + ! If false, uses the preferred remapping algorithm for initialization. If true, + ! use an older, less robust algorithm for remapping. +TEMP_SALT_INIT_VERTICAL_REMAP_ONLY = False ! [Boolean] default = False + ! If true, initial conditions are on the model horizontal grid. Extrapolation + ! over missing ocean values is done using an ICE-9 procedure with vertical ALE + ! remapping . +HOR_REGRID_2018_ANSWERS = False ! [Boolean] default = False + ! If true, use the order of arithmetic for horizonal regridding that recovers + ! the answers from the end of 2018. Otherwise, use rotationally symmetric forms + ! of the same expressions. +HOR_REGRID_ANSWER_DATE = 99991231 ! default = 99991231 + ! The vintage of the order of arithmetic for horizontal regridding. Dates + ! before 20190101 give the same answers as the code did in late 2018, while + ! later versions add parentheses for rotational symmetry. If both + ! HOR_REGRID_2018_ANSWERS and HOR_REGRID_ANSWER_DATE are specified, the latter + ! takes precedence. +DEPRESS_INITIAL_SURFACE = False ! [Boolean] default = False + ! If true, depress the initial surface to avoid huge tsunamis when a large + ! surface pressure is applied. +TRIM_IC_FOR_P_SURF = False ! [Boolean] default = False + ! If true, cuts way the top of the column for initial conditions at the depth + ! where the hydrostatic pressure matches the imposed surface pressure which is + ! read from file. +VELOCITY_CONFIG = "zero" ! default = "zero" + ! A string that determines how the initial velocities are specified for a new + ! run: + ! file - read velocities from the file specified + ! by (VELOCITY_FILE). + ! zero - the fluid is initially at rest. + ! uniform - the flow is uniform (determined by + ! parameters INITIAL_U_CONST and INITIAL_V_CONST). + ! rossby_front - a mixed layer front in thermal wind balance. + ! soliton - Equatorial Rossby soliton. + ! USER - call a user modified routine. +CONVERT_THICKNESS_UNITS = False ! [Boolean] default = False + ! If true, convert the thickness initial conditions from units of m to kg m-2 + ! or vice versa, depending on whether BOUSSINESQ is defined. This does not apply + ! if a restart file is read. +REGRID_ACCELERATE_INIT = False ! [Boolean] default = False + ! If true, runs REGRID_ACCELERATE_ITERATIONS iterations of the regridding + ! algorithm to push the initial grid to be consistent with the initial + ! condition. Useful only for state-based and iterative coordinates. +ODA_INCUPD = False ! [Boolean] default = False + ! If true, oda incremental updates will be applied everywhere in the domain. +SPONGE = False ! [Boolean] default = False + ! If true, sponges may be applied anywhere in the domain. The exact location and + ! properties of those sponges are specified via SPONGE_CONFIG. + +! === module MOM_diag_mediator === +NUM_DIAG_COORDS = 1 ! default = 1 + ! The number of diagnostic vertical coordinates to use. For each coordinate, an + ! entry in DIAG_COORDS must be provided. +USE_GRID_SPACE_DIAGNOSTIC_AXES = False ! [Boolean] default = False + ! If true, use a grid index coordinate convention for diagnostic axes. +DIAG_COORDS = "z Z ZSTAR" ! default = "z Z ZSTAR" + ! A list of string tuples associating diag_table modules to a coordinate + ! definition used for diagnostics. Each string is of the form "MODULE_SUFFIX + ! PARAMETER_SUFFIX COORDINATE_NAME". +DIAG_MISVAL = 1.0E+20 ! [not defined] default = 1.0E+20 + ! Set the default missing value to use for diagnostics. +DIAG_AS_CHKSUM = False ! [Boolean] default = False + ! Instead of writing diagnostics to the diag manager, write a text file + ! containing the checksum (bitcount) of the array. +AVAILABLE_DIAGS_FILE = "available_diags.000000" ! default = "available_diags.000000" + ! A file into which to write a list of all available ocean diagnostics that can + ! be included in a diag_table. +DIAG_COORD_DEF_Z = "WOA09" ! default = "WOA09" + ! Determines how to specify the coordinate resolution. Valid options are: + ! PARAM - use the vector-parameter DIAG_COORD_RES_Z + ! UNIFORM[:N] - uniformly distributed + ! FILE:string - read from a file. The string specifies + ! the filename and variable name, separated + ! by a comma or space, e.g. FILE:lev.nc,dz + ! or FILE:lev.nc,interfaces=zw + ! WOA09[:N] - the WOA09 vertical grid (approximately) + ! FNC1:string - FNC1:dz_min,H_total,power,precision + ! HYBRID:string - read from a file. The string specifies + ! the filename and two variable names, separated + ! by a comma or space, for sigma-2 and dz. e.g. + ! HYBRID:vgrid.nc,sigma2,dz + +! === module MOM_MEKE === +USE_MEKE = False ! [Boolean] default = False + ! If true, turns on the MEKE scheme which calculates a sub-grid mesoscale eddy + ! kinetic energy budget. + +! === module MOM_lateral_mixing_coeffs === +USE_VARIABLE_MIXING = False ! [Boolean] default = False + ! If true, the variable mixing code will be called. This allows diagnostics to + ! be created even if the scheme is not used. If KHTR_SLOPE_CFF>0 or + ! KhTh_Slope_Cff>0, this is set to true regardless of what is in the parameter + ! file. +USE_VISBECK = False ! [Boolean] default = False + ! If true, use the Visbeck et al. (1997) formulation for + ! thickness diffusivity. +RESOLN_SCALED_KH = False ! [Boolean] default = False + ! If true, the Laplacian lateral viscosity is scaled away when the first + ! baroclinic deformation radius is well resolved. +DEPTH_SCALED_KHTH = False ! [Boolean] default = False + ! If true, KHTH is scaled away when the depth is shallowerthan a reference + ! depth: KHTH = MIN(1,H/H0)**N * KHTH, where H0 is a reference depth, controlled + ! via DEPTH_SCALED_KHTH_H0, and the exponent (N) is controlled via + ! DEPTH_SCALED_KHTH_EXP. +RESOLN_SCALED_KHTH = False ! [Boolean] default = False + ! If true, the interface depth diffusivity is scaled away when the first + ! baroclinic deformation radius is well resolved. +RESOLN_SCALED_KHTR = False ! [Boolean] default = False + ! If true, the epipycnal tracer diffusivity is scaled away when the first + ! baroclinic deformation radius is well resolved. +RESOLN_USE_EBT = False ! [Boolean] default = False + ! If true, uses the equivalent barotropic wave speed instead of first baroclinic + ! wave for calculating the resolution fn. +KHTH_USE_EBT_STRUCT = False ! [Boolean] default = False + ! If true, uses the equivalent barotropic structure as the vertical structure of + ! thickness diffusivity. +KHTH_SLOPE_CFF = 0.0 ! [nondim] default = 0.0 + ! The nondimensional coefficient in the Visbeck formula for the interface depth + ! diffusivity +KHTR_SLOPE_CFF = 0.0 ! [nondim] default = 0.0 + ! The nondimensional coefficient in the Visbeck formula for the epipycnal tracer + ! diffusivity +USE_STORED_SLOPES = False ! [Boolean] default = False + ! If true, the isopycnal slopes are calculated once and stored for re-use. This + ! uses more memory but avoids calling the equation of state more times than + ! should be necessary. +VERY_SMALL_FREQUENCY = 1.0E-17 ! [s-1] default = 1.0E-17 + ! A miniscule frequency that is used to avoid division by 0. The default value + ! is roughly (pi / (the age of the universe)). +USE_STANLEY_ISO = False ! [Boolean] default = False + ! If true, turn on Stanley SGS T variance parameterization in isopycnal slope + ! code. +USE_QG_LEITH_GM = False ! [Boolean] default = False + ! If true, use the QG Leith viscosity as the GM coefficient. + +! === module MOM_set_visc === +SET_VISC_2018_ANSWERS = False ! [Boolean] default = False + ! If true, use the order of arithmetic and expressions that recover the answers + ! from the end of 2018. Otherwise, use updated and more robust forms of the + ! same expressions. +SET_VISC_ANSWER_DATE = 99991231 ! default = 99991231 + ! The vintage of the order of arithmetic and expressions in the set viscosity + ! calculations. Values below 20190101 recover the answers from the end of 2018, + ! while higher values use updated and more robust forms of the same expressions. + ! If both SET_VISC_2018_ANSWERS and SET_VISC_ANSWER_DATE are specified, the + ! latter takes precedence. +BOTTOMDRAGLAW = True ! [Boolean] default = True + ! If true, the bottom stress is calculated with a drag law of the form + ! c_drag*|u|*u. The velocity magnitude may be an assumed value or it may be + ! based on the actual velocity in the bottommost HBBL, depending on LINEAR_DRAG. +DRAG_AS_BODY_FORCE = False ! [Boolean] default = False + ! If true, the bottom stress is imposed as an explicit body force applied over a + ! fixed distance from the bottom, rather than as an implicit calculation based + ! on an enhanced near-bottom viscosity. The thickness of the bottom boundary + ! layer is HBBL. +CHANNEL_DRAG = False ! [Boolean] default = False + ! If true, the bottom drag is exerted directly on each layer proportional to the + ! fraction of the bottom it overlies. +LINEAR_DRAG = False ! [Boolean] default = False + ! If LINEAR_DRAG and BOTTOMDRAGLAW are defined the drag law is + ! cdrag*DRAG_BG_VEL*u. +PRANDTL_TURB = 1.0 ! [nondim] default = 1.0 + ! The turbulent Prandtl number applied to shear instability. +DYNAMIC_VISCOUS_ML = False ! [Boolean] default = False + ! If true, use a bulk Richardson number criterion to determine the mixed layer + ! thickness for viscosity. +OMEGA = 7.2921E-05 ! [s-1] default = 7.2921E-05 + ! The rotation rate of the earth. +HBBL = 10.0 ! [m] + ! The thickness of a bottom boundary layer with a viscosity of KVBBL if + ! BOTTOMDRAGLAW is not defined, or the thickness over which near-bottom + ! velocities are averaged for the drag law if BOTTOMDRAGLAW is defined but + ! LINEAR_DRAG is not. +CDRAG = 0.0 ! [nondim] default = 0.003 + ! CDRAG is the drag coefficient relating the magnitude of the velocity field to + ! the bottom stress. CDRAG is only used if BOTTOMDRAGLAW is defined. +BBL_USE_TIDAL_BG = False ! [Boolean] default = False + ! Flag to use the tidal RMS amplitude in place of constant background velocity + ! for computing u* in the BBL. This flag is only used when BOTTOMDRAGLAW is true + ! and LINEAR_DRAG is false. +DRAG_BG_VEL = 0.1 ! [m s-1] default = 0.0 + ! DRAG_BG_VEL is either the assumed bottom velocity (with LINEAR_DRAG) or an + ! unresolved velocity that is combined with the resolved velocity to estimate + ! the velocity magnitude. DRAG_BG_VEL is only used when BOTTOMDRAGLAW is + ! defined. +BBL_USE_EOS = True ! [Boolean] default = True + ! If true, use the equation of state in determining the properties of the bottom + ! boundary layer. Otherwise use the layer target potential densities. The + ! default of this parameter is the value of USE_EOS. +BBL_THICK_MIN = 0.1 ! [m] default = 0.0 + ! The minimum bottom boundary layer thickness that can be used with + ! BOTTOMDRAGLAW. This might be Kv/(cdrag*drag_bg_vel) to give Kv as the minimum + ! near-bottom viscosity. +HTBL_SHELF_MIN = 0.1 ! [m] default = 0.1 + ! The minimum top boundary layer thickness that can be used with BOTTOMDRAGLAW. + ! This might be Kv/(cdrag*drag_bg_vel) to give Kv as the minimum near-top + ! viscosity. +HTBL_SHELF = 10.0 ! [m] default = 10.0 + ! The thickness over which near-surface velocities are averaged for the drag law + ! under an ice shelf. By default this is the same as HBBL +KV = 1.0E-04 ! [m2 s-1] + ! The background kinematic viscosity in the interior. The molecular value, ~1e-6 + ! m2 s-1, may be used. +KV_BBL_MIN = 1.0E-04 ! [m2 s-1] default = 1.0E-04 + ! The minimum viscosities in the bottom boundary layer. +KV_TBL_MIN = 1.0E-04 ! [m2 s-1] default = 1.0E-04 + ! The minimum viscosities in the top boundary layer. +CORRECT_BBL_BOUNDS = False ! [Boolean] default = False + ! If true, uses the correct bounds on the BBL thickness and viscosity so that + ! the bottom layer feels the intended drag. + +! === module MOM_thickness_diffuse === +KHTH = 0.0 ! [m2 s-1] default = 0.0 + ! The background horizontal thickness diffusivity. +KHTH_MIN = 0.0 ! [m2 s-1] default = 0.0 + ! The minimum horizontal thickness diffusivity. +KHTH_MAX = 0.0 ! [m2 s-1] default = 0.0 + ! The maximum horizontal thickness diffusivity. +KHTH_MAX_CFL = 0.8 ! [nondimensional] default = 0.8 + ! The maximum value of the local diffusive CFL ratio that is permitted for the + ! thickness diffusivity. 1.0 is the marginally unstable value in a pure layered + ! model, but much smaller numbers (e.g. 0.1) seem to work better for ALE-based + ! models. +KH_ETA_CONST = 0.0 ! [m2 s-1] default = 0.0 + ! The background horizontal diffusivity of the interface heights (without + ! considering the layer density structure). If diffusive CFL limits are + ! encountered, the diffusivities of the isopycnals and the interfaces heights + ! are scaled back proportionately. +KH_ETA_VEL_SCALE = 0.0 ! [m s-1] default = 0.0 + ! A velocity scale that is multiplied by the grid spacing to give a contribution + ! to the horizontal diffusivity of the interface heights (without considering + ! the layer density structure). +DETANGLE_INTERFACES = False ! [Boolean] default = False + ! If defined add 3-d structured enhanced interface height diffusivities to + ! horizontally smooth jagged layers. +KHTH_SLOPE_MAX = 0.01 ! [nondim] default = 0.01 + ! A slope beyond which the calculated isopycnal slope is not reliable and is + ! scaled away. +KD_SMOOTH = 1.0E-06 ! [m2 s-1] default = 1.0E-06 + ! A diapycnal diffusivity that is used to interpolate more sensible values of T + ! & S into thin layers. +KHTH_USE_FGNV_STREAMFUNCTION = False ! [Boolean] default = False + ! If true, use the streamfunction formulation of Ferrari et al., 2010, which + ! effectively emphasizes graver vertical modes by smoothing in the vertical. +USE_STANLEY_GM = False ! [Boolean] default = False + ! If true, turn on Stanley SGS T variance parameterization in GM code. +MEKE_GM_SRC_ALT = False ! [Boolean] default = False + ! If true, use the GM energy conversion form S^2*N^2*kappa rather than the + ! streamfunction for the GM source term. +MEKE_GEOMETRIC = False ! [Boolean] default = False + ! If true, uses the GM coefficient formulation from the GEOMETRIC framework + ! (Marshall et al., 2012). +USE_KH_IN_MEKE = False ! [Boolean] default = False + ! If true, uses the thickness diffusivity calculated here to diffuse MEKE. +USE_GME = False ! [Boolean] default = False + ! If true, use the GM+E backscatter scheme in association with the Gent and + ! McWilliams parameterization. +USE_GM_WORK_BUG = False ! [Boolean] default = False + ! If true, compute the top-layer work tendency on the u-grid with the incorrect + ! sign, for legacy reproducibility. +STOCH_EOS = False ! [Boolean] default = False + ! If true, stochastic perturbations are applied to the EOS in the PGF. +STANLEY_COEFF = -1.0 ! [not defined] default = -1.0 + ! Coefficient correlating the temperature gradient and SGS T variance. +STANLEY_A = 1.0 ! [not defined] default = 1.0 + ! Coefficient a which scales chi in stochastic perturbation of the SGS T + ! variance. + +! === module MOM_dynamics_unsplit === +FIX_UNSPLIT_DT_VISC_BUG = True ! [Boolean] default = True + ! If true, use the correct timestep in the viscous terms applied in the first + ! predictor step with the unsplit time stepping scheme, and in the calculation + ! of the turbulent mixed layer properties for viscosity with unsplit or + ! unsplit_RK2. +TIDES = False ! [Boolean] default = False + ! If true, apply tidal momentum forcing. + +! === module MOM_continuity === +CONTINUITY_SCHEME = "PPM" ! default = "PPM" + ! CONTINUITY_SCHEME selects the discretization for the continuity solver. The + ! only valid value currently is: + ! PPM - use a positive-definite (or monotonic) + ! piecewise parabolic reconstruction solver. + +! === module MOM_continuity_PPM === +MONOTONIC_CONTINUITY = False ! [Boolean] default = False + ! If true, CONTINUITY_PPM uses the Colella and Woodward monotonic limiter. The + ! default (false) is to use a simple positive definite limiter. +SIMPLE_2ND_PPM_CONTINUITY = False ! [Boolean] default = False + ! If true, CONTINUITY_PPM uses a simple 2nd order (arithmetic mean) + ! interpolation of the edge values. This may give better PV conservation + ! properties. While it formally reduces the accuracy of the continuity solver + ! itself in the strongly advective limit, it does not reduce the overall order + ! of accuracy of the dynamic core. +UPWIND_1ST_CONTINUITY = True ! [Boolean] default = False + ! If true, CONTINUITY_PPM becomes a 1st-order upwind continuity solver. This + ! scheme is highly diffusive but may be useful for debugging or in single-column + ! mode where its minimal stencil is useful. +ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.75E-09 + ! The tolerance for the differences between the barotropic and baroclinic + ! estimates of the sea surface height due to the fluxes through each face. The + ! total tolerance for SSH is 4 times this value. The default is + ! 0.5*NK*ANGSTROM, and this should not be set less than about + ! 10^-15*MAXIMUM_DEPTH. +VELOCITY_TOLERANCE = 3.0E+08 ! [m s-1] default = 3.0E+08 + ! The tolerance for barotropic velocity discrepancies between the barotropic + ! solution and the sum of the layer thicknesses. +CONT_PPM_AGGRESS_ADJUST = False ! [Boolean] default = False + ! If true, allow the adjusted velocities to have a relative CFL change up to + ! 0.5. +CONT_PPM_VOLUME_BASED_CFL = False ! [Boolean] default = False + ! If true, use the ratio of the open face lengths to the tracer cell areas when + ! estimating CFL numbers. The default is set by CONT_PPM_AGGRESS_ADJUST. +CONTINUITY_CFL_LIMIT = 0.5 ! [nondim] default = 0.5 + ! The maximum CFL of the adjusted velocities. +CONT_PPM_BETTER_ITER = True ! [Boolean] default = True + ! If true, stop corrective iterations using a velocity based criterion and only + ! stop if the iteration is better than all predecessors. +CONT_PPM_USE_VISC_REM_MAX = True ! [Boolean] default = True + ! If true, use more appropriate limiting bounds for corrections in strongly + ! viscous columns. +CONT_PPM_MARGINAL_FACE_AREAS = True ! [Boolean] default = True + ! If true, use the marginal face areas from the continuity solver for use as the + ! weights in the barotropic solver. Otherwise use the transport averaged areas. + +! === module MOM_CoriolisAdv === +NOSLIP = False ! [Boolean] default = False + ! If true, no slip boundary conditions are used; otherwise free slip boundary + ! conditions are assumed. The implementation of the free slip BCs on a C-grid is + ! much cleaner than the no slip BCs. The use of free slip BCs is strongly + ! encouraged, and no slip BCs are not used with the biharmonic viscosity. +CORIOLIS_EN_DIS = True ! [Boolean] default = False + ! If true, two estimates of the thickness fluxes are used to estimate the + ! Coriolis term, and the one that dissipates energy relative to the other one is + ! used. +CORIOLIS_SCHEME = "SADOURNY75_ENERGY" ! default = "SADOURNY75_ENERGY" + ! CORIOLIS_SCHEME selects the discretization for the Coriolis terms. Valid + ! values are: + ! SADOURNY75_ENERGY - Sadourny, 1975; energy cons. + ! ARAKAWA_HSU90 - Arakawa & Hsu, 1990 + ! SADOURNY75_ENSTRO - Sadourny, 1975; enstrophy cons. + ! ARAKAWA_LAMB81 - Arakawa & Lamb, 1981; En. + Enst. + ! ARAKAWA_LAMB_BLEND - A blend of Arakawa & Lamb with + ! Arakawa & Hsu and Sadourny energy +BOUND_CORIOLIS = True ! [Boolean] default = False + ! If true, the Coriolis terms at u-points are bounded by the four estimates of + ! (f+rv)v from the four neighboring v-points, and similarly at v-points. This + ! option is always effectively false with CORIOLIS_EN_DIS defined and + ! CORIOLIS_SCHEME set to SADOURNY75_ENERGY. +KE_SCHEME = "KE_ARAKAWA" ! default = "KE_ARAKAWA" + ! KE_SCHEME selects the discretization for acceleration due to the kinetic + ! energy gradient. Valid values are: + ! KE_ARAKAWA, KE_SIMPLE_GUDONOV, KE_GUDONOV +PV_ADV_SCHEME = "PV_ADV_CENTERED" ! default = "PV_ADV_CENTERED" + ! PV_ADV_SCHEME selects the discretization for PV advection. Valid values are: + ! PV_ADV_CENTERED - centered (aka Sadourny, 75) + ! PV_ADV_UPWIND1 - upwind, first order + +! === module MOM_PressureForce === +ANALYTIC_FV_PGF = True ! [Boolean] default = True + ! If true the pressure gradient forces are calculated with a finite volume form + ! that analytically integrates the equations of state in pressure to avoid any + ! possibility of numerical thermobaric instability, as described in Adcroft et + ! al., O. Mod. (2008). + +! === module MOM_PressureForce_FV === +MASS_WEIGHT_IN_PRESSURE_GRADIENT = False ! [Boolean] default = False + ! If true, use mass weighting when interpolating T/S for integrals near the + ! bathymetry in FV pressure gradient calculations. +USE_INACCURATE_PGF_RHO_ANOM = False ! [Boolean] default = False + ! If true, use a form of the PGF that uses the reference density in an + ! inaccurate way. This is not recommended. +RECONSTRUCT_FOR_PRESSURE = True ! [Boolean] default = True + ! If True, use vertical reconstruction of T & S within the integrals of the FV + ! pressure gradient calculation. If False, use the constant-by-layer algorithm. + ! The default is set by USE_REGRIDDING. +PRESSURE_RECONSTRUCTION_SCHEME = 1 ! default = 1 + ! Order of vertical reconstruction of T/S to use in the integrals within the FV + ! pressure gradient calculation. + ! 0: PCM or no reconstruction. + ! 1: PLM reconstruction. + ! 2: PPM reconstruction. +BOUNDARY_EXTRAPOLATION_PRESSURE = True ! [Boolean] default = True + ! If true, the reconstruction of T & S for pressure in boundary cells is + ! extrapolated, rather than using PCM in these cells. If true, the same order + ! polynomial is used as is used for the interior cells. +USE_STANLEY_PGF = False ! [Boolean] default = False + ! If true, turn on Stanley SGS T variance parameterization in PGF code. + +! === module MOM_hor_visc === +HOR_VISC_2018_ANSWERS = False ! [Boolean] default = False + ! If true, use the order of arithmetic and expressions that recover the answers + ! from the end of 2018. Otherwise, use updated and more robust forms of the + ! same expressions. +HOR_VISC_ANSWER_DATE = 99991231 ! default = 99991231 + ! The vintage of the order of arithmetic and expressions in the horizontal + ! viscosity calculations. Values below 20190101 recover the answers from the + ! end of 2018, while higher values use updated and more robust forms of the same + ! expressions. If both HOR_VISC_2018_ANSWERS and HOR_VISC_ANSWER_DATE are + ! specified, the latter takes precedence. +LAPLACIAN = False ! [Boolean] default = False + ! If true, use a Laplacian horizontal viscosity. +BIHARMONIC = False ! [Boolean] default = True + ! If true, use a biharmonic horizontal viscosity. BIHARMONIC may be used with + ! LAPLACIAN. +USE_LAND_MASK_FOR_HVISC = True ! [Boolean] default = True + ! If true, use the land mask for the computation of thicknesses at velocity + ! locations. This eliminates the dependence on arbitrary values over land or + ! outside of the domain. + +! === module MOM_vert_friction === +VERT_FRICTION_2018_ANSWERS = False ! [Boolean] default = False + ! If true, use the order of arithmetic and expressions that recover the answers + ! from the end of 2018. Otherwise, use expressions that do not use an arbitrary + ! hard-coded maximum viscous coupling coefficient between layers. +VERT_FRICTION_ANSWER_DATE = 99991231 ! default = 99991231 + ! The vintage of the order of arithmetic and expressions in the viscous + ! calculations. Values below 20190101 recover the answers from the end of 2018, + ! while higher values use expressions that do not use an arbitrary hard-coded + ! maximum viscous coupling coefficient between layers. If both + ! VERT_FRICTION_2018_ANSWERS and VERT_FRICTION_ANSWER_DATE are specified, the + ! latter takes precedence. +DIRECT_STRESS = False ! [Boolean] default = False + ! If true, the wind stress is distributed over the topmost HMIX_STRESS of fluid + ! (like in HYCOM), and KVML may be set to a very small value. +HARMONIC_VISC = True ! [Boolean] default = False + ! If true, use the harmonic mean thicknesses for calculating the vertical + ! viscosity. +HARMONIC_BL_SCALE = 0.0 ! [nondim] default = 0.0 + ! A scale to determine when water is in the boundary layers based solely on + ! harmonic mean thicknesses for the purpose of determining the extent to which + ! the thicknesses used in the viscosities are upwinded. +HMIX_FIXED = 0.01 ! [m] + ! The prescribed depth over which the near-surface viscosity and diffusivity are + ! elevated when the bulk mixed layer is not used. +KVML = 0.0 ! [m2 s-1] default = 1.0E-04 + ! The kinematic viscosity in the mixed layer. A typical value is ~1e-2 m2 s-1. + ! KVML is not used if BULKMIXEDLAYER is true. The default is set by KV. +MAXVEL = 6.0 ! [m s-1] default = 3.0E+08 + ! The maximum velocity allowed before the velocity components are truncated. +CFL_BASED_TRUNCATIONS = True ! [Boolean] default = True + ! If true, base truncations on the CFL number, and not an absolute speed. +CFL_TRUNCATE = 0.5 ! [nondim] default = 0.5 + ! The value of the CFL number that will cause velocity components to be + ! truncated; instability can occur past 0.5. +CFL_REPORT = 0.5 ! [nondim] default = 0.5 + ! The value of the CFL number that causes accelerations to be reported; the + ! default is CFL_TRUNCATE. +CFL_TRUNCATE_RAMP_TIME = 0.0 ! [s] default = 0.0 + ! The time over which the CFL truncation value is ramped up at the beginning of + ! the run. +CFL_TRUNCATE_START = 0.0 ! [nondim] default = 0.0 + ! The start value of the truncation CFL number used when ramping up CFL_TRUNC. +STOKES_MIXING_COMBINED = False ! [Boolean] default = False + ! Flag to use Stokes drift Mixing via the Lagrangian current (Eulerian plus + ! Stokes drift). Still needs work and testing, so not recommended for use. +VEL_UNDERFLOW = 1.0E-30 ! [m s-1] default = 0.0 + ! A negligibly small velocity magnitude below which velocity components are set + ! to 0. A reasonable value might be 1e-30 m/s, which is less than an Angstrom + ! divided by the age of the universe. + +! === module MOM_mixed_layer_restrat === +MIXEDLAYER_RESTRAT = False ! [Boolean] default = False + ! If true, a density-gradient dependent re-stratifying flow is imposed in the + ! mixed layer. Can be used in ALE mode without restriction but in layer mode can + ! only be used if BULKMIXEDLAYER is true. + +! === module MOM_diagnostics === +DIAG_EBT_MONO_N2_COLUMN_FRACTION = 0.0 ! [nondim] default = 0.0 + ! The lower fraction of water column over which N2 is limited as monotonic for + ! the purposes of calculating the equivalent barotropic wave speed. +DIAG_EBT_MONO_N2_DEPTH = -1.0 ! [m] default = -1.0 + ! The depth below which N2 is limited as monotonic for the purposes of + ! calculating the equivalent barotropic wave speed. +INTERNAL_WAVE_SPEED_TOL = 0.001 ! [nondim] default = 0.001 + ! The fractional tolerance for finding the wave speeds. +INTERNAL_WAVE_SPEED_MIN = 0.0 ! [m s-1] default = 0.0 + ! A floor in the first mode speed below which 0 used instead. +INTERNAL_WAVE_SPEED_BETTER_EST = True ! [Boolean] default = True + ! If true, use a more robust estimate of the first mode wave speed as the + ! starting point for iterations. + +! === module MOM_diabatic_driver === +! The following parameters are used for diabatic processes. +USE_LEGACY_DIABATIC_DRIVER = True ! [Boolean] default = True + ! If true, use a legacy version of the diabatic subroutine. This is temporary + ! and is needed to avoid change in answers. +ENERGETICS_SFC_PBL = False ! [Boolean] default = False + ! If true, use an implied energetics planetary boundary layer scheme to + ! determine the diffusivity and viscosity in the surface boundary layer. +EPBL_IS_ADDITIVE = True ! [Boolean] default = True + ! If true, the diffusivity from ePBL is added to all other diffusivities. + ! Otherwise, the larger of kappa-shear and ePBL diffusivities are used. +INTERNAL_TIDES = False ! [Boolean] default = False + ! If true, use the code that advances a separate set of equations for the + ! internal tide energy density. +MASSLESS_MATCH_TARGETS = True ! [Boolean] default = True + ! If true, the temperature and salinity of massless layers are kept consistent + ! with their target densities. Otherwise the properties of massless layers + ! evolve diffusively to match massive neighboring layers. +AGGREGATE_FW_FORCING = True ! [Boolean] default = True + ! If true, the net incoming and outgoing fresh water fluxes are combined and + ! applied as either incoming or outgoing depending on the sign of the net. If + ! false, the net incoming fresh water flux is added to the model and thereafter + ! the net outgoing is removed from the topmost non-vanished layers of the + ! updated state. +MIX_BOUNDARY_TRACERS = True ! [Boolean] default = True + ! If true, mix the passive tracers in massless layers at the bottom into the + ! interior as though a diffusivity of KD_MIN_TR were operating. +MIX_BOUNDARY_TRACER_ALE = False ! [Boolean] default = False + ! If true and in ALE mode, mix the passive tracers in massless layers at the + ! bottom into the interior as though a diffusivity of KD_MIN_TR were operating. +KD_MIN_TR = 2.0E-06 ! [m2 s-1] default = 2.0E-06 + ! A minimal diffusivity that should always be applied to tracers, especially in + ! massless layers near the bottom. The default is 0.1*KD. +KD_BBL_TR = 0.0 ! [m2 s-1] default = 0.0 + ! A bottom boundary layer tracer diffusivity that will allow for explicitly + ! specified bottom fluxes. The entrainment at the bottom is at least + ! sqrt(Kd_BBL_tr*dt) over the same distance. +TRACER_TRIDIAG = False ! [Boolean] default = False + ! If true, use the passive tracer tridiagonal solver for T and S +MINIMUM_FORCING_DEPTH = 0.001 ! [m] default = 0.001 + ! The smallest depth over which forcing can be applied. This only takes effect + ! when near-surface layers become thin relative to this scale, in which case the + ! forcing tendencies scaled down by distributing the forcing over this depth + ! scale. +EVAP_CFL_LIMIT = 0.8 ! [nondim] default = 0.8 + ! The largest fraction of a layer than can be lost to forcing (e.g. evaporation, + ! sea-ice formation) in one time-step. The unused mass loss is passed down + ! through the column. +MLD_EN_VALS = 3*0.0 ! [J/m2] default = 0.0 + ! The energy values used to compute MLDs. If not set (or all set to 0.), the + ! default will overwrite to 25., 2500., 250000. +DIAG_MLD_DENSITY_DIFF = 0.1 ! [kg/m3] default = 0.1 + ! The density difference used to determine a diagnostic mixed layer depth, + ! MLD_user, following the definition of Levitus 1982. The MLD is the depth at + ! which the density is larger than the surface density by the specified amount. +DIAG_DEPTH_SUBML_N2 = 50.0 ! [m] default = 50.0 + ! The distance over which to calculate a diagnostic of the stratification at the + ! base of the mixed layer. + +! === module MOM_CVMix_KPP === +! This is the MOM wrapper to CVMix:KPP +! See http://cvmix.github.io/ +USE_KPP = True ! [Boolean] default = False + ! If true, turns on the [CVMix] KPP scheme of Large et al., 1994, to calculate + ! diffusivities and non-local transport in the OBL. +KPP% +PASSIVE = False ! [Boolean] default = False + ! If True, puts KPP into a passive-diagnostic mode. +APPLY_NONLOCAL_TRANSPORT = True ! [Boolean] default = True + ! If True, applies the non-local transport to all tracers. If False, calculates + ! the non-local transport and tendencies but purely for diagnostic purposes. +N_SMOOTH = 0 ! default = 0 + ! The number of times the 1-1-4-1-1 Laplacian filter is applied on OBL depth. +RI_CRIT = 0.3 ! [nondim] default = 0.3 + ! Critical bulk Richardson number used to define depth of the surface Ocean + ! Boundary Layer (OBL). +VON_KARMAN = 0.4 ! [nondim] default = 0.4 + ! von Karman constant. +ENHANCE_DIFFUSION = True ! [Boolean] default = True + ! If True, adds enhanced diffusion at the based of the boundary layer. +INTERP_TYPE = "cubic" ! default = "quadratic" + ! Type of interpolation to determine the OBL depth. + ! Allowed types are: linear, quadratic, cubic. +INTERP_TYPE2 = "LMD94" ! default = "LMD94" + ! Type of interpolation to compute diff and visc at OBL_depth. + ! Allowed types are: linear, quadratic, cubic or LMD94. +COMPUTE_EKMAN = False ! [Boolean] default = False + ! If True, limit OBL depth to be no deeper than Ekman depth. +COMPUTE_MONIN_OBUKHOV = False ! [Boolean] default = False + ! If True, limit the OBL depth to be no deeper than Monin-Obukhov depth. +CS = 98.96 ! [nondim] default = 98.96 + ! Parameter for computing velocity scale function. +CS2 = 6.32739901508 ! [nondim] default = 6.32739901508 + ! Parameter for computing non-local term. +DEEP_OBL_OFFSET = 0.0 ! [m] default = 0.0 + ! If non-zero, the distance above the bottom to which the OBL is clipped if it + ! would otherwise reach the bottom. The smaller of this and 0.1D is used. +FIXED_OBLDEPTH = False ! [Boolean] default = False + ! If True, fix the OBL depth to FIXED_OBLDEPTH_VALUE rather than using the OBL + ! depth from CVMix. This option is just for testing purposes. +FIXED_OBLDEPTH_VALUE = 30.0 ! [m] default = 30.0 + ! Value for the fixed OBL depth when fixedOBLdepth==True. This parameter is for + ! just for testing purposes. It will over-ride the OBLdepth computed from CVMix. +SURF_LAYER_EXTENT = 0.1 ! [nondim] default = 0.1 + ! Fraction of OBL depth considered in the surface layer. +MINIMUM_OBL_DEPTH = 0.0 ! [m] default = 0.0 + ! If non-zero, a minimum depth to use for KPP OBL depth. Independent of this + ! parameter, the OBL depth is always at least as deep as the first layer. +MINIMUM_VT2 = 1.0E-10 ! [m2/s2] default = 1.0E-10 + ! Min of the unresolved velocity Vt2 used in Rib CVMix calculation. + ! Scaling: MINIMUM_VT2 = const1*d*N*ws, with d=1m, N=1e-5/s, ws=1e-6 m/s. +CORRECT_SURFACE_LAYER_AVERAGE = False ! [Boolean] default = False + ! If true, applies a correction step to the averaging of surface layer + ! properties. This option is obsolete. +FIRST_GUESS_SURFACE_LAYER_DEPTH = 0.0 ! [m] default = 0.0 + ! The first guess at the depth of the surface layer used for averaging the + ! surface layer properties. If =0, the top model level properties will be used + ! for the surface layer. If CORRECT_SURFACE_LAYER_AVERAGE=True, a subsequent + ! correction is applied. This parameter is obsolete +NLT_SHAPE = "PARABOLIC" ! default = "CVMix" + ! MOM6 method to set nonlocal transport profile. Over-rides the result from + ! CVMix. Allowed values are: + ! CVMix - Uses the profiles from CVMix specified by MATCH_TECHNIQUE + ! LINEAR - A linear profile, 1-sigma + ! PARABOLIC - A parablic profile, (1-sigma)^2 + ! CUBIC - A cubic profile, (1-sigma)^2(1+2*sigma) + ! CUBIC_LMD - The original KPP profile +MATCH_TECHNIQUE = "SimpleShapes" ! default = "SimpleShapes" + ! CVMix method to set profile function for diffusivity and NLT, as well as + ! matching across OBL base. Allowed values are: + ! SimpleShapes = sigma*(1-sigma)^2 for both diffusivity and NLT + ! MatchGradient = sigma*(1-sigma)^2 for NLT; diffusivity profile from + ! matching + ! MatchBoth = match gradient for both diffusivity and NLT + ! ParabolicNonLocal = sigma*(1-sigma)^2 for diffusivity; (1-sigma)^2 for NLT +KPP_ZERO_DIFFUSIVITY = False ! [Boolean] default = False + ! If True, zeroes the KPP diffusivity and viscosity; for testing purpose. +KPP_IS_ADDITIVE = True ! [Boolean] default = True + ! If true, adds KPP diffusivity to diffusivity from other schemes. + ! If false, KPP is the only diffusivity wherever KPP is non-zero. +KPP_SHORTWAVE_METHOD = "MXL_SW" ! default = "MXL_SW" + ! Determines contribution of shortwave radiation to KPP surface buoyancy flux. + ! Options include: + ! ALL_SW: use total shortwave radiation + ! MXL_SW: use shortwave radiation absorbed by mixing layer + ! LV1_SW: use shortwave radiation absorbed by top model layer +CVMix_ZERO_H_WORK_AROUND = 0.0 ! [m] default = 0.0 + ! A minimum thickness used to avoid division by small numbers in the vicinity of + ! vanished layers. This is independent of MIN_THICKNESS used in other parts of + ! MOM. +USE_KPP_LT_K = False ! default = False + ! Flag for Langmuir turbulence enhancement of turbulentmixing coefficient. +STOKES_MIXING = False ! default = False + ! Flag for Langmuir turbulence enhancement of turbulentmixing coefficient. +USE_KPP_LT_VT2 = False ! default = False + ! Flag for Langmuir turbulence enhancement of Vt2in Bulk Richardson Number. +%KPP + +! === module MOM_CVMix_conv === +! Parameterization of enhanced mixing due to convection via CVMix +USE_CVMix_CONVECTION = False ! [Boolean] default = False + ! If true, turns on the enhanced mixing due to convection via CVMix. This scheme + ! increases diapycnal diffs./viscs. at statically unstable interfaces. Relevant + ! parameters are contained in the CVMix_CONVECTION% parameter block. + +! === module MOM_set_diffusivity === +FLUX_RI_MAX = 0.2 ! [nondim] default = 0.2 + ! The flux Richardson number where the stratification is large enough that N2 > + ! omega2. The full expression for the Flux Richardson number is usually + ! FLUX_RI_MAX*N2/(N2+OMEGA2). +SET_DIFF_2018_ANSWERS = False ! [Boolean] default = False + ! If true, use the order of arithmetic and expressions that recover the answers + ! from the end of 2018. Otherwise, use updated and more robust forms of the + ! same expressions. +SET_DIFF_ANSWER_DATE = 99991231 ! default = 99991231 + ! The vintage of the order of arithmetic and expressions in the set diffusivity + ! calculations. Values below 20190101 recover the answers from the end of 2018, + ! while higher values use updated and more robust forms of the same expressions. + ! If both SET_DIFF_2018_ANSWERS and SET_DIFF_ANSWER_DATE are specified, the + ! latter takes precedence. + +! === module MOM_tidal_mixing === +! Vertical Tidal Mixing Parameterization +USE_CVMix_TIDAL = False ! [Boolean] default = False + ! If true, turns on tidal mixing via CVMix +INT_TIDE_DISSIPATION = False ! [Boolean] default = False + ! If true, use an internal tidal dissipation scheme to drive diapycnal mixing, + ! along the lines of St. Laurent et al. (2002) and Simmons et al. (2004). +ML_RADIATION = True ! [Boolean] default = False + ! If true, allow a fraction of TKE available from wind work to penetrate below + ! the base of the mixed layer with a vertical decay scale determined by the + ! minimum of: (1) The depth of the mixed layer, (2) an Ekman length scale. +ML_RAD_EFOLD_COEFF = 0.2 ! [nondim] default = 0.2 + ! A coefficient that is used to scale the penetration depth for turbulence below + ! the base of the mixed layer. This is only used if ML_RADIATION is true. +ML_RAD_BUG = False ! [Boolean] default = False + ! If true use code with a bug that reduces the energy available in the + ! transition layer by a factor of the inverse of the energy deposition + ! lenthscale (in m). +ML_RAD_KD_MAX = 0.001 ! [m2 s-1] default = 0.001 + ! The maximum diapycnal diffusivity due to turbulence radiated from the base of + ! the mixed layer. This is only used if ML_RADIATION is true. +ML_RAD_COEFF = 0.2 ! [nondim] default = 0.2 + ! The coefficient which scales MSTAR*USTAR^3 to obtain the energy available for + ! mixing below the base of the mixed layer. This is only used if ML_RADIATION is + ! true. +ML_RAD_APPLY_TKE_DECAY = True ! [Boolean] default = True + ! If true, apply the same exponential decay to ML_rad as is applied to the other + ! surface sources of TKE in the mixed layer code. This is only used if + ! ML_RADIATION is true. +MSTAR = 1.2 ! [nondim] default = 1.2 + ! The ratio of the friction velocity cubed to the TKE input to the mixed layer. +TKE_DECAY = 10.0 ! [nondim] default = 2.5 + ! The ratio of the natural Ekman depth to the TKE decay scale. +ML_OMEGA_FRAC = 1.0 ! [nondim] default = 0.0 + ! When setting the decay scale for turbulence, use this fraction of the absolute + ! rotation rate blended with the local value of f, as sqrt((1-of)*f^2 + + ! of*4*omega^2). +BBL_EFFIC = 0.2 ! [nondim] default = 0.2 + ! The efficiency with which the energy extracted by bottom drag drives BBL + ! diffusion. This is only used if BOTTOMDRAGLAW is true. +BBL_MIXING_MAX_DECAY = 200.0 ! [m] default = 200.0 + ! The maximum decay scale for the BBL diffusion, or 0 to allow the mixing to + ! penetrate as far as stratification and rotation permit. The default for now + ! is 200 m. This is only used if BOTTOMDRAGLAW is true. +BBL_MIXING_AS_MAX = True ! [Boolean] default = True + ! If true, take the maximum of the diffusivity from the BBL mixing and the other + ! diffusivities. Otherwise, diffusivity from the BBL_mixing is simply added. +USE_LOTW_BBL_DIFFUSIVITY = False ! [Boolean] default = False + ! If true, uses a simple, imprecise but non-coordinate dependent, model of BBL + ! mixing diffusivity based on Law of the Wall. Otherwise, uses the original BBL + ! scheme. +SIMPLE_TKE_TO_KD = False ! [Boolean] default = False + ! If true, uses a simple estimate of Kd/TKE that will work for arbitrary + ! vertical coordinates. If false, calculates Kd/TKE and bounds based on exact + ! energetics for an isopycnal layer-formulation. + +! === module MOM_bkgnd_mixing === +! Adding static vertical background mixing coefficients +KD = 2.0E-05 ! [m2 s-1] default = 0.0 + ! The background diapycnal diffusivity of density in the interior. Zero or the + ! molecular value, ~1e-7 m2 s-1, may be used. +KD_MIN = 2.0E-06 ! [m2 s-1] default = 2.0E-07 + ! The minimum diapycnal diffusivity. +KDML = 2.0E-05 ! [m2 s-1] default = 2.0E-05 + ! If BULKMIXEDLAYER is false, KDML is the elevated diapycnal diffusivity in the + ! topmost HMIX of fluid. KDML is only used if BULKMIXEDLAYER is false. +BRYAN_LEWIS_DIFFUSIVITY = False ! [Boolean] default = False + ! If true, use a Bryan & Lewis (JGR 1979) like tanh profile of background + ! diapycnal diffusivity with depth. This is done via CVMix. +HORIZ_VARYING_BACKGROUND = False ! [Boolean] default = False + ! If true, apply vertically uniform, latitude-dependent background diffusivity, + ! as described in Danabasoglu et al., 2012 +PRANDTL_BKGND = 1.0 ! [nondim] default = 1.0 + ! Turbulent Prandtl number used to convert vertical background diffusivities + ! into viscosities. +HENYEY_IGW_BACKGROUND = False ! [Boolean] default = False + ! If true, use a latitude-dependent scaling for the near surface background + ! diffusivity, as described in Harrison & Hallberg, JPO 2008. +HENYEY_IGW_BACKGROUND_NEW = False ! [Boolean] default = False + ! If true, use a better latitude-dependent scaling for the background + ! diffusivity, as described in Harrison & Hallberg, JPO 2008. +KD_TANH_LAT_FN = False ! [Boolean] default = False + ! If true, use a tanh dependence of Kd_sfc on latitude, like CM2.1/CM2M. There + ! is no physical justification for this form, and it can not be used with + ! HENYEY_IGW_BACKGROUND. +KD_MAX = -1.0 ! [m2 s-1] default = -1.0 + ! The maximum permitted increment for the diapycnal diffusivity from TKE-based + ! parameterizations, or a negative value for no limit. +KD_ADD = 0.0 ! [m2 s-1] default = 0.0 + ! A uniform diapycnal diffusivity that is added everywhere without any filtering + ! or scaling. +USER_CHANGE_DIFFUSIVITY = False ! [Boolean] default = False + ! If true, call user-defined code to change the diffusivity. +DISSIPATION_MIN = 0.0 ! [W m-3] default = 0.0 + ! The minimum dissipation by which to determine a lower bound of Kd (a floor). +DISSIPATION_N0 = 0.0 ! [W m-3] default = 0.0 + ! The intercept when N=0 of the N-dependent expression used to set a minimum + ! dissipation by which to determine a lower bound of Kd (a floor): A in eps_min + ! = A + B*N. +DISSIPATION_N1 = 0.0 ! [J m-3] default = 0.0 + ! The coefficient multiplying N, following Gargett, used to set a minimum + ! dissipation by which to determine a lower bound of Kd (a floor): B in eps_min + ! = A + B*N +DISSIPATION_KD_MIN = 0.0 ! [m2 s-1] default = 0.0 + ! The minimum vertical diffusivity applied as a floor. +DOUBLE_DIFFUSION = False ! [Boolean] default = False + ! If true, increase diffusivites for temperature or salinity based on the + ! double-diffusive parameterization described in Large et al. (1994). + +! === module MOM_kappa_shear === +! Parameterization of shear-driven turbulence following Jackson, Hallberg and Legg, JPO 2008 +USE_JACKSON_PARAM = True ! [Boolean] default = False + ! If true, use the Jackson-Hallberg-Legg (JPO 2008) shear mixing + ! parameterization. +VERTEX_SHEAR = False ! [Boolean] default = False + ! If true, do the calculations of the shear-driven mixing at the cell vertices + ! (i.e., the vorticity points). +RINO_CRIT = 0.25 ! [nondim] default = 0.25 + ! The critical Richardson number for shear mixing. +SHEARMIX_RATE = 0.089 ! [nondim] default = 0.089 + ! A nondimensional rate scale for shear-driven entrainment. Jackson et al find + ! values in the range of 0.085-0.089. +MAX_RINO_IT = 25 ! [nondim] default = 50 + ! The maximum number of iterations that may be used to estimate the Richardson + ! number driven mixing. +KD_KAPPA_SHEAR_0 = 2.0E-05 ! [m2 s-1] default = 2.0E-05 + ! The background diffusivity that is used to smooth the density and shear + ! profiles before solving for the diffusivities. The default is the greater of + ! KD and 1e-7 m2 s-1. +KD_TRUNC_KAPPA_SHEAR = 2.0E-07 ! [m2 s-1] default = 2.0E-07 + ! The value of shear-driven diffusivity that is considered negligible and is + ! rounded down to 0. The default is 1% of KD_KAPPA_SHEAR_0. +FRI_CURVATURE = -0.97 ! [nondim] default = -0.97 + ! The nondimensional curvature of the function of the Richardson number in the + ! kappa source term in the Jackson et al. scheme. +TKE_N_DECAY_CONST = 0.24 ! [nondim] default = 0.24 + ! The coefficient for the decay of TKE due to stratification (i.e. proportional + ! to N*tke). The values found by Jackson et al. are 0.24-0.28. +TKE_SHEAR_DECAY_CONST = 0.14 ! [nondim] default = 0.14 + ! The coefficient for the decay of TKE due to shear (i.e. proportional to + ! |S|*tke). The values found by Jackson et al. are 0.14-0.12. +KAPPA_BUOY_SCALE_COEF = 0.82 ! [nondim] default = 0.82 + ! The coefficient for the buoyancy length scale in the kappa equation. The + ! values found by Jackson et al. are in the range of 0.81-0.86. +KAPPA_N_OVER_S_SCALE_COEF2 = 0.0 ! [nondim] default = 0.0 + ! The square of the ratio of the coefficients of the buoyancy and shear scales + ! in the diffusivity equation, Set this to 0 (the default) to eliminate the + ! shear scale. This is only used if USE_JACKSON_PARAM is true. +KAPPA_SHEAR_TOL_ERR = 0.1 ! [nondim] default = 0.1 + ! The fractional error in kappa that is tolerated. Iteration stops when changes + ! between subsequent iterations are smaller than this everywhere in a column. + ! The peak diffusivities usually converge most rapidly, and have much smaller + ! errors than this. +TKE_BACKGROUND = 0.0 ! [m2 s-2] default = 0.0 + ! A background level of TKE used in the first iteration of the kappa equation. + ! TKE_BACKGROUND could be 0. +KAPPA_SHEAR_ELIM_MASSLESS = True ! [Boolean] default = True + ! If true, massless layers are merged with neighboring massive layers in this + ! calculation. The default is true and I can think of no good reason why it + ! should be false. This is only used if USE_JACKSON_PARAM is true. +MAX_KAPPA_SHEAR_IT = 13 ! [nondim] default = 13 + ! The maximum number of iterations that may be used to estimate the + ! time-averaged diffusivity. +KAPPA_SHEAR_MAX_KAP_SRC_CHG = 10.0 ! [nondim] default = 10.0 + ! The maximum permitted increase in the kappa source within an iteration + ! relative to the local source; this must be greater than 1. The lower limit + ! for the permitted fractional decrease is (1 - 0.5/kappa_src_max_chg). These + ! limits could perhaps be made dynamic with an improved iterative solver. +KAPPA_SHEAR_ITER_BUG = False ! [Boolean] default = False + ! If true, use an older, dimensionally inconsistent estimate of the derivative + ! of diffusivity with energy in the Newton's method iteration. The bug causes + ! undercorrections when dz > 1 m. +KAPPA_SHEAR_ALL_LAYER_TKE_BUG = False ! [Boolean] default = False + ! If true, report back the latest estimate of TKE instead of the time average + ! TKE when there is mass in all layers. Otherwise always report the time + ! averaged TKE, as is currently done when there are some massless layers. +USE_RESTRICTIVE_TOLERANCE_CHECK = False ! [Boolean] default = False + ! If true, uses the more restrictive tolerance check to determine if a timestep + ! is acceptable for the KS_it outer iteration loop. False uses the original + ! less restrictive check. + +! === module MOM_CVMix_shear === +! Parameterization of shear-driven turbulence via CVMix (various options) +USE_LMD94 = False ! [Boolean] default = False + ! If true, use the Large-McWilliams-Doney (JGR 1994) shear mixing + ! parameterization. +USE_PP81 = False ! [Boolean] default = False + ! If true, use the Pacanowski and Philander (JPO 1981) shear mixing + ! parameterization. + +! === module MOM_CVMix_ddiff === +! Parameterization of mixing due to double diffusion processes via CVMix +USE_CVMIX_DDIFF = False ! [Boolean] default = False + ! If true, turns on double diffusive processes via CVMix. Note that double + ! diffusive processes on viscosity are ignored in CVMix, see + ! http://cvmix.github.io/ for justification. + +! === module MOM_diabatic_aux === +! The following parameters are used for auxiliary diabatic processes. +RECLAIM_FRAZIL = False ! [Boolean] default = True + ! If true, try to use any frazil heat deficit to cool any overlying layers down + ! to the freezing point, thereby avoiding the creation of thin ice when the SST + ! is above the freezing point. +PRESSURE_DEPENDENT_FRAZIL = False ! [Boolean] default = False + ! If true, use a pressure dependent freezing temperature when making frazil. The + ! default is false, which will be faster but is inappropriate with ice-shelf + ! cavities. +USE_RIVER_HEAT_CONTENT = False ! [Boolean] default = False + ! If true, use the fluxes%runoff_Hflx field to set the heat carried by runoff, + ! instead of using SST*CP*liq_runoff. +USE_CALVING_HEAT_CONTENT = False ! [Boolean] default = False + ! If true, use the fluxes%calving_Hflx field to set the heat carried by runoff, + ! instead of using SST*CP*froz_runoff. +VAR_PEN_SW = True ! [Boolean] default = False + ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine + ! the e-folding depth of incoming short wave radiation. +CHL_FROM_FILE = True ! [Boolean] default = True + ! If true, chl_a is read from a file. +CHL_FILE = "forcing_monthly.nc" ! + ! CHL_FILE is the file containing chl_a concentrations in the variable CHL_A. It + ! is used when VAR_PEN_SW and CHL_FROM_FILE are true. +CHL_VARNAME = "CHL_A" ! default = "CHL_A" + ! Name of CHL_A variable in CHL_FILE. + +! === module MOM_regularize_layers === +REGULARIZE_SURFACE_LAYERS = False ! [Boolean] default = False + ! If defined, vertically restructure the near-surface layers when they have too + ! much lateral variations to allow for sensible lateral barotropic transports. + +! === module MOM_opacity === +OPACITY_SCHEME = "MANIZZA_05" ! default = "MANIZZA_05" + ! This character string specifies how chlorophyll concentrations are translated + ! into opacities. Currently valid options include: + ! MANIZZA_05 - Use Manizza et al., GRL, 2005. + ! MOREL_88 - Use Morel, JGR, 1988. +BLUE_FRAC_SW = 0.5 ! [nondim] default = 0.5 + ! The fraction of the penetrating shortwave radiation that is in the blue band. +PEN_SW_NBANDS = 3 ! default = 1 + ! The number of bands of penetrating shortwave radiation. +OPTICS_2018_ANSWERS = False ! [Boolean] default = False + ! If true, use the order of arithmetic and expressions that recover the answers + ! from the end of 2018. Otherwise, use updated expressions for handling the + ! absorption of small remaining shortwave fluxes. +OPTICS_ANSWER_DATE = 99991231 ! default = 99991231 + ! The vintage of the order of arithmetic and expressions in the optics + ! calculations. Values below 20190101 recover the answers from the end of 2018, + ! while higher values use updated and more robust forms of the same expressions. + ! If both OPTICS_2018_ANSWERS and OPTICS_ANSWER_DATE are specified, the latter + ! takes precedence. +PEN_SW_FLUX_ABSORB = 2.5E-11 ! [degC m s-1] default = 2.5E-11 + ! A minimum remaining shortwave heating rate that will be simply absorbed in the + ! next sufficiently thick layers for computational efficiency, instead of + ! continuing to penetrate. The default, 2.5e-11 degC m s-1, is about 1e-4 W m-2 + ! or 0.08 degC m century-1, but 0 is also a valid value. +PEN_SW_ABSORB_MINTHICK = 1.0 ! [m] default = 1.0 + ! A thickness that is used to absorb the remaining penetrating shortwave heat + ! flux when it drops below PEN_SW_FLUX_ABSORB. +OPACITY_LAND_VALUE = 10.0 ! [m-1] default = 10.0 + ! The value to use for opacity over land. The default is 10 m-1 - a value for + ! muddy water. + +! === module MOM_tracer_advect === +TRACER_ADVECTION_SCHEME = "PLM" ! default = "PLM" + ! The horizontal transport scheme for tracers: + ! PLM - Piecewise Linear Method + ! PPM:H3 - Piecewise Parabolic Method (Huyhn 3rd order) + ! PPM - Piecewise Parabolic Method (Colella-Woodward) + +! === module MOM_tracer_hor_diff === +KHTR = 600.0 ! [m2 s-1] default = 0.0 + ! The background along-isopycnal tracer diffusivity. +KHTR_MIN = 0.0 ! [m2 s-1] default = 0.0 + ! The minimum along-isopycnal tracer diffusivity. +KHTR_MAX = 0.0 ! [m2 s-1] default = 0.0 + ! The maximum along-isopycnal tracer diffusivity. +KHTR_PASSIVITY_COEFF = 0.0 ! [nondim] default = 0.0 + ! The coefficient that scales deformation radius over grid-spacing in passivity, + ! where passivity is the ratio between along isopycnal mixing of tracers to + ! thickness mixing. A non-zero value enables this parameterization. +KHTR_PASSIVITY_MIN = 0.5 ! [nondim] default = 0.5 + ! The minimum passivity which is the ratio between along isopycnal mixing of + ! tracers to thickness mixing. +DIFFUSE_ML_TO_INTERIOR = False ! [Boolean] default = False + ! If true, enable epipycnal mixing between the surface boundary layer and the + ! interior. +CHECK_DIFFUSIVE_CFL = False ! [Boolean] default = False + ! If true, use enough iterations the diffusion to ensure that the diffusive + ! equivalent of the CFL limit is not violated. If false, always use the greater + ! of 1 or MAX_TR_DIFFUSION_CFL iteration. +MAX_TR_DIFFUSION_CFL = -1.0 ! [nondim] default = -1.0 + ! If positive, locally limit the along-isopycnal tracer diffusivity to keep the + ! diffusive CFL locally at or below this value. The number of diffusive + ! iterations is often this value or the next greater integer. +RECALC_NEUTRAL_SURF = False ! [Boolean] default = False + ! If true, then recalculate the neutral surfaces if the + ! diffusive CFL is exceeded. If false, assume that the + ! positions of the surfaces do not change + +! === module MOM_neutral_diffusion === +! This module implements neutral diffusion of tracers +USE_NEUTRAL_DIFFUSION = False ! [Boolean] default = False + ! If true, enables the neutral diffusion module. + +! === module MOM_lateral_boundary_diffusion === +! This module implements lateral diffusion of tracers near boundaries +USE_LATERAL_BOUNDARY_DIFFUSION = False ! [Boolean] default = False + ! If true, enables the lateral boundary tracer's diffusion module. +OBSOLETE_DIAGNOSTIC_IS_FATAL = True ! [Boolean] default = True + ! If an obsolete diagnostic variable appears in the diag_table, cause a FATAL + ! error rather than issue a WARNING. + +! === module MOM_sum_output === +CALCULATE_APE = True ! [Boolean] default = True + ! If true, calculate the available potential energy of the interfaces. Setting + ! this to false reduces the memory footprint of high-PE-count models + ! dramatically. +WRITE_STOCKS = True ! [Boolean] default = True + ! If true, write the integrated tracer amounts to stdout when the energy files + ! are written. +MAXTRUNC = 5000 ! [truncations save_interval-1] default = 0 + ! The run will be stopped, and the day set to a very large value if the velocity + ! is truncated more than MAXTRUNC times between energy saves. Set MAXTRUNC to 0 + ! to stop if there is any truncation of velocities. +MAX_ENERGY = 0.0 ! [m2 s-2] default = 0.0 + ! The maximum permitted average energy per unit mass; the model will be stopped + ! if there is more energy than this. If zero or negative, this is set to + ! 10*MAXVEL^2. +ENERGYFILE = "ocean.stats" ! default = "ocean.stats" + ! The file to use to write the energies and globally summed diagnostics. +DATE_STAMPED_STDOUT = True ! [Boolean] default = True + ! If true, use dates (not times) in messages to stdout +TIMEUNIT = 8.64E+04 ! [s] default = 8.64E+04 + ! The time unit in seconds a number of input fields +READ_DEPTH_LIST = False ! [Boolean] default = False + ! Read the depth list from a file if it exists or create that file otherwise. +DEPTH_LIST_MIN_INC = 1.0E-10 ! [m] default = 1.0E-10 + ! The minimum increment between the depths of the entries in the depth-list + ! file. +ENERGYSAVEDAYS = 10.0 ! [days] default = 1.0 + ! The interval in units of TIMEUNIT between saves of the energies of the run and + ! other globally summed diagnostics. +ENERGYSAVEDAYS_GEOMETRIC = 0.0 ! [days] default = 0.0 + ! The starting interval in units of TIMEUNIT for the first call to save the + ! energies of the run and other globally summed diagnostics. The interval + ! increases by a factor of 2. after each call to write_energy. + +! === module ocean_stochastics_init === +DO_SPPT = False ! [Boolean] default = False + ! If true, then stochastically perturb the thermodynamic tendemcies of T,S, amd + ! h. Amplitude and correlations are controlled by the nam_stoch namelist in the + ! UFS model only. +PERT_EPBL = False ! [Boolean] default = False + ! If true, then stochastically perturb the kinetic energy production and + ! dissipation terms. Amplitude and correlations are controlled by the nam_stoch + ! namelist in the UFS model only. + +! === module MOM_surface_forcing === +VARIABLE_WINDS = True ! [Boolean] default = True + ! If true, the winds vary in time after the initialization. +VARIABLE_BUOYFORCE = True ! [Boolean] default = True + ! If true, the buoyancy forcing varies in time after the initialization of the + ! model. +BUOY_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how buoyancy forcing is specified. Valid + ! options include (file), (data_override), (zero), (const), (linear), (MESO), + ! (SCM_CVmix_tests), (BFB), (dumbbell), (USER) and (NONE). +WIND_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how wind forcing is specified. Valid + ! options include (file), (data_override), (2gyre), (1gyre), (gyres), (zero), + ! (const), (Neverworld), (scurves), (ideal_hurr), (SCM_ideal_hurr), + ! (SCM_CVmix_tests) and (USER). +RESTOREBUOY = False ! [Boolean] default = False + ! If true, the buoyancy fluxes drive the model back toward some specified + ! surface state with a rate given by FLUXCONST. +LATENT_HEAT_FUSION = 3.34E+05 ! [J/kg] default = 3.34E+05 + ! The latent heat of fusion. +LATENT_HEAT_VAPORIZATION = 2.5E+06 ! [J/kg] default = 2.5E+06 + ! The latent heat of fusion. +GUST_CONST = 0.0 ! [Pa] default = 0.0 + ! The background gustiness in the winds. +FIX_USTAR_GUSTLESS_BUG = True ! [Boolean] default = True + ! If true correct a bug in the time-averaging of the gustless wind friction + ! velocity +READ_GUST_2D = True ! [Boolean] default = False + ! If true, use a 2-dimensional gustiness supplied from an input file +GUST_2D_FILE = "forcing_monthly.nc" ! + ! The file in which the wind gustiness is found in variable gustiness. + +! === module MOM_restart === +USE_WAVES = False ! [Boolean] default = False + ! If true, enables surface wave modules. + +! === module MOM_main (MOM_driver) === +DT_FORCING = 3600.0 ! [s] default = 3600.0 + ! The time step for changing forcing, coupling with other components, or + ! potentially writing certain diagnostics. The default value is given by DT. +DAYMAX = 1825.0 ! [days] + ! The final time of the whole simulation, in units of TIMEUNIT seconds. This + ! also sets the potential end time of the present run segment if the end time is + ! not set via ocean_solo_nml in input.nml. +SINGLE_STEPPING_CALL = True ! [Boolean] default = True + ! If true, advance the state of MOM with a single step including both dynamics + ! and thermodynamics. If false the two phases are advanced with separate calls. +RESTART_CONTROL = 0 ! default = 1 + ! An integer whose bits encode which restart files are written. Add 2 (bit 1) + ! for a time-stamped file, and odd (bit 0) for a non-time-stamped file. A + ! non-time-stamped restart file is saved at the end of the run segment for any + ! non-negative value. +RESTINT = 3650.0 ! [days] default = 0.0 + ! The interval between saves of the restart file in units of TIMEUNIT. Use 0 + ! (the default) to not save incremental restart files at all. +WRITE_CPU_STEPS = 1000 ! default = 1000 + ! The number of coupled timesteps between writing the cpu time. If this is not + ! positive, do not check cpu time, and the segment run-length can not be set via + ! an elapsed CPU time. + +! === module MOM_write_cputime === +MAXCPU = 2.88E+04 ! [wall-clock seconds] default = -1.0 + ! The maximum amount of cpu time per processor for which MOM should run before + ! saving a restart file and quitting with a return value that indicates that a + ! further run is required to complete the simulation. If automatic restarts are + ! not desired, use a negative value for MAXCPU. MAXCPU has units of wall-clock + ! seconds, so the actual CPU time used is larger by a factor of the number of + ! processors used. +CPU_TIME_FILE = "CPU_stats" ! default = "CPU_stats" + ! The file into which CPU time is written. + +! === module MOM_file_parser === +SEND_LOG_TO_STDOUT = False ! [Boolean] default = False + ! If true, all log messages are also sent to stdout. +DOCUMENT_FILE = "MOM_parameter_doc" ! default = "MOM_parameter_doc" + ! The basename for files where run-time parameters, their settings, units and + ! defaults are documented. Blank will disable all parameter documentation. +COMPLETE_DOCUMENTATION = True ! [Boolean] default = True + ! If true, all run-time parameters are documented in MOM_parameter_doc.all . +MINIMAL_DOCUMENTATION = True ! [Boolean] default = True + ! If true, non-default run-time parameters are documented in + ! MOM_parameter_doc.short . diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.debugging b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.debugging new file mode 100644 index 00000000..e7b54212 --- /dev/null +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.debugging @@ -0,0 +1,83 @@ +! This file was written by the model and records the debugging parameters used at run-time. + +! === module MOM_unit_scaling === +! Parameters for doing unit scaling of variables. +Z_RESCALE_POWER = 0 ! [nondim] default = 0 + ! An integer power of 2 that is used to rescale the model's internal units of + ! depths and heights. Valid values range from -300 to 300. +L_RESCALE_POWER = 0 ! [nondim] default = 0 + ! An integer power of 2 that is used to rescale the model's internal units of + ! lateral distances. Valid values range from -300 to 300. +T_RESCALE_POWER = 0 ! [nondim] default = 0 + ! An integer power of 2 that is used to rescale the model's internal units of + ! time. Valid values range from -300 to 300. +R_RESCALE_POWER = 0 ! [nondim] default = 0 + ! An integer power of 2 that is used to rescale the model's internal units of + ! density. Valid values range from -300 to 300. +Q_RESCALE_POWER = 0 ! [nondim] default = 0 + ! An integer power of 2 that is used to rescale the model's internal units of + ! heat content. Valid values range from -300 to 300. +C_RESCALE_POWER = 0 ! [nondim] default = 0 + ! An integer power of 2 that is used to rescale the model's internal units of + ! temperature. Valid values range from -300 to 300. +S_RESCALE_POWER = 0 ! [nondim] default = 0 + ! An integer power of 2 that is used to rescale the model's internal units of + ! salinity. Valid values range from -300 to 300. + +! === module MOM === +VERBOSITY = 2 ! default = 2 + ! Integer controlling level of messaging + ! 0 = Only FATAL messages + ! 2 = Only FATAL, WARNING, NOTE [default] + ! 9 = All) +DO_UNIT_TESTS = False ! [Boolean] default = False + ! If True, exercises unit tests at model start up. +DEBUG = False ! [Boolean] default = False + ! If true, write out verbose debugging data. +DEBUG_TRUNCATIONS = False ! [Boolean] default = False + ! If true, calculate all diagnostics that are useful for debugging truncations. +ROTATE_INDEX = False ! [Boolean] default = False + ! Enable rotation of the horizontal indices. +DEBUG_CHKSUMS = False ! [Boolean] default = False + ! If true, checksums are performed on arrays in the various vec_chksum routines. +DEBUG_REDUNDANT = False ! [Boolean] default = False + ! If true, debug redundant data points during calls to the various vec_chksum + ! routines. + +! === module MOM_verticalGrid === +! Parameters providing information about the vertical grid. +H_RESCALE_POWER = 0 ! [nondim] default = 0 + ! An integer power of 2 that is used to rescale the model's intenal units of + ! thickness. Valid values range from -300 to 300. + +! === module MOM_vert_friction === +U_TRUNC_FILE = "U_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to zonal + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. +V_TRUNC_FILE = "V_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to meridional + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. + +! === module MOM_PointAccel === +MAX_TRUNC_FILE_SIZE_PER_PE = 50 ! default = 50 + ! The maximum number of columns of truncations that any PE will write out during + ! a run. +DEBUG_FULL_COLUMN = False ! [Boolean] default = False + ! If true, write out the accelerations in all massive layers; otherwise just + ! document the ones with large velocities. + +! === module MOM_diabatic_driver === +! The following parameters are used for diabatic processes. +DEBUG_CONSERVATION = False ! [Boolean] default = False + ! If true, monitor conservation and extrema. + +! === module MOM_kappa_shear === +! Parameterization of shear-driven turbulence following Jackson, Hallberg and Legg, JPO 2008 + +! === module MOM_file_parser === +REPORT_UNUSED_PARAMS = True ! [Boolean] default = True + ! If true, report any parameter lines that are not used in the run. +FATAL_UNUSED_PARAMS = False ! [Boolean] default = False + ! If true, kill the run if there are any unused parameters. diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.layout b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.layout new file mode 100644 index 00000000..d0bf9724 --- /dev/null +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.layout @@ -0,0 +1,56 @@ +! This file was written by the model and records the layout parameters used at run-time. + +! === module MOM === +GLOBAL_INDEXING = False ! [Boolean] default = False + ! If true, use a global lateral indexing convention, so that corresponding + ! points on different processors have the same index. This does not work with + ! static memory. + +! === module MOM_domains === +!SYMMETRIC_MEMORY_ = True ! [Boolean] + ! If defined, the velocity point data domain includes every face of the + ! thickness points. In other words, some arrays are larger than others, + ! depending on where they are on the staggered grid. Also, the starting index + ! of the velocity-point arrays is usually 0, not 1. This can only be set at + ! compile time. +NONBLOCKING_UPDATES = False ! [Boolean] default = False + ! If true, non-blocking halo updates may be used. +THIN_HALO_UPDATES = True ! [Boolean] default = True + ! If true, optional arguments may be used to specify the width of the halos that + ! are updated with each call. +!STATIC_MEMORY_ = False ! [Boolean] + ! If STATIC_MEMORY_ is defined, the principle variables will have sizes that are + ! statically determined at compile time. Otherwise the sizes are not determined + ! until run time. The STATIC option is substantially faster, but does not allow + ! the PE count to be changed at run time. This can only be set at compile time. +MASKTABLE = "MOM_mask_table" ! default = "MOM_mask_table" + ! A text file to specify n_mask, layout and mask_list. This feature masks out + ! processors that contain only land points. The first line of mask_table is the + ! number of regions to be masked out. The second line is the layout of the model + ! and must be consistent with the actual model layout. The following (n_mask) + ! lines give the logical positions of the processors that are masked out. The + ! mask_table can be created by tools like check_mask. The following example of + ! mask_table masks out 2 processors, (1,2) and (3,6), out of the 24 in a 4x6 + ! layout: + ! 2 + ! 4,6 + ! 1,2 + ! 3,6 +NIPROC = 1 ! + ! The number of processors in the x-direction. With STATIC_MEMORY_ this is set + ! in MOM_memory.h at compile time. +NJPROC = 1 ! + ! The number of processors in the y-direction. With STATIC_MEMORY_ this is set + ! in MOM_memory.h at compile time. +LAYOUT = 1, 1 ! + ! The processor layout that was actually used. +IO_LAYOUT = 1, 1 ! default = 1 + ! The processor layout to be used, or 0,0 to automatically set the io_layout to + ! be the same as the layout. + +! === module MOM_grid === +! Parameters providing information about the lateral grid. +NIBLOCK = 1 ! default = 1 + ! The number of blocks in the x-direction on each processor (for openmp). +NJBLOCK = 1 ! default = 1 + ! The number of blocks in the y-direction on each processor (for openmp). diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short new file mode 100644 index 00000000..0c7b86a4 --- /dev/null +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short @@ -0,0 +1,381 @@ +! This file was written by the model and records the non-default parameters used at run-time. + +! === module MOM === +SPLIT = False ! [Boolean] default = True + ! Use the split time stepping if true. +USE_REGRIDDING = True ! [Boolean] default = False + ! If True, use the ALE algorithm (regridding/remapping). If False, use the + ! layered isopycnal algorithm. +DT = 3600.0 ! [s] + ! The (baroclinic) dynamics time step. The time-step that is actually used will + ! be an integer fraction of the forcing time-step (DT_FORCING in ocean-only mode + ! or the coupling timestep in coupled mode.) +FRAZIL = True ! [Boolean] default = False + ! If true, water freezes if it gets too cold, and the accumulated heat deficit + ! is returned in the surface state. FRAZIL is only used if + ! ENABLE_THERMODYNAMICS is true. +C_P = 3925.0 ! [J kg-1 K-1] default = 3991.86795711963 + ! The heat capacity of sea water, approximated as a constant. This is only used + ! if ENABLE_THERMODYNAMICS is true. The default value is from the TEOS-10 + ! definition of conservative temperature. +SAVE_INITIAL_CONDS = True ! [Boolean] default = False + ! If true, write the initial conditions to a file given by IC_OUTPUT_FILE. + +! === module MOM_domains === +REENTRANT_Y = True ! [Boolean] default = False + ! If true, the domain is meridionally reentrant. +NIGLOBAL = 2 ! + ! The total number of thickness grid points in the x-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NJGLOBAL = 2 ! + ! The total number of thickness grid points in the y-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NIHALO = 2 ! default = 4 + ! The number of halo points on each side in the x-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +NJHALO = 2 ! default = 4 + ! The number of halo points on each side in the y-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. + +! === module MOM_fixed_initialization === +INPUTDIR = "INPUT/BATS" ! default = "." + ! The directory in which input files are found. + +! === module MOM_grid_init === +GRID_CONFIG = "cartesian" ! + ! A character string that determines the method for defining the horizontal + ! grid. Current options are: + ! mosaic - read the grid from a mosaic (supergrid) + ! file set by GRID_FILE. + ! cartesian - use a (flat) Cartesian grid. + ! spherical - use a simple spherical grid. + ! mercator - use a Mercator spherical grid. +SOUTHLAT = 31.0 ! [degrees] + ! The southern latitude of the domain or the equivalent starting value for the + ! y-axis. +LENLAT = 1.0 ! [degrees] + ! The latitudinal or y-direction length of the domain. +WESTLON = -30.0 ! [degrees] default = 0.0 + ! The western longitude of the domain or the equivalent starting value for the + ! x-axis. +LENLON = 1.0 ! [degrees] + ! The longitudinal or x-direction length of the domain. +TOPO_CONFIG = "flat" ! + ! This specifies how bathymetry is specified: + ! file - read bathymetric information from the file + ! specified by (TOPO_FILE). + ! flat - flat bottom set to MAXIMUM_DEPTH. + ! bowl - an analytically specified bowl-shaped basin + ! ranging between MAXIMUM_DEPTH and MINIMUM_DEPTH. + ! spoon - a similar shape to 'bowl', but with an vertical + ! wall at the southern face. + ! halfpipe - a zonally uniform channel with a half-sine + ! profile in the meridional direction. + ! bbuilder - build topography from list of functions. + ! benchmark - use the benchmark test case topography. + ! Neverworld - use the Neverworld test case topography. + ! DOME - use a slope and channel configuration for the + ! DOME sill-overflow test case. + ! ISOMIP - use a slope and channel configuration for the + ! ISOMIP test case. + ! DOME2D - use a shelf and slope configuration for the + ! DOME2D gravity current/overflow test case. + ! Kelvin - flat but with rotated land mask. + ! seamount - Gaussian bump for spontaneous motion test case. + ! dumbbell - Sloshing channel with reservoirs on both ends. + ! shelfwave - exponential slope for shelfwave test case. + ! Phillips - ACC-like idealized topography used in the Phillips config. + ! dense - Denmark Strait-like dense water formation and overflow. + ! USER - call a user modified routine. +MINIMUM_DEPTH = 0.5 ! [m] default = 0.0 + ! The minimum depth of the ocean. +MAXIMUM_DEPTH = 6000.0 ! [m] + ! The maximum depth of the ocean. +ROTATION = "betaplane" ! default = "2omegasinlat" + ! This specifies how the Coriolis parameter is specified: + ! 2omegasinlat - Use twice the planetary rotation rate + ! times the sine of latitude. + ! betaplane - Use a beta-plane or f-plane. + ! USER - call a user modified routine. +F_0 = 7.59943E-05 ! [s-1] default = 0.0 + ! The reference value of the Coriolis parameter with the betaplane option. + +! === module MOM_verticalGrid === +! Parameters providing information about the vertical grid. +NK = 75 ! [nondim] + ! The number of model layers. + +! === module MOM_EOS === + +! === module MOM_tracer_flow_control === +USE_MARBL_TRACERS = True ! [Boolean] default = False + ! If true, use the MARBL tracer package. + +! === module MARBL_tracers === + +! === module MARBL_tracers === +MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c221027.nc" + ! The file in which the MARBL tracers initial values can be found. +MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc" + ! The file in which the iron sediment flux forcing field can be found. +MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211109.nc" + ! The file in which the iron vent flux forcing field can be found. + +! === module MOM_coord_initialization === +REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" + ! Coordinate mode for vertical regridding. Choose among the following + ! possibilities: LAYER - Isopycnal or stacked shallow water layers + ! ZSTAR, Z* - stretched geopotential z* + ! SIGMA_SHELF_ZSTAR - stretched geopotential z* ignoring shelf + ! SIGMA - terrain following coordinates + ! RHO - continuous isopycnal + ! HYCOM1 - HyCOM-like hybrid coordinate + ! HYBGEN - Hybrid coordinate from the Hycom hybgen code + ! SLIGHT - stretched coordinates above continuous isopycnal + ! ADAPTIVE - optimize for smooth neutral density surfaces +ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_cm4.nc,dz" ! default = "UNIFORM" + ! Determines how to specify the coordinate resolution. Valid options are: + ! PARAM - use the vector-parameter ALE_RESOLUTION + ! UNIFORM[:N] - uniformly distributed + ! FILE:string - read from a file. The string specifies + ! the filename and variable name, separated + ! by a comma or space, e.g. FILE:lev.nc,dz + ! or FILE:lev.nc,interfaces=zw + ! WOA09[:N] - the WOA09 vertical grid (approximately) + ! FNC1:string - FNC1:dz_min,H_total,power,precision + ! HYBRID:string - read from a file. The string specifies + ! the filename and two variable names, separated + ! by a comma or space, for sigma-2 and dz. e.g. + ! HYBRID:vgrid.nc,sigma2,dz +!ALE_RESOLUTION = 4*2.0, 2*2.01, 2*2.02, 2.05, 2.07, 2.09, 2.13, 2.18, 2.24, 2.3, 2.4, 2.5, 2.62, 2.78, 2.95, 3.17, 3.42, 3.71, 4.07, 4.48, 4.97, 5.55, 6.23, 7.04, 7.99, 9.11, 10.43, 11.98, 13.8, 15.94, 18.42, 21.32, 24.66, 28.51, 32.91, 37.92, 43.56, 49.87, 56.88, 64.55, 72.91, 81.87, 91.4, 101.39, 111.73, 122.29, 132.93, 143.47, 153.78, 163.7, 173.07, 181.78, 189.76, 196.89, 203.17, 208.58, 213.14, 216.87, 219.87, 222.19, 223.94, 225.18, 226.04, 226.59, 226.9, 227.07, 227.14, 2*227.16, 227.17 ! [m] + ! The distribution of vertical resolution for the target + ! grid used for Eulerian-like coordinates. For example, + ! in z-coordinate mode, the parameter is a list of level + ! thicknesses (in m). In sigma-coordinate mode, the list + ! is of non-dimensional fractions of the water column. + +! === module MOM_state_initialization === +INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False + ! If true, initialize the layer thicknesses, temperatures, and salinities from a + ! Z-space file on a latitude-longitude grid. + +! === module MOM_initialize_layers_from_Z === +TEMP_SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "temp_salt_z.nc" + ! The name of the z-space input file used to initialize temperatures (T) and + ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and + ! SALT_Z_INIT_FILE must be set. +Z_INIT_FILE_PTEMP_VAR = "PTEMP" ! default = "ptemp" + ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. +Z_INIT_FILE_SALT_VAR = "SALT" ! default = "salt" + ! The name of the salinity variable in SALT_Z_INIT_FILE. +Z_INIT_HOMOGENIZE = True ! [Boolean] default = False + ! If True, then horizontally homogenize the interpolated initial conditions. +Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False + ! If True, then remap straight to model coordinate from file. + +! === module MOM_diag_mediator === + +! === module MOM_lateral_mixing_coeffs === + +! === module MOM_set_visc === +HBBL = 10.0 ! [m] + ! The thickness of a bottom boundary layer with a viscosity of KVBBL if + ! BOTTOMDRAGLAW is not defined, or the thickness over which near-bottom + ! velocities are averaged for the drag law if BOTTOMDRAGLAW is defined but + ! LINEAR_DRAG is not. +CDRAG = 0.0 ! [nondim] default = 0.003 + ! CDRAG is the drag coefficient relating the magnitude of the velocity field to + ! the bottom stress. CDRAG is only used if BOTTOMDRAGLAW is defined. +DRAG_BG_VEL = 0.1 ! [m s-1] default = 0.0 + ! DRAG_BG_VEL is either the assumed bottom velocity (with LINEAR_DRAG) or an + ! unresolved velocity that is combined with the resolved velocity to estimate + ! the velocity magnitude. DRAG_BG_VEL is only used when BOTTOMDRAGLAW is + ! defined. +BBL_THICK_MIN = 0.1 ! [m] default = 0.0 + ! The minimum bottom boundary layer thickness that can be used with + ! BOTTOMDRAGLAW. This might be Kv/(cdrag*drag_bg_vel) to give Kv as the minimum + ! near-bottom viscosity. +KV = 1.0E-04 ! [m2 s-1] + ! The background kinematic viscosity in the interior. The molecular value, ~1e-6 + ! m2 s-1, may be used. + +! === module MOM_thickness_diffuse === + +! === module MOM_dynamics_unsplit === + +! === module MOM_continuity === + +! === module MOM_continuity_PPM === +UPWIND_1ST_CONTINUITY = True ! [Boolean] default = False + ! If true, CONTINUITY_PPM becomes a 1st-order upwind continuity solver. This + ! scheme is highly diffusive but may be useful for debugging or in single-column + ! mode where its minimal stencil is useful. +ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.75E-09 + ! The tolerance for the differences between the barotropic and baroclinic + ! estimates of the sea surface height due to the fluxes through each face. The + ! total tolerance for SSH is 4 times this value. The default is + ! 0.5*NK*ANGSTROM, and this should not be set less than about + ! 10^-15*MAXIMUM_DEPTH. + +! === module MOM_CoriolisAdv === +CORIOLIS_EN_DIS = True ! [Boolean] default = False + ! If true, two estimates of the thickness fluxes are used to estimate the + ! Coriolis term, and the one that dissipates energy relative to the other one is + ! used. +BOUND_CORIOLIS = True ! [Boolean] default = False + ! If true, the Coriolis terms at u-points are bounded by the four estimates of + ! (f+rv)v from the four neighboring v-points, and similarly at v-points. This + ! option is always effectively false with CORIOLIS_EN_DIS defined and + ! CORIOLIS_SCHEME set to SADOURNY75_ENERGY. + +! === module MOM_PressureForce === + +! === module MOM_PressureForce_FV === + +! === module MOM_hor_visc === +BIHARMONIC = False ! [Boolean] default = True + ! If true, use a biharmonic horizontal viscosity. BIHARMONIC may be used with + ! LAPLACIAN. + +! === module MOM_vert_friction === +HARMONIC_VISC = True ! [Boolean] default = False + ! If true, use the harmonic mean thicknesses for calculating the vertical + ! viscosity. +HMIX_FIXED = 0.01 ! [m] + ! The prescribed depth over which the near-surface viscosity and diffusivity are + ! elevated when the bulk mixed layer is not used. +KVML = 0.0 ! [m2 s-1] default = 1.0E-04 + ! The kinematic viscosity in the mixed layer. A typical value is ~1e-2 m2 s-1. + ! KVML is not used if BULKMIXEDLAYER is true. The default is set by KV. +MAXVEL = 6.0 ! [m s-1] default = 3.0E+08 + ! The maximum velocity allowed before the velocity components are truncated. +VEL_UNDERFLOW = 1.0E-30 ! [m s-1] default = 0.0 + ! A negligibly small velocity magnitude below which velocity components are set + ! to 0. A reasonable value might be 1e-30 m/s, which is less than an Angstrom + ! divided by the age of the universe. + +! === module MOM_diagnostics === + +! === module MOM_diabatic_driver === +! The following parameters are used for diabatic processes. + +! === module MOM_CVMix_KPP === +! This is the MOM wrapper to CVMix:KPP +! See http://cvmix.github.io/ +USE_KPP = True ! [Boolean] default = False + ! If true, turns on the [CVMix] KPP scheme of Large et al., 1994, to calculate + ! diffusivities and non-local transport in the OBL. +KPP% +INTERP_TYPE = "cubic" ! default = "quadratic" + ! Type of interpolation to determine the OBL depth. + ! Allowed types are: linear, quadratic, cubic. +NLT_SHAPE = "PARABOLIC" ! default = "CVMix" + ! MOM6 method to set nonlocal transport profile. Over-rides the result from + ! CVMix. Allowed values are: + ! CVMix - Uses the profiles from CVMix specified by MATCH_TECHNIQUE + ! LINEAR - A linear profile, 1-sigma + ! PARABOLIC - A parablic profile, (1-sigma)^2 + ! CUBIC - A cubic profile, (1-sigma)^2(1+2*sigma) + ! CUBIC_LMD - The original KPP profile +%KPP + +! === module MOM_set_diffusivity === +ML_RADIATION = True ! [Boolean] default = False + ! If true, allow a fraction of TKE available from wind work to penetrate below + ! the base of the mixed layer with a vertical decay scale determined by the + ! minimum of: (1) The depth of the mixed layer, (2) an Ekman length scale. +TKE_DECAY = 10.0 ! [nondim] default = 2.5 + ! The ratio of the natural Ekman depth to the TKE decay scale. +ML_OMEGA_FRAC = 1.0 ! [nondim] default = 0.0 + ! When setting the decay scale for turbulence, use this fraction of the absolute + ! rotation rate blended with the local value of f, as sqrt((1-of)*f^2 + + ! of*4*omega^2). + +! === module MOM_bkgnd_mixing === +! Adding static vertical background mixing coefficients +KD = 2.0E-05 ! [m2 s-1] default = 0.0 + ! The background diapycnal diffusivity of density in the interior. Zero or the + ! molecular value, ~1e-7 m2 s-1, may be used. +KD_MIN = 2.0E-06 ! [m2 s-1] default = 2.0E-07 + ! The minimum diapycnal diffusivity. + +! === module MOM_kappa_shear === +! Parameterization of shear-driven turbulence following Jackson, Hallberg and Legg, JPO 2008 +USE_JACKSON_PARAM = True ! [Boolean] default = False + ! If true, use the Jackson-Hallberg-Legg (JPO 2008) shear mixing + ! parameterization. +MAX_RINO_IT = 25 ! [nondim] default = 50 + ! The maximum number of iterations that may be used to estimate the Richardson + ! number driven mixing. + +! === module MOM_diabatic_aux === +! The following parameters are used for auxiliary diabatic processes. +RECLAIM_FRAZIL = False ! [Boolean] default = True + ! If true, try to use any frazil heat deficit to cool any overlying layers down + ! to the freezing point, thereby avoiding the creation of thin ice when the SST + ! is above the freezing point. +VAR_PEN_SW = True ! [Boolean] default = False + ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine + ! the e-folding depth of incoming short wave radiation. +CHL_FILE = "forcing_monthly.nc" ! + ! CHL_FILE is the file containing chl_a concentrations in the variable CHL_A. It + ! is used when VAR_PEN_SW and CHL_FROM_FILE are true. + +! === module MOM_opacity === +PEN_SW_NBANDS = 3 ! default = 1 + ! The number of bands of penetrating shortwave radiation. + +! === module MOM_tracer_advect === + +! === module MOM_tracer_hor_diff === +KHTR = 600.0 ! [m2 s-1] default = 0.0 + ! The background along-isopycnal tracer diffusivity. + +! === module MOM_sum_output === +MAXTRUNC = 5000 ! [truncations save_interval-1] default = 0 + ! The run will be stopped, and the day set to a very large value if the velocity + ! is truncated more than MAXTRUNC times between energy saves. Set MAXTRUNC to 0 + ! to stop if there is any truncation of velocities. +ENERGYSAVEDAYS = 10.0 ! [days] default = 1.0 + ! The interval in units of TIMEUNIT between saves of the energies of the run and + ! other globally summed diagnostics. + +! === module ocean_stochastics_init === + +! === module MOM_surface_forcing === +BUOY_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how buoyancy forcing is specified. Valid + ! options include (file), (data_override), (zero), (const), (linear), (MESO), + ! (SCM_CVmix_tests), (BFB), (dumbbell), (USER) and (NONE). +WIND_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how wind forcing is specified. Valid + ! options include (file), (data_override), (2gyre), (1gyre), (gyres), (zero), + ! (const), (Neverworld), (scurves), (ideal_hurr), (SCM_ideal_hurr), + ! (SCM_CVmix_tests) and (USER). +READ_GUST_2D = True ! [Boolean] default = False + ! If true, use a 2-dimensional gustiness supplied from an input file +GUST_2D_FILE = "forcing_monthly.nc" ! + ! The file in which the wind gustiness is found in variable gustiness. + +! === module MOM_main (MOM_driver) === +DAYMAX = 1825.0 ! [days] + ! The final time of the whole simulation, in units of TIMEUNIT seconds. This + ! also sets the potential end time of the present run segment if the end time is + ! not set via ocean_solo_nml in input.nml. +RESTART_CONTROL = 0 ! default = 1 + ! An integer whose bits encode which restart files are written. Add 2 (bit 1) + ! for a time-stamped file, and odd (bit 0) for a non-time-stamped file. A + ! non-time-stamped restart file is saved at the end of the run segment for any + ! non-negative value. +RESTINT = 3650.0 ! [days] default = 0.0 + ! The interval between saves of the restart file in units of TIMEUNIT. Use 0 + ! (the default) to not save incremental restart files at all. + +! === module MOM_write_cputime === +MAXCPU = 2.88E+04 ! [wall-clock seconds] default = -1.0 + ! The maximum amount of cpu time per processor for which MOM should run before + ! saving a restart file and quitting with a return value that indicates that a + ! further run is required to complete the simulation. If automatic restarts are + ! not desired, use a negative value for MAXCPU. MAXCPU has units of wall-clock + ! seconds, so the actual CPU time used is larger by a factor of the number of + ! processors used. diff --git a/standalone/examples/single_column/KPP_MARBL/RESTART/.gitignore b/standalone/examples/single_column/KPP_MARBL/RESTART/.gitignore new file mode 100644 index 00000000..d6b7ef32 --- /dev/null +++ b/standalone/examples/single_column/KPP_MARBL/RESTART/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/standalone/examples/single_column/KPP_MARBL/data_table b/standalone/examples/single_column/KPP_MARBL/data_table new file mode 100644 index 00000000..36cb27c2 --- /dev/null +++ b/standalone/examples/single_column/KPP_MARBL/data_table @@ -0,0 +1,11 @@ +# gridname, fieldname_code, fieldname_file, file_name, ongrid, factor +"OCN" , "lw", "netlwdn_sfc", "./INPUT/BATS/forcing.nc", .true., 1.0 +"OCN" , "sw", "netswdn_sfc", "./INPUT/BATS/forcing.nc", .true., 1.0 +"OCN" , "rain", "precip", "./INPUT/BATS/forcing.nc", .true., 1.0 +"OCN" , "evap", "evap", "./INPUT/BATS/forcing.nc", .true., 1.0 +"OCN" , "sens", "shflx", "./INPUT/BATS/forcing.nc", .true., 1.0 +"OCN" , "taux", "STRESS_X", "./INPUT/BATS/forcing.nc", .true., 1.0 +"OCN" , "tauy", "STRESS_Y", "./INPUT/BATS/forcing.nc", .true., 1.0 +"OCN" , "snow", "snow", "./INPUT/BATS/forcing.nc", .true., 0.0 +"OCN" , "runoff", "", "" , .true., 0.0 +"OCN" , "calving", "", "" , .true., 0.0 diff --git a/standalone/examples/single_column/KPP_MARBL/diag_table b/standalone/examples/single_column/KPP_MARBL/diag_table new file mode 100644 index 00000000..c434a9d6 --- /dev/null +++ b/standalone/examples/single_column/KPP_MARBL/diag_table @@ -0,0 +1,670 @@ +"MOM Experiment" +1 1 1 0 0 0 +"prog", 1,"days",1,"days","Time" +"prog_z", 1,"days",1,"days","Time" +"surffluxes", 1,"days",1,"days","Time" +"ave_prog", 1,"days",1,"days","Time", +#"mom", 5,"days",1,"days","Time", +#"bt_mom", 5,"days",1,"days","Time", +"visc", 1,"days",1,"days","Time", +#"energy", 5,"days",1,"days","Time", +"marbl", 1,"days",1,"days","Time", + + +#This is the field section of the diag_table. + +# Prognostic Ocean fields: +#========================= + +"ocean_model","u","u","prog","all",.false.,"none",2 +"ocean_model","v","v","prog","all",.false.,"none",2 +"ocean_model","h","h","prog","all",.false.,"none",1 +"ocean_model","e","e","prog","all",.false.,"none",2 +"ocean_model","temp","temp","prog","all",.false.,"none",2 +"ocean_model","salt","salt","prog","all",.false.,"none",2 +#"ocean_model","Rml","Rml","prog","all",.false.,"none",2 +#"ocean_model","h_ML","h_ML","prog","all",.false.,"none",2 + +#"ocean_model","RV","RV","prog","all",.false.,"none",2 +#"ocean_model","PV","PV","prog","all",.false.,"none",2 +#"ocean_model","e_D","e_D","prog","all",.false.,"none",2 + +"ocean_model","u","u","ave_prog","all",.true.,"none",2 +"ocean_model","v","v","ave_prog","all",.true.,"none",2 +#"ocean_model","h","h","ave_prog","all",.true.,"none",1 +"ocean_model","e","e","ave_prog","all",.true.,"none",2 +"ocean_model","temp","temp","ave_prog","all",.true.,"none",2 +"ocean_model","salt","salt","ave_prog","all",.true.,"none",2 +#"ocean_model","Rml","Rml","ave_prog","all",.true.,"none",2 + +# Z level output +#================ +"ocean_model_z","u","u","prog_z","all",.false.,"none",2 +"ocean_model_z","v","v","prog_z","all",.false.,"none",2 +"ocean_model_z","temp","temp","prog_z","all",.false.,"none",2 +"ocean_model_z","salt","salt","prog_z","all",.false.,"none",2 +# Auxilary Tracers: +#================== +#"ocean_model","vintage","vintage","prog","all",.false.,"none",2 +#"ocean_model","age","age","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","prog","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog","all",.false.,"none",2 +"ocean_model","O2","O2","prog","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog","all",.false.,"none",2 +"ocean_model","DON","DON","prog","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog","all",.false.,"none",2 +"ocean_model","zooC","zooC","prog","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog","all",.false.,"none",2 +"ocean_model","spC","spC","prog","all",.false.,"none",2 +"ocean_model","spP","spP","prog","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog","all",.false.,"none",2 +"ocean_model","spCaCO3","spCaCO3","prog","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","ave_prog","all",.true.,"none",2 +"ocean_model","NO3","NO3","ave_prog","all",.true.,"none",2 +"ocean_model","SiO3","SiO3","ave_prog","all",.true.,"none",2 +"ocean_model","NH4","NH4","ave_prog","all",.true.,"none",2 +"ocean_model","Fe","Fe","ave_prog","all",.true.,"none",2 +"ocean_model","Lig","Lig","ave_prog","all",.true.,"none",2 +"ocean_model","O2","O2","ave_prog","all",.true.,"none",2 +"ocean_model","DIC","DIC","ave_prog","all",.true.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","ALK","ALK","ave_prog","all",.true.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","DOC","DOC","ave_prog","all",.true.,"none",2 +"ocean_model","DON","DON","ave_prog","all",.true.,"none",2 +"ocean_model","DOP","DOP","ave_prog","all",.true.,"none",2 +"ocean_model","DOPr","DOPr","ave_prog","all",.true.,"none",2 +"ocean_model","DONr","DONr","ave_prog","all",.true.,"none",2 +"ocean_model","DOCr","DOCr","ave_prog","all",.true.,"none",2 +"ocean_model","zooC","zooC","ave_prog","all",.true.,"none",2 +"ocean_model","spChl","spChl","ave_prog","all",.true.,"none",2 +"ocean_model","spC","spC","ave_prog","all",.true.,"none",2 +"ocean_model","spP","spP","ave_prog","all",.true.,"none",2 +"ocean_model","spFe","spFe","ave_prog","all",.true.,"none",2 +"ocean_model","spCaCO3","spCaCO3","ave_prog","all",.true.,"none",2 +"ocean_model","diatChl","diatChl","ave_prog","all",.true.,"none",2 +"ocean_model","diatC","diatC","ave_prog","all",.true.,"none",2 +"ocean_model","diatP","diatP","ave_prog","all",.true.,"none",2 +"ocean_model","diatFe","diatFe","ave_prog","all",.true.,"none",2 +"ocean_model","diatSi","diatSi","ave_prog","all",.true.,"none",2 +"ocean_model","diazChl","diazChl","ave_prog","all",.true.,"none",2 +"ocean_model","diazC","diazC","ave_prog","all",.true.,"none",2 +"ocean_model","diazP","diazP","ave_prog","all",.true.,"none",2 +"ocean_model","diazFe","diazFe","ave_prog","all",.true.,"none",2 +"ocean_model","PO4","PO4","prog_z","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog_z","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog_z","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog_z","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog_z","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog_z","all",.false.,"none",2 +"ocean_model","O2","O2","prog_z","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog_z","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog_z","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog_z","all",.false.,"none",2 +"ocean_model","DON","DON","prog_z","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog_z","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog_z","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog_z","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog_z","all",.false.,"none",2 +"ocean_model","zooC","zooC","prog_z","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog_z","all",.false.,"none",2 +"ocean_model","spC","spC","prog_z","all",.false.,"none",2 +"ocean_model","spP","spP","prog_z","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog_z","all",.false.,"none",2 +"ocean_model","spCaCO3","spCaCO3","prog_z","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog_z","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog_z","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog_z","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog_z","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog_z","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog_z","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog_z","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog_z","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog_z","all",.false.,"none",2 + + +# Continuity Equation Terms: +#=========================== +#"ocean_model","dhdt","dhdt","cont","all",.true.,"none",2 +"ocean_model","wd","wd","prog","all",.false.,"none",2 +#"ocean_model","uh","uh","cont","all",.true.,"none",2 +#"ocean_model","vh","vh","cont","all",.true.,"none",2 +#"ocean_model","uhGM","uhGM","cont","all",.true.,"none",2 +#"ocean_model","vhGM","vhGM","cont","all",.true.,"none",2 +#"ocean_model","uhbt","uhbt","cont","all",.true.,"none",2 +#"ocean_model","vhbt","vhbt","cont","all",.true.,"none",2 +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","KH_u","KH_u","cont","all",.true.,"none",2 +#"ocean_model","KH_v","KH_v","cont","all",.true.,"none",2 +#"ocean_model","GMwork","GMwork","cont","all",.true.,"none",2 + +# Continuity Equation Terms In Pure Potential Density Coordiantes: +#================================================================= +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 + +# +# Tracer Fluxes: +#================== +#"ocean_model","T_adx", "T_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_ady", "T_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffx","T_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffy","T_diffy","ave_prog","all",.true.,"none",2 +#"ocean_model","S_adx", "S_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_ady", "S_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffx","S_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffy","S_diffy","ave_prog","all",.true.,"none",2 + + +# Momentum Balance Terms: +#======================= +#"ocean_model","dudt","dudt","mom","all",.true.,"none",2 +#"ocean_model","dvdt","dvdt","mom","all",.true.,"none",2 +#"ocean_model","CAu","CAu","mom","all",.true.,"none",2 +#"ocean_model","CAv","CAv","mom","all",.true.,"none",2 +#"ocean_model","PFu","PFu","mom","all",.true.,"none",2 +#"ocean_model","PFv","PFv","mom","all",.true.,"none",2 +#"ocean_model","du_dt_visc","du_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","dv_dt_visc","dv_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","diffu","diffu","mom","all",.true.,"none",2 +#"ocean_model","diffv","diffv","mom","all",.true.,"none",2 +#"ocean_model","dudt_dia","dudt_dia","mom","all",.true.,"none",2 +#"ocean_model","dvdt_dia","dvdt_dia","mom","all",.true.,"none",2 +# Subterms that should not be added to a closed budget. +#"ocean_model","gKEu","gKEu","mom","all",.true.,"none",2 +#"ocean_model","gKEv","gKEv","mom","all",.true.,"none",2 +#"ocean_model","rvxu","rvxu","mom","all",.true.,"none",2 +#"ocean_model","rvxv","rvxv","mom","all",.true.,"none",2 +#"ocean_model","PFu_bc","PFu_bc","mom","all",.true.,"none",2 +#"ocean_model","PFv_bc","PFv_bc","mom","all",.true.,"none",2 + +# Barotropic Momentum Balance Terms: +# (only available with split time stepping.) +#=========================================== +#"ocean_model","PFuBT","PFuBT","bt_mom","all",.true.,"none",2 +#"ocean_model","PFvBT","PFvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CoruBT","CoruBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CorvBT","CorvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","ubtforce","ubtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","vbtforce","vbtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","u_accel_bt","u_accel_bt","bt_mom","all",.true.,"none",2 +#"ocean_model","v_accel_bt","v_accel_bt","bt_mom","all",.true.,"none",2 +# +# Viscosities and diffusivities: +#=============================== +#"ocean_model","Kd_effective","Kd_effective","visc","all",.true.,"none",2 +"ocean_model","Kd_interface","Kd_interface","visc","all",.true.,"none",2 +#"ocean_model","Ahh","Ahh","visc","all",.true.,"none",2 +#"ocean_model","Ahq","Ahq","visc","all",.true.,"none",2 +#"ocean_model","Khh","Khh","visc","all",.true.,"none",2 +#"ocean_model","Khq","Khq","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_u","bbl_thick_u","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_u","kv_bbl_u","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_v","bbl_thick_v","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_v","kv_bbl_v","visc","all",.true.,"none",2 +#"ocean_model","av_visc","av_visc","visc","all",.true.,"none",2 +#"ocean_model","au_visc","au_visc","visc","all",.true.,"none",2 +#"ocean_model","FrictWork","FrictWork","visc","all",.true.,"none",2 +"ocean_model","KPP_uStar","KPP_uStar","visc","all",.true.,"none",2 +"ocean_model","KPP_buoyFlux","KPP_buoyFlux","visc","all",.true.,"none",2 +"ocean_model","KPP_QminusSW","KPP_QminusSW","visc","all",.true.,"none",2 +"ocean_model","KPP_netSalt","KPP_netSalt","visc","all",.true.,"none",2 +"ocean_model","KPP_OBLdepth","KPP_OBLdepth","visc","all",.true.,"none",2 +"ocean_model","KPP_sigma","KPP_sigma","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkRi","KPP_BulkRi","visc","all",.true.,"none",2 +"ocean_model","KPP_Ws","KPP_Ws","visc","all",.true.,"none",2 +"ocean_model","KPP_N","KPP_N","visc","all",.true.,"none",2 +"ocean_model","KPP_N2","KPP_N2","visc","all",.true.,"none",2 +"ocean_model","KPP_Vt2","KPP_Vt2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkUz2","KPP_BulkUz2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkDrho","KPP_BulkDrho","visc","all",.true.,"none",2 +"ocean_model","KPP_Kd_in","KPP_Kd_in","visc","all",.true.,"none",2 +"ocean_model","KPP_Kheat","KPP_Kheat","visc","all",.true.,"none",2 +"ocean_model","KPP_Ksalt","KPP_Ksalt","visc","all",.true.,"none",2 +"ocean_model","KPP_Kv","KPP_Kv","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_heat","KPP_NLtransport_heat","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_salt","KPP_NLtransport_salt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dTdt","KPP_NLT_dTdt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dSdt","KPP_NLT_dSdt","visc","all",.true.,"none",2 +"ocean_model","KPP_Tsurf","KPP_Tsurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Ssurf","KPP_Ssurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Usurf","KPP_Usurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Vsurf","KPP_Vsurf","visc","all",.true.,"none",2 +"ocean_model","MLD_003","MLD_003","visc","all",.true.,"none",2 + +# +# Kinetic Energy Balance Terms: +#============================= +#"ocean_model","KE","KE","energy","all",.true.,"none",2 +#"ocean_model","dKE_dt","dKE_dt","energy","all",.true.,"none",2 +#"ocean_model","PE_to_KE","PE_to_KE","energy","all",.true.,"none",2 +#"ocean_model","KE_Coradv","KE_Coradv","energy","all",.true.,"none",2 +#"ocean_model","KE_adv","KE_adv","energy","all",.true.,"none",2 +#"ocean_model","KE_visc","KE_visc","energy","all",.true.,"none",2 +#"ocean_model","KE_horvisc","KE_horvisc","energy","all",.true.,"none",2 +#"ocean_model","KE_dia","KE_dia","energy","all",.true.,"none",2 + +# Surface Forcing: +#================= +"ocean_model","taux","taux","surffluxes","all",.true.,"none",2 +"ocean_model","tauy","tauy","surffluxes","all",.true.,"none",2 +"ocean_model","ustar","ustar","surffluxes","all",.true.,"none",2 +"ocean_model","PRCmE","PRCmE","surffluxes","all",.true.,"none",2 +"ocean_model","evap","evap","surffluxes","all",.true.,"none",2 +"ocean_model","precip","precip","surffluxes","all",.true.,"none",2 +"ocean_model","fprec","fprec","surffluxes","all",.true.,"none",2 +"ocean_model","lprec","lprec","surffluxes","all",.true.,"none",2 +"ocean_model","net_heat_surface","net_heat_surface","surffluxes","all",.true.,"none",2 +"ocean_model","SW","SW","surffluxes","all",.true.,"none",2 +"ocean_model","LwLatSens","LwLatSens","surffluxes","all",.true.,"none",2 +#"ocean_model","p_surf","p_surf","surffluxes","all",.true.,"none",2 +#"ocean_model","salt_flux","salt_flux","surffluxes","all",.true.,"none",2 + +# MARBL diagnostics: +#=================== +"ocean_model", "ECOSYS_IFRAC", "ECOSYS_IFRAC", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_XKW", "ECOSYS_XKW", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_ATM_PRESS", "ECOSYS_ATM_PRESS", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_O2", "PV_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_O2", "SCHMIDT_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "O2SAT", "O2SAT", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR", "CO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR", "DCO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "pCO2SURF", "pCO2SURF", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2", "DpCO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_CO2", "PV_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_CO2", "SCHMIDT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_CO2", "FG_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH", "PH", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_CO2", "ATM_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR_ALT_CO2", "CO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR_ALT_CO2", "DCO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pCO2SURF_ALT_CO2", "pCO2SURF_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2_ALT_CO2", "DpCO2_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_ALT_CO2", "FG_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH_ALT_CO2", "PH_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_ALT_CO2", "ATM_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "IRON_FLUX", "IRON_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "DUST_FLUX", "DUST_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NOx_FLUX", "NOx_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHy_FLUX", "NHy_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHx_SURFACE_EMIS", "NHx_SURFACE_EMIS", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatcalc", "zsatcalc", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatarag", "zsatarag", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN", "O2_ZMIN", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN_DEPTH", "O2_ZMIN_DEPTH", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint", "photoC_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint_100m", "photoC_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint", "photoC_NO3_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint_100m", "photoC_NO3_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint", "DOC_prod_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint_100m", "DOC_prod_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint", "DOC_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint_100m", "DOC_remin_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint", "DOCr_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint_100m", "DOCr_remin_zint_100m", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ctot", "Jint_Ctot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ntot", "Jint_Ntot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ptot", "Jint_Ptot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Sitot", "Jint_Sitot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Fetot", "Jint_Fetot", "marbl", "all", .false., "none", 2 +#"ocean_model", "calcToFloor", "calcToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed", "calcToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed_ALT_CO2", "calcToSed_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pocToFloor", "pocToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "pocToSed", "pocToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "ponToSed", "ponToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "SedDenitrif", "SedDenitrif", "marbl", "all", .false., "none", 2 +#"ocean_model", "OtherRemin", "OtherRemin", "marbl", "all", .false., "none", 2 +"ocean_model", "popToSed", "popToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "bsiToSed", "bsiToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "dustToSed", "dustToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "pfeToSed", "pfeToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_surf", "sp_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_Cweight_avg_100m", "sp_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_surf", "sp_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_Cweight_avg_100m", "sp_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_surf", "sp_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_Cweight_avg_100m", "sp_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_surf", "sp_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_Cweight_avg_100m", "sp_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint", "photoC_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint_100m", "photoC_sp_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_sp_zint", "photoC_NO3_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_CaCO3_form_zint", "sp_CaCO3_form_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_CaCO3_form_zint_100m", "sp_CaCO3_form_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint", "graze_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint_100m", "graze_sp_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint", "graze_sp_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint_100m", "graze_sp_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint", "graze_sp_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint_100m", "graze_sp_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint", "graze_sp_zoo_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint_100m", "graze_sp_zoo_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint", "sp_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint_100m", "sp_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint", "sp_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint_100m", "sp_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint", "sp_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint_100m", "sp_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint", "sp_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint_100m", "sp_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_N_lim_surf", "diat_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_N_lim_Cweight_avg_100m", "diat_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 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"none", 2 +"ocean_model", "graze_sp_zoo", "graze_sp_zoo", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss", "sp_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc", "sp_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc", "sp_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg", "sp_agg", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_CaCO3_form", "sp_CaCO3_form", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Qp", "diat_Qp", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat", "photoC_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diat", "photoC_NO3_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "photoFe_diat", "photoFe_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNO3_diat", "photoNO3_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNH4_diat", "photoNH4_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "DOP_diat_uptake", "DOP_diat_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "PO4_diat_uptake", "PO4_diat_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat", "graze_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc", "graze_diat_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc", "graze_diat_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat_zootot", "graze_diat_zootot", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo", "graze_diat_zoo", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss", "diat_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc", "diat_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc", "diat_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_agg", "diat_agg", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_bSi_form", "diat_bSi_form", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Qp", "diaz_Qp", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diaz", "photoC_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diaz", "photoC_NO3_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "photoFe_diaz", "photoFe_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNO3_diaz", "photoNO3_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNH4_diaz", "photoNH4_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "DOP_diaz_uptake", "DOP_diaz_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "PO4_diaz_uptake", "PO4_diaz_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz", "graze_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc", "graze_diaz_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc", "graze_diaz_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zootot", "graze_diaz_zootot", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zoo", "graze_diaz_zoo", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss", "diaz_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_poc", "diaz_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_doc", "diaz_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_agg", "diaz_agg", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Nfix", "diaz_Nfix", "marbl", "all", .false., "none", 2 +"ocean_model", "bSi_form", "bSi_form", "marbl", "all", .false., "none", 2 +"ocean_model", "CaCO3_form", "CaCO3_form", "marbl", "all", .false., "none", 2 +"ocean_model", "Nfix", "Nfix", "marbl", "all", .false., "none", 2 +"ocean_model", "zoo_loss", "zoo_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "zoo_loss_basal", "zoo_loss_basal", "marbl", "all", .false., "none", 2 +"ocean_model", "zoo_loss_poc", "zoo_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "zoo_loss_doc", "zoo_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_zoo", "graze_zoo", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_zoo_poc", "graze_zoo_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_zoo_doc", "graze_zoo_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_zoo_zootot", "graze_zoo_zootot", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_zoo_zoo", "graze_zoo_zoo", "marbl", "all", .false., "none", 2 +"ocean_model", "x_graze_zoo", "x_graze_zoo", "marbl", "all", .false., "none", 2 +#"ocean_model", "PO4_RESTORE_TEND", "PO4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NO3_RESTORE_TEND", "NO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "SiO3_RESTORE_TEND", "SiO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NH4_RESTORE_TEND", "NH4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Fe_RESTORE_TEND", "Fe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Lig_RESTORE_TEND", "Lig_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "O2_RESTORE_TEND", "O2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_RESTORE_TEND", "DIC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_ALT_CO2_RESTORE_TEND", "DIC_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_RESTORE_TEND", "ALK_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_ALT_CO2_RESTORE_TEND", "ALK_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOC_RESTORE_TEND", "DOC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DON_RESTORE_TEND", "DON_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOP_RESTORE_TEND", "DOP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOPr_RESTORE_TEND", "DOPr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DONr_RESTORE_TEND", "DONr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOCr_RESTORE_TEND", "DOCr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "zooC_RESTORE_TEND", "zooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spChl_RESTORE_TEND", "spChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spC_RESTORE_TEND", "spC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spP_RESTORE_TEND", "spP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spFe_RESTORE_TEND", "spFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spCaCO3_RESTORE_TEND", "spCaCO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatChl_RESTORE_TEND", "diatChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatC_RESTORE_TEND", "diatC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatP_RESTORE_TEND", "diatP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatFe_RESTORE_TEND", "diatFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatSi_RESTORE_TEND", "diatSi_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazChl_RESTORE_TEND", "diazChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazC_RESTORE_TEND", "diazC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazP_RESTORE_TEND", "diazP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazFe_RESTORE_TEND", "diazFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 + +#============================================================================================= +# +#====> This file can be used with diag_manager/v2.0a (or higher) <==== +# +# +# FORMATS FOR FILE ENTRIES (not all input values are used) +# ------------------------ +# +#"file_name", output_freq, "output_units", format, "time_units", "time_long_name", ... +# (opt) new_file_frequecy, (opt) "new_file_freq_units", "new_file_start_date" +# +# +#output_freq: > 0 output frequency in "output_units" +# = 0 output frequency every time step +# =-1 output frequency at end of run +# +#output_units = units used for output frequency +# (years, months, days, minutes, hours, seconds) +# +#time_units = units used to label the time axis +# (days, minutes, hours, seconds) +# +# +# FORMAT FOR FIELD ENTRIES (not all input values are used) +# ------------------------ +# +#"module_name", "field_name", "output_name", "file_name" "time_sampling", time_avg, "other_opts", packing +# +#time_avg = .true. or .false. +# +#packing = 1 double precision +# = 2 float +# = 4 packed 16-bit integers +# = 8 packed 1-byte (not tested?) diff --git a/standalone/examples/single_column/KPP_MARBL/input.nml b/standalone/examples/single_column/KPP_MARBL/input.nml new file mode 100644 index 00000000..01e68431 --- /dev/null +++ b/standalone/examples/single_column/KPP_MARBL/input.nml @@ -0,0 +1,21 @@ + &MOM_input_nml + output_directory = './', + input_filename = 'n' + restart_input_dir = 'INPUT/', + restart_output_dir = 'RESTART/', + parameter_filename = 'MOM_input', + 'MOM_override', + 'MOM_override2' / + + &diag_manager_nml + max_output_fields = 750, + / + + &ocean_solo_nml + calendar = 'NOLEAP' +/ + + &fms_nml + clock_grain = 'LOOP', + domains_stack_size = 710000, + stack_size = 0 / diff --git a/standalone/examples/single_column/KPP_MARBL/marbl_in b/standalone/examples/single_column/KPP_MARBL/marbl_in new file mode 100644 index 00000000..e69de29b diff --git a/standalone/examples/single_column/KPP_MARBL/single_column_z.ipynb b/standalone/examples/single_column/KPP_MARBL/single_column_z.ipynb new file mode 100644 index 00000000..5ba27191 --- /dev/null +++ b/standalone/examples/single_column/KPP_MARBL/single_column_z.ipynb @@ -0,0 +1,581 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:847c08f9e6b1a07efd1e43efe9fa87aa4bed23875cde3d67a2c275d725f76697" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

MOM6 diagnostics for KPP single column test case

\n", + "\n", + " Results from this notebook: \n", + "1. Basic diagnostics of KPP boundary layer and prognostic fields. \n", + "\n", + "Caveats regarding this notebook:\n", + "1. This notebook is written for MOM6-examples/ocean_only/single_column_z. \n", + "\n", + "Hopes for the use of this notebook: \n", + "1. To provide a starting point to document single column model tests;\n", + "2. To illustrate a self-contained iPython notebook of use for MOM6 analysis. \n", + "\n", + "This iPython notebook was originally developed at NOAA/GFDL, and it is provided freely to the MOM6 community. GFDL scientists developing MOM6 make extensive use of Python for diagnostics. We solicit modifications/fixes that are useful to the MOM6 community." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import matplotlib.pyplot as plt # For plotting\n", + "import netCDF4 # For reading data\n", + "pylab.rcParams['figure.figsize'] = (17.0, 10.0) # Large figures\n", + "\n", + "path = '../single_column_z/'\n", + "deltaz = 'CM4'\n", + "fname_deltaz = '_CM4'\n", + "expt_name = 'single_column_z'\n", + "\n", + "dpi=200" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "visc = netCDF4.Dataset(path+'visc.nc')\n", + "for v in visc.variables: print v,\n", + "\n", + "# recall data layout is (t,z,y,x)\n", + "\n", + "time = visc.variables['Time'][:]\n", + "\n", + "# KPP boundary layer depth as function of time (metre)\n", + "h = visc.variables['KPP_OBLdepth'][:,0,0]\n", + "\n", + "# KPP tracer diffusivity as function of time and depth (m2/sec)\n", + "Kt = visc.variables['KPP_Kheat'][:,:,0,0]\n", + "\n", + "# KPP velocity diffusivity as function of time and depth (m2/sec)\n", + "Ku = visc.variables['KPP_Kv'][:,:,0,0]\n", + "\n", + "# surface (and penetrating) buoyancy flux, as used by [CVmix] KPP (m2/s3)\n", + "KPP_buoyFlux = visc.variables['KPP_buoyFlux'][:,:,0,0]\n", + "\n", + "# Temperature tendency due to non-local transport of heat, as calculated by KPP (K/s)\n", + "KPP_dTdt = visc.variables['KPP_dTdt'][:,:,0,0]\n", + "\n", + "# Saln tendency due to non-local transport of heat, as calculated by KPP (ppt/s)\n", + "KPP_dSdt = visc.variables['KPP_dSdt'][:,:,0,0]\n", + "\n", + "# friction velocity used by KPP (m/s)\n", + "KPP_uStar = visc.variables['KPP_uStar'][:,0,0]\n", + "\n", + "# Squared buoyancy frequency (1/s^2)\n", + "KPP_N2 = visc.variables['KPP_N2'][:,:,0,0]\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "xh yh zi Time nv zl xq yq Kd_interface KPP_uStar KPP_buoyFlux KPP_QminusSW KPP_netSalt KPP_OBLdepth KPP_sigma KPP_BulkRi KPP_Ws KPP_N KPP_N2 KPP_Vt2 KPP_BulkUz2 KPP_BulkDrho KPP_Kd_in KPP_Kheat KPP_Ksalt KPP_Kv KPP_NLtransport_heat KPP_NLtransport_salt KPP_dTdt KPP_dSdt KPP_Tsurf KPP_Ssurf KPP_Usurf KPP_Vsurf MLD_003 average_T1 average_T2 average_DT Time_bounds\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "prog = netCDF4.Dataset(path+'prog.nc')\n", + "for v in prog.variables: print v,\n", + " \n", + "# depth of cell interface \n", + "zi = prog.variables['zi'][:]\n", + "\n", + "# depth of cell center \n", + "zl = prog.variables['zl'][:]\n", + "\n", + "# zonal velocity as function of time and depth\n", + "u = prog.variables['u'][:,:,0,0]\n", + "\n", + "# zonal velocity as function of time and depth\n", + "v = prog.variables['v'][:,:,0,0]\n", + "\n", + "# temperature as function of time and depth \n", + "temp = prog.variables['temp'][:,:,0,0]\n", + "\n", + "\n", + "# saln as function of time and depth \n", + "saln = prog.variables['salt'][:,:,0,0]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "xq yh zl Time xh yq zi u v h e temp salt wd\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# thickness of cells \n", + "dz = zi[1:]-zi[:-1]\n", + "field = zi[1:]+dz[:]\n", + "#depth = np.ma.masked_array(data, mask=[data>400.])\n", + "depth = field \n", + "\n", + "#print dz\n", + "#print dz[1901:2000]\n", + "print zi\n", + "\n", + "figure(1)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "S = plt.plot(dz,-depth,'*',linewidth=3)\n", + "plt.ylim((-400,0))\n", + "plt.xlim((0, 50))\n", + "yticks = [-400, -350, -300, -250, -200, -150, -100, -50, 0]\n", + "ax.set_yticks(yticks)\n", + "#xticks = [0, .1, .2, .3, .4, .5]\n", + "#xticks = [0, .2, .4, .6, .8, 1.0, 1.2]\n", + "#xticks = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260]\n", + "xticks = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]\n", + "ax.set_xticks(xticks)\n", + "#plt.xlim((0, 40)\n", + "ax.set_xticks(xticks)\n", + "plt.grid()\n", + "plt.xlabel('Delta z (m)',fontsize=18)\n", + "plt.ylabel('depth (m)',fontsize=18)\n", + "plt.title(r'$\\Delta z$ for configuration '+deltaz,fontsize=20)\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[ 0.00000000e+00 2.00000000e+00 4.00000000e+00 6.00000000e+00\n", + " 8.00000000e+00 1.00100000e+01 1.20200000e+01 1.40400000e+01\n", + " 1.60600000e+01 1.81100000e+01 2.01800000e+01 2.22700000e+01\n", + " 2.44000000e+01 2.65800000e+01 2.88200000e+01 3.11200000e+01\n", + " 3.35200000e+01 3.60200000e+01 3.86400000e+01 4.14200000e+01\n", + " 4.43700000e+01 4.75400000e+01 5.09600000e+01 5.46700000e+01\n", + " 5.87400000e+01 6.32200000e+01 6.81900000e+01 7.37400000e+01\n", + " 7.99700000e+01 8.70100000e+01 9.50000000e+01 1.04110000e+02\n", + " 1.14540000e+02 1.26520000e+02 1.40320000e+02 1.56260000e+02\n", + " 1.74680000e+02 1.96000000e+02 2.20660000e+02 2.49170000e+02\n", + " 2.82080000e+02 3.20000000e+02 3.63560000e+02 4.13430000e+02\n", + " 4.70310000e+02 5.34860000e+02 6.07770000e+02 6.89640000e+02\n", + " 7.81040000e+02 8.82430000e+02 9.94160000e+02 1.11645000e+03\n", + " 1.24938000e+03 1.39285000e+03 1.54663000e+03 1.71033000e+03\n", + " 1.88340000e+03 2.06518000e+03 2.25494000e+03 2.45183000e+03\n", + " 2.65500000e+03 2.86358000e+03 3.07672000e+03 3.29359000e+03\n", + " 3.51346000e+03 3.73565000e+03 3.95959000e+03 4.18477000e+03\n", + " 4.41081000e+03 4.63740000e+03 4.86430000e+03 5.09137000e+03\n", + " 5.31851000e+03 5.54567000e+03 5.77283000e+03 6.00000000e+03]\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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89i3fBQUFpe4/U2XPHT16tDp27ChJatGihXr27Om5haXoP6iatL1x4/vatGmn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\nx44d670WAAAAAIC6CnjxXldPPfWUnnrqKY+xfv36yeVyKS0tzT3Wt29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CsrKyNHToUPXp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\nACDIUbwDAAAAABDk/j8pcgXb1INZ2QAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# KPP related fields \n", + "\n", + "plt.subplot(2,2,1)\n", + "data = KPP_buoyFlux\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, 16, levels=numpy.arange(-2e-7, 2e-7, 1e-8))\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-30,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Upper ocn buoy flux $(m^2/s^3)$ for '+expt_name,fontsize=18)\n", + "#plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = log10(sqrt(KPP_N2))\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, 16, levels=numpy.arange(-5,-1,.25))\n", + "#plt.contourf(time, -zi, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$Log_{10}(N)$ $(1/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = KPP_buoyFlux[:,0]\n", + "CS = plt.plot(time,data,'*',linewidth=3)\n", + "plt.xlim((0, 370))\n", + "plt.xlim((0, 370))\n", + "plt.grid()\n", + "plt.title(r'Surface buoyancy flux for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "plt.ylabel(r'Buoyancy flux $(m^2/s^3)$',fontsize=14)\n", + "\n", + "\n", + "plt.subplot(2,2,4)\n", + "CS = plt.plot(time,KPP_uStar,'*',linewidth=3)\n", + "#plt.ylim((-400,0))\n", + "plt.xlim((0, 370))\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "plt.ylabel(r'$u^{*}$ (m/s))',fontsize=14)\n", + "plt.title(r'$u^{*}$ used in KPP for '+expt_name,fontsize=18)\n", + "#ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "#ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "-c:16: RuntimeWarning: invalid value encountered in sqrt\n", + "-c:16: RuntimeWarning: divide by zero encountered in log10\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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C3fbc3FwhSZJITk72aKdfv34iKirKo42yvCt98ccff4g6deqIK664Qpw8ebJMbdnY+GPI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\ney/q16+P+vXrY/DgwTh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RtfMSI17tjIuLU+hj9eqrr2qcrzIU1ZU+9u4NDQ3F4sWL8c03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v7jjz+iQ4cOaNeuHR566CGMGzcObdu2dZmuFI3MvnMEBTVDSsoQBAWFgk9nEXuBzafTatlH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8V1AsbE\nwY18xpigrl27htDQUHz//fe4++67Bfve69ev49tvv8WoUaPw448/apfFscalS5cQHh6OwsJCKJVK\nwdLEGGOMMdO4TsCYOLiRzxjzCCEhIbhx4wbmz5+PjIwMsZPDGGOMMZFwnYAx87iRzxhjjDHGGGOM\nSQRPvMcYY4wxxhhjjEkEN/IZY4wxxhhjjDGJ4EY+Y4wxxhhjjDEmEdzIZ4wxxhhjjDHGJIIb+Ywx\nxhhjjDHGmERwI58xxhhjjDHGGJMIbuQzxhhjjDHGGGMS8f8Z4ivz1EjQ2gAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# KPP related fields \n", + "\n", + "plt.subplot(2,2,1)\n", + "data = Kt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, levels=numpy.arange(0,.5,.05))\n", + "plt.colorbar()\n", + "plt.ylim((-300,0))\n", + "plt.xlim((0, 370))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$\\kappa_{\\Theta} (m^2/s)$ for '+expt_name,fontsize=18)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = Ku\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, levels=numpy.arange(0,.5,.05))\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$\\kappa_u (m^2/s)$ for '+expt_name,fontsize=18)\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = KPP_dTdt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Non-local $\\Theta$ tendency $(K/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "plt.subplot(2,2,4)\n", + "data = KPP_dSdt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Non-local Saln tendency $(ppt/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 7, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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WAiSwdlW/eqdc/YO54aq+YY0Bi6p14sY2VvzxJSI2pZJ2rlFA5AKeMBqAiBxj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\nGDRoELp161blOA0bNkRpaanN+ImIiOo75gRmzAmIvBM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+ "text": [ + "" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Boundary layer depth and SST \n", + "\n", + "\n", + "figure(4)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "CS = plt.plot(time, -h,'-',linewidth=3)\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=18)\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=18)\n", + "plt.title(r'KPP boundary layer depth for '+expt_name,fontsize=18)\n", + "plt.xlim((0, 400))\n", + "xticks = [0, 50, 100, 150, 200, 250, 300, 350, 400]\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "plt.ylim((-300, 0))\n", + "yticks = [-300, -250, -200, -150, -100, -50, 0]\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "\n", + "figure(5)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "CS = plt.plot(time, temp[:,0],'-',linewidth=3)\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=18)\n", + "plt.ylabel('SST(C)',fontsize=18)\n", + "plt.title(r'SST for '+expt_name,fontsize=18)\n", + "plt.xlim((0, 400))\n", + "xticks = [0, 50, 100, 150, 200, 250, 300, 350, 400]\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "plt.ylim((18, 28))\n", + "yticks = [18, 20, 22, 24, 26, 28]\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 8, + "text": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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tt9/Gyy+/jM8//9zpdgiCyDqQ+z1BEIQDvPfee4iLi8Ps2bNN7pG2WLBgAcqVKwc/Pz+E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\nsd/9+sl1S5Vy3srNmGvfPTE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9QTgCiXqC0IjeYpusWeqBrOmCrzZ+5q735mRES70zol7v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\n27dvV58+fbzG4+PjtW/fPhUUFAT1PATOaqDrhDydp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mjb/4ovPef9UqUdQCwIIF5tt4qenUKSAqSowLFhTT1Y0rpRMReQr2aBPZiL0y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\nrkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# u,v,temp\n", + "\n", + "plt.subplot(2,2,1)\n", + "data = u\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, levels=numpy.arange(-.10,.11,.01))\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title('u (m/s) for expt = '+expt_name,fontsize=16)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = v\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, levels=numpy.arange(-.10,.11,.01))\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title('v (m/s) for expt = '+expt_name,fontsize=16)\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = temp\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "CS = plt.contourf(time, -zl, field.T,levels=numpy.arange(18,30,1))\n", + "plt.colorbar()\n", + "#C = plt.contour(time, -zl, temp.T, 8,linewidth=.5, colors='black')\n", + "#plt.clabel(C, inline=1, fontsize=10)\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title(r'$\\Theta$ ($\\degree$C) for expt = '+expt_name,fontsize=16)\n", + "plt.xlabel('Time (days)',fontsize=14);\n", + "\n", + "\n", + "plt.subplot(2,2,4)\n", + "data = saln[:,:]\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "CS = plt.contourf(time, -zl, field.T,levels=numpy.arange(36.3,37,.01))\n", + "#CS = plt.contourf(time, -zl, field.T)\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title(r'$S$ ($ppt$) for expt = '+expt_name,fontsize=16);\n", + "plt.xlabel('Time (days)',fontsize=14);\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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ZZ0XH//3vfzOO4wTnVkpGOVtczvletGiRSLbmzZuzyMhIduLECeF4Xl6eW13t27cvq1Gj\nBisuLhaOnT17llWrVo3NnTtXQUm488svv7CEhASWnp7OysvLfUqDCA407dzA8NPCmjRpInm+d+/e\nqFWrlvC7bdu2AICBAweKrmvbti1OnDghOrZu3ToMGzZM+J2WloaysjJMnDgRn3zyicepwo0bN5ac\nsuaNb7/9FmfOnMFtt90mOt61a1e0a9cO27ZtEx3v06cPGjZs6DXdvLw81KpVC8OGDcPVq1eFv/79\n+wMAtm/fLlx78803Y+rUqbjnnnuwYsUKvPjii2jTpo1bmunp6aL1aLVq1cJNN90kSkste/fuVfTn\nCavVijp16mDKlClYs2aNqkjeffr0Qe3atYXf/Fp/vm4cOXIEv/76K0aPHi26b9CgQaJ6JkV+fj4q\nKiowbtw40TtITk5Go0aNFJfbZ599hoqKCrepjc5s374dbdq0QVpamuj4+PHjcebMGdjtdkXP8kbD\nhg3RvXt34Tf/ffH1yvn4tWvXcPr0adHxgQMHiqYdupa3Gn7++WeMHDkSgwYNwr///W/V9xMEYRwm\nTJiA0tJSfP7558Kxd955B/379/e6DvrUqVOyNoE/bZZrDBg1uqZJkyY+2QSAoz0fOnSoSDfVrl0b\nw4cPd7MJ6tSpoygS+xdffIHff/8d48ePF+mjmJgYdOrUSaSPWrVqhddeew1vv/027rrrLkycOFEy\nEFxUVBSGDh0qOnbLLbfg9OnT+Pbbb9VmG4BjeZUSmyA1NdVjOmlpaVi0aBFeeeUVHD58WPGa84YN\nG+L6668XfkdERKBDhw4iHbV9+3YMGTIEkZGRwrHGjRujT58+HtP+/vvv8dNPP+G2224TvYMaNWog\nPT1dsU3w7bffori4GP/3f/8new2flqttOX78eNF5LRg8eLDwf47j0KZNGyQmJqJ58+bCcf67c/0m\nOnXqJFpC2rBhQzRv3twnm+Dy5csYOXIkLBYL1q9fTzF+TEa1YAtA+AbHcSJlBTgaTgCSx53Xvfz2\n22/YunUrPvzwQ+FYZmYmVq9ejZdeegkjRoxARUUFevfujRdeeMFrw6+Us2fPAoBk1PYmTZqIHH6O\n49CoUSNF6f7yyy/4888/ERUV5XaO4zjhuTwTJkzAsmXL0LRpU6FxlpLHlcaNG7ulpYbOnTv7fC9P\nw4YN8fnnn+PJJ5/ElClT8Mcff6BDhw6YO3cuxo0bJ3ufp/rC1w2+/Bs3biy6zmKxeO0E4bfBc41N\nwD9babmVlZUBgMdI7WfPnpWsQ/wxf96RM2q+LwBua8uUXueN3377DUOHDkXLli2xevVqVfcSBGE8\nevfujfj4eLzzzju48cYbUVRUhP3796ta/ymFP22WawwYX3WNWs6dO6fIJgCABg0aKEqT10dyDmLd\nunVFv4cMGYJGjRrh3LlzeOCBByTvadCggdsabt5O8FXntG7dWpGjzMfQkSMnJwdz587FggULMGPG\nDDRp0gRTp07FE0884XHduWu9ABz63rlenD9/3s0mAIDo6GiPsvPv4I477sAdd9zhdr5Vq1Ye88Sj\n1CYA3G1LrW0CQPpb8tUm4K9VaxMAwOTJk2G32/HFF19QID0TQs63zlSrVg3Xrl1zO/7HH3+gXr16\nHu/le9L4oBlasXHjRtSoUcOtN3zcuHEYN24cLl26hG3btuGhhx7CoEGDUFpaiurVqwvXnTlzRtRb\nqhTemZbar/yXX35BcnKy6jQBhxKoV6+eaBTBGeceyT///BN33XUXOnXqhO+//x7//Oc/JQOMSMl4\n5swZvxo5pT2TfJAXObp164b169ejoqIC+/btw8KFC3H77bejQ4cO6NKli8/y8Q72r7/+6iaPN+XF\nl8u2bdskFYzScuOV/MmTJ2WVc6NGjSQDl/HvzFOnTUREhNv3+Mcff0heq3T0QE+uXbuGsWPH4rff\nfkN+fr5fOwYQBGEc7rjjDixevBhLly7FO++8g7p16yra4qt58+ayNoGvbdbJkyfx5ZdfuulCpbrm\nzJkzQpA2tTRq1Mht1hDgaM9d23Kl+eP1zZo1a4RRSGdcne97770XFRUVaN26NaZMmYKdO3cKDhTP\nuXPnUFFRIXJm+fegdKDAlRtvvFHRqOzWrVtxww03yJ5v1qwZli1bhmXLluHHH3/Em2++iaeeegqN\nGjXC/fffL3ufkvJs2LChZH379ddfPW7Zxb+DF154QVJ2pfaQs00gh7NtGRsbKxxXYhNUq1ZNMmq+\nnF1gBJ5++mmsWbMGeXl56NChQ7DFIXyAnG+diY2NxcmTJ3HhwgXUr18fgCMy5aFDh5Cenu7x3vj4\neDRs2BCHDx/WVKZ169Zh0KBBso1fzZo1MWjQIPz444+YMWOGqEG7dOkSjh49imnTpql+bocOHdC0\naVO89957uP3224Xj+/btww8//ICZM2f6lJ/Bgwfj2WefRXl5OXr27Onx2pkzZ+LkyZM4cOAAPvro\nI9x///0YNGgQBgwYILpu586dOH36tDAF8OLFi8jNzUVGRoZwTWRkJM6fP69YTm9TytVisViQlpaG\nJ598EuvWrYPdbvfL+e7YsSOaNGmCnJwc0YyATz75BJcuXfJ4b2ZmJiwWC0pKSmRnEyiBT2flypVC\n5FhXMjIykJOTg6+++ko0K2PNmjVo2rSpx63zYmNj3aaly0XRN8JeoLNmzcLOnTuxY8cOUScSQRDm\nJisrC0899RQ+/PBDvPvuuxg1apTk7C1XunTpImsT+NpmrV+/HjExMejVq5fkeW+65ptvvvFZ9/Tt\n2xe5ubn4888/heVNFy9exEcffSTSt4Dy/PXq1Qt169bFjz/+iFtvvdXjtatXr8aqVavw/vvvIyEh\nAT179sScOXPc9i4vLy/HRx99JJodsHbtWjRr1kxwgPip2eXl5Yo6SrOzsxU5ee3bt/d6DU+bNm3w\n5JNP4rXXXvMaVVxJed5www3YuHEj/vrrL6F+njlzBjt27PC4RKJDhw6Ij49HUVGRxw4Ab/DpvPHG\nG5g4caLkNX379gUAvPfee5g1a5ZwfM2aNQDgVo+ciYuLQ15enqhjZc+ePcKIu9H44IMPMGfOHCxb\ntixgO+wQ2kPOt86MGTMGCxYswB133IEHHngAZ8+excKFC9G4cWOvvY4cx2HQoEGiLa54fO3hvnz5\nMvLy8ty2EJs3bx7Onj2LjIwMNGnSBMXFxXjxxRdhtVpFPYkFBQW4du2aaN2LUiIiIjB//nxMnToV\nU6ZMwfjx43HixAk88sgjaN++Pe666y6f8te3b1+MHz8ew4YNw+zZs5GWlgaO4/Djjz8iPz8f8+fP\nR7t27fDBBx9g5cqVWLVqFeLj4zFjxgxs2rQJEydOxMGDB0VTzRs3bozBgwfjiSeeQI0aNbBo0SJc\nuHABjz76qHBNYmIili1bhv/9739o0aIF6tWr51FJajF9Pzc3F2+99RaGDx+OVq1a4cKFC3j55ZcR\nFRWF3r17y96npDwjIiIwZ84c3HfffZg5cyZGjRqFn376CQsXLkT9+vU9Tl9r06YNZs+ejcmTJ+PI\nkSO44YYbUKNGDRQXF2PLli3IysoSFKQnWrdujQceeAALFizA77//Lqyx2717N9q3b49bbrkFkyZN\nwpIlSzBy5Eg8/fTTiI2NxerVq/Hxxx8jOzvbo0Fxyy23YPHixXj++efRvXt37Ny5U7T9ijPBHvle\ns2YNXnrpJTzyyCO4dOkSdu/eLZxr0aKF6LskCMJctGvXDj169MDs2bNx4sQJTJgwQdF9Q4YMwfTp\n0/HHH3+gTp06onO+tlmuMWAA5brm4sWL2LdvHxYvXqzoWa4yzpkzBz169MDgwYOFLT+feeYZ/Pbb\nb3j88cc93itHvXr18Nxzz2H69Ok4ffo0Bg8ejNq1a+PEiRPYsWMH0tPTMW7cOGEgYfLkyUKskwUL\nFuCf//wnBgwYIHLa6tatiwcffBBlZWVo3bo13nvvPeTm5mLp0qXCNYmJiQCA//znP8jMzATHcejW\nrZusnGqcajkuXLiAm266CePHj4fNZkNFRQU++ugjlJWVuc1udEWuPJ2PP/roo+jZsyeGDx8ubN86\nf/58NG7c2KNNwHEcXnnlFYwYMQLl5eUYM2aMMIr+xRdf4LrrrlM84LJ48WKMGjUKQ4cOxT333IP6\n9evDbrfjzJkzeOyxx5CYmIjx48fjX//6FyoqKtC9e3cUFhbi8ccfx2233Sa8FyluueUWvP3225g8\neTImTpyIY8eO4fnnn0f9+vUV1zd/rlPzzf7000+YMGECBgwYgKSkJJFNUL9+fVitVsVpEUEmoOHd\nwpR3332XtWnThkVGRrKUlBSWn5+vaKsxxhxbgFksFtG2F4w5IjG6RhzfsmULs1gsbPPmzaLjkyZN\nYi1atGCMMfbJJ5+w6tWrswsXLoiu+fjjj9mNN97ImjRpwiIiIliTJk3YHXfcwUpKSkTX3X333axn\nz55e5ZaKds6zatUqlpyczCIjI1l0dDSbMGGCW4RXqfx5oqKigi1ZsoQlJyezqKgoVrNmTZaYmMju\nv/9+9ttvv7Hi4mLWqFEjtzTPnDnDmjdvLorIyT97xYoVwntLTU11i1J96tQpNmTIEFa3bl3GcZyi\n9+kv3377LRs9ejRr2bIlq169OqtXrx7r37+/sGUcY46onBaLxW2rManylHpHixcvZq1atWJRUVEs\nLS2NFRQUsKZNm7JZs2Z5fAZjjL3zzjvs+uuvZ7Vr12aRkZGsXbt2bMqUKW71yBuvvfYa69y5M4uM\njGR16tRhffr0EUVlPXnyJMvKymKNGzdmkZGRLDk5mb377ruiNKRkvHTpEps8eTJr0KABq1u3Lhs3\nbhzbs2ePWwRU52/GtbzmzJkj+Zwff/zR43VSkVY98cQTT8hGv5X6rgiCMBevvPIK4zhOsq2R4/z5\n86xOnTpu7YivbdaFCxdYZGQky83NFV2jRNcw5rBvateuzc6dO+dV9t69e0vqycLCQta/f39Wp04d\nVrt2bda/f3+3bcTk8ueJjRs3sn79+rF69eqx6tWrs/j4eHbHHXcwu93Orl69yq6//nrWsWNH9uef\nf4ruGzBgAGvRooWw28rEiRNZXFwc++KLL1haWhqLiopi8fHx7KWXXhLdd+3aNXbvvfeymJgYZrFY\nmMViUSWvL5SXl7PJkyczq9XKoqKiWFRUFOvSpQt7/fXXhWukdI9ceUrZpvn5+SwlJYVFRkayNm3a\nsOzsbDZ27FiWmprq8RmMMfbFF1+woUOHsoYNG7Jq1aqx2NhYNnr0aLd65I3PP/+c9evXj9WpU4dF\nRkaypKQktmLFCuH85cuX2WOPPcZatWolvOs5c+awq1evepXx+eefZ7GxsSwqKoqlp6ezffv2SUY7\nd9XzfHm57nrDP2flypUer2PMPaq6J7Zs2cI4jmMWi8XNJgiE/UloB8eYARY2Eh7p0aMH+vbti2ef\nfdbvtKZNm4Yff/wRmzZtUn3v2bNnER8fj9WrV7tF/QwlEhIS0KdPH7z99tvBFsUQHDp0CMnJyXjn\nnXdEywUIgiCIwPPwww9j69at2LNnj99prVmzBlOnTsWZM2d8ipjcq1cv9OrVK6R3YZg0aRI+//xz\nVbuLhDJ//fUX2rZti8GDB2P58uXBFocgTIdptxrLy8tDUlISbDYbFi1aFGxxdOWFF15Adna2ZBAw\ntSxdutQnxxsAnn32WfTo0SOkHW8g+NONg8mxY8fwyCOPYOPGjSgoKMDKlSsxbNgwtG7d2m0LMoIg\nCCMRLnbBo48+iuLiYtl4FWoYN24cLly44JPjnZeXhx9//BGPPfaY33IYnXC2C2bNmoX3338fO3fu\nxIcffoiBAweirKzM5zg9BBHumHLNd3l5OaZNm4aCggI0bdoUPXv2xIABA/wKNmVk0tPTVQX20otn\nnnkm2CIEBCME2goWUVFR2Lt3L7Kzs3HhwgVER0cjMzMTzzzzjKJgQJ5gjElG/ufhOM4twmwo4217\nEW/byxAEUUU42QUNGjTAqVOngi0GBg0aJBmpPNTgOC6s7YLz58/jgQcewOnTp1GzZk306NEDn332\nGTp16uR32qQHq6CyCB9M+SYLCwuRmJgoBBwaO3YscnNzQ1LJEoHn6NGjwRYhaDRt2hT5+fm6pH3n\nnXd6nMqqRudDAAAgAElEQVSfkZEhu11cqPHmm2+KAgy6wnGcx44KgiDEkF1A6MUbb7wRbBGCyuuv\nv65LuqQHq/j555/RunVrj9d423KOMA+mdL5LSkrQokUL4XdcXJxbRPBw7qUkCDOyZcsW+m4rYYyZ\noiy0norZqFEjnDt3TtU9DRs29LoPPRH6eLMLzPA9EQRRhVn0YKBQsmNMsNFjeUZtjsOfKq43g01g\nyjXfSj9Gxpgp/+bOnRt0GUh28/2ZWX4zy252+X2VXQ/OnTuH5YCqP7XOOhGaKLELgv2tUftivj8z\ny29m2c0ufzjKrhd/IvRsAlM633FxcTh+/Ljw+/jx46Ieb4IgCIIgwgeyCwiCIAgzYMpp52lpaTh8\n+DBKS0sRExODtWvXYtmyZe4XHuVQmhCNcfgvbLADAMYgB5lHC4A3ARwAMAlYMnIKAGDm/7IBAPkj\ne8MOW+X1HwAA7LAiB2Ow7Oj9jntTHH/5CVXXOmODHTYUec0Ln67jeoeMv2Mf8L95VRelAEsSplRe\nb8MY5Ahpxx4tQ2lCNOywwoYixB4tc+SrMm8CByDkjb+Ovw+Ao0wAlCZEC+nyv+OyfxWSWTxlqpC/\nzP8VAPdXnphUWSZrALB5QArQZ+Qm4drsu2diyvIljvKrTFeQNQXok7AJY5CDmUezHeVbme8+Izeh\n4KtMLE6d6jgHR1ncn70MvafkY8eIAULeBBlSILz3gq8y0Ts1H2swXsgzT+z/HHlcMnIK7LCh+eJ5\nwP55WLJ+CmaOyBZkW7EBmFzZq7eC4zD5cSB/Xm/Mx+NC+jtaDUBRMVCPRSN2bhlWzK8q+sktHf+u\nqNylZPLjwJJ5UzBzbjZWzHdJu6VTXiY58ioqE5f3CQD56x2y1MHHwNx5wrPTAVgfF19but4p/yMc\n+S/aAOzkZX0c6DNvE4oqbLBa7ML7WzbX8d6K5jtdOxzC9zNzRLajnFoCpccc9YqvUzxcPsPiKVXv\nkS/f/ITewPkCYMQ8x7GtAPc0A1YUAZOtKFneGJ9wZUKepjCHfAAw2pIjfA+xc6vq05J5U4RvJXNu\nAXAAWLJ+Cp6seBxzLPOF7zP2aJnwPXMbGbASmFKxBMtG3C/kZ+qxxci+eyZKljd2PKOyjgllmhCN\nQsSDy34C+BLANCA6pRRWi71SNruoLPjvbsBXO4CuRcA+KxanThW+/xyMEWTnf+84OsDxraU46t74\nijVCel9bkhH7vzKUjoxGDkYjB2PwOOaL2p8cjBbnGZXf0d3LgFNAOvogc0SljPw3NDIa8zHHqS3M\nEeqDkraNIAKJErsgH30wH4+jqMKG0Zaq+myvrNUAhLblv5ZxyDxagNIEx3flquc/qBgjXAdASLfs\nQCx6p+aLdLodNtH/ndPg5XDF+fp9KMRm5KPgq0xEp5Si7EAsolNKAVS1gfy1dtiQ/dVMQQa+feRt\nDTtsKPgqE9jreE7vKfnCs7KzZwKvAbgHQDfH+eiUUqHNdH7G43hS0KH5Ix06yDnP96cuQ++v8nEJ\nhUhHHwAQbB3AYVvFtipDUbFDTy2ZNwU5cJQpn89fj8VV6b5JjjZLqn0bh/+igDsGLJuMKVOWYAxy\nhLScy7psRaxDB83NFqULQGRH8LbLGORg3r7mmHc3sHh5VRt9jHO0lekANrHKdnRFEYCdwLLJYJkc\nkAFBjxe1Bqwt4WhX1ztskx1Hq2yXxiNKHHlFlW2Un9AbA7J3ON5HVwBpQPRk8bvgy1ywSyvl584x\n9E7NRx18jFK8LNg+4/BfoW7ZYRO+A/43AGR/NRNTUh22mnPd5+sYAJENXbreoc/GV6wR0uKxw4bs\n7JmInlwqOpeDMSj4KhObUvs49HOKow4BcNiVlWnjAgA2D9wIVmXHzS0T7BBBP1fW9zHIcdjxvA0M\nCDrThiKHzXMAjjo3HJi6fnFVPb6/ykYDgMk/AUhg4LLheJ+8bZHgsC3SuPurrn0cKJ0XXZk3hw4u\nOAmcRKXNW3mvq30NOOwf57oqyFP5rCcrHsdoS47juJPuHoMPEHu0THQN/x6dy5lvzwDAarGLbCdX\n+DYtrbLeON6h492WHXDE0uDL2bXddH7vhHdMOfIdFRWFpUuXYuDAgUhOTsaoUaOQmpoaOAFSxD+l\nnG9/6JlRXdP0AklGtPdr1MA3FIo/7BTvl8iRkejf/aqpfNYKL9MlnTsMPNE2o7m/EunLl/KnMnrI\nnEjTRRKP+KJEDF/2nmif4fg3kHWfIDQm6HaBjjTPaBtsEdxwdiA84Sy7FraSfPuc7nfazs8QnsO3\nj75Q6XDxjjcAYcAjELjqJWdHzAxkRAZbAj/omhFsCXzCarGb254xCaZ0vgFg8ODBOHz4MOx2Ox55\n5BHfEnEZwTIKhnW+u3m/JKMxZMvU6D1jGf2hvj7s1UMS+FQv22Zcp70cCrDBXiWvj99TxvWaiaM/\nB9wPBavsNaFDRrAlIAhN0MQuMCDXOTmw/AiU0ZDT7+czhgPQxvGWSkN3Z/b2DHWdwC52kh1WVbYm\nnx8tyqttxnVO6QXO6ZfCo/0noVMBIMNld1OlHT6GoFtGsCUQwc9kdP2/FMMzgr+1cahjWuc7nOBH\nPrUeYVf2zOA22L7AT+tV2ssrlKufnTHWlt6vMQJmfKda4U3peEKp4uengxq9s4kgiNBAU9ugq3ZJ\nSaG6XQzSAIlW7bcdNmWd087LwxTOdvOEs/xK0hON9iuVwSk/ZQdiRdOQA0Wgn+eJQNhWWtQNIviQ\n8x1AXD9M99/+NSKi9KQaeT2UWLBnDqh8vkcHKsWlYZNLW22etUrHX5yep4mSCPa7NxBGMgBcCefO\nFoIIRfgOvkATDOcKgGpdo1pOFen7lH+901eJmmf4Io+rI68K17JSWHaKnleZlqqBEgUzPoNFIBxx\nI9s2Zoacb5Pjy8dnh1Wbj1YL58spDRvsstOPnNGiMXB2wvn0+BFzpembbf2UryhVovyKu7B29oKw\nRl0KUpgEETiM+L15ard9cd6d86jWsTJVUCYXu8bfjg5h5FvxtHOnuuQhToqRCHb9l3xHOgwOBHTU\nWYEtTJgXcr4NhmgKtAYIjYVEepo7SSky/w8Ufj5TcuaAgjRFa56V3FN5vjQh2j1EjFwwvwDPWlA8\nPVuhXM5Ky7DTpvaJf2oXvocINHl5eUhKSoLNZsOiRYvczpeXl2Ps2LFISkpCeno6jh07BgD466+/\ncPvtt6Njx45ISUnBtm3bAi06QShGSTut11prf6/lO8DlRiFd8+ZVb3Dy9oyijgMZB9lqsQPd/BtN\nL02IFtLX2lG1w+aTTvVn8EDID7S1Iz3ZO8J709ApFb0Ll2d6KlOpOhSOgw58OZiiUw3GsgvI+faC\n3h+ULw2xQwlYnX77LqPz8w3rFPmJa8MgjHpXKkPXLVR8QlCsjnfhl+NmxOncGskkehcyaSqph5oF\nHaLeZXdc6rLZKC8vx7Rp05CXl4eDBw8iJycH+/fvF13z8ssvo3nz5jh06BAeeugh3HfffQCAV155\nBVFRUThy5Ajy8/MxY8YMXL16NRjZIAjCTxTpcy+6zQa7aGRVMs197oec0xZmG7o65DKomYHniho7\nTqkelXSutOpMkNPBSmwOI9pKhCExml0Q8s631uuq/Xk2IY2WU+A9Th1X6Gj5vKbLabTcyMHXdPkG\nfFSCsu9ezphRidn2ow729D0lGF3GwsJCJCYmIjY2FtWqVcPYsWORm5srumbjxo3IysoCAAwfPhy7\ndu1CRUUFvv32W/Tr1w8A0KRJE8TExGDPnj0BzwNB6IVZRqm0QnF75WRD6PV8h33iMtrqo+7k9+kW\nwaelchcWtW26YNv6ILvR9YfZoPJUhtHsgpB3vgkxPn+onoJOKFEgBlkLK8LFGReNfPuiEFUqbzus\nipx0r4H0FMjkK34FTqkkGDMqws3A1AyV2+EEg28BbHD6c6WkpAQtWrQQfsfFxaGkpET2GovFgujo\naPzyyy9ISkrChg0bcO3aNRw9ehT79u3D8ePHdcsLQQSCsBgI8EfXebnXeQq8sw1lbanMpuJnK/r8\nHpR0RivMv1axakoTor3qdl9jAwhoHFdINwI0Aq80ar0r/uzyYga82QSA8eyCan7dTRga5U6gDZko\n0Fka7eAbaC2UiNcyUrPmu9KZt8OK2JQCoNhv8VTL4lN6Mq2V2xRxFVO07bDBllAkji7v1AETCIfc\nVQkFq4fY8KPvHjrWjNir3qHyj+cjl/Mcx/mULsdxuOeee3DkyBF07twZcXFx6NWrl8/pEaGPnFEb\njM4/I36rivFDt6nKt7dBBDj0t9z745+VDmCnTDLRKaWAY6koShOiRcva5HCd/u33u/QzQreyDgV9\nOnRKE6IRC8/belpbQpl9ZZJp6XbYhHpiqr3MDYI3mwAwnl1AzrevCA21iRWeDA7l49lhMJvDLolk\nEDple35LvvcUp0Y0BWKnNgVVysI1OJtSJXLA6f+o/K1kzZVLx4A3bLAHtF6L6ppz2XgIoGN0tOhp\nVvUeTGJkKMHfQHdxcXGiXunjx4+Lerz5a4qLixETE4OKigqUlZWhSZMmsFgseOWVV4Tr+vXrh44d\nO/opEUEQgM6dEl72J7da7CiA/Brn0oRoxB6Qdnz4GWB8e6zI+XOKm+HsXAGVAwde5p0qaft5PWOH\nFbEu9piz069FB7ANRYqWcbrFzvkSwGQ/H74XQKr3y2jGW+gSanZB+E47N4Ox6sd6oKChYtqqZqOf\nHpxoPlCKXG+inDPKB0cpHalMRrkpWJ4aDGELEi/IdghI3aswPdepc3LwyowvD6lr+XxYWyp7vvhe\n9/IP1BTJUOlhDosppSpIS0vD4cOHUVpaiitXrmDt2rUYPHiw6JohQ4Zg1apVAID169ejZ8+esFgs\nuHTpEi5dugQA2L59Oy5duoTOnTsHPA8EQfiAHyO+dlhROjLaTT86O3TC/73EdZGyBfTeW9tToEw1\nAdzEweW86xbn8pKbjaimM9oI+izoO51QIFjNMZpdEL7Otwf0GPVT06AovVbpdaEWxdwGu6L9Lz1F\n8vRWJnZYRY7kCg9TTCSnN6c4GnCva7onyZ9y3Z5LcPBdgrvpUV+d86R0NoAU0kaI1b/1b07IRmFV\ng5rrJZSiHsYC9eD7TlRUFJYuXYqBAwciOTkZo0aNQmpqKubOnYuPPnJMSJs+fTpOnDiBpKQkPPfc\nc3jxxRcBAKdPn0aXLl2QkpKCBQsW4P333w9mVggTEIqz3/Qm0O2b0tgldliRgzGS94v+P0niZs4K\nkdsmoZtdHXuRfkwR//a0rE4qXd4+UDO9XSnSW2u5PEdFkDc+b1Ij8oH8nrRa/+4MtQfGxGh2AU07\nJwAEwEH3cw2SUtQ4Qt4UFR85Ha7T62WcNdfAaEpGgh3l7jQCK7O/uCjthCLH9Dg1TqPcKLmrM+l0\nndgBr5zW5jKdXk1wOY91IIA9vT4ZfjoGDHQeaSC0YfDgwW692vPmzRP+HxkZibVr17rdFx8fjyNH\njuguH0GEK1LTlwH/nBYb7ChIy/T5fm+2AP8Mt4CsnnSwzDne4eOX95UmRAPn1MkrhfMUd286xZPT\nGai944OO2WaVEn5jJLuARr5DHLWjcgGZ8mOAKTVy+XReJ5WD0d4TcnEo3bYRcf7XA55G1vl0JaOx\nV/7ri0Mp5zjLpeW8T2m6sww8qqec+67Ao1NKRUaC0inkfN48rYGjUWeCIHiMMA2W8B9lI9/SOsn1\n3vyE3go71pXjen3ZgVjJKOdSU+Ld7AOVeCobT2nKPZf/LXevlIMvukeibP3Ry5J2mTMaO+JKR9S1\njhZPmAdyvk1CMHoaXRsGf0boolNK/W5IvG5toTIIntw6KOcebjtsitf/ON/j7Kg6kw5xuapbjlDV\nYy6sSfcjWrvr1HrnfEpNpefXxKWLjjvkF+SQnGrnuEa8lkxmSxH+fj54ToBmTIQC/jgJho/IThCE\nYrzp6lDfeoinanqzTH4l1klr6fC4BlqTg39fsm14pT6Uem+uDrjcdWrRrNNJZtaYqI7KOcS+xj0K\n4VFt6gwMDcj5JrzjowPkrHR8UgaVDWiwpuWKIptK4Gn9lODYToKgQOSceLfjTlueeJdRPpCKp/Xm\ngqL2QUlZWwLW4RDda4dVyKtzJ4FzHrzWAWdF66SwQyUwGkEQhBEw84ia7BaSOm6vJTmyK+MYKrV1\nnPf+tlrsbvpeS7tHzai4/DZrVdPalT7TU5pCOXlxvEsTooFuKuqsRo63L06uUabjm/n7DhfI+daJ\nQH+ERvno5QhGY+A6NdkVf8rMOZo6r0Td4JXHSOlRcD4d63DH/9Od7/GwBk0YhZYa9faieFzT5EfQ\nlYzuC/mcBGBSlYzOjrxclFd+BMCGIslRVueZAmqNBKOO2gZbAcoFtQu1AIwEQYQH/to5cg6tmnSd\nI567zdRSiatTrHSkXImMatLxln+5LcX4ZwGQ7/xQ0iniYrcI0/orZyXIdkSk6BM0zRNGt7UJc0DO\nd4gjG1kzQGjtgPjSG+kqg5LgKiJ86EkVRx91yCw7Ej1J7Pzy78mTwnP8X/n2IQBktmSzer1GMp0U\n9+c755WX1Vvvt1S63mQJtLL1hWA73mqggG8EET54dZZMgK8drppOt/eir1x1q/RSrqqOaV+cOq30\njGs6anWsWjlEnQMSnfau0duF9+YUL8jVnjKTznUm0M68t2/ArOVoNsLS+dbKCfUnHVq3YQ6kGkbn\n9+7ckEmtdRYcVKUOvMye5XIBTfzBLXibwntKR0YjP6G3W499DkZ7HK1XclzUMdKVHENvhMvaTYLQ\nEvpuCB65znhvOtZrDBo4z84SL8FTXf/SIDv1Wskot6/2ghZ2hlcdLmPz5GC0yKZQ3eGiIuAtj9QM\nQKU2SLBHxH1xmpXe4+sWdYQ8Yel8hyOGmWrqEulcrVxCAycRxMM5LX8im7qhoPH25lzy06qtLSXW\nTqU41lBbW1aNHisZ1Xab9gX38vS+n7n6dd9ya7/k1pV5z0dV0DahU6obgDR3I9nrvukKn2kYOG06\n4Ty9Z9fZEgRBeMa13VHTnlCHoTRaTakWp1nVnikpd91G9dIA3AOAs3p8hqf9sl3rmFAHK6e0+1uv\n7LAp2ovbBnvlGnR1utzbMj/n9J3/daQrb+/4VW/kbJsABGTTIsgwEbqEt/Otco0pIYHBo0qKeoUr\nHX/VDoiP087dlKrzGjEnhPXOCqOuequvntZvy42gW1tWOf/eUBK8RV6RSq9Ddj7uPC3Sm/Lydj6U\ng7XR6B1B6IthOq01xga7KToJ/Cl/j1OnfdTp4t9OuqwbRNP4PTmTShwy0TVpULREwPkesWOrjX3r\na8A2q0V+ZJ6PXSPn6LsdT5H4U0Ao2PjUcR5ahLfzjdBVrkZD9x5AP6KO+pI2AEW9yDzO0c/dg6w4\nRUaHusikUgaBdTgwWeEIMQD/tvTwglql5xzIzhOyCt0pD6LzAewkMq1TbPCONIIgAoDM1lBq0NTZ\nURhE1Nd211enxllPR6eUCjpLtXMN6anjNtgdo97dlI8qu8rnK/wadLlReam0fSl/pTFhZHV9ivuz\n1c7+UwzpR0JDqgVbgHDDKL1XWshRmhCNWBh7ZNF11FsrpKZ8e8VDxHPbyCLEHi1T5XjzuMmQAtEo\nfywKPD7fl/cYiJ7k3qn5NG1LJ/w1SBzvv0AbYVxQurRAoFgXMQgipDD16F+KOpvF66izRp3NdthQ\nVOEICFZUYQP+z/80RWjQEeKKICuCt0TCdcq5KpzeW2lCNFDhnqbUtSJbKNAcADnvfqLKLjCBTRD2\nI99GJRgj8pp1DLg0Mt62hPJJqUpMw1KaTiCNENdo4HLv1Q6rKIiZXzL6MpItMe3dF1yDsCm9x7XT\ngR/Z9sn59tDJ4Rcad+AQBGEMqJMvyATIMeHfs7Me9kcvuOosTFOfpsfp8U6j3lJrpV3xqjO7Ocmq\nADX5kHu2mmBwiu/XaaZeKCL1rqm9Cz7kfPuCgT56ZYG5Aj/aHqzp/LINudP+2WqwwS7cs4LjFD1X\n6liVU22VvMaXEW9ePrf0UiR+e8i7MO09APXa13y6QspDRwzUvhEEoQ/BXh6jlY2gxCmVRaO2jn+2\nL0HfPL0H1+nmWus9NQHS9ESJTSDYTi5RzF0jcQv2jNNvyZ1dfFr3b4yZq75ANpOxIOc7AATyg1Wj\n0LRyhHx1bNXgkFUc2bTsQGxAnEY7rEIQs3QAORjtaMhU7pPqLaKnM343lJMgrCH3NqrtS8RzvfA6\nnd+TnGrrYQBGsj29RyMHPKJYGARBaIV057Q1aHpHyw5n3vnjo4Rridbp+ROBW2qGWqAR9JIQLNkl\nSKvzdWptUhV1gX/eZMbczikpHzm59HaQyQE3DuR8mwAz97YB+kzzVquU+MbYl7K0wwbr8MrnDq86\nrsZ58lQGmkw1d8I5ejr/W9FWVBKR2LXA5+njlbjKLvXu5SLGGxVTr78kCMLQ+D2iqbJj2S9knB7d\nHYVJ0EbnOQVe1UtmfxxmufQ81REjO2m8PePNsa7atlUiYrpOSJa3yZerka2iDyHtfNPojQMtPh5f\nom2GFJMqt/Ca5HsSWk23VqIYeeXEzxjwtjzBiN+Kcz49b5+mo7NtkBkBwYCULkGEHkZ2rPxFrkNc\nKlp3aUI08hN6a/p8pfpZblswqbT8mlavQg4159SihS5xnf0o2DQSu85IbWmqJkK76qCfPmBEm4sI\nHBTtnKhs1HTelksD/G3AfVnvLZBSOeqdUnXO7ufWGmpRqgx5peM6ou559N0KJEgHxPMHLRS4HVZk\ntizAzmLPafLvV0kZC/t/873SThHi1eBWpwI5YkQQBOEPQWyv7LAhM6VAdbvryVn9wOu9RcKzlcTL\nUSJLATJ9vpeXJRDYYJccblMSwM1XGfXIo7fOdiXbommOQTrp/XlXRGAh55sIGILDoxK5aI3elJ6U\nUvG1YSpNiEbspDJRT7laxzLQjaKa6exayeaPs+1NhnTATcnZYXNS8H6MgHtTnt0AHBMf8rXnWs+R\nelK8BEGYBtG2mPq2XY70P3D5rQEm7XAN5AwIrZ7lq2MtuR0rQQSRkJ52rhX+GMtGWn9qCFl8bPS0\nmbbk53vUaU201njbusNMCHmZVDkVzGnmgfg6A9RtP/Fvyxt9tgkkCCI08aYbPMU0MatekZqObGS0\nLmfX9Iz2HrW2ryTXe7voOOdBIWtLz0vcCOPVGbNCzrcrBp42rRiJPBhK4bhEquSR2wdcTZpSuK4V\n8gV+jZi3LcX0RM1znEeElV5v6I4FH/Yut6FIt2An3ka9dVNQGrZPhn7fBGEy5PZrNuJ3Jts+BXoU\n1ykoqF54aou1ioejdZpaEAw5gumYqY4yniJz3ODoNUtRPGij/RJEQgw530HE20ekJKKj6VDhDJUm\nRIumnEuWl+ue1joi5cDr0TMdjj2Lnjo1hK3QJkkrSan91AnlhHUgRYJwQs6BDlcCsh1i0LYb0+Zd\nB7r9DKSOU2OLGMFuUVQ2Rp/dZfLo6IQyyPkmTIE2gbv8U1pa7cWtNhoq4X2rOKUj9+FWrtQZQRCE\nEeFnk/mLL226EWKcKE0jHHSWtzz6MjNPan24HdaqbUkV3q+0c4V0LaEGCrimEDN+WIaaaq4nKhpS\nf9C7DoSDkvUVPhq7z+9A7XR1I6Kyx96MbRYA9SMTxbpIQRAhT0BGtgMArzutFs/RufVoE0NBb6td\nphYsJOVUsVSCf/+xKAAQnHdnh9W4NoaRUWMXmMAmCN+Rb6NPPfEXo+ZPpVzeGkd/es6D0fCaQcEF\nA7lyEU8pV9eZxAfJM+y3EGBM64wTBKEZRjL8qwKZhslAgZ9QG+5O79R8yeMeba2UKttRsCE92Akh\nt/zTR0Kls84IhK/zLQUZ6erxUmZSDaBc4DItMJpyImdbG9Rsm+aGiwMuGJ8GXlulJkhQIOo81WOC\n0B4164VD8Rvk43nkYIwu6QcrnoUebbLRbBsj4OuSA4cNapXcgsx5V5VQIRTbDrNDzncI4a1xDmbj\nLbe+y/8o5JX3G7ix9HWNt5EbzEDKJuV4K10D5ta7TRAEEUIYWU8oQcu22SZoBvPtYR0I9JQ12J0D\n6naDsfo1q04LGZRCAVFDE3K+wwQtGgUtGm6+x1Ergt3ga4HeyttMxoGW8HWDN+60LodgT5UM1/dK\nEETooH45kfF0vhna4kDs8W207c2kttNy/nMcM+iSBwPMzDNs2YQA5HyHAcHYviqgH62XUW8tFYI/\nEc7NoKCNingPSjX7l1tF/2pNILaf8xcjGqsEQQQfI4yqqWmfvG0FZ7S2N5jIlUW4lZFUfv3RiZ7u\n1axs/XC8w+39mhVyvsMQT42HnoZ6lSOk7hlyjUl0Sqnb6KaeaNmo+ZqWURypYMghFelUyfRzvaaY\naYVWBrBR6gZBEMbGhiJ5HaQierQW8O2W1tt2Ober/HFftqxSi/MyKSO2yUaUKdjwsQdEfwA4bkUQ\npQo8ZQdigy1C2EDOt0po7WgVck6MpzLyx/GRU878VHa57TKoJ5DKwJVQ+Y7pvRIE4St+tx9+xFrR\ns+0KVrtIjq0x8bSbimgAR+PdUUg/E3IY1vmeNWsWbDYbbDYbhg4dirKyMuHcwoULYbPZkJSUhE2b\nNgVRSsKNIAQ+89WhD5SiDDWFbKb8+DpdHaiMeqo3Pn4vRpguamTy8vKQlJQEm82GRYsWuZ0vLy/H\n2LFjkZSUhPT0dBw7dkw4d/DgQfTp0wddunRBUlISysvLAyk6IYMRbYKwN64PQFedL6Xb7bAh9miZ\nxNVEOKLVNyhsTRrmhHKbZiS7wLDO97Bhw3D48GHY7XZ06tQJTz31FABg3759+PDDD3Ho0CHk5eVh\n6tSpuHz5cpClNR9SDlSgpmP58lypBoF3QJRNPQ6Ow2gmR1UPjJB/1/rh+K1O0dL+luahvLwc06ZN\nQ15eHg4ePIicnBzs379fdM3LL7+M5s2b49ChQ3jooYdw3333AQD++usv3HbbbVi5ciX279+PgoIC\nVIHrRuIAACAASURBVK9ePRjZIFwgm0BMKHbAaTP1vEiztEIZvXWz2cpf2JHHZA64EWwsM2A0u8Cw\nzne/fv1gsTjES09PR2mpw/jNzc3FuHHjEBERgdjYWCQmJmLPnj3BFJVQQbjs700QoYgZvrPCwkIk\nJiYiNjYW1apVw9ixY5Gbmyu6ZuPGjcjKygIADB8+HLt27UJFRQXy8vLQvXt3tG/fHgBQv359QQ8R\nwYVsgvCEXyLkre2RW3JmNidQCVq2w2Zo05XiS17cIp/LTD1XulSNr296dI4ZLV6NmTCaXWAKqyI7\nOxsjRowAAJSWliIuLk44FxcXh5KSkmCJpphgNXBKFY+ZG2Azy+6JQOYrFA0UvXEdTVeCodeay/T4\nm61ulJSUoEWLFsJvKR3hfI3FYkF0dDR++eUXfPvtt7h8+TIyMjKQlJQkjK4SxiIUbIJA4o8uCcQI\nu7odLDzPoCMHRZpgzHY0E1Iz5ADz6T9CGqPZBdX8TsEPMjMzcerUKbfjTz/9NIYNGwYAWLBgAWrU\nqIHbb79ddfovPPEnzqAYxXgHDTLq47qMtn7LHAgcgcOKgi2GMlIgbItghxWxKPApGentIKS3cTJb\no6hmH0pCW5yD8Ln+PzOlwBB7aZqFH7aewL6txSjGmaDKsfVXYKuHJZ8cx/mc9rVr17Br1y7s3bsX\nNWvWxI033oiuXbti8ODBPqdJKEdvm+CdJxz2AABT2QRqMZNeUSKrL52WzrYC4R/hVJalCdGIReBi\nCgSqXPV4h19vvYB9W/MMbxMAxrMLgup85+fnezz/1ltvITc3F59//rlwLC4uDsePHxd+u/ZmODPr\niVqwoyWOIgvXOTXwgpNo4LUddlgN54DLKkldA65IRzBXi5mMEUIarfYtlapTgnGnsi6Hi0ECAG0z\nrsPljKqyy5u3T58HeXkHGZV/PPPmi8+76ojjx4+76Yi4uDgUFxcjJiYGFRUVKCsrQ0xMDFq2bIkb\nbrgBjRo1AgAMGTIEBw4cIOc7QOhtE2Q94bAHAIhsgnBFK4M8nNpBInSxV34RcoNIVM/FJGfUx/cZ\ng/S3CQCPdkEGPNsEgPHsAsNOO8/Ly8Ozzz6LDRs2ICoqSjg+ZMgQvPfee7h69SpKSkpw+PBhdO/e\nPeDyGWX6KDUG5sWMHQJmrG/OMyXc5Ndya5FujsBs/rxXI5SvUdo2X0lLS8Phw4dRWlqKK1euYO3a\ntW5KcsiQIVi1ahUAYP369ejZsycsFgtuvPFGfP3117h06RKuXr2Kbdu2wWqlaaxGwOg2QSAwc+BH\nX9ZsByPNUMYI+sUMOLavtQFIV3w9QPXNyBjNLgjqyLcnZsyYgcuXLyMzMxMA0LNnT7z66qvo2rUr\nRo4cic6dO8NisWDZsmUUjVYhek4dUmKwazWKTRD+wkc2jT1AW9YA/i0ZMRpRUVFYunQpBg4ciIqK\nCmRlZSE1NRVz585Ft27dMGzYMEyfPh1ZWVlISkpC3bp1sXr1agBA8+bN8Y9//ANpaWm4cuUKhgwZ\ngptvvjnIOSIAsgm0Jng7gIRXZxbZPeaCOihCE6PZBYZ1vr///nvZc48++igeffTRAEoTmlAjQxBe\nMMjSlECvQzM7gwcPduvVnjdvnvD/yMhIrF27VvLe22+/3af1xIS+kE0QPph5hJ8IPIFek25DEY55\nv8wr4dYRFWyMZBcYdtp5oAhW5Q91x9fv/Dk5PUp7jal3mVCD87YipAQJgggXQrW9q4p4Htr2FaEf\nrlHPfSEQOwQQ5ibsne9QwEiKxizrRY1UZmoxs+ymhiKjE0TIU3YgNtgimJ5gdoTL6cdw7JwPxzz7\ng1t5cY5OKiWdVeFkl1G98h9yvv3E6B+ckojpSvIg15NnFmcbMP67IvTF9f3z676rgqs44TLdPNx6\nskN1ZIwgCIJQT6g7XJ63hC3SVCeSfiXI+daRYDt7WjneRsVWOUGI/38w5SDMiR1WdYqQRr8JgkBo\ndMh50v+hkD+C8Be97DtywMMbcr4JU+BqJPjaIGqxnodQTrA7JpQ+n+oEQRCEsYKdBbNdDrbuIoKH\nEKHeIAFXidCDnG9CMc4jzZ7ge/S0Ul7kGBGukGHkO6pH+wmCCBlC4ds303I3fyH7Rx3+lhfZFkQg\nMOxWY0RoKMlAYPTGkvb5NC6+KmqzfJuq80c9/QQRtvCj3vy/Rp56bpY2mDAxXd0PFVXYZIctqaOE\nUAqNfPtBuDf+ejc0FLWU0BOv9ddEjigpfYIgwoVwt72I0MQGu09xZawWZbNSCeNAzncYoCTwmtZo\n6QxQo0KELBI96wRBEHoSrM46LZ9LHY7GJVRtNn/rHHUaETw07TwAkJLwnVBtxL1BdSYISI10Vx4L\n13oYFEw044AwJ/Q9mwc7rEEZQCDCFxvsDhssLdiSEAIhZhfQyLdGyDlL4eZEad2zx5ef679Gwogy\nEX4SwIaeHAGCIEIZrXUk6VwiIHRz6OfYo2Wiw652rllGtMnWMA7kfBOa4c8abU/K1MiONxFCVE4B\nFxQpjXoLUBkQRGhgFj3q3OaYxbnRGrO8KyOgtY6ywY7eqfnCb1/ehRFnbJAuNwbkfBMEEbaI9n2X\nmmKmYgQ8OqXU0NGBCYJQT6g6QGbLl6u8ZpOfMB/+OKrk5BKeCFvnO7z2iTRur7GzAjWynGaHDBUF\ndHP6v47TzrV6F3ord6ozBBFaBPOb1qK9os7N4EIOpQOp7yhUy4bsAH0IW+fbTGhV+Y36ERlVLsLY\naK3s+L1tBUIswAdBEEQoQDYDEQiM6FBT3Q8NyPkmAoZrQ0aNCKGGQNYXfmYM/6+o7oaYU25EA4Mg\nCPOiZ1vNt1eh3m6RfWQMMo8WyO69vYLjAiYH1YfQgpxvQnOokSDMhK1y5Tc/pbHyl+Bk22FT1HHE\n3x/qRiFBEOaGdDQRTvhV32Ucb0/pkw1AeCPknW9SMgHAw0ggNUKEnuhZv0oTot3jECgc9Q5mvadv\njiAIV/SyhcIpfo6vqC17slt9IxC6z2qx6/9+FDj8hLkJeeebUI4WDYoejRIpIiJgdBX/5OueaMuQ\nFOlrzA4Z0QQR+oRKexXu0HsMLulO/5d7F6EYRJg697WhWrAFIIyPWyAqgghhxBH43aecS8FfQwYR\nQRgXo32f4WTI+ppX5zY4UOUVTu+FkGcMPnD85wCAkUEVJWBQ3Q8MNPIdgpQmREtPmdWRUOzhI8IL\npQEBPY0Qq1FczmvESeERBBGq+Nu+Ga3TxBWjyxdOaKlLY4+WyU4Bt7bU7DGmgbb60w4a+SYMgx02\nZKIg2GIQGmFKhzLN8Y8nY8rXjiZJpz1N+tpgll3QO9JCLJo8QZgJq8WOogpyJonAYpYODEE3pwAo\ndkw//1LBfbSsy09CzC6gkW/CeBjgIzOl4+gHZlF8utPNw7kUeK2butcbA3wbBBHK0DIrIpQIN1tG\nc+T0forD8bYOV5gGyM4iqiDn2wcC0YOl1UeqdBSLGgVzQe9LH7wa3inGLXujykUQZiQcvyezTCsl\nh5JQip7fsXU4vHeIC4632Ba3Vy44I8ITmnYeJgT7IydlSZgBrYxPvb83+p4IgvAXJe0UtTVEuFKa\nEI3YA2XCb9G3wDvdk+Tvt8MGW0IRYlEmPkbfVNgT1s43rcHwTrCddoIwFH5OOw/6emqCIEyNFtPi\nSa8T4YBm9Vxm2jlSgPyE3qqT81Uu+m5Dh7B2vvWCerb8IAWO6JK0tpUIAh6/W6qTBEGYHLMZ8Ly8\ndtgwBjl+p0MQqpDR+/zgnZJ6RQN9hCu05pswHaRECT1Q0mEWVCWqh/MvE22dIIjwxZ/lN6SfCSMQ\niHpoh9Xrc/jZbmb6LmKPOqbJm0lms0HON0EQign3GR1qp407gqpYqeebIAiiEjLqzUs42gBS+tvZ\nFvCnTHz5FtQ+LxzfmdEJS+fbtbKHuiIIZv5CvWyJ8IAcaHORl5eHpKQk2Gw2LFq0yO18eXk5xo4d\ni6SkJKSnp+PYsWMAgMLCQqSkpCAlJQVWqxVvv/12oEUnggDpqcBh9LImR4Xg0Urvq+q0P+D341Rj\n9G9SK4xkF4Sl800Yk3BpAAhCwNO+4oRPlJeXY9q0acjLy8PBgweRk5OD/fv3i655+eWX0bx5cxw6\ndAgPPfQQ7rvvPgBAcnIy9u/fjwMHDmDLli24//77cfny5WBkgyDCFn7aOznChK9oWXeknGfXrcKk\nnkfbiRkHo9kF5HwbGK0/XH/TsqEINhTplj5AgSn0gBp/5XhS2ErWd3m+V7r32/GVy39XWuIsf6jW\ni8LCQiQmJiI2NhbVqlXD2LFjkZubK7pm48aNyMrKAgAMHz4cu3btAmMMUVFR4DgOAHDp0iU0bNgQ\nNWrUCHgeCMLM2GEDuvp2LznchFHgl41pkY6a44T2GM0uCBvnOxwbdC0/bLnyk5qWI/VcamQIM8LX\ne3/qr5nqfih0fpWUlKBFixbC77i4OJSUlMheY7FYEB0djV9++QUAsGfPHiQmJiIxMREvvPBC4AQn\nDAdtDegF2gGCCAPMpMNdCUffRwqj2QVhv9WYmT8qJQQ6f3o8jxoPwgioqdu03aB+bN0NbC2UP8/3\nUPtK9+7d8c033+DIkSMYNGgQMjIyUL9+fb/SJAizYYM95O0jgvCGt1FrLfQ82Qr+4c0mAIxnF4S9\n8+0NMqJ9hy871QrcqTedyp4wE9T55D/eRt/bJQDtxlf9nrekTHQ+Li4Ox48fF34fP35c1OPNX1Nc\nXIyYmBhUVFSgrKwMTZo0EV3TsWNHtGnTBkeOHEGPHj18zA0RygT72wz284ngQe/eHHjzIeg9KsOT\nXeDNJgCMZxeEzbRztYTD2kg98bVB4af5hcL0VyK80Wv5BbVHnklLS8Phw4dRWlqKK1euYO3atRg8\neLDomiFDhmDVqlUAgPXr16Nnz56wWCw4fvw4rl27BgA4duwYioqK0LZt24DngSB8IRTbBnJOzIfZ\n6qHedcxs5aEGs3yfRrMLaOQ7TND74w/lxoUg/MW599v5/9EppcEUKySJiorC0qVLMXDgQFRUVCAr\nKwupqamYO3cuunXrhmHDhmH69OnIyspCUlIS6tati9WrVwMAtmzZgueeew4cx4HjOLz66quIjqaO\nQKIKmo4dHIoqbDRcRBCETxjNLiDnm9AVMlKIYKKk/gVqaYmrLDbYUWTQ78Ps3+3gwYPderXnzZsn\n/D8yMhJr1651u2/ChAmYMGGC7vIRhFGgKbEEoR6jdMIVVdiErfkIzxjJLqB+RIJwgQwOgiAIgiAI\nQg6yFQlfIec7zPClpy6QvWp22Gi9N2FqvH1jzgrbagncHt8EQRBaQG0WQRCE75DzHQb44nDrsb+p\nuq2aaH9VwjgorbtGmIZGEARhRuRGEsNphJF0SPjgrV6rqQvUIWYuaM03QRBEkPDXqAwno5QgiMBg\nxHbFnxl4ejq05CwT/uAciBVA1Va7KdLXE6GB4Ue+n3/+eVgsFpw9e1Y4dt999yExMRGpqanYv3+/\nps+jKc/q0XKU2g4bjXoTpsKIhipBhCqBtglCAXIQCcLYCAHcyOkOCwztfB8/fhz5+flo1aqVcOyD\nDz5AcXExvvnmG6xcuRJ33nmnT2mTg6cv/ip7/n4px4acHSLQ6FHnbJVdTQRBKENPm8BMqBkFVquL\n9WqTqAOAIBSSAnLCQxxDO9+zZs3Cs88+Kzq2ceNGZGVlAQC6dOmCq1evoqSkJBjimQIzKjwzykwQ\nvkDON0Eoh2wCgiDMhio97+R4ky0cuhh2zff69esRFxeHzp07i46XlJSgRYsWwu+4uDiUlJQgLi7O\nLY0XnvgTduxDMS7hh4xzQEboV2Q7rBR4gSAIn5FT+D9sPYF9W4tRjDMBloggtLEJ8p5w2AOXcAbN\nM9riuoy2usttZMzU+WcmWQnCV0oTohF7oMwUS2C/3noBm7fuC7YYpiSozndmZiZOnTrldnzBggVY\nuHAhNm3aJBxjjEn+HwA4jpNMf9YTtZCDrvgDY9AWOV6bbjNUdqNCPXTaQWVJBANvHXdtM67D5Qyb\nYATnzSOlS2iL3jbBoCcc9gA5coQUVC8IQ6DRlHO19Tk6pVTV9ckZ9VHdaVCTbALlBNX5zs/Plzx+\n+PBhHD16FMnJyQAcPdtdu3ZFYWEh4uLicPz4cfTo0UM4J9XDTQQGO2yaKyw90iQIogojd/Coj8dR\noIscROAhmyC46K13/dXtZBsQoY4dVsSSTnNDnV1g/PIz5JrvTp064fTp0zh69CiOHj2KuLg4fPXV\nV2jatCmGDBmCd999FwDw1VdfISIiArGxsUGWWB4jG7kEQRgDCgBJEPKEkk1AEAThidKEaMEmkOts\n0stmoGWrgcGQzrcrzlPIRo8ejdjYWCQmJmLy5Ml44403giiZudCrI4A6GAhCe+i7IghpyCYIfWiE\nmwgVlOpyf3U+Oc7mwbAB15z56aefRL9ffvnlIEkSegh7CwYIX6aNBVpGgtAKJfVdqgeb6jtByKO3\nTRDujh+ff7/aoTSNhKnEzHZAuNcnQjl8HTdrXSeUYYqRb0IaNR+n1LVG+rgDsdaMIAiCIMyKmv29\njYoSXSxnD5ATSxCBIfZoWbBFCGnI+fYRWqNJEKFJMA08b4Yp7chAEISe+NL+eWu3yGkmtCBc6pHy\naeo0a86shJ3zHS4V0w5rSOU1XBpdIjzhv9VA1PNQahcIgvCNQOtU0uEE4R3Sz+GB4jXf165dw7Fj\nx/D777+jXr16aNmyJSIiIvSUTXcCPXpN22QQBEEQoUAo2gSEfgTLqSBnhiAIo+Fx5PvixYtYvnw5\n+vfvjzp16qBt27bo0qUL2rRpgzp16qB///5Yvnw5/vzzz0DJS+gAdQhUQWVBEAQhTajYBDbYqa3X\nCaFcu1UdIweYINRB30xoI+t8P//884iPj8eSJUvQo0cP/Pe//8XevXvx3XffYe/evVi9ejXS0tKw\nZMkStGzZEs8//3wg5SZUQB8xQRgTVd9min5yEIQ3yCYgCIIwHtSRaD5kp53v2rULn376KVJTUyXP\np6amYuTIkVi4cCH27duHp59+Gg8++KBughL64Lp9h69T4/2ZUq/0XmpgiEBBdY0gxJBNQBAEoT9y\nnfI0kBY6yDrfH3zwgeJEunbtqup6wnyQM0IQ+mAkhUq7OBBykE0QPmiyzzdBEIqhby28UBxwjQg8\nvo9CqzOgXUe/CeNDnSFEqKK+LSrQRQ6CMCtG0OnRKaVBfX4gCHYZE0S4oO5bM75NoMj5/uuvv7Bk\nyRJs3rwZ58+fB2NMOMdxHPbs2aObgEYhFBtZpQ6cWkfPCIqf0A96v/phpLI1ihyE8SCbQIwRvxUj\ntSUE4Qmqp0S4ocj5njhxIjZv3oxRo0ahUaNGonMcx+kiGBG+0KguQRCEcSGbgCCIUEAre1PvDgTq\noAgtFDnfH330ET777DP06tVLb3kIwlRQg0joAV+vdO+I6ub9EoJwhWwCIlDQCD5BEKGGx32+eeLi\n4hAdHa23LIaHRmSlIcVImBn6rglCHWQTEARBaIcaO4RsbvOjyPl+8skn8a9//Qu//fab3vIQEtCH\nRoQ6VMflkVLK1GFABBOyCUIbO2zUJhNEmED2ROBR5HwPHjwY58+fR+PGjdGqVSskJCQIf61bt9Zb\nRr+hiiWPP2VDCpog9IW+L8KImN0mIOTx1OaoaY+sFrK7CIIgpFC05jsrKwvfffcd/vGPf5gquArt\nWascWldFEObA1y0IA50mEbqY1SYgggO1LwRBEFUocr4/++wz7NixA6mpqXrLExCcFUEwHE5ycgmC\n8AXqUCSMQKjZBOGGsyPsb8d7MNskcuoJwnfIFwkeiqadt2jRApGRkXrLYjoCUXH9fYaS+12VF32Q\nBBE8yJj0n7y8PCQlJcFms2HRokVu58vLyzF27FgkJSUhPT0dx44dE84tXLgQNpsNSUlJ2LRpUyDF\nNg1kExDBJpztFNIRBKEeI9kFipzvxYsXY/bs2SguLvb7geFOOCgMf/JISoUg3LHDitKEwESXNnsb\nVV5ejmnTpiEvLw8HDx5ETk4O9u/fL7rm5ZdfRvPmzXHo0CE89NBDuO+++wAA+/btw4cffohDhw4h\nLy8PU6dOxeXLl4ORDUNDNkFoE+x4LhT5mSA8Q/VeHUazCxQ537fccgs2bdqE+Ph41K5dG3Xr1hX+\n6tWr55cAhH+Y/QM0u/yEcaG6FZ4UFhYiMTERsbGxqFatGsaOHYvc3FzRNRs3bkRWVhYAYPjw4di1\naxcqKiqQm5uLcePGISIiArGxsUhMTMSePXuCkQ1DQzYBYSSorSfCEar3yjGaXaBozfdLL73k10OM\nCK0VMg6h/h5CPX8EEUh+2HoCP2w9KXu+pKTk/9m797go6v1/4C/wgiAkqIAK3sorN0Gz1IOGHi8B\nmZZ67KJHzTK7aHY52U9LRTOzb2VZJ01PZmU3yzRLRTFFU5MUI0XT7il4v2CgiCCf3x+4615md2cv\nszOz+3o+Hlvu7Ozse4fZmfd7Pp/5DJo3b258Hhsbi9zcXJvzBAYGolGjRjh58iSKi4vRp08fs/cW\nFRV59gv4AF/MCYCaEbp/qvZuQqvmYKc8NhG5TytFsL+OCeMoJwC0lxfIKr5Hjx7t1oeQtmlhx8HR\n1om0Q83fosPPTotDizST51n5Zi9ztG3lMScgZ7HBg0hftJST243FQU4AaC8vsNnt/O+//3ZqQc7O\nr1f+embJG3hgJrLtADpq6mCoVbGxsThy5Ijx+ZEjR8zOeBvmMVyvXF1djTNnziAyMtLqvZZny/0Z\ncwJt4P2zpXHfSP7Im/sDPefoWssLbBbfbdq0wbPPPovffvvN7gJ+/fVXTJ06FW3atHErECIiUo+v\nJK9du3ZFYWEhiouLUVlZieXLlyM9Pd1snoyMDCxbtgwA8OWXX6J79+6oVasWMjIy8Omnn6KqqgpF\nRUUoLCzETTfdpMbX0BzmBM7zld+Us/ScpBNpndR+xVbDoDO/RV8+sae1vMBmt/Nt27ZhxowZ6Nix\nI9q2bYuuXbuiRYsWCA0NRVlZGf766y/s2rULv/76K4YOHYpt27a5FQh5l5ykgK38RO7zRHdLdtmU\nr169eliwYAEGDBiA6upqjBw5Ep07d8b06dNx4403YuDAgXj00UcxcuRIJCYmIiwsDB999BEAoEuX\nLrjjjjuQlJSEwMBAvP3226hTp47K30gbmBOQM7RwKZnanw/wRATpS8wfZ1x6nxZ+a/ZoLS+wWXy3\na9cOH330EV555RV8/vnn2Lp1K1avXo2ysjKEhoaibdu2eOihh3DnnXciJibGrSCIiEiaofCWe/sf\ny2TP9LmrRbwWEmlnpKenW53VzsrKMv47KCgIy5cvl3zvlClTMGXKFEXj0yPmBPL4cuuR1vEkJZH3\n6CknALSVFzgccK1p06aYMGECJkyY4LEPVZPekkjyHn8+aPvzd/dHBxDn9VGdyTf4Wk5A2uatfM3Z\nYyDzSALU2w68faKpUXKx1z7LH8i6zzf5Lrk7Dj0XZzxIEhEREZGvcFSA6zlv93UsvolcpERRzxMF\nRETe48w+V0vJrJZi0RIeQ4lI6/y6+OZOmoj0Rsmkm/tEIlITTyp4FvfpJIXbhbr8uvgmeXgwJCIi\n8iw1jq1yPpOJOZFjWv2daDUuuobFN3mUsz96Fvakd758oOPvk4iIyHc4c1xnDqAMWcV37969MXPm\nTKvpZ8+eRZ8+fTweFOmTLxchRGrj74u0gjmB/5BKvg+gowqREGmHlsaKUHr5HOnc82QV31u2bMFr\nr72GoUOHory83Dj98uXLyM3NVSo2zdLCgYeJONcB+SZvbtc8qJIrmBOQs1wpEFzdFzI3ICVpcfs6\ngDhNxkXSHN7n22Djxo0YN24cUlNT8dVXX6FZs2ZKxkV+hN1aiOzz5EFV6wdorcdHNZgTkJZxP0Jk\nztv3BvckX/s9y77mOzY2Ft9++y3atGmDrl27Yvfu3UrGRV7maxs2kb/R60GV9Ik5ATmi1D6J+Qo5\notQ2wm2PPMGpAdeCg4PxySefYNy4cUhLS8Py5cuViotIFu4Iia5hAU7e5Is5QcdA/obkKm7dSO0Q\nfArzGSL/ILvbuUFAQACmT5+O+Ph4jBo1SomYiIj8kjPJl567kJHv8PWcwFcLIu47yIDbgj558u8W\nhwM+u6/TIlnF96ZNmxAREWE2bejQoejQoQPy8/MVCYzIHu4kSAv8aTvkwZkMmBP4Lrm/c7mt3txv\nEBGZk1V8p6WlSU5PSEhAQkKCJ+MhIiIPYgs5eRpzAiIi9fCYrm9OXfNN+uHtM81xV290QL6PrRjO\nUeJ3wb8BESnFF47lvvAdiMg3sfgm0hkmFeQJHFiK6BruV7XFEycY+TclX+TOdm3vd8Xfi/douvh+\n44030KlTJyQmJuI///mPcfqcOXMQFxeHxMREbNiwQcUIfQNb0ZTDnRnpjWF/wG2XtIY5gXvU+k2b\nfi73K0Tk75we7dxb1qxZg/Xr1yM/Px+1a9fGmTNnAAD5+fn44osvsG/fPhw/fhypqak4dOgQ6tat\nq3LE+sTCm8g1evjtcLAj8hXMCZznid+/o/d7e/+ip/0ZTzSQVujpd+MPNNvy/b///Q+TJ09G7do1\n5wcaNaoZWXPNmjW46667UKtWLcTExCA+Ph7ff/+9mqH6hTj8JHM+HmyIvEXOAdXwm+TBl/SMOYH+\naKGlnYjMMRdQn2Zbvg8ePIj169djwoQJCA4OxiuvvIIePXqguLgYffr0Mc4XGxuLoqIiyWV8MOMw\nTqEcp3AYddNqA2n+vcFp9YDE1jkibR0QpUZI/zH3PPJzs3EYp1SKivyZJ3KC7Bn5OIxyZOMA2qQ1\ndZgTaOk3qTdazTeIyDN+zT2KX3OPAQBO4bDK0eiLqsV3v379cPz4cavps2fPRnV1NUpLS1FQUIBd\nu3ZhyJAh+PPPP51a/sgZLa6OwR2HNjgg61BwAB2d+gzyPsuESI1bKTEpI19gOeiave26U1oDSpBB\naQAAIABJREFU/JJ2q/G3lp2lzP2cf6rmb8tfKZ0T3DqjC8owFLficwBgeegFnjy5rpXjrppx8KSG\nvvjC36tj4AHJ43KbtGZok9YMQM1vIj8rW7EYfC0vULX4zsnJsfna/PnzceeddwIAunbtirp16+LE\niROIjY3FkSNHjPMVFRWhefPmisfqLq0cNExp6f6/bP0mUo6c6za1si8g/+VPOQHZxv0REfkyzV7z\nnZmZiU2bNgEAfv75Z1y8eBHR0dHIyMjAp59+iqqqKhQVFaGwsBA33XSTRz6Trd7SeBAkMqfWiSL+\nFslfqZET2MPfovcYejASqcmftsHi1o3MnvPWpJ6l2Wu+H330Udx3331ISEgAACxduhSBgYHo0qUL\n7rjjDiQlJSEwMBBvv/026tSpo3K0JBcTFiIichZzAu2R22DhTs82fyp4iLSM+bvnaLb4rlOnDj74\n4APJ16ZMmYIpU6a4tXxf7dak1Pc6gI6yRzz3Nk98X1/cFogcibvapsQEl7RO6ZxAS3z1eOTs9+J+\niYh8kWa7nXsLd+7awL8DkXf5aoJPRMrT6mV6ruzXuC8kssbfhXL8vvgmfWGRTqQsHnCJtEnp36bU\n8tXaH/BYT6QNzAk8j8U3qY4HWZKDBwAiIm3h8ZtImp5zFkPPFsN30PN30SIW30TkcdxR64OtvxP/\nfuSLWCjWUOP3zX0KEVENny6+eaC9hgc+/8TfgOdxnerX2bNn0a9fPyQlJWHAgAEoKSmRnO+9995D\nfHw84uPj8f777xun9+/fHykpKWjfvj1GjhyJiooKb4VOpBpv5w/MV7SLxz/fZ3mbMV+nRl7g08W3\nI9yJ6Iuv/718/fuRdvlLsjt9+nRkZmZi7969SE9Px/Tp063mOXbsGGbNmoW8vDzk5eVh5syZOHHi\nBABg5cqV+OGHH3Do0CGUl5fjo48+8vZXINIMrR2z/GU/RqSEOBwwFt6mv21f/12pkRf4dfFNRKQk\nXz9o6c3atWsxcuRIAMCIESOwZs0aq3lycnKQnp6O0NBQhIaG4tZbb0VOTg4AoH79+gCAyspKXL58\nGU2bNvVe8ERERORRauQFmr3PN3kOCwAi36C1liY1VOZ+h8ot37n03lOnTqFRo5oz+40bN8bJkyet\n5ikuLkZsbKzxeWxsLIqKiozPBwwYgF27dqFfv3649dZbXYqDiLwjDge43yRyk5brCHdyAkCdvIDF\nt4u0vDPXamzu/Hi1+p2IyLPOFMTYnyF8KDBo6LXnM181e7lfv344fvy41dtmz57tifCwfv16VFRU\nYPjw4XjvvfcwatQojyyXSGu0nHD7GuY4RLbZzQsc5ASA9vICFt8+jgdPIpLiaN+g132HoSuYlMjI\nSJw+fRqNGzfGqVOnEBUVZTVPbGws8vLyjM+PHDmCHj16mM0TFBSEIUOGYPv27Sy+dYitoc7T+vrS\n6/6K5OHft4ZS68HX16/W8gKfv+Zb6wcMNehtndiL19EOQ2/flbzL1w84ZC4jIwPLli0DACxbtgwZ\nGRlW8/Tt2xfZ2dkoLS1FaWkpsrOz0bdvX1y4cAGnTp0CAFRVVeHrr79GYmKiV+MnIpKL+Y969Jxb\nGO7x7S/UyAvY8i2BOyxp/vaDJCLfkpWVheHDh2PJkiVo0qQJli9fDgDIz8/HwoULsXjxYjRt2hRT\np07FzTffDACYNm0aoqOjceLECdx2222orKxEeXk5+vfvj/Hjx6v5dYiIiDzGH/N8NfICFt+kaXo+\nEaLnM59Evqhhw4aS3c+6dOmCxYsXG5+PGTMGY8aMMZsnOjoau3btUjxGIr3QyvHZ3rFWrRh5/CfS\nBzXyAp/vdk5ERPIxaSQiXtJFRKQMFt/kFUzoiYhIK3hMIiKqwZNp3sXimzSLOwPSGl/fJg0FCQsT\nItID7quISG9YfJNu8aBLWuaN7VPuZ/C3QkRERI74eiODFrD4JiIiIvIDrp6I8+YJPCb/5G+0ss1r\nJQ5fx+KbZDuAOP4w4Xs7J7aKEhGRgbvHBB5TiHyXr+XAamDxTYpz5UDMH7c0JjVERNrTMZD7Zm/j\n8ZCI9IjFNxGRjsRd7YNCRL6Dv2l9YMOA7+BvjtRSW+0AiIjIeT6bOOxWOwAiIiLX+OyxWU0+lhew\n5ZsUxZ0QERERkXuYTxH5BhbfRERERKQ6duvWP54kILKPxbcf4w6SyD94KqHlPoNIOfx9ERH5Phbf\n5JC3z0TzzDeR9vB3SURawRMVRKRXLL7JaVpIwnngJSIiko/HTbLEbYLI+1h8ExGRXVo44UZERETq\nY07gHhbf5BRnfnB6OaOqlzhJP7hNEZEt7uwfuG8h8k9a++2zAHcdi2+ShT8yIt/C3zQRERGRd7H4\nJiIiIiIiIlIYi28iDWArJBGRurTWrdPf8DhI/ojbvf9h8U2awp0Q+SO52z2LAyL38DdERERqYvFN\nRERERLrgaydQ2OhA5F9qqx2AL+MOlYjISbvUDoCIiIg0w8fyArZ8E5GqeJKKiLRCD62qeoiRiIik\nsfgmRTA5ICIif9Ix0PFxz5eOjTxxSuRf+Jv3DBbfJBt/dES+hb9pIiIi38XjvPb4RfHtS2eaieTi\nDpeIiJTg6eMLj1dE5C/8ovgm36KHkyl6iJEIYNJLRP6Lx2oi8ja/K76ZaDqP64yIiIgMWLQSEbnG\n74pvInKdXk7E6CVOIiIiIr1gfuU+zRbf27dvR3JyMhISEtCpUyfs2LEDACCEwMSJExEfH4/OnTvj\nhx9+UDlS/+CNHxt/0ESkpLNnz6Jfv35ISkrCgAEDUFJSIjnfe++9h/j4eMTHx+P99983Tr98+TLG\njRuHpKQkdOzYEStWrPBW6H6POYH2sPWbiPROjbxAs8X35MmTMXfuXBQWFuLFF1/E5MmTAQBffPEF\nDh8+jP379+Odd97BmDFjVIuRxSIRkX5Mnz4dmZmZ2Lt3L9LT0zF9+nSreY4dO4ZZs2YhLy8PeXl5\nmDlzJk6ePAkAmDp1Klq3bo29e/fip59+Qp8+fbz9FfyWHnICUp4eCn4txMj8lEgeNfICzRbfzZs3\nx/nz5wEAJSUlaNmyJQBgzZo1GDlyJAAgJSUFVVVVKCoqUi1OIiLSh7Vr1xqPHyNGjMCaNWus5snJ\nyUF6ejpCQ0MRGhqKW2+9FRs2bIAQAh999BGeeuop47wRERFei93fMScgIq3TwokXV+g1bk9QIy+o\n7bnwPevFF19EamoqnnrqKVRXV+O7774DABQXF6N58+bG+WJjY1FUVITY2FirZWTPyMcpHMZhnEKb\ntKZAGs8EEhG54tfco/g195jaYQDHcmseLjh16hQaNWoEAGjcuLHxzLWp4uJis+OJ4Rhz8uRJ1KtX\nDxMmTMC2bdtw/fXXY9GiRWjSpIlLsZBzPJET5M/IBgCU4zCS0hoAaV4JnVTC1l/yd0r+Bo7m/orD\nufkAgFM4rNjnOORGTgCokxeoWnz369cPx48ft5o+e/ZszJ8/H/Pnz8cdd9yBzz77DPfddx9ycnIA\n1FzjZSogIEBy+bfO6IIDiDOe0fHf8zpERO5pk9YMbdKaGZ9nZ+Ur80EOF5sG86opy+xVe8cVVwUE\nBKC6uhp//PEH+vfvj4ULF2LevHl47LHH8Omnn7q8XDKndE7QZcatAIChKAMA/GIjDn9uBSIikqNZ\nWhvEpV0GUFPk52dlK/dhdvOCNNjLCQDt5QWqFt+GA6eUe+65Bxs3bgQADB061HgdV2xsLI4cOYKb\nb74ZAGye4XaVs2eJTIt7IiJSl73jSmRkJE6fPo3GjRvj1KlTiIqKsponNjYWeXl5xudHjhxBjx49\nEBkZiXr16uHOO+8EUHNcWrhwoee/gB/TYk5ARETm9NarRGt5gWav+W7ZsiW2bNkCANi0aRNat24N\nAMjIyMCHH34IANizZw9q1aqFmJgYj3ymJzcmvW2YROQZPBmnXRkZGVi2bBkAYNmyZcjIyLCap2/f\nvsjOzkZpaSlKS0uRnZ2Nvn37onbt2ujXrx82b94MAPjmm2/QsWNHr8bvz9TICYiItIB5hXLUyAs0\ne8334sWL8fDDD6OyshJBQUF45513AABDhgzB5s2bER8fj6CgILz77rsqR0pERHqQlZWF4cOHY8mS\nJWjSpAmWL18OAMjPz8fChQuxePFiNG3aFFOnTjW2pE6bNg3R0dEAgDfeeAMjR47EhAkTEBERgQ8+\n+EC17+JvmBMQqYuNSp7Hdao+NfICzRbfPXr0QEFBgeRrb775ppejISLSJx7cr2nYsKFk97MuXbpg\n8eLFxudjxoyRvGVVixYtjK2v5F3MCYh8D49PyvHGuvWFS2/VyAs02+3cG/ijJyIiIlIPczHyJG5P\npHV+XXwTEcml1AFd7nL1fnaZiIiIfAtPdjiPxTeZsZfg8wemLhZfRERERET6xeLbj7B4IyIiIiIi\nUgeLbx/gjaKard5ERERERESuY/FN5ASehCDi74CIiIjIFSy+iYiIiIg0ipcNEvkOFt+kCRzxmYiI\n/AWPZc7jOiOSh73TtK222gGQd/CgRUS6UKB2AERERKQZPpYXsOWbiIiIyAeocaKdJ/eJiORj8U1E\nRERERCTBl7tx8+SZ97H4JiLyUb6cMBCR+pi4ExE5h8W3H+DBkWzhtkFERERE5B0svr2ArU9ERETa\nweMyERGpgcU3ERERERERkcJYfPs4dismIiK6hq3eRESkFhbfRERERER28KQNEXkCi28/408t4TxQ\nEnkef1dERERErmHxTUREREROMT2Z708n9omI3MHim4jIB7GFmoiIbOEJEyJ1sPj2Ydyx6oOvFUnc\n7oiIiIiIrNVWOwA98rViiYhIM8RPakdAREREWuFjeQFbvomISBaeeCQiIiJyHYtvIiIiIiIiP8LL\nBNXB4puIiIjIDiapRKQGZ/c97KGmfT5ffPNWGJ7D9Udq8fdtT8nv7+/rloiIiMhbfL74JiIiIiLS\nGrZSEvkfFt8exJ0oERERkTxK5U3Mx4hIq1h8ExERERERESmMxTcREfmFs2fPol+/fkhKSsKAAQNQ\nUlIiOd97772H+Ph4xMfH4/333zeb3qFDB8THx+OZZ57xVthERESkADXyAhbfJAsHZSIivZs+fToy\nMzOxd+9epKenY/r06VbzHDt2DLNmzUJeXh7y8vIwc+ZMnDx5EidOnMCUKVOwfft27N+/H8ePH8fX\nX3+twrcgItIm5oqkN2rkBSy+iYjIL6xduxYjR44EAIwYMQJr1qyxmicnJwfp6ekIDQ1FaGgobr31\nVmzYsAG//fYbOnbsiEaNGgEA/vnPf2L16tVejZ/IX7GoIyIlqJEX1PbsVyAO8kFEpKTvAexy6Z2n\nTp0yHiQbN26MkydPWs1TXFyM2NhY4/PY2FgUFRUhPT0d+/fvx19//YWYmBisWrUKFy5ccCkOIiIi\n8gTXcwJAnbyAxTcRkUo40q+U7TLmSbb5Sr9+/XD8+HGr6bNnz3YjJqBRo0Z48803cccddyAgIAC9\nevXCwYMH3VomEREROeIoL7CdEwDaywtYfJPq5BYK7HZGRI7k5OTYfC0yMhKnT59G48aNcerUKURF\nRVnNExsbi7y8POPzI0eOoEePHgCAIUOGYMiQIQCApUuXIiAgwMPRExGRP9DbSXK9xWtKa3kBr/km\nIiK/kJGRgWXLlgEAli1bhoyMDKt5+vbti+zsbJSWlqK0tBTZ2dno27cvAOD06dMAgNLSUvz3v//F\n6NGjvRY7ec4BxOk6kSQiIs9QIy9gyzcR+SX2pPA/WVlZGD58OJYsWYImTZpg+fLlAID8/HwsXLgQ\nixcvRtOmTTF16lTcfPPNAIBp06YhOjoaADBhwgQUFhaiVq1amDJlCpKSklT7LkREROQeNfICFt9E\nPoitOkTWGjZsKNn9rEuXLli8eLHx+ZgxYzBmzBir+T7++GNF4yMiIvIGNkDUUCMvYLdzsotFHBER\nkW/iMZ6IyLtYfBMREREREREpjMW3Bv2ae1TtEFx2NPdXtUNwmZ7XO6Dv+PW83QD6jl/P2w2RP+D+\nRT16jl/P2w1wLX49do/W83aj59j1QvXi+7PPPkN8fDxq1aqFPXv2mL02Z84cxMXFITExERs2bDBO\nz87ORmJiIuLi4jB37lxvh6y4X3OPqR2Cy47peGev5/UO6Dt+PW83gH7jj8MBXW835HuYE1jT6/4F\n0PdxCdB3/HrebgB9x6/n7UbPseuF6gOuJSYmYuXKlXjwwQfNpufn5+OLL77Avn37cPz4caSmpuLn\nn39GdXU1HnroIWzbtg3R0dHo3r07+vfvj5SUFJW+AVnS01lKPcVKROTrmBMQEZEvU7347tChg+T0\nNWvW4K677kKtWrUQExOD+Ph45OXlobq6GvHx8YiJiQEADB8+HGvWrOGBloiISOeYExARkS9Tvfi2\npbi4GH369DE+j42NRVFREYQQaN68udn03NxcyWVMClgk+/O2ORXdNjvPPCM7K1+BpbrC+W+Xn6VA\nGF6infXuGj3Hn5+VrXYIbtFz/Nrabh5wau6IiAiF4iAt8UROsChgktJhAlAmJ9DS/qUms5L/LbW1\nf3GenuPX0nbjCj3Hr+ftRnuxy88L9JATeKX47tevH44fP241/YUXXsDAgQMV+UwhhCLLJSIiZXC/\n7R+YExARkRy+uO/2SvEtdfNyR2JjY3HkyBHj86KiIjRv3hzV1dVm048cOWJ21puIiIi0izkBERH5\nK9VHOzdlenYjIyMDn376KaqqqlBUVITCwkLcdNNN6Nq1KwoLC1FcXIzKykosX74c6enpKkZNRERE\nnsacgIiIfI3q13yvXLkSEydOxOnTp5GZmYmUlBSsW7cOXbp0wR133IGkpCQEBgbi7bffRp06dVCn\nTh0sWLAAAwYMQHV1NUaOHInOnTur/TWIiIjITcwJiIjIpwlSXcuWLUViYqJITk4WXbt2FUIIcebM\nGdG3b1+RmJgo+vfvL86dO6dylDXGjBkjoqKiREJCgnGavVgnTJgg4uLiREpKitizZ48aIRtJxT59\n+nQRExMjkpOTRXJysli7dq3xtRdeeEF07NhRJCQkiPXr16sRspnDhw+Lnj17ioSEBNGuXTsxd+5c\nIYQ+1r+t2PWw/svLy8WNN94okpOTRdu2bcWkSZOEEEL8/vvvolu3biIhIUEMHz5cXL58WQghxKVL\nl8S//vUvkZCQIHr06CH+/PNP1WIXwnb8o0aNEq1btzau+4KCAiGEENXV1ZrZboj8EXMC79FzXsCc\nQD16zguYE6iPxbcGtGrVSpw5c8Zs2qOPPirmzZsnhBBi3rx5YuLEiWqEZmXr1q1iz549ZgcqW7F+\n/vnnYtCgQUIIIfbs2SM6derk/YBNSMU+Y8YM8corr1jNu3v3bnHjjTeKqqoqUVRUJFq1aiUqKiq8\nGa6V48ePi3379gkhhCgtLRVt27YVBQUFulj/tmLXy/q/ePGiEEKIyspKcfPNN4tNmzaJ2267Taxc\nuVIIIcRjjz0mXn31VSGEEC+//LJ47LHHhBBCrFy5Utx+++3qBG1CKv7Ro0eLFStWWM2rpe2GyB8x\nJ/AePecFzAnUzcn0nBcwJ1CXpq759mfCYjS/tWvXYuTIkQCAESNGYM2aNWqEZaVnz55Ww/jbinXN\nmjXG6SkpKcZr9dQiFTsgPZKi1D1lv//+e2+EaVN0dDQSEhIAAKGhoUhKSkJxcbEu1r+t2AF9rP/g\n4GAAwOXLl3HlyhVERUVh586dGDx4MADz9W7697j99tuxY8cO1UfrlIofkF73pvGrvd0Q+SvmBN6h\n57yAOYG6OZme8wLmBOpi8a0BAQEB6NevH5KSkvDmm28CAE6dOoVGjRoBABo3boyTJ0+qGaJdtmIt\nLi62uv+qFn+w//3vf9GxY0eMGDECZ8+eBVATe2xsrHEercX+559/YteuXUhNTdXd+jfE3rNnTwD6\nWP/V1dVITk5GdHQ0evfujYiICDRu3Nj4ekxMjDE+wyjMABAYGIhGjRqp/vu1jD8+Ph4AMHXqVHTs\n2BGPPvooKioqAJjHD6i/7on8DXMC9enhuGSKOYH36TkvYE6gLhbfGrBz507s2bMH33zzDd59911s\n3LhR7ZA8xvIsWkBAgEqRSHvkkUfw22+/4cCBA7jhhhswceJEtUNyqKysDEOHDsXrr7+O6667zu68\nWlv/ZWVlGDZsGF5//XWEhYXpZv0HBgaioKAARUVF2Lp1K3Jzc9UOySlS8c+dOxcHDx7Ejz/+iPLy\ncsyaNcs4v9a2GyJ/wpxAXXo5LhkwJ1CHnvMC5gTqYvGtAYbuHpGRkRg6dCh27dqFyMhInD59GkDN\nWWTDPFpkK1ap+7KanrnUgsaNGyMgIAABAQF48MEHsWvXLgC27ymrtsrKSgwZMgT33nuvsWuTXta/\nIfZ77rnHGLve1n+DBg2QmZmJ33//3bjOAfN1Gxsbi8OHDwOoObt85swZREZGqhKvJUP8O3fuNG4n\ndevWxdixY+2ue639bol8GXMCdenpuMScQH16zguYE6iDxbfKLl68iIsXLwIALly4gOzsbMTHxyMj\nIwPLli0DACxbtgwZGRlqhmmXrVgzMjLw4YcfAgD27NljvFZHS0y7/axYscLY9cbWPWXVJITA2LFj\nERcXh8cff9w4XQ/r31bselj/Z86cQWlpKQCgvLwcOTk5SE5ORrdu3bBq1SoA1uvd8Pf48ssv0b17\ndwQGqrerlYo/MTHRuO6FEPjiiy/M1r1Wthsif8OcQH16OC4BzAnUXPd6zguYE2iAN0d3I2u///67\nSEpKEp06dRJt27YVzz33nBDC/FYR/fr108xtRe666y7RtGlTUadOHREbGyuWLFliN9ZHHnnEeHuC\n/Px8FSO3jv2dd94RI0aMEElJSaJDhw5iwIABoqioyDj/7NmzRceOHUV8fLzIzs5WMfIa3377rQgI\nCBCdOnUy3gpi3bp1ulj/UrGvXbtWF+t/7969Ijk5WXTq1Em0b99eZGVlCSHs31Jk2LBhIiEhQXTv\n3l388ccfqsUuhO34e/fuLTp16iTatWsnhg8fLs6fP298j1a2GyJ/w5zAu/ScFzAnUI+e8wLmBOoL\nEELlYXiJiIiIiIiIfBy7nRMREREREREpjMU3ERERERERkcJYfBMREREREREpjMU3ERERERERkcJY\nfBMREREREREpjMU3ERERERERkcJYfBMREREREREpjMU3ERERERERkcJYfBMREREREREpjMU3ERER\nERERkcJYfBMREREREREpjMU3ERERERERkcJYfBMREREREREpjMU3ERERERERkcJYfBMREREREREp\njMU3ERERERERkcJYfBMREREREREpjMU3ERERERERkcJYfBMREREREREpjMU3ERERERERkcJYfBMR\nEREREREpjMW3zj344IMYOnSoU+/57rvv8K9//QsxMTEICgpC48aN0b9/f3zwwQeorq4GACxYsABx\ncXGoqqqStcw333wTLVu2RO3atREREeH099C6VatWYd68eWqHYWXGjBkIDFT2Z+yNz3CXHmIkIlKC\nK3mAq5gbXKPVvABQ/pioh2OuHmIk/1Rb7QD82a5du7Bs2TK0bt0aQUFB+PPPP3H99dfjgQcekLXD\n2L9/P5YsWYIff/xR9me+9tprePLJJ/HPf/4TL730Elq2bIlz585h/fr1GD9+PBo0aIDbb78dDzzw\nAF588UUsWrQIDz/8sN1l/vXXX5g4cSJGjRqFBx54APXq1ZMdj16sWrUK33zzDR5//HG1QzHzwAMP\nICMjQ/HPCQgIUPwz3KWHGImI7NmzZw8WLVqEhg0bAgAiIyPRqFEjNGvWDH379rWa35U8wB3MDa7R\nal4AeCc30MMxVw8xkv9h8a2St99+G0uXLsX69etx3XXXGafPmTMH6enpWL16NYKCguwu46WXXkJa\nWhri4uKM0zZs2IDVq1ejfv36CA4OxowZM4yvbd26FU888QQmTpyI1157zWxZAwcOxFNPPYXS0lIA\nQO3atTFu3Di89NJLGD9+vN2TAb/99hsA4N///jd69Oghex3YU1FR4fD7ExATE4OYmBjFP0cIofhn\nuEsPMRIR2fLNN99g3rx5WLFihfH4t3HjRtx+++04c+aM5Huk8gBPuXLlCtq1a4ctW7YgNjYWgLq5\nAfMC+byRG+jhmKuHGMkPCfK67777TtStW1ccOnRI8vV//OMfYsKECXaX8ffff4vg4GCxePFi47SK\nigpx5513Gp+///77YtWqVcbnGRkZIjIyUlRUVMiK8+effxYBAQFi/fr1NucZNWqUCAgIMHuMGTPG\n+Pq6detEt27dRHBwsGjQoIEYPHiw1feePn26CAgIEIWFhaJ///4iNDRUDBo0yOZnFhQUiIEDB4qI\niAgRHBws/vGPf4hvv/3W+HpZWZlo3769uOmmm0RlZaVx+vr160VAQIB46623rD573759Ii0tTYSE\nhIimTZuKadOmierqapvfsXXr1rLWobv2798vbr31VtGgQQNRu3Zt0axZMzFo0CBRVVVlFr/l9/nl\nl19ERkaGqF+/voiMjBRPP/208fsYfPTRR6J9+/aiXr16IjExUXz55ZfilltuEWlpaWbzWX6GEI7/\nBnIVFBSIwYMHi0aNGok6deqINm3aiKysLLN5nNmGDEaNGiVatWpl9XmW38/wvp9++kn07dtXhISE\niJYtW4olS5YIIYRYtGiRaNu2rahXr57o1q2bOHDggOTnylnftmzevNlq+zI8Ro8eLWsZRKRv1dXV\nomXLliI/P9/qtQEDBki+RyoP8KS8vDwRGRlpNd3d3ECJvEAIz+UGes8LTL+D5XNP5QZSeYEQ3ssN\nXMkLhPBObuCJvEAI5ga+jBdDqGDWrFno3r072rVrJ/n6mDFjsHDhQhw7dszmMjZt2oRLly4hLS3N\nOO3s2bO44YYbjM9vuukm/PTTTwBqzmBv3rwZ/fv3R926dWXF2bZtWzRr1gxr1qyxOc8Op0KxAAAg\nAElEQVS0adMwf/58AMBbb72FnTt34rnnngMAZGdnIzMzE1FRUVi9ejWWLFmCX375BampqTh69KjV\nsu68805kZGQgOzsbTz31lOTn7dmzBz169EB5eTnef/99fPXVV2jatCn69u2LPXv2AADq16+PTz75\nBD/++KMxlhMnTuDf//43Bg0ahIceeshquUOGDMHgwYOxfv163H///Xj++efx//7f/zN+x4yMDERG\nRmLnzp3YuXMnVq5caXfdVVVVOXxcuXLF7jKuXLmC9PR0lJaW4v3338fWrVvx+uuvIyoqynhtPiDd\nrWro0KG47bbbsH79eowZMwb/93//h7feesv4+urVq3Hvvfeic+fOWLt2LZ599lk888wz+OWXXxx2\n05LzN5Dj+++/R/fu3XH8+HG8/fbb2Lx5M6ZMmYLz588b53F2GzIl9T0CAgIkp999990YPnw41q1b\nh27dumHs2LGYNGkSPv74Y7zxxhv45JNPcPToUQwfPlzysxytb3u6dOli3K4Mj9mzZwOAIq1ZRKQ9\nJ06cwOHDh1FSUmL12l133SX5Hqk8wJM2b96MPn36WE13JzdQIi8AlMkNPJ0XAO7nBnLzAsB3cwN3\n8gJb60WJ3MCdvABgbuDT1K7+/U1FRYUICgoSEydOFHPmzBHl5eVmj1mzZon8/HwREBAgli5danM5\n06ZNE3Xr1rWaPmzYMHHhwgUhhBAzZswQu3fvFkIIcfz4cREQECCmTJniVLz9+/cXqampdufJyckR\nAQEBYsuWLWbTu3TpIpKSkszO9BUVFYl69eqJJ554wjjNcJZw0aJFDuPp06ePSE5ONjvDW11dLZKS\nksTgwYPN5p03b56oVauW2LhxoxgwYICIjY0VZ86cMZvH8Nmvv/662fQJEyaIoKAgce7cOSFEzdnS\n2NhYh/EJYf9spemjd+/edpdTXFwsAgICxJo1a2zOY+vs9scff2w2X+fOncUtt9xi9rxr165m8+zf\nv18yLsvPcOZvYE/Pnj1Fu3btxKVLl2zO4+w2ZGDv7Lbp9zO879NPPzVOKy0tFbVr1xbR0dHi4sWL\nxukLFy40ns22fL+j9e2MQ4cOiYiICDFs2DCX3k9E+lNdXS1atWolIiIixCOPPCI+++wz4/HHFlt5\nQFVVlZg1a5a49957xfr168Vrr70m3njjDXHPPfeIP/74w+HrK1asEJMmTRLR0dEiMzNTTJo0yarX\nj6u5gRJ5gRCezQ2UyAuE8ExuICcvMP0Ols89lRtItSp7KzdwNS8Qwju5gRJ5gRDMDXwJr/n2stOn\nT+Py5csIDw/HX3/9hZCQELPX69Wrh7vvvhsA7LZ8Hzt2DI0aNbKa/vLLL+P5559HrVq1cNNNN6FL\nly5uxdu4cWPk5eU5/b4LFy7ghx9+wMyZM83OJsbExKBXr17YsmWL1XvuuOMOu8ssLy/H1q1bkZWV\nBSGE2Wirffv2xdKlS83mnzRpEnJycpCZmYmqqirk5OQYB7GxZDlS7LBhw/Dmm28iLy8PAwYMcPR1\nzdx4443YvXu3w/nCwsLsvt6kSRM0b94ckydPxokTJ5Camoq2bdvKiiEzM9PseVxcHHbu3AkAKCsr\nQ0FBAZ5//nmredq3b293uc7+DWy5ePEiduzYgWnTptm8hs+VbchV6enpxn+HhoaiadOm6Nq1K4KD\ng43T27RpAwA4evSo8d8G9ta3M86dO4eBAweiXbt2+OCDD5x+PxHpU0BAANatW4dnnnkG7733Ht56\n6y3Ur18fq1atwj//+U/J99jKA9auXYs777wTK1euxNSpU7F161YEBwejVq1aePrppzFy5Ei7ry9f\nvhyZmZlYvHgxXn31Vcleeq7kBkrkBYByuYEn8wLAM7mBO3kBoP/cwJt5AeBebuCpvABgbuBrWHx7\nWUREBGrVqoULFy5gwoQJGDNmjNnr5eXl+PvvvwHUHNzsERIDSbRo0QIvvPCC1fRGjRohODgYf/31\nl9MxuzJa5Llz5yCEQFRUlNVrkZGR+OWXX6ym2yqMDc6ePYsrV67g2WefxbPPPisrzhEjRmDdunVI\nSUlB7969bS47MjJS8vnZs2ftxiQlNDQUSUlJDudztF4DAwOxceNGzJgxA//5z39w9uxZNG/eHE8+\n+SQmTpxo973169c3e16rVi3jwbCkpARCCMntSyqRM+XK30DKuXPnUF1djSZNmtidx9ltyFVS60tq\nGgDJW+zYW99yVVZWYujQobh8+bKsAReJyLd06NABq1atwpUrV7B9+3Y88MADeO6552wW34B0HhAe\nHo64uDg8+eSTGDt2rLFQuHDhAvbv34+IiAi7rwPAjh07cN1119m8PA5wPjdQIi8AlMsNPJkXAJ7J\nDdzJCwD95wbezAsA93IDT+QFAHMDX8Ti28uCg4PRq1cv/PDDDzav2Vi6dCkCAwPRv39/m8tp2rSp\nUweA2rVrIy0tDRs2bMDly5dlX/d96tQpNG3aVPbnGERERCAgIAAnT560eu3kyZMOd+RSwsPDERgY\niCeffNLmtbemjh8/jsceewxdunTBnj17MH/+fJsHp5MnT5qNDHrq1CkA8g78lnJzcyWvk7OUlpaG\nTZs22Z2nbdu2+PDDDwEAhYWFeOONNzBp0iS0aNECgwcPdjo2oGY9BgQE4PTp01avnT59Gs2aNbP7\nXmf+BrZEREQgMDDQbu8Od7ah2rVrS143V1ZWZnZ3AS155JFHkJ+fj+3bt0smFkTkm77//nuEh4cb\nC91atWqhV69eePzxx7FgwQKb77OVB/Ts2ROVlZXYvn272Z1Nvv/+e9xwww1ITU21+zoA5OTk2L2W\n3JXcQIm8AFAuN/BkXgB4LjdQIi8A9JEbuLsNMTcgLeCAayqYNm0atm3bht9//13y9XfffRejR49G\nixYtbC4jJSUFlZWVOHTokOzPfeaZZ3DmzBk8/fTTkq//8ccf2Ldvn9m0/fv3IyUlRfZnGNSvXx9d\nunTBZ599ZnZm/ujRo/j2229dGiCmfv366NmzJ3788Ud07txZ8mEghMCoUaMQHByMjRs3YtKkSZg8\nebLV9zP4/PPPzZ4vX74c9erVw8033wwACAoKwuXLl2XFaeha5ujx9ttvO/X9ExIS8OqrrwKAsXXC\nFaGhoUhJScEXX3xhNn3//v34+eef7b7Xmb+BPSEhIUhNTcVHH32EiooKm5/l6jYUExODY8eOmQ3e\nVlRUZPPvrwRnWoXmzZuHd999F5988gni4+MVjIqItObrr7+WnP7HH39g4MCBNt9nLw/YtWsXwsLC\njN2FL126hJycHIwdO1bW6xs3bjTuY7/99ltcunTJbPmu5AZK5AWG5SqRG3gyLwCUyQ08lRcA+sgN\n3N2G1M4NnO0twtzAN7HlWwW33HIL5s6di3//+99Yt26d2fU9c+fORUBAAN544w27y+jTpw+CgoKQ\nm5vr8Focg549e+LVV1/FE088gQMHDmD06NFo3rw5zp07h2+++QbvvPMOPv74YyQmJgIAfvnlFxw7\ndszsmhdnzJo1C5mZmRgyZAgeeughlJWVYdq0abjuuuvw5JNPurTMV199Fb169cLtt9+OMWPGICoq\nCufOncPu3btRVVVlvFbplVdewTfffIPNmzejQYMGePHFF5Gbm4u7774bu3fvRr169cyWu2DBAggh\n0LlzZ2zcuBFvvfUWnn76aYSHhwMA4uPjsXjxYrz77rtITExEUFCQcT1ZCg0NlX2gsWfv3r145pln\nMGTIELRt2xbl5eX44IMPEBgYKOvsuT3Tp0/HoEGDcM899+D+++/H6dOnMWPGDDRp0sTufVsB+X8D\nR15++WXccsst6N27N5588klER0fjt99+w969e/HKK68AcH0bGjp0KGbPno0RI0bg8ccfx9mzZzFn\nzhw0btxY1n0/5czjqWXs2LEDTz31FEaNGoXw8HCza8KioqJw/fXXux0LEWnXxo0bceXKFeNIxgCw\ne/dufPvtt9iwYYPN99nLAzZt2mTWMv3KK68gPT0dgwYNkvV6YWEhevTogYqKCmzbtg09e/Y0zutO\nbqBEXgAokxt4Mi8APJMbKJkXAPrIDdzZhtTODZx5P3MDH+atkd3I2pYtW8S4cePESy+9JObNmyce\ne+wx8dprr4krV67Iev+9997rcMRsKTt27BDDhg0TTZs2FXXq1BENGzYUAwYMEB9++KHZ6JGzZ88W\nLVq0cBhPTk6OCAwMtBrtXAghsrOzRffu3c3uxfjzzz+bzTNjxgwRGBgo+3v/9NNP4q677hJRUVGi\ndu3aIioqSmRkZIivv/5aCCFEfn6+CAoKEs8995zZ+w4dOiTq168vHn74YeM0w6iU+/fvF7179xbB\nwcHG+3maunDhgrj77rtFRESE1+7neeLECXHvvfeKG264QdStW1eEhISIHj16iNWrVxvnMaw7y+eW\n63L06NFWMRvu5RkUFCQSEhLEqlWrxM0332x2r3ipzxDC8d9Arh9++EEMHDhQhIeHizp16oh27dqJ\n2bNnm83jzDZk6sMPPxQ33HCDCAoKEsnJySInJ0ekpaWZ/WZsra9WrVqJkSNHmk3bvHmzCAwMFN98\n843D90utb1uWLl1qc9Rbw31xicg3XbhwQYwbN06sX79ejB8/Xjz11FNi4sSJ4vHHHxfnz593+H5b\neUDv3r3F+PHjxZw5c8S0adPElClTzPZTjl5/+umnxXPPPSdefPFF8ffff5st293cQIm8QAjP5QZ6\nzwuEUD43kDrmCuG93MDVvEAI5XOD6dOnu50XCMHcwJcFCOGBJh5SRWFhIVJSUlBQUODx7iiVlZVo\n06YN/vOf/+DRRx/16LK1ZMaMGZg5cyaqqqocntX1dSdPnkSrVq0wdepUTJ06Ve1wiIjIAak8oKKi\nAhEREdi3b5/xGm5Tjl63xx9yA+YF5pgbEHmWbvcq2dnZSExMRFxcHObOnat2OKpISEjAfffdh6ys\nLI8v+5133kFoaCjGjx/v8WWT+i5duoSJEyfiyy+/xPbt2/HRRx+hX79+CAkJwf333692eERETvPH\nvEAqD/juu+/QsGFDm4W1o9ftYW7g25gbEClPly3fFRUV6NChA7Zt24bo6Gh0794dixYtcmlgMPJv\nWVlZmDlzJiorK/3qDHdlZSWGDBmCvLw8nDlzBtdddx169eqFF154weYo/M5wdDuN2rX9Z7gJrgsi\n5TEvqLFu3To899xzKC4uxsMPP4znnnvOqdfJf/MCgLmBN3Fd+C9dFt9bt27FSy+9ZBwh9OWXX8al\nS5ck7y1IRN71559/OhwIJDc3F7169fJSROrhuiDyDuYFRNrG4+E1XBf+TZenVYqKitC8eXPj89jY\nWOTm5prN4+xw/kTkPbfccovaIWiGnteFp8/dNmzYEOfOnXPqPREREZL3Oib/4igvYE5ApH16Ph56\nmh7XhRLtuc7mBXrICXTZn0buQVQIocvH9OnTVY+Bsevvoef49Ry73uN3NXYlnDt3DqsBpx7OFuvk\nm+TkBWr/1rh/0d9Dz/HrOXa9x++PsSvl3LlzeB6Q/dBDTqDL4js2NhZHjhwxPj9y5IjZGW8iIiLy\nH8wLiIhID3TZ7bxr164oLCxEcXExoqKisHz5crz99ttW8wU87MaHLHXjve4o/wrAz8jK+kqlANxl\nI/bggebPy2V+P8v3OWO0jekjav6X3GMnAGAgvkIG1qIIxzENlU5/TAbWGv/d7WCB0++38pmL79sK\n4KCTI9/L+ayPXQnGSacALJcR+91ufs4w2y/t7JDs8O1rkSE5fQu+RbXEtvMV5G+/BTu6OZ5pmezF\nybcLyDppMW2pAp9DpCA5ecEArFYpOmmlCJM13xH8iQ3Y7PbnlSPY7HPDUIpglLu1LAB2l2EaexhK\nzf5viKUUYVf/W2o1zZTlc4MCJCMKJ82WEYKLVp9jazmmz4NRbhbnPuzGYvxuXKaliwiR/DuGodRs\nftP5TiIKPyDFap5f0Qbf5fUG9kt8yfoAIgCcA1AM4DsAJVf/XQLgGACrY9AFZGWdABANNAUQfnU5\n4RLLB659bszVeQzzhZk8DzVZTgSABmZfukZ4BULDa9ZhWYnJuikJAsIrjE/N5ikJAopQ8zD4Acha\nYnjv1deOXI2z5OrD9LscqwSwx2RCQwDXXf13NBB8dX7T72f63QyfE3v1eazJ9ztvEVt9k39fuPq+\nQgClAJoD+A7IOnz1uSnTTcV0nZpOM/0Mwzo2fV+pIe6a9RwSYr5d2vqdXHu79W/L9L3HUYQvsVPy\nfabLd3XfQTotvuvVq4cFCxZgwIABqK6uxsiRI9G5c2e1w/KgBLUDcIN+Y++cForjagfhhrRWakfg\nurQQtSNwT8u0FmqH4LpmaWpHQOQ2PeYFYSiVVYBfl9bJC9EoQ8+xA0DHtEivfVYYSiFjc3BCD08u\nzPvapzn5hjpKRHFNfdQU2nLEpikYiLJC07S93/QFuiy+ASA9PR3p6elqh6GQRLUDcIM+Y1+LDCBN\n7Sjck9Za7Qhcl1bf8Txa1iqtpdohuC4mTe0IiDzCV/OCBmmOe+XIEYxysxZrb/BU7GppmdbSbkuh\nx4VXAPWDHBd5YahpbbVL58V3hzS1I3Bd8zQZfx9t0mLx3VDtADxMl9d8E5EXudvVWwfkdDknIiLt\n8XRhbOj27mly4jR0xbbLVrdxdym1XCIyw+KbiIiIiMiEuwX4RTh/PVVIyMWa63yJyGex+Ca/58yg\nWEREROQf7BXgrhTXjnite7vWhFeYD95mj1YvU2PPAZJJt9d8ExERERF5w0WESI547nENYD6ytpRw\n1Iz8bRAMcPBpoObq4LNqB0Eexmu+iXwQW7+JiIjIQInrvh1RteVbCy3KWoiBSGEsvslvybqvMhER\nEfkFb48GL0lOza9mF+cSyL/lFhFZYfFNdJUnWr/tjZrNEbW1q9vBArVDICIiDfBkAX4RIbKvDQ9D\nKYINfcf10gJ8XnpyWUkYykq833PAI8rUDoB8Ha/5JiIiIiJdClbgYudyBCuyXFnCKwAEyZgPQLHp\nhDoAKhUJSdI5uDUye1lJmLxbq5Hfu07tADyMLd/k17zd9Zyt30RERNpXjmCr676VGOHcVBhK5RWk\noVf/Xx81RXg44HaDvd5G69ZbvERXsfgmMqF013PdulvtAIiIiDxPzsBqzg6+5s5gbSEhDkZUN3RJ\nD0NNAWpagKPO1YfCSuy9JqPVXkoYeI9z8gssvsnvceA1IiIiffKle2OHobTm+0TA/nXf4RYPQwFu\nbP12sQB31Jpsej205aBrvvNnII1p6MRDD1h8E1ngbceIiIh8hzsFuqEV2/B/ZwZRc0UYSmvu9S1v\nZvMC3IxC5YhpAX7Oxjyutn4D+hlsjshFLL6JYN367UwBvhYZng6HiIiINMLT9/yWOhkQgpru5sEo\nd3y7MbOu5jBvBXfn2m+1C1+5Jx2IdIzFN9FVnux+zuu+iYiI9E2J+347GkW9GY5eHfFcguk10aGo\nKbbDYN4CbizA6xg+0DNMr/M2bf22cbsxIq24dOkSunbtipSUFLRr1w6PP/44AEAIgalTpyIpKQnx\n8fGYP3++5PsnTZqE9u3bIzExEcuXL7d6feLEiQgLk3+CjrcaI9+1FMBoienLAIxw/PavMBAD8ZVH\nQ9K9uwF8rHYQRLYdOXIE9957L86dO4fLly9j7NixePrpp7F9+3Y88sgjqKqqQq1atbBgwQL06NFD\n7XCJSANKESara7rc+aTmNW09D8FFya7rhumh4aUoM9xuzLI1OgI13b1NW70NYkz+XQ44fe13OOwP\npmbKMJ+btxuzYrm8C6iJqwzAEdc/i/TL3Ysn6tWrh61btyI4OBhVVVVITU3F5s2bceDAAZw9exZ7\n9+4FAJw5c8bqvStWrMCBAwfw008/4dKlS+jZsyd69+6NyMhIAMDu3btRUlKCgIAA2fGw5ZvIDs1c\n/z3s6oOI7Kpbty7eeust7Nu3D/n5+fjf//6HH3/8EZMnT8bcuXNRWFiIF198EZMnT1Y7VCLSEctr\nv+W+x/BwhsMRz00LVKnB14CaFm+5rd6mXdjdUWrycFZ4RU0LvmXXc6e6wvvaHaHJU4KDa34Mly9f\nxpUrVxAVFYX//e9/mDJlinGeRo0aWb3v0KFD6NWrFwIDAxESEoKUlBTk5OQAAK5cuYKnn34aL730\nEoQQsmNh8U1kwtWu51LXfdvqeu50l/RhFv/2ZCHOgp58THR0NBISEgAAoaGhSEpKQnFxMZo3b47z\n52v6R5aUlKBly5ZqhklEGmOrQC5HsLH7uaeu/XbUem4c8dwew3XfoTDvgm5aiHuaVKu4EoOuEcmU\nB+ANk4ct1dXVSE5ORnR0NHr37o34+Hj8/vvvWLRoEZKSktCnTx8cPHjQ6n2JiYnIzs5GeXk5Tp8+\njc2bN+PIkZouGG+++SYGDRqEJk2aOBUzu52Tf5LZ9RzwTPdzy4Lb8LzbwQLXFzoMwGcWz4l83D4A\nhTLn/fPPP7Fr1y68++67iI+PR2pqKp566ilUV1fju+++UzJMItIhOd3Knel67ooQXLw24nmRnRml\nivNWqGl1Lsa14ttRN/Jwi39LzV8C6WK+BDUnAc7D8WBptq5jl2L63WwV9+Q37PVn6Hf1YWCrAA8M\nDERBQQHOnz+PAQMGIDc3F5cuXULDhg2xd+9erFy5EqNGjUJeXp7Z+wYOHIjdu3eja9euaNiwIW6+\n+WYEBATg6NGj+Pzzz5Gbm+tUqzfAlm8iKwU7urnUAr4WGU6PfG63FVyrXc058BqpKBE1m6DhYUtZ\nWRmGDRuG119/HWFhYRg7dizmz5+Pw4cPY968ebjvvvu8EzARaYIz12pLdRX35OBrYSg1jm4uJQon\na/5h6HJtKEYbmDwM0w3zGFrBw1Bz7bfpiOhKtIKXOZ7FipzWcMvOBYbvGCrxGpGTGjRogMzMTOzc\nuRPNmzfHnXfeCQAYPHgwCgulT+1nZWWhsLAQW7duRVBQEDp06ICCggL8+uuvaNOmDa6//npcvHgR\n7dq1kxUDW77JO8q/AoI1cv20gROt385YiwxkYC0AF0c9d6bgVqs4N1Q93hx8jUW/XxjY0ck3/GQ9\nqbKyEkOGDME999yDwYMHAwB27tyJjRs3AgCGDh2KMWPGuBkpEWmBkq3QzrZye6pVPAyl1oWmZcty\nA9S0OFu2gLfCtWuuLYtuR89tTbPnwtUYHLV+O9MN3fDdpIRf/UzJFv2GAP4GcFb+Z5HPO3PmDOrW\nrYuwsDCUl5cjJycHkydPRmZmJjZt2oQxY8Zgy5YtaNu2rdV7q6ur8ffffyM8PBwHDx5EXl4eFi5c\niKCgIBw7dsw4X1hYGH7++WdZ8bDlm/zbMtsvuXPrMWdawHV9WzI5TZBEXiSEwNixYxEXF2e8nQgA\ntGzZElu2bAEAbNq0Ca1bt1YrRCJSkTvXbXvy2m+HRXp4RU1Ra1lcG24tBpi3gAPXWoibX32Ew3wE\ndNvBmC/XdLqrSiFdcNsrwk27pksV8kq04PsYT9+T3hccPXoUvXr1QnJyMlJSUtC3b19kZmZi5syZ\nWLFiBRISEvDkk09iyZIlAGpGMH/ggQcA1AzQ9o9//APJycm4//778cknnyAoyHobdma0c7Z8E9lR\nsKMbknvsVDsMZVleO+5ppoX5x3amEXnA9u3bsWzZMiQlJSElJQUA8MILL2Dx4sV4+OGHUVlZiaCg\nILzzzjsqR0pEemDZml2OYLuFs6H4sTdPMMrtFkkhuIioZidwsrBFzQRDIWr6Fqm3G25BZtklvAQ1\nrcUGShWxhtZqR9d/y2W6nAuouQbecF26Zct3MHDtFuru3pyKtKShM9uTRI+JxMRE/PDDD1bTGzRo\ngK+//tpq+o033ogbb7wRQM1tyvbv3+/wY//++2/ZIbL4Jt+2FNL3+jblhe7nfsuyRVyqhZz3DicP\nSk1NRXV1teRrBQVuDHBIRKpSczA0V5brqAUyDKWShbrptJP2ul9fW1BNK7NpgRKLayOg/wn7xbZl\nmFKDrjkq1s/BvIXetAu6rdZvewOwGb6TJYf3Ia+DmuG55BdCRN7GbudEgN3u54Dr9/t2dgA2zRgG\n529rJqfQtvdedl8nIiKNUqo7r2VRb3gehZM1RWgsrnUJD6+wfgDm3dANA5QZBmCzHHDN9N9hFtPD\nJeaBxTwGoRavOTsquSu3IjN8pt1B5CxbvdkKTtrClm8iAxst4O52PddFC7ijAtv0dXtd1D1RPFu2\nhLMgJyIiO0xbkb3BowOr2XkttNVplJWE2W8pDq+oed3e1zfcKsxe4S013fI1A8vCWwmmrd8NcK24\nD8e126kZnpveCq3c0PpNpE1s+SYyZacF3ND67UoruKMW8J0dkvU98JqnsSWciEgRSo4O7ussC3wl\nC/4wlCIk5CJCw0uNhXdoeKnVA4B5Kzhg3QJuWnhbDqwmZwR0e9NtcdRdHpBu/Tb9HqZxGrq1h1+d\nXt/kNUet4J67SxypoE4D+Q89YMs3+b6lcHzdtyk714C72v0ckNcCbijAux0ssJomxXQ+r1F6gDYi\nIiKNMhTcShfeFxGCZjiKoyHN7M5rKMDLSsKuFq5B11qMDQVrLKSvlXbUzdxyPsC5Vu9SuH9vbtMW\ncMNo7oaTCRcs5jW2fpt2Na/jZgBEnsXim0iKg0HYvsJADMRXTi/WVgu4ZVEutxXccj6vFeMswImI\niIxcKcbtdZcPwUUEo9xhAX7xYgiAmiLcrAA3dQE19//+8+pzW9eAm06zO7CZxXsvoKYwthx4DfBc\nAW5gOup5scl00+9RXgdApZsfSqQMFt9EbpJqDXelMPeEnR2SvVuAG7AQJyLSDS11PZeKRan4vH1t\nuLvCUGp2azOp2ENCLgKoKcKtCnDDKOjncK3FGHB8HXcZrK+ltmzxNl1GfbhO6np2w3XsUgxd6W2d\nHDC8Vn61xTvYZDrvE65P7mxfGsRrvsk/LHXhPSbXfxfs6Gb18lcYaLMbur3XpJkRXbUAACAASURB\nVHhyVHRHreaG68s9ep25M6OiExERqcQThb23CvgwlCIY5caYw1BqfFgyFOFm14EbwoxATeEZC+sC\nNBTWhbVhmpzC25Rlq7cnheHateymsdWXeISjpug2LbwJgLZOvPkrtnwT2ePmPcCd6Z7uyVHRnSmq\nPdpazu7oRERkh1L34vY20+/hbDFeLmMEMMN131L3ADe8bjk9JOSidAs4YP9WYFLFaQnkFd1yWiVd\n6Xpur/U7HDWt8/VNnhuUgMU2aRqLb/IfS+HcwGsGHijAAXld0XVxWzJHpFrAWZATEZGPcVR0yy2y\n7V33bXgtGOU2P9v0JIDNAtzZVmnLrt2uFt7usCzADV/Zshu92XsUjonITSy+iWRy937fclvBDV3Q\nvVmEK36tuKMu6SzOiYhIR8oRbFUQm77mKaaFdTDKjcuWanU3FPKGLugGZQCsBmFz5AJsF7Jyim5P\n3fZJqgU8Atfik9vSbTkyOumHTm4hJheLb/IvS+Fa6/dVhmu/XS3CnW0FN6V0MS51mzOvsSzOWYwT\nEfksLXQ9VyIGTxbdlgyFtWkBbpgOXCvCTVvSDa3gRqZFjL1u6EBNgW1ZsNorupW83tu0ADdt/W5u\nMo+tTgiG1nvFW+kBHFH4M8gnsPgm/7MUzhfgFl3PPdEKDjg3Krq3inFN3FecI6kTEfk0LYw67kwM\nloW6t+IPwUWrzzO0uFsW4bYKcMCF1u9zkFewShXdzrRUWo50bm8+0xZwwyByUqOeS/1p5Nw6jcgL\nWHyT95R/BQTLHwFc69xtBQdcK8INvN0yDpgX5rq7pzhHYyciIheZFrZqsbw+3F4ruN0C3HALMgA4\nb+PDDEW1VAu5vVZuqcJbifMUDVAzertUUV1y9TNLYd0l3dBNXf1zP+SnWHyTf1oKt7qfmzK9DZk3\nuqPbYlqMe7MQ90oRzlHUiYhIA7zZZV6qNVuqAAekW8ElB3MzHQHdtAi3ZCjKnelO7uy1uXJbvU3n\nLwm6VlgbbitGvo3XfBO5odz14tLjlsK5AlzGqOeeuCbcnQLcwJuDtnmtNdyyALfXks1CnYiIbHC3\ny7hpYeuNQjwEF423HbN1izOpVnCz+K5e+m0cAd1QxBpYfg1HLeOWbBVInm5hNo09Atdav8tM54H5\nQGyGlnClsTWdZGDxTaQAd1rDPVWAAz7YGi6367hhvs8snhMRkU+yd8suJbnbHd3ZmG1d2w1Yt4Jb\nvm46CrqxVrUcyOxaYDXcaXW099WcbfW2fG9JUE1sjgaOsyzCzZbjegiKUnpwOFIVi2/yb0vh8dZv\nS660hnuiG7oly2vEAe8M2qbK6OkGLLqJiEgBlq3eShf+htZvS1InHSxvSWbaUi99H3BY387LsEhX\nG/aVKrwtmd52DLhWYIei5gyD6XRH9wXXajHu73zsZESg2gEQqW6pk/Mvc+1jTFvD5foKA42FuBLW\nIsP4UMrODsnGBxERkZq0MMq6uyxb2aVa3U3vQW543fB/Qwt4aLjJ+8Irrj3MF37t4Tgwx/N6qvAO\nr7j2OfVxrUALx7UiOvTqw3R6uMU8RF7G4psI0HQBDihfhANQvAgHzAtxFuNERORNvlB4G3iyADcr\nwgF5hbjUwxFPtngbNMC1QeHkFuGQ+Hco3MeCnmTQbPH9xBNPIC4uDnFxcbjttttw5swZ42tz5sxB\nXFwcEhMTsWHDBhWjJJ+y1Mn53SjA3S3CvdEa7g2WxTgLcsLdTj7ILzAnIGdZFtpKFN7eKOZNi2rD\nPb+lXpN6DsgrwAEbRTggXYS7wsllGOKxfFgtMwzXCnA5RbjlNNPp5JMuXbqErl27IiUlBe3atcPj\njz8OABBCYOrUqUhKSkJ8fDzmz59v9d4rV67gwQcfRPv27dGuXTuMHz8e1dXVAIDLly9j3LhxSEpK\nQseOHbFixQpZ8Wj2mu+BAwfi5ZdfRmBgIJ555hk8//zzmDdvHvLz8/HFF19g3759OH78OFJTU3Ho\n0CHUrVtX7ZDJFyyF4teAG3hiZHTAs9eFm1LjPuIArApwVa8ZJyJNYE5Actga9EzJItmbtx4DbF/7\nbeBoJHSpa8AB4OLFmmWaFrhlJSbrzbJ4trxGXIoLRbvkCQCL183iMmiAmpHZTe9PbijATa8JNzCM\nOMfCW/uuc+/t9erVw9atWxEcHIyqqiqkpqZi8+bNOHDgAM6ePYu9e/cCgNlJXYPNmzdj//79OHjw\nIIQQ/5+9e4+Lqs7/B/46kHJZFG9BBqSWV2YYGYotiwpZla+wbYRa5hV1++7D3dDV3PXxyG+bdtHN\nR353tf1aotn0zWqrdU37WhaFeCtSVETWn9smkgxeWlEJAgTk/P7AMw5z4wxzOefMvJ6PxzwYZs6Z\nec8I8nnN54a0tDQUFxcjIyMDy5Ytw5AhQ1BQUAAAuHy5q9X/Oqi253vs2LEICeko77777kNNTQ0A\nYOfOnZg6dSpCQ0MRFxcHnU6HgwcPKlkq+ZKatiZzpps94BJPesIB//SGA/6ZH+6IWnrF/f26iegG\ntgkI6F6I9kfvtJLD2eUGf+secEfnWveCS5z2hgOdh6Y7u7jB5XM5OLZTHdLbb70yu/X+5NYLdjka\nig4Xt1FAiIjo+PCppaUF165dQ0xMDDZt2oSnn37ackz//v3tzouLi0NLSwuuXr2KpqYmtLa2Ii4u\nDqIo4p133sGSJUssx/bt29fufEdU2/NtraCgAFOnTgUA1NTUICMjw3JffHw8zGaz4xMPLb9x/dZ0\nIC7dZzVSADHBvd5vwKMecEnZl/d0uxdcYh3AfdUjDvh/CzNJychk9oQr5Vox0F6sdBVE3W4TnFr+\nruV633Q9+qUn+bZQUkwjIhGJRr+GYl/1gMt5DY5WPHe19Zqz7cpse8ElUth12OPsIbmB29F5nerp\nhY6V2aUecMB1L7j1tmPOtiIjp+qKy/BD8TGly0Dx+Y5LV9rb25GSkoJTp05h/vz50Ol0qKysREFB\nAbZv344BAwZg/fr1GDlyZKfzRo0ahQkTJmDgwIEQRRH5+fkYMWIEzp8/j/DwcOTn52P//v24/fbb\nUVBQgFtuuaXLWhQN3+PHj8f58/bv2MqVK/HQQx0h4sUXX0TPnj0xffp0958gdbmHFVLQMqF7ARzw\nKIR7OhTdmq+HpUuU2ktcwjDuJ6HpHRdJ2wqlKqEA5es2wR3LuUgA+Zane37L5WjouZwA7mj4uXW9\ntiEc6BzEnQ5J74buhm7bx7Bsl3YlrHMABzqHcGlE8E/geBg6uSU6PRnR6TfaY+YVb/rwyZzflR4N\npI+48f0KJ03CkJAQlJWVoa6uDpmZmSguLkZzczP69euH8vJybNu2DbNnz8bXX3/d6by9e/di9+7d\nqKmpgSiKGD9+PDIzM3H77bfj9OnTmDBhAl577TX86U9/wsKFC/Hee+91+XIUDd+FhYUu73/zzTex\nc+dOFBUVWW6Lj49HdXW15Xuz2YyEhASf1UhBzAT3Azigml5wib9COKBMj7iz4egM5UTawjYBBQrr\nwOtJEHe3596TAO7s/K56wyVywrg3AneXpAAOdA7hznrBrdn2iLsjyoNzyW+io6ORnZ2NkpISJCQk\nIDc3FwCQk5ODGTPsG+9ffvklJk6ciMjIjp//iRMn4sCBAxgzZgzCw8Mt50+ePBmvvfaarBpUO+d7\n165dWL16NXbs2IHw8HDL7VlZWXjvvffQ1tYGs9mMiooK/PSnP1WwUgpopm6e5+E8cMDzueC2/DU3\nXKLE/HBrXEWdKHCwTUBaVY9elou757liu/K5xFHYt73NdgV06/ttv7c8X2Sjw3nhEmerkztdqdxL\nLI9rPc/cdusz27ngtquiW6+O7gn2pqtSbW0t6us7fk6amppQWFiIpKQkZGdnWz7M3bNnD4YNG2Z3\n7h133IE9e/bg2rVraG1txZ49ezB06FCEhoZi/Pjx2L17NwDgiy++wKhRo2TVo9o53/n5+WhpacH4\n8eMBAGPGjMH69etx55134pFHHoHBYEBISAg2bNiAHj16KFwtBTQTut8DDnilFxzwzlB0ib/mhgPK\nzQ+3xeHqRNrFNgG5S5r3rSaOArWj1cl9wVUPuKP7ndXlrCdcSU5XQHfWCw50Hoou+YnNVwoIZ8+e\nxaxZsyCKIpqbmzFt2jRkZ2cjLS0N06dPx5o1axAWFobNmzcDAEpLS7FhwwZs3LgRkyZNQlFRkWUu\neGZmJiZNmgQAeOWVVzBz5kzk5+ejb9++eOutt2TVI4iiKPrmpSpLEARgvgcvzeS1UtyjhdW9/S3C\nPz21LuV54TE8DOGAdwO4I/4Ymi5RMohLpAAup2e8O7347owykDXKwQsjKmQxyTimSYC3/3wIggDx\nOTfP+QO8XgcFHkEQMEHcrnQZdvy9SrZ12AIcr4Dt7uPIfYyuhl/LDaHuDuNWWwD3Jldbjjl7H21v\nt/2ZcHReV/8magjiduHbeis02x+ZOjh2GR1D0X+CjnBuHdil+4DO4fxH3Bhu3mB1/YqD57UuUdpr\nPMrmNuvHl2qwPq/++vd9riKqT73D0Qjdnerg6jzb9QFsf++/EjJ88rdYEASIT3d9nOX4lepvE6i2\n55tIVUzwPIB7aS444LsQHujzw21xODoRkTp5a/VwNfaA+4Oz1c7d7QGXbgOch3BnC7R5wtUQd0fP\nZdf7LS3CBtwIr856wSXydooi8gjDN5FcJqgigAOBFcIB+15lNfSKExFRYHDUQxwIgdzRqudyyAng\ngH3YljM6wVFodhXI5YRsV+fKDuCA/BBO5EMM30TuMME7ARzQVAgH/DskXQ294kREFLi6E1ptqT3A\ny93rG7AP4K7Od3eeuicBW85jO9oOzRLCpUXYbEO49aAK262sbOeCE3kRwzeRu0zwzhxwL/WCA74P\n4QCDOBERkTUpwKs5hLsK4LacBXBA2cXiumIbwAEnveCA86HopF4u9vnWIoZvIiV5sRcc8E8IB/w/\nLF3CIE5ERGqj9hAud/434DiAu3oM6T6JUkFcVgAH5A9F7w5p8TTu900uqHafbyJVM3n58by8irW3\n9wh3xt97h1uT9hFXci9xIiIiiTeGsvubo0XtnK1g72wPcHeP8RVHw9sd7i8u9YJbs90bXEk/dn0I\naRd7vom6ywTvDD+XeHEYusRfPeGAcsPSAS7YRkRE6qDWXnB35n8DNwK4s15wwHUvt20A91ePuLMe\ncACuh6FLpEO4CJt6cNg5EVmY4P0ADvgshAP+DeL+DuEShnEiIlKSGkO4uwEccD4MvavHc3SsNV+G\ncakH3KMQTuQjDN+kfk0fARH+H9YsmwneDeCAT3rBJcHSG26NYZyIiJSgxhDuTHcDOOB+mO7O0HR3\nn8NRLzjgYi440BHCe6Fzz3c0uAI6eQ3nfBN5g8kHj7kFXp8Lbs1f88IlSs4Pt8X54oGruroaDzzw\nAJKSkjBixAisXr3act8rr7yC0aNHIykpCb/73e8UrJKIgk0jIlUxJ7y787Ej0OR0Lrj0uL6e6y09\nh+3FFWfbnEX1qXc9H9zZUOefyCy2j8zjKOiw55u0Qe29377ko6HoEn/2hEvU0iMOdARx9oQHjp49\ne2L9+vXQ6/VoaGhASkoKMjMzYTab8emnn+Lw4cO46aabUFtbq3SpRNQN9eil2IJe3mAdwNXYG97V\nUHJXveDS+YB/Vz3v6jmdDUMHXPWCh8nfhqwPuMK5L8n9wEMj2PNN2tGkbEjrksnHj+/DXnDA/z3h\nErX0hlNgiI2NhV6vBwBERUXBYDCgpqYGmzZtwtKlS3HTTR2fOffv31/JMomCjtJ7QauR1Bve3R7x\n7p4nZ8VyV1z1gFs/hr8/JPF6L7i1vt2tiqgzhm8ibzL5+PF9PBQdUD6EqzGIc3i6ehSfBpbvvnFx\npaqqCocOHUJaWhpOnjyJTz/9FMnJyRgzZgy+/PJL/xRMRCSDu0Hc0yHs3gjg7oRwfwXxrp4rMrJR\nXgh3tB1ZVzjUnGTgsHPSFi0MPzfB+wuw2fLhgmwSf6+Qbs3fw9I59Fw70od0XCQrnATwhoYGTJ48\nGWvXrkXv3r3R3t6O+vp6lJWV4dChQ5g0aRK+++47CILgn8KJyGukXnQtDz93RQ1zwwF5q5l3NQzd\n9vEkvh4J4fWh6F1h8CaZGL6JfMEE/wRwwOchHFBmXrhETfPDyQ+muHn8H+xvam1txaRJkzB9+nTk\n5OQAABISEpCbmwsASE1NRc+ePXHhwgXccsstHhZMREoJ9BDua3LCtdwADjjeE9zV4wL+CeGunsPZ\niugOyZl7HAXO//a2ANvnm8POSXvUPvdbYvLT8/hhKLpEqSHpEuuh6f4ans4h59oiiiLmzZuHxMRE\nLFq0yHJ7dnY2ioqKAADffPMNGhsbERMTo1SZRGTDkxDmeA3sGxfyjNwPN+QMQ3f02L4emi5nLrjt\nUPSoPvXo9o+O1AvOHz1ygOGbyJdMfnwuPwVwQPkQLlHrHHFSzoEDB7Blyxbs3r0bRqMRRqMRu3bt\nwpNPPonKykro9Xrk5ubCZDIhJIR/AomCAQO4c3IDry8DuO3z+CKMy92WzNl8cNm9rwE6/JyjS7yH\nw85Jm7Qw91tigu+HoEv8OBQdUHZeuDVfDU1nr7f2pKWlob293eF9b731lp+rISK10PoWZWogZwg6\n0L1h6K6e05qnH6TIGe7udCi69Yrnl7t4Imn7sV6Qv2UZORZgW40xfBP5gwn+C+CAXxZks6XkvHBr\ntj3h3Q3jDN5ERIGFc8Qdkxuq3T3WmyHc+vmtdTeMy5kL3uDqAfqicwCXAuKP6Jj3bX0yAzhZYfgm\n8hcT/B/AAcVCOKB8EAe4YBsREXXGEO4ZdwI44JsQbl2LNXfq6rIXvM9VoD7M9YPY9sr+BDcWXOsD\nLr5GdjjhjbRLKwuvWTPBv/PAAb8uyGZLLXPDJXIXbGOvNxFR4OOCbDe4+0FEdz648HQ+uBzdmSvu\n8pxeVpcgn/etVc3NzUhNTYXRaMTw4cMti7GKoohly5bBYDBAp9Nh3bp1dudarx9jNBoRERGBHTt2\nAAC2bduGxMRE6PV63HPPPfjnP/8pqx72fBMpwQT/9oIDivWEA+rrDZdIAfwhfMS9vomIghR7wrvH\n3R5wwLe94Na6s5WZ7evp2O/bpudbCuB1sB96bi3q+lep55uf8XSfh1uNhYeHY+/evYiIiEBbWxvS\n0tKwe/dunDhxApcuXUJ5eTkAoLa21u7csWPH4ujRowCAy5cvY+jQoZgwYQIAYMGCBfj8888xYsQI\nvPrqq3jhhRdkrS3Dnm/SNi32fktMCj2vgj3hgPp6w4EbIfxjZLHXm4goSHF7Mvd19wOLCDSpsie8\ny15wiRQI+zq4WOtOL3iALTCmBhERHR/2tLS04Nq1a4iJicGmTZvw9NNPW47p37+/y8f44IMPkJWV\nhfDwcABAfHw86urqAABXrlzBoEGDZNXCnm8iJZng/x5wiQKLsllTywJtREREtqwDOHvEXetOD7jE\nnz3hbs8HjwQa+ly9ceOVsBsB3NWPhHV4djTnm8PSvab4cMelK+3t7UhJScGpU6cwf/586HQ6VFZW\noqCgANu3b8eAAQOwfv16jBw50ulj/PWvf8WSJUss3//lL3/BhAkTEBkZid69e6OkRF57lj3fpH1a\n7v0GlOsBBxTvBQdu9ISrrTeciIgICJ4ecU8+ZPD0Awp/9IR7vHe4dRAHPB4OTTJFO7+kZwDLf3fj\n4kxISAjKyspgNpuxd+9eFBcXo7m5Gf369UN5eTny8/Mxe/Zsp+efO3cOFRUVyMzMBNAR5mfOnIld\nu3ahuroac+bMweLFi2W9HIZvIjUwKfz8KgjhAIO4KpiULoCISL2sg3igh3F3eRxuoYEQ3udq5xAe\nbfVVukhDz/tYXUgVoqOjkZ2djZKSEiQkJCA3NxcAkJOTg4qKCqfnvf/++8jNzUVoaCgA4MKFC2hp\naUFqaioA4NFHH8X+/ftl1cDwTYFB673fgDIrodtSSQgH1Dk3nIiIyBrDuD1vDNNXSwiP6lNvudic\nfGMIuqMecNt52wzgiqmtrUV9fce/X1NTEwoLC5GUlITs7GwUFRUBAPbs2YNhw4Y5fYx3330Xjz/+\nuOX7AQMGoL6+Hv/6178AAIWFhRg6dKisejjnm0htTFBuHrhEwZXRbflrpfSP8BD3ASciIo9wrrh3\n+WNOuLP54L1Qj0ZEWr7vWP28l+1B9vO/rcO49arnDOCKOHv2LGbNmgVRFNHc3Ixp06YhOzsbaWlp\nmD59OtasWYOwsDBs3rwZAFBaWooNGzZg48aNAICqqirU1NTgwQcftDxmjx49UFBQgIcffhiCIKBP\nnz548803ZdUjiKIoev9lKk8QBGC+By/N5LVS3BMIPbhKinC+d7Pm5CldgBUVhHBbvgji3g7frvYS\ntyWrl98foxJMMo9rEuDtPx+CIED8f26eMwper4MCjyAImCBuV7oMO/7uKbUNMN3tWbR+HHceo6sw\nah2C6tEroMKrVl6LN38mffXz7csgbltzPXqhsTHS7riGK706FmBz/CAdW5EBgPn6BegcwKXrUVbn\nSfdJveZ90RHkrUuqv/59n6uI6lOPyMhGu6fv7s9aV+dZ/07a/t5/JWT45G+xIAgQz7px/K3qbxNw\n2DkFlkD68MKkdAFWVDIU3ZovhqW7E5b9+VhERKRtwTgs3RtzwB3x5ZB0RzVHRjY6DLl2879vPIjj\noeh9nFynoMJh50RqZoJ6esBVNBTdmreHpTsKzRyOTkRE3hCMw9I92YrMFV8OSZdqtq49MrLR0gve\naQi6FMBte8KjAVxG595tSYPXSyaNYPimwNP0UWANPzdd/5qnYA3WVBrCAd/ND7cO5FIQdxXS2etN\nROS5QBt6biuYgrivAjjQeQi0N4O4swAOAI2NkZZF2OxCOADAyZB0CnoM3xSYAi2AA+rqBQdUHcIB\n/wRxd+4jIlJaBJp8Ol+Wuk8Kd4Ecwn0ZwCW+6g23rd1pL7itvgB+dPKg7P2W5WqA7afOOd8UuAJp\n/rfEBHXNBQdUtT2ZM9w/nIiItCDQty3z14cLvpgX7mguuMRuKzKpFzwaHQuo2W49BjhebI0CHnu+\nibTIBHX1ggOq7wmX+GvrMiIi6izQh5F7m7+Hpfsr8PujB1ziaU+4ba1d9YBLGq70uh7Awzp6vy/D\nPoBf6VZJpHEM3xTYAnH4ucQE9QVwQDMhHGAQJyIibfB1EPd3T7s/Azjg3eHorgK4pNNQdNth05c9\nLiGo1Ec6WrHOGfWP5eewcwp8gTj8XGJSugAXNDAc3RqHpBMRkRZ4e2i6UkPclRgF0Z3h6I7qdDUE\nXRLVp77z8HNJX3T0gvcBh5sHIfZ8E2md6frXPAVrcMU6gKu8N7zsy3vYA66wkpHJbp5R5pM6iIi0\nwFFwlhNq1TKnXKrV3/W42xPuqKfeUQ84ALtecKcvTVqIjQE8qLDnm4JDIPd+S0xKFyCDBnrD2QNO\nRIHM24tQuUstoS+QWfeMO7uojVJrAbjTEy6nBxzoCOFSEO/U+62+t50UwPBNwSNYArhJ4RrkUHkI\n58roRBTIGMBJjZRcjE9uCJcbwAGboejWAbwXbqyCTl1qRKTsixYwfBMFIpPSBcjEEE5EpAgGcFIj\npVfD93YAB6xWQZcCuESa+20dwhnIAx7DNwWXYOj9lpiULsANDOFEREGPgZwAdQTwrkK4swDubCG2\nTgG8z9Ubvd99rx/obC9wCjiqD99r1qxBSEgILl26ZLltwYIF0Ol0SElJwdGjRxWsjkjlTNBmCFdp\nEGcIJ1IW2wSBhWGbnFE6gANd94I7q7HLAG6rr+ObqYOcNQzUupaBI6oO39XV1SgsLMSgQYMst23d\nuhVnzpzBP/7xD7z++uuYM2eOghWSJgVT77fEBG2FcEATIZxBnMh/2CYITFppMJP/BVIAl3TqAZd6\nv4GOAM4QHhRUHb4XL16M1atXd7rt448/xsyZMwEARqMRbW1tMJvNSpRHpD0mpQvoBhWHcIBBnMhf\n2CYITgznwU3rAVy6z3rxNbsAbo0BPOCpdp/v7du3Iz4+HgaDodPtZrMZCQkJlu/j4+NhNpsRHx9v\n/yCHlt+4fms6EJfuk1pJg5o+AiIeUroKZZiuf81TsIbu0MB+4Y4CeMDsG36tGGgvVroKClLeaBOc\nWv6u5XrfdD36pSf5rmAi8hpHe2z7WwSaXO4J7qpG6T7rfcCj+tSj4cr146OtDq7zVsW+VVdchh+K\njyldhiYpGr7Hjx+P8+fP293+4osvYtWqVfjss88st4mi6PA6AAiC4PgJUpd7pU4KUMEcwIGOEJ6n\ncA3dpYEgLrEO5JoO4qHpHRdJ2wqlKqEA5es2wR3LH/dSpUTkb1oP4NYiIxtvBHAAqA/rfICr3m+V\nDASJTk9GdHqy5Xvzijd99lxK/7t7m6Lhu7Cw0OHtFRUVOH36NEaPHg2g45PtO++8E19//TXi4+NR\nXV2Nu+++23Kfw15vIuqaCdoN4BIpiKs8hAMdQVwK4BymTtQZ2wRE5IpWAjhgHxhta5cC+PU7O9NI\n7zd1jyrnfOv1ely4cAGnT5/G6dOnER8fjyNHjiA2NhZZWVl4++23AQBHjhxBaGgo4uLiFK6YNCsY\nF1+zZYI254LbUvnccAnnhxO5h20CUjpwkXqoYQ64HHL3Ao/qU2+//zcFNFWGb1vWQ8gmTZqEuLg4\n6HQ6/PKXv8Qbb7yhYGVEAcSkdAFeovLtyojIM2wTEAU3pQN4VwuwSboK4NaLsNnt/01e09zcjNTU\nVBiNRgwfPhyLFi0C0DFladmyZTAYDNDpdFi3bp3dubt374bRaLRcIiIisGPHDgDA6dOnMWbMGCQl\nJWHq1KlobW2VVY8g2k6WChCCIADzPXhpJq+V4h72xCojmOd+O5KndAFepoEh6bL44wMFk8zjmgS7\nubaeEgQBX4nJXR9oZYxQ5vU6KPAIgoAJ4naly7Cj1R5d62G3coNIV2yD9IN/AQAAIABJREFUiqNh\nu0TW1PD742oIusRRndJt0tBzy+JrV8KAetwYeh4Nx/O8+1xFVJ/6zgH+uu7+rnR1Xj16WY6x/b3/\nSsjwyd9iQRBQJI6RfXyG8JXDOpqamhAREYG2tjakpaVh1apVOHHiBCoqKvDqq68CAGpra9G/f3+n\nj3358mUMHToUNTU1CA8Px0MPPYR58+YhJycHv/3tbzFo0CBLsHdFEz3fRD7HDz06MyldgJexN5yI\nSNPUELRIXay38lKKnA+fXPWAS+E5qk995yHo7P32qoiIjg9JWlpacO3aNcTExGDTpk14+umnLce4\nCt4A8MEHHyArKwvh4eFoa2tDSUkJcnJyAAAzZszAzp07ZdWi2q3GiEhhputf8xSswRc0tFI6ERHd\nYN3zRiRReiG2rhZhAxzXaL0FmWXxtY47wB9zecqK63Cs+Icuj2tvb0dKSgpOnTqF+fPnQ6fTobKy\nEgUFBdi+fTsGDBiA9evXY+TIkU4f469//SuWLFkCAPj+++8xYMAAy31xcXEwm82yapYdvq9du4bv\nvvsO9fX16N27N2677TaEhobKPZ1I/YJ96zFnTNe/5ilYg68wiBN1C9sEpBQGcHIkUAJ4x/7f17ce\nY+83ANejXu5I74U70m98/+YKxwE4JCQEZWVlqKurQ2ZmJoqLi9Hc3Ix+/fqhvLwc27Ztw+zZs/H1\n1187PP/cuXOoqKhAZmamJy+loxZXd/7444/YuHEjxo0bh6ioKAwdOhRGoxF33HEHoqKiMG7cOGzc\nuBGNjfbzDYg0icPPnTMpXYCPcWg6kUtsE5BacAg6qVF3h6BLLPO3pYXXyOuio6ORnZ2NkpISJCQk\nIDc3FwCQk5ODiooKp+e9//77yM3NtXzIHBMTg4sXL1rud2ebS6fhe82aNRg8eDDWrl2Lu+++G+++\n+y5KS0vxzTffoLS0FO+88w5SU1Oxdu1a3HbbbVizZo2sJyRSPQZw50wI/BAOMIRrWHV1NR544AEk\nJSVhxIgRWL16daf716xZg5CQEFy6dEmhCrWJbQIiUjs1jIjoTgB3WTdDuMdqa2tRX9/xHjc1NaGw\nsBBJSUnIzs5GUVERAGDPnj0YNmyY08d499138fjjj1u+v+mmm3DPPffgww8/BABs2bIFWVlZsupx\nutr5pEmTsGzZMqSkpHT5IIcPH8bKlSuxdetWWU/qD1ztnDzC4eddy1O6AD9Sy5B0rnZux3a18wsX\nLuDf//439Ho9GhoakJKSgg8++ACjR49GdXU1nnjiCfzzn//E4cOH0a9fP6/WH8gCoU3A1c69R4nV\nzuWcQwSo4/fK3VXQra83NkZ2rH4uDT93JIhWO98uTpB9/MPCZ3Z1HD9+HLNmzYIoimhubsa0adPw\nhz/8AXV1dZg+fTqqqqoQFhaGjRs3IiUlBaWlpdiwYQM2btwIAKiqqsL999+P6urqTo97+vRpTJs2\nDQ0NDdDpdHjrrbfQo0ePrl8TtxpzwuS1UtzD8K0eDODy5CldgB8pHcIZvu10tdXY5MmTMW/ePEyc\nOBFTpkzBM888g4cffpjhO8gwfHuXWsK3o/OIAHX8bnU3gDN83yAIAraIk2QfP0PYqvrtR7naOZEz\nXIBNHtP1r3kK1uAvXKBNcUeKG3CkuEHWsVVVVTh06BDeeOMNbN++HfHx8TAYDD6ukIiIlKb0AmyA\n+4uwWS++BgANgOsATpokK3w3Nzdj7dq1+OKLL3DlypVOnygIgoCDBw/6rEAi0ggTgiOAS6QgzhDu\nVynpUUhJj7J8//qK8w6Pa2howJQpU7B27VqEhoZi5cqVKCwstNyv9k/G1YxtAlITrn5Ozmg9gDdc\ncVK7tBc4aZKs8D179mx88cUXyM3NtRumJwiCTwojUgX2frvHdP1rnoI1+Bt7w73qY8hbsOSGMrtb\nWltbMWnSJEybNg05OTk4fvw4qqqqMHr0aAAdq5LeeeedOHjwIGJiYrxQdXBhm4DUhgGcnJF+LrS0\nDZl0PapPPSzjvK6EMXQHCFnh+6OPPsLnn3+Oe++919f1EKkPA7j7TNe/5ilYgxIYxBUniiLmzZuH\nxMRELFq0CACQlJSECxcuWI4ZMmQI53x7gG0CUiMGcFIzOQHcpSAO3o2IVLoEr3K5z7ckPj4e/fv3\n93UtRBRoTAiOrckc4XZlijhw4AC2bNmC3bt3w2g0wmg04pNPPul0DHtnPcM2ARFpjRo+mOlqcULr\nGnuhYzG1qD7K103eJavn+/nnn8eyZcuwefNm9O7d29c1EakPe789Y7r+NU/BGpTC3nC/SktLQ3t7\nu8tjKisr/VRNYGKbgIioe7rqAVfDPHXyLVnhe+LEidi4cSMGDBiAgQMHIiTkRoe5IAhsyFBwYAD3\nnAnBGcAlDOIUANgmICItUkuwdXcIum3vt9OF2AKUGv7NvElW+J45cya++eYbLFmyhIurUHBjAPec\n6frXPAVrUAOulk4axTYBEWmVFgK4VGMv1AORHft+U+CQFb4///xz7Nu3DykpKb6uh4iChen61zwF\na1CDLWAAJ01hm4CItExLAdyRqD71Qdf7HUhkLbiWkJCAsDBu8k4EoKP3m7zHhOBdlE3ChdlIQ9gm\nILVSQ6AibVDDAmyAvEXYIiMb/VQN+YOs8P3nP/8ZS5cuxZkzZ3xdD5E2MIB7nwnBHcK5OjppBNsE\nRBQI1B7AreuLjGy0XIJNPXrJvmiBrGHnU6ZMwdWrVzF48GBERETYLa7yww8/+KxAItXi/G/fMFld\nz1OoBiJyim0CIiLv6u4QdNIeWeH7lVde8XUdRNrEAO5bputf8xSsQUnW88HZK04qwTYBqZVaejJJ\nO9QUbB0FcEf1BWPvdyCRFb7z8vJ8XAaRhjGA+54JwRPAbUM2QzepDNsEpEYM3kSByZ1t2bTA6Zxv\nd4eNcZgZEfmUCcE9J5xIQWwTkJoxeFOgcDX/W7qQtjkN30OHDsV//dd/4dSpUy4f4Ntvv8WyZcsw\ndOhQrxdHpBlcgM1/TGAQJ/IztglIrRhGyFNq+xmyDeBqq48843TY+f79+7F8+XKMGjUKw4YNQ2pq\nKm677TZERUWhoaEB3333HQ4dOoRvv/0WkydPxv79+/1ZN5H6cPi5/5muf81TsAaiIMA2AakRQwkF\nqq4WYGtEpJ8rIm8RRFEUXR1w7tw5/O1vf8PevXvxr3/9Cw0NDYiKisKwYcPwwAMPIDc3F3Fxcf6q\nVzZBEID5Ll+aayavleIe9qBqHwO4cvKULiAAmGQe1ySgiz8fbhMEAc+IT7t1zvPCSq/XQc5puU0w\nQdyudBl21LLQk7usQ0FX+xTLZRukXb03DN3kC2r7fbQN39b11aMXGhsjHS6+1t3fj67Oq0cvyzG2\nv/dfCRk++VssCAJ+La6Rffx64SnVtwm6XHBt4MCByM/PR35+vj/qIdI+9oArx3T9a56CNRAFMLYJ\nSEkM3RRMbHu/pZ9/tX1IQO6Rtdo5EZGmmKyu5ylUAxEReQ2DN/mamrYdk8jdfoy0w+mCa0TkAU4f\nUA8TuDgbEZFGcYVnIse437c8zc3NSE1NhdFoxPDhw7Fo0SIAgCiKWLZsGQwGA3Q6HdatW+fw/DNn\nziAzMxPJyclITEzEmTNnAABTp05FYmIiRo4ciZkzZ+Lq1auy6mHPN5GvcPi5upisrucpVAMREcnC\nwE3UIdh7vz19neHh4di7dy8iIiLQ1taGtLQ07N69GydOnMClS5dQXl4OAKitrXV4/qOPPoqXX34Z\naWlpuHr1Ktrb2wEAv/zlLzFu3DgAwLRp0/Daa69h4cKFXdbD8E3kS1IPOEO4upiuf81TsAYiIiIi\n8rmIiI4PL1paWnDt2jXExMRg8eLF2LFjh+WY/v37251XVlaGyMhIpKWlAQDCwsIs90nBGwDGjBmD\n6upqWbVw2DmRP3AYujqZwCHpREREpOqeZEe7CjgaHRJsI0bOF/8Tx5Z/ZLk4097ejuTkZMTGxmLs\n2LHQ6XSorKxEQUEBDAYDMjIycPLkSbvzTp48icjISEycOBF6vR4LFy7EtWvXOh3T2toKk8mEhx9+\nWFbNssL32LFj8dxzz9ndfunSJWRkZMh6IiIi1TKBQZxIJrYJyBfUHHyISDmNiHR66Z1uxLDlj1ku\nzoSEhKCsrAxmsxl79+5FcXExmpub0a9fP5SXlyM/Px+zZ8+2O6+9vR379+/Hq6++imPHjqGmpgYF\nBQWdjvnNb36DBx98EPfdd5+s1yNr2PmePXtw7NgxlJeX46233urUdV9cXCzriYiCHueAa4PJ6nqe\nQjUEsY/g7u/ISp/UQc6xTUBE5H/Wc7+Dac63N0VHRyM7OxslJSVISEhAbm4uACAnJwczZsywO/62\n225DcnIyBg8ebDlu3759lvtXrFiBixcv2gVyV2QPO//8889RVVWFtLQ0nD17VvYTEJEVDj/XFhPY\nG07kANsERET+Zz38PNiGmHdXbW0t6us73qumpiYUFhYiKSkJ2dnZKCoqAtDxofKwYcPszk1NTcX3\n33+PixcvAgC++OILjBo1CgCwadMmfPbZZ3jnnXfcqkd2+I6Pj8e+ffswdOhQpKamorS01K0nIqLr\nGMC1xwQGcSIrbBNQMKq3bHzmvQupg5b+LRzN/wYYxp05e/YsHnjgASQnJ8NoNGLcuHHIzs7Gc889\nh61bt0Kv1+Opp57C5s2bAQClpaV44oknAHQssLZu3Tr87Gc/w8iRI9Ha2opf//rXAID58+fj+++/\nx5gxY2A0GvHCCy/Iqset1c4jIiLw17/+Fc899xzS09OxciWH+xF1C4ega5fJ6nqeQjUQqQDbBBTI\n/BXG5DwPQxU5EwzDzz19fUlJSTh69Kjd7dHR0fi///s/u9vvuusu3HXXXZbvx40bh2PHjtkd19ra\n2q163N5qTBAEPPvss9DpdA4nphORTAzg2meyup6nUA1ECmKbgAKNGoNMVzUxnAcf272/+TOgHbLC\nd1FREfr27dvptsmTJ2PkyJE4fPiwTwojCgoM4IHDZHU9T6EaPGVSugDSArYJKFCpMXjL4apuhrKu\nafXfXcJ/Y22RFb7T09Md3q7X66HX671ZD1HwYQAPPKbrX/MUrIHIR9gmoECj9fDlirPXxsDWQcv/\n9hFo0nT9wcrtYedE5AMM4IHJZHU9T6EaiIjIoWAOLsEeyoP5315rAu3fiuGbSC0YwAObyep6nkI1\nEBERgMBr0HtLoIfyQPt3D4YF1wKN7K3GlPDKK69g9OjRSEpKwu9+9zvL7atWrUJiYiKSkpLw2Wef\nKVghkZdxG7LgYAK3LiNyE9sE5C0MK+7T8lZpWquXAptqe7537tyJTz/9FIcPH8ZNN92E2tpaAMDh\nw4fx97//HcePH8f58+eRlpaGf/7zn+jZs6fCFRN5CXvAg4vp+tc8BWsgUjm2CchbGMC8S60rsQfT\nv3Og935br+oeCFQbvjdt2oSlS5fipps6Suzfvz+Ajj/AU6dORWhoKOLi4qDT6XDw4EGkpaUpWS6R\ndzGABx+T1fU8hWogUim2CcgbAjmgqJU777m7QZ3/njdYv3d8X9RNteH75MmT+PTTT5Gfn4+IiAis\nWbMG9957L2pqapCRkWE5Lj4+Hmaz2fGDHFp+4/qt6UBcui9LJvIuaQg6Q3jwMVldz1OoBlvXioH2\nYqWroCDljTbBqeXvWq73TdejX3qSz+sm9WAgUT/+G3mHP3rC64rL8EPxMZ8+R6BSNHyPHz8e58+f\nt7v9xRdfRHt7O+rr61FWVoZDhw5h0qRJqKqqcu8JUpd7pU4iRbEXPLiZrn/NU7AGAAhN77hI2lYo\nVQkFKF+3Ce5Y/riXKiWtYaijQBWBJofDsn0dwKPTkxGdnmz53rziTZ89V6BRNHwXFhY6vW/dunXI\nzc0FAKSmpqJnz564cOEC4uPjUV1dbTnObDYjISHB57USKYoBnExQPoAT+RDbBERE7lMqgPtLILwG\na6pd7Tw7OxtFRUUAgG+++QaNjY2IjY1FVlYW3nvvPbS1tcFsNqOiogI//elPFa6WyA+aPuJq6MHO\npHQBRMpgm4C6K9Aa7kTdYbtOPSlHtXO+n3zyScydOxd6vR4AYDKZEBISgjvvvBOPPPIIDAYDQkJC\nsGHDBvTo0UPhaon8iL3gRBRk2CYgInKfs6At3S59OMVA7j+CKIqi0kX4giAIwHwPXprJa6W4hz2b\nJBcDePDK88Fjmtw8vkmAt/98CIKAZPErt84pE8Z4vQ4KPIIgYIK4Xeky7Gi1V9Z6iGsEmrz2uN4M\nAFp9b4m6w5PtuOrRS9bvnvVxtr/3XwkZPvlbLAgCYsTvZB//vTBI9W0C1Q47J6Iu8IMa8haT0gV4\nT3V1NR544AEkJSVhxIgRWL16NQBg8eLFSExMRGJiIn7+859b9okmosDD4E3BxpMPwdjr7V8M30Ra\nxgAenEwIqMDsTT179sT69etx/PhxHD58GJs2bcKxY8fw0EMPoaKiAidOnIBer8cLL7ygdKlE5GXS\njFYiIrVi+CbSOi7EFrxMKnkMFYmNjbXMC46KioLBYMDZs2cxduxYhIR0/Mm77777UFNTo2SZROSA\nJ8GZoZvIt9hD7h2qXXCNiNzEhdiCk+n61zwFa1CpqqoqHDp0CG+88Uan2wsKCjB16lSFqiIib2Lo\nJuogDT33ZP63GjU2RipdglcxfBMFEgbw4GWC4wBusrru6H6NaSg+gobiI10f19CAKVOmYO3atejV\n60bj/MUXX0TPnj0xffp0X5ZJFNCc7SvsTwzdRI6p4feTnGP4Jgo0DODByyTj/jyfV+GRsi/vcX1A\nz3uACVbfr3jd7pDW1lZMmjQJ06ZNQ05OjuX2N998Ezt37rTsF01E2sPQTURaxjnfRIGI88DJGZOT\n6wFCFEXMmzcPiYmJWLRokeX2Xbt2YfXq1dixYwfCw8MVrJCI3CEtosbF1Ijk8+YWgFrX3NyM1NRU\nGI1GDB8+3NI2EEURy5Ytg8FggE6nw7p16xyeHxoaCqPRCKPR2OkDfcmCBQs6jbDrCnu+iQIZe8HJ\nEZPSBfjOgQMHsGXLFhgMBhiNRgDAypUrsWDBArS0tGD8+PEAgDFjxmD9+vVKlkpEDjBgE3lHoMwB\nb7ji2f8J4eHh2Lt3LyIiItDW1oa0tDTs3r0bJ06cwKVLl1BeXg4ATrcgjYyMxNGjRx3eV1paiitX\nrkAQBNn1MHwTBTqpB5whnIJAWloa2tvb7W7/17/+pUA1REREygqUEO6JiIiO197S0oJr164hJiYG\nixcvxo4dOyzH9O/f363HvHbtGn7/+9/jnXfewbZt22Sfx2HnRMGCw9CJiIiIglJADkX/ci+w5oUb\nFyfa29uRnJyM2NhYjB07FjqdDpWVlSgoKIDBYEBGRgZOnjzp8Nzm5mbcddddSElJwfvvv2+5/S9/\n+Qsefvhh3HLLLW6VzJ5vomDCXnAiIiKioKTJldCvhDm/L3F8x0Xy344DeEhICMrKylBXV4fMzEwU\nFxejubkZ/fr1Q3l5ObZt24bZs2fj66+/tju3pqYGMTExOH36NDIyMjB69Gj06tULf/vb31BcXAxR\nFN16Oez5JgpG7AUnIiIiCjoB2QMuU3R0NLKzs1FSUoKEhATk5uYCAHJyclBRUeHwnJiYGADAkCFD\nMGHCBBw5cgRlZWX49ttvMXToUNx+++1obGzE8OHDZdXA8E0UrBjAiYiIiIJOMAXw2tpa1NfXAwCa\nmppQWFiIpKQkZGdnW7Ye3bNnD4YNG2Z3bl1dHVpbWy2Ps2fPHuh0OmRlZeHcuXM4ffo0Tp8+jcjI\nSHzzzTey6uGwc6JgxtXQiYiIiIJOsCzEdvbsWcyaNQuiKKK5uRnTpk1DdnY20tLSMH36dKxZswZh\nYWHYvHkzgI4VzDds2ICNGzfiH//4B371q18hJCQEV69exYIFC2AwGOyew53VzgXR3YHqGiEIAjDf\ng5dm8lop7mFvJCmFIZzc0SS4Pc+pK4IgAAfcfMz7vF8HBR5BEDBB3K50GXa0vK2W1GAPph40okDU\nnfBt+3v/lZDhk7/FgiAAJW487j3qbxNw2DkRdeAHP0REREREPsPwTUQ3MIATEREREfkEwzcRdcYA\nTkRERBQUOHXEv7jgGhHZ437gREREREFB1ft/1yldgHex55uInGMvOBEREVHAYw+4fzB8E5FrTR8x\nhBMREREReYjDzolIHu4JTkRERET+dFnpAryLPd9EJB97wYmIiIiIuoXhm4jcxxBOREREROQWhm8i\n6j4GcCIiIqKAwEXXfI9zvonIM5wLTkRERES+8KPSBXgXwzcReY77ghMRERFpnm3vt2r3/9YoDjsn\nIu/hMHQiIiKigMGh6N7Fnm8i8i72gpMntihdABEREVmLQJNyPeBXlHlaX2HPNxH5BldEJyIiIgoI\n7AH3DoZvIvItBnAiIiIizWMA9xzDNxH5HnvBiYiIiCjIcc43EfkP54MTERERkVwNShfgXez5JiL/\nYy84EREREQUZhm8iUgaHohMRERFREGH4JiJlMYQTEWkOF14iIi1obm5GamoqjEYjhg8fjkWLFgEA\nRFHEsmXLYDAYoNPpsG7dOqeP8cMPPyA+Ph75+fmW21paWvCf//mfMBgMGDVqFLZu3SqrHs75JiJ1\n4HxwIiJNYQAnIp/zcJ/v8PBw7N27FxEREWhra0NaWhp2796NEydO4NKlSygvLwcA1NbWOn2MZ555\nBg8++GCn25YtW4YhQ4agoKAAAHD58mVZ9TB8E5G6MIQTERERqZIWP3SLiIgA0NFbfe3aNcTExGDx\n4sXYsWOH5Zj+/fs7PPfw4cP4/vvv8R//8R8oLS0F0NFr/s4776CqqspyXN++fWXVwmHnRKRO0nB0\nDkknIiIiIlvVxcBXy29cnGhvb0dycjJiY2MxduxY6HQ6VFZWoqCgAAaDARkZGTh58qTD85YsWYI1\na9Z0uv3ChQsIDw9Hfn4+9Ho9fvGLX+D8+fOySmb4JiL1YwgnIiIiCj5XXFx6pQOjlt+4OBESEoKy\nsjKYzWbs3bsXxcXFaG5uRr9+/VBeXo78/HzMnj3b7rz169cjKysLt956K0RRtNze3t6O06dPY8KE\nCaioqMDYsWOxcOFCWS+Hw86JSDuaPuJwdCIiIiJyW3R0NLKzs1FSUoKEhATk5uYCAHJycjBjxgy7\n40tKSrBv3z6sX78eDQ0NaGlpQa9evfD8888jPDzccv7kyZPx2muvyaqBPd9EpC0cjk5EREREMtTW\n1qK+vh4A0NTUhMLCQiQlJSE7OxtFRUUAgD179mDYsGF2527ZsgXfffcdTp8+jZdffhmzZs3CypUr\nERoaivHjx2P37t0AgC+++AKjRo2SVY9qw/eBAweQnJwMvV6P0aNH48svvwTQMcF9wYIF0Ol0SElJ\nwdGjRxWulIgUwwBOFBTYJiAiou44e/YsHnjgASQnJ8NoNGLcuHHIzs7Gc889h61bt0Kv1+Opp57C\n5s2bAQClpaV44oknHD6WIAiW66+88gqWL18OvV6P119/HX/+859l1SOI1gPYVSQtLQ3PPPMMMjMz\n8cknn2DlypXYt28ftm7dirfeegsffvghjh49ijlz5qCsrMzufEEQgPkevDRT90/1CMMEUfdwOLp/\nNQnw9p+Pbv2//ar36yD18UabYIK4XYHKXatHL6VLICLy2FdChk/+FguCAEx243H/pv42gWp7vhMS\nElBXVwcAuHLlCgYNGgQA2LlzJ2bOnAkAMBqNaGtrg9lsVqxOIlIJDkUnClhsExARUSBQ7YJrf/zj\nH5GWloYlS5agvb0dX331FQCgpqYGCQkJluPi4+NhNpsRHx9v/yCHlt+4fms6EJfu05qJSAW4T7hv\nXCsG2ouVroKClDfaBKeWv2u53jddj37pSb4vnIgoANUVl+GH4mNKl6FJiobv8ePHO9wT7cUXX8S6\ndeuwbt06PPLII/jggw8wd+5cFBYWAoDdcALr8fedpC73dslEpBUM4d4Vmt5xkbStUKoSClC+bhPc\nsfxx7xdNRBSEotOTEZ2ebPnevOJNBavRFkXDt/SH05Fp06bh888/B9CxfPucOXMAdHyqXV1djbvv\nvhsAnPd6ExEBnYeiM4gHvOrqakyfPh2XL19GS0sL5s2bh9///ve4dOkSHnvsMVy4cAEDBw7Ee++9\nhz59+ihdLllhm4CIiOxcUboA71LtnO9BgwZhz549AICioiIMGTIEAJCVlYW3334bAHDkyBGEhoYi\nLi5OsTqJSEM4Lzzg9ezZE+vXr8fx48dx+PBhbNq0CceOHcOzzz6L7OxslJeXY+LEiXj22WeVLpXc\nwDYBEREFAtXO+d64cSN+/etfo7W1FWFhYXj99dcBAJMmTcLu3buh0+kQFhaGN954Q+FKiUhz2Bse\nsGJjYxEbGwsAiIqKgsFgQE1NDT7++GMcPHgQADBjxgzcc889WLt2rZKlkhvYJiAiokCg2q3GPMWt\nxojILQzh7lFqq7GaYuBs8Y3vS1c4raOqqgoPPvggjh8/jvj4ePzwww+W+3r37t3pewps3GqMiMh3\nfLrV2F1uPG6p+rcaU23PNxGRX7E3XB1MXR2Qfv0icbzwW0NDAyZPnoy1a9eid+/e3qiMiIiIyCMM\n30REthjENa21tRWTJk3C9OnTkZOTAwC4+eabcfHiRQwYMAD//ve/ERMTo3CVREREFGxUu+AaEZEq\nSIu0cUqIJoiiiHnz5iExMRGLFi2y3J6VlYUtW7YAALZs2YKsrCylSiQiIqIgxZ5vIiK52COuegcO\nHMCWLVtgMBhgNBoBAKtWrcKKFSvw2GOPYfPmzbjlllvw/vvvK1wpERERdelHpQvwLoZvIqLuYBBX\npbS0NLS3tzu8z9U+0kRERES+xvBNROQpBnEiIiIi6gLDNxGRNzGIExEREXnHFaUL8C6GbyIiX2EQ\nJyIiIqLruNo5EZE/cMV0IiIioqDGnm8iIn9ibzgRERFRUGL4JiKzo6JBAAAXqklEQVRSCoM4ERER\nkXOc801ERF7HIE5EREQU0Bi+iYjUxnZuOMM4ERERkeZxwTUiIrWTFmvjgm1EREREsjU3NyM1NRVG\noxHDhw/HokWLAACiKGLZsmUwGAzQ6XRYt26d3bnfffcdUlJSLOeuXr3act9rr72GESNGQKfTYdy4\ncbhw4YKsegRRFEXvvDR1EQQBmO/BSzN5rRT3sHFNRO5Qqle8SYC3/3wIggBEuPmYPqiDAo8gCJgg\nble6DDv16KV0CUREHvtKyPDJ32JBEAC487iO2wRNTU2IiIhAW1sb0tLSsGrVKpw4cQIVFRV49dVX\nAQC1tbXo379/p/NaW1shCAJuuukm/Pjjj9DpdCguLkZsbCxuvfVWnDp1Cv369cPSpUvRs2dPPP/8\n811WyGHnRERaxrniRERERE5FREQAAFpaWnDt2jXExMRg8eLF2LFjh+UY2+ANAD169LBcb2pqQo8e\nPdC7d2+EhYWhf//+aGhoQN++fVFXV4e77rpLVi0cdk5EFCish6dzFA0REREFtGIAy60ujrW3tyM5\nORmxsbEYO3YsdDodKisrUVBQAIPBgIyMDJw8edLhuWazGQaDAbfddhsWLVqEfv36ISQkBGvXroVe\nr0dcXBxOnjyJuXPnyqqY4ZuIKFAxjBOpWi/UK10CEZHKtbq43AdgmdXFsZCQEJSVlcFsNmPv3r0o\nLi5Gc3Mz+vXrh/LycuTn52P27NkOz42Pj0d5eTlOnTqFP//5z/j222/xww8/YMGCBTh27BjOnj2L\npKQkrFq1StarYfgmIgoWDONEREQUpKKjo5GdnY2SkhIkJCQgNzcXAJCTk4OKigqX5w4cOBD3338/\nSktLceLECQwZMgRDhgwBAEyZMgX79++XVQPDNxFRsGIQJyIiogBWW1uL+vqOUUZNTU0oLCxEUlIS\nsrOzUVRUBADYs2cPhg0bZnfuuXPncPXqVQDA5cuXsW/fPuj1egwaNAgnTpzAxYsXAQCFhYUYOnSo\nrHq44BoREXFvcSIiIgo4Z8+exaxZsyCKIpqbmzFt2jRkZ2cjLS0N06dPx5o1axAWFobNmzcDAEpL\nS7FhwwZs3LgRx48fx5IlSyAIAlpbW/HUU09Br9cDAJYvX457770XoaGhGDRoELZs2SKrHm415ozJ\na6W4hz1QRKQ2joI4txojDVHrVmMAtxsjIu3z7VZj59044xbVtwk47JyIiIiIiIjIxzjsnIiIXPPn\nXuIc/UNEREQBij3fRERERERERD7Gnm8iIpKv6SMuxkZERER+8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+ "text": [ + "" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/standalone/examples/single_column/KPP_MARBL/vgrid_cm4.cdl b/standalone/examples/single_column/KPP_MARBL/vgrid_cm4.cdl new file mode 100644 index 00000000..f3b0163e --- /dev/null +++ b/standalone/examples/single_column/KPP_MARBL/vgrid_cm4.cdl @@ -0,0 +1,38 @@ +netcdf vgrid { +dimensions: + nz = 75 ; + zt = 50 ; + zw = 51 ; +variables: + double dz(nz) ; + dz:long_name = "z coordinate level thickness" ; + dz:units = "m" ; + double zt(zt) ; + zt:long_name = "Diagnostic z coordinate level position" ; + zt:comment = "Used for diagnostics only" ; + zt:units = "m" ; + double zw(zw) ; + zw:long_name = "Diagnostic z coordinate interface position" ; + zw:comment = "Used for diagnostics only" ; + zw:units = "m" ; + +// global attributes: + :filename = "vgrid_cm4.nc" ; +data: + + dz = 2.00, 2.00, 2.00, 2.00, 2.01, 2.01, 2.02, 2.02, 2.05, 2.07, 2.09, 2.13, 2.18, 2.24, 2.30, 2.40, 2.50, 2.62, 2.78, 2.95, 3.17, 3.42, 3.71, 4.07, 4.48, 4.97, 5.55, 6.23, 7.04, 7.99, 9.11, 10.43, 11.98, 13.80, 15.94, 18.42, 21.32, 24.66, 28.51, 32.91, 37.92, 43.56, 49.87, 56.88, 64.55, 72.91, 81.87, 91.40, 101.39, 111.73, 122.29, 132.93, 143.47, 153.78, 163.70, 173.07, 181.78, 189.76, 196.89, 203.17, 208.58, 213.14, 216.87, 219.87, 222.19, 223.94, 225.18, 226.04, 226.59, 226.90, 227.07, 227.14, 227.16, 227.16, 227.17; + + zt = 5.05, 15.15, 25.25, 35.4, 45.6, 55.85, 66.25, 76.85, 87.65, 98.75, + 110.25, 122.3, 135.15, 149.1, 164.55, 182, 202.15, 225.95, 254.6, 289.55, + 332.5, 385.4, 450.25, 528.9, 622.85, 732.85, 858.9, 1000.25, 1155.4, + 1322.5, 1499.55, 1684.6, 1875.95, 2072.15, 2272, 2474.55, 2679.1, + 2885.15, 3092.3, 3300.25, 3508.75, 3717.65, 3926.85, 4136.25, 4345.85, + 4555.6, 4765.4, 4975.25, 5185.15, 5394.6 ; + + zw = 0, 10.1, 20.2, 30.3, 40.5, 50.7, 61, 71.5, 82.2, 93.1, 104.4, 116.1, + 128.5, 141.8, 156.4, 172.7, 191.3, 213, 238.9, 270.3, 308.8, 356.2, + 414.6, 485.9, 571.9, 673.8, 791.9, 925.9, 1074.6, 1236.2, 1408.8, 1590.3, + 1778.9, 1973, 2171.3, 2372.7, 2576.4, 2781.8, 2988.5, 3196.1, 3404.4, + 3613.1, 3822.2, 4031.5, 4241, 4450.7, 4660.5, 4870.3, 5080.2, 5290.1, + 5499.1 ; +} diff --git a/standalone/examples/single_column/KPP_MARBL/vgrid_cm4_10.cdl b/standalone/examples/single_column/KPP_MARBL/vgrid_cm4_10.cdl new file mode 100644 index 00000000..1f4286aa --- /dev/null +++ b/standalone/examples/single_column/KPP_MARBL/vgrid_cm4_10.cdl @@ -0,0 +1,38 @@ +netcdf vgrid { +dimensions: + nz = 75 ; + zt = 50 ; + zw = 51 ; +variables: + double dz(nz) ; + dz:long_name = "z coordinate level thickness" ; + dz:units = "m" ; + double zt(zt) ; + zt:long_name = "Diagnostic z coordinate level position" ; + zt:comment = "Used for diagnostics only" ; + zt:units = "m" ; + double zw(zw) ; + zw:long_name = "Diagnostic z coordinate interface position" ; + zw:comment = "Used for diagnostics only" ; + zw:units = "m" ; + +// global attributes: + :filename = "vgrid_cm4_10.nc" ; +data: + + dz = 10.00, 10.00, 10.00, 10.01, 10.02, 10.03, 10.05, 10.09, 10.13, 10.20, 10.29, 10.39, 10.53, 10.71, 10.91, 11.15, 11.46, 11.80, 12.22, 12.69, 13.24, 13.88, 14.62, 15.45, 16.40, 17.50, 18.72, 20.13, 21.70, 23.48, 25.47, 27.71, 30.22, 33.00, 36.08, 39.51, 43.26, 47.38, 51.86, 56.72, 61.95, 67.53, 73.47, 79.72, 86.25, 93.00, 99.95, 106.99, 114.09, 121.13, 128.08, 134.83, 141.32, 147.47, 153.23, 158.55, 163.40, 167.73, 171.57, 174.87, 177.70, 180.06, 181.97, 183.49, 184.66, 185.55, 186.17, 186.60, 186.87, 187.04, 187.11, 187.15, 187.16, 187.16, 187.17; + + zt = 5.05, 15.15, 25.25, 35.4, 45.6, 55.85, 66.25, 76.85, 87.65, 98.75, + 110.25, 122.3, 135.15, 149.1, 164.55, 182, 202.15, 225.95, 254.6, 289.55, + 332.5, 385.4, 450.25, 528.9, 622.85, 732.85, 858.9, 1000.25, 1155.4, + 1322.5, 1499.55, 1684.6, 1875.95, 2072.15, 2272, 2474.55, 2679.1, + 2885.15, 3092.3, 3300.25, 3508.75, 3717.65, 3926.85, 4136.25, 4345.85, + 4555.6, 4765.4, 4975.25, 5185.15, 5394.6 ; + + zw = 0, 10.1, 20.2, 30.3, 40.5, 50.7, 61, 71.5, 82.2, 93.1, 104.4, 116.1, + 128.5, 141.8, 156.4, 172.7, 191.3, 213, 238.9, 270.3, 308.8, 356.2, + 414.6, 485.9, 571.9, 673.8, 791.9, 925.9, 1074.6, 1236.2, 1408.8, 1590.3, + 1778.9, 1973, 2171.3, 2372.7, 2576.4, 2781.8, 2988.5, 3196.1, 3404.4, + 3613.1, 3822.2, 4031.5, 4241, 4450.7, 4660.5, 4870.3, 5080.2, 5290.1, + 5499.1 ; +} diff --git a/standalone/examples/single_column/KPP_MARBL/vgrid_cm4_4.cdl b/standalone/examples/single_column/KPP_MARBL/vgrid_cm4_4.cdl new file mode 100644 index 00000000..9e5f2138 --- /dev/null +++ b/standalone/examples/single_column/KPP_MARBL/vgrid_cm4_4.cdl @@ -0,0 +1,38 @@ +netcdf vgrid { +dimensions: + nz = 75 ; + zt = 50 ; + zw = 51 ; +variables: + double dz(nz) ; + dz:long_name = "z coordinate level thickness" ; + dz:units = "m" ; + double zt(zt) ; + zt:long_name = "Diagnostic z coordinate level position" ; + zt:comment = "Used for diagnostics only" ; + zt:units = "m" ; + double zw(zw) ; + zw:long_name = "Diagnostic z coordinate interface position" ; + zw:comment = "Used for diagnostics only" ; + zw:units = "m" ; + +// global attributes: + :filename = "vgrid_cm4_4.nc" ; +data: + + dz = 4.00, 4.00, 4.00, 4.00, 4.01, 4.02, 4.03, 4.05, 4.07, 4.11, 4.16, 4.22, 4.29, 4.39, 4.51, 4.65, 4.81, 5.02, 5.26, 5.54, 5.87, 6.26, 6.71, 7.24, 7.85, 8.57, 9.40, 10.36, 11.47, 12.76, 14.26, 15.97, 17.95, 20.22, 22.81, 25.79, 29.15, 32.95, 37.24, 42.02, 47.33, 53.20, 59.62, 66.57, 74.05, 82.03, 90.42, 99.17, 108.19, 117.37, 126.60, 135.76, 144.72, 153.37, 161.60, 169.30, 176.40, 182.82, 188.55, 193.56, 197.83, 201.44, 204.37, 206.72, 208.54, 209.90, 210.88, 211.54, 211.96, 212.22, 212.34, 212.40, 212.41, 212.41, 212.42; + + zt = 5.05, 15.15, 25.25, 35.4, 45.6, 55.85, 66.25, 76.85, 87.65, 98.75, + 110.25, 122.3, 135.15, 149.1, 164.55, 182, 202.15, 225.95, 254.6, 289.55, + 332.5, 385.4, 450.25, 528.9, 622.85, 732.85, 858.9, 1000.25, 1155.4, + 1322.5, 1499.55, 1684.6, 1875.95, 2072.15, 2272, 2474.55, 2679.1, + 2885.15, 3092.3, 3300.25, 3508.75, 3717.65, 3926.85, 4136.25, 4345.85, + 4555.6, 4765.4, 4975.25, 5185.15, 5394.6 ; + + zw = 0, 10.1, 20.2, 30.3, 40.5, 50.7, 61, 71.5, 82.2, 93.1, 104.4, 116.1, + 128.5, 141.8, 156.4, 172.7, 191.3, 213, 238.9, 270.3, 308.8, 356.2, + 414.6, 485.9, 571.9, 673.8, 791.9, 925.9, 1074.6, 1236.2, 1408.8, 1590.3, + 1778.9, 1973, 2171.3, 2372.7, 2576.4, 2781.8, 2988.5, 3196.1, 3404.4, + 3613.1, 3822.2, 4031.5, 4241, 4450.7, 4660.5, 4870.3, 5080.2, 5290.1, + 5499.1 ; +} From 6a5ec398df7028bb7054e7b7de030ed009654ea6 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 1 Feb 2023 16:15:07 -0700 Subject: [PATCH 064/136] Improvements to standalone KPP_MARBL 1. use data_override to provide dust fluxes, black carbon flux, and u10_sqr 2. set marbl_in to use 4p2z tuning 3. clean up diag_table (introduced marbl2.nc output file because with 4p2z we have too many fields for FMS to handle) --- Externals.cfg | 2 +- .../INPUT/BATS/IC_FILE_COLUMN.nc | Bin 164131 -> 181607 bytes .../INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc | Bin 0 -> 507792 bytes .../single_column/KPP_MARBL/data_table | 4 + .../single_column/KPP_MARBL/diag_table | 257 +++++++++++------ .../examples/single_column/KPP_MARBL/marbl_in | 265 ++++++++++++++++++ 6 files changed, 448 insertions(+), 80 deletions(-) create mode 100644 standalone/examples/single_column/INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc diff --git a/Externals.cfg b/Externals.cfg index 98125a99..ec4869ed 100644 --- a/Externals.cfg +++ b/Externals.cfg @@ -2,7 +2,7 @@ branch = mnlevy1981/add_MARBL protocol = git local_path = MOM6 -repo_url = git@github.com:mnlevy1981/MOM6.git +repo_url = https://github.com/mnlevy1981/MOM6.git required = True externals = ../Externals_MOM.cfg diff --git a/standalone/examples/single_column/INPUT/BATS/IC_FILE_COLUMN.nc b/standalone/examples/single_column/INPUT/BATS/IC_FILE_COLUMN.nc index 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zp6b~V@btYr%JUXk*sb&_r{fZ_DfshBRlJc^37TSz4~=0j*H#Jd(IDH-m1;uz{PIxs$@oY<6xSSxlMpH){O(hVL*9fA6gznI z=u0-j{k6;{!4*n?>6t>Z$KHjZ2ydQ;3N!nLW$Q!1;qNTbnIVq;v3nluk-jDqTM z?BI(3y$wjN%K;n2AOk$sBKvkz2@}vPjc! zavzsE{Yq3SbTGvD`yJt{kMPsN%kIC5P4FXDHEY=D1yEbx2wr*G41TAmCK6PeV4<;I zv6ZC|=01Pgr|D4&iZY}EGIb85$fm7}krWCcCY*D{_@g6G^$X(F2ts6hXk_V^>;>}m zC2CYit$E}c?!PAw}EFI`eEn^ye(;;L}fqL%eEV!2X#E--dL#cAY#iDe%9k54+F#;&_1RR`@+hi>y2+4>P2tqpXcC z*EKVuJ8;np7%ff3IcXB_H6;~zxiBMWexw8L!Wm2D2r zB;uVgmRjVaxKa5tr4~8-Vx}o_8ZbsI>7AlUBZ}U3ICPP4i+}nqYS~R#l>O#%pNtV- ztRvQGiGQJj0Q-=yp6B(oy!xPoHgbC{J_EGWNBY8lvUnlnf zUl~+fC`r!yOw@@m2m6oG`--baH~+5WB-+ZnrhJr3d3eA5d?U+s Date: Fri, 24 Feb 2023 16:08:23 -0700 Subject: [PATCH 068/136] CHL_FROM_FILE=FALSE when MARBL is active Updated the default value for CHL_FROM_FILE in MOM_input; it will still be True when running without MARBL, but with MARBL turned on we will use the MARBL chlorophyll to compute shortwave absorption. --- param_templates/MOM_input.yaml | 8 ++++++++ param_templates/json/MOM_input.json | 7 +++++++ 2 files changed, 15 insertions(+) diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index 335ec295..dc6f6fed 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -348,6 +348,14 @@ Global: value: bool(re.search("MOM6%[^_]*MARBL", $COMPSET ))== True: True else: False + CHL_FROM_FILE: + description: | + "[Boolean] default = True + If true, read chlorophyll from a file. Otherwise MARBL will provide it. + " + datatype: logical + value: + $OCN_BGC_CONFIG != "none": False DUST_RATIO_THRES: description: | "TODO: Add description diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index 3678c0ca..f45573bf 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -249,6 +249,13 @@ "else": false } }, + "CHL_FROM_FILE": { + "description": "\"[Boolean] default = True\n If true, read chlorophyll from a file. Otherwise MARBL will provide it.\n\"\n", + "datatype": "logical", + "value": { + "$OCN_BGC_CONFIG != \"none\"": false + } + }, "DUST_RATIO_THRES": { "description": "\"TODO: Add description\n\"\n", "datatype": "real", From 7a0313cf8bd30c8a77e5319d92bd106e3d37fda1 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 7 Mar 2023 16:06:50 -0700 Subject: [PATCH 069/136] Make it easier to turn off ice categories setting ICE_NCAT=0 will make sure MARBL is not expecting multiple categories of ice from the coupler --- cime_config/buildnml | 7 ++++--- param_templates/MOM_input.yaml | 2 +- param_templates/json/MOM_input.json | 2 +- 3 files changed, 6 insertions(+), 5 deletions(-) diff --git a/cime_config/buildnml b/cime_config/buildnml index 1f0dad50..f2cccdd6 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -92,9 +92,10 @@ def prep_input(case): # calling set_value will() also change the value in env_run.xml; we only do that # if the value needs to be updated CPL_I2O_PER_CAT_curr = case.get_value("CPL_I2O_PER_CAT") # returns python logical, not string - if (CPL_I2O_PER_CAT_curr != use_MARBL): - case.set_value("CPL_I2O_PER_CAT", use_MARBL) - append_status(f"xmlchange via MOM buildnml: CPL_I2O_PER_CAT={use_MARBL}", + CPL_I20_PER_CAT_new = (ice_ncat>0 and use_MARBL) + if (CPL_I2O_PER_CAT_curr != CPL_I20_PER_CAT_new): + case.set_value("CPL_I2O_PER_CAT", CPL_I20_PER_CAT_new) + append_status(f"xmlchange via MOM buildnml: CPL_I2O_PER_CAT={CPL_I20_PER_CAT_new}", "CaseStatus") diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index dc6f6fed..d150b2d1 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -384,7 +384,7 @@ Global: datatype: logical units: Boolean value: - bool(re.search("MOM6%[^_]*MARBL", $COMPSET ))== True: True + bool(re.search("MOM6%[^_]*MARBL", $COMPSET )) and $ICE_NCAT > 0: True else: False ICE_NCAT: description: | diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index f45573bf..d4b7f52f 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -282,7 +282,7 @@ "datatype": "logical", "units": "Boolean", "value": { - "bool(re.search(\"MOM6%[^_]*MARBL\", $COMPSET ))== True": true, + "bool(re.search(\"MOM6%[^_]*MARBL\", $COMPSET )) and $ICE_NCAT > 0": true, "else": false } }, From 2203d9c70705e97f552382c89a9b007fd9e45d71 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 22 Mar 2023 20:26:17 -0600 Subject: [PATCH 070/136] Updates for stand-alone examples 1. Bumped marbl up to 0.42.0 2. Updated build template to work with either cheyenne or casper 3. Changes in standalone/examples/single_column/KPP_MARBL: = updated IC_FILE_COLUMN.nc because we are using different API to read it in - updated MARBL_FORCING_FILE_COLUMN.nc to include SWDN - updated data_table to read SWDN from MARBL_FORCING_FILE_COLUMN.nc - updated diag_table to include some SW variables --- Externals_MOM.cfg | 2 +- standalone/.gitignore | 3 + standalone/build/build_examples-cheyenne.sh | 5 +- .../CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc | Bin 181607 -> 189958 bytes .../BATS/MARBL_FORCING_FILE_COLUMN.nc | Bin 507792 -> 2143943 bytes .../INPUT/CESM_MOM_COLUMNS/BATS/MOM_override | 1 + .../KPP_MARBL/MOM_parameter_doc.all | 111 +++++++++++++----- .../KPP_MARBL/MOM_parameter_doc.short | 26 ++-- .../single_column/KPP_MARBL/data_table | 3 +- .../single_column/KPP_MARBL/diag_table | 2 + standalone/templates/cheyenne-intel.mk | 6 +- 11 files changed, 109 insertions(+), 50 deletions(-) create mode 100644 standalone/.gitignore diff --git a/Externals_MOM.cfg b/Externals_MOM.cfg index 58e33681..87509f30 100644 --- a/Externals_MOM.cfg +++ b/Externals_MOM.cfg @@ -10,7 +10,7 @@ required = True [MARBL] protocol = git -tag = marbl0.40.3 +tag = marbl0.42.0 repo_url = https://github.com/marbl-ecosys/MARBL local_path = pkg/MARBL required = True diff --git a/standalone/.gitignore b/standalone/.gitignore new file mode 100644 index 00000000..f6d44f04 --- /dev/null +++ b/standalone/.gitignore @@ -0,0 +1,3 @@ +examples/**/*.nc +!examples/**/INPUT/**/*.nc +examples/**/logfile.* diff --git a/standalone/build/build_examples-cheyenne.sh b/standalone/build/build_examples-cheyenne.sh index 8fb38d3b..1f22aa74 100755 --- a/standalone/build/build_examples-cheyenne.sh +++ b/standalone/build/build_examples-cheyenne.sh @@ -14,6 +14,7 @@ FMS_ROOT=${CESM_ROOT}/libraries/FMS SHR_ROOT=${CESM_ROOT}/components/cdeps/share COMPILER=intel +TEMPLATE=${TEMPLATE_DIR}/cheyenne-${COMPILER}.mk # 1) Build FMS @@ -22,7 +23,7 @@ cd ${INTERFACE_ROOT}/standalone/build mkdir -p ${COMPILER}/FMS cd ${COMPILER}/FMS ${MKMF_ROOT}/list_paths ${FMS_ROOT}/src -${MKMF_ROOT}/mkmf -t ${TEMPLATE_DIR}/cheyenne-${COMPILER}.mk -p libfms.a -c "-Duse_libMPI -Duse_netCDF -DSPMD" path_names +${MKMF_ROOT}/mkmf -t ${TEMPLATE} -p libfms.a -c "-Duse_libMPI -Duse_netCDF -DSPMD" path_names make -j36 NETCDF=3 REPRO=1 libfms.a # 2) Build MOM6 @@ -30,5 +31,5 @@ cd ${INTERFACE_ROOT}/standalone/build mkdir -p ${COMPILER}/MOM6 cd ${COMPILER}/MOM6 ${MKMF_ROOT}/list_paths -l ${MOM_ROOT}/{config_src/infra/FMS2,config_src/memory/dynamic_symmetric,config_src/drivers/solo_driver,pkg/MARBL/src,config_src/external,src/{*,*/*}}/ -${MKMF_ROOT}/mkmf -t ${TEMPLATE_DIR}/cheyenne-${COMPILER}.mk -o '-I../FMS' -p MOM6 -l '-L../FMS -lfms' -c '-Duse_libMPI -Duse_netCDF -DSPMD' path_names +${MKMF_ROOT}/mkmf -t ${TEMPLATE} -o '-I../FMS' -p MOM6 -l '-L../FMS -lfms' -c '-Duse_libMPI -Duse_netCDF -DSPMD' path_names make -j36 NETCDF=3 REPRO=1 MOM6 diff --git 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zYy<(=?XduMdmMn>9uHu*Cji*(i2!zc5`f(v^9ej+96$iD+hYOj_Ba4X56cX0EIs-s>3@jdRS1#RAQuHbxk{3LF_ z?Y&R@7&k00)9C2P=APHt-19nLM3L9!1sEOm&3iiW$Y&m8d+Pb0*SXP>*SXP>*FW5s>nFB$jC{RJ z-a%TwF!Ny9mHF8p>S^-PH-4I2t6%AhJxwm-`}I~_=*6DNu@lGdIKA@+dY0J#;aSr0 z?jQVFA}Hv&`~LH#@`A098#w>F3$}jxg63(Z`~u`JLr(1ed`R*?Z@KZK_u$xf>ytO# J)${Y0{TFXzjHUnp diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override index 1f2b1a2b..aeb918ce 100644 --- a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override +++ b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override @@ -2,5 +2,6 @@ F_0 = 7.59943e-05 INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" CESM_INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" +CHL_FROM_FILE = FALSE READ_NDEP = FALSE READ_RIV_FLUXES = FALSE diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all index e2cec46d..d6a4494e 100644 --- a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all @@ -22,9 +22,9 @@ USE_CONTEMP_ABSSAL = False ! [Boolean] default = False ! practical salinity before exchanging them with the coupler and/or reporting ! T&S diagnostics. ADIABATIC = False ! [Boolean] default = False - ! There are no diapycnal mass fluxes if ADIABATIC is true. This assumes that KD - ! = KDML = 0.0 and that there is no buoyancy forcing, but makes the model faster - ! by eliminating subroutine calls. + ! There are no diapycnal mass fluxes if ADIABATIC is true. This assumes that KD + ! = 0.0 and that there is no buoyancy forcing, but makes the model faster by + ! eliminating subroutine calls. DO_DYNAMICS = True ! [Boolean] default = True ! If False, skips the dynamics calls that update u & v, as well as the gravity ! wave adjustment to h. This may be a fragile feature, but can be useful during @@ -43,10 +43,13 @@ BULKMIXEDLAYER = False ! [Boolean] default = False ! least NKML+NKBL+1 layers if BULKMIXEDLAYER is true. BULKMIXEDLAYER can not be ! used with USE_REGRIDDING. The default is influenced by ENABLE_THERMODYNAMICS. THICKNESSDIFFUSE = False ! [Boolean] default = False - ! If true, interface heights are diffused with a coefficient of KHTH. -THICKNESSDIFFUSE_FIRST = False ! [Boolean] default = False - ! If true, do thickness diffusion before dynamics. This is only used if - ! THICKNESSDIFFUSE is true. + ! If true, isopycnal surfaces are diffused with a Laplacian coefficient of KHTH. +APPLY_INTERFACE_FILTER = False ! [Boolean] default = False + ! If true, model interface heights are subjected to a grid-scale dependent + ! spatial smoothing, often with biharmonic filter. +USE_POROUS_BARRIER = True ! [Boolean] default = True + ! If true, use porous barrier to constrain the widths and face areas at the + ! edges of the grid cells. BATHYMETRY_AT_VEL = False ! [Boolean] default = False ! If true, there are separate values for the basin depths at velocity points. ! Otherwise the effects of topography are entirely determined from thickness @@ -187,7 +190,7 @@ REFERENCE_HEIGHT = 0.0 ! [m] default = 0.0 ! A reference value for geometric height fields, such as bathyT. ! === module MOM_fixed_initialization === -INPUTDIR = "INPUT/BATS" ! default = "." +INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" ! default = "." ! The directory in which input files are found. ! === module MOM_grid_init === @@ -267,6 +270,8 @@ CHANNEL_CONFIG = "none" ! default = "none" ! test case. ! file - Read open face widths everywhere from a ! NetCDF file on the model grid. +SUBGRID_TOPO_AT_VEL = False ! [Boolean] default = False + ! If true, use variables from TOPO_AT_VEL_FILE as parameters for porous barrier. ROTATION = "betaplane" ! default = "2omegasinlat" ! This specifies how the Coriolis parameter is specified: ! 2omegasinlat - Use twice the planetary rotation rate @@ -385,6 +390,8 @@ NDEP_SCALE_FACTOR = 1.0E+05 ! [not defined] default = 1.0E+05 ! Scale factor applied to nitrogen deposition terms BOT_FLUX_MIX_THICKNESS = 1.0 ! [m] default = 1.0 ! Bottom fluxes are uniformly mixed over layer of this thickness +CHL_FROM_FILE = False ! [Boolean] default = True + ! If true, chl_a is read from a file. USE_ICE_CATEGORIES = False ! [Boolean] default = False ! If true, allocate memory for shortwave and ice fraction split by ice thickness ! category. @@ -392,7 +399,7 @@ ICE_NCAT = 0 ! default = 0 ! Number of ice thickness categories in shortwave and ice fraction forcings. ! === module MARBL_tracers === -MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c221027.nc" +MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_latlon_1x1_180W_c230317.nc" ! The file in which the MARBL tracers initial values can be found. MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc" ! The file in which the iron sediment flux forcing field can be found. @@ -737,10 +744,10 @@ DYNAMIC_VISCOUS_ML = False ! [Boolean] default = False OMEGA = 7.2921E-05 ! [s-1] default = 7.2921E-05 ! The rotation rate of the earth. HBBL = 10.0 ! [m] - ! The thickness of a bottom boundary layer with a viscosity of KVBBL if - ! BOTTOMDRAGLAW is not defined, or the thickness over which near-bottom - ! velocities are averaged for the drag law if BOTTOMDRAGLAW is defined but - ! LINEAR_DRAG is not. + ! The thickness of a bottom boundary layer with a viscosity increased by + ! KV_EXTRA_BBL if BOTTOMDRAGLAW is not defined, or the thickness over which + ! near-bottom velocities are averaged for the drag law if BOTTOMDRAGLAW is + ! defined but LINEAR_DRAG is not. CDRAG = 0.0 ! [nondim] default = 0.003 ! CDRAG is the drag coefficient relating the magnitude of the velocity field to ! the bottom stress. CDRAG is only used if BOTTOMDRAGLAW is defined. @@ -836,6 +843,23 @@ STANLEY_A = 1.0 ! [not defined] default = 1.0 ! Coefficient a which scales chi in stochastic perturbation of the SGS T ! variance. +! === module MOM_porous_barriers === +PORBAR_ANSWER_DATE = 99991231 ! default = 99991231 + ! The vintage of the porous barrier weight function calculations. Values below + ! 20220806 recover the old answers in which the layer averaged weights are not + ! strictly limited by an upper-bound of 1.0 . +PORBAR_MASKING_DEPTH = 0.0 ! [m] default = 0.0 + ! If the effective average depth at the velocity cell is shallower than this + ! number, then porous barrier is not applied at that location. + ! PORBAR_MASKING_DEPTH is assumed to be positive below the sea surface. +PORBAR_ETA_INTERP = "MAX" ! default = "MAX" + ! A string describing the method that decides how the interface heights at the + ! velocity points are calculated. Valid values are: + ! MAX (the default) - maximum of the adjacent cells + ! MIN - minimum of the adjacent cells + ! ARITHMETIC - arithmetic mean of the adjacent cells + ! HARMONIC - harmonic mean of the adjacent cells + ! === module MOM_dynamics_unsplit === FIX_UNSPLIT_DT_VISC_BUG = True ! [Boolean] default = True ! If true, use the correct timestep in the viscous terms applied in the first @@ -992,7 +1016,20 @@ VERT_FRICTION_ANSWER_DATE = 99991231 ! default = 99991231 ! latter takes precedence. DIRECT_STRESS = False ! [Boolean] default = False ! If true, the wind stress is distributed over the topmost HMIX_STRESS of fluid - ! (like in HYCOM), and KVML may be set to a very small value. + ! (like in HYCOM), and an added mixed layer viscosity or a physically based + ! boundary layer turbulence parameterization is not needed for stability. +FIXED_DEPTH_LOTW_ML = False ! [Boolean] default = False + ! If true, use a Law-of-the-wall prescription for the mixed layer viscosity + ! within a boundary layer that is the lesser of HMIX_FIXED and the total depth + ! of the ocean in a column. +LOTW_VISCOUS_ML_FLOOR = False ! [Boolean] default = False + ! If true, use a Law-of-the-wall prescription to set a lower bound on the + ! viscous coupling between layers within the surface boundary layer, based the + ! distance of interfaces from the surface. This only acts when there are large + ! changes in the thicknesses of successive layers or when the viscosity is set + ! externally and the wind stress has subsequently increased. +VON_KARMAN_CONST = 0.41 ! [nondim] default = 0.41 + ! The value the von Karman constant as used for mixed layer viscosity. HARMONIC_VISC = True ! [Boolean] default = False ! If true, use the harmonic mean thicknesses for calculating the vertical ! viscosity. @@ -1003,9 +1040,12 @@ HARMONIC_BL_SCALE = 0.0 ! [nondim] default = 0.0 HMIX_FIXED = 0.01 ! [m] ! The prescribed depth over which the near-surface viscosity and diffusivity are ! elevated when the bulk mixed layer is not used. -KVML = 0.0 ! [m2 s-1] default = 1.0E-04 - ! The kinematic viscosity in the mixed layer. A typical value is ~1e-2 m2 s-1. - ! KVML is not used if BULKMIXEDLAYER is true. The default is set by KV. +KV_ML_INVZ2 = 0.0 ! [m2 s-1] default = 0.0 + ! An extra kinematic viscosity in a mixed layer of thickness HMIX_FIXED, with + ! the actual viscosity scaling as 1/(z*HMIX_FIXED)^2, where z is the distance + ! from the surface, to allow for finite wind stresses to be transmitted through + ! infinitesimally thin surface layers. This is an older option for numerical + ! convenience without a strong physical basis, and its use is now discouraged. MAXVEL = 6.0 ! [m s-1] default = 3.0E+08 ! The maximum velocity allowed before the velocity components are truncated. CFL_BASED_TRUNCATIONS = True ! [Boolean] default = True @@ -1294,9 +1334,6 @@ KD = 2.0E-05 ! [m2 s-1] default = 0.0 ! molecular value, ~1e-7 m2 s-1, may be used. KD_MIN = 2.0E-06 ! [m2 s-1] default = 2.0E-07 ! The minimum diapycnal diffusivity. -KDML = 2.0E-05 ! [m2 s-1] default = 2.0E-05 - ! If BULKMIXEDLAYER is false, KDML is the elevated diapycnal diffusivity in the - ! topmost HMIX of fluid. KDML is only used if BULKMIXEDLAYER is false. BRYAN_LEWIS_DIFFUSIVITY = False ! [Boolean] default = False ! If true, use a Bryan & Lewis (JGR 1979) like tanh profile of background ! diapycnal diffusivity with depth. This is done via CVMix. @@ -1309,9 +1346,6 @@ PRANDTL_BKGND = 1.0 ! [nondim] default = 1.0 HENYEY_IGW_BACKGROUND = False ! [Boolean] default = False ! If true, use a latitude-dependent scaling for the near surface background ! diffusivity, as described in Harrison & Hallberg, JPO 2008. -HENYEY_IGW_BACKGROUND_NEW = False ! [Boolean] default = False - ! If true, use a better latitude-dependent scaling for the background - ! diffusivity, as described in Harrison & Hallberg, JPO 2008. KD_TANH_LAT_FN = False ! [Boolean] default = False ! If true, use a tanh dependence of Kd_sfc on latitude, like CM2.1/CM2M. There ! is no physical justification for this form, and it can not be used with @@ -1447,13 +1481,6 @@ USE_CALVING_HEAT_CONTENT = False ! [Boolean] default = False VAR_PEN_SW = True ! [Boolean] default = False ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine ! the e-folding depth of incoming short wave radiation. -CHL_FROM_FILE = True ! [Boolean] default = True - ! If true, chl_a is read from a file. -CHL_FILE = "forcing_monthly.nc" ! - ! CHL_FILE is the file containing chl_a concentrations in the variable CHL_A. It - ! is used when VAR_PEN_SW and CHL_FROM_FILE are true. -CHL_VARNAME = "CHL_A" ! default = "CHL_A" - ! Name of CHL_A variable in CHL_FILE. ! === module MOM_regularize_layers === REGULARIZE_SURFACE_LAYERS = False ! [Boolean] default = False @@ -1618,6 +1645,30 @@ READ_GUST_2D = True ! [Boolean] default = False ! If true, use a 2-dimensional gustiness supplied from an input file GUST_2D_FILE = "forcing_monthly.nc" ! ! The file in which the wind gustiness is found in variable gustiness. +DUST_RATIO_THRES = 69.00594 ! [not defined] default = 69.00594 + ! TODO: Add description +DUST_RATIO_TO_FE_BIOAVAIL_FRAC = 0.0027298983931818 ! [not defined] default = 0.0027298983931818 + ! TODO: Add description +FE_BIOAVAIL_FRAC_OFFSET = 0.0146756 ! [not defined] default = 0.0146756 + ! TODO: Add description +ATM_FE_TO_BC_RATIO = 1.0 ! [not defined] default = 1.0 + ! TODO: Add description +ATM_BC_FE_BIOAVAIL_FRAC = 0.06 ! [not defined] default = 0.06 + ! TODO: Add description +SEAICE_FE_TO_BC_RATIO = 1.0 ! [not defined] default = 1.0 + ! TODO: Add description +SEAICE_BC_FE_BIOAVAIL_FRAC = 0.06 ! [not defined] default = 0.06 + ! TODO: Add description +IRON_FRAC_IN_ATM_FINE_DUST = 0.035 ! [not defined] default = 0.035 + ! Fraction of fine dust from the atmosphere that is iron +IRON_FRAC_IN_ATM_COARSE_DUST = 0.035 ! [not defined] default = 0.035 + ! Fraction of coarse dust from the atmosphere that is iron +IRON_FRAC_IN_SEAICE_DUST = 0.035 ! [not defined] default = 0.035 + ! Fraction of dust from sea ice that is iron +READ_NDEP = False ! [Boolean] default = True + ! If true, use nitrogen deposition supplied from an input file +READ_RIV_FLUXES = False ! [Boolean] default = True + ! If true, use nitrogen deposition supplied from an input file ! === module MOM_restart === USE_WAVES = False ! [Boolean] default = False diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short index 0c7b86a4..c971bdce 100644 --- a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short @@ -38,7 +38,7 @@ NJHALO = 2 ! default = 4 ! varies with the calling component and static or dynamic memory configuration. ! === module MOM_fixed_initialization === -INPUTDIR = "INPUT/BATS" ! default = "." +INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" ! default = "." ! The directory in which input files are found. ! === module MOM_grid_init === @@ -112,9 +112,11 @@ USE_MARBL_TRACERS = True ! [Boolean] default = False ! If true, use the MARBL tracer package. ! === module MARBL_tracers === +CHL_FROM_FILE = False ! [Boolean] default = True + ! If true, chl_a is read from a file. ! === module MARBL_tracers === -MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c221027.nc" +MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_latlon_1x1_180W_c230317.nc" ! The file in which the MARBL tracers initial values can be found. MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc" ! The file in which the iron sediment flux forcing field can be found. @@ -179,10 +181,10 @@ Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False ! === module MOM_set_visc === HBBL = 10.0 ! [m] - ! The thickness of a bottom boundary layer with a viscosity of KVBBL if - ! BOTTOMDRAGLAW is not defined, or the thickness over which near-bottom - ! velocities are averaged for the drag law if BOTTOMDRAGLAW is defined but - ! LINEAR_DRAG is not. + ! The thickness of a bottom boundary layer with a viscosity increased by + ! KV_EXTRA_BBL if BOTTOMDRAGLAW is not defined, or the thickness over which + ! near-bottom velocities are averaged for the drag law if BOTTOMDRAGLAW is + ! defined but LINEAR_DRAG is not. CDRAG = 0.0 ! [nondim] default = 0.003 ! CDRAG is the drag coefficient relating the magnitude of the velocity field to ! the bottom stress. CDRAG is only used if BOTTOMDRAGLAW is defined. @@ -201,6 +203,8 @@ KV = 1.0E-04 ! [m2 s-1] ! === module MOM_thickness_diffuse === +! === module MOM_porous_barriers === + ! === module MOM_dynamics_unsplit === ! === module MOM_continuity === @@ -244,9 +248,6 @@ HARMONIC_VISC = True ! [Boolean] default = False HMIX_FIXED = 0.01 ! [m] ! The prescribed depth over which the near-surface viscosity and diffusivity are ! elevated when the bulk mixed layer is not used. -KVML = 0.0 ! [m2 s-1] default = 1.0E-04 - ! The kinematic viscosity in the mixed layer. A typical value is ~1e-2 m2 s-1. - ! KVML is not used if BULKMIXEDLAYER is true. The default is set by KV. MAXVEL = 6.0 ! [m s-1] default = 3.0E+08 ! The maximum velocity allowed before the velocity components are truncated. VEL_UNDERFLOW = 1.0E-30 ! [m s-1] default = 0.0 @@ -317,9 +318,6 @@ RECLAIM_FRAZIL = False ! [Boolean] default = True VAR_PEN_SW = True ! [Boolean] default = False ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine ! the e-folding depth of incoming short wave radiation. -CHL_FILE = "forcing_monthly.nc" ! - ! CHL_FILE is the file containing chl_a concentrations in the variable CHL_A. It - ! is used when VAR_PEN_SW and CHL_FROM_FILE are true. ! === module MOM_opacity === PEN_SW_NBANDS = 3 ! default = 1 @@ -356,6 +354,10 @@ READ_GUST_2D = True ! [Boolean] default = False ! If true, use a 2-dimensional gustiness supplied from an input file GUST_2D_FILE = "forcing_monthly.nc" ! ! The file in which the wind gustiness is found in variable gustiness. +READ_NDEP = False ! [Boolean] default = True + ! If true, use nitrogen deposition supplied from an input file +READ_RIV_FLUXES = False ! [Boolean] default = True + ! If true, use nitrogen deposition supplied from an input file ! === module MOM_main (MOM_driver) === DAYMAX = 1825.0 ! [days] diff --git a/standalone/examples/single_column/KPP_MARBL/data_table b/standalone/examples/single_column/KPP_MARBL/data_table index 5a71f07e..a4c9c3fa 100644 --- a/standalone/examples/single_column/KPP_MARBL/data_table +++ b/standalone/examples/single_column/KPP_MARBL/data_table @@ -1,7 +1,6 @@ # gridname, fieldname_code, fieldname_file, file_name, ongrid, factor "OCN" , "lw", "LWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -#"OCN" , "sw", "SWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "sw", "netswdn_sfc", "./INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc", .true., 1.0 +"OCN" , "sw", "SWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 "OCN" , "rain", "RAIN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 "OCN" , "evap", "EVAP", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 "OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 diff --git a/standalone/examples/single_column/KPP_MARBL/diag_table b/standalone/examples/single_column/KPP_MARBL/diag_table index ef40b3eb..8f6227fa 100644 --- a/standalone/examples/single_column/KPP_MARBL/diag_table +++ b/standalone/examples/single_column/KPP_MARBL/diag_table @@ -301,12 +301,14 @@ "ocean_model","lprec","lprec","surffluxes","all",.true.,"none",2 "ocean_model","net_heat_surface","net_heat_surface","surffluxes","all",.true.,"none",2 "ocean_model","SW","SW","surffluxes","all",.true.,"none",2 +"ocean_model","SW_pen","SW_pen","surffluxes","all",.true.,"none",2 "ocean_model","LwLatSens","LwLatSens","surffluxes","all",.true.,"none",2 #"ocean_model","p_surf","p_surf","surffluxes","all",.true.,"none",2 #"ocean_model","salt_flux","salt_flux","surffluxes","all",.true.,"none",2 # MARBL diagnostics: #=================== +"ocean_model", "rsdoabsorb", "rsdoabsorb", "marbl", "all", .false., "none", 2 "ocean_model", "ECOSYS_IFRAC", "ECOSYS_IFRAC", "marbl", "all", .false., "none", 2 "ocean_model", "ECOSYS_XKW", "ECOSYS_XKW", "marbl", "all", .false., "none", 2 "ocean_model", "ECOSYS_ATM_PRESS", "ECOSYS_ATM_PRESS", "marbl", "all", .false., "none", 2 diff --git a/standalone/templates/cheyenne-intel.mk b/standalone/templates/cheyenne-intel.mk index 8b66e07b..1860b474 100644 --- a/standalone/templates/cheyenne-intel.mk +++ b/standalone/templates/cheyenne-intel.mk @@ -6,10 +6,10 @@ # commands # ############ -FC = ifort +FC = mpif90 CC = icc CXX = icpc -LD = ifort +LD = mpif90 ############ # flags # @@ -80,7 +80,7 @@ else endif #LIBS += -lmpi -LIBS += -lmpi -lsma +#LIBS += -lmpi -lsma #LIBS += -lmkl_blas95_lp64 -lmkl_lapack95_lp64 -lmkl_intel_lp64 -lmkl_core -lmkl_sequential LDFLAGS += $(LIBS) From e38f049b11148f9b21a952bd42e20e4eafaeec7f Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 29 Mar 2023 16:21:38 -0600 Subject: [PATCH 071/136] Two small updates 1. OCN_BGC_CONFIG can be "none"; that is the default unless the compset longname includes MOM6%MARBL. USE_MARBL_TRACERS then triggers off OCN_BGC_CONFIG instead of the compset. This was necessary to prevent CHL_FROM_FILE=FALSE in non-MARBL configurations. 2. Removed READ_NDEP from MOM_override in the single_column KPP_MARBL test. NDEP now needs to come from the solo_driver cap, where it can be set via the data_table. (Currently, we don't have a dataset for it but that should change in a later commit.) --- cime_config/config_component.xml | 7 +++++-- param_templates/MOM_input.yaml | 2 +- param_templates/json/MOM_input.json | 2 +- .../single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override | 1 - 4 files changed, 7 insertions(+), 5 deletions(-) diff --git a/cime_config/config_component.xml b/cime_config/config_component.xml index 6e10de29..7927389e 100644 --- a/cime_config/config_component.xml +++ b/cime_config/config_component.xml @@ -70,8 +70,11 @@ char - latest - latest + none,latest + none + + latest + run_component_mom env_run.xml Version of ocean BGC tunings to use (default values can be overwritten by user_nl_mom and user_nl_marbl). diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index d150b2d1..5ff84373 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -346,7 +346,7 @@ Global: datatype: logical units: Boolean value: - bool(re.search("MOM6%[^_]*MARBL", $COMPSET ))== True: True + $OCN_BGC_CONFIG != "none": True else: False CHL_FROM_FILE: description: | diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index d4b7f52f..618851ec 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -245,7 +245,7 @@ "datatype": "logical", "units": "Boolean", "value": { - "bool(re.search(\"MOM6%[^_]*MARBL\", $COMPSET ))== True": true, + "$OCN_BGC_CONFIG != \"none\"": true, "else": false } }, diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override index aeb918ce..54550022 100644 --- a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override +++ b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override @@ -3,5 +3,4 @@ F_0 = 7.59943e-05 INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" CESM_INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" CHL_FROM_FILE = FALSE -READ_NDEP = FALSE READ_RIV_FLUXES = FALSE From 838afa93d25fdd9827c19b3e0669b32234835ebb Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 31 Mar 2023 08:32:18 -0600 Subject: [PATCH 072/136] Added NDEP and SEAICE forcings from A compset This updates data_table to provide NDEP_NHx, NDEP_NOy, SEAICE_DUST_FLUX, and SEAICE_BC_FLUX. 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TS_IC_FILE_COLUMN.nc Required setting TEMP_SALT_INIT_VERTICAL_REMAP_ONLY=TRUE to avoid horizontal interpolation (which is the correct thing to do anyway). Also added a similar option for MARBL_TRACERS and set that to TRUE since IC_FILE_COLUMN.nc is also on the correct grid. --- .../INPUT/CESM_MOM_COLUMNS/BATS/MOM_override | 2 ++ .../CESM_MOM_COLUMNS/BATS/TS_IC_FILE_COLUMN.nc | Bin 0 -> 25010 bytes .../INPUT/CESM_MOM_COLUMNS/BATS/WOA_column.nc | Bin 15052 -> 0 bytes .../examples/single_column/KPP_MARBL/MOM_input | 8 ++++---- .../KPP_MARBL/MOM_parameter_doc.all | 16 ++++++++++------ .../KPP_MARBL/MOM_parameter_doc.short | 14 +++++++++++--- 6 files changed, 27 insertions(+), 13 deletions(-) create mode 100644 standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/TS_IC_FILE_COLUMN.nc delete mode 100644 standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/WOA_column.nc diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override index 54550022..97be9bc6 100644 --- 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[Boolean] default = False ! Z-space file on a latitude-longitude grid. ! === module MOM_initialize_layers_from_Z === -TEMP_SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "temp_salt_z.nc" +TEMP_SALT_Z_INIT_FILE = "TS_IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" ! The name of the z-space input file used to initialize temperatures (T) and ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and ! SALT_Z_INIT_FILE must be set. -Z_INIT_FILE_PTEMP_VAR = "PTEMP" ! default = "ptemp" - ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. -Z_INIT_FILE_SALT_VAR = "SALT" ! default = "salt" +Z_INIT_FILE_PTEMP_VAR = "theta0" ! default = "ptemp" + ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. +Z_INIT_FILE_SALT_VAR = "s_an" ! default = "salt" ! The name of the salinity variable in SALT_Z_INIT_FILE. Z_INIT_HOMOGENIZE = True ! [Boolean] default = False ! If True, then horizontally homogenize the interpolated initial conditions. diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all index cea2916d..4f5df014 100644 --- a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all @@ -399,6 +399,10 @@ ICE_NCAT = 0 ! default = 0 ! === module MARBL_tracers === MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_latlon_1x1_180W_c230317.nc" ! The file in which the MARBL tracers initial values can be found. +MARBL_TRACERS_INIT_VERTICAL_REMAP_ONLY = True ! [Boolean] default = False + ! If true, initial conditions are on the model horizontal grid. Extrapolation + ! over missing ocean values is done using an ICE-9 procedure with vertical ALE + ! remapping . MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc" ! The file in which the iron sediment flux forcing field can be found. MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211109.nc" @@ -549,17 +553,17 @@ INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False ! Z-space file on a latitude-longitude grid. ! === module MOM_initialize_layers_from_Z === -TEMP_SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "temp_salt_z.nc" +TEMP_SALT_Z_INIT_FILE = "TS_IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" ! The name of the z-space input file used to initialize temperatures (T) and ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and ! SALT_Z_INIT_FILE must be set. -TEMP_Z_INIT_FILE = "WOA_column.nc" ! default = "WOA_column.nc" +TEMP_Z_INIT_FILE = "TS_IC_FILE_COLUMN.nc" ! default = "TS_IC_FILE_COLUMN.nc" ! The name of the z-space input file used to initialize temperatures, only. -SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "WOA_column.nc" +SALT_Z_INIT_FILE = "TS_IC_FILE_COLUMN.nc" ! default = "TS_IC_FILE_COLUMN.nc" ! The name of the z-space input file used to initialize temperatures, only. -Z_INIT_FILE_PTEMP_VAR = "PTEMP" ! default = "ptemp" +Z_INIT_FILE_PTEMP_VAR = "theta0" ! default = "ptemp" ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. -Z_INIT_FILE_SALT_VAR = "SALT" ! default = "salt" +Z_INIT_FILE_SALT_VAR = "s_an" ! default = "salt" ! The name of the salinity variable in SALT_Z_INIT_FILE. Z_INIT_HOMOGENIZE = True ! [Boolean] default = False ! If True, then horizontally homogenize the interpolated initial conditions. @@ -576,7 +580,7 @@ Z_INIT_REMAP_FULL_COLUMN = False ! [Boolean] default = False Z_INIT_REMAP_OLD_ALG = False ! [Boolean] default = False ! If false, uses the preferred remapping algorithm for initialization. If true, ! use an older, less robust algorithm for remapping. -TEMP_SALT_INIT_VERTICAL_REMAP_ONLY = False ! [Boolean] default = False +TEMP_SALT_INIT_VERTICAL_REMAP_ONLY = True ! [Boolean] default = False ! If true, initial conditions are on the model horizontal grid. Extrapolation ! over missing ocean values is done using an ICE-9 procedure with vertical ALE ! remapping . diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short index 57b0f437..a27b1c12 100644 --- a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short @@ -118,6 +118,10 @@ CHL_FROM_FILE = False ! [Boolean] default = True ! === module MARBL_tracers === MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_latlon_1x1_180W_c230317.nc" ! The file in which the MARBL tracers initial values can be found. +MARBL_TRACERS_INIT_VERTICAL_REMAP_ONLY = True ! [Boolean] default = False + ! If true, initial conditions are on the model horizontal grid. Extrapolation + ! over missing ocean values is done using an ICE-9 procedure with vertical ALE + ! remapping . MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc" ! The file in which the iron sediment flux forcing field can be found. MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211109.nc" @@ -162,18 +166,22 @@ INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False ! Z-space file on a latitude-longitude grid. ! === module MOM_initialize_layers_from_Z === -TEMP_SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "temp_salt_z.nc" +TEMP_SALT_Z_INIT_FILE = "TS_IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" ! The name of the z-space input file used to initialize temperatures (T) and ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and ! SALT_Z_INIT_FILE must be set. -Z_INIT_FILE_PTEMP_VAR = "PTEMP" ! default = "ptemp" +Z_INIT_FILE_PTEMP_VAR = "theta0" ! default = "ptemp" ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. -Z_INIT_FILE_SALT_VAR = "SALT" ! default = "salt" +Z_INIT_FILE_SALT_VAR = "s_an" ! default = "salt" ! The name of the salinity variable in SALT_Z_INIT_FILE. Z_INIT_HOMOGENIZE = True ! [Boolean] default = False ! If True, then horizontally homogenize the interpolated initial conditions. Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False ! If True, then remap straight to model coordinate from file. +TEMP_SALT_INIT_VERTICAL_REMAP_ONLY = True ! [Boolean] default = False + ! If true, initial conditions are on the model horizontal grid. Extrapolation + ! over missing ocean values is done using an ICE-9 procedure with vertical ALE + ! remapping . ! === module MOM_diag_mediator === From e66025ffb00e2df06e6f90a195044e91ddad5417 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 31 Mar 2023 12:46:38 -0600 Subject: [PATCH 074/136] Add support for submitting build script to PBS Also added ability to override the default directory name that just matches compiler choice (so Cheyenne can build to intel-cheyenne/ while Casper builds to intel-casper/) --- standalone/build/.gitignore | 2 ++ standalone/build/build_examples-cheyenne.sh | 18 +++++++++++++---- standalone/build/submit_casper | 22 +++++++++++++++++++++ standalone/build/submit_cheyenne | 22 +++++++++++++++++++++ 4 files changed, 60 insertions(+), 4 deletions(-) create mode 100644 standalone/build/submit_casper create mode 100644 standalone/build/submit_cheyenne diff --git a/standalone/build/.gitignore b/standalone/build/.gitignore index 970ab8d2..2d43e94f 100644 --- a/standalone/build/.gitignore +++ b/standalone/build/.gitignore @@ -1,2 +1,4 @@ intel/ +intel-*/ gnu/ +gnu-*/ diff --git a/standalone/build/build_examples-cheyenne.sh b/standalone/build/build_examples-cheyenne.sh index 1f22aa74..d0c0450f 100755 --- a/standalone/build/build_examples-cheyenne.sh +++ b/standalone/build/build_examples-cheyenne.sh @@ -2,6 +2,8 @@ ### For NCAR machines, this script should be run as ### $ qcmd -- ./build_examples.sh +echo "Starting build at `date`" + # Save various paths to use as shortcuts cd ../.. INTERFACE_ROOT=`pwd -P` @@ -16,20 +18,28 @@ SHR_ROOT=${CESM_ROOT}/components/cdeps/share COMPILER=intel TEMPLATE=${TEMPLATE_DIR}/cheyenne-${COMPILER}.mk +if [ -e $1 ]; then + BLD_ROOT=${COMPILER} +else + BLD_ROOT=$1 +fi + # 1) Build FMS # (b) build FMS cd ${INTERFACE_ROOT}/standalone/build -mkdir -p ${COMPILER}/FMS -cd ${COMPILER}/FMS +mkdir -p ${BLD_ROOT}/FMS +cd ${BLD_ROOT}/FMS ${MKMF_ROOT}/list_paths ${FMS_ROOT}/src ${MKMF_ROOT}/mkmf -t ${TEMPLATE} -p libfms.a -c "-Duse_libMPI -Duse_netCDF -DSPMD" path_names make -j36 NETCDF=3 REPRO=1 libfms.a # 2) Build MOM6 cd ${INTERFACE_ROOT}/standalone/build -mkdir -p ${COMPILER}/MOM6 -cd ${COMPILER}/MOM6 +mkdir -p ${BLD_ROOT}/MOM6 +cd ${BLD_ROOT}/MOM6 ${MKMF_ROOT}/list_paths -l ${MOM_ROOT}/{config_src/infra/FMS2,config_src/memory/dynamic_symmetric,config_src/drivers/solo_driver,pkg/MARBL/src,config_src/external,src/{*,*/*}}/ ${MKMF_ROOT}/mkmf -t ${TEMPLATE} -o '-I../FMS' -p MOM6 -l '-L../FMS -lfms' -c '-Duse_libMPI -Duse_netCDF -DSPMD' path_names make -j36 NETCDF=3 REPRO=1 MOM6 + +echo "Finished build at `date`" diff --git a/standalone/build/submit_casper b/standalone/build/submit_casper new file mode 100644 index 00000000..88eb8026 --- /dev/null +++ b/standalone/build/submit_casper @@ -0,0 +1,22 @@ +#!/bin/bash + +### Job Name +#PBS -N standalone_build_casper + +### Project code +#PBS -A P93300070 +#PBS -l walltime=00:30:00 +#PBS -q casper + +### Merge output and error files +#PBS -j oe +#PBS -k eod + +### Select 1 nodes with 18 CPUs +#PBS -l select=1:ncpus=18:mpiprocs=18 + +### Send email on abort, begin and end +#PBS -m abe + +### Run the executable +./build_examples-cheyenne.sh intel-casper diff --git a/standalone/build/submit_cheyenne b/standalone/build/submit_cheyenne new file mode 100644 index 00000000..33d275a1 --- /dev/null +++ b/standalone/build/submit_cheyenne @@ -0,0 +1,22 @@ +#!/bin/bash + +### Job Name +#PBS -N standalone_build_cheyenne + +### Project code +#PBS -A P93300070 +#PBS -l walltime=00:30:00 +#PBS -q share + +### Merge output and error files +#PBS -j oe +#PBS -k eod + +### Select 1 nodes with 18 CPUs +#PBS -l select=1:ncpus=18:mpiprocs=18 + +### Send email on abort, begin and end +#PBS -m abe + +### Run the executable +./build_examples-cheyenne.sh intel-cheyenne From 461a710305e0cf1e09770268d1d97711d3b2dd23 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 11 Apr 2023 15:19:45 -0600 Subject: [PATCH 075/136] Bugfix plus parameter reversion 1. MOM6 expects sign convention of sens and evap to be opposite what CESM provides, so we need to scale by -1 when we read it in 2. Need TEMP_SALT_INIT_VERTICAL_REMAP_ONLY and MARBL_TRACERS_INIT_VERTICAL_REMAP_ONLY both FALSE to get correct vertical profile in IC file 3. Need to use WOA_column.nc with TEMP_SALT_INIT_VERTICAL_REMAP_ONLY set to false, for some reason TS_IC_FILE_COLUMN.nc causes code to crash when being read in --- .../INPUT/CESM_MOM_COLUMNS/BATS/MOM_override | 4 ++-- .../INPUT/CESM_MOM_COLUMNS/BATS/WOA_column.nc | Bin 0 -> 15052 bytes .../examples/single_column/KPP_MARBL/MOM_input | 8 ++++---- .../KPP_MARBL/MOM_parameter_doc.all | 14 +++++++------- .../KPP_MARBL/MOM_parameter_doc.short | 14 +++----------- .../examples/single_column/KPP_MARBL/data_table | 4 ++-- 6 files changed, 18 insertions(+), 26 deletions(-) create mode 100644 standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/WOA_column.nc diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override index 97be9bc6..210e5944 100644 --- a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override +++ b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override @@ -2,7 +2,7 @@ F_0 = 7.59943e-05 INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" CESM_INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" -TEMP_SALT_INIT_VERTICAL_REMAP_ONLY=TRUE -MARBL_TRACERS_INIT_VERTICAL_REMAP_ONLY=TRUE +TEMP_SALT_INIT_VERTICAL_REMAP_ONLY=FALSE +MARBL_TRACERS_INIT_VERTICAL_REMAP_ONLY=FALSE CHL_FROM_FILE = FALSE READ_RIV_FLUXES = FALSE diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/WOA_column.nc b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/WOA_column.nc new file mode 100644 index 0000000000000000000000000000000000000000..2ddf27d6e77f9e2c759264db8f42ab0658e3f987 GIT binary patch literal 15052 zcmd7Zc~liwo(FJLATGu7s*QULG44TvOOkPm_xBS}1eYkTtrC={K4g>4U89ghLPj@D1qnLJ_Td!V)Z*h9UnSU7T`0;md-P-Hc z@7Akx80r_$$Vs1?%itn42_8L4o731{rVyj-;^#jmWVlvya(Y2>o)H}$FQc}cb4cJw ze;E}AZJSMJ*&?mcVNq6D)|PRbIGRFd#zZDYMf*h0lr!YiLTV}*`!tS4+m5s3k{E4E z&~_pxZ5v9sb#|Q98Xsy6i%*bs*Ql8A#K;(}*~wLEB$D3J%G%@>8)J)32#t>on`w)l zt!>-%qRKc-N}UAJHFM)Kadw3Lh2ZPSidqg^j!lIPc0UR&1= za)!}gv~$zWSG%_V>fAbphb@fvinm42w0il zc$~j%_tn?#J53(>Z|C$vuq@ZTFWNb2=U1oyIaNA24NxjO%N99_H{PnpJTZL zOYHT^miFKz>P8>Od?WUE)Q#xps2g##u+I=i9AB;1jX2dyuNyJUah^uBI+izLq+@v_ z&T}kp#6-vPMqJ`p-iRw4%NsFejFUDABfcA}*NwP3L$4cg?H;{u#0)*_3nOMe)Z2}i z<+!d!+|WjEH)2kyUN>THxL!A6q1MIa@fmTe<9-=&yMDif5kGLOZ^Sak`bON7r*F@Q ze_E&4jktfMUN_>wB)x9j?*Mz%NsN6{pYIqj#GB{!diWMS&T(AF$T507)>Du39M>oQ zu3k^n*Q0H1vdmtJrfsP8Y1#&g?{z1W%r;aE-h1pqXzQnI2^AGDB9ynkf>8EPYYAok zx{Odp?}>!cc61|@(&1-9(Ut{-Mv2RWyu;QJQbM*95Z~SW#^mFZ#R8R$Un$V$p3Mcy zu@wol_S_wT7FO5 z|D%lxz9}+$uaXK-SOyiK*4|VA@{6+w<=wNANl;_T1G!`7654p_BBAW6V}#ao0-=od z!wIETx)4hKrahs^RsMv6=6530?caYU zb!NFhm>aY*<_4{dxj`#qZqUk*8?>@1H)y#6a)VZ`uaD8@GZALC?z9;cohl&{=U6HN z1$~1F?V})+`EL*tSN+&S8OCSw{tq7K#M=2 zE@cAZQ@BTD&JMB7NVX~uU5~0%c z?S!`PJWXgz&^v_kcSRD~bo&~ijZTdTZTO4`WhMN9Q06cHB$N@=pU|3D7ZOUoH-k{5 z*F-`i%1a4#bNQB#YeXUeajj~u$>II43slbWhNQ1d0}1Vr_Br%aB0zf_>orH=&4pZSVF>*F2^wCZMG0UG-G zuWx^Jd6B2d7w5eQoxL!d(20R(2$kPzN2qx02112xKb7yBwQ}cF5z4v4gt7|z63V$3u-E*~J!)0Q8u+ZGbK)}sxfuQUHa=-iGBLI-`85_*4GB%z{is|n@yUrs2eQ;f_H zwbobnC6rl_K`8yQ523WU&j=;AT1qJ5NI9Xv;>m=%#GNDL`s$Yi#FdIzlZqNwfyy4; 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[Boolean] default = False ! Z-space file on a latitude-longitude grid. ! === module MOM_initialize_layers_from_Z === -TEMP_SALT_Z_INIT_FILE = "TS_IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" +TEMP_SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "temp_salt_z.nc" ! The name of the z-space input file used to initialize temperatures (T) and ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and ! SALT_Z_INIT_FILE must be set. -Z_INIT_FILE_PTEMP_VAR = "theta0" ! default = "ptemp" - ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. -Z_INIT_FILE_SALT_VAR = "s_an" ! default = "salt" +Z_INIT_FILE_PTEMP_VAR = "PTEMP" ! default = "ptemp" + ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. +Z_INIT_FILE_SALT_VAR = "SALT" ! default = "salt" ! The name of the salinity variable in SALT_Z_INIT_FILE. Z_INIT_HOMOGENIZE = True ! [Boolean] default = False ! If True, then horizontally homogenize the interpolated initial conditions. diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all index 4f5df014..557d3976 100644 --- a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all @@ -399,7 +399,7 @@ ICE_NCAT = 0 ! default = 0 ! === module MARBL_tracers === MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_latlon_1x1_180W_c230317.nc" ! The file in which the MARBL tracers initial values can be found. -MARBL_TRACERS_INIT_VERTICAL_REMAP_ONLY = True ! [Boolean] default = False +MARBL_TRACERS_INIT_VERTICAL_REMAP_ONLY = False ! [Boolean] default = False ! If true, initial conditions are on the model horizontal grid. Extrapolation ! over missing ocean values is done using an ICE-9 procedure with vertical ALE ! remapping . @@ -553,17 +553,17 @@ INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False ! Z-space file on a latitude-longitude grid. ! === module MOM_initialize_layers_from_Z === -TEMP_SALT_Z_INIT_FILE = "TS_IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" +TEMP_SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "temp_salt_z.nc" ! The name of the z-space input file used to initialize temperatures (T) and ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and ! SALT_Z_INIT_FILE must be set. -TEMP_Z_INIT_FILE = "TS_IC_FILE_COLUMN.nc" ! default = "TS_IC_FILE_COLUMN.nc" +TEMP_Z_INIT_FILE = "WOA_column.nc" ! default = "WOA_column.nc" ! The name of the z-space input file used to initialize temperatures, only. -SALT_Z_INIT_FILE = "TS_IC_FILE_COLUMN.nc" ! default = "TS_IC_FILE_COLUMN.nc" +SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "WOA_column.nc" ! The name of the z-space input file used to initialize temperatures, only. -Z_INIT_FILE_PTEMP_VAR = "theta0" ! default = "ptemp" +Z_INIT_FILE_PTEMP_VAR = "PTEMP" ! default = "ptemp" ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. -Z_INIT_FILE_SALT_VAR = "s_an" ! default = "salt" +Z_INIT_FILE_SALT_VAR = "SALT" ! default = "salt" ! The name of the salinity variable in SALT_Z_INIT_FILE. Z_INIT_HOMOGENIZE = True ! [Boolean] default = False ! If True, then horizontally homogenize the interpolated initial conditions. @@ -580,7 +580,7 @@ Z_INIT_REMAP_FULL_COLUMN = False ! [Boolean] default = False Z_INIT_REMAP_OLD_ALG = False ! [Boolean] default = False ! If false, uses the preferred remapping algorithm for initialization. If true, ! use an older, less robust algorithm for remapping. -TEMP_SALT_INIT_VERTICAL_REMAP_ONLY = True ! [Boolean] default = False +TEMP_SALT_INIT_VERTICAL_REMAP_ONLY = False ! [Boolean] default = False ! If true, initial conditions are on the model horizontal grid. Extrapolation ! over missing ocean values is done using an ICE-9 procedure with vertical ALE ! remapping . diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short index a27b1c12..57b0f437 100644 --- a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short @@ -118,10 +118,6 @@ CHL_FROM_FILE = False ! [Boolean] default = True ! === module MARBL_tracers === MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_latlon_1x1_180W_c230317.nc" ! The file in which the MARBL tracers initial values can be found. -MARBL_TRACERS_INIT_VERTICAL_REMAP_ONLY = True ! [Boolean] default = False - ! If true, initial conditions are on the model horizontal grid. Extrapolation - ! over missing ocean values is done using an ICE-9 procedure with vertical ALE - ! remapping . MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc" ! The file in which the iron sediment flux forcing field can be found. MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211109.nc" @@ -166,22 +162,18 @@ INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False ! Z-space file on a latitude-longitude grid. ! === module MOM_initialize_layers_from_Z === -TEMP_SALT_Z_INIT_FILE = "TS_IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" +TEMP_SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "temp_salt_z.nc" ! The name of the z-space input file used to initialize temperatures (T) and ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and ! SALT_Z_INIT_FILE must be set. -Z_INIT_FILE_PTEMP_VAR = "theta0" ! default = "ptemp" +Z_INIT_FILE_PTEMP_VAR = "PTEMP" ! default = "ptemp" ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. -Z_INIT_FILE_SALT_VAR = "s_an" ! default = "salt" +Z_INIT_FILE_SALT_VAR = "SALT" ! default = "salt" ! The name of the salinity variable in SALT_Z_INIT_FILE. Z_INIT_HOMOGENIZE = True ! [Boolean] default = False ! If True, then horizontally homogenize the interpolated initial conditions. Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False ! If True, then remap straight to model coordinate from file. -TEMP_SALT_INIT_VERTICAL_REMAP_ONLY = True ! [Boolean] default = False - ! If true, initial conditions are on the model horizontal grid. Extrapolation - ! over missing ocean values is done using an ICE-9 procedure with vertical ALE - ! remapping . ! === module MOM_diag_mediator === diff --git a/standalone/examples/single_column/KPP_MARBL/data_table b/standalone/examples/single_column/KPP_MARBL/data_table index a9ae7a83..a3e02730 100644 --- a/standalone/examples/single_column/KPP_MARBL/data_table +++ b/standalone/examples/single_column/KPP_MARBL/data_table @@ -2,8 +2,8 @@ "OCN" , "lw", "LWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 "OCN" , "sw", "SWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 "OCN" , "rain", "RAIN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "evap", "EVAP", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "evap", "EVAP", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., -1.0 +"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., -1.0 "OCN" , "taux", "TAUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 "OCN" , "tauy", "TAUY", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 "OCN" , "snow", "SNOW", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 From 60d131cae2c1bcd70edfa15ff826b3aaff7c0d0b Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 14 Apr 2023 15:53:51 -0600 Subject: [PATCH 076/136] Updated INPUT files 1. All fields in IC_FILE_COLUMN.nc (including Temp and Salt) are grabbed from the same MOM_IC.nc file output by a CESM-MOM6 run with MARBL, so they should all be consistent -- this means we don't need TS_IC_FILE_COLUMN.nc or WOA_column.nc anymore 2. I flipped the sign of SENS_HEAT_FLUX and EVAP in MARBL_FORCING_FILE_COLUMN.nc and added a standard name to note they are positive up 3. I removed the INIT_VERTICAL_REMAP_ONLY flags from MOM_override since we want to use the default values --- .../CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc | Bin 191237 -> 134712 bytes .../BATS/MARBL_FORCING_FILE_COLUMN.nc | Bin 2800499 -> 3125511 bytes .../INPUT/CESM_MOM_COLUMNS/BATS/MOM_override | 2 -- .../BATS/TS_IC_FILE_COLUMN.nc | Bin 25010 -> 0 bytes .../INPUT/CESM_MOM_COLUMNS/BATS/WOA_column.nc | Bin 15052 -> 0 bytes .../single_column/KPP_MARBL/MOM_input | 6 +++--- .../KPP_MARBL/MOM_parameter_doc.all | 10 +++++----- .../KPP_MARBL/MOM_parameter_doc.short | 6 +++--- .../single_column/KPP_MARBL/data_table | 4 ++-- 9 files changed, 13 insertions(+), 15 deletions(-) delete mode 100644 standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/TS_IC_FILE_COLUMN.nc delete mode 100644 standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/WOA_column.nc diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc index 666c0df4bfcbbb57916f45bc43e1e9f35f57caaa..9d23e9d0f9f46124063b616c51ed75699fec85f2 100644 GIT binary patch literal 134712 zcmeF42V507_xI-lq9PU)1Qiv*0*Xj)%AH(#?N-k zRqVa@3Mv**Z1^U368Cy{^XxwRi2Gk(^4ax!&o7xXXOc|IWQM7Zw)RaG+A7F2YQ&KA z1t;@nGGa*|XFR+(@|lODt+$NgHW^K^CTobZu2}Lvl$!7y)|;n-3?o3lRMZ(c2w541 zIG3iP4k{w175aj#n3l43t#S zKfhDOAuvbx9O^9&ziF93po|L22m}It6M6qglT}6$D`~JW4wL|6+=MayBFiuhnxP!o z?!Q&kG6xifE|Xj)F3P;d&dAp4(YIEFmXKwX8AbR%6XYJA92z#n*d{J6F)T7RBsn~Z zX#v-R>WpDD#DT(fixVL-6nii*CRs+FuQVH1k&lnK#!&5`U~HY-?A(2vJlunPY(%bh zjA93*%Q6l41>8k$cH;7AMW}pqNV3?eEG*Lu#s+l~n{8xFWZ1V+!39uK?1mzhWfXX) z(Q&augTA&Msx9Q%H6%GQIVCJyQ5)9eZy~3|MkXhTLuiT%hJ_DJ3=dBVij7N5j!WJFZ%Aq+Ul>&a^4gzaTD?@3<(rlHuFc9YZ(*aO6w2dkC$7_GMO*e?H>fc?>aRgA1@;1F7pgIr;!AJrJ!~9Z_&w+sw12_0j>n&Okhoap&l<&l z(&kG?f8Qj>anB|E@Aou@+24SV(+-DA_Oym|xu>CO``b;DyzZ?Vv(3KP<+nP&fc6QP z9C z=g7hX+!0(o^0;|$i!yRBFF_uc{NrFw~#_cB92C4TwP%+NdQ8SyphxkU!99;6_o@<4;fc$|5u( zJTxjPCFYy+q{z_5#LUpl*51a%(9XitHqXC_>kZOMI?RNld~J{uJc{)%pCb94m3!!B z{I$i5kvkb#fq=9W?oQ-5&d~I~)}pfMSIU>1sXfAX!1gA5@Sk~nJ&noJThs{^{jaW0 z@Tn3m#3ynv6E_9EO2b(UGh2Bn>F>Oj>0lB~Y8xJN8uO|bUO-Xfj}K&MH$xTY8j=>C z2qQaEi6Bqb)g4}I96XW8;6O$w12B0&Q4)$g2LJeBi7D)%gLS1BCX8AmDAK zaJg_^2si*&>o6!3T+$AhbwCsjtWdt~P!Is+yF((uh7&NYi{i_*i3M&b-#;GQfNPz! 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a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override +++ b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override @@ -2,7 +2,5 @@ F_0 = 7.59943e-05 INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" CESM_INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" -TEMP_SALT_INIT_VERTICAL_REMAP_ONLY=FALSE -MARBL_TRACERS_INIT_VERTICAL_REMAP_ONLY=FALSE CHL_FROM_FILE = FALSE READ_RIV_FLUXES = FALSE diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/TS_IC_FILE_COLUMN.nc b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/TS_IC_FILE_COLUMN.nc deleted file mode 100644 index 8b5075cf299ae40ebd5260d580f607fdfdf46fb9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 25010 zcmeHP3w%_?xu4BKOtObZRzN^42gFLngiQ!2Sln|a58y)HsHo_&$!?O>&2HQW5HBc# zC?LvHL6L_@kP21sac!$s=oL|^)~Z}9RRn7hA8oZ46|I%~eP?Di-zI>;a@Bi(JHPz* zf97%K@y*ORGvE0p^TrgFbjj+Qm6nmglsh8b!(I{Mk`cQPJo0+^n4+q*E<4iF23Uc1 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[Boolean] default = False ! Z-space file on a latitude-longitude grid. ! === module MOM_initialize_layers_from_Z === -TEMP_SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "temp_salt_z.nc" +TEMP_SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" ! The name of the z-space input file used to initialize temperatures (T) and ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and ! SALT_Z_INIT_FILE must be set. -Z_INIT_FILE_PTEMP_VAR = "PTEMP" ! default = "ptemp" +Z_INIT_FILE_PTEMP_VAR = "Temp" ! default = "ptemp" ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. -Z_INIT_FILE_SALT_VAR = "SALT" ! default = "salt" +Z_INIT_FILE_SALT_VAR = "Salt" ! default = "salt" ! The name of the salinity variable in SALT_Z_INIT_FILE. Z_INIT_HOMOGENIZE = True ! [Boolean] default = False ! If True, then horizontally homogenize the interpolated initial conditions. diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all index 557d3976..431ae5f4 100644 --- a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all @@ -553,17 +553,17 @@ INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False ! Z-space file on a latitude-longitude grid. ! === module MOM_initialize_layers_from_Z === -TEMP_SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "temp_salt_z.nc" +TEMP_SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" ! The name of the z-space input file used to initialize temperatures (T) and ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and ! SALT_Z_INIT_FILE must be set. -TEMP_Z_INIT_FILE = "WOA_column.nc" ! default = "WOA_column.nc" +TEMP_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "IC_FILE_COLUMN.nc" ! The name of the z-space input file used to initialize temperatures, only. -SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "WOA_column.nc" +SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "IC_FILE_COLUMN.nc" ! The name of the z-space input file used to initialize temperatures, only. -Z_INIT_FILE_PTEMP_VAR = "PTEMP" ! default = "ptemp" +Z_INIT_FILE_PTEMP_VAR = "Temp" ! default = "ptemp" ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. -Z_INIT_FILE_SALT_VAR = "SALT" ! default = "salt" +Z_INIT_FILE_SALT_VAR = "Salt" ! default = "salt" ! The name of the salinity variable in SALT_Z_INIT_FILE. Z_INIT_HOMOGENIZE = True ! [Boolean] default = False ! If True, then horizontally homogenize the interpolated initial conditions. diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short index 57b0f437..1120a6a7 100644 --- a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short @@ -162,13 +162,13 @@ INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False ! Z-space file on a latitude-longitude grid. ! === module MOM_initialize_layers_from_Z === -TEMP_SALT_Z_INIT_FILE = "WOA_column.nc" ! default = "temp_salt_z.nc" +TEMP_SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" ! The name of the z-space input file used to initialize temperatures (T) and ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and ! SALT_Z_INIT_FILE must be set. -Z_INIT_FILE_PTEMP_VAR = "PTEMP" ! default = "ptemp" +Z_INIT_FILE_PTEMP_VAR = "Temp" ! default = "ptemp" ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. -Z_INIT_FILE_SALT_VAR = "SALT" ! default = "salt" +Z_INIT_FILE_SALT_VAR = "Salt" ! default = "salt" ! The name of the salinity variable in SALT_Z_INIT_FILE. Z_INIT_HOMOGENIZE = True ! [Boolean] default = False ! If True, then horizontally homogenize the interpolated initial conditions. diff --git a/standalone/examples/single_column/KPP_MARBL/data_table b/standalone/examples/single_column/KPP_MARBL/data_table index a3e02730..a9ae7a83 100644 --- a/standalone/examples/single_column/KPP_MARBL/data_table +++ b/standalone/examples/single_column/KPP_MARBL/data_table @@ -2,8 +2,8 @@ "OCN" , "lw", "LWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 "OCN" , "sw", "SWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 "OCN" , "rain", "RAIN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "evap", "EVAP", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., -1.0 -"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., -1.0 +"OCN" , "evap", "EVAP", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 "OCN" , "taux", "TAUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 "OCN" , "tauy", "TAUY", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 "OCN" , "snow", "SNOW", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 From fe38760128a54526df87d2559bc39a31d4f245e6 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 19 Apr 2023 11:35:15 -0600 Subject: [PATCH 077/136] Update MOM_parameter_doc files 251e0f9 in MOM6 added a couple new parameter variables for setting atmospheric co2 in MARBL, and moved the existing constant value from MARBL_tracers to MARBL_forcing --- .../single_column/KPP_MARBL/MOM_parameter_doc.all | 14 +++++++++----- .../KPP_MARBL/MOM_parameter_doc.short | 2 +- 2 files changed, 10 insertions(+), 6 deletions(-) diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all index 431ae5f4..e52882ff 100644 --- a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all @@ -382,10 +382,6 @@ USE_NW2_TRACERS = False ! [Boolean] default = False ! === module MARBL_tracers === MARBL_SETTINGS_FILE = "marbl_in" ! default = "marbl_in" ! The name of a file from which to read the run-time settings for MARBL. -ATM_CO2_CONST = 284.317 ! [ppm] default = 284.317 - ! Value to send to MARBL as xco2 -ATM_ALT_CO2_CONST = 284.317 ! [ppm] default = 284.317 - ! Value to send to MARBL as xco2_alt_co2 BOT_FLUX_MIX_THICKNESS = 1.0 ! [m] default = 1.0 ! Bottom fluxes are uniformly mixed over layer of this thickness CHL_FROM_FILE = False ! [Boolean] default = True @@ -397,7 +393,7 @@ ICE_NCAT = 0 ! default = 0 ! Number of ice thickness categories in shortwave and ice fraction forcings. ! === module MARBL_tracers === -MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_latlon_1x1_180W_c230317.nc" +MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_latlon_1x1_180W_c230331.nc" ! The file in which the MARBL tracers initial values can be found. MARBL_TRACERS_INIT_VERTICAL_REMAP_ONLY = False ! [Boolean] default = False ! If true, initial conditions are on the model horizontal grid. Extrapolation @@ -1667,6 +1663,14 @@ IRON_FRAC_IN_ATM_COARSE_DUST = 0.035 ! [not defined] default = 0.035 ! Fraction of coarse dust from the atmosphere that is iron IRON_FRAC_IN_SEAICE_DUST = 0.035 ! [not defined] default = 0.035 ! Fraction of dust from sea ice that is iron +ATM_CO2_OPT = "const" ! default = "const" + ! Source of atmospheric CO2 [const, diagnostic, or prognostic] +ATM_CO2_CONST = 284.317 ! [ppm] default = 284.317 + ! Value to send to MARBL as xco2 +ATM_ALT_CO2_OPT = "const" ! default = "const" + ! Source of alternate atmospheric CO2 [const, diagnostic, or prognostic] +ATM_ALT_CO2_CONST = 284.317 ! [ppm] default = 284.317 + ! Value to send to MARBL as xco2_alt_co2 READ_RIV_FLUXES = False ! [Boolean] default = True ! If true, use nitrogen deposition supplied from an input file diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short index 1120a6a7..ef5162d5 100644 --- a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short @@ -116,7 +116,7 @@ CHL_FROM_FILE = False ! [Boolean] default = True ! If true, chl_a is read from a file. ! === module MARBL_tracers === -MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_latlon_1x1_180W_c230317.nc" +MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_latlon_1x1_180W_c230331.nc" ! The file in which the MARBL tracers initial values can be found. MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc" ! The file in which the iron sediment flux forcing field can be found. From 3bc41ed49ce7cb81b6cec367de7d0a86fa47958d Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 20 Apr 2023 11:06:16 -0600 Subject: [PATCH 078/136] Improve default values for CO2 variables I added OCN_CO2_TYPE to config_compset.xml, with similar logic to the same variable in the POP2 cime_config/ directory. MOM_input then uses this variable to set ATM_CO2_OPT (while ATM_ALT_CO2_OPT still defaults to "constant"). I also added ATM_CO2_CONST and ATM_ALT_CO2_CONST to MOM_input, and they both use CCSM_CO2_PPMV as a default value instead of relying on the hard-coded default in MARBL_forcing_mod.F90 --- cime_config/config_component.xml | 20 ++++++++++++++++++++ param_templates/MOM_input.yaml | 25 +++++++++++++++++++++++++ param_templates/json/MOM_input.json | 21 +++++++++++++++++++++ 3 files changed, 66 insertions(+) diff --git a/cime_config/config_component.xml b/cime_config/config_component.xml index 7927389e..fefd07d1 100644 --- a/cime_config/config_component.xml +++ b/cime_config/config_component.xml @@ -68,6 +68,26 @@ + + + char + constant,prognostic,diagnostic,box_atm_co2 + constant + + diagnostic + prognostic + diagnostic + diagnostic + diagnostic + diagnostic + + run_component_pop + env_run.xml + Determines provenance of atmospheric CO2 for gas flux computation. + This option is used in the POP ecosystem model. + The default is constant. + + char none,latest diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index 5ff84373..66512377 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -356,6 +356,31 @@ Global: datatype: logical value: $OCN_BGC_CONFIG != "none": False + ATM_CO2_OPT: + description: | + "default comes from env_run.xml (OCN_CO2_TYPE) + Source of atmospheric CO2 concentration for MARBL + valid values: constant, prognostic, diagnostic + " + datatype: string + value: + $OCN_BGC_CONFIG != "none": $OCN_CO2_TYPE + ATM_CO2_CONST: + description: | + "default comes from env_run.xml (CCSM_CO2_PPMV) + Atmospheric CO2 concentration for MARBL [ppm] + " + datatype: real + value: + $OCN_BGC_CONFIG != "none": $CCSM_CO2_PPMV + ATM_ALT_CO2_CONST: + description: | + "default comes from env_run.xml (CCSM_CO2_PPMV) + Alternate atmospheric CO2 concentration for MARBL [ppm] + " + datatype: real + value: + $OCN_BGC_CONFIG != "none": $CCSM_CO2_PPMV DUST_RATIO_THRES: description: | "TODO: Add description diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index 618851ec..14dc5f77 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -256,6 +256,27 @@ "$OCN_BGC_CONFIG != \"none\"": false } }, + "ATM_CO2_OPT": { + "description": "\"default comes from env_run.xml (OCN_CO2_TYPE)\n Source of atmospheric CO2 concentration for MARBL\n valid values: constant, prognostic, diagnostic\n\"\n", + "datatype": "string", + "value": { + "$OCN_BGC_CONFIG != \"none\"": "$OCN_CO2_TYPE" + } + }, + "ATM_CO2_CONST": { + "description": "\"default comes from env_run.xml (CCSM_CO2_PPMV)\n Atmospheric CO2 concentration for MARBL [ppm]\n\"\n", + "datatype": "real", + "value": { + "$OCN_BGC_CONFIG != \"none\"": "$CCSM_CO2_PPMV" + } + }, + "ATM_ALT_CO2_CONST": { + "description": "\"default comes from env_run.xml (CCSM_CO2_PPMV)\n Alternate atmospheric CO2 concentration for MARBL [ppm]\n\"\n", + "datatype": "real", + "value": { + "$OCN_BGC_CONFIG != \"none\"": "$CCSM_CO2_PPMV" + } + }, "DUST_RATIO_THRES": { "description": "\"TODO: Add description\n\"\n", "datatype": "real", From 2842f737efa6f89c00ec95953aa0de10bade10b3 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 20 Apr 2023 16:19:32 -0600 Subject: [PATCH 079/136] Logic to add ALT_CO2 diagnostics when necessary If ATM_CO2_OPT and ATM_ALT_CO2_OPT differ, or if they are both "constant" but ATM_CO2_CONST and ATM_ALT_CO2_CONST differ, then we want to include ALT_CO2 diagnostics. Otherwise we don't need them. --- cime_config/buildnml | 25 ++++++++++++++++++++++--- 1 file changed, 22 insertions(+), 3 deletions(-) diff --git a/cime_config/buildnml b/cime_config/buildnml index f2cccdd6..57aed2fa 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -137,7 +137,24 @@ def prep_input(case): # For now, user has no control over lMARBL_output_all # TODO: mimic what is done in POP and allow it via user_nl_mom? lMARBL_output_all = case.get_value("TEST") - generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, vert_grid, lMARBL_output_all, ice_ncat) + # Include ALT_CO2 diagnostics if atm_co2_opt and atm_alt_co2_opt differ + # (or if they are both "constant" but atm_co2_const != atm_alt_co2_const) + atm_co2_opt = MOM_input_final._data["Global"]["ATM_CO2_OPT"]["value"].strip().lower().replace('"', '') + try: + atm_alt_co2_opt = MOM_input_final._data["Global"]["ATM_ALT_CO2_OPT"]["value"].strip().lower().replace('"', '') + except: + atm_alt_co2_opt = "constant" + if (atm_co2_opt == atm_alt_co2_opt): + if atm_co2_opt == "constant": + lMARBL_output_alt_co2 = (MOM_input_final._data["Global"]["ATM_CO2_CONST"]["value"] != + MOM_input_final._data["Global"]["ATM_ALT_CO2_CONST"]["value"]) + else: + lMARBL_output_alt_co2 = False + else: + lMARBL_output_alt_co2 = True + generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, + diag_table_buildconf, vert_grid, lMARBL_output_all, lMARBL_output_alt_co2, + ice_ncat) if os.path.isfile(diag_table_buildconf): diag_table.append(FType_diag_table.from_json(diag_table_buildconf)) diag_table.write(diag_table_dest, case, MOM_input_final) @@ -185,7 +202,8 @@ def _copy_files_to_momconf(case): for filename in ["MOM_input", "MOM_override", "diag_table", "input.nml"]: shutil.copy(os.path.join(rundir,filename), momconfdir) -def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, vert_grid, lMARBL_output_all, ice_ncat): +def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfdir, MARBL_dir, diag_table_buildconf, + vert_grid, lMARBL_output_all, lMARBL_output_alt_co2, ice_ncat): """ Create Buildconf/momconf/diag_table_MARBL.json @@ -244,7 +262,8 @@ def generate_diag_table_MARBL(caseroot, MARBL_settings, SourceMods_dir, momconfd # 4. Generate diag_table based on contents of ecosys_diagnostics # and knowledge of which diags are 2D from MARBL_diags_to_diag_table import diagnostics_to_diag_table - diagnostics_to_diag_table(ecosys_diags_buildconf, diag_table_buildconf, diag2D_list, vert_grid, lMARBL_output_all, False) + diagnostics_to_diag_table(ecosys_diags_buildconf, diag_table_buildconf, diag2D_list, vert_grid, + lMARBL_output_all, lMARBL_output_alt_co2) # pylint: disable=unused-argument From 979bf5a24855965202a5c8fc51d1063ad7ac75a7 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 21 Apr 2023 16:09:21 -0600 Subject: [PATCH 080/136] Some code clean-up following review 1. OCN_BGC_CONFIG -> MARBL_CONFIG in config_component.xml (and OCN_BGC_VERT_HIST -> MARBL_VERT_HIST) 2. MARBL_CONFIG uses "off" instead of "none" when MARBL is not active 3. Cleaned up a couple of references to POP that I missed after a copy / paste --- cime_config/buildnml | 6 +++--- cime_config/config_component.xml | 12 ++++++------ param_templates/MOM_input.yaml | 18 +++++++++--------- param_templates/input_data_list.yaml | 11 +++++------ param_templates/input_nml.yaml | 10 +++++----- param_templates/json/MOM_input.json | 18 +++++++++--------- param_templates/json/input_data_list.json | 12 +++++------- param_templates/json/input_nml.json | 10 +++++----- 8 files changed, 47 insertions(+), 50 deletions(-) diff --git a/cime_config/buildnml b/cime_config/buildnml index 57aed2fa..1c15c913 100755 --- a/cime_config/buildnml +++ b/cime_config/buildnml @@ -41,8 +41,8 @@ def prep_input(case): rundir = case.get_value("RUNDIR") comp_root_dir_ocn = case.get_value("COMP_ROOT_DIR_OCN") srcroot = case.get_value("SRCROOT") - ocn_bgc_config = case.get_value("OCN_BGC_CONFIG") - vert_grid = case.get_value("OCN_BGC_HIST_VERT_GRID") + marbl_config = case.get_value("MARBL_CONFIG") + vert_grid = case.get_value("MARBL_HIST_VERT_GRID") caseroot = case.get_value("CASEROOT") ocn_grid = case.get_value("OCN_GRID") run_type = case.get_value("RUN_TYPE") @@ -119,7 +119,7 @@ def prep_input(case): MARBL_dir = os.path.join(srcroot, "components", "mom", "MOM6", "pkg", "MARBL") from MARBL_settings import MARBL_settings_for_MOM MARBL_settings = MARBL_settings_for_MOM(MARBL_dir, caseroot, ocn_grid, run_type, - continue_run, ocn_bgc_config) + continue_run, marbl_config) MARBL_settings.write_settings_file(os.path.join(rundir, "marbl_in")) # 7. Create diag_table: diff --git a/cime_config/config_component.xml b/cime_config/config_component.xml index fefd07d1..d41a5638 100644 --- a/cime_config/config_component.xml +++ b/cime_config/config_component.xml @@ -81,17 +81,17 @@ diagnostic diagnostic - run_component_pop + run_component_mom env_run.xml Determines provenance of atmospheric CO2 for gas flux computation. - This option is used in the POP ecosystem model. + This option is used in MARBL. The default is constant. - + char - none,latest - none + off,latest + off latest @@ -101,7 +101,7 @@ - + char interpolated,native,both interpolated diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index 66512377..d6a8517a 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -346,7 +346,7 @@ Global: datatype: logical units: Boolean value: - $OCN_BGC_CONFIG != "none": True + $MARBL_CONFIG != "off": True else: False CHL_FROM_FILE: description: | @@ -355,7 +355,7 @@ Global: " datatype: logical value: - $OCN_BGC_CONFIG != "none": False + $MARBL_CONFIG != "off": False ATM_CO2_OPT: description: | "default comes from env_run.xml (OCN_CO2_TYPE) @@ -364,7 +364,7 @@ Global: " datatype: string value: - $OCN_BGC_CONFIG != "none": $OCN_CO2_TYPE + $MARBL_CONFIG != "off": $OCN_CO2_TYPE ATM_CO2_CONST: description: | "default comes from env_run.xml (CCSM_CO2_PPMV) @@ -372,7 +372,7 @@ Global: " datatype: real value: - $OCN_BGC_CONFIG != "none": $CCSM_CO2_PPMV + $MARBL_CONFIG != "off": $CCSM_CO2_PPMV ATM_ALT_CO2_CONST: description: | "default comes from env_run.xml (CCSM_CO2_PPMV) @@ -380,28 +380,28 @@ Global: " datatype: real value: - $OCN_BGC_CONFIG != "none": $CCSM_CO2_PPMV + $MARBL_CONFIG != "off": $CCSM_CO2_PPMV DUST_RATIO_THRES: description: | "TODO: Add description " datatype: real value: - $OCN_BGC_CONFIG != "none" and $COMP_ATM == "cam": 60.0 + $MARBL_CONFIG != "off" and $COMP_ATM == "cam": 60.0 DUST_RATIO_TO_FE_BIOAVAIL_FRAC: description: | "TODO: Add description " datatype: real value: - $OCN_BGC_CONFIG != "none" and $COMP_ATM == "cam": 1.0 / 170.0 + $MARBL_CONFIG != "off" and $COMP_ATM == "cam": 1.0 / 170.0 FE_BIOAVAIL_FRAC_OFFSET: description: | "TODO: Add description " datatype: real value: - $OCN_BGC_CONFIG != "none" and $COMP_ATM == "cam": 0.01 + $MARBL_CONFIG != "off" and $COMP_ATM == "cam": 0.01 USE_ICE_CATEGORIES: description: | "[Boolean] default = False @@ -3385,7 +3385,7 @@ Global: datatype: logical units: Boolean value: - $OCN_BGC_CONFIG != "none": True + $MARBL_CONFIG != "off": True USE_WAVES: description: | "default = False diff --git a/param_templates/input_data_list.yaml b/param_templates/input_data_list.yaml index 9fe6fc8a..279d4c90 100644 --- a/param_templates/input_data_list.yaml +++ b/param_templates/input_data_list.yaml @@ -41,12 +41,11 @@ mom.input_data_list: diag_coord_def_rho2: $OCN_GRID == "tx0.66v1": "${INPUTDIR}/ocean_rho2_190917.nc" marbl_tracers_ic_file: - $OCN_BGC_CONFIG != "None": - $OCN_GRID == "tx0.66v1": "${INPUTDIR}/ecosys_jan_IC_omip_MOM_tx0.66v1_c190925.nc" + $MARBL_CONFIG != "off": "${INPUTDIR}/ecosys_jan_IC_omip_latlon_1x1_180W_c230331.nc" marbl_fesedflux_file: - $OCN_BGC_CONFIG != "None": - $OCN_GRID == "tx0.66v1": "${INPUTDIR}/fesedflux_total_reduce_oxic_tx0.66v1.c201204.nc" + $MARBL_CONFIG != "off": + $OCN_GRID == "tx0.66v1": "${INPUTDIR}/fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc" marbl_feventflux_file: - $OCN_BGC_CONFIG != "None": - $OCN_GRID == "tx0.66v1": "${INPUTDIR}/feventflux_5gmol_tx0.66v1.c201204.nc" + $MARBL_CONFIG != "off": + $OCN_GRID == "tx0.66v1": "${INPUTDIR}/feventflux_5gmol_tx0.66v1.c211109.nc" ... diff --git a/param_templates/input_nml.yaml b/param_templates/input_nml.yaml index bef5ed62..f93dad77 100644 --- a/param_templates/input_nml.yaml +++ b/param_templates/input_nml.yaml @@ -25,17 +25,17 @@ diag_manager_nml: value: .true. max_input_fields: value: - $OCN_BGC_CONFIG != "none" and $TEST == True: 900 + $MARBL_CONFIG != "off" and $TEST == True: 900 else: 600 max_output_fields: value: - $OCN_BGC_CONFIG != "none" and $TEST != True: 600 - $OCN_BGC_CONFIG != "none" and $TEST == True: 1200 + $MARBL_CONFIG != "off" and $TEST != True: 600 + $MARBL_CONFIG != "off" and $TEST == True: 1200 else: 300 max_num_axis_sets: value: - $OCN_BGC_CONFIG != "none" and $TEST != True: 30 - $OCN_BGC_CONFIG != "none" and $TEST == True: 26 + $MARBL_CONFIG != "off" and $TEST != True: 30 + $MARBL_CONFIG != "off" and $TEST == True: 26 else: 24 mpp_io_nml: diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index 14dc5f77..83fb41b4 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -245,7 +245,7 @@ "datatype": "logical", "units": "Boolean", "value": { - "$OCN_BGC_CONFIG != \"none\"": true, + "$MARBL_CONFIG != \"off\"": true, "else": false } }, @@ -253,49 +253,49 @@ "description": "\"[Boolean] default = True\n If true, read chlorophyll from a file. Otherwise MARBL will provide it.\n\"\n", "datatype": "logical", "value": { - "$OCN_BGC_CONFIG != \"none\"": false + "$MARBL_CONFIG != \"off\"": false } }, "ATM_CO2_OPT": { "description": "\"default comes from env_run.xml (OCN_CO2_TYPE)\n Source of atmospheric CO2 concentration for MARBL\n valid values: constant, prognostic, diagnostic\n\"\n", "datatype": "string", "value": { - "$OCN_BGC_CONFIG != \"none\"": "$OCN_CO2_TYPE" + "$MARBL_CONFIG != \"off\"": "$OCN_CO2_TYPE" } }, "ATM_CO2_CONST": { "description": "\"default comes from env_run.xml (CCSM_CO2_PPMV)\n Atmospheric CO2 concentration for MARBL [ppm]\n\"\n", "datatype": "real", "value": { - "$OCN_BGC_CONFIG != \"none\"": "$CCSM_CO2_PPMV" + "$MARBL_CONFIG != \"off\"": "$CCSM_CO2_PPMV" } }, "ATM_ALT_CO2_CONST": { "description": "\"default comes from env_run.xml (CCSM_CO2_PPMV)\n Alternate atmospheric CO2 concentration for MARBL [ppm]\n\"\n", "datatype": "real", "value": { - "$OCN_BGC_CONFIG != \"none\"": "$CCSM_CO2_PPMV" + "$MARBL_CONFIG != \"off\"": "$CCSM_CO2_PPMV" } }, "DUST_RATIO_THRES": { "description": "\"TODO: Add description\n\"\n", "datatype": "real", "value": { - "$OCN_BGC_CONFIG != \"none\" and $COMP_ATM == \"cam\"": 60.0 + "$MARBL_CONFIG != \"off\" and $COMP_ATM == \"cam\"": 60.0 } }, "DUST_RATIO_TO_FE_BIOAVAIL_FRAC": { "description": "\"TODO: Add description\n\"\n", "datatype": "real", "value": { - "$OCN_BGC_CONFIG != \"none\" and $COMP_ATM == \"cam\"": "1.0 / 170.0" + "$MARBL_CONFIG != \"off\" and $COMP_ATM == \"cam\"": "1.0 / 170.0" } }, "FE_BIOAVAIL_FRAC_OFFSET": { "description": "\"TODO: Add description\n\"\n", "datatype": "real", "value": { - "$OCN_BGC_CONFIG != \"none\" and $COMP_ATM == \"cam\"": 0.01 + "$MARBL_CONFIG != \"off\" and $COMP_ATM == \"cam\"": 0.01 } }, "USE_ICE_CATEGORIES": { @@ -2713,7 +2713,7 @@ "datatype": "logical", "units": "Boolean", "value": { - "$OCN_BGC_CONFIG != \"none\"": true + "$MARBL_CONFIG != \"off\"": true } }, "USE_WAVES": { diff --git a/param_templates/json/input_data_list.json b/param_templates/json/input_data_list.json index 3bdab722..a5bf674c 100644 --- a/param_templates/json/input_data_list.json +++ b/param_templates/json/input_data_list.json @@ -54,18 +54,16 @@ "$OCN_GRID == \"tx0.66v1\"": "${INPUTDIR}/ocean_rho2_190917.nc" }, "marbl_tracers_ic_file": { - "$OCN_BGC_CONFIG != \"None\"": { - "$OCN_GRID == \"tx0.66v1\"": "${INPUTDIR}/ecosys_jan_IC_omip_MOM_tx0.66v1_c190925.nc" - } + "$MARBL_CONFIG != \"off\"": "${INPUTDIR}/ecosys_jan_IC_omip_latlon_1x1_180W_c230331.nc" }, "marbl_fesedflux_file": { - "$OCN_BGC_CONFIG != \"None\"": { - "$OCN_GRID == \"tx0.66v1\"": "${INPUTDIR}/fesedflux_total_reduce_oxic_tx0.66v1.c201204.nc" + "$MARBL_CONFIG != \"off\"": { + "$OCN_GRID == \"tx0.66v1\"": "${INPUTDIR}/fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc" } }, "marbl_feventflux_file": { - "$OCN_BGC_CONFIG != \"None\"": { - "$OCN_GRID == \"tx0.66v1\"": "${INPUTDIR}/feventflux_5gmol_tx0.66v1.c201204.nc" + "$MARBL_CONFIG != \"off\"": { + "$OCN_GRID == \"tx0.66v1\"": "${INPUTDIR}/feventflux_5gmol_tx0.66v1.c211109.nc" } } } diff --git a/param_templates/json/input_nml.json b/param_templates/json/input_nml.json index d1497c79..ad71ac35 100644 --- a/param_templates/json/input_nml.json +++ b/param_templates/json/input_nml.json @@ -33,21 +33,21 @@ }, "max_input_fields": { "value": { - "$OCN_BGC_CONFIG != \"none\" and $TEST == True": 900, + "$MARBL_CONFIG != \"off\" and $TEST == True": 900, "else": 600 } }, "max_output_fields": { "value": { - "$OCN_BGC_CONFIG != \"none\" and $TEST != True": 600, - "$OCN_BGC_CONFIG != \"none\" and $TEST == True": 1200, + "$MARBL_CONFIG != \"off\" and $TEST != True": 600, + "$MARBL_CONFIG != \"off\" and $TEST == True": 1200, "else": 300 } }, "max_num_axis_sets": { "value": { - "$OCN_BGC_CONFIG != \"none\" and $TEST != True": 30, - "$OCN_BGC_CONFIG != \"none\" and $TEST == True": 26, + "$MARBL_CONFIG != \"off\" and $TEST != True": 30, + "$MARBL_CONFIG != \"off\" and $TEST == True": 26, "else": 24 } } From c477897e69d60d3004d98510e45bb3cea42b3917 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 25 Apr 2023 15:20:20 -0600 Subject: [PATCH 081/136] ParamGen computes DUST_RATIO_TO_FE_BIOAVAIL_FRAC I was missing a minus sign, so instead of evaluating 1/170, MOM_input actually contained the fraction 1 / 170 and then MOM6 was only reading 1.0. --- param_templates/MOM_input.yaml | 2 +- param_templates/json/MOM_input.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index d6a8517a..16ffb3f8 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -394,7 +394,7 @@ Global: " datatype: real value: - $MARBL_CONFIG != "off" and $COMP_ATM == "cam": 1.0 / 170.0 + $MARBL_CONFIG != "off" and $COMP_ATM == "cam": = 1.0 / 170.0 FE_BIOAVAIL_FRAC_OFFSET: description: | "TODO: Add description diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index 83fb41b4..98204a22 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -288,7 +288,7 @@ "description": "\"TODO: Add description\n\"\n", "datatype": "real", "value": { - "$MARBL_CONFIG != \"off\" and $COMP_ATM == \"cam\"": "1.0 / 170.0" + "$MARBL_CONFIG != \"off\" and $COMP_ATM == \"cam\"": "= 1.0 / 170.0" } }, "FE_BIOAVAIL_FRAC_OFFSET": { From e3597345b37b17a3e7229c318d334f0a18eaaa31 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 2 May 2023 12:32:19 -0600 Subject: [PATCH 082/136] Updates to support 4p2z --- cime_config/config_component.xml | 4 ++-- param_templates/MOM_input.yaml | 8 ++++++++ param_templates/json/MOM_input.json | 8 ++++++++ 3 files changed, 18 insertions(+), 2 deletions(-) diff --git a/cime_config/config_component.xml b/cime_config/config_component.xml index d41a5638..b060eedf 100644 --- a/cime_config/config_component.xml +++ b/cime_config/config_component.xml @@ -90,10 +90,10 @@ char - off,latest + off,latest,latest+4p2z off - latest + latest+4p2z run_component_mom env_run.xml diff --git a/param_templates/MOM_input.yaml b/param_templates/MOM_input.yaml index 16ffb3f8..33d7b277 100644 --- a/param_templates/MOM_input.yaml +++ b/param_templates/MOM_input.yaml @@ -348,6 +348,14 @@ Global: value: $MARBL_CONFIG != "off": True else: False + MARBL_TRACERS_IC_FILE: + description: | + "Name of file containing initial conditions + for the MARBL tracer package." + datatype: string + value: + $MARBL_CONFIG == "latest": ecosys_jan_IC_omip_latlon_1x1_180W_c230331.nc + $MARBL_CONFIG == "latest+4p2z": ecosys_jan_IC_omip_latlon_1x1_180W_c230501.nc CHL_FROM_FILE: description: | "[Boolean] default = True diff --git a/param_templates/json/MOM_input.json b/param_templates/json/MOM_input.json index 98204a22..0aed2868 100644 --- a/param_templates/json/MOM_input.json +++ b/param_templates/json/MOM_input.json @@ -249,6 +249,14 @@ "else": false } }, + "MARBL_TRACERS_IC_FILE": { + "description": "\"Name of file containing initial conditions\nfor the MARBL tracer package.\"\n", + "datatype": "string", + "value": { + "$MARBL_CONFIG == \"latest\"": "ecosys_jan_IC_omip_latlon_1x1_180W_c230331.nc", + "$MARBL_CONFIG == \"latest+4p2z\"": "ecosys_jan_IC_omip_latlon_1x1_180W_c230501.nc" + } + }, "CHL_FROM_FILE": { "description": "\"[Boolean] default = True\n If true, read chlorophyll from a file. Otherwise MARBL will provide it.\n\"\n", "datatype": "logical", From 54e3921f778e5507ff4021514504778c56ae8609 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 2 May 2023 12:45:59 -0600 Subject: [PATCH 083/136] Update MOM_parameter_doc.all I had changed a valid value from "const" to "constant" in MOM6's MARBL driver, but the old version of this file still used "const" so running the model introduced a change. --- .../single_column/KPP_MARBL/MOM_parameter_doc.all | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all index e52882ff..c60c3b79 100644 --- a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all +++ b/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all @@ -1663,12 +1663,12 @@ IRON_FRAC_IN_ATM_COARSE_DUST = 0.035 ! [not defined] default = 0.035 ! Fraction of coarse dust from the atmosphere that is iron IRON_FRAC_IN_SEAICE_DUST = 0.035 ! [not defined] default = 0.035 ! Fraction of dust from sea ice that is iron -ATM_CO2_OPT = "const" ! default = "const" - ! Source of atmospheric CO2 [const, diagnostic, or prognostic] +ATM_CO2_OPT = "constant" ! default = "constant" + ! Source of atmospheric CO2 [constant, diagnostic, or prognostic] ATM_CO2_CONST = 284.317 ! [ppm] default = 284.317 ! Value to send to MARBL as xco2 -ATM_ALT_CO2_OPT = "const" ! default = "const" - ! Source of alternate atmospheric CO2 [const, diagnostic, or prognostic] +ATM_ALT_CO2_OPT = "constant" ! default = "constant" + ! Source of alternate atmospheric CO2 [constant, diagnostic, or prognostic] ATM_ALT_CO2_CONST = 284.317 ! [ppm] default = 284.317 ! Value to send to MARBL as xco2_alt_co2 READ_RIV_FLUXES = False ! [Boolean] default = True From 299e90c645c5c93b9c74f49c55b514fa2fb1649d Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 2 May 2023 12:54:17 -0600 Subject: [PATCH 084/136] single_column/KPP_MARBL -> single_column_MARBL/BATS I want to create a suite of single column tests with MARBL enabled, each using forcing from a different observational data location (well, a CESM-MOM6 grid cell close to where the obs come from). BATS is the only site we have currently, future commits will add more. --- .../KPP_MARBL => single_column_KPP/BATS}/INPUT | 0 .../BATS}/MOM_input | 3 --- .../BATS}/MOM_override | 0 .../BATS}/MOM_override2 | 0 .../BATS}/MOM_parameter_doc.all | 0 .../BATS}/MOM_parameter_doc.debugging | 0 .../BATS}/MOM_parameter_doc.layout | 0 .../BATS}/MOM_parameter_doc.short | 0 .../BATS}/RESTART/.gitignore | 0 .../BATS}/data_table | 0 .../BATS}/diag_table | 0 .../BATS}/input.nml | 0 .../BATS}/marbl_in | 0 .../BATS}/single_column_z.ipynb | 0 .../BATS}/vgrid_cm4.cdl | 0 .../BATS}/vgrid_cm4_10.cdl | 0 .../BATS}/vgrid_cm4_4.cdl | 0 .../CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc | Bin .../BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin .../BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin .../BATS/MARBL_FORCING_FILE_COLUMN.nc | Bin .../INPUT/CESM_MOM_COLUMNS/BATS/MOM_override | 0 .../INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc | Bin .../CESM_MOM_COLUMNS/BATS/forcing_monthly.nc | Bin .../single_column_KPP/INPUT/grid_spec.nc | Bin 0 -> 37348 bytes .../single_column_KPP/INPUT/vgrid_cm4.nc | Bin 0 -> 1968 bytes 26 files changed, 3 deletions(-) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/INPUT (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/MOM_input (99%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/MOM_override (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/MOM_override2 (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/MOM_parameter_doc.all (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/MOM_parameter_doc.debugging (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/MOM_parameter_doc.layout (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/MOM_parameter_doc.short (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/RESTART/.gitignore (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/data_table (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/diag_table (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/input.nml (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/marbl_in (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/single_column_z.ipynb (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/vgrid_cm4.cdl (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/vgrid_cm4_10.cdl (100%) rename standalone/examples/{single_column/KPP_MARBL => single_column_KPP/BATS}/vgrid_cm4_4.cdl (100%) rename standalone/examples/{single_column => single_column_KPP}/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc (100%) rename standalone/examples/{single_column => single_column_KPP}/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc (100%) rename standalone/examples/{single_column => single_column_KPP}/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc (100%) rename standalone/examples/{single_column => single_column_KPP}/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc (100%) rename standalone/examples/{single_column => single_column_KPP}/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override (100%) rename standalone/examples/{single_column => single_column_KPP}/INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc (100%) rename standalone/examples/{single_column => single_column_KPP}/INPUT/CESM_MOM_COLUMNS/BATS/forcing_monthly.nc (100%) create mode 100644 standalone/examples/single_column_KPP/INPUT/grid_spec.nc create mode 100644 standalone/examples/single_column_KPP/INPUT/vgrid_cm4.nc diff --git a/standalone/examples/single_column/KPP_MARBL/INPUT b/standalone/examples/single_column_KPP/BATS/INPUT similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/INPUT rename to standalone/examples/single_column_KPP/BATS/INPUT diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_input b/standalone/examples/single_column_KPP/BATS/MOM_input similarity index 99% rename from standalone/examples/single_column/KPP_MARBL/MOM_input rename to standalone/examples/single_column_KPP/BATS/MOM_input index ffbaa3d7..9bc40aed 100644 --- a/standalone/examples/single_column/KPP_MARBL/MOM_input +++ b/standalone/examples/single_column_KPP/BATS/MOM_input @@ -325,9 +325,6 @@ RECLAIM_FRAZIL = False ! [Boolean] default = True VAR_PEN_SW = True ! [Boolean] default = False ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine ! the e-folding depth of incoming short wave radiation. -CHL_FILE = "forcing_monthly.nc" ! - ! CHL_FILE is the file containing chl_a concentrations in the variable CHL_A. It - ! is used when VAR_PEN_SW and CHL_FROM_FILE are true. ! === module MOM_opacity === PEN_SW_NBANDS = 3 ! default = 1 diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_override b/standalone/examples/single_column_KPP/BATS/MOM_override similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/MOM_override rename to standalone/examples/single_column_KPP/BATS/MOM_override diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_override2 b/standalone/examples/single_column_KPP/BATS/MOM_override2 similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/MOM_override2 rename to standalone/examples/single_column_KPP/BATS/MOM_override2 diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all b/standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.all similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.all rename to standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.all diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.debugging b/standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.debugging similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.debugging rename to standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.debugging diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.layout b/standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.layout similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.layout rename to standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.layout diff --git a/standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short b/standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.short similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/MOM_parameter_doc.short rename to standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.short diff --git a/standalone/examples/single_column/KPP_MARBL/RESTART/.gitignore b/standalone/examples/single_column_KPP/BATS/RESTART/.gitignore similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/RESTART/.gitignore rename to standalone/examples/single_column_KPP/BATS/RESTART/.gitignore diff --git a/standalone/examples/single_column/KPP_MARBL/data_table b/standalone/examples/single_column_KPP/BATS/data_table similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/data_table rename to standalone/examples/single_column_KPP/BATS/data_table diff --git a/standalone/examples/single_column/KPP_MARBL/diag_table b/standalone/examples/single_column_KPP/BATS/diag_table similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/diag_table rename to standalone/examples/single_column_KPP/BATS/diag_table diff --git a/standalone/examples/single_column/KPP_MARBL/input.nml b/standalone/examples/single_column_KPP/BATS/input.nml similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/input.nml rename to standalone/examples/single_column_KPP/BATS/input.nml diff --git a/standalone/examples/single_column/KPP_MARBL/marbl_in b/standalone/examples/single_column_KPP/BATS/marbl_in similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/marbl_in rename to standalone/examples/single_column_KPP/BATS/marbl_in diff --git a/standalone/examples/single_column/KPP_MARBL/single_column_z.ipynb b/standalone/examples/single_column_KPP/BATS/single_column_z.ipynb similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/single_column_z.ipynb rename to standalone/examples/single_column_KPP/BATS/single_column_z.ipynb diff --git a/standalone/examples/single_column/KPP_MARBL/vgrid_cm4.cdl b/standalone/examples/single_column_KPP/BATS/vgrid_cm4.cdl similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/vgrid_cm4.cdl rename to standalone/examples/single_column_KPP/BATS/vgrid_cm4.cdl diff --git a/standalone/examples/single_column/KPP_MARBL/vgrid_cm4_10.cdl b/standalone/examples/single_column_KPP/BATS/vgrid_cm4_10.cdl similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/vgrid_cm4_10.cdl rename to standalone/examples/single_column_KPP/BATS/vgrid_cm4_10.cdl diff --git a/standalone/examples/single_column/KPP_MARBL/vgrid_cm4_4.cdl b/standalone/examples/single_column_KPP/BATS/vgrid_cm4_4.cdl similarity index 100% rename from standalone/examples/single_column/KPP_MARBL/vgrid_cm4_4.cdl rename to standalone/examples/single_column_KPP/BATS/vgrid_cm4_4.cdl diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc b/standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc rename to standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc b/standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc rename to standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc b/standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc rename to standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc b/standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc rename to standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override b/standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override similarity index 100% rename from standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override rename to standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc 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zOVJi(f5SYZf5F;sp{`wudD>MxkQTD*Zi+QRb|dQAA?Vqrc-Dt)^shzT9Q6)M1IT+9 zzc4CIV;yIP9;U4lDd*FwTf*-yLzEy^} zy&8J%E%4&Gz`bkWeL4^E&+_57egWS8BzOnz!ydW~ynPLL$7Z44SN-t6b`bBZf?n4q z_ndk1*i zZ1@L%hJSbh_Q-YM$19+70{>vBV-)dIYEEbVK>hBg==0o{$m`XS_svhhzpDkW?-}y@ xI}ks=3jV+t@-FNFZ|ErO#l|!f=u%ND1C;g$MB)gg|3u#9{yb5jldiN#`!{ZJnPvb0 literal 0 HcmV?d00001 From 02dee28ca9b1d643e21996ff39a29dd7dd70b270 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Tue, 2 May 2023 17:07:03 -0600 Subject: [PATCH 085/136] single_column_MARBL, not single_column_KPP --- .../BATS/INPUT | 0 .../BATS/MOM_input | 0 .../BATS/MOM_override | 0 .../BATS/MOM_override2 | 0 .../BATS/MOM_parameter_doc.all | 0 .../BATS/MOM_parameter_doc.debugging | 0 .../BATS/MOM_parameter_doc.layout | 0 .../BATS/MOM_parameter_doc.short | 0 .../BATS/RESTART/.gitignore | 0 .../BATS/data_table | 0 .../BATS/diag_table | 0 .../BATS/input.nml | 0 .../BATS/marbl_in | 0 .../BATS/single_column_z.ipynb | 0 .../BATS/vgrid_cm4.cdl | 0 .../BATS/vgrid_cm4_10.cdl | 0 .../BATS/vgrid_cm4_4.cdl | 0 .../INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc | Bin .../BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin .../BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin .../BATS/MARBL_FORCING_FILE_COLUMN.nc | Bin .../INPUT/CESM_MOM_COLUMNS/BATS/MOM_override | 0 .../INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc | Bin .../INPUT/CESM_MOM_COLUMNS/BATS/forcing_monthly.nc | Bin .../INPUT/grid_spec.nc | Bin .../INPUT/vgrid_cm4.nc | Bin 26 files changed, 0 insertions(+), 0 deletions(-) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/INPUT (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/MOM_input (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/MOM_override (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/MOM_override2 (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/MOM_parameter_doc.all (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/MOM_parameter_doc.debugging (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/MOM_parameter_doc.layout (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/MOM_parameter_doc.short (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/RESTART/.gitignore (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/data_table (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/diag_table (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/input.nml (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/marbl_in (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/single_column_z.ipynb (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/vgrid_cm4.cdl (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/vgrid_cm4_10.cdl (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/BATS/vgrid_cm4_4.cdl (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/INPUT/CESM_MOM_COLUMNS/BATS/forcing_monthly.nc (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/INPUT/grid_spec.nc (100%) rename standalone/examples/{single_column_KPP => single_column_MARBL}/INPUT/vgrid_cm4.nc (100%) diff --git a/standalone/examples/single_column_KPP/BATS/INPUT b/standalone/examples/single_column_MARBL/BATS/INPUT similarity index 100% rename from standalone/examples/single_column_KPP/BATS/INPUT rename to standalone/examples/single_column_MARBL/BATS/INPUT diff --git a/standalone/examples/single_column_KPP/BATS/MOM_input b/standalone/examples/single_column_MARBL/BATS/MOM_input similarity index 100% rename from standalone/examples/single_column_KPP/BATS/MOM_input rename to standalone/examples/single_column_MARBL/BATS/MOM_input diff --git a/standalone/examples/single_column_KPP/BATS/MOM_override b/standalone/examples/single_column_MARBL/BATS/MOM_override similarity index 100% rename from standalone/examples/single_column_KPP/BATS/MOM_override rename to standalone/examples/single_column_MARBL/BATS/MOM_override diff --git a/standalone/examples/single_column_KPP/BATS/MOM_override2 b/standalone/examples/single_column_MARBL/BATS/MOM_override2 similarity index 100% rename from standalone/examples/single_column_KPP/BATS/MOM_override2 rename to standalone/examples/single_column_MARBL/BATS/MOM_override2 diff --git a/standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.all b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all similarity index 100% rename from standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.all rename to standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all diff --git a/standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.debugging b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.debugging similarity index 100% rename from standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.debugging rename to standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.debugging diff --git a/standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.layout b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.layout similarity index 100% rename from standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.layout rename to standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.layout diff --git a/standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.short b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short similarity index 100% rename from standalone/examples/single_column_KPP/BATS/MOM_parameter_doc.short rename to standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short diff --git a/standalone/examples/single_column_KPP/BATS/RESTART/.gitignore b/standalone/examples/single_column_MARBL/BATS/RESTART/.gitignore similarity index 100% rename from standalone/examples/single_column_KPP/BATS/RESTART/.gitignore rename to standalone/examples/single_column_MARBL/BATS/RESTART/.gitignore diff --git a/standalone/examples/single_column_KPP/BATS/data_table b/standalone/examples/single_column_MARBL/BATS/data_table similarity index 100% rename from standalone/examples/single_column_KPP/BATS/data_table rename to standalone/examples/single_column_MARBL/BATS/data_table diff --git a/standalone/examples/single_column_KPP/BATS/diag_table b/standalone/examples/single_column_MARBL/BATS/diag_table similarity index 100% rename from standalone/examples/single_column_KPP/BATS/diag_table rename to standalone/examples/single_column_MARBL/BATS/diag_table diff --git a/standalone/examples/single_column_KPP/BATS/input.nml b/standalone/examples/single_column_MARBL/BATS/input.nml similarity index 100% rename from standalone/examples/single_column_KPP/BATS/input.nml rename to standalone/examples/single_column_MARBL/BATS/input.nml diff --git a/standalone/examples/single_column_KPP/BATS/marbl_in b/standalone/examples/single_column_MARBL/BATS/marbl_in similarity index 100% rename from standalone/examples/single_column_KPP/BATS/marbl_in rename to standalone/examples/single_column_MARBL/BATS/marbl_in diff --git a/standalone/examples/single_column_KPP/BATS/single_column_z.ipynb b/standalone/examples/single_column_MARBL/BATS/single_column_z.ipynb similarity index 100% rename from standalone/examples/single_column_KPP/BATS/single_column_z.ipynb rename to standalone/examples/single_column_MARBL/BATS/single_column_z.ipynb diff --git a/standalone/examples/single_column_KPP/BATS/vgrid_cm4.cdl b/standalone/examples/single_column_MARBL/BATS/vgrid_cm4.cdl similarity index 100% rename from standalone/examples/single_column_KPP/BATS/vgrid_cm4.cdl rename to standalone/examples/single_column_MARBL/BATS/vgrid_cm4.cdl diff --git a/standalone/examples/single_column_KPP/BATS/vgrid_cm4_10.cdl b/standalone/examples/single_column_MARBL/BATS/vgrid_cm4_10.cdl similarity index 100% rename from standalone/examples/single_column_KPP/BATS/vgrid_cm4_10.cdl rename to standalone/examples/single_column_MARBL/BATS/vgrid_cm4_10.cdl diff --git a/standalone/examples/single_column_KPP/BATS/vgrid_cm4_4.cdl b/standalone/examples/single_column_MARBL/BATS/vgrid_cm4_4.cdl similarity index 100% rename from standalone/examples/single_column_KPP/BATS/vgrid_cm4_4.cdl rename to standalone/examples/single_column_MARBL/BATS/vgrid_cm4_4.cdl diff --git a/standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override b/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override similarity index 100% rename from standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override rename to standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override diff --git a/standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc b/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc similarity index 100% rename from standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc rename to standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc diff --git a/standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/forcing_monthly.nc b/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/forcing_monthly.nc similarity index 100% rename from standalone/examples/single_column_KPP/INPUT/CESM_MOM_COLUMNS/BATS/forcing_monthly.nc rename to standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/forcing_monthly.nc diff --git a/standalone/examples/single_column_KPP/INPUT/grid_spec.nc b/standalone/examples/single_column_MARBL/INPUT/grid_spec.nc similarity index 100% rename from standalone/examples/single_column_KPP/INPUT/grid_spec.nc rename to standalone/examples/single_column_MARBL/INPUT/grid_spec.nc diff --git a/standalone/examples/single_column_KPP/INPUT/vgrid_cm4.nc b/standalone/examples/single_column_MARBL/INPUT/vgrid_cm4.nc similarity index 100% rename from standalone/examples/single_column_KPP/INPUT/vgrid_cm4.nc rename to standalone/examples/single_column_MARBL/INPUT/vgrid_cm4.nc From 855c108a9989a77a93f6b9a63eeb5d8aa6e73eb8 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 3 May 2023 13:50:50 -0600 Subject: [PATCH 086/136] Update forcing files and MOM_input for BATS Lots of changes to be closer to POP setup: 1. 65 levels, not 75 2. Created TOPO_FILE_COLUMN.nc so we can set bottom depth to be something other than 6000. 3. Changed MINIMUM_DEPTH and MASKING_DEPTH to match POP settings (though that shouldn't affect the run) 4. Cleaned up some _FillValues and other metadata in the input directory --- .../single_column_MARBL/BATS/MOM_input | 29 ++++++++++++---- .../BATS/MOM_parameter_doc.all | 31 ++++++++++++------ .../BATS/MOM_parameter_doc.short | 22 +++++++++---- .../CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc | Bin 134712 -> 134712 bytes .../BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 20765 -> 20765 bytes .../BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 16739 -> 16739 bytes .../BATS/MARBL_FORCING_FILE_COLUMN.nc | Bin 3125511 -> 3125511 bytes .../CESM_MOM_COLUMNS/BATS/TOPO_FILE_COLUMN.nc | Bin 0 -> 11889 bytes .../INPUT/vgrid_65L_20200626.nc | Bin 0 -> 1164 bytes .../single_column_MARBL/INPUT/vgrid_cm4.nc | Bin 1968 -> 0 bytes 10 files changed, 58 insertions(+), 24 deletions(-) create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/TOPO_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/vgrid_65L_20200626.nc delete mode 100644 standalone/examples/single_column_MARBL/INPUT/vgrid_cm4.nc diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_input b/standalone/examples/single_column_MARBL/BATS/MOM_input index 9bc40aed..6d9068c5 100644 --- a/standalone/examples/single_column_MARBL/BATS/MOM_input +++ b/standalone/examples/single_column_MARBL/BATS/MOM_input @@ -78,7 +78,7 @@ WESTLON = -30.0 ! [degrees] default = 0.0 ! x-axis. LENLON = 1.0 ! [degrees] ! The longitudinal or x-direction length of the domain. -TOPO_CONFIG = "flat" ! +TOPO_CONFIG = "file" ! ! This specifies how bathymetry is specified: ! file - read bathymetric information from the file ! specified by (TOPO_FILE). @@ -105,10 +105,25 @@ TOPO_CONFIG = "flat" ! ! Phillips - ACC-like idealized topography used in the Phillips config. ! dense - Denmark Strait-like dense water formation and overflow. ! USER - call a user modified routine. -MINIMUM_DEPTH = 0.5 ! [m] default = 0.0 - ! The minimum depth of the ocean. -MAXIMUM_DEPTH = 6000.0 ! [m] - ! The maximum depth of the ocean. +TOPO_FILE = "TOPO_FILE_COLUMN.nc" ! default = "topog.nc" + ! The file from which the bathymetry is read. + +MAXIMUM_DEPTH = 6000.0 + ! "[m] + ! The maximum depth of the ocean." + +MINIMUM_DEPTH = 9.5 + ! "[m] default = 0.0 + ! If MASKING_DEPTH is unspecified, then anything shallower than + ! MINIMUM_DEPTH is assumed to be land and all fluxes are masked out. + ! If MASKING_DEPTH is specified, then all depths shallower than + ! MINIMUM_DEPTH but depper than MASKING_DEPTH are rounded to + ! MINIMUM_DEPTH." + +MASKING_DEPTH = 0.0 + ! "[m] default = -9999.0 + ! The depth below which to mask points as land points, for which all + ! fluxes are zeroed out. MASKING_DEPTH is ignored if negative." ROTATION = "betaplane" ! default = "2omegasinlat" ! This specifies how the Coriolis parameter is specified: ! 2omegasinlat - Use twice the planetary rotation rate @@ -118,7 +133,7 @@ ROTATION = "betaplane" ! default = "2omegasinlat" ! === module MOM_verticalGrid === ! Parameters providing information about the vertical grid. -NK = 75 ! [nondim] +NK =65 ! [nondim] ! The number of model layers. ! === module MOM_EOS === @@ -136,7 +151,7 @@ REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" ! HYCOM1 - HyCOM-like hybrid coordinate ! SLIGHT - stretched coordinates above continuous isopycnal ! ADAPTIVE - optimize for smooth neutral density surfaces -ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_cm4.nc,dz" ! default = "UNIFORM" +ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_65L_20200626.nc,dz" ! default = "UNIFORM" ! Determines how to specify the coordinate resolution. Valid options are: ! PARAM - use the vector-parameter ALE_RESOLUTION ! UNIFORM[:N] - uniformly distributed diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all index c60c3b79..566e4ce9 100644 --- a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all +++ b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all @@ -219,7 +219,7 @@ LENLON = 1.0 ! [degrees] ! The longitudinal or x-direction length of the domain. RAD_EARTH = 6.378E+06 ! [m] default = 6.378E+06 ! The radius of the Earth. -TOPO_CONFIG = "flat" ! +TOPO_CONFIG = "file" ! ! This specifies how bathymetry is specified: ! file - read bathymetric information from the file ! specified by (TOPO_FILE). @@ -246,8 +246,22 @@ TOPO_CONFIG = "flat" ! ! Phillips - ACC-like idealized topography used in the Phillips config. ! dense - Denmark Strait-like dense water formation and overflow. ! USER - call a user modified routine. -MINIMUM_DEPTH = 0.5 ! [m] default = 0.0 - ! The minimum depth of the ocean. +TOPO_FILE = "TOPO_FILE_COLUMN.nc" ! default = "topog.nc" + ! The file from which the bathymetry is read. +TOPO_VARNAME = "depth" ! default = "depth" + ! The name of the bathymetry variable in TOPO_FILE. +TOPO_EDITS_FILE = "" ! default = "" + ! The file from which to read a list of i,j,z topography overrides. +ALLOW_LANDMASK_CHANGES = False ! [Boolean] default = False + ! If true, allow topography overrides to change land mask. +MINIMUM_DEPTH = 9.5 ! [m] default = 0.0 + ! If MASKING_DEPTH is unspecified, then anything shallower than MINIMUM_DEPTH is + ! assumed to be land and all fluxes are masked out. If MASKING_DEPTH is + ! specified, then all depths shallower than MINIMUM_DEPTH but deeper than + ! MASKING_DEPTH are rounded to MINIMUM_DEPTH. +MASKING_DEPTH = 0.0 ! [m] default = -9999.0 + ! The depth below which to mask points as land points, for which all fluxes are + ! zeroed out. MASKING_DEPTH is ignored if it has the special default value. MAXIMUM_DEPTH = 6000.0 ! [m] ! The maximum depth of the ocean. @@ -256,9 +270,6 @@ MAXIMUM_DEPTH = 6000.0 ! [m] ! if any. OBC_NUMBER_OF_SEGMENTS = 0 ! default = 0 ! The number of open boundary segments. -MASKING_DEPTH = -9999.0 ! [m] default = -9999.0 - ! The depth below which to mask points as land points, for which all fluxes are - ! zeroed out. MASKING_DEPTH is ignored if it has the special default value. CHANNEL_CONFIG = "none" ! default = "none" ! A parameter that determines which set of channels are ! restricted to specific widths. Options are: @@ -303,7 +314,7 @@ ANGSTROM = 1.0E-10 ! [m] default = 1.0E-10 ! The minimum layer thickness, usually one-Angstrom. H_TO_M = 1.0 ! [m H-1] default = 1.0 ! A constant that translates the model's internal units of thickness into m. -NK = 75 ! [nondim] +NK = 65 ! [nondim] ! The number of model layers. ! === module MOM_tracer_registry === @@ -445,7 +456,7 @@ REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" ! ADAPTIVE - optimize for smooth neutral density surfaces REGRIDDING_COORDINATE_UNITS = "m" ! default = "m" ! Units of the regridding coordinate. -ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_cm4.nc,dz" ! default = "UNIFORM" +ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_65L_20200626.nc,dz" ! default = "UNIFORM" ! Determines how to specify the coordinate resolution. Valid options are: ! PARAM - use the vector-parameter ALE_RESOLUTION ! UNIFORM[:N] - uniformly distributed @@ -459,7 +470,7 @@ ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_cm4.nc,dz" ! default = "UNIFORM" ! the filename and two variable names, separated ! by a comma or space, for sigma-2 and dz. e.g. ! HYBRID:vgrid.nc,sigma2,dz -!ALE_RESOLUTION = 4*2.0, 2*2.01, 2*2.02, 2.05, 2.07, 2.09, 2.13, 2.18, 2.24, 2.3, 2.4, 2.5, 2.62, 2.78, 2.95, 3.17, 3.42, 3.71, 4.07, 4.48, 4.97, 5.55, 6.23, 7.04, 7.99, 9.11, 10.43, 11.98, 13.8, 15.94, 18.42, 21.32, 24.66, 28.51, 32.91, 37.92, 43.56, 49.87, 56.88, 64.55, 72.91, 81.87, 91.4, 101.39, 111.73, 122.29, 132.93, 143.47, 153.78, 163.7, 173.07, 181.78, 189.76, 196.89, 203.17, 208.58, 213.14, 216.87, 219.87, 222.19, 223.94, 225.18, 226.04, 226.59, 226.9, 227.07, 227.14, 2*227.16, 227.17 ! [m] +!ALE_RESOLUTION = 4*2.5, 2.77, 3.38, 4.01, 4.65, 5.29, 5.95, 6.61, 7.28, 7.97, 8.66, 9.37, 10.08, 10.81, 11.54, 12.29, 13.06, 13.85, 14.69, 15.59, 16.56, 17.61, 18.76, 20.02, 21.42, 23.0, 24.77, 26.79, 29.1, 31.76, 34.87, 38.5, 42.79, 47.9, 54.01, 61.37, 70.25, 80.95, 93.75, 108.8, 126.04, 145.04, 164.81, 184.05, 201.34, 215.66, 226.64, 234.5, 239.84, 243.31, 245.52, 246.88, 247.72, 248.23, 248.54, 248.73, 248.84, 248.64, 248.68, 248.71, 248.72, 248.73 ! [m] ! The distribution of vertical resolution for the target ! grid used for Eulerian-like coordinates. For example, ! in z-coordinate mode, the parameter is a list of level @@ -888,7 +899,7 @@ UPWIND_1ST_CONTINUITY = True ! [Boolean] default = False ! If true, CONTINUITY_PPM becomes a 1st-order upwind continuity solver. This ! scheme is highly diffusive but may be useful for debugging or in single-column ! mode where its minimal stencil is useful. -ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.75E-09 +ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.25E-09 ! The tolerance for the differences between the barotropic and baroclinic ! estimates of the sea surface height due to the fluxes through each face. The ! total tolerance for SSH is 4 times this value. The default is diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short index ef5162d5..909e318a 100644 --- a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short +++ b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short @@ -60,7 +60,7 @@ WESTLON = -30.0 ! [degrees] default = 0.0 ! x-axis. LENLON = 1.0 ! [degrees] ! The longitudinal or x-direction length of the domain. -TOPO_CONFIG = "flat" ! +TOPO_CONFIG = "file" ! ! This specifies how bathymetry is specified: ! file - read bathymetric information from the file ! specified by (TOPO_FILE). @@ -87,8 +87,16 @@ TOPO_CONFIG = "flat" ! ! Phillips - ACC-like idealized topography used in the Phillips config. ! dense - Denmark Strait-like dense water formation and overflow. ! USER - call a user modified routine. -MINIMUM_DEPTH = 0.5 ! [m] default = 0.0 - ! The minimum depth of the ocean. +TOPO_FILE = "TOPO_FILE_COLUMN.nc" ! default = "topog.nc" + ! The file from which the bathymetry is read. +MINIMUM_DEPTH = 9.5 ! [m] default = 0.0 + ! If MASKING_DEPTH is unspecified, then anything shallower than MINIMUM_DEPTH is + ! assumed to be land and all fluxes are masked out. If MASKING_DEPTH is + ! specified, then all depths shallower than MINIMUM_DEPTH but deeper than + ! MASKING_DEPTH are rounded to MINIMUM_DEPTH. +MASKING_DEPTH = 0.0 ! [m] default = -9999.0 + ! The depth below which to mask points as land points, for which all fluxes are + ! zeroed out. MASKING_DEPTH is ignored if it has the special default value. MAXIMUM_DEPTH = 6000.0 ! [m] ! The maximum depth of the ocean. ROTATION = "betaplane" ! default = "2omegasinlat" @@ -102,7 +110,7 @@ F_0 = 7.59943E-05 ! [s-1] default = 0.0 ! === module MOM_verticalGrid === ! Parameters providing information about the vertical grid. -NK = 75 ! [nondim] +NK = 65 ! [nondim] ! The number of model layers. ! === module MOM_EOS === @@ -135,7 +143,7 @@ REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" ! HYBGEN - Hybrid coordinate from the Hycom hybgen code ! SLIGHT - stretched coordinates above continuous isopycnal ! ADAPTIVE - optimize for smooth neutral density surfaces -ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_cm4.nc,dz" ! default = "UNIFORM" +ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_65L_20200626.nc,dz" ! default = "UNIFORM" ! Determines how to specify the coordinate resolution. Valid options are: ! PARAM - use the vector-parameter ALE_RESOLUTION ! UNIFORM[:N] - uniformly distributed @@ -149,7 +157,7 @@ ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_cm4.nc,dz" ! default = "UNIFORM" ! the filename and two variable names, separated ! by a comma or space, for sigma-2 and dz. e.g. ! HYBRID:vgrid.nc,sigma2,dz -!ALE_RESOLUTION = 4*2.0, 2*2.01, 2*2.02, 2.05, 2.07, 2.09, 2.13, 2.18, 2.24, 2.3, 2.4, 2.5, 2.62, 2.78, 2.95, 3.17, 3.42, 3.71, 4.07, 4.48, 4.97, 5.55, 6.23, 7.04, 7.99, 9.11, 10.43, 11.98, 13.8, 15.94, 18.42, 21.32, 24.66, 28.51, 32.91, 37.92, 43.56, 49.87, 56.88, 64.55, 72.91, 81.87, 91.4, 101.39, 111.73, 122.29, 132.93, 143.47, 153.78, 163.7, 173.07, 181.78, 189.76, 196.89, 203.17, 208.58, 213.14, 216.87, 219.87, 222.19, 223.94, 225.18, 226.04, 226.59, 226.9, 227.07, 227.14, 2*227.16, 227.17 ! [m] +!ALE_RESOLUTION = 4*2.5, 2.77, 3.38, 4.01, 4.65, 5.29, 5.95, 6.61, 7.28, 7.97, 8.66, 9.37, 10.08, 10.81, 11.54, 12.29, 13.06, 13.85, 14.69, 15.59, 16.56, 17.61, 18.76, 20.02, 21.42, 23.0, 24.77, 26.79, 29.1, 31.76, 34.87, 38.5, 42.79, 47.9, 54.01, 61.37, 70.25, 80.95, 93.75, 108.8, 126.04, 145.04, 164.81, 184.05, 201.34, 215.66, 226.64, 234.5, 239.84, 243.31, 245.52, 246.88, 247.72, 248.23, 248.54, 248.73, 248.84, 248.64, 248.68, 248.71, 248.72, 248.73 ! [m] ! The distribution of vertical resolution for the target ! grid used for Eulerian-like coordinates. For example, ! in z-coordinate mode, the parameter is a list of level @@ -214,7 +222,7 @@ UPWIND_1ST_CONTINUITY = True ! [Boolean] default = False ! If true, CONTINUITY_PPM becomes a 1st-order upwind continuity solver. This ! scheme is highly diffusive but may be useful for debugging or in single-column ! mode where its minimal stencil is useful. -ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.75E-09 +ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.25E-09 ! The tolerance for the differences between the barotropic and baroclinic ! estimates of the sea surface height due to the fluxes through each face. The ! total tolerance for SSH is 4 times this value. 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We generate MOM_override in the same notebook that we use to generate IC files and all the forcing we need; this way we can make sure F_0 (Coriolis parameter) is consistent with the latitude associated with our column 2. Instead of reading in a gustiness file, we use the same constant (0.02) that gets set for MOM6 in a CESM run 3. SOUTHLAT and WESTLON now match the latitude and longitude of our run (I think this only affects the latitude and longitude in diagnostic output) 4. I added support for running at the EQPAC JGOFS site --- .../single_column_MARBL/BATS/MOM_input | 10 +- .../BATS/MOM_parameter_doc.all | 12 +- .../BATS/MOM_parameter_doc.short | 12 +- .../examples/single_column_MARBL/EQPAC/INPUT | 1 + .../single_column_MARBL/EQPAC/MOM_input | 406 +++++++++ .../single_column_MARBL/EQPAC/MOM_override | 1 + .../single_column_MARBL/EQPAC/MOM_override2 | 4 + .../EQPAC/RESTART/.gitignore | 2 + .../single_column_MARBL/EQPAC/data_table | 19 + .../single_column_MARBL/EQPAC/diag_table | 771 ++++++++++++++++++ .../single_column_MARBL/EQPAC/input.nml | 21 + .../single_column_MARBL/EQPAC/marbl_in | 265 ++++++ .../INPUT/CESM_MOM_COLUMNS/BATS/MOM_override | 4 +- .../INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc | Bin 75468 -> 0 bytes .../CESM_MOM_COLUMNS/BATS/forcing_monthly.nc | Bin 3280 -> 0 bytes .../CESM_MOM_COLUMNS/EQPAC/IC_FILE_COLUMN.nc | Bin 0 -> 134712 bytes .../EQPAC/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 0 -> 20765 bytes .../EQPAC/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 0 -> 16739 bytes .../EQPAC/MARBL_FORCING_FILE_COLUMN.nc | Bin 0 -> 3125511 bytes .../INPUT/CESM_MOM_COLUMNS/EQPAC/MOM_override | 6 + .../EQPAC/TOPO_FILE_COLUMN.nc | Bin 0 -> 11889 bytes .../CESM_MOM_COLUMNS/HOT/IC_FILE_COLUMN.nc | Bin 0 -> 134712 bytes .../HOT/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 0 -> 20765 bytes .../HOT/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 0 -> 16739 bytes .../HOT/MARBL_FORCING_FILE_COLUMN.nc | Bin 0 -> 3125511 bytes .../INPUT/CESM_MOM_COLUMNS/HOT/MOM_override | 6 + .../CESM_MOM_COLUMNS/HOT/TOPO_FILE_COLUMN.nc | Bin 0 -> 11889 bytes 27 files changed, 1519 insertions(+), 21 deletions(-) create mode 120000 standalone/examples/single_column_MARBL/EQPAC/INPUT create mode 100644 standalone/examples/single_column_MARBL/EQPAC/MOM_input create mode 120000 standalone/examples/single_column_MARBL/EQPAC/MOM_override create mode 100644 standalone/examples/single_column_MARBL/EQPAC/MOM_override2 create mode 100644 standalone/examples/single_column_MARBL/EQPAC/RESTART/.gitignore create mode 100644 standalone/examples/single_column_MARBL/EQPAC/data_table create mode 100644 standalone/examples/single_column_MARBL/EQPAC/diag_table create mode 100644 standalone/examples/single_column_MARBL/EQPAC/input.nml create mode 100644 standalone/examples/single_column_MARBL/EQPAC/marbl_in delete mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc delete mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/forcing_monthly.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/IC_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MARBL_FESEDFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MARBL_FEVENTFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MARBL_FORCING_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MOM_override create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/TOPO_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/IC_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MARBL_FESEDFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MARBL_FEVENTFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MARBL_FORCING_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MOM_override create mode 100644 standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/TOPO_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_input b/standalone/examples/single_column_MARBL/BATS/MOM_input index 6d9068c5..9032fe9b 100644 --- a/standalone/examples/single_column_MARBL/BATS/MOM_input +++ b/standalone/examples/single_column_MARBL/BATS/MOM_input @@ -68,12 +68,12 @@ GRID_CONFIG = "cartesian" ! ! cartesian - use a (flat) Cartesian grid. ! spherical - use a simple spherical grid. ! mercator - use a Mercator spherical grid. -SOUTHLAT = 31.0 ! [degrees] +SOUTHLAT = 31.660667 ! [degrees] ! The southern latitude of the domain or the equivalent starting value for the ! y-axis. LENLAT = 1.0 ! [degrees] ! The latitudinal or y-direction length of the domain. -WESTLON = -30.0 ! [degrees] default = 0.0 +WESTLON = -64.018700 ! [degrees] default = 0.0 ! The western longitude of the domain or the equivalent starting value for the ! x-axis. LENLON = 1.0 ! [degrees] @@ -367,10 +367,10 @@ BUOY_CONFIG = "data_override" ! default = "zero" WIND_CONFIG = "data_override" ! default = "zero" ! The character string that indicates how wind forcing is specified. Valid ! options include (file), (2gyre), (1gyre), (gyres), (zero), and (USER). -READ_GUST_2D = True ! [Boolean] default = False +READ_GUST_2D = False ! [Boolean] default = False ! If true, use a 2-dimensional gustiness supplied from an input file -GUST_2D_FILE = "forcing_monthly.nc" ! - ! The file in which the wind gustiness is found in variable gustiness. +GUST_CONST = 0.02 ! [Pa] default = 0.0 + ! The background gustiness in the winds. ! === module MOM_main (MOM_driver) === DAYMAX = 1825.0 ! [days] diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all index 566e4ce9..4b774020 100644 --- a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all +++ b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all @@ -207,12 +207,12 @@ AXIS_UNITS = "degrees" ! default = "degrees" ! degrees - degrees of latitude and longitude ! m - meters ! k - kilometers -SOUTHLAT = 31.0 ! [degrees] +SOUTHLAT = 31.660667 ! [degrees] ! The southern latitude of the domain or the equivalent starting value for the ! y-axis. LENLAT = 1.0 ! [degrees] ! The latitudinal or y-direction length of the domain. -WESTLON = -30.0 ! [degrees] default = 0.0 +WESTLON = -64.0187 ! [degrees] default = 0.0 ! The western longitude of the domain or the equivalent starting value for the ! x-axis. LENLON = 1.0 ! [degrees] @@ -289,7 +289,7 @@ ROTATION = "betaplane" ! default = "2omegasinlat" ! times the sine of latitude. ! betaplane - Use a beta-plane or f-plane. ! USER - call a user modified routine. -F_0 = 7.59943E-05 ! [s-1] default = 0.0 +F_0 = 7.63418E-05 ! [s-1] default = 0.0 ! The reference value of the Coriolis parameter with the betaplane option. BETA = 0.0 ! [m-1 s-1] default = 0.0 ! The northward gradient of the Coriolis parameter with the betaplane option. @@ -1645,15 +1645,13 @@ LATENT_HEAT_FUSION = 3.34E+05 ! [J/kg] default = 3.34E+05 ! The latent heat of fusion. LATENT_HEAT_VAPORIZATION = 2.5E+06 ! [J/kg] default = 2.5E+06 ! The latent heat of fusion. -GUST_CONST = 0.0 ! [Pa] default = 0.0 +GUST_CONST = 0.02 ! [Pa] default = 0.0 ! The background gustiness in the winds. FIX_USTAR_GUSTLESS_BUG = True ! [Boolean] default = True ! If true correct a bug in the time-averaging of the gustless wind friction ! velocity -READ_GUST_2D = True ! [Boolean] default = False +READ_GUST_2D = False ! [Boolean] default = False ! If true, use a 2-dimensional gustiness supplied from an input file -GUST_2D_FILE = "forcing_monthly.nc" ! - ! The file in which the wind gustiness is found in variable gustiness. DUST_RATIO_THRES = 69.00594 ! [not defined] default = 69.00594 ! TODO: Add description DUST_RATIO_TO_FE_BIOAVAIL_FRAC = 0.0027298983931818 ! [not defined] default = 0.0027298983931818 diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short index 909e318a..4552ca3a 100644 --- a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short +++ b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short @@ -50,12 +50,12 @@ GRID_CONFIG = "cartesian" ! ! cartesian - use a (flat) Cartesian grid. ! spherical - use a simple spherical grid. ! mercator - use a Mercator spherical grid. -SOUTHLAT = 31.0 ! [degrees] +SOUTHLAT = 31.660667 ! [degrees] ! The southern latitude of the domain or the equivalent starting value for the ! y-axis. LENLAT = 1.0 ! [degrees] ! The latitudinal or y-direction length of the domain. -WESTLON = -30.0 ! [degrees] default = 0.0 +WESTLON = -64.0187 ! [degrees] default = 0.0 ! The western longitude of the domain or the equivalent starting value for the ! x-axis. LENLON = 1.0 ! [degrees] @@ -105,7 +105,7 @@ ROTATION = "betaplane" ! default = "2omegasinlat" ! times the sine of latitude. ! betaplane - Use a beta-plane or f-plane. ! USER - call a user modified routine. -F_0 = 7.59943E-05 ! [s-1] default = 0.0 +F_0 = 7.63418E-05 ! [s-1] default = 0.0 ! The reference value of the Coriolis parameter with the betaplane option. ! === module MOM_verticalGrid === @@ -358,10 +358,8 @@ WIND_CONFIG = "data_override" ! default = "zero" ! options include (file), (data_override), (2gyre), (1gyre), (gyres), (zero), ! (const), (Neverworld), (scurves), (ideal_hurr), (SCM_ideal_hurr), ! (SCM_CVmix_tests) and (USER). -READ_GUST_2D = True ! [Boolean] default = False - ! If true, use a 2-dimensional gustiness supplied from an input file -GUST_2D_FILE = "forcing_monthly.nc" ! - ! The file in which the wind gustiness is found in variable gustiness. +GUST_CONST = 0.02 ! [Pa] default = 0.0 + ! The background gustiness in the winds. READ_RIV_FLUXES = False ! [Boolean] default = True ! If true, use nitrogen deposition supplied from an input file diff --git a/standalone/examples/single_column_MARBL/EQPAC/INPUT b/standalone/examples/single_column_MARBL/EQPAC/INPUT new file mode 120000 index 00000000..3a2d233e --- /dev/null +++ b/standalone/examples/single_column_MARBL/EQPAC/INPUT @@ -0,0 +1 @@ +../INPUT \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/EQPAC/MOM_input b/standalone/examples/single_column_MARBL/EQPAC/MOM_input new file mode 100644 index 00000000..893d7ec5 --- /dev/null +++ b/standalone/examples/single_column_MARBL/EQPAC/MOM_input @@ -0,0 +1,406 @@ +! This input file provides the adjustable run-time parameters for version 6 of the Modular Ocean Model (MOM6). +! Where appropriate, parameters use usually given in MKS units. + +! This particular file is for the example in single_column/KPP. + +! This MOM_input file typically contains only the non-default values that are needed to reproduce this example. +! A full list of parameters for this example can be found in the corresponding MOM_parameter_doc.all file +! which is generated by the model at run-time. + +! === module MOM_domains === +REENTRANT_Y = True ! [Boolean] default = False + ! If true, the domain is meridionally reentrant. +NIGLOBAL = 2 ! + ! The total number of thickness grid points in the x-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NJGLOBAL = 2 ! + ! The total number of thickness grid points in the y-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NIHALO = 2 ! default = 4 + ! The number of halo points on each side in the x-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +NJHALO = 2 ! default = 4 + ! The number of halo points on each side in the y-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +! LAYOUT = 1, 1 ! + ! The processor layout that was actually used. + +! === module MOM === +SPLIT = False ! [Boolean] default = True + ! Use the split time stepping if true. +USE_REGRIDDING = True ! [Boolean] default = False + ! If True, use the ALE algorithm (regridding/remapping). If False, use the + ! layered isopycnal algorithm. +DT = 3600.0 ! [s] + ! The (baroclinic) dynamics time step. The time-step that is actually used will + ! be an integer fraction of the forcing time-step (DT_FORCING in ocean-only mode + ! or the coupling timestep in coupled mode.) +FRAZIL = True ! [Boolean] default = False + ! If true, water freezes if it gets too cold, and the accumulated heat deficit + ! is returned in the surface state. FRAZIL is only used if + ! ENABLE_THERMODYNAMICS is true. +C_P = 3925.0 ! [J kg-1 K-1] default = 3991.86795711963 + ! The heat capacity of sea water, approximated as a constant. This is only used + ! if ENABLE_THERMODYNAMICS is true. The default value is from the TEOS-10 + ! definition of conservative temperature. +SAVE_INITIAL_CONDS = True ! [Boolean] default = False + ! If true, write the initial conditions to a file given by IC_OUTPUT_FILE. + +! === module MOM_fixed_initialization === +USE_MARBL_TRACERS = True + ! "[Boolean] default = False + ! If true, use the MARBL tracer package." +! === module MARBL_tracers === +MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c211008.nc" + ! The file in which the MARBL tracers initial values can be found. +MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211020.nc" + ! The file in which the iron sediment flux forcing field can be found. +MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211020.nc" + ! The file in which the iron vent flux forcing field can be found. + + +! === module MOM_grid_init === +GRID_CONFIG = "cartesian" ! + ! A character string that determines the method for defining the horizontal + ! grid. Current options are: + ! mosaic - read the grid from a mosaic (supergrid) + ! file set by GRID_FILE. + ! cartesian - use a (flat) Cartesian grid. + ! spherical - use a simple spherical grid. + ! mercator - use a Mercator spherical grid. +SOUTHLAT = 0.06250 ! [degrees] + ! The southern latitude of the domain or the equivalent starting value for the + ! y-axis. +LENLAT = 1.0 ! [degrees] + ! The latitudinal or y-direction length of the domain. +WESTLON = -140.0 ! [degrees] default = 0.0 + ! The western longitude of the domain or the equivalent starting value for the + ! x-axis. +LENLON = 1.0 ! [degrees] + ! The longitudinal or x-direction length of the domain. +TOPO_CONFIG = "file" ! + ! This specifies how bathymetry is specified: + ! file - read bathymetric information from the file + ! specified by (TOPO_FILE). + ! flat - flat bottom set to MAXIMUM_DEPTH. + ! bowl - an analytically specified bowl-shaped basin + ! ranging between MAXIMUM_DEPTH and MINIMUM_DEPTH. + ! spoon - a similar shape to 'bowl', but with an vertical + ! wall at the southern face. + ! halfpipe - a zonally uniform channel with a half-sine + ! profile in the meridional direction. + ! bbuilder - build topography from list of functions. + ! benchmark - use the benchmark test case topography. + ! Neverworld - use the Neverworld test case topography. + ! DOME - use a slope and channel configuration for the + ! DOME sill-overflow test case. + ! ISOMIP - use a slope and channel configuration for the + ! ISOMIP test case. + ! DOME2D - use a shelf and slope configuration for the + ! DOME2D gravity current/overflow test case. + ! Kelvin - flat but with rotated land mask. + ! seamount - Gaussian bump for spontaneous motion test case. + ! dumbbell - Sloshing channel with reservoirs on both ends. + ! shelfwave - exponential slope for shelfwave test case. + ! Phillips - ACC-like idealized topography used in the Phillips config. + ! dense - Denmark Strait-like dense water formation and overflow. + ! USER - call a user modified routine. +TOPO_FILE = "TOPO_FILE_COLUMN.nc" ! default = "topog.nc" + ! The file from which the bathymetry is read. + +MAXIMUM_DEPTH = 6000.0 + ! "[m] + ! The maximum depth of the ocean." + +MINIMUM_DEPTH = 9.5 + ! "[m] default = 0.0 + ! If MASKING_DEPTH is unspecified, then anything shallower than + ! MINIMUM_DEPTH is assumed to be land and all fluxes are masked out. + ! If MASKING_DEPTH is specified, then all depths shallower than + ! MINIMUM_DEPTH but depper than MASKING_DEPTH are rounded to + ! MINIMUM_DEPTH." + +MASKING_DEPTH = 0.0 + ! "[m] default = -9999.0 + ! The depth below which to mask points as land points, for which all + ! fluxes are zeroed out. MASKING_DEPTH is ignored if negative." +ROTATION = "betaplane" ! default = "2omegasinlat" + ! This specifies how the Coriolis parameter is specified: + ! 2omegasinlat - Use twice the planetary rotation rate + ! times the sine of latitude. + ! betaplane - Use a beta-plane or f-plane. + ! USER - call a user modified routine. + +! === module MOM_verticalGrid === +! Parameters providing information about the vertical grid. +NK =65 ! [nondim] + ! The number of model layers. + +! === module MOM_EOS === + +! === module MOM_tracer_flow_control === + +! === module MOM_coord_initialization === +REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" + ! Coordinate mode for vertical regridding. Choose among the following + ! possibilities: LAYER - Isopycnal or stacked shallow water layers + ! ZSTAR, Z* - stretched geopotential z* + ! SIGMA_SHELF_ZSTAR - stretched geopotential z* ignoring shelf + ! SIGMA - terrain following coordinates + ! RHO - continuous isopycnal + ! HYCOM1 - HyCOM-like hybrid coordinate + ! SLIGHT - stretched coordinates above continuous isopycnal + ! ADAPTIVE - optimize for smooth neutral density surfaces +ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_65L_20200626.nc,dz" ! default = "UNIFORM" + ! Determines how to specify the coordinate resolution. Valid options are: + ! PARAM - use the vector-parameter ALE_RESOLUTION + ! UNIFORM[:N] - uniformly distributed + ! FILE:string - read from a file. The string specifies + ! the filename and variable name, separated + ! by a comma or space, e.g. FILE:lev.nc,dz + ! or FILE:lev.nc,interfaces=zw + ! WOA09[:N] - the WOA09 vertical grid (approximately) + ! FNC1:string - FNC1:dz_min,H_total,power,precision + ! HYBRID:string - read from a file. The string specifies + ! the filename and two variable names, separated + ! by a comma or space, for sigma-2 and dz. e.g. + ! HYBRID:vgrid.nc,sigma2,dz +!ALE_RESOLUTION = 4*2.0, 2*2.01, 2*2.02, 2.05, 2.07, 2.09, 2.13, 2.18, 2.24, 2.3, 2.4, 2.5, 2.62, 2.78, 2.95, 3.17, 3.42, 3.71, 4.07, 4.48, 4.97, 5.55, 6.23, 7.04, 7.99, 9.11, 10.43, 11.98, 13.8, 15.94, 18.42, 21.32, 24.66, 28.51, 32.91, 37.92, 43.56, 49.87, 56.88, 64.55, 72.91, 81.87, 91.4, 101.39, 111.73, 122.29, 132.93, 143.47, 153.78, 163.7, 173.07, 181.78, 189.76, 196.89, 203.17, 208.58, 213.14, 216.87, 219.87, 222.19, 223.94, 225.18, 226.04, 226.59, 226.9, 227.07, 227.14, 2*227.16, 227.17 ! [m] + ! The distribution of vertical resolution for the target + ! grid used for Eulerian-like coordinates. For example, + ! in z-coordinate mode, the parameter is a list of level + ! thicknesses (in m). In sigma-coordinate mode, the list + ! is of non-dimensional fractions of the water column. + +! === module MOM_state_initialization === +INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False + ! If true, initialize the layer thicknesses, temperatures, and salinities from a + ! Z-space file on a latitude-longitude grid. + +! === module MOM_initialize_layers_from_Z === +TEMP_SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" + ! The name of the z-space input file used to initialize temperatures (T) and + ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and + ! SALT_Z_INIT_FILE must be set. +Z_INIT_FILE_PTEMP_VAR = "Temp" ! default = "ptemp" + ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. +Z_INIT_FILE_SALT_VAR = "Salt" ! default = "salt" + ! The name of the salinity variable in SALT_Z_INIT_FILE. +Z_INIT_HOMOGENIZE = True ! [Boolean] default = False + ! If True, then horizontally homogenize the interpolated initial conditions. +Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False + ! If True, then remap straight to model coordinate from file. + +! === module MOM_diag_mediator === + +! === module MOM_lateral_mixing_coeffs === + +! === module MOM_set_visc === +HBBL = 10.0 ! [m] + ! The thickness of a bottom boundary layer with a viscosity of KVBBL if + ! BOTTOMDRAGLAW is not defined, or the thickness over which near-bottom + ! velocities are averaged for the drag law if BOTTOMDRAGLAW is defined but + ! LINEAR_DRAG is not. +CDRAG = 0.0 ! [nondim] default = 0.003 + ! CDRAG is the drag coefficient relating the magnitude of the velocity field to + ! the bottom stress. CDRAG is only used if BOTTOMDRAGLAW is defined. +DRAG_BG_VEL = 0.1 ! [m s-1] default = 0.0 + ! DRAG_BG_VEL is either the assumed bottom velocity (with LINEAR_DRAG) or an + ! unresolved velocity that is combined with the resolved velocity to estimate + ! the velocity magnitude. DRAG_BG_VEL is only used when BOTTOMDRAGLAW is + ! defined. +BBL_THICK_MIN = 0.1 ! [m] default = 0.0 + ! The minimum bottom boundary layer thickness that can be used with + ! BOTTOMDRAGLAW. This might be Kv/(cdrag*drag_bg_vel) to give Kv as the minimum + ! near-bottom viscosity. +KV = 1.0E-04 ! [m2 s-1] + ! The background kinematic viscosity in the interior. The molecular value, ~1e-6 + ! m2 s-1, may be used. + +! === module MOM_thickness_diffuse === + +! === module MOM_dynamics_unsplit === + +! === module MOM_continuity === + +! === module MOM_continuity_PPM === +UPWIND_1ST_CONTINUITY = True ! [Boolean] default = False + ! If true, CONTINUITY_PPM becomes a 1st-order upwind continuity solver. This + ! scheme is highly diffusive but may be useful for debugging or in single-column + ! mode where its minimal stencil is useful. +ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.75E-09 + ! The tolerance for the differences between the barotropic and baroclinic + ! estimates of the sea surface height due to the fluxes through each face. The + ! total tolerance for SSH is 4 times this value. The default is + ! 0.5*NK*ANGSTROM, and this should not be set less than about + ! 10^-15*MAXIMUM_DEPTH. +ETA_TOLERANCE_AUX = 0.001 ! [m] default = 1.0E-06 + ! The tolerance for free-surface height discrepancies between the barotropic + ! solution and the sum of the layer thicknesses when calculating the auxiliary + ! corrected velocities. By default, this is the same as ETA_TOLERANCE, but can + ! be made larger for efficiency. + +! === module MOM_CoriolisAdv === +CORIOLIS_EN_DIS = True ! [Boolean] default = False + ! If true, two estimates of the thickness fluxes are used to estimate the + ! Coriolis term, and the one that dissipates energy relative to the other one is + ! used. +BOUND_CORIOLIS = True ! [Boolean] default = False + ! If true, the Coriolis terms at u-points are bounded by the four estimates of + ! (f+rv)v from the four neighboring v-points, and similarly at v-points. This + ! option is always effectively false with CORIOLIS_EN_DIS defined and + ! CORIOLIS_SCHEME set to SADOURNY75_ENERGY. + +! === module MOM_PressureForce === + +! === module MOM_PressureForce_AFV === + +! === module MOM_hor_visc === +BIHARMONIC = False ! [Boolean] default = True + ! If true, use a biharmonic horizontal viscosity. BIHARMONIC may be used with + ! LAPLACIAN. + +! === module MOM_vert_friction === +HARMONIC_VISC = True ! [Boolean] default = False + ! If true, use the harmonic mean thicknesses for calculating the vertical + ! viscosity. +HMIX_FIXED = 0.01 ! [m] + ! The prescribed depth over which the near-surface viscosity and diffusivity are + ! elevated when the bulk mixed layer is not used. +KVML = 0.0 ! [m2 s-1] default = 1.0E-04 + ! The kinematic viscosity in the mixed layer. A typical value is ~1e-2 m2 s-1. + ! KVML is not used if BULKMIXEDLAYER is true. The default is set by KV. +MAXVEL = 6.0 ! [m s-1] default = 3.0E+08 + ! The maximum velocity allowed before the velocity components are truncated. +VEL_UNDERFLOW = 1.0E-30 ! [m s-1] default = 0.0 + ! A negligibly small velocity magnitude below which velocity components are set + ! to 0. A reasonable value might be 1e-30 m/s, which is less than an Angstrom + ! divided by the age of the universe. + +! === module MOM_diagnostics === + +! === module MOM_diabatic_driver === +! The following parameters are used for diabatic processes. + +! === module MOM_CVMix_KPP === +! This is the MOM wrapper to CVMix:KPP +! See http://cvmix.github.io/ +USE_KPP = True ! [Boolean] default = False + ! If true, turns on the [CVMix] KPP scheme of Large et al., 1994, to calculate + ! diffusivities and non-local transport in the OBL. +KPP% +INTERP_TYPE = "cubic" ! default = "quadratic" + ! Type of interpolation to determine the OBL depth. + ! Allowed types are: linear, quadratic, cubic. +NLT_SHAPE = "PARABOLIC" ! default = "CVMix" + ! MOM6 method to set nonlocal transport profile. Over-rides the result from + ! CVMix. Allowed values are: + ! CVMix - Uses the profiles from CVMix specified by MATCH_TECHNIQUE + ! LINEAR - A linear profile, 1-sigma + ! PARABOLIC - A parablic profile, (1-sigma)^2 + ! CUBIC - A cubic profile, (1-sigma)^2(1+2*sigma) + ! CUBIC_LMD - The original KPP profile +%KPP + +! === module MOM_set_diffusivity === +ML_RADIATION = True ! [Boolean] default = False + ! If true, allow a fraction of TKE available from wind work to penetrate below + ! the base of the mixed layer with a vertical decay scale determined by the + ! minimum of: (1) The depth of the mixed layer, (2) an Ekman length scale. +TKE_DECAY = 10.0 ! [nondim] default = 2.5 + ! The ratio of the natural Ekman depth to the TKE decay scale. +ML_OMEGA_FRAC = 1.0 ! [nondim] default = 0.0 + ! When setting the decay scale for turbulence, use this fraction of the absolute + ! rotation rate blended with the local value of f, as sqrt((1-of)*f^2 + + ! of*4*omega^2). + +! === module MOM_bkgnd_mixing === +! Adding static vertical background mixing coefficients +KD = 2.0E-05 ! [m2 s-1] default = 0.0 + ! The background diapycnal diffusivity of density in the interior. Zero or the + ! molecular value, ~1e-7 m2 s-1, may be used. +KD_MIN = 2.0E-06 ! [m2 s-1] default = 2.0E-07 + ! The minimum diapycnal diffusivity. + +! === module MOM_kappa_shear === +! Parameterization of shear-driven turbulence following Jackson, Hallberg and Legg, JPO 2008 +USE_JACKSON_PARAM = True ! [Boolean] default = False + ! If true, use the Jackson-Hallberg-Legg (JPO 2008) shear mixing + ! parameterization. +MAX_RINO_IT = 25 ! [nondim] default = 50 + ! The maximum number of iterations that may be used to estimate the Richardson + ! number driven mixing. + +! === module MOM_diabatic_aux === +! The following parameters are used for auxiliary diabatic processes. +RECLAIM_FRAZIL = False ! [Boolean] default = True + ! If true, try to use any frazil heat deficit to cool any overlying layers down + ! to the freezing point, thereby avoiding the creation of thin ice when the SST + ! is above the freezing point. +VAR_PEN_SW = True ! [Boolean] default = False + ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine + ! the e-folding depth of incoming short wave radiation. + +! === module MOM_opacity === +PEN_SW_NBANDS = 3 ! default = 1 + ! The number of bands of penetrating shortwave radiation. + +! === module MOM_tracer_advect === + +! === module MOM_tracer_hor_diff === +KHTR = 600.0 ! [m2 s-1] default = 0.0 + ! The background along-isopycnal tracer diffusivity. + +! === module MOM_sum_output === +MAXTRUNC = 5000 ! [truncations save_interval-1] default = 0 + ! The run will be stopped, and the day set to a very large value if the velocity + ! is truncated more than MAXTRUNC times between energy saves. Set MAXTRUNC to 0 + ! to stop if there is any truncation of velocities. +ENERGYSAVEDAYS = 10.0 ! [days] default = 1.0 + ! The interval in units of TIMEUNIT between saves of the energies of the run and + ! other globally summed diagnostics. + +! === module MOM_surface_forcing === +BUOY_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how buoyancy forcing is specified. Valid + ! options include (file), (zero), (linear), (USER), (BFB) and (NONE). +WIND_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how wind forcing is specified. Valid + ! options include (file), (2gyre), (1gyre), (gyres), (zero), and (USER). +READ_GUST_2D = False ! [Boolean] default = False + ! If true, use a 2-dimensional gustiness supplied from an input file +GUST_CONST = 0.02 ! [Pa] default = 0.0 + ! The background gustiness in the winds. + +! === module MOM_main (MOM_driver) === +DAYMAX = 1825.0 ! [days] + ! The final time of the whole simulation, in units of TIMEUNIT seconds. This + ! also sets the potential end time of the present run segment if the end time is + ! not set via ocean_solo_nml in input.nml. +RESTART_CONTROL = 0 ! default = 1 + ! An integer whose bits encode which restart files are written. Add 2 (bit 1) + ! for a time-stamped file, and odd (bit 0) for a non-time-stamped file. A + ! non-time-stamped restart file is saved at the end of the run segment for any + ! non-negative value. +RESTINT = 3650.0 ! [days] default = 0.0 + ! The interval between saves of the restart file in units of TIMEUNIT. Use 0 + ! (the default) to not save incremental restart files at all. + +! === module MOM_write_cputime === +MAXCPU = 2.88E+04 ! [wall-clock seconds] default = -1.0 + ! The maximum amount of cpu time per processor for which MOM should run before + ! saving a restart file and quitting with a return value that indicates that a + ! further run is required to complete the simulation. If automatic restarts are + ! not desired, use a negative value for MAXCPU. MAXCPU has units of wall-clock + ! seconds, so the actual CPU time used is larger by a factor of the number of + ! processors used. + +! Debugging parameters set to non-default values +U_TRUNC_FILE = "U_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to zonal + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. +V_TRUNC_FILE = "V_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to meridional + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. diff --git a/standalone/examples/single_column_MARBL/EQPAC/MOM_override b/standalone/examples/single_column_MARBL/EQPAC/MOM_override new file mode 120000 index 00000000..71bc5b9c --- /dev/null +++ b/standalone/examples/single_column_MARBL/EQPAC/MOM_override @@ -0,0 +1 @@ +INPUT/CESM_MOM_COLUMNS/EQPAC/MOM_override \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/EQPAC/MOM_override2 b/standalone/examples/single_column_MARBL/EQPAC/MOM_override2 new file mode 100644 index 00000000..94a755f0 --- /dev/null +++ b/standalone/examples/single_column_MARBL/EQPAC/MOM_override2 @@ -0,0 +1,4 @@ +! Generated by run_suite.csh +!#override DAYMAX = 365 +!#override ALE_COORDINATE_CONFIG = "FILE:INPUT/vgrid_cm4_4.nc,dz" ! 4 meter surface +!#override ALE_COORDINATE_CONFIG = "FILE:INPUT/vgrid_cm4.nc,dz" ! 2 meter surface diff --git a/standalone/examples/single_column_MARBL/EQPAC/RESTART/.gitignore b/standalone/examples/single_column_MARBL/EQPAC/RESTART/.gitignore new file mode 100644 index 00000000..d6b7ef32 --- /dev/null +++ b/standalone/examples/single_column_MARBL/EQPAC/RESTART/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/standalone/examples/single_column_MARBL/EQPAC/data_table b/standalone/examples/single_column_MARBL/EQPAC/data_table new file mode 100644 index 00000000..a9ae7a83 --- /dev/null +++ b/standalone/examples/single_column_MARBL/EQPAC/data_table @@ -0,0 +1,19 @@ +# gridname, fieldname_code, fieldname_file, file_name, ongrid, factor +"OCN" , "lw", "LWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sw", "SWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "rain", "RAIN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "evap", "EVAP", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "taux", "TAUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "tauy", "TAUY", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "snow", "SNOW", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 +"OCN" , "runoff", "", "", .true., 0.0 +"OCN" , "calving", "", "", .true., 0.0 +"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_dust_flux", "SEAICE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_bc_flux", "SEAICE_BC_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "nhx_dep", "NDEP_NHx", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "noy_dep", "NDEP_NOy", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "u10_sqr", "U10_SQR", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 diff --git a/standalone/examples/single_column_MARBL/EQPAC/diag_table b/standalone/examples/single_column_MARBL/EQPAC/diag_table new file mode 100644 index 00000000..8f6227fa --- /dev/null +++ b/standalone/examples/single_column_MARBL/EQPAC/diag_table @@ -0,0 +1,771 @@ +"MOM Experiment" +1 1 1 0 0 0 +"prog", 1,"days",1,"days","Time" +"prog_z", 1,"days",1,"days","Time" +"surffluxes", 1,"days",1,"days","Time" +"ave_prog", 1,"days",1,"days","Time", +#"mom", 5,"days",1,"days","Time", +#"bt_mom", 5,"days",1,"days","Time", +"visc", 1,"days",1,"days","Time", +#"energy", 5,"days",1,"days","Time", +"marbl", 1,"days",1,"days","Time", +"marbl2", 1,"days",1,"days","Time", + + +#This is the field section of the diag_table. + +# Prognostic Ocean fields: +#========================= + +"ocean_model","u","u","prog","all",.false.,"none",2 +"ocean_model","v","v","prog","all",.false.,"none",2 +"ocean_model","h","h","prog","all",.false.,"none",1 +"ocean_model","e","e","prog","all",.false.,"none",2 +"ocean_model","temp","temp","prog","all",.false.,"none",2 +"ocean_model","salt","salt","prog","all",.false.,"none",2 +#"ocean_model","Rml","Rml","prog","all",.false.,"none",2 +#"ocean_model","h_ML","h_ML","prog","all",.false.,"none",2 + +#"ocean_model","RV","RV","prog","all",.false.,"none",2 +#"ocean_model","PV","PV","prog","all",.false.,"none",2 +#"ocean_model","e_D","e_D","prog","all",.false.,"none",2 + +"ocean_model","u","u","ave_prog","all",.true.,"none",2 +"ocean_model","v","v","ave_prog","all",.true.,"none",2 +#"ocean_model","h","h","ave_prog","all",.true.,"none",1 +"ocean_model","e","e","ave_prog","all",.true.,"none",2 +"ocean_model","temp","temp","ave_prog","all",.true.,"none",2 +"ocean_model","salt","salt","ave_prog","all",.true.,"none",2 +#"ocean_model","Rml","Rml","ave_prog","all",.true.,"none",2 + +# Z level output +#================ +"ocean_model_z","u","u","prog_z","all",.false.,"none",2 +"ocean_model_z","v","v","prog_z","all",.false.,"none",2 +"ocean_model_z","temp","temp","prog_z","all",.false.,"none",2 +"ocean_model_z","salt","salt","prog_z","all",.false.,"none",2 +# Auxilary Tracers: +#================== +#"ocean_model","vintage","vintage","prog","all",.false.,"none",2 +#"ocean_model","age","age","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","prog","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog","all",.false.,"none",2 +"ocean_model","O2","O2","prog","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog","all",.false.,"none",2 +"ocean_model","DON","DON","prog","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog","all",.false.,"none",2 +"ocean_model","spC","spC","prog","all",.false.,"none",2 +"ocean_model","spP","spP","prog","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","ave_prog","all",.true.,"none",2 +"ocean_model","NO3","NO3","ave_prog","all",.true.,"none",2 +"ocean_model","SiO3","SiO3","ave_prog","all",.true.,"none",2 +"ocean_model","NH4","NH4","ave_prog","all",.true.,"none",2 +"ocean_model","Fe","Fe","ave_prog","all",.true.,"none",2 +"ocean_model","Lig","Lig","ave_prog","all",.true.,"none",2 +"ocean_model","O2","O2","ave_prog","all",.true.,"none",2 +"ocean_model","DIC","DIC","ave_prog","all",.true.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","ALK","ALK","ave_prog","all",.true.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","DOC","DOC","ave_prog","all",.true.,"none",2 +"ocean_model","DON","DON","ave_prog","all",.true.,"none",2 +"ocean_model","DOP","DOP","ave_prog","all",.true.,"none",2 +"ocean_model","DOPr","DOPr","ave_prog","all",.true.,"none",2 +"ocean_model","DONr","DONr","ave_prog","all",.true.,"none",2 +"ocean_model","DOCr","DOCr","ave_prog","all",.true.,"none",2 +"ocean_model","microzooC","microzooC","ave_prog","all",.true.,"none",2 +"ocean_model","mesozooC","mesozooC","ave_prog","all",.true.,"none",2 +"ocean_model","spChl","spChl","ave_prog","all",.true.,"none",2 +"ocean_model","spC","spC","ave_prog","all",.true.,"none",2 +"ocean_model","spP","spP","ave_prog","all",.true.,"none",2 +"ocean_model","spFe","spFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoChl","coccoChl","ave_prog","all",.true.,"none",2 +"ocean_model","coccoC","coccoC","ave_prog","all",.true.,"none",2 +"ocean_model","coccoP","coccoP","ave_prog","all",.true.,"none",2 +"ocean_model","coccoFe","coccoFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","ave_prog","all",.true.,"none",2 +"ocean_model","diatChl","diatChl","ave_prog","all",.true.,"none",2 +"ocean_model","diatC","diatC","ave_prog","all",.true.,"none",2 +"ocean_model","diatP","diatP","ave_prog","all",.true.,"none",2 +"ocean_model","diatFe","diatFe","ave_prog","all",.true.,"none",2 +"ocean_model","diatSi","diatSi","ave_prog","all",.true.,"none",2 +"ocean_model","diazChl","diazChl","ave_prog","all",.true.,"none",2 +"ocean_model","diazC","diazC","ave_prog","all",.true.,"none",2 +"ocean_model","diazP","diazP","ave_prog","all",.true.,"none",2 +"ocean_model","diazFe","diazFe","ave_prog","all",.true.,"none",2 +"ocean_model","PO4","PO4","prog_z","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog_z","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog_z","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog_z","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog_z","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog_z","all",.false.,"none",2 +"ocean_model","O2","O2","prog_z","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog_z","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog_z","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog_z","all",.false.,"none",2 +"ocean_model","DON","DON","prog_z","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog_z","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog_z","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog_z","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog_z","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog_z","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog_z","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog_z","all",.false.,"none",2 +"ocean_model","spC","spC","prog_z","all",.false.,"none",2 +"ocean_model","spP","spP","prog_z","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog_z","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog_z","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog_z","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog_z","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog_z","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog_z","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog_z","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog_z","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog_z","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog_z","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog_z","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog_z","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog_z","all",.false.,"none",2 + + +# Continuity Equation Terms: +#=========================== +#"ocean_model","dhdt","dhdt","cont","all",.true.,"none",2 +"ocean_model","wd","wd","prog","all",.false.,"none",2 +#"ocean_model","uh","uh","cont","all",.true.,"none",2 +#"ocean_model","vh","vh","cont","all",.true.,"none",2 +#"ocean_model","uhGM","uhGM","cont","all",.true.,"none",2 +#"ocean_model","vhGM","vhGM","cont","all",.true.,"none",2 +#"ocean_model","uhbt","uhbt","cont","all",.true.,"none",2 +#"ocean_model","vhbt","vhbt","cont","all",.true.,"none",2 +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","KH_u","KH_u","cont","all",.true.,"none",2 +#"ocean_model","KH_v","KH_v","cont","all",.true.,"none",2 +#"ocean_model","GMwork","GMwork","cont","all",.true.,"none",2 + +# Continuity Equation Terms In Pure Potential Density Coordiantes: +#================================================================= +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 + +# +# Tracer Fluxes: +#================== +#"ocean_model","T_adx", "T_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_ady", "T_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffx","T_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffy","T_diffy","ave_prog","all",.true.,"none",2 +#"ocean_model","S_adx", "S_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_ady", "S_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffx","S_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffy","S_diffy","ave_prog","all",.true.,"none",2 + + +# Momentum Balance Terms: +#======================= +#"ocean_model","dudt","dudt","mom","all",.true.,"none",2 +#"ocean_model","dvdt","dvdt","mom","all",.true.,"none",2 +#"ocean_model","CAu","CAu","mom","all",.true.,"none",2 +#"ocean_model","CAv","CAv","mom","all",.true.,"none",2 +#"ocean_model","PFu","PFu","mom","all",.true.,"none",2 +#"ocean_model","PFv","PFv","mom","all",.true.,"none",2 +#"ocean_model","du_dt_visc","du_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","dv_dt_visc","dv_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","diffu","diffu","mom","all",.true.,"none",2 +#"ocean_model","diffv","diffv","mom","all",.true.,"none",2 +#"ocean_model","dudt_dia","dudt_dia","mom","all",.true.,"none",2 +#"ocean_model","dvdt_dia","dvdt_dia","mom","all",.true.,"none",2 +# Subterms that should not be added to a closed budget. +#"ocean_model","gKEu","gKEu","mom","all",.true.,"none",2 +#"ocean_model","gKEv","gKEv","mom","all",.true.,"none",2 +#"ocean_model","rvxu","rvxu","mom","all",.true.,"none",2 +#"ocean_model","rvxv","rvxv","mom","all",.true.,"none",2 +#"ocean_model","PFu_bc","PFu_bc","mom","all",.true.,"none",2 +#"ocean_model","PFv_bc","PFv_bc","mom","all",.true.,"none",2 + +# Barotropic Momentum Balance Terms: +# (only available with split time stepping.) +#=========================================== +#"ocean_model","PFuBT","PFuBT","bt_mom","all",.true.,"none",2 +#"ocean_model","PFvBT","PFvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CoruBT","CoruBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CorvBT","CorvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","ubtforce","ubtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","vbtforce","vbtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","u_accel_bt","u_accel_bt","bt_mom","all",.true.,"none",2 +#"ocean_model","v_accel_bt","v_accel_bt","bt_mom","all",.true.,"none",2 +# +# Viscosities and diffusivities: +#=============================== +#"ocean_model","Kd_effective","Kd_effective","visc","all",.true.,"none",2 +"ocean_model","Kd_interface","Kd_interface","visc","all",.true.,"none",2 +#"ocean_model","Ahh","Ahh","visc","all",.true.,"none",2 +#"ocean_model","Ahq","Ahq","visc","all",.true.,"none",2 +#"ocean_model","Khh","Khh","visc","all",.true.,"none",2 +#"ocean_model","Khq","Khq","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_u","bbl_thick_u","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_u","kv_bbl_u","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_v","bbl_thick_v","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_v","kv_bbl_v","visc","all",.true.,"none",2 +#"ocean_model","av_visc","av_visc","visc","all",.true.,"none",2 +#"ocean_model","au_visc","au_visc","visc","all",.true.,"none",2 +#"ocean_model","FrictWork","FrictWork","visc","all",.true.,"none",2 +"ocean_model","KPP_uStar","KPP_uStar","visc","all",.true.,"none",2 +"ocean_model","KPP_buoyFlux","KPP_buoyFlux","visc","all",.true.,"none",2 +"ocean_model","KPP_QminusSW","KPP_QminusSW","visc","all",.true.,"none",2 +"ocean_model","KPP_netSalt","KPP_netSalt","visc","all",.true.,"none",2 +"ocean_model","KPP_OBLdepth","KPP_OBLdepth","visc","all",.true.,"none",2 +"ocean_model","KPP_sigma","KPP_sigma","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkRi","KPP_BulkRi","visc","all",.true.,"none",2 +"ocean_model","KPP_Ws","KPP_Ws","visc","all",.true.,"none",2 +"ocean_model","KPP_N","KPP_N","visc","all",.true.,"none",2 +"ocean_model","KPP_N2","KPP_N2","visc","all",.true.,"none",2 +"ocean_model","KPP_Vt2","KPP_Vt2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkUz2","KPP_BulkUz2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkDrho","KPP_BulkDrho","visc","all",.true.,"none",2 +"ocean_model","KPP_Kd_in","KPP_Kd_in","visc","all",.true.,"none",2 +"ocean_model","KPP_Kheat","KPP_Kheat","visc","all",.true.,"none",2 +"ocean_model","KPP_Ksalt","KPP_Ksalt","visc","all",.true.,"none",2 +"ocean_model","KPP_Kv","KPP_Kv","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_heat","KPP_NLtransport_heat","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_salt","KPP_NLtransport_salt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dTdt","KPP_NLT_dTdt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dSdt","KPP_NLT_dSdt","visc","all",.true.,"none",2 +"ocean_model","KPP_Tsurf","KPP_Tsurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Ssurf","KPP_Ssurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Usurf","KPP_Usurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Vsurf","KPP_Vsurf","visc","all",.true.,"none",2 +"ocean_model","MLD_003","MLD_003","visc","all",.true.,"none",2 + +# +# Kinetic Energy Balance Terms: +#============================= +#"ocean_model","KE","KE","energy","all",.true.,"none",2 +#"ocean_model","dKE_dt","dKE_dt","energy","all",.true.,"none",2 +#"ocean_model","PE_to_KE","PE_to_KE","energy","all",.true.,"none",2 +#"ocean_model","KE_Coradv","KE_Coradv","energy","all",.true.,"none",2 +#"ocean_model","KE_adv","KE_adv","energy","all",.true.,"none",2 +#"ocean_model","KE_visc","KE_visc","energy","all",.true.,"none",2 +#"ocean_model","KE_horvisc","KE_horvisc","energy","all",.true.,"none",2 +#"ocean_model","KE_dia","KE_dia","energy","all",.true.,"none",2 + +# Surface Forcing: +#================= +"ocean_model","taux","taux","surffluxes","all",.true.,"none",2 +"ocean_model","tauy","tauy","surffluxes","all",.true.,"none",2 +"ocean_model","ustar","ustar","surffluxes","all",.true.,"none",2 +"ocean_model","PRCmE","PRCmE","surffluxes","all",.true.,"none",2 +"ocean_model","evap","evap","surffluxes","all",.true.,"none",2 +"ocean_model","precip","precip","surffluxes","all",.true.,"none",2 +"ocean_model","fprec","fprec","surffluxes","all",.true.,"none",2 +"ocean_model","lprec","lprec","surffluxes","all",.true.,"none",2 +"ocean_model","net_heat_surface","net_heat_surface","surffluxes","all",.true.,"none",2 +"ocean_model","SW","SW","surffluxes","all",.true.,"none",2 +"ocean_model","SW_pen","SW_pen","surffluxes","all",.true.,"none",2 +"ocean_model","LwLatSens","LwLatSens","surffluxes","all",.true.,"none",2 +#"ocean_model","p_surf","p_surf","surffluxes","all",.true.,"none",2 +#"ocean_model","salt_flux","salt_flux","surffluxes","all",.true.,"none",2 + +# MARBL diagnostics: +#=================== +"ocean_model", "rsdoabsorb", "rsdoabsorb", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_IFRAC", "ECOSYS_IFRAC", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_XKW", "ECOSYS_XKW", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_ATM_PRESS", "ECOSYS_ATM_PRESS", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_O2", "PV_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_O2", "SCHMIDT_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "O2SAT", "O2SAT", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR", "CO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR", "DCO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "pCO2SURF", "pCO2SURF", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2", "DpCO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_CO2", "PV_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_CO2", "SCHMIDT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_CO2", "FG_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH", "PH", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_CO2", "ATM_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR_ALT_CO2", "CO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR_ALT_CO2", "DCO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pCO2SURF_ALT_CO2", "pCO2SURF_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2_ALT_CO2", "DpCO2_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_ALT_CO2", "FG_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH_ALT_CO2", "PH_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_ALT_CO2", "ATM_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "IRON_FLUX", "IRON_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "DUST_FLUX", "DUST_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NOx_FLUX", "NOx_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHy_FLUX", "NHy_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHx_SURFACE_EMIS", "NHx_SURFACE_EMIS", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatcalc", "zsatcalc", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatarag", "zsatarag", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN", "O2_ZMIN", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN_DEPTH", "O2_ZMIN_DEPTH", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint", "photoC_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint_100m", "photoC_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint", "photoC_NO3_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint_100m", "photoC_NO3_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint", "DOC_prod_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint_100m", "DOC_prod_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint", "DOC_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint_100m", "DOC_remin_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint", "DOCr_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint_100m", "DOCr_remin_zint_100m", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ctot", "Jint_Ctot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ntot", "Jint_Ntot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ptot", "Jint_Ptot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Sitot", "Jint_Sitot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Fetot", "Jint_Fetot", "marbl", "all", .false., "none", 2 +#"ocean_model", "calcToFloor", "calcToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed", "calcToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed_ALT_CO2", "calcToSed_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pocToFloor", "pocToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "pocToSed", "pocToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "ponToSed", "ponToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "SedDenitrif", "SedDenitrif", "marbl", "all", .false., "none", 2 +#"ocean_model", "OtherRemin", "OtherRemin", "marbl", "all", .false., "none", 2 +"ocean_model", "popToSed", "popToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "bsiToSed", "bsiToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "dustToSed", "dustToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "pfeToSed", "pfeToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_surf", "sp_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_Cweight_avg_100m", "sp_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_surf", "sp_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_Cweight_avg_100m", "sp_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_surf", "sp_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_Cweight_avg_100m", "sp_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_surf", "sp_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_Cweight_avg_100m", "sp_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint", "photoC_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint_100m", "photoC_sp_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_sp_zint", "photoC_NO3_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint", "graze_sp_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint_100m", "graze_sp_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint", "graze_sp_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint_100m", "graze_sp_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint", "graze_sp_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint_100m", "graze_sp_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint", "graze_sp_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint_100m", "graze_sp_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint", "sp_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint_100m", "sp_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint", "sp_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint_100m", "sp_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint", "sp_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint_100m", "sp_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint", "sp_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint_100m", "sp_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_surf", "cocco_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_Cweight_avg_100m", "cocco_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_surf", "cocco_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_Cweight_avg_100m", "cocco_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_surf", "cocco_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_Cweight_avg_100m", "cocco_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_surf", "cocco_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_Cweight_avg_100m", "cocco_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint", "photoC_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint_100m", "photoC_cocco_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_cocco_zint", "photoC_NO3_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint", "cocco_CaCO3_form_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint_100m", "cocco_CaCO3_form_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint", "graze_cocco_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint_100m", "graze_cocco_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint", "graze_cocco_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint_100m", "graze_cocco_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint", "graze_cocco_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint_100m", "graze_cocco_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint", "graze_cocco_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint_100m", "graze_cocco_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint", "cocco_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint_100m", "cocco_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint", "cocco_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint_100m", "cocco_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint", "cocco_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint_100m", "cocco_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg_zint", "cocco_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg_zint_100m", "cocco_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_N_lim_surf", "diat_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_N_lim_Cweight_avg_100m", "diat_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_P_lim_surf", "diat_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_P_lim_Cweight_avg_100m", "diat_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Fe_lim_surf", "diat_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Fe_lim_Cweight_avg_100m", "diat_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_SiO3_lim_surf", "diat_SiO3_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_SiO3_lim_Cweight_avg_100m", "diat_SiO3_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_light_lim_surf", "diat_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_light_lim_Cweight_avg_100m", "diat_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat_zint", "photoC_diat_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat_zint_100m", "photoC_diat_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diat_zint", "photoC_NO3_diat_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat_zint", "graze_diat_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zint_100m", "graze_diat_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc_zint", "graze_diat_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc_zint_100m", "graze_diat_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc_zint", "graze_diat_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc_zint_100m", "graze_diat_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo_zint", "graze_diat_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo_zint_100m", "graze_diat_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "diat_loss_zint", "diat_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_zint_100m", "diat_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc_zint", "diat_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc_zint_100m", "diat_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc_zint", "diat_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc_zint_100m", 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"sp_loss_poc", "sp_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc", "sp_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg", "sp_agg", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Qp", "cocco_Qp", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco", "photoC_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_cocco", "photoC_NO3_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "photoFe_cocco", "photoFe_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNO3_cocco", "photoNO3_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNH4_cocco", "photoNH4_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "DOP_cocco_uptake", "DOP_cocco_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "PO4_cocco_uptake", "PO4_cocco_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_cocco", "graze_cocco", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc", "graze_cocco_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc", "graze_cocco_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zootot", "graze_cocco_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_microzoo", "graze_cocco_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_mesozoo", "graze_cocco_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "cocco_loss", "cocco_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc", "cocco_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc", "cocco_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg", "cocco_agg", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form", "cocco_CaCO3_form", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Qp", "diat_Qp", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat", "photoC_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diat", "photoC_NO3_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "photoFe_diat", "photoFe_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNO3_diat", "photoNO3_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNH4_diat", "photoNH4_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "DOP_diat_uptake", "DOP_diat_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "PO4_diat_uptake", "PO4_diat_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat", "graze_diat", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc", "graze_diat_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc", "graze_diat_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zootot", "graze_diat_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_microzoo", "graze_diat_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_mesozoo", "graze_diat_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "diat_loss", "diat_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc", "diat_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc", "diat_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_agg", "diat_agg", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_bSi_form", "diat_bSi_form", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Qp", "diaz_Qp", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diaz", "photoC_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diaz", "photoC_NO3_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "photoFe_diaz", "photoFe_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNO3_diaz", "photoNO3_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNH4_diaz", "photoNH4_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "DOP_diaz_uptake", "DOP_diaz_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "PO4_diaz_uptake", "PO4_diaz_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz", "graze_diaz", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc", "graze_diaz_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc", "graze_diaz_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zootot", "graze_diaz_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_microzoo", "graze_diaz_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_mesozoo", "graze_diaz_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "diaz_loss", "diaz_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_poc", "diaz_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_doc", "diaz_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_agg", "diaz_agg", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Nfix", "diaz_Nfix", "marbl", "all", .false., "none", 2 +"ocean_model", "bSi_form", "bSi_form", "marbl", "all", .false., "none", 2 +"ocean_model", "CaCO3_form", "CaCO3_form", "marbl", "all", .false., "none", 2 +"ocean_model", "Nfix", "Nfix", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss", "microzoo_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss", "mesozoo_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_basal", "microzoo_loss_basal", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_basal", "mesozoo_loss_basal", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_poc", "microzoo_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_poc", "mesozoo_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_doc", "microzoo_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_doc", "mesozoo_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_microzoo", "graze_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo", "graze_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_poc", "graze_microzoo_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_poc", "graze_mesozoo_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_doc", "graze_microzoo_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_doc", "graze_mesozoo_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_zootot", "graze_microzoo_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_zootot", "graze_mesozoo_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_microzoo", "graze_microzoo_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_mesozoo", "graze_microzoo_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_microzoo", "graze_mesozoo_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_mesozoo", "graze_mesozoo_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_microzoo", "x_graze_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_mesozoo", "x_graze_mesozoo", "marbl2", "all", .false., "none", 2 +#"ocean_model", "PO4_RESTORE_TEND", "PO4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NO3_RESTORE_TEND", "NO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "SiO3_RESTORE_TEND", "SiO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NH4_RESTORE_TEND", "NH4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Fe_RESTORE_TEND", "Fe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Lig_RESTORE_TEND", "Lig_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "O2_RESTORE_TEND", "O2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_RESTORE_TEND", "DIC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_ALT_CO2_RESTORE_TEND", "DIC_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_RESTORE_TEND", "ALK_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_ALT_CO2_RESTORE_TEND", "ALK_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOC_RESTORE_TEND", "DOC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DON_RESTORE_TEND", "DON_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOP_RESTORE_TEND", "DOP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOPr_RESTORE_TEND", "DOPr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DONr_RESTORE_TEND", "DONr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOCr_RESTORE_TEND", "DOCr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "microzooC_RESTORE_TEND", "microzooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "mesozooC_RESTORE_TEND", "mesozooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spChl_RESTORE_TEND", "spChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spC_RESTORE_TEND", "spC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spP_RESTORE_TEND", "spP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spFe_RESTORE_TEND", "spFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoChl_RESTORE_TEND", "coccoChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoC_RESTORE_TEND", "coccoC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoP_RESTORE_TEND", "coccoP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoFe_RESTORE_TEND", "coccoFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoCaCO3_RESTORE_TEND", "coccoCaCO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatChl_RESTORE_TEND", "diatChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatC_RESTORE_TEND", "diatC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatP_RESTORE_TEND", "diatP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatFe_RESTORE_TEND", "diatFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatSi_RESTORE_TEND", "diatSi_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazChl_RESTORE_TEND", "diazChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazC_RESTORE_TEND", "diazC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazP_RESTORE_TEND", "diazP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazFe_RESTORE_TEND", "diazFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 + +#============================================================================================= +# +#====> This file can be used with diag_manager/v2.0a (or higher) <==== +# +# +# FORMATS FOR FILE ENTRIES (not all input values are used) +# ------------------------ +# +#"file_name", output_freq, "output_units", format, "time_units", "time_long_name", ... +# (opt) new_file_frequecy, (opt) "new_file_freq_units", "new_file_start_date" +# +# +#output_freq: > 0 output frequency in "output_units" +# = 0 output frequency every time step +# =-1 output frequency at end of run +# +#output_units = units used for output frequency +# (years, months, days, minutes, hours, seconds) +# +#time_units = units used to label the time axis +# (days, minutes, hours, seconds) +# +# +# FORMAT FOR FIELD ENTRIES (not all input values are used) +# ------------------------ +# +#"module_name", "field_name", "output_name", "file_name" "time_sampling", time_avg, "other_opts", packing +# +#time_avg = .true. or .false. +# +#packing = 1 double precision +# = 2 float +# = 4 packed 16-bit integers +# = 8 packed 1-byte (not tested?) diff --git a/standalone/examples/single_column_MARBL/EQPAC/input.nml b/standalone/examples/single_column_MARBL/EQPAC/input.nml new file mode 100644 index 00000000..01e68431 --- /dev/null +++ b/standalone/examples/single_column_MARBL/EQPAC/input.nml @@ -0,0 +1,21 @@ + &MOM_input_nml + output_directory = './', + input_filename = 'n' + restart_input_dir = 'INPUT/', + restart_output_dir = 'RESTART/', + parameter_filename = 'MOM_input', + 'MOM_override', + 'MOM_override2' / + + &diag_manager_nml + max_output_fields = 750, + / + + &ocean_solo_nml + calendar = 'NOLEAP' +/ + + &fms_nml + clock_grain = 'LOOP', + domains_stack_size = 710000, + stack_size = 0 / diff --git a/standalone/examples/single_column_MARBL/EQPAC/marbl_in b/standalone/examples/single_column_MARBL/EQPAC/marbl_in new file mode 100644 index 00000000..f4c8add0 --- /dev/null +++ b/standalone/examples/single_column_MARBL/EQPAC/marbl_in @@ -0,0 +1,265 @@ +f_graze_CaCO3_remin = 0.5 +QCaCO3_max = 1.0 + +zoo_mort2_exp = 2.0 + +PFT_defaults = 'user-specified' +autotroph_cnt = 4 +zooplankton_cnt = 2 +max_grazer_prey_cnt = 4 + +! autotrophs +autotroph_settings(1)%Ea = 0.32 +autotroph_settings(1)%Nfixer = .false. +autotroph_settings(1)%PCref_per_day = 4.8 +autotroph_settings(1)%Qp_fixed = 8.5470085470085479e-03 +autotroph_settings(1)%agg_rate_max = 0.5 +autotroph_settings(1)%agg_rate_min = 0.01 +autotroph_settings(1)%alphaPI_per_day = 0.35 +autotroph_settings(1)%exp_calcifier = .false. +autotroph_settings(1)%gQfe_0 = 3.0000000000000001e-05 +autotroph_settings(1)%gQfe_min = 2.5e-06 +autotroph_settings(1)%imp_calcifier = .false. +autotroph_settings(1)%is_carbon_limited = .false. +autotroph_settings(1)%kCO2 = 0 +autotroph_settings(1)%kDOP = 0.3 +autotroph_settings(1)%kFe = 3e-05 +autotroph_settings(1)%kNH4 = 0.01 +autotroph_settings(1)%kNO3 = 0.2 +autotroph_settings(1)%kPO4 = 0.01 +autotroph_settings(1)%kSiO3 = 0 +autotroph_settings(1)%lname = "Small Phyto" +autotroph_settings(1)%loss_poc = 0 +autotroph_settings(1)%loss_thres = 0.01 +autotroph_settings(1)%loss_thres2 = 0 +autotroph_settings(1)%mort2_per_day = 0.01 +autotroph_settings(1)%mort_per_day = 0.1 +autotroph_settings(1)%silicifier = .false. +autotroph_settings(1)%sname = "sp" +autotroph_settings(1)%temp_func_form_opt = "q_10" +autotroph_settings(1)%temp_thres = -10 +autotroph_settings(1)%thetaN_max = 2.5 +autotroph_settings(2)%Ea = 0.32 +autotroph_settings(2)%Nfixer = .false. +autotroph_settings(2)%PCref_per_day = 5.3 +autotroph_settings(2)%Qp_fixed = 8.5470085470085479e-03 +autotroph_settings(2)%agg_rate_max = 0.5 +autotroph_settings(2)%agg_rate_min = 0.02 +autotroph_settings(2)%alphaPI_per_day = 0.39 +autotroph_settings(2)%exp_calcifier = .false. +autotroph_settings(2)%gQfe_0 = 3.0000000000000001e-05 +autotroph_settings(2)%gQfe_min = 2.5e-06 +autotroph_settings(2)%imp_calcifier = .false. +autotroph_settings(2)%is_carbon_limited = .false. +autotroph_settings(2)%kCO2 = 0 +autotroph_settings(2)%kDOP = 0.5 +autotroph_settings(2)%kFe = 5e-05 +autotroph_settings(2)%kNH4 = 0.05 +autotroph_settings(2)%kNO3 = 0.4 +autotroph_settings(2)%kPO4 = 0.05 +autotroph_settings(2)%kSiO3 = 1.6 +autotroph_settings(2)%lname = "Diatom" +autotroph_settings(2)%loss_poc = 0 +autotroph_settings(2)%loss_thres = 0.02 +autotroph_settings(2)%loss_thres2 = 0 +autotroph_settings(2)%mort2_per_day = 0.01 +autotroph_settings(2)%mort_per_day = 0.1 +autotroph_settings(2)%silicifier = .true. +autotroph_settings(2)%sname = "diat" +autotroph_settings(2)%temp_func_form_opt = "q_10" +autotroph_settings(2)%temp_thres = -10 +autotroph_settings(2)%thetaN_max = 4 +autotroph_settings(3)%Ea = 0.32 +autotroph_settings(3)%Nfixer = .true. +autotroph_settings(3)%PCref_per_day = 1.6 +autotroph_settings(3)%Qp_fixed = 2.7350427350427355e-03 +autotroph_settings(3)%agg_rate_max = 0.5 +autotroph_settings(3)%agg_rate_min = 0.01 +autotroph_settings(3)%alphaPI_per_day = 0.31 +autotroph_settings(3)%exp_calcifier = .false. +autotroph_settings(3)%gQfe_0 = 6.0000000000000002e-05 +autotroph_settings(3)%gQfe_min = 2.5e-06 +autotroph_settings(3)%imp_calcifier = .false. +autotroph_settings(3)%is_carbon_limited = .false. +autotroph_settings(3)%kCO2 = 0 +autotroph_settings(3)%kDOP = 0.075 +autotroph_settings(3)%kFe = 4.5e-05 +autotroph_settings(3)%kNH4 = 0.2 +autotroph_settings(3)%kNO3 = 2 +autotroph_settings(3)%kPO4 = 0.015 +autotroph_settings(3)%kSiO3 = 0 +autotroph_settings(3)%lname = "Diazotroph" +autotroph_settings(3)%loss_poc = 0 +autotroph_settings(3)%loss_thres = 0.02 +autotroph_settings(3)%loss_thres2 = 0.001 +autotroph_settings(3)%mort2_per_day = 0.01 +autotroph_settings(3)%mort_per_day = 0.1 +autotroph_settings(3)%silicifier = .false. +autotroph_settings(3)%sname = "diaz" +autotroph_settings(3)%temp_func_form_opt = "q_10" +autotroph_settings(3)%temp_thres = 15 +autotroph_settings(3)%thetaN_max = 2.5 +autotroph_settings(4)%Ea = 0.32 +autotroph_settings(4)%Nfixer = .false. +autotroph_settings(4)%PCref_per_day = 4.7 +autotroph_settings(4)%Qp_fixed = 8.5470085470085479e-03 +autotroph_settings(4)%agg_rate_max = 0.5 +autotroph_settings(4)%agg_rate_min = 0.01 +autotroph_settings(4)%alphaPI_per_day = 0.31 +autotroph_settings(4)%exp_calcifier = .true. +autotroph_settings(4)%gQfe_0 = 3.0000000000000001e-05 +autotroph_settings(4)%gQfe_min = 2.5e-06 +autotroph_settings(4)%imp_calcifier = .false. +autotroph_settings(4)%is_carbon_limited = .true. +autotroph_settings(4)%kCO2 = 1 +autotroph_settings(4)%kDOP = 0.25 +autotroph_settings(4)%kFe = 3.2e-05 +autotroph_settings(4)%kNH4 = 0.01 +autotroph_settings(4)%kNO3 = 0.41 +autotroph_settings(4)%kPO4 = 0.006 +autotroph_settings(4)%kSiO3 = 0 +autotroph_settings(4)%lname = "Coccolithophores" +autotroph_settings(4)%loss_poc = 0 +autotroph_settings(4)%loss_thres = 0.01 +autotroph_settings(4)%loss_thres2 = 0 +autotroph_settings(4)%mort2_per_day = 0.01 +autotroph_settings(4)%mort_per_day = 0.1 +autotroph_settings(4)%silicifier = .false. +autotroph_settings(4)%sname = "cocco" +autotroph_settings(4)%temp_func_form_opt = "power" +autotroph_settings(4)%temp_thres = 0 +autotroph_settings(4)%thetaN_max = 3.8 + + + + + +! zoo1: small zooplankton -- these graze on primarily on sp, a bit on cocco/diaz +zooplankton_settings(1)%sname = 'microzoo' +zooplankton_settings(1)%lname = 'Microzooplankton' +zooplankton_settings(1)%z_mort_0_per_day = 0.1 +zooplankton_settings(1)%loss_thres = 0.075 +zooplankton_settings(1)%z_mort2_0_per_day = 0.0028 +zooplankton_settings(1)%Ea = 0.55 +zooplankton_settings(1)%temp_func_form_opt = "q_10" +zooplankton_settings(1)%basal_respiration_rate_per_day = 0 + +! zoo2: medium/large zooplankton -- these graze on medium size phytoplankton and zoo2 +zooplankton_settings(2)%sname = 'mesozoo' +zooplankton_settings(2)%lname = 'Mesozooplankton' +zooplankton_settings(2)%z_mort_0_per_day = 0.035 +zooplankton_settings(2)%loss_thres = 0.075 +zooplankton_settings(2)%z_mort2_0_per_day = 0.02 +zooplankton_settings(2)%Ea = 0.55 +zooplankton_settings(2)%temp_func_form_opt = "q_10" +zooplankton_settings(2)%basal_respiration_rate_per_day = 0 + +! Microzoo settings + +grazing_relationship_settings(1,1)%sname = 'grz_sp_microzoo' +grazing_relationship_settings(1,1)%lname = 'Grazing of sp by microzoo' +grazing_relationship_settings(1,1)%auto_ind_cnt = 1 +grazing_relationship_settings(1,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(1,1)%grazing_function = 2 +grazing_relationship_settings(1,1)%z_umax_0_per_day =4.1 +grazing_relationship_settings(1,1)%z_grz = 1.4 +grazing_relationship_settings(1,1)%graze_zoo = 0.35 +grazing_relationship_settings(1,1)%graze_poc = 0.01 +grazing_relationship_settings(1,1)%graze_doc = 0.057 +grazing_relationship_settings(1,1)%f_zoo_detr = 0.17 +grazing_relationship_settings(1,1)%auto_ind(1) = 1 + +grazing_relationship_settings(2,1)%sname = 'grz_diaz_microzoo' +grazing_relationship_settings(2,1)%lname = 'Grazing of diaz by microzoo' +grazing_relationship_settings(2,1)%auto_ind_cnt = 1 +grazing_relationship_settings(2,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(2,1)%f_zoo_detr = 0.17 +grazing_relationship_settings(2,1)%graze_doc = 0.057 +grazing_relationship_settings(2,1)%graze_poc = 0.01 +grazing_relationship_settings(2,1)%graze_zoo = 0.35 +grazing_relationship_settings(2,1)%grazing_function = 1 +grazing_relationship_settings(2,1)%z_grz = 1.2 +grazing_relationship_settings(2,1)%z_umax_0_per_day = 1 +grazing_relationship_settings(2,1)%auto_ind(1) = 3 + +grazing_relationship_settings(3,1)%sname = 'grz_cocco_microzoo' +grazing_relationship_settings(3,1)%lname = 'Grazing of cocco by microzoo' +grazing_relationship_settings(3,1)%auto_ind_cnt = 1 +grazing_relationship_settings(3,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(3,1)%grazing_function = 1 +grazing_relationship_settings(3,1)%z_umax_0_per_day = 1.7 +grazing_relationship_settings(3,1)%z_grz = 1.5 +grazing_relationship_settings(3,1)%graze_zoo = 0.25 +grazing_relationship_settings(3,1)%graze_poc = 0.18 +grazing_relationship_settings(3,1)%graze_doc = 0.09 +grazing_relationship_settings(3,1)%f_zoo_detr = 0.27 +grazing_relationship_settings(3,1)%auto_ind(1) = 4 + + +grazing_relationship_settings(4,1)%sname = "null" +grazing_relationship_settings(4,1)%lname = "null" +grazing_relationship_settings(4,1)%auto_ind_cnt = 0 +grazing_relationship_settings(4,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(4,1)%f_zoo_detr = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%graze_doc = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%graze_poc = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%graze_zoo = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%grazing_function = 1 +grazing_relationship_settings(4,1)%z_grz = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%z_umax_0_per_day = 0.0000000000000000e+00 + +! Mesozoo settings + +grazing_relationship_settings(1,2)%sname = 'grz_diat_mesozoo' +grazing_relationship_settings(1,2)%lname = 'Grazing of diat by mesozoo' +grazing_relationship_settings(1,2)%auto_ind_cnt = 1 +grazing_relationship_settings(1,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(1,2)%grazing_function = 1 +grazing_relationship_settings(1,2)%z_umax_0_per_day = 0.9 +grazing_relationship_settings(1,2)%z_grz = 1.0 +grazing_relationship_settings(1,2)%graze_zoo = 0.25 +grazing_relationship_settings(1,2)%graze_poc = 0.28 +grazing_relationship_settings(1,2)%graze_doc = 0.09 +grazing_relationship_settings(1,2)%f_zoo_detr = 0.40 +grazing_relationship_settings(1,2)%auto_ind(1) = 2 + + +grazing_relationship_settings(2,2)%sname = 'grz_cocco_mesozoo' +grazing_relationship_settings(2,2)%lname = 'Grazing of cocco by mesozoo' +grazing_relationship_settings(2,2)%auto_ind_cnt = 1 +grazing_relationship_settings(2,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(2,2)%grazing_function = 1 +grazing_relationship_settings(2,2)%z_umax_0_per_day = 0.015 +grazing_relationship_settings(2,2)%z_grz = 1.4 +grazing_relationship_settings(2,2)%graze_zoo = 0.25 +grazing_relationship_settings(2,2)%graze_poc = 0.18 +grazing_relationship_settings(2,2)%graze_doc = 0.09 +grazing_relationship_settings(2,2)%f_zoo_detr = 0.27 +grazing_relationship_settings(2,2)%auto_ind(1) = 4 + +grazing_relationship_settings(3,2)%sname = 'grz_diaz_mesozoo' +grazing_relationship_settings(3,2)%lname = 'Grazing of diaz by mesozoo' +grazing_relationship_settings(3,2)%auto_ind_cnt = 1 +grazing_relationship_settings(3,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(3,2)%grazing_function = 1 +grazing_relationship_settings(3,2)%z_umax_0_per_day = 0.5 +grazing_relationship_settings(3,2)%z_grz = 1.2 +grazing_relationship_settings(3,2)%graze_zoo = 0.25 +grazing_relationship_settings(3,2)%graze_poc = 0.18 +grazing_relationship_settings(3,2)%graze_doc = 0.09 +grazing_relationship_settings(3,2)%f_zoo_detr = 0.40 +grazing_relationship_settings(3,2)%auto_ind(1) = 3 + +grazing_relationship_settings(4,2)%sname = 'grz_microzoo_mesozoo' +grazing_relationship_settings(4,2)%lname = 'Predation of microzoo by mesozoo' +grazing_relationship_settings(4,2)%auto_ind_cnt = 0 +grazing_relationship_settings(4,2)%zoo_ind_cnt = 1 +grazing_relationship_settings(4,2)%grazing_function = 1 +grazing_relationship_settings(4,2)%z_umax_0_per_day = 0.2 +grazing_relationship_settings(4,2)%z_grz = 1.3 +grazing_relationship_settings(4,2)%graze_zoo = 0.25 +grazing_relationship_settings(4,2)%graze_poc = 0.28 +grazing_relationship_settings(4,2)%graze_doc = 0.09 +grazing_relationship_settings(4,2)%f_zoo_detr = 0.27 +grazing_relationship_settings(4,2)%zoo_ind(1) = 1 + diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override b/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override index 54550022..cdc19ad8 100644 --- a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override +++ b/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override @@ -1,5 +1,5 @@ -! 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I don't think we want to keep MOM_parameter_doc files in the repository, though I'm open to putting them back in if others disagree. --- .../BATS/MOM_parameter_doc.all | 1735 ----------------- .../BATS/MOM_parameter_doc.debugging | 83 - .../BATS/MOM_parameter_doc.layout | 56 - .../BATS/MOM_parameter_doc.short | 387 ---- .../single_column_MARBL/BATS/vgrid_cm4.cdl | 38 - .../single_column_MARBL/BATS/vgrid_cm4_10.cdl | 38 - .../single_column_MARBL/BATS/vgrid_cm4_4.cdl | 38 - 7 files changed, 2375 deletions(-) delete mode 100644 standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all delete mode 100644 standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.debugging delete mode 100644 standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.layout delete mode 100644 standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short delete mode 100644 standalone/examples/single_column_MARBL/BATS/vgrid_cm4.cdl delete mode 100644 standalone/examples/single_column_MARBL/BATS/vgrid_cm4_10.cdl delete mode 100644 standalone/examples/single_column_MARBL/BATS/vgrid_cm4_4.cdl diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all deleted file mode 100644 index 4b774020..00000000 --- a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.all +++ /dev/null @@ -1,1735 +0,0 @@ -! This file was written by the model and records all non-layout or debugging parameters used at run-time. - -! === module MOM === -SPLIT = False ! [Boolean] default = True - ! Use the split time stepping if true. -USE_RK2 = False ! [Boolean] default = False - ! If true, use RK2 instead of RK3 in the unsplit time stepping. -CALC_RHO_FOR_SEA_LEVEL = False ! [Boolean] default = False - ! If true, the in-situ density is used to calculate the effective sea level that - ! is returned to the coupler. If false, the Boussinesq parameter RHO_0 is used. -ENABLE_THERMODYNAMICS = True ! [Boolean] default = True - ! If true, Temperature and salinity are used as state variables. -USE_EOS = True ! [Boolean] default = True - ! If true, density is calculated from temperature and salinity with an equation - ! of state. If USE_EOS is true, ENABLE_THERMODYNAMICS must be true as well. -DIABATIC_FIRST = False ! [Boolean] default = False - ! If true, apply diabatic and thermodynamic processes, including buoyancy - ! forcing and mass gain or loss, before stepping the dynamics forward. -USE_CONTEMP_ABSSAL = False ! [Boolean] default = False - ! If true, the prognostics T&S are the conservative temperature and absolute - ! salinity. Care should be taken to convert them to potential temperature and - ! practical salinity before exchanging them with the coupler and/or reporting - ! T&S diagnostics. -ADIABATIC = False ! [Boolean] default = False - ! There are no diapycnal mass fluxes if ADIABATIC is true. This assumes that KD - ! = 0.0 and that there is no buoyancy forcing, but makes the model faster by - ! eliminating subroutine calls. -DO_DYNAMICS = True ! [Boolean] default = True - ! If False, skips the dynamics calls that update u & v, as well as the gravity - ! wave adjustment to h. This may be a fragile feature, but can be useful during - ! development -OFFLINE_TRACER_MODE = False ! [Boolean] default = False - ! If true, barotropic and baroclinic dynamics, thermodynamics are all bypassed - ! with all the fields necessary to integrate the tracer advection and diffusion - ! equation are read in from files stored from a previous integration of the - ! prognostic model. NOTE: This option only used in the ocean_solo_driver. -USE_REGRIDDING = True ! [Boolean] default = False - ! If True, use the ALE algorithm (regridding/remapping). If False, use the - ! layered isopycnal algorithm. -BULKMIXEDLAYER = False ! [Boolean] default = False - ! If true, use a Kraus-Turner-like bulk mixed layer with transitional buffer - ! layers. Layers 1 through NKML+NKBL have variable densities. There must be at - ! least NKML+NKBL+1 layers if BULKMIXEDLAYER is true. BULKMIXEDLAYER can not be - ! used with USE_REGRIDDING. The default is influenced by ENABLE_THERMODYNAMICS. -THICKNESSDIFFUSE = False ! [Boolean] default = False - ! If true, isopycnal surfaces are diffused with a Laplacian coefficient of KHTH. -APPLY_INTERFACE_FILTER = False ! [Boolean] default = False - ! If true, model interface heights are subjected to a grid-scale dependent - ! spatial smoothing, often with biharmonic filter. -USE_POROUS_BARRIER = True ! [Boolean] default = True - ! If true, use porous barrier to constrain the widths and face areas at the - ! edges of the grid cells. -BATHYMETRY_AT_VEL = False ! [Boolean] default = False - ! If true, there are separate values for the basin depths at velocity points. - ! Otherwise the effects of topography are entirely determined from thickness - ! points. -DT = 3600.0 ! [s] - ! The (baroclinic) dynamics time step. The time-step that is actually used will - ! be an integer fraction of the forcing time-step (DT_FORCING in ocean-only mode - ! or the coupling timestep in coupled mode.) -DT_THERM = 3600.0 ! [s] default = 3600.0 - ! The thermodynamic and tracer advection time step. Ideally DT_THERM should be - ! an integer multiple of DT and less than the forcing or coupling time-step, - ! unless THERMO_SPANS_COUPLING is true, in which case DT_THERM can be an integer - ! multiple of the coupling timestep. By default DT_THERM is set to DT. -THERMO_SPANS_COUPLING = False ! [Boolean] default = False - ! If true, the MOM will take thermodynamic and tracer timesteps that can be - ! longer than the coupling timestep. The actual thermodynamic timestep that is - ! used in this case is the largest integer multiple of the coupling timestep - ! that is less than or equal to DT_THERM. -HMIX_SFC_PROP = 1.0 ! [m] default = 1.0 - ! If BULKMIXEDLAYER is false, HMIX_SFC_PROP is the depth over which to average - ! to find surface properties like SST and SSS or density (but not surface - ! velocities). -HMIX_UV_SFC_PROP = 0.0 ! [m] default = 0.0 - ! If BULKMIXEDLAYER is false, HMIX_UV_SFC_PROP is the depth over which to - ! average to find surface flow properties, SSU, SSV. A non-positive value - ! indicates no averaging. -HFREEZE = -1.0 ! [m] default = -1.0 - ! If HFREEZE > 0, melt potential will be computed. The actual depth over which - ! melt potential is computed will be min(HFREEZE, OBLD), where OBLD is the - ! boundary layer depth. If HFREEZE <= 0 (default), melt potential will not be - ! computed. -INTERPOLATE_P_SURF = False ! [Boolean] default = False - ! If true, linearly interpolate the surface pressure over the coupling time - ! step, using the specified value at the end of the step. -FRAZIL = True ! [Boolean] default = False - ! If true, water freezes if it gets too cold, and the accumulated heat deficit - ! is returned in the surface state. FRAZIL is only used if - ! ENABLE_THERMODYNAMICS is true. -DO_GEOTHERMAL = False ! [Boolean] default = False - ! If true, apply geothermal heating. -BOUND_SALINITY = False ! [Boolean] default = False - ! If true, limit salinity to being positive. (The sea-ice model may ask for more - ! salt than is available and drive the salinity negative otherwise.) -SALINITY_UNDERFLOW = 0.0 ! [PPT] default = 0.0 - ! A tiny value of salinity below which the it is set to 0. For reference, one - ! molecule of salt per square meter of ocean is of order 1e-29 ppt. -TEMPERATURE_UNDERFLOW = 0.0 ! [degC] default = 0.0 - ! A tiny magnitude of temperatures below which they are set to 0. -C_P = 3925.0 ! [J kg-1 K-1] default = 3991.86795711963 - ! The heat capacity of sea water, approximated as a constant. This is only used - ! if ENABLE_THERMODYNAMICS is true. The default value is from the TEOS-10 - ! definition of conservative temperature. -USE_PSURF_IN_EOS = True ! [Boolean] default = True - ! If true, always include the surface pressure contributions in equation of - ! state calculations. -P_REF = 2.0E+07 ! [Pa] default = 2.0E+07 - ! The pressure that is used for calculating the coordinate density. (1 Pa = 1e4 - ! dbar, so 2e7 is commonly used.) This is only used if USE_EOS and - ! ENABLE_THERMODYNAMICS are true. -FIRST_DIRECTION = 0 ! default = 0 - ! An integer that indicates which direction goes first in parts of the code that - ! use directionally split updates, with even numbers (or 0) used for x- first - ! and odd numbers used for y-first. -ALTERNATE_FIRST_DIRECTION = False ! [Boolean] default = False - ! If true, after every dynamic timestep alternate whether the x- or y- direction - ! updates occur first in directionally split parts of the calculation. If this - ! is true, FIRST_DIRECTION applies at the start of a new run or if the next - ! first direction can not be found in the restart file. -CHECK_BAD_SURFACE_VALS = False ! [Boolean] default = False - ! If true, check the surface state for ridiculous values. -DEFAULT_ANSWER_DATE = 99991231 ! default = 99991231 - ! This sets the default value for the various _ANSWER_DATE parameters. -DEFAULT_2018_ANSWERS = False ! [Boolean] default = False - ! This sets the default value for the various _2018_ANSWERS parameters. -SURFACE_2018_ANSWERS = False ! [Boolean] default = False - ! If true, use expressions for the surface properties that recover the answers - ! from the end of 2018. Otherwise, use more appropriate expressions that differ - ! at roundoff for non-Boussinesq cases. -SURFACE_ANSWER_DATE = 99991231 ! default = 99991231 - ! The vintage of the expressions for the surface properties. Values below - ! 20190101 recover the answers from the end of 2018, while higher values use - ! updated and more robust forms of the same expressions. If both - ! SURFACE_2018_ANSWERS and SURFACE_ANSWER_DATE are specified, the latter takes - ! precedence. -USE_DIABATIC_TIME_BUG = False ! [Boolean] default = False - ! If true, uses the wrong calendar time for diabatic processes, as was done in - ! MOM6 versions prior to February 2018. This is not recommended. -SAVE_INITIAL_CONDS = True ! [Boolean] default = False - ! If true, write the initial conditions to a file given by IC_OUTPUT_FILE. -IC_OUTPUT_FILE = "MOM_IC" ! default = "MOM_IC" - ! The file into which to write the initial conditions. -WRITE_GEOM = 1 ! default = 1 - ! If =0, never write the geometry and vertical grid files. If =1, write the - ! geometry and vertical grid files only for a new simulation. If =2, always - ! write the geometry and vertical grid files. Other values are invalid. -USE_DBCLIENT = False ! [Boolean] default = False - ! If true, initialize a client to a remote database that can be used for online - ! analysis and machine-learning inference. -ICE_SHELF = False ! [Boolean] default = False - ! If true, enables the ice shelf model. -USE_PARTICLES = False ! [Boolean] default = False - ! If true, use the particles package. -ENSEMBLE_OCEAN = False ! [Boolean] default = False - ! If False, The model is being run in serial mode as a single realization. If - ! True, The current model realization is part of a larger ensemble and at the - ! end of step MOM, we will perform a gather of the ensemble members for - ! statistical evaluation and/or data assimilation. -HOMOGENIZE_FORCINGS = False ! [Boolean] default = False - ! If True, homogenize the forces and fluxes. - -! === module MOM_domains === -REENTRANT_X = True ! [Boolean] default = True - ! If true, the domain is zonally reentrant. -REENTRANT_Y = True ! [Boolean] default = False - ! If true, the domain is meridionally reentrant. -TRIPOLAR_N = False ! [Boolean] default = False - ! Use tripolar connectivity at the northern edge of the domain. With - ! TRIPOLAR_N, NIGLOBAL must be even. -NIGLOBAL = 2 ! - ! The total number of thickness grid points in the x-direction in the physical - ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. -NJGLOBAL = 2 ! - ! The total number of thickness grid points in the y-direction in the physical - ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. -NIHALO = 2 ! default = 4 - ! The number of halo points on each side in the x-direction. How this is set - ! varies with the calling component and static or dynamic memory configuration. -NJHALO = 2 ! default = 4 - ! The number of halo points on each side in the y-direction. How this is set - ! varies with the calling component and static or dynamic memory configuration. - -! === module MOM_hor_index === -! Sets the horizontal array index types. - -! === module MOM_grid === -! Parameters providing information about the lateral grid. -REFERENCE_HEIGHT = 0.0 ! [m] default = 0.0 - ! A reference value for geometric height fields, such as bathyT. - -! === module MOM_fixed_initialization === -INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" ! default = "." - ! The directory in which input files are found. - -! === module MOM_grid_init === -GRID_CONFIG = "cartesian" ! - ! A character string that determines the method for defining the horizontal - ! grid. Current options are: - ! mosaic - read the grid from a mosaic (supergrid) - ! file set by GRID_FILE. - ! cartesian - use a (flat) Cartesian grid. - ! spherical - use a simple spherical grid. - ! mercator - use a Mercator spherical grid. -AXIS_UNITS = "degrees" ! default = "degrees" - ! The units for the Cartesian axes. Valid entries are: - ! degrees - degrees of latitude and longitude - ! m - meters - ! k - kilometers -SOUTHLAT = 31.660667 ! [degrees] - ! The southern latitude of the domain or the equivalent starting value for the - ! y-axis. -LENLAT = 1.0 ! [degrees] - ! The latitudinal or y-direction length of the domain. -WESTLON = -64.0187 ! [degrees] default = 0.0 - ! The western longitude of the domain or the equivalent starting value for the - ! x-axis. -LENLON = 1.0 ! [degrees] - ! The longitudinal or x-direction length of the domain. -RAD_EARTH = 6.378E+06 ! [m] default = 6.378E+06 - ! The radius of the Earth. -TOPO_CONFIG = "file" ! - ! This specifies how bathymetry is specified: - ! file - read bathymetric information from the file - ! specified by (TOPO_FILE). - ! flat - flat bottom set to MAXIMUM_DEPTH. - ! bowl - an analytically specified bowl-shaped basin - ! ranging between MAXIMUM_DEPTH and MINIMUM_DEPTH. - ! spoon - a similar shape to 'bowl', but with an vertical - ! wall at the southern face. - ! halfpipe - a zonally uniform channel with a half-sine - ! profile in the meridional direction. - ! bbuilder - build topography from list of functions. - ! benchmark - use the benchmark test case topography. - ! Neverworld - use the Neverworld test case topography. - ! DOME - use a slope and channel configuration for the - ! DOME sill-overflow test case. - ! ISOMIP - use a slope and channel configuration for the - ! ISOMIP test case. - ! DOME2D - use a shelf and slope configuration for the - ! DOME2D gravity current/overflow test case. - ! Kelvin - flat but with rotated land mask. - ! seamount - Gaussian bump for spontaneous motion test case. - ! dumbbell - Sloshing channel with reservoirs on both ends. - ! shelfwave - exponential slope for shelfwave test case. - ! Phillips - ACC-like idealized topography used in the Phillips config. - ! dense - Denmark Strait-like dense water formation and overflow. - ! USER - call a user modified routine. -TOPO_FILE = "TOPO_FILE_COLUMN.nc" ! default = "topog.nc" - ! The file from which the bathymetry is read. -TOPO_VARNAME = "depth" ! default = "depth" - ! The name of the bathymetry variable in TOPO_FILE. -TOPO_EDITS_FILE = "" ! default = "" - ! The file from which to read a list of i,j,z topography overrides. -ALLOW_LANDMASK_CHANGES = False ! [Boolean] default = False - ! If true, allow topography overrides to change land mask. -MINIMUM_DEPTH = 9.5 ! [m] default = 0.0 - ! If MASKING_DEPTH is unspecified, then anything shallower than MINIMUM_DEPTH is - ! assumed to be land and all fluxes are masked out. If MASKING_DEPTH is - ! specified, then all depths shallower than MINIMUM_DEPTH but deeper than - ! MASKING_DEPTH are rounded to MINIMUM_DEPTH. -MASKING_DEPTH = 0.0 ! [m] default = -9999.0 - ! The depth below which to mask points as land points, for which all fluxes are - ! zeroed out. MASKING_DEPTH is ignored if it has the special default value. -MAXIMUM_DEPTH = 6000.0 ! [m] - ! The maximum depth of the ocean. - -! === module MOM_open_boundary === -! Controls where open boundaries are located, what kind of boundary condition to impose, and what data to apply, -! if any. -OBC_NUMBER_OF_SEGMENTS = 0 ! default = 0 - ! The number of open boundary segments. -CHANNEL_CONFIG = "none" ! default = "none" - ! A parameter that determines which set of channels are - ! restricted to specific widths. Options are: - ! none - All channels have the grid width. - ! global_1deg - Sets 16 specific channels appropriate - ! for a 1-degree model, as used in CM2G. - ! list - Read the channel locations and widths from a - ! text file, like MOM_channel_list in the MOM_SIS - ! test case. - ! file - Read open face widths everywhere from a - ! NetCDF file on the model grid. -SUBGRID_TOPO_AT_VEL = False ! [Boolean] default = False - ! If true, use variables from TOPO_AT_VEL_FILE as parameters for porous barrier. -ROTATION = "betaplane" ! default = "2omegasinlat" - ! This specifies how the Coriolis parameter is specified: - ! 2omegasinlat - Use twice the planetary rotation rate - ! times the sine of latitude. - ! betaplane - Use a beta-plane or f-plane. - ! USER - call a user modified routine. -F_0 = 7.63418E-05 ! [s-1] default = 0.0 - ! The reference value of the Coriolis parameter with the betaplane option. -BETA = 0.0 ! [m-1 s-1] default = 0.0 - ! The northward gradient of the Coriolis parameter with the betaplane option. -BETA_LAT_REF = 0.0 ! [degrees] default = 0.0 - ! The reference latitude (origin) of the beta-plane -GRID_ROTATION_ANGLE_BUGS = False ! [Boolean] default = False - ! If true, use an older algorithm to calculate the sine and cosines needed - ! rotate between grid-oriented directions and true north and east. Differences - ! arise at the tripolar fold. - -! === module MOM_verticalGrid === -! Parameters providing information about the vertical grid. -G_EARTH = 9.8 ! [m s-2] default = 9.8 - ! The gravitational acceleration of the Earth. -RHO_0 = 1035.0 ! [kg m-3] default = 1035.0 - ! The mean ocean density used with BOUSSINESQ true to calculate accelerations - ! and the mass for conservation properties, or with BOUSSINSEQ false to convert - ! some parameters from vertical units of m to kg m-2. -BOUSSINESQ = True ! [Boolean] default = True - ! If true, make the Boussinesq approximation. -ANGSTROM = 1.0E-10 ! [m] default = 1.0E-10 - ! The minimum layer thickness, usually one-Angstrom. -H_TO_M = 1.0 ! [m H-1] default = 1.0 - ! A constant that translates the model's internal units of thickness into m. -NK = 65 ! [nondim] - ! The number of model layers. - -! === module MOM_tracer_registry === - -! === module MOM_EOS === -EQN_OF_STATE = "WRIGHT" ! default = "WRIGHT" - ! EQN_OF_STATE determines which ocean equation of state should be used. - ! Currently, the valid choices are "LINEAR", "UNESCO", "WRIGHT", "NEMO" and - ! "TEOS10". This is only used if USE_EOS is true. -EOS_QUADRATURE = False ! [Boolean] default = False - ! If true, always use the generic (quadrature) code code for the integrals of - ! density. -TFREEZE_FORM = "LINEAR" ! default = "LINEAR" - ! TFREEZE_FORM determines which expression should be used for the freezing - ! point. Currently, the valid choices are "LINEAR", "MILLERO_78", "TEOS10" -TFREEZE_S0_P0 = 0.0 ! [deg C] default = 0.0 - ! When TFREEZE_FORM=LINEAR, this is the freezing potential temperature at S=0, - ! P=0. -DTFREEZE_DS = -0.054 ! [deg C PSU-1] default = -0.054 - ! When TFREEZE_FORM=LINEAR, this is the derivative of the freezing potential - ! temperature with salinity. -DTFREEZE_DP = 0.0 ! [deg C Pa-1] default = 0.0 - ! When TFREEZE_FORM=LINEAR, this is the derivative of the freezing potential - ! temperature with pressure. - -! === module MOM_restart === -PARALLEL_RESTARTFILES = False ! [Boolean] default = False - ! If true, the IO layout is used to group processors that write to the same - ! restart file or each processor writes its own (numbered) restart file. If - ! false, a single restart file is generated combining output from all PEs. -RESTARTFILE = "MOM.res" ! default = "MOM.res" - ! The name-root of the restart file. -MAX_FIELDS = 100 ! default = 100 - ! The maximum number of restart fields that can be used. -RESTART_CHECKSUMS_REQUIRED = True ! [Boolean] default = True - ! If true, require the restart checksums to match and error out otherwise. Users - ! may want to avoid this comparison if for example the restarts are made from a - ! run with a different mask_table than the current run, in which case the - ! checksums will not match and cause crash. - -! === module MOM_tracer_flow_control === -USE_USER_TRACER_EXAMPLE = False ! [Boolean] default = False - ! If true, use the USER_tracer_example tracer package. -USE_DOME_TRACER = False ! [Boolean] default = False - ! If true, use the DOME_tracer tracer package. -USE_ISOMIP_TRACER = False ! [Boolean] default = False - ! If true, use the ISOMIP_tracer tracer package. -USE_RGC_TRACER = False ! [Boolean] default = False - ! If true, use the RGC_tracer tracer package. -USE_IDEAL_AGE_TRACER = False ! [Boolean] default = False - ! If true, use the ideal_age_example tracer package. -USE_MARBL_TRACERS = True ! [Boolean] default = False - ! If true, use the MARBL tracer package. -USE_REGIONAL_DYES = False ! [Boolean] default = False - ! If true, use the regional_dyes tracer package. -USE_OIL_TRACER = False ! [Boolean] default = False - ! If true, use the oil_tracer tracer package. -USE_ADVECTION_TEST_TRACER = False ! [Boolean] default = False - ! If true, use the advection_test_tracer tracer package. -USE_OCMIP2_CFC = False ! [Boolean] default = False - ! If true, use the MOM_OCMIP2_CFC tracer package. -USE_CFC_CAP = False ! [Boolean] default = False - ! If true, use the MOM_CFC_cap tracer package. -USE_generic_tracer = False ! [Boolean] default = False - ! If true and _USE_GENERIC_TRACER is defined as a preprocessor macro, use the - ! MOM_generic_tracer packages. -USE_PSEUDO_SALT_TRACER = False ! [Boolean] default = False - ! If true, use the pseudo salt tracer, typically run as a diagnostic. -USE_BOUNDARY_IMPULSE_TRACER = False ! [Boolean] default = False - ! If true, use the boundary impulse tracer. -USE_DYED_OBC_TRACER = False ! [Boolean] default = False - ! If true, use the dyed_obc_tracer tracer package. -USE_NW2_TRACERS = False ! [Boolean] default = False - ! If true, use the NeverWorld2 tracers. - -! === module MARBL_tracers === -MARBL_SETTINGS_FILE = "marbl_in" ! default = "marbl_in" - ! The name of a file from which to read the run-time settings for MARBL. -BOT_FLUX_MIX_THICKNESS = 1.0 ! [m] default = 1.0 - ! Bottom fluxes are uniformly mixed over layer of this thickness -CHL_FROM_FILE = False ! [Boolean] default = True - ! If true, chl_a is read from a file. -USE_ICE_CATEGORIES = False ! [Boolean] default = False - ! If true, allocate memory for shortwave and ice fraction split by ice thickness - ! category. -ICE_NCAT = 0 ! default = 0 - ! Number of ice thickness categories in shortwave and ice fraction forcings. - -! === module MARBL_tracers === -MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_latlon_1x1_180W_c230331.nc" - ! The file in which the MARBL tracers initial values can be found. -MARBL_TRACERS_INIT_VERTICAL_REMAP_ONLY = False ! [Boolean] default = False - ! If true, initial conditions are on the model horizontal grid. Extrapolation - ! over missing ocean values is done using an ICE-9 procedure with vertical ALE - ! remapping . -MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc" - ! The file in which the iron sediment flux forcing field can be found. -MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211109.nc" - ! The file in which the iron vent flux forcing field can be found. -MARBL_FESEDFLUX_SCALE_FACTOR = 1.157407407407407E-06 ! [not defined] default = 1.157407407407407E-06 - ! Conversion factor between FESEDFLUX file and MARBL units (umol / m^2 / d -> - ! nmol / cm^2 / s) - -! === module MOM_coord_initialization === -COORD_CONFIG = "none" ! default = "none" - ! This specifies how layers are to be defined: - ! ALE or none - used to avoid defining layers in ALE mode - ! file - read coordinate information from the file - ! specified by (COORD_FILE). - ! BFB - Custom coords for buoyancy-forced basin case - ! based on SST_S, T_BOT and DRHO_DT. - ! linear - linear based on interfaces not layers - ! layer_ref - linear based on layer densities - ! ts_ref - use reference temperature and salinity - ! ts_range - use range of temperature and salinity - ! (T_REF and S_REF) to determine surface density - ! and GINT calculate internal densities. - ! gprime - use reference density (RHO_0) for surface - ! density and GINT calculate internal densities. - ! ts_profile - use temperature and salinity profiles - ! (read from COORD_FILE) to set layer densities. - ! USER - call a user modified routine. -GFS = 9.8 ! [m s-2] default = 9.8 - ! The reduced gravity at the free surface. -REMAP_UV_USING_OLD_ALG = False ! [Boolean] default = False - ! If true, uses the old remapping-via-a-delta-z method for remapping u and v. If - ! false, uses the new method that remaps between grids described by an old and - ! new thickness. -REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" - ! Coordinate mode for vertical regridding. Choose among the following - ! possibilities: LAYER - Isopycnal or stacked shallow water layers - ! ZSTAR, Z* - stretched geopotential z* - ! SIGMA_SHELF_ZSTAR - stretched geopotential z* ignoring shelf - ! SIGMA - terrain following coordinates - ! RHO - continuous isopycnal - ! HYCOM1 - HyCOM-like hybrid coordinate - ! HYBGEN - Hybrid coordinate from the Hycom hybgen code - ! SLIGHT - stretched coordinates above continuous isopycnal - ! ADAPTIVE - optimize for smooth neutral density surfaces -REGRIDDING_COORDINATE_UNITS = "m" ! default = "m" - ! Units of the regridding coordinate. -ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_65L_20200626.nc,dz" ! default = "UNIFORM" - ! Determines how to specify the coordinate resolution. Valid options are: - ! PARAM - use the vector-parameter ALE_RESOLUTION - ! UNIFORM[:N] - uniformly distributed - ! FILE:string - read from a file. The string specifies - ! the filename and variable name, separated - ! by a comma or space, e.g. FILE:lev.nc,dz - ! or FILE:lev.nc,interfaces=zw - ! WOA09[:N] - the WOA09 vertical grid (approximately) - ! FNC1:string - FNC1:dz_min,H_total,power,precision - ! HYBRID:string - read from a file. The string specifies - ! the filename and two variable names, separated - ! by a comma or space, for sigma-2 and dz. e.g. - ! HYBRID:vgrid.nc,sigma2,dz -!ALE_RESOLUTION = 4*2.5, 2.77, 3.38, 4.01, 4.65, 5.29, 5.95, 6.61, 7.28, 7.97, 8.66, 9.37, 10.08, 10.81, 11.54, 12.29, 13.06, 13.85, 14.69, 15.59, 16.56, 17.61, 18.76, 20.02, 21.42, 23.0, 24.77, 26.79, 29.1, 31.76, 34.87, 38.5, 42.79, 47.9, 54.01, 61.37, 70.25, 80.95, 93.75, 108.8, 126.04, 145.04, 164.81, 184.05, 201.34, 215.66, 226.64, 234.5, 239.84, 243.31, 245.52, 246.88, 247.72, 248.23, 248.54, 248.73, 248.84, 248.64, 248.68, 248.71, 248.72, 248.73 ! [m] - ! The distribution of vertical resolution for the target - ! grid used for Eulerian-like coordinates. For example, - ! in z-coordinate mode, the parameter is a list of level - ! thicknesses (in m). In sigma-coordinate mode, the list - ! is of non-dimensional fractions of the water column. -MIN_THICKNESS = 0.001 ! [m] default = 0.001 - ! When regridding, this is the minimum layer thickness allowed. -REMAPPING_SCHEME = "PLM" ! default = "PLM" - ! This sets the reconstruction scheme used for vertical remapping for all - ! variables. It can be one of the following schemes: PCM (1st-order - ! accurate) - ! PLM (2nd-order accurate) - ! PLM_HYBGEN (2nd-order accurate) - ! PPM_H4 (3rd-order accurate) - ! PPM_IH4 (3rd-order accurate) - ! PPM_HYBGEN (3rd-order accurate) - ! WENO_HYBGEN (3rd-order accurate) - ! PQM_IH4IH3 (4th-order accurate) - ! PQM_IH6IH5 (5th-order accurate) -VELOCITY_REMAPPING_SCHEME = "PLM" ! default = "PLM" - ! This sets the reconstruction scheme used for vertical remapping of velocities. - ! By default it is the same as REMAPPING_SCHEME. It can be one of the following - ! schemes: PCM (1st-order accurate) - ! PLM (2nd-order accurate) - ! PLM_HYBGEN (2nd-order accurate) - ! PPM_H4 (3rd-order accurate) - ! PPM_IH4 (3rd-order accurate) - ! PPM_HYBGEN (3rd-order accurate) - ! WENO_HYBGEN (3rd-order accurate) - ! PQM_IH4IH3 (4th-order accurate) - ! PQM_IH6IH5 (5th-order accurate) -FATAL_CHECK_RECONSTRUCTIONS = False ! [Boolean] default = False - ! If true, cell-by-cell reconstructions are checked for consistency and if - ! non-monotonicity or an inconsistency is detected then a FATAL error is issued. -FATAL_CHECK_REMAPPING = False ! [Boolean] default = False - ! If true, the results of remapping are checked for conservation and new extrema - ! and if an inconsistency is detected then a FATAL error is issued. -REMAP_BOUND_INTERMEDIATE_VALUES = False ! [Boolean] default = False - ! If true, the values on the intermediate grid used for remapping are forced to - ! be bounded, which might not be the case due to round off. -REMAP_BOUNDARY_EXTRAP = False ! [Boolean] default = False - ! If true, values at the interfaces of boundary cells are extrapolated instead - ! of piecewise constant -REMAPPING_2018_ANSWERS = False ! [Boolean] default = False - ! If true, use the order of arithmetic and expressions that recover the answers - ! from the end of 2018. Otherwise, use updated and more robust forms of the - ! same expressions. -REMAPPING_ANSWER_DATE = 99991231 ! default = 99991231 - ! The vintage of the expressions and order of arithmetic to use for remapping. - ! Values below 20190101 result in the use of older, less accurate expressions - ! that were in use at the end of 2018. Higher values result in the use of more - ! robust and accurate forms of mathematically equivalent expressions. If both - ! REMAPPING_2018_ANSWERS and REMAPPING_ANSWER_DATE are specified, the latter - ! takes precedence. -PARTIAL_CELL_VELOCITY_REMAP = False ! [Boolean] default = False - ! If true, use partial cell thicknesses at velocity points that are masked out - ! where they extend below the shallower of the neighboring bathymetry for - ! remapping velocity. -REMAP_AFTER_INITIALIZATION = True ! [Boolean] default = True - ! If true, applies regridding and remapping immediately after initialization so - ! that the state is ALE consistent. This is a legacy step and should not be - ! needed if the initialization is consistent with the coordinate mode. -REGRID_TIME_SCALE = 0.0 ! [s] default = 0.0 - ! The time-scale used in blending between the current (old) grid and the target - ! (new) grid. A short time-scale favors the target grid (0. or anything less - ! than DT_THERM) has no memory of the old grid. A very long time-scale makes the - ! model more Lagrangian. -REGRID_FILTER_SHALLOW_DEPTH = 0.0 ! [m] default = 0.0 - ! The depth above which no time-filtering is applied. Above this depth final - ! grid exactly matches the target (new) grid. -REGRID_FILTER_DEEP_DEPTH = 0.0 ! [m] default = 0.0 - ! The depth below which full time-filtering is applied with time-scale - ! REGRID_TIME_SCALE. Between depths REGRID_FILTER_SHALLOW_DEPTH and - ! REGRID_FILTER_SHALLOW_DEPTH the filter weights adopt a cubic profile. -REMAP_VEL_MASK_BBL_THICK = -0.001 ! [m] default = -0.001 - ! A thickness of a bottom boundary layer below which velocities in thin layers - ! are zeroed out after remapping, following practice with Hybgen remapping, or a - ! negative value to avoid such filtering altogether. - -! === module MOM_state_initialization === -FATAL_INCONSISTENT_RESTART_TIME = False ! [Boolean] default = False - ! If true and a time_in value is provided to MOM_initialize_state, verify that - ! the time read from a restart file is the same as time_in, and issue a fatal - ! error if it is not. Otherwise, simply set the time to time_in if present. -INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False - ! If true, initialize the layer thicknesses, temperatures, and salinities from a - ! Z-space file on a latitude-longitude grid. - -! === module MOM_initialize_layers_from_Z === -TEMP_SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" - ! The name of the z-space input file used to initialize temperatures (T) and - ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and - ! SALT_Z_INIT_FILE must be set. -TEMP_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "IC_FILE_COLUMN.nc" - ! The name of the z-space input file used to initialize temperatures, only. -SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "IC_FILE_COLUMN.nc" - ! The name of the z-space input file used to initialize temperatures, only. -Z_INIT_FILE_PTEMP_VAR = "Temp" ! default = "ptemp" - ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. -Z_INIT_FILE_SALT_VAR = "Salt" ! default = "salt" - ! The name of the salinity variable in SALT_Z_INIT_FILE. -Z_INIT_HOMOGENIZE = True ! [Boolean] default = False - ! If True, then horizontally homogenize the interpolated initial conditions. -Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False - ! If True, then remap straight to model coordinate from file. -Z_INIT_REMAPPING_SCHEME = "PPM_IH4" ! default = "PPM_IH4" - ! The remapping scheme to use if using Z_INIT_ALE_REMAPPING is True. -Z_INIT_REMAP_GENERAL = False ! [Boolean] default = False - ! If false, only initializes to z* coordinates. If true, allows initialization - ! directly to general coordinates. -Z_INIT_REMAP_FULL_COLUMN = False ! [Boolean] default = False - ! If false, only reconstructs profiles for valid data points. If true, inserts - ! vanished layers below the valid data. -Z_INIT_REMAP_OLD_ALG = False ! [Boolean] default = False - ! If false, uses the preferred remapping algorithm for initialization. If true, - ! use an older, less robust algorithm for remapping. -TEMP_SALT_INIT_VERTICAL_REMAP_ONLY = False ! [Boolean] default = False - ! If true, initial conditions are on the model horizontal grid. Extrapolation - ! over missing ocean values is done using an ICE-9 procedure with vertical ALE - ! remapping . -HOR_REGRID_2018_ANSWERS = False ! [Boolean] default = False - ! If true, use the order of arithmetic for horizonal regridding that recovers - ! the answers from the end of 2018. Otherwise, use rotationally symmetric forms - ! of the same expressions. -HOR_REGRID_ANSWER_DATE = 99991231 ! default = 99991231 - ! The vintage of the order of arithmetic for horizontal regridding. Dates - ! before 20190101 give the same answers as the code did in late 2018, while - ! later versions add parentheses for rotational symmetry. If both - ! HOR_REGRID_2018_ANSWERS and HOR_REGRID_ANSWER_DATE are specified, the latter - ! takes precedence. -DEPRESS_INITIAL_SURFACE = False ! [Boolean] default = False - ! If true, depress the initial surface to avoid huge tsunamis when a large - ! surface pressure is applied. -TRIM_IC_FOR_P_SURF = False ! [Boolean] default = False - ! If true, cuts way the top of the column for initial conditions at the depth - ! where the hydrostatic pressure matches the imposed surface pressure which is - ! read from file. -VELOCITY_CONFIG = "zero" ! default = "zero" - ! A string that determines how the initial velocities are specified for a new - ! run: - ! file - read velocities from the file specified - ! by (VELOCITY_FILE). - ! zero - the fluid is initially at rest. - ! uniform - the flow is uniform (determined by - ! parameters INITIAL_U_CONST and INITIAL_V_CONST). - ! rossby_front - a mixed layer front in thermal wind balance. - ! soliton - Equatorial Rossby soliton. - ! USER - call a user modified routine. -CONVERT_THICKNESS_UNITS = False ! [Boolean] default = False - ! If true, convert the thickness initial conditions from units of m to kg m-2 - ! or vice versa, depending on whether BOUSSINESQ is defined. This does not apply - ! if a restart file is read. -REGRID_ACCELERATE_INIT = False ! [Boolean] default = False - ! If true, runs REGRID_ACCELERATE_ITERATIONS iterations of the regridding - ! algorithm to push the initial grid to be consistent with the initial - ! condition. Useful only for state-based and iterative coordinates. -ODA_INCUPD = False ! [Boolean] default = False - ! If true, oda incremental updates will be applied everywhere in the domain. -SPONGE = False ! [Boolean] default = False - ! If true, sponges may be applied anywhere in the domain. The exact location and - ! properties of those sponges are specified via SPONGE_CONFIG. - -! === module MOM_diag_mediator === -NUM_DIAG_COORDS = 1 ! default = 1 - ! The number of diagnostic vertical coordinates to use. For each coordinate, an - ! entry in DIAG_COORDS must be provided. -USE_GRID_SPACE_DIAGNOSTIC_AXES = False ! [Boolean] default = False - ! If true, use a grid index coordinate convention for diagnostic axes. -DIAG_COORDS = "z Z ZSTAR" ! default = "z Z ZSTAR" - ! A list of string tuples associating diag_table modules to a coordinate - ! definition used for diagnostics. Each string is of the form "MODULE_SUFFIX - ! PARAMETER_SUFFIX COORDINATE_NAME". -DIAG_MISVAL = 1.0E+20 ! [not defined] default = 1.0E+20 - ! Set the default missing value to use for diagnostics. -DIAG_AS_CHKSUM = False ! [Boolean] default = False - ! Instead of writing diagnostics to the diag manager, write a text file - ! containing the checksum (bitcount) of the array. -AVAILABLE_DIAGS_FILE = "available_diags.000000" ! default = "available_diags.000000" - ! A file into which to write a list of all available ocean diagnostics that can - ! be included in a diag_table. -DIAG_COORD_DEF_Z = "WOA09" ! default = "WOA09" - ! Determines how to specify the coordinate resolution. Valid options are: - ! PARAM - use the vector-parameter DIAG_COORD_RES_Z - ! UNIFORM[:N] - uniformly distributed - ! FILE:string - read from a file. The string specifies - ! the filename and variable name, separated - ! by a comma or space, e.g. FILE:lev.nc,dz - ! or FILE:lev.nc,interfaces=zw - ! WOA09[:N] - the WOA09 vertical grid (approximately) - ! FNC1:string - FNC1:dz_min,H_total,power,precision - ! HYBRID:string - read from a file. The string specifies - ! the filename and two variable names, separated - ! by a comma or space, for sigma-2 and dz. e.g. - ! HYBRID:vgrid.nc,sigma2,dz - -! === module MOM_MEKE === -USE_MEKE = False ! [Boolean] default = False - ! If true, turns on the MEKE scheme which calculates a sub-grid mesoscale eddy - ! kinetic energy budget. - -! === module MOM_lateral_mixing_coeffs === -USE_VARIABLE_MIXING = False ! [Boolean] default = False - ! If true, the variable mixing code will be called. This allows diagnostics to - ! be created even if the scheme is not used. If KHTR_SLOPE_CFF>0 or - ! KhTh_Slope_Cff>0, this is set to true regardless of what is in the parameter - ! file. -USE_VISBECK = False ! [Boolean] default = False - ! If true, use the Visbeck et al. (1997) formulation for - ! thickness diffusivity. -RESOLN_SCALED_KH = False ! [Boolean] default = False - ! If true, the Laplacian lateral viscosity is scaled away when the first - ! baroclinic deformation radius is well resolved. -DEPTH_SCALED_KHTH = False ! [Boolean] default = False - ! If true, KHTH is scaled away when the depth is shallowerthan a reference - ! depth: KHTH = MIN(1,H/H0)**N * KHTH, where H0 is a reference depth, controlled - ! via DEPTH_SCALED_KHTH_H0, and the exponent (N) is controlled via - ! DEPTH_SCALED_KHTH_EXP. -RESOLN_SCALED_KHTH = False ! [Boolean] default = False - ! If true, the interface depth diffusivity is scaled away when the first - ! baroclinic deformation radius is well resolved. -RESOLN_SCALED_KHTR = False ! [Boolean] default = False - ! If true, the epipycnal tracer diffusivity is scaled away when the first - ! baroclinic deformation radius is well resolved. -RESOLN_USE_EBT = False ! [Boolean] default = False - ! If true, uses the equivalent barotropic wave speed instead of first baroclinic - ! wave for calculating the resolution fn. -KHTH_USE_EBT_STRUCT = False ! [Boolean] default = False - ! If true, uses the equivalent barotropic structure as the vertical structure of - ! thickness diffusivity. -KHTH_SLOPE_CFF = 0.0 ! [nondim] default = 0.0 - ! The nondimensional coefficient in the Visbeck formula for the interface depth - ! diffusivity -KHTR_SLOPE_CFF = 0.0 ! [nondim] default = 0.0 - ! The nondimensional coefficient in the Visbeck formula for the epipycnal tracer - ! diffusivity -USE_STORED_SLOPES = False ! [Boolean] default = False - ! If true, the isopycnal slopes are calculated once and stored for re-use. This - ! uses more memory but avoids calling the equation of state more times than - ! should be necessary. -VERY_SMALL_FREQUENCY = 1.0E-17 ! [s-1] default = 1.0E-17 - ! A miniscule frequency that is used to avoid division by 0. The default value - ! is roughly (pi / (the age of the universe)). -USE_STANLEY_ISO = False ! [Boolean] default = False - ! If true, turn on Stanley SGS T variance parameterization in isopycnal slope - ! code. -USE_QG_LEITH_GM = False ! [Boolean] default = False - ! If true, use the QG Leith viscosity as the GM coefficient. - -! === module MOM_set_visc === -SET_VISC_2018_ANSWERS = False ! [Boolean] default = False - ! If true, use the order of arithmetic and expressions that recover the answers - ! from the end of 2018. Otherwise, use updated and more robust forms of the - ! same expressions. -SET_VISC_ANSWER_DATE = 99991231 ! default = 99991231 - ! The vintage of the order of arithmetic and expressions in the set viscosity - ! calculations. Values below 20190101 recover the answers from the end of 2018, - ! while higher values use updated and more robust forms of the same expressions. - ! If both SET_VISC_2018_ANSWERS and SET_VISC_ANSWER_DATE are specified, the - ! latter takes precedence. -BOTTOMDRAGLAW = True ! [Boolean] default = True - ! If true, the bottom stress is calculated with a drag law of the form - ! c_drag*|u|*u. The velocity magnitude may be an assumed value or it may be - ! based on the actual velocity in the bottommost HBBL, depending on LINEAR_DRAG. -DRAG_AS_BODY_FORCE = False ! [Boolean] default = False - ! If true, the bottom stress is imposed as an explicit body force applied over a - ! fixed distance from the bottom, rather than as an implicit calculation based - ! on an enhanced near-bottom viscosity. The thickness of the bottom boundary - ! layer is HBBL. -CHANNEL_DRAG = False ! [Boolean] default = False - ! If true, the bottom drag is exerted directly on each layer proportional to the - ! fraction of the bottom it overlies. -LINEAR_DRAG = False ! [Boolean] default = False - ! If LINEAR_DRAG and BOTTOMDRAGLAW are defined the drag law is - ! cdrag*DRAG_BG_VEL*u. -PRANDTL_TURB = 1.0 ! [nondim] default = 1.0 - ! The turbulent Prandtl number applied to shear instability. -DYNAMIC_VISCOUS_ML = False ! [Boolean] default = False - ! If true, use a bulk Richardson number criterion to determine the mixed layer - ! thickness for viscosity. -OMEGA = 7.2921E-05 ! [s-1] default = 7.2921E-05 - ! The rotation rate of the earth. -HBBL = 10.0 ! [m] - ! The thickness of a bottom boundary layer with a viscosity increased by - ! KV_EXTRA_BBL if BOTTOMDRAGLAW is not defined, or the thickness over which - ! near-bottom velocities are averaged for the drag law if BOTTOMDRAGLAW is - ! defined but LINEAR_DRAG is not. -CDRAG = 0.0 ! [nondim] default = 0.003 - ! CDRAG is the drag coefficient relating the magnitude of the velocity field to - ! the bottom stress. CDRAG is only used if BOTTOMDRAGLAW is defined. -BBL_USE_TIDAL_BG = False ! [Boolean] default = False - ! Flag to use the tidal RMS amplitude in place of constant background velocity - ! for computing u* in the BBL. This flag is only used when BOTTOMDRAGLAW is true - ! and LINEAR_DRAG is false. -DRAG_BG_VEL = 0.1 ! [m s-1] default = 0.0 - ! DRAG_BG_VEL is either the assumed bottom velocity (with LINEAR_DRAG) or an - ! unresolved velocity that is combined with the resolved velocity to estimate - ! the velocity magnitude. DRAG_BG_VEL is only used when BOTTOMDRAGLAW is - ! defined. -BBL_USE_EOS = True ! [Boolean] default = True - ! If true, use the equation of state in determining the properties of the bottom - ! boundary layer. Otherwise use the layer target potential densities. The - ! default of this parameter is the value of USE_EOS. -BBL_THICK_MIN = 0.1 ! [m] default = 0.0 - ! The minimum bottom boundary layer thickness that can be used with - ! BOTTOMDRAGLAW. This might be Kv/(cdrag*drag_bg_vel) to give Kv as the minimum - ! near-bottom viscosity. -HTBL_SHELF_MIN = 0.1 ! [m] default = 0.1 - ! The minimum top boundary layer thickness that can be used with BOTTOMDRAGLAW. - ! This might be Kv/(cdrag*drag_bg_vel) to give Kv as the minimum near-top - ! viscosity. -HTBL_SHELF = 10.0 ! [m] default = 10.0 - ! The thickness over which near-surface velocities are averaged for the drag law - ! under an ice shelf. By default this is the same as HBBL -KV = 1.0E-04 ! [m2 s-1] - ! The background kinematic viscosity in the interior. The molecular value, ~1e-6 - ! m2 s-1, may be used. -KV_BBL_MIN = 1.0E-04 ! [m2 s-1] default = 1.0E-04 - ! The minimum viscosities in the bottom boundary layer. -KV_TBL_MIN = 1.0E-04 ! [m2 s-1] default = 1.0E-04 - ! The minimum viscosities in the top boundary layer. -CORRECT_BBL_BOUNDS = False ! [Boolean] default = False - ! If true, uses the correct bounds on the BBL thickness and viscosity so that - ! the bottom layer feels the intended drag. - -! === module MOM_thickness_diffuse === -KHTH = 0.0 ! [m2 s-1] default = 0.0 - ! The background horizontal thickness diffusivity. -KHTH_MIN = 0.0 ! [m2 s-1] default = 0.0 - ! The minimum horizontal thickness diffusivity. -KHTH_MAX = 0.0 ! [m2 s-1] default = 0.0 - ! The maximum horizontal thickness diffusivity. -KHTH_MAX_CFL = 0.8 ! [nondimensional] default = 0.8 - ! The maximum value of the local diffusive CFL ratio that is permitted for the - ! thickness diffusivity. 1.0 is the marginally unstable value in a pure layered - ! model, but much smaller numbers (e.g. 0.1) seem to work better for ALE-based - ! models. -KH_ETA_CONST = 0.0 ! [m2 s-1] default = 0.0 - ! The background horizontal diffusivity of the interface heights (without - ! considering the layer density structure). If diffusive CFL limits are - ! encountered, the diffusivities of the isopycnals and the interfaces heights - ! are scaled back proportionately. -KH_ETA_VEL_SCALE = 0.0 ! [m s-1] default = 0.0 - ! A velocity scale that is multiplied by the grid spacing to give a contribution - ! to the horizontal diffusivity of the interface heights (without considering - ! the layer density structure). -DETANGLE_INTERFACES = False ! [Boolean] default = False - ! If defined add 3-d structured enhanced interface height diffusivities to - ! horizontally smooth jagged layers. -KHTH_SLOPE_MAX = 0.01 ! [nondim] default = 0.01 - ! A slope beyond which the calculated isopycnal slope is not reliable and is - ! scaled away. -KD_SMOOTH = 1.0E-06 ! [m2 s-1] default = 1.0E-06 - ! A diapycnal diffusivity that is used to interpolate more sensible values of T - ! & S into thin layers. -KHTH_USE_FGNV_STREAMFUNCTION = False ! [Boolean] default = False - ! If true, use the streamfunction formulation of Ferrari et al., 2010, which - ! effectively emphasizes graver vertical modes by smoothing in the vertical. -USE_STANLEY_GM = False ! [Boolean] default = False - ! If true, turn on Stanley SGS T variance parameterization in GM code. -MEKE_GM_SRC_ALT = False ! [Boolean] default = False - ! If true, use the GM energy conversion form S^2*N^2*kappa rather than the - ! streamfunction for the GM source term. -MEKE_GEOMETRIC = False ! [Boolean] default = False - ! If true, uses the GM coefficient formulation from the GEOMETRIC framework - ! (Marshall et al., 2012). -USE_KH_IN_MEKE = False ! [Boolean] default = False - ! If true, uses the thickness diffusivity calculated here to diffuse MEKE. -USE_GME = False ! [Boolean] default = False - ! If true, use the GM+E backscatter scheme in association with the Gent and - ! McWilliams parameterization. -USE_GM_WORK_BUG = False ! [Boolean] default = False - ! If true, compute the top-layer work tendency on the u-grid with the incorrect - ! sign, for legacy reproducibility. -STOCH_EOS = False ! [Boolean] default = False - ! If true, stochastic perturbations are applied to the EOS in the PGF. -STANLEY_COEFF = -1.0 ! [not defined] default = -1.0 - ! Coefficient correlating the temperature gradient and SGS T variance. -STANLEY_A = 1.0 ! [not defined] default = 1.0 - ! Coefficient a which scales chi in stochastic perturbation of the SGS T - ! variance. - -! === module MOM_porous_barriers === -PORBAR_ANSWER_DATE = 99991231 ! default = 99991231 - ! The vintage of the porous barrier weight function calculations. Values below - ! 20220806 recover the old answers in which the layer averaged weights are not - ! strictly limited by an upper-bound of 1.0 . -PORBAR_MASKING_DEPTH = 0.0 ! [m] default = 0.0 - ! If the effective average depth at the velocity cell is shallower than this - ! number, then porous barrier is not applied at that location. - ! PORBAR_MASKING_DEPTH is assumed to be positive below the sea surface. -PORBAR_ETA_INTERP = "MAX" ! default = "MAX" - ! A string describing the method that decides how the interface heights at the - ! velocity points are calculated. Valid values are: - ! MAX (the default) - maximum of the adjacent cells - ! MIN - minimum of the adjacent cells - ! ARITHMETIC - arithmetic mean of the adjacent cells - ! HARMONIC - harmonic mean of the adjacent cells - -! === module MOM_dynamics_unsplit === -FIX_UNSPLIT_DT_VISC_BUG = True ! [Boolean] default = True - ! If true, use the correct timestep in the viscous terms applied in the first - ! predictor step with the unsplit time stepping scheme, and in the calculation - ! of the turbulent mixed layer properties for viscosity with unsplit or - ! unsplit_RK2. -TIDES = False ! [Boolean] default = False - ! If true, apply tidal momentum forcing. - -! === module MOM_continuity === -CONTINUITY_SCHEME = "PPM" ! default = "PPM" - ! CONTINUITY_SCHEME selects the discretization for the continuity solver. The - ! only valid value currently is: - ! PPM - use a positive-definite (or monotonic) - ! piecewise parabolic reconstruction solver. - -! === module MOM_continuity_PPM === -MONOTONIC_CONTINUITY = False ! [Boolean] default = False - ! If true, CONTINUITY_PPM uses the Colella and Woodward monotonic limiter. The - ! default (false) is to use a simple positive definite limiter. -SIMPLE_2ND_PPM_CONTINUITY = False ! [Boolean] default = False - ! If true, CONTINUITY_PPM uses a simple 2nd order (arithmetic mean) - ! interpolation of the edge values. This may give better PV conservation - ! properties. While it formally reduces the accuracy of the continuity solver - ! itself in the strongly advective limit, it does not reduce the overall order - ! of accuracy of the dynamic core. -UPWIND_1ST_CONTINUITY = True ! [Boolean] default = False - ! If true, CONTINUITY_PPM becomes a 1st-order upwind continuity solver. This - ! scheme is highly diffusive but may be useful for debugging or in single-column - ! mode where its minimal stencil is useful. -ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.25E-09 - ! The tolerance for the differences between the barotropic and baroclinic - ! estimates of the sea surface height due to the fluxes through each face. The - ! total tolerance for SSH is 4 times this value. The default is - ! 0.5*NK*ANGSTROM, and this should not be set less than about - ! 10^-15*MAXIMUM_DEPTH. -VELOCITY_TOLERANCE = 3.0E+08 ! [m s-1] default = 3.0E+08 - ! The tolerance for barotropic velocity discrepancies between the barotropic - ! solution and the sum of the layer thicknesses. -CONT_PPM_AGGRESS_ADJUST = False ! [Boolean] default = False - ! If true, allow the adjusted velocities to have a relative CFL change up to - ! 0.5. -CONT_PPM_VOLUME_BASED_CFL = False ! [Boolean] default = False - ! If true, use the ratio of the open face lengths to the tracer cell areas when - ! estimating CFL numbers. The default is set by CONT_PPM_AGGRESS_ADJUST. -CONTINUITY_CFL_LIMIT = 0.5 ! [nondim] default = 0.5 - ! The maximum CFL of the adjusted velocities. -CONT_PPM_BETTER_ITER = True ! [Boolean] default = True - ! If true, stop corrective iterations using a velocity based criterion and only - ! stop if the iteration is better than all predecessors. -CONT_PPM_USE_VISC_REM_MAX = True ! [Boolean] default = True - ! If true, use more appropriate limiting bounds for corrections in strongly - ! viscous columns. -CONT_PPM_MARGINAL_FACE_AREAS = True ! [Boolean] default = True - ! If true, use the marginal face areas from the continuity solver for use as the - ! weights in the barotropic solver. Otherwise use the transport averaged areas. - -! === module MOM_CoriolisAdv === -NOSLIP = False ! [Boolean] default = False - ! If true, no slip boundary conditions are used; otherwise free slip boundary - ! conditions are assumed. The implementation of the free slip BCs on a C-grid is - ! much cleaner than the no slip BCs. The use of free slip BCs is strongly - ! encouraged, and no slip BCs are not used with the biharmonic viscosity. -CORIOLIS_EN_DIS = True ! [Boolean] default = False - ! If true, two estimates of the thickness fluxes are used to estimate the - ! Coriolis term, and the one that dissipates energy relative to the other one is - ! used. -CORIOLIS_SCHEME = "SADOURNY75_ENERGY" ! default = "SADOURNY75_ENERGY" - ! CORIOLIS_SCHEME selects the discretization for the Coriolis terms. Valid - ! values are: - ! SADOURNY75_ENERGY - Sadourny, 1975; energy cons. - ! ARAKAWA_HSU90 - Arakawa & Hsu, 1990 - ! SADOURNY75_ENSTRO - Sadourny, 1975; enstrophy cons. - ! ARAKAWA_LAMB81 - Arakawa & Lamb, 1981; En. + Enst. - ! ARAKAWA_LAMB_BLEND - A blend of Arakawa & Lamb with - ! Arakawa & Hsu and Sadourny energy -BOUND_CORIOLIS = True ! [Boolean] default = False - ! If true, the Coriolis terms at u-points are bounded by the four estimates of - ! (f+rv)v from the four neighboring v-points, and similarly at v-points. This - ! option is always effectively false with CORIOLIS_EN_DIS defined and - ! CORIOLIS_SCHEME set to SADOURNY75_ENERGY. -KE_SCHEME = "KE_ARAKAWA" ! default = "KE_ARAKAWA" - ! KE_SCHEME selects the discretization for acceleration due to the kinetic - ! energy gradient. Valid values are: - ! KE_ARAKAWA, KE_SIMPLE_GUDONOV, KE_GUDONOV -PV_ADV_SCHEME = "PV_ADV_CENTERED" ! default = "PV_ADV_CENTERED" - ! PV_ADV_SCHEME selects the discretization for PV advection. Valid values are: - ! PV_ADV_CENTERED - centered (aka Sadourny, 75) - ! PV_ADV_UPWIND1 - upwind, first order - -! === module MOM_PressureForce === -ANALYTIC_FV_PGF = True ! [Boolean] default = True - ! If true the pressure gradient forces are calculated with a finite volume form - ! that analytically integrates the equations of state in pressure to avoid any - ! possibility of numerical thermobaric instability, as described in Adcroft et - ! al., O. Mod. (2008). - -! === module MOM_PressureForce_FV === -MASS_WEIGHT_IN_PRESSURE_GRADIENT = False ! [Boolean] default = False - ! If true, use mass weighting when interpolating T/S for integrals near the - ! bathymetry in FV pressure gradient calculations. -USE_INACCURATE_PGF_RHO_ANOM = False ! [Boolean] default = False - ! If true, use a form of the PGF that uses the reference density in an - ! inaccurate way. This is not recommended. -RECONSTRUCT_FOR_PRESSURE = True ! [Boolean] default = True - ! If True, use vertical reconstruction of T & S within the integrals of the FV - ! pressure gradient calculation. If False, use the constant-by-layer algorithm. - ! The default is set by USE_REGRIDDING. -PRESSURE_RECONSTRUCTION_SCHEME = 1 ! default = 1 - ! Order of vertical reconstruction of T/S to use in the integrals within the FV - ! pressure gradient calculation. - ! 0: PCM or no reconstruction. - ! 1: PLM reconstruction. - ! 2: PPM reconstruction. -BOUNDARY_EXTRAPOLATION_PRESSURE = True ! [Boolean] default = True - ! If true, the reconstruction of T & S for pressure in boundary cells is - ! extrapolated, rather than using PCM in these cells. If true, the same order - ! polynomial is used as is used for the interior cells. -USE_STANLEY_PGF = False ! [Boolean] default = False - ! If true, turn on Stanley SGS T variance parameterization in PGF code. - -! === module MOM_hor_visc === -HOR_VISC_2018_ANSWERS = False ! [Boolean] default = False - ! If true, use the order of arithmetic and expressions that recover the answers - ! from the end of 2018. Otherwise, use updated and more robust forms of the - ! same expressions. -HOR_VISC_ANSWER_DATE = 99991231 ! default = 99991231 - ! The vintage of the order of arithmetic and expressions in the horizontal - ! viscosity calculations. Values below 20190101 recover the answers from the - ! end of 2018, while higher values use updated and more robust forms of the same - ! expressions. If both HOR_VISC_2018_ANSWERS and HOR_VISC_ANSWER_DATE are - ! specified, the latter takes precedence. -LAPLACIAN = False ! [Boolean] default = False - ! If true, use a Laplacian horizontal viscosity. -BIHARMONIC = False ! [Boolean] default = True - ! If true, use a biharmonic horizontal viscosity. BIHARMONIC may be used with - ! LAPLACIAN. -USE_LAND_MASK_FOR_HVISC = True ! [Boolean] default = True - ! If true, use the land mask for the computation of thicknesses at velocity - ! locations. This eliminates the dependence on arbitrary values over land or - ! outside of the domain. - -! === module MOM_vert_friction === -VERT_FRICTION_2018_ANSWERS = False ! [Boolean] default = False - ! If true, use the order of arithmetic and expressions that recover the answers - ! from the end of 2018. Otherwise, use expressions that do not use an arbitrary - ! hard-coded maximum viscous coupling coefficient between layers. -VERT_FRICTION_ANSWER_DATE = 99991231 ! default = 99991231 - ! The vintage of the order of arithmetic and expressions in the viscous - ! calculations. Values below 20190101 recover the answers from the end of 2018, - ! while higher values use expressions that do not use an arbitrary hard-coded - ! maximum viscous coupling coefficient between layers. If both - ! VERT_FRICTION_2018_ANSWERS and VERT_FRICTION_ANSWER_DATE are specified, the - ! latter takes precedence. -DIRECT_STRESS = False ! [Boolean] default = False - ! If true, the wind stress is distributed over the topmost HMIX_STRESS of fluid - ! (like in HYCOM), and an added mixed layer viscosity or a physically based - ! boundary layer turbulence parameterization is not needed for stability. -FIXED_DEPTH_LOTW_ML = False ! [Boolean] default = False - ! If true, use a Law-of-the-wall prescription for the mixed layer viscosity - ! within a boundary layer that is the lesser of HMIX_FIXED and the total depth - ! of the ocean in a column. -LOTW_VISCOUS_ML_FLOOR = False ! [Boolean] default = False - ! If true, use a Law-of-the-wall prescription to set a lower bound on the - ! viscous coupling between layers within the surface boundary layer, based the - ! distance of interfaces from the surface. This only acts when there are large - ! changes in the thicknesses of successive layers or when the viscosity is set - ! externally and the wind stress has subsequently increased. -VON_KARMAN_CONST = 0.41 ! [nondim] default = 0.41 - ! The value the von Karman constant as used for mixed layer viscosity. -HARMONIC_VISC = True ! [Boolean] default = False - ! If true, use the harmonic mean thicknesses for calculating the vertical - ! viscosity. -HARMONIC_BL_SCALE = 0.0 ! [nondim] default = 0.0 - ! A scale to determine when water is in the boundary layers based solely on - ! harmonic mean thicknesses for the purpose of determining the extent to which - ! the thicknesses used in the viscosities are upwinded. -HMIX_FIXED = 0.01 ! [m] - ! The prescribed depth over which the near-surface viscosity and diffusivity are - ! elevated when the bulk mixed layer is not used. -KV_ML_INVZ2 = 0.0 ! [m2 s-1] default = 0.0 - ! An extra kinematic viscosity in a mixed layer of thickness HMIX_FIXED, with - ! the actual viscosity scaling as 1/(z*HMIX_FIXED)^2, where z is the distance - ! from the surface, to allow for finite wind stresses to be transmitted through - ! infinitesimally thin surface layers. This is an older option for numerical - ! convenience without a strong physical basis, and its use is now discouraged. -MAXVEL = 6.0 ! [m s-1] default = 3.0E+08 - ! The maximum velocity allowed before the velocity components are truncated. -CFL_BASED_TRUNCATIONS = True ! [Boolean] default = True - ! If true, base truncations on the CFL number, and not an absolute speed. -CFL_TRUNCATE = 0.5 ! [nondim] default = 0.5 - ! The value of the CFL number that will cause velocity components to be - ! truncated; instability can occur past 0.5. -CFL_REPORT = 0.5 ! [nondim] default = 0.5 - ! The value of the CFL number that causes accelerations to be reported; the - ! default is CFL_TRUNCATE. -CFL_TRUNCATE_RAMP_TIME = 0.0 ! [s] default = 0.0 - ! The time over which the CFL truncation value is ramped up at the beginning of - ! the run. -CFL_TRUNCATE_START = 0.0 ! [nondim] default = 0.0 - ! The start value of the truncation CFL number used when ramping up CFL_TRUNC. -STOKES_MIXING_COMBINED = False ! [Boolean] default = False - ! Flag to use Stokes drift Mixing via the Lagrangian current (Eulerian plus - ! Stokes drift). Still needs work and testing, so not recommended for use. -VEL_UNDERFLOW = 1.0E-30 ! [m s-1] default = 0.0 - ! A negligibly small velocity magnitude below which velocity components are set - ! to 0. A reasonable value might be 1e-30 m/s, which is less than an Angstrom - ! divided by the age of the universe. - -! === module MOM_mixed_layer_restrat === -MIXEDLAYER_RESTRAT = False ! [Boolean] default = False - ! If true, a density-gradient dependent re-stratifying flow is imposed in the - ! mixed layer. Can be used in ALE mode without restriction but in layer mode can - ! only be used if BULKMIXEDLAYER is true. - -! === module MOM_diagnostics === -DIAG_EBT_MONO_N2_COLUMN_FRACTION = 0.0 ! [nondim] default = 0.0 - ! The lower fraction of water column over which N2 is limited as monotonic for - ! the purposes of calculating the equivalent barotropic wave speed. -DIAG_EBT_MONO_N2_DEPTH = -1.0 ! [m] default = -1.0 - ! The depth below which N2 is limited as monotonic for the purposes of - ! calculating the equivalent barotropic wave speed. -INTERNAL_WAVE_SPEED_TOL = 0.001 ! [nondim] default = 0.001 - ! The fractional tolerance for finding the wave speeds. -INTERNAL_WAVE_SPEED_MIN = 0.0 ! [m s-1] default = 0.0 - ! A floor in the first mode speed below which 0 used instead. -INTERNAL_WAVE_SPEED_BETTER_EST = True ! [Boolean] default = True - ! If true, use a more robust estimate of the first mode wave speed as the - ! starting point for iterations. - -! === module MOM_diabatic_driver === -! The following parameters are used for diabatic processes. -USE_LEGACY_DIABATIC_DRIVER = True ! [Boolean] default = True - ! If true, use a legacy version of the diabatic subroutine. This is temporary - ! and is needed to avoid change in answers. -ENERGETICS_SFC_PBL = False ! [Boolean] default = False - ! If true, use an implied energetics planetary boundary layer scheme to - ! determine the diffusivity and viscosity in the surface boundary layer. -EPBL_IS_ADDITIVE = True ! [Boolean] default = True - ! If true, the diffusivity from ePBL is added to all other diffusivities. - ! Otherwise, the larger of kappa-shear and ePBL diffusivities are used. -INTERNAL_TIDES = False ! [Boolean] default = False - ! If true, use the code that advances a separate set of equations for the - ! internal tide energy density. -MASSLESS_MATCH_TARGETS = True ! [Boolean] default = True - ! If true, the temperature and salinity of massless layers are kept consistent - ! with their target densities. Otherwise the properties of massless layers - ! evolve diffusively to match massive neighboring layers. -AGGREGATE_FW_FORCING = True ! [Boolean] default = True - ! If true, the net incoming and outgoing fresh water fluxes are combined and - ! applied as either incoming or outgoing depending on the sign of the net. If - ! false, the net incoming fresh water flux is added to the model and thereafter - ! the net outgoing is removed from the topmost non-vanished layers of the - ! updated state. -MIX_BOUNDARY_TRACERS = True ! [Boolean] default = True - ! If true, mix the passive tracers in massless layers at the bottom into the - ! interior as though a diffusivity of KD_MIN_TR were operating. -MIX_BOUNDARY_TRACER_ALE = False ! [Boolean] default = False - ! If true and in ALE mode, mix the passive tracers in massless layers at the - ! bottom into the interior as though a diffusivity of KD_MIN_TR were operating. -KD_MIN_TR = 2.0E-06 ! [m2 s-1] default = 2.0E-06 - ! A minimal diffusivity that should always be applied to tracers, especially in - ! massless layers near the bottom. The default is 0.1*KD. -KD_BBL_TR = 0.0 ! [m2 s-1] default = 0.0 - ! A bottom boundary layer tracer diffusivity that will allow for explicitly - ! specified bottom fluxes. The entrainment at the bottom is at least - ! sqrt(Kd_BBL_tr*dt) over the same distance. -TRACER_TRIDIAG = False ! [Boolean] default = False - ! If true, use the passive tracer tridiagonal solver for T and S -MINIMUM_FORCING_DEPTH = 0.001 ! [m] default = 0.001 - ! The smallest depth over which forcing can be applied. This only takes effect - ! when near-surface layers become thin relative to this scale, in which case the - ! forcing tendencies scaled down by distributing the forcing over this depth - ! scale. -EVAP_CFL_LIMIT = 0.8 ! [nondim] default = 0.8 - ! The largest fraction of a layer than can be lost to forcing (e.g. evaporation, - ! sea-ice formation) in one time-step. The unused mass loss is passed down - ! through the column. -MLD_EN_VALS = 3*0.0 ! [J/m2] default = 0.0 - ! The energy values used to compute MLDs. If not set (or all set to 0.), the - ! default will overwrite to 25., 2500., 250000. -DIAG_MLD_DENSITY_DIFF = 0.1 ! [kg/m3] default = 0.1 - ! The density difference used to determine a diagnostic mixed layer depth, - ! MLD_user, following the definition of Levitus 1982. The MLD is the depth at - ! which the density is larger than the surface density by the specified amount. -DIAG_DEPTH_SUBML_N2 = 50.0 ! [m] default = 50.0 - ! The distance over which to calculate a diagnostic of the stratification at the - ! base of the mixed layer. - -! === module MOM_CVMix_KPP === -! This is the MOM wrapper to CVMix:KPP -! See http://cvmix.github.io/ -USE_KPP = True ! [Boolean] default = False - ! If true, turns on the [CVMix] KPP scheme of Large et al., 1994, to calculate - ! diffusivities and non-local transport in the OBL. -KPP% -PASSIVE = False ! [Boolean] default = False - ! If True, puts KPP into a passive-diagnostic mode. -APPLY_NONLOCAL_TRANSPORT = True ! [Boolean] default = True - ! If True, applies the non-local transport to all tracers. If False, calculates - ! the non-local transport and tendencies but purely for diagnostic purposes. -N_SMOOTH = 0 ! default = 0 - ! The number of times the 1-1-4-1-1 Laplacian filter is applied on OBL depth. -RI_CRIT = 0.3 ! [nondim] default = 0.3 - ! Critical bulk Richardson number used to define depth of the surface Ocean - ! Boundary Layer (OBL). -VON_KARMAN = 0.4 ! [nondim] default = 0.4 - ! von Karman constant. -ENHANCE_DIFFUSION = True ! [Boolean] default = True - ! If True, adds enhanced diffusion at the based of the boundary layer. -INTERP_TYPE = "cubic" ! default = "quadratic" - ! Type of interpolation to determine the OBL depth. - ! Allowed types are: linear, quadratic, cubic. -INTERP_TYPE2 = "LMD94" ! default = "LMD94" - ! Type of interpolation to compute diff and visc at OBL_depth. - ! Allowed types are: linear, quadratic, cubic or LMD94. -COMPUTE_EKMAN = False ! [Boolean] default = False - ! If True, limit OBL depth to be no deeper than Ekman depth. -COMPUTE_MONIN_OBUKHOV = False ! [Boolean] default = False - ! If True, limit the OBL depth to be no deeper than Monin-Obukhov depth. -CS = 98.96 ! [nondim] default = 98.96 - ! Parameter for computing velocity scale function. -CS2 = 6.32739901508 ! [nondim] default = 6.32739901508 - ! Parameter for computing non-local term. -DEEP_OBL_OFFSET = 0.0 ! [m] default = 0.0 - ! If non-zero, the distance above the bottom to which the OBL is clipped if it - ! would otherwise reach the bottom. The smaller of this and 0.1D is used. -FIXED_OBLDEPTH = False ! [Boolean] default = False - ! If True, fix the OBL depth to FIXED_OBLDEPTH_VALUE rather than using the OBL - ! depth from CVMix. This option is just for testing purposes. -FIXED_OBLDEPTH_VALUE = 30.0 ! [m] default = 30.0 - ! Value for the fixed OBL depth when fixedOBLdepth==True. This parameter is for - ! just for testing purposes. It will over-ride the OBLdepth computed from CVMix. -SURF_LAYER_EXTENT = 0.1 ! [nondim] default = 0.1 - ! Fraction of OBL depth considered in the surface layer. -MINIMUM_OBL_DEPTH = 0.0 ! [m] default = 0.0 - ! If non-zero, a minimum depth to use for KPP OBL depth. Independent of this - ! parameter, the OBL depth is always at least as deep as the first layer. -MINIMUM_VT2 = 1.0E-10 ! [m2/s2] default = 1.0E-10 - ! Min of the unresolved velocity Vt2 used in Rib CVMix calculation. - ! Scaling: MINIMUM_VT2 = const1*d*N*ws, with d=1m, N=1e-5/s, ws=1e-6 m/s. -CORRECT_SURFACE_LAYER_AVERAGE = False ! [Boolean] default = False - ! If true, applies a correction step to the averaging of surface layer - ! properties. This option is obsolete. -FIRST_GUESS_SURFACE_LAYER_DEPTH = 0.0 ! [m] default = 0.0 - ! The first guess at the depth of the surface layer used for averaging the - ! surface layer properties. If =0, the top model level properties will be used - ! for the surface layer. If CORRECT_SURFACE_LAYER_AVERAGE=True, a subsequent - ! correction is applied. This parameter is obsolete -NLT_SHAPE = "PARABOLIC" ! default = "CVMix" - ! MOM6 method to set nonlocal transport profile. Over-rides the result from - ! CVMix. Allowed values are: - ! CVMix - Uses the profiles from CVMix specified by MATCH_TECHNIQUE - ! LINEAR - A linear profile, 1-sigma - ! PARABOLIC - A parablic profile, (1-sigma)^2 - ! CUBIC - A cubic profile, (1-sigma)^2(1+2*sigma) - ! CUBIC_LMD - The original KPP profile -MATCH_TECHNIQUE = "SimpleShapes" ! default = "SimpleShapes" - ! CVMix method to set profile function for diffusivity and NLT, as well as - ! matching across OBL base. Allowed values are: - ! SimpleShapes = sigma*(1-sigma)^2 for both diffusivity and NLT - ! MatchGradient = sigma*(1-sigma)^2 for NLT; diffusivity profile from - ! matching - ! MatchBoth = match gradient for both diffusivity and NLT - ! ParabolicNonLocal = sigma*(1-sigma)^2 for diffusivity; (1-sigma)^2 for NLT -KPP_ZERO_DIFFUSIVITY = False ! [Boolean] default = False - ! If True, zeroes the KPP diffusivity and viscosity; for testing purpose. -KPP_IS_ADDITIVE = True ! [Boolean] default = True - ! If true, adds KPP diffusivity to diffusivity from other schemes. - ! If false, KPP is the only diffusivity wherever KPP is non-zero. -KPP_SHORTWAVE_METHOD = "MXL_SW" ! default = "MXL_SW" - ! Determines contribution of shortwave radiation to KPP surface buoyancy flux. - ! Options include: - ! ALL_SW: use total shortwave radiation - ! MXL_SW: use shortwave radiation absorbed by mixing layer - ! LV1_SW: use shortwave radiation absorbed by top model layer -CVMix_ZERO_H_WORK_AROUND = 0.0 ! [m] default = 0.0 - ! A minimum thickness used to avoid division by small numbers in the vicinity of - ! vanished layers. This is independent of MIN_THICKNESS used in other parts of - ! MOM. -USE_KPP_LT_K = False ! default = False - ! Flag for Langmuir turbulence enhancement of turbulentmixing coefficient. -STOKES_MIXING = False ! default = False - ! Flag for Langmuir turbulence enhancement of turbulentmixing coefficient. -USE_KPP_LT_VT2 = False ! default = False - ! Flag for Langmuir turbulence enhancement of Vt2in Bulk Richardson Number. -%KPP - -! === module MOM_CVMix_conv === -! Parameterization of enhanced mixing due to convection via CVMix -USE_CVMix_CONVECTION = False ! [Boolean] default = False - ! If true, turns on the enhanced mixing due to convection via CVMix. This scheme - ! increases diapycnal diffs./viscs. at statically unstable interfaces. Relevant - ! parameters are contained in the CVMix_CONVECTION% parameter block. - -! === module MOM_set_diffusivity === -FLUX_RI_MAX = 0.2 ! [nondim] default = 0.2 - ! The flux Richardson number where the stratification is large enough that N2 > - ! omega2. The full expression for the Flux Richardson number is usually - ! FLUX_RI_MAX*N2/(N2+OMEGA2). -SET_DIFF_2018_ANSWERS = False ! [Boolean] default = False - ! If true, use the order of arithmetic and expressions that recover the answers - ! from the end of 2018. Otherwise, use updated and more robust forms of the - ! same expressions. -SET_DIFF_ANSWER_DATE = 99991231 ! default = 99991231 - ! The vintage of the order of arithmetic and expressions in the set diffusivity - ! calculations. Values below 20190101 recover the answers from the end of 2018, - ! while higher values use updated and more robust forms of the same expressions. - ! If both SET_DIFF_2018_ANSWERS and SET_DIFF_ANSWER_DATE are specified, the - ! latter takes precedence. - -! === module MOM_tidal_mixing === -! Vertical Tidal Mixing Parameterization -USE_CVMix_TIDAL = False ! [Boolean] default = False - ! If true, turns on tidal mixing via CVMix -INT_TIDE_DISSIPATION = False ! [Boolean] default = False - ! If true, use an internal tidal dissipation scheme to drive diapycnal mixing, - ! along the lines of St. Laurent et al. (2002) and Simmons et al. (2004). -ML_RADIATION = True ! [Boolean] default = False - ! If true, allow a fraction of TKE available from wind work to penetrate below - ! the base of the mixed layer with a vertical decay scale determined by the - ! minimum of: (1) The depth of the mixed layer, (2) an Ekman length scale. -ML_RAD_EFOLD_COEFF = 0.2 ! [nondim] default = 0.2 - ! A coefficient that is used to scale the penetration depth for turbulence below - ! the base of the mixed layer. This is only used if ML_RADIATION is true. -ML_RAD_BUG = False ! [Boolean] default = False - ! If true use code with a bug that reduces the energy available in the - ! transition layer by a factor of the inverse of the energy deposition - ! lenthscale (in m). -ML_RAD_KD_MAX = 0.001 ! [m2 s-1] default = 0.001 - ! The maximum diapycnal diffusivity due to turbulence radiated from the base of - ! the mixed layer. This is only used if ML_RADIATION is true. -ML_RAD_COEFF = 0.2 ! [nondim] default = 0.2 - ! The coefficient which scales MSTAR*USTAR^3 to obtain the energy available for - ! mixing below the base of the mixed layer. This is only used if ML_RADIATION is - ! true. -ML_RAD_APPLY_TKE_DECAY = True ! [Boolean] default = True - ! If true, apply the same exponential decay to ML_rad as is applied to the other - ! surface sources of TKE in the mixed layer code. This is only used if - ! ML_RADIATION is true. -MSTAR = 1.2 ! [nondim] default = 1.2 - ! The ratio of the friction velocity cubed to the TKE input to the mixed layer. -TKE_DECAY = 10.0 ! [nondim] default = 2.5 - ! The ratio of the natural Ekman depth to the TKE decay scale. -ML_OMEGA_FRAC = 1.0 ! [nondim] default = 0.0 - ! When setting the decay scale for turbulence, use this fraction of the absolute - ! rotation rate blended with the local value of f, as sqrt((1-of)*f^2 + - ! of*4*omega^2). -BBL_EFFIC = 0.2 ! [nondim] default = 0.2 - ! The efficiency with which the energy extracted by bottom drag drives BBL - ! diffusion. This is only used if BOTTOMDRAGLAW is true. -BBL_MIXING_MAX_DECAY = 200.0 ! [m] default = 200.0 - ! The maximum decay scale for the BBL diffusion, or 0 to allow the mixing to - ! penetrate as far as stratification and rotation permit. The default for now - ! is 200 m. This is only used if BOTTOMDRAGLAW is true. -BBL_MIXING_AS_MAX = True ! [Boolean] default = True - ! If true, take the maximum of the diffusivity from the BBL mixing and the other - ! diffusivities. Otherwise, diffusivity from the BBL_mixing is simply added. -USE_LOTW_BBL_DIFFUSIVITY = False ! [Boolean] default = False - ! If true, uses a simple, imprecise but non-coordinate dependent, model of BBL - ! mixing diffusivity based on Law of the Wall. Otherwise, uses the original BBL - ! scheme. -SIMPLE_TKE_TO_KD = False ! [Boolean] default = False - ! If true, uses a simple estimate of Kd/TKE that will work for arbitrary - ! vertical coordinates. If false, calculates Kd/TKE and bounds based on exact - ! energetics for an isopycnal layer-formulation. - -! === module MOM_bkgnd_mixing === -! Adding static vertical background mixing coefficients -KD = 2.0E-05 ! [m2 s-1] default = 0.0 - ! The background diapycnal diffusivity of density in the interior. Zero or the - ! molecular value, ~1e-7 m2 s-1, may be used. -KD_MIN = 2.0E-06 ! [m2 s-1] default = 2.0E-07 - ! The minimum diapycnal diffusivity. -BRYAN_LEWIS_DIFFUSIVITY = False ! [Boolean] default = False - ! If true, use a Bryan & Lewis (JGR 1979) like tanh profile of background - ! diapycnal diffusivity with depth. This is done via CVMix. -HORIZ_VARYING_BACKGROUND = False ! [Boolean] default = False - ! If true, apply vertically uniform, latitude-dependent background diffusivity, - ! as described in Danabasoglu et al., 2012 -PRANDTL_BKGND = 1.0 ! [nondim] default = 1.0 - ! Turbulent Prandtl number used to convert vertical background diffusivities - ! into viscosities. -HENYEY_IGW_BACKGROUND = False ! [Boolean] default = False - ! If true, use a latitude-dependent scaling for the near surface background - ! diffusivity, as described in Harrison & Hallberg, JPO 2008. -KD_TANH_LAT_FN = False ! [Boolean] default = False - ! If true, use a tanh dependence of Kd_sfc on latitude, like CM2.1/CM2M. There - ! is no physical justification for this form, and it can not be used with - ! HENYEY_IGW_BACKGROUND. -KD_MAX = -1.0 ! [m2 s-1] default = -1.0 - ! The maximum permitted increment for the diapycnal diffusivity from TKE-based - ! parameterizations, or a negative value for no limit. -KD_ADD = 0.0 ! [m2 s-1] default = 0.0 - ! A uniform diapycnal diffusivity that is added everywhere without any filtering - ! or scaling. -USER_CHANGE_DIFFUSIVITY = False ! [Boolean] default = False - ! If true, call user-defined code to change the diffusivity. -DISSIPATION_MIN = 0.0 ! [W m-3] default = 0.0 - ! The minimum dissipation by which to determine a lower bound of Kd (a floor). -DISSIPATION_N0 = 0.0 ! [W m-3] default = 0.0 - ! The intercept when N=0 of the N-dependent expression used to set a minimum - ! dissipation by which to determine a lower bound of Kd (a floor): A in eps_min - ! = A + B*N. -DISSIPATION_N1 = 0.0 ! [J m-3] default = 0.0 - ! The coefficient multiplying N, following Gargett, used to set a minimum - ! dissipation by which to determine a lower bound of Kd (a floor): B in eps_min - ! = A + B*N -DISSIPATION_KD_MIN = 0.0 ! [m2 s-1] default = 0.0 - ! The minimum vertical diffusivity applied as a floor. -DOUBLE_DIFFUSION = False ! [Boolean] default = False - ! If true, increase diffusivites for temperature or salinity based on the - ! double-diffusive parameterization described in Large et al. (1994). - -! === module MOM_kappa_shear === -! Parameterization of shear-driven turbulence following Jackson, Hallberg and Legg, JPO 2008 -USE_JACKSON_PARAM = True ! [Boolean] default = False - ! If true, use the Jackson-Hallberg-Legg (JPO 2008) shear mixing - ! parameterization. -VERTEX_SHEAR = False ! [Boolean] default = False - ! If true, do the calculations of the shear-driven mixing at the cell vertices - ! (i.e., the vorticity points). -RINO_CRIT = 0.25 ! [nondim] default = 0.25 - ! The critical Richardson number for shear mixing. -SHEARMIX_RATE = 0.089 ! [nondim] default = 0.089 - ! A nondimensional rate scale for shear-driven entrainment. Jackson et al find - ! values in the range of 0.085-0.089. -MAX_RINO_IT = 25 ! [nondim] default = 50 - ! The maximum number of iterations that may be used to estimate the Richardson - ! number driven mixing. -KD_KAPPA_SHEAR_0 = 2.0E-05 ! [m2 s-1] default = 2.0E-05 - ! The background diffusivity that is used to smooth the density and shear - ! profiles before solving for the diffusivities. The default is the greater of - ! KD and 1e-7 m2 s-1. -KD_TRUNC_KAPPA_SHEAR = 2.0E-07 ! [m2 s-1] default = 2.0E-07 - ! The value of shear-driven diffusivity that is considered negligible and is - ! rounded down to 0. The default is 1% of KD_KAPPA_SHEAR_0. -FRI_CURVATURE = -0.97 ! [nondim] default = -0.97 - ! The nondimensional curvature of the function of the Richardson number in the - ! kappa source term in the Jackson et al. scheme. -TKE_N_DECAY_CONST = 0.24 ! [nondim] default = 0.24 - ! The coefficient for the decay of TKE due to stratification (i.e. proportional - ! to N*tke). The values found by Jackson et al. are 0.24-0.28. -TKE_SHEAR_DECAY_CONST = 0.14 ! [nondim] default = 0.14 - ! The coefficient for the decay of TKE due to shear (i.e. proportional to - ! |S|*tke). The values found by Jackson et al. are 0.14-0.12. -KAPPA_BUOY_SCALE_COEF = 0.82 ! [nondim] default = 0.82 - ! The coefficient for the buoyancy length scale in the kappa equation. The - ! values found by Jackson et al. are in the range of 0.81-0.86. -KAPPA_N_OVER_S_SCALE_COEF2 = 0.0 ! [nondim] default = 0.0 - ! The square of the ratio of the coefficients of the buoyancy and shear scales - ! in the diffusivity equation, Set this to 0 (the default) to eliminate the - ! shear scale. This is only used if USE_JACKSON_PARAM is true. -KAPPA_SHEAR_TOL_ERR = 0.1 ! [nondim] default = 0.1 - ! The fractional error in kappa that is tolerated. Iteration stops when changes - ! between subsequent iterations are smaller than this everywhere in a column. - ! The peak diffusivities usually converge most rapidly, and have much smaller - ! errors than this. -TKE_BACKGROUND = 0.0 ! [m2 s-2] default = 0.0 - ! A background level of TKE used in the first iteration of the kappa equation. - ! TKE_BACKGROUND could be 0. -KAPPA_SHEAR_ELIM_MASSLESS = True ! [Boolean] default = True - ! If true, massless layers are merged with neighboring massive layers in this - ! calculation. The default is true and I can think of no good reason why it - ! should be false. This is only used if USE_JACKSON_PARAM is true. -MAX_KAPPA_SHEAR_IT = 13 ! [nondim] default = 13 - ! The maximum number of iterations that may be used to estimate the - ! time-averaged diffusivity. -KAPPA_SHEAR_MAX_KAP_SRC_CHG = 10.0 ! [nondim] default = 10.0 - ! The maximum permitted increase in the kappa source within an iteration - ! relative to the local source; this must be greater than 1. The lower limit - ! for the permitted fractional decrease is (1 - 0.5/kappa_src_max_chg). These - ! limits could perhaps be made dynamic with an improved iterative solver. -KAPPA_SHEAR_ITER_BUG = False ! [Boolean] default = False - ! If true, use an older, dimensionally inconsistent estimate of the derivative - ! of diffusivity with energy in the Newton's method iteration. The bug causes - ! undercorrections when dz > 1 m. -KAPPA_SHEAR_ALL_LAYER_TKE_BUG = False ! [Boolean] default = False - ! If true, report back the latest estimate of TKE instead of the time average - ! TKE when there is mass in all layers. Otherwise always report the time - ! averaged TKE, as is currently done when there are some massless layers. -USE_RESTRICTIVE_TOLERANCE_CHECK = False ! [Boolean] default = False - ! If true, uses the more restrictive tolerance check to determine if a timestep - ! is acceptable for the KS_it outer iteration loop. False uses the original - ! less restrictive check. - -! === module MOM_CVMix_shear === -! Parameterization of shear-driven turbulence via CVMix (various options) -USE_LMD94 = False ! [Boolean] default = False - ! If true, use the Large-McWilliams-Doney (JGR 1994) shear mixing - ! parameterization. -USE_PP81 = False ! [Boolean] default = False - ! If true, use the Pacanowski and Philander (JPO 1981) shear mixing - ! parameterization. - -! === module MOM_CVMix_ddiff === -! Parameterization of mixing due to double diffusion processes via CVMix -USE_CVMIX_DDIFF = False ! [Boolean] default = False - ! If true, turns on double diffusive processes via CVMix. Note that double - ! diffusive processes on viscosity are ignored in CVMix, see - ! http://cvmix.github.io/ for justification. - -! === module MOM_diabatic_aux === -! The following parameters are used for auxiliary diabatic processes. -RECLAIM_FRAZIL = False ! [Boolean] default = True - ! If true, try to use any frazil heat deficit to cool any overlying layers down - ! to the freezing point, thereby avoiding the creation of thin ice when the SST - ! is above the freezing point. -PRESSURE_DEPENDENT_FRAZIL = False ! [Boolean] default = False - ! If true, use a pressure dependent freezing temperature when making frazil. The - ! default is false, which will be faster but is inappropriate with ice-shelf - ! cavities. -USE_RIVER_HEAT_CONTENT = False ! [Boolean] default = False - ! If true, use the fluxes%runoff_Hflx field to set the heat carried by runoff, - ! instead of using SST*CP*liq_runoff. -USE_CALVING_HEAT_CONTENT = False ! [Boolean] default = False - ! If true, use the fluxes%calving_Hflx field to set the heat carried by runoff, - ! instead of using SST*CP*froz_runoff. -VAR_PEN_SW = True ! [Boolean] default = False - ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine - ! the e-folding depth of incoming short wave radiation. - -! === module MOM_regularize_layers === -REGULARIZE_SURFACE_LAYERS = False ! [Boolean] default = False - ! If defined, vertically restructure the near-surface layers when they have too - ! much lateral variations to allow for sensible lateral barotropic transports. - -! === module MOM_opacity === -OPACITY_SCHEME = "MANIZZA_05" ! default = "MANIZZA_05" - ! This character string specifies how chlorophyll concentrations are translated - ! into opacities. Currently valid options include: - ! MANIZZA_05 - Use Manizza et al., GRL, 2005. - ! MOREL_88 - Use Morel, JGR, 1988. -BLUE_FRAC_SW = 0.5 ! [nondim] default = 0.5 - ! The fraction of the penetrating shortwave radiation that is in the blue band. -PEN_SW_NBANDS = 3 ! default = 1 - ! The number of bands of penetrating shortwave radiation. -OPTICS_2018_ANSWERS = False ! [Boolean] default = False - ! If true, use the order of arithmetic and expressions that recover the answers - ! from the end of 2018. Otherwise, use updated expressions for handling the - ! absorption of small remaining shortwave fluxes. -OPTICS_ANSWER_DATE = 99991231 ! default = 99991231 - ! The vintage of the order of arithmetic and expressions in the optics - ! calculations. Values below 20190101 recover the answers from the end of 2018, - ! while higher values use updated and more robust forms of the same expressions. - ! If both OPTICS_2018_ANSWERS and OPTICS_ANSWER_DATE are specified, the latter - ! takes precedence. -PEN_SW_FLUX_ABSORB = 2.5E-11 ! [degC m s-1] default = 2.5E-11 - ! A minimum remaining shortwave heating rate that will be simply absorbed in the - ! next sufficiently thick layers for computational efficiency, instead of - ! continuing to penetrate. The default, 2.5e-11 degC m s-1, is about 1e-4 W m-2 - ! or 0.08 degC m century-1, but 0 is also a valid value. -PEN_SW_ABSORB_MINTHICK = 1.0 ! [m] default = 1.0 - ! A thickness that is used to absorb the remaining penetrating shortwave heat - ! flux when it drops below PEN_SW_FLUX_ABSORB. -OPACITY_LAND_VALUE = 10.0 ! [m-1] default = 10.0 - ! The value to use for opacity over land. The default is 10 m-1 - a value for - ! muddy water. - -! === module MOM_tracer_advect === -TRACER_ADVECTION_SCHEME = "PLM" ! default = "PLM" - ! The horizontal transport scheme for tracers: - ! PLM - Piecewise Linear Method - ! PPM:H3 - Piecewise Parabolic Method (Huyhn 3rd order) - ! PPM - Piecewise Parabolic Method (Colella-Woodward) - -! === module MOM_tracer_hor_diff === -KHTR = 600.0 ! [m2 s-1] default = 0.0 - ! The background along-isopycnal tracer diffusivity. -KHTR_MIN = 0.0 ! [m2 s-1] default = 0.0 - ! The minimum along-isopycnal tracer diffusivity. -KHTR_MAX = 0.0 ! [m2 s-1] default = 0.0 - ! The maximum along-isopycnal tracer diffusivity. -KHTR_PASSIVITY_COEFF = 0.0 ! [nondim] default = 0.0 - ! The coefficient that scales deformation radius over grid-spacing in passivity, - ! where passivity is the ratio between along isopycnal mixing of tracers to - ! thickness mixing. A non-zero value enables this parameterization. -KHTR_PASSIVITY_MIN = 0.5 ! [nondim] default = 0.5 - ! The minimum passivity which is the ratio between along isopycnal mixing of - ! tracers to thickness mixing. -DIFFUSE_ML_TO_INTERIOR = False ! [Boolean] default = False - ! If true, enable epipycnal mixing between the surface boundary layer and the - ! interior. -CHECK_DIFFUSIVE_CFL = False ! [Boolean] default = False - ! If true, use enough iterations the diffusion to ensure that the diffusive - ! equivalent of the CFL limit is not violated. If false, always use the greater - ! of 1 or MAX_TR_DIFFUSION_CFL iteration. -MAX_TR_DIFFUSION_CFL = -1.0 ! [nondim] default = -1.0 - ! If positive, locally limit the along-isopycnal tracer diffusivity to keep the - ! diffusive CFL locally at or below this value. The number of diffusive - ! iterations is often this value or the next greater integer. -RECALC_NEUTRAL_SURF = False ! [Boolean] default = False - ! If true, then recalculate the neutral surfaces if the - ! diffusive CFL is exceeded. If false, assume that the - ! positions of the surfaces do not change - -! === module MOM_neutral_diffusion === -! This module implements neutral diffusion of tracers -USE_NEUTRAL_DIFFUSION = False ! [Boolean] default = False - ! If true, enables the neutral diffusion module. - -! === module MOM_lateral_boundary_diffusion === -! This module implements lateral diffusion of tracers near boundaries -USE_LATERAL_BOUNDARY_DIFFUSION = False ! [Boolean] default = False - ! If true, enables the lateral boundary tracer's diffusion module. -OBSOLETE_DIAGNOSTIC_IS_FATAL = True ! [Boolean] default = True - ! If an obsolete diagnostic variable appears in the diag_table, cause a FATAL - ! error rather than issue a WARNING. - -! === module MOM_sum_output === -CALCULATE_APE = True ! [Boolean] default = True - ! If true, calculate the available potential energy of the interfaces. Setting - ! this to false reduces the memory footprint of high-PE-count models - ! dramatically. -WRITE_STOCKS = True ! [Boolean] default = True - ! If true, write the integrated tracer amounts to stdout when the energy files - ! are written. -MAXTRUNC = 5000 ! [truncations save_interval-1] default = 0 - ! The run will be stopped, and the day set to a very large value if the velocity - ! is truncated more than MAXTRUNC times between energy saves. Set MAXTRUNC to 0 - ! to stop if there is any truncation of velocities. -MAX_ENERGY = 0.0 ! [m2 s-2] default = 0.0 - ! The maximum permitted average energy per unit mass; the model will be stopped - ! if there is more energy than this. If zero or negative, this is set to - ! 10*MAXVEL^2. -ENERGYFILE = "ocean.stats" ! default = "ocean.stats" - ! The file to use to write the energies and globally summed diagnostics. -DATE_STAMPED_STDOUT = True ! [Boolean] default = True - ! If true, use dates (not times) in messages to stdout -TIMEUNIT = 8.64E+04 ! [s] default = 8.64E+04 - ! The time unit in seconds a number of input fields -READ_DEPTH_LIST = False ! [Boolean] default = False - ! Read the depth list from a file if it exists or create that file otherwise. -DEPTH_LIST_MIN_INC = 1.0E-10 ! [m] default = 1.0E-10 - ! The minimum increment between the depths of the entries in the depth-list - ! file. -ENERGYSAVEDAYS = 10.0 ! [days] default = 1.0 - ! The interval in units of TIMEUNIT between saves of the energies of the run and - ! other globally summed diagnostics. -ENERGYSAVEDAYS_GEOMETRIC = 0.0 ! [days] default = 0.0 - ! The starting interval in units of TIMEUNIT for the first call to save the - ! energies of the run and other globally summed diagnostics. The interval - ! increases by a factor of 2. after each call to write_energy. - -! === module ocean_stochastics_init === -DO_SPPT = False ! [Boolean] default = False - ! If true, then stochastically perturb the thermodynamic tendemcies of T,S, amd - ! h. Amplitude and correlations are controlled by the nam_stoch namelist in the - ! UFS model only. -PERT_EPBL = False ! [Boolean] default = False - ! If true, then stochastically perturb the kinetic energy production and - ! dissipation terms. Amplitude and correlations are controlled by the nam_stoch - ! namelist in the UFS model only. - -! === module MOM_surface_forcing === -VARIABLE_WINDS = True ! [Boolean] default = True - ! If true, the winds vary in time after the initialization. -VARIABLE_BUOYFORCE = True ! [Boolean] default = True - ! If true, the buoyancy forcing varies in time after the initialization of the - ! model. -BUOY_CONFIG = "data_override" ! default = "zero" - ! The character string that indicates how buoyancy forcing is specified. Valid - ! options include (file), (data_override), (zero), (const), (linear), (MESO), - ! (SCM_CVmix_tests), (BFB), (dumbbell), (USER) and (NONE). -WIND_CONFIG = "data_override" ! default = "zero" - ! The character string that indicates how wind forcing is specified. Valid - ! options include (file), (data_override), (2gyre), (1gyre), (gyres), (zero), - ! (const), (Neverworld), (scurves), (ideal_hurr), (SCM_ideal_hurr), - ! (SCM_CVmix_tests) and (USER). -RESTOREBUOY = False ! [Boolean] default = False - ! If true, the buoyancy fluxes drive the model back toward some specified - ! surface state with a rate given by FLUXCONST. -LATENT_HEAT_FUSION = 3.34E+05 ! [J/kg] default = 3.34E+05 - ! The latent heat of fusion. -LATENT_HEAT_VAPORIZATION = 2.5E+06 ! [J/kg] default = 2.5E+06 - ! The latent heat of fusion. -GUST_CONST = 0.02 ! [Pa] default = 0.0 - ! The background gustiness in the winds. -FIX_USTAR_GUSTLESS_BUG = True ! [Boolean] default = True - ! If true correct a bug in the time-averaging of the gustless wind friction - ! velocity -READ_GUST_2D = False ! [Boolean] default = False - ! If true, use a 2-dimensional gustiness supplied from an input file -DUST_RATIO_THRES = 69.00594 ! [not defined] default = 69.00594 - ! TODO: Add description -DUST_RATIO_TO_FE_BIOAVAIL_FRAC = 0.0027298983931818 ! [not defined] default = 0.0027298983931818 - ! TODO: Add description -FE_BIOAVAIL_FRAC_OFFSET = 0.0146756 ! [not defined] default = 0.0146756 - ! TODO: Add description -ATM_FE_TO_BC_RATIO = 1.0 ! [not defined] default = 1.0 - ! TODO: Add description -ATM_BC_FE_BIOAVAIL_FRAC = 0.06 ! [not defined] default = 0.06 - ! TODO: Add description -SEAICE_FE_TO_BC_RATIO = 1.0 ! [not defined] default = 1.0 - ! TODO: Add description -SEAICE_BC_FE_BIOAVAIL_FRAC = 0.06 ! [not defined] default = 0.06 - ! TODO: Add description -IRON_FRAC_IN_ATM_FINE_DUST = 0.035 ! [not defined] default = 0.035 - ! Fraction of fine dust from the atmosphere that is iron -IRON_FRAC_IN_ATM_COARSE_DUST = 0.035 ! [not defined] default = 0.035 - ! Fraction of coarse dust from the atmosphere that is iron -IRON_FRAC_IN_SEAICE_DUST = 0.035 ! [not defined] default = 0.035 - ! Fraction of dust from sea ice that is iron -ATM_CO2_OPT = "constant" ! default = "constant" - ! Source of atmospheric CO2 [constant, diagnostic, or prognostic] -ATM_CO2_CONST = 284.317 ! [ppm] default = 284.317 - ! Value to send to MARBL as xco2 -ATM_ALT_CO2_OPT = "constant" ! default = "constant" - ! Source of alternate atmospheric CO2 [constant, diagnostic, or prognostic] -ATM_ALT_CO2_CONST = 284.317 ! [ppm] default = 284.317 - ! Value to send to MARBL as xco2_alt_co2 -READ_RIV_FLUXES = False ! [Boolean] default = True - ! If true, use nitrogen deposition supplied from an input file - -! === module MOM_restart === -USE_WAVES = False ! [Boolean] default = False - ! If true, enables surface wave modules. - -! === module MOM_main (MOM_driver) === -DT_FORCING = 3600.0 ! [s] default = 3600.0 - ! The time step for changing forcing, coupling with other components, or - ! potentially writing certain diagnostics. The default value is given by DT. -DAYMAX = 1825.0 ! [days] - ! The final time of the whole simulation, in units of TIMEUNIT seconds. This - ! also sets the potential end time of the present run segment if the end time is - ! not set via ocean_solo_nml in input.nml. -SINGLE_STEPPING_CALL = True ! [Boolean] default = True - ! If true, advance the state of MOM with a single step including both dynamics - ! and thermodynamics. If false the two phases are advanced with separate calls. -RESTART_CONTROL = 0 ! default = 1 - ! An integer whose bits encode which restart files are written. Add 2 (bit 1) - ! for a time-stamped file, and odd (bit 0) for a non-time-stamped file. A - ! non-time-stamped restart file is saved at the end of the run segment for any - ! non-negative value. -RESTINT = 3650.0 ! [days] default = 0.0 - ! The interval between saves of the restart file in units of TIMEUNIT. Use 0 - ! (the default) to not save incremental restart files at all. -WRITE_CPU_STEPS = 1000 ! default = 1000 - ! The number of coupled timesteps between writing the cpu time. If this is not - ! positive, do not check cpu time, and the segment run-length can not be set via - ! an elapsed CPU time. - -! === module MOM_write_cputime === -MAXCPU = 2.88E+04 ! [wall-clock seconds] default = -1.0 - ! The maximum amount of cpu time per processor for which MOM should run before - ! saving a restart file and quitting with a return value that indicates that a - ! further run is required to complete the simulation. If automatic restarts are - ! not desired, use a negative value for MAXCPU. MAXCPU has units of wall-clock - ! seconds, so the actual CPU time used is larger by a factor of the number of - ! processors used. -CPU_TIME_FILE = "CPU_stats" ! default = "CPU_stats" - ! The file into which CPU time is written. - -! === module MOM_file_parser === -SEND_LOG_TO_STDOUT = False ! [Boolean] default = False - ! If true, all log messages are also sent to stdout. -DOCUMENT_FILE = "MOM_parameter_doc" ! default = "MOM_parameter_doc" - ! The basename for files where run-time parameters, their settings, units and - ! defaults are documented. Blank will disable all parameter documentation. -COMPLETE_DOCUMENTATION = True ! [Boolean] default = True - ! If true, all run-time parameters are documented in MOM_parameter_doc.all . -MINIMAL_DOCUMENTATION = True ! [Boolean] default = True - ! If true, non-default run-time parameters are documented in - ! MOM_parameter_doc.short . diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.debugging b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.debugging deleted file mode 100644 index e7b54212..00000000 --- a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.debugging +++ /dev/null @@ -1,83 +0,0 @@ -! This file was written by the model and records the debugging parameters used at run-time. - -! === module MOM_unit_scaling === -! Parameters for doing unit scaling of variables. -Z_RESCALE_POWER = 0 ! [nondim] default = 0 - ! An integer power of 2 that is used to rescale the model's internal units of - ! depths and heights. Valid values range from -300 to 300. -L_RESCALE_POWER = 0 ! [nondim] default = 0 - ! An integer power of 2 that is used to rescale the model's internal units of - ! lateral distances. Valid values range from -300 to 300. -T_RESCALE_POWER = 0 ! [nondim] default = 0 - ! An integer power of 2 that is used to rescale the model's internal units of - ! time. Valid values range from -300 to 300. -R_RESCALE_POWER = 0 ! [nondim] default = 0 - ! An integer power of 2 that is used to rescale the model's internal units of - ! density. Valid values range from -300 to 300. -Q_RESCALE_POWER = 0 ! [nondim] default = 0 - ! An integer power of 2 that is used to rescale the model's internal units of - ! heat content. Valid values range from -300 to 300. -C_RESCALE_POWER = 0 ! [nondim] default = 0 - ! An integer power of 2 that is used to rescale the model's internal units of - ! temperature. Valid values range from -300 to 300. -S_RESCALE_POWER = 0 ! [nondim] default = 0 - ! An integer power of 2 that is used to rescale the model's internal units of - ! salinity. Valid values range from -300 to 300. - -! === module MOM === -VERBOSITY = 2 ! default = 2 - ! Integer controlling level of messaging - ! 0 = Only FATAL messages - ! 2 = Only FATAL, WARNING, NOTE [default] - ! 9 = All) -DO_UNIT_TESTS = False ! [Boolean] default = False - ! If True, exercises unit tests at model start up. -DEBUG = False ! [Boolean] default = False - ! If true, write out verbose debugging data. -DEBUG_TRUNCATIONS = False ! [Boolean] default = False - ! If true, calculate all diagnostics that are useful for debugging truncations. -ROTATE_INDEX = False ! [Boolean] default = False - ! Enable rotation of the horizontal indices. -DEBUG_CHKSUMS = False ! [Boolean] default = False - ! If true, checksums are performed on arrays in the various vec_chksum routines. -DEBUG_REDUNDANT = False ! [Boolean] default = False - ! If true, debug redundant data points during calls to the various vec_chksum - ! routines. - -! === module MOM_verticalGrid === -! Parameters providing information about the vertical grid. -H_RESCALE_POWER = 0 ! [nondim] default = 0 - ! An integer power of 2 that is used to rescale the model's intenal units of - ! thickness. Valid values range from -300 to 300. - -! === module MOM_vert_friction === -U_TRUNC_FILE = "U_velocity_truncations" ! default = "" - ! The absolute path to a file into which the accelerations leading to zonal - ! velocity truncations are written. Undefine this for efficiency if this - ! diagnostic is not needed. -V_TRUNC_FILE = "V_velocity_truncations" ! default = "" - ! The absolute path to a file into which the accelerations leading to meridional - ! velocity truncations are written. Undefine this for efficiency if this - ! diagnostic is not needed. - -! === module MOM_PointAccel === -MAX_TRUNC_FILE_SIZE_PER_PE = 50 ! default = 50 - ! The maximum number of columns of truncations that any PE will write out during - ! a run. -DEBUG_FULL_COLUMN = False ! [Boolean] default = False - ! If true, write out the accelerations in all massive layers; otherwise just - ! document the ones with large velocities. - -! === module MOM_diabatic_driver === -! The following parameters are used for diabatic processes. -DEBUG_CONSERVATION = False ! [Boolean] default = False - ! If true, monitor conservation and extrema. - -! === module MOM_kappa_shear === -! Parameterization of shear-driven turbulence following Jackson, Hallberg and Legg, JPO 2008 - -! === module MOM_file_parser === -REPORT_UNUSED_PARAMS = True ! [Boolean] default = True - ! If true, report any parameter lines that are not used in the run. -FATAL_UNUSED_PARAMS = False ! [Boolean] default = False - ! If true, kill the run if there are any unused parameters. diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.layout b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.layout deleted file mode 100644 index d0bf9724..00000000 --- a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.layout +++ /dev/null @@ -1,56 +0,0 @@ -! This file was written by the model and records the layout parameters used at run-time. - -! === module MOM === -GLOBAL_INDEXING = False ! [Boolean] default = False - ! If true, use a global lateral indexing convention, so that corresponding - ! points on different processors have the same index. This does not work with - ! static memory. - -! === module MOM_domains === -!SYMMETRIC_MEMORY_ = True ! [Boolean] - ! If defined, the velocity point data domain includes every face of the - ! thickness points. In other words, some arrays are larger than others, - ! depending on where they are on the staggered grid. Also, the starting index - ! of the velocity-point arrays is usually 0, not 1. This can only be set at - ! compile time. -NONBLOCKING_UPDATES = False ! [Boolean] default = False - ! If true, non-blocking halo updates may be used. -THIN_HALO_UPDATES = True ! [Boolean] default = True - ! If true, optional arguments may be used to specify the width of the halos that - ! are updated with each call. -!STATIC_MEMORY_ = False ! [Boolean] - ! If STATIC_MEMORY_ is defined, the principle variables will have sizes that are - ! statically determined at compile time. Otherwise the sizes are not determined - ! until run time. The STATIC option is substantially faster, but does not allow - ! the PE count to be changed at run time. This can only be set at compile time. -MASKTABLE = "MOM_mask_table" ! default = "MOM_mask_table" - ! A text file to specify n_mask, layout and mask_list. This feature masks out - ! processors that contain only land points. The first line of mask_table is the - ! number of regions to be masked out. The second line is the layout of the model - ! and must be consistent with the actual model layout. The following (n_mask) - ! lines give the logical positions of the processors that are masked out. The - ! mask_table can be created by tools like check_mask. The following example of - ! mask_table masks out 2 processors, (1,2) and (3,6), out of the 24 in a 4x6 - ! layout: - ! 2 - ! 4,6 - ! 1,2 - ! 3,6 -NIPROC = 1 ! - ! The number of processors in the x-direction. With STATIC_MEMORY_ this is set - ! in MOM_memory.h at compile time. -NJPROC = 1 ! - ! The number of processors in the y-direction. With STATIC_MEMORY_ this is set - ! in MOM_memory.h at compile time. -LAYOUT = 1, 1 ! - ! The processor layout that was actually used. -IO_LAYOUT = 1, 1 ! default = 1 - ! The processor layout to be used, or 0,0 to automatically set the io_layout to - ! be the same as the layout. - -! === module MOM_grid === -! Parameters providing information about the lateral grid. -NIBLOCK = 1 ! default = 1 - ! The number of blocks in the x-direction on each processor (for openmp). -NJBLOCK = 1 ! default = 1 - ! The number of blocks in the y-direction on each processor (for openmp). diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short b/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short deleted file mode 100644 index 4552ca3a..00000000 --- a/standalone/examples/single_column_MARBL/BATS/MOM_parameter_doc.short +++ /dev/null @@ -1,387 +0,0 @@ -! This file was written by the model and records the non-default parameters used at run-time. - -! === module MOM === -SPLIT = False ! [Boolean] default = True - ! Use the split time stepping if true. -USE_REGRIDDING = True ! [Boolean] default = False - ! If True, use the ALE algorithm (regridding/remapping). If False, use the - ! layered isopycnal algorithm. -DT = 3600.0 ! [s] - ! The (baroclinic) dynamics time step. The time-step that is actually used will - ! be an integer fraction of the forcing time-step (DT_FORCING in ocean-only mode - ! or the coupling timestep in coupled mode.) -FRAZIL = True ! [Boolean] default = False - ! If true, water freezes if it gets too cold, and the accumulated heat deficit - ! is returned in the surface state. FRAZIL is only used if - ! ENABLE_THERMODYNAMICS is true. -C_P = 3925.0 ! [J kg-1 K-1] default = 3991.86795711963 - ! The heat capacity of sea water, approximated as a constant. This is only used - ! if ENABLE_THERMODYNAMICS is true. The default value is from the TEOS-10 - ! definition of conservative temperature. -SAVE_INITIAL_CONDS = True ! [Boolean] default = False - ! If true, write the initial conditions to a file given by IC_OUTPUT_FILE. - -! === module MOM_domains === -REENTRANT_Y = True ! [Boolean] default = False - ! If true, the domain is meridionally reentrant. -NIGLOBAL = 2 ! - ! The total number of thickness grid points in the x-direction in the physical - ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. -NJGLOBAL = 2 ! - ! The total number of thickness grid points in the y-direction in the physical - ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. -NIHALO = 2 ! default = 4 - ! The number of halo points on each side in the x-direction. How this is set - ! varies with the calling component and static or dynamic memory configuration. -NJHALO = 2 ! default = 4 - ! The number of halo points on each side in the y-direction. How this is set - ! varies with the calling component and static or dynamic memory configuration. - -! === module MOM_fixed_initialization === -INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" ! default = "." - ! The directory in which input files are found. - -! === module MOM_grid_init === -GRID_CONFIG = "cartesian" ! - ! A character string that determines the method for defining the horizontal - ! grid. Current options are: - ! mosaic - read the grid from a mosaic (supergrid) - ! file set by GRID_FILE. - ! cartesian - use a (flat) Cartesian grid. - ! spherical - use a simple spherical grid. - ! mercator - use a Mercator spherical grid. -SOUTHLAT = 31.660667 ! [degrees] - ! The southern latitude of the domain or the equivalent starting value for the - ! y-axis. -LENLAT = 1.0 ! [degrees] - ! The latitudinal or y-direction length of the domain. -WESTLON = -64.0187 ! [degrees] default = 0.0 - ! The western longitude of the domain or the equivalent starting value for the - ! x-axis. -LENLON = 1.0 ! [degrees] - ! The longitudinal or x-direction length of the domain. -TOPO_CONFIG = "file" ! - ! This specifies how bathymetry is specified: - ! file - read bathymetric information from the file - ! specified by (TOPO_FILE). - ! flat - flat bottom set to MAXIMUM_DEPTH. - ! bowl - an analytically specified bowl-shaped basin - ! ranging between MAXIMUM_DEPTH and MINIMUM_DEPTH. - ! spoon - a similar shape to 'bowl', but with an vertical - ! wall at the southern face. - ! halfpipe - a zonally uniform channel with a half-sine - ! profile in the meridional direction. - ! bbuilder - build topography from list of functions. - ! benchmark - use the benchmark test case topography. - ! Neverworld - use the Neverworld test case topography. - ! DOME - use a slope and channel configuration for the - ! DOME sill-overflow test case. - ! ISOMIP - use a slope and channel configuration for the - ! ISOMIP test case. - ! DOME2D - use a shelf and slope configuration for the - ! DOME2D gravity current/overflow test case. - ! Kelvin - flat but with rotated land mask. - ! seamount - Gaussian bump for spontaneous motion test case. - ! dumbbell - Sloshing channel with reservoirs on both ends. - ! shelfwave - exponential slope for shelfwave test case. - ! Phillips - ACC-like idealized topography used in the Phillips config. - ! dense - Denmark Strait-like dense water formation and overflow. - ! USER - call a user modified routine. -TOPO_FILE = "TOPO_FILE_COLUMN.nc" ! default = "topog.nc" - ! The file from which the bathymetry is read. -MINIMUM_DEPTH = 9.5 ! [m] default = 0.0 - ! If MASKING_DEPTH is unspecified, then anything shallower than MINIMUM_DEPTH is - ! assumed to be land and all fluxes are masked out. If MASKING_DEPTH is - ! specified, then all depths shallower than MINIMUM_DEPTH but deeper than - ! MASKING_DEPTH are rounded to MINIMUM_DEPTH. -MASKING_DEPTH = 0.0 ! [m] default = -9999.0 - ! The depth below which to mask points as land points, for which all fluxes are - ! zeroed out. MASKING_DEPTH is ignored if it has the special default value. -MAXIMUM_DEPTH = 6000.0 ! [m] - ! The maximum depth of the ocean. -ROTATION = "betaplane" ! default = "2omegasinlat" - ! This specifies how the Coriolis parameter is specified: - ! 2omegasinlat - Use twice the planetary rotation rate - ! times the sine of latitude. - ! betaplane - Use a beta-plane or f-plane. - ! USER - call a user modified routine. -F_0 = 7.63418E-05 ! [s-1] default = 0.0 - ! The reference value of the Coriolis parameter with the betaplane option. - -! === module MOM_verticalGrid === -! Parameters providing information about the vertical grid. -NK = 65 ! [nondim] - ! The number of model layers. - -! === module MOM_EOS === - -! === module MOM_tracer_flow_control === -USE_MARBL_TRACERS = True ! [Boolean] default = False - ! If true, use the MARBL tracer package. - -! === module MARBL_tracers === -CHL_FROM_FILE = False ! [Boolean] default = True - ! If true, chl_a is read from a file. - -! === module MARBL_tracers === -MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_latlon_1x1_180W_c230331.nc" - ! The file in which the MARBL tracers initial values can be found. -MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc" - ! The file in which the iron sediment flux forcing field can be found. -MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211109.nc" - ! The file in which the iron vent flux forcing field can be found. - -! === module MOM_coord_initialization === -REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" - ! Coordinate mode for vertical regridding. Choose among the following - ! possibilities: LAYER - Isopycnal or stacked shallow water layers - ! ZSTAR, Z* - stretched geopotential z* - ! SIGMA_SHELF_ZSTAR - stretched geopotential z* ignoring shelf - ! SIGMA - terrain following coordinates - ! RHO - continuous isopycnal - ! HYCOM1 - HyCOM-like hybrid coordinate - ! HYBGEN - Hybrid coordinate from the Hycom hybgen code - ! SLIGHT - stretched coordinates above continuous isopycnal - ! ADAPTIVE - optimize for smooth neutral density surfaces -ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_65L_20200626.nc,dz" ! default = "UNIFORM" - ! Determines how to specify the coordinate resolution. Valid options are: - ! PARAM - use the vector-parameter ALE_RESOLUTION - ! UNIFORM[:N] - uniformly distributed - ! FILE:string - read from a file. The string specifies - ! the filename and variable name, separated - ! by a comma or space, e.g. FILE:lev.nc,dz - ! or FILE:lev.nc,interfaces=zw - ! WOA09[:N] - the WOA09 vertical grid (approximately) - ! FNC1:string - FNC1:dz_min,H_total,power,precision - ! HYBRID:string - read from a file. The string specifies - ! the filename and two variable names, separated - ! by a comma or space, for sigma-2 and dz. e.g. - ! HYBRID:vgrid.nc,sigma2,dz -!ALE_RESOLUTION = 4*2.5, 2.77, 3.38, 4.01, 4.65, 5.29, 5.95, 6.61, 7.28, 7.97, 8.66, 9.37, 10.08, 10.81, 11.54, 12.29, 13.06, 13.85, 14.69, 15.59, 16.56, 17.61, 18.76, 20.02, 21.42, 23.0, 24.77, 26.79, 29.1, 31.76, 34.87, 38.5, 42.79, 47.9, 54.01, 61.37, 70.25, 80.95, 93.75, 108.8, 126.04, 145.04, 164.81, 184.05, 201.34, 215.66, 226.64, 234.5, 239.84, 243.31, 245.52, 246.88, 247.72, 248.23, 248.54, 248.73, 248.84, 248.64, 248.68, 248.71, 248.72, 248.73 ! [m] - ! The distribution of vertical resolution for the target - ! grid used for Eulerian-like coordinates. For example, - ! in z-coordinate mode, the parameter is a list of level - ! thicknesses (in m). In sigma-coordinate mode, the list - ! is of non-dimensional fractions of the water column. - -! === module MOM_state_initialization === -INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False - ! If true, initialize the layer thicknesses, temperatures, and salinities from a - ! Z-space file on a latitude-longitude grid. - -! === module MOM_initialize_layers_from_Z === -TEMP_SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" - ! The name of the z-space input file used to initialize temperatures (T) and - ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and - ! SALT_Z_INIT_FILE must be set. -Z_INIT_FILE_PTEMP_VAR = "Temp" ! default = "ptemp" - ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. -Z_INIT_FILE_SALT_VAR = "Salt" ! default = "salt" - ! The name of the salinity variable in SALT_Z_INIT_FILE. -Z_INIT_HOMOGENIZE = True ! [Boolean] default = False - ! If True, then horizontally homogenize the interpolated initial conditions. -Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False - ! If True, then remap straight to model coordinate from file. - -! === module MOM_diag_mediator === - -! === module MOM_lateral_mixing_coeffs === - -! === module MOM_set_visc === -HBBL = 10.0 ! [m] - ! The thickness of a bottom boundary layer with a viscosity increased by - ! KV_EXTRA_BBL if BOTTOMDRAGLAW is not defined, or the thickness over which - ! near-bottom velocities are averaged for the drag law if BOTTOMDRAGLAW is - ! defined but LINEAR_DRAG is not. -CDRAG = 0.0 ! [nondim] default = 0.003 - ! CDRAG is the drag coefficient relating the magnitude of the velocity field to - ! the bottom stress. CDRAG is only used if BOTTOMDRAGLAW is defined. -DRAG_BG_VEL = 0.1 ! [m s-1] default = 0.0 - ! DRAG_BG_VEL is either the assumed bottom velocity (with LINEAR_DRAG) or an - ! unresolved velocity that is combined with the resolved velocity to estimate - ! the velocity magnitude. DRAG_BG_VEL is only used when BOTTOMDRAGLAW is - ! defined. -BBL_THICK_MIN = 0.1 ! [m] default = 0.0 - ! The minimum bottom boundary layer thickness that can be used with - ! BOTTOMDRAGLAW. This might be Kv/(cdrag*drag_bg_vel) to give Kv as the minimum - ! near-bottom viscosity. -KV = 1.0E-04 ! [m2 s-1] - ! The background kinematic viscosity in the interior. The molecular value, ~1e-6 - ! m2 s-1, may be used. - -! === module MOM_thickness_diffuse === - -! === module MOM_porous_barriers === - -! === module MOM_dynamics_unsplit === - -! === module MOM_continuity === - -! === module MOM_continuity_PPM === -UPWIND_1ST_CONTINUITY = True ! [Boolean] default = False - ! If true, CONTINUITY_PPM becomes a 1st-order upwind continuity solver. This - ! scheme is highly diffusive but may be useful for debugging or in single-column - ! mode where its minimal stencil is useful. -ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.25E-09 - ! The tolerance for the differences between the barotropic and baroclinic - ! estimates of the sea surface height due to the fluxes through each face. The - ! total tolerance for SSH is 4 times this value. The default is - ! 0.5*NK*ANGSTROM, and this should not be set less than about - ! 10^-15*MAXIMUM_DEPTH. - -! === module MOM_CoriolisAdv === -CORIOLIS_EN_DIS = True ! [Boolean] default = False - ! If true, two estimates of the thickness fluxes are used to estimate the - ! Coriolis term, and the one that dissipates energy relative to the other one is - ! used. -BOUND_CORIOLIS = True ! [Boolean] default = False - ! If true, the Coriolis terms at u-points are bounded by the four estimates of - ! (f+rv)v from the four neighboring v-points, and similarly at v-points. This - ! option is always effectively false with CORIOLIS_EN_DIS defined and - ! CORIOLIS_SCHEME set to SADOURNY75_ENERGY. - -! === module MOM_PressureForce === - -! === module MOM_PressureForce_FV === - -! === module MOM_hor_visc === -BIHARMONIC = False ! [Boolean] default = True - ! If true, use a biharmonic horizontal viscosity. BIHARMONIC may be used with - ! LAPLACIAN. - -! === module MOM_vert_friction === -HARMONIC_VISC = True ! [Boolean] default = False - ! If true, use the harmonic mean thicknesses for calculating the vertical - ! viscosity. -HMIX_FIXED = 0.01 ! [m] - ! The prescribed depth over which the near-surface viscosity and diffusivity are - ! elevated when the bulk mixed layer is not used. -MAXVEL = 6.0 ! [m s-1] default = 3.0E+08 - ! The maximum velocity allowed before the velocity components are truncated. -VEL_UNDERFLOW = 1.0E-30 ! [m s-1] default = 0.0 - ! A negligibly small velocity magnitude below which velocity components are set - ! to 0. A reasonable value might be 1e-30 m/s, which is less than an Angstrom - ! divided by the age of the universe. - -! === module MOM_diagnostics === - -! === module MOM_diabatic_driver === -! The following parameters are used for diabatic processes. - -! === module MOM_CVMix_KPP === -! This is the MOM wrapper to CVMix:KPP -! See http://cvmix.github.io/ -USE_KPP = True ! [Boolean] default = False - ! If true, turns on the [CVMix] KPP scheme of Large et al., 1994, to calculate - ! diffusivities and non-local transport in the OBL. -KPP% -INTERP_TYPE = "cubic" ! default = "quadratic" - ! Type of interpolation to determine the OBL depth. - ! Allowed types are: linear, quadratic, cubic. -NLT_SHAPE = "PARABOLIC" ! default = "CVMix" - ! MOM6 method to set nonlocal transport profile. Over-rides the result from - ! CVMix. Allowed values are: - ! CVMix - Uses the profiles from CVMix specified by MATCH_TECHNIQUE - ! LINEAR - A linear profile, 1-sigma - ! PARABOLIC - A parablic profile, (1-sigma)^2 - ! CUBIC - A cubic profile, (1-sigma)^2(1+2*sigma) - ! CUBIC_LMD - The original KPP profile -%KPP - -! === module MOM_set_diffusivity === -ML_RADIATION = True ! [Boolean] default = False - ! If true, allow a fraction of TKE available from wind work to penetrate below - ! the base of the mixed layer with a vertical decay scale determined by the - ! minimum of: (1) The depth of the mixed layer, (2) an Ekman length scale. -TKE_DECAY = 10.0 ! [nondim] default = 2.5 - ! The ratio of the natural Ekman depth to the TKE decay scale. -ML_OMEGA_FRAC = 1.0 ! [nondim] default = 0.0 - ! When setting the decay scale for turbulence, use this fraction of the absolute - ! rotation rate blended with the local value of f, as sqrt((1-of)*f^2 + - ! of*4*omega^2). - -! === module MOM_bkgnd_mixing === -! Adding static vertical background mixing coefficients -KD = 2.0E-05 ! [m2 s-1] default = 0.0 - ! The background diapycnal diffusivity of density in the interior. Zero or the - ! molecular value, ~1e-7 m2 s-1, may be used. -KD_MIN = 2.0E-06 ! [m2 s-1] default = 2.0E-07 - ! The minimum diapycnal diffusivity. - -! === module MOM_kappa_shear === -! Parameterization of shear-driven turbulence following Jackson, Hallberg and Legg, JPO 2008 -USE_JACKSON_PARAM = True ! [Boolean] default = False - ! If true, use the Jackson-Hallberg-Legg (JPO 2008) shear mixing - ! parameterization. -MAX_RINO_IT = 25 ! [nondim] default = 50 - ! The maximum number of iterations that may be used to estimate the Richardson - ! number driven mixing. - -! === module MOM_diabatic_aux === -! The following parameters are used for auxiliary diabatic processes. -RECLAIM_FRAZIL = False ! [Boolean] default = True - ! If true, try to use any frazil heat deficit to cool any overlying layers down - ! to the freezing point, thereby avoiding the creation of thin ice when the SST - ! is above the freezing point. -VAR_PEN_SW = True ! [Boolean] default = False - ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine - ! the e-folding depth of incoming short wave radiation. - -! === module MOM_opacity === -PEN_SW_NBANDS = 3 ! default = 1 - ! The number of bands of penetrating shortwave radiation. - -! === module MOM_tracer_advect === - -! === module MOM_tracer_hor_diff === -KHTR = 600.0 ! [m2 s-1] default = 0.0 - ! The background along-isopycnal tracer diffusivity. - -! === module MOM_sum_output === -MAXTRUNC = 5000 ! [truncations save_interval-1] default = 0 - ! The run will be stopped, and the day set to a very large value if the velocity - ! is truncated more than MAXTRUNC times between energy saves. Set MAXTRUNC to 0 - ! to stop if there is any truncation of velocities. -ENERGYSAVEDAYS = 10.0 ! [days] default = 1.0 - ! The interval in units of TIMEUNIT between saves of the energies of the run and - ! other globally summed diagnostics. - -! === module ocean_stochastics_init === - -! === module MOM_surface_forcing === -BUOY_CONFIG = "data_override" ! default = "zero" - ! The character string that indicates how buoyancy forcing is specified. Valid - ! options include (file), (data_override), (zero), (const), (linear), (MESO), - ! (SCM_CVmix_tests), (BFB), (dumbbell), (USER) and (NONE). -WIND_CONFIG = "data_override" ! default = "zero" - ! The character string that indicates how wind forcing is specified. Valid - ! options include (file), (data_override), (2gyre), (1gyre), (gyres), (zero), - ! (const), (Neverworld), (scurves), (ideal_hurr), (SCM_ideal_hurr), - ! (SCM_CVmix_tests) and (USER). -GUST_CONST = 0.02 ! [Pa] default = 0.0 - ! The background gustiness in the winds. -READ_RIV_FLUXES = False ! [Boolean] default = True - ! If true, use nitrogen deposition supplied from an input file - -! === module MOM_main (MOM_driver) === -DAYMAX = 1825.0 ! [days] - ! The final time of the whole simulation, in units of TIMEUNIT seconds. This - ! also sets the potential end time of the present run segment if the end time is - ! not set via ocean_solo_nml in input.nml. -RESTART_CONTROL = 0 ! default = 1 - ! An integer whose bits encode which restart files are written. Add 2 (bit 1) - ! for a time-stamped file, and odd (bit 0) for a non-time-stamped file. A - ! non-time-stamped restart file is saved at the end of the run segment for any - ! non-negative value. -RESTINT = 3650.0 ! [days] default = 0.0 - ! The interval between saves of the restart file in units of TIMEUNIT. Use 0 - ! (the default) to not save incremental restart files at all. - -! === module MOM_write_cputime === -MAXCPU = 2.88E+04 ! [wall-clock seconds] default = -1.0 - ! The maximum amount of cpu time per processor for which MOM should run before - ! saving a restart file and quitting with a return value that indicates that a - ! further run is required to complete the simulation. If automatic restarts are - ! not desired, use a negative value for MAXCPU. MAXCPU has units of wall-clock - ! seconds, so the actual CPU time used is larger by a factor of the number of - ! processors used. diff --git a/standalone/examples/single_column_MARBL/BATS/vgrid_cm4.cdl b/standalone/examples/single_column_MARBL/BATS/vgrid_cm4.cdl deleted file mode 100644 index f3b0163e..00000000 --- a/standalone/examples/single_column_MARBL/BATS/vgrid_cm4.cdl +++ /dev/null @@ -1,38 +0,0 @@ -netcdf vgrid { -dimensions: - nz = 75 ; - zt = 50 ; - zw = 51 ; -variables: - double dz(nz) ; - dz:long_name = "z coordinate level thickness" ; - dz:units = "m" ; - double zt(zt) ; - zt:long_name = "Diagnostic z coordinate level position" ; - zt:comment = "Used for diagnostics only" ; - zt:units = "m" ; - double zw(zw) ; - zw:long_name = "Diagnostic z coordinate interface position" ; - zw:comment = "Used for diagnostics only" ; - zw:units = "m" ; - -// global attributes: - :filename = "vgrid_cm4.nc" ; -data: - - dz = 2.00, 2.00, 2.00, 2.00, 2.01, 2.01, 2.02, 2.02, 2.05, 2.07, 2.09, 2.13, 2.18, 2.24, 2.30, 2.40, 2.50, 2.62, 2.78, 2.95, 3.17, 3.42, 3.71, 4.07, 4.48, 4.97, 5.55, 6.23, 7.04, 7.99, 9.11, 10.43, 11.98, 13.80, 15.94, 18.42, 21.32, 24.66, 28.51, 32.91, 37.92, 43.56, 49.87, 56.88, 64.55, 72.91, 81.87, 91.40, 101.39, 111.73, 122.29, 132.93, 143.47, 153.78, 163.70, 173.07, 181.78, 189.76, 196.89, 203.17, 208.58, 213.14, 216.87, 219.87, 222.19, 223.94, 225.18, 226.04, 226.59, 226.90, 227.07, 227.14, 227.16, 227.16, 227.17; - - zt = 5.05, 15.15, 25.25, 35.4, 45.6, 55.85, 66.25, 76.85, 87.65, 98.75, - 110.25, 122.3, 135.15, 149.1, 164.55, 182, 202.15, 225.95, 254.6, 289.55, - 332.5, 385.4, 450.25, 528.9, 622.85, 732.85, 858.9, 1000.25, 1155.4, - 1322.5, 1499.55, 1684.6, 1875.95, 2072.15, 2272, 2474.55, 2679.1, - 2885.15, 3092.3, 3300.25, 3508.75, 3717.65, 3926.85, 4136.25, 4345.85, - 4555.6, 4765.4, 4975.25, 5185.15, 5394.6 ; - - zw = 0, 10.1, 20.2, 30.3, 40.5, 50.7, 61, 71.5, 82.2, 93.1, 104.4, 116.1, - 128.5, 141.8, 156.4, 172.7, 191.3, 213, 238.9, 270.3, 308.8, 356.2, - 414.6, 485.9, 571.9, 673.8, 791.9, 925.9, 1074.6, 1236.2, 1408.8, 1590.3, - 1778.9, 1973, 2171.3, 2372.7, 2576.4, 2781.8, 2988.5, 3196.1, 3404.4, - 3613.1, 3822.2, 4031.5, 4241, 4450.7, 4660.5, 4870.3, 5080.2, 5290.1, - 5499.1 ; -} diff --git a/standalone/examples/single_column_MARBL/BATS/vgrid_cm4_10.cdl b/standalone/examples/single_column_MARBL/BATS/vgrid_cm4_10.cdl deleted file mode 100644 index 1f4286aa..00000000 --- a/standalone/examples/single_column_MARBL/BATS/vgrid_cm4_10.cdl +++ /dev/null @@ -1,38 +0,0 @@ -netcdf vgrid { -dimensions: - nz = 75 ; - zt = 50 ; - zw = 51 ; -variables: - double dz(nz) ; - dz:long_name = "z coordinate level thickness" ; - dz:units = "m" ; - double zt(zt) ; - zt:long_name = "Diagnostic z coordinate level position" ; - zt:comment = "Used for diagnostics only" ; - zt:units = "m" ; - double zw(zw) ; - zw:long_name = "Diagnostic z coordinate interface position" ; - zw:comment = "Used for diagnostics only" ; - zw:units = "m" ; - -// global attributes: - :filename = "vgrid_cm4_10.nc" ; -data: - - dz = 10.00, 10.00, 10.00, 10.01, 10.02, 10.03, 10.05, 10.09, 10.13, 10.20, 10.29, 10.39, 10.53, 10.71, 10.91, 11.15, 11.46, 11.80, 12.22, 12.69, 13.24, 13.88, 14.62, 15.45, 16.40, 17.50, 18.72, 20.13, 21.70, 23.48, 25.47, 27.71, 30.22, 33.00, 36.08, 39.51, 43.26, 47.38, 51.86, 56.72, 61.95, 67.53, 73.47, 79.72, 86.25, 93.00, 99.95, 106.99, 114.09, 121.13, 128.08, 134.83, 141.32, 147.47, 153.23, 158.55, 163.40, 167.73, 171.57, 174.87, 177.70, 180.06, 181.97, 183.49, 184.66, 185.55, 186.17, 186.60, 186.87, 187.04, 187.11, 187.15, 187.16, 187.16, 187.17; - - zt = 5.05, 15.15, 25.25, 35.4, 45.6, 55.85, 66.25, 76.85, 87.65, 98.75, - 110.25, 122.3, 135.15, 149.1, 164.55, 182, 202.15, 225.95, 254.6, 289.55, - 332.5, 385.4, 450.25, 528.9, 622.85, 732.85, 858.9, 1000.25, 1155.4, - 1322.5, 1499.55, 1684.6, 1875.95, 2072.15, 2272, 2474.55, 2679.1, - 2885.15, 3092.3, 3300.25, 3508.75, 3717.65, 3926.85, 4136.25, 4345.85, - 4555.6, 4765.4, 4975.25, 5185.15, 5394.6 ; - - zw = 0, 10.1, 20.2, 30.3, 40.5, 50.7, 61, 71.5, 82.2, 93.1, 104.4, 116.1, - 128.5, 141.8, 156.4, 172.7, 191.3, 213, 238.9, 270.3, 308.8, 356.2, - 414.6, 485.9, 571.9, 673.8, 791.9, 925.9, 1074.6, 1236.2, 1408.8, 1590.3, - 1778.9, 1973, 2171.3, 2372.7, 2576.4, 2781.8, 2988.5, 3196.1, 3404.4, - 3613.1, 3822.2, 4031.5, 4241, 4450.7, 4660.5, 4870.3, 5080.2, 5290.1, - 5499.1 ; -} diff --git a/standalone/examples/single_column_MARBL/BATS/vgrid_cm4_4.cdl b/standalone/examples/single_column_MARBL/BATS/vgrid_cm4_4.cdl deleted file mode 100644 index 9e5f2138..00000000 --- a/standalone/examples/single_column_MARBL/BATS/vgrid_cm4_4.cdl +++ /dev/null @@ -1,38 +0,0 @@ -netcdf vgrid { -dimensions: - nz = 75 ; - zt = 50 ; - zw = 51 ; -variables: - double dz(nz) ; - dz:long_name = "z coordinate level thickness" ; - dz:units = "m" ; - double zt(zt) ; - zt:long_name = "Diagnostic z coordinate level position" ; - zt:comment = "Used for diagnostics only" ; - zt:units = "m" ; - double zw(zw) ; - zw:long_name = "Diagnostic z coordinate interface position" ; - zw:comment = "Used for diagnostics only" ; - zw:units = "m" ; - -// global attributes: - :filename = "vgrid_cm4_4.nc" ; -data: - - dz = 4.00, 4.00, 4.00, 4.00, 4.01, 4.02, 4.03, 4.05, 4.07, 4.11, 4.16, 4.22, 4.29, 4.39, 4.51, 4.65, 4.81, 5.02, 5.26, 5.54, 5.87, 6.26, 6.71, 7.24, 7.85, 8.57, 9.40, 10.36, 11.47, 12.76, 14.26, 15.97, 17.95, 20.22, 22.81, 25.79, 29.15, 32.95, 37.24, 42.02, 47.33, 53.20, 59.62, 66.57, 74.05, 82.03, 90.42, 99.17, 108.19, 117.37, 126.60, 135.76, 144.72, 153.37, 161.60, 169.30, 176.40, 182.82, 188.55, 193.56, 197.83, 201.44, 204.37, 206.72, 208.54, 209.90, 210.88, 211.54, 211.96, 212.22, 212.34, 212.40, 212.41, 212.41, 212.42; - - zt = 5.05, 15.15, 25.25, 35.4, 45.6, 55.85, 66.25, 76.85, 87.65, 98.75, - 110.25, 122.3, 135.15, 149.1, 164.55, 182, 202.15, 225.95, 254.6, 289.55, - 332.5, 385.4, 450.25, 528.9, 622.85, 732.85, 858.9, 1000.25, 1155.4, - 1322.5, 1499.55, 1684.6, 1875.95, 2072.15, 2272, 2474.55, 2679.1, - 2885.15, 3092.3, 3300.25, 3508.75, 3717.65, 3926.85, 4136.25, 4345.85, - 4555.6, 4765.4, 4975.25, 5185.15, 5394.6 ; - - zw = 0, 10.1, 20.2, 30.3, 40.5, 50.7, 61, 71.5, 82.2, 93.1, 104.4, 116.1, - 128.5, 141.8, 156.4, 172.7, 191.3, 213, 238.9, 270.3, 308.8, 356.2, - 414.6, 485.9, 571.9, 673.8, 791.9, 925.9, 1074.6, 1236.2, 1408.8, 1590.3, - 1778.9, 1973, 2171.3, 2372.7, 2576.4, 2781.8, 2988.5, 3196.1, 3404.4, - 3613.1, 3822.2, 4031.5, 4241, 4450.7, 4660.5, 4870.3, 5080.2, 5290.1, - 5499.1 ; -} From 34e665ddaf5fb873523b90e9f1e68a97f719125e Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 4 May 2023 15:22:32 -0600 Subject: [PATCH 089/136] Add inputs for HOT --- .../examples/single_column_MARBL/HOT/INPUT | 1 + .../single_column_MARBL/HOT/MOM_input | 406 +++++++++ .../single_column_MARBL/HOT/MOM_override | 1 + .../single_column_MARBL/HOT/MOM_override2 | 4 + .../HOT/RESTART/.gitignore | 2 + .../single_column_MARBL/HOT/data_table | 19 + .../single_column_MARBL/HOT/diag_table | 771 ++++++++++++++++++ .../single_column_MARBL/HOT/input.nml | 21 + .../examples/single_column_MARBL/HOT/marbl_in | 265 ++++++ 9 files changed, 1490 insertions(+) create mode 120000 standalone/examples/single_column_MARBL/HOT/INPUT create mode 100644 standalone/examples/single_column_MARBL/HOT/MOM_input create mode 120000 standalone/examples/single_column_MARBL/HOT/MOM_override create mode 100644 standalone/examples/single_column_MARBL/HOT/MOM_override2 create mode 100644 standalone/examples/single_column_MARBL/HOT/RESTART/.gitignore create mode 100644 standalone/examples/single_column_MARBL/HOT/data_table create mode 100644 standalone/examples/single_column_MARBL/HOT/diag_table create mode 100644 standalone/examples/single_column_MARBL/HOT/input.nml create mode 100644 standalone/examples/single_column_MARBL/HOT/marbl_in diff --git a/standalone/examples/single_column_MARBL/HOT/INPUT b/standalone/examples/single_column_MARBL/HOT/INPUT new file mode 120000 index 00000000..3a2d233e --- /dev/null +++ b/standalone/examples/single_column_MARBL/HOT/INPUT @@ -0,0 +1 @@ +../INPUT \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/HOT/MOM_input b/standalone/examples/single_column_MARBL/HOT/MOM_input new file mode 100644 index 00000000..ee53ca4d --- /dev/null +++ b/standalone/examples/single_column_MARBL/HOT/MOM_input @@ -0,0 +1,406 @@ +! This input file provides the adjustable run-time parameters for version 6 of the Modular Ocean Model (MOM6). +! Where appropriate, parameters use usually given in MKS units. + +! This particular file is for the example in single_column/KPP. + +! This MOM_input file typically contains only the non-default values that are needed to reproduce this example. +! A full list of parameters for this example can be found in the corresponding MOM_parameter_doc.all file +! which is generated by the model at run-time. + +! === module MOM_domains === +REENTRANT_Y = True ! [Boolean] default = False + ! If true, the domain is meridionally reentrant. +NIGLOBAL = 2 ! + ! The total number of thickness grid points in the x-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NJGLOBAL = 2 ! + ! The total number of thickness grid points in the y-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NIHALO = 2 ! default = 4 + ! The number of halo points on each side in the x-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +NJHALO = 2 ! default = 4 + ! The number of halo points on each side in the y-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +! LAYOUT = 1, 1 ! + ! The processor layout that was actually used. + +! === module MOM === +SPLIT = False ! [Boolean] default = True + ! Use the split time stepping if true. +USE_REGRIDDING = True ! [Boolean] default = False + ! If True, use the ALE algorithm (regridding/remapping). If False, use the + ! layered isopycnal algorithm. +DT = 3600.0 ! [s] + ! The (baroclinic) dynamics time step. The time-step that is actually used will + ! be an integer fraction of the forcing time-step (DT_FORCING in ocean-only mode + ! or the coupling timestep in coupled mode.) +FRAZIL = True ! [Boolean] default = False + ! If true, water freezes if it gets too cold, and the accumulated heat deficit + ! is returned in the surface state. FRAZIL is only used if + ! ENABLE_THERMODYNAMICS is true. +C_P = 3925.0 ! [J kg-1 K-1] default = 3991.86795711963 + ! The heat capacity of sea water, approximated as a constant. This is only used + ! if ENABLE_THERMODYNAMICS is true. The default value is from the TEOS-10 + ! definition of conservative temperature. +SAVE_INITIAL_CONDS = True ! [Boolean] default = False + ! If true, write the initial conditions to a file given by IC_OUTPUT_FILE. + +! === module MOM_fixed_initialization === +USE_MARBL_TRACERS = True + ! "[Boolean] default = False + ! If true, use the MARBL tracer package." +! === module MARBL_tracers === +MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c211008.nc" + ! The file in which the MARBL tracers initial values can be found. +MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211020.nc" + ! The file in which the iron sediment flux forcing field can be found. +MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211020.nc" + ! The file in which the iron vent flux forcing field can be found. + + +! === module MOM_grid_init === +GRID_CONFIG = "cartesian" ! + ! A character string that determines the method for defining the horizontal + ! grid. Current options are: + ! mosaic - read the grid from a mosaic (supergrid) + ! file set by GRID_FILE. + ! cartesian - use a (flat) Cartesian grid. + ! spherical - use a simple spherical grid. + ! mercator - use a Mercator spherical grid. +SOUTHLAT = 22.86669 ! [degrees] + ! The southern latitude of the domain or the equivalent starting value for the + ! y-axis. +LENLAT = 1.0 ! [degrees] + ! The latitudinal or y-direction length of the domain. +WESTLON = -157.99992 ! [degrees] default = 0.0 + ! The western longitude of the domain or the equivalent starting value for the + ! x-axis. +LENLON = 1.0 ! [degrees] + ! The longitudinal or x-direction length of the domain. +TOPO_CONFIG = "file" ! + ! This specifies how bathymetry is specified: + ! file - read bathymetric information from the file + ! specified by (TOPO_FILE). + ! flat - flat bottom set to MAXIMUM_DEPTH. + ! bowl - an analytically specified bowl-shaped basin + ! ranging between MAXIMUM_DEPTH and MINIMUM_DEPTH. + ! spoon - a similar shape to 'bowl', but with an vertical + ! wall at the southern face. + ! halfpipe - a zonally uniform channel with a half-sine + ! profile in the meridional direction. + ! bbuilder - build topography from list of functions. + ! benchmark - use the benchmark test case topography. + ! Neverworld - use the Neverworld test case topography. + ! DOME - use a slope and channel configuration for the + ! DOME sill-overflow test case. + ! ISOMIP - use a slope and channel configuration for the + ! ISOMIP test case. + ! DOME2D - use a shelf and slope configuration for the + ! DOME2D gravity current/overflow test case. + ! Kelvin - flat but with rotated land mask. + ! seamount - Gaussian bump for spontaneous motion test case. + ! dumbbell - Sloshing channel with reservoirs on both ends. + ! shelfwave - exponential slope for shelfwave test case. + ! Phillips - ACC-like idealized topography used in the Phillips config. + ! dense - Denmark Strait-like dense water formation and overflow. + ! USER - call a user modified routine. +TOPO_FILE = "TOPO_FILE_COLUMN.nc" ! default = "topog.nc" + ! The file from which the bathymetry is read. + +MAXIMUM_DEPTH = 6000.0 + ! "[m] + ! The maximum depth of the ocean." + +MINIMUM_DEPTH = 9.5 + ! "[m] default = 0.0 + ! If MASKING_DEPTH is unspecified, then anything shallower than + ! MINIMUM_DEPTH is assumed to be land and all fluxes are masked out. + ! If MASKING_DEPTH is specified, then all depths shallower than + ! MINIMUM_DEPTH but depper than MASKING_DEPTH are rounded to + ! MINIMUM_DEPTH." + +MASKING_DEPTH = 0.0 + ! "[m] default = -9999.0 + ! The depth below which to mask points as land points, for which all + ! fluxes are zeroed out. MASKING_DEPTH is ignored if negative." +ROTATION = "betaplane" ! default = "2omegasinlat" + ! This specifies how the Coriolis parameter is specified: + ! 2omegasinlat - Use twice the planetary rotation rate + ! times the sine of latitude. + ! betaplane - Use a beta-plane or f-plane. + ! USER - call a user modified routine. + +! === module MOM_verticalGrid === +! Parameters providing information about the vertical grid. +NK =65 ! [nondim] + ! The number of model layers. + +! === module MOM_EOS === + +! === module MOM_tracer_flow_control === + +! === module MOM_coord_initialization === +REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" + ! Coordinate mode for vertical regridding. Choose among the following + ! possibilities: LAYER - Isopycnal or stacked shallow water layers + ! ZSTAR, Z* - stretched geopotential z* + ! SIGMA_SHELF_ZSTAR - stretched geopotential z* ignoring shelf + ! SIGMA - terrain following coordinates + ! RHO - continuous isopycnal + ! HYCOM1 - HyCOM-like hybrid coordinate + ! SLIGHT - stretched coordinates above continuous isopycnal + ! ADAPTIVE - optimize for smooth neutral density surfaces +ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_65L_20200626.nc,dz" ! default = "UNIFORM" + ! Determines how to specify the coordinate resolution. Valid options are: + ! PARAM - use the vector-parameter ALE_RESOLUTION + ! UNIFORM[:N] - uniformly distributed + ! FILE:string - read from a file. The string specifies + ! the filename and variable name, separated + ! by a comma or space, e.g. FILE:lev.nc,dz + ! or FILE:lev.nc,interfaces=zw + ! WOA09[:N] - the WOA09 vertical grid (approximately) + ! FNC1:string - FNC1:dz_min,H_total,power,precision + ! HYBRID:string - read from a file. The string specifies + ! the filename and two variable names, separated + ! by a comma or space, for sigma-2 and dz. e.g. + ! HYBRID:vgrid.nc,sigma2,dz +!ALE_RESOLUTION = 4*2.0, 2*2.01, 2*2.02, 2.05, 2.07, 2.09, 2.13, 2.18, 2.24, 2.3, 2.4, 2.5, 2.62, 2.78, 2.95, 3.17, 3.42, 3.71, 4.07, 4.48, 4.97, 5.55, 6.23, 7.04, 7.99, 9.11, 10.43, 11.98, 13.8, 15.94, 18.42, 21.32, 24.66, 28.51, 32.91, 37.92, 43.56, 49.87, 56.88, 64.55, 72.91, 81.87, 91.4, 101.39, 111.73, 122.29, 132.93, 143.47, 153.78, 163.7, 173.07, 181.78, 189.76, 196.89, 203.17, 208.58, 213.14, 216.87, 219.87, 222.19, 223.94, 225.18, 226.04, 226.59, 226.9, 227.07, 227.14, 2*227.16, 227.17 ! [m] + ! The distribution of vertical resolution for the target + ! grid used for Eulerian-like coordinates. For example, + ! in z-coordinate mode, the parameter is a list of level + ! thicknesses (in m). In sigma-coordinate mode, the list + ! is of non-dimensional fractions of the water column. + +! === module MOM_state_initialization === +INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False + ! If true, initialize the layer thicknesses, temperatures, and salinities from a + ! Z-space file on a latitude-longitude grid. + +! === module MOM_initialize_layers_from_Z === +TEMP_SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" + ! The name of the z-space input file used to initialize temperatures (T) and + ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and + ! SALT_Z_INIT_FILE must be set. +Z_INIT_FILE_PTEMP_VAR = "Temp" ! default = "ptemp" + ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. +Z_INIT_FILE_SALT_VAR = "Salt" ! default = "salt" + ! The name of the salinity variable in SALT_Z_INIT_FILE. +Z_INIT_HOMOGENIZE = True ! [Boolean] default = False + ! If True, then horizontally homogenize the interpolated initial conditions. +Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False + ! If True, then remap straight to model coordinate from file. + +! === module MOM_diag_mediator === + +! === module MOM_lateral_mixing_coeffs === + +! === module MOM_set_visc === +HBBL = 10.0 ! [m] + ! The thickness of a bottom boundary layer with a viscosity of KVBBL if + ! BOTTOMDRAGLAW is not defined, or the thickness over which near-bottom + ! velocities are averaged for the drag law if BOTTOMDRAGLAW is defined but + ! LINEAR_DRAG is not. +CDRAG = 0.0 ! [nondim] default = 0.003 + ! CDRAG is the drag coefficient relating the magnitude of the velocity field to + ! the bottom stress. CDRAG is only used if BOTTOMDRAGLAW is defined. +DRAG_BG_VEL = 0.1 ! [m s-1] default = 0.0 + ! DRAG_BG_VEL is either the assumed bottom velocity (with LINEAR_DRAG) or an + ! unresolved velocity that is combined with the resolved velocity to estimate + ! the velocity magnitude. DRAG_BG_VEL is only used when BOTTOMDRAGLAW is + ! defined. +BBL_THICK_MIN = 0.1 ! [m] default = 0.0 + ! The minimum bottom boundary layer thickness that can be used with + ! BOTTOMDRAGLAW. This might be Kv/(cdrag*drag_bg_vel) to give Kv as the minimum + ! near-bottom viscosity. +KV = 1.0E-04 ! [m2 s-1] + ! The background kinematic viscosity in the interior. The molecular value, ~1e-6 + ! m2 s-1, may be used. + +! === module MOM_thickness_diffuse === + +! === module MOM_dynamics_unsplit === + +! === module MOM_continuity === + +! === module MOM_continuity_PPM === +UPWIND_1ST_CONTINUITY = True ! [Boolean] default = False + ! If true, CONTINUITY_PPM becomes a 1st-order upwind continuity solver. This + ! scheme is highly diffusive but may be useful for debugging or in single-column + ! mode where its minimal stencil is useful. +ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.75E-09 + ! The tolerance for the differences between the barotropic and baroclinic + ! estimates of the sea surface height due to the fluxes through each face. The + ! total tolerance for SSH is 4 times this value. The default is + ! 0.5*NK*ANGSTROM, and this should not be set less than about + ! 10^-15*MAXIMUM_DEPTH. +ETA_TOLERANCE_AUX = 0.001 ! [m] default = 1.0E-06 + ! The tolerance for free-surface height discrepancies between the barotropic + ! solution and the sum of the layer thicknesses when calculating the auxiliary + ! corrected velocities. By default, this is the same as ETA_TOLERANCE, but can + ! be made larger for efficiency. + +! === module MOM_CoriolisAdv === +CORIOLIS_EN_DIS = True ! [Boolean] default = False + ! If true, two estimates of the thickness fluxes are used to estimate the + ! Coriolis term, and the one that dissipates energy relative to the other one is + ! used. +BOUND_CORIOLIS = True ! [Boolean] default = False + ! If true, the Coriolis terms at u-points are bounded by the four estimates of + ! (f+rv)v from the four neighboring v-points, and similarly at v-points. This + ! option is always effectively false with CORIOLIS_EN_DIS defined and + ! CORIOLIS_SCHEME set to SADOURNY75_ENERGY. + +! === module MOM_PressureForce === + +! === module MOM_PressureForce_AFV === + +! === module MOM_hor_visc === +BIHARMONIC = False ! [Boolean] default = True + ! If true, use a biharmonic horizontal viscosity. BIHARMONIC may be used with + ! LAPLACIAN. + +! === module MOM_vert_friction === +HARMONIC_VISC = True ! [Boolean] default = False + ! If true, use the harmonic mean thicknesses for calculating the vertical + ! viscosity. +HMIX_FIXED = 0.01 ! [m] + ! The prescribed depth over which the near-surface viscosity and diffusivity are + ! elevated when the bulk mixed layer is not used. +KVML = 0.0 ! [m2 s-1] default = 1.0E-04 + ! The kinematic viscosity in the mixed layer. A typical value is ~1e-2 m2 s-1. + ! KVML is not used if BULKMIXEDLAYER is true. The default is set by KV. +MAXVEL = 6.0 ! [m s-1] default = 3.0E+08 + ! The maximum velocity allowed before the velocity components are truncated. +VEL_UNDERFLOW = 1.0E-30 ! [m s-1] default = 0.0 + ! A negligibly small velocity magnitude below which velocity components are set + ! to 0. A reasonable value might be 1e-30 m/s, which is less than an Angstrom + ! divided by the age of the universe. + +! === module MOM_diagnostics === + +! === module MOM_diabatic_driver === +! The following parameters are used for diabatic processes. + +! === module MOM_CVMix_KPP === +! This is the MOM wrapper to CVMix:KPP +! See http://cvmix.github.io/ +USE_KPP = True ! [Boolean] default = False + ! If true, turns on the [CVMix] KPP scheme of Large et al., 1994, to calculate + ! diffusivities and non-local transport in the OBL. +KPP% +INTERP_TYPE = "cubic" ! default = "quadratic" + ! Type of interpolation to determine the OBL depth. + ! Allowed types are: linear, quadratic, cubic. +NLT_SHAPE = "PARABOLIC" ! default = "CVMix" + ! MOM6 method to set nonlocal transport profile. Over-rides the result from + ! CVMix. Allowed values are: + ! CVMix - Uses the profiles from CVMix specified by MATCH_TECHNIQUE + ! LINEAR - A linear profile, 1-sigma + ! PARABOLIC - A parablic profile, (1-sigma)^2 + ! CUBIC - A cubic profile, (1-sigma)^2(1+2*sigma) + ! CUBIC_LMD - The original KPP profile +%KPP + +! === module MOM_set_diffusivity === +ML_RADIATION = True ! [Boolean] default = False + ! If true, allow a fraction of TKE available from wind work to penetrate below + ! the base of the mixed layer with a vertical decay scale determined by the + ! minimum of: (1) The depth of the mixed layer, (2) an Ekman length scale. +TKE_DECAY = 10.0 ! [nondim] default = 2.5 + ! The ratio of the natural Ekman depth to the TKE decay scale. +ML_OMEGA_FRAC = 1.0 ! [nondim] default = 0.0 + ! When setting the decay scale for turbulence, use this fraction of the absolute + ! rotation rate blended with the local value of f, as sqrt((1-of)*f^2 + + ! of*4*omega^2). + +! === module MOM_bkgnd_mixing === +! Adding static vertical background mixing coefficients +KD = 2.0E-05 ! [m2 s-1] default = 0.0 + ! The background diapycnal diffusivity of density in the interior. Zero or the + ! molecular value, ~1e-7 m2 s-1, may be used. +KD_MIN = 2.0E-06 ! [m2 s-1] default = 2.0E-07 + ! The minimum diapycnal diffusivity. + +! === module MOM_kappa_shear === +! Parameterization of shear-driven turbulence following Jackson, Hallberg and Legg, JPO 2008 +USE_JACKSON_PARAM = True ! [Boolean] default = False + ! If true, use the Jackson-Hallberg-Legg (JPO 2008) shear mixing + ! parameterization. +MAX_RINO_IT = 25 ! [nondim] default = 50 + ! The maximum number of iterations that may be used to estimate the Richardson + ! number driven mixing. + +! === module MOM_diabatic_aux === +! The following parameters are used for auxiliary diabatic processes. +RECLAIM_FRAZIL = False ! [Boolean] default = True + ! If true, try to use any frazil heat deficit to cool any overlying layers down + ! to the freezing point, thereby avoiding the creation of thin ice when the SST + ! is above the freezing point. +VAR_PEN_SW = True ! [Boolean] default = False + ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine + ! the e-folding depth of incoming short wave radiation. + +! === module MOM_opacity === +PEN_SW_NBANDS = 3 ! default = 1 + ! The number of bands of penetrating shortwave radiation. + +! === module MOM_tracer_advect === + +! === module MOM_tracer_hor_diff === +KHTR = 600.0 ! [m2 s-1] default = 0.0 + ! The background along-isopycnal tracer diffusivity. + +! === module MOM_sum_output === +MAXTRUNC = 5000 ! [truncations save_interval-1] default = 0 + ! The run will be stopped, and the day set to a very large value if the velocity + ! is truncated more than MAXTRUNC times between energy saves. Set MAXTRUNC to 0 + ! to stop if there is any truncation of velocities. +ENERGYSAVEDAYS = 10.0 ! [days] default = 1.0 + ! The interval in units of TIMEUNIT between saves of the energies of the run and + ! other globally summed diagnostics. + +! === module MOM_surface_forcing === +BUOY_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how buoyancy forcing is specified. Valid + ! options include (file), (zero), (linear), (USER), (BFB) and (NONE). +WIND_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how wind forcing is specified. Valid + ! options include (file), (2gyre), (1gyre), (gyres), (zero), and (USER). +READ_GUST_2D = False ! [Boolean] default = False + ! If true, use a 2-dimensional gustiness supplied from an input file +GUST_CONST = 0.02 ! [Pa] default = 0.0 + ! The background gustiness in the winds. + +! === module MOM_main (MOM_driver) === +DAYMAX = 1825.0 ! [days] + ! The final time of the whole simulation, in units of TIMEUNIT seconds. This + ! also sets the potential end time of the present run segment if the end time is + ! not set via ocean_solo_nml in input.nml. +RESTART_CONTROL = 0 ! default = 1 + ! An integer whose bits encode which restart files are written. Add 2 (bit 1) + ! for a time-stamped file, and odd (bit 0) for a non-time-stamped file. A + ! non-time-stamped restart file is saved at the end of the run segment for any + ! non-negative value. +RESTINT = 3650.0 ! [days] default = 0.0 + ! The interval between saves of the restart file in units of TIMEUNIT. Use 0 + ! (the default) to not save incremental restart files at all. + +! === module MOM_write_cputime === +MAXCPU = 2.88E+04 ! [wall-clock seconds] default = -1.0 + ! The maximum amount of cpu time per processor for which MOM should run before + ! saving a restart file and quitting with a return value that indicates that a + ! further run is required to complete the simulation. If automatic restarts are + ! not desired, use a negative value for MAXCPU. MAXCPU has units of wall-clock + ! seconds, so the actual CPU time used is larger by a factor of the number of + ! processors used. + +! Debugging parameters set to non-default values +U_TRUNC_FILE = "U_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to zonal + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. +V_TRUNC_FILE = "V_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to meridional + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. diff --git a/standalone/examples/single_column_MARBL/HOT/MOM_override b/standalone/examples/single_column_MARBL/HOT/MOM_override new file mode 120000 index 00000000..3d53a3d8 --- /dev/null +++ b/standalone/examples/single_column_MARBL/HOT/MOM_override @@ -0,0 +1 @@ +INPUT/CESM_MOM_COLUMNS/HOT/MOM_override \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/HOT/MOM_override2 b/standalone/examples/single_column_MARBL/HOT/MOM_override2 new file mode 100644 index 00000000..94a755f0 --- /dev/null +++ b/standalone/examples/single_column_MARBL/HOT/MOM_override2 @@ -0,0 +1,4 @@ +! Generated by run_suite.csh +!#override DAYMAX = 365 +!#override ALE_COORDINATE_CONFIG = "FILE:INPUT/vgrid_cm4_4.nc,dz" ! 4 meter surface +!#override ALE_COORDINATE_CONFIG = "FILE:INPUT/vgrid_cm4.nc,dz" ! 2 meter surface diff --git a/standalone/examples/single_column_MARBL/HOT/RESTART/.gitignore b/standalone/examples/single_column_MARBL/HOT/RESTART/.gitignore new file mode 100644 index 00000000..d6b7ef32 --- /dev/null +++ b/standalone/examples/single_column_MARBL/HOT/RESTART/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/standalone/examples/single_column_MARBL/HOT/data_table b/standalone/examples/single_column_MARBL/HOT/data_table new file mode 100644 index 00000000..a9ae7a83 --- /dev/null +++ b/standalone/examples/single_column_MARBL/HOT/data_table @@ -0,0 +1,19 @@ +# gridname, fieldname_code, fieldname_file, file_name, ongrid, factor +"OCN" , "lw", "LWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sw", "SWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "rain", "RAIN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "evap", "EVAP", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "taux", "TAUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "tauy", "TAUY", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "snow", "SNOW", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 +"OCN" , "runoff", "", "", .true., 0.0 +"OCN" , "calving", "", "", .true., 0.0 +"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_dust_flux", "SEAICE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_bc_flux", "SEAICE_BC_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "nhx_dep", "NDEP_NHx", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "noy_dep", "NDEP_NOy", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "u10_sqr", "U10_SQR", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 diff --git a/standalone/examples/single_column_MARBL/HOT/diag_table b/standalone/examples/single_column_MARBL/HOT/diag_table new file mode 100644 index 00000000..8f6227fa --- /dev/null +++ b/standalone/examples/single_column_MARBL/HOT/diag_table @@ -0,0 +1,771 @@ +"MOM Experiment" +1 1 1 0 0 0 +"prog", 1,"days",1,"days","Time" +"prog_z", 1,"days",1,"days","Time" +"surffluxes", 1,"days",1,"days","Time" +"ave_prog", 1,"days",1,"days","Time", +#"mom", 5,"days",1,"days","Time", +#"bt_mom", 5,"days",1,"days","Time", +"visc", 1,"days",1,"days","Time", +#"energy", 5,"days",1,"days","Time", +"marbl", 1,"days",1,"days","Time", +"marbl2", 1,"days",1,"days","Time", + + +#This is the field section of the diag_table. + +# Prognostic Ocean fields: +#========================= + +"ocean_model","u","u","prog","all",.false.,"none",2 +"ocean_model","v","v","prog","all",.false.,"none",2 +"ocean_model","h","h","prog","all",.false.,"none",1 +"ocean_model","e","e","prog","all",.false.,"none",2 +"ocean_model","temp","temp","prog","all",.false.,"none",2 +"ocean_model","salt","salt","prog","all",.false.,"none",2 +#"ocean_model","Rml","Rml","prog","all",.false.,"none",2 +#"ocean_model","h_ML","h_ML","prog","all",.false.,"none",2 + +#"ocean_model","RV","RV","prog","all",.false.,"none",2 +#"ocean_model","PV","PV","prog","all",.false.,"none",2 +#"ocean_model","e_D","e_D","prog","all",.false.,"none",2 + +"ocean_model","u","u","ave_prog","all",.true.,"none",2 +"ocean_model","v","v","ave_prog","all",.true.,"none",2 +#"ocean_model","h","h","ave_prog","all",.true.,"none",1 +"ocean_model","e","e","ave_prog","all",.true.,"none",2 +"ocean_model","temp","temp","ave_prog","all",.true.,"none",2 +"ocean_model","salt","salt","ave_prog","all",.true.,"none",2 +#"ocean_model","Rml","Rml","ave_prog","all",.true.,"none",2 + +# Z level output +#================ +"ocean_model_z","u","u","prog_z","all",.false.,"none",2 +"ocean_model_z","v","v","prog_z","all",.false.,"none",2 +"ocean_model_z","temp","temp","prog_z","all",.false.,"none",2 +"ocean_model_z","salt","salt","prog_z","all",.false.,"none",2 +# Auxilary Tracers: +#================== +#"ocean_model","vintage","vintage","prog","all",.false.,"none",2 +#"ocean_model","age","age","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","prog","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog","all",.false.,"none",2 +"ocean_model","O2","O2","prog","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog","all",.false.,"none",2 +"ocean_model","DON","DON","prog","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog","all",.false.,"none",2 +"ocean_model","spC","spC","prog","all",.false.,"none",2 +"ocean_model","spP","spP","prog","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","ave_prog","all",.true.,"none",2 +"ocean_model","NO3","NO3","ave_prog","all",.true.,"none",2 +"ocean_model","SiO3","SiO3","ave_prog","all",.true.,"none",2 +"ocean_model","NH4","NH4","ave_prog","all",.true.,"none",2 +"ocean_model","Fe","Fe","ave_prog","all",.true.,"none",2 +"ocean_model","Lig","Lig","ave_prog","all",.true.,"none",2 +"ocean_model","O2","O2","ave_prog","all",.true.,"none",2 +"ocean_model","DIC","DIC","ave_prog","all",.true.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","ALK","ALK","ave_prog","all",.true.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","DOC","DOC","ave_prog","all",.true.,"none",2 +"ocean_model","DON","DON","ave_prog","all",.true.,"none",2 +"ocean_model","DOP","DOP","ave_prog","all",.true.,"none",2 +"ocean_model","DOPr","DOPr","ave_prog","all",.true.,"none",2 +"ocean_model","DONr","DONr","ave_prog","all",.true.,"none",2 +"ocean_model","DOCr","DOCr","ave_prog","all",.true.,"none",2 +"ocean_model","microzooC","microzooC","ave_prog","all",.true.,"none",2 +"ocean_model","mesozooC","mesozooC","ave_prog","all",.true.,"none",2 +"ocean_model","spChl","spChl","ave_prog","all",.true.,"none",2 +"ocean_model","spC","spC","ave_prog","all",.true.,"none",2 +"ocean_model","spP","spP","ave_prog","all",.true.,"none",2 +"ocean_model","spFe","spFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoChl","coccoChl","ave_prog","all",.true.,"none",2 +"ocean_model","coccoC","coccoC","ave_prog","all",.true.,"none",2 +"ocean_model","coccoP","coccoP","ave_prog","all",.true.,"none",2 +"ocean_model","coccoFe","coccoFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","ave_prog","all",.true.,"none",2 +"ocean_model","diatChl","diatChl","ave_prog","all",.true.,"none",2 +"ocean_model","diatC","diatC","ave_prog","all",.true.,"none",2 +"ocean_model","diatP","diatP","ave_prog","all",.true.,"none",2 +"ocean_model","diatFe","diatFe","ave_prog","all",.true.,"none",2 +"ocean_model","diatSi","diatSi","ave_prog","all",.true.,"none",2 +"ocean_model","diazChl","diazChl","ave_prog","all",.true.,"none",2 +"ocean_model","diazC","diazC","ave_prog","all",.true.,"none",2 +"ocean_model","diazP","diazP","ave_prog","all",.true.,"none",2 +"ocean_model","diazFe","diazFe","ave_prog","all",.true.,"none",2 +"ocean_model","PO4","PO4","prog_z","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog_z","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog_z","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog_z","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog_z","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog_z","all",.false.,"none",2 +"ocean_model","O2","O2","prog_z","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog_z","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog_z","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog_z","all",.false.,"none",2 +"ocean_model","DON","DON","prog_z","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog_z","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog_z","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog_z","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog_z","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog_z","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog_z","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog_z","all",.false.,"none",2 +"ocean_model","spC","spC","prog_z","all",.false.,"none",2 +"ocean_model","spP","spP","prog_z","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog_z","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog_z","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog_z","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog_z","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog_z","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog_z","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog_z","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog_z","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog_z","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog_z","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog_z","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog_z","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog_z","all",.false.,"none",2 + + +# Continuity Equation Terms: +#=========================== +#"ocean_model","dhdt","dhdt","cont","all",.true.,"none",2 +"ocean_model","wd","wd","prog","all",.false.,"none",2 +#"ocean_model","uh","uh","cont","all",.true.,"none",2 +#"ocean_model","vh","vh","cont","all",.true.,"none",2 +#"ocean_model","uhGM","uhGM","cont","all",.true.,"none",2 +#"ocean_model","vhGM","vhGM","cont","all",.true.,"none",2 +#"ocean_model","uhbt","uhbt","cont","all",.true.,"none",2 +#"ocean_model","vhbt","vhbt","cont","all",.true.,"none",2 +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","KH_u","KH_u","cont","all",.true.,"none",2 +#"ocean_model","KH_v","KH_v","cont","all",.true.,"none",2 +#"ocean_model","GMwork","GMwork","cont","all",.true.,"none",2 + +# Continuity Equation Terms In Pure Potential Density Coordiantes: +#================================================================= +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 + +# +# Tracer Fluxes: +#================== +#"ocean_model","T_adx", "T_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_ady", "T_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffx","T_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffy","T_diffy","ave_prog","all",.true.,"none",2 +#"ocean_model","S_adx", "S_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_ady", "S_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffx","S_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffy","S_diffy","ave_prog","all",.true.,"none",2 + + +# Momentum Balance Terms: +#======================= +#"ocean_model","dudt","dudt","mom","all",.true.,"none",2 +#"ocean_model","dvdt","dvdt","mom","all",.true.,"none",2 +#"ocean_model","CAu","CAu","mom","all",.true.,"none",2 +#"ocean_model","CAv","CAv","mom","all",.true.,"none",2 +#"ocean_model","PFu","PFu","mom","all",.true.,"none",2 +#"ocean_model","PFv","PFv","mom","all",.true.,"none",2 +#"ocean_model","du_dt_visc","du_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","dv_dt_visc","dv_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","diffu","diffu","mom","all",.true.,"none",2 +#"ocean_model","diffv","diffv","mom","all",.true.,"none",2 +#"ocean_model","dudt_dia","dudt_dia","mom","all",.true.,"none",2 +#"ocean_model","dvdt_dia","dvdt_dia","mom","all",.true.,"none",2 +# Subterms that should not be added to a closed budget. +#"ocean_model","gKEu","gKEu","mom","all",.true.,"none",2 +#"ocean_model","gKEv","gKEv","mom","all",.true.,"none",2 +#"ocean_model","rvxu","rvxu","mom","all",.true.,"none",2 +#"ocean_model","rvxv","rvxv","mom","all",.true.,"none",2 +#"ocean_model","PFu_bc","PFu_bc","mom","all",.true.,"none",2 +#"ocean_model","PFv_bc","PFv_bc","mom","all",.true.,"none",2 + +# Barotropic Momentum Balance Terms: +# (only available with split time stepping.) +#=========================================== +#"ocean_model","PFuBT","PFuBT","bt_mom","all",.true.,"none",2 +#"ocean_model","PFvBT","PFvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CoruBT","CoruBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CorvBT","CorvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","ubtforce","ubtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","vbtforce","vbtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","u_accel_bt","u_accel_bt","bt_mom","all",.true.,"none",2 +#"ocean_model","v_accel_bt","v_accel_bt","bt_mom","all",.true.,"none",2 +# +# Viscosities and diffusivities: +#=============================== +#"ocean_model","Kd_effective","Kd_effective","visc","all",.true.,"none",2 +"ocean_model","Kd_interface","Kd_interface","visc","all",.true.,"none",2 +#"ocean_model","Ahh","Ahh","visc","all",.true.,"none",2 +#"ocean_model","Ahq","Ahq","visc","all",.true.,"none",2 +#"ocean_model","Khh","Khh","visc","all",.true.,"none",2 +#"ocean_model","Khq","Khq","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_u","bbl_thick_u","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_u","kv_bbl_u","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_v","bbl_thick_v","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_v","kv_bbl_v","visc","all",.true.,"none",2 +#"ocean_model","av_visc","av_visc","visc","all",.true.,"none",2 +#"ocean_model","au_visc","au_visc","visc","all",.true.,"none",2 +#"ocean_model","FrictWork","FrictWork","visc","all",.true.,"none",2 +"ocean_model","KPP_uStar","KPP_uStar","visc","all",.true.,"none",2 +"ocean_model","KPP_buoyFlux","KPP_buoyFlux","visc","all",.true.,"none",2 +"ocean_model","KPP_QminusSW","KPP_QminusSW","visc","all",.true.,"none",2 +"ocean_model","KPP_netSalt","KPP_netSalt","visc","all",.true.,"none",2 +"ocean_model","KPP_OBLdepth","KPP_OBLdepth","visc","all",.true.,"none",2 +"ocean_model","KPP_sigma","KPP_sigma","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkRi","KPP_BulkRi","visc","all",.true.,"none",2 +"ocean_model","KPP_Ws","KPP_Ws","visc","all",.true.,"none",2 +"ocean_model","KPP_N","KPP_N","visc","all",.true.,"none",2 +"ocean_model","KPP_N2","KPP_N2","visc","all",.true.,"none",2 +"ocean_model","KPP_Vt2","KPP_Vt2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkUz2","KPP_BulkUz2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkDrho","KPP_BulkDrho","visc","all",.true.,"none",2 +"ocean_model","KPP_Kd_in","KPP_Kd_in","visc","all",.true.,"none",2 +"ocean_model","KPP_Kheat","KPP_Kheat","visc","all",.true.,"none",2 +"ocean_model","KPP_Ksalt","KPP_Ksalt","visc","all",.true.,"none",2 +"ocean_model","KPP_Kv","KPP_Kv","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_heat","KPP_NLtransport_heat","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_salt","KPP_NLtransport_salt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dTdt","KPP_NLT_dTdt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dSdt","KPP_NLT_dSdt","visc","all",.true.,"none",2 +"ocean_model","KPP_Tsurf","KPP_Tsurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Ssurf","KPP_Ssurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Usurf","KPP_Usurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Vsurf","KPP_Vsurf","visc","all",.true.,"none",2 +"ocean_model","MLD_003","MLD_003","visc","all",.true.,"none",2 + +# +# Kinetic Energy Balance Terms: +#============================= +#"ocean_model","KE","KE","energy","all",.true.,"none",2 +#"ocean_model","dKE_dt","dKE_dt","energy","all",.true.,"none",2 +#"ocean_model","PE_to_KE","PE_to_KE","energy","all",.true.,"none",2 +#"ocean_model","KE_Coradv","KE_Coradv","energy","all",.true.,"none",2 +#"ocean_model","KE_adv","KE_adv","energy","all",.true.,"none",2 +#"ocean_model","KE_visc","KE_visc","energy","all",.true.,"none",2 +#"ocean_model","KE_horvisc","KE_horvisc","energy","all",.true.,"none",2 +#"ocean_model","KE_dia","KE_dia","energy","all",.true.,"none",2 + +# Surface Forcing: +#================= +"ocean_model","taux","taux","surffluxes","all",.true.,"none",2 +"ocean_model","tauy","tauy","surffluxes","all",.true.,"none",2 +"ocean_model","ustar","ustar","surffluxes","all",.true.,"none",2 +"ocean_model","PRCmE","PRCmE","surffluxes","all",.true.,"none",2 +"ocean_model","evap","evap","surffluxes","all",.true.,"none",2 +"ocean_model","precip","precip","surffluxes","all",.true.,"none",2 +"ocean_model","fprec","fprec","surffluxes","all",.true.,"none",2 +"ocean_model","lprec","lprec","surffluxes","all",.true.,"none",2 +"ocean_model","net_heat_surface","net_heat_surface","surffluxes","all",.true.,"none",2 +"ocean_model","SW","SW","surffluxes","all",.true.,"none",2 +"ocean_model","SW_pen","SW_pen","surffluxes","all",.true.,"none",2 +"ocean_model","LwLatSens","LwLatSens","surffluxes","all",.true.,"none",2 +#"ocean_model","p_surf","p_surf","surffluxes","all",.true.,"none",2 +#"ocean_model","salt_flux","salt_flux","surffluxes","all",.true.,"none",2 + +# MARBL diagnostics: +#=================== +"ocean_model", "rsdoabsorb", "rsdoabsorb", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_IFRAC", "ECOSYS_IFRAC", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_XKW", "ECOSYS_XKW", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_ATM_PRESS", "ECOSYS_ATM_PRESS", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_O2", "PV_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_O2", "SCHMIDT_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "O2SAT", "O2SAT", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR", "CO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR", "DCO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "pCO2SURF", "pCO2SURF", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2", "DpCO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_CO2", "PV_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_CO2", "SCHMIDT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_CO2", "FG_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH", "PH", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_CO2", "ATM_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR_ALT_CO2", "CO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR_ALT_CO2", "DCO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pCO2SURF_ALT_CO2", "pCO2SURF_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2_ALT_CO2", "DpCO2_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_ALT_CO2", "FG_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH_ALT_CO2", "PH_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_ALT_CO2", "ATM_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "IRON_FLUX", "IRON_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "DUST_FLUX", "DUST_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NOx_FLUX", "NOx_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHy_FLUX", "NHy_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHx_SURFACE_EMIS", "NHx_SURFACE_EMIS", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatcalc", "zsatcalc", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatarag", "zsatarag", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN", "O2_ZMIN", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN_DEPTH", "O2_ZMIN_DEPTH", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint", "photoC_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint_100m", "photoC_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint", "photoC_NO3_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint_100m", "photoC_NO3_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint", "DOC_prod_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint_100m", "DOC_prod_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint", "DOC_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint_100m", "DOC_remin_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint", "DOCr_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint_100m", "DOCr_remin_zint_100m", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ctot", "Jint_Ctot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ntot", "Jint_Ntot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ptot", "Jint_Ptot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Sitot", "Jint_Sitot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Fetot", "Jint_Fetot", "marbl", "all", .false., "none", 2 +#"ocean_model", "calcToFloor", "calcToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed", "calcToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed_ALT_CO2", "calcToSed_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pocToFloor", "pocToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "pocToSed", "pocToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "ponToSed", "ponToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "SedDenitrif", "SedDenitrif", "marbl", "all", .false., "none", 2 +#"ocean_model", "OtherRemin", "OtherRemin", "marbl", "all", .false., "none", 2 +"ocean_model", "popToSed", "popToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "bsiToSed", "bsiToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "dustToSed", "dustToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "pfeToSed", "pfeToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_surf", "sp_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_Cweight_avg_100m", "sp_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_surf", "sp_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_Cweight_avg_100m", "sp_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_surf", "sp_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_Cweight_avg_100m", "sp_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_surf", "sp_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_Cweight_avg_100m", "sp_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint", "photoC_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint_100m", "photoC_sp_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_sp_zint", "photoC_NO3_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint", "graze_sp_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint_100m", "graze_sp_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint", "graze_sp_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint_100m", "graze_sp_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint", "graze_sp_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint_100m", "graze_sp_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint", "graze_sp_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint_100m", "graze_sp_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint", "sp_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint_100m", "sp_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint", "sp_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint_100m", "sp_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint", "sp_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint_100m", "sp_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint", "sp_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint_100m", "sp_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_surf", "cocco_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_Cweight_avg_100m", "cocco_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_surf", "cocco_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_Cweight_avg_100m", "cocco_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_surf", "cocco_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_Cweight_avg_100m", "cocco_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_surf", "cocco_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_Cweight_avg_100m", "cocco_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint", "photoC_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint_100m", "photoC_cocco_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_cocco_zint", "photoC_NO3_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint", "cocco_CaCO3_form_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint_100m", "cocco_CaCO3_form_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint", "graze_cocco_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint_100m", "graze_cocco_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint", "graze_cocco_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint_100m", "graze_cocco_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint", "graze_cocco_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint_100m", "graze_cocco_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint", "graze_cocco_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint_100m", "graze_cocco_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint", "cocco_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint_100m", "cocco_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint", "cocco_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint_100m", "cocco_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint", "cocco_loss_doc_zint", 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"photoFe_sp", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNO3_sp", "photoNO3_sp", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNH4_sp", "photoNH4_sp", "marbl", "all", .false., "none", 2 +"ocean_model", "DOP_sp_uptake", "DOP_sp_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "PO4_sp_uptake", "PO4_sp_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp", "graze_sp", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc", "graze_sp_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc", "graze_sp_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zootot", "graze_sp_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_microzoo", "graze_sp_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_mesozoo", "graze_sp_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "sp_loss", "sp_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc", "sp_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc", "sp_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg", "sp_agg", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Qp", "cocco_Qp", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco", "photoC_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_cocco", "photoC_NO3_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "photoFe_cocco", "photoFe_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNO3_cocco", "photoNO3_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNH4_cocco", "photoNH4_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "DOP_cocco_uptake", "DOP_cocco_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "PO4_cocco_uptake", "PO4_cocco_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_cocco", "graze_cocco", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc", "graze_cocco_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc", "graze_cocco_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zootot", "graze_cocco_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_microzoo", "graze_cocco_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_mesozoo", "graze_cocco_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "cocco_loss", "cocco_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc", "cocco_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc", "cocco_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg", "cocco_agg", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form", "cocco_CaCO3_form", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Qp", "diat_Qp", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat", "photoC_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diat", "photoC_NO3_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "photoFe_diat", "photoFe_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNO3_diat", "photoNO3_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNH4_diat", "photoNH4_diat", "marbl", "all", .false., "none", 2 +"ocean_model", "DOP_diat_uptake", "DOP_diat_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "PO4_diat_uptake", "PO4_diat_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat", "graze_diat", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc", "graze_diat_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc", "graze_diat_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zootot", "graze_diat_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_microzoo", "graze_diat_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_mesozoo", "graze_diat_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "diat_loss", "diat_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc", "diat_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc", "diat_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_agg", "diat_agg", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_bSi_form", "diat_bSi_form", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Qp", "diaz_Qp", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diaz", "photoC_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diaz", "photoC_NO3_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "photoFe_diaz", "photoFe_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNO3_diaz", "photoNO3_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNH4_diaz", "photoNH4_diaz", "marbl", "all", .false., "none", 2 +"ocean_model", "DOP_diaz_uptake", "DOP_diaz_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "PO4_diaz_uptake", "PO4_diaz_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz", "graze_diaz", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc", "graze_diaz_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc", "graze_diaz_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zootot", "graze_diaz_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_microzoo", "graze_diaz_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_mesozoo", "graze_diaz_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "diaz_loss", "diaz_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_poc", "diaz_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_doc", "diaz_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_agg", "diaz_agg", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Nfix", "diaz_Nfix", "marbl", "all", .false., "none", 2 +"ocean_model", "bSi_form", "bSi_form", "marbl", "all", .false., "none", 2 +"ocean_model", "CaCO3_form", "CaCO3_form", "marbl", "all", .false., "none", 2 +"ocean_model", "Nfix", "Nfix", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss", "microzoo_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss", "mesozoo_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_basal", "microzoo_loss_basal", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_basal", "mesozoo_loss_basal", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_poc", "microzoo_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_poc", "mesozoo_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_doc", "microzoo_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_doc", "mesozoo_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_microzoo", "graze_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo", "graze_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_poc", "graze_microzoo_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_poc", "graze_mesozoo_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_doc", "graze_microzoo_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_doc", "graze_mesozoo_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_zootot", "graze_microzoo_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_zootot", "graze_mesozoo_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_microzoo", "graze_microzoo_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_mesozoo", "graze_microzoo_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_microzoo", "graze_mesozoo_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_mesozoo", "graze_mesozoo_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_microzoo", "x_graze_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_mesozoo", "x_graze_mesozoo", "marbl2", "all", .false., "none", 2 +#"ocean_model", "PO4_RESTORE_TEND", "PO4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NO3_RESTORE_TEND", "NO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "SiO3_RESTORE_TEND", "SiO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NH4_RESTORE_TEND", "NH4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Fe_RESTORE_TEND", "Fe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Lig_RESTORE_TEND", "Lig_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "O2_RESTORE_TEND", "O2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_RESTORE_TEND", "DIC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_ALT_CO2_RESTORE_TEND", "DIC_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_RESTORE_TEND", "ALK_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_ALT_CO2_RESTORE_TEND", "ALK_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOC_RESTORE_TEND", "DOC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DON_RESTORE_TEND", "DON_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOP_RESTORE_TEND", "DOP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOPr_RESTORE_TEND", "DOPr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DONr_RESTORE_TEND", "DONr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOCr_RESTORE_TEND", "DOCr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "microzooC_RESTORE_TEND", "microzooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "mesozooC_RESTORE_TEND", "mesozooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spChl_RESTORE_TEND", "spChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spC_RESTORE_TEND", "spC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spP_RESTORE_TEND", "spP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spFe_RESTORE_TEND", "spFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoChl_RESTORE_TEND", "coccoChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoC_RESTORE_TEND", "coccoC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoP_RESTORE_TEND", "coccoP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoFe_RESTORE_TEND", "coccoFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoCaCO3_RESTORE_TEND", "coccoCaCO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatChl_RESTORE_TEND", "diatChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatC_RESTORE_TEND", "diatC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatP_RESTORE_TEND", "diatP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatFe_RESTORE_TEND", "diatFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatSi_RESTORE_TEND", "diatSi_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazChl_RESTORE_TEND", "diazChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazC_RESTORE_TEND", "diazC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazP_RESTORE_TEND", "diazP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazFe_RESTORE_TEND", "diazFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 + +#============================================================================================= +# +#====> This file can be used with diag_manager/v2.0a (or higher) <==== +# +# +# FORMATS FOR FILE ENTRIES (not all input values are used) +# ------------------------ +# +#"file_name", output_freq, "output_units", format, "time_units", "time_long_name", ... +# (opt) new_file_frequecy, (opt) "new_file_freq_units", "new_file_start_date" +# +# +#output_freq: > 0 output frequency in "output_units" +# = 0 output frequency every time step +# =-1 output frequency at end of run +# +#output_units = units used for output frequency +# (years, months, days, minutes, hours, seconds) +# +#time_units = units used to label the time axis +# (days, minutes, hours, seconds) +# +# +# FORMAT FOR FIELD ENTRIES (not all input values are used) +# ------------------------ +# +#"module_name", "field_name", "output_name", "file_name" "time_sampling", time_avg, "other_opts", packing +# +#time_avg = .true. or .false. +# +#packing = 1 double precision +# = 2 float +# = 4 packed 16-bit integers +# = 8 packed 1-byte (not tested?) diff --git a/standalone/examples/single_column_MARBL/HOT/input.nml b/standalone/examples/single_column_MARBL/HOT/input.nml new file mode 100644 index 00000000..01e68431 --- /dev/null +++ b/standalone/examples/single_column_MARBL/HOT/input.nml @@ -0,0 +1,21 @@ + &MOM_input_nml + output_directory = './', + input_filename = 'n' + restart_input_dir = 'INPUT/', + restart_output_dir = 'RESTART/', + parameter_filename = 'MOM_input', + 'MOM_override', + 'MOM_override2' / + + &diag_manager_nml + max_output_fields = 750, + / + + &ocean_solo_nml + calendar = 'NOLEAP' +/ + + &fms_nml + clock_grain = 'LOOP', + domains_stack_size = 710000, + stack_size = 0 / diff --git a/standalone/examples/single_column_MARBL/HOT/marbl_in b/standalone/examples/single_column_MARBL/HOT/marbl_in new file mode 100644 index 00000000..f4c8add0 --- /dev/null +++ b/standalone/examples/single_column_MARBL/HOT/marbl_in @@ -0,0 +1,265 @@ +f_graze_CaCO3_remin = 0.5 +QCaCO3_max = 1.0 + +zoo_mort2_exp = 2.0 + +PFT_defaults = 'user-specified' +autotroph_cnt = 4 +zooplankton_cnt = 2 +max_grazer_prey_cnt = 4 + +! autotrophs +autotroph_settings(1)%Ea = 0.32 +autotroph_settings(1)%Nfixer = .false. +autotroph_settings(1)%PCref_per_day = 4.8 +autotroph_settings(1)%Qp_fixed = 8.5470085470085479e-03 +autotroph_settings(1)%agg_rate_max = 0.5 +autotroph_settings(1)%agg_rate_min = 0.01 +autotroph_settings(1)%alphaPI_per_day = 0.35 +autotroph_settings(1)%exp_calcifier = .false. +autotroph_settings(1)%gQfe_0 = 3.0000000000000001e-05 +autotroph_settings(1)%gQfe_min = 2.5e-06 +autotroph_settings(1)%imp_calcifier = .false. +autotroph_settings(1)%is_carbon_limited = .false. +autotroph_settings(1)%kCO2 = 0 +autotroph_settings(1)%kDOP = 0.3 +autotroph_settings(1)%kFe = 3e-05 +autotroph_settings(1)%kNH4 = 0.01 +autotroph_settings(1)%kNO3 = 0.2 +autotroph_settings(1)%kPO4 = 0.01 +autotroph_settings(1)%kSiO3 = 0 +autotroph_settings(1)%lname = "Small Phyto" +autotroph_settings(1)%loss_poc = 0 +autotroph_settings(1)%loss_thres = 0.01 +autotroph_settings(1)%loss_thres2 = 0 +autotroph_settings(1)%mort2_per_day = 0.01 +autotroph_settings(1)%mort_per_day = 0.1 +autotroph_settings(1)%silicifier = .false. +autotroph_settings(1)%sname = "sp" +autotroph_settings(1)%temp_func_form_opt = "q_10" +autotroph_settings(1)%temp_thres = -10 +autotroph_settings(1)%thetaN_max = 2.5 +autotroph_settings(2)%Ea = 0.32 +autotroph_settings(2)%Nfixer = .false. +autotroph_settings(2)%PCref_per_day = 5.3 +autotroph_settings(2)%Qp_fixed = 8.5470085470085479e-03 +autotroph_settings(2)%agg_rate_max = 0.5 +autotroph_settings(2)%agg_rate_min = 0.02 +autotroph_settings(2)%alphaPI_per_day = 0.39 +autotroph_settings(2)%exp_calcifier = .false. +autotroph_settings(2)%gQfe_0 = 3.0000000000000001e-05 +autotroph_settings(2)%gQfe_min = 2.5e-06 +autotroph_settings(2)%imp_calcifier = .false. +autotroph_settings(2)%is_carbon_limited = .false. +autotroph_settings(2)%kCO2 = 0 +autotroph_settings(2)%kDOP = 0.5 +autotroph_settings(2)%kFe = 5e-05 +autotroph_settings(2)%kNH4 = 0.05 +autotroph_settings(2)%kNO3 = 0.4 +autotroph_settings(2)%kPO4 = 0.05 +autotroph_settings(2)%kSiO3 = 1.6 +autotroph_settings(2)%lname = "Diatom" +autotroph_settings(2)%loss_poc = 0 +autotroph_settings(2)%loss_thres = 0.02 +autotroph_settings(2)%loss_thres2 = 0 +autotroph_settings(2)%mort2_per_day = 0.01 +autotroph_settings(2)%mort_per_day = 0.1 +autotroph_settings(2)%silicifier = .true. +autotroph_settings(2)%sname = "diat" +autotroph_settings(2)%temp_func_form_opt = "q_10" +autotroph_settings(2)%temp_thres = -10 +autotroph_settings(2)%thetaN_max = 4 +autotroph_settings(3)%Ea = 0.32 +autotroph_settings(3)%Nfixer = .true. +autotroph_settings(3)%PCref_per_day = 1.6 +autotroph_settings(3)%Qp_fixed = 2.7350427350427355e-03 +autotroph_settings(3)%agg_rate_max = 0.5 +autotroph_settings(3)%agg_rate_min = 0.01 +autotroph_settings(3)%alphaPI_per_day = 0.31 +autotroph_settings(3)%exp_calcifier = .false. +autotroph_settings(3)%gQfe_0 = 6.0000000000000002e-05 +autotroph_settings(3)%gQfe_min = 2.5e-06 +autotroph_settings(3)%imp_calcifier = .false. +autotroph_settings(3)%is_carbon_limited = .false. +autotroph_settings(3)%kCO2 = 0 +autotroph_settings(3)%kDOP = 0.075 +autotroph_settings(3)%kFe = 4.5e-05 +autotroph_settings(3)%kNH4 = 0.2 +autotroph_settings(3)%kNO3 = 2 +autotroph_settings(3)%kPO4 = 0.015 +autotroph_settings(3)%kSiO3 = 0 +autotroph_settings(3)%lname = "Diazotroph" +autotroph_settings(3)%loss_poc = 0 +autotroph_settings(3)%loss_thres = 0.02 +autotroph_settings(3)%loss_thres2 = 0.001 +autotroph_settings(3)%mort2_per_day = 0.01 +autotroph_settings(3)%mort_per_day = 0.1 +autotroph_settings(3)%silicifier = .false. +autotroph_settings(3)%sname = "diaz" +autotroph_settings(3)%temp_func_form_opt = "q_10" +autotroph_settings(3)%temp_thres = 15 +autotroph_settings(3)%thetaN_max = 2.5 +autotroph_settings(4)%Ea = 0.32 +autotroph_settings(4)%Nfixer = .false. +autotroph_settings(4)%PCref_per_day = 4.7 +autotroph_settings(4)%Qp_fixed = 8.5470085470085479e-03 +autotroph_settings(4)%agg_rate_max = 0.5 +autotroph_settings(4)%agg_rate_min = 0.01 +autotroph_settings(4)%alphaPI_per_day = 0.31 +autotroph_settings(4)%exp_calcifier = .true. +autotroph_settings(4)%gQfe_0 = 3.0000000000000001e-05 +autotroph_settings(4)%gQfe_min = 2.5e-06 +autotroph_settings(4)%imp_calcifier = .false. +autotroph_settings(4)%is_carbon_limited = .true. +autotroph_settings(4)%kCO2 = 1 +autotroph_settings(4)%kDOP = 0.25 +autotroph_settings(4)%kFe = 3.2e-05 +autotroph_settings(4)%kNH4 = 0.01 +autotroph_settings(4)%kNO3 = 0.41 +autotroph_settings(4)%kPO4 = 0.006 +autotroph_settings(4)%kSiO3 = 0 +autotroph_settings(4)%lname = "Coccolithophores" +autotroph_settings(4)%loss_poc = 0 +autotroph_settings(4)%loss_thres = 0.01 +autotroph_settings(4)%loss_thres2 = 0 +autotroph_settings(4)%mort2_per_day = 0.01 +autotroph_settings(4)%mort_per_day = 0.1 +autotroph_settings(4)%silicifier = .false. +autotroph_settings(4)%sname = "cocco" +autotroph_settings(4)%temp_func_form_opt = "power" +autotroph_settings(4)%temp_thres = 0 +autotroph_settings(4)%thetaN_max = 3.8 + + + + + +! zoo1: small zooplankton -- these graze on primarily on sp, a bit on cocco/diaz +zooplankton_settings(1)%sname = 'microzoo' +zooplankton_settings(1)%lname = 'Microzooplankton' +zooplankton_settings(1)%z_mort_0_per_day = 0.1 +zooplankton_settings(1)%loss_thres = 0.075 +zooplankton_settings(1)%z_mort2_0_per_day = 0.0028 +zooplankton_settings(1)%Ea = 0.55 +zooplankton_settings(1)%temp_func_form_opt = "q_10" +zooplankton_settings(1)%basal_respiration_rate_per_day = 0 + +! zoo2: medium/large zooplankton -- these graze on medium size phytoplankton and zoo2 +zooplankton_settings(2)%sname = 'mesozoo' +zooplankton_settings(2)%lname = 'Mesozooplankton' +zooplankton_settings(2)%z_mort_0_per_day = 0.035 +zooplankton_settings(2)%loss_thres = 0.075 +zooplankton_settings(2)%z_mort2_0_per_day = 0.02 +zooplankton_settings(2)%Ea = 0.55 +zooplankton_settings(2)%temp_func_form_opt = "q_10" +zooplankton_settings(2)%basal_respiration_rate_per_day = 0 + +! Microzoo settings + +grazing_relationship_settings(1,1)%sname = 'grz_sp_microzoo' +grazing_relationship_settings(1,1)%lname = 'Grazing of sp by microzoo' +grazing_relationship_settings(1,1)%auto_ind_cnt = 1 +grazing_relationship_settings(1,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(1,1)%grazing_function = 2 +grazing_relationship_settings(1,1)%z_umax_0_per_day =4.1 +grazing_relationship_settings(1,1)%z_grz = 1.4 +grazing_relationship_settings(1,1)%graze_zoo = 0.35 +grazing_relationship_settings(1,1)%graze_poc = 0.01 +grazing_relationship_settings(1,1)%graze_doc = 0.057 +grazing_relationship_settings(1,1)%f_zoo_detr = 0.17 +grazing_relationship_settings(1,1)%auto_ind(1) = 1 + +grazing_relationship_settings(2,1)%sname = 'grz_diaz_microzoo' +grazing_relationship_settings(2,1)%lname = 'Grazing of diaz by microzoo' +grazing_relationship_settings(2,1)%auto_ind_cnt = 1 +grazing_relationship_settings(2,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(2,1)%f_zoo_detr = 0.17 +grazing_relationship_settings(2,1)%graze_doc = 0.057 +grazing_relationship_settings(2,1)%graze_poc = 0.01 +grazing_relationship_settings(2,1)%graze_zoo = 0.35 +grazing_relationship_settings(2,1)%grazing_function = 1 +grazing_relationship_settings(2,1)%z_grz = 1.2 +grazing_relationship_settings(2,1)%z_umax_0_per_day = 1 +grazing_relationship_settings(2,1)%auto_ind(1) = 3 + +grazing_relationship_settings(3,1)%sname = 'grz_cocco_microzoo' +grazing_relationship_settings(3,1)%lname = 'Grazing of cocco by microzoo' +grazing_relationship_settings(3,1)%auto_ind_cnt = 1 +grazing_relationship_settings(3,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(3,1)%grazing_function = 1 +grazing_relationship_settings(3,1)%z_umax_0_per_day = 1.7 +grazing_relationship_settings(3,1)%z_grz = 1.5 +grazing_relationship_settings(3,1)%graze_zoo = 0.25 +grazing_relationship_settings(3,1)%graze_poc = 0.18 +grazing_relationship_settings(3,1)%graze_doc = 0.09 +grazing_relationship_settings(3,1)%f_zoo_detr = 0.27 +grazing_relationship_settings(3,1)%auto_ind(1) = 4 + + +grazing_relationship_settings(4,1)%sname = "null" +grazing_relationship_settings(4,1)%lname = "null" +grazing_relationship_settings(4,1)%auto_ind_cnt = 0 +grazing_relationship_settings(4,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(4,1)%f_zoo_detr = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%graze_doc = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%graze_poc = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%graze_zoo = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%grazing_function = 1 +grazing_relationship_settings(4,1)%z_grz = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%z_umax_0_per_day = 0.0000000000000000e+00 + +! Mesozoo settings + +grazing_relationship_settings(1,2)%sname = 'grz_diat_mesozoo' +grazing_relationship_settings(1,2)%lname = 'Grazing of diat by mesozoo' +grazing_relationship_settings(1,2)%auto_ind_cnt = 1 +grazing_relationship_settings(1,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(1,2)%grazing_function = 1 +grazing_relationship_settings(1,2)%z_umax_0_per_day = 0.9 +grazing_relationship_settings(1,2)%z_grz = 1.0 +grazing_relationship_settings(1,2)%graze_zoo = 0.25 +grazing_relationship_settings(1,2)%graze_poc = 0.28 +grazing_relationship_settings(1,2)%graze_doc = 0.09 +grazing_relationship_settings(1,2)%f_zoo_detr = 0.40 +grazing_relationship_settings(1,2)%auto_ind(1) = 2 + + +grazing_relationship_settings(2,2)%sname = 'grz_cocco_mesozoo' +grazing_relationship_settings(2,2)%lname = 'Grazing of cocco by mesozoo' +grazing_relationship_settings(2,2)%auto_ind_cnt = 1 +grazing_relationship_settings(2,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(2,2)%grazing_function = 1 +grazing_relationship_settings(2,2)%z_umax_0_per_day = 0.015 +grazing_relationship_settings(2,2)%z_grz = 1.4 +grazing_relationship_settings(2,2)%graze_zoo = 0.25 +grazing_relationship_settings(2,2)%graze_poc = 0.18 +grazing_relationship_settings(2,2)%graze_doc = 0.09 +grazing_relationship_settings(2,2)%f_zoo_detr = 0.27 +grazing_relationship_settings(2,2)%auto_ind(1) = 4 + +grazing_relationship_settings(3,2)%sname = 'grz_diaz_mesozoo' +grazing_relationship_settings(3,2)%lname = 'Grazing of diaz by mesozoo' +grazing_relationship_settings(3,2)%auto_ind_cnt = 1 +grazing_relationship_settings(3,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(3,2)%grazing_function = 1 +grazing_relationship_settings(3,2)%z_umax_0_per_day = 0.5 +grazing_relationship_settings(3,2)%z_grz = 1.2 +grazing_relationship_settings(3,2)%graze_zoo = 0.25 +grazing_relationship_settings(3,2)%graze_poc = 0.18 +grazing_relationship_settings(3,2)%graze_doc = 0.09 +grazing_relationship_settings(3,2)%f_zoo_detr = 0.40 +grazing_relationship_settings(3,2)%auto_ind(1) = 3 + +grazing_relationship_settings(4,2)%sname = 'grz_microzoo_mesozoo' +grazing_relationship_settings(4,2)%lname = 'Predation of microzoo by mesozoo' +grazing_relationship_settings(4,2)%auto_ind_cnt = 0 +grazing_relationship_settings(4,2)%zoo_ind_cnt = 1 +grazing_relationship_settings(4,2)%grazing_function = 1 +grazing_relationship_settings(4,2)%z_umax_0_per_day = 0.2 +grazing_relationship_settings(4,2)%z_grz = 1.3 +grazing_relationship_settings(4,2)%graze_zoo = 0.25 +grazing_relationship_settings(4,2)%graze_poc = 0.28 +grazing_relationship_settings(4,2)%graze_doc = 0.09 +grazing_relationship_settings(4,2)%f_zoo_detr = 0.27 +grazing_relationship_settings(4,2)%zoo_ind(1) = 1 + From f1920a819fd0c41d1f55a773e229db292b9db91a Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 4 May 2023 16:08:25 -0600 Subject: [PATCH 090/136] Some directory restructuring and a bug fix Removed the CESM_MOM_COLUMNS subdirectory from INPUT/ since INPUT is now specific to the single_column_MARBL/ examples. This required updating MOM_override and data_table to point to the correct directories. Also, while cleaning up the data_table file in HOT/ and EQPAC/ I noticed they were both configured to use the BATS forcing, so I fixed that bug. --- .../single_column_MARBL/BATS/MOM_override | 2 +- .../single_column_MARBL/BATS/data_table | 32 +++++++++--------- .../single_column_MARBL/EQPAC/MOM_override | 2 +- .../single_column_MARBL/EQPAC/data_table | 32 +++++++++--------- .../single_column_MARBL/HOT/MOM_override | 2 +- .../single_column_MARBL/HOT/data_table | 32 +++++++++--------- .../BATS/IC_FILE_COLUMN.nc | Bin .../BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin .../BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin .../BATS/MARBL_FORCING_FILE_COLUMN.nc | Bin .../{CESM_MOM_COLUMNS => }/BATS/MOM_override | 4 +-- .../BATS/TOPO_FILE_COLUMN.nc | Bin .../EQPAC/IC_FILE_COLUMN.nc | Bin .../EQPAC/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin .../EQPAC/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin .../EQPAC/MARBL_FORCING_FILE_COLUMN.nc | Bin .../{CESM_MOM_COLUMNS => }/EQPAC/MOM_override | 4 +-- .../EQPAC/TOPO_FILE_COLUMN.nc | Bin .../HOT/IC_FILE_COLUMN.nc | Bin .../HOT/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin .../HOT/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin .../HOT/MARBL_FORCING_FILE_COLUMN.nc | Bin .../{CESM_MOM_COLUMNS => }/HOT/MOM_override | 4 +-- .../HOT/TOPO_FILE_COLUMN.nc | Bin 24 files changed, 57 insertions(+), 57 deletions(-) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/BATS/IC_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/BATS/MARBL_FORCING_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/BATS/MOM_override (54%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/BATS/TOPO_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/EQPAC/IC_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/EQPAC/MARBL_FESEDFLUX_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/EQPAC/MARBL_FEVENTFLUX_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/EQPAC/MARBL_FORCING_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/EQPAC/MOM_override (53%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/EQPAC/TOPO_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/HOT/IC_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/HOT/MARBL_FESEDFLUX_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/HOT/MARBL_FEVENTFLUX_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/HOT/MARBL_FORCING_FILE_COLUMN.nc (100%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/HOT/MOM_override (54%) rename standalone/examples/single_column_MARBL/INPUT/{CESM_MOM_COLUMNS => }/HOT/TOPO_FILE_COLUMN.nc (100%) diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_override b/standalone/examples/single_column_MARBL/BATS/MOM_override index e80e6628..eb851e2b 120000 --- a/standalone/examples/single_column_MARBL/BATS/MOM_override +++ b/standalone/examples/single_column_MARBL/BATS/MOM_override @@ -1 +1 @@ -INPUT/CESM_MOM_COLUMNS/BATS/MOM_override \ No newline at end of file +INPUT/BATS/MOM_override \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/BATS/data_table b/standalone/examples/single_column_MARBL/BATS/data_table index a9ae7a83..0992358e 100644 --- a/standalone/examples/single_column_MARBL/BATS/data_table +++ b/standalone/examples/single_column_MARBL/BATS/data_table @@ -1,19 +1,19 @@ # gridname, fieldname_code, fieldname_file, file_name, ongrid, factor -"OCN" , "lw", "LWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "sw", "SWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "rain", "RAIN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "evap", "EVAP", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "taux", "TAUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "tauy", "TAUY", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "snow", "SNOW", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 +"OCN" , "lw", "LWDN", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sw", "SWDN", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "rain", "RAIN", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "evap", "EVAP", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "taux", "TAUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "tauy", "TAUY", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "snow", "SNOW", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 "OCN" , "runoff", "", "", .true., 0.0 "OCN" , "calving", "", "", .true., 0.0 -"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "seaice_dust_flux", "SEAICE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "seaice_bc_flux", "SEAICE_BC_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "nhx_dep", "NDEP_NHx", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "noy_dep", "NDEP_NOy", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "u10_sqr", "U10_SQR", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_dust_flux", "SEAICE_DUST_FLUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_bc_flux", "SEAICE_BC_FLUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "nhx_dep", "NDEP_NHx", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "noy_dep", "NDEP_NOy", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "u10_sqr", "U10_SQR", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 diff --git a/standalone/examples/single_column_MARBL/EQPAC/MOM_override b/standalone/examples/single_column_MARBL/EQPAC/MOM_override index 71bc5b9c..7038a107 120000 --- a/standalone/examples/single_column_MARBL/EQPAC/MOM_override +++ b/standalone/examples/single_column_MARBL/EQPAC/MOM_override @@ -1 +1 @@ -INPUT/CESM_MOM_COLUMNS/EQPAC/MOM_override \ No newline at end of file +INPUT/EQPAC/MOM_override \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/EQPAC/data_table b/standalone/examples/single_column_MARBL/EQPAC/data_table index a9ae7a83..5d659e63 100644 --- a/standalone/examples/single_column_MARBL/EQPAC/data_table +++ b/standalone/examples/single_column_MARBL/EQPAC/data_table @@ -1,19 +1,19 @@ # gridname, fieldname_code, fieldname_file, file_name, ongrid, factor -"OCN" , "lw", "LWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "sw", "SWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "rain", "RAIN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "evap", "EVAP", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "taux", "TAUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "tauy", "TAUY", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "snow", "SNOW", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 +"OCN" , "lw", "LWDN", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sw", "SWDN", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "rain", "RAIN", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "evap", "EVAP", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "taux", "TAUX", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "tauy", "TAUY", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "snow", "SNOW", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 "OCN" , "runoff", "", "", .true., 0.0 "OCN" , "calving", "", "", .true., 0.0 -"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "seaice_dust_flux", "SEAICE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "seaice_bc_flux", "SEAICE_BC_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "nhx_dep", "NDEP_NHx", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "noy_dep", "NDEP_NOy", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "u10_sqr", "U10_SQR", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_dust_flux", "SEAICE_DUST_FLUX", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_bc_flux", "SEAICE_BC_FLUX", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "nhx_dep", "NDEP_NHx", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "noy_dep", "NDEP_NOy", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "u10_sqr", "U10_SQR", "./INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 diff --git a/standalone/examples/single_column_MARBL/HOT/MOM_override b/standalone/examples/single_column_MARBL/HOT/MOM_override index 3d53a3d8..be2f203b 120000 --- a/standalone/examples/single_column_MARBL/HOT/MOM_override +++ b/standalone/examples/single_column_MARBL/HOT/MOM_override @@ -1 +1 @@ -INPUT/CESM_MOM_COLUMNS/HOT/MOM_override \ No newline at end of file +INPUT/HOT/MOM_override \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/HOT/data_table b/standalone/examples/single_column_MARBL/HOT/data_table index a9ae7a83..08a0342a 100644 --- a/standalone/examples/single_column_MARBL/HOT/data_table +++ b/standalone/examples/single_column_MARBL/HOT/data_table @@ -1,19 +1,19 @@ # gridname, fieldname_code, fieldname_file, file_name, ongrid, factor -"OCN" , "lw", "LWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "sw", "SWDN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "rain", "RAIN", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "evap", "EVAP", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "taux", "TAUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "tauy", "TAUY", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "snow", "SNOW", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 +"OCN" , "lw", "LWDN", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sw", "SWDN", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "rain", "RAIN", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "evap", "EVAP", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "taux", "TAUX", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "tauy", "TAUY", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "snow", "SNOW", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 "OCN" , "runoff", "", "", .true., 0.0 "OCN" , "calving", "", "", .true., 0.0 -"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "seaice_dust_flux", "SEAICE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "seaice_bc_flux", "SEAICE_BC_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "nhx_dep", "NDEP_NHx", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "noy_dep", "NDEP_NOy", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "u10_sqr", "U10_SQR", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_dust_flux", "SEAICE_DUST_FLUX", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_bc_flux", "SEAICE_BC_FLUX", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "nhx_dep", "NDEP_NHx", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "noy_dep", "NDEP_NOy", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "u10_sqr", "U10_SQR", "./INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/BATS/IC_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/BATS/IC_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override b/standalone/examples/single_column_MARBL/INPUT/BATS/MOM_override similarity index 54% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override rename to standalone/examples/single_column_MARBL/INPUT/BATS/MOM_override index cdc19ad8..a95139a0 100644 --- a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override +++ b/standalone/examples/single_column_MARBL/INPUT/BATS/MOM_override @@ -1,6 +1,6 @@ ! Generated by Generate_IC_Files.ipynb F_0 = 7.63418e-05 -INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" -CESM_INPUTDIR = "INPUT/CESM_MOM_COLUMNS/BATS" +INPUTDIR = "INPUT/BATS" +CESM_INPUTDIR = "INPUT/BATS" CHL_FROM_FILE = FALSE READ_RIV_FLUXES = FALSE diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/TOPO_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/BATS/TOPO_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/BATS/TOPO_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/BATS/TOPO_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/IC_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/EQPAC/IC_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/IC_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/EQPAC/IC_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MARBL_FESEDFLUX_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/EQPAC/MARBL_FESEDFLUX_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MARBL_FESEDFLUX_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/EQPAC/MARBL_FESEDFLUX_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MARBL_FEVENTFLUX_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/EQPAC/MARBL_FEVENTFLUX_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MARBL_FEVENTFLUX_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/EQPAC/MARBL_FEVENTFLUX_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MARBL_FORCING_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MARBL_FORCING_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MOM_override b/standalone/examples/single_column_MARBL/INPUT/EQPAC/MOM_override similarity index 53% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MOM_override rename to standalone/examples/single_column_MARBL/INPUT/EQPAC/MOM_override index fb469cb0..6d81d258 100644 --- a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/MOM_override +++ b/standalone/examples/single_column_MARBL/INPUT/EQPAC/MOM_override @@ -1,6 +1,6 @@ ! Generated by Generate_IC_Files.ipynb F_0 = 1.58655e-07 -INPUTDIR = "INPUT/CESM_MOM_COLUMNS/EQPAC" -CESM_INPUTDIR = "INPUT/CESM_MOM_COLUMNS/EQPAC" +INPUTDIR = "INPUT/EQPAC" +CESM_INPUTDIR = "INPUT/EQPAC" CHL_FROM_FILE = FALSE READ_RIV_FLUXES = FALSE diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/TOPO_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/EQPAC/TOPO_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/EQPAC/TOPO_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/EQPAC/TOPO_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/IC_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/HOT/IC_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/IC_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/HOT/IC_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MARBL_FESEDFLUX_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/HOT/MARBL_FESEDFLUX_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MARBL_FESEDFLUX_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/HOT/MARBL_FESEDFLUX_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MARBL_FEVENTFLUX_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/HOT/MARBL_FEVENTFLUX_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MARBL_FEVENTFLUX_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/HOT/MARBL_FEVENTFLUX_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MARBL_FORCING_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MARBL_FORCING_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MOM_override b/standalone/examples/single_column_MARBL/INPUT/HOT/MOM_override similarity index 54% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MOM_override rename to standalone/examples/single_column_MARBL/INPUT/HOT/MOM_override index 5ee2f7a0..b5613d8b 100644 --- a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/MOM_override +++ b/standalone/examples/single_column_MARBL/INPUT/HOT/MOM_override @@ -1,6 +1,6 @@ ! Generated by Generate_IC_Files.ipynb F_0 = 5.65179e-05 -INPUTDIR = "INPUT/CESM_MOM_COLUMNS/HOT" -CESM_INPUTDIR = "INPUT/CESM_MOM_COLUMNS/HOT" +INPUTDIR = "INPUT/HOT" +CESM_INPUTDIR = "INPUT/HOT" CHL_FROM_FILE = FALSE READ_RIV_FLUXES = FALSE diff --git a/standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/TOPO_FILE_COLUMN.nc b/standalone/examples/single_column_MARBL/INPUT/HOT/TOPO_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column_MARBL/INPUT/CESM_MOM_COLUMNS/HOT/TOPO_FILE_COLUMN.nc rename to standalone/examples/single_column_MARBL/INPUT/HOT/TOPO_FILE_COLUMN.nc From c5102d4abfe67759bc647aa052f3283b17e5b2b1 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 4 May 2023 21:44:04 -0600 Subject: [PATCH 091/136] Moved marbl_in to INPUT/ Also replaced it with updated 4p2z tuning --- .../single_column_MARBL/BATS/MOM_input | 1 + .../single_column_MARBL/EQPAC/MOM_input | 1 + .../single_column_MARBL/EQPAC/marbl_in | 265 -------------- .../single_column_MARBL/HOT/MOM_input | 1 + .../examples/single_column_MARBL/HOT/marbl_in | 265 -------------- .../{BATS => INPUT}/marbl_in | 326 ++++++++++++------ 6 files changed, 216 insertions(+), 643 deletions(-) delete mode 100644 standalone/examples/single_column_MARBL/EQPAC/marbl_in delete mode 100644 standalone/examples/single_column_MARBL/HOT/marbl_in rename standalone/examples/single_column_MARBL/{BATS => INPUT}/marbl_in (64%) diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_input b/standalone/examples/single_column_MARBL/BATS/MOM_input index 9032fe9b..d89bee60 100644 --- a/standalone/examples/single_column_MARBL/BATS/MOM_input +++ b/standalone/examples/single_column_MARBL/BATS/MOM_input @@ -51,6 +51,7 @@ USE_MARBL_TRACERS = True ! "[Boolean] default = False ! If true, use the MARBL tracer package." ! === module MARBL_tracers === +MARBL_SETTINGS_FILE = "INPUT/marbl_in" ! default = "marbl_in" MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c211008.nc" ! The file in which the MARBL tracers initial values can be found. MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211020.nc" diff --git a/standalone/examples/single_column_MARBL/EQPAC/MOM_input b/standalone/examples/single_column_MARBL/EQPAC/MOM_input index 893d7ec5..80519997 100644 --- a/standalone/examples/single_column_MARBL/EQPAC/MOM_input +++ b/standalone/examples/single_column_MARBL/EQPAC/MOM_input @@ -51,6 +51,7 @@ USE_MARBL_TRACERS = True ! "[Boolean] default = False ! If true, use the MARBL tracer package." ! === module MARBL_tracers === +MARBL_SETTINGS_FILE = "INPUT/marbl_in" ! default = "marbl_in" MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c211008.nc" ! The file in which the MARBL tracers initial values can be found. MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211020.nc" diff --git a/standalone/examples/single_column_MARBL/EQPAC/marbl_in b/standalone/examples/single_column_MARBL/EQPAC/marbl_in deleted file mode 100644 index f4c8add0..00000000 --- a/standalone/examples/single_column_MARBL/EQPAC/marbl_in +++ /dev/null @@ -1,265 +0,0 @@ -f_graze_CaCO3_remin = 0.5 -QCaCO3_max = 1.0 - -zoo_mort2_exp = 2.0 - -PFT_defaults = 'user-specified' -autotroph_cnt = 4 -zooplankton_cnt = 2 -max_grazer_prey_cnt = 4 - -! autotrophs -autotroph_settings(1)%Ea = 0.32 -autotroph_settings(1)%Nfixer = .false. -autotroph_settings(1)%PCref_per_day = 4.8 -autotroph_settings(1)%Qp_fixed = 8.5470085470085479e-03 -autotroph_settings(1)%agg_rate_max = 0.5 -autotroph_settings(1)%agg_rate_min = 0.01 -autotroph_settings(1)%alphaPI_per_day = 0.35 -autotroph_settings(1)%exp_calcifier = .false. -autotroph_settings(1)%gQfe_0 = 3.0000000000000001e-05 -autotroph_settings(1)%gQfe_min = 2.5e-06 -autotroph_settings(1)%imp_calcifier = .false. -autotroph_settings(1)%is_carbon_limited = .false. -autotroph_settings(1)%kCO2 = 0 -autotroph_settings(1)%kDOP = 0.3 -autotroph_settings(1)%kFe = 3e-05 -autotroph_settings(1)%kNH4 = 0.01 -autotroph_settings(1)%kNO3 = 0.2 -autotroph_settings(1)%kPO4 = 0.01 -autotroph_settings(1)%kSiO3 = 0 -autotroph_settings(1)%lname = "Small Phyto" -autotroph_settings(1)%loss_poc = 0 -autotroph_settings(1)%loss_thres = 0.01 -autotroph_settings(1)%loss_thres2 = 0 -autotroph_settings(1)%mort2_per_day = 0.01 -autotroph_settings(1)%mort_per_day = 0.1 -autotroph_settings(1)%silicifier = .false. -autotroph_settings(1)%sname = "sp" -autotroph_settings(1)%temp_func_form_opt = "q_10" -autotroph_settings(1)%temp_thres = -10 -autotroph_settings(1)%thetaN_max = 2.5 -autotroph_settings(2)%Ea = 0.32 -autotroph_settings(2)%Nfixer = .false. -autotroph_settings(2)%PCref_per_day = 5.3 -autotroph_settings(2)%Qp_fixed = 8.5470085470085479e-03 -autotroph_settings(2)%agg_rate_max = 0.5 -autotroph_settings(2)%agg_rate_min = 0.02 -autotroph_settings(2)%alphaPI_per_day = 0.39 -autotroph_settings(2)%exp_calcifier = .false. -autotroph_settings(2)%gQfe_0 = 3.0000000000000001e-05 -autotroph_settings(2)%gQfe_min = 2.5e-06 -autotroph_settings(2)%imp_calcifier = .false. -autotroph_settings(2)%is_carbon_limited = .false. -autotroph_settings(2)%kCO2 = 0 -autotroph_settings(2)%kDOP = 0.5 -autotroph_settings(2)%kFe = 5e-05 -autotroph_settings(2)%kNH4 = 0.05 -autotroph_settings(2)%kNO3 = 0.4 -autotroph_settings(2)%kPO4 = 0.05 -autotroph_settings(2)%kSiO3 = 1.6 -autotroph_settings(2)%lname = "Diatom" -autotroph_settings(2)%loss_poc = 0 -autotroph_settings(2)%loss_thres = 0.02 -autotroph_settings(2)%loss_thres2 = 0 -autotroph_settings(2)%mort2_per_day = 0.01 -autotroph_settings(2)%mort_per_day = 0.1 -autotroph_settings(2)%silicifier = .true. -autotroph_settings(2)%sname = "diat" -autotroph_settings(2)%temp_func_form_opt = "q_10" -autotroph_settings(2)%temp_thres = -10 -autotroph_settings(2)%thetaN_max = 4 -autotroph_settings(3)%Ea = 0.32 -autotroph_settings(3)%Nfixer = .true. -autotroph_settings(3)%PCref_per_day = 1.6 -autotroph_settings(3)%Qp_fixed = 2.7350427350427355e-03 -autotroph_settings(3)%agg_rate_max = 0.5 -autotroph_settings(3)%agg_rate_min = 0.01 -autotroph_settings(3)%alphaPI_per_day = 0.31 -autotroph_settings(3)%exp_calcifier = .false. -autotroph_settings(3)%gQfe_0 = 6.0000000000000002e-05 -autotroph_settings(3)%gQfe_min = 2.5e-06 -autotroph_settings(3)%imp_calcifier = .false. -autotroph_settings(3)%is_carbon_limited = .false. -autotroph_settings(3)%kCO2 = 0 -autotroph_settings(3)%kDOP = 0.075 -autotroph_settings(3)%kFe = 4.5e-05 -autotroph_settings(3)%kNH4 = 0.2 -autotroph_settings(3)%kNO3 = 2 -autotroph_settings(3)%kPO4 = 0.015 -autotroph_settings(3)%kSiO3 = 0 -autotroph_settings(3)%lname = "Diazotroph" -autotroph_settings(3)%loss_poc = 0 -autotroph_settings(3)%loss_thres = 0.02 -autotroph_settings(3)%loss_thres2 = 0.001 -autotroph_settings(3)%mort2_per_day = 0.01 -autotroph_settings(3)%mort_per_day = 0.1 -autotroph_settings(3)%silicifier = .false. -autotroph_settings(3)%sname = "diaz" -autotroph_settings(3)%temp_func_form_opt = "q_10" -autotroph_settings(3)%temp_thres = 15 -autotroph_settings(3)%thetaN_max = 2.5 -autotroph_settings(4)%Ea = 0.32 -autotroph_settings(4)%Nfixer = .false. -autotroph_settings(4)%PCref_per_day = 4.7 -autotroph_settings(4)%Qp_fixed = 8.5470085470085479e-03 -autotroph_settings(4)%agg_rate_max = 0.5 -autotroph_settings(4)%agg_rate_min = 0.01 -autotroph_settings(4)%alphaPI_per_day = 0.31 -autotroph_settings(4)%exp_calcifier = .true. -autotroph_settings(4)%gQfe_0 = 3.0000000000000001e-05 -autotroph_settings(4)%gQfe_min = 2.5e-06 -autotroph_settings(4)%imp_calcifier = .false. -autotroph_settings(4)%is_carbon_limited = .true. -autotroph_settings(4)%kCO2 = 1 -autotroph_settings(4)%kDOP = 0.25 -autotroph_settings(4)%kFe = 3.2e-05 -autotroph_settings(4)%kNH4 = 0.01 -autotroph_settings(4)%kNO3 = 0.41 -autotroph_settings(4)%kPO4 = 0.006 -autotroph_settings(4)%kSiO3 = 0 -autotroph_settings(4)%lname = "Coccolithophores" -autotroph_settings(4)%loss_poc = 0 -autotroph_settings(4)%loss_thres = 0.01 -autotroph_settings(4)%loss_thres2 = 0 -autotroph_settings(4)%mort2_per_day = 0.01 -autotroph_settings(4)%mort_per_day = 0.1 -autotroph_settings(4)%silicifier = .false. -autotroph_settings(4)%sname = "cocco" -autotroph_settings(4)%temp_func_form_opt = "power" -autotroph_settings(4)%temp_thres = 0 -autotroph_settings(4)%thetaN_max = 3.8 - - - - - -! zoo1: small zooplankton -- these graze on primarily on sp, a bit on cocco/diaz -zooplankton_settings(1)%sname = 'microzoo' -zooplankton_settings(1)%lname = 'Microzooplankton' -zooplankton_settings(1)%z_mort_0_per_day = 0.1 -zooplankton_settings(1)%loss_thres = 0.075 -zooplankton_settings(1)%z_mort2_0_per_day = 0.0028 -zooplankton_settings(1)%Ea = 0.55 -zooplankton_settings(1)%temp_func_form_opt = "q_10" -zooplankton_settings(1)%basal_respiration_rate_per_day = 0 - -! zoo2: medium/large zooplankton -- these graze on medium size phytoplankton and zoo2 -zooplankton_settings(2)%sname = 'mesozoo' -zooplankton_settings(2)%lname = 'Mesozooplankton' -zooplankton_settings(2)%z_mort_0_per_day = 0.035 -zooplankton_settings(2)%loss_thres = 0.075 -zooplankton_settings(2)%z_mort2_0_per_day = 0.02 -zooplankton_settings(2)%Ea = 0.55 -zooplankton_settings(2)%temp_func_form_opt = "q_10" -zooplankton_settings(2)%basal_respiration_rate_per_day = 0 - -! Microzoo settings - -grazing_relationship_settings(1,1)%sname = 'grz_sp_microzoo' -grazing_relationship_settings(1,1)%lname = 'Grazing of sp by microzoo' -grazing_relationship_settings(1,1)%auto_ind_cnt = 1 -grazing_relationship_settings(1,1)%zoo_ind_cnt = 0 -grazing_relationship_settings(1,1)%grazing_function = 2 -grazing_relationship_settings(1,1)%z_umax_0_per_day =4.1 -grazing_relationship_settings(1,1)%z_grz = 1.4 -grazing_relationship_settings(1,1)%graze_zoo = 0.35 -grazing_relationship_settings(1,1)%graze_poc = 0.01 -grazing_relationship_settings(1,1)%graze_doc = 0.057 -grazing_relationship_settings(1,1)%f_zoo_detr = 0.17 -grazing_relationship_settings(1,1)%auto_ind(1) = 1 - -grazing_relationship_settings(2,1)%sname = 'grz_diaz_microzoo' -grazing_relationship_settings(2,1)%lname = 'Grazing of diaz by microzoo' -grazing_relationship_settings(2,1)%auto_ind_cnt = 1 -grazing_relationship_settings(2,1)%zoo_ind_cnt = 0 -grazing_relationship_settings(2,1)%f_zoo_detr = 0.17 -grazing_relationship_settings(2,1)%graze_doc = 0.057 -grazing_relationship_settings(2,1)%graze_poc = 0.01 -grazing_relationship_settings(2,1)%graze_zoo = 0.35 -grazing_relationship_settings(2,1)%grazing_function = 1 -grazing_relationship_settings(2,1)%z_grz = 1.2 -grazing_relationship_settings(2,1)%z_umax_0_per_day = 1 -grazing_relationship_settings(2,1)%auto_ind(1) = 3 - -grazing_relationship_settings(3,1)%sname = 'grz_cocco_microzoo' -grazing_relationship_settings(3,1)%lname = 'Grazing of cocco by microzoo' -grazing_relationship_settings(3,1)%auto_ind_cnt = 1 -grazing_relationship_settings(3,1)%zoo_ind_cnt = 0 -grazing_relationship_settings(3,1)%grazing_function = 1 -grazing_relationship_settings(3,1)%z_umax_0_per_day = 1.7 -grazing_relationship_settings(3,1)%z_grz = 1.5 -grazing_relationship_settings(3,1)%graze_zoo = 0.25 -grazing_relationship_settings(3,1)%graze_poc = 0.18 -grazing_relationship_settings(3,1)%graze_doc = 0.09 -grazing_relationship_settings(3,1)%f_zoo_detr = 0.27 -grazing_relationship_settings(3,1)%auto_ind(1) = 4 - - -grazing_relationship_settings(4,1)%sname = "null" -grazing_relationship_settings(4,1)%lname = "null" -grazing_relationship_settings(4,1)%auto_ind_cnt = 0 -grazing_relationship_settings(4,1)%zoo_ind_cnt = 0 -grazing_relationship_settings(4,1)%f_zoo_detr = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%graze_doc = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%graze_poc = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%graze_zoo = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%grazing_function = 1 -grazing_relationship_settings(4,1)%z_grz = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%z_umax_0_per_day = 0.0000000000000000e+00 - -! Mesozoo settings - -grazing_relationship_settings(1,2)%sname = 'grz_diat_mesozoo' -grazing_relationship_settings(1,2)%lname = 'Grazing of diat by mesozoo' -grazing_relationship_settings(1,2)%auto_ind_cnt = 1 -grazing_relationship_settings(1,2)%zoo_ind_cnt = 0 -grazing_relationship_settings(1,2)%grazing_function = 1 -grazing_relationship_settings(1,2)%z_umax_0_per_day = 0.9 -grazing_relationship_settings(1,2)%z_grz = 1.0 -grazing_relationship_settings(1,2)%graze_zoo = 0.25 -grazing_relationship_settings(1,2)%graze_poc = 0.28 -grazing_relationship_settings(1,2)%graze_doc = 0.09 -grazing_relationship_settings(1,2)%f_zoo_detr = 0.40 -grazing_relationship_settings(1,2)%auto_ind(1) = 2 - - -grazing_relationship_settings(2,2)%sname = 'grz_cocco_mesozoo' -grazing_relationship_settings(2,2)%lname = 'Grazing of cocco by mesozoo' -grazing_relationship_settings(2,2)%auto_ind_cnt = 1 -grazing_relationship_settings(2,2)%zoo_ind_cnt = 0 -grazing_relationship_settings(2,2)%grazing_function = 1 -grazing_relationship_settings(2,2)%z_umax_0_per_day = 0.015 -grazing_relationship_settings(2,2)%z_grz = 1.4 -grazing_relationship_settings(2,2)%graze_zoo = 0.25 -grazing_relationship_settings(2,2)%graze_poc = 0.18 -grazing_relationship_settings(2,2)%graze_doc = 0.09 -grazing_relationship_settings(2,2)%f_zoo_detr = 0.27 -grazing_relationship_settings(2,2)%auto_ind(1) = 4 - -grazing_relationship_settings(3,2)%sname = 'grz_diaz_mesozoo' -grazing_relationship_settings(3,2)%lname = 'Grazing of diaz by mesozoo' -grazing_relationship_settings(3,2)%auto_ind_cnt = 1 -grazing_relationship_settings(3,2)%zoo_ind_cnt = 0 -grazing_relationship_settings(3,2)%grazing_function = 1 -grazing_relationship_settings(3,2)%z_umax_0_per_day = 0.5 -grazing_relationship_settings(3,2)%z_grz = 1.2 -grazing_relationship_settings(3,2)%graze_zoo = 0.25 -grazing_relationship_settings(3,2)%graze_poc = 0.18 -grazing_relationship_settings(3,2)%graze_doc = 0.09 -grazing_relationship_settings(3,2)%f_zoo_detr = 0.40 -grazing_relationship_settings(3,2)%auto_ind(1) = 3 - -grazing_relationship_settings(4,2)%sname = 'grz_microzoo_mesozoo' -grazing_relationship_settings(4,2)%lname = 'Predation of microzoo by mesozoo' -grazing_relationship_settings(4,2)%auto_ind_cnt = 0 -grazing_relationship_settings(4,2)%zoo_ind_cnt = 1 -grazing_relationship_settings(4,2)%grazing_function = 1 -grazing_relationship_settings(4,2)%z_umax_0_per_day = 0.2 -grazing_relationship_settings(4,2)%z_grz = 1.3 -grazing_relationship_settings(4,2)%graze_zoo = 0.25 -grazing_relationship_settings(4,2)%graze_poc = 0.28 -grazing_relationship_settings(4,2)%graze_doc = 0.09 -grazing_relationship_settings(4,2)%f_zoo_detr = 0.27 -grazing_relationship_settings(4,2)%zoo_ind(1) = 1 - diff --git a/standalone/examples/single_column_MARBL/HOT/MOM_input b/standalone/examples/single_column_MARBL/HOT/MOM_input index ee53ca4d..a37293f6 100644 --- a/standalone/examples/single_column_MARBL/HOT/MOM_input +++ b/standalone/examples/single_column_MARBL/HOT/MOM_input @@ -51,6 +51,7 @@ USE_MARBL_TRACERS = True ! "[Boolean] default = False ! If true, use the MARBL tracer package." ! === module MARBL_tracers === +MARBL_SETTINGS_FILE = "INPUT/marbl_in" ! default = "marbl_in" MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c211008.nc" ! The file in which the MARBL tracers initial values can be found. MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211020.nc" diff --git a/standalone/examples/single_column_MARBL/HOT/marbl_in b/standalone/examples/single_column_MARBL/HOT/marbl_in deleted file mode 100644 index f4c8add0..00000000 --- a/standalone/examples/single_column_MARBL/HOT/marbl_in +++ /dev/null @@ -1,265 +0,0 @@ -f_graze_CaCO3_remin = 0.5 -QCaCO3_max = 1.0 - -zoo_mort2_exp = 2.0 - -PFT_defaults = 'user-specified' -autotroph_cnt = 4 -zooplankton_cnt = 2 -max_grazer_prey_cnt = 4 - -! autotrophs -autotroph_settings(1)%Ea = 0.32 -autotroph_settings(1)%Nfixer = .false. -autotroph_settings(1)%PCref_per_day = 4.8 -autotroph_settings(1)%Qp_fixed = 8.5470085470085479e-03 -autotroph_settings(1)%agg_rate_max = 0.5 -autotroph_settings(1)%agg_rate_min = 0.01 -autotroph_settings(1)%alphaPI_per_day = 0.35 -autotroph_settings(1)%exp_calcifier = .false. -autotroph_settings(1)%gQfe_0 = 3.0000000000000001e-05 -autotroph_settings(1)%gQfe_min = 2.5e-06 -autotroph_settings(1)%imp_calcifier = .false. -autotroph_settings(1)%is_carbon_limited = .false. -autotroph_settings(1)%kCO2 = 0 -autotroph_settings(1)%kDOP = 0.3 -autotroph_settings(1)%kFe = 3e-05 -autotroph_settings(1)%kNH4 = 0.01 -autotroph_settings(1)%kNO3 = 0.2 -autotroph_settings(1)%kPO4 = 0.01 -autotroph_settings(1)%kSiO3 = 0 -autotroph_settings(1)%lname = "Small Phyto" -autotroph_settings(1)%loss_poc = 0 -autotroph_settings(1)%loss_thres = 0.01 -autotroph_settings(1)%loss_thres2 = 0 -autotroph_settings(1)%mort2_per_day = 0.01 -autotroph_settings(1)%mort_per_day = 0.1 -autotroph_settings(1)%silicifier = .false. -autotroph_settings(1)%sname = "sp" -autotroph_settings(1)%temp_func_form_opt = "q_10" -autotroph_settings(1)%temp_thres = -10 -autotroph_settings(1)%thetaN_max = 2.5 -autotroph_settings(2)%Ea = 0.32 -autotroph_settings(2)%Nfixer = .false. -autotroph_settings(2)%PCref_per_day = 5.3 -autotroph_settings(2)%Qp_fixed = 8.5470085470085479e-03 -autotroph_settings(2)%agg_rate_max = 0.5 -autotroph_settings(2)%agg_rate_min = 0.02 -autotroph_settings(2)%alphaPI_per_day = 0.39 -autotroph_settings(2)%exp_calcifier = .false. -autotroph_settings(2)%gQfe_0 = 3.0000000000000001e-05 -autotroph_settings(2)%gQfe_min = 2.5e-06 -autotroph_settings(2)%imp_calcifier = .false. -autotroph_settings(2)%is_carbon_limited = .false. -autotroph_settings(2)%kCO2 = 0 -autotroph_settings(2)%kDOP = 0.5 -autotroph_settings(2)%kFe = 5e-05 -autotroph_settings(2)%kNH4 = 0.05 -autotroph_settings(2)%kNO3 = 0.4 -autotroph_settings(2)%kPO4 = 0.05 -autotroph_settings(2)%kSiO3 = 1.6 -autotroph_settings(2)%lname = "Diatom" -autotroph_settings(2)%loss_poc = 0 -autotroph_settings(2)%loss_thres = 0.02 -autotroph_settings(2)%loss_thres2 = 0 -autotroph_settings(2)%mort2_per_day = 0.01 -autotroph_settings(2)%mort_per_day = 0.1 -autotroph_settings(2)%silicifier = .true. -autotroph_settings(2)%sname = "diat" -autotroph_settings(2)%temp_func_form_opt = "q_10" -autotroph_settings(2)%temp_thres = -10 -autotroph_settings(2)%thetaN_max = 4 -autotroph_settings(3)%Ea = 0.32 -autotroph_settings(3)%Nfixer = .true. -autotroph_settings(3)%PCref_per_day = 1.6 -autotroph_settings(3)%Qp_fixed = 2.7350427350427355e-03 -autotroph_settings(3)%agg_rate_max = 0.5 -autotroph_settings(3)%agg_rate_min = 0.01 -autotroph_settings(3)%alphaPI_per_day = 0.31 -autotroph_settings(3)%exp_calcifier = .false. -autotroph_settings(3)%gQfe_0 = 6.0000000000000002e-05 -autotroph_settings(3)%gQfe_min = 2.5e-06 -autotroph_settings(3)%imp_calcifier = .false. -autotroph_settings(3)%is_carbon_limited = .false. -autotroph_settings(3)%kCO2 = 0 -autotroph_settings(3)%kDOP = 0.075 -autotroph_settings(3)%kFe = 4.5e-05 -autotroph_settings(3)%kNH4 = 0.2 -autotroph_settings(3)%kNO3 = 2 -autotroph_settings(3)%kPO4 = 0.015 -autotroph_settings(3)%kSiO3 = 0 -autotroph_settings(3)%lname = "Diazotroph" -autotroph_settings(3)%loss_poc = 0 -autotroph_settings(3)%loss_thres = 0.02 -autotroph_settings(3)%loss_thres2 = 0.001 -autotroph_settings(3)%mort2_per_day = 0.01 -autotroph_settings(3)%mort_per_day = 0.1 -autotroph_settings(3)%silicifier = .false. -autotroph_settings(3)%sname = "diaz" -autotroph_settings(3)%temp_func_form_opt = "q_10" -autotroph_settings(3)%temp_thres = 15 -autotroph_settings(3)%thetaN_max = 2.5 -autotroph_settings(4)%Ea = 0.32 -autotroph_settings(4)%Nfixer = .false. -autotroph_settings(4)%PCref_per_day = 4.7 -autotroph_settings(4)%Qp_fixed = 8.5470085470085479e-03 -autotroph_settings(4)%agg_rate_max = 0.5 -autotroph_settings(4)%agg_rate_min = 0.01 -autotroph_settings(4)%alphaPI_per_day = 0.31 -autotroph_settings(4)%exp_calcifier = .true. -autotroph_settings(4)%gQfe_0 = 3.0000000000000001e-05 -autotroph_settings(4)%gQfe_min = 2.5e-06 -autotroph_settings(4)%imp_calcifier = .false. -autotroph_settings(4)%is_carbon_limited = .true. -autotroph_settings(4)%kCO2 = 1 -autotroph_settings(4)%kDOP = 0.25 -autotroph_settings(4)%kFe = 3.2e-05 -autotroph_settings(4)%kNH4 = 0.01 -autotroph_settings(4)%kNO3 = 0.41 -autotroph_settings(4)%kPO4 = 0.006 -autotroph_settings(4)%kSiO3 = 0 -autotroph_settings(4)%lname = "Coccolithophores" -autotroph_settings(4)%loss_poc = 0 -autotroph_settings(4)%loss_thres = 0.01 -autotroph_settings(4)%loss_thres2 = 0 -autotroph_settings(4)%mort2_per_day = 0.01 -autotroph_settings(4)%mort_per_day = 0.1 -autotroph_settings(4)%silicifier = .false. -autotroph_settings(4)%sname = "cocco" -autotroph_settings(4)%temp_func_form_opt = "power" -autotroph_settings(4)%temp_thres = 0 -autotroph_settings(4)%thetaN_max = 3.8 - - - - - -! zoo1: small zooplankton -- these graze on primarily on sp, a bit on cocco/diaz -zooplankton_settings(1)%sname = 'microzoo' -zooplankton_settings(1)%lname = 'Microzooplankton' -zooplankton_settings(1)%z_mort_0_per_day = 0.1 -zooplankton_settings(1)%loss_thres = 0.075 -zooplankton_settings(1)%z_mort2_0_per_day = 0.0028 -zooplankton_settings(1)%Ea = 0.55 -zooplankton_settings(1)%temp_func_form_opt = "q_10" -zooplankton_settings(1)%basal_respiration_rate_per_day = 0 - -! zoo2: medium/large zooplankton -- these graze on medium size phytoplankton and zoo2 -zooplankton_settings(2)%sname = 'mesozoo' -zooplankton_settings(2)%lname = 'Mesozooplankton' -zooplankton_settings(2)%z_mort_0_per_day = 0.035 -zooplankton_settings(2)%loss_thres = 0.075 -zooplankton_settings(2)%z_mort2_0_per_day = 0.02 -zooplankton_settings(2)%Ea = 0.55 -zooplankton_settings(2)%temp_func_form_opt = "q_10" -zooplankton_settings(2)%basal_respiration_rate_per_day = 0 - -! Microzoo settings - -grazing_relationship_settings(1,1)%sname = 'grz_sp_microzoo' -grazing_relationship_settings(1,1)%lname = 'Grazing of sp by microzoo' -grazing_relationship_settings(1,1)%auto_ind_cnt = 1 -grazing_relationship_settings(1,1)%zoo_ind_cnt = 0 -grazing_relationship_settings(1,1)%grazing_function = 2 -grazing_relationship_settings(1,1)%z_umax_0_per_day =4.1 -grazing_relationship_settings(1,1)%z_grz = 1.4 -grazing_relationship_settings(1,1)%graze_zoo = 0.35 -grazing_relationship_settings(1,1)%graze_poc = 0.01 -grazing_relationship_settings(1,1)%graze_doc = 0.057 -grazing_relationship_settings(1,1)%f_zoo_detr = 0.17 -grazing_relationship_settings(1,1)%auto_ind(1) = 1 - -grazing_relationship_settings(2,1)%sname = 'grz_diaz_microzoo' -grazing_relationship_settings(2,1)%lname = 'Grazing of diaz by microzoo' -grazing_relationship_settings(2,1)%auto_ind_cnt = 1 -grazing_relationship_settings(2,1)%zoo_ind_cnt = 0 -grazing_relationship_settings(2,1)%f_zoo_detr = 0.17 -grazing_relationship_settings(2,1)%graze_doc = 0.057 -grazing_relationship_settings(2,1)%graze_poc = 0.01 -grazing_relationship_settings(2,1)%graze_zoo = 0.35 -grazing_relationship_settings(2,1)%grazing_function = 1 -grazing_relationship_settings(2,1)%z_grz = 1.2 -grazing_relationship_settings(2,1)%z_umax_0_per_day = 1 -grazing_relationship_settings(2,1)%auto_ind(1) = 3 - -grazing_relationship_settings(3,1)%sname = 'grz_cocco_microzoo' -grazing_relationship_settings(3,1)%lname = 'Grazing of cocco by microzoo' -grazing_relationship_settings(3,1)%auto_ind_cnt = 1 -grazing_relationship_settings(3,1)%zoo_ind_cnt = 0 -grazing_relationship_settings(3,1)%grazing_function = 1 -grazing_relationship_settings(3,1)%z_umax_0_per_day = 1.7 -grazing_relationship_settings(3,1)%z_grz = 1.5 -grazing_relationship_settings(3,1)%graze_zoo = 0.25 -grazing_relationship_settings(3,1)%graze_poc = 0.18 -grazing_relationship_settings(3,1)%graze_doc = 0.09 -grazing_relationship_settings(3,1)%f_zoo_detr = 0.27 -grazing_relationship_settings(3,1)%auto_ind(1) = 4 - - -grazing_relationship_settings(4,1)%sname = "null" -grazing_relationship_settings(4,1)%lname = "null" -grazing_relationship_settings(4,1)%auto_ind_cnt = 0 -grazing_relationship_settings(4,1)%zoo_ind_cnt = 0 -grazing_relationship_settings(4,1)%f_zoo_detr = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%graze_doc = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%graze_poc = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%graze_zoo = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%grazing_function = 1 -grazing_relationship_settings(4,1)%z_grz = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%z_umax_0_per_day = 0.0000000000000000e+00 - -! Mesozoo settings - -grazing_relationship_settings(1,2)%sname = 'grz_diat_mesozoo' -grazing_relationship_settings(1,2)%lname = 'Grazing of diat by mesozoo' -grazing_relationship_settings(1,2)%auto_ind_cnt = 1 -grazing_relationship_settings(1,2)%zoo_ind_cnt = 0 -grazing_relationship_settings(1,2)%grazing_function = 1 -grazing_relationship_settings(1,2)%z_umax_0_per_day = 0.9 -grazing_relationship_settings(1,2)%z_grz = 1.0 -grazing_relationship_settings(1,2)%graze_zoo = 0.25 -grazing_relationship_settings(1,2)%graze_poc = 0.28 -grazing_relationship_settings(1,2)%graze_doc = 0.09 -grazing_relationship_settings(1,2)%f_zoo_detr = 0.40 -grazing_relationship_settings(1,2)%auto_ind(1) = 2 - - -grazing_relationship_settings(2,2)%sname = 'grz_cocco_mesozoo' -grazing_relationship_settings(2,2)%lname = 'Grazing of cocco by mesozoo' -grazing_relationship_settings(2,2)%auto_ind_cnt = 1 -grazing_relationship_settings(2,2)%zoo_ind_cnt = 0 -grazing_relationship_settings(2,2)%grazing_function = 1 -grazing_relationship_settings(2,2)%z_umax_0_per_day = 0.015 -grazing_relationship_settings(2,2)%z_grz = 1.4 -grazing_relationship_settings(2,2)%graze_zoo = 0.25 -grazing_relationship_settings(2,2)%graze_poc = 0.18 -grazing_relationship_settings(2,2)%graze_doc = 0.09 -grazing_relationship_settings(2,2)%f_zoo_detr = 0.27 -grazing_relationship_settings(2,2)%auto_ind(1) = 4 - -grazing_relationship_settings(3,2)%sname = 'grz_diaz_mesozoo' -grazing_relationship_settings(3,2)%lname = 'Grazing of diaz by mesozoo' -grazing_relationship_settings(3,2)%auto_ind_cnt = 1 -grazing_relationship_settings(3,2)%zoo_ind_cnt = 0 -grazing_relationship_settings(3,2)%grazing_function = 1 -grazing_relationship_settings(3,2)%z_umax_0_per_day = 0.5 -grazing_relationship_settings(3,2)%z_grz = 1.2 -grazing_relationship_settings(3,2)%graze_zoo = 0.25 -grazing_relationship_settings(3,2)%graze_poc = 0.18 -grazing_relationship_settings(3,2)%graze_doc = 0.09 -grazing_relationship_settings(3,2)%f_zoo_detr = 0.40 -grazing_relationship_settings(3,2)%auto_ind(1) = 3 - -grazing_relationship_settings(4,2)%sname = 'grz_microzoo_mesozoo' -grazing_relationship_settings(4,2)%lname = 'Predation of microzoo by mesozoo' -grazing_relationship_settings(4,2)%auto_ind_cnt = 0 -grazing_relationship_settings(4,2)%zoo_ind_cnt = 1 -grazing_relationship_settings(4,2)%grazing_function = 1 -grazing_relationship_settings(4,2)%z_umax_0_per_day = 0.2 -grazing_relationship_settings(4,2)%z_grz = 1.3 -grazing_relationship_settings(4,2)%graze_zoo = 0.25 -grazing_relationship_settings(4,2)%graze_poc = 0.28 -grazing_relationship_settings(4,2)%graze_doc = 0.09 -grazing_relationship_settings(4,2)%f_zoo_detr = 0.27 -grazing_relationship_settings(4,2)%zoo_ind(1) = 1 - diff --git a/standalone/examples/single_column_MARBL/BATS/marbl_in b/standalone/examples/single_column_MARBL/INPUT/marbl_in similarity index 64% rename from standalone/examples/single_column_MARBL/BATS/marbl_in rename to standalone/examples/single_column_MARBL/INPUT/marbl_in index f4c8add0..d1db93ca 100644 --- a/standalone/examples/single_column_MARBL/BATS/marbl_in +++ b/standalone/examples/single_column_MARBL/INPUT/marbl_in @@ -1,12 +1,92 @@ -f_graze_CaCO3_remin = 0.5 -QCaCO3_max = 1.0 +! config PFTs +PFT_defaults = "user-specified" +autotroph_cnt = 4 +max_grazer_prey_cnt = 4 +zooplankton_cnt = 2 +! config flags +ciso_lsource_sink = .true. +ciso_on = .false. +ladjust_bury_coeff = .false. +lcheck_forcing = .false. +lcompute_nhx_surface_emis = .true. +lecovars_full_depth_tavg = .false. +lflux_gas_co2 = .true. +lflux_gas_o2 = .true. +lo2_consumption_scalef = .true. +lp_remin_scalef = .false. +lsource_sink = .true. +lvariable_PtoC = .true. + +! config strings +init_bury_coeff_opt = "settings_file" + +! general parameters +Jint_Ctot_thres_molpm2pyr = 1e-09 +QCaCO3_max = 1.0 +auto_mort2_exp = 1.75 +bftt_dz_sum_thres = 1e-14 +bury_coeff_rmean_timescale_years = 10 +caco3_bury_thres_depth = 3.0000000000000000e+05 +caco3_bury_thres_omega_calc = 1.0 +caco3_bury_thres_opt = "omega_calc" +ciso_fract_factors = "Laws" +f_graze_CaCO3_remin = 0.5 +gQ_Fe_kFe_thres = 10.0 +gQ_Si_kSi_thres = 5.0 +gQsi_0 = 0.137 +gQsi_max = 0.685 +gQsi_min = 0.0457 +o2_sf_o2_range_hi = 45.0 +o2_sf_o2_range_lo = 5.0 +o2_sf_val_lo_o2 = 3.0 +parm_CaCO3_gamma = 0.1 +parm_Fe_bioavail = 1.0 +parm_Fe_desorption_rate0 = 9.9999999999999995e-07 +parm_Lig_degrade_rate0 = 9.4e-05 +parm_SiO2_gamma = 0.1 +parm_f_prod_sp_CaCO3 = 0.07 +parm_hPOC_CaCO3_ratio = 0.05 +parm_hPOC_SiO2_ratio = 0.05 +parm_hPOC_dust_ratio = 0.05 +parm_labile_ratio = 0.94 +parm_o2_min = 5.0 +parm_o2_min_delta = 5.0 +parm_sed_denitrif_coeff = 1 +particulate_flux_ref_depth = 100 zoo_mort2_exp = 2.0 -PFT_defaults = 'user-specified' -autotroph_cnt = 4 -zooplankton_cnt = 2 -max_grazer_prey_cnt = 4 +! general parameters (bury coeffs) +POM_bury_frac_max = 0.8 +PON_bury_coeff = 0.5 +bSi_bury_frac_max = 1.0 +parm_init_POC_bury_coeff = 1.1 +parm_init_POP_bury_coeff = 1.1 +parm_init_bSi_bury_coeff = 1.0 + +! general parameters (dissolution) +parm_CaCO3_diss = 5.0000000000000000e+04 +parm_POC_diss = 1.0000000000000000e+04 +parm_SiO2_diss = 6.5000000000000000e+04 + +! general parameters (nitrification) +parm_kappa_nitrif_per_day = 0.06 +parm_nitrif_par_lim = 1.0 + +! general parameters (scavenging) +parm_FeLig_scavenge_rate0 = 1.3 +parm_Fe_scavenge_rate0 = 24.0 +parm_Lig_scavenge_rate0 = 0.015 + +! Scale lengths +parm_scalelen_vals(1) = 1 +parm_scalelen_vals(2) = 3.2 +parm_scalelen_vals(3) = 4.8 +parm_scalelen_vals(4) = 5.3 +parm_scalelen_z(1) = 1.0000000000000000e+04 +parm_scalelen_z(2) = 2.5000000000000000e+04 +parm_scalelen_z(3) = 5.0000000000000000e+04 +parm_scalelen_z(4) = 1.0000000000000000e+05 ! autotrophs autotroph_settings(1)%Ea = 0.32 @@ -130,136 +210,156 @@ autotroph_settings(4)%temp_func_form_opt = "power" autotroph_settings(4)%temp_thres = 0 autotroph_settings(4)%thetaN_max = 3.8 - - - - -! zoo1: small zooplankton -- these graze on primarily on sp, a bit on cocco/diaz -zooplankton_settings(1)%sname = 'microzoo' -zooplankton_settings(1)%lname = 'Microzooplankton' -zooplankton_settings(1)%z_mort_0_per_day = 0.1 -zooplankton_settings(1)%loss_thres = 0.075 -zooplankton_settings(1)%z_mort2_0_per_day = 0.0028 +! zooplankton zooplankton_settings(1)%Ea = 0.55 -zooplankton_settings(1)%temp_func_form_opt = "q_10" zooplankton_settings(1)%basal_respiration_rate_per_day = 0 - -! zoo2: medium/large zooplankton -- these graze on medium size phytoplankton and zoo2 -zooplankton_settings(2)%sname = 'mesozoo' -zooplankton_settings(2)%lname = 'Mesozooplankton' -zooplankton_settings(2)%z_mort_0_per_day = 0.035 -zooplankton_settings(2)%loss_thres = 0.075 -zooplankton_settings(2)%z_mort2_0_per_day = 0.02 +zooplankton_settings(1)%lname = "Microzooplankton" +zooplankton_settings(1)%loss_thres = 0.075 +zooplankton_settings(1)%sname = "microzoo" +zooplankton_settings(1)%temp_func_form_opt = "q_10" +zooplankton_settings(1)%z_mort2_0_per_day = 0.0028 +zooplankton_settings(1)%z_mort_0_per_day = 0.1 zooplankton_settings(2)%Ea = 0.55 -zooplankton_settings(2)%temp_func_form_opt = "q_10" zooplankton_settings(2)%basal_respiration_rate_per_day = 0 +zooplankton_settings(2)%lname = "Mesozooplankton" +zooplankton_settings(2)%loss_thres = 0.075 +zooplankton_settings(2)%sname = "mesozoo" +zooplankton_settings(2)%temp_func_form_opt = "q_10" +zooplankton_settings(2)%z_mort2_0_per_day = 0.02 +zooplankton_settings(2)%z_mort_0_per_day = 0.035 -! Microzoo settings - -grazing_relationship_settings(1,1)%sname = 'grz_sp_microzoo' -grazing_relationship_settings(1,1)%lname = 'Grazing of sp by microzoo' +! grazing +grazing_relationship_settings(1,1)%auto_ind(1) = 1 grazing_relationship_settings(1,1)%auto_ind_cnt = 1 -grazing_relationship_settings(1,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(1,1)%f_zoo_detr = 0.17 +grazing_relationship_settings(1,1)%graze_doc = 0.057 +grazing_relationship_settings(1,1)%graze_poc = 0.01 +grazing_relationship_settings(1,1)%graze_zoo = 0.35 grazing_relationship_settings(1,1)%grazing_function = 2 -grazing_relationship_settings(1,1)%z_umax_0_per_day =4.1 +grazing_relationship_settings(1,1)%lname = "Grazing of sp by microzoo" +grazing_relationship_settings(1,1)%sname = "grz_sp_microzoo" grazing_relationship_settings(1,1)%z_grz = 1.4 -grazing_relationship_settings(1,1)%graze_zoo = 0.35 -grazing_relationship_settings(1,1)%graze_poc = 0.01 -grazing_relationship_settings(1,1)%graze_doc = 0.057 -grazing_relationship_settings(1,1)%f_zoo_detr = 0.17 -grazing_relationship_settings(1,1)%auto_ind(1) = 1 - -grazing_relationship_settings(2,1)%sname = 'grz_diaz_microzoo' -grazing_relationship_settings(2,1)%lname = 'Grazing of diaz by microzoo' +grazing_relationship_settings(1,1)%z_umax_0_per_day = 4.1 +grazing_relationship_settings(1,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(1,2)%auto_ind(1) = 2 +grazing_relationship_settings(1,2)%auto_ind_cnt = 1 +grazing_relationship_settings(1,2)%f_zoo_detr = 0.4 +grazing_relationship_settings(1,2)%graze_doc = 0.09 +grazing_relationship_settings(1,2)%graze_poc = 0.28 +grazing_relationship_settings(1,2)%graze_zoo = 0.25 +grazing_relationship_settings(1,2)%grazing_function = 1 +grazing_relationship_settings(1,2)%lname = "Grazing of diat by mesozoo" +grazing_relationship_settings(1,2)%sname = "grz_diat_mesozoo" +grazing_relationship_settings(1,2)%z_grz = 1 +grazing_relationship_settings(1,2)%z_umax_0_per_day = 0.9 +grazing_relationship_settings(1,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(2,1)%auto_ind(1) = 3 grazing_relationship_settings(2,1)%auto_ind_cnt = 1 -grazing_relationship_settings(2,1)%zoo_ind_cnt = 0 grazing_relationship_settings(2,1)%f_zoo_detr = 0.17 grazing_relationship_settings(2,1)%graze_doc = 0.057 grazing_relationship_settings(2,1)%graze_poc = 0.01 grazing_relationship_settings(2,1)%graze_zoo = 0.35 grazing_relationship_settings(2,1)%grazing_function = 1 -grazing_relationship_settings(2,1)%z_grz = 1.2 +grazing_relationship_settings(2,1)%lname = "Grazing of diaz by microzoo" +grazing_relationship_settings(2,1)%sname = "grz_diaz_microzoo" +grazing_relationship_settings(2,1)%z_grz = 1.2 grazing_relationship_settings(2,1)%z_umax_0_per_day = 1 -grazing_relationship_settings(2,1)%auto_ind(1) = 3 - -grazing_relationship_settings(3,1)%sname = 'grz_cocco_microzoo' -grazing_relationship_settings(3,1)%lname = 'Grazing of cocco by microzoo' -grazing_relationship_settings(3,1)%auto_ind_cnt = 1 -grazing_relationship_settings(3,1)%zoo_ind_cnt = 0 -grazing_relationship_settings(3,1)%grazing_function = 1 -grazing_relationship_settings(3,1)%z_umax_0_per_day = 1.7 -grazing_relationship_settings(3,1)%z_grz = 1.5 -grazing_relationship_settings(3,1)%graze_zoo = 0.25 -grazing_relationship_settings(3,1)%graze_poc = 0.18 -grazing_relationship_settings(3,1)%graze_doc = 0.09 -grazing_relationship_settings(3,1)%f_zoo_detr = 0.27 -grazing_relationship_settings(3,1)%auto_ind(1) = 4 - - -grazing_relationship_settings(4,1)%sname = "null" -grazing_relationship_settings(4,1)%lname = "null" -grazing_relationship_settings(4,1)%auto_ind_cnt = 0 -grazing_relationship_settings(4,1)%zoo_ind_cnt = 0 -grazing_relationship_settings(4,1)%f_zoo_detr = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%graze_doc = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%graze_poc = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%graze_zoo = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%grazing_function = 1 -grazing_relationship_settings(4,1)%z_grz = 0.0000000000000000e+00 -grazing_relationship_settings(4,1)%z_umax_0_per_day = 0.0000000000000000e+00 - -! Mesozoo settings - -grazing_relationship_settings(1,2)%sname = 'grz_diat_mesozoo' -grazing_relationship_settings(1,2)%lname = 'Grazing of diat by mesozoo' -grazing_relationship_settings(1,2)%auto_ind_cnt = 1 -grazing_relationship_settings(1,2)%zoo_ind_cnt = 0 -grazing_relationship_settings(1,2)%grazing_function = 1 -grazing_relationship_settings(1,2)%z_umax_0_per_day = 0.9 -grazing_relationship_settings(1,2)%z_grz = 1.0 -grazing_relationship_settings(1,2)%graze_zoo = 0.25 -grazing_relationship_settings(1,2)%graze_poc = 0.28 -grazing_relationship_settings(1,2)%graze_doc = 0.09 -grazing_relationship_settings(1,2)%f_zoo_detr = 0.40 -grazing_relationship_settings(1,2)%auto_ind(1) = 2 - - -grazing_relationship_settings(2,2)%sname = 'grz_cocco_mesozoo' -grazing_relationship_settings(2,2)%lname = 'Grazing of cocco by mesozoo' +grazing_relationship_settings(2,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(2,2)%auto_ind(1) = 4 grazing_relationship_settings(2,2)%auto_ind_cnt = 1 -grazing_relationship_settings(2,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(2,2)%f_zoo_detr = 0.27 +grazing_relationship_settings(2,2)%graze_doc = 0.09 +grazing_relationship_settings(2,2)%graze_poc = 0.18 +grazing_relationship_settings(2,2)%graze_zoo = 0.25 grazing_relationship_settings(2,2)%grazing_function = 1 -grazing_relationship_settings(2,2)%z_umax_0_per_day = 0.015 +grazing_relationship_settings(2,2)%lname = "Grazing of cocco by mesozoo" +grazing_relationship_settings(2,2)%sname = "grz_cocco_mesozoo" grazing_relationship_settings(2,2)%z_grz = 1.4 -grazing_relationship_settings(2,2)%graze_zoo = 0.25 -grazing_relationship_settings(2,2)%graze_poc = 0.18 -grazing_relationship_settings(2,2)%graze_doc = 0.09 -grazing_relationship_settings(2,2)%f_zoo_detr = 0.27 -grazing_relationship_settings(2,2)%auto_ind(1) = 4 - -grazing_relationship_settings(3,2)%sname = 'grz_diaz_mesozoo' -grazing_relationship_settings(3,2)%lname = 'Grazing of diaz by mesozoo' +grazing_relationship_settings(2,2)%z_umax_0_per_day = 0.015 +grazing_relationship_settings(2,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(3,1)%auto_ind(1) = 4 +grazing_relationship_settings(3,1)%auto_ind_cnt = 1 +grazing_relationship_settings(3,1)%f_zoo_detr = 0.27 +grazing_relationship_settings(3,1)%graze_doc = 0.09 +grazing_relationship_settings(3,1)%graze_poc = 0.18 +grazing_relationship_settings(3,1)%graze_zoo = 0.25 +grazing_relationship_settings(3,1)%grazing_function = 1 +grazing_relationship_settings(3,1)%lname = "Grazing of cocco by microzoo" +grazing_relationship_settings(3,1)%sname = "grz_cocco_microzoo" +grazing_relationship_settings(3,1)%z_grz = 1.5 +grazing_relationship_settings(3,1)%z_umax_0_per_day = 1.7 +grazing_relationship_settings(3,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(3,2)%auto_ind(1) = 3 grazing_relationship_settings(3,2)%auto_ind_cnt = 1 -grazing_relationship_settings(3,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(3,2)%f_zoo_detr = 0.4 +grazing_relationship_settings(3,2)%graze_doc = 0.09 +grazing_relationship_settings(3,2)%graze_poc = 0.18 +grazing_relationship_settings(3,2)%graze_zoo = 0.25 grazing_relationship_settings(3,2)%grazing_function = 1 -grazing_relationship_settings(3,2)%z_umax_0_per_day = 0.5 +grazing_relationship_settings(3,2)%lname = "Grazing of diaz by mesozoo" +grazing_relationship_settings(3,2)%sname = "grz_diaz_mesozoo" grazing_relationship_settings(3,2)%z_grz = 1.2 -grazing_relationship_settings(3,2)%graze_zoo = 0.25 -grazing_relationship_settings(3,2)%graze_poc = 0.18 -grazing_relationship_settings(3,2)%graze_doc = 0.09 -grazing_relationship_settings(3,2)%f_zoo_detr = 0.40 -grazing_relationship_settings(3,2)%auto_ind(1) = 3 - -grazing_relationship_settings(4,2)%sname = 'grz_microzoo_mesozoo' -grazing_relationship_settings(4,2)%lname = 'Predation of microzoo by mesozoo' +grazing_relationship_settings(3,2)%z_umax_0_per_day = 0.5 +grazing_relationship_settings(3,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(4,1)%auto_ind_cnt = 0 +grazing_relationship_settings(4,1)%f_zoo_detr = 0 +grazing_relationship_settings(4,1)%graze_doc = 0 +grazing_relationship_settings(4,1)%graze_poc = 0 +grazing_relationship_settings(4,1)%graze_zoo = 0 +grazing_relationship_settings(4,1)%grazing_function = 1 +grazing_relationship_settings(4,1)%lname = "null" +grazing_relationship_settings(4,1)%sname = "null" +grazing_relationship_settings(4,1)%z_grz = 0 +grazing_relationship_settings(4,1)%z_umax_0_per_day = 0 +grazing_relationship_settings(4,1)%zoo_ind_cnt = 0 grazing_relationship_settings(4,2)%auto_ind_cnt = 0 -grazing_relationship_settings(4,2)%zoo_ind_cnt = 1 +grazing_relationship_settings(4,2)%f_zoo_detr = 0.27 +grazing_relationship_settings(4,2)%graze_doc = 0.09 +grazing_relationship_settings(4,2)%graze_poc = 0.28 +grazing_relationship_settings(4,2)%graze_zoo = 0.25 grazing_relationship_settings(4,2)%grazing_function = 1 -grazing_relationship_settings(4,2)%z_umax_0_per_day = 0.2 +grazing_relationship_settings(4,2)%lname = "Grazing of microzoo by mesozoo" +grazing_relationship_settings(4,2)%sname = "grz_microzoo_mesozoo" grazing_relationship_settings(4,2)%z_grz = 1.3 -grazing_relationship_settings(4,2)%graze_zoo = 0.25 -grazing_relationship_settings(4,2)%graze_poc = 0.28 -grazing_relationship_settings(4,2)%graze_doc = 0.09 -grazing_relationship_settings(4,2)%f_zoo_detr = 0.27 +grazing_relationship_settings(4,2)%z_umax_0_per_day = 0.2 grazing_relationship_settings(4,2)%zoo_ind(1) = 1 +grazing_relationship_settings(4,2)%zoo_ind_cnt = 1 +! tracer restoring +tracer_restore_vars(1) = "PO4" +tracer_restore_vars(2) = "NO3" +tracer_restore_vars(3) = "SiO3" +tracer_restore_vars(4) = "ALK" +tracer_restore_vars(5) = "ALK_ALT_CO2" +tracer_restore_vars(6) = "" +tracer_restore_vars(7) = "" +tracer_restore_vars(8) = "" +tracer_restore_vars(9) = "" +tracer_restore_vars(10) = "" +tracer_restore_vars(11) = "" +tracer_restore_vars(12) = "" +tracer_restore_vars(13) = "" +tracer_restore_vars(14) = "" +tracer_restore_vars(15) = "" +tracer_restore_vars(16) = "" +tracer_restore_vars(17) = "" +tracer_restore_vars(18) = "" +tracer_restore_vars(19) = "" +tracer_restore_vars(20) = "" +tracer_restore_vars(21) = "" +tracer_restore_vars(22) = "" +tracer_restore_vars(23) = "" +tracer_restore_vars(24) = "" +tracer_restore_vars(25) = "" +tracer_restore_vars(26) = "" +tracer_restore_vars(27) = "" +tracer_restore_vars(28) = "" +tracer_restore_vars(29) = "" +tracer_restore_vars(30) = "" +tracer_restore_vars(31) = "" +tracer_restore_vars(32) = "" +tracer_restore_vars(33) = "" +tracer_restore_vars(34) = "" +tracer_restore_vars(35) = "" +tracer_restore_vars(36) = "" +tracer_restore_vars(37) = "" From 3ee92e133a93b3c768e65c9c8d3979fb9699de4b Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 5 May 2023 14:55:24 -0600 Subject: [PATCH 092/136] Adding more data for single-column locations Also moved SOUTHLAT and WESTLON from MOM_input to MOM_override, which makes it easier for the gen_IC python notebook to make sure they are set correctly (a bunch of tests in the previous commit were using the wrong values) --- .../examples/single_column_MARBL/.gitignore | 7 + .../examples/single_column_MARBL/ARAB/INPUT | 1 + .../single_column_MARBL/ARAB/MOM_input | 401 +++++++++ .../single_column_MARBL/ARAB/MOM_override | 1 + .../single_column_MARBL/ARAB/MOM_override2 | 4 + .../ARAB/RESTART/.gitignore | 2 + .../single_column_MARBL/ARAB/data_table | 19 + .../single_column_MARBL/ARAB/diag_table | 771 ++++++++++++++++++ .../single_column_MARBL/ARAB/input.nml | 21 + .../ARAB/single_column_z.ipynb | 581 +++++++++++++ .../single_column_MARBL/BATS/MOM_input | 6 - .../single_column_MARBL/EQPAC/MOM_input | 6 - .../single_column_MARBL/HOT/MOM_input | 6 - .../INPUT/ARAB/IC_FILE_COLUMN.nc | Bin 0 -> 134712 bytes .../INPUT/ARAB/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 0 -> 20765 bytes .../ARAB/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 0 -> 16739 bytes .../INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc | Bin 0 -> 3125511 bytes .../INPUT/ARAB/MOM_override | 8 + .../INPUT/ARAB/TOPO_FILE_COLUMN.nc | Bin 0 -> 11889 bytes .../INPUT/BATS/MOM_override | 2 + .../INPUT/EQPAC/MOM_override | 2 + .../INPUT/HOT/MOM_override | 2 + .../INPUT/KERFIX/IC_FILE_COLUMN.nc | Bin 0 -> 134712 bytes .../KERFIX/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 0 -> 20765 bytes .../KERFIX/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 0 -> 16739 bytes .../INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc | Bin 0 -> 3125511 bytes .../INPUT/KERFIX/MOM_override | 8 + .../INPUT/KERFIX/TOPO_FILE_COLUMN.nc | Bin 0 -> 11889 bytes .../INPUT/NABE/IC_FILE_COLUMN.nc | Bin 0 -> 134712 bytes .../INPUT/NABE/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 0 -> 20765 bytes .../NABE/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 0 -> 16739 bytes .../INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc | Bin 0 -> 3125511 bytes .../INPUT/NABE/MOM_override | 8 + .../INPUT/NABE/TOPO_FILE_COLUMN.nc | Bin 0 -> 11889 bytes .../INPUT/STNP/IC_FILE_COLUMN.nc | Bin 0 -> 134712 bytes .../INPUT/STNP/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 0 -> 20765 bytes .../STNP/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 0 -> 16739 bytes .../INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc | Bin 0 -> 3125511 bytes .../INPUT/STNP/MOM_override | 8 + .../INPUT/STNP/TOPO_FILE_COLUMN.nc | Bin 0 -> 11889 bytes .../examples/single_column_MARBL/KERFIX/INPUT | 1 + .../single_column_MARBL/KERFIX/MOM_input | 401 +++++++++ .../single_column_MARBL/KERFIX/MOM_override | 1 + .../single_column_MARBL/KERFIX/MOM_override2 | 4 + .../KERFIX/RESTART/.gitignore | 2 + .../single_column_MARBL/KERFIX/data_table | 19 + .../single_column_MARBL/KERFIX/diag_table | 771 ++++++++++++++++++ .../single_column_MARBL/KERFIX/input.nml | 21 + .../KERFIX/single_column_z.ipynb | 581 +++++++++++++ .../examples/single_column_MARBL/NABE/INPUT | 1 + .../single_column_MARBL/NABE/MOM_input | 401 +++++++++ .../single_column_MARBL/NABE/MOM_override | 1 + .../single_column_MARBL/NABE/MOM_override2 | 4 + .../NABE/RESTART/.gitignore | 2 + .../single_column_MARBL/NABE/data_table | 19 + .../single_column_MARBL/NABE/diag_table | 771 ++++++++++++++++++ .../single_column_MARBL/NABE/input.nml | 21 + .../NABE/single_column_z.ipynb | 581 +++++++++++++ .../examples/single_column_MARBL/STNP/INPUT | 1 + .../single_column_MARBL/STNP/MOM_input | 401 +++++++++ .../single_column_MARBL/STNP/MOM_override | 1 + .../single_column_MARBL/STNP/MOM_override2 | 4 + .../STNP/RESTART/.gitignore | 2 + .../single_column_MARBL/STNP/data_table | 19 + .../single_column_MARBL/STNP/diag_table | 771 ++++++++++++++++++ .../single_column_MARBL/STNP/input.nml | 21 + .../STNP/single_column_z.ipynb | 581 +++++++++++++ 67 files changed, 7249 insertions(+), 18 deletions(-) create mode 100644 standalone/examples/single_column_MARBL/.gitignore create mode 120000 standalone/examples/single_column_MARBL/ARAB/INPUT create mode 100644 standalone/examples/single_column_MARBL/ARAB/MOM_input create mode 120000 standalone/examples/single_column_MARBL/ARAB/MOM_override create mode 100644 standalone/examples/single_column_MARBL/ARAB/MOM_override2 create mode 100644 standalone/examples/single_column_MARBL/ARAB/RESTART/.gitignore create mode 100644 standalone/examples/single_column_MARBL/ARAB/data_table create mode 100644 standalone/examples/single_column_MARBL/ARAB/diag_table create mode 100644 standalone/examples/single_column_MARBL/ARAB/input.nml create mode 100644 standalone/examples/single_column_MARBL/ARAB/single_column_z.ipynb create mode 100644 standalone/examples/single_column_MARBL/INPUT/ARAB/IC_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/ARAB/MARBL_FESEDFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/ARAB/MARBL_FEVENTFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/ARAB/MOM_override create mode 100644 standalone/examples/single_column_MARBL/INPUT/ARAB/TOPO_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/KERFIX/IC_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/KERFIX/MARBL_FESEDFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/KERFIX/MARBL_FEVENTFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/KERFIX/MOM_override create mode 100644 standalone/examples/single_column_MARBL/INPUT/KERFIX/TOPO_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/NABE/IC_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/NABE/MARBL_FESEDFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/NABE/MARBL_FEVENTFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/NABE/MOM_override create mode 100644 standalone/examples/single_column_MARBL/INPUT/NABE/TOPO_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/STNP/IC_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/STNP/MARBL_FESEDFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/STNP/MARBL_FEVENTFLUX_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc create mode 100644 standalone/examples/single_column_MARBL/INPUT/STNP/MOM_override create mode 100644 standalone/examples/single_column_MARBL/INPUT/STNP/TOPO_FILE_COLUMN.nc create mode 120000 standalone/examples/single_column_MARBL/KERFIX/INPUT create mode 100644 standalone/examples/single_column_MARBL/KERFIX/MOM_input create mode 120000 standalone/examples/single_column_MARBL/KERFIX/MOM_override create mode 100644 standalone/examples/single_column_MARBL/KERFIX/MOM_override2 create mode 100644 standalone/examples/single_column_MARBL/KERFIX/RESTART/.gitignore create mode 100644 standalone/examples/single_column_MARBL/KERFIX/data_table create mode 100644 standalone/examples/single_column_MARBL/KERFIX/diag_table create mode 100644 standalone/examples/single_column_MARBL/KERFIX/input.nml create mode 100644 standalone/examples/single_column_MARBL/KERFIX/single_column_z.ipynb create mode 120000 standalone/examples/single_column_MARBL/NABE/INPUT create mode 100644 standalone/examples/single_column_MARBL/NABE/MOM_input create mode 120000 standalone/examples/single_column_MARBL/NABE/MOM_override create mode 100644 standalone/examples/single_column_MARBL/NABE/MOM_override2 create mode 100644 standalone/examples/single_column_MARBL/NABE/RESTART/.gitignore create mode 100644 standalone/examples/single_column_MARBL/NABE/data_table create mode 100644 standalone/examples/single_column_MARBL/NABE/diag_table create mode 100644 standalone/examples/single_column_MARBL/NABE/input.nml create mode 100644 standalone/examples/single_column_MARBL/NABE/single_column_z.ipynb create mode 120000 standalone/examples/single_column_MARBL/STNP/INPUT create mode 100644 standalone/examples/single_column_MARBL/STNP/MOM_input create mode 120000 standalone/examples/single_column_MARBL/STNP/MOM_override create mode 100644 standalone/examples/single_column_MARBL/STNP/MOM_override2 create mode 100644 standalone/examples/single_column_MARBL/STNP/RESTART/.gitignore create mode 100644 standalone/examples/single_column_MARBL/STNP/data_table create mode 100644 standalone/examples/single_column_MARBL/STNP/diag_table create mode 100644 standalone/examples/single_column_MARBL/STNP/input.nml create mode 100644 standalone/examples/single_column_MARBL/STNP/single_column_z.ipynb diff --git a/standalone/examples/single_column_MARBL/.gitignore b/standalone/examples/single_column_MARBL/.gitignore new file mode 100644 index 00000000..e6d2123b --- /dev/null +++ b/standalone/examples/single_column_MARBL/.gitignore @@ -0,0 +1,7 @@ +*/available_diags.000000 +*/CPU_stats +*/exitcode +*/logfile.000000.out +*/MOM_parameter_doc.* +*/ocean.stats +*/time_stamp.out diff --git a/standalone/examples/single_column_MARBL/ARAB/INPUT b/standalone/examples/single_column_MARBL/ARAB/INPUT new file mode 120000 index 00000000..3a2d233e --- /dev/null +++ b/standalone/examples/single_column_MARBL/ARAB/INPUT @@ -0,0 +1 @@ +../INPUT \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/ARAB/MOM_input b/standalone/examples/single_column_MARBL/ARAB/MOM_input new file mode 100644 index 00000000..8172d0d1 --- /dev/null +++ b/standalone/examples/single_column_MARBL/ARAB/MOM_input @@ -0,0 +1,401 @@ +! This input file provides the adjustable run-time parameters for version 6 of the Modular Ocean Model (MOM6). +! Where appropriate, parameters use usually given in MKS units. + +! This particular file is for the example in single_column/KPP. + +! This MOM_input file typically contains only the non-default values that are needed to reproduce this example. +! A full list of parameters for this example can be found in the corresponding MOM_parameter_doc.all file +! which is generated by the model at run-time. + +! === module MOM_domains === +REENTRANT_Y = True ! [Boolean] default = False + ! If true, the domain is meridionally reentrant. +NIGLOBAL = 2 ! + ! The total number of thickness grid points in the x-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NJGLOBAL = 2 ! + ! The total number of thickness grid points in the y-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NIHALO = 2 ! default = 4 + ! The number of halo points on each side in the x-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +NJHALO = 2 ! default = 4 + ! The number of halo points on each side in the y-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +! LAYOUT = 1, 1 ! + ! The processor layout that was actually used. + +! === module MOM === +SPLIT = False ! [Boolean] default = True + ! Use the split time stepping if true. +USE_REGRIDDING = True ! [Boolean] default = False + ! If True, use the ALE algorithm (regridding/remapping). If False, use the + ! layered isopycnal algorithm. +DT = 3600.0 ! [s] + ! The (baroclinic) dynamics time step. The time-step that is actually used will + ! be an integer fraction of the forcing time-step (DT_FORCING in ocean-only mode + ! or the coupling timestep in coupled mode.) +FRAZIL = True ! [Boolean] default = False + ! If true, water freezes if it gets too cold, and the accumulated heat deficit + ! is returned in the surface state. FRAZIL is only used if + ! ENABLE_THERMODYNAMICS is true. +C_P = 3925.0 ! [J kg-1 K-1] default = 3991.86795711963 + ! The heat capacity of sea water, approximated as a constant. This is only used + ! if ENABLE_THERMODYNAMICS is true. The default value is from the TEOS-10 + ! definition of conservative temperature. +SAVE_INITIAL_CONDS = True ! [Boolean] default = False + ! If true, write the initial conditions to a file given by IC_OUTPUT_FILE. + +! === module MOM_fixed_initialization === +USE_MARBL_TRACERS = True + ! "[Boolean] default = False + ! If true, use the MARBL tracer package." +! === module MARBL_tracers === +MARBL_SETTINGS_FILE = "INPUT/marbl_in" ! default = "marbl_in" +MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c211008.nc" + ! The file in which the MARBL tracers initial values can be found. +MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211020.nc" + ! The file in which the iron sediment flux forcing field can be found. +MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211020.nc" + ! The file in which the iron vent flux forcing field can be found. + + +! === module MOM_grid_init === +GRID_CONFIG = "cartesian" ! + ! A character string that determines the method for defining the horizontal + ! grid. Current options are: + ! mosaic - read the grid from a mosaic (supergrid) + ! file set by GRID_FILE. + ! cartesian - use a (flat) Cartesian grid. + ! spherical - use a simple spherical grid. + ! mercator - use a Mercator spherical grid. +LENLAT = 1.0 ! [degrees] + ! The latitudinal or y-direction length of the domain. +LENLON = 1.0 ! [degrees] + ! The longitudinal or x-direction length of the domain. +TOPO_CONFIG = "file" ! + ! This specifies how bathymetry is specified: + ! file - read bathymetric information from the file + ! specified by (TOPO_FILE). + ! flat - flat bottom set to MAXIMUM_DEPTH. + ! bowl - an analytically specified bowl-shaped basin + ! ranging between MAXIMUM_DEPTH and MINIMUM_DEPTH. + ! spoon - a similar shape to 'bowl', but with an vertical + ! wall at the southern face. + ! halfpipe - a zonally uniform channel with a half-sine + ! profile in the meridional direction. + ! bbuilder - build topography from list of functions. + ! benchmark - use the benchmark test case topography. + ! Neverworld - use the Neverworld test case topography. + ! DOME - use a slope and channel configuration for the + ! DOME sill-overflow test case. + ! ISOMIP - use a slope and channel configuration for the + ! ISOMIP test case. + ! DOME2D - use a shelf and slope configuration for the + ! DOME2D gravity current/overflow test case. + ! Kelvin - flat but with rotated land mask. + ! seamount - Gaussian bump for spontaneous motion test case. + ! dumbbell - Sloshing channel with reservoirs on both ends. + ! shelfwave - exponential slope for shelfwave test case. + ! Phillips - ACC-like idealized topography used in the Phillips config. + ! dense - Denmark Strait-like dense water formation and overflow. + ! USER - call a user modified routine. +TOPO_FILE = "TOPO_FILE_COLUMN.nc" ! default = "topog.nc" + ! The file from which the bathymetry is read. + +MAXIMUM_DEPTH = 6000.0 + ! "[m] + ! The maximum depth of the ocean." + +MINIMUM_DEPTH = 9.5 + ! "[m] default = 0.0 + ! If MASKING_DEPTH is unspecified, then anything shallower than + ! MINIMUM_DEPTH is assumed to be land and all fluxes are masked out. + ! If MASKING_DEPTH is specified, then all depths shallower than + ! MINIMUM_DEPTH but depper than MASKING_DEPTH are rounded to + ! MINIMUM_DEPTH." + +MASKING_DEPTH = 0.0 + ! "[m] default = -9999.0 + ! The depth below which to mask points as land points, for which all + ! fluxes are zeroed out. MASKING_DEPTH is ignored if negative." +ROTATION = "betaplane" ! default = "2omegasinlat" + ! This specifies how the Coriolis parameter is specified: + ! 2omegasinlat - Use twice the planetary rotation rate + ! times the sine of latitude. + ! betaplane - Use a beta-plane or f-plane. + ! USER - call a user modified routine. + +! === module MOM_verticalGrid === +! Parameters providing information about the vertical grid. +NK =65 ! [nondim] + ! The number of model layers. + +! === module MOM_EOS === + +! === module MOM_tracer_flow_control === + +! === module MOM_coord_initialization === +REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" + ! Coordinate mode for vertical regridding. Choose among the following + ! possibilities: LAYER - Isopycnal or stacked shallow water layers + ! ZSTAR, Z* - stretched geopotential z* + ! SIGMA_SHELF_ZSTAR - stretched geopotential z* ignoring shelf + ! SIGMA - terrain following coordinates + ! RHO - continuous isopycnal + ! HYCOM1 - HyCOM-like hybrid coordinate + ! SLIGHT - stretched coordinates above continuous isopycnal + ! ADAPTIVE - optimize for smooth neutral density surfaces +ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_65L_20200626.nc,dz" ! default = "UNIFORM" + ! Determines how to specify the coordinate resolution. Valid options are: + ! PARAM - use the vector-parameter ALE_RESOLUTION + ! UNIFORM[:N] - uniformly distributed + ! FILE:string - read from a file. The string specifies + ! the filename and variable name, separated + ! by a comma or space, e.g. FILE:lev.nc,dz + ! or FILE:lev.nc,interfaces=zw + ! WOA09[:N] - the WOA09 vertical grid (approximately) + ! FNC1:string - FNC1:dz_min,H_total,power,precision + ! HYBRID:string - read from a file. The string specifies + ! the filename and two variable names, separated + ! by a comma or space, for sigma-2 and dz. e.g. + ! HYBRID:vgrid.nc,sigma2,dz +!ALE_RESOLUTION = 4*2.0, 2*2.01, 2*2.02, 2.05, 2.07, 2.09, 2.13, 2.18, 2.24, 2.3, 2.4, 2.5, 2.62, 2.78, 2.95, 3.17, 3.42, 3.71, 4.07, 4.48, 4.97, 5.55, 6.23, 7.04, 7.99, 9.11, 10.43, 11.98, 13.8, 15.94, 18.42, 21.32, 24.66, 28.51, 32.91, 37.92, 43.56, 49.87, 56.88, 64.55, 72.91, 81.87, 91.4, 101.39, 111.73, 122.29, 132.93, 143.47, 153.78, 163.7, 173.07, 181.78, 189.76, 196.89, 203.17, 208.58, 213.14, 216.87, 219.87, 222.19, 223.94, 225.18, 226.04, 226.59, 226.9, 227.07, 227.14, 2*227.16, 227.17 ! [m] + ! The distribution of vertical resolution for the target + ! grid used for Eulerian-like coordinates. For example, + ! in z-coordinate mode, the parameter is a list of level + ! thicknesses (in m). In sigma-coordinate mode, the list + ! is of non-dimensional fractions of the water column. + +! === module MOM_state_initialization === +INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False + ! If true, initialize the layer thicknesses, temperatures, and salinities from a + ! Z-space file on a latitude-longitude grid. + +! === module MOM_initialize_layers_from_Z === +TEMP_SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" + ! The name of the z-space input file used to initialize temperatures (T) and + ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and + ! SALT_Z_INIT_FILE must be set. +Z_INIT_FILE_PTEMP_VAR = "Temp" ! default = "ptemp" + ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. +Z_INIT_FILE_SALT_VAR = "Salt" ! default = "salt" + ! The name of the salinity variable in SALT_Z_INIT_FILE. +Z_INIT_HOMOGENIZE = True ! [Boolean] default = False + ! If True, then horizontally homogenize the interpolated initial conditions. +Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False + ! If True, then remap straight to model coordinate from file. + +! === module MOM_diag_mediator === + +! === module MOM_lateral_mixing_coeffs === + +! === module MOM_set_visc === +HBBL = 10.0 ! [m] + ! The thickness of a bottom boundary layer with a viscosity of KVBBL if + ! BOTTOMDRAGLAW is not defined, or the thickness over which near-bottom + ! velocities are averaged for the drag law if BOTTOMDRAGLAW is defined but + ! LINEAR_DRAG is not. +CDRAG = 0.0 ! [nondim] default = 0.003 + ! CDRAG is the drag coefficient relating the magnitude of the velocity field to + ! the bottom stress. CDRAG is only used if BOTTOMDRAGLAW is defined. +DRAG_BG_VEL = 0.1 ! [m s-1] default = 0.0 + ! DRAG_BG_VEL is either the assumed bottom velocity (with LINEAR_DRAG) or an + ! unresolved velocity that is combined with the resolved velocity to estimate + ! the velocity magnitude. DRAG_BG_VEL is only used when BOTTOMDRAGLAW is + ! defined. +BBL_THICK_MIN = 0.1 ! [m] default = 0.0 + ! The minimum bottom boundary layer thickness that can be used with + ! BOTTOMDRAGLAW. This might be Kv/(cdrag*drag_bg_vel) to give Kv as the minimum + ! near-bottom viscosity. +KV = 1.0E-04 ! [m2 s-1] + ! The background kinematic viscosity in the interior. The molecular value, ~1e-6 + ! m2 s-1, may be used. + +! === module MOM_thickness_diffuse === + +! === module MOM_dynamics_unsplit === + +! === module MOM_continuity === + +! === module MOM_continuity_PPM === +UPWIND_1ST_CONTINUITY = True ! [Boolean] default = False + ! If true, CONTINUITY_PPM becomes a 1st-order upwind continuity solver. This + ! scheme is highly diffusive but may be useful for debugging or in single-column + ! mode where its minimal stencil is useful. +ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.75E-09 + ! The tolerance for the differences between the barotropic and baroclinic + ! estimates of the sea surface height due to the fluxes through each face. The + ! total tolerance for SSH is 4 times this value. The default is + ! 0.5*NK*ANGSTROM, and this should not be set less than about + ! 10^-15*MAXIMUM_DEPTH. +ETA_TOLERANCE_AUX = 0.001 ! [m] default = 1.0E-06 + ! The tolerance for free-surface height discrepancies between the barotropic + ! solution and the sum of the layer thicknesses when calculating the auxiliary + ! corrected velocities. By default, this is the same as ETA_TOLERANCE, but can + ! be made larger for efficiency. + +! === module MOM_CoriolisAdv === +CORIOLIS_EN_DIS = True ! [Boolean] default = False + ! If true, two estimates of the thickness fluxes are used to estimate the + ! Coriolis term, and the one that dissipates energy relative to the other one is + ! used. +BOUND_CORIOLIS = True ! [Boolean] default = False + ! If true, the Coriolis terms at u-points are bounded by the four estimates of + ! (f+rv)v from the four neighboring v-points, and similarly at v-points. This + ! option is always effectively false with CORIOLIS_EN_DIS defined and + ! CORIOLIS_SCHEME set to SADOURNY75_ENERGY. + +! === module MOM_PressureForce === + +! === module MOM_PressureForce_AFV === + +! === module MOM_hor_visc === +BIHARMONIC = False ! [Boolean] default = True + ! If true, use a biharmonic horizontal viscosity. BIHARMONIC may be used with + ! LAPLACIAN. + +! === module MOM_vert_friction === +HARMONIC_VISC = True ! [Boolean] default = False + ! If true, use the harmonic mean thicknesses for calculating the vertical + ! viscosity. +HMIX_FIXED = 0.01 ! [m] + ! The prescribed depth over which the near-surface viscosity and diffusivity are + ! elevated when the bulk mixed layer is not used. +KVML = 0.0 ! [m2 s-1] default = 1.0E-04 + ! The kinematic viscosity in the mixed layer. A typical value is ~1e-2 m2 s-1. + ! KVML is not used if BULKMIXEDLAYER is true. The default is set by KV. +MAXVEL = 6.0 ! [m s-1] default = 3.0E+08 + ! The maximum velocity allowed before the velocity components are truncated. +VEL_UNDERFLOW = 1.0E-30 ! [m s-1] default = 0.0 + ! A negligibly small velocity magnitude below which velocity components are set + ! to 0. A reasonable value might be 1e-30 m/s, which is less than an Angstrom + ! divided by the age of the universe. + +! === module MOM_diagnostics === + +! === module MOM_diabatic_driver === +! The following parameters are used for diabatic processes. + +! === module MOM_CVMix_KPP === +! This is the MOM wrapper to CVMix:KPP +! See http://cvmix.github.io/ +USE_KPP = True ! [Boolean] default = False + ! If true, turns on the [CVMix] KPP scheme of Large et al., 1994, to calculate + ! diffusivities and non-local transport in the OBL. +KPP% +INTERP_TYPE = "cubic" ! default = "quadratic" + ! Type of interpolation to determine the OBL depth. + ! Allowed types are: linear, quadratic, cubic. +NLT_SHAPE = "PARABOLIC" ! default = "CVMix" + ! MOM6 method to set nonlocal transport profile. Over-rides the result from + ! CVMix. Allowed values are: + ! CVMix - Uses the profiles from CVMix specified by MATCH_TECHNIQUE + ! LINEAR - A linear profile, 1-sigma + ! PARABOLIC - A parablic profile, (1-sigma)^2 + ! CUBIC - A cubic profile, (1-sigma)^2(1+2*sigma) + ! CUBIC_LMD - The original KPP profile +%KPP + +! === module MOM_set_diffusivity === +ML_RADIATION = True ! [Boolean] default = False + ! If true, allow a fraction of TKE available from wind work to penetrate below + ! the base of the mixed layer with a vertical decay scale determined by the + ! minimum of: (1) The depth of the mixed layer, (2) an Ekman length scale. +TKE_DECAY = 10.0 ! [nondim] default = 2.5 + ! The ratio of the natural Ekman depth to the TKE decay scale. +ML_OMEGA_FRAC = 1.0 ! [nondim] default = 0.0 + ! When setting the decay scale for turbulence, use this fraction of the absolute + ! rotation rate blended with the local value of f, as sqrt((1-of)*f^2 + + ! of*4*omega^2). + +! === module MOM_bkgnd_mixing === +! Adding static vertical background mixing coefficients +KD = 2.0E-05 ! [m2 s-1] default = 0.0 + ! The background diapycnal diffusivity of density in the interior. Zero or the + ! molecular value, ~1e-7 m2 s-1, may be used. +KD_MIN = 2.0E-06 ! [m2 s-1] default = 2.0E-07 + ! The minimum diapycnal diffusivity. + +! === module MOM_kappa_shear === +! Parameterization of shear-driven turbulence following Jackson, Hallberg and Legg, JPO 2008 +USE_JACKSON_PARAM = True ! [Boolean] default = False + ! If true, use the Jackson-Hallberg-Legg (JPO 2008) shear mixing + ! parameterization. +MAX_RINO_IT = 25 ! [nondim] default = 50 + ! The maximum number of iterations that may be used to estimate the Richardson + ! number driven mixing. + +! === module MOM_diabatic_aux === +! The following parameters are used for auxiliary diabatic processes. +RECLAIM_FRAZIL = False ! [Boolean] default = True + ! If true, try to use any frazil heat deficit to cool any overlying layers down + ! to the freezing point, thereby avoiding the creation of thin ice when the SST + ! is above the freezing point. +VAR_PEN_SW = True ! [Boolean] default = False + ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine + ! the e-folding depth of incoming short wave radiation. + +! === module MOM_opacity === +PEN_SW_NBANDS = 3 ! default = 1 + ! The number of bands of penetrating shortwave radiation. + +! === module MOM_tracer_advect === + +! === module MOM_tracer_hor_diff === +KHTR = 600.0 ! [m2 s-1] default = 0.0 + ! The background along-isopycnal tracer diffusivity. + +! === module MOM_sum_output === +MAXTRUNC = 5000 ! [truncations save_interval-1] default = 0 + ! The run will be stopped, and the day set to a very large value if the velocity + ! is truncated more than MAXTRUNC times between energy saves. Set MAXTRUNC to 0 + ! to stop if there is any truncation of velocities. +ENERGYSAVEDAYS = 10.0 ! [days] default = 1.0 + ! The interval in units of TIMEUNIT between saves of the energies of the run and + ! other globally summed diagnostics. + +! === module MOM_surface_forcing === +BUOY_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how buoyancy forcing is specified. Valid + ! options include (file), (zero), (linear), (USER), (BFB) and (NONE). +WIND_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how wind forcing is specified. Valid + ! options include (file), (2gyre), (1gyre), (gyres), (zero), and (USER). +READ_GUST_2D = False ! [Boolean] default = False + ! If true, use a 2-dimensional gustiness supplied from an input file +GUST_CONST = 0.02 ! [Pa] default = 0.0 + ! The background gustiness in the winds. + +! === module MOM_main (MOM_driver) === +DAYMAX = 1825.0 ! [days] + ! The final time of the whole simulation, in units of TIMEUNIT seconds. This + ! also sets the potential end time of the present run segment if the end time is + ! not set via ocean_solo_nml in input.nml. +RESTART_CONTROL = 0 ! default = 1 + ! An integer whose bits encode which restart files are written. Add 2 (bit 1) + ! for a time-stamped file, and odd (bit 0) for a non-time-stamped file. A + ! non-time-stamped restart file is saved at the end of the run segment for any + ! non-negative value. +RESTINT = 3650.0 ! [days] default = 0.0 + ! The interval between saves of the restart file in units of TIMEUNIT. Use 0 + ! (the default) to not save incremental restart files at all. + +! === module MOM_write_cputime === +MAXCPU = 2.88E+04 ! [wall-clock seconds] default = -1.0 + ! The maximum amount of cpu time per processor for which MOM should run before + ! saving a restart file and quitting with a return value that indicates that a + ! further run is required to complete the simulation. If automatic restarts are + ! not desired, use a negative value for MAXCPU. MAXCPU has units of wall-clock + ! seconds, so the actual CPU time used is larger by a factor of the number of + ! processors used. + +! Debugging parameters set to non-default values +U_TRUNC_FILE = "U_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to zonal + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. +V_TRUNC_FILE = "V_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to meridional + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. diff --git a/standalone/examples/single_column_MARBL/ARAB/MOM_override b/standalone/examples/single_column_MARBL/ARAB/MOM_override new file mode 120000 index 00000000..7076a2e2 --- /dev/null +++ b/standalone/examples/single_column_MARBL/ARAB/MOM_override @@ -0,0 +1 @@ +INPUT/ARAB/MOM_override \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/ARAB/MOM_override2 b/standalone/examples/single_column_MARBL/ARAB/MOM_override2 new file mode 100644 index 00000000..94a755f0 --- /dev/null +++ b/standalone/examples/single_column_MARBL/ARAB/MOM_override2 @@ -0,0 +1,4 @@ +! Generated by run_suite.csh +!#override DAYMAX = 365 +!#override ALE_COORDINATE_CONFIG = "FILE:INPUT/vgrid_cm4_4.nc,dz" ! 4 meter surface +!#override ALE_COORDINATE_CONFIG = "FILE:INPUT/vgrid_cm4.nc,dz" ! 2 meter surface diff --git a/standalone/examples/single_column_MARBL/ARAB/RESTART/.gitignore b/standalone/examples/single_column_MARBL/ARAB/RESTART/.gitignore new file mode 100644 index 00000000..d6b7ef32 --- /dev/null +++ b/standalone/examples/single_column_MARBL/ARAB/RESTART/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/standalone/examples/single_column_MARBL/ARAB/data_table b/standalone/examples/single_column_MARBL/ARAB/data_table new file mode 100644 index 00000000..7c904d8c --- /dev/null +++ b/standalone/examples/single_column_MARBL/ARAB/data_table @@ -0,0 +1,19 @@ +# gridname, fieldname_code, fieldname_file, file_name, ongrid, factor +"OCN" , "lw", "LWDN", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sw", "SWDN", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "rain", "RAIN", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "evap", "EVAP", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "taux", "TAUX", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "tauy", "TAUY", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "snow", "SNOW", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 +"OCN" , "runoff", "", "", .true., 0.0 +"OCN" , "calving", "", "", .true., 0.0 +"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_dust_flux", "SEAICE_DUST_FLUX", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_bc_flux", "SEAICE_BC_FLUX", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "nhx_dep", "NDEP_NHx", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "noy_dep", "NDEP_NOy", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "u10_sqr", "U10_SQR", "./INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 diff --git a/standalone/examples/single_column_MARBL/ARAB/diag_table b/standalone/examples/single_column_MARBL/ARAB/diag_table new file mode 100644 index 00000000..8f6227fa --- /dev/null +++ b/standalone/examples/single_column_MARBL/ARAB/diag_table @@ -0,0 +1,771 @@ +"MOM Experiment" +1 1 1 0 0 0 +"prog", 1,"days",1,"days","Time" +"prog_z", 1,"days",1,"days","Time" +"surffluxes", 1,"days",1,"days","Time" +"ave_prog", 1,"days",1,"days","Time", +#"mom", 5,"days",1,"days","Time", +#"bt_mom", 5,"days",1,"days","Time", +"visc", 1,"days",1,"days","Time", +#"energy", 5,"days",1,"days","Time", +"marbl", 1,"days",1,"days","Time", +"marbl2", 1,"days",1,"days","Time", + + +#This is the field section of the diag_table. + +# Prognostic Ocean fields: +#========================= + +"ocean_model","u","u","prog","all",.false.,"none",2 +"ocean_model","v","v","prog","all",.false.,"none",2 +"ocean_model","h","h","prog","all",.false.,"none",1 +"ocean_model","e","e","prog","all",.false.,"none",2 +"ocean_model","temp","temp","prog","all",.false.,"none",2 +"ocean_model","salt","salt","prog","all",.false.,"none",2 +#"ocean_model","Rml","Rml","prog","all",.false.,"none",2 +#"ocean_model","h_ML","h_ML","prog","all",.false.,"none",2 + +#"ocean_model","RV","RV","prog","all",.false.,"none",2 +#"ocean_model","PV","PV","prog","all",.false.,"none",2 +#"ocean_model","e_D","e_D","prog","all",.false.,"none",2 + +"ocean_model","u","u","ave_prog","all",.true.,"none",2 +"ocean_model","v","v","ave_prog","all",.true.,"none",2 +#"ocean_model","h","h","ave_prog","all",.true.,"none",1 +"ocean_model","e","e","ave_prog","all",.true.,"none",2 +"ocean_model","temp","temp","ave_prog","all",.true.,"none",2 +"ocean_model","salt","salt","ave_prog","all",.true.,"none",2 +#"ocean_model","Rml","Rml","ave_prog","all",.true.,"none",2 + +# Z level output +#================ +"ocean_model_z","u","u","prog_z","all",.false.,"none",2 +"ocean_model_z","v","v","prog_z","all",.false.,"none",2 +"ocean_model_z","temp","temp","prog_z","all",.false.,"none",2 +"ocean_model_z","salt","salt","prog_z","all",.false.,"none",2 +# Auxilary Tracers: +#================== +#"ocean_model","vintage","vintage","prog","all",.false.,"none",2 +#"ocean_model","age","age","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","prog","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog","all",.false.,"none",2 +"ocean_model","O2","O2","prog","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog","all",.false.,"none",2 +"ocean_model","DON","DON","prog","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog","all",.false.,"none",2 +"ocean_model","spC","spC","prog","all",.false.,"none",2 +"ocean_model","spP","spP","prog","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","ave_prog","all",.true.,"none",2 +"ocean_model","NO3","NO3","ave_prog","all",.true.,"none",2 +"ocean_model","SiO3","SiO3","ave_prog","all",.true.,"none",2 +"ocean_model","NH4","NH4","ave_prog","all",.true.,"none",2 +"ocean_model","Fe","Fe","ave_prog","all",.true.,"none",2 +"ocean_model","Lig","Lig","ave_prog","all",.true.,"none",2 +"ocean_model","O2","O2","ave_prog","all",.true.,"none",2 +"ocean_model","DIC","DIC","ave_prog","all",.true.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","ALK","ALK","ave_prog","all",.true.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","DOC","DOC","ave_prog","all",.true.,"none",2 +"ocean_model","DON","DON","ave_prog","all",.true.,"none",2 +"ocean_model","DOP","DOP","ave_prog","all",.true.,"none",2 +"ocean_model","DOPr","DOPr","ave_prog","all",.true.,"none",2 +"ocean_model","DONr","DONr","ave_prog","all",.true.,"none",2 +"ocean_model","DOCr","DOCr","ave_prog","all",.true.,"none",2 +"ocean_model","microzooC","microzooC","ave_prog","all",.true.,"none",2 +"ocean_model","mesozooC","mesozooC","ave_prog","all",.true.,"none",2 +"ocean_model","spChl","spChl","ave_prog","all",.true.,"none",2 +"ocean_model","spC","spC","ave_prog","all",.true.,"none",2 +"ocean_model","spP","spP","ave_prog","all",.true.,"none",2 +"ocean_model","spFe","spFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoChl","coccoChl","ave_prog","all",.true.,"none",2 +"ocean_model","coccoC","coccoC","ave_prog","all",.true.,"none",2 +"ocean_model","coccoP","coccoP","ave_prog","all",.true.,"none",2 +"ocean_model","coccoFe","coccoFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","ave_prog","all",.true.,"none",2 +"ocean_model","diatChl","diatChl","ave_prog","all",.true.,"none",2 +"ocean_model","diatC","diatC","ave_prog","all",.true.,"none",2 +"ocean_model","diatP","diatP","ave_prog","all",.true.,"none",2 +"ocean_model","diatFe","diatFe","ave_prog","all",.true.,"none",2 +"ocean_model","diatSi","diatSi","ave_prog","all",.true.,"none",2 +"ocean_model","diazChl","diazChl","ave_prog","all",.true.,"none",2 +"ocean_model","diazC","diazC","ave_prog","all",.true.,"none",2 +"ocean_model","diazP","diazP","ave_prog","all",.true.,"none",2 +"ocean_model","diazFe","diazFe","ave_prog","all",.true.,"none",2 +"ocean_model","PO4","PO4","prog_z","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog_z","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog_z","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog_z","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog_z","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog_z","all",.false.,"none",2 +"ocean_model","O2","O2","prog_z","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog_z","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog_z","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog_z","all",.false.,"none",2 +"ocean_model","DON","DON","prog_z","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog_z","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog_z","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog_z","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog_z","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog_z","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog_z","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog_z","all",.false.,"none",2 +"ocean_model","spC","spC","prog_z","all",.false.,"none",2 +"ocean_model","spP","spP","prog_z","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog_z","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog_z","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog_z","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog_z","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog_z","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog_z","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog_z","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog_z","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog_z","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog_z","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog_z","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog_z","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog_z","all",.false.,"none",2 + + +# Continuity Equation Terms: +#=========================== +#"ocean_model","dhdt","dhdt","cont","all",.true.,"none",2 +"ocean_model","wd","wd","prog","all",.false.,"none",2 +#"ocean_model","uh","uh","cont","all",.true.,"none",2 +#"ocean_model","vh","vh","cont","all",.true.,"none",2 +#"ocean_model","uhGM","uhGM","cont","all",.true.,"none",2 +#"ocean_model","vhGM","vhGM","cont","all",.true.,"none",2 +#"ocean_model","uhbt","uhbt","cont","all",.true.,"none",2 +#"ocean_model","vhbt","vhbt","cont","all",.true.,"none",2 +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","KH_u","KH_u","cont","all",.true.,"none",2 +#"ocean_model","KH_v","KH_v","cont","all",.true.,"none",2 +#"ocean_model","GMwork","GMwork","cont","all",.true.,"none",2 + +# Continuity Equation Terms In Pure Potential Density Coordiantes: +#================================================================= +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 + +# +# Tracer Fluxes: +#================== +#"ocean_model","T_adx", "T_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_ady", "T_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffx","T_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffy","T_diffy","ave_prog","all",.true.,"none",2 +#"ocean_model","S_adx", "S_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_ady", "S_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffx","S_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffy","S_diffy","ave_prog","all",.true.,"none",2 + + +# Momentum Balance Terms: +#======================= +#"ocean_model","dudt","dudt","mom","all",.true.,"none",2 +#"ocean_model","dvdt","dvdt","mom","all",.true.,"none",2 +#"ocean_model","CAu","CAu","mom","all",.true.,"none",2 +#"ocean_model","CAv","CAv","mom","all",.true.,"none",2 +#"ocean_model","PFu","PFu","mom","all",.true.,"none",2 +#"ocean_model","PFv","PFv","mom","all",.true.,"none",2 +#"ocean_model","du_dt_visc","du_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","dv_dt_visc","dv_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","diffu","diffu","mom","all",.true.,"none",2 +#"ocean_model","diffv","diffv","mom","all",.true.,"none",2 +#"ocean_model","dudt_dia","dudt_dia","mom","all",.true.,"none",2 +#"ocean_model","dvdt_dia","dvdt_dia","mom","all",.true.,"none",2 +# Subterms that should not be added to a closed budget. +#"ocean_model","gKEu","gKEu","mom","all",.true.,"none",2 +#"ocean_model","gKEv","gKEv","mom","all",.true.,"none",2 +#"ocean_model","rvxu","rvxu","mom","all",.true.,"none",2 +#"ocean_model","rvxv","rvxv","mom","all",.true.,"none",2 +#"ocean_model","PFu_bc","PFu_bc","mom","all",.true.,"none",2 +#"ocean_model","PFv_bc","PFv_bc","mom","all",.true.,"none",2 + +# Barotropic Momentum Balance Terms: +# (only available with split time stepping.) +#=========================================== +#"ocean_model","PFuBT","PFuBT","bt_mom","all",.true.,"none",2 +#"ocean_model","PFvBT","PFvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CoruBT","CoruBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CorvBT","CorvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","ubtforce","ubtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","vbtforce","vbtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","u_accel_bt","u_accel_bt","bt_mom","all",.true.,"none",2 +#"ocean_model","v_accel_bt","v_accel_bt","bt_mom","all",.true.,"none",2 +# +# Viscosities and diffusivities: +#=============================== +#"ocean_model","Kd_effective","Kd_effective","visc","all",.true.,"none",2 +"ocean_model","Kd_interface","Kd_interface","visc","all",.true.,"none",2 +#"ocean_model","Ahh","Ahh","visc","all",.true.,"none",2 +#"ocean_model","Ahq","Ahq","visc","all",.true.,"none",2 +#"ocean_model","Khh","Khh","visc","all",.true.,"none",2 +#"ocean_model","Khq","Khq","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_u","bbl_thick_u","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_u","kv_bbl_u","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_v","bbl_thick_v","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_v","kv_bbl_v","visc","all",.true.,"none",2 +#"ocean_model","av_visc","av_visc","visc","all",.true.,"none",2 +#"ocean_model","au_visc","au_visc","visc","all",.true.,"none",2 +#"ocean_model","FrictWork","FrictWork","visc","all",.true.,"none",2 +"ocean_model","KPP_uStar","KPP_uStar","visc","all",.true.,"none",2 +"ocean_model","KPP_buoyFlux","KPP_buoyFlux","visc","all",.true.,"none",2 +"ocean_model","KPP_QminusSW","KPP_QminusSW","visc","all",.true.,"none",2 +"ocean_model","KPP_netSalt","KPP_netSalt","visc","all",.true.,"none",2 +"ocean_model","KPP_OBLdepth","KPP_OBLdepth","visc","all",.true.,"none",2 +"ocean_model","KPP_sigma","KPP_sigma","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkRi","KPP_BulkRi","visc","all",.true.,"none",2 +"ocean_model","KPP_Ws","KPP_Ws","visc","all",.true.,"none",2 +"ocean_model","KPP_N","KPP_N","visc","all",.true.,"none",2 +"ocean_model","KPP_N2","KPP_N2","visc","all",.true.,"none",2 +"ocean_model","KPP_Vt2","KPP_Vt2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkUz2","KPP_BulkUz2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkDrho","KPP_BulkDrho","visc","all",.true.,"none",2 +"ocean_model","KPP_Kd_in","KPP_Kd_in","visc","all",.true.,"none",2 +"ocean_model","KPP_Kheat","KPP_Kheat","visc","all",.true.,"none",2 +"ocean_model","KPP_Ksalt","KPP_Ksalt","visc","all",.true.,"none",2 +"ocean_model","KPP_Kv","KPP_Kv","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_heat","KPP_NLtransport_heat","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_salt","KPP_NLtransport_salt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dTdt","KPP_NLT_dTdt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dSdt","KPP_NLT_dSdt","visc","all",.true.,"none",2 +"ocean_model","KPP_Tsurf","KPP_Tsurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Ssurf","KPP_Ssurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Usurf","KPP_Usurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Vsurf","KPP_Vsurf","visc","all",.true.,"none",2 +"ocean_model","MLD_003","MLD_003","visc","all",.true.,"none",2 + +# +# Kinetic Energy Balance Terms: +#============================= +#"ocean_model","KE","KE","energy","all",.true.,"none",2 +#"ocean_model","dKE_dt","dKE_dt","energy","all",.true.,"none",2 +#"ocean_model","PE_to_KE","PE_to_KE","energy","all",.true.,"none",2 +#"ocean_model","KE_Coradv","KE_Coradv","energy","all",.true.,"none",2 +#"ocean_model","KE_adv","KE_adv","energy","all",.true.,"none",2 +#"ocean_model","KE_visc","KE_visc","energy","all",.true.,"none",2 +#"ocean_model","KE_horvisc","KE_horvisc","energy","all",.true.,"none",2 +#"ocean_model","KE_dia","KE_dia","energy","all",.true.,"none",2 + +# Surface Forcing: +#================= +"ocean_model","taux","taux","surffluxes","all",.true.,"none",2 +"ocean_model","tauy","tauy","surffluxes","all",.true.,"none",2 +"ocean_model","ustar","ustar","surffluxes","all",.true.,"none",2 +"ocean_model","PRCmE","PRCmE","surffluxes","all",.true.,"none",2 +"ocean_model","evap","evap","surffluxes","all",.true.,"none",2 +"ocean_model","precip","precip","surffluxes","all",.true.,"none",2 +"ocean_model","fprec","fprec","surffluxes","all",.true.,"none",2 +"ocean_model","lprec","lprec","surffluxes","all",.true.,"none",2 +"ocean_model","net_heat_surface","net_heat_surface","surffluxes","all",.true.,"none",2 +"ocean_model","SW","SW","surffluxes","all",.true.,"none",2 +"ocean_model","SW_pen","SW_pen","surffluxes","all",.true.,"none",2 +"ocean_model","LwLatSens","LwLatSens","surffluxes","all",.true.,"none",2 +#"ocean_model","p_surf","p_surf","surffluxes","all",.true.,"none",2 +#"ocean_model","salt_flux","salt_flux","surffluxes","all",.true.,"none",2 + +# MARBL diagnostics: +#=================== +"ocean_model", "rsdoabsorb", "rsdoabsorb", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_IFRAC", "ECOSYS_IFRAC", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_XKW", "ECOSYS_XKW", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_ATM_PRESS", "ECOSYS_ATM_PRESS", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_O2", "PV_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_O2", "SCHMIDT_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "O2SAT", "O2SAT", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR", "CO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR", "DCO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "pCO2SURF", "pCO2SURF", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2", "DpCO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_CO2", "PV_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_CO2", "SCHMIDT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_CO2", "FG_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH", "PH", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_CO2", "ATM_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR_ALT_CO2", "CO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR_ALT_CO2", "DCO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pCO2SURF_ALT_CO2", "pCO2SURF_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2_ALT_CO2", "DpCO2_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_ALT_CO2", "FG_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH_ALT_CO2", "PH_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_ALT_CO2", "ATM_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "IRON_FLUX", "IRON_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "DUST_FLUX", "DUST_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NOx_FLUX", "NOx_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHy_FLUX", "NHy_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHx_SURFACE_EMIS", "NHx_SURFACE_EMIS", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatcalc", "zsatcalc", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatarag", "zsatarag", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN", "O2_ZMIN", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN_DEPTH", "O2_ZMIN_DEPTH", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint", "photoC_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint_100m", "photoC_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint", "photoC_NO3_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint_100m", "photoC_NO3_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint", "DOC_prod_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint_100m", "DOC_prod_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint", "DOC_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint_100m", "DOC_remin_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint", "DOCr_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint_100m", "DOCr_remin_zint_100m", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ctot", "Jint_Ctot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ntot", "Jint_Ntot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ptot", "Jint_Ptot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Sitot", "Jint_Sitot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Fetot", "Jint_Fetot", "marbl", "all", .false., "none", 2 +#"ocean_model", "calcToFloor", "calcToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed", "calcToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed_ALT_CO2", "calcToSed_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pocToFloor", "pocToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "pocToSed", "pocToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "ponToSed", "ponToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "SedDenitrif", "SedDenitrif", "marbl", "all", .false., "none", 2 +#"ocean_model", "OtherRemin", "OtherRemin", "marbl", "all", .false., "none", 2 +"ocean_model", "popToSed", "popToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "bsiToSed", "bsiToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "dustToSed", "dustToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "pfeToSed", "pfeToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_surf", "sp_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_Cweight_avg_100m", "sp_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_surf", "sp_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_Cweight_avg_100m", "sp_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_surf", "sp_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_Cweight_avg_100m", "sp_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_surf", "sp_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_Cweight_avg_100m", "sp_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint", "photoC_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint_100m", "photoC_sp_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_sp_zint", "photoC_NO3_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint", "graze_sp_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint_100m", "graze_sp_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint", "graze_sp_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint_100m", "graze_sp_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint", "graze_sp_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint_100m", "graze_sp_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint", "graze_sp_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint_100m", "graze_sp_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint", "sp_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint_100m", "sp_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint", "sp_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint_100m", "sp_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint", "sp_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint_100m", "sp_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint", "sp_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint_100m", "sp_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_surf", "cocco_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_Cweight_avg_100m", "cocco_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_surf", "cocco_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_Cweight_avg_100m", "cocco_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_surf", "cocco_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_Cweight_avg_100m", "cocco_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_surf", "cocco_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_Cweight_avg_100m", "cocco_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint", "photoC_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint_100m", "photoC_cocco_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_cocco_zint", "photoC_NO3_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint", "cocco_CaCO3_form_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint_100m", "cocco_CaCO3_form_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint", "graze_cocco_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint_100m", "graze_cocco_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint", "graze_cocco_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint_100m", "graze_cocco_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint", "graze_cocco_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint_100m", "graze_cocco_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint", "graze_cocco_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint_100m", "graze_cocco_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint", "cocco_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint_100m", "cocco_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint", "cocco_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint_100m", "cocco_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint", "cocco_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint_100m", "cocco_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg_zint", "cocco_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg_zint_100m", "cocco_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_N_lim_surf", "diat_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_N_lim_Cweight_avg_100m", "diat_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_P_lim_surf", "diat_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_P_lim_Cweight_avg_100m", "diat_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Fe_lim_surf", "diat_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Fe_lim_Cweight_avg_100m", "diat_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_SiO3_lim_surf", "diat_SiO3_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_SiO3_lim_Cweight_avg_100m", "diat_SiO3_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_light_lim_surf", "diat_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_light_lim_Cweight_avg_100m", "diat_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat_zint", "photoC_diat_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat_zint_100m", "photoC_diat_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diat_zint", "photoC_NO3_diat_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat_zint", "graze_diat_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zint_100m", "graze_diat_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc_zint", "graze_diat_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc_zint_100m", "graze_diat_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc_zint", "graze_diat_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc_zint_100m", "graze_diat_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo_zint", "graze_diat_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo_zint_100m", "graze_diat_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "diat_loss_zint", "diat_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_zint_100m", "diat_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc_zint", "diat_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc_zint_100m", "diat_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc_zint", "diat_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc_zint_100m", "diat_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_agg_zint", "diat_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_agg_zint_100m", "diat_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_N_lim_surf", "diaz_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_N_lim_Cweight_avg_100m", "diaz_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_P_lim_surf", "diaz_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_P_lim_Cweight_avg_100m", "diaz_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Fe_lim_surf", "diaz_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Fe_lim_Cweight_avg_100m", "diaz_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_light_lim_surf", "diaz_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_light_lim_Cweight_avg_100m", "diaz_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diaz_zint", "photoC_diaz_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diaz_zint_100m", "photoC_diaz_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diaz_zint", "photoC_NO3_diaz_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zint", "graze_diaz_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zint_100m", "graze_diaz_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc_zint", "graze_diaz_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc_zint_100m", "graze_diaz_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc_zint", "graze_diaz_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc_zint_100m", "graze_diaz_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zoo_zint", "graze_diaz_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zoo_zint_100m", "graze_diaz_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "diaz_loss_zint", "diaz_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_zint_100m", "diaz_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_poc_zint", "diaz_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_poc_zint_100m", "diaz_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_doc_zint", "diaz_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_doc_zint_100m", "diaz_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_agg_zint", "diaz_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_agg_zint_100m", "diaz_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "CaCO3_form_zint", "CaCO3_form_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "CaCO3_form_zint_100m", "CaCO3_form_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_zint", "microzoo_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_zint", "mesozoo_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_zint_100m", "microzoo_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_zint_100m", "mesozoo_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_basal_zint", "microzoo_loss_basal_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_basal_zint", "mesozoo_loss_basal_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_basal_zint_100m", "microzoo_loss_basal_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_basal_zint_100m", "mesozoo_loss_basal_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_poc_zint", "microzoo_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_poc_zint", "mesozoo_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_poc_zint_100m", "microzoo_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_poc_zint_100m", "mesozoo_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_doc_zint", "microzoo_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_doc_zint", "mesozoo_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_doc_zint_100m", "microzoo_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_doc_zint_100m", "mesozoo_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_zint", "graze_microzoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_zint", "graze_mesozoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_zint_100m", "graze_microzoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_zint_100m", "graze_mesozoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_poc_zint", "graze_microzoo_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_poc_zint", 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+#"ocean_model", "PO4_RESTORE_TEND", "PO4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NO3_RESTORE_TEND", "NO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "SiO3_RESTORE_TEND", "SiO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NH4_RESTORE_TEND", "NH4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Fe_RESTORE_TEND", "Fe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Lig_RESTORE_TEND", "Lig_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "O2_RESTORE_TEND", "O2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_RESTORE_TEND", "DIC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_ALT_CO2_RESTORE_TEND", "DIC_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_RESTORE_TEND", "ALK_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_ALT_CO2_RESTORE_TEND", "ALK_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOC_RESTORE_TEND", "DOC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DON_RESTORE_TEND", "DON_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOP_RESTORE_TEND", "DOP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOPr_RESTORE_TEND", "DOPr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DONr_RESTORE_TEND", "DONr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOCr_RESTORE_TEND", "DOCr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "microzooC_RESTORE_TEND", "microzooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "mesozooC_RESTORE_TEND", "mesozooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spChl_RESTORE_TEND", "spChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spC_RESTORE_TEND", "spC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spP_RESTORE_TEND", "spP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spFe_RESTORE_TEND", "spFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoChl_RESTORE_TEND", "coccoChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoC_RESTORE_TEND", "coccoC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoP_RESTORE_TEND", "coccoP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoFe_RESTORE_TEND", "coccoFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoCaCO3_RESTORE_TEND", "coccoCaCO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatChl_RESTORE_TEND", "diatChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatC_RESTORE_TEND", "diatC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatP_RESTORE_TEND", "diatP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatFe_RESTORE_TEND", "diatFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatSi_RESTORE_TEND", "diatSi_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazChl_RESTORE_TEND", "diazChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazC_RESTORE_TEND", "diazC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazP_RESTORE_TEND", "diazP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazFe_RESTORE_TEND", "diazFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 + +#============================================================================================= +# +#====> This file can be used with diag_manager/v2.0a (or higher) <==== +# +# +# FORMATS FOR FILE ENTRIES (not all input values are used) +# ------------------------ +# +#"file_name", output_freq, "output_units", format, "time_units", "time_long_name", ... +# (opt) new_file_frequecy, (opt) "new_file_freq_units", "new_file_start_date" +# +# +#output_freq: > 0 output frequency in "output_units" +# = 0 output frequency every time step +# =-1 output frequency at end of run +# +#output_units = units used for output frequency +# (years, months, days, minutes, hours, seconds) +# +#time_units = units used to label the time axis +# (days, minutes, hours, seconds) +# +# +# FORMAT FOR FIELD ENTRIES (not all input values are used) +# ------------------------ +# +#"module_name", "field_name", "output_name", "file_name" "time_sampling", time_avg, "other_opts", packing +# +#time_avg = .true. or .false. +# +#packing = 1 double precision +# = 2 float +# = 4 packed 16-bit integers +# = 8 packed 1-byte (not tested?) diff --git a/standalone/examples/single_column_MARBL/ARAB/input.nml b/standalone/examples/single_column_MARBL/ARAB/input.nml new file mode 100644 index 00000000..01e68431 --- /dev/null +++ b/standalone/examples/single_column_MARBL/ARAB/input.nml @@ -0,0 +1,21 @@ + &MOM_input_nml + output_directory = './', + input_filename = 'n' + restart_input_dir = 'INPUT/', + restart_output_dir = 'RESTART/', + parameter_filename = 'MOM_input', + 'MOM_override', + 'MOM_override2' / + + &diag_manager_nml + max_output_fields = 750, + / + + &ocean_solo_nml + calendar = 'NOLEAP' +/ + + &fms_nml + clock_grain = 'LOOP', + domains_stack_size = 710000, + stack_size = 0 / diff --git a/standalone/examples/single_column_MARBL/ARAB/single_column_z.ipynb b/standalone/examples/single_column_MARBL/ARAB/single_column_z.ipynb new file mode 100644 index 00000000..5ba27191 --- /dev/null +++ b/standalone/examples/single_column_MARBL/ARAB/single_column_z.ipynb @@ -0,0 +1,581 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:847c08f9e6b1a07efd1e43efe9fa87aa4bed23875cde3d67a2c275d725f76697" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

      MOM6 diagnostics for KPP single column test case

      \n", + "\n", + " Results from this notebook: \n", + "1. Basic diagnostics of KPP boundary layer and prognostic fields. \n", + "\n", + "Caveats regarding this notebook:\n", + "1. This notebook is written for MOM6-examples/ocean_only/single_column_z. \n", + "\n", + "Hopes for the use of this notebook: \n", + "1. To provide a starting point to document single column model tests;\n", + "2. To illustrate a self-contained iPython notebook of use for MOM6 analysis. \n", + "\n", + "This iPython notebook was originally developed at NOAA/GFDL, and it is provided freely to the MOM6 community. GFDL scientists developing MOM6 make extensive use of Python for diagnostics. We solicit modifications/fixes that are useful to the MOM6 community." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import matplotlib.pyplot as plt # For plotting\n", + "import netCDF4 # For reading data\n", + "pylab.rcParams['figure.figsize'] = (17.0, 10.0) # Large figures\n", + "\n", + "path = '../single_column_z/'\n", + "deltaz = 'CM4'\n", + "fname_deltaz = '_CM4'\n", + "expt_name = 'single_column_z'\n", + "\n", + "dpi=200" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "visc = netCDF4.Dataset(path+'visc.nc')\n", + "for v in visc.variables: print v,\n", + "\n", + "# recall data layout is (t,z,y,x)\n", + "\n", + "time = visc.variables['Time'][:]\n", + "\n", + "# KPP boundary layer depth as function of time (metre)\n", + "h = visc.variables['KPP_OBLdepth'][:,0,0]\n", + "\n", + "# KPP tracer diffusivity as function of time and depth (m2/sec)\n", + "Kt = visc.variables['KPP_Kheat'][:,:,0,0]\n", + "\n", + "# KPP velocity diffusivity as function of time and depth (m2/sec)\n", + "Ku = visc.variables['KPP_Kv'][:,:,0,0]\n", + "\n", + "# surface (and penetrating) buoyancy flux, as used by [CVmix] KPP (m2/s3)\n", + "KPP_buoyFlux = visc.variables['KPP_buoyFlux'][:,:,0,0]\n", + "\n", + "# Temperature tendency due to non-local transport of heat, as calculated by KPP (K/s)\n", + "KPP_dTdt = visc.variables['KPP_dTdt'][:,:,0,0]\n", + "\n", + "# Saln tendency due to non-local transport of heat, as calculated by KPP (ppt/s)\n", + "KPP_dSdt = visc.variables['KPP_dSdt'][:,:,0,0]\n", + "\n", + "# friction velocity used by KPP (m/s)\n", + "KPP_uStar = visc.variables['KPP_uStar'][:,0,0]\n", + "\n", + "# Squared buoyancy frequency (1/s^2)\n", + "KPP_N2 = visc.variables['KPP_N2'][:,:,0,0]\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "xh yh zi Time nv zl xq yq Kd_interface KPP_uStar KPP_buoyFlux KPP_QminusSW KPP_netSalt KPP_OBLdepth KPP_sigma KPP_BulkRi KPP_Ws KPP_N KPP_N2 KPP_Vt2 KPP_BulkUz2 KPP_BulkDrho KPP_Kd_in KPP_Kheat KPP_Ksalt KPP_Kv KPP_NLtransport_heat KPP_NLtransport_salt KPP_dTdt KPP_dSdt KPP_Tsurf KPP_Ssurf KPP_Usurf KPP_Vsurf MLD_003 average_T1 average_T2 average_DT Time_bounds\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "prog = netCDF4.Dataset(path+'prog.nc')\n", + "for v in prog.variables: print v,\n", + " \n", + "# depth of cell interface \n", + "zi = prog.variables['zi'][:]\n", + "\n", + "# depth of cell center \n", + "zl = prog.variables['zl'][:]\n", + "\n", + "# zonal velocity as function of time and depth\n", + "u = prog.variables['u'][:,:,0,0]\n", + "\n", + "# zonal velocity as function of time and depth\n", + "v = prog.variables['v'][:,:,0,0]\n", + "\n", + "# temperature as function of time and depth \n", + "temp = prog.variables['temp'][:,:,0,0]\n", + "\n", + "\n", + "# saln as function of time and depth \n", + "saln = prog.variables['salt'][:,:,0,0]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "xq yh zl Time xh yq zi u v h e temp salt wd\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# thickness of cells \n", + "dz = zi[1:]-zi[:-1]\n", + "field = zi[1:]+dz[:]\n", + "#depth = np.ma.masked_array(data, mask=[data>400.])\n", + "depth = field \n", + "\n", + "#print dz\n", + "#print dz[1901:2000]\n", + "print zi\n", + "\n", + "figure(1)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "S = plt.plot(dz,-depth,'*',linewidth=3)\n", + "plt.ylim((-400,0))\n", + "plt.xlim((0, 50))\n", + "yticks = [-400, -350, -300, -250, -200, -150, -100, -50, 0]\n", + "ax.set_yticks(yticks)\n", + "#xticks = [0, .1, .2, .3, .4, .5]\n", + "#xticks = [0, .2, .4, .6, .8, 1.0, 1.2]\n", + "#xticks = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260]\n", + "xticks = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]\n", + "ax.set_xticks(xticks)\n", + "#plt.xlim((0, 40)\n", + "ax.set_xticks(xticks)\n", + "plt.grid()\n", + "plt.xlabel('Delta z (m)',fontsize=18)\n", + "plt.ylabel('depth (m)',fontsize=18)\n", + "plt.title(r'$\\Delta z$ for configuration '+deltaz,fontsize=20)\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[ 0.00000000e+00 2.00000000e+00 4.00000000e+00 6.00000000e+00\n", + " 8.00000000e+00 1.00100000e+01 1.20200000e+01 1.40400000e+01\n", + " 1.60600000e+01 1.81100000e+01 2.01800000e+01 2.22700000e+01\n", + " 2.44000000e+01 2.65800000e+01 2.88200000e+01 3.11200000e+01\n", + " 3.35200000e+01 3.60200000e+01 3.86400000e+01 4.14200000e+01\n", + " 4.43700000e+01 4.75400000e+01 5.09600000e+01 5.46700000e+01\n", + " 5.87400000e+01 6.32200000e+01 6.81900000e+01 7.37400000e+01\n", + " 7.99700000e+01 8.70100000e+01 9.50000000e+01 1.04110000e+02\n", + " 1.14540000e+02 1.26520000e+02 1.40320000e+02 1.56260000e+02\n", + " 1.74680000e+02 1.96000000e+02 2.20660000e+02 2.49170000e+02\n", + " 2.82080000e+02 3.20000000e+02 3.63560000e+02 4.13430000e+02\n", + " 4.70310000e+02 5.34860000e+02 6.07770000e+02 6.89640000e+02\n", + " 7.81040000e+02 8.82430000e+02 9.94160000e+02 1.11645000e+03\n", + " 1.24938000e+03 1.39285000e+03 1.54663000e+03 1.71033000e+03\n", + " 1.88340000e+03 2.06518000e+03 2.25494000e+03 2.45183000e+03\n", + " 2.65500000e+03 2.86358000e+03 3.07672000e+03 3.29359000e+03\n", + " 3.51346000e+03 3.73565000e+03 3.95959000e+03 4.18477000e+03\n", + " 4.41081000e+03 4.63740000e+03 4.86430000e+03 5.09137000e+03\n", + " 5.31851000e+03 5.54567000e+03 5.77283000e+03 6.00000000e+03]\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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89i3fBQUFpe4/U2XPHT16tDp27ChJatGihXr27Om5haXoP6iatL1x4/vatGmn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\nx44d670WAAAAAIC6CnjxXldPPfWUnnrqKY+xfv36yeVyKS0tzT3Wt29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CsrKyNHToUPXp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\nACDIUbwDAAAAABDk/j8pcgXb1INZ2QAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# KPP related fields \n", + "\n", + "plt.subplot(2,2,1)\n", + "data = KPP_buoyFlux\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, 16, levels=numpy.arange(-2e-7, 2e-7, 1e-8))\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-30,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Upper ocn buoy flux $(m^2/s^3)$ for '+expt_name,fontsize=18)\n", + "#plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = log10(sqrt(KPP_N2))\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, 16, levels=numpy.arange(-5,-1,.25))\n", + "#plt.contourf(time, -zi, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$Log_{10}(N)$ $(1/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = KPP_buoyFlux[:,0]\n", + "CS = plt.plot(time,data,'*',linewidth=3)\n", + "plt.xlim((0, 370))\n", + "plt.xlim((0, 370))\n", + "plt.grid()\n", + "plt.title(r'Surface buoyancy flux for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "plt.ylabel(r'Buoyancy flux $(m^2/s^3)$',fontsize=14)\n", + "\n", + "\n", + "plt.subplot(2,2,4)\n", + "CS = plt.plot(time,KPP_uStar,'*',linewidth=3)\n", + "#plt.ylim((-400,0))\n", + "plt.xlim((0, 370))\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "plt.ylabel(r'$u^{*}$ (m/s))',fontsize=14)\n", + "plt.title(r'$u^{*}$ used in KPP for '+expt_name,fontsize=18)\n", + "#ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "#ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "-c:16: RuntimeWarning: invalid value encountered in sqrt\n", + "-c:16: RuntimeWarning: divide by zero encountered in log10\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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C3fbc3FwhSZJITk72aKdfv34iKirKo42yvCt98ccff4g6deqIK664Qpw8ebJMbdnY+GPI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\ney/q16+P+vXrY/DgwTh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RtfMSI17tjIuLU+hj9eqrr2qcrzIU1ZU+9u4NDQ3F4sWL8c03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v7jjz+iQ4cOaNeuHR566CGMGzcObdu2dZmuFI3MvnMEBTVDSsoQBAWFgk9nEXuBzafTatlH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8V1AsbE\nwY18xpigrl27htDQUHz//fe4++67Bfve69ev49tvv8WoUaPw448/apfFscalS5cQHh6OwsJCKJVK\nwdLEGGOMMdO4TsCYOLiRzxjzCCEhIbhx4wbmz5+PjIwMsZPDGGOMMZFwnYAx87iRzxhjjDHGGGOM\nSQRPvMcYY4wxxhhjjEkEN/IZY4wxxhhjjDGJ4EY+Y4wxxhhjjDEmEdzIZ4wxxhhjjDHGJIIb+Ywx\nxhhjjDHGmERwI58xxhhjjDHGGJMIbuQzxhhjjDHGGGMS8f8Z4ivz1EjQ2gAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# KPP related fields \n", + "\n", + "plt.subplot(2,2,1)\n", + "data = Kt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, levels=numpy.arange(0,.5,.05))\n", + "plt.colorbar()\n", + "plt.ylim((-300,0))\n", + "plt.xlim((0, 370))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$\\kappa_{\\Theta} (m^2/s)$ for '+expt_name,fontsize=18)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = Ku\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, levels=numpy.arange(0,.5,.05))\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$\\kappa_u (m^2/s)$ for '+expt_name,fontsize=18)\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = KPP_dTdt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Non-local $\\Theta$ tendency $(K/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "plt.subplot(2,2,4)\n", + "data = KPP_dSdt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Non-local Saln tendency $(ppt/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 7, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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WAiSwdlW/eqdc/YO54aq+YY0Bi6p14sY2VvzxJSI2pZJ2rlFA5AKeMBqAiBxj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\nGDRoELp161blOA0bNkRpaanN+ImIiOo75gRmzAmIvBM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+ "text": [ + "" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Boundary layer depth and SST \n", + "\n", + "\n", + "figure(4)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "CS = plt.plot(time, -h,'-',linewidth=3)\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=18)\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=18)\n", + "plt.title(r'KPP boundary layer depth for '+expt_name,fontsize=18)\n", + "plt.xlim((0, 400))\n", + "xticks = [0, 50, 100, 150, 200, 250, 300, 350, 400]\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "plt.ylim((-300, 0))\n", + "yticks = [-300, -250, -200, -150, -100, -50, 0]\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "\n", + "figure(5)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "CS = plt.plot(time, temp[:,0],'-',linewidth=3)\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=18)\n", + "plt.ylabel('SST(C)',fontsize=18)\n", + "plt.title(r'SST for '+expt_name,fontsize=18)\n", + "plt.xlim((0, 400))\n", + "xticks = [0, 50, 100, 150, 200, 250, 300, 350, 400]\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "plt.ylim((18, 28))\n", + "yticks = [18, 20, 22, 24, 26, 28]\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 8, + "text": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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tt9/Gyy+/jM8//9zpdgiCyDqQ+z1BEIQDvPfee4iLi8Ps2bNN7pG2WLBgAcqVKwc/Pz+E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\nsd/9+sl1S5Vy3srNmGvfPTE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9QTgCiXqC0IjeYpusWeqBrOmCrzZ+5q735mRES70zol7v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\n27dvV58+fbzG4+PjtW/fPhUUFAT1PATOaqDrhDydp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mjb/4ovPef9UqUdQCwIIF5tt4qenUKSAqSowLFhTT1Y0rpRMReQr2aBPZiL0y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\nrkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# u,v,temp\n", + "\n", + "plt.subplot(2,2,1)\n", + "data = u\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, levels=numpy.arange(-.10,.11,.01))\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title('u (m/s) for expt = '+expt_name,fontsize=16)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = v\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, levels=numpy.arange(-.10,.11,.01))\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title('v (m/s) for expt = '+expt_name,fontsize=16)\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = temp\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "CS = plt.contourf(time, -zl, field.T,levels=numpy.arange(18,30,1))\n", + "plt.colorbar()\n", + "#C = plt.contour(time, -zl, temp.T, 8,linewidth=.5, colors='black')\n", + "#plt.clabel(C, inline=1, fontsize=10)\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title(r'$\\Theta$ ($\\degree$C) for expt = '+expt_name,fontsize=16)\n", + "plt.xlabel('Time (days)',fontsize=14);\n", + "\n", + "\n", + "plt.subplot(2,2,4)\n", + "data = saln[:,:]\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "CS = plt.contourf(time, -zl, field.T,levels=numpy.arange(36.3,37,.01))\n", + "#CS = plt.contourf(time, -zl, field.T)\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title(r'$S$ ($ppt$) for expt = '+expt_name,fontsize=16);\n", + "plt.xlabel('Time (days)',fontsize=14);\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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ZZ0XH//3vfzOO4wTnVkpGOVtczvletGiRSLbmzZuzyMhIduLECeF4Xl6eW13t27cvq1Gj\nBisuLhaOnT17llWrVo3NnTtXQUm488svv7CEhASWnp7OysvLfUqDCA407dzA8NPCmjRpInm+d+/e\nqFWrlvC7bdu2AICBAweKrmvbti1OnDghOrZu3ToMGzZM+J2WloaysjJMnDgRn3zyicepwo0bN5ac\nsuaNb7/9FmfOnMFtt90mOt61a1e0a9cO27ZtEx3v06cPGjZs6DXdvLw81KpVC8OGDcPVq1eFv/79\n+wMAtm/fLlx78803Y+rUqbjnnnuwYsUKvPjii2jTpo1bmunp6aL1aLVq1cJNN90kSkste/fuVfTn\nCavVijp16mDKlClYs2aNqkjeffr0Qe3atYXf/Fp/vm4cOXIEv/76K0aPHi26b9CgQaJ6JkV+fj4q\nKiowbtw40TtITk5Go0aNFJfbZ599hoqKCrepjc5s374dbdq0QVpamuj4+PHjcebMGdjtdkXP8kbD\nhg3RvXt34Tf/ffH1yvn4tWvXcPr0adHxgQMHiqYdupa3Gn7++WeMHDkSgwYNwr///W/V9xMEYRwm\nTJiA0tJSfP7558Kxd955B/379/e6DvrUqVOyNoE/bZZrDBg1uqZJkyY+2QSAoz0fOnSoSDfVrl0b\nw4cPd7MJ6tSpoygS+xdffIHff/8d48ePF+mjmJgYdOrUSaSPWrVqhddeew1vv/027rrrLkycOFEy\nEFxUVBSGDh0qOnbLLbfg9OnT+Pbbb9VmG4BjeZUSmyA1NdVjOmlpaVi0aBFeeeUVHD58WPGa84YN\nG+L6668XfkdERKBDhw4iHbV9+3YMGTIEkZGRwrHGjRujT58+HtP+/vvv8dNPP+G2224TvYMaNWog\nPT1dsU3w7bffori4GP/3f/8new2flqttOX78eNF5LRg8eLDwf47j0KZNGyQmJqJ58+bCcf67c/0m\nOnXqJFpC2rBhQzRv3twnm+Dy5csYOXIkLBYL1q9fTzF+TEa1YAtA+AbHcSJlBTgaTgCSx53Xvfz2\n22/YunUrPvzwQ+FYZmYmVq9ejZdeegkjRoxARUUFevfujRdeeMFrw6+Us2fPAoBk1PYmTZqIHH6O\n49CoUSNF6f7yyy/4888/ERUV5XaO4zjhuTwTJkzAsmXL0LRpU6FxlpLHlcaNG7ulpYbOnTv7fC9P\nw4YN8fnnn+PJJ5/ElClT8Mcff6BDhw6YO3cuxo0bJ3ufp/rC1w2+/Bs3biy6zmKxeO0E4bfBc41N\nwD9babmVlZUBgMdI7WfPnpWsQ/wxf96RM2q+LwBua8uUXueN3377DUOHDkXLli2xevVqVfcSBGE8\nevfujfj4eLzzzju48cYbUVRUhP3796ta/ymFP22WawwYX3WNWs6dO6fIJgCABg0aKEqT10dyDmLd\nunVFv4cMGYJGjRrh3LlzeOCBByTvadCggdsabt5O8FXntG7dWpGjzMfQkSMnJwdz587FggULMGPG\nDDRp0gRTp07FE0884XHduWu9ABz63rlenD9/3s0mAIDo6GiPsvPv4I477sAdd9zhdr5Vq1Ye88Sj\n1CYA3G1LrW0CQPpb8tUm4K9VaxMAwOTJk2G32/HFF19QID0TQs63zlSrVg3Xrl1zO/7HH3+gXr16\nHu/le9L4oBlasXHjRtSoUcOtN3zcuHEYN24cLl26hG3btuGhhx7CoEGDUFpaiurVqwvXnTlzRtRb\nqhTemZbar/yXX35BcnKy6jQBhxKoV6+eaBTBGeceyT///BN33XUXOnXqhO+//x7//Oc/JQOMSMl4\n5swZvxo5pT2TfJAXObp164b169ejoqIC+/btw8KFC3H77bejQ4cO6NKli8/y8Q72r7/+6iaPN+XF\nl8u2bdskFYzScuOV/MmTJ2WVc6NGjSQDl/HvzFOnTUREhNv3+Mcff0heq3T0QE+uXbuGsWPH4rff\nfkN+fr5fOwYQBGEc7rjjDixevBhLly7FO++8g7p16yra4qt58+ayNoGvbdbJkyfx5ZdfuulCpbrm\nzJkzQpA2tTRq1Mht1hDgaM9d23Kl+eP1zZo1a4RRSGdcne97770XFRUVaN26NaZMmYKdO3cKDhTP\nuXPnUFFRIXJm+fegdKDAlRtvvFHRqOzWrVtxww03yJ5v1qwZli1bhmXLluHHH3/Em2++iaeeegqN\nGjXC/fffL3ufkvJs2LChZH379ddfPW7Zxb+DF154QVJ2pfaQs00gh7NtGRsbKxxXYhNUq1ZNMmq+\nnF1gBJ5++mmsWbMGeXl56NChQ7DFIXyAnG+diY2NxcmTJ3HhwgXUr18fgCMy5aFDh5Cenu7x3vj4\neDRs2BCHDx/WVKZ169Zh0KBBso1fzZo1MWjQIPz444+YMWOGqEG7dOkSjh49imnTpql+bocOHdC0\naVO89957uP3224Xj+/btww8//ICZM2f6lJ/Bgwfj2WefRXl5OXr27Onx2pkzZ+LkyZM4cOAAPvro\nI9x///0YNGgQBgwYILpu586dOH36tDAF8OLFi8jNzUVGRoZwTWRkJM6fP69YTm9TytVisViQlpaG\nJ598EuvWrYPdbvfL+e7YsSOaNGmCnJwc0YyATz75BJcuXfJ4b2ZmJiwWC0pKSmRnEyiBT2flypVC\n5FhXMjIykJOTg6+++ko0K2PNmjVo2rSpx63zYmNj3aaly0XRN8JeoLNmzcLOnTuxY8cOUScSQRDm\nJisrC0899RQ+/PBDvPvuuxg1apTk7C1XunTpImsT+NpmrV+/HjExMejVq5fkeW+65ptvvvFZ9/Tt\n2xe5ubn4888/heVNFy9exEcffSTSt4Dy/PXq1Qt169bFjz/+iFtvvdXjtatXr8aqVavw/vvvIyEh\nAT179sScOXPc9i4vLy/HRx99JJodsHbtWjRr1kxwgPip2eXl5Yo6SrOzsxU5ee3bt/d6DU+bNm3w\n5JNP4rXXXvMaVVxJed5www3YuHEj/vrrL6F+njlzBjt27PC4RKJDhw6Ij49HUVGRxw4Ab/DpvPHG\nG5g4caLkNX379gUAvPfee5g1a5ZwfM2aNQDgVo+ciYuLQ15enqhjZc+ePcKIu9H44IMPMGfOHCxb\ntixgO+wQ2kPOt86MGTMGCxYswB133IEHHngAZ8+excKFC9G4cWOvvY4cx2HQoEGiLa54fO3hvnz5\nMvLy8ty2EJs3bx7Onj2LjIwMNGnSBMXFxXjxxRdhtVpFPYkFBQW4du2aaN2LUiIiIjB//nxMnToV\nU6ZMwfjx43HixAk88sgjaN++Pe666y6f8te3b1+MHz8ew4YNw+zZs5GWlgaO4/Djjz8iPz8f8+fP\nR7t27fDBBx9g5cqVWLVqFeLj4zFjxgxs2rQJEydOxMGDB0VTzRs3bozBgwfjiSeeQI0aNbBo0SJc\nuHABjz76qHBNYmIili1bhv/9739o0aIF6tWr51FJajF9Pzc3F2+99RaGDx+OVq1a4cKFC3j55ZcR\nFRWF3r17y96npDwjIiIwZ84c3HfffZg5cyZGjRqFn376CQsXLkT9+vU9Tl9r06YNZs+ejcmTJ+PI\nkSO44YYbUKNGDRQXF2PLli3IysoSFKQnWrdujQceeAALFizA77//Lqyx2717N9q3b49bbrkFkyZN\nwpIlSzBy5Eg8/fTTiI2NxerVq/Hxxx8jOzvbo0Fxyy23YPHixXj++efRvXt37Ny5U7T9ijPBHvle\ns2YNXnrpJTzyyCO4dOkSdu/eLZxr0aKF6LskCMJctGvXDj169MDs2bNx4sQJTJgwQdF9Q4YMwfTp\n0/HHH3+gTp06onO+tlmuMWAA5brm4sWL2LdvHxYvXqzoWa4yzpkzBz169MDgwYOFLT+feeYZ/Pbb\nb3j88cc93itHvXr18Nxzz2H69Ok4ffo0Bg8ejNq1a+PEiRPYsWMH0tPTMW7cOGEgYfLkyUKskwUL\nFuCf//wnBgwYIHLa6tatiwcffBBlZWVo3bo13nvvPeTm5mLp0qXCNYmJiQCA//znP8jMzATHcejW\nrZusnGqcajkuXLiAm266CePHj4fNZkNFRQU++ugjlJWVuc1udEWuPJ2PP/roo+jZsyeGDx8ubN86\nf/58NG7c2KNNwHEcXnnlFYwYMQLl5eUYM2aMMIr+xRdf4LrrrlM84LJ48WKMGjUKQ4cOxT333IP6\n9evDbrfjzJkzeOyxx5CYmIjx48fjX//6FyoqKtC9e3cUFhbi8ccfx2233Sa8FyluueUWvP3225g8\neTImTpyIY8eO4fnnn0f9+vUV1zd/rlPzzf7000+YMGECBgwYgKSkJJFNUL9+fVitVsVpEUEmoOHd\nwpR3332XtWnThkVGRrKUlBSWn5+vaKsxxhxbgFksFtG2F4w5IjG6RhzfsmULs1gsbPPmzaLjkyZN\nYi1atGCMMfbJJ5+w6tWrswsXLoiu+fjjj9mNN97ImjRpwiIiIliTJk3YHXfcwUpKSkTX3X333axn\nz55e5ZaKds6zatUqlpyczCIjI1l0dDSbMGGCW4RXqfx5oqKigi1ZsoQlJyezqKgoVrNmTZaYmMju\nv/9+9ttvv7Hi4mLWqFEjtzTPnDnDmjdvLorIyT97xYoVwntLTU11i1J96tQpNmTIEFa3bl3GcZyi\n9+kv3377LRs9ejRr2bIlq169OqtXrx7r37+/sGUcY46onBaLxW2rManylHpHixcvZq1atWJRUVEs\nLS2NFRQUsKZNm7JZs2Z5fAZjjL3zzjvs+uuvZ7Vr12aRkZGsXbt2bMqUKW71yBuvvfYa69y5M4uM\njGR16tRhffr0EUVlPXnyJMvKymKNGzdmkZGRLDk5mb377ruiNKRkvHTpEps8eTJr0KABq1u3Lhs3\nbhzbs2ePWwRU52/GtbzmzJkj+Zwff/zR43VSkVY98cQTT8hGv5X6rgiCMBevvPIK4zhOsq2R4/z5\n86xOnTpu7YivbdaFCxdYZGQky83NFV2jRNcw5rBvateuzc6dO+dV9t69e0vqycLCQta/f39Wp04d\nVrt2bda/f3+3bcTk8ueJjRs3sn79+rF69eqx6tWrs/j4eHbHHXcwu93Orl69yq6//nrWsWNH9uef\nf4ruGzBgAGvRooWw28rEiRNZXFwc++KLL1haWhqLiopi8fHx7KWXXhLdd+3aNXbvvfeymJgYZrFY\nmMViUSWvL5SXl7PJkyczq9XKoqKiWFRUFOvSpQt7/fXXhWukdI9ceUrZpvn5+SwlJYVFRkayNm3a\nsOzsbDZ27FiWmprq8RmMMfbFF1+woUOHsoYNG7Jq1aqx2NhYNnr0aLd65I3PP/+c9evXj9WpU4dF\nRkaypKQktmLFCuH85cuX2WOPPcZatWolvOs5c+awq1evepXx+eefZ7GxsSwqKoqlp6ezffv2SUY7\nd9XzfHm57nrDP2flypUer2PMPaq6J7Zs2cI4jmMWi8XNJgiE/UloB8eYARY2Eh7p0aMH+vbti2ef\nfdbvtKZNm4Yff/wRmzZtUn3v2bNnER8fj9WrV7tF/QwlEhIS0KdPH7z99tvBFsUQHDp0CMnJyXjn\nnXdEywUIgiCIwPPwww9j69at2LNnj99prVmzBlOnTsWZM2d8ipjcq1cv9OrVK6R3YZg0aRI+//xz\nVbuLhDJ//fUX2rZti8GDB2P58uXBFocgTIdptxrLy8tDUlISbDYbFi1aFGxxdOWFF15Adna2ZBAw\ntSxdutQnxxsAnn32WfTo0SOkHW8g+NONg8mxY8fwyCOPYOPGjSgoKMDKlSsxbNgwtG7d2m0LMoIg\nCCMRLnbBo48+iuLiYtl4FWoYN24cLly44JPjnZeXhx9//BGPPfaY33IYnXC2C2bNmoX3338fO3fu\nxIcffoiBAweirKzM5zg9BBHumHLNd3l5OaZNm4aCggI0bdoUPXv2xIABA/wKNmVk0tPTVQX20otn\nnnkm2CIEBCME2goWUVFR2Lt3L7Kzs3HhwgVER0cjMzMTzzzzjKJgQJ5gjElG/ufhOM4twmwo4217\nEW/byxAEUUU42QUNGjTAqVOngi0GBg0aJBmpPNTgOC6s7YLz58/jgQcewOnTp1GzZk306NEDn332\nGTp16uR32qQHq6CyCB9M+SYLCwuRmJgoBBwaO3YscnNzQ1LJEoHn6NGjwRYhaDRt2hT5+fm6pH3n\nnXd6nMqqRudDAAAgAElEQVSfkZEhu11cqPHmm2+KAgy6wnGcx44KgiDEkF1A6MUbb7wRbBGCyuuv\nv65LuqQHq/j555/RunVrj9d423KOMA+mdL5LSkrQokUL4XdcXJxbRPBw7qUkCDOyZcsW+m4rYYyZ\noiy0norZqFEjnDt3TtU9DRs29LoPPRH6eLMLzPA9EQRRhVn0YKBQsmNMsNFjeUZtjsOfKq43g01g\nyjXfSj9Gxpgp/+bOnRt0GUh28/2ZWX4zy252+X2VXQ/OnTuH5YCqP7XOOhGaKLELgv2tUftivj8z\ny29m2c0ufzjKrhd/IvRsAlM633FxcTh+/Ljw+/jx46Ieb4IgCIIgwgeyCwiCIAgzYMpp52lpaTh8\n+DBKS0sRExODtWvXYtmyZe4XHuVQmhCNcfgvbLADAMYgB5lHC4A3ARwAMAlYMnIKAGDm/7IBAPkj\ne8MOW+X1HwAA7LAiB2Ow7Oj9jntTHH/5CVXXOmODHTYUec0Ln67jeoeMv2Mf8L95VRelAEsSplRe\nb8MY5Ahpxx4tQ2lCNOywwoYixB4tc+SrMm8CByDkjb+Ovw+Ao0wAlCZEC+nyv+OyfxWSWTxlqpC/\nzP8VAPdXnphUWSZrALB5QArQZ+Qm4drsu2diyvIljvKrTFeQNQXok7AJY5CDmUezHeVbme8+Izeh\n4KtMLE6d6jgHR1ncn70MvafkY8eIAULeBBlSILz3gq8y0Ts1H2swXsgzT+z/HHlcMnIK7LCh+eJ5\nwP55WLJ+CmaOyBZkW7EBmFzZq7eC4zD5cSB/Xm/Mx+NC+jtaDUBRMVCPRSN2bhlWzK8q+sktHf+u\nqNylZPLjwJJ5UzBzbjZWzHdJu6VTXiY58ioqE5f3CQD56x2y1MHHwNx5wrPTAVgfF19but4p/yMc\n+S/aAOzkZX0c6DNvE4oqbLBa7ML7WzbX8d6K5jtdOxzC9zNzRLajnFoCpccc9YqvUzxcPsPiKVXv\nkS/f/ITewPkCYMQ8x7GtAPc0A1YUAZOtKFneGJ9wZUKepjCHfAAw2pIjfA+xc6vq05J5U4RvJXNu\nAXAAWLJ+Cp6seBxzLPOF7zP2aJnwPXMbGbASmFKxBMtG3C/kZ+qxxci+eyZKljd2PKOyjgllmhCN\nQsSDy34C+BLANCA6pRRWi71SNruoLPjvbsBXO4CuRcA+KxanThW+/xyMEWTnf+84OsDxraU46t74\nijVCel9bkhH7vzKUjoxGDkYjB2PwOOaL2p8cjBbnGZXf0d3LgFNAOvogc0SljPw3NDIa8zHHqS3M\nEeqDkraNIAKJErsgH30wH4+jqMKG0Zaq+myvrNUAhLblv5ZxyDxagNIEx3flquc/qBgjXAdASLfs\nQCx6p+aLdLodNtH/ndPg5XDF+fp9KMRm5KPgq0xEp5Si7EAsolNKAVS1gfy1dtiQ/dVMQQa+feRt\nDTtsKPgqE9jreE7vKfnCs7KzZwKvAbgHQDfH+eiUUqHNdH7G43hS0KH5Ix06yDnP96cuQ++v8nEJ\nhUhHHwAQbB3AYVvFtipDUbFDTy2ZNwU5cJQpn89fj8VV6b5JjjZLqn0bh/+igDsGLJuMKVOWYAxy\nhLScy7psRaxDB83NFqULQGRH8LbLGORg3r7mmHc3sHh5VRt9jHO0lekANrHKdnRFEYCdwLLJYJkc\nkAFBjxe1Bqwt4WhX1ztskx1Hq2yXxiNKHHlFlW2Un9AbA7J3ON5HVwBpQPRk8bvgy1ywSyvl584x\n9E7NRx18jFK8LNg+4/BfoW7ZYRO+A/43AGR/NRNTUh22mnPd5+sYAJENXbreoc/GV6wR0uKxw4bs\n7JmInlwqOpeDMSj4KhObUvs49HOKow4BcNiVlWnjAgA2D9wIVmXHzS0T7BBBP1fW9zHIcdjxvA0M\nCDrThiKHzXMAjjo3HJi6fnFVPb6/ykYDgMk/AUhg4LLheJ+8bZHgsC3SuPurrn0cKJ0XXZk3hw4u\nOAmcRKXNW3mvq30NOOwf57oqyFP5rCcrHsdoS47juJPuHoMPEHu0THQN/x6dy5lvzwDAarGLbCdX\n+DYtrbLeON6h492WHXDE0uDL2bXddH7vhHdMOfIdFRWFpUuXYuDAgUhOTsaoUaOQmpoaOAFSxD+l\nnG9/6JlRXdP0AklGtPdr1MA3FIo/7BTvl8iRkejf/aqpfNYKL9MlnTsMPNE2o7m/EunLl/KnMnrI\nnEjTRRKP+KJEDF/2nmif4fg3kHWfIDQm6HaBjjTPaBtsEdxwdiA84Sy7FraSfPuc7nfazs8QnsO3\nj75Q6XDxjjcAYcAjELjqJWdHzAxkRAZbAj/omhFsCXzCarGb254xCaZ0vgFg8ODBOHz4MOx2Ox55\n5BHfEnEZwTIKhnW+u3m/JKMxZMvU6D1jGf2hvj7s1UMS+FQv22Zcp70cCrDBXiWvj99TxvWaiaM/\nB9wPBavsNaFDRrAlIAhN0MQuMCDXOTmw/AiU0ZDT7+czhgPQxvGWSkN3Z/b2DHWdwC52kh1WVbYm\nnx8tyqttxnVO6QXO6ZfCo/0noVMBIMNld1OlHT6GoFtGsCUQwc9kdP2/FMMzgr+1cahjWuc7nOBH\nPrUeYVf2zOA22L7AT+tV2ssrlKufnTHWlt6vMQJmfKda4U3peEKp4uengxq9s4kgiNBAU9ugq3ZJ\nSaG6XQzSAIlW7bcdNmWd087LwxTOdvOEs/xK0hON9iuVwSk/ZQdiRdOQA0Wgn+eJQNhWWtQNIviQ\n8x1AXD9M99/+NSKi9KQaeT2UWLBnDqh8vkcHKsWlYZNLW22etUrHX5yep4mSCPa7NxBGMgBcCefO\nFoIIRfgOvkATDOcKgGpdo1pOFen7lH+901eJmmf4Io+rI68K17JSWHaKnleZlqqBEgUzPoNFIBxx\nI9s2Zoacb5Pjy8dnh1Wbj1YL58spDRvsstOPnNGiMXB2wvn0+BFzpembbf2UryhVovyKu7B29oKw\nRl0KUpgEETiM+L15ard9cd6d86jWsTJVUCYXu8bfjg5h5FvxtHOnuuQhToqRCHb9l3xHOgwOBHTU\nWYEtTJgXcr4NhmgKtAYIjYVEepo7SSky/w8Ufj5TcuaAgjRFa56V3FN5vjQh2j1EjFwwvwDPWlA8\nPVuhXM5Ky7DTpvaJf2oXvocINHl5eUhKSoLNZsOiRYvczpeXl2Ps2LFISkpCeno6jh07BgD466+/\ncPvtt6Njx45ISUnBtm3bAi06QShGSTut11prf6/lO8DlRiFd8+ZVb3Dy9oyijgMZB9lqsQPd/BtN\nL02IFtLX2lG1w+aTTvVn8EDID7S1Iz3ZO8J709ApFb0Ll2d6KlOpOhSOgw58OZiiUw3GsgvI+faC\n3h+ULw2xQwlYnX77LqPz8w3rFPmJa8MgjHpXKkPXLVR8QlCsjnfhl+NmxOncGskkehcyaSqph5oF\nHaLeZXdc6rLZKC8vx7Rp05CXl4eDBw8iJycH+/fvF13z8ssvo3nz5jh06BAeeugh3HfffQCAV155\nBVFRUThy5Ajy8/MxY8YMXL16NRjZIAjCTxTpcy+6zQa7aGRVMs197oec0xZmG7o65DKomYHniho7\nTqkelXSutOpMkNPBSmwOI9pKhCExml0Q8s631uuq/Xk2IY2WU+A9Th1X6Gj5vKbLabTcyMHXdPkG\nfFSCsu9ezphRidn2ow729D0lGF3GwsJCJCYmIjY2FtWqVcPYsWORm5srumbjxo3IysoCAAwfPhy7\ndu1CRUUFvv32W/Tr1w8A0KRJE8TExGDPnj0BzwNB6IVZRqm0QnF75WRD6PV8h33iMtrqo+7k9+kW\nwaelchcWtW26YNv6ILvR9YfZoPJUhtHsgpB3vgkxPn+onoJOKFEgBlkLK8LFGReNfPuiEFUqbzus\nipx0r4H0FMjkK34FTqkkGDMqws3A1AyV2+EEg28BbHD6c6WkpAQtWrQQfsfFxaGkpET2GovFgujo\naPzyyy9ISkrChg0bcO3aNRw9ehT79u3D8ePHdcsLQQSCsBgI8EfXebnXeQq8sw1lbanMpuJnK/r8\nHpR0RivMv1axakoTor3qdl9jAwhoHFdINwI0Aq80ar0r/uzyYga82QSA8eyCan7dTRga5U6gDZko\n0Fka7eAbaC2UiNcyUrPmu9KZt8OK2JQCoNhv8VTL4lN6Mq2V2xRxFVO07bDBllAkji7v1AETCIfc\nVQkFq4fY8KPvHjrWjNir3qHyj+cjl/Mcx/mULsdxuOeee3DkyBF07twZcXFx6NWrl8/pEaGPnFEb\njM4/I36rivFDt6nKt7dBBDj0t9z745+VDmCnTDLRKaWAY6koShOiRcva5HCd/u33u/QzQreyDgV9\nOnRKE6IRC8/belpbQpl9ZZJp6XbYhHpiqr3MDYI3mwAwnl1AzrevCA21iRWeDA7l49lhMJvDLolk\nEDple35LvvcUp0Y0BWKnNgVVysI1OJtSJXLA6f+o/K1kzZVLx4A3bLAHtF6L6ppz2XgIoGN0tOhp\nVvUeTGJkKMHfQHdxcXGiXunjx4+Lerz5a4qLixETE4OKigqUlZWhSZMmsFgseOWVV4Tr+vXrh44d\nO/opEUEQgM6dEl72J7da7CiA/Brn0oRoxB6Qdnz4GWB8e6zI+XOKm+HsXAGVAwde5p0qaft5PWOH\nFbEu9piz069FB7ANRYqWcbrFzvkSwGQ/H74XQKr3y2jGW+gSanZB+E47N4Ox6sd6oKChYtqqZqOf\nHpxoPlCKXG+inDPKB0cpHalMRrkpWJ4aDGELEi/IdghI3aswPdepc3LwyowvD6lr+XxYWyp7vvhe\n9/IP1BTJUOlhDosppSpIS0vD4cOHUVpaiitXrmDt2rUYPHiw6JohQ4Zg1apVAID169ejZ8+esFgs\nuHTpEi5dugQA2L59Oy5duoTOnTsHPA8EQfiAHyO+dlhROjLaTT86O3TC/73EdZGyBfTeW9tToEw1\nAdzEweW86xbn8pKbjaimM9oI+izoO51QIFjNMZpdEL7Otwf0GPVT06AovVbpdaEWxdwGu6L9Lz1F\n8vRWJnZYRY7kCg9TTCSnN6c4GnCva7onyZ9y3Z5LcPBdgrvpUV+d86R0NoAU0kaI1b/1b07IRmFV\ng5rrJZSiHsYC9eD7TlRUFJYuXYqBAwciOTkZo0aNQmpqKubOnYuPPnJMSJs+fTpOnDiBpKQkPPfc\nc3jxxRcBAKdPn0aXLl2QkpKCBQsW4P333w9mVggTEIqz3/Qm0O2b0tgldliRgzGS94v+P0niZs4K\nkdsmoZtdHXuRfkwR//a0rE4qXd4+UDO9XSnSW2u5PEdFkDc+b1Ij8oH8nrRa/+4MtQfGxGh2AU07\nJwAEwEH3cw2SUtQ4Qt4UFR85Ha7T62WcNdfAaEpGgh3l7jQCK7O/uCjthCLH9Dg1TqPcKLmrM+l0\nndgBr5zW5jKdXk1wOY91IIA9vT4ZfjoGDHQeaSC0YfDgwW692vPmzRP+HxkZibVr17rdFx8fjyNH\njuguH0GEK1LTlwH/nBYb7ChIy/T5fm+2AP8Mt4CsnnSwzDne4eOX95UmRAPn1MkrhfMUd286xZPT\nGai944OO2WaVEn5jJLuARr5DHLWjcgGZ8mOAKTVy+XReJ5WD0d4TcnEo3bYRcf7XA55G1vl0JaOx\nV/7ri0Mp5zjLpeW8T2m6sww8qqec+67Ao1NKRUaC0inkfN48rYGjUWeCIHiMMA2W8B9lI9/SOsn1\n3vyE3go71pXjen3ZgVjJKOdSU+Ld7AOVeCobT2nKPZf/LXevlIMvukeibP3Ry5J2mTMaO+JKR9S1\njhZPmAdyvk1CMHoaXRsGf0boolNK/W5IvG5toTIIntw6KOcebjtsitf/ON/j7Kg6kw5xuapbjlDV\nYy6sSfcjWrvr1HrnfEpNpefXxKWLjjvkF+SQnGrnuEa8lkxmSxH+fj54ToBmTIQC/jgJho/IThCE\nYrzp6lDfeoinanqzTH4l1klr6fC4BlqTg39fsm14pT6Uem+uDrjcdWrRrNNJZtaYqI7KOcS+xj0K\n4VFt6gwMDcj5JrzjowPkrHR8UgaVDWiwpuWKIptK4Gn9lODYToKgQOSceLfjTlueeJdRPpCKp/Xm\ngqL2QUlZWwLW4RDda4dVyKtzJ4FzHrzWAWdF66SwQyUwGkEQhBEw84ia7BaSOm6vJTmyK+MYKrV1\nnPf+tlrsbvpeS7tHzai4/DZrVdPalT7TU5pCOXlxvEsTooFuKuqsRo63L06uUabjm/n7DhfI+daJ\nQH+ERvno5QhGY+A6NdkVf8rMOZo6r0Td4JXHSOlRcD4d63DH/9Od7/GwBk0YhZYa9faieFzT5EfQ\nlYzuC/mcBGBSlYzOjrxclFd+BMCGIslRVueZAmqNBKOO2gZbAcoFtQu1AIwEQYQH/to5cg6tmnSd\nI567zdRSiatTrHSkXImMatLxln+5LcX4ZwGQ7/xQ0iniYrcI0/orZyXIdkSk6BM0zRNGt7UJc0DO\nd4gjG1kzQGjtgPjSG+kqg5LgKiJ86EkVRx91yCw7Ej1J7Pzy78mTwnP8X/n2IQBktmSzer1GMp0U\n9+c755WX1Vvvt1S63mQJtLL1hWA73mqggG8EET54dZZMgK8drppOt/eir1x1q/RSrqqOaV+cOq30\njGs6anWsWjlEnQMSnfau0duF9+YUL8jVnjKTznUm0M68t2/ArOVoNsLS+dbKCfUnHVq3YQ6kGkbn\n9+7ckEmtdRYcVKUOvMye5XIBTfzBLXibwntKR0YjP6G3W499DkZ7HK1XclzUMdKVHENvhMvaTYLQ\nEvpuCB65znhvOtZrDBo4z84SL8FTXf/SIDv1Wskot6/2ghZ2hlcdLmPz5GC0yKZQ3eGiIuAtj9QM\nQKU2SLBHxH1xmpXe4+sWdYQ8Yel8hyOGmWrqEulcrVxCAycRxMM5LX8im7qhoPH25lzy06qtLSXW\nTqU41lBbW1aNHisZ1Xab9gX38vS+n7n6dd9ya7/k1pV5z0dV0DahU6obgDR3I9nrvukKn2kYOG06\n4Ty9Z9fZEgRBeMa13VHTnlCHoTRaTakWp1nVnikpd91G9dIA3AOAs3p8hqf9sl3rmFAHK6e0+1uv\n7LAp2ovbBnvlGnR1utzbMj/n9J3/daQrb+/4VW/kbJsABGTTIsgwEbqEt/Otco0pIYHBo0qKeoUr\nHX/VDoiP087dlKrzGjEnhPXOCqOuequvntZvy42gW1tWOf/eUBK8RV6RSq9Ddj7uPC3Sm/Lydj6U\ng7XR6B1B6IthOq01xga7KToJ/Cl/j1OnfdTp4t9OuqwbRNP4PTmTShwy0TVpULREwPkesWOrjX3r\na8A2q0V+ZJ6PXSPn6LsdT5H4U0Ao2PjUcR5ahLfzjdBVrkZD9x5AP6KO+pI2AEW9yDzO0c/dg6w4\nRUaHusikUgaBdTgwWeEIMQD/tvTwglql5xzIzhOyCt0pD6LzAewkMq1TbPCONIIgAoDM1lBq0NTZ\nURhE1Nd211enxllPR6eUCjpLtXMN6anjNtgdo97dlI8qu8rnK/wadLlReam0fSl/pTFhZHV9ivuz\n1c7+UwzpR0JDqgVbgHDDKL1XWshRmhCNWBh7ZNF11FsrpKZ8e8VDxHPbyCLEHi1T5XjzuMmQAtEo\nfywKPD7fl/cYiJ7k3qn5NG1LJ/w1SBzvv0AbYVxQurRAoFgXMQgipDD16F+KOpvF66izRp3NdthQ\nVOEICFZUYQP+z/80RWjQEeKKICuCt0TCdcq5KpzeW2lCNFDhnqbUtSJbKNAcADnvfqLKLjCBTRD2\nI99GJRgj8pp1DLg0Mt62hPJJqUpMw1KaTiCNENdo4HLv1Q6rKIiZXzL6MpItMe3dF1yDsCm9x7XT\ngR/Z9sn59tDJ4Rcad+AQBGEMqJMvyATIMeHfs7Me9kcvuOosTFOfpsfp8U6j3lJrpV3xqjO7Ocmq\nADX5kHu2mmBwiu/XaaZeKCL1rqm9Cz7kfPuCgT56ZYG5Aj/aHqzp/LINudP+2WqwwS7cs4LjFD1X\n6liVU22VvMaXEW9ePrf0UiR+e8i7MO09APXa13y6QspDRwzUvhEEoQ/BXh6jlY2gxCmVRaO2jn+2\nL0HfPL0H1+nmWus9NQHS9ESJTSDYTi5RzF0jcQv2jNNvyZ1dfFr3b4yZq75ANpOxIOc7AATyg1Wj\n0LRyhHx1bNXgkFUc2bTsQGxAnEY7rEIQs3QAORjtaMhU7pPqLaKnM343lJMgrCH3NqrtS8RzvfA6\nnd+TnGrrYQBGsj29RyMHPKJYGARBaIV057Q1aHpHyw5n3vnjo4Rridbp+ROBW2qGWqAR9JIQLNkl\nSKvzdWptUhV1gX/eZMbczikpHzm59HaQyQE3DuR8mwAz97YB+kzzVquU+MbYl7K0wwbr8MrnDq86\nrsZ58lQGmkw1d8I5ejr/W9FWVBKR2LXA5+njlbjKLvXu5SLGGxVTr78kCMLQ+D2iqbJj2S9knB7d\nHYVJ0EbnOQVe1UtmfxxmufQ81REjO2m8PePNsa7atlUiYrpOSJa3yZerka2iDyHtfNPojQMtPh5f\nom2GFJMqt/Ca5HsSWk23VqIYeeXEzxjwtjzBiN+Kcz49b5+mo7NtkBkBwYCULkGEHkZ2rPxFrkNc\nKlp3aUI08hN6a/p8pfpZblswqbT8mlavQg4159SihS5xnf0o2DQSu85IbWmqJkK76qCfPmBEm4sI\nHBTtnKhs1HTelksD/G3AfVnvLZBSOeqdUnXO7ufWGmpRqgx5peM6ou559N0KJEgHxPMHLRS4HVZk\ntizAzmLPafLvV0kZC/t/873SThHi1eBWpwI5YkQQBOEPQWyv7LAhM6VAdbvryVn9wOu9RcKzlcTL\nUSJLATJ9vpeXJRDYYJccblMSwM1XGfXIo7fOdiXbommOQTrp/XlXRGAh55sIGILDoxK5aI3elJ6U\nUvG1YSpNiEbspDJRT7laxzLQjaKa6exayeaPs+1NhnTATcnZYXNS8H6MgHtTnt0AHBMf8rXnWs+R\nelK8BEGYBtG2mPq2XY70P3D5rQEm7XAN5AwIrZ7lq2MtuR0rQQSRkJ52rhX+GMtGWn9qCFl8bPS0\nmbbk53vUaU201njbusNMCHmZVDkVzGnmgfg6A9RtP/Fvyxt9tgkkCCI08aYbPMU0MatekZqObGS0\nLmfX9Iz2HrW2ryTXe7voOOdBIWtLz0vcCOPVGbNCzrcrBp42rRiJPBhK4bhEquSR2wdcTZpSuK4V\n8gV+jZi3LcX0RM1znEeElV5v6I4FH/Yut6FIt2An3ka9dVNQGrZPhn7fBGEy5PZrNuJ3Jts+BXoU\n1ykoqF54aou1ioejdZpaEAw5gumYqY4yniJz3ODoNUtRPGij/RJEQgw530HE20ekJKKj6VDhDJUm\nRIumnEuWl+ue1joi5cDr0TMdjj2Lnjo1hK3QJkkrSan91AnlhHUgRYJwQs6BDlcCsh1i0LYb0+Zd\nB7r9DKSOU2OLGMFuUVQ2Rp/dZfLo6IQyyPkmTIE2gbv8U1pa7cWtNhoq4X2rOKUj9+FWrtQZQRCE\nEeFnk/mLL226EWKcKE0jHHSWtzz6MjNPan24HdaqbUkV3q+0c4V0LaEGCrimEDN+WIaaaq4nKhpS\nf9C7DoSDkvUVPhq7z+9A7XR1I6Kyx96MbRYA9SMTxbpIQRAhT0BGtgMArzutFs/RufVoE0NBb6td\nphYsJOVUsVSCf/+xKAAQnHdnh9W4NoaRUWMXmMAmCN+Rb6NPPfEXo+ZPpVzeGkd/es6D0fCaQcEF\nA7lyEU8pV9eZxAfJM+y3EGBM64wTBKEZRjL8qwKZhslAgZ9QG+5O79R8yeMeba2UKttRsCE92Akh\nt/zTR0Kls84IhK/zLQUZ6erxUmZSDaBc4DItMJpyImdbG9Rsm+aGiwMuGJ8GXlulJkhQIOo81WOC\n0B4164VD8Rvk43nkYIwu6QcrnoUebbLRbBsj4OuSA4cNapXcgsx5V5VQIRTbDrNDzncI4a1xDmbj\nLbe+y/8o5JX3G7ix9HWNt5EbzEDKJuV4K10D5ta7TRAEEUIYWU8oQcu22SZoBvPtYR0I9JQ12J0D\n6naDsfo1q04LGZRCAVFDE3K+wwQtGgUtGm6+x1Ergt3ga4HeyttMxoGW8HWDN+60LodgT5UM1/dK\nEETooH45kfF0vhna4kDs8W207c2kttNy/nMcM+iSBwPMzDNs2YQA5HyHAcHYviqgH62XUW8tFYI/\nEc7NoKCNingPSjX7l1tF/2pNILaf8xcjGqsEQQQfI4yqqWmfvG0FZ7S2N5jIlUW4lZFUfv3RiZ7u\n1axs/XC8w+39mhVyvsMQT42HnoZ6lSOk7hlyjUl0Sqnb6KaeaNmo+ZqWURypYMghFelUyfRzvaaY\naYVWBrBR6gZBEMbGhiJ5HaQierQW8O2W1tt2Ober/HFftqxSi/MyKSO2yUaUKdjwsQdEfwA4bkUQ\npQo8ZQdigy1C2EDOt0po7WgVck6MpzLyx/GRU878VHa57TKoJ5DKwJVQ+Y7pvRIE4St+tx9+xFrR\ns+0KVrtIjq0x8bSbimgAR+PdUUg/E3IY1vmeNWsWbDYbbDYbhg4dirKyMuHcwoULYbPZkJSUhE2b\nNgVRSsKNIAQ+89WhD5SiDDWFbKb8+DpdHaiMeqo3Pn4vRpguamTy8vKQlJQEm82GRYsWuZ0vLy/H\n2LFjkZSUhPT0dBw7dkw4d/DgQfTp0wddunRBUlISysvLAyk6IYMRbYKwN64PQFedL6Xb7bAh9miZ\nxNVEOKLVNyhsTRrmhHKbZiS7wLDO97Bhw3D48GHY7XZ06tQJTz31FABg3759+PDDD3Ho0CHk5eVh\n6tSpuHz5cpClNR9SDlSgpmP58lypBoF3QJRNPQ6Ow2gmR1UPjJB/1/rh+K1O0dL+luahvLwc06ZN\nQ15eHg4ePIicnBzs379fdM3LL7+M5s2b49ChQ3jooYdw3333AQD++usv3HbbbVi5ciX279+PgoIC\nVIHrRuIAACAASURBVK9ePRjZIFwgm0BMKHbAaTP1vEiztEIZvXWz2cpf2JHHZA64EWwsM2A0u8Cw\nzne/fv1gsTjES09PR2mpw/jNzc3FuHHjEBERgdjYWCQmJmLPnj3BFJVQQbjs700QoYgZvrPCwkIk\nJiYiNjYW1apVw9ixY5Gbmyu6ZuPGjcjKygIADB8+HLt27UJFRQXy8vLQvXt3tG/fHgBQv359QQ8R\nwYVsgvCEXyLkre2RW3JmNidQCVq2w2Zo05XiS17cIp/LTD1XulSNr296dI4ZLV6NmTCaXWAKqyI7\nOxsjRowAAJSWliIuLk44FxcXh5KSkmCJpphgNXBKFY+ZG2Azy+6JQOYrFA0UvXEdTVeCodeay/T4\nm61ulJSUoEWLFsJvKR3hfI3FYkF0dDR++eUXfPvtt7h8+TIyMjKQlJQkjK4SxiIUbIJA4o8uCcQI\nu7odLDzPoCMHRZpgzHY0E1Iz5ADz6T9CGqPZBdX8TsEPMjMzcerUKbfjTz/9NIYNGwYAWLBgAWrU\nqIHbb79ddfovPPEnzqAYxXgHDTLq47qMtn7LHAgcgcOKgi2GMlIgbItghxWxKPApGentIKS3cTJb\no6hmH0pCW5yD8Ln+PzOlwBB7aZqFH7aewL6txSjGmaDKsfVXYKuHJZ8cx/mc9rVr17Br1y7s3bsX\nNWvWxI033oiuXbti8ODBPqdJKEdvm+CdJxz2AABT2QRqMZNeUSKrL52WzrYC4R/hVJalCdGIReBi\nCgSqXPV4h19vvYB9W/MMbxMAxrMLgup85+fnezz/1ltvITc3F59//rlwLC4uDsePHxd+u/ZmODPr\niVqwoyWOIgvXOTXwgpNo4LUddlgN54DLKkldA65IRzBXi5mMEUIarfYtlapTgnGnsi6Hi0ECAG0z\nrsPljKqyy5u3T58HeXkHGZV/PPPmi8+76ojjx4+76Yi4uDgUFxcjJiYGFRUVKCsrQ0xMDFq2bIkb\nbrgBjRo1AgAMGTIEBw4cIOc7QOhtE2Q94bAHAIhsgnBFK4M8nNpBInSxV34RcoNIVM/FJGfUx/cZ\ng/S3CQCPdkEGPNsEgPHsAsNOO8/Ly8Ozzz6LDRs2ICoqSjg+ZMgQvPfee7h69SpKSkpw+PBhdO/e\nPeDyGWX6KDUG5sWMHQJmrG/OMyXc5Ndya5FujsBs/rxXI5SvUdo2X0lLS8Phw4dRWlqKK1euYO3a\ntW5KcsiQIVi1ahUAYP369ejZsycsFgtuvPFGfP3117h06RKuXr2Kbdu2wWqlaaxGwOg2QSAwc+BH\nX9ZsByPNUMYI+sUMOLavtQFIV3w9QPXNyBjNLgjqyLcnZsyYgcuXLyMzMxMA0LNnT7z66qvo2rUr\nRo4cic6dO8NisWDZsmUUjVYhek4dUmKwazWKTRD+wkc2jT1AW9YA/i0ZMRpRUVFYunQpBg4ciIqK\nCmRlZSE1NRVz585Ft27dMGzYMEyfPh1ZWVlISkpC3bp1sXr1agBA8+bN8Y9//ANpaWm4cuUKhgwZ\ngptvvjnIOSIAsgm0Jng7gIRXZxbZPeaCOihCE6PZBYZ1vr///nvZc48++igeffTRAEoTmlAjQxBe\nMMjSlECvQzM7gwcPduvVnjdvnvD/yMhIrF27VvLe22+/3af1xIS+kE0QPph5hJ8IPIFek25DEY55\nv8wr4dYRFWyMZBcYdtp5oAhW5Q91x9fv/Dk5PUp7jal3mVCD87YipAQJgggXQrW9q4p4Htr2FaEf\nrlHPfSEQOwQQ5ibsne9QwEiKxizrRY1UZmoxs+ymhiKjE0TIU3YgNtgimJ5gdoTL6cdw7JwPxzz7\ng1t5cY5OKiWdVeFkl1G98h9yvv3E6B+ckojpSvIg15NnFmcbMP67IvTF9f3z676rgqs44TLdPNx6\nskN1ZIwgCIJQT6g7XJ63hC3SVCeSfiXI+daRYDt7WjneRsVWOUGI/38w5SDMiR1WdYqQRr8JgkBo\ndMh50v+hkD+C8Be97DtywMMbcr4JU+BqJPjaIGqxnodQTrA7JpQ+n+oEQRCEsYKdBbNdDrbuIoKH\nEKHeIAFXidCDnG9CMc4jzZ7ge/S0Ul7kGBGukGHkO6pH+wmCCBlC4ds303I3fyH7Rx3+lhfZFkQg\nMOxWY0RoKMlAYPTGkvb5NC6+KmqzfJuq80c9/QQRtvCj3vy/Rp56bpY2mDAxXd0PFVXYZIctqaOE\nUAqNfPtBuDf+ejc0FLWU0BOv9ddEjigpfYIgwoVwt72I0MQGu09xZawWZbNSCeNAzncYoCTwmtZo\n6QxQo0KELBI96wRBEHoSrM46LZ9LHY7GJVRtNn/rHHUaETw07TwAkJLwnVBtxL1BdSYISI10Vx4L\n13oYFEw044AwJ/Q9mwc7rEEZQCDCFxvsDhssLdiSEAIhZhfQyLdGyDlL4eZEad2zx5ef679Gwogy\nEX4SwIaeHAGCIEIZrXUk6VwiIHRz6OfYo2Wiw652rllGtMnWMA7kfBOa4c8abU/K1MiONxFCVE4B\nFxQpjXoLUBkQRGhgFj3q3OaYxbnRGrO8KyOgtY6ywY7eqfnCb1/ehRFnbJAuNwbkfBMEEbaI9n2X\nmmKmYgQ8OqXU0NGBCYJQT6g6QGbLl6u8ZpOfMB/+OKrk5BKeCFvnO7z2iTRur7GzAjWynGaHDBUF\ndHP6v47TzrV6F3ord6ozBBFaBPOb1qK9os7N4EIOpQOp7yhUy4bsAH0IW+fbTGhV+Y36ERlVLsLY\naK3s+L1tBUIswAdBEEQoQDYDEQiM6FBT3Q8NyPkmAoZrQ0aNCKGGQNYXfmYM/6+o7oaYU25EA4Mg\nCPOiZ1vNt1eh3m6RfWQMMo8WyO69vYLjAiYH1YfQgpxvQnOokSDMhK1y5Tc/pbHyl+Bk22FT1HHE\n3x/qRiFBEOaGdDQRTvhV32Ucb0/pkw1AeCPknW9SMgHAw0ggNUKEnuhZv0oTot3jECgc9Q5mvadv\njiAIV/SyhcIpfo6vqC17slt9IxC6z2qx6/9+FDj8hLkJeeebUI4WDYoejRIpIiJgdBX/5OueaMuQ\nFOlrzA4Z0QQR+oRKexXu0HsMLulO/5d7F6EYRJg697WhWrAFIIyPWyAqgghhxBH43aecS8FfQwYR\nQRgXo32f4WTI+ppX5zY4UOUVTu+FkGcMPnD85wCAkUEVJWBQ3Q8MNPIdgpQmREtPmdWRUOzhI8IL\npQEBPY0Qq1FczmvESeERBBGq+Nu+Ga3TxBWjyxdOaKlLY4+WyU4Bt7bU7DGmgbb60w4a+SYMgx02\nZKIg2GIQGmFKhzLN8Y8nY8rXjiZJpz1N+tpgll3QO9JCLJo8QZgJq8WOogpyJonAYpYODEE3pwAo\ndkw//1LBfbSsy09CzC6gkW/CeBjgIzOl4+gHZlF8utPNw7kUeK2butcbA3wbBBHK0DIrIpQIN1tG\nc+T0forD8bYOV5gGyM4iqiDn2wcC0YOl1UeqdBSLGgVzQe9LH7wa3inGLXujykUQZiQcvyezTCsl\nh5JQip7fsXU4vHeIC4632Ba3Vy44I8ITmnYeJgT7IydlSZgBrYxPvb83+p4IgvAXJe0UtTVEuFKa\nEI3YA2XCb9G3wDvdk+Tvt8MGW0IRYlEmPkbfVNgT1s43rcHwTrCddoIwFH5OOw/6emqCIEyNFtPi\nSa8T4YBm9Vxm2jlSgPyE3qqT81Uu+m5Dh7B2vvWCerb8IAWO6JK0tpUIAh6/W6qTBEGYHLMZ8Ly8\ndtgwBjl+p0MQqpDR+/zgnZJ6RQN9hCu05pswHaRECT1Q0mEWVCWqh/MvE22dIIjwxZ/lN6SfCSMQ\niHpoh9Xrc/jZbmb6LmKPOqbJm0lms0HON0EQign3GR1qp407gqpYqeebIAiiEjLqzUs42gBS+tvZ\nFvCnTHz5FtQ+LxzfmdEJS+fbtbKHuiIIZv5CvWyJ8IAcaHORl5eHpKQk2Gw2LFq0yO18eXk5xo4d\ni6SkJKSnp+PYsWMAgMLCQqSkpCAlJQVWqxVvv/12oEUnggDpqcBh9LImR4Xg0Urvq+q0P+D341Rj\n9G9SK4xkF4Sl800Yk3BpAAhCwNO+4oRPlJeXY9q0acjLy8PBgweRk5OD/fv3i655+eWX0bx5cxw6\ndAgPPfQQ7rvvPgBAcnIy9u/fjwMHDmDLli24//77cfny5WBkgyDCFn7aOznChK9oWXeknGfXrcKk\nnkfbiRkHo9kF5HwbGK0/XH/TsqEINhTplj5AgSn0gBp/5XhS2ErWd3m+V7r32/GVy39XWuIsf6jW\ni8LCQiQmJiI2NhbVqlXD2LFjkZubK7pm48aNyMrKAgAMHz4cu3btAmMMUVFR4DgOAHDp0iU0bNgQ\nNWrUCHgeCMLM2GEDuvp2LznchFHgl41pkY6a44T2GM0uCBvnOxwbdC0/bLnyk5qWI/VcamQIM8LX\ne3/qr5nqfih0fpWUlKBFixbC77i4OJSUlMheY7FYEB0djV9++QUAsGfPHiQmJiIxMREvvPBC4AQn\nDAdtDegF2gGCCAPMpMNdCUffRwqj2QVhv9WYmT8qJQQ6f3o8jxoPwgioqdu03aB+bN0NbC2UP8/3\nUPtK9+7d8c033+DIkSMYNGgQMjIyUL9+fb/SJAizYYM95O0jgvCGt1FrLfQ82Qr+4c0mAIxnF4S9\n8+0NMqJ9hy871QrcqTedyp4wE9T55D/eRt/bJQDtxlf9nrekTHQ+Li4Ox48fF34fP35c1OPNX1Nc\nXIyYmBhUVFSgrKwMTZo0EV3TsWNHtGnTBkeOHEGPHj18zA0RygT72wz284ngQe/eHHjzIeg9KsOT\nXeDNJgCMZxeEzbRztYTD2kg98bVB4af5hcL0VyK80Wv5BbVHnklLS8Phw4dRWlqKK1euYO3atRg8\neLDomiFDhmDVqlUAgPXr16Nnz56wWCw4fvw4rl27BgA4duwYioqK0LZt24DngSB8IRTbBnJOzIfZ\n6qHedcxs5aEGs3yfRrMLaOQ7TND74w/lxoUg/MW599v5/9EppcEUKySJiorC0qVLMXDgQFRUVCAr\nKwupqamYO3cuunXrhmHDhmH69OnIyspCUlIS6tati9WrVwMAtmzZgueeew4cx4HjOLz66quIjqaO\nQKIKmo4dHIoqbDRcRBCETxjNLiDnm9AVMlKIYKKk/gVqaYmrLDbYUWTQ78Ps3+3gwYPderXnzZsn\n/D8yMhJr1651u2/ChAmYMGGC7vIRhFGgKbEEoR6jdMIVVdiErfkIzxjJLqB+RIJwgQwOgiAIgiAI\nQg6yFQlfIec7zPClpy6QvWp22Gi9N2FqvH1jzgrbagncHt8EQRBaQG0WQRCE75DzHQb44nDrsb+p\nuq2aaH9VwjgorbtGmIZGEARhRuRGEsNphJF0SPjgrV6rqQvUIWYuaM03QRBEkPDXqAwno5QgiMBg\nxHbFnxl4ejq05CwT/uAciBVA1Va7KdLXE6GB4Ue+n3/+eVgsFpw9e1Y4dt999yExMRGpqanYv3+/\nps+jKc/q0XKU2g4bjXoTpsKIhipBhCqBtglCAXIQCcLYCAHcyOkOCwztfB8/fhz5+flo1aqVcOyD\nDz5AcXExvvnmG6xcuRJ33nmnT2mTg6cv/ip7/n4px4acHSLQ6FHnbJVdTQRBKENPm8BMqBkFVquL\n9WqTqAOAIBSSAnLCQxxDO9+zZs3Cs88+Kzq2ceNGZGVlAQC6dOmCq1evoqSkJBjimQIzKjwzykwQ\nvkDON0Eoh2wCgiDMhio97+R4ky0cuhh2zff69esRFxeHzp07i46XlJSgRYsWwu+4uDiUlJQgLi7O\nLY0XnvgTduxDMS7hh4xzQEboV2Q7rBR4gSAIn5FT+D9sPYF9W4tRjDMBloggtLEJ8p5w2AOXcAbN\nM9riuoy2usttZMzU+WcmWQnCV0oTohF7oMwUS2C/3noBm7fuC7YYpiSozndmZiZOnTrldnzBggVY\nuHAhNm3aJBxjjEn+HwA4jpNMf9YTtZCDrvgDY9AWOV6bbjNUdqNCPXTaQWVJBANvHXdtM67D5Qyb\nYATnzSOlS2iL3jbBoCcc9gA5coQUVC8IQ6DRlHO19Tk6pVTV9ckZ9VHdaVCTbALlBNX5zs/Plzx+\n+PBhHD16FMnJyQAcPdtdu3ZFYWEh4uLicPz4cfTo0UM4J9XDTQQGO2yaKyw90iQIogojd/Coj8dR\noIscROAhmyC46K13/dXtZBsQoY4dVsSSTnNDnV1g/PIz5JrvTp064fTp0zh69CiOHj2KuLg4fPXV\nV2jatCmGDBmCd999FwDw1VdfISIiArGxsUGWWB4jG7kEQRgDCgBJEPKEkk1AEAThidKEaMEmkOts\n0stmoGWrgcGQzrcrzlPIRo8ejdjYWCQmJmLy5Ml44403giiZudCrI4A6GAhCe+i7IghpyCYIfWiE\nmwgVlOpyf3U+Oc7mwbAB15z56aefRL9ffvnlIEkSegh7CwYIX6aNBVpGgtAKJfVdqgeb6jtByKO3\nTRDujh+ff7/aoTSNhKnEzHZAuNcnQjl8HTdrXSeUYYqRb0IaNR+n1LVG+rgDsdaMIAiCIMyKmv29\njYoSXSxnD5ATSxCBIfZoWbBFCGnI+fYRWqNJEKFJMA08b4Yp7chAEISe+NL+eWu3yGkmtCBc6pHy\naeo0a86shJ3zHS4V0w5rSOU1XBpdIjzhv9VA1PNQahcIgvCNQOtU0uEE4R3Sz+GB4jXf165dw7Fj\nx/D777+jXr16aNmyJSIiIvSUTXcCPXpN22QQBEEQoUAo2gSEfgTLqSBnhiAIo+Fx5PvixYtYvnw5\n+vfvjzp16qBt27bo0qUL2rRpgzp16qB///5Yvnw5/vzzz0DJS+gAdQhUQWVBEAQhTajYBDbYqa3X\nCaFcu1UdIweYINRB30xoI+t8P//884iPj8eSJUvQo0cP/Pe//8XevXvx3XffYe/evVi9ejXS0tKw\nZMkStGzZEs8//3wg5SZUQB8xQRgTVd9min5yEIQ3yCYgCIIwHtSRaD5kp53v2rULn376KVJTUyXP\np6amYuTIkVi4cCH27duHp59+Gg8++KBughL64Lp9h69T4/2ZUq/0XmpgiEBBdY0gxJBNQBAEoT9y\nnfI0kBY6yDrfH3zwgeJEunbtqup6wnyQM0IQ+mAkhUq7OBBykE0QPmiyzzdBEIqhby28UBxwjQg8\nvo9CqzOgXUe/CeNDnSFEqKK+LSrQRQ6CMCtG0OnRKaVBfX4gCHYZE0S4oO5bM75NoMj5/uuvv7Bk\nyRJs3rwZ58+fB2NMOMdxHPbs2aObgEYhFBtZpQ6cWkfPCIqf0A96v/phpLI1ihyE8SCbQIwRvxUj\ntSUE4Qmqp0S4ocj5njhxIjZv3oxRo0ahUaNGonMcx+kiGBG+0KguQRCEcSGbgCCIUEAre1PvDgTq\noAgtFDnfH330ET777DP06tVLb3kIwlRQg0joAV+vdO+I6ub9EoJwhWwCIlDQCD5BEKGGx32+eeLi\n4hAdHa23LIaHRmSlIcVImBn6rglCHWQTEARBaIcaO4RsbvOjyPl+8skn8a9//Qu//fab3vIQEtCH\nRoQ6VMflkVLK1GFABBOyCUIbO2zUJhNEmED2ROBR5HwPHjwY58+fR+PGjdGqVSskJCQIf61bt9Zb\nRr+hiiWPP2VDCpog9IW+L8KImN0mIOTx1OaoaY+sFrK7CIIgpFC05jsrKwvfffcd/vGPf5gquArt\nWascWldFEObA1y0IA50mEbqY1SYgggO1LwRBEFUocr4/++wz7NixA6mpqXrLExCcFUEwHE5ycgmC\n8AXqUCSMQKjZBOGGsyPsb8d7MNskcuoJwnfIFwkeiqadt2jRApGRkXrLYjoCUXH9fYaS+12VF32Q\nBBE8yJj0n7y8PCQlJcFms2HRokVu58vLyzF27FgkJSUhPT0dx44dE84tXLgQNpsNSUlJ2LRpUyDF\nNg1kExDBJpztFNIRBKEeI9kFipzvxYsXY/bs2SguLvb7geFOOCgMf/JISoUg3LHDitKEwESXNnsb\nVV5ejmnTpiEvLw8HDx5ETk4O9u/fL7rm5ZdfRvPmzXHo0CE89NBDuO+++wAA+/btw4cffohDhw4h\nLy8PU6dOxeXLl4ORDUNDNkFoE+x4LhT5mSA8Q/VeHUazCxQ537fccgs2bdqE+Ph41K5dG3Xr1hX+\n6tWr55cAhH+Y/QM0u/yEcaG6FZ4UFhYiMTERsbGxqFatGsaOHYvc3FzRNRs3bkRWVhYAYPjw4di1\naxcqKiqQm5uLcePGISIiArGxsUhMTMSePXuCkQ1DQzYBYSSorSfCEar3yjGaXaBozfdLL73k10OM\nCK0VMg6h/h5CPX8EEUh+2HoCP2w9KXu+pKTk/9m797go6v1/4C/wgiAkqIAK3sorN0Gz1IOGHi8B\nmZZ67KJHzTK7aHY52U9LRTOzb2VZJ01PZmU3yzRLRTFFU5MUI0XT7il4v2CgiCCf3x+4615md2cv\nszOz+3o+Hlvu7Ozse4fZmfd7Pp/5DJo3b258Hhsbi9zcXJvzBAYGolGjRjh58iSKi4vRp08fs/cW\nFRV59gv4AF/MCYCaEbp/qvZuQqvmYKc8NhG5TytFsL+OCeMoJwC0lxfIKr5Hjx7t1oeQtmlhx8HR\n1om0Q83fosPPTotDizST51n5Zi9ztG3lMScgZ7HBg0hftJST243FQU4AaC8vsNnt/O+//3ZqQc7O\nr1f+embJG3hgJrLtADpq6mCoVbGxsThy5Ijx+ZEjR8zOeBvmMVyvXF1djTNnziAyMtLqvZZny/0Z\ncwJt4P2zpXHfSP7Im/sDPefoWssLbBbfbdq0wbPPPovffvvN7gJ+/fVXTJ06FW3atHErECIiUo+v\nJK9du3ZFYWEhiouLUVlZieXLlyM9Pd1snoyMDCxbtgwA8OWXX6J79+6oVasWMjIy8Omnn6KqqgpF\nRUUoLCzETTfdpMbX0BzmBM7zld+Us/ScpBNpndR+xVbDoDO/RV8+sae1vMBmt/Nt27ZhxowZ6Nix\nI9q2bYuuXbuiRYsWCA0NRVlZGf766y/s2rULv/76K4YOHYpt27a5FQh5l5ykgK38RO7zRHdLdtmU\nr169eliwYAEGDBiA6upqjBw5Ep07d8b06dNx4403YuDAgXj00UcxcuRIJCYmIiwsDB999BEAoEuX\nLrjjjjuQlJSEwMBAvP3226hTp47K30gbmBOQM7RwKZnanw/wRATpS8wfZ1x6nxZ+a/ZoLS+wWXy3\na9cOH330EV555RV8/vnn2Lp1K1avXo2ysjKEhoaibdu2eOihh3DnnXciJibGrSCIiEiaofCWe/sf\ny2TP9LmrRbwWEmlnpKenW53VzsrKMv47KCgIy5cvl3zvlClTMGXKFEXj0yPmBPL4cuuR1vEkJZH3\n6CknALSVFzgccK1p06aYMGECJkyY4LEPVZPekkjyHn8+aPvzd/dHBxDn9VGdyTf4Wk5A2uatfM3Z\nYyDzSALU2w68faKpUXKx1z7LH8i6zzf5Lrk7Dj0XZzxIEhEREZGvcFSA6zlv93UsvolcpERRzxMF\nRETe48w+V0vJrJZi0RIeQ4lI6/y6+OZOmoj0Rsmkm/tEIlITTyp4FvfpJIXbhbr8uvgmeXgwJCIi\n8iw1jq1yPpOJOZFjWv2daDUuuobFN3mUsz96Fvakd758oOPvk4iIyHc4c1xnDqAMWcV37969MXPm\nTKvpZ8+eRZ8+fTweFOmTLxchRGrj74u0gjmB/5BKvg+gowqREGmHlsaKUHr5HOnc82QV31u2bMFr\nr72GoUOHory83Dj98uXLyM3NVSo2zdLCgYeJONcB+SZvbtc8qJIrmBOQs1wpEFzdFzI3ICVpcfs6\ngDhNxkXSHN7n22Djxo0YN24cUlNT8dVXX6FZs2ZKxkV+hN1aiOzz5EFV6wdorcdHNZgTkJZxP0Jk\nztv3BvckX/s9y77mOzY2Ft9++y3atGmDrl27Yvfu3UrGRV7maxs2kb/R60GV9Ik5ATmi1D6J+Qo5\notQ2wm2PPMGpAdeCg4PxySefYNy4cUhLS8Py5cuViotIFu4Iia5hAU7e5Is5QcdA/obkKm7dSO0Q\nfArzGSL/ILvbuUFAQACmT5+O+Ph4jBo1SomYiIj8kjPJl567kJHv8PWcwFcLIu47yIDbgj558u8W\nhwM+u6/TIlnF96ZNmxAREWE2bejQoejQoQPy8/MVCYzIHu4kSAv8aTvkwZkMmBP4Lrm/c7mt3txv\nEBGZk1V8p6WlSU5PSEhAQkKCJ+MhIiIPYgs5eRpzAiIi9fCYrm9OXfNN+uHtM81xV290QL6PrRjO\nUeJ3wb8BESnFF47lvvAdiMg3sfgm0hkmFeQJHFiK6BruV7XFEycY+TclX+TOdm3vd8Xfi/douvh+\n44030KlTJyQmJuI///mPcfqcOXMQFxeHxMREbNiwQcUIfQNb0ZTDnRnpjWF/wG2XtIY5gXvU+k2b\nfi73K0Tk75we7dxb1qxZg/Xr1yM/Px+1a9fGmTNnAAD5+fn44osvsG/fPhw/fhypqak4dOgQ6tat\nq3LE+sTCm8g1evjtcLAj8hXMCZznid+/o/d7e/+ip/0ZTzSQVujpd+MPNNvy/b///Q+TJ09G7do1\n5wcaNaoZWXPNmjW46667UKtWLcTExCA+Ph7ff/+9mqH6hTj8JHM+HmyIvEXOAdXwm+TBl/SMOYH+\naKGlnYjMMRdQn2Zbvg8ePIj169djwoQJCA4OxiuvvIIePXqguLgYffr0Mc4XGxuLoqIiyWV8MOMw\nTqEcp3AYddNqA2n+vcFp9YDE1jkibR0QpUZI/zH3PPJzs3EYp1SKivyZJ3KC7Bn5OIxyZOMA2qQ1\ndZgTaOk3qTdazTeIyDN+zT2KX3OPAQBO4bDK0eiLqsV3v379cPz4cavps2fPRnV1NUpLS1FQUIBd\nu3ZhyJAh+PPPP51a/sgZLa6OwR2HNjgg61BwAB2d+gzyPsuESI1bKTEpI19gOeiave26U1oDSpBB\naQAAIABJREFU/JJ2q/G3lp2lzP2cf6rmb8tfKZ0T3DqjC8owFLficwBgeegFnjy5rpXjrppx8KSG\nvvjC36tj4AHJ43KbtGZok9YMQM1vIj8rW7EYfC0vULX4zsnJsfna/PnzceeddwIAunbtirp16+LE\niROIjY3FkSNHjPMVFRWhefPmisfqLq0cNExp6f6/bP0mUo6c6za1si8g/+VPOQHZxv0REfkyzV7z\nnZmZiU2bNgEAfv75Z1y8eBHR0dHIyMjAp59+iqqqKhQVFaGwsBA33XSTRz6Trd7SeBAkMqfWiSL+\nFslfqZET2MPfovcYejASqcmftsHi1o3MnvPWpJ6l2Wu+H330Udx3331ISEgAACxduhSBgYHo0qUL\n7rjjDiQlJSEwMBBvv/026tSpo3K0JBcTFiIichZzAu2R22DhTs82fyp4iLSM+bvnaLb4rlOnDj74\n4APJ16ZMmYIpU6a4tXxf7dak1Pc6gI6yRzz3Nk98X1/cFogcibvapsQEl7RO6ZxAS3z1eOTs9+J+\niYh8kWa7nXsLd+7awL8DkXf5aoJPRMrT6mV6ruzXuC8kssbfhXL8vvgmfWGRTqQsHnCJtEnp36bU\n8tXaH/BYT6QNzAk8j8U3qY4HWZKDBwAiIm3h8ZtImp5zFkPPFsN30PN30SIW30TkcdxR64OtvxP/\nfuSLWCjWUOP3zX0KEVENny6+eaC9hgc+/8TfgOdxnerX2bNn0a9fPyQlJWHAgAEoKSmRnO+9995D\nfHw84uPj8f777xun9+/fHykpKWjfvj1GjhyJiooKb4VOpBpv5w/MV7SLxz/fZ3mbMV+nRl7g08W3\nI9yJ6Iuv/718/fuRdvlLsjt9+nRkZmZi7969SE9Px/Tp063mOXbsGGbNmoW8vDzk5eVh5syZOHHi\nBABg5cqV+OGHH3Do0CGUl5fjo48+8vZXINIMrR2z/GU/RqSEOBwwFt6mv21f/12pkRf4dfFNRKQk\nXz9o6c3atWsxcuRIAMCIESOwZs0aq3lycnKQnp6O0NBQhIaG4tZbb0VOTg4AoH79+gCAyspKXL58\nGU2bNvVe8ERERORRauQFmr3PN3kOCwAi36C1liY1VOZ+h8ot37n03lOnTqFRo5oz+40bN8bJkyet\n5ikuLkZsbKzxeWxsLIqKiozPBwwYgF27dqFfv3649dZbXYqDiLwjDge43yRyk5brCHdyAkCdvIDF\nt4u0vDPXamzu/Hi1+p2IyLPOFMTYnyF8KDBo6LXnM181e7lfv344fvy41dtmz57tifCwfv16VFRU\nYPjw4XjvvfcwatQojyyXSGu0nHD7GuY4RLbZzQsc5ASA9vICFt8+jgdPIpLiaN+g132HoSuYlMjI\nSJw+fRqNGzfGqVOnEBUVZTVPbGws8vLyjM+PHDmCHj16mM0TFBSEIUOGYPv27Sy+dYitoc7T+vrS\n6/6K5OHft4ZS68HX16/W8gKfv+Zb6wcMNehtndiL19EOQ2/flbzL1w84ZC4jIwPLli0DACxbtgwZ\nGRlW8/Tt2xfZ2dkoLS1FaWkpsrOz0bdvX1y4cAGnTp0CAFRVVeHrr79GYmKiV+MnIpKL+Y969Jxb\nGO7x7S/UyAvY8i2BOyxp/vaDJCLfkpWVheHDh2PJkiVo0qQJli9fDgDIz8/HwoULsXjxYjRt2hRT\np07FzTffDACYNm0aoqOjceLECdx2222orKxEeXk5+vfvj/Hjx6v5dYiIiDzGH/N8NfICFt+kaXo+\nEaLnM59Evqhhw4aS3c+6dOmCxYsXG5+PGTMGY8aMMZsnOjoau3btUjxGIr3QyvHZ3rFWrRh5/CfS\nBzXyAp/vdk5ERPIxaSQiXtJFRKQMFt/kFUzoiYhIK3hMIiKqwZNp3sXimzSLOwPSGl/fJg0FCQsT\nItID7quISG9YfJNu8aBLWuaN7VPuZ/C3QkRERI74eiODFrD4JiIiIvIDrp6I8+YJPCb/5G+0ss1r\nJQ5fx+KbZDuAOP4w4Xs7J7aKEhGRgbvHBB5TiHyXr+XAamDxTYpz5UDMH7c0JjVERNrTMZD7Zm/j\n8ZCI9IjFNxGRjsRd7YNCRL6Dv2l9YMOA7+BvjtRSW+0AiIjIeT6bOOxWOwAiIiLX+OyxWU0+lhew\n5ZsUxZ0QERERkXuYTxH5BhbfRERERKQ6duvWP54kILKPxbcf4w6SyD94KqHlPoNIOfx9ERH5Phbf\n5JC3z0TzzDeR9vB3SURawRMVRKRXLL7JaVpIwnngJSIiko/HTbLEbYLI+1h8ExGRXVo44UZERETq\nY07gHhbf5BRnfnB6OaOqlzhJP7hNEZEt7uwfuG8h8k9a++2zAHcdi2+ShT8yIt/C3zQRERGRd7H4\nJiIiIiIiIlIYi28iDWArJBGRurTWrdPf8DhI/ojbvf9h8U2awp0Q+SO52z2LAyL38DdERERqYvFN\nRERERLrgaydQ2OhA5F9qqx2AL+MOlYjISbvUDoCIiIg0w8fyArZ8E5GqeJKKiLRCD62qeoiRiIik\nsfgmRTA5ICIif9Ix0PFxz5eOjTxxSuRf+Jv3DBbfJBt/dES+hb9pIiIi38XjvPb4RfHtS2eaieTi\nDpeIiJTg6eMLj1dE5C/8ovgm36KHkyl6iJEIYNJLRP6Lx2oi8ja/K76ZaDqP64yIiIgMWLQSEbnG\n74pvInKdXk7E6CVOIiIiIr1gfuU+zRbf27dvR3JyMhISEtCpUyfs2LEDACCEwMSJExEfH4/OnTvj\nhx9+UDlS/+CNHxt/0ESkpLNnz6Jfv35ISkrCgAEDUFJSIjnfe++9h/j4eMTHx+P99983Tr98+TLG\njRuHpKQkdOzYEStWrPBW6H6POYH2sPWbiPROjbxAs8X35MmTMXfuXBQWFuLFF1/E5MmTAQBffPEF\nDh8+jP379+Odd97BmDFjVIuRxSIRkX5Mnz4dmZmZ2Lt3L9LT0zF9+nSreY4dO4ZZs2YhLy8PeXl5\nmDlzJk6ePAkAmDp1Klq3bo29e/fip59+Qp8+fbz9FfyWHnICUp4eCn4txMj8lEgeNfICzRbfzZs3\nx/nz5wEAJSUlaNmyJQBgzZo1GDlyJAAgJSUFVVVVKCoqUi1OIiLSh7Vr1xqPHyNGjMCaNWus5snJ\nyUF6ejpCQ0MRGhqKW2+9FRs2bIAQAh999BGeeuop47wRERFei93fMScgIq3TwokXV+g1bk9QIy+o\n7bnwPevFF19EamoqnnrqKVRXV+O7774DABQXF6N58+bG+WJjY1FUVITY2FirZWTPyMcpHMZhnEKb\ntKZAGs8EEhG54tfco/g195jaYQDHcmseLjh16hQaNWoEAGjcuLHxzLWp4uJis+OJ4Rhz8uRJ1KtX\nDxMmTMC2bdtw/fXXY9GiRWjSpIlLsZBzPJET5M/IBgCU4zCS0hoAaV4JnVTC1l/yd0r+Bo7m/orD\nufkAgFM4rNjnOORGTgCokxeoWnz369cPx48ft5o+e/ZszJ8/H/Pnz8cdd9yBzz77DPfddx9ycnIA\n1FzjZSogIEBy+bfO6IIDiDOe0fHf8zpERO5pk9YMbdKaGZ9nZ+Ur80EOF5sG86opy+xVe8cVVwUE\nBKC6uhp//PEH+vfvj4ULF2LevHl47LHH8Omnn7q8XDKndE7QZcatAIChKAMA/GIjDn9uBSIikqNZ\nWhvEpV0GUFPk52dlK/dhdvOCNNjLCQDt5QWqFt+GA6eUe+65Bxs3bgQADB061HgdV2xsLI4cOYKb\nb74ZAGye4XaVs2eJTIt7IiJSl73jSmRkJE6fPo3GjRvj1KlTiIqKsponNjYWeXl5xudHjhxBjx49\nEBkZiXr16uHOO+8EUHNcWrhwoee/gB/TYk5ARETm9NarRGt5gWav+W7ZsiW2bNkCANi0aRNat24N\nAMjIyMCHH34IANizZw9q1aqFmJgYj3ymJzcmvW2YROQZPBmnXRkZGVi2bBkAYNmyZcjIyLCap2/f\nvsjOzkZpaSlKS0uRnZ2Nvn37onbt2ujXrx82b94MAPjmm2/QsWNHr8bvz9TICYiItIB5hXLUyAs0\ne8334sWL8fDDD6OyshJBQUF45513AABDhgzB5s2bER8fj6CgILz77rsqR0pERHqQlZWF4cOHY8mS\nJWjSpAmWL18OAMjPz8fChQuxePFiNG3aFFOnTjW2pE6bNg3R0dEAgDfeeAMjR47EhAkTEBERgQ8+\n+EC17+JvmBMQqYuNSp7Hdao+NfICzRbfPXr0QEFBgeRrb775ppejISLSJx7cr2nYsKFk97MuXbpg\n8eLFxudjxoyRvGVVixYtjK2v5F3MCYh8D49PyvHGuvWFS2/VyAs02+3cG/ijJyIiIlIPczHyJG5P\npHV+XXwTEcml1AFd7nL1fnaZiIiIfAtPdjiPxTeZsZfg8wemLhZfRERERET6xeLbj7B4IyIiIiIi\nUgeLbx/gjaKard5ERERERESuY/FN5ASehCDi74CIiIjIFSy+iYiIiIg0ipcNEvkOFt+kCRzxmYiI\n/AWPZc7jOiOSh73TtK222gGQd/CgRUS6UKB2AERERKQZPpYXsOWbiIiIyAeocaKdJ/eJiORj8U1E\nRERERCTBl7tx8+SZ97H4JiLyUb6cMBCR+pi4ExE5h8W3H+DBkWzhtkFERERE5B0svr2ArU9ERETa\nweMyERGpgcU3ERERERERkcJYfPs4dismIiK6hq3eRESkFhbfRERERER28KQNEXkCi28/408t4TxQ\nEnkef1dERERErmHxTUREREROMT2Z708n9omI3MHim4jIB7GFmoiIbOEJEyJ1sPj2Ydyx6oOvFUnc\n7oiIiIiIrNVWOwA98rViiYhIM8RPakdAREREWuFjeQFbvomISBaeeCQiIiJyHYtvIiIiIiIiP8LL\nBNXB4puIiIjIDiapRKQGZ/c97KGmfT5ffPNWGJ7D9Udq8fdtT8nv7+/rloiIiMhbfL74JiIiIiLS\nGrZSEvkfFt8exJ0oERERkTxK5U3Mx4hIq1h8ExERERERESmMxTcREfmFs2fPol+/fkhKSsKAAQNQ\nUlIiOd97772H+Ph4xMfH4/333zeb3qFDB8THx+OZZ57xVthERESkADXyAhbfJAsHZSIivZs+fToy\nMzOxd+9epKenY/r06VbzHDt2DLNmzUJeXh7y8vIwc+ZMnDx5EidOnMCUKVOwfft27N+/H8ePH8fX\nX3+twrcgItIm5oqkN2rkBSy+iYjIL6xduxYjR44EAIwYMQJr1qyxmicnJwfp6ekIDQ1FaGgobr31\nVmzYsAG//fYbOnbsiEaNGgEA/vnPf2L16tVejZ/IX7GoIyIlqJEX1PbsVyAO8kFEpKTvAexy6Z2n\nTp0yHiQbN26MkydPWs1TXFyM2NhY4/PY2FgUFRUhPT0d+/fvx19//YWYmBisWrUKFy5ccCkOIiIi\n8gTXcwJAnbyAxTcRkUo40q+U7TLmSbb5Sr9+/XD8+HGr6bNnz3YjJqBRo0Z48803cccddyAgIAC9\nevXCwYMH3VomEREROeIoL7CdEwDaywtYfJPq5BYK7HZGRI7k5OTYfC0yMhKnT59G48aNcerUKURF\nRVnNExsbi7y8POPzI0eOoEePHgCAIUOGYMiQIQCApUuXIiAgwMPRExGRP9DbSXK9xWtKa3kBr/km\nIiK/kJGRgWXLlgEAli1bhoyMDKt5+vbti+zsbJSWlqK0tBTZ2dno27cvAOD06dMAgNLSUvz3v//F\n6NGjvRY7ec4BxOk6kSQiIs9QIy9gyzcR+SX2pPA/WVlZGD58OJYsWYImTZpg+fLlAID8/HwsXLgQ\nixcvRtOmTTF16lTcfPPNAIBp06YhOjoaADBhwgQUFhaiVq1amDJlCpKSklT7LkREROQeNfICFt9E\nPoitOkTWGjZsKNn9rEuXLli8eLHx+ZgxYzBmzBir+T7++GNF4yMiIvIGNkDUUCMvYLdzsotFHBER\nkW/iMZ6IyLtYfBMREREREREpjMW3Bv2ae1TtEFx2NPdXtUNwmZ7XO6Dv+PW83QD6jl/P2w2RP+D+\nRT16jl/P2w1wLX49do/W83aj59j1QvXi+7PPPkN8fDxq1aqFPXv2mL02Z84cxMXFITExERs2bDBO\nz87ORmJiIuLi4jB37lxvh6y4X3OPqR2Cy47peGev5/UO6Dt+PW83gH7jj8MBXW835HuYE1jT6/4F\n0PdxCdB3/HrebgB9x6/n7UbPseuF6gOuJSYmYuXKlXjwwQfNpufn5+OLL77Avn37cPz4caSmpuLn\nn39GdXU1HnroIWzbtg3R0dHo3r07+vfvj5SUFJW+AVnS01lKPcVKROTrmBMQEZEvU7347tChg+T0\nNWvW4K677kKtWrUQExOD+Ph45OXlobq6GvHx8YiJiQEADB8+HGvWrOGBloiISOeYExARkS9Tvfi2\npbi4GH369DE+j42NRVFREYQQaN68udn03NxcyWVMClgk+/O2ORXdNjvPPCM7K1+BpbrC+W+Xn6VA\nGF6infXuGj3Hn5+VrXYIbtFz/Nrabh5wau6IiAiF4iAt8UROsChgktJhAlAmJ9DS/qUms5L/LbW1\nf3GenuPX0nbjCj3Hr+ftRnuxy88L9JATeKX47tevH44fP241/YUXXsDAgQMV+UwhhCLLJSIiZXC/\n7R+YExARkRy+uO/2SvEtdfNyR2JjY3HkyBHj86KiIjRv3hzV1dVm048cOWJ21puIiIi0izkBERH5\nK9VHOzdlenYjIyMDn376KaqqqlBUVITCwkLcdNNN6Nq1KwoLC1FcXIzKykosX74c6enpKkZNRERE\nnsacgIiIfI3q13yvXLkSEydOxOnTp5GZmYmUlBSsW7cOXbp0wR133IGkpCQEBgbi7bffRp06dVCn\nTh0sWLAAAwYMQHV1NUaOHInOnTur/TWIiIjITcwJiIjIpwlSXcuWLUViYqJITk4WXbt2FUIIcebM\nGdG3b1+RmJgo+vfvL86dO6dylDXGjBkjoqKiREJCgnGavVgnTJgg4uLiREpKitizZ48aIRtJxT59\n+nQRExMjkpOTRXJysli7dq3xtRdeeEF07NhRJCQkiPXr16sRspnDhw+Lnj17ioSEBNGuXTsxd+5c\nIYQ+1r+t2PWw/svLy8WNN94okpOTRdu2bcWkSZOEEEL8/vvvolu3biIhIUEMHz5cXL58WQghxKVL\nl8S//vUvkZCQIHr06CH+/PNP1WIXwnb8o0aNEq1btzau+4KCAiGEENXV1ZrZboj8EXMC79FzXsCc\nQD16zguYE6iPxbcGtGrVSpw5c8Zs2qOPPirmzZsnhBBi3rx5YuLEiWqEZmXr1q1iz549ZgcqW7F+\n/vnnYtCgQUIIIfbs2SM6derk/YBNSMU+Y8YM8corr1jNu3v3bnHjjTeKqqoqUVRUJFq1aiUqKiq8\nGa6V48ePi3379gkhhCgtLRVt27YVBQUFulj/tmLXy/q/ePGiEEKIyspKcfPNN4tNmzaJ2267Taxc\nuVIIIcRjjz0mXn31VSGEEC+//LJ47LHHhBBCrFy5Utx+++3qBG1CKv7Ro0eLFStWWM2rpe2GyB8x\nJ/AePecFzAnUzcn0nBcwJ1CXpq759mfCYjS/tWvXYuTIkQCAESNGYM2aNWqEZaVnz55Ww/jbinXN\nmjXG6SkpKcZr9dQiFTsgPZKi1D1lv//+e2+EaVN0dDQSEhIAAKGhoUhKSkJxcbEu1r+t2AF9rP/g\n4GAAwOXLl3HlyhVERUVh586dGDx4MADz9W7697j99tuxY8cO1UfrlIofkF73pvGrvd0Q+SvmBN6h\n57yAOYG6OZme8wLmBOpi8a0BAQEB6NevH5KSkvDmm28CAE6dOoVGjRoBABo3boyTJ0+qGaJdtmIt\nLi62uv+qFn+w//3vf9GxY0eMGDECZ8+eBVATe2xsrHEercX+559/YteuXUhNTdXd+jfE3rNnTwD6\nWP/V1dVITk5GdHQ0evfujYiICDRu3Nj4ekxMjDE+wyjMABAYGIhGjRqp/vu1jD8+Ph4AMHXqVHTs\n2BGPPvooKioqAJjHD6i/7on8DXMC9enhuGSKOYH36TkvYE6gLhbfGrBz507s2bMH33zzDd59911s\n3LhR7ZA8xvIsWkBAgEqRSHvkkUfw22+/4cCBA7jhhhswceJEtUNyqKysDEOHDsXrr7+O6667zu68\nWlv/ZWVlGDZsGF5//XWEhYXpZv0HBgaioKAARUVF2Lp1K3Jzc9UOySlS8c+dOxcHDx7Ejz/+iPLy\ncsyaNcs4v9a2GyJ/wpxAXXo5LhkwJ1CHnvMC5gTqYvGtAYbuHpGRkRg6dCh27dqFyMhInD59GkDN\nWWTDPFpkK1ap+7KanrnUgsaNGyMgIAABAQF48MEHsWvXLgC27ymrtsrKSgwZMgT33nuvsWuTXta/\nIfZ77rnHGLve1n+DBg2QmZmJ33//3bjOAfN1Gxsbi8OHDwOoObt85swZREZGqhKvJUP8O3fuNG4n\ndevWxdixY+2ue639bol8GXMCdenpuMScQH16zguYE6iDxbfKLl68iIsXLwIALly4gOzsbMTHxyMj\nIwPLli0DACxbtgwZGRlqhmmXrVgzMjLw4YcfAgD27NljvFZHS0y7/axYscLY9cbWPWXVJITA2LFj\nERcXh8cff9w4XQ/r31bselj/Z86cQWlpKQCgvLwcOTk5SE5ORrdu3bBq1SoA1uvd8Pf48ssv0b17\ndwQGqrerlYo/MTHRuO6FEPjiiy/M1r1Wthsif8OcQH16OC4BzAnUXPd6zguYE2iAN0d3I2u///67\nSEpKEp06dRJt27YVzz33nBDC/FYR/fr108xtRe666y7RtGlTUadOHREbGyuWLFliN9ZHHnnEeHuC\n/Px8FSO3jv2dd94RI0aMEElJSaJDhw5iwIABoqioyDj/7NmzRceOHUV8fLzIzs5WMfIa3377rQgI\nCBCdOnUy3gpi3bp1ulj/UrGvXbtWF+t/7969Ijk5WXTq1Em0b99eZGVlCSHs31Jk2LBhIiEhQXTv\n3l388ccfqsUuhO34e/fuLTp16iTatWsnhg8fLs6fP298j1a2GyJ/w5zAu/ScFzAnUI+e8wLmBOoL\nEELlYXiJiIiIiIiIfBy7nRMREREREREpjMU3ERERERERkcJYfBMREREREREpjMU3ERERERERkcJY\nfBMREREREREpjMU3ERERERERkcJYfBMREREREREpjMU3ERERERERkcJYfBMREREREREpjMU3ERER\nERERkcJYfBMREREREREpjMU3ERERERERkcJYfBMREREREREpjMU3ERERERERkcJYfBMREREREREp\njMU3ERERERERkcJYfBMREREREREpjMU3ERERERERkcJYfBMREREREREpjMU3ERERERERkcJYfBMR\nEREREREpjMW3zj344IMYOnSoU+/57rvv8K9//QsxMTEICgpC48aN0b9/f3zwwQeorq4GACxYsABx\ncXGoqqqStcw333wTLVu2RO3atREREeH099C6VatWYd68eWqHYWXGjBkIDFT2Z+yNz3CXHmIkIlKC\nK3mAq5gbXKPVvABQ/pioh2OuHmIk/1Rb7QD82a5du7Bs2TK0bt0aQUFB+PPPP3H99dfjgQcekLXD\n2L9/P5YsWYIff/xR9me+9tprePLJJ/HPf/4TL730Elq2bIlz585h/fr1GD9+PBo0aIDbb78dDzzw\nAF588UUsWrQIDz/8sN1l/vXXX5g4cSJGjRqFBx54APXq1ZMdj16sWrUK33zzDR5//HG1QzHzwAMP\nICMjQ/HPCQgIUPwz3KWHGImI7NmzZw8WLVqEhg0bAgAiIyPRqFEjNGvWDH379rWa35U8wB3MDa7R\nal4AeCc30MMxVw8xkv9h8a2St99+G0uXLsX69etx3XXXGafPmTMH6enpWL16NYKCguwu46WXXkJa\nWhri4uKM0zZs2IDVq1ejfv36CA4OxowZM4yvbd26FU888QQmTpyI1157zWxZAwcOxFNPPYXS0lIA\nQO3atTFu3Di89NJLGD9+vN2TAb/99hsA4N///jd69Oghex3YU1FR4fD7ExATE4OYmBjFP0cIofhn\nuEsPMRIR2fLNN99g3rx5WLFihfH4t3HjRtx+++04c+aM5Huk8gBPuXLlCtq1a4ctW7YgNjYWgLq5\nAfMC+byRG+jhmKuHGMkPCfK67777TtStW1ccOnRI8vV//OMfYsKECXaX8ffff4vg4GCxePFi47SK\nigpx5513Gp+///77YtWqVcbnGRkZIjIyUlRUVMiK8+effxYBAQFi/fr1NucZNWqUCAgIMHuMGTPG\n+Pq6detEt27dRHBwsGjQoIEYPHiw1feePn26CAgIEIWFhaJ///4iNDRUDBo0yOZnFhQUiIEDB4qI\niAgRHBws/vGPf4hvv/3W+HpZWZlo3769uOmmm0RlZaVx+vr160VAQIB46623rD573759Ii0tTYSE\nhIimTZuKadOmierqapvfsXXr1rLWobv2798vbr31VtGgQQNRu3Zt0axZMzFo0CBRVVVlFr/l9/nl\nl19ERkaGqF+/voiMjBRPP/208fsYfPTRR6J9+/aiXr16IjExUXz55ZfilltuEWlpaWbzWX6GEI7/\nBnIVFBSIwYMHi0aNGok6deqINm3aiKysLLN5nNmGDEaNGiVatWpl9XmW38/wvp9++kn07dtXhISE\niJYtW4olS5YIIYRYtGiRaNu2rahXr57o1q2bOHDggOTnylnftmzevNlq+zI8Ro8eLWsZRKRv1dXV\nomXLliI/P9/qtQEDBki+RyoP8KS8vDwRGRlpNd3d3ECJvEAIz+UGes8LTL+D5XNP5QZSeYEQ3ssN\nXMkLhPBObuCJvEAI5ga+jBdDqGDWrFno3r072rVrJ/n6mDFjsHDhQhw7dszmMjZt2oRLly4hLS3N\nOO3s2bO44YYbjM9vuukm/PTTTwBqzmBv3rwZ/fv3R926dWXF2bZtWzRr1gxr1qyxOc8Op0KxAAAg\nAElEQVS0adMwf/58AMBbb72FnTt34rnnngMAZGdnIzMzE1FRUVi9ejWWLFmCX375BampqTh69KjV\nsu68805kZGQgOzsbTz31lOTn7dmzBz169EB5eTnef/99fPXVV2jatCn69u2LPXv2AADq16+PTz75\nBD/++KMxlhMnTuDf//43Bg0ahIceeshquUOGDMHgwYOxfv163H///Xj++efx//7f/zN+x4yMDERG\nRmLnzp3YuXMnVq5caXfdVVVVOXxcuXLF7jKuXLmC9PR0lJaW4v3338fWrVvx+uuvIyoqynhtPiDd\nrWro0KG47bbbsH79eowZMwb/93//h7feesv4+urVq3Hvvfeic+fOWLt2LZ599lk888wz+OWXXxx2\n05LzN5Dj+++/R/fu3XH8+HG8/fbb2Lx5M6ZMmYLz588b53F2GzIl9T0CAgIkp999990YPnw41q1b\nh27dumHs2LGYNGkSPv74Y7zxxhv45JNPcPToUQwfPlzysxytb3u6dOli3K4Mj9mzZwOAIq1ZRKQ9\nJ06cwOHDh1FSUmL12l133SX5Hqk8wJM2b96MPn36WE13JzdQIi8AlMkNPJ0XAO7nBnLzAsB3cwN3\n8gJb60WJ3MCdvABgbuDT1K7+/U1FRYUICgoSEydOFHPmzBHl5eVmj1mzZon8/HwREBAgli5danM5\n06ZNE3Xr1rWaPmzYMHHhwgUhhBAzZswQu3fvFkIIcfz4cREQECCmTJniVLz9+/cXqampdufJyckR\nAQEBYsuWLWbTu3TpIpKSkszO9BUVFYl69eqJJ554wjjNcJZw0aJFDuPp06ePSE5ONjvDW11dLZKS\nksTgwYPN5p03b56oVauW2LhxoxgwYICIjY0VZ86cMZvH8Nmvv/662fQJEyaIoKAgce7cOSFEzdnS\n2NhYh/EJYf9spemjd+/edpdTXFwsAgICxJo1a2zOY+vs9scff2w2X+fOncUtt9xi9rxr165m8+zf\nv18yLsvPcOZvYE/Pnj1Fu3btxKVLl2zO4+w2ZGDv7Lbp9zO879NPPzVOKy0tFbVr1xbR0dHi4sWL\nxukLFy40ns22fL+j9e2MQ4cOiYiICDFs2DCX3k9E+lNdXS1atWolIiIixCOPPCI+++wz4/HHFlt5\nQFVVlZg1a5a49957xfr168Vrr70m3njjDXHPPfeIP/74w+HrK1asEJMmTRLR0dEiMzNTTJo0yarX\nj6u5gRJ5gRCezQ2UyAuE8ExuICcvMP0Ols89lRtItSp7KzdwNS8Qwju5gRJ5gRDMDXwJr/n2stOn\nT+Py5csIDw/HX3/9hZCQELPX69Wrh7vvvhsA7LZ8Hzt2DI0aNbKa/vLLL+P5559HrVq1cNNNN6FL\nly5uxdu4cWPk5eU5/b4LFy7ghx9+wMyZM83OJsbExKBXr17YsmWL1XvuuOMOu8ssLy/H1q1bkZWV\nBSGE2Wirffv2xdKlS83mnzRpEnJycpCZmYmqqirk5OQYB7GxZDlS7LBhw/Dmm28iLy8PAwYMcPR1\nzdx4443YvXu3w/nCwsLsvt6kSRM0b94ckydPxokTJ5Camoq2bdvKiiEzM9PseVxcHHbu3AkAKCsr\nQ0FBAZ5//nmredq3b293uc7+DWy5ePEiduzYgWnTptm8hs+VbchV6enpxn+HhoaiadOm6Nq1K4KD\ng43T27RpAwA4evSo8d8G9ta3M86dO4eBAweiXbt2+OCDD5x+PxHpU0BAANatW4dnnnkG7733Ht56\n6y3Ur18fq1atwj//+U/J99jKA9auXYs777wTK1euxNSpU7F161YEBwejVq1aePrppzFy5Ei7ry9f\nvhyZmZlYvHgxXn31Vcleeq7kBkrkBYByuYEn8wLAM7mBO3kBoP/cwJt5AeBebuCpvABgbuBrWHx7\nWUREBGrVqoULFy5gwoQJGDNmjNnr5eXl+PvvvwHUHNzsERIDSbRo0QIvvPCC1fRGjRohODgYf/31\nl9MxuzJa5Llz5yCEQFRUlNVrkZGR+OWXX6ym2yqMDc6ePYsrV67g2WefxbPPPisrzhEjRmDdunVI\nSUlB7969bS47MjJS8vnZs2ftxiQlNDQUSUlJDudztF4DAwOxceNGzJgxA//5z39w9uxZNG/eHE8+\n+SQmTpxo973169c3e16rVi3jwbCkpARCCMntSyqRM+XK30DKuXPnUF1djSZNmtidx9ltyFVS60tq\nGgDJW+zYW99yVVZWYujQobh8+bKsAReJyLd06NABq1atwpUrV7B9+3Y88MADeO6552wW34B0HhAe\nHo64uDg8+eSTGDt2rLFQuHDhAvbv34+IiAi7rwPAjh07cN1119m8PA5wPjdQIi8AlMsNPJkXAJ7J\nDdzJCwD95wbezAsA93IDT+QFAHMDX8Ti28uCg4PRq1cv/PDDDzav2Vi6dCkCAwPRv39/m8tp2rSp\nUweA2rVrIy0tDRs2bMDly5dlX/d96tQpNG3aVPbnGERERCAgIAAnT560eu3kyZMOd+RSwsPDERgY\niCeffNLmtbemjh8/jsceewxdunTBnj17MH/+fJsHp5MnT5qNDHrq1CkA8g78lnJzcyWvk7OUlpaG\nTZs22Z2nbdu2+PDDDwEAhYWFeOONNzBp0iS0aNECgwcPdjo2oGY9BgQE4PTp01avnT59Gs2aNbP7\nXmf+BrZEREQgMDDQbu8Od7ah2rVrS143V1ZWZnZ3AS155JFHkJ+fj+3bt0smFkTkm77//nuEh4cb\nC91atWqhV69eePzxx7FgwQKb77OVB/Ts2ROVlZXYvn272Z1Nvv/+e9xwww1ITU21+zoA5OTk2L2W\n3JXcQIm8AFAuN/BkXgB4LjdQIi8A9JEbuLsNMTcgLeCAayqYNm0atm3bht9//13y9XfffRejR49G\nixYtbC4jJSUFlZWVOHTokOzPfeaZZ3DmzBk8/fTTkq//8ccf2Ldvn9m0/fv3IyUlRfZnGNSvXx9d\nunTBZ599ZnZm/ujRo/j2229dGiCmfv366NmzJ3788Ud07txZ8mEghMCoUaMQHByMjRs3YtKkSZg8\nebLV9zP4/PPPzZ4vX74c9erVw8033wwACAoKwuXLl2XFaeha5ujx9ttvO/X9ExIS8OqrrwKAsXXC\nFaGhoUhJScEXX3xhNn3//v34+eef7b7Xmb+BPSEhIUhNTcVHH32EiooKm5/l6jYUExODY8eOmQ3e\nVlRUZPPvrwRnWoXmzZuHd999F5988gni4+MVjIqItObrr7+WnP7HH39g4MCBNt9nLw/YtWsXwsLC\njN2FL126hJycHIwdO1bW6xs3bjTuY7/99ltcunTJbPmu5AZK5AWG5SqRG3gyLwCUyQ08lRcA+sgN\n3N2G1M4NnO0twtzAN7HlWwW33HIL5s6di3//+99Yt26d2fU9c+fORUBAAN544w27y+jTpw+CgoKQ\nm5vr8Focg549e+LVV1/FE088gQMHDmD06NFo3rw5zp07h2+++QbvvPMOPv74YyQmJgIAfvnlFxw7\ndszsmhdnzJo1C5mZmRgyZAgeeughlJWVYdq0abjuuuvw5JNPurTMV199Fb169cLtt9+OMWPGICoq\nCufOncPu3btRVVVlvFbplVdewTfffIPNmzejQYMGePHFF5Gbm4u7774bu3fvRr169cyWu2DBAggh\n0LlzZ2zcuBFvvfUWnn76aYSHhwMA4uPjsXjxYrz77rtITExEUFCQcT1ZCg0NlX2gsWfv3r145pln\nMGTIELRt2xbl5eX44IMPEBgYKOvsuT3Tp0/HoEGDcM899+D+++/H6dOnMWPGDDRp0sTufVsB+X8D\nR15++WXccsst6N27N5588klER0fjt99+w969e/HKK68AcH0bGjp0KGbPno0RI0bg8ccfx9mzZzFn\nzhw0btxY1n0/5czjqWXs2LEDTz31FEaNGoXw8HCza8KioqJw/fXXux0LEWnXxo0bceXKFeNIxgCw\ne/dufPvtt9iwYYPN99nLAzZt2mTWMv3KK68gPT0dgwYNkvV6YWEhevTogYqKCmzbtg09e/Y0zutO\nbqBEXgAokxt4Mi8APJMbKJkXAPrIDdzZhtTODZx5P3MDH+atkd3I2pYtW8S4cePESy+9JObNmyce\ne+wx8dprr4krV67Iev+9997rcMRsKTt27BDDhg0TTZs2FXXq1BENGzYUAwYMEB9++KHZ6JGzZ88W\nLVq0cBhPTk6OCAwMtBrtXAghsrOzRffu3c3uxfjzzz+bzTNjxgwRGBgo+3v/9NNP4q677hJRUVGi\ndu3aIioqSmRkZIivv/5aCCFEfn6+CAoKEs8995zZ+w4dOiTq168vHn74YeM0w6iU+/fvF7179xbB\nwcHG+3maunDhgrj77rtFRESE1+7neeLECXHvvfeKG264QdStW1eEhISIHj16iNWrVxvnMaw7y+eW\n63L06NFWMRvu5RkUFCQSEhLEqlWrxM0332x2r3ipzxDC8d9Arh9++EEMHDhQhIeHizp16oh27dqJ\n2bNnm83jzDZk6sMPPxQ33HCDCAoKEsnJySInJ0ekpaWZ/WZsra9WrVqJkSNHmk3bvHmzCAwMFN98\n843D90utb1uWLl1qc9Rbw31xicg3XbhwQYwbN06sX79ejB8/Xjz11FNi4sSJ4vHHHxfnz593+H5b\neUDv3r3F+PHjxZw5c8S0adPElClTzPZTjl5/+umnxXPPPSdefPFF8ffff5st293cQIm8QAjP5QZ6\nzwuEUD43kDrmCuG93MDVvEAI5XOD6dOnu50XCMHcwJcFCOGBJh5SRWFhIVJSUlBQUODx7iiVlZVo\n06YN/vOf/+DRRx/16LK1ZMaMGZg5cyaqqqocntX1dSdPnkSrVq0wdepUTJ06Ve1wiIjIAak8oKKi\nAhEREdi3b5/xGm5Tjl63xx9yA+YF5pgbEHmWbvcq2dnZSExMRFxcHObOnat2OKpISEjAfffdh6ys\nLI8v+5133kFoaCjGjx/v8WWT+i5duoSJEyfiyy+/xPbt2/HRRx+hX79+CAkJwf333692eERETvPH\nvEAqD/juu+/QsGFDm4W1o9ftYW7g25gbEClPly3fFRUV6NChA7Zt24bo6Gh0794dixYtcmlgMPJv\nWVlZmDlzJiorK/3qDHdlZSWGDBmCvLw8nDlzBtdddx169eqFF154weYo/M5wdDuN2rX9Z7gJrgsi\n5TEvqLFu3To899xzKC4uxsMPP4znnnvOqdfJf/MCgLmBN3Fd+C9dFt9bt27FSy+9ZBwh9OWXX8al\nS5ck7y1IRN71559/OhwIJDc3F7169fJSROrhuiDyDuYFRNrG4+E1XBf+TZenVYqKitC8eXPj89jY\nWOTm5prN4+xw/kTkPbfccovaIWiGnteFp8/dNmzYEOfOnXPqPREREZL3Oib/4igvYE5ApH16Ph56\nmh7XhRLtuc7mBXrICXTZn0buQVQIocvH9OnTVY+Bsevvoef49Ry73uN3NXYlnDt3DqsBpx7OFuvk\nm+TkBWr/1rh/0d9Dz/HrOXa9x++PsSvl3LlzeB6Q/dBDTqDL4js2NhZHjhwxPj9y5IjZGW8iIiLy\nH8wLiIhID3TZ7bxr164oLCxEcXExoqKisHz5crz99ttW8wU87MaHLHXjve4o/wrAz8jK+kqlANxl\nI/bggebPy2V+P8v3OWO0jekjav6X3GMnAGAgvkIG1qIIxzENlU5/TAbWGv/d7WCB0++38pmL79sK\n4KCTI9/L+ayPXQnGSacALJcR+91ufs4w2y/t7JDs8O1rkSE5fQu+RbXEtvMV5G+/BTu6OZ5pmezF\nybcLyDppMW2pAp9DpCA5ecEArFYpOmmlCJM13xH8iQ3Y7PbnlSPY7HPDUIpglLu1LAB2l2EaexhK\nzf5viKUUYVf/W2o1zZTlc4MCJCMKJ82WEYKLVp9jazmmz4NRbhbnPuzGYvxuXKaliwiR/DuGodRs\nftP5TiIKPyDFap5f0Qbf5fUG9kt8yfoAIgCcA1AM4DsAJVf/XQLgGACrY9AFZGWdABANNAUQfnU5\n4RLLB659bszVeQzzhZk8DzVZTgSABmZfukZ4BULDa9ZhWYnJuikJAsIrjE/N5ikJAopQ8zD4Acha\nYnjv1deOXI2z5OrD9LscqwSwx2RCQwDXXf13NBB8dX7T72f63QyfE3v1eazJ9ztvEVt9k39fuPq+\nQgClAJoD+A7IOnz1uSnTTcV0nZpOM/0Mwzo2fV+pIe6a9RwSYr5d2vqdXHu79W/L9L3HUYQvsVPy\nfabLd3XfQTotvuvVq4cFCxZgwIABqK6uxsiRI9G5c2e1w/KgBLUDcIN+Y++cForjagfhhrRWakfg\nurQQtSNwT8u0FmqH4LpmaWpHQOQ2PeYFYSiVVYBfl9bJC9EoQ8+xA0DHtEivfVYYSiFjc3BCD08u\nzPvapzn5hjpKRHFNfdQU2nLEpikYiLJC07S93/QFuiy+ASA9PR3p6elqh6GQRLUDcIM+Y1+LDCBN\n7Sjck9Za7Qhcl1bf8Txa1iqtpdohuC4mTe0IiDzCV/OCBmmOe+XIEYxysxZrb/BU7GppmdbSbkuh\nx4VXAPWDHBd5YahpbbVL58V3hzS1I3Bd8zQZfx9t0mLx3VDtADxMl9d8E5EXudvVWwfkdDknIiLt\n8XRhbOj27mly4jR0xbbLVrdxdym1XCIyw+KbiIiIiMiEuwX4RTh/PVVIyMWa63yJyGex+Ca/58yg\nWEREROQf7BXgrhTXjnite7vWhFeYD95mj1YvU2PPAZJJt9d8ExERERF5w0WESI547nENYD6ytpRw\n1Iz8bRAMcPBpoObq4LNqB0Eexmu+iXwQW7+JiIjIQInrvh1RteVbCy3KWoiBSGEsvslvybqvMhER\nEfkFb48GL0lOza9mF+cSyL/lFhFZYfFNdJUnWr/tjZrNEbW1q9vBArVDICIiDfBkAX4RIbKvDQ9D\nKYINfcf10gJ8XnpyWUkYykq833PAI8rUDoB8Ha/5JiIiIiJdClbgYudyBCuyXFnCKwAEyZgPQLHp\nhDoAKhUJSdI5uDUye1lJmLxbq5Hfu07tADyMLd/k17zd9Zyt30RERNpXjmCr676VGOHcVBhK5RWk\noVf/Xx81RXg44HaDvd5G69ZbvERXsfgmMqF013PdulvtAIiIiDxPzsBqzg6+5s5gbSEhDkZUN3RJ\nD0NNAWpagKPO1YfCSuy9JqPVXkoYeI9z8gssvsnvceA1IiIiffKle2OHobTm+0TA/nXf4RYPQwFu\nbP12sQB31Jpsej205aBrvvNnII1p6MRDD1h8E1ngbceIiIh8hzsFuqEV2/B/ZwZRc0UYSmvu9S1v\nZvMC3IxC5YhpAX7Oxjyutn4D+hlsjshFLL6JYN367UwBvhYZng6HiIiINMLT9/yWOhkQgpru5sEo\nd3y7MbOu5jBvBXfn2m+1C1+5Jx2IdIzFN9FVnux+zuu+iYiI9E2J+347GkW9GY5eHfFcguk10aGo\nKbbDYN4CbizA6xg+0DNMr/M2bf22cbsxIq24dOkSunbtipSUFLRr1w6PP/44AEAIgalTpyIpKQnx\n8fGYP3++5PsnTZqE9u3bIzExEcuXL7d6feLEiQgLk3+CjrcaI9+1FMBoienLAIxw/PavMBAD8ZVH\nQ9K9uwF8rHYQRLYdOXIE9957L86dO4fLly9j7NixePrpp7F9+3Y88sgjqKqqQq1atbBgwQL06NFD\n7XCJSANKESara7rc+aTmNW09D8FFya7rhumh4aUoM9xuzLI1OgI13b1NW70NYkz+XQ44fe13OOwP\npmbKMJ+btxuzYrm8C6iJqwzAEdc/i/TL3Ysn6tWrh61btyI4OBhVVVVITU3F5s2bceDAAZw9exZ7\n9+4FAJw5c8bqvStWrMCBAwfw008/4dKlS+jZsyd69+6NyMhIAMDu3btRUlKCgIAA2fGw5ZvIDs1c\n/z3s6oOI7Kpbty7eeust7Nu3D/n5+fjf//6HH3/8EZMnT8bcuXNRWFiIF198EZMnT1Y7VCLSEctr\nv+W+x/BwhsMRz00LVKnB14CaFm+5rd6mXdjdUWrycFZ4RU0LvmXXc6e6wvvaHaHJU4KDa34Mly9f\nxpUrVxAVFYX//e9/mDJlinGeRo0aWb3v0KFD6NWrFwIDAxESEoKUlBTk5OQAAK5cuYKnn34aL730\nEoQQsmNh8U1kwtWu51LXfdvqeu50l/RhFv/2ZCHOgp58THR0NBISEgAAoaGhSEpKQnFxMZo3b47z\n52v6R5aUlKBly5ZqhklEGmOrQC5HsLH7uaeu/XbUem4c8dwew3XfoTDvgm5aiHuaVKu4EoOuEcmU\nB+ANk4ct1dXVSE5ORnR0NHr37o34+Hj8/vvvWLRoEZKSktCnTx8cPHjQ6n2JiYnIzs5GeXk5Tp8+\njc2bN+PIkZouGG+++SYGDRqEJk2aOBUzu52Tf5LZ9RzwTPdzy4Lb8LzbwQLXFzoMwGcWz4l83D4A\nhTLn/fPPP7Fr1y68++67iI+PR2pqKp566ilUV1fju+++UzJMItIhOd3Knel67ooQXLw24nmRnRml\nivNWqGl1Lsa14ttRN/Jwi39LzV8C6WK+BDUnAc7D8WBptq5jl2L63WwV9+Q37PVn6Hf1YWCrAA8M\nDERBQQHOnz+PAQMGIDc3F5cuXULDhg2xd+9erFy5EqNGjUJeXp7Z+wYOHIjdu3eja9euaNiwIW6+\n+WYEBATg6NGj+Pzzz5Gbm+tUqzfAlm8iKwU7urnUAr4WGU6PfG63FVyrXc058BqpKBE1m6DhYUtZ\nWRmGDRuG119/HWFhYRg7dizmz5+Pw4cPY968ebjvvvu8EzARaYIz12pLdRX35OBrYSg1jm4uJQon\na/5h6HJtKEYbmDwM0w3zGFrBw1Bz7bfpiOhKtIKXOZ7FipzWcMvOBYbvGCrxGpGTGjRogMzMTOzc\nuRPNmzfHnXfeCQAYPHgwCgulT+1nZWWhsLAQW7duRVBQEDp06ICCggL8+uuvaNOmDa6//npcvHgR\n7dq1kxUDW77JO8q/AoI1cv20gROt385YiwxkYC0AF0c9d6bgVqs4N1Q93hx8jUW/XxjY0ck3/GQ9\nqbKyEkOGDME999yDwYMHAwB27tyJjRs3AgCGDh2KMWPGuBkpEWmBkq3QzrZye6pVPAyl1oWmZcty\nA9S0OFu2gLfCtWuuLYtuR89tTbPnwtUYHLV+O9MN3fDdpIRf/UzJFv2GAP4GcFb+Z5HPO3PmDOrW\nrYuwsDCUl5cjJycHkydPRmZmJjZt2oQxY8Zgy5YtaNu2rdV7q6ur8ffffyM8PBwHDx5EXl4eFi5c\niKCgIBw7dsw4X1hYGH7++WdZ8bDlm/zbMtsvuXPrMWdawHV9WzI5TZBEXiSEwNixYxEXF2e8nQgA\ntGzZElu2bAEAbNq0Ca1bt1YrRCJSkTvXbXvy2m+HRXp4RU1Ra1lcG24tBpi3gAPXWoibX32Ew3wE\ndNvBmC/XdLqrSiFdcNsrwk27pksV8kq04PsYT9+T3hccPXoUvXr1QnJyMlJSUtC3b19kZmZi5syZ\nWLFiBRISEvDkk09iyZIlAGpGMH/ggQcA1AzQ9o9//APJycm4//778cknnyAoyHobdma0c7Z8E9lR\nsKMbknvsVDsMZVleO+5ppoX5x3amEXnA9u3bsWzZMiQlJSElJQUA8MILL2Dx4sV4+OGHUVlZiaCg\nILzzzjsqR0pEemDZml2OYLuFs6H4sTdPMMrtFkkhuIioZidwsrBFzQRDIWr6Fqm3G25BZtklvAQ1\nrcUGShWxhtZqR9d/y2W6nAuouQbecF26Zct3MHDtFuru3pyKtKShM9uTRI+JxMRE/PDDD1bTGzRo\ngK+//tpq+o033ogbb7wRQM1tyvbv3+/wY//++2/ZIbL4Jt+2FNL3+jblhe7nfsuyRVyqhZz3DicP\nSk1NRXV1teRrBQVuDHBIRKpSczA0V5brqAUyDKWShbrptJP2ul9fW1BNK7NpgRKLayOg/wn7xbZl\nmFKDrjkq1s/BvIXetAu6rdZvewOwGb6TJYf3Ia+DmuG55BdCRN7GbudEgN3u54Dr9/t2dgA2zRgG\n529rJqfQtvdedl8nIiKNUqo7r2VRb3gehZM1RWgsrnUJD6+wfgDm3dANA5QZBmCzHHDN9N9hFtPD\nJeaBxTwGoRavOTsquSu3IjN8pt1B5CxbvdkKTtrClm8iAxst4O52PddFC7ijAtv0dXtd1D1RPFu2\nhLMgJyIiO0xbkb3BowOr2XkttNVplJWE2W8pDq+oed3e1zfcKsxe4S013fI1A8vCWwmmrd8NcK24\nD8e126kZnpveCq3c0PpNpE1s+SYyZacF3ND67UoruKMW8J0dkvU98JqnsSWciEgRSo4O7ussC3wl\nC/4wlCIk5CJCw0uNhXdoeKnVA4B5Kzhg3QJuWnhbDqwmZwR0e9NtcdRdHpBu/Tb9HqZxGrq1h1+d\nXt/kNUet4J67SxypoE4D+Q89YMs3+b6lcHzdtyk714C72v0ckNcCbijAux0ssJomxXQ+r1F6gDYi\nIiKNMhTcShfeFxGCZjiKoyHN7M5rKMDLSsKuFq5B11qMDQVrLKSvlXbUzdxyPsC5Vu9SuH9vbtMW\ncMNo7oaTCRcs5jW2fpt2Na/jZgBEnsXim0iKg0HYvsJADMRXTi/WVgu4ZVEutxXccj6vFeMswImI\niIxcKcbtdZcPwUUEo9xhAX7xYgiAmiLcrAA3dQE19//+8+pzW9eAm06zO7CZxXsvoKYwthx4DfBc\nAW5gOup5scl00+9RXgdApZsfSqQMFt9EbpJqDXelMPeEnR2SvVuAG7AQJyLSDS11PZeKRan4vH1t\nuLvCUGp2azOp2ENCLgKoKcKtCnDDKOjncK3FGHB8HXcZrK+ltmzxNl1GfbhO6np2w3XsUgxd6W2d\nHDC8Vn61xTvYZDrvE65P7mxfGsRrvsk/LHXhPSbXfxfs6Gb18lcYaLMbur3XpJkRXbUAACAASURB\nVHhyVHRHreaG68s9ep25M6OiExERqcQThb23CvgwlCIY5caYw1BqfFgyFOFm14EbwoxATeEZC+sC\nNBTWhbVhmpzC25Rlq7cnheHateymsdWXeISjpug2LbwJgLZOvPkrtnwT2ePmPcCd6Z7uyVHRnSmq\nPdpazu7oRERkh1L34vY20+/hbDFeLmMEMMN131L3ADe8bjk9JOSidAs4YP9WYFLFaQnkFd1yWiVd\n6Xpur/U7HDWt8/VNnhuUgMU2aRqLb/IfS+HcwGsGHijAAXld0XVxWzJHpFrAWZATEZGPcVR0yy2y\n7V33bXgtGOU2P9v0JIDNAtzZVmnLrt2uFt7usCzADV/Zshu92XsUjonITSy+iWRy937fclvBDV3Q\nvVmEK36tuKMu6SzOiYhIR8oRbFUQm77mKaaFdTDKjcuWanU3FPKGLugGZQCsBmFz5AJsF7Jyim5P\n3fZJqgU8Atfik9vSbTkyOumHTm4hJheLb/IvS+Fa6/dVhmu/XS3CnW0FN6V0MS51mzOvsSzOWYwT\nEfksLXQ9VyIGTxbdlgyFtWkBbpgOXCvCTVvSDa3gRqZFjL1u6EBNgW1ZsNorupW83tu0ADdt/W5u\nMo+tTgiG1nvFW+kBHFH4M8gnsPgm/7MUzhfgFl3PPdEKDjg3Krq3inFN3FecI6kTEfk0LYw67kwM\nloW6t+IPwUWrzzO0uFsW4bYKcMCF1u9zkFewShXdzrRUWo50bm8+0xZwwyByUqOeS/1p5Nw6jcgL\nWHyT95R/BQTLHwFc69xtBQdcK8INvN0yDpgX5rq7pzhHYyciIheZFrZqsbw+3F4ruN0C3HALMgA4\nb+PDDEW1VAu5vVZuqcJbifMUDVAzertUUV1y9TNLYd0l3dBNXf1zP+SnWHyTf1oKt7qfmzK9DZk3\nuqPbYlqMe7MQ90oRzlHUiYhIA7zZZV6qNVuqAAekW8ElB3MzHQHdtAi3ZCjKnelO7uy1uXJbvU3n\nLwm6VlgbbitGvo3XfBO5odz14tLjlsK5AlzGqOeeuCbcnQLcwJuDtnmtNdyyALfXks1CnYiIbHC3\ny7hpYeuNQjwEF423HbN1izOpVnCz+K5e+m0cAd1QxBpYfg1HLeOWbBVInm5hNo09Atdav8tM54H5\nQGyGlnClsTWdZGDxTaQAd1rDPVWAAz7YGi6367hhvs8snhMRkU+yd8suJbnbHd3ZmG1d2w1Yt4Jb\nvm46CrqxVrUcyOxaYDXcaXW099WcbfW2fG9JUE1sjgaOsyzCzZbjegiKUnpwOFIVi2/yb0vh8dZv\nS660hnuiG7oly2vEAe8M2qbK6OkGLLqJiEgBlq3eShf+htZvS1InHSxvSWbaUi99H3BY387LsEhX\nG/aVKrwtmd52DLhWYIei5gyD6XRH9wXXajHu73zsZESg2gEQqW6pk/Mvc+1jTFvD5foKA42FuBLW\nIsP4UMrODsnGBxERkZq0MMq6uyxb2aVa3U3vQW543fB/Qwt4aLjJ+8Irrj3MF37t4Tgwx/N6qvAO\nr7j2OfVxrUALx7UiOvTqw3R6uMU8RF7G4psI0HQBDihfhANQvAgHzAtxFuNERORNvlB4G3iyADcr\nwgF5hbjUwxFPtngbNMC1QeHkFuGQ+Hco3MeCnmTQbPH9xBNPIC4uDnFxcbjttttw5swZ42tz5sxB\nXFwcEhMTsWHDBhWjJJ+y1Mn53SjA3S3CvdEa7g2WxTgLcsLdTj7ILzAnIGdZFtpKFN7eKOZNi2rD\nPb+lXpN6DsgrwAEbRTggXYS7wsllGOKxfFgtMwzXCnA5RbjlNNPp5JMuXbqErl27IiUlBe3atcPj\njz8OABBCYOrUqUhKSkJ8fDzmz59v9d4rV67gwQcfRPv27dGuXTuMHz8e1dXVAIDLly9j3LhxSEpK\nQseOHbFixQpZ8Wj2mu+BAwfi5ZdfRmBgIJ555hk8//zzmDdvHvLz8/HFF19g3759OH78OFJTU3Ho\n0CHUrVtX7ZDJFyyF4teAG3hiZHTAs9eFm1LjPuIArApwVa8ZJyJNYE5Actga9EzJItmbtx4DbF/7\nbeBoJHSpa8AB4OLFmmWaFrhlJSbrzbJ4trxGXIoLRbvkCQCL183iMmiAmpHZTe9PbijATa8JNzCM\nOMfCW/uuc+/t9erVw9atWxEcHIyqqiqkpqZi8+bNOHDgAM6ePYu9e/cCgNlJXYPNmzdj//79OHjw\nIIQQ/5+9e4+Lqs7/B/46kHJZFG9BBqSWV2YYGYotiwpZla+wbYRa5hV1++7D3dDV3PXxyG+bdtHN\nR353tf1aotn0zWqrdU37WhaFeCtSVETWn9smkgxeWlEJAgTk/P7AMw5z4wxzOefMvJ6PxzwYZs6Z\nec8I8nnN54a0tDQUFxcjIyMDy5Ytw5AhQ1BQUAAAuHy5q9X/Oqi253vs2LEICeko77777kNNTQ0A\nYOfOnZg6dSpCQ0MRFxcHnU6HgwcPKlkq+ZKatiZzpps94BJPesIB//SGA/6ZH+6IWnrF/f26iegG\ntgkI6F6I9kfvtJLD2eUGf+secEfnWveCS5z2hgOdh6Y7u7jB5XM5OLZTHdLbb70yu/X+5NYLdjka\nig4Xt1FAiIjo+PCppaUF165dQ0xMDDZt2oSnn37ackz//v3tzouLi0NLSwuuXr2KpqYmtLa2Ii4u\nDqIo4p133sGSJUssx/bt29fufEdU2/NtraCgAFOnTgUA1NTUICMjw3JffHw8zGaz4xMPLb9x/dZ0\nIC7dZzVSADHBvd5vwKMecEnZl/d0uxdcYh3AfdUjDvh/CzNJychk9oQr5Vox0F6sdBVE3W4TnFr+\nruV633Q9+qUn+bZQUkwjIhGJRr+GYl/1gMt5DY5WPHe19Zqz7cpse8ElUth12OPsIbmB29F5nerp\nhY6V2aUecMB1L7j1tmPOtiIjp+qKy/BD8TGly0Dx+Y5LV9rb25GSkoJTp05h/vz50Ol0qKysREFB\nAbZv344BAwZg/fr1GDlyZKfzRo0ahQkTJmDgwIEQRRH5+fkYMWIEzp8/j/DwcOTn52P//v24/fbb\nUVBQgFtuuaXLWhQN3+PHj8f58/bv2MqVK/HQQx0h4sUXX0TPnj0xffp0958gdbmHFVLQMqF7ARzw\nKIR7OhTdmq+HpUuU2ktcwjDuJ6HpHRdJ2wqlKqEA5es2wR3LuUgA+Zane37L5WjouZwA7mj4uXW9\ntiEc6BzEnQ5J74buhm7bx7Bsl3YlrHMABzqHcGlE8E/geBg6uSU6PRnR6TfaY+YVb/rwyZzflR4N\npI+48f0KJ03CkJAQlJWVoa6uDpmZmSguLkZzczP69euH8vJybNu2DbNnz8bXX3/d6by9e/di9+7d\nqKmpgSiKGD9+PDIzM3H77bfj9OnTmDBhAl577TX86U9/wsKFC/Hee+91+XIUDd+FhYUu73/zzTex\nc+dOFBUVWW6Lj49HdXW15Xuz2YyEhASf1UhBzAT3Azigml5wib9COKBMj7iz4egM5UTawjYBBQrr\nwOtJEHe3596TAO7s/K56wyVywrg3AneXpAAOdA7hznrBrdn2iLsjyoNzyW+io6ORnZ2NkpISJCQk\nIDc3FwCQk5ODGTPsG+9ffvklJk6ciMjIjp//iRMn4sCBAxgzZgzCw8Mt50+ePBmvvfaarBpUO+d7\n165dWL16NXbs2IHw8HDL7VlZWXjvvffQ1tYGs9mMiooK/PSnP1WwUgpopm6e5+E8cMDzueC2/DU3\nXKLE/HBrXEWdKHCwTUBaVY9elou757liu/K5xFHYt73NdgV06/ttv7c8X2Sjw3nhEmerkztdqdxL\nLI9rPc/cdusz27ngtquiW6+O7gn2pqtSbW0t6us7fk6amppQWFiIpKQkZGdnWz7M3bNnD4YNG2Z3\n7h133IE9e/bg2rVraG1txZ49ezB06FCEhoZi/Pjx2L17NwDgiy++wKhRo2TVo9o53/n5+WhpacH4\n8eMBAGPGjMH69etx55134pFHHoHBYEBISAg2bNiAHj16KFwtBTQTut8DDnilFxzwzlB0ib/mhgPK\nzQ+3xeHqRNrFNgG5S5r3rSaOArWj1cl9wVUPuKP7ndXlrCdcSU5XQHfWCw50Hoou+YnNVwoIZ8+e\nxaxZsyCKIpqbmzFt2jRkZ2cjLS0N06dPx5o1axAWFobNmzcDAEpLS7FhwwZs3LgRkyZNQlFRkWUu\neGZmJiZNmgQAeOWVVzBz5kzk5+ejb9++eOutt2TVI4iiKPrmpSpLEARgvgcvzeS1UtyjhdW9/S3C\nPz21LuV54TE8DOGAdwO4I/4Ymi5RMohLpAAup2e8O7347owykDXKwQsjKmQxyTimSYC3/3wIggDx\nOTfP+QO8XgcFHkEQMEHcrnQZdvy9SrZ12AIcr4Dt7uPIfYyuhl/LDaHuDuNWWwD3Jldbjjl7H21v\nt/2ZcHReV/8magjiduHbeis02x+ZOjh2GR1D0X+CjnBuHdil+4DO4fxH3Bhu3mB1/YqD57UuUdpr\nPMrmNuvHl2qwPq/++vd9riKqT73D0Qjdnerg6jzb9QFsf++/EjJ88rdYEASIT3d9nOX4lepvE6i2\n55tIVUzwPIB7aS444LsQHujzw21xODoRkTp5a/VwNfaA+4Oz1c7d7QGXbgOch3BnC7R5wtUQd0fP\nZdf7LS3CBtwIr856wSXydooi8gjDN5FcJqgigAOBFcIB+15lNfSKExFRYHDUQxwIgdzRqudyyAng\ngH3YljM6wVFodhXI5YRsV+fKDuCA/BBO5EMM30TuMME7ARzQVAgH/DskXQ294kREFLi6E1ptqT3A\ny93rG7AP4K7Od3eeuicBW85jO9oOzRLCpUXYbEO49aAK262sbOeCE3kRwzeRu0zwzhxwL/WCA74P\n4QCDOBERkTUpwKs5hLsK4LacBXBA2cXiumIbwAEnveCA86HopF4u9vnWIoZvIiV5sRcc8E8IB/w/\nLF3CIE5ERGqj9hAud/434DiAu3oM6T6JUkFcVgAH5A9F7w5p8TTu900uqHafbyJVM3n58by8irW3\n9wh3xt97h1uT9hFXci9xIiIiiTeGsvubo0XtnK1g72wPcHeP8RVHw9sd7i8u9YJbs90bXEk/dn0I\naRd7vom6ywTvDD+XeHEYusRfPeGAcsPSAS7YRkRE6qDWXnB35n8DNwK4s15wwHUvt20A91ePuLMe\ncACuh6FLpEO4CJt6cNg5EVmY4P0ADvgshAP+DeL+DuEShnEiIlKSGkO4uwEccD4MvavHc3SsNV+G\ncakH3KMQTuQjDN+kfk0fARH+H9YsmwneDeCAT3rBJcHSG26NYZyIiJSgxhDuTHcDOOB+mO7O0HR3\nn8NRLzjgYi440BHCe6Fzz3c0uAI6eQ3nfBN5g8kHj7kFXp8Lbs1f88IlSs4Pt8X54oGruroaDzzw\nAJKSkjBixAisXr3act8rr7yC0aNHIykpCb/73e8UrJKIgk0jIlUxJ7y787Ej0OR0Lrj0uL6e6y09\nh+3FFWfbnEX1qXc9H9zZUOefyCy2j8zjKOiw55u0Qe29377ko6HoEn/2hEvU0iMOdARx9oQHjp49\ne2L9+vXQ6/VoaGhASkoKMjMzYTab8emnn+Lw4cO46aabUFtbq3SpRNQN9eil2IJe3mAdwNXYG97V\nUHJXveDS+YB/Vz3v6jmdDUMHXPWCh8nfhqwPuMK5L8n9wEMj2PNN2tGkbEjrksnHj+/DXnDA/z3h\nErX0hlNgiI2NhV6vBwBERUXBYDCgpqYGmzZtwtKlS3HTTR2fOffv31/JMomCjtJ7QauR1Bve3R7x\n7p4nZ8VyV1z1gFs/hr8/JPF6L7i1vt2tiqgzhm8ibzL5+PF9PBQdUD6EqzGIc3i6ehSfBpbvvnFx\npaqqCocOHUJaWhpOnjyJTz/9FMnJyRgzZgy+/PJL/xRMRCSDu0Hc0yHs3gjg7oRwfwXxrp4rMrJR\nXgh3tB1ZVzjUnGTgsHPSFi0MPzfB+wuw2fLhgmwSf6+Qbs3fw9I59Fw70od0XCQrnATwhoYGTJ48\nGWvXrkXv3r3R3t6O+vp6lJWV4dChQ5g0aRK+++47CILgn8KJyGukXnQtDz93RQ1zwwF5q5l3NQzd\n9vEkvh4J4fWh6F1h8CaZGL6JfMEE/wRwwOchHFBmXrhETfPDyQ+muHn8H+xvam1txaRJkzB9+nTk\n5OQAABISEpCbmwsASE1NRc+ePXHhwgXccsstHhZMREoJ9BDua3LCtdwADjjeE9zV4wL+CeGunsPZ\niugOyZl7HAXO//a2ANvnm8POSXvUPvdbYvLT8/hhKLpEqSHpEuuh6f4ans4h59oiiiLmzZuHxMRE\nLFq0yHJ7dnY2ioqKAADffPMNGhsbERMTo1SZRGTDkxDmeA3sGxfyjNwPN+QMQ3f02L4emi5nLrjt\nUPSoPvXo9o+O1AvOHz1ygOGbyJdMfnwuPwVwQPkQLlHrHHFSzoEDB7Blyxbs3r0bRqMRRqMRu3bt\nwpNPPonKykro9Xrk5ubCZDIhJIR/AomCAQO4c3IDry8DuO3z+CKMy92WzNl8cNm9rwE6/JyjS7yH\nw85Jm7Qw91tigu+HoEv8OBQdUHZeuDVfDU1nr7f2pKWlob293eF9b731lp+rISK10PoWZWogZwg6\n0L1h6K6e05qnH6TIGe7udCi69Yrnl7t4Imn7sV6Qv2UZORZgW40xfBP5gwn+C+CAXxZks6XkvHBr\ntj3h3Q3jDN5ERIGFc8Qdkxuq3T3WmyHc+vmtdTeMy5kL3uDqAfqicwCXAuKP6Jj3bX0yAzhZYfgm\n8hcT/B/AAcVCOKB8EAe4YBsREXXGEO4ZdwI44JsQbl2LNXfq6rIXvM9VoD7M9YPY9sr+BDcWXOsD\nLr5GdjjhjbRLKwuvWTPBv/PAAb8uyGZLLXPDJXIXbGOvNxFR4OOCbDe4+0FEdz648HQ+uBzdmSvu\n8pxeVpcgn/etVc3NzUhNTYXRaMTw4cMti7GKoohly5bBYDBAp9Nh3bp1dudarx9jNBoRERGBHTt2\nAAC2bduGxMRE6PV63HPPPfjnP/8pqx72fBMpwQT/9oIDivWEA+rrDZdIAfwhfMS9vomIghR7wrvH\n3R5wwLe94Na6s5WZ7evp2O/bpudbCuB1sB96bi3q+lep55uf8XSfh1uNhYeHY+/evYiIiEBbWxvS\n0tKwe/dunDhxApcuXUJ5eTkAoLa21u7csWPH4ujRowCAy5cvY+jQoZgwYQIAYMGCBfj8888xYsQI\nvPrqq3jhhRdkrS3Dnm/SNi32fktMCj2vgj3hgPp6w4EbIfxjZLHXm4goSHF7Mvd19wOLCDSpsie8\ny15wiRQI+zq4WOtOL3iALTCmBhERHR/2tLS04Nq1a4iJicGmTZvw9NNPW47p37+/y8f44IMPkJWV\nhfDwcABAfHw86urqAABXrlzBoEGDZNXCnm8iJZng/x5wiQKLsllTywJtREREtqwDOHvEXetOD7jE\nnz3hbs8HjwQa+ly9ceOVsBsB3NWPhHV4djTnm8PSvab4cMelK+3t7UhJScGpU6cwf/586HQ6VFZW\noqCgANu3b8eAAQOwfv16jBw50ulj/PWvf8WSJUss3//lL3/BhAkTEBkZid69e6OkRF57lj3fpH1a\n7v0GlOsBBxTvBQdu9ISrrTeciIgICJ4ecU8+ZPD0Awp/9IR7vHe4dRAHPB4OTTJFO7+kZwDLf3fj\n4kxISAjKyspgNpuxd+9eFBcXo7m5Gf369UN5eTny8/Mxe/Zsp+efO3cOFRUVyMzMBNAR5mfOnIld\nu3ahuroac+bMweLFi2W9HIZvIjUwKfz8KgjhAIO4KpiULoCISL2sg3igh3F3eRxuoYEQ3udq5xAe\nbfVVukhDz/tYXUgVoqOjkZ2djZKSEiQkJCA3NxcAkJOTg4qKCqfnvf/++8jNzUVoaCgA4MKFC2hp\naUFqaioA4NFHH8X+/ftl1cDwTYFB673fgDIrodtSSQgH1Dk3nIiIyBrDuD1vDNNXSwiP6lNvudic\nfGMIuqMecNt52wzgiqmtrUV9fce/X1NTEwoLC5GUlITs7GwUFRUBAPbs2YNhw4Y5fYx3330Xjz/+\nuOX7AQMGoL6+Hv/6178AAIWFhRg6dKisejjnm0htTFBuHrhEwZXRbflrpfSP8BD3ASciIo9wrrh3\n+WNOuLP54L1Qj0ZEWr7vWP28l+1B9vO/rcO49arnDOCKOHv2LGbNmgVRFNHc3Ixp06YhOzsbaWlp\nmD59OtasWYOwsDBs3rwZAFBaWooNGzZg48aNAICqqirU1NTgwQcftDxmjx49UFBQgIcffhiCIKBP\nnz548803ZdUjiKIoev9lKk8QBGC+By/N5LVS3BMIPbhKinC+d7Pm5CldgBUVhHBbvgji3g7frvYS\ntyWrl98foxJMMo9rEuDtPx+CIED8f26eMwper4MCjyAImCBuV7oMO/7uKbUNMN3tWbR+HHceo6sw\nah2C6tEroMKrVl6LN38mffXz7csgbltzPXqhsTHS7riGK706FmBz/CAdW5EBgPn6BegcwKXrUVbn\nSfdJveZ90RHkrUuqv/59n6uI6lOPyMhGu6fv7s9aV+dZ/07a/t5/JWT45G+xIAgQz7px/K3qbxNw\n2DkFlkD68MKkdAFWVDIU3ZovhqW7E5b9+VhERKRtwTgs3RtzwB3x5ZB0RzVHRjY6DLl2879vPIjj\noeh9nFynoMJh50RqZoJ6esBVNBTdmreHpTsKzRyOTkRE3hCMw9I92YrMFV8OSZdqtq49MrLR0gve\naQi6FMBte8KjAVxG595tSYPXSyaNYPimwNP0UWANPzdd/5qnYA3WVBrCAd/ND7cO5FIQdxXS2etN\nROS5QBt6biuYgrivAjjQeQi0N4O4swAOAI2NkZZF2OxCOADAyZB0CnoM3xSYAi2AA+rqBQdUHcIB\n/wRxd+4jIlJaBJp8Ol+Wuk8Kd4Ecwn0ZwCW+6g23rd1pL7itvgB+dPKg7P2W5WqA7afOOd8UuAJp\n/rfEBHXNBQdUtT2ZM9w/nIiItCDQty3z14cLvpgX7mguuMRuKzKpFzwaHQuo2W49BjhebI0CHnu+\nibTIBHX1ggOq7wmX+GvrMiIi6izQh5F7m7+Hpfsr8PujB1ziaU+4ba1d9YBLGq70uh7Awzp6vy/D\nPoBf6VZJpHEM3xTYAnH4ucQE9QVwQDMhHGAQJyIibfB1EPd3T7s/Azjg3eHorgK4pNNQdNth05c9\nLiGo1Ec6WrHOGfWP5eewcwp8gTj8XGJSugAXNDAc3RqHpBMRkRZ4e2i6UkPclRgF0Z3h6I7qdDUE\nXRLVp77z8HNJX3T0gvcBh5sHIfZ8E2md6frXPAVrcMU6gKu8N7zsy3vYA66wkpHJbp5R5pM6iIi0\nwFFwlhNq1TKnXKrV3/W42xPuqKfeUQ84ALtecKcvTVqIjQE8qLDnm4JDIPd+S0xKFyCDBnrD2QNO\nRIHM24tQuUstoS+QWfeMO7uojVJrAbjTEy6nBxzoCOFSEO/U+62+t50UwPBNwSNYArhJ4RrkUHkI\n58roRBTIGMBJjZRcjE9uCJcbwAGboejWAbwXbqyCTl1qRKTsixYwfBMFIpPSBcjEEE5EpAgGcFIj\npVfD93YAB6xWQZcCuESa+20dwhnIAx7DNwWXYOj9lpiULsANDOFEREGPgZwAdQTwrkK4swDubCG2\nTgG8z9Ubvd99rx/obC9wCjiqD99r1qxBSEgILl26ZLltwYIF0Ol0SElJwdGjRxWsjkjlTNBmCFdp\nEGcIJ1IW2wSBhWGbnFE6gANd94I7q7HLAG6rr+ObqYOcNQzUupaBI6oO39XV1SgsLMSgQYMst23d\nuhVnzpzBP/7xD7z++uuYM2eOghWSJgVT77fEBG2FcEATIZxBnMh/2CYITFppMJP/BVIAl3TqAZd6\nv4GOAM4QHhRUHb4XL16M1atXd7rt448/xsyZMwEARqMRbW1tMJvNSpRHpD0mpQvoBhWHcIBBnMhf\n2CYITgznwU3rAVy6z3rxNbsAbo0BPOCpdp/v7du3Iz4+HgaDodPtZrMZCQkJlu/j4+NhNpsRHx9v\n/yCHlt+4fms6EJfuk1pJg5o+AiIeUroKZZiuf81TsIbu0MB+4Y4CeMDsG36tGGgvVroKClLeaBOc\nWv6u5XrfdD36pSf5rmAi8hpHe2z7WwSaXO4J7qpG6T7rfcCj+tSj4cr146OtDq7zVsW+VVdchh+K\njyldhiYpGr7Hjx+P8+fP293+4osvYtWqVfjss88st4mi6PA6AAiC4PgJUpd7pU4KUMEcwIGOEJ6n\ncA3dpYEgLrEO5JoO4qHpHRdJ2wqlKqEA5es2wR3LH/dSpUTkb1oP4NYiIxtvBHAAqA/rfICr3m+V\nDASJTk9GdHqy5Xvzijd99lxK/7t7m6Lhu7Cw0OHtFRUVOH36NEaPHg2g45PtO++8E19//TXi4+NR\nXV2Nu+++23Kfw15vIuqaCdoN4BIpiKs8hAMdQVwK4BymTtQZ2wRE5IpWAjhgHxhta5cC+PU7O9NI\n7zd1jyrnfOv1ely4cAGnT5/G6dOnER8fjyNHjiA2NhZZWVl4++23AQBHjhxBaGgo4uLiFK6YNCsY\nF1+zZYI254LbUvnccAnnhxO5h20CUjpwkXqoYQ64HHL3Ao/qU2+//zcFNFWGb1vWQ8gmTZqEuLg4\n6HQ6/PKXv8Qbb7yhYGVEAcSkdAFeovLtyojIM2wTEAU3pQN4VwuwSboK4NaLsNnt/01e09zcjNTU\nVBiNRgwfPhyLFi0C0DFladmyZTAYDNDpdFi3bp3dubt374bRaLRcIiIisGPHDgDA6dOnMWbMGCQl\nJWHq1KlobW2VVY8g2k6WChCCIADzPXhpJq+V4h72xCojmOd+O5KndAFepoEh6bL44wMFk8zjmgS7\nubaeEgQBX4nJXR9oZYxQ5vU6KPAIgoAJ4naly7Cj1R5d62G3coNIV2yD9IN/AQAAIABJREFUiqNh\nu0TW1PD742oIusRRndJt0tBzy+JrV8KAetwYeh4Nx/O8+1xFVJ/6zgH+uu7+rnR1Xj16WY6x/b3/\nSsjwyd9iQRBQJI6RfXyG8JXDOpqamhAREYG2tjakpaVh1apVOHHiBCoqKvDqq68CAGpra9G/f3+n\nj3358mUMHToUNTU1CA8Px0MPPYR58+YhJycHv/3tbzFo0CBLsHdFEz3fRD7HDz06MyldgJexN5yI\nSNPUELRIXay38lKKnA+fXPWAS+E5qk995yHo7P32qoiIjg9JWlpacO3aNcTExGDTpk14+umnLce4\nCt4A8MEHHyArKwvh4eFoa2tDSUkJcnJyAAAzZszAzp07ZdWi2q3GiEhhputf8xSswRc0tFI6ERHd\nYN3zRiRReiG2rhZhAxzXaL0FmWXxtY47wB9zecqK63Cs+Icuj2tvb0dKSgpOnTqF+fPnQ6fTobKy\nEgUFBdi+fTsGDBiA9evXY+TIkU4f469//SuWLFkCAPj+++8xYMAAy31xcXEwm82yapYdvq9du4bv\nvvsO9fX16N27N2677TaEhobKPZ1I/YJ96zFnTNe/5ilYg68wiBN1C9sEpBQGcHIkUAJ4x/7f17ce\nY+83ANejXu5I74U70m98/+YKxwE4JCQEZWVlqKurQ2ZmJoqLi9Hc3Ix+/fqhvLwc27Ztw+zZs/H1\n1187PP/cuXOoqKhAZmamJy+loxZXd/7444/YuHEjxo0bh6ioKAwdOhRGoxF33HEHoqKiMG7cOGzc\nuBGNjfbzDYg0icPPnTMpXYCPcWg6kUtsE5BacAg6qVF3h6BLLPO3pYXXyOuio6ORnZ2NkpISJCQk\nIDc3FwCQk5ODiooKp+e9//77yM3NtXzIHBMTg4sXL1rud2ebS6fhe82aNRg8eDDWrl2Lu+++G+++\n+y5KS0vxzTffoLS0FO+88w5SU1Oxdu1a3HbbbVizZo2sJyRSPQZw50wI/BAOMIRrWHV1NR544AEk\nJSVhxIgRWL16daf716xZg5CQEFy6dEmhCrWJbQIiUjs1jIjoTgB3WTdDuMdqa2tRX9/xHjc1NaGw\nsBBJSUnIzs5GUVERAGDPnj0YNmyY08d499138fjjj1u+v+mmm3DPPffgww8/BABs2bIFWVlZsupx\nutr5pEmTsGzZMqSkpHT5IIcPH8bKlSuxdetWWU/qD1ztnDzC4eddy1O6AD9Sy5B0rnZux3a18wsX\nLuDf//439Ho9GhoakJKSgg8++ACjR49GdXU1nnjiCfzzn//E4cOH0a9fP6/WH8gCoU3A1c69R4nV\nzuWcQwSo4/fK3VXQra83NkZ2rH4uDT93JIhWO98uTpB9/MPCZ3Z1HD9+HLNmzYIoimhubsa0adPw\nhz/8AXV1dZg+fTqqqqoQFhaGjRs3IiUlBaWlpdiwYQM2btwIAKiqqsL999+P6urqTo97+vRpTJs2\nDQ0NDdDpdHjrrbfQo0ePrl8TtxpzwuS1UtzD8K0eDODy5CldgB8pHcIZvu10tdXY5MmTMW/ePEyc\nOBFTpkzBM888g4cffpjhO8gwfHuXWsK3o/OIAHX8bnU3gDN83yAIAraIk2QfP0PYqvrtR7naOZEz\nXIBNHtP1r3kK1uAvXKBNcUeKG3CkuEHWsVVVVTh06BDeeOMNbN++HfHx8TAYDD6ukIiIlKb0AmyA\n+4uwWS++BgANgOsATpokK3w3Nzdj7dq1+OKLL3DlypVOnygIgoCDBw/6rEAi0ggTgiOAS6QgzhDu\nVynpUUhJj7J8//qK8w6Pa2howJQpU7B27VqEhoZi5cqVKCwstNyv9k/G1YxtAlITrn5Ozmg9gDdc\ncVK7tBc4aZKs8D179mx88cUXyM3NtRumJwiCTwojUgX2frvHdP1rnoI1+Bt7w73qY8hbsOSGMrtb\nWltbMWnSJEybNg05OTk4fvw4qqqqMHr0aAAdq5LeeeedOHjwIGJiYrxQdXBhm4DUhgGcnJF+LrS0\nDZl0PapPPSzjvK6EMXQHCFnh+6OPPsLnn3+Oe++919f1EKkPA7j7TNe/5ilYgxIYxBUniiLmzZuH\nxMRELFq0CACQlJSECxcuWI4ZMmQI53x7gG0CUiMGcFIzOQHcpSAO3o2IVLoEr3K5z7ckPj4e/fv3\n93UtRBRoTAiOrckc4XZlijhw4AC2bNmC3bt3w2g0wmg04pNPPul0DHtnPcM2ARFpjRo+mOlqcULr\nGnuhYzG1qD7K103eJavn+/nnn8eyZcuwefNm9O7d29c1EakPe789Y7r+NU/BGpTC3nC/SktLQ3t7\nu8tjKisr/VRNYGKbgIioe7rqAVfDPHXyLVnhe+LEidi4cSMGDBiAgQMHIiTkRoe5IAhsyFBwYAD3\nnAnBGcAlDOIUANgmICItUkuwdXcIum3vt9OF2AKUGv7NvElW+J45cya++eYbLFmyhIurUHBjAPec\n6frXPAVrUAOulk4axTYBEWmVFgK4VGMv1AORHft+U+CQFb4///xz7Nu3DykpKb6uh4iChen61zwF\na1CDLWAAJ01hm4CItExLAdyRqD71Qdf7HUhkLbiWkJCAsDBu8k4EoKP3m7zHhOBdlE3ChdlIQ9gm\nILVSQ6AibVDDAmyAvEXYIiMb/VQN+YOs8P3nP/8ZS5cuxZkzZ3xdD5E2MIB7nwnBHcK5OjppBNsE\nRBQI1B7AreuLjGy0XIJNPXrJvmiBrGHnU6ZMwdWrVzF48GBERETYLa7yww8/+KxAItXi/G/fMFld\nz1OoBiJyim0CIiLv6u4QdNIeWeH7lVde8XUdRNrEAO5bputf8xSsQUnW88HZK04qwTYBqZVaejJJ\nO9QUbB0FcEf1BWPvdyCRFb7z8vJ8XAaRhjGA+54JwRPAbUM2QzepDNsEpEYM3kSByZ1t2bTA6Zxv\nd4eNcZgZEfmUCcE9J5xIQWwTkJoxeFOgcDX/W7qQtjkN30OHDsV//dd/4dSpUy4f4Ntvv8WyZcsw\ndOhQrxdHpBlcgM1/TGAQJ/IztglIrRhGyFNq+xmyDeBqq48843TY+f79+7F8+XKMGjUKw4YNQ2pq\nKm677TZERUWhoaEB3333HQ4dOoRvv/0WkydPxv79+/1ZN5H6cPi5/5muf81TsAaiIMA2AakRQwkF\nqq4WYGtEpJ8rIm8RRFEUXR1w7tw5/O1vf8PevXvxr3/9Cw0NDYiKisKwYcPwwAMPIDc3F3Fxcf6q\nVzZBEID5Ll+aayavleIe9qBqHwO4cvKULiAAmGQe1ySgiz8fbhMEAc+IT7t1zvPCSq/XQc5puU0w\nQdyudBl21LLQk7usQ0FX+xTLZRukXb03DN3kC2r7fbQN39b11aMXGhsjHS6+1t3fj67Oq0cvyzG2\nv/dfCRk++VssCAJ+La6Rffx64SnVtwm6XHBt4MCByM/PR35+vj/qIdI+9oArx3T9a56CNRAFMLYJ\nSEkM3RRMbHu/pZ9/tX1IQO6Rtdo5EZGmmKyu5ylUAxEReQ2DN/mamrYdk8jdfoy0w+mCa0TkAU4f\nUA8TuDgbEZFGcYVnIse437c8zc3NSE1NhdFoxPDhw7Fo0SIAgCiKWLZsGQwGA3Q6HdatW+fw/DNn\nziAzMxPJyclITEzEmTNnAABTp05FYmIiRo4ciZkzZ+Lq1auy6mHPN5GvcPi5upisrucpVAMREcnC\nwE3UIdh7vz19neHh4di7dy8iIiLQ1taGtLQ07N69GydOnMClS5dQXl4OAKitrXV4/qOPPoqXX34Z\naWlpuHr1Ktrb2wEAv/zlLzFu3DgAwLRp0/Daa69h4cKFXdbD8E3kS1IPOEO4upiuf81TsAYiIiIi\n8rmIiI4PL1paWnDt2jXExMRg8eLF2LFjh+WY/v37251XVlaGyMhIpKWlAQDCwsIs90nBGwDGjBmD\n6upqWbVw2DmRP3AYujqZwCHpREREpOqeZEe7CjgaHRJsI0bOF/8Tx5Z/ZLk4097ejuTkZMTGxmLs\n2LHQ6XSorKxEQUEBDAYDMjIycPLkSbvzTp48icjISEycOBF6vR4LFy7EtWvXOh3T2toKk8mEhx9+\nWFbNssL32LFj8dxzz9ndfunSJWRkZMh6IiIi1TKBQZxIJrYJyBfUHHyISDmNiHR66Z1uxLDlj1ku\nzoSEhKCsrAxmsxl79+5FcXExmpub0a9fP5SXlyM/Px+zZ8+2O6+9vR379+/Hq6++imPHjqGmpgYF\nBQWdjvnNb36DBx98EPfdd5+s1yNr2PmePXtw7NgxlJeX46233urUdV9cXCzriYiCHueAa4PJ6nqe\nQjUEsY/g7u/ISp/UQc6xTUBE5H/Wc7+Dac63N0VHRyM7OxslJSVISEhAbm4uACAnJwczZsywO/62\n225DcnIyBg8ebDlu3759lvtXrFiBixcv2gVyV2QPO//8889RVVWFtLQ0nD17VvYTEJEVDj/XFhPY\nG07kANsERET+Zz38PNiGmHdXbW0t6us73qumpiYUFhYiKSkJ2dnZKCoqAtDxofKwYcPszk1NTcX3\n33+PixcvAgC++OILjBo1CgCwadMmfPbZZ3jnnXfcqkd2+I6Pj8e+ffswdOhQpKamorS01K0nIqLr\nGMC1xwQGcSIrbBNQMKq3bHzmvQupg5b+LRzN/wYYxp05e/YsHnjgASQnJ8NoNGLcuHHIzs7Gc889\nh61bt0Kv1+Opp57C5s2bAQClpaV44oknAHQssLZu3Tr87Gc/w8iRI9Ha2opf//rXAID58+fj+++/\nx5gxY2A0GvHCCy/Iqset1c4jIiLw17/+Fc899xzS09OxciWH+xF1C4ega5fJ6nqeQjUQqQDbBBTI\n/BXG5DwPQxU5EwzDzz19fUlJSTh69Kjd7dHR0fi///s/u9vvuusu3HXXXZbvx40bh2PHjtkd19ra\n2q163N5qTBAEPPvss9DpdA4nphORTAzg2meyup6nUA1ECmKbgAKNGoNMVzUxnAcf272/+TOgHbLC\nd1FREfr27dvptsmTJ2PkyJE4fPiwTwojCgoM4IHDZHU9T6EaPGVSugDSArYJKFCpMXjL4apuhrKu\nafXfXcJ/Y22RFb7T09Md3q7X66HX671ZD1HwYQAPPKbrX/MUrIHIR9gmoECj9fDlirPXxsDWQcv/\n9hFo0nT9wcrtYedE5AMM4IHJZHU9T6EaiIjIoWAOLsEeyoP5315rAu3fiuGbSC0YwAObyep6nkI1\nEBERgMBr0HtLoIfyQPt3D4YF1wKN7K3GlPDKK69g9OjRSEpKwu9+9zvL7atWrUJiYiKSkpLw2Wef\nKVghkZdxG7LgYAK3LiNyE9sE5C0MK+7T8lZpWquXAptqe7537tyJTz/9FIcPH8ZNN92E2tpaAMDh\nw4fx97//HcePH8f58+eRlpaGf/7zn+jZs6fCFRN5CXvAg4vp+tc8BWsgUjm2CchbGMC8S60rsQfT\nv3Og935br+oeCFQbvjdt2oSlS5fipps6Suzfvz+Ajj/AU6dORWhoKOLi4qDT6XDw4EGkpaUpWS6R\ndzGABx+T1fU8hWogUim2CcgbAjmgqJU777m7QZ3/njdYv3d8X9RNteH75MmT+PTTT5Gfn4+IiAis\nWbMG9957L2pqapCRkWE5Lj4+Hmaz2fGDHFp+4/qt6UBcui9LJvIuaQg6Q3jwMVldz1OoBlvXioH2\nYqWroCDljTbBqeXvWq73TdejX3qSz+sm9WAgUT/+G3mHP3rC64rL8EPxMZ8+R6BSNHyPHz8e58+f\nt7v9xRdfRHt7O+rr61FWVoZDhw5h0qRJqKqqcu8JUpd7pU4iRbEXPLiZrn/NU7AGAAhN77hI2lYo\nVQkFKF+3Ce5Y/riXKiWtYaijQBWBJofDsn0dwKPTkxGdnmz53rziTZ89V6BRNHwXFhY6vW/dunXI\nzc0FAKSmpqJnz564cOEC4uPjUV1dbTnObDYjISHB57USKYoBnExQPoAT+RDbBERE7lMqgPtLILwG\na6pd7Tw7OxtFRUUAgG+++QaNjY2IjY1FVlYW3nvvPbS1tcFsNqOiogI//elPFa6WyA+aPuJq6MHO\npHQBRMpgm4C6K9Aa7kTdYbtOPSlHtXO+n3zyScydOxd6vR4AYDKZEBISgjvvvBOPPPIIDAYDQkJC\nsGHDBvTo0UPhaon8iL3gRBRk2CYgInKfs6At3S59OMVA7j+CKIqi0kX4giAIwHwPXprJa6W4hz2b\nJBcDePDK88Fjmtw8vkmAt/98CIKAZPErt84pE8Z4vQ4KPIIgYIK4Xeky7Gi1V9Z6iGsEmrz2uN4M\nAFp9b4m6w5PtuOrRS9bvnvVxtr/3XwkZPvlbLAgCYsTvZB//vTBI9W0C1Q47J6Iu8IMa8haT0gV4\nT3V1NR544AEkJSVhxIgRWL16NQBg8eLFSExMRGJiIn7+859b9okmosDD4E3BxpMPwdjr7V8M30Ra\nxgAenEwIqMDsTT179sT69etx/PhxHD58GJs2bcKxY8fw0EMPoaKiAidOnIBer8cLL7ygdKlE5GXS\njFYiIrVi+CbSOi7EFrxMKnkMFYmNjbXMC46KioLBYMDZs2cxduxYhIR0/Mm77777UFNTo2SZROSA\nJ8GZoZvIt9hD7h2qXXCNiNzEhdiCk+n61zwFa1CpqqoqHDp0CG+88Uan2wsKCjB16lSFqiIib2Lo\nJuogDT33ZP63GjU2RipdglcxfBMFEgbw4GWC4wBusrru6H6NaSg+gobiI10f19CAKVOmYO3atejV\n60bj/MUXX0TPnj0xffp0X5ZJFNCc7SvsTwzdRI6p4feTnGP4Jgo0DODByyTj/jyfV+GRsi/vcX1A\nz3uACVbfr3jd7pDW1lZMmjQJ06ZNQ05OjuX2N998Ezt37rTsF01E2sPQTURaxjnfRIGI88DJGZOT\n6wFCFEXMmzcPiYmJWLRokeX2Xbt2YfXq1dixYwfCw8MVrJCI3CEtosbF1Ijk8+YWgFrX3NyM1NRU\nGI1GDB8+3NI2EEURy5Ytg8FggE6nw7p16xyeHxoaCqPRCKPR2OkDfcmCBQs6jbDrCnu+iQIZe8HJ\nEZPSBfjOgQMHsGXLFhgMBhiNRgDAypUrsWDBArS0tGD8+PEAgDFjxmD9+vVKlkpEDjBgE3lHoMwB\nb7ji2f8J4eHh2Lt3LyIiItDW1oa0tDTs3r0bJ06cwKVLl1BeXg4ATrcgjYyMxNGjRx3eV1paiitX\nrkAQBNn1MHwTBTqpB5whnIJAWloa2tvb7W7/17/+pUA1REREygqUEO6JiIiO197S0oJr164hJiYG\nixcvxo4dOyzH9O/f363HvHbtGn7/+9/jnXfewbZt22Sfx2HnRMGCw9CJiIiIglJADkX/ci+w5oUb\nFyfa29uRnJyM2NhYjB07FjqdDpWVlSgoKIDBYEBGRgZOnjzp8Nzm5mbcddddSElJwfvvv2+5/S9/\n+Qsefvhh3HLLLW6VzJ5vomDCXnAiIiKioKTJldCvhDm/L3F8x0Xy344DeEhICMrKylBXV4fMzEwU\nFxejubkZ/fr1Q3l5ObZt24bZs2fj66+/tju3pqYGMTExOH36NDIyMjB69Gj06tULf/vb31BcXAxR\nFN16Oez5JgpG7AUnIiIiCjoB2QMuU3R0NLKzs1FSUoKEhATk5uYCAHJyclBRUeHwnJiYGADAkCFD\nMGHCBBw5cgRlZWX49ttvMXToUNx+++1obGzE8OHDZdXA8E0UrBjAiYiIiIJOMAXw2tpa1NfXAwCa\nmppQWFiIpKQkZGdnW7Ye3bNnD4YNG2Z3bl1dHVpbWy2Ps2fPHuh0OmRlZeHcuXM4ffo0Tp8+jcjI\nSHzzzTey6uGwc6JgxtXQiYiIiIJOsCzEdvbsWcyaNQuiKKK5uRnTpk1DdnY20tLSMH36dKxZswZh\nYWHYvHkzgI4VzDds2ICNGzfiH//4B371q18hJCQEV69exYIFC2AwGOyew53VzgXR3YHqGiEIAjDf\ng5dm8lop7mFvJCmFIZzc0SS4Pc+pK4IgAAfcfMz7vF8HBR5BEDBB3K50GXa0vK2W1GAPph40okDU\nnfBt+3v/lZDhk7/FgiAAJW487j3qbxNw2DkRdeAHP0REREREPsPwTUQ3MIATEREREfkEwzcRdcYA\nTkRERBQUOHXEv7jgGhHZ437gREREREFB1ft/1yldgHex55uInGMvOBEREVHAYw+4fzB8E5FrTR8x\nhBMREREReYjDzolIHu4JTkRERET+dFnpAryLPd9EJB97wYmIiIiIuoXhm4jcxxBOREREROQWhm8i\n6j4GcCIiIqKAwEXXfI9zvonIM5wLTkRERES+8KPSBXgXwzcReY77ghMRERFpnm3vt2r3/9YoDjsn\nIu/hMHQiIiKigMGh6N7Fnm8i8i72gpMntihdABEREVmLQJNyPeBXlHlaX2HPNxH5BldEJyIiIgoI\n7AH3DoZvIvItBnAiIiIizWMA9xzDNxH5HnvBiYiIiCjIcc43EfkP54MTERERkVwNShfgXez5JiL/\nYy84EREREQUZhm8iUgaHohMRERFREGH4JiJlMYQTEWkOF14iIi1obm5GamoqjEYjhg8fjkWLFgEA\nRFHEsmXLYDAYoNPpsG7dOqeP8cMPPyA+Ph75+fmW21paWvCf//mfMBgMGDVqFLZu3SqrHs75JiJ1\n4HxwIiJNYQAnIp/zcJ/v8PBw7N27FxEREWhra0NaWhp2796NEydO4NKlSygvLwcA1NbWOn2MZ555\nBg8++GCn25YtW4YhQ4agoKAAAHD58mVZ9TB8E5G6MIQTERERqZIWP3SLiIgA0NFbfe3aNcTExGDx\n4sXYsWOH5Zj+/fs7PPfw4cP4/vvv8R//8R8oLS0F0NFr/s4776CqqspyXN++fWXVwmHnRKRO0nB0\nDkknIiIiIlvVxcBXy29cnGhvb0dycjJiY2MxduxY6HQ6VFZWoqCgAAaDARkZGTh58qTD85YsWYI1\na9Z0uv3ChQsIDw9Hfn4+9Ho9fvGLX+D8+fOySmb4JiL1YwgnIiIiCj5XXFx6pQOjlt+4OBESEoKy\nsjKYzWbs3bsXxcXFaG5uRr9+/VBeXo78/HzMnj3b7rz169cjKysLt956K0RRtNze3t6O06dPY8KE\nCaioqMDYsWOxcOFCWS+Hw86JSDuaPuJwdCIiIiJyW3R0NLKzs1FSUoKEhATk5uYCAHJycjBjxgy7\n40tKSrBv3z6sX78eDQ0NaGlpQa9evfD8888jPDzccv7kyZPx2muvyaqBPd9EpC0cjk5EREREMtTW\n1qK+vh4A0NTUhMLCQiQlJSE7OxtFRUUAgD179mDYsGF2527ZsgXfffcdTp8+jZdffhmzZs3CypUr\nERoaivHjx2P37t0AgC+++AKjRo2SVY9qw/eBAweQnJwMvV6P0aNH48svvwTQMcF9wYIF0Ol0SElJ\nwdGjRxWulIgUwwBOFBTYJiAiou44e/YsHnjgASQnJ8NoNGLcuHHIzs7Gc889h61bt0Kv1+Opp57C\n5s2bAQClpaV44oknHD6WIAiW66+88gqWL18OvV6P119/HX/+859l1SOI1gPYVSQtLQ3PPPMMMjMz\n8cknn2DlypXYt28ftm7dirfeegsffvghjh49ijlz5qCsrMzufEEQgPkevDRT90/1CMMEUfdwOLp/\nNQnw9p+Pbv2//ar36yD18UabYIK4XYHKXatHL6VLICLy2FdChk/+FguCAEx243H/pv42gWp7vhMS\nElBXVwcAuHLlCgYNGgQA2LlzJ2bOnAkAMBqNaGtrg9lsVqxOIlIJDkUnClhsExARUSBQ7YJrf/zj\nH5GWloYlS5agvb0dX331FQCgpqYGCQkJluPi4+NhNpsRHx9v/yCHlt+4fms6EJfu05qJSAW4T7hv\nXCsG2ouVroKClDfaBKeWv2u53jddj37pSb4vnIgoANUVl+GH4mNKl6FJiobv8ePHO9wT7cUXX8S6\ndeuwbt06PPLII/jggw8wd+5cFBYWAoDdcALr8fedpC73dslEpBUM4d4Vmt5xkbStUKoSClC+bhPc\nsfxx7xdNRBSEotOTEZ2ebPnevOJNBavRFkXDt/SH05Fp06bh888/B9CxfPucOXMAdHyqXV1djbvv\nvhsAnPd6ExEBnYeiM4gHvOrqakyfPh2XL19GS0sL5s2bh9///ve4dOkSHnvsMVy4cAEDBw7Ee++9\nhz59+ihdLllhm4CIiOxcUboA71LtnO9BgwZhz549AICioiIMGTIEAJCVlYW3334bAHDkyBGEhoYi\nLi5OsTqJSEM4Lzzg9ezZE+vXr8fx48dx+PBhbNq0CceOHcOzzz6L7OxslJeXY+LEiXj22WeVLpXc\nwDYBEREFAtXO+d64cSN+/etfo7W1FWFhYXj99dcBAJMmTcLu3buh0+kQFhaGN954Q+FKiUhz2Bse\nsGJjYxEbGwsAiIqKgsFgQE1NDT7++GMcPHgQADBjxgzcc889WLt2rZKlkhvYJiAiokCg2q3GPMWt\nxojILQzh7lFqq7GaYuBs8Y3vS1c4raOqqgoPPvggjh8/jvj4ePzwww+W+3r37t3pewps3GqMiMh3\nfLrV2F1uPG6p+rcaU23PNxGRX7E3XB1MXR2Qfv0icbzwW0NDAyZPnoy1a9eid+/e3qiMiIiIyCMM\n30REthjENa21tRWTJk3C9OnTkZOTAwC4+eabcfHiRQwYMAD//ve/ERMTo3CVREREFGxUu+AaEZEq\nSIu0cUqIJoiiiHnz5iExMRGLFi2y3J6VlYUtW7YAALZs2YKsrCylSiQiIqIgxZ5vIiK52COuegcO\nHMCWLVtgMBhgNBoBAKtWrcKKFSvw2GOPYfPmzbjlllvw/vvvK1wpERERdelHpQvwLoZvIqLuYBBX\npbS0NLS3tzu8z9U+0kRERES+xvBNROQpBnEiIiIi6gLDNxGRNzGIExEREXnHFaUL8C6GbyIiX2EQ\nJyIiIqLruNo5EZE/cMV0IiIioqDGnm8iIn9ibzgRERFRUGL4JiKzo6JBAAAXqklEQVRSCoM4ERER\nkXOc801ERF7HIE5EREQU0Bi+iYjUxnZuOMM4ERERkeZxwTUiIrWTFmvjgm1EREREsjU3NyM1NRVG\noxHDhw/HokWLAACiKGLZsmUwGAzQ6XRYt26d3bnfffcdUlJSLOeuXr3act9rr72GESNGQKfTYdy4\ncbhw4YKsegRRFEXvvDR1EQQBmO/BSzN5rRT3sHFNRO5Qqle8SYC3/3wIggBEuPmYPqiDAo8gCJgg\nble6DDv16KV0CUREHvtKyPDJ32JBEAC487iO2wRNTU2IiIhAW1sb0tLSsGrVKpw4cQIVFRV49dVX\nAQC1tbXo379/p/NaW1shCAJuuukm/Pjjj9DpdCguLkZsbCxuvfVWnDp1Cv369cPSpUvRs2dPPP/8\n811WyGHnRERaxrniRERERE5FREQAAFpaWnDt2jXExMRg8eLF2LFjh+UY2+ANAD169LBcb2pqQo8e\nPdC7d2+EhYWhf//+aGhoQN++fVFXV4e77rpLVi0cdk5EFCish6dzFA0REREFtGIAy60ujrW3tyM5\nORmxsbEYO3YsdDodKisrUVBQAIPBgIyMDJw8edLhuWazGQaDAbfddhsWLVqEfv36ISQkBGvXroVe\nr0dcXBxOnjyJuXPnyqqY4ZuIKFAxjBOpWi/UK10CEZHKtbq43AdgmdXFsZCQEJSVlcFsNmPv3r0o\nLi5Gc3Mz+vXrh/LycuTn52P27NkOz42Pj0d5eTlOnTqFP//5z/j222/xww8/YMGCBTh27BjOnj2L\npKQkrFq1StarYfgmIgoWDONEREQUpKKjo5GdnY2SkhIkJCQgNzcXAJCTk4OKigqX5w4cOBD3338/\nSktLceLECQwZMgRDhgwBAEyZMgX79++XVQPDNxFRsGIQJyIiogBWW1uL+vqOUUZNTU0oLCxEUlIS\nsrOzUVRUBADYs2cPhg0bZnfuuXPncPXqVQDA5cuXsW/fPuj1egwaNAgnTpzAxYsXAQCFhYUYOnSo\nrHq44BoREXFvcSIiIgo4Z8+exaxZsyCKIpqbmzFt2jRkZ2cjLS0N06dPx5o1axAWFobNmzcDAEpL\nS7FhwwZs3LgRx48fx5IlSyAIAlpbW/HUU09Br9cDAJYvX457770XoaGhGDRoELZs2SKrHm415ozJ\na6W4hz1QRKQ2joI4txojDVHrVmMAtxsjIu3z7VZj59044xbVtwk47JyIiIiIiIjIxzjsnIiIXPPn\nXuIc/UNEREQBij3fRERERERERD7Gnm8iIpKv6SMuxkZERER+8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+ "text": [ + "" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/BATS/MOM_input b/standalone/examples/single_column_MARBL/BATS/MOM_input index d89bee60..8172d0d1 100644 --- a/standalone/examples/single_column_MARBL/BATS/MOM_input +++ b/standalone/examples/single_column_MARBL/BATS/MOM_input @@ -69,14 +69,8 @@ GRID_CONFIG = "cartesian" ! ! cartesian - use a (flat) Cartesian grid. ! spherical - use a simple spherical grid. ! mercator - use a Mercator spherical grid. -SOUTHLAT = 31.660667 ! [degrees] - ! The southern latitude of the domain or the equivalent starting value for the - ! y-axis. LENLAT = 1.0 ! [degrees] ! The latitudinal or y-direction length of the domain. -WESTLON = -64.018700 ! [degrees] default = 0.0 - ! The western longitude of the domain or the equivalent starting value for the - ! x-axis. LENLON = 1.0 ! [degrees] ! The longitudinal or x-direction length of the domain. TOPO_CONFIG = "file" ! diff --git a/standalone/examples/single_column_MARBL/EQPAC/MOM_input b/standalone/examples/single_column_MARBL/EQPAC/MOM_input index 80519997..8172d0d1 100644 --- a/standalone/examples/single_column_MARBL/EQPAC/MOM_input +++ b/standalone/examples/single_column_MARBL/EQPAC/MOM_input @@ -69,14 +69,8 @@ GRID_CONFIG = "cartesian" ! ! cartesian - use a (flat) Cartesian grid. ! spherical - use a simple spherical grid. ! mercator - use a Mercator spherical grid. -SOUTHLAT = 0.06250 ! [degrees] - ! The southern latitude of the domain or the equivalent starting value for the - ! y-axis. LENLAT = 1.0 ! [degrees] ! The latitudinal or y-direction length of the domain. -WESTLON = -140.0 ! [degrees] default = 0.0 - ! The western longitude of the domain or the equivalent starting value for the - ! x-axis. LENLON = 1.0 ! [degrees] ! The longitudinal or x-direction length of the domain. TOPO_CONFIG = "file" ! diff --git a/standalone/examples/single_column_MARBL/HOT/MOM_input b/standalone/examples/single_column_MARBL/HOT/MOM_input index a37293f6..8172d0d1 100644 --- a/standalone/examples/single_column_MARBL/HOT/MOM_input +++ b/standalone/examples/single_column_MARBL/HOT/MOM_input @@ -69,14 +69,8 @@ GRID_CONFIG = "cartesian" ! ! cartesian - use a (flat) Cartesian grid. ! spherical - use a simple spherical grid. ! mercator - use a Mercator spherical grid. -SOUTHLAT = 22.86669 ! [degrees] - ! The southern latitude of the domain or the equivalent starting value for the - ! y-axis. LENLAT = 1.0 ! [degrees] ! The latitudinal or y-direction length of the domain. -WESTLON = -157.99992 ! [degrees] default = 0.0 - ! The western longitude of the domain or the equivalent starting value for the - ! x-axis. LENLON = 1.0 ! [degrees] ! The longitudinal or x-direction length of the domain. 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This input file provides the adjustable run-time parameters for version 6 of the Modular Ocean Model (MOM6). +! Where appropriate, parameters use usually given in MKS units. + +! This particular file is for the example in single_column/KPP. + +! This MOM_input file typically contains only the non-default values that are needed to reproduce this example. +! A full list of parameters for this example can be found in the corresponding MOM_parameter_doc.all file +! which is generated by the model at run-time. + +! === module MOM_domains === +REENTRANT_Y = True ! [Boolean] default = False + ! If true, the domain is meridionally reentrant. +NIGLOBAL = 2 ! + ! The total number of thickness grid points in the x-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NJGLOBAL = 2 ! + ! The total number of thickness grid points in the y-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NIHALO = 2 ! default = 4 + ! The number of halo points on each side in the x-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +NJHALO = 2 ! default = 4 + ! The number of halo points on each side in the y-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +! LAYOUT = 1, 1 ! + ! The processor layout that was actually used. + +! === module MOM === +SPLIT = False ! [Boolean] default = True + ! Use the split time stepping if true. +USE_REGRIDDING = True ! [Boolean] default = False + ! If True, use the ALE algorithm (regridding/remapping). If False, use the + ! layered isopycnal algorithm. +DT = 3600.0 ! [s] + ! The (baroclinic) dynamics time step. The time-step that is actually used will + ! be an integer fraction of the forcing time-step (DT_FORCING in ocean-only mode + ! or the coupling timestep in coupled mode.) +FRAZIL = True ! [Boolean] default = False + ! If true, water freezes if it gets too cold, and the accumulated heat deficit + ! is returned in the surface state. FRAZIL is only used if + ! ENABLE_THERMODYNAMICS is true. +C_P = 3925.0 ! [J kg-1 K-1] default = 3991.86795711963 + ! The heat capacity of sea water, approximated as a constant. This is only used + ! if ENABLE_THERMODYNAMICS is true. The default value is from the TEOS-10 + ! definition of conservative temperature. +SAVE_INITIAL_CONDS = True ! [Boolean] default = False + ! If true, write the initial conditions to a file given by IC_OUTPUT_FILE. + +! === module MOM_fixed_initialization === +USE_MARBL_TRACERS = True + ! "[Boolean] default = False + ! If true, use the MARBL tracer package." +! === module MARBL_tracers === +MARBL_SETTINGS_FILE = "INPUT/marbl_in" ! default = "marbl_in" +MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c211008.nc" + ! The file in which the MARBL tracers initial values can be found. +MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211020.nc" + ! The file in which the iron sediment flux forcing field can be found. +MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211020.nc" + ! The file in which the iron vent flux forcing field can be found. + + +! === module MOM_grid_init === +GRID_CONFIG = "cartesian" ! + ! A character string that determines the method for defining the horizontal + ! grid. Current options are: + ! mosaic - read the grid from a mosaic (supergrid) + ! file set by GRID_FILE. + ! cartesian - use a (flat) Cartesian grid. + ! spherical - use a simple spherical grid. + ! mercator - use a Mercator spherical grid. +LENLAT = 1.0 ! [degrees] + ! The latitudinal or y-direction length of the domain. +LENLON = 1.0 ! [degrees] + ! The longitudinal or x-direction length of the domain. +TOPO_CONFIG = "file" ! + ! This specifies how bathymetry is specified: + ! file - read bathymetric information from the file + ! specified by (TOPO_FILE). + ! flat - flat bottom set to MAXIMUM_DEPTH. + ! bowl - an analytically specified bowl-shaped basin + ! ranging between MAXIMUM_DEPTH and MINIMUM_DEPTH. + ! spoon - a similar shape to 'bowl', but with an vertical + ! wall at the southern face. + ! halfpipe - a zonally uniform channel with a half-sine + ! profile in the meridional direction. + ! bbuilder - build topography from list of functions. + ! benchmark - use the benchmark test case topography. + ! Neverworld - use the Neverworld test case topography. + ! DOME - use a slope and channel configuration for the + ! DOME sill-overflow test case. + ! ISOMIP - use a slope and channel configuration for the + ! ISOMIP test case. + ! DOME2D - use a shelf and slope configuration for the + ! DOME2D gravity current/overflow test case. + ! Kelvin - flat but with rotated land mask. + ! seamount - Gaussian bump for spontaneous motion test case. + ! dumbbell - Sloshing channel with reservoirs on both ends. + ! shelfwave - exponential slope for shelfwave test case. + ! Phillips - ACC-like idealized topography used in the Phillips config. + ! dense - Denmark Strait-like dense water formation and overflow. + ! USER - call a user modified routine. +TOPO_FILE = "TOPO_FILE_COLUMN.nc" ! default = "topog.nc" + ! The file from which the bathymetry is read. + +MAXIMUM_DEPTH = 6000.0 + ! "[m] + ! The maximum depth of the ocean." + +MINIMUM_DEPTH = 9.5 + ! "[m] default = 0.0 + ! If MASKING_DEPTH is unspecified, then anything shallower than + ! MINIMUM_DEPTH is assumed to be land and all fluxes are masked out. + ! If MASKING_DEPTH is specified, then all depths shallower than + ! MINIMUM_DEPTH but depper than MASKING_DEPTH are rounded to + ! MINIMUM_DEPTH." + +MASKING_DEPTH = 0.0 + ! "[m] default = -9999.0 + ! The depth below which to mask points as land points, for which all + ! fluxes are zeroed out. MASKING_DEPTH is ignored if negative." +ROTATION = "betaplane" ! default = "2omegasinlat" + ! This specifies how the Coriolis parameter is specified: + ! 2omegasinlat - Use twice the planetary rotation rate + ! times the sine of latitude. + ! betaplane - Use a beta-plane or f-plane. + ! USER - call a user modified routine. + +! === module MOM_verticalGrid === +! Parameters providing information about the vertical grid. +NK =65 ! [nondim] + ! The number of model layers. + +! === module MOM_EOS === + +! === module MOM_tracer_flow_control === + +! === module MOM_coord_initialization === +REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" + ! Coordinate mode for vertical regridding. Choose among the following + ! possibilities: LAYER - Isopycnal or stacked shallow water layers + ! ZSTAR, Z* - stretched geopotential z* + ! SIGMA_SHELF_ZSTAR - stretched geopotential z* ignoring shelf + ! SIGMA - terrain following coordinates + ! RHO - continuous isopycnal + ! HYCOM1 - HyCOM-like hybrid coordinate + ! SLIGHT - stretched coordinates above continuous isopycnal + ! ADAPTIVE - optimize for smooth neutral density surfaces +ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_65L_20200626.nc,dz" ! default = "UNIFORM" + ! Determines how to specify the coordinate resolution. Valid options are: + ! PARAM - use the vector-parameter ALE_RESOLUTION + ! UNIFORM[:N] - uniformly distributed + ! FILE:string - read from a file. The string specifies + ! the filename and variable name, separated + ! by a comma or space, e.g. FILE:lev.nc,dz + ! or FILE:lev.nc,interfaces=zw + ! WOA09[:N] - the WOA09 vertical grid (approximately) + ! FNC1:string - FNC1:dz_min,H_total,power,precision + ! HYBRID:string - read from a file. The string specifies + ! the filename and two variable names, separated + ! by a comma or space, for sigma-2 and dz. e.g. + ! HYBRID:vgrid.nc,sigma2,dz +!ALE_RESOLUTION = 4*2.0, 2*2.01, 2*2.02, 2.05, 2.07, 2.09, 2.13, 2.18, 2.24, 2.3, 2.4, 2.5, 2.62, 2.78, 2.95, 3.17, 3.42, 3.71, 4.07, 4.48, 4.97, 5.55, 6.23, 7.04, 7.99, 9.11, 10.43, 11.98, 13.8, 15.94, 18.42, 21.32, 24.66, 28.51, 32.91, 37.92, 43.56, 49.87, 56.88, 64.55, 72.91, 81.87, 91.4, 101.39, 111.73, 122.29, 132.93, 143.47, 153.78, 163.7, 173.07, 181.78, 189.76, 196.89, 203.17, 208.58, 213.14, 216.87, 219.87, 222.19, 223.94, 225.18, 226.04, 226.59, 226.9, 227.07, 227.14, 2*227.16, 227.17 ! [m] + ! The distribution of vertical resolution for the target + ! grid used for Eulerian-like coordinates. For example, + ! in z-coordinate mode, the parameter is a list of level + ! thicknesses (in m). In sigma-coordinate mode, the list + ! is of non-dimensional fractions of the water column. + +! === module MOM_state_initialization === +INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False + ! If true, initialize the layer thicknesses, temperatures, and salinities from a + ! Z-space file on a latitude-longitude grid. + +! === module MOM_initialize_layers_from_Z === +TEMP_SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" + ! The name of the z-space input file used to initialize temperatures (T) and + ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and + ! SALT_Z_INIT_FILE must be set. +Z_INIT_FILE_PTEMP_VAR = "Temp" ! default = "ptemp" + ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. +Z_INIT_FILE_SALT_VAR = "Salt" ! default = "salt" + ! The name of the salinity variable in SALT_Z_INIT_FILE. +Z_INIT_HOMOGENIZE = True ! [Boolean] default = False + ! If True, then horizontally homogenize the interpolated initial conditions. +Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False + ! If True, then remap straight to model coordinate from file. + +! === module MOM_diag_mediator === + +! === module MOM_lateral_mixing_coeffs === + +! === module MOM_set_visc === +HBBL = 10.0 ! [m] + ! The thickness of a bottom boundary layer with a viscosity of KVBBL if + ! BOTTOMDRAGLAW is not defined, or the thickness over which near-bottom + ! velocities are averaged for the drag law if BOTTOMDRAGLAW is defined but + ! LINEAR_DRAG is not. +CDRAG = 0.0 ! [nondim] default = 0.003 + ! CDRAG is the drag coefficient relating the magnitude of the velocity field to + ! the bottom stress. CDRAG is only used if BOTTOMDRAGLAW is defined. +DRAG_BG_VEL = 0.1 ! [m s-1] default = 0.0 + ! DRAG_BG_VEL is either the assumed bottom velocity (with LINEAR_DRAG) or an + ! unresolved velocity that is combined with the resolved velocity to estimate + ! the velocity magnitude. DRAG_BG_VEL is only used when BOTTOMDRAGLAW is + ! defined. +BBL_THICK_MIN = 0.1 ! [m] default = 0.0 + ! The minimum bottom boundary layer thickness that can be used with + ! BOTTOMDRAGLAW. This might be Kv/(cdrag*drag_bg_vel) to give Kv as the minimum + ! near-bottom viscosity. +KV = 1.0E-04 ! [m2 s-1] + ! The background kinematic viscosity in the interior. The molecular value, ~1e-6 + ! m2 s-1, may be used. + +! === module MOM_thickness_diffuse === + +! === module MOM_dynamics_unsplit === + +! === module MOM_continuity === + +! === module MOM_continuity_PPM === +UPWIND_1ST_CONTINUITY = True ! [Boolean] default = False + ! If true, CONTINUITY_PPM becomes a 1st-order upwind continuity solver. This + ! scheme is highly diffusive but may be useful for debugging or in single-column + ! mode where its minimal stencil is useful. +ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.75E-09 + ! The tolerance for the differences between the barotropic and baroclinic + ! estimates of the sea surface height due to the fluxes through each face. The + ! total tolerance for SSH is 4 times this value. The default is + ! 0.5*NK*ANGSTROM, and this should not be set less than about + ! 10^-15*MAXIMUM_DEPTH. +ETA_TOLERANCE_AUX = 0.001 ! [m] default = 1.0E-06 + ! The tolerance for free-surface height discrepancies between the barotropic + ! solution and the sum of the layer thicknesses when calculating the auxiliary + ! corrected velocities. By default, this is the same as ETA_TOLERANCE, but can + ! be made larger for efficiency. + +! === module MOM_CoriolisAdv === +CORIOLIS_EN_DIS = True ! [Boolean] default = False + ! If true, two estimates of the thickness fluxes are used to estimate the + ! Coriolis term, and the one that dissipates energy relative to the other one is + ! used. +BOUND_CORIOLIS = True ! [Boolean] default = False + ! If true, the Coriolis terms at u-points are bounded by the four estimates of + ! (f+rv)v from the four neighboring v-points, and similarly at v-points. This + ! option is always effectively false with CORIOLIS_EN_DIS defined and + ! CORIOLIS_SCHEME set to SADOURNY75_ENERGY. + +! === module MOM_PressureForce === + +! === module MOM_PressureForce_AFV === + +! === module MOM_hor_visc === +BIHARMONIC = False ! [Boolean] default = True + ! If true, use a biharmonic horizontal viscosity. BIHARMONIC may be used with + ! LAPLACIAN. + +! === module MOM_vert_friction === +HARMONIC_VISC = True ! [Boolean] default = False + ! If true, use the harmonic mean thicknesses for calculating the vertical + ! viscosity. +HMIX_FIXED = 0.01 ! [m] + ! The prescribed depth over which the near-surface viscosity and diffusivity are + ! elevated when the bulk mixed layer is not used. +KVML = 0.0 ! [m2 s-1] default = 1.0E-04 + ! The kinematic viscosity in the mixed layer. A typical value is ~1e-2 m2 s-1. + ! KVML is not used if BULKMIXEDLAYER is true. The default is set by KV. +MAXVEL = 6.0 ! [m s-1] default = 3.0E+08 + ! The maximum velocity allowed before the velocity components are truncated. +VEL_UNDERFLOW = 1.0E-30 ! [m s-1] default = 0.0 + ! A negligibly small velocity magnitude below which velocity components are set + ! to 0. A reasonable value might be 1e-30 m/s, which is less than an Angstrom + ! divided by the age of the universe. + +! === module MOM_diagnostics === + +! === module MOM_diabatic_driver === +! The following parameters are used for diabatic processes. + +! === module MOM_CVMix_KPP === +! This is the MOM wrapper to CVMix:KPP +! See http://cvmix.github.io/ +USE_KPP = True ! [Boolean] default = False + ! If true, turns on the [CVMix] KPP scheme of Large et al., 1994, to calculate + ! diffusivities and non-local transport in the OBL. +KPP% +INTERP_TYPE = "cubic" ! default = "quadratic" + ! Type of interpolation to determine the OBL depth. + ! Allowed types are: linear, quadratic, cubic. +NLT_SHAPE = "PARABOLIC" ! default = "CVMix" + ! MOM6 method to set nonlocal transport profile. Over-rides the result from + ! CVMix. Allowed values are: + ! CVMix - Uses the profiles from CVMix specified by MATCH_TECHNIQUE + ! LINEAR - A linear profile, 1-sigma + ! PARABOLIC - A parablic profile, (1-sigma)^2 + ! CUBIC - A cubic profile, (1-sigma)^2(1+2*sigma) + ! CUBIC_LMD - The original KPP profile +%KPP + +! === module MOM_set_diffusivity === +ML_RADIATION = True ! [Boolean] default = False + ! If true, allow a fraction of TKE available from wind work to penetrate below + ! the base of the mixed layer with a vertical decay scale determined by the + ! minimum of: (1) The depth of the mixed layer, (2) an Ekman length scale. +TKE_DECAY = 10.0 ! [nondim] default = 2.5 + ! The ratio of the natural Ekman depth to the TKE decay scale. +ML_OMEGA_FRAC = 1.0 ! [nondim] default = 0.0 + ! When setting the decay scale for turbulence, use this fraction of the absolute + ! rotation rate blended with the local value of f, as sqrt((1-of)*f^2 + + ! of*4*omega^2). + +! === module MOM_bkgnd_mixing === +! Adding static vertical background mixing coefficients +KD = 2.0E-05 ! [m2 s-1] default = 0.0 + ! The background diapycnal diffusivity of density in the interior. Zero or the + ! molecular value, ~1e-7 m2 s-1, may be used. +KD_MIN = 2.0E-06 ! [m2 s-1] default = 2.0E-07 + ! The minimum diapycnal diffusivity. + +! === module MOM_kappa_shear === +! Parameterization of shear-driven turbulence following Jackson, Hallberg and Legg, JPO 2008 +USE_JACKSON_PARAM = True ! [Boolean] default = False + ! If true, use the Jackson-Hallberg-Legg (JPO 2008) shear mixing + ! parameterization. +MAX_RINO_IT = 25 ! [nondim] default = 50 + ! The maximum number of iterations that may be used to estimate the Richardson + ! number driven mixing. + +! === module MOM_diabatic_aux === +! The following parameters are used for auxiliary diabatic processes. +RECLAIM_FRAZIL = False ! [Boolean] default = True + ! If true, try to use any frazil heat deficit to cool any overlying layers down + ! to the freezing point, thereby avoiding the creation of thin ice when the SST + ! is above the freezing point. +VAR_PEN_SW = True ! [Boolean] default = False + ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine + ! the e-folding depth of incoming short wave radiation. + +! === module MOM_opacity === +PEN_SW_NBANDS = 3 ! default = 1 + ! The number of bands of penetrating shortwave radiation. + +! === module MOM_tracer_advect === + +! === module MOM_tracer_hor_diff === +KHTR = 600.0 ! [m2 s-1] default = 0.0 + ! The background along-isopycnal tracer diffusivity. + +! === module MOM_sum_output === +MAXTRUNC = 5000 ! [truncations save_interval-1] default = 0 + ! The run will be stopped, and the day set to a very large value if the velocity + ! is truncated more than MAXTRUNC times between energy saves. Set MAXTRUNC to 0 + ! to stop if there is any truncation of velocities. +ENERGYSAVEDAYS = 10.0 ! [days] default = 1.0 + ! The interval in units of TIMEUNIT between saves of the energies of the run and + ! other globally summed diagnostics. + +! === module MOM_surface_forcing === +BUOY_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how buoyancy forcing is specified. Valid + ! options include (file), (zero), (linear), (USER), (BFB) and (NONE). +WIND_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how wind forcing is specified. Valid + ! options include (file), (2gyre), (1gyre), (gyres), (zero), and (USER). +READ_GUST_2D = False ! [Boolean] default = False + ! If true, use a 2-dimensional gustiness supplied from an input file +GUST_CONST = 0.02 ! [Pa] default = 0.0 + ! The background gustiness in the winds. + +! === module MOM_main (MOM_driver) === +DAYMAX = 1825.0 ! [days] + ! The final time of the whole simulation, in units of TIMEUNIT seconds. This + ! also sets the potential end time of the present run segment if the end time is + ! not set via ocean_solo_nml in input.nml. +RESTART_CONTROL = 0 ! default = 1 + ! An integer whose bits encode which restart files are written. Add 2 (bit 1) + ! for a time-stamped file, and odd (bit 0) for a non-time-stamped file. A + ! non-time-stamped restart file is saved at the end of the run segment for any + ! non-negative value. +RESTINT = 3650.0 ! [days] default = 0.0 + ! The interval between saves of the restart file in units of TIMEUNIT. Use 0 + ! (the default) to not save incremental restart files at all. + +! === module MOM_write_cputime === +MAXCPU = 2.88E+04 ! [wall-clock seconds] default = -1.0 + ! The maximum amount of cpu time per processor for which MOM should run before + ! saving a restart file and quitting with a return value that indicates that a + ! further run is required to complete the simulation. If automatic restarts are + ! not desired, use a negative value for MAXCPU. MAXCPU has units of wall-clock + ! seconds, so the actual CPU time used is larger by a factor of the number of + ! processors used. + +! Debugging parameters set to non-default values +U_TRUNC_FILE = "U_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to zonal + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. +V_TRUNC_FILE = "V_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to meridional + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. diff --git a/standalone/examples/single_column_MARBL/KERFIX/MOM_override b/standalone/examples/single_column_MARBL/KERFIX/MOM_override new file mode 120000 index 00000000..99c7b645 --- /dev/null +++ b/standalone/examples/single_column_MARBL/KERFIX/MOM_override @@ -0,0 +1 @@ +INPUT/KERFIX/MOM_override \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/KERFIX/MOM_override2 b/standalone/examples/single_column_MARBL/KERFIX/MOM_override2 new file mode 100644 index 00000000..94a755f0 --- /dev/null +++ b/standalone/examples/single_column_MARBL/KERFIX/MOM_override2 @@ -0,0 +1,4 @@ +! Generated by run_suite.csh +!#override DAYMAX = 365 +!#override ALE_COORDINATE_CONFIG = "FILE:INPUT/vgrid_cm4_4.nc,dz" ! 4 meter surface +!#override ALE_COORDINATE_CONFIG = "FILE:INPUT/vgrid_cm4.nc,dz" ! 2 meter surface diff --git a/standalone/examples/single_column_MARBL/KERFIX/RESTART/.gitignore b/standalone/examples/single_column_MARBL/KERFIX/RESTART/.gitignore new file mode 100644 index 00000000..d6b7ef32 --- /dev/null +++ b/standalone/examples/single_column_MARBL/KERFIX/RESTART/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/standalone/examples/single_column_MARBL/KERFIX/data_table b/standalone/examples/single_column_MARBL/KERFIX/data_table new file mode 100644 index 00000000..5bc90fee --- /dev/null +++ b/standalone/examples/single_column_MARBL/KERFIX/data_table @@ -0,0 +1,19 @@ +# gridname, fieldname_code, fieldname_file, file_name, ongrid, factor +"OCN" , "lw", "LWDN", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sw", "SWDN", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "rain", "RAIN", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "evap", "EVAP", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "taux", "TAUX", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "tauy", "TAUY", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "snow", "SNOW", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 +"OCN" , "runoff", "", "", .true., 0.0 +"OCN" , "calving", "", "", .true., 0.0 +"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_dust_flux", "SEAICE_DUST_FLUX", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_bc_flux", "SEAICE_BC_FLUX", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "nhx_dep", "NDEP_NHx", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "noy_dep", "NDEP_NOy", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "u10_sqr", "U10_SQR", "./INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 diff --git a/standalone/examples/single_column_MARBL/KERFIX/diag_table b/standalone/examples/single_column_MARBL/KERFIX/diag_table new file mode 100644 index 00000000..8f6227fa --- /dev/null +++ b/standalone/examples/single_column_MARBL/KERFIX/diag_table @@ -0,0 +1,771 @@ +"MOM Experiment" +1 1 1 0 0 0 +"prog", 1,"days",1,"days","Time" +"prog_z", 1,"days",1,"days","Time" +"surffluxes", 1,"days",1,"days","Time" +"ave_prog", 1,"days",1,"days","Time", +#"mom", 5,"days",1,"days","Time", +#"bt_mom", 5,"days",1,"days","Time", +"visc", 1,"days",1,"days","Time", +#"energy", 5,"days",1,"days","Time", +"marbl", 1,"days",1,"days","Time", +"marbl2", 1,"days",1,"days","Time", + + +#This is the field section of the diag_table. + +# Prognostic Ocean fields: +#========================= + +"ocean_model","u","u","prog","all",.false.,"none",2 +"ocean_model","v","v","prog","all",.false.,"none",2 +"ocean_model","h","h","prog","all",.false.,"none",1 +"ocean_model","e","e","prog","all",.false.,"none",2 +"ocean_model","temp","temp","prog","all",.false.,"none",2 +"ocean_model","salt","salt","prog","all",.false.,"none",2 +#"ocean_model","Rml","Rml","prog","all",.false.,"none",2 +#"ocean_model","h_ML","h_ML","prog","all",.false.,"none",2 + +#"ocean_model","RV","RV","prog","all",.false.,"none",2 +#"ocean_model","PV","PV","prog","all",.false.,"none",2 +#"ocean_model","e_D","e_D","prog","all",.false.,"none",2 + +"ocean_model","u","u","ave_prog","all",.true.,"none",2 +"ocean_model","v","v","ave_prog","all",.true.,"none",2 +#"ocean_model","h","h","ave_prog","all",.true.,"none",1 +"ocean_model","e","e","ave_prog","all",.true.,"none",2 +"ocean_model","temp","temp","ave_prog","all",.true.,"none",2 +"ocean_model","salt","salt","ave_prog","all",.true.,"none",2 +#"ocean_model","Rml","Rml","ave_prog","all",.true.,"none",2 + +# Z level output +#================ +"ocean_model_z","u","u","prog_z","all",.false.,"none",2 +"ocean_model_z","v","v","prog_z","all",.false.,"none",2 +"ocean_model_z","temp","temp","prog_z","all",.false.,"none",2 +"ocean_model_z","salt","salt","prog_z","all",.false.,"none",2 +# Auxilary Tracers: +#================== +#"ocean_model","vintage","vintage","prog","all",.false.,"none",2 +#"ocean_model","age","age","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","prog","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog","all",.false.,"none",2 +"ocean_model","O2","O2","prog","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog","all",.false.,"none",2 +"ocean_model","DON","DON","prog","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog","all",.false.,"none",2 +"ocean_model","spC","spC","prog","all",.false.,"none",2 +"ocean_model","spP","spP","prog","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","ave_prog","all",.true.,"none",2 +"ocean_model","NO3","NO3","ave_prog","all",.true.,"none",2 +"ocean_model","SiO3","SiO3","ave_prog","all",.true.,"none",2 +"ocean_model","NH4","NH4","ave_prog","all",.true.,"none",2 +"ocean_model","Fe","Fe","ave_prog","all",.true.,"none",2 +"ocean_model","Lig","Lig","ave_prog","all",.true.,"none",2 +"ocean_model","O2","O2","ave_prog","all",.true.,"none",2 +"ocean_model","DIC","DIC","ave_prog","all",.true.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","ALK","ALK","ave_prog","all",.true.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","DOC","DOC","ave_prog","all",.true.,"none",2 +"ocean_model","DON","DON","ave_prog","all",.true.,"none",2 +"ocean_model","DOP","DOP","ave_prog","all",.true.,"none",2 +"ocean_model","DOPr","DOPr","ave_prog","all",.true.,"none",2 +"ocean_model","DONr","DONr","ave_prog","all",.true.,"none",2 +"ocean_model","DOCr","DOCr","ave_prog","all",.true.,"none",2 +"ocean_model","microzooC","microzooC","ave_prog","all",.true.,"none",2 +"ocean_model","mesozooC","mesozooC","ave_prog","all",.true.,"none",2 +"ocean_model","spChl","spChl","ave_prog","all",.true.,"none",2 +"ocean_model","spC","spC","ave_prog","all",.true.,"none",2 +"ocean_model","spP","spP","ave_prog","all",.true.,"none",2 +"ocean_model","spFe","spFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoChl","coccoChl","ave_prog","all",.true.,"none",2 +"ocean_model","coccoC","coccoC","ave_prog","all",.true.,"none",2 +"ocean_model","coccoP","coccoP","ave_prog","all",.true.,"none",2 +"ocean_model","coccoFe","coccoFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","ave_prog","all",.true.,"none",2 +"ocean_model","diatChl","diatChl","ave_prog","all",.true.,"none",2 +"ocean_model","diatC","diatC","ave_prog","all",.true.,"none",2 +"ocean_model","diatP","diatP","ave_prog","all",.true.,"none",2 +"ocean_model","diatFe","diatFe","ave_prog","all",.true.,"none",2 +"ocean_model","diatSi","diatSi","ave_prog","all",.true.,"none",2 +"ocean_model","diazChl","diazChl","ave_prog","all",.true.,"none",2 +"ocean_model","diazC","diazC","ave_prog","all",.true.,"none",2 +"ocean_model","diazP","diazP","ave_prog","all",.true.,"none",2 +"ocean_model","diazFe","diazFe","ave_prog","all",.true.,"none",2 +"ocean_model","PO4","PO4","prog_z","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog_z","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog_z","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog_z","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog_z","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog_z","all",.false.,"none",2 +"ocean_model","O2","O2","prog_z","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog_z","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog_z","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog_z","all",.false.,"none",2 +"ocean_model","DON","DON","prog_z","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog_z","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog_z","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog_z","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog_z","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog_z","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog_z","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog_z","all",.false.,"none",2 +"ocean_model","spC","spC","prog_z","all",.false.,"none",2 +"ocean_model","spP","spP","prog_z","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog_z","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog_z","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog_z","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog_z","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog_z","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog_z","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog_z","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog_z","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog_z","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog_z","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog_z","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog_z","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog_z","all",.false.,"none",2 + + +# Continuity Equation Terms: +#=========================== +#"ocean_model","dhdt","dhdt","cont","all",.true.,"none",2 +"ocean_model","wd","wd","prog","all",.false.,"none",2 +#"ocean_model","uh","uh","cont","all",.true.,"none",2 +#"ocean_model","vh","vh","cont","all",.true.,"none",2 +#"ocean_model","uhGM","uhGM","cont","all",.true.,"none",2 +#"ocean_model","vhGM","vhGM","cont","all",.true.,"none",2 +#"ocean_model","uhbt","uhbt","cont","all",.true.,"none",2 +#"ocean_model","vhbt","vhbt","cont","all",.true.,"none",2 +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","KH_u","KH_u","cont","all",.true.,"none",2 +#"ocean_model","KH_v","KH_v","cont","all",.true.,"none",2 +#"ocean_model","GMwork","GMwork","cont","all",.true.,"none",2 + +# Continuity Equation Terms In Pure Potential Density Coordiantes: +#================================================================= +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 + +# +# Tracer Fluxes: +#================== +#"ocean_model","T_adx", "T_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_ady", "T_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffx","T_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffy","T_diffy","ave_prog","all",.true.,"none",2 +#"ocean_model","S_adx", "S_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_ady", "S_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffx","S_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffy","S_diffy","ave_prog","all",.true.,"none",2 + + +# Momentum Balance Terms: +#======================= +#"ocean_model","dudt","dudt","mom","all",.true.,"none",2 +#"ocean_model","dvdt","dvdt","mom","all",.true.,"none",2 +#"ocean_model","CAu","CAu","mom","all",.true.,"none",2 +#"ocean_model","CAv","CAv","mom","all",.true.,"none",2 +#"ocean_model","PFu","PFu","mom","all",.true.,"none",2 +#"ocean_model","PFv","PFv","mom","all",.true.,"none",2 +#"ocean_model","du_dt_visc","du_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","dv_dt_visc","dv_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","diffu","diffu","mom","all",.true.,"none",2 +#"ocean_model","diffv","diffv","mom","all",.true.,"none",2 +#"ocean_model","dudt_dia","dudt_dia","mom","all",.true.,"none",2 +#"ocean_model","dvdt_dia","dvdt_dia","mom","all",.true.,"none",2 +# Subterms that should not be added to a closed budget. +#"ocean_model","gKEu","gKEu","mom","all",.true.,"none",2 +#"ocean_model","gKEv","gKEv","mom","all",.true.,"none",2 +#"ocean_model","rvxu","rvxu","mom","all",.true.,"none",2 +#"ocean_model","rvxv","rvxv","mom","all",.true.,"none",2 +#"ocean_model","PFu_bc","PFu_bc","mom","all",.true.,"none",2 +#"ocean_model","PFv_bc","PFv_bc","mom","all",.true.,"none",2 + +# Barotropic Momentum Balance Terms: +# (only available with split time stepping.) +#=========================================== +#"ocean_model","PFuBT","PFuBT","bt_mom","all",.true.,"none",2 +#"ocean_model","PFvBT","PFvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CoruBT","CoruBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CorvBT","CorvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","ubtforce","ubtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","vbtforce","vbtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","u_accel_bt","u_accel_bt","bt_mom","all",.true.,"none",2 +#"ocean_model","v_accel_bt","v_accel_bt","bt_mom","all",.true.,"none",2 +# +# Viscosities and diffusivities: +#=============================== +#"ocean_model","Kd_effective","Kd_effective","visc","all",.true.,"none",2 +"ocean_model","Kd_interface","Kd_interface","visc","all",.true.,"none",2 +#"ocean_model","Ahh","Ahh","visc","all",.true.,"none",2 +#"ocean_model","Ahq","Ahq","visc","all",.true.,"none",2 +#"ocean_model","Khh","Khh","visc","all",.true.,"none",2 +#"ocean_model","Khq","Khq","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_u","bbl_thick_u","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_u","kv_bbl_u","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_v","bbl_thick_v","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_v","kv_bbl_v","visc","all",.true.,"none",2 +#"ocean_model","av_visc","av_visc","visc","all",.true.,"none",2 +#"ocean_model","au_visc","au_visc","visc","all",.true.,"none",2 +#"ocean_model","FrictWork","FrictWork","visc","all",.true.,"none",2 +"ocean_model","KPP_uStar","KPP_uStar","visc","all",.true.,"none",2 +"ocean_model","KPP_buoyFlux","KPP_buoyFlux","visc","all",.true.,"none",2 +"ocean_model","KPP_QminusSW","KPP_QminusSW","visc","all",.true.,"none",2 +"ocean_model","KPP_netSalt","KPP_netSalt","visc","all",.true.,"none",2 +"ocean_model","KPP_OBLdepth","KPP_OBLdepth","visc","all",.true.,"none",2 +"ocean_model","KPP_sigma","KPP_sigma","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkRi","KPP_BulkRi","visc","all",.true.,"none",2 +"ocean_model","KPP_Ws","KPP_Ws","visc","all",.true.,"none",2 +"ocean_model","KPP_N","KPP_N","visc","all",.true.,"none",2 +"ocean_model","KPP_N2","KPP_N2","visc","all",.true.,"none",2 +"ocean_model","KPP_Vt2","KPP_Vt2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkUz2","KPP_BulkUz2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkDrho","KPP_BulkDrho","visc","all",.true.,"none",2 +"ocean_model","KPP_Kd_in","KPP_Kd_in","visc","all",.true.,"none",2 +"ocean_model","KPP_Kheat","KPP_Kheat","visc","all",.true.,"none",2 +"ocean_model","KPP_Ksalt","KPP_Ksalt","visc","all",.true.,"none",2 +"ocean_model","KPP_Kv","KPP_Kv","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_heat","KPP_NLtransport_heat","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_salt","KPP_NLtransport_salt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dTdt","KPP_NLT_dTdt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dSdt","KPP_NLT_dSdt","visc","all",.true.,"none",2 +"ocean_model","KPP_Tsurf","KPP_Tsurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Ssurf","KPP_Ssurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Usurf","KPP_Usurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Vsurf","KPP_Vsurf","visc","all",.true.,"none",2 +"ocean_model","MLD_003","MLD_003","visc","all",.true.,"none",2 + +# +# Kinetic Energy Balance Terms: +#============================= +#"ocean_model","KE","KE","energy","all",.true.,"none",2 +#"ocean_model","dKE_dt","dKE_dt","energy","all",.true.,"none",2 +#"ocean_model","PE_to_KE","PE_to_KE","energy","all",.true.,"none",2 +#"ocean_model","KE_Coradv","KE_Coradv","energy","all",.true.,"none",2 +#"ocean_model","KE_adv","KE_adv","energy","all",.true.,"none",2 +#"ocean_model","KE_visc","KE_visc","energy","all",.true.,"none",2 +#"ocean_model","KE_horvisc","KE_horvisc","energy","all",.true.,"none",2 +#"ocean_model","KE_dia","KE_dia","energy","all",.true.,"none",2 + +# Surface Forcing: +#================= +"ocean_model","taux","taux","surffluxes","all",.true.,"none",2 +"ocean_model","tauy","tauy","surffluxes","all",.true.,"none",2 +"ocean_model","ustar","ustar","surffluxes","all",.true.,"none",2 +"ocean_model","PRCmE","PRCmE","surffluxes","all",.true.,"none",2 +"ocean_model","evap","evap","surffluxes","all",.true.,"none",2 +"ocean_model","precip","precip","surffluxes","all",.true.,"none",2 +"ocean_model","fprec","fprec","surffluxes","all",.true.,"none",2 +"ocean_model","lprec","lprec","surffluxes","all",.true.,"none",2 +"ocean_model","net_heat_surface","net_heat_surface","surffluxes","all",.true.,"none",2 +"ocean_model","SW","SW","surffluxes","all",.true.,"none",2 +"ocean_model","SW_pen","SW_pen","surffluxes","all",.true.,"none",2 +"ocean_model","LwLatSens","LwLatSens","surffluxes","all",.true.,"none",2 +#"ocean_model","p_surf","p_surf","surffluxes","all",.true.,"none",2 +#"ocean_model","salt_flux","salt_flux","surffluxes","all",.true.,"none",2 + +# MARBL diagnostics: +#=================== +"ocean_model", "rsdoabsorb", "rsdoabsorb", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_IFRAC", "ECOSYS_IFRAC", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_XKW", "ECOSYS_XKW", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_ATM_PRESS", "ECOSYS_ATM_PRESS", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_O2", "PV_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_O2", "SCHMIDT_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "O2SAT", "O2SAT", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR", "CO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR", "DCO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "pCO2SURF", "pCO2SURF", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2", "DpCO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_CO2", "PV_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_CO2", "SCHMIDT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_CO2", "FG_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH", "PH", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_CO2", "ATM_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR_ALT_CO2", "CO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR_ALT_CO2", "DCO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pCO2SURF_ALT_CO2", "pCO2SURF_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2_ALT_CO2", "DpCO2_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_ALT_CO2", "FG_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH_ALT_CO2", "PH_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_ALT_CO2", "ATM_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "IRON_FLUX", "IRON_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "DUST_FLUX", "DUST_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NOx_FLUX", "NOx_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHy_FLUX", "NHy_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHx_SURFACE_EMIS", "NHx_SURFACE_EMIS", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatcalc", "zsatcalc", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatarag", "zsatarag", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN", "O2_ZMIN", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN_DEPTH", "O2_ZMIN_DEPTH", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint", "photoC_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint_100m", "photoC_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint", "photoC_NO3_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint_100m", "photoC_NO3_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint", "DOC_prod_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint_100m", "DOC_prod_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint", "DOC_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint_100m", "DOC_remin_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint", "DOCr_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint_100m", "DOCr_remin_zint_100m", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ctot", "Jint_Ctot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ntot", "Jint_Ntot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ptot", "Jint_Ptot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Sitot", "Jint_Sitot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Fetot", "Jint_Fetot", "marbl", "all", .false., "none", 2 +#"ocean_model", "calcToFloor", "calcToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed", "calcToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed_ALT_CO2", "calcToSed_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pocToFloor", "pocToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "pocToSed", "pocToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "ponToSed", "ponToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "SedDenitrif", "SedDenitrif", "marbl", "all", .false., "none", 2 +#"ocean_model", "OtherRemin", "OtherRemin", "marbl", "all", .false., "none", 2 +"ocean_model", "popToSed", "popToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "bsiToSed", "bsiToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "dustToSed", "dustToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "pfeToSed", "pfeToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_surf", "sp_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_Cweight_avg_100m", "sp_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_surf", "sp_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_Cweight_avg_100m", "sp_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_surf", "sp_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_Cweight_avg_100m", "sp_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_surf", "sp_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_Cweight_avg_100m", "sp_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint", "photoC_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint_100m", "photoC_sp_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_sp_zint", "photoC_NO3_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint", "graze_sp_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint_100m", "graze_sp_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint", "graze_sp_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint_100m", "graze_sp_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint", "graze_sp_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint_100m", "graze_sp_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint", "graze_sp_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint_100m", "graze_sp_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint", "sp_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint_100m", "sp_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint", "sp_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint_100m", "sp_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint", "sp_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint_100m", "sp_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint", "sp_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint_100m", "sp_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_surf", "cocco_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_Cweight_avg_100m", "cocco_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_surf", "cocco_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_Cweight_avg_100m", "cocco_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_surf", "cocco_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_Cweight_avg_100m", "cocco_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_surf", "cocco_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_Cweight_avg_100m", "cocco_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint", "photoC_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint_100m", "photoC_cocco_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_cocco_zint", "photoC_NO3_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint", "cocco_CaCO3_form_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint_100m", "cocco_CaCO3_form_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint", "graze_cocco_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint_100m", "graze_cocco_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint", "graze_cocco_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint_100m", "graze_cocco_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint", "graze_cocco_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint_100m", "graze_cocco_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint", "graze_cocco_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint_100m", "graze_cocco_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint", "cocco_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint_100m", "cocco_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint", "cocco_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint_100m", "cocco_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint", "cocco_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint_100m", "cocco_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg_zint", "cocco_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg_zint_100m", "cocco_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_N_lim_surf", "diat_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_N_lim_Cweight_avg_100m", "diat_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_P_lim_surf", "diat_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_P_lim_Cweight_avg_100m", "diat_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Fe_lim_surf", "diat_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Fe_lim_Cweight_avg_100m", "diat_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_SiO3_lim_surf", "diat_SiO3_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_SiO3_lim_Cweight_avg_100m", "diat_SiO3_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_light_lim_surf", "diat_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_light_lim_Cweight_avg_100m", "diat_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat_zint", "photoC_diat_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat_zint_100m", "photoC_diat_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diat_zint", "photoC_NO3_diat_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat_zint", "graze_diat_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zint_100m", "graze_diat_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc_zint", "graze_diat_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc_zint_100m", "graze_diat_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc_zint", "graze_diat_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc_zint_100m", "graze_diat_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo_zint", "graze_diat_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo_zint_100m", "graze_diat_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "diat_loss_zint", "diat_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_zint_100m", "diat_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc_zint", "diat_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc_zint_100m", "diat_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc_zint", "diat_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc_zint_100m", "diat_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_agg_zint", "diat_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_agg_zint_100m", "diat_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_N_lim_surf", "diaz_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_N_lim_Cweight_avg_100m", "diaz_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_P_lim_surf", "diaz_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_P_lim_Cweight_avg_100m", "diaz_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Fe_lim_surf", "diaz_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Fe_lim_Cweight_avg_100m", "diaz_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_light_lim_surf", "diaz_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_light_lim_Cweight_avg_100m", "diaz_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diaz_zint", "photoC_diaz_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diaz_zint_100m", "photoC_diaz_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diaz_zint", "photoC_NO3_diaz_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zint", "graze_diaz_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zint_100m", "graze_diaz_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc_zint", "graze_diaz_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc_zint_100m", "graze_diaz_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc_zint", "graze_diaz_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc_zint_100m", "graze_diaz_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zoo_zint", "graze_diaz_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zoo_zint_100m", "graze_diaz_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "diaz_loss_zint", "diaz_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_zint_100m", "diaz_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_poc_zint", "diaz_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_poc_zint_100m", "diaz_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_doc_zint", "diaz_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_doc_zint_100m", "diaz_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_agg_zint", "diaz_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_agg_zint_100m", "diaz_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "CaCO3_form_zint", "CaCO3_form_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "CaCO3_form_zint_100m", "CaCO3_form_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_zint", "microzoo_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_zint", "mesozoo_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_zint_100m", "microzoo_loss_zint_100m", "marbl", 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"mesozoo_loss", "mesozoo_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_basal", "microzoo_loss_basal", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_basal", "mesozoo_loss_basal", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_poc", "microzoo_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_poc", "mesozoo_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_doc", "microzoo_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_doc", "mesozoo_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_microzoo", "graze_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo", "graze_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_poc", "graze_microzoo_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_poc", "graze_mesozoo_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_doc", "graze_microzoo_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_doc", "graze_mesozoo_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_zootot", "graze_microzoo_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_zootot", "graze_mesozoo_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_microzoo", "graze_microzoo_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_mesozoo", "graze_microzoo_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_microzoo", "graze_mesozoo_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_mesozoo", "graze_mesozoo_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_microzoo", "x_graze_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_mesozoo", "x_graze_mesozoo", "marbl2", "all", .false., "none", 2 +#"ocean_model", "PO4_RESTORE_TEND", "PO4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NO3_RESTORE_TEND", "NO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "SiO3_RESTORE_TEND", "SiO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NH4_RESTORE_TEND", "NH4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Fe_RESTORE_TEND", "Fe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Lig_RESTORE_TEND", "Lig_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "O2_RESTORE_TEND", "O2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_RESTORE_TEND", "DIC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_ALT_CO2_RESTORE_TEND", "DIC_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_RESTORE_TEND", "ALK_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_ALT_CO2_RESTORE_TEND", "ALK_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOC_RESTORE_TEND", "DOC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DON_RESTORE_TEND", "DON_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOP_RESTORE_TEND", "DOP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOPr_RESTORE_TEND", "DOPr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DONr_RESTORE_TEND", "DONr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOCr_RESTORE_TEND", "DOCr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "microzooC_RESTORE_TEND", "microzooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "mesozooC_RESTORE_TEND", "mesozooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spChl_RESTORE_TEND", "spChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spC_RESTORE_TEND", "spC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spP_RESTORE_TEND", "spP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spFe_RESTORE_TEND", "spFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoChl_RESTORE_TEND", "coccoChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoC_RESTORE_TEND", "coccoC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoP_RESTORE_TEND", "coccoP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoFe_RESTORE_TEND", "coccoFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoCaCO3_RESTORE_TEND", "coccoCaCO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatChl_RESTORE_TEND", "diatChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatC_RESTORE_TEND", "diatC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatP_RESTORE_TEND", "diatP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatFe_RESTORE_TEND", "diatFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatSi_RESTORE_TEND", "diatSi_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazChl_RESTORE_TEND", "diazChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazC_RESTORE_TEND", "diazC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazP_RESTORE_TEND", "diazP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazFe_RESTORE_TEND", "diazFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 + +#============================================================================================= +# +#====> This file can be used with diag_manager/v2.0a (or higher) <==== +# +# +# FORMATS FOR FILE ENTRIES (not all input values are used) +# ------------------------ +# +#"file_name", output_freq, "output_units", format, "time_units", "time_long_name", ... +# (opt) new_file_frequecy, (opt) "new_file_freq_units", "new_file_start_date" +# +# +#output_freq: > 0 output frequency in "output_units" +# = 0 output frequency every time step +# =-1 output frequency at end of run +# +#output_units = units used for output frequency +# (years, months, days, minutes, hours, seconds) +# +#time_units = units used to label the time axis +# (days, minutes, hours, seconds) +# +# +# FORMAT FOR FIELD ENTRIES (not all input values are used) +# ------------------------ +# +#"module_name", "field_name", "output_name", "file_name" "time_sampling", time_avg, "other_opts", packing +# +#time_avg = .true. or .false. +# +#packing = 1 double precision +# = 2 float +# = 4 packed 16-bit integers +# = 8 packed 1-byte (not tested?) diff --git a/standalone/examples/single_column_MARBL/KERFIX/input.nml b/standalone/examples/single_column_MARBL/KERFIX/input.nml new file mode 100644 index 00000000..01e68431 --- /dev/null +++ b/standalone/examples/single_column_MARBL/KERFIX/input.nml @@ -0,0 +1,21 @@ + &MOM_input_nml + output_directory = './', + input_filename = 'n' + restart_input_dir = 'INPUT/', + restart_output_dir = 'RESTART/', + parameter_filename = 'MOM_input', + 'MOM_override', + 'MOM_override2' / + + &diag_manager_nml + max_output_fields = 750, + / + + &ocean_solo_nml + calendar = 'NOLEAP' +/ + + &fms_nml + clock_grain = 'LOOP', + domains_stack_size = 710000, + stack_size = 0 / diff --git a/standalone/examples/single_column_MARBL/KERFIX/single_column_z.ipynb b/standalone/examples/single_column_MARBL/KERFIX/single_column_z.ipynb new file mode 100644 index 00000000..5ba27191 --- /dev/null +++ b/standalone/examples/single_column_MARBL/KERFIX/single_column_z.ipynb @@ -0,0 +1,581 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:847c08f9e6b1a07efd1e43efe9fa87aa4bed23875cde3d67a2c275d725f76697" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

      MOM6 diagnostics for KPP single column test case

      \n", + "\n", + " Results from this notebook: \n", + "1. Basic diagnostics of KPP boundary layer and prognostic fields. \n", + "\n", + "Caveats regarding this notebook:\n", + "1. This notebook is written for MOM6-examples/ocean_only/single_column_z. \n", + "\n", + "Hopes for the use of this notebook: \n", + "1. To provide a starting point to document single column model tests;\n", + "2. To illustrate a self-contained iPython notebook of use for MOM6 analysis. \n", + "\n", + "This iPython notebook was originally developed at NOAA/GFDL, and it is provided freely to the MOM6 community. GFDL scientists developing MOM6 make extensive use of Python for diagnostics. We solicit modifications/fixes that are useful to the MOM6 community." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import matplotlib.pyplot as plt # For plotting\n", + "import netCDF4 # For reading data\n", + "pylab.rcParams['figure.figsize'] = (17.0, 10.0) # Large figures\n", + "\n", + "path = '../single_column_z/'\n", + "deltaz = 'CM4'\n", + "fname_deltaz = '_CM4'\n", + "expt_name = 'single_column_z'\n", + "\n", + "dpi=200" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "visc = netCDF4.Dataset(path+'visc.nc')\n", + "for v in visc.variables: print v,\n", + "\n", + "# recall data layout is (t,z,y,x)\n", + "\n", + "time = visc.variables['Time'][:]\n", + "\n", + "# KPP boundary layer depth as function of time (metre)\n", + "h = visc.variables['KPP_OBLdepth'][:,0,0]\n", + "\n", + "# KPP tracer diffusivity as function of time and depth (m2/sec)\n", + "Kt = visc.variables['KPP_Kheat'][:,:,0,0]\n", + "\n", + "# KPP velocity diffusivity as function of time and depth (m2/sec)\n", + "Ku = visc.variables['KPP_Kv'][:,:,0,0]\n", + "\n", + "# surface (and penetrating) buoyancy flux, as used by [CVmix] KPP (m2/s3)\n", + "KPP_buoyFlux = visc.variables['KPP_buoyFlux'][:,:,0,0]\n", + "\n", + "# Temperature tendency due to non-local transport of heat, as calculated by KPP (K/s)\n", + "KPP_dTdt = visc.variables['KPP_dTdt'][:,:,0,0]\n", + "\n", + "# Saln tendency due to non-local transport of heat, as calculated by KPP (ppt/s)\n", + "KPP_dSdt = visc.variables['KPP_dSdt'][:,:,0,0]\n", + "\n", + "# friction velocity used by KPP (m/s)\n", + "KPP_uStar = visc.variables['KPP_uStar'][:,0,0]\n", + "\n", + "# Squared buoyancy frequency (1/s^2)\n", + "KPP_N2 = visc.variables['KPP_N2'][:,:,0,0]\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "xh yh zi Time nv zl xq yq Kd_interface KPP_uStar KPP_buoyFlux KPP_QminusSW KPP_netSalt KPP_OBLdepth KPP_sigma KPP_BulkRi KPP_Ws KPP_N KPP_N2 KPP_Vt2 KPP_BulkUz2 KPP_BulkDrho KPP_Kd_in KPP_Kheat KPP_Ksalt KPP_Kv KPP_NLtransport_heat KPP_NLtransport_salt KPP_dTdt KPP_dSdt KPP_Tsurf KPP_Ssurf KPP_Usurf KPP_Vsurf MLD_003 average_T1 average_T2 average_DT Time_bounds\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "prog = netCDF4.Dataset(path+'prog.nc')\n", + "for v in prog.variables: print v,\n", + " \n", + "# depth of cell interface \n", + "zi = prog.variables['zi'][:]\n", + "\n", + "# depth of cell center \n", + "zl = prog.variables['zl'][:]\n", + "\n", + "# zonal velocity as function of time and depth\n", + "u = prog.variables['u'][:,:,0,0]\n", + "\n", + "# zonal velocity as function of time and depth\n", + "v = prog.variables['v'][:,:,0,0]\n", + "\n", + "# temperature as function of time and depth \n", + "temp = prog.variables['temp'][:,:,0,0]\n", + "\n", + "\n", + "# saln as function of time and depth \n", + "saln = prog.variables['salt'][:,:,0,0]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "xq yh zl Time xh yq zi u v h e temp salt wd\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# thickness of cells \n", + "dz = zi[1:]-zi[:-1]\n", + "field = zi[1:]+dz[:]\n", + "#depth = np.ma.masked_array(data, mask=[data>400.])\n", + "depth = field \n", + "\n", + "#print dz\n", + "#print dz[1901:2000]\n", + "print zi\n", + "\n", + "figure(1)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "S = plt.plot(dz,-depth,'*',linewidth=3)\n", + "plt.ylim((-400,0))\n", + "plt.xlim((0, 50))\n", + "yticks = [-400, -350, -300, -250, -200, -150, -100, -50, 0]\n", + "ax.set_yticks(yticks)\n", + "#xticks = [0, .1, .2, .3, .4, .5]\n", + "#xticks = [0, .2, .4, .6, .8, 1.0, 1.2]\n", + "#xticks = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260]\n", + "xticks = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]\n", + "ax.set_xticks(xticks)\n", + "#plt.xlim((0, 40)\n", + "ax.set_xticks(xticks)\n", + "plt.grid()\n", + "plt.xlabel('Delta z (m)',fontsize=18)\n", + "plt.ylabel('depth (m)',fontsize=18)\n", + "plt.title(r'$\\Delta z$ for configuration '+deltaz,fontsize=20)\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[ 0.00000000e+00 2.00000000e+00 4.00000000e+00 6.00000000e+00\n", + " 8.00000000e+00 1.00100000e+01 1.20200000e+01 1.40400000e+01\n", + " 1.60600000e+01 1.81100000e+01 2.01800000e+01 2.22700000e+01\n", + " 2.44000000e+01 2.65800000e+01 2.88200000e+01 3.11200000e+01\n", + " 3.35200000e+01 3.60200000e+01 3.86400000e+01 4.14200000e+01\n", + " 4.43700000e+01 4.75400000e+01 5.09600000e+01 5.46700000e+01\n", + " 5.87400000e+01 6.32200000e+01 6.81900000e+01 7.37400000e+01\n", + " 7.99700000e+01 8.70100000e+01 9.50000000e+01 1.04110000e+02\n", + " 1.14540000e+02 1.26520000e+02 1.40320000e+02 1.56260000e+02\n", + " 1.74680000e+02 1.96000000e+02 2.20660000e+02 2.49170000e+02\n", + " 2.82080000e+02 3.20000000e+02 3.63560000e+02 4.13430000e+02\n", + " 4.70310000e+02 5.34860000e+02 6.07770000e+02 6.89640000e+02\n", + " 7.81040000e+02 8.82430000e+02 9.94160000e+02 1.11645000e+03\n", + " 1.24938000e+03 1.39285000e+03 1.54663000e+03 1.71033000e+03\n", + " 1.88340000e+03 2.06518000e+03 2.25494000e+03 2.45183000e+03\n", + " 2.65500000e+03 2.86358000e+03 3.07672000e+03 3.29359000e+03\n", + " 3.51346000e+03 3.73565000e+03 3.95959000e+03 4.18477000e+03\n", + " 4.41081000e+03 4.63740000e+03 4.86430000e+03 5.09137000e+03\n", + " 5.31851000e+03 5.54567000e+03 5.77283000e+03 6.00000000e+03]\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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89i3fBQUFpe4/U2XPHT16tDp27ChJatGihXr27Om5haXoP6iatL1x4/vatGmn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\nx44d670WAAAAAIC6CnjxXldPPfWUnnrqKY+xfv36yeVyKS0tzT3Wt29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CsrKyNHToUPXp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\nACDIUbwDAAAAABDk/j8pcgXb1INZ2QAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# KPP related fields \n", + "\n", + "plt.subplot(2,2,1)\n", + "data = KPP_buoyFlux\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, 16, levels=numpy.arange(-2e-7, 2e-7, 1e-8))\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-30,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Upper ocn buoy flux $(m^2/s^3)$ for '+expt_name,fontsize=18)\n", + "#plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = log10(sqrt(KPP_N2))\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, 16, levels=numpy.arange(-5,-1,.25))\n", + "#plt.contourf(time, -zi, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$Log_{10}(N)$ $(1/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = KPP_buoyFlux[:,0]\n", + "CS = plt.plot(time,data,'*',linewidth=3)\n", + "plt.xlim((0, 370))\n", + "plt.xlim((0, 370))\n", + "plt.grid()\n", + "plt.title(r'Surface buoyancy flux for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "plt.ylabel(r'Buoyancy flux $(m^2/s^3)$',fontsize=14)\n", + "\n", + "\n", + "plt.subplot(2,2,4)\n", + "CS = plt.plot(time,KPP_uStar,'*',linewidth=3)\n", + "#plt.ylim((-400,0))\n", + "plt.xlim((0, 370))\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "plt.ylabel(r'$u^{*}$ (m/s))',fontsize=14)\n", + "plt.title(r'$u^{*}$ used in KPP for '+expt_name,fontsize=18)\n", + "#ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "#ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "-c:16: RuntimeWarning: invalid value encountered in sqrt\n", + "-c:16: RuntimeWarning: divide by zero encountered in log10\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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C3fbc3FwhSZJITk72aKdfv34iKirKo42yvCt98ccff4g6deqIK664Qpw8ebJMbdnY+GPI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\ney/q16+P+vXrY/DgwTh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RtfMSI17tjIuLU+hj9eqrr2qcrzIU1ZU+9u4NDQ3F4sWL8c03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v7jjz+iQ4cOaNeuHR566CGMGzcObdu2dZmuFI3MvnMEBTVDSsoQBAWFgk9nEXuBzafTatlH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8V1AsbE\nwY18xpigrl27htDQUHz//fe4++67Bfve69ev49tvv8WoUaPw448/apfFscalS5cQHh6OwsJCKJVK\nwdLEGGOMMdO4TsCYOLiRzxjzCCEhIbhx4wbmz5+PjIwMsZPDGGOMMZFwnYAx87iRzxhjjDHGGGOM\nSQRPvMcYY4wxxhhjjEkEN/IZY4wxxhhjjDGJ4EY+Y4wxxhhjjDEmEdzIZ4wxxhhjjDHGJIIb+Ywx\nxhhjjDHGmERwI58xxhhjjDHGGJMIbuQzxhhjjDHGGGMS8f8Z4ivz1EjQ2gAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# KPP related fields \n", + "\n", + "plt.subplot(2,2,1)\n", + "data = Kt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, levels=numpy.arange(0,.5,.05))\n", + "plt.colorbar()\n", + "plt.ylim((-300,0))\n", + "plt.xlim((0, 370))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$\\kappa_{\\Theta} (m^2/s)$ for '+expt_name,fontsize=18)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = Ku\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, levels=numpy.arange(0,.5,.05))\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$\\kappa_u (m^2/s)$ for '+expt_name,fontsize=18)\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = KPP_dTdt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Non-local $\\Theta$ tendency $(K/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "plt.subplot(2,2,4)\n", + "data = KPP_dSdt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Non-local Saln tendency $(ppt/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 7, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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WAiSwdlW/eqdc/YO54aq+YY0Bi6p14sY2VvzxJSI2pZJ2rlFA5AKeMBqAiBxj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\nGDRoELp161blOA0bNkRpaanN+ImIiOo75gRmzAmIvBM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+ "text": [ + "" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Boundary layer depth and SST \n", + "\n", + "\n", + "figure(4)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "CS = plt.plot(time, -h,'-',linewidth=3)\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=18)\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=18)\n", + "plt.title(r'KPP boundary layer depth for '+expt_name,fontsize=18)\n", + "plt.xlim((0, 400))\n", + "xticks = [0, 50, 100, 150, 200, 250, 300, 350, 400]\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "plt.ylim((-300, 0))\n", + "yticks = [-300, -250, -200, -150, -100, -50, 0]\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "\n", + "figure(5)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "CS = plt.plot(time, temp[:,0],'-',linewidth=3)\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=18)\n", + "plt.ylabel('SST(C)',fontsize=18)\n", + "plt.title(r'SST for '+expt_name,fontsize=18)\n", + "plt.xlim((0, 400))\n", + "xticks = [0, 50, 100, 150, 200, 250, 300, 350, 400]\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "plt.ylim((18, 28))\n", + "yticks = [18, 20, 22, 24, 26, 28]\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 8, + "text": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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tt9/Gyy+/jM8//9zpdgiCyDqQ+z1BEIQDvPfee4iLi8Ps2bNN7pG2WLBgAcqVKwc/Pz+E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\nsd/9+sl1S5Vy3srNmGvfPTE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9QTgCiXqC0IjeYpusWeqBrOmCrzZ+5q735mRES70zol7v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\n27dvV58+fbzG4+PjtW/fPhUUFAT1PATOaqDrhDydp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mjb/4ovPef9UqUdQCwIIF5tt4qenUKSAqSowLFhTT1Y0rpRMReQr2aBPZiL0y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\nrkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# u,v,temp\n", + "\n", + "plt.subplot(2,2,1)\n", + "data = u\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, levels=numpy.arange(-.10,.11,.01))\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title('u (m/s) for expt = '+expt_name,fontsize=16)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = v\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, levels=numpy.arange(-.10,.11,.01))\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title('v (m/s) for expt = '+expt_name,fontsize=16)\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = temp\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "CS = plt.contourf(time, -zl, field.T,levels=numpy.arange(18,30,1))\n", + "plt.colorbar()\n", + "#C = plt.contour(time, -zl, temp.T, 8,linewidth=.5, colors='black')\n", + "#plt.clabel(C, inline=1, fontsize=10)\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title(r'$\\Theta$ ($\\degree$C) for expt = '+expt_name,fontsize=16)\n", + "plt.xlabel('Time (days)',fontsize=14);\n", + "\n", + "\n", + "plt.subplot(2,2,4)\n", + "data = saln[:,:]\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "CS = plt.contourf(time, -zl, field.T,levels=numpy.arange(36.3,37,.01))\n", + "#CS = plt.contourf(time, -zl, field.T)\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title(r'$S$ ($ppt$) for expt = '+expt_name,fontsize=16);\n", + "plt.xlabel('Time (days)',fontsize=14);\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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ZZ0XH//3vfzOO4wTnVkpGOVtczvletGiRSLbmzZuzyMhIduLECeF4Xl6eW13t27cvq1Gj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\nXuIc/UNEREQBij3fRERERERERD7Gnm8iIpKv6SMuxkZERER+8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"input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/NABE/INPUT b/standalone/examples/single_column_MARBL/NABE/INPUT new file mode 120000 index 00000000..3a2d233e --- /dev/null +++ b/standalone/examples/single_column_MARBL/NABE/INPUT @@ -0,0 +1 @@ +../INPUT \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/NABE/MOM_input b/standalone/examples/single_column_MARBL/NABE/MOM_input new file mode 100644 index 00000000..8172d0d1 --- /dev/null +++ b/standalone/examples/single_column_MARBL/NABE/MOM_input @@ -0,0 +1,401 @@ +! This input file provides the adjustable run-time parameters for version 6 of the Modular Ocean Model (MOM6). +! Where appropriate, parameters use usually given in MKS units. + +! This particular file is for the example in single_column/KPP. + +! This MOM_input file typically contains only the non-default values that are needed to reproduce this example. +! A full list of parameters for this example can be found in the corresponding MOM_parameter_doc.all file +! which is generated by the model at run-time. + +! === module MOM_domains === +REENTRANT_Y = True ! [Boolean] default = False + ! If true, the domain is meridionally reentrant. +NIGLOBAL = 2 ! + ! The total number of thickness grid points in the x-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NJGLOBAL = 2 ! + ! The total number of thickness grid points in the y-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NIHALO = 2 ! default = 4 + ! The number of halo points on each side in the x-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +NJHALO = 2 ! default = 4 + ! The number of halo points on each side in the y-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +! LAYOUT = 1, 1 ! + ! The processor layout that was actually used. + +! === module MOM === +SPLIT = False ! [Boolean] default = True + ! Use the split time stepping if true. +USE_REGRIDDING = True ! [Boolean] default = False + ! If True, use the ALE algorithm (regridding/remapping). If False, use the + ! layered isopycnal algorithm. +DT = 3600.0 ! [s] + ! The (baroclinic) dynamics time step. The time-step that is actually used will + ! be an integer fraction of the forcing time-step (DT_FORCING in ocean-only mode + ! or the coupling timestep in coupled mode.) +FRAZIL = True ! [Boolean] default = False + ! If true, water freezes if it gets too cold, and the accumulated heat deficit + ! is returned in the surface state. FRAZIL is only used if + ! ENABLE_THERMODYNAMICS is true. +C_P = 3925.0 ! [J kg-1 K-1] default = 3991.86795711963 + ! The heat capacity of sea water, approximated as a constant. This is only used + ! if ENABLE_THERMODYNAMICS is true. The default value is from the TEOS-10 + ! definition of conservative temperature. +SAVE_INITIAL_CONDS = True ! [Boolean] default = False + ! If true, write the initial conditions to a file given by IC_OUTPUT_FILE. + +! === module MOM_fixed_initialization === +USE_MARBL_TRACERS = True + ! "[Boolean] default = False + ! If true, use the MARBL tracer package." +! === module MARBL_tracers === +MARBL_SETTINGS_FILE = "INPUT/marbl_in" ! default = "marbl_in" +MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c211008.nc" + ! The file in which the MARBL tracers initial values can be found. +MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211020.nc" + ! The file in which the iron sediment flux forcing field can be found. +MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211020.nc" + ! The file in which the iron vent flux forcing field can be found. + + +! === module MOM_grid_init === +GRID_CONFIG = "cartesian" ! + ! A character string that determines the method for defining the horizontal + ! grid. Current options are: + ! mosaic - read the grid from a mosaic (supergrid) + ! file set by GRID_FILE. + ! cartesian - use a (flat) Cartesian grid. + ! spherical - use a simple spherical grid. + ! mercator - use a Mercator spherical grid. +LENLAT = 1.0 ! [degrees] + ! The latitudinal or y-direction length of the domain. +LENLON = 1.0 ! [degrees] + ! The longitudinal or x-direction length of the domain. +TOPO_CONFIG = "file" ! + ! This specifies how bathymetry is specified: + ! file - read bathymetric information from the file + ! specified by (TOPO_FILE). + ! flat - flat bottom set to MAXIMUM_DEPTH. + ! bowl - an analytically specified bowl-shaped basin + ! ranging between MAXIMUM_DEPTH and MINIMUM_DEPTH. + ! spoon - a similar shape to 'bowl', but with an vertical + ! wall at the southern face. + ! halfpipe - a zonally uniform channel with a half-sine + ! profile in the meridional direction. + ! bbuilder - build topography from list of functions. + ! benchmark - use the benchmark test case topography. + ! Neverworld - use the Neverworld test case topography. + ! DOME - use a slope and channel configuration for the + ! DOME sill-overflow test case. + ! ISOMIP - use a slope and channel configuration for the + ! ISOMIP test case. + ! DOME2D - use a shelf and slope configuration for the + ! DOME2D gravity current/overflow test case. + ! Kelvin - flat but with rotated land mask. + ! seamount - Gaussian bump for spontaneous motion test case. + ! dumbbell - Sloshing channel with reservoirs on both ends. + ! shelfwave - exponential slope for shelfwave test case. + ! Phillips - ACC-like idealized topography used in the Phillips config. + ! dense - Denmark Strait-like dense water formation and overflow. + ! USER - call a user modified routine. +TOPO_FILE = "TOPO_FILE_COLUMN.nc" ! default = "topog.nc" + ! The file from which the bathymetry is read. + +MAXIMUM_DEPTH = 6000.0 + ! "[m] + ! The maximum depth of the ocean." + +MINIMUM_DEPTH = 9.5 + ! "[m] default = 0.0 + ! If MASKING_DEPTH is unspecified, then anything shallower than + ! MINIMUM_DEPTH is assumed to be land and all fluxes are masked out. + ! If MASKING_DEPTH is specified, then all depths shallower than + ! MINIMUM_DEPTH but depper than MASKING_DEPTH are rounded to + ! MINIMUM_DEPTH." + +MASKING_DEPTH = 0.0 + ! "[m] default = -9999.0 + ! The depth below which to mask points as land points, for which all + ! fluxes are zeroed out. MASKING_DEPTH is ignored if negative." +ROTATION = "betaplane" ! default = "2omegasinlat" + ! This specifies how the Coriolis parameter is specified: + ! 2omegasinlat - Use twice the planetary rotation rate + ! times the sine of latitude. + ! betaplane - Use a beta-plane or f-plane. + ! USER - call a user modified routine. + +! === module MOM_verticalGrid === +! Parameters providing information about the vertical grid. +NK =65 ! [nondim] + ! The number of model layers. + +! === module MOM_EOS === + +! === module MOM_tracer_flow_control === + +! === module MOM_coord_initialization === +REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" + ! Coordinate mode for vertical regridding. Choose among the following + ! possibilities: LAYER - Isopycnal or stacked shallow water layers + ! ZSTAR, Z* - stretched geopotential z* + ! SIGMA_SHELF_ZSTAR - stretched geopotential z* ignoring shelf + ! SIGMA - terrain following coordinates + ! RHO - continuous isopycnal + ! HYCOM1 - HyCOM-like hybrid coordinate + ! SLIGHT - stretched coordinates above continuous isopycnal + ! ADAPTIVE - optimize for smooth neutral density surfaces +ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_65L_20200626.nc,dz" ! default = "UNIFORM" + ! Determines how to specify the coordinate resolution. Valid options are: + ! PARAM - use the vector-parameter ALE_RESOLUTION + ! UNIFORM[:N] - uniformly distributed + ! FILE:string - read from a file. The string specifies + ! the filename and variable name, separated + ! by a comma or space, e.g. FILE:lev.nc,dz + ! or FILE:lev.nc,interfaces=zw + ! WOA09[:N] - the WOA09 vertical grid (approximately) + ! FNC1:string - FNC1:dz_min,H_total,power,precision + ! HYBRID:string - read from a file. The string specifies + ! the filename and two variable names, separated + ! by a comma or space, for sigma-2 and dz. e.g. + ! HYBRID:vgrid.nc,sigma2,dz +!ALE_RESOLUTION = 4*2.0, 2*2.01, 2*2.02, 2.05, 2.07, 2.09, 2.13, 2.18, 2.24, 2.3, 2.4, 2.5, 2.62, 2.78, 2.95, 3.17, 3.42, 3.71, 4.07, 4.48, 4.97, 5.55, 6.23, 7.04, 7.99, 9.11, 10.43, 11.98, 13.8, 15.94, 18.42, 21.32, 24.66, 28.51, 32.91, 37.92, 43.56, 49.87, 56.88, 64.55, 72.91, 81.87, 91.4, 101.39, 111.73, 122.29, 132.93, 143.47, 153.78, 163.7, 173.07, 181.78, 189.76, 196.89, 203.17, 208.58, 213.14, 216.87, 219.87, 222.19, 223.94, 225.18, 226.04, 226.59, 226.9, 227.07, 227.14, 2*227.16, 227.17 ! [m] + ! The distribution of vertical resolution for the target + ! grid used for Eulerian-like coordinates. For example, + ! in z-coordinate mode, the parameter is a list of level + ! thicknesses (in m). In sigma-coordinate mode, the list + ! is of non-dimensional fractions of the water column. + +! === module MOM_state_initialization === +INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False + ! If true, initialize the layer thicknesses, temperatures, and salinities from a + ! Z-space file on a latitude-longitude grid. + +! === module MOM_initialize_layers_from_Z === +TEMP_SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" + ! The name of the z-space input file used to initialize temperatures (T) and + ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and + ! SALT_Z_INIT_FILE must be set. +Z_INIT_FILE_PTEMP_VAR = "Temp" ! default = "ptemp" + ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. +Z_INIT_FILE_SALT_VAR = "Salt" ! default = "salt" + ! The name of the salinity variable in SALT_Z_INIT_FILE. +Z_INIT_HOMOGENIZE = True ! [Boolean] default = False + ! If True, then horizontally homogenize the interpolated initial conditions. +Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False + ! If True, then remap straight to model coordinate from file. + +! === module MOM_diag_mediator === + +! === module MOM_lateral_mixing_coeffs === + +! === module MOM_set_visc === +HBBL = 10.0 ! [m] + ! The thickness of a bottom boundary layer with a viscosity of KVBBL if + ! BOTTOMDRAGLAW is not defined, or the thickness over which near-bottom + ! velocities are averaged for the drag law if BOTTOMDRAGLAW is defined but + ! LINEAR_DRAG is not. +CDRAG = 0.0 ! [nondim] default = 0.003 + ! CDRAG is the drag coefficient relating the magnitude of the velocity field to + ! the bottom stress. CDRAG is only used if BOTTOMDRAGLAW is defined. +DRAG_BG_VEL = 0.1 ! [m s-1] default = 0.0 + ! DRAG_BG_VEL is either the assumed bottom velocity (with LINEAR_DRAG) or an + ! unresolved velocity that is combined with the resolved velocity to estimate + ! the velocity magnitude. DRAG_BG_VEL is only used when BOTTOMDRAGLAW is + ! defined. +BBL_THICK_MIN = 0.1 ! [m] default = 0.0 + ! The minimum bottom boundary layer thickness that can be used with + ! BOTTOMDRAGLAW. This might be Kv/(cdrag*drag_bg_vel) to give Kv as the minimum + ! near-bottom viscosity. +KV = 1.0E-04 ! [m2 s-1] + ! The background kinematic viscosity in the interior. The molecular value, ~1e-6 + ! m2 s-1, may be used. + +! === module MOM_thickness_diffuse === + +! === module MOM_dynamics_unsplit === + +! === module MOM_continuity === + +! === module MOM_continuity_PPM === +UPWIND_1ST_CONTINUITY = True ! [Boolean] default = False + ! If true, CONTINUITY_PPM becomes a 1st-order upwind continuity solver. This + ! scheme is highly diffusive but may be useful for debugging or in single-column + ! mode where its minimal stencil is useful. +ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.75E-09 + ! The tolerance for the differences between the barotropic and baroclinic + ! estimates of the sea surface height due to the fluxes through each face. The + ! total tolerance for SSH is 4 times this value. The default is + ! 0.5*NK*ANGSTROM, and this should not be set less than about + ! 10^-15*MAXIMUM_DEPTH. +ETA_TOLERANCE_AUX = 0.001 ! [m] default = 1.0E-06 + ! The tolerance for free-surface height discrepancies between the barotropic + ! solution and the sum of the layer thicknesses when calculating the auxiliary + ! corrected velocities. By default, this is the same as ETA_TOLERANCE, but can + ! be made larger for efficiency. + +! === module MOM_CoriolisAdv === +CORIOLIS_EN_DIS = True ! [Boolean] default = False + ! If true, two estimates of the thickness fluxes are used to estimate the + ! Coriolis term, and the one that dissipates energy relative to the other one is + ! used. +BOUND_CORIOLIS = True ! [Boolean] default = False + ! If true, the Coriolis terms at u-points are bounded by the four estimates of + ! (f+rv)v from the four neighboring v-points, and similarly at v-points. This + ! option is always effectively false with CORIOLIS_EN_DIS defined and + ! CORIOLIS_SCHEME set to SADOURNY75_ENERGY. + +! === module MOM_PressureForce === + +! === module MOM_PressureForce_AFV === + +! === module MOM_hor_visc === +BIHARMONIC = False ! [Boolean] default = True + ! If true, use a biharmonic horizontal viscosity. BIHARMONIC may be used with + ! LAPLACIAN. + +! === module MOM_vert_friction === +HARMONIC_VISC = True ! [Boolean] default = False + ! If true, use the harmonic mean thicknesses for calculating the vertical + ! viscosity. +HMIX_FIXED = 0.01 ! [m] + ! The prescribed depth over which the near-surface viscosity and diffusivity are + ! elevated when the bulk mixed layer is not used. +KVML = 0.0 ! [m2 s-1] default = 1.0E-04 + ! The kinematic viscosity in the mixed layer. A typical value is ~1e-2 m2 s-1. + ! KVML is not used if BULKMIXEDLAYER is true. The default is set by KV. +MAXVEL = 6.0 ! [m s-1] default = 3.0E+08 + ! The maximum velocity allowed before the velocity components are truncated. +VEL_UNDERFLOW = 1.0E-30 ! [m s-1] default = 0.0 + ! A negligibly small velocity magnitude below which velocity components are set + ! to 0. A reasonable value might be 1e-30 m/s, which is less than an Angstrom + ! divided by the age of the universe. + +! === module MOM_diagnostics === + +! === module MOM_diabatic_driver === +! The following parameters are used for diabatic processes. + +! === module MOM_CVMix_KPP === +! This is the MOM wrapper to CVMix:KPP +! See http://cvmix.github.io/ +USE_KPP = True ! [Boolean] default = False + ! If true, turns on the [CVMix] KPP scheme of Large et al., 1994, to calculate + ! diffusivities and non-local transport in the OBL. +KPP% +INTERP_TYPE = "cubic" ! default = "quadratic" + ! Type of interpolation to determine the OBL depth. + ! Allowed types are: linear, quadratic, cubic. +NLT_SHAPE = "PARABOLIC" ! default = "CVMix" + ! MOM6 method to set nonlocal transport profile. Over-rides the result from + ! CVMix. Allowed values are: + ! CVMix - Uses the profiles from CVMix specified by MATCH_TECHNIQUE + ! LINEAR - A linear profile, 1-sigma + ! PARABOLIC - A parablic profile, (1-sigma)^2 + ! CUBIC - A cubic profile, (1-sigma)^2(1+2*sigma) + ! CUBIC_LMD - The original KPP profile +%KPP + +! === module MOM_set_diffusivity === +ML_RADIATION = True ! [Boolean] default = False + ! If true, allow a fraction of TKE available from wind work to penetrate below + ! the base of the mixed layer with a vertical decay scale determined by the + ! minimum of: (1) The depth of the mixed layer, (2) an Ekman length scale. +TKE_DECAY = 10.0 ! [nondim] default = 2.5 + ! The ratio of the natural Ekman depth to the TKE decay scale. +ML_OMEGA_FRAC = 1.0 ! [nondim] default = 0.0 + ! When setting the decay scale for turbulence, use this fraction of the absolute + ! rotation rate blended with the local value of f, as sqrt((1-of)*f^2 + + ! of*4*omega^2). + +! === module MOM_bkgnd_mixing === +! Adding static vertical background mixing coefficients +KD = 2.0E-05 ! [m2 s-1] default = 0.0 + ! The background diapycnal diffusivity of density in the interior. Zero or the + ! molecular value, ~1e-7 m2 s-1, may be used. +KD_MIN = 2.0E-06 ! [m2 s-1] default = 2.0E-07 + ! The minimum diapycnal diffusivity. + +! === module MOM_kappa_shear === +! Parameterization of shear-driven turbulence following Jackson, Hallberg and Legg, JPO 2008 +USE_JACKSON_PARAM = True ! [Boolean] default = False + ! If true, use the Jackson-Hallberg-Legg (JPO 2008) shear mixing + ! parameterization. +MAX_RINO_IT = 25 ! [nondim] default = 50 + ! The maximum number of iterations that may be used to estimate the Richardson + ! number driven mixing. + +! === module MOM_diabatic_aux === +! The following parameters are used for auxiliary diabatic processes. +RECLAIM_FRAZIL = False ! [Boolean] default = True + ! If true, try to use any frazil heat deficit to cool any overlying layers down + ! to the freezing point, thereby avoiding the creation of thin ice when the SST + ! is above the freezing point. +VAR_PEN_SW = True ! [Boolean] default = False + ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine + ! the e-folding depth of incoming short wave radiation. + +! === module MOM_opacity === +PEN_SW_NBANDS = 3 ! default = 1 + ! The number of bands of penetrating shortwave radiation. + +! === module MOM_tracer_advect === + +! === module MOM_tracer_hor_diff === +KHTR = 600.0 ! [m2 s-1] default = 0.0 + ! The background along-isopycnal tracer diffusivity. + +! === module MOM_sum_output === +MAXTRUNC = 5000 ! [truncations save_interval-1] default = 0 + ! The run will be stopped, and the day set to a very large value if the velocity + ! is truncated more than MAXTRUNC times between energy saves. Set MAXTRUNC to 0 + ! to stop if there is any truncation of velocities. +ENERGYSAVEDAYS = 10.0 ! [days] default = 1.0 + ! The interval in units of TIMEUNIT between saves of the energies of the run and + ! other globally summed diagnostics. + +! === module MOM_surface_forcing === +BUOY_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how buoyancy forcing is specified. Valid + ! options include (file), (zero), (linear), (USER), (BFB) and (NONE). +WIND_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how wind forcing is specified. Valid + ! options include (file), (2gyre), (1gyre), (gyres), (zero), and (USER). +READ_GUST_2D = False ! [Boolean] default = False + ! If true, use a 2-dimensional gustiness supplied from an input file +GUST_CONST = 0.02 ! [Pa] default = 0.0 + ! The background gustiness in the winds. + +! === module MOM_main (MOM_driver) === +DAYMAX = 1825.0 ! [days] + ! The final time of the whole simulation, in units of TIMEUNIT seconds. This + ! also sets the potential end time of the present run segment if the end time is + ! not set via ocean_solo_nml in input.nml. +RESTART_CONTROL = 0 ! default = 1 + ! An integer whose bits encode which restart files are written. Add 2 (bit 1) + ! for a time-stamped file, and odd (bit 0) for a non-time-stamped file. A + ! non-time-stamped restart file is saved at the end of the run segment for any + ! non-negative value. +RESTINT = 3650.0 ! [days] default = 0.0 + ! The interval between saves of the restart file in units of TIMEUNIT. Use 0 + ! (the default) to not save incremental restart files at all. + +! === module MOM_write_cputime === +MAXCPU = 2.88E+04 ! [wall-clock seconds] default = -1.0 + ! The maximum amount of cpu time per processor for which MOM should run before + ! saving a restart file and quitting with a return value that indicates that a + ! further run is required to complete the simulation. If automatic restarts are + ! not desired, use a negative value for MAXCPU. MAXCPU has units of wall-clock + ! seconds, so the actual CPU time used is larger by a factor of the number of + ! processors used. + +! Debugging parameters set to non-default values +U_TRUNC_FILE = "U_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to zonal + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. +V_TRUNC_FILE = "V_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to meridional + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. diff --git a/standalone/examples/single_column_MARBL/NABE/MOM_override b/standalone/examples/single_column_MARBL/NABE/MOM_override new file mode 120000 index 00000000..774371ab --- /dev/null +++ b/standalone/examples/single_column_MARBL/NABE/MOM_override @@ -0,0 +1 @@ +INPUT/NABE/MOM_override \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/NABE/MOM_override2 b/standalone/examples/single_column_MARBL/NABE/MOM_override2 new file mode 100644 index 00000000..94a755f0 --- /dev/null +++ b/standalone/examples/single_column_MARBL/NABE/MOM_override2 @@ -0,0 +1,4 @@ +! Generated by run_suite.csh +!#override DAYMAX = 365 +!#override ALE_COORDINATE_CONFIG = "FILE:INPUT/vgrid_cm4_4.nc,dz" ! 4 meter surface +!#override ALE_COORDINATE_CONFIG = "FILE:INPUT/vgrid_cm4.nc,dz" ! 2 meter surface diff --git a/standalone/examples/single_column_MARBL/NABE/RESTART/.gitignore b/standalone/examples/single_column_MARBL/NABE/RESTART/.gitignore new file mode 100644 index 00000000..d6b7ef32 --- /dev/null +++ b/standalone/examples/single_column_MARBL/NABE/RESTART/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/standalone/examples/single_column_MARBL/NABE/data_table b/standalone/examples/single_column_MARBL/NABE/data_table new file mode 100644 index 00000000..d50bebf8 --- /dev/null +++ b/standalone/examples/single_column_MARBL/NABE/data_table @@ -0,0 +1,19 @@ +# gridname, fieldname_code, fieldname_file, file_name, ongrid, factor +"OCN" , "lw", "LWDN", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sw", "SWDN", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "rain", "RAIN", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "evap", "EVAP", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "taux", "TAUX", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "tauy", "TAUY", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "snow", "SNOW", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 +"OCN" , "runoff", "", "", .true., 0.0 +"OCN" , "calving", "", "", .true., 0.0 +"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_dust_flux", "SEAICE_DUST_FLUX", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_bc_flux", "SEAICE_BC_FLUX", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "nhx_dep", "NDEP_NHx", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "noy_dep", "NDEP_NOy", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "u10_sqr", "U10_SQR", "./INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 diff --git a/standalone/examples/single_column_MARBL/NABE/diag_table b/standalone/examples/single_column_MARBL/NABE/diag_table new file mode 100644 index 00000000..8f6227fa --- /dev/null +++ b/standalone/examples/single_column_MARBL/NABE/diag_table @@ -0,0 +1,771 @@ +"MOM Experiment" +1 1 1 0 0 0 +"prog", 1,"days",1,"days","Time" +"prog_z", 1,"days",1,"days","Time" +"surffluxes", 1,"days",1,"days","Time" +"ave_prog", 1,"days",1,"days","Time", +#"mom", 5,"days",1,"days","Time", +#"bt_mom", 5,"days",1,"days","Time", +"visc", 1,"days",1,"days","Time", +#"energy", 5,"days",1,"days","Time", +"marbl", 1,"days",1,"days","Time", +"marbl2", 1,"days",1,"days","Time", + + +#This is the field section of the diag_table. + +# Prognostic Ocean fields: +#========================= + +"ocean_model","u","u","prog","all",.false.,"none",2 +"ocean_model","v","v","prog","all",.false.,"none",2 +"ocean_model","h","h","prog","all",.false.,"none",1 +"ocean_model","e","e","prog","all",.false.,"none",2 +"ocean_model","temp","temp","prog","all",.false.,"none",2 +"ocean_model","salt","salt","prog","all",.false.,"none",2 +#"ocean_model","Rml","Rml","prog","all",.false.,"none",2 +#"ocean_model","h_ML","h_ML","prog","all",.false.,"none",2 + +#"ocean_model","RV","RV","prog","all",.false.,"none",2 +#"ocean_model","PV","PV","prog","all",.false.,"none",2 +#"ocean_model","e_D","e_D","prog","all",.false.,"none",2 + +"ocean_model","u","u","ave_prog","all",.true.,"none",2 +"ocean_model","v","v","ave_prog","all",.true.,"none",2 +#"ocean_model","h","h","ave_prog","all",.true.,"none",1 +"ocean_model","e","e","ave_prog","all",.true.,"none",2 +"ocean_model","temp","temp","ave_prog","all",.true.,"none",2 +"ocean_model","salt","salt","ave_prog","all",.true.,"none",2 +#"ocean_model","Rml","Rml","ave_prog","all",.true.,"none",2 + +# Z level output +#================ +"ocean_model_z","u","u","prog_z","all",.false.,"none",2 +"ocean_model_z","v","v","prog_z","all",.false.,"none",2 +"ocean_model_z","temp","temp","prog_z","all",.false.,"none",2 +"ocean_model_z","salt","salt","prog_z","all",.false.,"none",2 +# Auxilary Tracers: +#================== +#"ocean_model","vintage","vintage","prog","all",.false.,"none",2 +#"ocean_model","age","age","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","prog","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog","all",.false.,"none",2 +"ocean_model","O2","O2","prog","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog","all",.false.,"none",2 +"ocean_model","DON","DON","prog","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog","all",.false.,"none",2 +"ocean_model","spC","spC","prog","all",.false.,"none",2 +"ocean_model","spP","spP","prog","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","ave_prog","all",.true.,"none",2 +"ocean_model","NO3","NO3","ave_prog","all",.true.,"none",2 +"ocean_model","SiO3","SiO3","ave_prog","all",.true.,"none",2 +"ocean_model","NH4","NH4","ave_prog","all",.true.,"none",2 +"ocean_model","Fe","Fe","ave_prog","all",.true.,"none",2 +"ocean_model","Lig","Lig","ave_prog","all",.true.,"none",2 +"ocean_model","O2","O2","ave_prog","all",.true.,"none",2 +"ocean_model","DIC","DIC","ave_prog","all",.true.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","ALK","ALK","ave_prog","all",.true.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","DOC","DOC","ave_prog","all",.true.,"none",2 +"ocean_model","DON","DON","ave_prog","all",.true.,"none",2 +"ocean_model","DOP","DOP","ave_prog","all",.true.,"none",2 +"ocean_model","DOPr","DOPr","ave_prog","all",.true.,"none",2 +"ocean_model","DONr","DONr","ave_prog","all",.true.,"none",2 +"ocean_model","DOCr","DOCr","ave_prog","all",.true.,"none",2 +"ocean_model","microzooC","microzooC","ave_prog","all",.true.,"none",2 +"ocean_model","mesozooC","mesozooC","ave_prog","all",.true.,"none",2 +"ocean_model","spChl","spChl","ave_prog","all",.true.,"none",2 +"ocean_model","spC","spC","ave_prog","all",.true.,"none",2 +"ocean_model","spP","spP","ave_prog","all",.true.,"none",2 +"ocean_model","spFe","spFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoChl","coccoChl","ave_prog","all",.true.,"none",2 +"ocean_model","coccoC","coccoC","ave_prog","all",.true.,"none",2 +"ocean_model","coccoP","coccoP","ave_prog","all",.true.,"none",2 +"ocean_model","coccoFe","coccoFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","ave_prog","all",.true.,"none",2 +"ocean_model","diatChl","diatChl","ave_prog","all",.true.,"none",2 +"ocean_model","diatC","diatC","ave_prog","all",.true.,"none",2 +"ocean_model","diatP","diatP","ave_prog","all",.true.,"none",2 +"ocean_model","diatFe","diatFe","ave_prog","all",.true.,"none",2 +"ocean_model","diatSi","diatSi","ave_prog","all",.true.,"none",2 +"ocean_model","diazChl","diazChl","ave_prog","all",.true.,"none",2 +"ocean_model","diazC","diazC","ave_prog","all",.true.,"none",2 +"ocean_model","diazP","diazP","ave_prog","all",.true.,"none",2 +"ocean_model","diazFe","diazFe","ave_prog","all",.true.,"none",2 +"ocean_model","PO4","PO4","prog_z","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog_z","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog_z","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog_z","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog_z","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog_z","all",.false.,"none",2 +"ocean_model","O2","O2","prog_z","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog_z","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog_z","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog_z","all",.false.,"none",2 +"ocean_model","DON","DON","prog_z","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog_z","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog_z","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog_z","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog_z","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog_z","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog_z","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog_z","all",.false.,"none",2 +"ocean_model","spC","spC","prog_z","all",.false.,"none",2 +"ocean_model","spP","spP","prog_z","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog_z","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog_z","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog_z","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog_z","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog_z","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog_z","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog_z","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog_z","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog_z","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog_z","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog_z","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog_z","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog_z","all",.false.,"none",2 + + +# Continuity Equation Terms: +#=========================== +#"ocean_model","dhdt","dhdt","cont","all",.true.,"none",2 +"ocean_model","wd","wd","prog","all",.false.,"none",2 +#"ocean_model","uh","uh","cont","all",.true.,"none",2 +#"ocean_model","vh","vh","cont","all",.true.,"none",2 +#"ocean_model","uhGM","uhGM","cont","all",.true.,"none",2 +#"ocean_model","vhGM","vhGM","cont","all",.true.,"none",2 +#"ocean_model","uhbt","uhbt","cont","all",.true.,"none",2 +#"ocean_model","vhbt","vhbt","cont","all",.true.,"none",2 +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","KH_u","KH_u","cont","all",.true.,"none",2 +#"ocean_model","KH_v","KH_v","cont","all",.true.,"none",2 +#"ocean_model","GMwork","GMwork","cont","all",.true.,"none",2 + +# Continuity Equation Terms In Pure Potential Density Coordiantes: +#================================================================= +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 + +# +# Tracer Fluxes: +#================== +#"ocean_model","T_adx", "T_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_ady", "T_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffx","T_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffy","T_diffy","ave_prog","all",.true.,"none",2 +#"ocean_model","S_adx", "S_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_ady", "S_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffx","S_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffy","S_diffy","ave_prog","all",.true.,"none",2 + + +# Momentum Balance Terms: +#======================= +#"ocean_model","dudt","dudt","mom","all",.true.,"none",2 +#"ocean_model","dvdt","dvdt","mom","all",.true.,"none",2 +#"ocean_model","CAu","CAu","mom","all",.true.,"none",2 +#"ocean_model","CAv","CAv","mom","all",.true.,"none",2 +#"ocean_model","PFu","PFu","mom","all",.true.,"none",2 +#"ocean_model","PFv","PFv","mom","all",.true.,"none",2 +#"ocean_model","du_dt_visc","du_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","dv_dt_visc","dv_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","diffu","diffu","mom","all",.true.,"none",2 +#"ocean_model","diffv","diffv","mom","all",.true.,"none",2 +#"ocean_model","dudt_dia","dudt_dia","mom","all",.true.,"none",2 +#"ocean_model","dvdt_dia","dvdt_dia","mom","all",.true.,"none",2 +# Subterms that should not be added to a closed budget. +#"ocean_model","gKEu","gKEu","mom","all",.true.,"none",2 +#"ocean_model","gKEv","gKEv","mom","all",.true.,"none",2 +#"ocean_model","rvxu","rvxu","mom","all",.true.,"none",2 +#"ocean_model","rvxv","rvxv","mom","all",.true.,"none",2 +#"ocean_model","PFu_bc","PFu_bc","mom","all",.true.,"none",2 +#"ocean_model","PFv_bc","PFv_bc","mom","all",.true.,"none",2 + +# Barotropic Momentum Balance Terms: +# (only available with split time stepping.) +#=========================================== +#"ocean_model","PFuBT","PFuBT","bt_mom","all",.true.,"none",2 +#"ocean_model","PFvBT","PFvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CoruBT","CoruBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CorvBT","CorvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","ubtforce","ubtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","vbtforce","vbtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","u_accel_bt","u_accel_bt","bt_mom","all",.true.,"none",2 +#"ocean_model","v_accel_bt","v_accel_bt","bt_mom","all",.true.,"none",2 +# +# Viscosities and diffusivities: +#=============================== +#"ocean_model","Kd_effective","Kd_effective","visc","all",.true.,"none",2 +"ocean_model","Kd_interface","Kd_interface","visc","all",.true.,"none",2 +#"ocean_model","Ahh","Ahh","visc","all",.true.,"none",2 +#"ocean_model","Ahq","Ahq","visc","all",.true.,"none",2 +#"ocean_model","Khh","Khh","visc","all",.true.,"none",2 +#"ocean_model","Khq","Khq","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_u","bbl_thick_u","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_u","kv_bbl_u","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_v","bbl_thick_v","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_v","kv_bbl_v","visc","all",.true.,"none",2 +#"ocean_model","av_visc","av_visc","visc","all",.true.,"none",2 +#"ocean_model","au_visc","au_visc","visc","all",.true.,"none",2 +#"ocean_model","FrictWork","FrictWork","visc","all",.true.,"none",2 +"ocean_model","KPP_uStar","KPP_uStar","visc","all",.true.,"none",2 +"ocean_model","KPP_buoyFlux","KPP_buoyFlux","visc","all",.true.,"none",2 +"ocean_model","KPP_QminusSW","KPP_QminusSW","visc","all",.true.,"none",2 +"ocean_model","KPP_netSalt","KPP_netSalt","visc","all",.true.,"none",2 +"ocean_model","KPP_OBLdepth","KPP_OBLdepth","visc","all",.true.,"none",2 +"ocean_model","KPP_sigma","KPP_sigma","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkRi","KPP_BulkRi","visc","all",.true.,"none",2 +"ocean_model","KPP_Ws","KPP_Ws","visc","all",.true.,"none",2 +"ocean_model","KPP_N","KPP_N","visc","all",.true.,"none",2 +"ocean_model","KPP_N2","KPP_N2","visc","all",.true.,"none",2 +"ocean_model","KPP_Vt2","KPP_Vt2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkUz2","KPP_BulkUz2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkDrho","KPP_BulkDrho","visc","all",.true.,"none",2 +"ocean_model","KPP_Kd_in","KPP_Kd_in","visc","all",.true.,"none",2 +"ocean_model","KPP_Kheat","KPP_Kheat","visc","all",.true.,"none",2 +"ocean_model","KPP_Ksalt","KPP_Ksalt","visc","all",.true.,"none",2 +"ocean_model","KPP_Kv","KPP_Kv","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_heat","KPP_NLtransport_heat","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_salt","KPP_NLtransport_salt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dTdt","KPP_NLT_dTdt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dSdt","KPP_NLT_dSdt","visc","all",.true.,"none",2 +"ocean_model","KPP_Tsurf","KPP_Tsurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Ssurf","KPP_Ssurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Usurf","KPP_Usurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Vsurf","KPP_Vsurf","visc","all",.true.,"none",2 +"ocean_model","MLD_003","MLD_003","visc","all",.true.,"none",2 + +# +# Kinetic Energy Balance Terms: +#============================= +#"ocean_model","KE","KE","energy","all",.true.,"none",2 +#"ocean_model","dKE_dt","dKE_dt","energy","all",.true.,"none",2 +#"ocean_model","PE_to_KE","PE_to_KE","energy","all",.true.,"none",2 +#"ocean_model","KE_Coradv","KE_Coradv","energy","all",.true.,"none",2 +#"ocean_model","KE_adv","KE_adv","energy","all",.true.,"none",2 +#"ocean_model","KE_visc","KE_visc","energy","all",.true.,"none",2 +#"ocean_model","KE_horvisc","KE_horvisc","energy","all",.true.,"none",2 +#"ocean_model","KE_dia","KE_dia","energy","all",.true.,"none",2 + +# Surface Forcing: +#================= +"ocean_model","taux","taux","surffluxes","all",.true.,"none",2 +"ocean_model","tauy","tauy","surffluxes","all",.true.,"none",2 +"ocean_model","ustar","ustar","surffluxes","all",.true.,"none",2 +"ocean_model","PRCmE","PRCmE","surffluxes","all",.true.,"none",2 +"ocean_model","evap","evap","surffluxes","all",.true.,"none",2 +"ocean_model","precip","precip","surffluxes","all",.true.,"none",2 +"ocean_model","fprec","fprec","surffluxes","all",.true.,"none",2 +"ocean_model","lprec","lprec","surffluxes","all",.true.,"none",2 +"ocean_model","net_heat_surface","net_heat_surface","surffluxes","all",.true.,"none",2 +"ocean_model","SW","SW","surffluxes","all",.true.,"none",2 +"ocean_model","SW_pen","SW_pen","surffluxes","all",.true.,"none",2 +"ocean_model","LwLatSens","LwLatSens","surffluxes","all",.true.,"none",2 +#"ocean_model","p_surf","p_surf","surffluxes","all",.true.,"none",2 +#"ocean_model","salt_flux","salt_flux","surffluxes","all",.true.,"none",2 + +# MARBL diagnostics: +#=================== +"ocean_model", "rsdoabsorb", "rsdoabsorb", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_IFRAC", "ECOSYS_IFRAC", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_XKW", "ECOSYS_XKW", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_ATM_PRESS", "ECOSYS_ATM_PRESS", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_O2", "PV_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_O2", "SCHMIDT_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "O2SAT", "O2SAT", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR", "CO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR", "DCO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "pCO2SURF", "pCO2SURF", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2", "DpCO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_CO2", "PV_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_CO2", "SCHMIDT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_CO2", "FG_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH", "PH", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_CO2", "ATM_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR_ALT_CO2", "CO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR_ALT_CO2", "DCO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pCO2SURF_ALT_CO2", "pCO2SURF_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2_ALT_CO2", "DpCO2_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_ALT_CO2", "FG_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH_ALT_CO2", "PH_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_ALT_CO2", "ATM_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "IRON_FLUX", "IRON_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "DUST_FLUX", "DUST_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NOx_FLUX", "NOx_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHy_FLUX", "NHy_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHx_SURFACE_EMIS", "NHx_SURFACE_EMIS", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatcalc", "zsatcalc", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatarag", "zsatarag", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN", "O2_ZMIN", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN_DEPTH", "O2_ZMIN_DEPTH", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint", "photoC_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint_100m", "photoC_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint", "photoC_NO3_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint_100m", "photoC_NO3_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint", "DOC_prod_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint_100m", "DOC_prod_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint", "DOC_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint_100m", "DOC_remin_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint", "DOCr_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint_100m", "DOCr_remin_zint_100m", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ctot", "Jint_Ctot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ntot", "Jint_Ntot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ptot", "Jint_Ptot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Sitot", "Jint_Sitot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Fetot", "Jint_Fetot", "marbl", "all", .false., "none", 2 +#"ocean_model", "calcToFloor", "calcToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed", "calcToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed_ALT_CO2", "calcToSed_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pocToFloor", "pocToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "pocToSed", "pocToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "ponToSed", "ponToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "SedDenitrif", "SedDenitrif", "marbl", "all", .false., "none", 2 +#"ocean_model", "OtherRemin", "OtherRemin", "marbl", "all", .false., "none", 2 +"ocean_model", "popToSed", "popToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "bsiToSed", "bsiToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "dustToSed", "dustToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "pfeToSed", "pfeToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_surf", "sp_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_Cweight_avg_100m", "sp_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_surf", "sp_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_Cweight_avg_100m", "sp_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_surf", "sp_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_Cweight_avg_100m", "sp_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_surf", "sp_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_Cweight_avg_100m", "sp_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint", "photoC_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint_100m", "photoC_sp_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_sp_zint", "photoC_NO3_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint", "graze_sp_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint_100m", "graze_sp_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint", "graze_sp_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint_100m", "graze_sp_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint", "graze_sp_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint_100m", "graze_sp_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint", "graze_sp_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint_100m", "graze_sp_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint", "sp_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint_100m", "sp_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint", "sp_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint_100m", "sp_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint", "sp_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint_100m", "sp_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint", "sp_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint_100m", "sp_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_surf", "cocco_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_Cweight_avg_100m", "cocco_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_surf", "cocco_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_Cweight_avg_100m", "cocco_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_surf", "cocco_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_Cweight_avg_100m", "cocco_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_surf", "cocco_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_Cweight_avg_100m", "cocco_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint", "photoC_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint_100m", "photoC_cocco_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_cocco_zint", "photoC_NO3_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint", "cocco_CaCO3_form_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint_100m", "cocco_CaCO3_form_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint", "graze_cocco_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint_100m", "graze_cocco_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint", "graze_cocco_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint_100m", "graze_cocco_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint", "graze_cocco_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint_100m", "graze_cocco_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint", "graze_cocco_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint_100m", "graze_cocco_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint", "cocco_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint_100m", "cocco_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint", "cocco_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint_100m", "cocco_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint", "cocco_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint_100m", "cocco_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg_zint", "cocco_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg_zint_100m", "cocco_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_N_lim_surf", "diat_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_N_lim_Cweight_avg_100m", "diat_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_P_lim_surf", "diat_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_P_lim_Cweight_avg_100m", "diat_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Fe_lim_surf", "diat_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Fe_lim_Cweight_avg_100m", "diat_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_SiO3_lim_surf", "diat_SiO3_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_SiO3_lim_Cweight_avg_100m", "diat_SiO3_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_light_lim_surf", "diat_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_light_lim_Cweight_avg_100m", "diat_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat_zint", "photoC_diat_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat_zint_100m", "photoC_diat_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diat_zint", "photoC_NO3_diat_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat_zint", "graze_diat_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zint_100m", "graze_diat_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc_zint", "graze_diat_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc_zint_100m", "graze_diat_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc_zint", "graze_diat_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc_zint_100m", "graze_diat_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo_zint", "graze_diat_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo_zint_100m", "graze_diat_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "diat_loss_zint", "diat_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_zint_100m", "diat_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc_zint", "diat_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc_zint_100m", "diat_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc_zint", "diat_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc_zint_100m", "diat_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_agg_zint", "diat_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_agg_zint_100m", "diat_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_N_lim_surf", "diaz_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_N_lim_Cweight_avg_100m", "diaz_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_P_lim_surf", "diaz_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_P_lim_Cweight_avg_100m", "diaz_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Fe_lim_surf", "diaz_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Fe_lim_Cweight_avg_100m", "diaz_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_light_lim_surf", "diaz_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_light_lim_Cweight_avg_100m", "diaz_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diaz_zint", "photoC_diaz_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diaz_zint_100m", "photoC_diaz_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diaz_zint", "photoC_NO3_diaz_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zint", "graze_diaz_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zint_100m", "graze_diaz_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc_zint", "graze_diaz_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc_zint_100m", "graze_diaz_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc_zint", "graze_diaz_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc_zint_100m", "graze_diaz_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zoo_zint", "graze_diaz_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zoo_zint_100m", "graze_diaz_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "diaz_loss_zint", "diaz_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_zint_100m", "diaz_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_poc_zint", 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+"ocean_model", "graze_microzoo_doc", "graze_microzoo_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_doc", "graze_mesozoo_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_zootot", "graze_microzoo_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_zootot", "graze_mesozoo_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_microzoo", "graze_microzoo_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_mesozoo", "graze_microzoo_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_microzoo", "graze_mesozoo_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_mesozoo", "graze_mesozoo_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_microzoo", "x_graze_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_mesozoo", "x_graze_mesozoo", "marbl2", "all", .false., "none", 2 +#"ocean_model", "PO4_RESTORE_TEND", "PO4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NO3_RESTORE_TEND", "NO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "SiO3_RESTORE_TEND", "SiO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NH4_RESTORE_TEND", "NH4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Fe_RESTORE_TEND", "Fe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Lig_RESTORE_TEND", "Lig_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "O2_RESTORE_TEND", "O2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_RESTORE_TEND", "DIC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_ALT_CO2_RESTORE_TEND", "DIC_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_RESTORE_TEND", "ALK_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_ALT_CO2_RESTORE_TEND", "ALK_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOC_RESTORE_TEND", "DOC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DON_RESTORE_TEND", "DON_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOP_RESTORE_TEND", "DOP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOPr_RESTORE_TEND", "DOPr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DONr_RESTORE_TEND", "DONr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOCr_RESTORE_TEND", "DOCr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "microzooC_RESTORE_TEND", "microzooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "mesozooC_RESTORE_TEND", "mesozooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spChl_RESTORE_TEND", "spChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spC_RESTORE_TEND", "spC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spP_RESTORE_TEND", "spP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spFe_RESTORE_TEND", "spFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoChl_RESTORE_TEND", "coccoChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoC_RESTORE_TEND", "coccoC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoP_RESTORE_TEND", "coccoP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoFe_RESTORE_TEND", "coccoFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoCaCO3_RESTORE_TEND", "coccoCaCO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatChl_RESTORE_TEND", "diatChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatC_RESTORE_TEND", "diatC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatP_RESTORE_TEND", "diatP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatFe_RESTORE_TEND", "diatFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatSi_RESTORE_TEND", "diatSi_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazChl_RESTORE_TEND", "diazChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazC_RESTORE_TEND", "diazC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazP_RESTORE_TEND", "diazP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazFe_RESTORE_TEND", "diazFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 + +#============================================================================================= +# +#====> This file can be used with diag_manager/v2.0a (or higher) <==== +# +# +# FORMATS FOR FILE ENTRIES (not all input values are used) +# ------------------------ +# +#"file_name", output_freq, "output_units", format, "time_units", "time_long_name", ... +# (opt) new_file_frequecy, (opt) "new_file_freq_units", "new_file_start_date" +# +# +#output_freq: > 0 output frequency in "output_units" +# = 0 output frequency every time step +# =-1 output frequency at end of run +# +#output_units = units used for output frequency +# (years, months, days, minutes, hours, seconds) +# +#time_units = units used to label the time axis +# (days, minutes, hours, seconds) +# +# +# FORMAT FOR FIELD ENTRIES (not all input values are used) +# ------------------------ +# +#"module_name", "field_name", "output_name", "file_name" "time_sampling", time_avg, "other_opts", packing +# +#time_avg = .true. or .false. +# +#packing = 1 double precision +# = 2 float +# = 4 packed 16-bit integers +# = 8 packed 1-byte (not tested?) diff --git a/standalone/examples/single_column_MARBL/NABE/input.nml b/standalone/examples/single_column_MARBL/NABE/input.nml new file mode 100644 index 00000000..01e68431 --- /dev/null +++ b/standalone/examples/single_column_MARBL/NABE/input.nml @@ -0,0 +1,21 @@ + &MOM_input_nml + output_directory = './', + input_filename = 'n' + restart_input_dir = 'INPUT/', + restart_output_dir = 'RESTART/', + parameter_filename = 'MOM_input', + 'MOM_override', + 'MOM_override2' / + + &diag_manager_nml + max_output_fields = 750, + / + + &ocean_solo_nml + calendar = 'NOLEAP' +/ + + &fms_nml + clock_grain = 'LOOP', + domains_stack_size = 710000, + stack_size = 0 / diff --git a/standalone/examples/single_column_MARBL/NABE/single_column_z.ipynb b/standalone/examples/single_column_MARBL/NABE/single_column_z.ipynb new file mode 100644 index 00000000..5ba27191 --- /dev/null +++ b/standalone/examples/single_column_MARBL/NABE/single_column_z.ipynb @@ -0,0 +1,581 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:847c08f9e6b1a07efd1e43efe9fa87aa4bed23875cde3d67a2c275d725f76697" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

      MOM6 diagnostics for KPP single column test case

      \n", + "\n", + " Results from this notebook: \n", + "1. Basic diagnostics of KPP boundary layer and prognostic fields. \n", + "\n", + "Caveats regarding this notebook:\n", + "1. This notebook is written for MOM6-examples/ocean_only/single_column_z. \n", + "\n", + "Hopes for the use of this notebook: \n", + "1. To provide a starting point to document single column model tests;\n", + "2. To illustrate a self-contained iPython notebook of use for MOM6 analysis. \n", + "\n", + "This iPython notebook was originally developed at NOAA/GFDL, and it is provided freely to the MOM6 community. GFDL scientists developing MOM6 make extensive use of Python for diagnostics. We solicit modifications/fixes that are useful to the MOM6 community." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import matplotlib.pyplot as plt # For plotting\n", + "import netCDF4 # For reading data\n", + "pylab.rcParams['figure.figsize'] = (17.0, 10.0) # Large figures\n", + "\n", + "path = '../single_column_z/'\n", + "deltaz = 'CM4'\n", + "fname_deltaz = '_CM4'\n", + "expt_name = 'single_column_z'\n", + "\n", + "dpi=200" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "visc = netCDF4.Dataset(path+'visc.nc')\n", + "for v in visc.variables: print v,\n", + "\n", + "# recall data layout is (t,z,y,x)\n", + "\n", + "time = visc.variables['Time'][:]\n", + "\n", + "# KPP boundary layer depth as function of time (metre)\n", + "h = visc.variables['KPP_OBLdepth'][:,0,0]\n", + "\n", + "# KPP tracer diffusivity as function of time and depth (m2/sec)\n", + "Kt = visc.variables['KPP_Kheat'][:,:,0,0]\n", + "\n", + "# KPP velocity diffusivity as function of time and depth (m2/sec)\n", + "Ku = visc.variables['KPP_Kv'][:,:,0,0]\n", + "\n", + "# surface (and penetrating) buoyancy flux, as used by [CVmix] KPP (m2/s3)\n", + "KPP_buoyFlux = visc.variables['KPP_buoyFlux'][:,:,0,0]\n", + "\n", + "# Temperature tendency due to non-local transport of heat, as calculated by KPP (K/s)\n", + "KPP_dTdt = visc.variables['KPP_dTdt'][:,:,0,0]\n", + "\n", + "# Saln tendency due to non-local transport of heat, as calculated by KPP (ppt/s)\n", + "KPP_dSdt = visc.variables['KPP_dSdt'][:,:,0,0]\n", + "\n", + "# friction velocity used by KPP (m/s)\n", + "KPP_uStar = visc.variables['KPP_uStar'][:,0,0]\n", + "\n", + "# Squared buoyancy frequency (1/s^2)\n", + "KPP_N2 = visc.variables['KPP_N2'][:,:,0,0]\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "xh yh zi Time nv zl xq yq Kd_interface KPP_uStar KPP_buoyFlux KPP_QminusSW KPP_netSalt KPP_OBLdepth KPP_sigma KPP_BulkRi KPP_Ws KPP_N KPP_N2 KPP_Vt2 KPP_BulkUz2 KPP_BulkDrho KPP_Kd_in KPP_Kheat KPP_Ksalt KPP_Kv KPP_NLtransport_heat KPP_NLtransport_salt KPP_dTdt KPP_dSdt KPP_Tsurf KPP_Ssurf KPP_Usurf KPP_Vsurf MLD_003 average_T1 average_T2 average_DT Time_bounds\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "prog = netCDF4.Dataset(path+'prog.nc')\n", + "for v in prog.variables: print v,\n", + " \n", + "# depth of cell interface \n", + "zi = prog.variables['zi'][:]\n", + "\n", + "# depth of cell center \n", + "zl = prog.variables['zl'][:]\n", + "\n", + "# zonal velocity as function of time and depth\n", + "u = prog.variables['u'][:,:,0,0]\n", + "\n", + "# zonal velocity as function of time and depth\n", + "v = prog.variables['v'][:,:,0,0]\n", + "\n", + "# temperature as function of time and depth \n", + "temp = prog.variables['temp'][:,:,0,0]\n", + "\n", + "\n", + "# saln as function of time and depth \n", + "saln = prog.variables['salt'][:,:,0,0]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "xq yh zl Time xh yq zi u v h e temp salt wd\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# thickness of cells \n", + "dz = zi[1:]-zi[:-1]\n", + "field = zi[1:]+dz[:]\n", + "#depth = np.ma.masked_array(data, mask=[data>400.])\n", + "depth = field \n", + "\n", + "#print dz\n", + "#print dz[1901:2000]\n", + "print zi\n", + "\n", + "figure(1)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "S = plt.plot(dz,-depth,'*',linewidth=3)\n", + "plt.ylim((-400,0))\n", + "plt.xlim((0, 50))\n", + "yticks = [-400, -350, -300, -250, -200, -150, -100, -50, 0]\n", + "ax.set_yticks(yticks)\n", + "#xticks = [0, .1, .2, .3, .4, .5]\n", + "#xticks = [0, .2, .4, .6, .8, 1.0, 1.2]\n", + "#xticks = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260]\n", + "xticks = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]\n", + "ax.set_xticks(xticks)\n", + "#plt.xlim((0, 40)\n", + "ax.set_xticks(xticks)\n", + "plt.grid()\n", + "plt.xlabel('Delta z (m)',fontsize=18)\n", + "plt.ylabel('depth (m)',fontsize=18)\n", + "plt.title(r'$\\Delta z$ for configuration '+deltaz,fontsize=20)\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[ 0.00000000e+00 2.00000000e+00 4.00000000e+00 6.00000000e+00\n", + " 8.00000000e+00 1.00100000e+01 1.20200000e+01 1.40400000e+01\n", + " 1.60600000e+01 1.81100000e+01 2.01800000e+01 2.22700000e+01\n", + " 2.44000000e+01 2.65800000e+01 2.88200000e+01 3.11200000e+01\n", + " 3.35200000e+01 3.60200000e+01 3.86400000e+01 4.14200000e+01\n", + " 4.43700000e+01 4.75400000e+01 5.09600000e+01 5.46700000e+01\n", + " 5.87400000e+01 6.32200000e+01 6.81900000e+01 7.37400000e+01\n", + " 7.99700000e+01 8.70100000e+01 9.50000000e+01 1.04110000e+02\n", + " 1.14540000e+02 1.26520000e+02 1.40320000e+02 1.56260000e+02\n", + " 1.74680000e+02 1.96000000e+02 2.20660000e+02 2.49170000e+02\n", + " 2.82080000e+02 3.20000000e+02 3.63560000e+02 4.13430000e+02\n", + " 4.70310000e+02 5.34860000e+02 6.07770000e+02 6.89640000e+02\n", + " 7.81040000e+02 8.82430000e+02 9.94160000e+02 1.11645000e+03\n", + " 1.24938000e+03 1.39285000e+03 1.54663000e+03 1.71033000e+03\n", + " 1.88340000e+03 2.06518000e+03 2.25494000e+03 2.45183000e+03\n", + " 2.65500000e+03 2.86358000e+03 3.07672000e+03 3.29359000e+03\n", + " 3.51346000e+03 3.73565000e+03 3.95959000e+03 4.18477000e+03\n", + " 4.41081000e+03 4.63740000e+03 4.86430000e+03 5.09137000e+03\n", + " 5.31851000e+03 5.54567000e+03 5.77283000e+03 6.00000000e+03]\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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89i3fBQUFpe4/U2XPHT16tDp27ChJatGihXr27Om5haXoP6iatL1x4/vatGmn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\nx44d670WAAAAAIC6CnjxXldPPfWUnnrqKY+xfv36yeVyKS0tzT3Wt29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CsrKyNHToUPXp\n00fS5a9hmzBhgr766qsae9uv9ZVuTZs2lXT5U+uv9ve//10VFRVVXmGv7gPfapKamqply5ZpxowZ\neuaZZ7xeJ0k333yzYmJi9M0339RpXV2dPXtWubm5io+PN/o8AADUBa+8AwAQxPbs2eP+erWSkhJl\nZ2crIyNDx44dU79+/bRy5UqP+fPmzVNWVpaGDRumCRMmqE+fPgoNDVVeXp6++OIL3XnnnXrvvfdq\nfL7u3burWbNmevvtt9WkSRNFRESoTZs26tevn0aNGqWNGzcqPj5eTz75pEJDQ5WRkeF1r/v333+v\nmTNnqm3btvrXf/3XKl8b16lTJ/3bv/1brfcYM2aMFixYoLNnz3rVJ18fGzdulGVZGj16tJH7AwBQ\nHxTvAAAEocq3r69atUqffvqpXC6XGjdurA4dOqhv3756/PHHNWzYsCrrmjdvrqysLC1cuFCrV6/W\nH/7wB1VUVOiWW27R/fffr6effrra56nUqFEjrVq1Si+//LL+8z//U5cuXVJ8fLz69eunKVOmqKSk\nRIsXL9bzzz+v6OhoPfLII1q1apWioqKu+W86d+6cLMvS6dOnNWnSpCqPT548+ZrF+9SpU/Vf//Vf\n+vTTT/Xss8/W+m/xZry6xz766CP16tVL9957b617AQDATi6rto9sBQAACDKJiYnavn27Dh8+rJAQ\n/3YAHjx4UD179lR6erpGjhzp13sDAOALincAANCg5Ofnq0uXLlq8eHG1r+D74vHHH9fp06e1bds2\nv94XAABfUbwDAAAAABDk+LR5AAAAAACCHMU7AAAAAABBjuIdAAAAAIAgR/EOAAAAAECQo3gHAAAA\nACDIUbwDAAAAABDk/j8pcgXb1INZ2QAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# KPP related fields \n", + "\n", + "plt.subplot(2,2,1)\n", + "data = KPP_buoyFlux\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, 16, levels=numpy.arange(-2e-7, 2e-7, 1e-8))\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-30,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Upper ocn buoy flux $(m^2/s^3)$ for '+expt_name,fontsize=18)\n", + "#plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = log10(sqrt(KPP_N2))\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, 16, levels=numpy.arange(-5,-1,.25))\n", + "#plt.contourf(time, -zi, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$Log_{10}(N)$ $(1/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = KPP_buoyFlux[:,0]\n", + "CS = plt.plot(time,data,'*',linewidth=3)\n", + "plt.xlim((0, 370))\n", + "plt.xlim((0, 370))\n", + "plt.grid()\n", + "plt.title(r'Surface buoyancy flux for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "plt.ylabel(r'Buoyancy flux $(m^2/s^3)$',fontsize=14)\n", + "\n", + "\n", + "plt.subplot(2,2,4)\n", + "CS = plt.plot(time,KPP_uStar,'*',linewidth=3)\n", + "#plt.ylim((-400,0))\n", + "plt.xlim((0, 370))\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "plt.ylabel(r'$u^{*}$ (m/s))',fontsize=14)\n", + "plt.title(r'$u^{*}$ used in KPP for '+expt_name,fontsize=18)\n", + "#ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "#ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "-c:16: RuntimeWarning: invalid value encountered in sqrt\n", + "-c:16: RuntimeWarning: divide by zero encountered in log10\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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C3fbc3FwhSZJITk72aKdfv34iKirKo42yvCt98ccff4g6deqIK664Qpw8ebJMbdnY+GPI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\ney/q16+P+vXrY/DgwTh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RtfMSI17tjIuLU+hj9eqrr2qcrzIU1ZU+9u4NDQ3F4sWL8c03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v7jjz+iQ4cOaNeuHR566CGMGzcObdu2dZmuFI3MvnMEBTVDSsoQBAWFgk9nEXuBzafTatlH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8V1AsbE\nwY18xpigrl27htDQUHz//fe4++67Bfve69ev49tvv8WoUaPw448/apfFscalS5cQHh6OwsJCKJVK\nwdLEGGOMMdO4TsCYOLiRzxjzCCEhIbhx4wbmz5+PjIwMsZPDGGOMMZFwnYAx87iRzxhjjDHGGGOM\nSQRPvMcYY4wxxhhjjEkEN/IZY4wxxhhjjDGJ4EY+Y4wxxhhjjDEmEdzIZ4wxxhhjjDHGJIIb+Ywx\nxhhjjDHGmERwI58xxhhjjDHGGJMIbuQzxhhjjDHGGGMS8f8Z4ivz1EjQ2gAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# KPP related fields \n", + "\n", + "plt.subplot(2,2,1)\n", + "data = Kt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, levels=numpy.arange(0,.5,.05))\n", + "plt.colorbar()\n", + "plt.ylim((-300,0))\n", + "plt.xlim((0, 370))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$\\kappa_{\\Theta} (m^2/s)$ for '+expt_name,fontsize=18)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = Ku\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, levels=numpy.arange(0,.5,.05))\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$\\kappa_u (m^2/s)$ for '+expt_name,fontsize=18)\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = KPP_dTdt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Non-local $\\Theta$ tendency $(K/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "plt.subplot(2,2,4)\n", + "data = KPP_dSdt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Non-local Saln tendency $(ppt/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 7, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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WAiSwdlW/eqdc/YO54aq+YY0Bi6p14sY2VvzxJSI2pZJ2rlFA5AKeMBqAiBxj\nbw0AZ9qSqx0lPvxzmgARkWqxECBC1Q/Ctq6KW/og7siHaJMigJhjLR1np3N2tAoqd7HB0VV+q2+3\nt4I8kQGvBFjhgVcIiVRBxIfh6lMApbZpcRSAjXNJ7aulvh86cg65Y7G1Uj/f50ks/q6IwPzAa7EQ\n4ABLxQClOkT97oovt1Gg2u+KoX98UycxnP0d8bjfMY4qIHI/ex/inWjLGzEnIE/hdTkBkR0sBIgk\nds6/0Qonh9SvMC0AVE0kqt7O0OH4JDBMbeAUASIVy9XbLhSwiEC+zoVXvbx+/Q1eQSQi8ngsBDjI\n2hSB0dhoPlTfzjz/qm1ZfL4tK5wYMSDnPEQfuGpB3snjrxTxAzqRV3G0CBA+Ic8jbufr9UUM8mke\nnxMQyYiFAAkcuequ1JV6uRIBduhERETSSe0/nSkAiDKzslhARF7OcJHAFRcLeEHCa7EQYIfSH5Ql\nd9jWrryvkK84YKltSaMULJCruspKLSlFVb9X7JSJ3EpsbsBiu2XMCcjT8feKfAELAQqxNFxfyhx7\nw/N1giD5PGZkGrav5DoEROQiYosILDYQuZ5M/bXSowJM2nfFbQqJiEh2LASIUPUDsCML5kld+V+u\nuwQoccWCQw6JKqjhaoHsMbI4QCQvmT74u2PkAfMBokpuyQkk9slqyFvItVgIUIDSt/uTUowwJgdM\n4IlIDC99r0hPT0d0dDQiIyOxePFiq8d9+umn8PPzw5EjR4zbFi1ahMjISERHR+OLL75wRbhERESk\nEOYEFVgIEMuBOfJKD6WvWhBQsvggdUSDNZzLR2pR9b7Tln5nq25zye+0l344d5Xbt29jypQpSE9P\nx/Hjx/HJJ58gOzvb7LiSkhK888476Natm3Hb4cOHsWXLFpw4cQLp6emYNGkS7ty548rwyYM5cyXe\n10fjMScgNbGVF7g8J3AVe7cdVinmBJVYCJCZ2Yf/agWEcUip6PytDAfkPHwisknuTrlqe17Y4QNA\nVlYWoqKiEBISgoCAAIwcORJpaWlmx7366quYPXs2atSoYdyWlpaGUaNGwd/fHyEhIYiKisKhQ4dc\nGT65ioK//3J+kK8+KtChYoSX/q0TEdnDnKASCwEycraj5+rCRO5lawSAvRECil8FYOJu0VEAa6t8\nWVJUVISwsDDj49DQUBQVFZkcc+TIERQXF2PQoEEm24uLixEaGmrzuUS2KFkEsIU5BZFzxPT7UveR\nspgTSBN1hddzAAAgAElEQVTg7gA8Rq7e/kq3KwAkVHTEmzDGBUE5TpEE4I/XT0RuIlcxQMz7HYCC\nNRFu/zChi7Ozv9rjdRamMWk0GpttlJeXY8aMGVi3bp1xm2Dnji3kJUT+LchtHFKwHmMdfn74hDwU\nLI2wuE8NOQoRqVDV90s3XpywlRfoqj1mTmCb7xUClkK2VXp9QfiEPOBp95xbEHTQaPTuOTmRA6xV\n//l77F6hoaEoLCw0Pi4sLDS5GlBSUoKTJ09Cp9MBAM6dO4chQ4bgs88+M3tu9SsJRL7GXesVMCcg\nNbKUF/D32L2YE1Ti1ABLlrr2dI7ckpCIyBUK1li+6qgmnTt3Rk5ODoqLi1FaWorU1FTEx8cb9997\n7704f/48zpw5gzNnzqBbt27Yvn07YmNjMWjQIHz88ce4e/cuioqKkJOTgy5durjx1ZDL+PJ0HJGj\nJNw9YoiISCrmBJVYCFApS8UDFhSIPBPnCrpXcHAwVq1ahbi4OHTo0AHDhw9HTEwMEhMTsWPHDpvP\njY2NxbBhw9C+fXsMHDgQycnJCAwMdFHk5I0cvaIu5nmGY5gPEHkuj8wJDIVPHyiAMieo5HtTA7yQ\nThCgtzPfRVYz4bbpAkREjoiPjzep+ANAUlKSxWMzMzNNHs+ZMwdz5sxRLDYih+XqATRxdxRE5A18\noAhgwJygAgsBLhSxo0D5xfZy9RAEHVrA/nA9YQLQQuFwnOVxFVMiJ1T/feY8QSKyx9k5+WLn1guC\nDppIp06lOOYE5E24fgC5m29NDXBDpUtvYbVKT+KuRX+IyAP4UPWfyBOImVOvhnn3nHpARKR+vlUI\nIKucqbKHT8izmbiwgk9kmVvnCUopArh4AVUibyRX4T1iR4Es7dginLJ/jKPFAOYERNZ55PoB5LVY\nCHAzRzpSVyQBRERE5FnY/xMRkVxYCLBlqXfcOouIiIjUS8pFA4vFApG3AyQisovTCr2GagoB8+bN\nQ2hoKLRaLbRaLXbt2mXct2jRIkRGRiI6OhpffPGFtIar/jLL/Yu9Qt7mXMWZIUmOPJdDoMjXGYYC\nWvpypk2ncUoAeSjFcgIy4473IuYF5Os8Micgr6OauwZoNBrMmDEDM2bMMNl++PBhbNmyBSdOnMC5\nc+fQq1cvfP/99wgKCrLfqCdWtCQUDyJ2FOCccpEQEbEYQB5JkZyAiIhs88TPTuQw1YwIAABBEMy2\npaWlYdSoUfD390dISAiioqJw6NAhN0TnQyR8MGAFksg5/Bsisow5ARH5GuYEJCfVjAgAgBUrVuD9\n999HbGwsli9fjvr166O4uBj9+vUzHhMaGoqioiIrLawFzuuBzwC00SkfsJfx9FshEpHr6PV66PV6\nd4dBPkyWnACoyAtq6RSOlojImx3FvHl6dwdBEnlUIaB///44d858sPuCBQvwzDPPYO7cuQAq5gZO\nnz4dGzZskHiG8cD9OuAHAEMAQO9UvBaJHdrP4bZEpCbVhgPqdDrodDrj46SkJNfGQ17PJTkBUJEX\nAFAkJ1CbmWB+QkQO6Ih583TGR8wJ1MGjCgF79uwRddykSZPQt29fABXV/sLCQuO+oqIihIWF2W9E\nrR3dUgCLHXuqlOFEgqCDRqN37EREJCvD3y7/JsmXuDQn8FHMC4jUhzkByUU1awT89ttvxu8//fRT\nREVFAQAGDRqEjz/+GHfv3kVRURFycnLQpUsXd4VJRERECpM1J7C1+FWuXpWLY1m8hSAREVEVHjUi\nwJYXX3wRx48fx507dxAeHo7//ve/AIDY2FgMGzYM7du3h5+fH5KTkxEYGOjmaD3DOKRgPcbK2uZo\nbMTrWCBrm0RERFIwJyAiInKOagoBKSkpVvfNmTMHc+bMcWE0rjcOKXg9tz/QVqdI2+CHeyKyRoVX\nRMm7+XpOQERE5CzVTA1wGakJrxNrDQinxB8r95V9SwRBZzJf0JW3KKl+Xt4ehchc1b8Nl/ydqHRY\nNJEq5eohTHB3EJUsvce4My8gIlOW8nb+rZAUvlcIEJvUWvqA/4z1wwvWRNg9hoiIiMijzbS9ezQ2\nuiYOIiJSlO8VAhyQlxDOD/hERETkfmJvU0xERGSDatYI8CSbMMbdIXikqkOWiUg5UobMVt3PWw0R\nucBS2L2q7iuYFxApT8oUnur7mBf4No4IICIiIt8mZtog18sgIiIvwkIAERF5vfT0dERHRyMyMhKL\nFy82279y5Up06NAB7du3R6dOnXD48GEAQH5+PmrWrAmtVgutVoupU6e6OnQiIiKSEXOCCpwaYEmu\nXpHb9BERkevdvn0bU6ZMwddff43GjRuje/fuGDBgALRarfGYcePGGTv0HTt2YMaMGdi3bx8AoHXr\n1sjOznZL7OQhqo4GWArAE1b3tzZCYQW4rhERkRXMCSr51IgAOeeoyXU7P643UIm3PSFSluFvzNLt\nhtx5mzClZWVlISoqCiEhIQgICMDIkSORlpZmckydOnWM31+/fh1NmzZ1dZjkYor+jjtxa2GqxLyA\nSFm28oLqx3kL5gSVOCLAHhGVf+OtA4mIyKX0lyq+bCkqKkJYWJjxcWhoKPR6vdlxK1euxJtvvokb\nN27g4MGDxu35+fno2LEjatWqhfnz56Nfv35yhU9khjkFEZFjmBNI41MjAmRnr+Lv4qF5ETsKXHtC\nIiI309UH5rWu/LJEo9GIamvq1Kn48ccf8eabb2LChIoKcLNmzVBcXIyjR49ixYoVGDt2LK5cuSJX\n+KRShg/r/NBOROQ5mBNI45sjAhRaA2A9xuI1LJS9XSIinyW1oLrbfFNoaCgKCwuNjwsLC02uBlQ3\ncuRITJw4EQAQFBSEoKAgAIBWq0W7du2Ql5eHbt26SQyMvIK9/IFrDBERKUtKXsCcwCbfHRHA2wA5\nhfP2iNTN2hzAqtu95W+8c+fOyMnJQXFxMUpLS5Gamor4+HiTY/Lz843fp6WloW3btgCAS5cuoby8\n3HhMTk4OWre2cpmByEcxJyBSP3trCHnL3zhzgkq+OSJAok0Yg9EJG90dhkfg4oZEpDbBwcFYtWoV\n4uLiUF5ejrFjxyImJgaJiYno1KkTEhISsGzZMuzbtw/l5eVo0KAB1q9fDwDIzMxEYmIi/Pz8IAgC\nli9fjoYNG7r5FZEncMu0gBWuPyURkTdhTlDJqUJAaWkpAgMD5YrF83CIHxGRV4iPjzer+CclJRm/\nf/fddy0+709/+hP+9Kc/KRqbt/D6nICIiLwCc4IKTk0NqF27Nk6dOiVXLKrAK+JERETmfDEnMOLt\nAomISGVEjQiYNm2a2QqLgiCgrKwMCxcuxH333QeNRoPly5crEqS3Y3GBiIjUgjmBY9ZjrLtDICIi\nMhJVCFixYgXat2+P++67D4IgQKPRQBAECIKA06dPo1atWqJvxUCOzysUBB00kfaPkVM+IgBBgJ7/\nv0REBOYEnkQQdGiBPOv7FRigEYF85KGF/A0TkccTBB00Gr27wyCZiCoELFiwAO+99x7eeecdPPzw\nw8btgYGB+OCDDxAVFaVYgN4kLyHc3SEQERE5hTmB4wrWRAAT3B0FERGRyDUCXnnlFWzatAlPP/00\nXnnlFZSVlRn3ser/B84PJCIiH8CcgIiISP1ELxbYrVs3HD58GD/99BO6deuG06dPKxmX++Xqre5y\nZp6f3ec+43DTRERELuFzOQEREZGXkXT7wHvvvRcff/wx3nvvPfTq1cvkKoA34wI/NuTqATRxdxRE\nRORivpoTiLYU8k8DeAbACpnbJCIinySpEGAwceJE9OnTB//73/8QEhIid0yKsrXIhRwLYFSsA1Dg\nVBu2CKcA7LB+bseWIXScvYWKiEjd5F6AlLyPt+YEYvaLsR5jMQ4pTrVhSz4iPKYfFrOoMRGpG/MC\n7+FQIQAA2rRpgzZt2sgZi6qIXflfFwfod0to834ngiIiInIDn84JuEYQERGpkKhCQEJCgvH2QFUZ\nFgUy3D5o+/bt8kfobhz6TkREZOTTOQEREZGXEFUISEtLQ/PmzaHT6Sx2/oCPrxTMqwFEROQjmBOg\not+f6e4g3E/s6EgiIvI8ogoBs2bNwvr167F//35MmDAB48ePR2hoqNKxeSQpCwfmJYQjYoX86wVU\nrEPgero4IMMtZyYiIk/BnICIiEj9RN0+cPHixSgsLMRbb72Fb7/9Fq1bt0Z8fDw2b96M0tJSpWMk\nIiIiD+HzOYGN2wtbY/UiAkcUEhGRm4gqBABAQEAAhgwZgs8++wz5+fnQ6XR49dVX0axZM1y/fl3J\nGN2uYE2EqJEAnjpETpjAFT6JiEg+vpwTmKj6Qd5CgcBSXuAJtyQWBB3zAiIiHye6EFDVjRs3cPXq\nVZSUlOCee+6ROyaPJbbz1sUpHAgREZGH8NWcgFfz/8CfAxGRKokuBPz+++9Yu3Yt+vTpg+joaBQU\nFGD9+vX46aefUKdOHSVjJCIiIg/CnMAGB6YOEBERuZqoxQL/9re/ITU1FQ8++CCefvppbN++HfXq\n1VM6No/lCVMANmEMXsNCm8d4wvBDIiLyLswJiIiI1E9UIWDNmjUICwtDs2bNsGvXLqSnpxv3GW4b\nxHsGO2Y9xmIcUtwdBhERkSjMCYiIiNRPVCFg3LhxJvcE9sl7BotkuFKviwPypDyR9yQmIiIVYE4g\nQq4eaKuzuttsxJ6N6QQc3UdEREoQVQhYu3atwmFU2rx5M+bNm4e8vDx8++23iImJMe5btGgRUlJS\n4O/vj2XLlmHAgAEAgPT0dMyaNQtlZWV48skn8fLLL7ssXrueAbC74ttNGGPxEHbyRETKstdPrFy5\nEsnJyRAEAUFBQUhOTkZsbCwA632Pr2JOIA9PmGZo0VIAE9wdBBGRcpgTVHDorgFKio6OxtatW9Gn\nTx+T7YcPH8aWLVtw4sQJpKenY9KkSSgtLcXt27cxZcoUpKen4/jx4/jkk0+QnZ1t8xyO3DLH0GGb\nddwyLQoknJKlGSIiqkZMPzFu3DgcO3YMx48fR2JiImbMmAHAct9z584dd7wMn+SpOYHSBBV8ELd2\ncYOIyJMxJ6gkqhDwySefoEWLFrh27ZrZvitXriA8PBxbt26VJaCIiAg89NBDZtvT0tIwatQo+Pv7\nIyQkBFFRUcjKykJWVhaioqIQEhKCgIAAjBw5EmlpabLEYvTHrXFMigC2bpfzjLyntyYvIdw1JyIi\nUjEx/UTVle6vX7+Opk2bArDc9xw6dMil8Xsan88JHCTbCAAX5RhERN6IOUElUVMDkpOTMWvWLNSt\nW9dsX7169TB79mysWrUKw4YNkz1Ag+LiYvTr18/4ODQ0FEVFRRAEAWFhYSbb9Xq9xTbmzZtX5VE9\nAB2dC8rOaAAxK/tXV/HhvsDxmIiIXECv11t9r3VpHCcAfY7tY4qKikT1EytXrsSbb76JGzdu4ODB\ngwCs9z2+jDkBERFVxZxAnUQVAr777jv8+9//trq/b9++mDNnjuiT9u/fH+fOnTPbvnDhQiQkJIhu\nR6qqnX5Skl6x86jNeozFa+4OgohURafTQafTGR8nJSW5J47oii9jHB+aHyN24bqpU6di6tSp+PDD\nDzFhwgRkZmbKFKV3YU4gTcGaCIRPkLR8MBGRqjAnUCdRhYCbN2+iVq1aVvfXrFkTv//+u+iT7tmz\nR/SxBqGhoSgsLDQ+NlRzysvLTbYXFhaaVHk8gS4O0O92dxREROojfQqU+Yiq6v2HvX5i5MiRmDhx\nosXnVr+S4IuYExARkbtIywuYE9giao2A0NBQHDt2zOr+EydOIDQ0VLagDKrekmjQoEH4+OOPcffu\nXRQVFSEnJwddunRB586dkZOTg+LiYpSWliI1NRXx8fGyxyJZtTl8uriKfz12lWAiIi8lpp/Iz883\nfp+Wloa2bdsCsN73+DLmBDCfGijTwsFERKQs5gSVRI0IeOyxx/Daa6/h0UcfRXBwsMm+mzdvIikp\nCY899pgsAW3duhXTp0/HhQsX8Nhjj0Gr1WLXrl2IjY3FsGHD0L59e/j5+SE5ORmBgYEIDAzEqlWr\nEBcXh/LycowdO9bk9kJEROTbgoODLfYTiYmJ6NSpExISErBs2TLs27cP5eXlaNCgAdavXw8AVvse\nX8acQGHO3r6PiwkSEVnFnKCSqELAnDlzEBMTg44dO2LSpEmIiooCAOTk5GD16tW4fv26pPmAtgwb\nNszqAkNz5syxeJ74+HjJFX9rtwsSBB00Gr35jqUAZlb5vrpcPQDdH/PtpS0QSEREyrLUT1Sdw/ju\nu+9afa61vsdX+VJOIBeOBiQi8hzMCSqIKgQ0btwYBw4cwNSpUzFz5kzj8DyNRoO4uDisWLECTZo0\nUTRQVfijip+XEI6IHRVzUvISwhGxgncBICIi78CcwIWkjA5wdiQBERH5FFGFAABo0aIFPv/8c1y6\ndAk//vgjBEHAgw8+iPr16ysZn3eyNKKAiIhIJZgToGIkYFudy9YHcMmIQ+YnREQ+Q9RigVXl5uai\nffv26Nq1K3Jzc3Hr1i0l4vIshk5+KWxMC1AWhxUSEZGn8cmcQIrqOQM/aBMRkYeQXAgYOHAgfv75\nZ7PvyZTJrS3+WLhnPcbK0vYmjJGlHSIiImf4fE5g60KAlX1KFPZ5sYCIiKSSXAgg8Uw++HMVXyIi\nIiIiIvIALAQQERERqYDJaEMiIiInsBCgAClD9wvWRKhnSB9HNRARETnNpN930WKDREREVbEQ4Chn\nO24rz+f8fyIiInIGRw4QEZE9LARYIAg6ky8Aoj/4V7+6zw/2RERE6mUxJ5BChjsFyLXYMBERkQEL\nAQpxqADwR7Kgi5M3FiIiInIzd946kNMPiIioGhYCXCAvIVz2YXq8OkBERKRi7iwMEBGRz5NcCHjl\nlVdw3333mX1PvoEFCCIiMmBOoDxLCwqrZpFhIiLyWJILAXPmzDF29FW/9yl2hti568My1yMgIiJX\nYk7gerZGGLJYT0REYnFqgBS5eknz7Jyp2ItZJ4Af/ImIiFSA0wCIiMjDsBBARERERKLxQgQRkfqx\nEKAQS8PzCtZE8KoAERERERERuRULAXKr9kF/PcZKr5w/I1849nA+IRERkffhgoJERGSLQ4WAAwcO\nGL+/deuWbMGono31AziMjoiIvBFzAidIWHeIiIhITg4VAtq0aYNDhw4BAC5fvoyZM2fi1KlTsgbm\nSQRB5+4QbNJrNC49n+C9/9VERCQRcwIbLH3QV3iKoK27CiiBOQERkTo5VAj44YcfcOrUKQiCgKZN\nm+If//gHRo4cKXdsquXMcLzqQ/Vd/SGfiMgbpaenIzo6GpGRkVi8eLHZ/iVLliAqKgrt2rVDnz59\ncObMGeM+f39/aLVaaLVaDB061JVhqwJzAtfxpNGFnFpIRGrFnKBCgCNPGjx4MM6ePQtBEFBeXo7z\n588jKChI7th82zMAdlveJaXzLVgTAUyQJyQiIjW6ffs2pkyZgq+//hqNGzdG9+7dMWDAAGi1WuMx\n3bp1w/Tp01GjRg2sXr0aM2bMwNatWwEAtWrVQnZ2trvC93jMCRzgwKgA9udERM5jTlDJoREBkydP\nxhdffAE/Pz/4+fkhMzMTX331ldyxkYfgqAQiUrOsrCxERUUhJCQEAQEBGDlyJNLS0kyO6d27N2rU\nqAEA6NmzJ4qLi90RqioxJyAiIrVgTlDJoULAggUL0K5dOwCARqNBbGws8vPz5YxL9URPD1gK3lKQ\niEhBRUVFCAsLMz4ODQ1FUVGR1eOTk5MxZMgQ4+Nbt26hU6dOiImJQWpqqqKxqhFzAjLBBRCJyIMx\nJ6jk0NQAAHjwwQeN33fq1Ak5OTmyBORVlgKYIH0eXV5COCJ2FDh16oI1EQifkOdUG9W5egEiIiJ7\nDulv4ZDe9kr1GgmjmjZu3IgjR45g3759xm3FxcVo1KgRzpw5g379+qFDhw5o06aNwzF7I+YENuTq\ngbY6p5rgrQCJiOxjTiCNw4WA6gxXA8iUEh/IpZybiEjN7C6OpgPq66o8TlpodkhoaCgKCwuNjwsL\nC02uBhjs3bsXCxYswP79+xEYGGjc3qhRIwBAy5YtMWDAABw5ckS1nb6rMCewQoaiABGRL7OZF+iY\nE0jh0NSA6g4dOgSdTidHU76Bw+aIiFymc+fOyMnJQXFxMUpLS5Gamor4+HiTY7KzszF58mTs2LED\nDRs2NG6/evUqSktLAQAXL17Evn37EBUV5dL41YY5gRUu7Ps96e4CRESehDlBJacKAfn5+Rg1ahS6\nd+/OhYGqkmnOvy5OnnaIiHxZcHAwVq1ahbi4OHTo0AHDhw9HTEwMEhMTsXPnTgDASy+9hBs3bmDE\niBEmtwQ6efIkYmJi0KFDB/Ts2RPTp09H+/bt3flyPBZzApnYKRjwQz4RkeOYE1RyaGrAlStXMH/+\nfPz73/9Gly5dsHPnTjz22GNyx6ZOtob9SSgQ5CWEI2KFc+sEEBFRhfj4eLOKf1JSkvH7PXv2WHxe\njx49cOLECUVjUzvmBCJwJCARkcdgTlBB0oiA0tJSvPXWW2jVqhW2b9+ODz/8EPv370fnzp2Vio8M\nLBQRpC5CSEREJBfmBEREROoluhDwySefoG3btliwYAHmzp2LU6dOYdiwYUrGpm4K3xKw+kKAnEZA\nRESuwpzAhXiLYSIiUoDoqQGrVq1Cy5YtsXnzZtSrV0/JmDySIOgAABqNXvJzpazeX7AmApsmjMFo\nbJR8HiIiIldgTqAD4FhOYCTndAEWC4iISCLRIwK+/PJLTJ8+HUuXLsVPP/2kZExERETkwZgTuAjX\nFiAiIoVIWiMgISEB8+bNQ2ZmJhYtWoQrV64oFRcBwDPSDnfXmgFSRjwQEZF3YE7gelwbiIiI5CL5\n9oEBAQF4+umn8eyzz+K///0vkpOTjfdTlMPmzZsRFRUFf39/HDlyxLg9Pz8fNWvWhFarhVarxdSp\nU437Dh8+DK1Wi6ioKDz33HOyxSILB4frufL2QGI+yNtbg4DFACIi38OcwLMUrIng7QWJiEgUyYUA\ng3vuuQcvvvgiBg0ahHfffRcajUaWgKKjo7F161b06dPHbF/r1q2RnZ2N7OxsrFy50rj9qaeewpo1\na3Dy5EkUFBRg69atssSiCFvD/DgEkIiIVIg5gY/i2gRERKrlcCHAICwsDAsXLjSp1DsjIiICDz30\nkOjjz549i/Lycmi1WgDAE088gbS0NFliISIiIvGYE4jAoj8REXkApwsBBh06dJCrKavy8/PRsWNH\n9OjRAxkZGQCAoqIihIWFGY8JCQlBUVGR4rHYZOjkFersOQyfiIg8GXMCOxzJD5Za+d4V5yYiIq8j\n+vaBcurfvz/OnTtntn3hwoVISEiw+JxmzZqhuLgYdevWRXZ2NgYPHoyTJ09KOu+8efOM3+t0Ouh0\nOknPd5X1GItxSEFeQjiAApvH6uIA/W7XxEVEZKDX66HX690dBnkB5gQicAg+EXkw5gTq5JZCwJ49\neyQ/JygoCEFBQQAArVaLdu3aIS8vD2FhYSgsLDQeV1RUhNDQUIttVO30FVe14u6KDlziHQYcVbAm\nApjgmnMRkeeq/sEpKSnJfcGQqvlETiAj3jmAiDwNcwJ1km1qgBIEQTB+f+nSJZSXlwOoGA6Yk5OD\n1q1bIywsDH5+fsjOzgYAbNy4EYMGDXJLvN6OKxETEZG7MCfwHCxGEBGpn8cVArZu3YqwsDB88803\neOyxxxAfHw8AyMjIQPv27dG+fXskJCRg+fLlaNiwIQDggw8+wIQJExAVFYXmzZtj+PDh7nwJ1kmY\nl8dOloiIfJ1X5wQKYg5BRET2SJoaUF5ejvXr1yMjIwNXrlyBIAjQaDTGf7dv3+50QMOGDcOwYcPM\nto8YMQIjRoyw+JzY2Fhj9V91jMUBXcU/S8Gh90RE5PGYExAREamXpELA9OnT8d577+HRRx/F/fff\nb7JPrnsGkzldHICz7o6CiIioEnMC38Y7GBERqZukQkBKSgpSU1MxZMgQpeLxeIKgs7pPo9Gbb8zV\nA22tP8eWTRiD1+IWWt3vqk5YF+eS0xARkYowJ3AgJyAiIvIQktYICAoKglarVSoWcgPRxQQX3ZWA\niIjUgTmB69nssyWsQ0RERCSpEDBx4kRs2LBBqVjoD5Ku9PMDOhERuQFzAiIiIvWyOzVg2rRpxrl+\nZWVlWLlyJfbs2YOoqCj4+VXUEQwLAy1fvlzZaImIiMhtmBN4PuMdA7j4MBER2WB3RMCJEyeMX6dO\nnULHjh0hCAJOnjxpsu/EiROuiFedcvWSh+yZjAoQcdU/LyFcUvtERL4kPT0d0dHRiIyMxOLFi832\nL1myBFFRUWjXrh369OmDM2fOGPetW7cOUVFRiIqKwvr1610ZtsdhTkBERGrHnKCC3REBer3eBWGQ\nVdaKAEsBzHRlIERE6nT79m1MmTIFX3/9NRo3bozu3btjwIABJvPbu3XrhunTp6NGjRpYvXo1ZsyY\nga1bt+KXX37B66+/jqNHjwIAOnbsiLi4ODRu3NhdL8etmBO4gROLDhMRkSnmBJUkrRFw9uxZlJeX\nm20XBAFnz/L+dnIzDu9zxlLnmyAiUrOsrCxERUUhJCQEAQEBGDlyJNLS0kyO6d27N2rUqAEA6Nmz\nJ4qLiwEAe/bsQXx8POrUqYM6depg4MCB2LNnj8tfgydiTqAyDoxOJCLyNswJKkkqBLRo0QIXLlww\n237x4kW0bNlStqDIMbynLxGRuaKiIoSFhRkfh4aGoqioyOrxycnJxlviFRcXIzQ0VPRzfQlzAvdg\nX09E5DjmBJXsTg0Q49atW/D395ejKZIoLyEcurgC5Lk7ECIiN8jXF6BAb/vqs2FxOzE2btyII0eO\nYN++fc6G5rOYExARkTswJ5BGVCFg2rRpxu/nzJmDWrVqGR+Xl5fj22+/RXR0tPzRkWfgLQqJyE3s\nTpHS/fFlkNTW7JDQ0FAUFhYaHxcWFppcDTDYu3cvFixYgP379yMwMND43KysLJPn9ujRQ8Ir8D7M\nCfhQ0SQAACAASURBVDwY7xRARF7OZl6gA3MCCUQVAqqu/pubm4ugoCDjY39/f0RGRmLWrFnyR+er\nqi0EmJcQDrxcbb+YNoiICJ07d0ZOTg6Ki4vRqFEjpKamIjk52eSY7OxsTJ48Gbt370bDhg2N2x99\n9FEkJSWhpKQEQMVKw3PnznVp/J6GOQEREakVc4JKogoBhlWCx48fj+XLl6Nu3bpKxuTbqq0O7Cm3\nBcxLCJdn8UIiIhcLDg7GqlWrEBcXh/LycowdOxYxMTFITExE586dMXjwYLz00ku4ceMGRowYAQAI\nDw/Htm3b0LRpU/zjH/9A165dAQBz585V7erAcmFO4AbGRf50og7nOgJERJYxJ6gkaY2AtWvXKhQG\nmeHwPiIi2cTHxyM+Pt5kW1JSkvF7W6v+PvXUU3jqqacUi02tmBMQEZEaMSeoYLcQkJCQgI0bN6Ju\n3bpISEiARqOBIAhmx2k0Gmzfvl2RIH1VwZqIymJArh62rgRswhgXRERERL6MOYGbVZs6SERE5Ci7\nhYAGDRoYV1ds0KCB1eOkrMBIMuNifkRE5ALMCYiIiLyD3UJA1aF/HAboYTh9gIiIXIg5gYcyriFA\nREQkjqQ1Avr374++fftCp9OhS5cuCAiQ9HSvJwg6i9s1Gr3Dbdob8r8JY/AaFjrcPhERkSOYE9im\nRE4ghisWCuRihERE6ucn5eCuXbvi888/R79+/VCvXj0MGDAACxcuxMGDB3H37l2lYiQiIiIPw5zA\njTgCgIiInCSpEDB//nx8/fXXuHTpErZt24auXbti165dePjhh3HfffcpFSMpbam7AyAiIrVhTuCl\nWGQgIvIJkgoBBiUlJbhw4QJ+++03nDt3DgEBAejUqZPcsZGHyEsId3cIRETkoZgTEBERqY+kCX1T\npkyBXq9HQUEBunbtCp1Oh/fffx9du3ZFcHCwUjGSHfygTkRErsacwE04io+IiGQgqRCQnJyMhg0b\nYvbs2YiPj0dsbCz8/BwaVEC25OqBtjpJdwVYj7EKBlQF71RARERgTuCVOC2AiMhnSCoEnD59Gnq9\nHnq9Hu+//z6uXbuG3r17G1cNjomJUSpOn1X5Af+cW+Owd/cCIiLyLcwJyCoWFIiIPJ6k0n3r1q3x\nt7/9DRs2bMDZs2fxzTff4P7778fs2bPRuXNnpWL0TSI7UZeNBLCFwxSJiHwOcwIiIiL1kjQioLy8\nHN999x0yMjKg1+tx4MAB3L59G7GxsdDpdAqFSKrAKQNERD6FOYGP40UAIiJVk1QIqFevHm7dumXs\n5F944QX07NkTderUUSo+koIfxomIyEWYExAREamXpEJAamoqevXqxU7eExgWFIST8/clVPQL1kQ4\nfh4iIvIqzAk8GK/WExGRHZIKAQMHDlQqDiIiIlIR5gRERETqxfv8EBEREakBV+MnIiKZSBoRQK5X\nORy/yu0DXZwI8NaBREREKpCrB6BzcxBERKQGHBHgyVj5JyIiIim4PgAREYnAQoCa5eq5gB8RERER\nERFJIrkQUFZWhp9++gnHjh3DmTNnUFZWJmtAM2bMQGRkJCIjIzF48GBcvHjRuG/RokWIjIxEdHQ0\nvvjiC+P29PR0REdHIzIyEosXL5Y1HiIiUj97/cT+/fsRExODwMBAfPrppyb7/P39odVqodVqMXTo\nUFeFrArMCYiISG2YE1QQVQi4ceMG3nvvPTz66KOoU6cOWrduDa1Wi1atWqFOnTp49NFH8d577+H3\n3393OqCEhATk5OTg1KlTaNeuHebPnw8AOHz4MLZs2YITJ04gPT0dkyZNQmlpKW7fvo0pU6YgPT0d\nx48fxyeffILs7Gyn41CT9Rir+MiA9RiraPtEREoR00+Eh4dj3bp1GD16tNnza9WqhezsbGRnZ2Pb\ntm2uCttjMScgI05hJCKVYU5QyW4hYNmyZWjRogXeeecddO3aFR9++CG+++47nD59Gt999x02bdqE\nzp0745133kHz5s2xbNkypwLq27cv/PwqwurZsyeKi4sBAGlpaRg1ahT8/f0REhKCqKgoZGVlISsr\nC1FRUQgJCUFAQABGjhyJtLQ0p2KQmyDoLH5V3Wf4noiI5CWmnwgPD0d0dLSx/yHLmBM4z1pOUD0X\nsJoTiP3wzQ/pRERmmBNUsnvXgIMHD2L37t2IiYmxuD8mJgbDhg3DokWLcPjwYSxcuBAvvviiLMH9\n5z//wahRowAAxcXF6Nevn3FfaGgoioqKIAgCwsLCTLbr9XpZzk9EROpXVFTkVD9x69YtdOrUCeXl\n5Zg9ezb+8pe/KBClOjAnICIiNWNOUMluIaD6vAhbYmNjRR3fv39/nDt3zmz7woULkZCQAABYsGAB\ngoKCMGaMfLeumzdvnvF7nU4HnU4nW9uK4eq/ROSB9Hq9R3zAuqU/hFv6QzaP0Wg0Tp2juLgYjRo1\nwpkzZ9CvXz906NABbdq0capNtWJOQACYmxCRCeYE6swJ7BYClLBnzx6b+9etW4e0tDRkZGQYt4WG\nhqKwsND42FDNKS8vN9leWFhoUuWpqmqn7zN4T2EiUkD1D05JSUmKnMf++icRQPNxVR6vMDuiev9h\nq58AzJOERo0aAQBatmyJAQMG4MiRI6rt9D0RcwIiInVzVU4A2MsLmBNIIbkQUFRUhK+++gpXrlyB\nIAgm+6ZOnep0QOnp6XjjjTewb98+BAcHG7cPGjQIkydPxvPPP49z584hJycHXbp0QVlZGXJycozV\nmdTUVCQnJzsdBxEReYfOnTuL7icEQTDp265evYpatWohMDAQFy9exL59+/DMM8+4KnSPx5yAiIjU\nhDlBJUmFgDVr1mDy5MkICAjAvffea7Zfjk5/2rRpuHPnDvr37w8A6N69O1auXInY2FgMGzYM7du3\nh5+fH5KTkxEYGIjAwECsWrUKcXFxKC8vx9ixY63OXSTH8I4BRKRmwcHBFvuJxMREdOrUCQkJCfj2\n228xfPhwXL58GTt37sS8efNw4sQJnDx5EpMmTYKfnx9u376N6dOno3379u5+SR6BOQEREakNc4JK\nGqF6Cd+Gxo0bY9q0aXjllVfg7++vZFyy02g0ZlcrPJFGozfd0FZX8W+u3riCsMkxbXUIP5VXMUxm\nKSCcMm/H1t0INJF/HHPKekwtkAcAZueQ0gYReTcl3mM1Gg3wX4ltPq2O93pvwJxAeWY5QTXW8gIA\n1vMGWM8LDMfZu4uRod+veo7qbYhph4i8k1LvsZLzAuYENkm6J8Lt27cxfvx41XX43s7+HFoiIiJ5\nMScgIiJSL0mFgFGjRnnc/XiJiIjI9ZgTEBERqZekNQLefvttDB06FBkZGWjbti38/EzrCHPnzpU1\nOPIwvF0QERH9gTkBERGRekkqBPznP//BF198gSZNmuDUqcoJ4YIgQKPRsNN3J4U/pHP6ARERVcWc\ngIiISL0kFQLmz5+Pd999V9W3SSAiIiLnMSfwYLl6d0dAREQeTtIaAWVlZXj88ceVioWIiIhUgjkB\nERGRekkqBIwfPx6bNm1SKhayJFcvvrLPKwBEROQizAk8FHMBIiISQdLUgJs3b2LhwoXYvXs3IiMj\njQsDGeYDLl++XJEgSWG5egA6NwdBRERqwpyAiIhIvSQVAk6dOgWtVovy8nKcPHnSuN3Q6ZObcKFA\nIiJyMeYERERE6iWpEKDX6xUKg4iIiNSEOQEREZF62V0j4KeffpLUoNTjSWU495CIyGcxJyAiIvIO\ndgsB3bt3xxNPPIEvv/wSgiBYPEYQBOzduxdjxoxB9+7dZQ+SbHD2gzk/2BMRkUjMCciI+QMRkarZ\nnRqQm5uLpUuXYuTIkbhz5w5iYmLQvHlz1KlTByUlJTh79iyys7MRGBiISZMmITc31xVxExERkYsx\nJyAiIvIOdkcE1K9fHwsXLkRxcTE2btyIzp074+rVqzh9+jRKSkrQpUsXbNy4EcXFxVi4cCHq16/v\niriJiIjIxZgTEBEReQfRiwXWqFEDCQkJSEhIUDIenycIOpPHGo3e4nYiIiJ3YU7gGpb6fuYFREQk\nB7sjAqrq27cvXnvtNbPtly5dQr9+/WQLioiIiDwbcwIiIiL1knT7wH379uHYsWM4fvw4UlJSULNm\nTQDAnTt3eBshX2ZcMEjnxiCIiMiVmBMQERGpl6QRAQCwd+9e5Ofno1evXvj555+ViIk80VJ3B0BE\nRJ6GOQEREZE6SS4EhIaG4quvvkLr1q3RuXNnfPfdd0rERVLxNj5ERORizAmIiIjUSXIhAABq1qyJ\njz76CH//+9+h0+mQmpoqd1xE/8/encdFUf5xAP/sCogI3kcoKKamsKyCoqiocSmC4m2UYh55522a\nR4oX3ldlauVVHpV5piilAh6geGEKar9SlCNMATMQBWSf3x/jDnuzC7vsLnzfrxcv3Zlnnvnu7Bzf\neeaZGUIIIWaAcoJKiC4+EEKI2StVQwAACAQChIWFYffu3Zg3b54+YyK6ogMyIYRoFBkZCbFYDBcX\nF6xevVpp/Pnz59GuXTtYWlri0KFDcuO+++47iEQiiEQifP/99+UVslmhnIAQQoi5oJyAo9PDAqOi\nolC7dm25YYMHD0br1q1x/fp1vQZGTAg9H4AQYsby8/MxceJEXLx4EQ0bNkTnzp3Rs2dPuLu782Wa\nNm2K7777DuvWye/wMjIysGzZMty8eRMA4ObmhoCAADRs2LBcv4MpopyAEEKIuaGcoJhOPQK8vb1h\naWmpNNzV1RUjRozQW1CEEEKIvsTHx0MkEqFx48awsLBASEgIIiIi5Mo0bdoUYrEYQqH8YfH06dMI\nDAyEra0tbG1t0atXL5w+fbo8wzdZlBMQQggxN5QTFNOpRwAhhBBiUu7FAH/EaCySlpYGR0dH/rOD\ng4PWr7dLT0+Hg4OD3LRpaWmlCJQQQgghBkU5gU6oIYBoj55FQAgxNa29uT+pX5YoFREIBOUWDiGE\nEEKMhHICnVBDACGEENOlh2eUODg4IDU1lf+cmpoqdzVAkWyS4ODggPj4eLlpu3TpUvagCCGEEKK7\nMuYFlBMUK/VbAwghhBBz0KFDByQmJiI9PR2FhYU4cOAAAgMDVZZljIExxn/29/dHZGQkcnJykJOT\ng8jISPj7+5dX6IQQQgjRI8oJilFDACGEkArN2toaW7duRUBAANq2bYuBAweiXbt2CAsLw/HjxwEA\nV69ehaOjIw4ePIjx48dDLBYDAOzt7bFgwQJ4enrC09MTixYtMtunAxNCCCGVHeUExQRMtpmjAhMI\nBDDHryoQxAAAGPNWGqaKtJxsGdlptalfqYzLm//cVV1WmzoIIRWbIfaxAoEAcNaxzrvmua8n5ctc\ncwJAt7xAVU6gOG1JdWsqp6qstvkHIaTiMtQ+Vue8gHICjahHACGEEEIIIYQQUolQQwAhhBBCCCGE\nEFKJUEMAIYQQQgghhBBSiVBDACGEEEIIIYQQUolQQwDRzpsHBRJCCCGEEEIIMW8m1xAwc+ZMuLi4\nwMXFBX369EFWVhYA4OHDh6hWrRrc3d3h7u6OSZMm8dNcv34d7u7uEIlEmDZtmrFCJ4QQQogeUU5A\nCCGEGIbJNQQEBwcjMTERd+7cgaurK5YvX86Pa9GiBRISEpCQkIAtW7bww0eNGoWdO3ciKSkJjx49\nwpEjR4wResVFvQEIIYQYAeUEhBBCiGGYXEOAj48PhEIuLC8vL6Snp2ssn5KSAolEAnd3dwBAaGgo\nIiIiDB4nIYQQQgyLcgJCCCHEMCyMHYAm33zzDd5//33+88OHD+Hm5gYbGxssX74cvr6+SEtLg6Oj\nI1+mcePGSEtLU1nf4sWL+f97e3vD29vbUKETQkiFFhMTg5iYGGOHQSoRygkIIcQ0UU5gnozSENCj\nRw88fvxYafiKFSsQHBwMAAgPD4eVlRWGDRsGAGjUqBHS09NRo0YNJCQkoE+fPkhKStJpvrIHfXPB\nmLdWwwSCGIPHQgghUoonTkuWLDFeMMSsUU6gG23yAsWcgDFvyhMIIQZDOYF5MkpDwOnTpzWO/+67\n7xAREYGoqCh+mJWVFaysrAAA7u7ucHV1xb179+Do6IjU1FS+XFpaGhwcHAwTOCGEEEL0inICQggh\npPyZ3DMCIiMjsWbNGvzyyy+wtrbmh2dnZ0MikQDgugMmJiaiRYsWcHR0hFAoREJCAgBg3759CAoK\nMkrshBBCCNEfygkIIYQQwzC5ZwRMmTIFBQUF6NGjBwCgc+fO2LJlC6KiorB48WIIhUIwxvDFF1+g\nXr16AIBdu3Zh9OjRKCgogJ+fHwYOHGjMr0AIIYQQPaCcgBBCCDEMAWOMGTuI8iAQCFCRv6r03j/p\nfYKy9wKqup9Q3XSayqirT5s6CCEVmyH2sQKBAHDWsc67FXtfT/SjsuUEssMUh5c0naZyqspqm38Q\nQiouQ+1jdc4LKCfQyORuDSCEEEIIIYQQQojhUEMAIYQQQgghhBBSiVBDACGEEEIIIYQQUolQQwAh\nhBBCCCGEEFKJUEMAIYQQQgghhBBSiVBDACGEkAovMjISYrEYLi4uWL16tdL4/Px8hISEQCwWw8vL\nC48ePQLAvaO+WrVqcHd3h7u7OyZNmlTeoRNCCCFEjygn4FgYOwBCCCHEkPLz8zFx4kRcvHgRDRs2\nROfOndGzZ0+4u7vzZTZv3gx7e3v89NNPOHr0KKZOnYpjx44BAFq0aIGEhARjhU8IIYQQPaGcoBj1\nCCCEEFKhxcfHQyQSoXHjxrCwsEBISAgiIiLkypw8eRLDhw8HAPTt2xdxcXH07mFCCCGkgqGcoBj1\nCCCEEGK+XsQAeTEai6SlpcHR0ZH/7ODggJiYGLVlhEIh6tatiydPngDgugK6ubnBxsYGy5cvh6+v\nrz6/ASGEEEL0gXICnVBDACGEEPNV3Zv7k8pcolREIBCUuvpGjRohPT0dNWrUQEJCAvr06YOkpCTU\nqlWr1HUSQgghxAAoJ9AJNQQQQggxXXdjylyFg4MDUlNT+c+pqalyVwOkZVJSUtCgQQNIJBJkZWWh\nfv36EAqFsLKyAgC4u7vD1dUV9+7dQ6dOncocFyGEEEJ0VMa8gHKCYvSMAEIIIRVahw4dkJiYiPT0\ndBQWFuLAgQMIDAyUKxMUFIS9e/cCAI4dO4bOnTtDKBQiOzsbEokEANcdMDExES1atCj370AIIYSQ\nsqOcoBj1CCCEEFKhWVtbY+vWrQgICIBEIsHw4cPRrl07hIWFwcPDA8HBwZg8eTKGDx8OsVgMOzs7\n7N+/HwAQHR2NsLAwCIVCMMbwxRdfoF69ekb+RoQQQggpDcoJiglYRXwEogoCgaBCPu1RSiCIAQAw\n5i33WXaYNtNpKqOuPm3qIIRUbIbYx3L38UXrOJVPhd7XE/2obDmB7DDF4SVNp6mcqrLa5h+EkIrL\nUPtY3fMCygk0oVsDCCGEEEIIIYSQSoQaAgghhBBCCCGEkEqEGgIIIYQQQgghhJBKhBoCCCGEEEII\nIYSQSoQaAgghhBBCCCGEkEqEGgIIIYQQQgghhJBKhBoCCCGEEEIIIYSQSoQaAgghhBBCCCGEkEqE\nGgIIIYQQQgghhJBKRMAYY8YOojwIBAJUkq9KCCHlzhD7WIFAACBax6l8aF9PSkQ5ASGEGI6h9rG6\n5wWUE2hCPQIIIYQQQgghhJBKhBoCCCGEEEIIIYSQSoQaAgghhBBCCCGEkEqEGgIIIYQQQgghhJBK\nhBoCCCGEEEIIIYSQSoQaAgghhBBCCCGEkEqEGgIIIYQQQgghhJBKhBoCCCGEEEIIIYSQSoQaAkxc\nTEyMsUMoNYq9/Jlr3ADFbgzmGjchlZm5brfmGjdAsRuDucYNUOzEfJhcQ8Bnn32Gtm3bwtXVFd27\nd8eDBw/4cVOnToVIJEK7du2QkJDAD//uu+8gEokgEonw/fffGyNsgzHnDZJiL3/mGjdAsRuDucZd\nGpGRkRCLxXBxccHq1auVxufn5yMkJARisRheXl549OgRP27lypVwcXGBWCzGb7/9Vp5hV3qUEygz\n1+3WXOMGKHZjMNe4AYrdHFBOwDG5hoC5c+fi999/R2JiIoYMGYIlS5YAAA4dOoSUlBQkJSVhx44d\nGDVqFAAgIyMDy5YtQ3x8POLj47F06VL8888/xvwKhBBCTEh+fj4mTpyIyMhI3Lp1CwcPHpQ7cQSA\nzZs3w97eHrdv38bs2bMxdepUAMD169dx+PBh3L59G5GRkRg/fjwKCgqM8TUqJcoJCCGE6BPlBMVM\nriHA1taW/39ubi7s7e0BABERERg+fDgAwN3dHa9fv0ZaWhpOnz6NwMBA2NrawtbWFr169cLp06eN\nEjshhBDTEx8fD5FIhMaNG8PCwgIhISGIiIiQK3Py5En+GNO3b1/ExcVBIpEgIiIC77//PqpUqYLG\njRtDJBLhypUrxvgalRLlBIQQQvSJcoJiFsYOQJUFCxZgz549qFatGr9w09PT4ejoyJdxcHBAWloa\n0tPT4eDgoDRcFYFAYNjADUR6BcQcUezlz1zjBih2YzD9uH10Ki174iiVlpamdPxQ7P4oW0YoFKJu\n3bp48uQJ0tPT4evrKzetumMMMQzKCZSZ/narmrnGDVDsxmCucQMUu2FpnxdQTqCZURoCevTogceP\nHysNX7FiBYKDgxEeHo7w8HCsWrUK06dPx65duwAAjLFSz7Ms0xJCCCl/+tpvm/MJX2VAOQEhhBBt\n6GPfTTlBMaM0BGjbTW/o0KHo2bMnAK7FJTU1FZ6engCKW2ocHBwQHx/PT5OamoouXbroP2hCCCFm\nSXr8kEpNTZW7GiAtk5KSggYNGkAikSArKwv169dXmlbxSgIpO8oJCCGElBfKCYqZ3DMCkpOT+f8f\nO3YMYrEYABAUFIR9+/YBAG7cuMHfm+Hn54fIyEjk5OQgJycHkZGR8Pf3N0rshBBCTE+HDh2QmJiI\n9PR0FBYW4sCBAwgMDJQrExQUhL179wLgjj2dO3dGlSpVEBQUhJ9++om/Bz0xMREdO3Y0xteolCgn\nIIQQok+UExQzuWcEzJo1C/fv30dhYSGaNWuG7du3AwAGDRqE6OhoiEQiVK1ale8a2KhRIyxYsIC/\nKrBo0SI0bNjQaPETQggxLdbW1ti6dSsCAgIgkUgwfPhwtGvXDmFhYfDw8EBwcDAmT56M4cOHQywW\nw87ODvv37wcAtG/fHgMGDECbNm0gFArx9ddfw9LS0sjfqPKgnIAQQog+UU4gg1UCp06dYq6urszZ\n2ZmtWrXK2OFo1LRpUyYWi5mbmxvr0KEDY4yxrKws5u/vz8RiMevZsyd79uyZkaPkjBo1ijVo0IC5\nurrywzTFOmXKFObi4sLc3d3ZjRs3jBEyT1XsYWFhrHHjxszNzY25ubmxkydP8uNWrFjBnJ2dmaur\nK/v111+NETJjjLGUlBTWrVs35urqyt555x22evVqxph5LHd1sZv6cn/58iXz8PBgbm5urGXLlmz6\n9OmMMcYePHjAOnXqxFxdXVlISAgrKChgjDH26tUr9t577zFXV1fWpUsX9vDhQ6PErSn2ESNGsGbN\nmvHL/ObNm4wxxiQSicmsL4QYijnlBIyZT15AOUH5o5zAOMw1L6CcgCiq8A0Br169Yk5OTiwtLY0V\nFhYyDw8Pk16RnZycWFZWltywyZMns40bNzLGGNu4cSObOnWqMUJTcv78eXbjxg25A6e6WA8ePMj6\n9evHGGPsxo0brG3btuUfsAxVsS9evJitX79eqey1a9eYh4cHe/36NUtLS2NOTk4sPz+/PMPlPX78\nmN2+fZsxxlhOTg5r2bIlu3nzplksd3Wxm8Nyz8vLY4wxVlhYyDw9PVlUVBTr06cPO3LkCGOMsWnT\nprENGzYwxhhbt24dmzZtGmOMsSNHjrC+ffsaJWYpVbGPHDmSHTp0SKmsKa0vhBiCueUEjJlPXkA5\nQfmjnMA4y50x880LKCcgskzuGQH6ps27Ik0NU3gipuy7LENDQ00m/m7duqF27dpyw9TFqu6dz8ai\nKnZA9dNITemdoQ0bNoSrqysA7pUobdq0QXp6ulksd3WxA6a/3KtVqwYAKCgoQFFRERo0aIDLly+j\nf//+AOSXuap3z6r6fuVFVeyA6mUuG7ux1xdCDMEccwLAPPICygnKH+UExnt/u7nmBZQTEFkVviFA\n1bsiTXklFggE6NGjB9q0aYPNmzcDAJ4+fYq6desCAOrVq4cnT54YM0SN1MWq7p3Ppuarr76Cs7Mz\nQkNDkZ2dDQA6vZe6PD18+BBXr15F165dzW65S2Pv1q0bANNf7hKJBG5ubmjYsCF8fHxQu3Zt1KtX\njx/fuHFjPjZ17541FsXYRSIRAO7d7M7Ozpg8eTLy8/OVYgdMZ30hRF/McR0357zA3I5Nikz92CSL\ncoLyZa55AeUERFaFbwgwt3dFXr58GTdu3MDZs2exa9cunDlzxtgh6Y1ia6Op/TYff/wx7t+/jzt3\n7qB58+aYOnWqsUNSKzc3F4MHD8bnn3+OGjVqaCxrass9NzcXQ4YMweeffw47OzuzWO5CoRA3b95E\nWloazp8/j5iYGGOHpDVVsa9evRr37t3D77//jpcvX2LZsmV8eVNbXwjRJ3NcnytqXmDq+xpzODZJ\nUU5Q/sw1L6CcgMiq8A0B2rwr0pRIu+jUr18fgwcPxtWrV1G/fn1kZmYC4FrXpWVMkbpYVb13U7Zl\n1xTUq1cPAoEAAoEA48ePx9WrVwGojt2Y61BhYSEGDRqEYcOG8V3QzGW5S2MfOnQoH7u5LHcAqFmz\nJnr37o0HDx7wyxuQX67Sd88CkHv3rLFJY798+TK/flhZWeGjjz7SuMxNbTslpCzMLScAzDsvMJdj\nkyrmcmyinMC4zDUvoJyAAJWgIUCbd0Wairy8POTl5QEAXrx4gcjISIhEIrl3We7duxdBQUHGDFMj\ndbGqe+ezKZHtpnXo0CG+u5QpvTOUMYaPPvoILi4umDFjBj/cHJa7uthNfblnZWUhJycHAPDyih4p\negAAIABJREFU5UucPn0abm5u6NSpE44ePQpAeZkrvntWKDTOrlZV7GKxmF/mjDEcPnxYbpmbyvpC\niCGYU04AmH9eYA7HJnVM/dgEUE5grOVurnkB5QRESXk+mdBYTp48yUQiEXN2dmYrVqwwdjhqPXjw\ngLVp04a1bduWtWzZki1cuJAxJv8amB49epjEa4IYY+z9999n9vb2zNLSkjk4OLCdO3dqjPXjjz/m\nX0Fy/fp1I0auHPuOHTtYaGgoa9OmDWvdujULCAhgaWlpfPnw8HDm7OzMRCIRi4yMNFrcFy5cYAKB\ngLVt25Z/zcupU6fMYrmriv3kyZMmv9xv3brF3NzcWNu2bVmrVq3YkiVLGGOaXxM0ZMgQ5urqyjp3\n7sySk5ONErem2H18fFjbtm3ZO++8w0JCQtjz58/5aUxlfSHEUMwlJ2DMvPICygnKH+UExmGueQHl\nBESRgDEjPs6aEEIIIYQQQggh5arC3xpACCGEEEIIIYSQYtQQQAghhBBCCCGEVCLUEEAIIYQQQggh\nhFQi1BBACCGEEEIIIYRUItQQQAghhBBCCCGEVCLUEEAIIYQQQgghhFQi1BBACCGEEEIIIYRUItQQ\nQAghhBBCCCGEVCLUEEAIIYQQQgghhFQi1BBACCGEEEIIIYRUItQQQAghhBBCCCGEVCLUEEAIIYQQ\nQgghhFQi1BBACCGEEEIIIYRUItQQQAghhBBCCCGEVCLUEEAIIYQQQgghhFQi1BBACCGEEEIIIYRU\nItQQQAghhBBCCCGEVCLUEEAIIYQQQgghhFQi1BBACCGEEEIIIYRUIpWuISAmJgZCoRDfffedsUPh\nmWJMxuLk5AQfHx9jh2EwOTk5CA8PN3YYclauXImXL1/qNM2ff/6JgIAA1KhRA0KhEKNGjTJQdLor\n7+3JXLdfc42bEClTXIdNISZTiKG8UM5gmkqTVwCmm1tQXqEdc427MtNbQ4D0xxcKhdi+fbvqmQmF\nCA4O1tcsy0QgEBg7BCXlHdPNmzexePFiPHr0qFznq4lAIDDJ30YfXr9+jfHjx2PMmDEGnU///v11\nKj9q1CiMHTsWjDGtpxk6dCgSEhIQHh6OvXv3YsKECbqGaVDGWI/Mdb0117iJ4dDxvOy0jenu3bsI\nDQ2Fo6MjrKysUL16dbRu3RoffPABoqKiyiUGbVHOUL7KK2fQRnnkFYBp5xaUV2jPXOOujCwMUeni\nxYsRGhoKa2trpXG0cuhHYWEhtm7diqtXr6J58+YQCoV48OABPD09MW7cOFSpUqXEOm7evImlS5fC\n19cXTZs2LYeoS6brQcOcrFmzBsHBwWjYsKHSuMuXL2Pjxo04ffo0nj9/joCAALi4uGDdunV8mcGD\nB+Pw4cOwt7eHj48P9u7dq1TPH3/8gXr16ukU11tvvYXAwECsW7cOs2fPLrH8v//+i+vXr2PmzJmY\nMmWKTvMqD++++y5evnwJCwuD7N4IqVToeG44165dw7vvvotq1aph9OjRcHFxQWFhIZKSknDy5EnU\nr18fvr6+xg6TRzlD+dKUM+jLrVu3kJ6ejsDAQLVlyiOvAEw7t6C8glRUel+jPTw8cO3aNWzatAlz\n587Vd/UEwOPHj9G7d2/4+fnh+++/55MxiUSCTz/9FJ06dcKJEye0PnhU5AOpqcjMzMSBAwdw8+ZN\nleM7deqEn376Ca1bt4aHhwdOnjwpN54xBnd3d/j6+mLChAkQClV35tm7dy+GDx+uc3xDhw6Fh4cH\nxo4di1q1amks+/z5cwCAnZ2dzvMpiUQiQX5+PqpVq1bqOgQCAaysrPQYFSGVEx3PDWvJkiUoKCjA\njRs30KpVK7lxmzZtwj///GOkyDSjnMHwSsoZ9GXBggVo2rSpxoaA8sgrAMPlFpRXEKKe3p8R8N57\n76F9+/ZYvXo1srOzSyyfmZmJjz/+GI6OjqhatSqaNGmCyZMnK027e/duCIVCREdHY926dWjevDms\nra3RqlUrfP/992WOW9s4AKCgoABr1qyBm5sbqlevDhsbG7i7u2Pjxo18mZycHMyZMwceHh6oUaMG\nqlatipYtW2LevHmlum9K6vXr1+jfvz/q16+PNWvWyF2REQqFWLt2LWrXro2+ffvi9evXautZvHgx\nRo8eDQDw8fHhu4HK3o+Vn5+PFStWQCQSoVq1any9igem0vw2qampeO+991CzZk3UrFkTffv2xf37\n91WWNWQcJf2WR44c0dg9ViQSoWXLlirHydq5cycGDBigsUxaWhr+97//wc/PT254VlYWwsLCMHTo\nUEyaNEltIwAAXLhwAe+++26J8SgSCATo06cPdu/erbHcyJEj0axZMwBcEitdb86fP8+X0XWbPnv2\nLJYtW8b/XgcOHFA7/7y8PMyePRvNmjXju9GKRCLMmDGDL6PqHrXSrBsPHz7EoEGDUKNGDdSsWRP9\n+/fHw4cPtb4nVdv1Vlva7HcA3fZlihYvXgyhUIiUlBSlcYrfW7pMo6KisHz5cjg5OaFq1apwd3dH\nbGwsAODMmTPo1KkTrK2t0ahRIyxfvlypXn3v26XrpKo/U7nf1FzQ8ZxjqOP5H3/8gYYNGyo1AkjJ\nNuaXNYayLnPKGTia1pejR4/qJV8AtMsZykoikSA2NrbEXieGziuAknMLyis4FT2vAPSTWxjiGFNR\ncwu99wgQCoVYtWoVevTogfDwcKxfv15t2efPn6NLly549OgRJkyYADc3N9y8eRPbtm1DVFQUrly5\nAltbW7lp5s+fD8YYZsyYgSpVqmDbtm0YOXIkGjZsiICAgFLFrEscBQUFCAgIwLlz59C/f39MnDgR\nFhYWSEhIwOnTp/kdR3p6On7++Wf079+fv4IbExODNWvWICEhAZGRkaWKdffu3bhy5QrOnj2rtszs\n2bMREBCAXbt2YezYsSrLDBo0CI8fP8Y333yDBQsWwNnZGQDQvHlzANytB7169UJ8fDw++ugjzJ49\nG8+ePcP27dvh5eWF8+fPo3379nJ1avvb/Pvvv+jevTuePHmCyZMnw9nZGefPn4efnx/y8vLk6jRk\nHNr8ln379sVbb72FnTt3Kt2nd/nyZdy9excrVqxQ+1tIHTlyROO2AIC/H1T2oBwXF4eYmBgsXLgQ\nlpaWGqePjY1F586dS4xFHT8/P3z22WeYPn262jITJkyAu7s7ZsyYgYEDB2LgwIEAgNatWwMo3Tb9\nySefoEqVKpgxYwaqVq0KFxcXtfMfPXo0fv75Z4wePRpdunQBYwz37t3DpUuXlMqq6ras7bqRlZWF\nbt264dmzZ5gyZQpatWqF2NhYfh0tqUt0adZbTbTd75Rm+WtL3f2Rc+fOhaWlJebOnYtXr15h7dq1\n6NWrF7Zt24ZZs2Zh0qRJGD9+PA4cOIBFixahQYMGGDdunFI9+tq3q7pl5sSJE/jxxx/x1ltv6fal\nKzk6nhv2eO7o6Ijo6GicPHkSQUFBGsvqK4bSLnPKGUpeX44fP66XfAHQLmcoq99//x3Pnz9H9+7d\n1ZYpj7wCUJ9bODs7U17xRmXKKwD95Bb6PMZU2NyC6Ul0dDQTCARs/fr1jDHGevbsyaytrdmjR4/4\nMgKBgAUHB/Of58+fzwQCAfvuu+/k6tq1axcTCARs4cKFSsO6dOnCXr9+zQ9/8uQJs7a2ZgMHDtQp\nTtl56hLH6tWrmUAgYCtXrtQ4n4KCAiaRSJSGh4eHM4FAwK5cuaIxJnW6du3KrKys2P3799nKlStZ\nWloaS01N5f9WrlzJ/vrrL2ZhYcG6du2qsS7p9zt37pzSuA0bNjChUKg0Ljc3lzVv3px5e3sr1aPt\nbzNv3jwmEAjYgQMH5IZLfwcfH59yiUPb31Ia1507d+SGjxkzhllaWrKMjAyN0798+ZJZWlqynJwc\njeVGjBjBatasyYqKiphEImGrV69mO3fu1DiNrIkTJ7KkpCSV4zZt2sQWLlzI1q5dy8LDw9mff/6p\nVObZs2fM2tqaFRUVaZxPcnIyEwgEbMmSJUrjSrNNu7m5sfz8fG2+IrO2tmb9+vXTWEbV9qTrujF7\n9mwmEAjY0aNH5YYvXrxYaR1VNT9d1ltt6LquarP8VcUdFhbGBAKB3H5bqmnTpnLfW1qnl5eX3DoT\nGRnJBAIBs7KyYomJifzwwsJC1qRJE9a+fXuVsZV1367O1atXmY2NDevSpYvW61llR8dzeYY6np89\ne5ZZWVkxgUDAXFxc2OjRo9nWrVvZ3bt39R6DPpY55Qwlry9lzRcY05wzbNmyhc2aNYtNnjyZJSUl\nsbCwMLZs2TL23nvvsezsbK3KHDhwgIWGhjKxWMwaNmzIQkND2bhx41TGUl55BWPqcwvKKziVIa+Q\nrbcsuYWh8wrGKk5uYbCGgISEBCYUCtmHH37Il1FMHJydnVmjRo2U6pJIJMze3p65urryw6Q/6p49\ne5TKt2/fnolEIp3ilF1JdYmjTZs2rEGDBlrt1KQKCwtZdnY2e/r0KYuLi2MCgYBt3rxZY0zq2NnZ\nsUaNGrG0tDQmEAiU/oRCIUtLS2MNGjRgNWvW1FiXpoN6u3btmLu7O8vMzGRPnz6V+5s4cSKzsLBg\nr169kqtH29/G2dmZOTo6KpXNzMxkQqFQbqdgyDi0/S2Tk5OZUChks2bN4ofl5uYyOzs71rdvX43T\nMsbY3bt3S/wtGGOsSZMmLDg4mGVlZbFJkyYxgUDA1qxZU+J0jDGWn5/P/Pz8VI778ssv2bx58/jP\n7u7ubNOmTSrL2tnZsb/++kvjvDQ1BJRmm962bZvG+clq3Lgxa9q0qcpEWUrTAVvbdaN169asadOm\nSmWzs7OV1lFV89NlvdWGtuuqLstfXw0Be/fulSuXm5vLBAIB8/f3V6pj8ODBzMbGRm6YvvbtqqSk\npDB7e3v29ttvs6dPn5a6nsqGjufq6fN4zhhjt2/fZiNHjmQNGzaUO5Z3796d3b9/X28x6GOZU85Q\n8vpS1nyBMfU5Q0pKCtu8eTN78uQJEwgEbNGiRfy4jz/+mI0fP56lpqaWWEaqX79+bPLkyWrjKM+8\ngjH1uQXlFZzKkFcwpp/cwpB5BWMVK7cw2OMv3dzc8MEHH2Dfvn345JNPIBaLlcokJyejW7duSsMF\nAgFcXFxw+fJlpXGOjo5Kw2xtbfHs2TMA3D1PT548kRtvY2ODGjVqqI21pDhkuwf9+eef8PLy0nif\nttSWLVuwbds23LlzBxKJRG6cNF5dFRQUoEqVKrCxscGGDRvwzjvvyI3/888/YWNjA6FQiMLCwlLN\nA+BeafTq1SvUr19f5XiBQIDMzEw0btyYH1bSbyP14MEDlfeb1a1bV2l+hoxD29/SyckJ/v7+2LNn\nD1atWgULCwscOHAAubm5Wr3WJzs7u8SH39y/fx+pqanw9PTE119/jQ0bNiA2NhZfffUVZs2aVWKM\nmrqWnjp1CnXr1sWLFy9QvXp17Nixg+/WqcjOzg7Z2dl8d09dlWab1vaeSQDYuHEjRo4cCRcXF7Ro\n0QJdu3ZFnz590L9/f622SW3XjeTkZJXraO3atdGgQYMS51Oa9VYTbdfV0iz/snJwcJD7XL16dZXD\npePU3c+s7W+jrZycHPTp0wcvX77E2bNndX7qNSlGx3PDHM8BwNXVFbt27QIApKSkIC4uDt9++y2i\no6PRs2dP3Llzh39ImT5i0Pd2JkU5A6es+QKgPmeIj49Hnz59cOvWLdjY2GDhwoX8OBsbGxw7dgw9\nevQosQwAFBUV4dy5c/j222/VxkF5BeUV0rjLO68A9JNbGGJ/V9FyC4O+B2P58uU4ePAgPv30U6Wn\noJeWQM29JOzNU2xTUlLw9ttvy40bOXIkdu7cqfd5arJhwwZ88skn6N27N6ZPn45GjRrBysoKf//9\nNz788EOlg7i2XF1dcefOHdSsWVPtPVf5+fnIzMxEu3btSjUPgFueHh4eWLVqldoyiit/Sb+NOccx\nbtw4DBkyBL/88gsGDhyIHTt2wN7eHr179y5xWsZYieuQ9PkAQUFBGDlyJABgxowZGDVqFA4dOoQh\nQ4ZonP7HH3/Epk2bVI4LDg7GpEmTsH//fojFYqxatQru7u4qywqFQhQVFZXwjfRLl9fxDBkyBL6+\nvjhx4gRiY2Nx5swZ7N69Gx07dkR0dHSJTwU2xLqhrj5d11tToGk9VbfPUjeNrvtNff42RUVFCAkJ\nwb1793DixAm1CSrRHh3P9X88V9SkSRM0adIE77//Pvz8/BAdHY1z586hR48eeovBUPtAUzlWm0Ic\nZckXpPNVFdPgwYMBALt27ULXrl3ljp1JSUmoVq0aBg0aVGIZALh+/XqJzwegvILyCn0oTV6haTpd\n9uH6/m0qYm5h0IYAJycnTJw4EZ9//jnOnTunNL558+ZISkpSGs4YQ1JSklICoA17e3ucOXNGblij\nRo00TqNLHK1atcLvv/+OoqIiVKlSRW2de/bsQatWrXD8+HG54dHR0dp8DbWGDRuGWbNmIS4uDl27\ndlVZJjY2FkVFRSW+7kXTxvTOO+/g6dOnBnmHcfPmzXHnzh2l4ZmZmXjy5IncQ10MGYe2vyUA9OvX\nDw0aNMCOHTsgEokQFxeHuXPnatVaXKtWLeTm5mosExUVhTp16vCNAAD36p158+Zh06ZNGhsC/v33\nX7x48ULtw0omTJgAT09PxMTEYO/evRg8eDCePn2q8r3gubm5Wr3mRx1DbNOK6tatixEjRmDEiBEA\nuAfKrFmzBocPH8awYcPKXD8ANGvWDPfu3VManp2djX/++afEnb++11tt19WyLn9pwvPixQu54QUF\nBXjy5AlatGhRiujL39SpUxEZGYmtW7eiR48exg6nQqDjuf6P55p4enoiOjqav3JljBgUUc6g3fpS\nlnwBKDlnuHDhAnr27Ml/LiwsxMWLF/m3OmhTJioqCq1atVJ7JZryCsorpCivKFYRcwu9vz5Q0Wef\nfYYaNWpgzpw5SuP69++PjIwM7NmzR274nj178M8//5Tq1SlVq1aFr6+v3J/0iebq6BLHsGHDkJmZ\nqfJprrItTIWFhUotXUVFRVi9erXO30nWpEmT4OLiorGeNWvWQCwWY/z48Rrrkj7lU/ruVlkffvgh\nHj16pLbbWFZWlg5Ry+vXrx//BGRZGzZsKNc4NP2WiiwsLDBy5Ej8+uuvWLJkCQDgo48+0mo+Tk5O\n+O+//zS+4ik6Olqpy5ilpSUmTZqES5cu4dq1a2qn/fnnn/Hee+8pDS8sLETbtm0RFhbGP433zJkz\nePnypcrbRnJzc5GbmwsnJyetvpcqhtimpSQSCXJycpSGe3h4AIDS06PLIjg4GKmpqXw3SqkvvvhC\nq+n1vd5qu98p6/KXJtSnT5+WG75161YUFBToFLM6pbkiq4tNmzZh69atmD59eon7QKIbOp4X08fx\n/PTp0yqvlBYUFODXX3+FQCCASCQyaAy6oJxBu5yhLPkCoDlnKCwsRHx8PLy9vflhEREReP36Nd9L\nVJsyUVFRfM5RUFCg9Po1yisor5Ayh7wCoNyitAzaIwDgWtlmz54td5+S1Jw5c/Dzzz9j7NixuHHj\nBsRiMW7duoVt27ahdevWKpMNdcrSBUeXOKZNm4bjx49j7ty5iI+PR48ePWBhYYHbt2/jf//7H06d\nOgWAe9XOsmXL0KtXLwwYMAD5+fnYv39/mbsQVq1aFSdPnkRgYCAWLFiA5cuX8yu/RCLB/PnzkZ6e\njlOnTpX4urmOHTtCKBRixYoVyMrKgrW1Nd5++2107NgR06ZNw+nTpzFhwgT8+uuv8PX1RbVq1ZCW\nloaoqCj+Pa3aUPxt5syZg/3792PEiBG4evUqWrdujQsXLiA6Ohr16tWTK2/IOLT9LaXGjh2LtWvX\n4scff4S3t7fW97tVr14dYrEYt2/fRseOHZXGJyUl4cmTJ3IHbakJEyZg8eLFWL9+PX744QeV9R85\nckQpQQK4+5jy8/PldtIXL17EiBEjVN5/ePv2bYjFYpUt+trS5zat6L///kOTJk3Qr18/eHh4oFat\nWnj48CG2bNmCOnXqIDg4uNR1K64bn376Kfbv349hw4ZhypQpaNmyJeLi4hAVFYV69eqVeMDR53or\nrU+bdbWsy79nz55o2rQp5s2bh6ysLDRt2hRxcXG4cOGC0rZZWrrWoUv5xMREzJo1C2+99Rbc3d2V\nXvfTokULdOrUSaf5k2J0PNfv8XzGjBnIzs5G37594erqCjs7O6Snp+OHH35AUlISRowYwTcEGCoG\nKW2WOeUM2ucMpc0XAM05w40bN5CXl8f3GPj3338xb948bNu2DU2bNtW6TFpaGn+rwRdffKHUg5Ty\nCsorzCmvAHQ7bug6z4qcWxi8IQAAZs6ciS1btuDx48dyw2vUqIHY2FiEhYXh4MGD+Oqrr/DWW29h\n7NixWLJkCf9gCClN94uU5Z4RXeKwtLTEb7/9hvXr12P//v2IiIhAlSpV0Lp1a7lu3YsWLQJjDLt2\n7UJUVBQaN26MkJAQ/oEkJcWkSZMmTXD9+nVs3rwZI0eOhJOTExhjSE5OhpubG65du4aqVauWWI+j\noyN27tyJ1atXY/z48SgsLMTIkSPRsWNHWFhYICIiAlu2bMGePXswZ84cFBYWolGjRujYsaPcd9UU\nv6rfplatWrhw4QJmzpyJr7/+GgDg7e2NqKgo+Pn5yZU3ZBza/pZSzZs3h4+PD6KionRq3Qe4KxrR\n0dFyB/WkpCQsX74cCQkJEAgE2Lt3L27fvo1vvvkGAPC///0P06dPh0QiwYEDB/D8+XOsXLkSbdu2\n5et49OgR6tSpo7StAECdOnUwf/58nDp1CidPnsTz589RvXp1fpkrio6ORv/+/XX6Xor0tU2rUr16\ndYwfPx7R0dH45ZdfkJeXh/r168PPzw9LliyR68Koql5d1o26devi4sWLmDVrFr766isIBAJ4e3vj\nzJkz6Nq1q9I9g4rT67relkTbdbWsy9/Kygq//fYbpk6dinXr1sHKygoBAQGIjo6Gl5eXUvnSPAeg\nrL+NJllZWWCM4Z9//uG7eMoaOXKk2R6sTQUdz/V3PF+3bh0OHTqE2NhY7Nu3D69evULNmjXRtm1b\nzJo1S+8xlHWZU86gfc5QlnwBUJ0zAFyX/3fffRcxMTG4ePEi7t69i7Vr16JPnz46lVm4cCEOHTqE\n9PR0+Pr68g0EAOUVlFcYN69QVUdJVC1vfR1jgIqdWwiYvp9mQUgF1rt3b1y+fBl///23Vo0tUmlp\naRg4cCCuXLmi13hWrFiBdu3aoVevXmWuy8PDA8eOHdP6ibOV0fPnz1G3bl2MGzcOW7ZsMXY4hBBC\nTFRp8wVAfc7Qr18/uLq6Ijw8XO202pTRhPKK8kV5BTEmgz8joLxERkZCLBaXeP88IaX14MED/Prr\nrwgNDdX5oO7g4IDu3bvr7WnbUmfOnJF7IFBpnThxAu+++y4drGW8evVKadimTZsgkUgqzENiKpOS\njhH5+fkICQmBWCyGl5cXHj16xI9buXIlXFxcIBaL8dtvv5VYZ3JyMjp37gyxWIz333+fv3d2y5Yt\naNu2Ldq0aQMPDw9cv36dn+bWrVvo1q0b3N3dIRaLkZ+fb4jFUKlQXkCMpSz5AqA6Z2CMITY2VuOV\nR23KlITyCsOhvMJ0mEJOoG4e2dnZ8PHxgZ2dHaZMmcLX8/LlSwQEBEAkEqFVq1aYOXNm2W+tYBXA\nq1evmJOTE0tLS2OFhYXMw8OD3bhxw9hhkQri8uXLbNeuXax9+/bMxsaGPXr0qFT15OXlsUGDBrH/\n/vtPL3Fdv36dTZ06tcz1/Pfff2zAgAEsLy9PD1FVHP7+/mzMmDHs66+/Ztu2bWNDhgxhAoGAde3a\nlUkkkjLXX1RUxDIyMkr8Kygo0MO3MW9lXVbaHCPWrVvHpk2bxhhj7MiRI6xv376MMcauXbvGPDw8\n2OvXr1laWhpzcnJiBQUFGuvs06cPO3LkCGOMsWnTprENGzYwxhjLycnh5/fLL7+w7t27M8YYe/ny\nJROJROyPP/5gjDH277//sqKiIn0tvkqJ8gJiDPrKFxhTzhlu3brFBAIBe/z4sdpptCmjCeUVhkV5\nhWmQHh9SUlJYamoqa9u2Lfvtt9/kllFYWBgbO3Ysy8jIYD///LNBcgJ1eceLFy/YxYsX2bZt29jk\nyZP5uPPy8tjFixcZY4wVFBSwbt26saNHj5ZpWVSIhoBz586x3r1785/Xrl3Lli1bZsSISEUycuRI\nJhAIWNOmTdnBgwfLVFdGRgabPXu2XuKaMWMGu3r1apnr+eSTT9jff/+th4gqllWrVjGRSMRsbGyY\nlZUVa968OZs9ezbLzc3VS/3JyclMIBCU+Hfu3Dm9zM+clXVZaXOM8PX1ZdeuXWOMcclUvXr1WFFR\nEVuyZAlbt24dX653797swoULaussLCxk9erV44dfvXqV+fn5KcW0f/9+FhISwhjjEoBRo0aVYskQ\ndSgvIMagz3yBseKc4ccff2QuLi5MKBSy4OBgdv36daWy2pQpCeUVhkV5hWmQHh+kywsAAyC3jGSH\nRUdHGyQnUJV3yDYI7dq1S64hQNGsWbPYli1byrQsyuVhgYaWlpYGR0dH/rODgwNiYmLkyhj6tRKk\n4nv06BH/lN2yWrt2rV7q2bhxo17qWbdunV7qqcju37+PtWvX6u2305biayVNGdPzI2fq1KnDv0dd\nG++++y5sbW2VXgelzTFCtoxQKETdunXx5MkT/mFastOmpaWBMaayzqdPn6JevXr88MaNGyMtLY3/\nvGXLFmzYsAEvXrxAXFwcAOCPP/5AQUEBvL29kZWVhZCQEHz22Wdaf2+irKTfnHICYkj6zBcUHT9+\nHMePHy9zGXUoryg/FT2v0HdOAOieF2jKCezt7XHmzBlER0fj5s2bmDFjBl9mxIgR2LRpE2rXrg03\nNzeD5ATq8o6GDRsC0Hyc+vfff3HkyBGcOXNG62WhSoV4RoC2B3TG9YAwq7+wsDCjx0AX7ZwMAAAg\nAElEQVSxm8+fucZNsZt/3Ibw7Nkz7AJ0+pO+MkuWKZ30TZo0CX/99Rc2bNiA0aNHA+DeBx8XF4fD\nhw/jypUrOHnypNLrS4lutPnNjb39mcJ2S3FT7Kb6Z65xU+zcn6E8e/YMewGt/zTlBFWrVoWvry9E\nIhEaN24MX19f/s/GxgbdunWDr68vatWqZbDvUxqvX7/G0KFDMW3aNDRr1qxMdVWIHgEODg5ITU3l\nP6empsq1ykgtwgJcQ3v+sweu859vwh0Z8c1g75kMAHBDAjxwHdsxRuO83ZDATw+Ar0Nx+BhsBwC5\n+bkhgR8vLS87zgPXsREvIMECAFAZi+z8PMA9eGoUdgEAdmEUP11GfDMwz+LpDiNIrp6bcMc1tMdJ\nDEQjFMcvXUbSOAHgb3ArXTIaodlfGUhuYY8EuGEg5B+Etw938Q2S8Tea8WUXtZiPpeCeZCuIB9if\nAuweDvQA0PhPAC1023kserNspHUmoxGa7c3gxoUWz6tEBQXAypVAWhqQnw/8959OcShJSABiYoAh\nQwAHBz7WpXtXAPEAvpT5nvn5QG4uULdumWbZCMloiSi0wRUM/OsUcBlAKAP2CoBOwOEWgUq/kaEk\noxF2YRS/jvO/w943yXmo+t/5MILgjpsAgGb4u3QB7BVwyxkApoFbFvEAPAG8eYZScgt7bh5TMrjx\nnbiyi1rM57eFIBzGGGyX2z6CcBgeuM59p7/efJ9sYBEK5bZj6XdIgBv/Wbq9ANz2Kd0fnPxrEBcD\nuDik2xQA7rd8E690m5Ylnafs/kG63Ur3CbL7PaB4v5SDf3EFh/lhGfHcts08wX9P2XnITqv1tmUC\ntDlGODg4ICUlBQ0aNIBEIkFWVhbq16+vNK20BV8ikaiss0GDBsjMzJQr7/BmHyArJCQEY8eOBcC9\nErZ79+6oU6cOACAoKAg3b95EYGCgfhZAJaTNb+6PCPyI9xEPT2wEdyXIE/EIj10GnAci5vkhDl0Q\nD+7gGZ/nCU+beH56T8SjC7heHdLpFXm+2RHFwxMzsBGeiJebn5R0nLQuaQzoDvh7nZAr+wB/4iIi\n5OqXJY2X/6wibqlwLAUA2OVlQpUcG+5Klj8i+O+7ETP4OmdgI3rjDF9Gdn6eiOfrl/JHBM6gN18W\nAM6gN///BVik9B1kx2vDHxGIz/NEjk09ue/laROvW12RkcCJE1w+8McfwPTpQGlPCAoLgS+/BEQi\nICCgeHjsm2OIl8IxMSMDsLcv3bxkRMAfebiACFxUuY7qumxLyx8RmIGN/Pak6jdXXFekIuCP3ivP\nAucARJbuBFN23ZTKPcOt27b+3Doy1eYLhMcuQ6aXLZrlJSN3bj3AH2B1BfjPHyh4aYt6yOHXL+n6\nlAk71FuZC8xj/LiJWIl9giUItgYuvPSDJ+JRLzZXbluOz/PkY/DrG8HvG2Sdndob8OdilI2v/i/F\nV7ht/TPltm/pd5Rug9J9lCx1+ytA9f4lHEuxAIsQD08+bsX5AuW3PpWVqeQE6uZRknHjxqFly5aY\nOnVqqZeBVIVoCOjQoQMSExORnp6OBg0a4MCBA2rfaSpL8QRX1fiSqJtesWFAtgFA1bSK9dyE+5th\nn5c6Dk2NGJq+t7ZlPsZmeLQofur1QIXxj2EPO5myY1psx3aMkdvVB4UewpjQ7Yh/Mz/VhwH1tmOM\n3EnKLoyCWyj3eVl8OJZ6qptSwaFDwLFjwPjxgJUV8PHHwF9/AS1aAIwBul5RnDoVqFYN+Ppr4NIl\noHZtLIsPx7XQ9vAIvV78Pe/cAaSv6Ll+HVC3A4iMBPz8AEtLtbPMiG+GjHRfnL8cBvtODzGmxXYs\nBbeMpb+l4m+E3FzAwgKwttbt+yk6cQJo3ZpbXgC8EMufVALgf4eg0EMAoLE5YlD8Sb5BrpTNAAgK\nPQSP0OvF60YLAKGq1+lRX+7i/y89+ZYtp7gdyY5b1GI+3JCAu3X+RA7ayzXuqZpXQovi+mXX3cMt\nAuHeorjhQNrwAEBuuJTsPkH2/4r7FMUYZLcVVdOoIy0ju8/UdVs1Jm2OEUFBQdi7dy//uqvOnTuj\nSpUqCAoKwoQJEzB9+nQ8fvwYiYmJ6NixI4qKilTWWaVKFXTq1AlHjx5F//79sXfvXgQFcQ2vycnJ\nfOt9REQEnJ2dAQB+fn5Yv349Xr58CUtLS5w7dw4ff/xx+S6kCqa0eQEARHj5wdNL+QRbMemVikMX\nreqNQxeVJ+6qZHrZoh5ykellq1X5eHiqrVtd3KrKSE9wZE+YNIlDF6XUX6v5ycWqfPKg6qRIW56I\nB2y4eqWxFM9PyxOVggJg1Cju5P+tt4ArV4AVK4A1a0qXExw6BGzfDuTkAOvXA++9BwBY4LUQAIqb\nVRkDRowA9u4Fvv0W+Ogj1fX9+ScgkQCtWmmcbRy64DIKYck6w1NQvMy7IO7NequwPBgDsrPLfGEC\nDx8Ct24BvXsDVarAE/H8dsL9FtqfMMahCzAPwDxdppInPSGOsylu2PPsq7yeZnrZctuSTTzO+veG\nrX8mImz80HvRWUTAk5+/3Ik3PNG7+9nieb0ZNyxAJn5AaZ/iaROPeP8S1nF/rpFAKsKLa5y09c/k\nGxGUvqvC9heHLvzvXfy7l4208QSQb+w0F6aSE6iah1BY3FlfVc+Kzz77DM+fP8eOHTv0siwqREOA\ntbU1tm7dioCAAEgkEgwfPhzt2rVTKidNYmWTdQByJyxS2iTHJZGtt7T1WXl7aj2t9LuV1MABqG7k\nkG28kG7PmuqRnmioOrmQxi51Kn4gbnq6I2NDM2Am5Iafih2IwJmHNcar0s8/o4ZEjJshxTFeQ3tc\nQ3ucih8IxAJa7ZcYA776CvjsM2Agd6rsfekSdzXfzg545x1g61Zg2zagb1+gaVPN9V2+DDx6BDx4\nAEycyCUPa9cCscCp1QNxCgOxVPp1Z8/mEo3Hj4E5c4Bdu5Tre/YMCAoCFi0CFi9WP99YYM6FeITv\nsMQY9+1YNiwcS2cCp958J3SF3LJHcjLg7w8MGwYs1XBaxxiXlGzbBnToAGzaJD/+1Stg6FCuQeHc\nOUAsRsbAZnD5LwkB//yKr5uNB36pDkAmFun3l0i4HhHVqsHb25v/HhmxzYCLMuV0dGoDt5xl1yt1\nvXYUqTuxliV7YgwAVb3r4ILMibd0G8yIb4ZAT+7KuqrGBemw7RjD9xrajjFyZWV7VsjOU7aM4j5N\n1f7N3jNZ6ftIt1FN+0DFXkF8WfM55qs9RoSFhcHDwwPBwcGYPHkyhg8fDrFYDDs7O+zfvx8A0L59\newwYMABt2rSBUCjE119/DUtLS1haWqo97nzxxRcYOnQoFi5cCJFIxN8ru2HDBpw7dw4SiQR169bF\n999/DwCwt7fHJ598gg4dOqCwsBBBQUHo37+/cRZWBaFNXiA92VSVGBsjqVWcp7pGgNreYp3r1vbE\nmj9xtlF94syf1Ghxsq/I29sbF/FC6/I6n7w/fgz88gswTveTTSWHDgEuLsCnnwIAvKtXByZMAJ4+\nBX77DYiPB9LTueP8Bx9orquwkDtuhodz+UOvXkCfPoCNDd9ThHfqFHDtGnDjBuDrC/Tvr/qkfOlS\nIDYW+P13Lk9R48VFWyxcEYuWZ5Ox8+xwFFlVUR+nRALMmAHs3w/88w8g1HD38P37wIIFXB5x4IBy\nXrR+PZc3hIZyPSEACAuL0GnzVSR7O0Hx0Cq3bublyeUE3HVtbrw+rjdLtwXplXLZ7V82DunJbhy6\nwHNevMpGLymu4a647jrerkDVG3JlZBvrZOcjvXKviuwJt+xyUDVeE+l31KYRQNv9S2n2AabCVHIC\ndfMAACcnJ+Tk5KCgoABHjx7F6dOnYWtrixUrVsDZ2Zmve8qUKfxthqVRIRoCACAwMFDrbpRKCa0M\n2dsDyoumeVb1Vn4XrKbyit+tpHJS6k6KVM1buh8q6YSgqnen4vKxQAbU38dyKn6g7st940ZMQ1tM\nctrKx8DXE/umTEqK/FV3WYwBkyYBmZnAixfcSf4b3tu2AXv2AM+fcwfwd97hrp7//Td3C4E6+/Zx\nvQE2b+ZOjBcsADw8uJN42HEn47PSADgAN28Ct28Dhw8DWVmAqyt3FcLKSr7OCxcANzdgxw7g7l1g\n9GjuaoCtLVe3szNQpQpqvHyO+Y/i0K3bBfxyuS/O/uYHzFS+RQa//841fERGAt27A2fPam4ImDcP\niIgAVq/mGi3q1eN6Tkh7L5w7B7RpAwwYwNVz4AAm39+MhQ+W4ZZ9G/j+FQXgTXe4CzL13rkD9OzJ\nXX24eLG4IQDgGgFky5bGReCUl0wfiNji/2ZAvrFLcf2XXc81kTZwuXlz0yveaiT9vyLFbVj26r2q\n8oqNAOrGK9afEd+M+95equNXtX9RJDtPVftMc6HqGLFkyRL+/1WrVsWBAwdUTjt//nzMnz9fqzoB\noFmzZrh06ZLS8C/fJMSqDBs2DMOGDVM7nuhOl7xAlj6umOmLqpN3XRsCpCcZmhoDdLmqV1I96non\neHt7Y7nMLQF6d/YsMH06rIZORYFt8XGUvxos/V137eIawBWPtQBw/Dhw9Ch3zNu3rzj2wYO5Y/Tu\n3dwFgy5duEbs16+BwYPV99bLzOSOs2+/DQQHc3lB587Ad99xFwoUhYcDy5Zxx3wfHy6ekSPlyzDG\n3Xro7s79LVrE9VD47z8uH/DwAGrUAAD03H0WD1aMgGPMBXRbHYuYhd0BqFjHZ87k8orsbK5h4dYt\nLgZVbt3iLiLMmsV9r9BQrqdE587F8Z08CRw8yPV8mDMHb2U9RvDkSDChAD7LLwLxc/gehLzXr4Ex\nY7jca8IEeH/1ldzo+DzPNz09yk5TzxxVjV0bMaO4J4GKbSVetrcA4iH2rg3IrBLq9imlPZlW15tA\ndvtTukVIZrvUtA1rs38x50YAKVPICTTN4+HDhyqHSyQSlcNLq0I8LFBb+rjKr0ibxFhfZRTLqZum\nNPNTt2y0rUur+GNVfI4Fd9KnK8aApCQMvn4QFucLleLhrV8PfPKJ6jouXQJ+/ZVr9T97ljtASwmF\nXPe8qVO5hGD/fu7E+eBBbt6qJCVxJ8rnzxdfIXBy4lr+Z8xAlaLXGHP5W1hLW/cPHwbefx+oWhVo\n1IjrWh8VpVxvTAwwaBDXCODmBixcCCQmclclBgwAatcGfHzw9cHxiG7hg8v9OiNS3AuBsdy95Y55\nKYg91wWCC5Li+SYnc40V33zDNQzIPkyFMeDzz7meDStXcldYYmK4XgkHD3JXK/r3564eANwBPzCQ\nu1py7hywdSum/P0l/MefQd/Rv8DrYSx3dQGANXuJ9q7XuKse/v5c8rBhA9czQnHn1k31YtZJLOTX\nM+lfbPF6mxHfjOtB8obi1XTFdVtu2Jt6VE4vnS+KT/RVddeXkvZmkZ1HRnwzfriqngrq4pP9V3G4\nInM+uSekopJe/dN3o4S2tyYYax5l6omRlAS8fAnnY3+oj+WPP7jGdBVJORjjTqqtrIDvv+dux5PV\nujWwahWXV3z+OXf7YMuWQHS06ngYA4YP5xoATpwozjHmzeMazVNSIIq9g1rxz7nhGRlcA7n0osTA\ngVzPBEUPHgBFRdztjN9+y504HzvGXVhYtIjLJ1xcgPHj4XX0MmJCuiHms65wPXiXr2LAmON45+Sf\n3IcnT7gLDV5eXF7Rs6fyd4qN5eZz5Qp3vP/yS663xNKlXINF377cOIBrUHj1iuvRMHo0sH493g85\njMQhztgZNRw3Q8VcTvVmGdW5nw2XuLvcssrI4J7V9PPP3PfRI2nXeABy983L/l/29gVtG8fi0EVp\nO41DF2R62Wp9a4860t4C2mxT+ty2Nc2zIjQCkGIVpkeAIZR0VV1deV3HyZaRzqs0PRNUTaN2vjJX\n6NXNp6wnCDpNL41H2xwgLQ2wscH9Wk3R7ZcLwGxfvh4poaSI67L2/Dl3IvrmdRy8LVuAyZO5lnBN\n3tzHC8a4Fuvbt7kr4IoWLwbmzuUeBiRr82age3c8POgEu/wciJAEYBN34JZt8Q4J4Q7qsr0X/vuP\na4jYuZPrATBvHvcnKysLuHIF+Yt+xHZPrsv52ZZ+6PU4EsA4jH34LbpkX0Lb578DcOdO5CdNKk42\n2rUDjhzhDsIAcO8esGQJl0xUq8YlBtLGizZtuIaE7t25OKpUAX74geu1UL06d9CfNg0b+32B2424\nZXTCpQ+G9+8P2NkhueAWHv/vLWCRJZdIDRnCdZv86ScuaWjZEofis5BtUwf72g0D4KP5t1Hjgxv7\nYZHyGnsufqi+kELD1E1P9Q2FGnu/vLnirrK3DJS3a1XDAfU9lRR7LKgrJ/dZ2gtAsfGtlGR7OBBC\nSs+c7qPVVXk0MmiUmAgMGACXw3fx+7Diq5pyD0v76SfumThRUYDiK9QuX+bu4f/qK83d4q2suEZ4\ngOsNcOgQd/Ks6Nw5rgu94mv8OnXiTqI9PLBUko+qVfOB3zO5cr16Ffcu6NMHmDKFu9VQtuv9Dz8A\n3t5cLwAfH+5PVmEh1ygSGYmoD7rjqWN9pNs3Qq1Hz2Hz9AUs8ovQbtfvXNkgcDlBhw5c/gJw9W3d\nyvVYkD4/aOFC7jbF58+5XCckhBtuYcE1BrRqBXz4IZc7LFjAXdkXCLjnLbVuDUmLGrg0tSMgEODW\nByJ0GfQ119iQn4+xD28jrUljoGUHLi+ytuYuEPTsCfTvj8+yEgGBAH+5vw3M+lbjs5LUsXn6AoGz\nz+Dx9ro6nflIr5yXZrvVNI2mnjMlTaM0XOHEXFOdsr0ayvIcDm3mRcxDpWoIUJeQl6W+8qDLfEos\n++IFd8KmbhoVJzIaSU801HQ9Vqk0D9pRlJgIuLoi4UULuNS/A6D4nZ64CKAr0PnhJe4hP56e3EFf\n9j6+W7e4E1wNXXWVCATclfmDB5UbAu7e5Q76u3crT2drC1y5AodLl4CWLTG9QwdgaR2uFV7alQ4A\nxo3jrjT89hv316gRd1XCx0e+nKK6dYHAQIwIDMSIN4OaLPTFmhNzgIICfPRoB2Kavwsfu2iAuQFX\nr3IPMZRavJjr/ZCQwD3L4MgRrtukumUjFHKNAR98wN0mcOsWt5wBrlfAvn34wb14Wa9/dxaG1zgG\ntGuHHm2dkGgvBpNte7G05Hpm/PgjkJeHgb3qY9qeNPy0JwRY+SdQs6b6756SAjg6yq9Pz57h86PT\nkCexwT7HYZBcrCLfu6Ar+HVEltKJtKbjo+J42cYAFWR7EPA0lJfdrrS5TaE0PYpKamjMiG8GN0/t\nbhkihJSNPh/kJaVVol9QwP1rVTwNoHwSY9CEXx85AcCd/K5diwYLxqsvc+gQd8J7+jR30iobw/z5\nXHd3TY0AigYN4nKMr76S71UIcN38585VHg5wvQeDglDTwoI76R47ljueSU/GAe7YN3Uqd7Fi8mQu\nv3Bw4BoCfv1VfUyWllzvQTc3DAAwAAAseuNutwN4O/oRGtx5iofdmqBZ9COu/NWr3O0EUr17c8fj\ndu24E/M6dbiHGT9+zF0cUGXoUO7WyeXLudsp+/Thhjs5Af364bJPLv8bp3o25m536NABEAiwNugK\nLll2kX/ifGgo19vi2jV41+mOFewGBm46BtTeyd2aqM6zZ9wFije3Rkj5Lr0At+9vYf+A9/7P3nmH\nSVmea/w3fWZntvfK0lYWWBAFxKCCBVTsFaKS4kli1Gg8RFNMYiESNRpP4lGTnBgTSzR2jYLEiooF\nEBEWZJe6wPa+0/ucP96pO9/MzhbKwve7rr1g56u7MPO97/3ez/3wjYviU/RDhCbnUvkBg2WgYwd6\nTw24fRChnqHzHc1CpMzQOKaEAEhtpT3ZPgc7QyDV8ydbWUxWE8y4cWLl1fksEGpb02+fZMcPk9N3\nvg9p54mHXfqdQz/Rtm0wdSo79lRQ1bEj8npUmcH0ls0we7ZQ32+4QTyUr79e/P3HPxaDgOzswV33\n8svFqvfy5WKSHkoCvvde8cDuJ7KEUavh1OBs9IknxIN21SrxwAphMAh73uLFQl33esWK+e23D3qQ\ntP83FfBqAdxyC7tKJvDo3Bv59oYnYf9l4pqlpZGdzzhDlAdcfLEYbHz4oXAxJKOgQJRT9KeyEvbt\nozdmIHV88AtqL0hwPq1WrCYE+eNlwHe/EoOsCy8UA6IFC4Q4EmLjRvHvO3OmKNsI/VuuWMGrNZcw\nvX4z57a+xUrEgOSEMRup3VvDsvce4kHNrfjWqiOiQAip//cDvR8kRIWYCf9g308pHBsnLCS671TP\nk4BkYaAyMkcbR9LqVmgScdBX7O67Dx5/nOLXzqblhOG3q0vtfvpFrtXUiOfdhg2QOPcuOTabsJUv\nXEj2VT0ovf64Ea7S4xOlATffLCbu1dVCSH7lFfEM6e4WE/LBMHasOMfHH4tJ+l13iUnwuHGwY4eY\n0Caiqkr8ec89YlJcXS2eedHcdps436JFYvyxZg18+mlEfB8E1Yv+m+qnV9G3aRNPrvom1y74p3Ab\nbNggOiSEMJnEgsBjj4nyiCVLRClfIhEAxBjlttvEV3+ee46LlEouCu8L0d1nfxPZEstJJ4kv4Ha+\nCROvFL+LAwdEmUZxcWz5RiAgxlZffy3GUqHfZX09057byuoHFzD7zxvZeVElAJpuD9pOD/r9TsqN\njRw4Ob7NKyRZiY/6vyy1PbpWf7CfLanufzht+kfS56XM0DnmhACQGPyOwMR3JN0BB81p4PeL4JrP\nPmPKhG1sY+rAx6SavJ8iY7v3iof+U0/BjXdy49pHaMipDE/WUubLL2HBAnZYCji7Pl4Z13pdVLdt\nh1OrhfK8cKFoZ3PppUJE6O4WK/CDZfZsUU+/fLkQABYsEKFB3d3w8MOpnWPBAvHv0E+xDm+rrxeC\nQtowU3F+9Sv45jf5y1XP8O7Es/jDa7eIlf+FC+OFhZwcEZB0xhniQdrfMjkYBrOakow//lHUCW7Y\nIAYrjz0Ga9eK1Q6HQ6yoPPqo+H0tWCAGCA4H/P3v3HHTNq4oepFLNr3KSs5H5ffy3p/P5H/H3MSv\n6+/hg3mnsz7npPh8Cqkci+g/T5LYJ3rfZJ8jA9n0R8jGnyqpfs4cjGwVGZkjhUQdAw7mIDfRSuFh\nCSlsbweViln/9yX//nNkgn7IVg8DAVFrP20afPIJuTVdXHLHG7z6t0SqcQK++krUxZtMWItMZO3r\nhfGRzUqXn9w93WLSnp0tcnL27hUhuFdfLcTwJ5+UXr0fiCuuEOL5rl1iYvrjH4vJ9G23SQcS9ken\nE5kFen3889NoFKJ7U5O49+Hwne/AihX0VmTSNq2Qr66p4ZRvflPU9D/+ePz+N9wg/n/85z/SHY1S\nZaTGBLNmCafCBx+IkskPPhCLMXODD95//EOMAd59V+QYrFsnrv3RR3z802+w/ocncuadH7LHUoYv\nXc243+0jZ20vfo0SXblXUghIxaWT7H0yHDEgEalmF8gTdZlUOKbCAgfDQPX+yUL7RmoiP6IlASAU\nc4MBzj6bs0zvHpT7GIg8W6eobbNYKO/Zz3c3/J1Lal8VG1OdCAUC4gEwfz4rfzqRc9w7YjYfZ6lj\n92/HM6V1m1DYFQqxAj53rrC5Wa2iFZ4qSRudRCiV4oF5993CRv/Xv4oJ6oYNkJWV+nmkRIAQ+fnD\nFwFApPXeey///Ntl9Pwqm9J3XoXx44UNUYqcHDGYeuCBof1uRpqMDNFD+c9/Fu6JzEwhttjtwl1R\nXi7qEB98UFgnvV4x+HrySVrvKuKdqgUs6HsHTgkwq2cDGS4zP995Hz6FknnpH8Ze62NiRIHAALER\nCYkKCIx7HYYWjPlJgpDQFN4vJqeFmfs3pHSNZMSVTcjIyBwdmM1w6aWMf68hbtOhmEhobR4x+V60\nCNas4cr/vMQJ/9iM1uoe3Inef1+MLYDsqtn8ZEes62Dm+ZuZv2JtJPPHaBRdAP70J7E4cc01QxfA\nb75ZBPhlZoryvo8/Fs6/G25I/RxGY+LnrlI5fBEAxPjv739nzO+eYwXLOeW+j8Tv4JVXEjsM7rpL\niBSTJg3/+iPB6aeLhZjnnhNjg2uvFZkIjz4Kd94pfu8zZwrxxGQSHRAWLeKcm9/Ek6ahcXYJHWvE\nz5r/djeZn5vJ+tRM5Yf7B7z0uyPSvPDwEC0aTF9Ti8Inwpml3uOygHBscUw6AkYTI+oOsFjEh+JZ\nZ3HWnU/yx9NuSbxvKoP9/tbjREFqUa/nWzsgvxBOO42Lt77G8U1fofc4U7r9MNu3C+V87FjxAGhq\nEm18dDoA5nV+SFlfE8XmFpj8VOyxc+aIr+GwcKEI1JswQYgMCxYM73wHC6Uytt5w9mzxNRpRKIRD\n4LTTROlCVZVo7RRyNlx1Vdwh9QXHoQgEmDhpJ2e/9h+emH0t397wJH+d831O2/0RD0z66fDuyecj\n02Ghjyzp8oCBiHYQpPB+S/pZkMCNcMHXb/CDz/+P029YM7QSBbmcUEYmzMFaJT+UA++4lUmLBS66\nCP0zfyGroZfeykEI2iOAscMmsmbmz4ef/5xxTXaU/gClXzSLfvOp8v77EVt6VZWw5QfbeCncfnLW\n9pLr7oHb+k3mTCZhzR8OaWni+j6feCYdd5z4OhKJHq9oNKJz0Gjl4ovFQsy3vy1cAOvWiXEhCIeJ\nRKjzrgXjGP/uXmyztaTtcVC7eApam4eKzxqFi2RM3CGDwtDtwJGTpIRiAIYaHpjycYEAd11yD0+s\nXUr7lPy48wzmmjJHB7Ij4CBxRCZsm81ilXXePE7ZO/ilyUH/TBKTmzxbp3joX3QRd//nTj4cP48x\nPftId5qTn2vtWqG6g3jghurCNBqRprtjB9xxB8ZZVuaqPmFfdgV2bZqo2TsYTJw4MuFGMqlTXS3a\nDc6eLcIOB/r9KxS8NP1y/vN/Z3NLwx94Yva1nHbjR9y98E5O2bsWtd+T/PiBePllXnzqitQFAKm3\nXCIHwVCQOE9Vxw5ybV1J95GRkUnMUAa9IzWxPySig9kMmZnsPa2CMZ8cGPHrDaZbesEAACAASURB\nVERap0OMCebMgX37qH69nvpFEyhb1zTwwQsXQkeHsINv2BDJ4amqEgsGb73FhLd3k7nJgiXfhNIf\niDgCRprsbPFzyBxaHn1UZAe99lpEBEjACpZTd0EVM57czIxTvmbrhdWsfuAsVv3PAvaeFhWeOFQC\nAW6Z9Bi5O7oG3vcwkdlpJr3XRlqXfcTPLTsJRieyI+BIYiRC+pIN9EOOgPx81H4vGY4+zIbMoSX/\nD5GwEHD++WTX1/Pk7olofW5mHdjA+xPPTHzg7t2RlNwtW0SabYgFC0Q9/L//zZIrKjhl71puveBB\nrtj8IlfKk/Wji+hAowEILAP++yFm3Pot2k0FNGdGAhLrC47j9MIPeOe4hZEJ+sfBP1Od2H/xBbP2\nbxClKozQ/7NEzoL+nw3J3udR26o6dpBrPwiDEtktICNzdBAcF/SMyyZrX98hv3xap12MCfR6eOMN\n9Pfcw1eLlUx9cfvAB3/6qShnKygQCwLpwaTBs84Stf8bNnCOr4lNS2vYfmEV6S1Wpo5WV5yMNBMn\nivLAFPnxlD/BzuX8veEXNJ9YTEApnt11F1Qx5eXt8J1h3Mv+/Zg67JRtaKarKpd19pNGPMxvuFb+\nsh1CYEvrcsSdQ17pPzaRHQEpcESu7g+FkCNAoWB/dgXlvf3U/ySTi+H+DsZ37uK5p5dEhACFAu65\nh6dmfZvaohoR7DfQve/aJZT/PXtErXuIa66Bf/8bTjmFe1f9AoCXay5j8bdeGNY9yxwFKBR8VToj\nRgQAeGH6lVyx+cXIC6cyeDZtIsvZJwIwB1sWcLD4ROQCrHlU1LpWdewQ77lAYEin65+HIjsKZI5W\nUk7pHuaq12EJBExGcFzQW5FB5oEBnHkjzKXf/TfjPmiIrKTPnAmvvUZrTQH52zuTH+zzidyjrVvj\nxwSTJ0NhITQ3o7W4Oe3+T9lx7gSee/mKg+cIkBk95OfTNKskLAIA1F1Yxbg1+0gzD2OlfJPosFPy\nZetw7zBlUvk8WsdJ/ODWvzH+3T0RIaBz5B0BMqOTY8sRMMwV9xETBEL3cRDb9EkScgQAk6eXU9G7\nn23FUyP3chD55qbnuHjra7SmF4kwvCj2ZY+homeAoJa+PtH1oK5OPPTHjYtsmzNH1Ir9/vc8fOM/\n+etJ3ycwUim1MqMeyeC/y6+ge9LxbC+sZtWkRcx95xOe4L8kj//8jydx9dX/ZHfehKiTBuCrr1hf\nPosTGzeyNzf4/zHkLjhFTMjvfPtufn3ub3CuS6FmcKBqnRTfo3m2Tubt+Qi9x0FVxw4UgQAmlxWr\nPj1ynkP5uSMjcwwx6uyxwXHBheXXU7/qJyN66mQrjGkdNmY8tYWuiTlwbux+fRWZZO3vE5+ziVx9\n5qBosW2bGBtEjwkAfvITaGnhzeqNKAIBdpw3cTg/isxRxgqWx76QBbUL3+SOy+/l3X/MY8Lf99Jw\nYxneLE3csTP+sZmi2nbe+n2/fKivvuLArBJKNrYA0q39pv+zFlt+GrsWjo/bJkUqXQtSYczXB+gq\nzmJ8ZwM+tTLOEQCyK+BY5dgSAvoziMnvUeEKsFgiafXl5fGOgIPIZVteRu33UtmzL66Obn92BTOa\nBuhVHnrof/WVCAccE5XoolCInrfAPQt+PZK3LXO0UlHBrFs28Pb/LeSWj/5Arr2LlxdcJoL/+jGp\nvY7xnbtjhIASczMoFKycfB4nNm7kpelXxBxzzva3WPHWL5nWsoXXpl7MJ4fQMmByWwGYeeALXGod\nPWnZ5Nk6I0LAUDlMboAjNIpTRiaOoYgAh104CI0LyssPqSNg8mv1BBSQX98FS2PHBK4MHT6tirQu\nB/a8BB10QmOCrVtFWUFVVez2q68GYAd3jPStyxylvPjMxSz8xfv8cM4TZB0w4zWqaLilIm6/vB1d\n5O7sjj/BV1/x5bXHc/bP3kPhD8R4rjM7+rjoVys58Ymv2HbZpJSFgJHCYHWQtV9JRqOZlumFsiNA\nJoy8bHosYTZHaugqJEoDDhatrZT3HuCdqgWiPU5mZnhTYFnQEdA7gCPAbBbtc1auFG1uEvTmDSyL\n/ZKRScTu345n/MpneXfiWbxZfT7fOvBU3D5Gl5VMp5lSc2xw1WVbXoZ58/h47KmcsfP9uOMefONW\nHj/pezx+0veY2fhFZMNAesAg9AK9x4HK54173eQSQsCCHe+wI7+KTmNefE5Av44fv3vjNpQf+1K/\nuIzMUU70ythJrDtsE/Zk1w2t4A11FW+9f5aw15tMUFFxSIWAiat3U7t4ivim3+LACpbTOyaTzP1J\nMgvMZhEGvG0b7NwZ7wiIOlf0l4xMIpbrfsspv1vL3nljeON/z2HMn5sky+oymi1kNMW+V9K7LbB2\nLXUXTMRckh52BYQ47cW15Ozu4amVSyjZePBKB7QWl+TrBquTzANm8us62f+NMklHQDTVr9czdk1D\nwu2HXcCUGTGOPSHgSK1xPVj39UnUn4fLEdDYSENOJbVFNZF8gCj2Z1cwpmeAtFazGa68UiTDJnjg\ny8gMmpNO4r+WPMGzJ1zFItequM3FZvEwL+lrDr+m9zj4+fv3wS9/ycfjTmVC5y6Ko7ZzCpT2NfGv\nGUtYXzGbExs3DilDIMvewwePzU+4/aHXl/HtL56Me93otgGwaPsqaotr6ErLFTkBCVD5vNy25kHS\nXZbB36SMzDHO4ar5H4mBuN7mFK3vlErIyUHl8iacSIw0mY1m6i4IruJLpO33VmSSnSy80GyGigqY\nOlW0jpPHBTIjgVrNS09fzOc3zkRl9ZG2J37CnN5sJaPZGvPa5b9/FS69FHNpBnUXVlH9en3M9rym\nLvbOq2D3mWNJb7Wi70k+EU/EObe+w/h39khuK9jazvfnxS9ogHiv5+zuJmdPL/tOqUjoCAh9rkx8\naxcVCbqIHAkiwAX61L9kknPsCQHHGtH1/9GOgPLySF3+EEUIhd+P2pdCC7bWVlrTi/i6aLLkA785\no4Q8Wydab5IBiNks+gzPny8/8GVGlMAy2FI8jSmt2wAY27UHWoICgLk55k8Qlvv9WRVw/PF4btPw\nVvW5XLz1NbHxFEhz2dB5XfQYsvmibCYzD3wR3gYwoWMnd6++gwJLW9L7OnXvx8zf/SE6j1MyP6DQ\n2iYpoIUcATMbNwohwJgb20IwRLB1YaZTDLb1HmfidoZHqoAqI3OEcCQ6BgYizeyIjAkUCvrKM4ft\nClC5U3MWmVptNM4uAaNRWghIxRGQkQE//anICKisHOIdy8jEsoLlIlR7WjmmbTYU/gDVn9eFt2c0\nWzC2WVF6/eHXTn5jPVx3HStYzvaLqpj8Wn2MmyC3uRtLSToBlZKW4wvjAgXnrfiYce/tTX5jgQA1\nz39NUW275GZTmy1h5w+D1UHxlnZ6xmZhKTEN6Agw9DrROMV7Wc4NOLqRhYAQ/ayyR9zAN8V7ynD0\nce72VeR3t0eOCxHtCCgtjZncJOKmjx8m09Irue3ira/xp5evH/imWlpoTS/i47GnwkUXxW32K1U0\nZ5RQ1tuY+Byhh/4jj8BNNw18TRmZQbD3N2PIcvaS5e7hrv/cFW5TWNLXjEOtj3mvTOzcSX3BceHv\n/3Ty9dzxznIhICBEg+bMElAo+LpwMpXdDRjcEfV96canuf7TP7HirV/G3kQ/18Cpe0Q/w8IEgkGW\no1fyPRzKCACoLaqRLg3odx4AvdeZcB8ZmWOR0TgAlrrnik8OUPpF/GdFmsUeGRMA5tJ0MpqTO4OK\nNrfFrXZG898THx3YVRAIkN5qxVJsgu99D447Lm6X3ooMsvb1Jf43CI0JLroInn9eOBtkZEaIFSyn\nbWo+GbVWSjc084e5P4V68f8+vdmCT6fG1CaetQq/n+LdLeGcigMnleJO07DkvkhnorymLsylQnRr\nnlFE8ebIc93UauW0+z9l6QX/QmNzx9xHtOMoa18fWY1mTK2xboQQhl4nad0O1E6JkkGrcAq21hRg\ny0sbUAjQ97okzyNz9CELAYeLoQoNAxy35Kt/serx8/jJs7+P3xjtCNDr0SVbgQ/y44//yNhmaZUy\nz9ZJtr1nwHPQ2kpLRjF78sbDihWSuwxYHhB66E+aBMcfP/A1ZWQGg1LJtsIpTKncxnEd9fDhh/Dh\nh5SYm9lUOiOmNGBix0525kUSqD8Zdwp/mXMdt33wACDKApqC7Qp9KjWNWWUxXTHO3Pke/zzhaorM\nyesET9vzEW6VhmJLi+T2LEdvuHQhGpPLSmPw+luLpw5YGhAWAjwSQsCRKIrKyMjEkdAZEAjw/VP/\nwX/NfypuUh3jCAC8ejUqV/IV/fHv7WXSGzsSbs9otqC1DeAU7OnBY1DjNWjgD38QuT/96KvITLi6\nCUTGBEqlKBuUkRlh2moKcG41kFffhVerhl//GhwOtDYP7ZPzSA+WB+Q2d2PPSAu/lwIqJc++egVX\n/fZFdHbxXBVCgBDdesZlk703ssA27v0Gdp85FnteGsYkIX6VH+3Hq1GS3iItBOh7xbXihIJAAK3V\nTV9pOm1T87HnGpJeJ3QuWQg4NpCFgGj6D3hTGQAfYYPkDKcZn0KJRsqyH+0IUKtR+Qe28OXYu1Ek\n6EGe5rGntorY2sqvLipKGt7XmFlGaV9T4h36+mJCBmVkRpo5C6ZS01rLpPY6uO8++MUvKO1r4ovy\nmXGOgF3RrQSBV6Zdypk73wOCjoCMkvC2hpxKKnsamNS2nc/+OIdpLVt4Y8oF5NglUocBjddNkbmF\nSe11rBk/PyIY9CsPSCQEGN02aotr2J9VTk9ajnAESJUGhM7jFAMSg3doNYsyMkczh9LyfzAcCBqH\nFxQKVB5/zOvrOCnOEeBXK1H6/P1PEYOh2wHSQwKUHh8qr3/gCURrK4aiyqThfX1lGWQ0JXEn9PXF\n3LuMzEhzxdQ7KaptJ7+uk1d+fCGsXQsrV2IpNmEuzQi7Z0p3NtM8oTjmWHNpBrunj2XKJ9sByG3u\nwlxiAqCnMpOsBvHcXbzkZc64+yN2nzUWe64BQ3f8mFrh86Pw+Tlu5U52LJpIegJHQEgI6C8UaFwe\n/GolnVW5tB5fhDPbgK7PiSLJe93Q60TjSKH0V2bUc2wKASM1eT/CRACAdJeFvvRMVD6JSX6wXzAA\nKhVqf/KHtdLvI9vRizIg/WFh8DhSchXQ0iKp+EfTlDlAqUJI/ZeROVhMn86F2/6NW6WFH/0ILBa+\nuek5viw7gQJrO8qgcDahc1eMIwCEBT/T2UdF974YRwBAQ3Ylld0NXLH5RbQ+N/884WqaMkpj7PoK\nv1/UEwYC7LxvIv9+4kKePnEpe3PGxjoCosSAhI4At5UN5bOYeYvIJmhNL6KsL3HZTVJHgIyMzKhG\na3HhzNRJDvoNllhHgF+liKl7lsLQ7RCt0STQOMSYQu0aQAhoaYHi4qS7DFimII8JZA42kyaR1dBL\n+bomdp0wHn71K7jpJswl6ZhLTGGhqnRXM00TS+IO/3LB8Zz/7lto7B60Tg+OHAMAPZVZZDf0Ymy3\nUfXWbsyl6dSfNxFHjoG0rshKfeg9e+oDn3HDrL8x4d29rL11TkJHgKFXjMfTW2LfNwarA7dJy79e\nuIz68ybiVytx5KYlPA/IjoBjiWNTCEjGETi5Hwwml5U+Y6b05D36walSoQokdwSEJghKv/TAIM2d\nuiNgICGgOaMksRAQCMSWNcjIHAwuuYSz6/9DXcEk0eby+ecpVrv4x48m0m4q4Lh2Ef4zoXMXO/Nj\nhYCAUsl7E8/knPrV8UJA0BFwwddvcOsFD3L95X8WAX5RQsA//vUdLt/yEpPa69B7nGTbe3ho3jJa\n04ukSwgCATJcZvJsnbz45OUc37QpvMnksmLVmehILyCwDL4qPZ7jm79K+GMPNiNgbNeesCgiIyMj\nzeFsORiNzurGmamXnLwbzbGOgIBKidKXYLk/SFqUEND/59PYxQqi2jnA50MKYwJLsQlTq1WIpFLI\nQoDMwcZgYNeCcYx/v4H9k8rguuvg1FMZU3kqJ425nIJtHYBwBDRKCQFnHU/VW7vIaDLTVZIT7pjV\nW5lFdkMfVat2seussTzx/lJ6K7Ow5xrCtfvlnzdy7ZnPADDh7T0ofH4+/9EsOqtyE2YE6Pqc+JUK\nTnxiM+f9+D+RH8PqxGXSYs9L4x7lbwBoOb6Q4q8SlycKISC157zG7olrpygzejh2hYDDNeFPdt25\nwz99ustCb3rWwI6AFEoDQtblYTsCWlsHVP+bM0ti6rDx++Hll8XfnU5Qq0GrHfhaMjJDpayMD8af\nHgkCnDwZ9u2Db3yDv875Pr969x549FG603Kw6OMHoM+ecBXfXf93xvTsi3MEnLJ3LeM7d/PxuFMB\n6DFkk+XoDQ9yqzp2MGff55y+6wNWVS9i4u272Js7jpaMYsmMAJPLikNjoMuYy+VbXqaqY0fMNqvW\nFP5+T844Mh195FqlcwKyHSLnI1VHwAtPXcncvaNcMZWRGQaHaoI/EtfRWty4MrQoA8T1RI9zBKgV\nKAYQAgzdziSOgKAQMJAjIAUhwKdT48rQkdkZNcH4/HNoDLqbZCFA5hDw1dIa/AoomdgkFgj+9S/4\n619h6VKmPbcV3nuPua99TsPUMXHHfn3yJHRmF5NfqaOzNDf8ujNLj1+lYObfNkVaaAL23EiIX+7O\nbsrWN6GxuSlb38RfP/4O7909D3teGjqzS7I7h6HXSfe4bCat3Enxpsgk32B14jZpYvZtPqE4rnNB\nCLXDg8blS9kRUPP8Ns5d9k5K+8oceRy7QkAUY7obSHPZDvdtjAgmt5U+U6b0JN9mE616IKXSgJAQ\nkDQjIMnkIbAMAv8dEDbAwsKk14pzBOzZA5dfLh728gNf5hBx5j+W870HvxN5IT0dFAru/tePucr6\nBbz6KhWfvR2TdxFYJr7e/PM5lPU1MvPAF3w4bl54+77sMZyy9xP+eeLVeFXiYexTqbHo0sP1+aV9\nTcxo2sT83Wv4YMLp4WNbMoolHQFZzl569VnhLIICS6SdkNFtw6qLCAEBpZJNpTM4oelLyZ95sI6A\nEnMz+baOlPY9kli9ejU1NTVMnjyZ+++/P267y+Vi8eLF1NTUMHfuXPbti4SX3nvvvUyePJmamhre\nfvvtAc+5d+9eTj75ZGpqaliyZAkej5ggPfbYY0yfPp1p06Yxc+ZMNm7cOOA1ZA49Ko+X0h1JMmtG\nETqrG1e6Dp9KEVf/r7e5ImMCwK9SplYakGBMoLEHSwOSTCBWsDyl0gAAS0k6uc1RWSp33gn//Kf4\nu9ks5wbJHHSuPu8ZlA/9D3cb7hUvKJXiPVNWRtr3b4brr6fsx7dzz/l3h49ZwXJWsJx3lBeQ/b1b\nOefn7/Pe1fNjzts7JpOizW1su2xS+DV7rgFDsDQgo8mC1uFlxtO1tE/Jx5WhAyCgVGArMIY7FkSj\n73XRPlm04jS1R+Y0epsTtyl2Ia3pxCJKNkoHEYeyBlIVAtKbLRg7kocPyhy5yEIA8IfXb2FR3arB\nHXSELoilOy30mrKkhQCnE/R68XeVakBHQGilMFFpgMHjGHjy0NsLGg2YTEl3a8osjQ0L3Lo18qcs\nBMgcKubOhVNOiX89Kwt27ID33hOdK6RQq7l28RMsvO5t2jIiq10NOZUA/PnkH8bs3p2WQ66tC6Xf\nR5GllRMbN7Jgxzu8XbUwvE9repGkIyDL0UufIZOmzFJa0wspsEaEAJM71hEA8GXZCZzQmFwIMHgG\nDgtU+P0UWNuThg8eibhcLq6//npWr17Nli1beOmll9i0aVPMPo888gjFxcXU1tZy2223cfPNNwOw\nceNGXnnlFWpra1m9ejXXXXcdHo8n6Tlvvvlmfvazn1FbW0tRURGPPPIIAN/61rfYvHkzW7Zs4c47\n72TZsmUJr+F2x7aRkjl0TPtwK8t+8Mjhvo0ww3EGaC1u3CYtAZUSVT8hQOt0R8YEQEBCLOiPoScF\nR8BAE4jm5pSEAHNpOnlNUZ81W7fCli3BjfK4QOYQoNPBLbdIb7v/fjEuuPHGsO0/jhtu4IWnL2bl\ndefGvNxTmcVXS6fhTteFXxMZAeI5HMofmH/Px9SfF1uKaCkySdb363udtE/Jx1JoxBglBIQyAqJJ\n5ggwBIWAVMMCTW22mGwDmdGFLAQAubauxIPgVDsHHCHCQNgRIFUaEC0EjEBpQJrbHlsa8P77sHt3\n7E779sGYeMtUf1oyikXwWWilISQEbNkiK/8yo4a3/7KQ7fdXxzgGmjJLWfCDt/m6aErMvl1pueTY\nuym0tNGdloNNa+SdqgUxIkL/1oMhshy99BqyuOGyx7j/9J/FrNCbXFZsWmPM/l8XThZtESXIcvTi\nUapTcgTk2LtR+30x+QajgXXr1jFlyhRKS0tRq9UsXryYlStXxuyzatUqli5dCsCFF17Ip59+it/v\nZ+XKlSxZsgSVSkVpaSlTpkxh3bp1Cc/p9Xr5/PPPufjiiwG45pprwtcyRQmiVquV4uBkSOoa69ev\nPxS/GhkJMrrMaB0plL2NAoQjQCuCAPuNC/oLAX61csDSgLRkYYGhjIBgC8I0sx2efz5+xxTHBeaS\ndHKbg5813d1CQJCFAJnRRE4OV17zKu9yXkyXjPeWz+f9O06N2dWeayCtOygENJppPr4QU6uVL743\nI2Y/c1kGmfvjW2saep18fekk/vLpd9FZ3Cg94n1osDpxG2OFgJ6xWRg7bOH3bDT6Xhc+tTJlR4AQ\nAuSuQ6MVWQhADG5HXWJ2AuEh3WWhz5igNKCfI2BIpQFR1w07AuYivv76V3jiidiTNDRAZWXS6wA4\nNQYxeekKPvS3boVZs8RDv7kZcnOTn0BG5khFoeDd4xbEvRwKDAyFC/591nd5YP5tMfs0Z5SQ5rGT\ndUJPzOtZDlEa0JhVzoGs8lhHQDAsMJq29EIKLW2St5fl6KU1vSilz8BCqzjHkeQI+Ax4KOpLisbG\nRsrLy8Pfl5WV0djYmHAfpVJJbm4u7e3tNDU1UVZWFndsU1OT5Dk7OjrIy8sLv15aWhpzrccee4wJ\nEyawbNky7r1X2E0TXUPm8JDRbUXrPDpaZ0U7Avqv9mudnlghYIDSAIXPL1YLE2gFakdsacC4LXvh\nhz8UmT/RpDguiCkN2LYNZsyAXbvA5RLjgqj3mYzMaKJ1eiG2wtjndHRYYEaThS++N4O1t56MpSQ2\nKLtjUi559fHPYH2vE1t+Gj3jsoW7oFOs0uslMgJQKLAWmhKUGDixFJlkIeAY4ZgVAi7b/BL/9fnj\ngJjwphR6NwowuaQzAhR+P3g8kcC9qK4Bp+7+iN+u/EXcuQZ0BHj6OQKsVvjoo9idUlT+QQQG0hQs\nD9i6Fa66SggBa9bAaaeldA4ZmSOFUHZA6Ks/XWm55NqEENCYWcavFq3gi4pZsTspFNQVTGJSe13M\ny5nOPnoNWQC0mwpihID+GQEgSgxCk/j+ZDl7ac0oSskREBITjiRHwMnAsqgvKRSJbJuHgRtuuIFd\nu3bx0EMPce211x7u25EJonL7uOZCsXqd3m1B4zpyhIB1nDTkY0OOgIAy3vYf5wiIKg349rnPorXE\njotCtcOJHAHa4OqiKugI0Fudojww5PADcLuhsxNK4lPW+2MuMUVKA7ZuhZkzYexYWLUKvF4YP37A\nc8jIHEmE8gOi3QEh7LlpGKKEgLoLq3j7vjPj9uuYlEfB9vjwX32fC0eWeD9bC9IwtQeFAImMAABr\nkRFTW3w+mqHXibXImHLXAFObDY3TK+kuOJI5EnKDhnKN++67j6qqKqZOncof//jHYf8ejlkhoLp9\nO7MObIBAgGxHDzrfoRcC/vzideR3tw+84yBI1DVA63OLWqfQgFipRBkIoPD7Ke89wNyGyFJ/msuG\nwu+PCAHJMgKiVxFtNli/HhxRyuC+fSkp/yBqpunpEeUBO3YIIaCuDl54Ac46K6VzyMiMFsKOAHNs\nu8H+bC+ojhMCQqUBIISAfGtUaYBERkDCNoTBc7WkF6eUEVBoacOh1h9SR0DpzMF9SVFWVsaBAwfC\n3x84cCBmNT+0z/79ogzD7/fT1dVFfn5+3LEh50CicxYUFNDZ2Rmzf/Rqf4jFixezYcMGyfvr72CQ\nOfhorW6q39iB0uPD1GMdUAgYSt3+QBP6E9/+ktl/3ph0n1QJ3Z/W4sJt0sZM8kNoXJ640oBQ+8Dy\nzxrJbojYj7VWd9i2nEgI6O8I0NuC44PoBYIDB4QIoFYP+DM4cgyYeoIrlvX1Ip/l9NPh1lvFmOAI\nEvhkZIZLyBGg9PoxdtiwFElna3VU55FXF/sMVvj8aGzucOaArcCIsUNM8g1WBy4JIcBSZJJsRajv\ndWIpTh9ERoAVj149qnICjpTcoMFeY+PGjTzzzDNs2bKFzZs38+abb1JbWzus38UxKwRofB5y7N1B\ne7vrsDgCztj1PgU9IygEfBJ0BEiUBui9zpgHPgoFXqVwBaj93pigvkdfuZHfvnX7wF0D3HZ0Pnek\nrt9mE4mqwcEtMChHgFVrEuewWoVoUVAgQlosFjjxxJTOISNzpNLfHdCdlsOYnn2M6d6XVAioK5hE\nddt28U0wxzBGCEgvGLA0oN1UQL6tQ7Ind5Yj6AhIsTSgrmDSEeUISIVZs2axdetWmpqa8Hg8vPDC\nC5x7bmx406JFi3jmGdG3+fXXX+fkk09GpVKxaNEinn/+ebxeL42NjWzdupXZs2cnPKdKpWLOnDm8\n9tprADzzzDMsWrQIEKsCIVauXEl1dXX42lLXkDn4hCbn4XraHicZh8kRULH9AEUSvb2HExaos3pw\npWtxq7TM8n0Rsy0+LDBSGqD0+iO9wTdsYNmER8g8IL5XJCgNiGQECCHAYAuOOz7+OLLTIMYELpNW\ndDYAkRGQlwe33y7KAuTFAZlRTn93gCPHgKndRs6ubhy5afg1KsnjOiblkl/XGSPI6cwu3Ok6Akoh\njlkLjBiDjgCD1cle09i489gKpR0B+rAjYODSAJXbh87ipnt8dtjNMBo4Yepf3QAAIABJREFUUnKD\nBnuN+vp65syZg16vR6VSMW/ePN54441h/S4GlmSPUkJCwGB7aAMjFgyo9bkHDOwbFIEARrdNsjRA\n73GKyXUUPoXoHKAK+IQQEAgACqa1bOHyLS+F+6knKg0weMWbXud24dLpxQR+4kTYHxVu1tCQ8kPf\npjWKc/T2ipR2gJ/8BM44I6XVAxmZ0cSL065g9V/PQed1ccF/Jf4g315YzXfX/x2yI6/l2zpoyK4E\noFefhcllReN141FrMbnjwwI9ai0WXTo59m66TLF1tSaXlU5j3oClAXMaPmP2/vVsK5rCzMYvku57\npKHX6/nTn/7E2Wefjd/vZ+nSpZxwwgnceeedzJw5kwsuuIAf/ehHLF26lJqaGtLT03n22WcBOPHE\nE7nkkkuYNm0aSqWSv/zlL2g0GjQajeQ5AR5++GGuuuoqfv3rXzNlyhQefPBBAB566CE+/PBD/H4/\nubm5PPXUU0mvIXPoUHnEc87Q7SC92yomyQeZ/hN8tdsbXpEfKbQWF+YSE36VEoVURkDUuMCvUoT3\nUfoC4eRytmwhvc3GGXeLlf1UuwborU6oqhrymMBj1EREztC4oLRUdG+ZMSP5wTIyowx7roHtF1Vx\nc81f+OzmxEKwK1OPK0NHRpOZvnIRpG3ssGPPNYT3seWnhVsIGqwOzLnxwZqJHAE6qwdbXtqAQoDG\n5mbGU1uw5adhy087YnICPvbD2uTNTyRzg9asWZNwn/65QWeccUbMsY2NjQQCAclzJssNGuw1ampq\nuPPOO+nu7kav17Ny5UqOP/74wf2C+nHMzq40Pg/Zjp7wqvfhcARofB5UX/pg4sD7pkKa245To8ej\n1sTZ+eMcAYBXJToHqH1e9F6X+F34szmuo577T/8ZM5o2YXTbEpYGpLmD9UdupxACbDaoqYkE/rlc\nsHdv6o4AXdAREC0EGAzwjW8M4rcgIzM62F40mck//RoAmy5xe80d+VVUde6A4yKvTWqvC7cZDCiV\ndBrz+NsL/8WPLnkEg8eBXZMWd5629EIKrW1xQkCax05XWi6VPQ1J7/e2NQ9wae2r3H7uCs6u/0+K\nP+WRw7nnnhvnArj77kjvZ51OxwsvvCB57O23387tt9+e0jkBxo4dy2effRb3+v/+7/8mvL9E15A5\nNKiiHAGplAYcDNQe34Dt+waLyAjQiSDAfhN4jUTXgJAQIRwBQSGgro4vvzUNfa+T7edPHLhrQHRp\nwJgxosQvRF1dymMCt0mL1hYUZKLHBfKYQOZoRKHgpScvYvXvzsKaoCwgRGdVLrk7usNCQH5dJ51V\nkVBtW4GR6f+spWdsFgark7YxBXHnsBYaKdjWEfe6xu7BXJou3seBACSowCn/vImLbniL5uMLsece\nWiEgY17ibecFv0LcJzFcOZJyg1JFoVBQU1PDsmXLmD9/PgaDYdgiABzDpQFan1s4AuzCEZA0I+Ag\ntQbU+DzDcwT0u690lwWr1sRr1SppR0A/IcCoE50DQt0DSvuaoKmJPn0mv1l4B5d+91UasisjpQH9\nrmfwOPArFOg8wd+dzQYVFREh4MYb4cwzhcU/BSQdATIyRyGhEgHrL0xYf5H8gb8ve4xoITg3Mvie\n1F7H9sLq8PfbC6u5tPYVztr5Lq3pRQSU8R/trelFFFlibcdqnwdlwI9ZnzFgRkCurYs2UwGfVZ4s\nnFQJBEIZmdGIyh0UArodpHdb0B4WIcA7YPu+waK1uLnctBSfShV37v5dA05TzQuXBqiihYD6erZf\nfBz/fH0xX/zghEg5YD80Di9+RaR9oMHmgvLyyJjg88/hySfh6qtTune3UYPWGvx3kMcFMkcx4TIB\nxW/4RdFDkmGC0fRWZpK1L5LhkV/XRUd1ROTvmphD7s5upry0ndymLrpK4jtvWYtMpLfGlwZobcGW\noxpV+HNRirQuB/YcA21TC4L5BqMnI+BIyQ0azDVCx1x//fVs2bKFdevWUVxcHC4xHCrHrBAQKg04\n7I6AgG/EhIZ0lwWLLl10BPANkBEA4c4BIdGgtK8J6uqoK5gU3sWvUCbtGtCrz0LvClqKrdZYIeDl\nl+HRR1MO9Ak7Anp65Ae+jEwQm86ETWsMW2SNLiv51g72ZUdW1c68/n1WVp/HGTvfD5cM9EeqhaDR\nbcOuScOhMQxYHpVr72LBde+wZsLpQrTri+9jLCMzWlEGSwPSuh1kdFtQ+fwovSNYuocoBUgWGKg6\nGI4AixvS04UjYICuAahUKHz+8Ip/tCOgY5KYSPiVijhnQQiN3YMzSx9VGuCAsjLxWeHzwZtvinaC\nxx0neXx/YhwB8rhARiZMT2UW2Q294e/zt3eG36MAtYun8Mzri8ne20vx3jZaK6UdAVLtAzU2D540\nDV69Oml5QFqXna2XV/PSUxfFtD4cDRwpuUGDvQYQFhVaW1t54YUXWLx48bB+F8eUELCwLuIP0fg9\nZLgsFFrb8CsUg8sIGCE0/mE6Avphqg6GhKkkHAFSQoA6WBoQdAS8NacZ6uo4Y0FECAgoFNKlAYEA\nBo+DnrRsdB4XSp9PlAKUlQkhwOcTIX+58Spk3KmC4Wk2rTG+NEBG5hhAqr1gNPuyxzCmR7SVOa69\nnp15E/ErY4OE9uaM5Yxd77M3Nz4UCKQdAWluOzatEadaP2BGQK6tiy6jeD93peVGBD8ZmVHKhLd3\nk9EpBK3ojABTjw2/QnHIXQHRjoDhtAyMRmt1gymUEdDfEdBPCFCrUfoCYVfApGaTeK7v388t44Nt\nqhTJMgK8OLINYUfAGNt+yMyE9HTxXO/pSdkhuILluI1atDbZESBzbJLMFdA7pp8jYHtnjCMAoGds\nFtl7eyna20br2MK4c4iMgHhHgMbuwW1MRQhwiFwChWLUCQHRuUHTp0/n0ksvDecGhcL3fvSjH9Hc\n3ExNTQ0PPPAADz/8MBCb6XPOOeeEM30SnRNEbtD9999PTU0NbW1t3HTTTUO6BsAll1zC9OnTOf/8\n83n00UcpLIz/tx0MoyYj4K677uLxxx8nPz8fgN/+9rdh9ebee+/l6aefRqVS8fvf/56FCxdKnuPB\nN25l2qSzAdB6hco8rmsP7aaCkWkf+AkwN/XdJUsDBnmOaNLtUY4Af6zTQKo0AFVsaQBNTaK/78SJ\nEHzO+xVK6a4BLhcepQab1oje7STNaYe0NDHx7+4WKwDp4l5Sxao1gbVLfuDLyPSjIaeSyu4GNlTM\njisLCLE3ZyzV7XW8PO0yyXOsL5/NL96/l7/MuQ67ToQJGt02IQRo9MnF0ECAHHu3aPEJLL3qaT4p\nKhr+DyYjM0RGYkww/561bLlnGpwWyQjIaLKg9PmwZaaJnACj5KEHBY3bexAcAa4kjgBPnCNA6fKH\nhQCamkS4X1kZaEX7sYBSER4fxN2/3YMjJ+II0NrcYDRGxgXd3ZCdLX2wBG6TVggZXi/Y7WBKXkYl\nI3Os0FOZxQkNm8U3gQD5dZ209xMCzKXpGDtsOIwGbFnx7x1zWQZqp5eqVTvZsSgSVqYNOgI8ejVb\nnTUJ78HQ7cBSkg7Atsur0dg9Q52+HBaOhNygoVzj4+guLCPAqHEEKBQKli1bxqZNm9i0aVP4Fy3V\na9Htlk77zXCZw3/X+IXKPL5rNy3pxQOXBox0TkAggDZUGjBCmBxBR4BSGVcaoPO6pEsD/MHSgKws\naGwUX1E9rxOWBjgcODQGXGodOrcLk8MqHtK5wZXCnp5BPfBBdgTIyCRiX/aYcJjfCU1fUlsc/3Bu\nyKkEhCAgxb9mLGFHfhU//OzP4dfSPHbs2mBpQD9HQEX3Pk7ZIx44RrcNn1KFUyNSiT8dO1celMsc\nVkZiTODK0JFmFnWtoVrYnD29WHLS8eg0hzwwUHUwMgKsHuEIUCrizq1xSQgBvoDoHJCWJp7Fu3fH\njAkCSgUKf0CypaHG4Qk6Arwx1x7quMCnDS4kdHQIZ4FE9omMzLFI75hMshqEIyB7by+eNA2O3NiQ\n4IBKSV95Jq1jpUV7r17Ni89czAU/Wh3zunAEaPEa1HHdU6Y9uzXsCAo7AoDeMVl0VOePyM8mc2gZ\nVZ+qAYmVaalei+vXr5c8PsMZJQT4xAP+uPZ6mjNLDk1GQJSYEFqFDzkCZu9bh32eQeqolElz2kVa\neKLSgH7tA2NKA8aNE/19m5piH/qJSgMcDuzaNGEpdjsxOmwR5X+IQoBVZ5LDAmWOWUIBgv2/ABqy\nhSMA4PRdH/DBhNPjjg8JAKE/48oNFAremnQu01q2hF8KOwLU+riwwCs3v8CNnzwKBMsC0gYu85GR\nOZQMd0zgytBiDAoBoYyAvPpOLDnpuPXaQy4EiK4BwZ8pEODnRQ8x4e3dwzqn1u6BtDT0Kldca0Kt\n0x07LlCrUfiCjgCtVrTq+/zzuDFBotIAtcOLIzuBI2CI4wK3SSsWKOQxgcwxSDhEMOoLxGp+eosF\npcfHuA8a2HN6peTxPWOzwmUBUqUGDfPGYGqzoTNH5kBamztcGqBzRIQAjc3N4qtfRd8jxgrRQoDM\n6GXUlAYAPProozz++OOceOKJPPzww+Tk5CTstSjF/zj74D93AVDf1wxATetWnp65lIX1bw98A8Ow\n7fcnJESo/D4IBPjHv76DwTW8nAK1z4tHpUk9IyC6NGDCBPjqKzERLy0N75KwNMBuDzsC9G4nRufw\nhQCb1gidQUfA1KmDOlZG5mimIaeSC75+gxxbFxM6d7GhfFbcPqHwwESOAIBtRVNiHQFJMgKO66jH\n6Bb1g7n2SD5AiDVr1sT13ZWROZQMd0zwyI5uDjz7IfYdBrxGUX9ZtLWD1d89iymfbD8MQoA3PMk+\n9eVPSG+zYegZ3rhA6fWDRoNfpRAr/VFIlgZ4g0KAWg2VlbB2LcyKfN4ElArpMQFCdOguNIaTyDU2\njxgX5OQMXQgwajA0NQ36OBmZoxmfVoW1yER2Qy/j3m9gzxmVkvv1jM2iJTNxDXlAqaBjUi75dZ00\nzhZjf43dGy4NiHYE5O0Q4epamwdHrggLtEe5EPataeCuNXcN/4eTOaQcUULAggULaG1tjXt9xYoV\n3Hjjjdxxxx2AqA28+eabw0mLqfIzjYHfz78Nu87ICfvX0W3IxqPS8HnFHC7c9u/UTjJCJQIhIUAZ\n8JPmtlPdXkevKXNY96De7sWnVEl3DUiQEaAKRDkCXn9dtAQrLg7vkqw0wK4RjgCd24XJHiwNyMgA\npxNaW8XDfxCE2weq1bL6LyMTJLAM6DkFc/HVXLTtdT4ZOxevShO3n0uj59rFf2N/dkXCc20vqKa6\nbTsKv5+AUhnuGiCVETCpvQ63StQF59i74xwB8+fPZ/78+eHvo2vrZGRGgoM9Jlh6RiV1eZPY99My\nqlbuxJ6tJ63HyfpzZ1L1xS5OdH5JK8MLYhoM0V0DTlr5xYicU+n1g0pFQKWUdgRIlAaEjqGyEp5/\nHi65JLxLqDRACrXDizPbQHYwxEwXDCoMLxB0dw96XOA2yo4AGZloQiv7G8/8N5P+vYPx7zfw7m/m\nS+77xfdnsEmXvNd8++R88r+OCAFamxtP0BGgdUbE0Py6zvB2AEO3E0dO5PNjzPxK7pp/V/h7eUww\nOjiihIB33nknpf2uu+46Tj9dWGMT9XOUwqzLIMNlxq4zovF5aEsvZEP5LJwa/SFvHxh2BAQiqf3D\nReX34VWqxcO83+Rd73VChnTXAJXfJybwmZni4a+JTDICJCgNsIva4jhHgEIhHvS7dw+tNMAWTDCV\nH/oyMhGys1k79hT+99Wb+PaSJxPu9vfZ1yY9jdmQSa8hi/LeA+zPGSNKA3RGyYyA49rr2ZM7DpB2\nBMjIHGwO9pggOiNA7fZhLTSh73Px5VnHs/h3L4dr3Q8VGnckI0AVbF0YDu4bIgqfWN33q+In8FJd\nAxQ+vxAMQo4Auz2mNCBp1wC7J6Y0IOwIyM0VQcRDKPtzmzSiZFEeE8jIxLD18mq+eeXLNM4uoWes\n9PujcXYpexiX9Dztk/Mo3NYR/l5r8+BO0+A1qNFEOwLqu8LbId4RIDM6GTUZAe3t7eG/v/zyy0yZ\nMgUgaa/F/pj1GeGcAI3Pw19Ovo77zvi5CLw7xEKA1ifeXCq/D2XAj1epQu0b3qBD7ffiUwzOERAu\nDVCrYcyYmLIACJYGEIh3IQTDAp0a4QgIZwSAeOjv2jW00gA5I0BGRpKnT1zKuoqTEnYFSJWvCycz\npW0bIEoDQs6emIyA7m7ybZ2R0gBbV7hjgIzMkcBIjAlcGbqYjICOSbk8/9ylWLNNuHUaVK6RC/NN\nBZUn0jVA6ffjUylihACpgL6BCK3u9+8aoAwKDaij1oP6lwaMEeVG0eOCpF0DHEIICP3eYjIC9u0D\ngyFmoSEVZEeAjIw0u88ai7XAyKrfLxCLcEOkY3I+BVFCgMbuwWPU4umXERASAjQ2Dwp/AH2vE0eO\nnBEw2jmiHAHJ+MlPfsKWLVtwu92MGTOGv/3tb0Bsr0WlUhnTa7E/0UKA1udmQ/ksthdNpqq9/uAI\nAUkyBaIzAlR+H26VNr6V4CBR+X14VepBZQSEwwJDNkCPuK/AMlA8BH6lUtoRYLVi0xpxqYQjQBnw\nR1LEi4pg61aoSdx2RAqrNugIcDjkh76MTD+ee+ab4F8Mfxj6Ax9gS/E0pjdv5q3qRYnbB9bX05WW\nE5sRcLjCAucMcv+RcVTLHOGMxJjAmaEj3Sz+j6s8PrwGNVuvnAyAR69JyRGwjpOGNEGXQu3xMcFX\nCYDS58enVaXURSDZ9UOr+wFVbNcArdONW68lZhgvVRoAYUfACpbzf8rvJHQEaK0eHDkGNKGwwFBp\nQGhMMIQ6f7dJKxwBM2YM+lgZmaOZ5brfwu57aFHeNazztE4roGhzG0BYCPTqVMHSgIgQkF/fhT3H\ngNbmRt/rxGPU4lePmvVkmQSMGiHg6aefTrgtUa/F/ph1GWQ6Re2axucRwXog7O3e4QXyDJZQ+0JV\nwIcq4MOl1mFyW4d1zhhHwCC6Bqj8vlgbYBQJSwPa2mgzFQpHgMeFxuuJOAJmzICHHoIbbhjU/du0\nRrBYRMZAvtyGREYmjhFon7W+Yjbf3PQcINoH2rSiNCDNE/Xe37uX2uIaJrd9DQhHQLLsARmZQ81I\njAlcGVqK+oSzQOX24dOowtvcOk3Y4j4S9J+sS03e1R4vBN18Sp8fn0497NIAVQJHgNbpxqPTxAoB\najVKn1/sFxoTqFRQGMlJSNg1IBDA1Gald0wmKpdXtEgOlQbMmAGbN8O0aYO+f7dRA1t3wcKFgz5W\nRuaoZwTGBD2VWajdPjKazLjSdXjSNKBQ4DWo0dkji6TZe3tpmV6I1uYJlgXIboCjgWNKyulfGhAK\nwhrR0oAUwwSlHAEan3h4DpUYR0C/0gCd1zVwacAPfgA33hizS8KwwOZmmjNLhIjiisoIgEjC8CBD\ngaw6UyRMKD19UMfKyMikxobyWczeL9qpGd22cBtQj0pDhkMIpTQ3syO/KuwIKLQK4U9G5mhCZASI\nkhilxx/pWw94dBrUh7w0wBcjBHh1KjGRHyIKfwC/AlAq4xwBGpcXt76fUyJcGhDMCKiogNdeE2JA\nkIRhgT09eA0aHFl61E4fapdPrBaq1TB2rCgPGOSYAIKOgAMHYPLkQR8rIyOTAgoFjbNLKN3QjCbY\nOhDAWmAku61X7GO3o3Z46CvPQGPzYGqzYS00Hsablhkpjj0hwBUpDYh2BIxoaUAKYkCMEBAQIX8+\nZYJJd4qEHQFKZbwjYKCuASqVaCFYXR2zS8L2gS0ttGQUi7ZjbmekawBAqB5zkDZAh8Yg6pzk1oEy\nMgeNhpxKtD43JX1N4dIAFAoOZJVT0btf7NTczK68CRg8DhR+P8Vm8X6XkTmaiM4IUHl8+DSRIZHn\nMGQEaNyeWEeAVjUsR4DS6yegEj9TQKmIcwS49drYA1SiFCFcGqBQwPnnx+ySsH1gSwuWYhNevRq1\n04vW6haTeBDnmTVraKUBwUmJPC6QkTl4HJhdQtn6ZrQ2j3AEAH3lGRTsD2YHtLRgKUnHbdKitblJ\nb7FiKZYX7I4GRk1pwEjQ3xEQFgJUByEscAAxILprgNLvx6dU4VOKFXo3quQHJ0Dt94a7BqSUEdC/\nNKAfgWXw5HOKhI6AlqyzyLN1ovO4MLgcEEoVD6n/g3joB5YBKOAuIwRDn2RkZOIR75XB7694KPiC\nQsGG8lnM2r+BNLcoDQA4kFVOee8BthbXQFMTBzJn4VLpMHgcshAgc1TijOoaoPL48Wv6OwIObdcA\nlccHwWe30h/AG1UaMJQcAoXPj1+tRAX4VUrJjIAYgqUBoU4DUlyvuI5G/1vxG5qbI0KAy4s2uLIY\nzhSfNUvU+g+CFSwHkxvSvo7kFcjIyMQQaic42P1/yR3h15pmlTD3f9axZcmUsPjWV57BxFV7gjs0\nYS5Jx23UoLV5ULZYMZeYRuYHkDmsHFuOAF2UEOCXKA0Yhi1/sER3DVAFfPiUKrwq9YCdA/7y4g+o\n7NoruS1ZaUBKXQMk8CsShAW2tIjSAJUoDTC4HJHzKxTw5pswfXrSn0USoywEyMgcbGqLa5jaulWU\nBmjEUH1/VgXlvcG2a8HSH5vOiNFtk4UAmaOS6PaBKrcvpjTArdce8tIAtccLweetcAQoB3QEjHtv\nL6fdu1Zym9LrD4d5BVT9HQGeBKUBAVGOkGBMgFIpXRrQ0oK5JB2vToXa6UXt8OLVR53jxhvh5z9P\n+rNIYjQKp+II1ELLyMhI01ZTQOHWjhhHQG9FJgUHOsUOzc1YSkx4jFq0Nk/QESALAUcDx9QnayJH\ngE+lJqBQiAlxFNWtXw//ogmcAf0zAnwKIQSoAskHHud9vZJiS4vktgHDAgfqGiBBwtKA5mZa0otF\n2rjbidbjjg0jnDMn4TmTkpEhWwBlZA4yW4umMqVtWzgsECKOAACam2nKLMWmNZJv60Dj89CnzzyM\ndywjM/JECwFKidKA/mGBWQ29aGxuDhbqmIwAHz6dWqzOJ6F8XROFWzsktyl9Afwq0WVEhAUO4AhQ\nqVD4/JHSAMmTKlFIrZlEOwKcXlRuH15d1DkKC2HixKQ/iyTymEBG5qDTV5aBxu4h84BZtOxEOALy\nDwQ/W5qbMZdm4DZq0Ng9pLdYDm9pwLxBfMkk5dgTAqIzApQRNbx/TkC+pZ21j5wyMhf+hDhBINQ1\nQBnwo/L78CuUeNXqODEi5hivm2JLi/QK/SeEswakhICrKgdfGgAiITiuNMDvh7Y2WjOKcKl1aA+4\n0Ta5QauVPMegWLkSZs4c/nlkZGRiCCyLfG0rmsLUlq3hsMDAsighIBAIC302rZEJnbuEG2AYfYpl\nZI5E3EYNWqcHpdcvURqgFun3UZxz27tU/3vHsK+7jpMkX1dFdQ1Q+fx4E2QERJcJZO7vS9jOL9oR\n4FcpYkQFrdPNVH0/l49ajdIXECUEiRwBiboGBGuI/WrhGNA4vDEOiyHzne/A/fcP/zwyMjIxrGB5\n5EvxG9on51G2oTnsCHDkGNC4vOitjmBpgEkIAeGMANkRcDRwbAkBOmlHAMTnBKS7LLHttBIwtaUW\n7RDyBWIyAgL+lEoDyvoaUQYCCQMF1T4vPqVwBMSJBU6J9oFDLQ3o7ISMDNxqHW6VFp3XJUod+p9/\nKEycKE84ZGQOMnUFk5jQuYtMR1+cIyDb0QM6HXadMVYIkJE52lAocKQb0FpcqDz+eEdAv9IAncWN\nxu5JekqtxUVefWfSfRLV+2vc/dsHqlB5k5csZu9LLgSEwwJVsZZ+rdMjOSYI9REfdGlA0BGAQoFP\nq0JrdePVjUAMVWZmTPtCGRmZg0Pb1ALK1jdFAjoVCjrK80R5QHOzCAsMlQY0W2Qh4Cjh2BICErQP\nhHhHgMltRe91oZBafY/isZdv4BsNnw76Xvp3DQgLAUkcAWN69oljEpQPJHME4Eyha4AEkqUBLS1Q\nLCYGbpUWrc+N1jtCjgAZGZmDjkObRmNWGTWttTFCQHXbdq768lkoLQWQhQCZox5bRhp6sysuI0BK\nCNBa3WgcyXN8jlu5iwW/XDOke4luH6joFxaYiKx9fTGW/2iUPj++KEdA9H4ap1tyTKBMoTRAMk8p\n2DUAwKdVobO6R8YRICMjc0hon5JP6RcteIyRRdKO8jzm/+sj2LYNc2k6nmBYoOwIOHo4poSAdlNB\nuL4+un0gIGrdvc7w9yaXFQCdL/lqv9FtG1LHAamMAJ9HlVQIqOgRrb1ScQREhwWmuWxgsSTMCEha\nGoAC5a5+1+vpEV0BEAKK1ucWjgBZCJCRGTX88twV/N+cH7ArbwIAO/Mm8ofTbuHs+v+IYC+CQkDX\n0SEErF69mpqaGiZPnsz9ElZjl8vF4sWLqampYe7cuezbty+87d5772Xy5MnU1NTw9ttvD3jOvXv3\ncvLJJ1NTU8OSJUvweMTn/QMPPMCUKVOYOnUqp512Gnv3xga/ms1mysrKuOmmm0b6x5eRYB0n0VuY\nRXqLNS4jwK3XxmUE6KzuuNf6o7W540oKUkXtiXUEeHUDtA8MBAYsDQgEMwICKkWka0AgQE5rj+SY\nQOELiFDBwZYG9PRgzxXBoz6dGq0sBMjIjCq2XTqJ9T88kdorJ4df+8fya6ioa4Tjj6d1WiFuowZD\njwN9nwt7vvEw3q3MSHFMCQHr7qpiYsdOFH4/ar8vtjQgyhFw35s/I8feDYDB40h6zjSPPTypHwzh\nrgGBoBAQdASo/L6EAYMhR4BkRgCJHQEvPH0lNDRAUVHsAcFMggFLA/oLD1YrmEwElhFbGiALATIy\no4LAMnjhqSv57w//B/PtGQD4b1Vy/5s/44Jtb8ANNxBYJoSAya1fcyCr/DDf8fBwuVxcf/31rF69\nmi1btvDSSy+xadOmmH0eeeQRiouLqa2t5bbbbuPmm28GYOPGjbzyyivU1tayevVqrrvuOjweT9Jz\n3nzzzfzsZz+jtraWoqIiHnnkEQDmzJnDl19+ydatW7nqqqtYtixk0HEwAAAgAElEQVS2F+Svf/1r\n5s2T040OFe9yHo1VJeTu6JZsHxia0Fet3Mn4d/egtbpRO5I/7zV2DypP8lX8RESHBap8fnxaVVxY\nYHRZgaHbgc7mSSgEKHyBqIwAZbhrQNVbu/jhssehqqrfDahTLA2QeN1q5ccm0RXAq1Whs8hCgIzM\naOKnFX/klN9/xnfOeTacHfDwN25l/vMfwVNP8aucB3AbteTVdWEuSSeglMt4jwaOKSGA7GycGj3l\nvQfwKNUxtejhjIBAgJ98+HvGdouVGr3HmehsAKS57eFJ/WCIdgQoA34RFqhMXhpQ0SscAYlKA9T+\noCNAqYwRAjKdffDqqzB+fOwBqXQNUEqUBlgsYBKWoLAjQC4NkJE56rBpjZSam9lWOLpbeq5bt44p\nU6ZQWlqKWq1m8eLFrFy5MmafVatWsXTpUgAuvPBCPv30U/x+PytXrmTJkiWoVCpKS0uZMmUK69at\nS3hOr9fL559/zsUXXwzANddcE77Wqaeeii5Ylz137lyaovqqb9y4kfb2dhYuXHgofiUyQRqrSsnb\n0YXK48Mb3T4wqjRg4tt7GPd+gygNcCbv7KOxe1C5h9Z2UBXjCBClCskcAVn7Raljos4CSq8ffygj\nQBlxBOj7XKy99Btw1139biC10gDJTkJR4wKfTiU7AmRkjkI8Rg2ZzRbap+Qf7luRGSGOLSEA2JFf\nxZS2bTFuAIg4AoxuG2q/j7LeRmBgR4DB4xCT4EES6hoQkxEQLQRIuALKew/gVaoSlgao/FGOgKjS\nAL1HIh8AUioNSOgISBdtQ9xqLVqvXBogI3M0EsoP2FY0uoWAxsZGyssjroaysjIaGxsT7qNUKsnN\nzaW9vZ2mpibKysrijm1qapI8Z0dHB3l5eeHXS0tL467F/7N37vFR1Xfe/5zbzGRyJySACUQUFYiD\ncpWsrqV2lRJFQbuFsgSt1kW6Sm1XypauFdxFHre4bVkqy+6r1grSJbIKWjSPthXxApHSWBLRB6tc\nklBIAgQyk8yc6/PHmTlzzsw5c0kykMv3/XrxInPmdy4ZdM7v9z2fz+cLYPPmzbjrrrsAAKqq4rHH\nHsMzzzzTN78wkTJGIUBUoMaFBer3Y09HEJ6OYGqKgG65x4UAc1ggo2pQklgD8pvOQ+EYsE7WACXa\nNUDjGEMRwAfl+NaBgMkakKBrgFNYoGleEAkLVFxDbopJEIOaSJAgFQIGD30Q6TqwODL8alx7qtG2\nEOCRgyjo7gAAXHbhJIDUrAG9UgRo0YyAZIqAks5WtOaUOGcEqLKtNcAj23QMAHRrgJTYGqDBpn1g\n2BoAhK0BSkj/fagQQBCDioArG35XNk4UjLnUl+LInhb9TyKYftaJ5MUXX8Qf//hHvPPOOwCAZ599\nFlVVVbjsssug2T1tJTJG0zWlGH7kLE5XFEOJsQZE8gA8HUFwkgJ3QEoaFtgbRQAvyXp7XoTbByYJ\nC8xu7UKgJDtBRoAGldf/21c51lAE8EEZkluI3yEVa4BdRoAYngOF5wAKWQMIYlAiZuv/j5+mQsCg\noVeFAEmSIAg2N5N+zJHiq1FxKl4R8N7Ym7BlWzXuW/g8AKD0vD6zNAcIxqFpyO4Da0AkI0BhY8IC\n3wdwY/Rlib8Vp3JHxncECGNYA2IKAW455KwIEBW9ZWGirgGIuembCgEh3g2XLFIhgCAGIQFXNg6P\nmAiNvYRP9uxbrhvMCv+JsKY6fkxZWRmampqM101NTZan+ZExJ06cQElJCVRVxZkzZ1BcXBy3b0Q5\noKqq7TFLSkrQ3t5uGW9WFPz2t7/F2rVrsXfvXuP+uX//frz77rt49tln4ff7IYoicnNz8dRTTyX+\n5fsZA3FO0HLVZSj67CzarxpmUQQcu7YcVzx8DDM2/QGe8yF4LugZQsnCAoUuCXxPCgGaBk5Rre0D\nXRyEBAqEnNYA/CNzoiGAMTCKuX2gSREQUhB0mBOk1jUgZptpTgAAMlkDCGJQQoqAwUevZnfZ2dk4\nfPhwX13LReHz4Vfimtb/Z2kdCACrbl+HL4quwI3HdE1+2fnk1oBIkaAnhQAjLDA2I0BxmGRoGoYH\n2nE6d0RK1gA+HWuA5mwN+N5057BAIBoW6FZCVAggiEHGk1/NQ+PIay/1ZfSa6dOno7GxES0tLZAk\nCTU1NZgzZ45lTFVVFbZu3QoA2LVrFyorK8FxHKqqqrB9+3bIsozm5mY0NjZixowZjsfkOA4zZ87E\nzp07AQBbt25FVVUVAKC+vh4PPfQQXnvtNYt9YOvWrTh+/DiOHj2K9evXY8mSJQOuCAAMzDlBd64X\nYo4L+c0XLAvXY9eW49VNVRj31lFkdQSR19wJAAkX5gDg6qEiwAgYjCkEJFQEtHXBP8JZEcDJ5vaB\nrNE+MJE1gFU0vbCQwBpQpOZbt8UUAnRFQMiSuUAQxMDnn3PWQuFZtE0YnnwwMSBISRHwyCOPxEkr\nNU2Doih46qmnUFhYCIZhsGHDhoxcZF9y1jsMI/yn4xQBgN5e8Kq2zwCYFAEJwgIjRYKedA2I7MNq\nqiUjwCkIMC94AUHBgy7Bm9AaEAkLBABGVaGxbGJrQJKuAWBsrAGdncAYXSosci64FFIEEMSg5P77\n8U/nEwemDgQ8Hg82bdqE2bNnQ1VVVFdXY8qUKXjiiScwbdo0zJ07Fw8//DCqq6vh8/mQm5uLbdu2\nAQCmTp2K+fPnY9KkSWBZFps3b4YgCBAEwfaYALBhwwYsWrQIjz/+OCoqKrB+/XoAwPe//30EAgF8\n7WtfAwCUl5cbBQMz/c3KEMtgmhPcgDp0DctCzumApX0gABwomYbKtg/hPh9Czmm9pTCfJCyQ75bB\n9qBrACuFjxvTPtDpaT8AZLcF0DkyBwXHz1t+n7qwjIZRNKN9oMoxRqhgb60BiO1cZAoKBMIZAZ0i\n/CXUXowgBhVuN376yTKIOTTfHyykVAj4+c9/jkmTJqGwsBCapoFhGGiaBk3TcOTIEXi93n4/cYlw\nLqsQxf42nModGfdeW3YxprT8EQDgDS/ys2RnRYBX7ALQM0WAoEgAw8S1D3RSBJT4W9GWXQyVYR2t\nAYYiAIDM6aGCCtjE1gA1sTXANiE41hoQKQTYFRsIghi45OWhLTfvUl9FnzBnzpw4FcCaNWuMn91u\nN2pqamz3XbVqFVatWpXSMQFg7Nix2LdvX9z2t956K+l13nvvvbj33nuTjruUDKY5AQB0F3ow8tBp\nS0YAAHQU5yO7rQtZHUG4wtkAyRQBPc0I4EQFKsOAjRQCVL0QkCwj4OTUURj2xTnb91nZHBYYDfnj\nQwrEPJtCQCQsMKk1wHlOAACym4fbL0JxD7kYKoIY9JwdN+xSX0KfUFtbixUrVkBRFNx7771YuXKl\n5f1QKIQlS5bg8OHDyMvLw7Zt21BeXg4AWLduHbZs2QKO4/DMM88Y3X6cjnn06FEsWrQIfr8fFRUV\n2LJlCwRB6NE5Ojo68OCDD+LIkSMQRRHPPfccKisre/w5pPQtvXbtWvz3f/83fvazn1l6HAuCgF/+\n8peoqBg4idIdWQXIEQMQ+fhqVltOMca1/9myLZEiwCuFCwE96RoQXjgbGQFJwgKLA21oyym2T/EP\nYygCACgspysNwOuKgB5aA8Am7xrglkP6dZMigCAIYtAzmOYEABAs8MAdkOI87eeL85HdGoDbpIpJ\nlhHg6mFGACepkNwC3GZrQLKwwLYAOkfGWwNuQJ1xjEj7QJVjrNaAEidrgKpnCSSYE8QpAkxzAkBX\nBHjbuygjgCCIfkkoFMKyZcvw3nvvYcSIEaisrMRtt92GyZMnG2M2btyIUaNGYfv27di5cyeWL1+O\nXbt24eDBg3j55ZfR0NCAU6dO4aabbsKRI0egqqrjMZcvX46VK1di3rx5ePTRR7Fx40Z897vfTfsc\ngiDgwQcfxN13341vfOMbUFUVfr+/V59FShkBP/jBD7Bt2zY88MAD+MEPfgDF5D8fSFV/QFcEAIDE\nxlfD23KKURxoR6dbr2x3ePITZgT0ShGgSoDHYyzEjYwAp0KAvw2tOSVQUmkfiHAhQFXAqKp+TLsA\np55aA8yKAE5XBLhkah9IEAQxFBhMcwJAVwQAsIQFAkDnsBx4zocgZwlQGaA73w0+Q10DOEmB6BF6\nkBGQk6BrQIwiIGwN4EJ9bA2wCQt0+0XI1D6QIIh+SF1dHSoqKlBaWgqe57FgwQLs3r3bMub1119H\ndbWePnznnXfigw8+gKqq2L17NxYuXAiO41BaWoqKigrU1dU5HlOWZezfvx/z5s0DACxevNg4V7rn\nOHPmDD766CN84xvfAKC3Os7L651qM+Vv6ZkzZ+LgwYP44osvMHPmTBw5cqRXJ75UnPfkQ2UY24yA\nny/QUzCPF+qyjPbs4QkLAZH3emwN8HjAaXpYoG3XAEDvHPC+bg24a2ZiawCvyvjfeXoFXmX1cW4l\nhBDn1m/esXC6aiCZNSBRIaD1Oy64ZFH/DKgQQBCDDu17l/oKiP7IYJkTALoiAECcNUDlOLDDitA1\nLAtirhtdw73JrQHdco8KAayk6gF+MRkBjoUATUN2WxcWjnzEMUeAlVVcyY3Th7NRRYCQICwwJWtA\ngkLAWjypZwRQ1wCCGJSsxZNYiycv9WX0ikj3nwhlZWVobm52HMOyLIqKitDa2oqWlhZLJ6DIvi0t\nLbbHbGtrs4QDl5aWGudK9xx//vOfUVxcjK9//eu49tprsWTJkoujCIiQn5+P7du34+///u9x0003\nWZ4CDBQ0lsUFd55tIQDF8YWARO0DI9aAnoQFuhQRyMqytA+UWd5xkV/sbwOKbawB70d/5NSoxD9i\nDfBIQQQFG1sAkLI1IC4jwBwM5NKtAVQIIAiCGFoMhjkBEFUExIYFAgCKixHKd6O7wIPAcG/SsECh\nS4oG/6UBJykQs0yFADWsCHBY5LsvhCC7OcDrdVQEmNP/48ICPfYZAbo1IHHXgLiMAJuwQHcnFQII\ngrj47PkCWP276B87BqJyjWEYKIqCAwcOYMWKFWhsbMSwYcPwL//yL706bo+SXB588EHcfPPN2Ldv\nH0pLS3t1AZeCc97CuPaBAGwLARmzBigma0A4I4BhNUdrQIm/FSgph8KeSpgRYBQCwot8QZEQ4h1C\n/CLWAKXn1gC43XArIbCaZm8/IAiCIAY1A31OYCgC7BauxcXoVvzQWAZdw73Ib76Q8FiGNUDT7JV4\nDnCiCtEt6PtpWlgR4JwRkNMaQKAkGx6WTdg+MHJv10ztA7mQop8rlj60BpAigCCIjHGz81uzbgZm\nmV6vsSkGlJWVoampyXjd1NRkeZofGXPixAmUlJRAVVWcOXMGxcXFcftGnuqrqmp7zJKSErS3t1vG\nR572p3OOsrIyjBkzBqWlpZg+fToA4Gtf+1qvCwE9NnBdc801uO+++5Cfn598cD+jw1OQkiKgLac4\ns2GBYWuAuX2gUyFgeKAdGD48aVhgRM4XyQjwyEEE+cSKgIQZAUmsAeA4aGAgckJakx6CIAhi8DCQ\n5wROGQEArIqAYm/SsEChSwKrwfFJvhOspEB26fdRRtXCYYHO1gBvezcCw736PdpRERCV+JvDAoWg\njLGeo/E7cLoCIWEhIIk1ANALKqyqUSGAIIh+yfTp09HY2IiWlhZIkoSampq4DkBVVVXYunUrAGDX\nrl2orKwEx3GoqqrC9u3bIcsympub0djYiBkzZjgek+M4zJw502gVvHXrVlRVVfXoHGVlZRg+fLhh\nxfvtb3+LCRMm9OqzSEkRMHfuXKM9kJmItCLSPujVV1/t1cVcLM55C+0LAUVFUBkmWgjILs58RoCq\ngFVVqAwLjWUcCwG5oU4gN9c+IyBsD+iZNUCyFBDiYFkwWsw1xSQEh3g3VIYFGQMIgiAGP4NtTuCU\nEQAAKClB93kPVJZBoNgLIVlYYPh9TlSMoL5U4CQVssBFF+NJwgLdnSGIua6wr99+jHlBr3IM+JA+\nd+CDMmSPzfQvfKwetQ8cGW3JHGkbSIUAgiD6Ix6PB5s2bcLs2bOhqiqqq6sxZcoUPPHEE5g2bRrm\nzp2Lhx9+GNXV1fD5fMjNzcW2bdsAAFOnTsX8+fMxadIksCyLzZs3QxAECIJge0wA2LBhAxYtWoTH\nH38cFRUVWL9+PQCkfQ4A+MUvfoG/+7u/Q1dXF8rLy/Hiiy/26rNIqRCwe/dujBkzBrNmzbK9+QMD\nx2+hfQ/43+cL7RfcPI+z3mFRa0DOcJR1NMePC+MVu3DBndu7rgH+qCKA0TRdpm9DthgAcnLw4PUc\nDranoAgIP+13KyGMH5nIGtBtKSDEEWMN4BQZEEVLO0KRc0FlKB2YIAYrFBhImBlMc4K1eBK/KNRT\nm52sAUG4DWsAH5SdZf+aBr5bQjDXBU5UIHlTt8txkgJF4KOL8SRhga6AhFCOC2BZjFJLbMewigZw\nZmtAuEgRUvCAe2n8DhFrgKIBrp5bAyKfIxUCCGLwMtADA+fMmROnAlizZo3xs9vtRk1Nje2+q1at\nwqpVq1I6JgCMHTsW+/bti9vek3Ncd911OHDggO0+PSGlQsCKFSvwwgsvYO/evbj//vtx3333WdIM\nBxodWQXID563fe+Hc9bio9Lr8dz0b+K8Jx9XSZ85HscrdaEjq6B31oDOaEYAGOjBfTZkiwEgO9te\nqh8mThGg6ooA86LdukP61oBIQcI8CRJ5KgQQBEEMFQbbnKC7MAuAQ1jgvHlo6DyGwi86cG5sATSW\nASeptotcPihDcXGQPXzanQNYSYXk4qOKgLC0nnV42i8EJEjZgr1UP3LMGEWAOSzQdl5gtgZ4e24N\niLQNpEIAQRBE/yal1dvTTz+NpqYm/OQnP8GBAwcwbtw4zJkzBy+99BIkKf3E/EvNuSwHawCA/6pc\niqCQhQcWPoduIStx1wCxC+c9+frT/TSJdA1gNTWljICckN8oBCRqH2jOCGA1Vb/+RIUATUluDUD0\naU9uyJoODAAhzu0cSEgQBEEMKgbbnCBYoN+/VDtrwOTJOHZzOervuw7HvlQO2cM75gQIXRIkrwBF\n4MCl2TmAExXdmmBRBDiHBbr9IsRsXREAh24NjKIZ93aNi4YKCgkKAT2yBsR2DQhbA2Q3FQIIgiD6\nMyk/xuV5HnfddRd27dqFY8eOYdasWXj88cdx2WWX9bqH4cXmnLcQEptcstctZCXNCOiVIsDttmQE\nyByf2BqQna1X7J0UAaY2gEp4ke+WQ4A7cdeApNYANUYRkJ1tGSLyLvsuDARBEMSgZDDNCRIqAmKQ\nsnjw3fbFDqFb1gsBLi5tRQAnKZAFkyJAURJmBAgBEWK2oC/YU1QEmLsG2M4LwtYARkmza0DAOi8g\nawBBEMTAoEftAwOBAM6fP4/Ozk7kmkLjBgpnvEXOAXomgrwncfvAsDUg0RgnomGBgZQUAYYkP4E1\ngFeiT/bVIGt0DehLa4CgSIDLuugP8W5oGBh+UIIgBhgzL/UFEMkY6HOCUJ4bsotLaeEqe3jHwMCI\nIkBjmB4UAsJhgSwLRlHBpZARIIYzAhwLAaauARqbmjWA60nXAMk6L4goAagQQBAE0b9JWRHQ1dWF\n559/HjfffDN8Ph+OHz+OF154AV988QVyYqTi/Z0tU6vxz1/916TjuoWsxO0Dw9aAXnUN0KIZAQmt\nAaLJGuCQI2BRBIQzAtxyKGEhgFdlcJqq39ztiLEG2BUNRI4UAQRBEEOJwTQn0FgGP/10mSFpT4SU\nJSS2BmTxUFwsONF+ce4EawoLZBUNjKqh3j0FrGzfGtDlDysCElgDzAt6PSxQPxYfcigEhOcBnKik\nZw2QrfMCUgQQBEEMDFJSBHzrW99CTU0NrrrqKjzwwAN49dVXUVBQkOlryxhd7mx0ubOTjkukCHjy\njcfxt396CVunLsa49j+ndf5vfvgcprT8EcifCU79i14IYDkoLGdbCGBUVb8OrzehNcC8SDe3D0xk\nDXDLIcgsB94p4Tmma4BTIUAbIAnRBEEQRO8YbHMCADg3tjClcbKHs1UEFH5xDvdW/Rod5fngRCUt\nRUBeywV8ad37aPRVhAsBKlhNgywkVgScH5Of2BqgaM5hgQ7zAoVn9TaD6VgDqBBAEAQxIEmpEPDc\nc89h9OjRuOyyy/DGG2+gtrbWeC/SNmgg9QxOlURhgVe3HUGO6MfJvMvSVgTcd+B5PHTPf+LXM73g\nXv3YYg2wCwLMkroR5D3wsqwu1Xe46ZutAQrDRa0B2c6KAL0QwDv/hxBjDbArBFBQIEEQxNBhqM4J\nAGdFQH7zBWS3BtAydRSy27vSKgSUv9eEriIv/uPnyzD7mu+AlVQoHAuFd+4a4AqYFAGJMgIidkGz\nIsDJGgC9YMCHEtsFkxUC5EhYIBUCCIIg+jUpFQKWLFli6Qk8kHsGp0OisMAc0Y+vV9fgs+Kr8I36\nX6d13BJ/Kz4qvR7gPgenKWC1cFiggzUgWwzA786BF+hza4BbDkFhE9ysWRaM6d+bV+wVAQRBEMTQ\nYKjOCQBAzuIh2IQFuvwiPr/1Cvx6x9fwzVtfTKtrQHZrAO1XD0Mw26P79EUFKhsuBDiGBZoyAlKy\nBjBgFRWMGs4AEOwDkzWO1cMEe2UNoPaBBEEQA4GUCgHPP/98hi8jyksvvYTVq1fj008/xYEDBzBl\nyhTjvXXr1mHLli3gOA7PPPMMbrvtNgBAbW0tVqxYAUVRcO+992LlypV9ci0dWQUY1nXW9r1sMYBz\n3kKInCttRUBJZytac0oA7hg4NXlGQI7oR8AVtjIksgYo1vaBnBZWBCS1BiT4zyAFa0CId1tyBAiC\nIIjBy1CdEwBAd4EHWWfjHxC4AhLEbBcUN59214Cc1i4ESqL3eE5UoHKJCwFuf/KuAYxiVQQwigYu\nJENx82AdCjVqH1gDIooAKgQQBEH0b3rUNSCT+Hw+vPLKK1i6dKll+8GDB/Hyyy+joaEBp06dwk03\n3YQjR45AVVUsW7YM7733HkaMGIHKykrcdtttmDx5cq+v5VTuSBR0d8AtBRGK6TKQLQYQcGWnXQgQ\nZBG5oU6cyyrUb/iRsECWc2wfGDkXgIRdAyyKgIg1QErcNcCtJCkEpGANEHmXRTVAEARBEH1Bf5oT\nAMD5MfnIb7oQt10ISPrCHEi7EJDdGsBfJo/UX1gKAWxiRUB2kq4BNooAPijrnQ8criWpNSBSQNC0\n6M+UEUAQBDEgSalrwI4dO3D55ZfjwoX4m19HRwfKy8vxyiuv9MkFjR8/HldffXXc9t27d2PhwoXg\nOA6lpaWoqKhAXV0d6urqUFFRgdLSUvA8jwULFmD37t19ci0ay6IlvxRl55vj3ssJ+eF35UDkXXDJ\nqRcChgfa0Z49HBrLGu37IhkBCmMfFpgtBuB3hVOYWdY2RwAIL9LNioBMWAOoawBBEMSQZqjOCQCg\nY0weCk7E/95uv6hL9dGzQoC/xKu/4Dhw4YwAlUsUFigmtwYoWpwigA8pkD3Oxf+k1gCGARgGjLn2\nT4UAgiCIAUlKhYDNmzdjxYoVyMvLi3uvoKAA//RP/4RNmzb1+cWZaWlpQVlZmfG6rKwMzc3NaGlp\nwejRo+O29xUnCseg/NzxuO2xioDR506kdLwSf9gWABgyf3NGgJ3/PyeUmjWAU60ZAaymXjRrAAUG\nEgRBDA2G8pzg/Jh8FBw/H7ddiIT3QffIe9u74TlnnzEUS05rIM4aoLGMrghQnNoHSkmtAbGKAEbV\ndEWA23mBrlsDEigCAL0QoJquK7YQED5+ovMQBEEQl56UrAF/+MMfsHHjRsf3v/zlL2PVqlUpn/TW\nW2/FqVOn4rY/9dRTmDt3bsrHSZfVq1frP+wDcOUsYNyspPscLyzHGJtFfmRxLnM8XIqI/RtmYsZ3\nPkRLQZnNUaKU+FvRmhsuBISf7hsZAb20BpgVASrDRjMCkigC0rIGOIQFkjWAIIYWe/bswZ49ey71\nZRCXgME2J3gX72DMrHKUz7o86T4d5fnIPxFfCHD7TdYAgcNNz+xH0efn8Oa6W5IeM7u1C/6S6D1e\ntwZwCTMCXAERUrKuAYoGhBfjGsuAVTTDGuCEbg1IkBEQvsZEhQCZFAEEMeSgOcHAJKVCQHd3N7xe\nr+P7WVlZ6OrqSvmkb731VspjI5SVlaGpqcl43dzcjNGjR0NVVcv2pqYmy9MAM5Gb/urwa+bf9b+1\n7zmf90TBGIzpiC8ERJL8WU2FRwoiJ+RHTsif9PeIVQRECgGP/7WAR//o3DUg4DZNEhysARZFAGOy\nBjgpAlK1BpiCAAVVsi8EUFggQQwpZs2ahVmzZhmv16xZc+kuJgWSBciFQiEsWbIEhw8fRl5eHrZt\n24by8nIA6YfSHT16FIsWLYLf70dFRQW2bNkCQRCwd+9ePProo2hoaMD//M//4J577gEAKIqCb3/7\n29izZw80TcMtt9yCZ599FiybkmjvojP45gT63z/Ej4z3fovbbffpGJOPAptCgBAQ0V2aC0BfABec\nOA9XZyil38VOEZDPeZIUAkwZASl0DVA5Boyi6taARIqAZNYAIL5zgCSRNYAghjgXc07QfmNOGqOT\nr82GMinNMsrKyvCnP/3J8f2GhgaLRK+vMLckqqqqwvbt2yHLMpqbm9HY2IgZM2Zg+vTpaGxsREtL\nCyRJQk1NDebMmdNn13CicEycIoBXJPCqjBDvhsi54FZEcJoKr5R84hNXCND0jABwCboGhPMIIvsk\nVATEtA9MGBaYqjVATawIiHwOBEEQ/ZFQKIRly5ahtrYWhw4dwo4dO1BfX28Zs3HjRowaNQoNDQ1Y\nsWIFli9fDsAaSldbW4ulS5dCkqSEx1y+fDlWrlyJhoYGjBw50nh6Xl5ejl/96ldYtGiR5dxvv/02\nPv74Y3z66af49NNPcejQIbz99tsX4ZPpGUN5ThAoyYb7Qgh8TAtBV0CClB3NCHB1SRC64+/nsfBB\nGXy3hGB+uGDPcXrrQY7TF+WyGt+uD3q7QjHHlbxrgGENYEI7iUgAACAASURBVFNTBPSRNUBlGWhc\n/yxkEQRBEDopfUvffvvtePLJJxEMBuPe6+7uxpo1a3D77fbV83R55ZVXMHr0aOzfvx+33367cQOf\nOnUq5s+fj0mTJuGrX/0qNm/eDEEQ4PF4sGnTJsyePRvXXXcd7r77bkt7od5yoiA+I8CQ6jMMFJaD\nGk7OzZKS+wHtFAGspgIsC4V1DgtM1xoQaR+YJXX3rTVAleP6D1NYIEEQ/ZlUAuRef/11VFdXAwDu\nvPNOfPDBB1BVNe1QOlmWsX//fsybNw8AsHjxYuNc5eXl8Pl8cU/6S0tLIYoiQqEQuru7IUlSRhbS\nfcVQnhNoLIMLZXlxqgCXX7R0DQAAIaZYYEd2W1gNEEngD4cFgmWhsSxUBtZFN2AEESouLnnXACMs\nUO8aIHRLScICGV0RkK41wDQvUFwcqQEIgiAGAClZA1atWoUpU6bg+uuvx9KlS1FRUQEAaGxsxH/+\n53/C7/en5QdMxPz58zF//nzH67A7z5w5c/q04m+mNbcEwwPtlm0RWwAAgGEgci545FBKhYARnafx\n56Jx+guTNSCRIiC2EGBrDdA08GpUzhexBuSFLgD5+fYXE24fmMwaEFcIcMcrAuxCDgmCIPoDEdl4\nhLKysjgvo3kMy7IoKipCa2srWlpacMstt1j2bW5uhqZptsdsa2vD8OHDje2lpaVJw+omTJiA2267\nDaNGjYKmaXjkkUdwzTXX9OZXzihDeU4AAP6SbHjbuwHTP5ErIFm6BgAAn4IiIOd0AIFik80ibA0w\nFvDhFoKK6em6OZgwadcAwxqgdw1wXwghmO/wcCA8jg/JSa0BCTMC3DwVAgiCIAYAKRUCRowYgfff\nfx/f/va38dhjjxnyPIZhMHv2bPz85z/HyJEjM3qhl4qAKxvZYsCyzbIwB9IqBFx+9hi2TQ7LQiPW\nAFMhwG6Rny0G0OnWvYdOigC98wBjPGmKWAPyu887FwJSsQbEZATYdQ3Ye8XN4DQFfTPtIwiCSJ09\ndfqfRDCRp639lL179+Ltt99GS0sLNE3DrbfeitmzZ+Omm2661Jdmy1CeEwCAlC3AFbC2DXYF7BQB\nyQsBBcc60FFeEN1gVwhQNJhnBkY+QHh8KooALawI8JwPRW0INqh8imGBCdoHdg/LQt23pznvTxAE\nQfQLUioEAMDll1+O119/HWfPnsWf//xnaJqGq666CsOGDcvk9V1y7AoBlnZ+ACROv/mnUgi44uwX\n+Hz4lfqLiCIgSUZAthjAqdyRxj52hQBOVSCzPCIC/Yg1oCDYARQUxI2PHKsv2gf+9ppbnfcnCILo\nBUfHjUr4fvk44N6/i75e8x9/iRsTGyxnFyBXVlaGEydOoKSkBKqq4syZMyguLk47lK6kpATt7e2W\n8XYyf3Nx4oMPPsCcOXOMAL45c+bg/fff77eFAGDozgkAQMx2wRWIyQjwi8biXBH0gnwq1oBhX3Tg\n7JWF0Q0OigDLuSIdA4Dk1oAYRYCnI4hggbMiQONYcL3NCHBxKXVLIAiCIC4taSe5fPLJJ5g0aRJu\nuOEGfPLJJ7YewcGEkyLAsAYAhj8+WSHAJYcw8sIpnCgYo2+wUQTYFQJ4VTaKDU7WAF6VoTBRKV7E\nGlDQ3ZHQGuBSpPSsATZhgQRBEP2ZVALkqqqqsHXrVgDArl27UFlZCY7j0g6l4zgOM2fOxM6dOwEA\nW7duRVVVleVcmqZZgu+uvPJKvPPOO1AUBZIk4Z133sG4ceMy/Kn0DUNtTgAAYrYAIbYQ0ENrwLDP\nz8UXAiQ1YSGAlTWj2BDJFojNEQAAVrFRBHQEEysCOAa86fy2mKwBxnn7aYcLgiAIwpm0v7m/+tWv\n4uTJk3E/D1aMQoBp0hZnDeBdUBg2aSGg/NxxNBeUQeHCC+mYsECZ4/WFdgycqkBlwv9UDtYATlUg\nc6aKPKsrBxJaAwr1yYe5gBAHy4LRElsDCIIg+jNOAXJPPPEEXnvtNQDAww8/jJMnT8Ln8+HHP/4x\nNmzYAKBnoXQbNmzA008/DZ/Ph9OnT+ORRx4BABw4cACjR4/Gjh07sHTpUvh8PgDAPffcg3HjxmH8\n+PGYOHEixo8fb7QW7O8MtTkBYG8NEAKSxRqgskyKioBzOHuFjTUgvLC2KwQwigqNZSz72BYCYhUB\nqpbUGtBdmKX/kMQaEJkTmc9BEARBDCyG9Le39j3nbcy/63/LnACVYeFSRIi8fvOMtQaInAst+aVJ\nCwFXtn+Oz4uujG4Iy/wjigCFse8awGpq9Km9gzUgVhGgMiy8UpduO8jKsr+gq6/Wr+vM584XnYI1\ngCAIor9jFyBn7nPsdrtRU1Nju2+6oXRjx47Fvn374rZPnz7dYieIwLIsNm3alPR3IDLLWjzpuP2H\n+JHx2s4a4I7pGnChNDeljABbRYAom57k2xQCVM3ami82vC8yTtGix2EZMIqGrI4g2q8pcryez/9m\nLMb97mjK1gAqBBAEQQxcSMuVArH2gGwxAL/Lag04XlietBBwxdkvrIWAsMzfyAjg7MMC9SDA9BUB\nw7rO4rwnP9qWKBaGwZ+LrsQIf6vzRdt1DaCbPkEQBDFE0a0BsYoA0WIN6CjPh9CVWBHASgryWjqt\nYYEsa8kIUHhWl/ibYFTNqghwKASYF+kRa4D7fOKuAf+vKtrVyPnCWSoEEARBDAKoEJACsYWAHNGq\nCPjZX38HdWNuSF4IOPMFjhaNjW5IsX1grDXAKSPAHPqnMHohoCPLISgwzE9vfhRB3lkmSNYAgiAI\ngogiZgsWRQCjahC6JEheXRFw9Evl+PChqUkVAflNF9A5Msfaai+VjABFgxprDVBsugnZtA/M6ggi\nWOB8zz/tK9F/cCebF4R/pEIAQRDEgIUKASkQcGUjJ+Q3XsdmBPx35d/jdO4IeKWuhMcpPd+CpnxT\nUnU4LNDICHAoBFgUASlaAxSWQ1HgjK4ISMDPb3oYWf8nQQEjxhogKBLd9AmCIIghi5TtgssfVQTw\n3RIUN2/I9VsrinF4/njwSTIC8lo6cX50nnUjx4GVkmQEpKMIiBQUUlQEgGHwQ/WfgSuuSDjGUARI\nSVoNEgRBEP0WKgSkgN+dY7UGhKxdAwCgy+VNqggoPd+Ck/mXRTekqAiwZASkaQ1IpggA4GwdsDkf\nKQIIgiCIoUwox5oRYO4YEEH28OBExXaBHiGvpROdl1nnEqm0D9QzApIXAhjFbA1gwaagCNB3TDAn\niDkfR4oAgiAGILW1tfD5fJg4cSKefvrpuPdDoRAWLFgAn8+HG2+8EcePHzfeW7duHSZOnAifz4c3\n33wz6TGPHj2KyspK+Hw+LFy4EJIk9egcwWAQ06dPx+TJk3H11Vfju9/9bq8/ByoEpECsNaAg2IFO\nd65lTLeQlbQQcNmFkziZF1MI0KIZAQrrHBZosQZoqbUPLOpKrghISqw1gNoHEgRBEEOY2K4Bno4g\nQrnWQoDGMlDcPPigsz0g72QnLlxmnUvEFgI0jgErWxf5cYoAjtNtADFwMYoARlHDXQMSKAJSwTQv\nIGsAQRADjVAohGXLlqG2thaHDh3Cjh07UF9fbxmzceNGjBo1Cg0NDVixYgWWL18OADh48CBefvll\nNDQ0oLa2FkuXLoUkSQmPuXz5cqxcuRINDQ0YOXIkNm7c2KNzeDwe7N27F/X19Th8+DD27duHt99+\nu1efBRUCUiC2EDDx9GF8MmKCZUw3n6QQoGm47LxNISBWEZCsfSDHgVVtFAGaYs0ICFsDUlIEJMKu\na4Ag9O6YBEEQBDFAic0IKDnchrYJw+PGSVl8QntA7slOXChNXAiwzwhQU7IGMKaMAD0sUIOnI4hg\nQS8LAbFdA2hOQBDEAKKurg4VFRUoLS0Fz/NYsGABdu/ebRnz+uuvo7q6GgBw55134oMPPoCqqti9\nezcWLlwIjuNQWlqKiooK1NXVOR5TlmXs378f8+bNAwAsXrzYOFe65wCArHAnOFEUoSgKRowY0avP\nIu1CwA9+8AMUhvvPm38ezFgKAZqG607+CYdGTbKMiVUEjLzwF3z/91FZSEF3B0K8G13uaLZApBDA\naqpRCLB72p+KNYBX5OgYxHQN6A1kDSAIgiAcGIpzAjHbBcFUCBj5p9M4NSl+MiZlCZbAwC+tfRf5\nbeeN13knO9FppwgwhQU6dQ1QU2gfaM0IYI1rlj29vIdT1wCCIAYwzc3NGD06mtlWVlaG5uZmxzEs\ny6KoqAitra1oaWlBWVlZ3L4tLS22x2xra8Pw4dFCcWlpqXGudM8BAIqi4Prrr8eIESPw5S9/GRMn\nTuzVZ5H2t7e5l7JdX+XBiLkQMKLzNDhVQUt+qWVMbCHgqrbPsOCj7fi3W1YCCNsCzPkAQNQaoCqp\nhwU6WAPiFAFha0CvFQEsCwbUNYAgCIKIZyjOCWKtASMPtaLxb+MnY3IWD8GkCJj0Px/jsltOAsX6\n69yTfmdrQDphgY5dA6ztA71nunqvBgD0azO6Bmg0JyAIot/w/h4F7++JXyeZYZLloPRjOI7DRx99\nhPPnz2P27NnYs2cPZs2a1ePj0bd3CpgLAded/BP+dNl1cWE6sYUAQZUsnQZKz7dYbQFAvDWAs7cG\npNw1wKQIUBkWXqm794oAO2sA3fQJgiCIIYqYbQ0LHPWn0/jtv86KG6dbA6L3dE5SkeUPGq8dwwKD\nKYQFxlgD2CSKAI1l4OqWcaEsL25c2sRaA2hOQBDERaQONzi+x88CvjQr+vrHa34XN6asrAxNTU3G\n66amJsvT/MiYEydOoKSkBKqq4syZMyguLo7bN/JUX1VV22OWlJSgvb3dMj7ytD/dc5jJz8/H7bff\njv379/eqEEAZAQ5o34v++fZfRQsB155qRMMoX9z42EKASxaRI0YLAXFBgYCxqI+EBUqsAJciIhZL\nRgDLglVV/GTno/CYzsepVkVAt5CFL4aNxa5r7+rR728Qaw2gsECCIAhiiLEWTxp/vpO90pDZu7uC\nyGvpxJmriuL2kbIEuLqiBQNOVKKFAE1LKSxQFVj9tQlGiS8EjH/1CMa/dsQyzrxIlzw8VAb44FHn\nCXTKkDWAIIgBzPTp09HY2IiWlhZIkoSamhrMmTPHMqaqqgpbt24FAOzatQuVlZXgOA5VVVXYvn07\nZFlGc3MzGhsbMWPGDMdjchyHmTNnYufOnQCArVu3oqqqqkfnOHPmDDo7OwEA3d3deOutt+Dzxa9J\n04G+vVMhOxvZIb0QUNR1Bq05JXFDkikCRl34C/6SN8q6E8taFAEh3g23HIo7dmxGAKcpuO/A8/jZ\nX38Hx4rGAtCf1JsLAetnPYYff3kFOj29rP7Hdg0gRQBBEAQxlMnONqwBOecC6C70QOXjn6vIMYoA\nVlKR5e9GJ7Lh7hShMYCYG9PKj2UtGQGymwcXiikExLYP5DiUv9+EzpE5+HTu1dFxprBA/6hc/PjE\nd/pGEUBdAwiCGMB4PB5s2rQJs2fPhqqqqK6uxpQpU/DEE09g2rRpmDt3Lh5++GFUV1fD5/MhNzcX\n27ZtAwBMnToV8+fPx6RJk8CyLDZv3gxBECAIgu0xAWDDhg1YtGgRHn/8cVRUVGD9+vUAkPY5Tp48\niSVLlkDTNASDQSxatAi33357rz4L+vZOhexs5IinAAA5IT9O58SHAnW5vHGKgGwxAGgawDDIFgPw\nu20kgBFFAMsiKHgcCwGx1gBelVEQ7DDGxIYFXsjqpSUgAlkDCIK4hNTj+jT3+EtGroMgDLKz4fLr\nhYAsfzfEHJftMMkrWDICeFGBJxBEJ7IhBET7/TgOnKQAbv2eL3t48CGrZdDOGsCFFGR1BC3jzO0D\nAfRNEQAgawBBEAOeOXPmxKkA1qxZY/zsdrtRU1Nju++qVatsM3HsjgkAY8eOxb59++K2p3sOn88X\n1+awt/TIGvD+++8bPweDwQQjBwnZUWtATsgfv6CHjSJAkcBpKjyy/vnEPrEHADAMVIbR5fYchyDv\nMcabiQ0LZDUVgiKhoDtaCIgNC+wzqGsAQRAEkYAhNyfIygInKmAU3fPvWAiI6RrASlFrACertiqC\nWGuA7OHAB62KAFbVoMYUAviQAk+H9UECo2Rokc6yYMLTAioEEARBDFx6VAi45ppr8OGHHwIAzp07\nh8ceewyHDx/u0wvrV5gLAaJzIcArdRmvI17/iD1AUCRIXHyvXYXh9LFha4BdIYDTrBkBnKpAUK2F\ngNiwwD6DCgEEQRBEAobcnIBhIHkFuAISsvzdCDkWAmLCAk0ZAaykQhFs7tmxhQA3Dz4YowhQ1Liu\nAXxIhidGEcDKmkUR0GdQRgBBEMSgoEeFgM8++wyHDx+GpmkYNWoUfvjDH2LBggV9fW39B1MhIDfU\niU53btwQO0UAAGM/QZEgsTaFADZaCAjyDtYA1ZQRwHEQFAmspiE/GO1H7FRo6DUMY8kIEBSJbvoE\nQRCEwZCbEwCQsl0QAiKyOrsh5toXAmLbB3KSCk8grAiQFKhCKooAB2sAZ9qXZcEHZXjOx1gDJAUQ\nMjMvQHhewEkKzQkIgiAGKD369r7jjjtw4sQJaJoGVVXR1tYGl8v+RjgoyMtDfre+6M4J+eF3xSsC\nRM4FXpXBqgrUyOIeKSgCWA4uWQRYFgqn/3Nwimz8DMRbAyLFArMiwKWIELkM/BuQIoAgCIJIwJCb\nEwAI5rnhOR+C4g8mUASYrAGapmcE+PUHBroiwKEQIKkAa8oIiFUEOGQExFoDOFEBMvHvQIoAgiCI\nQUGPFAEPPfQQ3nzzTbAsC5Zl8fbbb+Pdd9/t62vrP4wbh6vaPwPgnBEAhrGoAiKKgEgLQUF1tgYI\nqmRU/+3sAbHWALtCQMYUAXaFgEw8YSAIgiAGJENuTgDg7LhCDP/sbOKwwCwefFgRwCr6wtnICJBU\nqClZA7i4QgCraNBY6z58SI4PC5TUzNyvYwsBNCcgCIIYkPSoELB27Vpce+21AACGYTB16lQcO3as\nL6+rf1FejuGBdmSH/I4ZAYDVHhBRBJitAXZhfoYiIHzTt7MHsJoKhYlaA9yKfSEgI4qAGGsAr5Ai\ngCAIgogy5OYEAFonDEfxJ+0JwwJlkyKAE/XAv4g1gJVVKCmFBfLgbdoHqrHWgJCiZwSY7teslCFF\nAMOACZ+GFAEEQRADlx4VAgDgqquuMn6eNm0aVFVNMHqAw3E4Unw1xrd+ipyQ3zYjAAC6eRtFQNga\nwKuyszVAsRYCPHIQN3/+Dr6z96cA9IwAlU1uDbhoigC66RMEQRAmhtScAECbUQjoRsghI0DK4iF0\nhRUBkr6YjyoC0sgICCsCFn59B1hJcbQGcLJqnA8IFx8yrgjQaE5AEAQxQOlxISCWyNOAwcr1N0/A\nhNOfIDfUaZsRANgrApJlBKgMaykEhHg3PFIQV7V9hqnNBwEkyAgIxlgDbMIIew0VAgiCIIg0Gexz\ngvkT/kkvBHQmtgbEKgKyLBkBNtYAlnW0BkzYdQRCtxxfCAhbAwBY7AEXzRpAcwKCIIgBSZ8UAj78\n8EPMmjWrLw7Vf5kwARNPH4ZX6kKXy2s7pMvljS8EiKZCgM1CvUvw6sWC8BP/oKBbA3hVBqfqE4fY\njIBIhkCcNYDPkAQQJmsAFQIIgiCIBAyVOUHJJ+3wJioEeAWjawAvpqgI8Hrh8ovWsMCwNYCVVTCK\nCkaJVwREigXmwMCMhQWaLINUCCAIghi49KoQcOzYMSxcuBCVlZWDPhgI11yDKS1/RLeQBZW178tr\nFxZoaR9oowg4l1WIHDEQZw3gVVlfdMMmIyCsCIh0MgAybA0wSTwpI4AgiIFIbW0tfD4fJk6ciKef\nfjru/VAohAULFsDn8+HGG2/E8ePHjffWrVuHiRMnwufz4c0330x6zKNHj6KyshI+nw8LFy6EJOn3\ng71792LKlCkQBAH/+7//a4yvr6/HDTfcgEmTJmHChAl44YUXMvERZJwhNScoKoIisBj1+V8Sdg3g\nw4oAVtLvo0ZGgKRCtcsIKCyEOyDFWQMYVQOranoxQNWgcTGFgHCxINJCkFE1PS+As5+v9ApSBBAE\nQQwKelQI6OjowGOPPYbx48fj5MmT+M1vfgPNFFAzKLn8ckw8ddjRFgDEWwMuuHOj1gCHrgHnvIX6\nDzaFgIgiwC4jIMS54qwB1D6QIAginlAohGXLlqG2thaHDh3Cjh07UF9fbxmzceNGjBo1Cg0NDVix\nYgWWL18OADh48CBefvllNDQ0oLa2FkuXLoUkSQmPuXz5cqxcuRINDQ0YOXIkNm7cCAAoLy/Hr371\nKyxatMhy7tzcXNTU1ODQoUP4/e9/jxUrVuDMmTMX4ZPpG4bknADAucsLUH64CWKOfRFejrEGBHNd\nUUWA7GANKLTOCYxCgKLfh1lFs7UGcCEZsovTAwOhZxIorgwUAQBLIYCjQgBBEMSAJa1CgCRJ+MlP\nfoIrr7wSr776Kn79619j7969mD59eqaur/8wZgxGn292DAoE9EKAV+oCoC/Mz3kLo2GBimzbNeBc\nlvWmH+LdhjXArAgwWwNcioS2nOKLExZI1gCCIAY4dXV1qKioQGlpKXiex4IFC7B7927LmNdffx3V\n1dUAgDvvvBMffPABVFXF7t27sXDhQnAch9LSUlRUVKCurs7xmLIsY//+/Zg3bx4AYPHixca5ysvL\n4fP5wLLWW++4ceNQXl4OABg1ahRGjx6N1tbWTH8svWZIzwkAnB+Tj5LmdoRy3bbv6xkBuhqEk1R0\nF2bBY2QEKFDsrAGxhQA3Bz6k6E/egagiINYaIKkIFHsNawAnKvbtCfsChgGoawBBEMSAJ+VCwI4d\nOzBhwgSsXbsWP/rRj3D48GHMnz8/k9fWvxgxAiInOLYOBOIVAeeyCqPWgGSKAFNGgEcKglccMgLC\nk4Mz3iLkBS+ACcv2KSyQIAjCnubmZowePdp4XVZWhubmZscxLMuiqKgIra2taGlpQVlZWdy+LS0t\ntsdsa2vD8OHDje2lpaVx50rEhx9+iK6uLkyYMCHt3/NiMuTnBAA6xuQBQIKwwKg1gBMVhPLdcAUl\nMKoGTlLtMwIKrXOCiCLAKAQoKlhFhRpTCACAruFeIyyQk1T7QkNfQNYAgiCIQUHK396bNm3C2LFj\n8dJLL6GgoCCT19Q/YVk0FYxOuRAgKFLY/5+4a0CHJ/xZppoRYCoYyCwPlyIixHoyFxZIhQCCIPox\njXvO4OM9ZxOOYRgm4fv9hb/85S9YsmTJgMgIGPJzAuiKAMC5ECBbFAEKZDcHMcsFoUty7hpQYJ0T\nxBUCjIwA0yI/Mn/Ic4OPnE+8ONYAKgQQBHGx+QB/lcbo32XsOgYDKX97/+53v8Nrr72G9evX4/77\n78cVV1yRyevql5woGIMQby8BBGwUASZrgNMT+9iMALM1gNPCGQEx1gAAkDgBEidAUCSEBA9cioiA\nK7tPfk8LLGukA0d+D7rpEwRxsfgIkxMPmAVcM8v0es1TcUPKysrQ1NRkvG5qarI8zY+MOXHiBEpK\nSqCqKs6cOYPi4uK4fSPKAVVVbY9ZUlKC9vZ2y3izoiBCbHHiwoULuOOOO7B27VrMmDEj8e/cD6A5\ngVkRYK/Gk7IECF3WhXkw2wOXXwQrO4cF6jtErAE8+FC0EMDYZQRE5gXZgt4yEHrhwbbQ0BeY5gWs\npNCcgCAIYoCSlm5s7ty5WL16Nd5++22sW7cOHR0dyXcaRJwoHJM0I8CsCOjwFKTUNQCAvSJACSsC\nzGGB4XESGy4EqJJx/Ey1DyRFAEEQA5np06ejsbERLS0tkCQJNTU1mDNnjmVMVVUVtm7dCgDYtWsX\nKisrwXEcqqqqsH37dsiyjObmZjQ2NmLGjBmOx+Q4DjNnzsTOnTsBAFu3bkVVVZXlXJqmWcL0RFHE\n/PnzsWTJEtxzzz0Z/jT6jqE+J4goAhJnBES7BigCh6DXDVdAdG4fmJcHlUFyRYBNIUD0CvrCHBlW\nBDAMKQIIgiAGAWl/e/M8jwceeACdnZ34r//6L+Tk5ODOO+/MxLX1O44XlmN0R5Pj+12CN6oIkEX4\n83KMVn+8miQsMDYjwGQNsGQEmBQBIucy2hS6FPHiZQQIGTgPQRBEhvB4PNi0aRNmz54NVVVRXV2N\nKVOm4IknnsC0adMwd+5cPPzww6iurobP50Nubi62bdsGAJg6dSrmz5+PSZMmgWVZbN68GYIgQBAE\n22MCwIYNG7Bo0SI8/vjjqKiowPr16wEABw4cwN13341z587hN7/5DVavXo2GhgbU1NTg3Xffxdmz\nZ/H8888DAH71q19h0qRJl+TzSoehPCc4V57YGiB5rRkBiouD5BbAhZSwh99moc6yCBZ44I3JCOBM\nhQBWie8aEDlfRBHAOmUQ9AWWrgEazQkIgiAGKD0u4+bm5uIf//Ef0dTUhP/4j//oMw/mSy+9hNWr\nV+PTTz/FgQMHjInVsWPHMGHCBIwfPx4AUFlZiWeffRaA3t7pW9/6FkRRxN/8zd/gZz/7WZ9cSyy/\nu+orCQsBFkWAKqHTnWsUAhwVAU7WAFNYoF1GQMQa4FLEhMfvNTHWAF4hRQBBEAOPOXPmxKkA1qxZ\nY/zsdrtRU1Nju++qVauwatWqlI4JAGPHjsW+ffvitk+fPt1iJ4iwePFiLF68OOnv0J8ZinOCruJs\n/Obvvwopy/6eKMVkBKgCC9Ej6F0AnLoGAOguzILXqWtA2BqgcjbWAK8ATgwrAjIcFmjpGuClOQFB\nEMRApNd3idGjR+Opp57CH//4x764Hvh8Przyyiu4+eab494bN24c6uvrUV9fb9zwAeCb3/wmnnvu\nOXz88cc4fvw4XnnllT65llj2XvklvDjVebJmyQiQRb0QoIQLAU5dAxJZA+zaB0YmByxvZAQAZA0g\nCIIgLj1DaU6gsQx+uvlhvZ2eDXKWYFgDOFGBHFYE8CE53DXAXrrfXejpkTXArAggawBBEASRjD4r\nF1933XV9cpzx48fj6quvTnn8iRMnoKoqJk/WA6XMma21ywAAIABJREFUPZsvNnGKAE/6igBzIcAI\nCzRnBJgVAaxwaawBdNMnCIIgEjAU5gR1uCHh+7KHBxeSAS3aLjBiDXAMCwQQTFQIUGwKASZrgJER\nkOmwQCoEEARBDHgG1Lf3sWPHcP3118Pr9eJf//Vfccstt8T1h07Us3n16tXGz7NmzcKsWbPSOr/2\nvfhtzL9Hf45VBPhdOdZCgF3XgKx4a0CO6LeEBXKaYmsNEHnXxbEGwGQNoEIAQRAA9uzZgz179lzq\nyyCGMJd6TvBb3B7+6XbL9h/iRwB0xYDi4nSPf/gJvRLpApDQGmAqBBjWgOjCm1FUa/tAIyyQR9aZ\nLgC6IiCjGQEaFQIIgohCc4KBySX59r711ltx6tSpuO1PPfUU5s6da7vPZZddhpaWFuTl5aG+vh53\n3HEHPv7447TOa77pZ4JYRcAFTx48UhCAc1hgR1a4Z7ApLHB4oN25faC5a0CMIkDkMmQNUMOKAE0D\nryrGNRAEMXSJXTiZ/fYEkQ6DdU4ARO0BrKRCcXEQPS69MJDAw99dmGXMCWIVAYnbB7osYYFkDSAI\n4mJBc4KBySX59n7rrbfS3sflcsHl0he6kydPxrXXXotPP/0Uo0ePjuvxbNez+WLQLWTBK+nV+LiM\nAIcn9jIn4Pt3PI1/83gAmKwBikNGQExYoDkjIFOKgIg1gFMVyCwHvo9CoAiCIAhisM4JgGhgIC/q\nUn09I0DvGiB77Iv3DV+fiBn5XwZwGoqLAyspRgigbUZAuDgvegVwZA0gCIIgUiRDurG+wdxn+ezZ\ns1DDT6aPHTuGxsZGjBs3DqNHjwbLsqivrwcAvPjii3E9my8WsYoAvzsnaUYAAPz4y9+3KALcckhX\nBIS7BnCqQ/tAPto+UFClzCgCTIUAJ1UDQRAEQWSagTYnAAApS4DQJelWABcHyc3rGQEJrAFffGUs\nMG2a/oJhoLh5CF36vZ5V9PaBqnlXU1ggezHCAqkQQBAEMShI69tbVVW88MIL+P3vf4+Ojg5omgaG\nYYy/X3311V5f0CuvvILly5ejvb0dt99+OyZPnow33ngDv//977F69WqwLAtN07BhwwYMHz4cAPDL\nX/4S999/P0RRxFe+8hXcfffdvb6OnmDbNUBO3DUglhDnhkcOglMViyJAYcM3dHPXADbaPtAli5lR\nBDCMkRFAhQCCIAgiAs0JkiNn8eC7wxkB4bBAPiQnDAuMO4abg9uv3+sNRYB5kW/YCLiL0z6QYcCY\n2wdSIYAgCGJAktZdYvny5Vi6dCnOnDmDYcOGoaioyPi7qKioTy5o/vz5aGpqQnd3N06dOoU33ngD\nAPC1r30NjY2NOHToEBoaGnDPPfcY+0ydOhX19fX4+OOPsWHDhj65jp7QJXijigBFgt+VA48cAqOq\n4FUlpUV0UPDAIwUtioCUrAEpFhrSJqwIYFUFb2/6MnLEQN+fgyAIghhw0JwgOaJXzwjgbDICnNoH\nxiJ7eLgihQC7jACOgyywUAXO0j4wo2GBqoa/+sl+THnhEBUCCIIYcNTW1sLn82HixIl4+umn494P\nhUJYsGABfD4fbrzxRhw/ftx4b926dZg4cSJ8Ph/efPPNpMc8evQoKisr4fP5sHDhQkiS1Ofn6Clp\nfXtv2bIFNTU1uOuuu3p94sGIRRGgiAjxboicAK/UBZnlHHsNmwnyujVA5vjEGQFsuBCghsMC5QyF\nBYYLAVlSN6Y3/aHvj08QBEEMSGhOkBw5nBGgL8z1jICsUFfYw5+iIsDDQwiErQF2GQGsXgRQBNbI\nCMhoWCDLglEVjProtP46QA8ICIIYOIRCISxbtgzvvfceRowYgcrKStx2221G21kA2LhxI0aNGoXt\n27dj586dWL58OXbt2oWDBw/i5ZdfRkNDA06dOoWbbroJR44cgaqqjsdcvnw5Vq5ciXnz5uHRRx/F\nxo0b8d3vfrdPz9FT0ioXu1yuXp1ssGPJCFAkiLwLId6NbDFg2zrQjhDvNsICbTMCIl0DOAEi54JL\nznD7QIYBo2nwSEF0ePJxxwOv9f05CIIgiAEHzQmSI2UJujVAUiG7WIhuAVwo3EUgVUWAmzesAUw4\nIyC2EKAILBSX2RqQwbBAhgE0gA/K2PcP0wCTGoMgCKK/U1dXh4qKCpSWloLneSxYsAC7d++2jHn9\n9ddRXV0NALjzzjvxwQcfQFVV7N69GwsXLgTHcSgtLUVFRQXq6uocjynLMvbv34958+YBABYvXmyc\nq6/O0RvSKgQ8+OCD2Lp1a69OOJiJVQRIrIAQ70ZOyJ/yIt3oGqDK9hkBsdYA9eKEBXrkIPzuHOye\neEffn4MgCIIYcNCcIDmSjSKADyl6RkDKigAuag2QdWuAylmtAboigLuoYYF8UMbnt14B9JENhCAI\norcc33MM765+x/hjR3NzM0aPHm28LisrQ3Nzs+MYlmVRVFSE1tZWtLS0WDrRRPZtaWmxPWZbW5uR\nXwMApaWlxrn66hy9Iak14JFHHgETlrQrioJnn30Wb731FioqKsCGF6WRYKBL7cW71NgpAoK8B7mh\nzpQLASKvP+Xn+cQZATLLWzICMhYWGC4EuOUQgryn749PEASRgD9g6qW+BMIEzQnSQ7cGyEbXAFET\nwF9QwElKymGBiouzdA2wtQbwLFSTNSCjYYGRQkBIgeShfACCIPoP5bMuR/msy43X767ZGzeGoTbo\nBkm/wRsaGiwf2PXXXw9N0/Dxxx8b2yI3/aFOl8sLr9gFj9QNdw8VASLngksR4xQBTtaAjIcFRqwB\nchBBgQoBBEEQQxmaE6SHmOOCt70r2jUAAvhgxBrQg0KAQ0ZAxBrAWsICM2cNYDRdESBTIYAgiItM\nHW7o1f5lZWVoamoyXjc1NVmetEfGnDhxAiUlJVBVFWfOnEFxcXHcvpGn+qqq2h6zpKQE7e3tlvGR\np/19dY7ekPQbfM+ePb06wVBC5N14Y8IcLN23GTLLQWNZvRAg+tPKCIgUAiKKAN6cERCeXEWsAZH2\ngYKSYWuAFESId/f98QmCIIgBA80J0qPhbyfiju/8Xxy/cTQUFweJ1TMC0uoa4ObhChcCGEUDo6hx\nXQOUsDWAFyNhgRfHGqC4M3QOgiCIDDF9+nQ0NjaipaUFJSUlqKmpwebNmy1jqqqqsHXrVkybNg27\ndu1CZWUlOI5DVVUVHnroITz66KM4deoUGhsbMWPGDCiKYntMjuMwc+ZM7Ny5E/PmzcPWrVtRVVXV\np+foDWmVck+cOIGysjJD/hdB0zQ0NTVhzJgxvbqYwcBTX1mFnb+cZyz8Q5yuCJC51D5qkXPBLYfA\nK2FFgKY369VMn7nCsHrXANZkDVAyaw3wyEGyBhAEQRAGNCdIzud/MxZitoBrdn+GE5VlkDg9IyCd\nrgGKi4vvGsCZ9mV1W4AisNaMgAxbA4SgTNYAgiAGHB6PB5s2bcLs2bOhqiqqq6sxZcoUPPHEE5g2\nbRrmzp2Lhx9+GNXV1fD5fMjNzcW2bdsA6O1p58+fj0mTJoFlWWzevBmCIEAQBNtjAsCGDRuwaNEi\nPP7446ioqMD69esBoE/P0VPS+ga//PLLcerUKZSUlFi2nzlzBmPHjoWiKL26mMHAgdHTkRvqhMjr\nT+eDgictRYCREaDKEFRZDwpkrDdzlWGjYYERa0CmFAFmawAVAgiCIIgwNCdIAYbB518Ziy89/YGe\nEQAX+JCshwX2ICOAS2ANiM0IyJhs39Q1gKwBBEEMRObMmYM5c+ZYtq1Zs8b42e12o6amxnbfVatW\nYdWqVSkdEwDGjh2Lffv2xW3vy3P0lD4pFweDQXAcycMAAAyDj0dUGE/n080ICHFuuJWQkQ/Aq3LU\nFhBGYblo+8CwNSDjigCJMgIIgiCI5NCcwEprRTEAQBVYvWtAUEmzfSAHVyBiDVDBqhpU1qZrgKV9\n4EUIC6RCAEEQxIAmpW/wRx55xPh51apV8Hq9xmtVVXHgwAH4fL6+v7oBgPY9m42Hr8XJl04ASL8Q\nYFYEAHo3AKN1YBiVYeO6BghKhsICyRpAEARBmKA5QWLW4knrhmvrAezSMwK4SEaAknL7wLiwQEWD\nxsUoAngWisCBuxhhgVQIIAiCGBSk9A3e0NBg/PzJJ5/A5YpK0DmOw8SJE7FixYq+v7qBSkUFpJff\nAhDOCBB71jUA0J/0xyoC4qwBmgaXIqVsP0gLhgEDjRQBBEEQBACaE6TN+PFQWUbvGiDoGQHpKAIU\nF2eEBbKKZt8+0MgIuAhhgdQ1gCAIYlCQ0jd4JCX4vvvuw4YNG5CXl5fJaxr4VFQYfv2IIkBm0w8L\nBOwLAWZrQI7oB6/KRpeCPiesCHArIVIEEARBEDQnSJesLJy9olBXBLgE8GFFQK/CAu26BriiigD+\nIoQF8iGFCgEEQRADmLS+wZ9//vkMXcYgo7ISP735UQCmsMAUFQFKuLuAWw4B0K0BtooANtw+UBZ1\nW0Am1ABAuBCgUftAgiAIwgLNCVJnzz/fhFO+EojHBfDB9MICZXfUGsAoKtjY9oFhRYAqRDMC0lEc\npA3LglU0cCE5c6oDgiAIIuMkLQTMnTsXL774IvLy8jB37lwwDAMt3NLODMMwePXVVzNykQOO3Fw8\ne+M/ADBlBKSxUBc5F7xSFwBdEWCXEWBYA1QJLkU0uhT0OYw+2ciSukkRQBDEgKW2thYrVqyAoii4\n9957sXLlSsv7oVAIS5YsweHDh5GXl4dt27ahvLwcALBu3Tps2bIFHMfhmWeewW233ZbwmEePHsWi\nRYvg9/tRUVGBLVu2QBCEhOc4dOgQ/uEf/gF+vx+yLOMPf/gD3O7+V3ylOUHPqL/3OgCAeCrSPlBN\nKyPA1WVVBKgx7QMVgbNYA7gMWwO4kAxV4KwFCYIgCGJAkfQuVFRUBCa8GCwqKsKwYcNQVFRk+4eI\nJ8SlFxYI6IGB2WIAgB4C6GQNiGQEZCwoMIzKMPBKXZQRQBDEgCQUCmHZsmWora3FoUOHsGPHDtTX\n11vGbNy4EaNGjUJDQwNWrFiB5cuXAwAOHjyIl19+GQ0NDaitrcXSpUshSVLCYy5fvhwrV65EQ0MD\nRo4ciY0bNyY8RzAYxKJFi/CLX/wC9fX1eO+99yAImftO7w00J+gdklsA14OMACGgdwiyzQjg9CKA\nyrPgwu/rYYGZswYIXRLZAgiCIAY4Sb/FzdI/kgGmT4jXwwI73blp7ZMfvIBQODjQzhogs7wRLCgo\nkpFJkAlUhoVX7EK3kJWxcxAEQWSKuro6VFRUoLS0FACwYMEC7N69G5MnTzbGvP766/i3f/s3AMCd\nd96JBx98EKqqYvfu3Vi4cCE4jkNpaSkqKipQV1cHVVVtj+nz+bB///9v797joizT/4F/5sShRU1F\nPDCYllk4gIIHxNRQIQXDNDVckYrSDt88tttqWiqmVv50W83dPJOK7sY3T7UYairWZlIqamhpX08w\nk+YpXUxlmJn798fIIzgHGGROzOf9evGKeeY5XPOE81xzzf1c9z58/vnnAIBRo0ZhypQpmDRpks1j\n5OXloVu3bmjfvj0AoFGjRi47N45iTnBvyv3NPQLkhpqPCDD4K6AqM8Lgp7DeI0BuLgJAJoPh9qgA\nebnJeSMC5HL43WCjQCIib+fQu3hiYiL69OmD+Ph4dOvWDUolLwLVuaUKQFDZdfwW2LjG21R8qL+l\nCqjRrAF+Rr1TRwQImEcE/HZfzV8DEZErXM4vwpX8o3bX0Wq1CAsLkx6r1Wqp4Z21deRyOZo2bYoL\nFy5Ap9Ohb9++VbbVarUQQljd58WLFxEcHCwtDw0NhVartXuM48ePQ6/XIz4+HpcvX0Zqaireeuut\n2p0QF2JO4Dh9gB8UZUbIhKh2REAsCgBA+kBvCFCaRwQYBUTltOB2jwAA5j4B5SbnNguUyaC6UY5y\nFgKIiLyaQ+/isbGx2Lp1K2bNmgWlUokePXogPj6eSYAdFSMCHLo1oKIQoAyAn8GyR4BRdvvWAHml\nWwOc1SwQ5sIDewQQkSdqGh+BpvER0uP/y8yxWKdiKLunMhqN2Lt3L/bv34/AwED069cPnTt3RlJS\nkrtDs4s5gePK/ZVQ3TJAyGU1bhZYpRBQMSKgco+A27MGAJD6BDi7WaDqOm8NICLydg69i8+ePRsA\ncOPGDezduxd79uzBF198gRkzZiAgIAClpaVOCdKb1aZHQEV3/jKlP1Qmyx4BJrl51gC90k8aEeC0\nZoG4UwjgrAFE5GqHEF39StVQq9UoKSmRHpeUlFT5Nr9ineLiYoSEhMBkMuHy5cto1qyZxbYV3+qb\nTCar+wwJCcGlS5eqrK9Wq20eIyQkBK1bt0bv3r3RpEkTAEBycjIOHTrk8YUA5gSOM/cIMMCklNd8\n+kB/c6pm8FdAZrRxa8DtfVVMIejUZoFyOZQ3DTD6c8YAIiJvVqtxY6Wlpbh06RIuXLiA8+fPQ6lU\nokuXLnUdW71Q21kDgDsjAmzeGiBXST0CnDkiQMhkuE9/gyMCiMgrde3aFUVFRdDpdCgvL0dOTo7F\nh+zk5GRkZ2cDALZs2YK4uDgoFAokJyfjk08+gcFggFarRVFREbp162ZznwqFAt27d8fmzZsBANnZ\n2UhOTrZ5DLlcjn79+uHw4cO4efMmDAYD9uzZg/DwcBeeoXvDnKDmzD0CKmYNqNkHaYO1EQF3FQLu\njAgwTyHo1GaBvDWAiKhecOhd/NVXX0V+fj7Onj2L2NhYxMfHY8WKFYiNjUVAAD8kWnNLFYAGZaUO\nzxpQ8V9r0wdKtwbc7hGgNBmcPGvA7VsDOGsAEXmhgIAAfPTRR+jfvz9MJhPS09MRExODGTNmoEuX\nLkhJScHYsWORnp6OyMhINGjQAOvXrwcAdO7cGUOGDEFUVBTkcjmWLl0KlUoFlUpldZ8AsGjRIowc\nORJvv/02NBoN5s+fDwA2j9GyZUv8+c9/RteuXVFeXo7k5GQMHjzYPSfLAcwJHGdQKSEzmqAoEzUf\nEVBRCPBXQG4UkBsFTArLWQMAwKSSQ1FuhNzg5FsDbrJZIBGRt3PoXXzp0qUIDg7GlClTkJSUhM6d\nO0Mud1LFuZ4oU/pDIUwO3xpQLlfCIFdabRa4PmYkdI1CEXa1BCqTuRBgkDvvgmySyc3TB3JEABF5\nqaSkJItRAJmZmdLv/v7+yMmx7C8AAFOnTsXUqVNrtE8AaNu2Lb799luL5faOkZaWhrS0NLuvwdMw\nJ6gFmQzG230CajoioGIIvs0RAV264HTYN+Z1VQrIy03mWQlq2IPAYZw+kIjcqOBGrLtDqDccehc/\nceIE8vPzkZ+fjxUrVuC///0vevXqJXUNrvg2hO441KoTDrXqiNNN2tZ4G73CD4bbhQCV0bJHwIwB\ns6T1/Ax6KI0GGBROLgTw1gAiIqqEOUHtHB36KFoe+rXqh3k7qs4aYKUQ0LMnjmL77XXlUOiNLikE\nlDVk3yAiIm/m0KfHdu3aoV27dhg9ejQA4KeffsK8efMwZcoUGI1GGI1GpwTpzQrVMYj+0yGHttEr\n/GBQKGGUK6yOCKhQcWuAylTu1BEBQibjrQFERFQFc4La+d/sIQ6tf+fWABsjAiqpmD5QUe7EQoBM\nBtXNchgC2CyQiMibOfTp0WQyYf/+/di1axfy8/PxzTffoKysDJ07d0Z8fLyTQvQ9lUcEWJs+sEK5\nQmW+NcDIWwOIiMi1mBO4xt3NAuVGE0wK6x/yjZV6BJiUTpo2Uy6H6gZ7BBAReTuH3sXvv/9+3Lp1\nS7rIT5o0CY899hiCgoKcFZ/XEq/XftsypT8M8upHBOgVfi5tFsjpA4mIqAJzgpqbg1mVHg10aFup\nR4C/AjKjsDsiwOh3p0eAc5sFlsPgz0IAEZE3c+hdPCcnBz179uRF3sn0ykojAqq5NcDPqHd6s0Dp\n1gCOCCAiotuYE7hG5R4BylsG+4WA29MHOrVHgEzGWQOIiOoBh97FBwwY4Kw4qJKKWwNq2iPAJbMG\n6G+wRwAREUmYE7hGRSHA6K+A3+/lkBnt9QiofGsAZw0gIiLbOM+PBypT+sOgqEGPALnrCgH+Rj1H\nBBAREblYxRD88srTBypsjwhwxfSBSr2RhQAiIi/HQoAHkkYEyBRWpw+U1lP6mW8NcPL0gQLmhIM9\nAoiIiFzL6GfOAYz+NqYPrLKuAkoX3BoAmHsWEBGR92IhwANJswYoqukRIHfN9IFlSn+s7vIsrgbe\n77RjEBERkSXjXbMG2O8RIIfc2dMH+vvjWqsGKBoW7pz9ExGRS3BclweSZg2Q1bxHQLncebMG9Bz7\nH5xr2FL6FoCIiIhco+LWgIpZA+RGE0w16BFgVDmpEPDKK1j04q+4dT9vFyQi8mYOFwKMRiPOnj2L\n0tJSNGzYEK1bt4ZCweFhdenuWQNs9QjQK1xza8C5Rq2ctm8iIvJezAmcz+qIAIX1D/lGPxfMGvCH\nP+AWWAQgIvJ2NbpK/P7771i+fDkSEhIQFBSEdu3aITo6Gg899BCCgoKQkJCA5cuX48aNG/cc0Ouv\nv44OHTqgQ4cOePLJJ3H58mXpuXfffRcdOnRAZGQktm/fLi3Py8tDZGQkOnTogPfff/+eY3A3vcIP\nBoV51gB7PQJcNWsAEZG7nCto69APOR9zAteSCgH+iup7BNxuFqgwmGwWC4iIyLorV64gMTERUVFR\n6N+/P65evWp1vdWrV0Oj0UCj0WDNmjXS8gMHDiA6OhoajQYTJkyo0X7Hjx8PjUaDmJgYFBYW1voY\nb731Fjp27IiIiAj07t0bp06dqvb1VnuVWLBgAdq0aYOFCxciNjYW//znP7F//36cOHEC+/fvx/r1\n69G1a1csXLgQrVu3xoIFC6o9qD0pKSkoKirCsWPHEBERgdmzZwMwv+iNGzfihx9+QF5eHl5++WWU\nl5ejrKwMr776KvLy8nDkyBF8+umnVU6iN5J6BNyeNcBuIcBkgMro3B4BREREAHMCd3C0R4DqlgEm\nuczmOkREZN2MGTMwcOBAHDlyBElJSZgxY4bFOufOncM777yDgoICFBQUYNasWbhw4QIAICMjA6tW\nrcLRo0dx9uxZbNq0ye5+N2zYgOLiYhw9ehQrV65ERkZGrY8xZcoUHD58GEVFRRg+fDgyMzOrfb3V\nFgL27t2Lbdu2oaioCHPmzMHgwYMRExODdu3aISYmBkOGDMG7776LoqIibNu2DXv37q3JebapT58+\nkMvNYT322GPQ6XQAgNzcXIwYMQIKhQKhoaHQaDTSydFoNAgNDYVSqURqaipyc3PvKQZ3q+gRIN0a\nILMxzFImg0GuQIDhFgsBRETkdMwJXK9yIcDcI0DAZGP6QJNKAeXNcufdFkBEVI9t3boV6enpAIBR\no0ZZvX7s2LEDSUlJCAoKQlBQEAYMGIDt27ejuLgYJpMJ0dHRFtvb2m9ubq60PDo6GgaDAVqttlbH\nCAoKkmK8fv06WrZsWe3rrfbT44YNG6rdSYXOnTs7tH51li1bhhEjRgAAdDod+vbtKz2nVquh1Woh\nhEBYWFiV5fn5+Vb3N3PmTOn3+Ph4xMfH11msdUmaPlCugJ9eD5Pc9gVdr/DDffobLAQQkUvl5+fb\nfK+l+os5gesJuQxGpRwGfyUU1Y0I8FPA74aBhQAicilPyQmMX30D49ff1Hr7ixcvomnTpgCA4OBg\n6Vv4ynQ6HdRqtfS44vqj0+mqXH9CQ0Oh1Wrt7vfubSrvy9FjAMC0adOwdu1a3Hfffdi3b1+1r9ct\nnx4TExNx/vx5i+Vz585FSkoKAGDOnDnw8/NDWlpanR238kXfk+mV5h4BBrnSbo8AwHx7wH3lN6BX\n+LkwQiLydXd/cKrJEDQia5gTVM/op4AhQAFZtT0C5FDdKHfejAFERFZ4Sk6g6P0YFL0fkx7r5/4/\ni3VsXXPmzJnj1NhsEULU2b7mzJmDOXPm4L333sOkSZOQlZVld32HCwFarRZff/01rl69ahH4//zP\n/9RoHzt27LD7/OrVq5Gbm4tdu3ZJy9RqNUpKSqrEERYWBpPJVGV5SUlJlUqJNypT+N8ZEWBn+kDA\nXAgILL+JG6r7XBghERERcwJXMfgrYPBXQm4QdgsBJpUC/v8t44gAIqq3rn8ZfE/b27vmNGvWDJcu\nXUJwcDAuXryIkJAQi3XUajUKCgqkxyUlJejRo4fN65K9/VZsExsbW2UbR45ReeRAhZEjR+KJJ56o\n9lw4VAhYtWoVXnnlFSiVSjRq1Mji+Zpe9O3Jy8vDvHnzsGfPHgQE3JmeJjk5Ga+88gomTpyI8+fP\no6ioCN26dYPRaERRURF0Oh1CQkKQk5ODpUuX3nMc7nT39IFlCn/b696+NeDXBs1dGCEREfk65gSu\nYx4RoDTPGmAUEDZ6BBj85PC7wR4BRES1kZycjOzsbEycOBHZ2dlITk62WCchIQGZmZkoLS0FYL5O\nTZ8+Hc2bN4dcLkdhYSGio6Oxbt06PPvss3b3W7F82LBhOHjwoNT3pl+/fg4f4/Tp02jb1jx70pYt\nWxAZGVnt63WoEPDmm29i+vTpePPNN502T/C4ceOg1+uRmJgIAIiLi8M//vEPdO7cGUOGDEFUVBTk\ncjmWLl0KlUoFlUqFjz76CP3794fJZEJ6ejpiYmKcEpurfN22F34LbIyBP+bCz6DHTWWgzXXLFSoE\nGm6yRwAREbkUcwLX+feHA3CtdcNqZw0wNwtkjwAiotrIzMxEamoqVq1ahRYtWiAnJweAeaaaJUuW\nYPny5WjZsiWmTZsmfYtf8QEdALKysvDCCy9Ar9ejX79+ePrpp+3ud+jQodi9ezc0Gg38/f2lofyt\nWrVy+Bh/+tOfcPLkSZSXl6Nt27ZYsWJFta/XoU+PZWVleP755512wQeAn3/+2eZzU6dOxdSpUy2W\nJyUlISkpyWkxudrFBiHY3aAv+h/fZr41wE6zwHK5+dYAFgKIiMiVmBO4TtHwDmhUfK1m0weyEEBE\nVCtNmjSxeutA586dsXz5culxRkaGNNXf3eucZ2DAAAAgAElEQVRZm7LW1n4BYPHixVaXO3qMjRs3\nWt2PPQ5dKUaMGOH10/B4E6NcUbNmgZw1gIiIXIw5gWsJhcw8faBJwGRnRICKtwYQEVENOPTp8W9/\n+xsGDx6MXbt2ITw8XJrbt8L06dPrNDhfV9EjwCiz/W2LXuGH+8pvoFyhcmFkRETeJS8vD2+88QaM\nRiOee+45TJ48ucrzZWVlePbZZ3Hs2DE0bNgQ69evxwMPPAAAePfdd7F27VooFAosWLBAasBja5+n\nT5/GyJEjcf36dWg0GqxduxYqlapWx/BkzAlcy6iUQ2EwAUJAKKx/0Df4KW7PGuC8URpERFQ/OFQI\nWLZsGbZv344WLVrg2LFj0nIhBGQyGS/6dcyRWQM4IoCIyLqysjK8+uqr+M9//oPmzZsjLi4OTzzx\nBKKjo6V1Fi9ejJYtW+KTTz7B5s2bMX78eGzZsgUHDhzAxo0b8cMPP+D8+fPo2bMnTpw4AZPJZHOf\n48ePx+TJkzF48GBMnDgRixcvxqRJkxw6xvHjx+Hn59nTwjIncC2hkENmNJl/tzkiQA7VzXKYlNaf\nJyIiquDQp8fZs2fjww8/xGuvveaseHyOeN367wCAm0qc+r6aHgEKFRrdusZCABGRDQUFBdBoNAgN\nDQUApKamIjc3t0ohYOvWrZg3bx4AYNCgQRgzZgxMJhNyc3MxYsQIqZOvRqNBQUEBTCaT1X1GRkZi\n3759+PzzzwEAo0aNwpQpUzBp0iSHjvHdd9+hZ8+erjxNDmNO4BxzMKvKfyXKq7hpWAwhl9npEaCA\n6qYBBn+OCCAiIvsc+vRoNBoxaNAgZ8VCd1Mqqx0RUDF9oEHBQgAR+aCD+eYfOyrP5QuY5+3Nz8+3\nuY5cLkfTpk1x4cIF6HQ69O3bt8q2Wq0WQgir+7x48SKCg+/McRwaGgqtVlurY3g65gQuplRCbhQQ\nwvaIAOPtWwP0f+DtgkREZJ9D3WSef/55rF+/3lmx0N0Uimp7BPDWACLyaTHxwOiZd36skMk4TNoZ\nmBO4mEIhzRpgUti7NYCzBhARUfUc+vR48+ZNzJ07F9u2bUOHDh2kxkAV9wMuWrTIKUH6rIAA/EH/\nO3sEEJHv+ubed6FWq1FSUiI9LikpqfJtfsU6xcXFCAkJgclkwuXLl9GsWTOLbSu+1TeZTFb3GRIS\ngkuXLlVZX61W1+oYno45gYv5+0OhNwKwMyJAJYfyJmcNICKi6jl0pTh27Biio6NhMplw9OhR/PDD\nD1V+qI41aoQ/6G/YLwTIVZw1gIjIjq5du6KoqAg6nQ7l5eXIycmxmGc+OTkZ2dnZAIAtW7YgLi4O\nCoUCycnJ+OSTT2AwGKDValFUVIRu3brZ3KdCoUD37t2xefNmAEB2djaSk5NrdQxPx5zAxeRy6P+g\nguqWwX6PAM4aQERENeDQ18h331NJTtaoEQDz7AG26JV+UJqMHBFARGRDQEAAPvroI/Tv3x8mkwnp\n6emIiYnBjBkz0KVLF6SkpGDs2LFIT09HZGQkGjRoIA1579y5M4YMGYKoqCjI5XIsXboUKpUKKpXK\n6j4BYNGiRRg5ciTefvttaDQazJ8/HwAcPoanY07gercaBSCgVG9z+kCjnwIKo+CIACIiqla1nx5P\nnTqFBx98sMY7dHR9sqNhQwCo9tYAACwEEBHZkZSUZDEKIDMzU/rd398fOTk5VredOnUqpk6dWqN9\nAkDbtm3x7bffWiyvzTE8DXMC9ypraJ5S0t70gQBYCCAiompVe6WIi4vDqFGjsHPnTgghrK4jhMCX\nX36JtLQ0xMXF1XmQPuv2iIDqbg0AWAggIiLnY07gXrcaBQCw1yPAPIKQhQAiIqpOtZ8ef/zxR8yf\nPx+pqanQ6/WIiYlB69atERQUhNLSUhQXF6OwsBAqlQovv/wyfvzxR1fE7RtqUAjQK83fDnD6QCIi\ncjbmBO5V1sgfACBsTIRh9GMhgIiIaqbaK0WTJk0wd+5c6HQ6rFu3Dl27dsW1a9dw4sQJlJaWolu3\nbli3bh10Oh3mzp2LJk2auCJu31CDHgEcEUBERK7CnMC9bjXyh0kuA2xMiWmUbg3glJlERGRfjT89\n+vv7IyUlBSkpKc6MhypzoEcAZw0gIiJXYU7gHrcaBdi8LQC40yOAswYQUb31pbsDqD8cGjvWp08f\nzJo1y2L5lStX0Ldv3zoLim5TqfC7331sFkhERB6HOYHr3Wrkb7cQwB4BRERUUw59etyzZw8OHz6M\nI0eOYO3atQgMDAQA6PV6TiPkJNcCGtnvEaC43SOAhQAiInIh5gSuV20hgD0CiIiohhy+Unz55Zc4\nc+YMevbsiV9++cUZMVEl1wIa2e8RwBEBRETkJswJXKusUQBMCnsjAjh9IBER1YzDVwq1Wo2vv/4a\n7dq1Q9euXbF//35nxEW3VTcigIUAIiJyF+YErlXdiAATbw0gIqIaqtWVIjAwEP/617/w0ksvIT4+\nHjk5OXUdF93234CGNbs1gNMHEhGRGzAncJ2yhrw1gIiI6katPz3KZDLMmDEDGo0Gzz33XF3GRJXU\n9NaAimkEiYiIXI05gWvcauQPobD9Id8ozRrAQgAREdnnUCFg165daNy4cZVlw4YNw6OPPooDBw7U\naWBkdi2wmlsDbhcAOCKAiIhciTmB61U/fSBHBBARUc049OkxPj7e6vKIiAhERETURTx0F/YIICKf\n9h93B0C2MCdwvep6BAi5DCa5jIUAIiKqFj89erhPOqXCKLN9a4BeyekDiYiIfMG1sIbYPqeP3XWM\nfgoWAoiIqFr89Ojhvm/dze7z0q0BLAQQERHVayaVAgdGR9tdx6iSsxBARETV4pXCy/HWACIiIqpg\nUnFEABERVY9XCg8nXr/zY23ZvwbfnjVAwVkDiIiI6rM5mFXlx9pyo0rOWQOIiGrhypUrSExMRFRU\nFPr374+rV69aXW/16tXQaDTQaDRYs2aNtPzAgQOIjo6GRqPBhAkTarTf8ePHQ6PRICYmBoWFhbU+\nBgB8+OGH6NixIyIjI/HGG29U+3p5pfB2fuwRQERERGbsEUBEVDszZszAwIEDceTIESQlJWHGjBkW\n65w7dw7vvPMOCgoKUFBQgFmzZuHChQsAgIyMDKxatQpHjx7F2bNnsWnTJrv73bBhA4qLi3H06FGs\nXLkSGRkZtT5Gbm4utm3bhgMHDuCHH37AlClTqn29vFJ4O5V5JIBRbruhIBEREfkGI28NICKqla1b\ntyI9PR0AMGrUKOTm5lqss2PHDiQlJSEoKAhBQUEYMGAAtm/fjuLiYphMJkRHR1tsb2u/ubm50vLo\n6GgYDAZotdpaHWP58uWYPHkylErzl8NNmzat9vXya2Rvp1LBIFcAMtvTCREREZFvMLFZIBH5Km0+\noMuv9eYXL16UPkAHBwdL38JXptPpoFarpcdqtRparRY6nQ5hYWHS8tDQUGi1Wrv7vXubyvty9BjH\njx/Htm3bMG7cOAQGBmLBggXo0aOH3dfLQoC38/PjbQFEREQEgLcGEJEPU8ebfyp8l2mxSmJiIs6f\nP2+xfM6cOc6Lyw4hRJ3sx2QyobS0FIcOHcL333+PoUOH4uzZs5DZ+bKYnyC9nUrFQgAREREB4PSB\nRFTPfXlvm+/YscPmc82aNcOlS5cQHByMixcvIiQkxGIdtVqNgoIC6XFJSQl69OgBtVqNkpISablW\nq5W+vbe134ptYmNjq2xTm2OEhYXh6aefBgB07doVfn5++PXXX9GiRQubr5dXCm+nUnHGACKiWvKG\nDsF6vR4vvfQSoqKiEB4ejg0bNtTlKaB6xqhSwKhi3yAiIkclJycjOzsbAJCdnY3k5GSLdRISEpCX\nl4fS0lKUlpYiLy8PCQkJCAsLg1wul67r69atQ1JSkt39JicnY926dQCAgwcPQqFQIDQ0FP369XP4\nGAMHDsSuXbsAACdOnMCNGzesFjIqYyHA23FEABFRrXlDh+Bp06ahbdu2OHLkCH788Uf07dvXFaeG\nvBR7BBAR1U5mZiZyc3MRFRWFL774ArNmmadpPXDgAMaMGQMAaNmyJaZNm4bY2FjExsZi+vTpaN68\nOQAgKysLL7zwAjQaDVq3bi19Q29rv0OHDkVoaCg0Gg1Gjx6NrKwsAECrVq0cPsbYsWNx6tQpRERE\n4Omnn8bHH38Mudz+tcDjPkG+/vrryMvLAwA8+OCDWL16NZo2bYozZ84gPDwcjz76KAAgLi4O//jH\nPwCY/+eMHj0aer0eCQkJWLhwodvidzn2CCAiqrWtW7fiu+++A2Duvtu9e3eLa0jl7r0ApO69vXv3\nttq9d8iQITb3a6tD8K5du6weIy0tDevXr8eZM2ekeBo3buzUc+JJmBM4ztwjgA2EiYgc1aRJE6u3\nDnTu3BnLly+XHmdkZEiF/LvXqzzSr7r9AsDixYutLnf0GCqVCmvXrrW6L1s8rmSckpKCoqIiHDt2\nDBEREZg9e7b0XLt27VBYWIjCwkLpgg/Y/kbGJ3BEABH5sov5wI8z7/w4urmHdwi+cOECAgICMG7c\nOERERGDQoEFWmxzVV8wJHMfpA4mIqCY87krRp08faRjDY489Bp1OZ3d9e/Mp+oSwMEwfMMvdURAR\nuUezeCB85p0fKxITExEZGWnx89lnn7kw0Dtq2iFYJpPBZDLh9OnTeOKJJ1BUVIQ+ffpU6UVQ3zEn\ncFzB/3SGtmsrd4dBREQezqO/Sl62bBlGjBghPT5z5gw6deqE++67D7Nnz0bfvn2rdEsEqn4j4xP8\n/PBxN8thI0RE9cLX974Lb+4Q3KxZMwQEBEj3AA4bNgxLliy5h7PhvZgT1MxPgx5xdwhEROQF3FII\nsDV/49y5c5GSkgLAPJejn58f0tLSAJibJuh0OjRs2BCFhYV48skncfToUYeOO3PmTOn3+Ph4xMfH\n1/o1EBH5svz8fOTn57s7jHtW0cl34sSJdjsEZ2ZmorS0FACQl5cnNe6p6N4bHR2NdevW4dlnn7W7\n34rlw4YNs+gQbO0YSqUSiYmJ2L17N/r06YOdO3ciPDzcRWfHNZgTEBF5t/qSE/gatxQC7H07A5in\nUMrNzZWmQAAAPz8/+Pn5ATA3WIqIiMBPP/2EsLAwi29kKt9nWVnliz4REdXe3R+cMjMz3RfMPcjM\nzERqaipWrVqFFi1aICcnB4C54dySJUuwfPnyKh2CAVjt3qvX69GvX78qHYKt7Xfo0KHYvXs3NBoN\n/P39rXYIvvsYH374IdLT0zFu3Dg0btzY4WZAno45ARGRd6svOYGv8bhbA/Ly8jBv3jzs2bMHAQEB\n0vIrV67g/vvvh1wux5kzZ1BUVIR27dohODjY5jcyRERE9nhDh+DWrVtjz549Nl9DfcacgIiIyDk8\nrhAwbtw46PV6JCYmArgzJdCuXbswc+ZMyOVyCCGwaNEiBAcHA7D9jQwRERF5L+YEREREzuFxhYCf\nf/7Z6vJhw4Zh2LBhVp+z9Y0MEREReS/mBERERM7hcdMHEhEREREREZHzsBBARERERERE5ENYCCAi\nIiIiIiLyISwEEBEREREREfkQj2sWSERERERERGThx/+6O4J6gyMCiIiIiIiIiHwICwFERERERERE\nPoSFACIiIiIiIiIfwh4BRETkuS5p3R0BERERUb0jE0IIdwfhCjKZDD7yUuuc7K9VH4vXra9jbTkR\n+QZnvMfKZDIAJQ5uFcb3eqoWc4J7Mw3Tpd/nYJbN5609R0T1n7PeY815wTUHtmjE93o7eGsAERER\nERERkQ9hIYCIiIiIiIjIh7AQQERERERERORDWAggIiIiIiIi8iEsBBARERERERH5EBYCiIiIiIiI\niHwICwFERERERETk065cuYLExERERUWhf//+uHr1qtX1Vq9eDY1GA41GgzVr1kjLDxw4gOjoaGg0\nGkyYMKFG+x0/fjw0Gg1iYmJQWFhY62Ps378fMTExiIiIwKBBg1BaWlrt62UhgIiIiIiIiHzajBkz\nMHDgQBw5cgRJSUmYMWOGxTrnzp3DO++8g4KCAhQUFGDWrFm4cOECACAjIwOrVq3C0aNHcfbsWWza\ntMnufjds2IDi4mIcPXoUK1euREZGRq2PkZGRgYULF6KoqAhDhgxBZmZmta+XhQAiIvJZnlT9HzBg\nABo3boyUlJQqxx4xYgQ6dOiARx99FOnp6SgrK6url09ERES3bd26Fenp6QCAUaNGITc312KdHTt2\nICkpCUFBQQgKCsKAAQOwfft2FBcXw2QyITo62mJ7W/vNzc2VlkdHR8NgMECr1dbqGCdPnkSvXr0A\nAAkJCfjss8+qfb0sBBARkc/ylOo/APzlL3/B2rVrLY4/evRoHDt2DD/99BOMRiOWLFnijFNBRETk\n0y5evIimTZsCAIKDg6VrfWU6nQ5qtVp6rFarodVqodPpEBYWJi0PDQ2FVqu1u9+7t6m8L0ePER4e\nji1btgAA/vd//xfFxcXVvl4WAoiIyGd5SvUfAPr27YugoCCL4yckJEi/x8XFQafT1dXLJyIiqke+\nBvBupR9LiYmJiIyMtPipyTfoziCEqJP9rFmzBn/7298QGRmJy5cvw9/fv9ptlHVyZCIiIi/kKdX/\nyvu3pby8HB9//DEWLVpUi1dKRERUH3xezfMRdp/dsWOHzeeaNWuGS5cuITg4GBcvXkRISIjFOmq1\nGgUFBdLjkpIS9OjRA2q1GiUlJdJyrVYrXe9t7bdim9jY2CrbOHKMivxBo9Fg9+7dAIAzZ85g69at\nds8DwBEBRETk1b4F8NdKP5Y8vfovk8lqtN1rr72Gxx9/HI899pgzwiIiIvJpycnJyM7OBgBkZ2cj\nOTnZYp2EhATk5eWhtLQUpaWlyMvLQ0JCAsLCwiCXy6XeP+vWrUNSUpLd/SYnJ2PdunUAgIMHD0Kh\nUCA0NBT9+vWr8TEq9nX58mUA5hxj7ty5GD16dLWvlyMCiIjIg31Zg3Wi7D7r6dX/yqMBbBUFMjMz\ncenSJSxbtszuayUiIqLayczMRGpqKlatWoUWLVogJycHgLkx8JIlS7B8+XK0bNkS06ZNk67j06dP\nR/PmzQEAWVlZeOGFF6DX69GvXz88/fTTdvc7dOhQ7N69GxqNBv7+/sjKygIAtGrVyuFjZGdnY8mS\nJVAqlUhJScFrr71W7euVibq6McHDyWSyOrsHw9fI7vqSTbxufR1ry4nINzjjPdb8oTjLwa0yHIpj\n3LhxeOihhzBx4kR88MEHOH36tMXQ+3PnzqFXr15SBb5Tp07Yu3cvmjdvjqioKKxevRrR0dEYPHgw\nnn32WTz99NM297thwwZkZ2dj06ZNOHjwIDIyMnD48GHpWPn5+ViwYAE+//zO0McVK1YgKysLO3fu\nREBAgIPng6xhTnBvpmG69PsczLL5vLXniKj+c9Z7rDkvyHZgi1F8r7eDIwKIiMhneUr1HwB69eqF\n48eP4/r16wgLC8OqVauQmJiIV199FW3atEFcXJy0j7feestl54iIiIjqH44IoGpxRAARVcdbRwSQ\nb2JOcG84IoCI7OGIAO/AZoFEREREREREPoSFACIiIiIiIiIfwkIAERERERERkQ9hIYCIiIiIiIjI\nh7AQQERERERERORDWAggIiIiIiIi8iEeVwh466230LFjR0RERKB37944deqU9Nz48eOh0WgQExOD\nwsJCafnq1auh0Wig0WiwZs0ad4TtNPn5+e4OodYYu+t5a9wAY3cHb42bfAdzAkve+u/WW+MGGLs7\neGvcAGMn7+FxhYApU6bg8OHDKCoqwvDhw5GZmQkA2LBhA4qLi3H06FGsXLkSGRkZAIBz587hnXfe\nQUFBAQoKCjBr1iz8+uuv7nwJdcqb/0Eydtfz1rgBxu4O3ho3+Q7mBJa89d+tt8YNMHZ38Na4AcZO\n3sPjCgFBQUHS79evX0fLli0BALm5uUhPTwcAREdHw2AwQKvVYseOHUhKSkJQUBCCgoIwYMAA7Nix\nwy2xExERUd1hTkBEROQcSncHYM20adOwdu1aBAYG4rvvvgMA6HQ6hIWFSeuo1WpotVrodDqo1WqL\n5UREROT9mBMQERHVPZkQQrj6oImJiTh//rzF8rlz5yIlJUV6/N577+H48ePIyspC//79MWvWLMTG\nxgIABgwYgJkzZ2L37t2Qy+WYPHmytA1gHk5YmUwmc9bLISIiAHV9OanN+3bjxo1x5cqVOo2DnIs5\nARFR/eOMj5iOvnczJ7DPLSMCajpMb+TIkXjiiScAmKv6JSUl0kVfq9UiLCwMarUaBQUF0jYlJSXo\n0aOHxb7cUO8gIqJ7wPdt38CcgIiIaoLv3XXL43oEnD59Wvp9y5YtiIyMBAAkJydj3bp1AICDBw9C\noVAgNDQU/fr1Q15eHkpLS1FaWoq8vDwkJCS4JXYiIiKqO8wJiIiInMPjegT86U9/wsmTJ1FeXo62\nbdtixYoVAIChQ4di9+7d0Gg08Pf3R1ZWFgCgVatWmDZtmvStwPTp09G8eXO3xU9ERER1gzkBERGR\nkwgf8MUXX4iIiAgRHh4u3nvvPXeHY9cDDzwgIiMjRadOnUTXrl2FEEJcvnxZJCQkiMjISPHEE0+I\n3377zc1RmmVkZIiQkBAREREhLbMX67hx40SHDh1EdHS0OHjwoDtClliLfcaMGSI0NFR06tRJdOrU\nSWzdulV6bu7cuSI8PFxERESIbdu2uSNkIYQQxcXFolevXiIiIkK0b99evP/++0II7zjvtmL39PN+\n8+ZN0aVLF9GpUyfx8MMPi4kTJwohhDh16pTo3r27iIiIEKmpqUKv1wshhLh165Z45plnREREhOjR\no4c4c+aMW+K2F/tzzz0n2rZtK53zQ4cOCSGEMJlMHvP3QuQs3pQTCOE9eQFzAtdjTuAe3poXMCeg\nu9X7QsCtW7dEmzZthFarFeXl5aJLly4e/Yfcpk0bcfny5SrLxo4dKz744AMhhBAffPCBGD9+vDtC\ns/DVV1+JgwcPVrlw2or1008/FU899ZQQQoiDBw+Kjh07uj7gSqzFPnPmTLFgwQKLdffv3y+6dOki\nDAaD0Gq1ok2bNqKsrMyV4UrOnz8vfvjhByGEEKWlpeLhhx8Whw4d8orzbit2bzjvN27cEEIIUV5e\nLmJjY8WuXbvEk08+KTZt2iSEEGLChAnir3/9qxBCiPnz54sJEyYIIYTYtGmTGDRokFtirmAt9uef\nf15s2LDBYl1P+nshcgZvywmE8J68gDmB6zEncM95F8J78wLmBFSZx/UIqGsFBQXQaDQIDQ2FUqlE\namoqcnNz3R2WXeKuRhhbt26V5kseNWqUx8Tfq1cvNG7cuMoyW7HamvPZXazFDlhvQpKbm4sRI0ZI\n96BqNBppCitXa968OSIiIgCY59eOioqCTqfzivNuK3bA8897YGAgAECv18NoNCIkJAT79u3D4MGD\nAVQ955X/XwwaNAh79+51a3Mba7ED1s955djd/fdC5AzemBMA3pEXMCdwPeYE7jnvgPfmBcwJqLJ6\nXwio6CRcwdPnFJbJZEhMTERUVBQWL14MALh48SKaNm0KAAgODsaFCxfcGaJdtmK1Neezp/n73/+O\n8PBwjBo1SppuxFPnpT5z5gy+//579OzZ0+vOe0XsvXr1AuD5591kMqFTp05o3rw5+vTpg8aNGyM4\nOFh6PjQ0VIqt8nuOXC5H06ZN3fpv9u7YNRoNAPPc7OHh4Rg7dizKysosYgc85++FqK5449+4N+cF\n3nZtupunX5sqY07gWt6aFzAnoMrqfSHA2+YK3rdvHw4ePIidO3ciKysLX375pbtDqjN3Vxs97f/N\na6+9hpMnT+LYsWN46KGHMH78eHeHZNP169cxbNgwLFy4EA0bNrS7rqed9+vXr2P48OFYuHAhGjRo\n4BXnXS6X49ChQ9Bqtfjqq6+Qn5/v7pBqzFrs77//Pn766SccPnwYN2/exDvvvCOt72l/L0R1yRv/\nnutrXuDp7zXecG2qwJzA9bw1L2BOQJXV+0JAxVzDFUpKSqpUtzxNxRCdZs2aYdiwYfj+++/RrFkz\nXLp0CYC5ul6xjieyFevd/x+0Wm2Vyq4nCA4Ohkwmg0wmw8svv4zvv/8egPXY3fk3VF5ejqFDhyIt\nLU0aguYt570i9pEjR0qxe8t5B4BGjRph4MCBOHXqlHS+garnVa1Wo7i4GIC58n758mU0a9bMLfFW\nVhH7vn37pL8PPz8/vPjii3bPuaf9OyW6F96WEwDenRd4y7XJGm+5NjEncC9vzQuYExDgA4WArl27\noqioCDqdDuXl5cjJyUFSUpK7w7Lqxo0buHHjBgDg999/R15eHjQaDZKTk5GdnQ0AyM7ORnJysjvD\ntMtWrLbmfPYklYdpbdiwQRoulZycjE8++US6N6qoqAjdunVzS4xCCLz44ovo0KEDJk2aJC33hvNu\nK3ZPP++XL19GaWkpAODmzZvYsWMHOnXqhO7du2Pz5s0ALM95xf+LLVu2IC4uDnK5e95qrcUeGRkp\nnXMhBDZu3FjlnHvK3wuRM3hTTgB4f17gDdcmWzz92gQwJ3DXeffWvIA5AVlwZWdCd9m6davQaDQi\nPDxczJ07193h2HTq1CkRFRUlOnbsKB5++GHx9ttvCyGqTgOTmJjoEdMECSHEiBEjRMuWLYVKpRJq\ntVqsWrXKbqyvvfaaNAXJgQMH3Bi5ZewrV64Uo0aNElFRUeLRRx8V/fv3F1qtVlp/zpw5Ijw8XGg0\nGpGXl+e2uL/++mshk8lEx44dpWlevvjiC68479Zi37p1q8ef9yNHjohOnTqJjh07ikceeURkZmYK\nIexPEzR8+HAREREh4uLixOnTp90St73Y+/TpIzp27Cjat28vUlNTxbVr16RtPOXvhchZvCUnEMK7\n8gLmBK7HnMA9vDUvYE5Ad5MJ4cZ21kRERERERETkUvX+1gAiIiIiIiIiuoOFACIiIiIiIiIfwkIA\nERERERERkQ9hIYCIiIiIiIjIh7AQQLDaTD4AAAdESURBVGTDxx9/jAYNGrjt+FeuXEHz5s1x6tQp\nm+vs378fcrlcmp/W2X799VcEBwdDq9W65HhERESegDmBJeYERN6NhQDySXK53O7PCy+8gD/+8Y84\nffq022KcP38+EhMT8eCDD7othrs1b94cI0eOxOzZs90dChERUZ1gTlA7zAmIvJvS3QEQucP58+el\n3z///HOMGTOmyrLAwED4+/vD39/fHeGhrKwMy5YtwyeffOKW49vz7LPP4vHHH8e8efPQsGFDd4dD\nRER0T5gT1B5zAiLvxREB5JNCQkKkn0aNGlksa9CggcUwwJkzZyIyMhJr1qxBmzZtEBQUhBdeeAHl\n5eX48MMPERYWhqCgIIwdOxZCCGk7vV6PyZMnIywsDAEBAYiKisLmzZvtxrd9+3bo9Xr07du3yvK8\nvDw8+uijCAwMRO/evXHixIkqz1+5cgXDhw9Hy5Yt4efnh4ceegh///vfpefXrFmD4OBg6PX6Ktul\npaXhqaeeAgCcOnUKSUlJaNCgAfz9/dG+fXv885//lNbt0qUL7r//fnz22Wc1OdVEREQejTkBcwIi\nX8RCAJEDzpw5g7y8PHz55Zf497//jQ0bNmDgwIE4duwYvvrqK+Tk5GDFihXIycmRtsnIyMC+ffvw\n6aef4v/+7/8wduxYjBgxAt9++63N43z11VeIiYmBTCaTlh0/fhxPPfUUBg0ahJ9++gl/+ctfMGXK\nlCrr3Lp1C48//jh27dqFn3/+GW+++Sb+/Oc/4/PPPwcAPPPMMzCZTNiyZYu0zbVr17B582aMHj0a\nAPDyyy/DZDJh7969OHXqFD766CM0a9asSnzdunVDfn7+PZ1LIiIib8acwIw5AZF34q0BRA4QQmDl\nypUIDAxEu3btMHDgQOTn52Pbtm2QyWRo27YtevfujZ07dyI1NRUnT57Ev/71L+h0OrRo0QIA8NJL\nL2HHjh1YsWIF4uLirB7nxIkTaN26dZVly5cvxyOPPIJ58+YBAB544AH8+OOPmDx5srROq1atMHbs\nWOnx6NGjkZ+fj5ycHKSkpCAgIABpaWlYtWoVhg8fDgBYv349GjVqhIEDBwIAfvnlF4wYMQKRkZEA\ngNDQUIv4wsLCUFhYWNvTSERE5PWYE5gxJyDyTiwEEDnggQceQGBgoPS4SZMmaN++fZUKfJMmTXDt\n2jUAwMGDByGEwMMPP1xlP3q9Ho8//rjN4/z+++8WF9ujR48iNja2yrLu3btXeVxeXo7MzExs3LgR\n586dg8FggF6vR+/evaV1xowZg5iYGPzyyy9o1aoVVq1aheeeew5yuXmA0Lhx4zB+/Hhs27YN8fHx\nGDRoELp161blOA0bNkRpaanN+ImIiOo75gRmzAmIvBM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+ "text": [ + "" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Boundary layer depth and SST \n", + "\n", + "\n", + "figure(4)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "CS = plt.plot(time, -h,'-',linewidth=3)\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=18)\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=18)\n", + "plt.title(r'KPP boundary layer depth for '+expt_name,fontsize=18)\n", + "plt.xlim((0, 400))\n", + "xticks = [0, 50, 100, 150, 200, 250, 300, 350, 400]\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "plt.ylim((-300, 0))\n", + "yticks = [-300, -250, -200, -150, -100, -50, 0]\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "\n", + "figure(5)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "CS = plt.plot(time, temp[:,0],'-',linewidth=3)\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=18)\n", + "plt.ylabel('SST(C)',fontsize=18)\n", + "plt.title(r'SST for '+expt_name,fontsize=18)\n", + "plt.xlim((0, 400))\n", + "xticks = [0, 50, 100, 150, 200, 250, 300, 350, 400]\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "plt.ylim((18, 28))\n", + "yticks = [18, 20, 22, 24, 26, 28]\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 8, + "text": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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tt9/Gyy+/jM8//9zpdgiCyDqQ+z1BEIQDvPfee4iLi8Ps2bNN7pG2WLBgAcqVKwc/Pz+E\nhYWhYsWKVpOWrVu3Drly5TItf1exYkWHsmFHR0dblEkiX4phlv5XqlTJom6FChXg7++viHd2hmLF\nilmUhYSEKOLEL126pOifWp9F0tLS8Omnn2LFihW4ePGiRUz8w4cPLY6JjIxU7d/AgQOxfPlyxMbG\nYsSIEaZY7erVq6N69eq235wNFixYgEWLFuH06dMWcf5i/3LmzIlu3bph2bJluHfvHsLDwxEXF4fL\nly8rllk7d+4cjEYjvvrqK3z11Veq5yxTpoxFmbX3rcalS5dQokQJC9Hl7++P8uXLIz4+3lR25swZ\nAFCddAG4+7NYH+CTNHny5LGoGxUVhR9//BFJSUmakxdq+Vw5iq3vQ+XKlRV1JMqWLau5/cjISIwa\nNQpz5sxBwYIFUa1aNTRq1Ahdu3ZFvXr1NLVh7X0DML13qY8VKlSwqFuxYkVN5zlz5ozN0CCDwYA7\nd+5ofv///fcfAKBmzZo2612+fBkBAQEWk4wAH4O1a9fiypUrmsOQtFKkSBHFvjRhVLRoUYu6OXLk\nUP2caRkbRzlx4gRee+01VKlSBd9//71TbRAEkfUgUU8QBOEAOXLkwObNm00WFKPRaNOSUqdOHUX2\ne1s0btxYkf0+I2AtTtVctGo5xhVGjx6N+fPno2fPnpg4cSIKFCiAgIAAHD16FO+8845qf7JlU7/F\n1atXD5UqVcKSJUswYsQI7N69G1evXsW4ceOc7t/nn3+OsWPHom3bthg5ciQKFy6MwMBA3Lx5E2+8\n8YZF/wYOHIhvvvkGK1aswOjRo7FkyRIEBwejV69epjrSxEW/fv0sVkqQUBPE1t63q0j9mTdvnurE\nCwALq6U7yShrkzt6fWfPno1BgwZh27Zt+Ouvv7B8+XLMmTMHw4YNw7x58+web+t9m09uuQJjDEWL\nFsWyZcus1lGbOPQ1tq6Prd8pR9tTu9buHpvbt2+jXbt2CA0NxebNmx3K10IQRNaGRD1BEISDBAcH\nY9OmTWjfvj3mzJkDxhi++OILX3dLNVP0qVOnAPBwAQCmTPYnTpywqHv27Fmkp6eb6gJcND579syi\n7vXr113qq2RhPnXqlIXFUuqzSGxsLFq2bIkVK1Yoyq9everU+QcMGIARI0bg4MGDWLJkCbJnz27V\nAq2F2NhYlC9f3pSUTGLPnj2q9WvWrInq1atjyZIl6NevH37++Wd06tRJkZyubNmyMBgMSE1NNWWH\ndzelS5fG7t27kZiYqBAQaWlpOHfunMLKLllSQ0JCNPfn9u3bePDggcVk1alTpxAREWGalPCVYJc+\nhydOnED9+vUVr0nfEfH74CyRkZGIjIzE8OHDkZycjFatWmH+/PkYP368WyZCpKX6JG8KkdOnT2tq\no1y5cti5cyfq1avnlqUfy5cvD4CHBphbxUVKly6N7du349y5c6ZjJE6cOAGDwaC6AoeE1NeEhASL\n11z9nfImiYmJePnll/HgwQP88ccfHp0gIwgi80Ex9QRBEE4QHByMjRs3okWLFpg7dy5Gjhzp6y5h\nx44dFg/ws2fPBgB07NgRAFCgQAHUr18fGzZsMLnHSkjLg3Xq1MlUFhUVhXv37uH48eOmMsaYwk1c\nK6Jwa9GiBbJnz44FCxYgJSXFVH779m2sXr3aQuSlpaVZLBOVmJiIOXPmONwPgGf5Dg4OxsyZM7F+\n/Xq88sorFlnkHUFaD14kPT0dn332mdVjBgwYgDNnzmDYsGFITk5G//79Fa/ny5cPbdq0wdq1axXX\nX0TMC+AMHTt2RGpqqoV7/3fffWeR1b1Vq1YoUKAAZs6cqZrxPSUlxWICiDFm4cmyY8cOnDp1yvSZ\nBGSXZVuZ5K3hyoRAixYtkDNnTsyfPx/Pnz83lSclJeGrr75CaGgoWrRo4XT7T58+tfjcBgUFmdz9\n1YSoM9SuXRuFChXC8uXL8fTpU1N5UlISFi9erKmNN954A8+fP8dHH32k+rqjOQG6dOmCwMBATJs2\nzaYruvR7M2PGDEX5+fPnsXHjRjRs2NCUJV+NAgUKIF++fBa5D65du4Z169apfj4yiteHBGMMPXv2\nxJEjR7By5UqXwoAIgsiakKWeIAjCSSRh36FDB8ybNw9Go1GTO62niIqKQtOmTTFixAgUKFAAmzZt\nwqZNm/DGG2+gbt26pnpz585FkyZN0LhxYwwfPhz58+fHli1bsGHDBvTo0UOxPnjfvn0xb948dOjQ\nAaNGjUJgYCB++uknp8SI6I4aFhaGadOmYezYsWjcuDF69+6NpKQkLFiwACVLlsTRo0cVx3bu3Bmx\nsbHo1q0bmjVrhsePH2Pp0qWKxGmOEBYWhi5dumDlypUwGAwWgtpRXnnlFUybNg2tW7dGp06dkJyc\njNWrV9t0/+3RowfeeecdrFq1CqVLl1a1fi9cuBANGzZEgwYN0Lt3b1SvXh1GoxH//fcftm7diq5d\nu2LSpElO97tv375YvHgx3nvvPVy6dAm1a9fG8ePHsWrVKpQpUwZpaWmmujly5MCKFSvQsWNHVK5c\nGX379kWpUqWQkJCA48ePY+PGjVizZg0aN25sOiY8PBwrV67EjRs30KhRI9OSdgULFsTkyZNN9aKj\noxEaGooFCxYgR44cyJkzJyIiIizWqlfDFRf03LlzY8aMGRg6dCgaNGiAN99807Sk3ZkzZ/D1118j\nNDTU6fZ3796N4cOHo2PHjoiOjkZgYCCOHDmCb775Bi+88IJD+Q9s4e/vj1mzZqFHjx6oX78+Bg4c\naFrSTpowsSdkX3nlFfTt2xeffPIJDh48iHbt2iFXrly4c+cO/vzzT5w/fx7nz5/X3KciRYpgzpw5\nGDp0KKpXr44+ffqgYMGCuHnzJjZv3oxFixahSpUqaN68OV599VUsXboUDx8+RJs2bXDnzh18+eWX\nCAgI0PSbOmDAAHz66afo2LEj2rdvj9u3b2PhwoWoUKECDh48aFHfnWEL7mDRokVYv349mjRpgmfP\nnlksb9egQQOb3goEQRAk6gXWrl2L9evXo2rVqjhx4gSaNWtmsYasO48jCEL/BAUFYcOGDejYsSO+\n+uorMMbw5ZdfAtBuDTIYDG6xHHXo0AHlypXDJ598gnPnziEiIgIffvghJk6cqKhXs2ZN7Nu3D5Mm\nTcKsWbOQkJCAMmXKYMaMGRaZyqOjo7Fhwwa8//77GDduHMLDw9GrVy/07dtXNSmXtfeh9h5Hjx6N\nkJAQfP755xg1ahSKFy+O4cOHI1euXBZZ6BcuXIjQ0FCsXbsW69atQ8mSJdGvXz/UqlULzZs319wP\nkYEDB2LlypUoW7asQohqwbz9Dz/80JRw77fffkORIkXw2muvoU+fPlbjz0NDQ/Haa6/hu+++s3rP\nKFq0KA4fPozPPvsMGzZswLfffgs/Pz+UKlUKjRs3RteuXRV9cvRzFBAQgJ07d+Kdd97B2rVrERsb\nizp16mD79u0YM2aMRXhDy5YtcfDgQXz66adYsmQJ4uPjkSNHDlSoUAH9+/e3SDgXEhKC3377DSNH\njsSoUaMAcOv47NmzFevQBwcH44cffsAHH3xg8lyIiYkxiXpHPlfWsFZv8ODBKFSoEGbOnGlaAaJa\ntWr45Zdf0L59e01tWKNatWpo0aIFduzYgW+++Qbp6ekoUaIEhg8fjg8//NChtsz7Yd6Xbt26ISAg\nANOmTcM777yDiIgI9O3bF3Xq1EG7du0sXOrV3suSJUvQtGlTLF68GBMnTkRiYiIiIiJQo0YNxSSM\nVgYNGoQyZcpg5syZ+PTTT5GQkIAiRYogJiZGkZBu1apVqFGjBpYtW4atW7ciZ86caNKkCaZNm6Yp\njn/atGlIT09HbGwstm/fjujoaCxatAiHDh3CoUOHLN63rc+TI+XW6jr6OZESTP7xxx/4/fffLdpb\nunQpiXqCIGxiYBltutJHxMXFoWvXrrh8+TJCQkLw/PlzREZGYu7cuejcubPbjyMIgiB8y8GDB1G3\nbl188sknJjHnbYYOHYrFixfj6tWrmS6GNiYmBteuXTOtdED4hi+//BIjRozAgQMHUKdOHV93hyAI\ngvAAFFP//0yePBmdOnUyuakFBweje/fuqmtJu+M4giAIwrcsWLAAgYGBPvOsevLkCVatWoU2bdpk\nOkFPeJ+0tDSLcI/ExEQsWrQI4eHhmlfhIAiCIPQHud8DSE5Oxl9//YVXXnlFUV6lShXMnDkT9+/f\nV03S4uxxBEEQhG9ITEzExo0bcerUKcTGxmLgwIFW1+X2FCdPnsTRo0cRGxuLZ8+e4f333/fq+b0J\nOQN6jwsXLqBjx454/fXXUbJkScTHx2PZsmU4d+4cFi5c6LalDu/evWuR/M+c0NBQp/NdZCYSEhIU\niQvV8Pf3R/78+b3UI4IgMisk6gFcvnwZ6enpFgJcWoLn3LlzFkvduHIcQRAE4Rvi4+PRvXt3BAcH\no0OHDhYZt73Bzz//jClTpiAiIgJz585VJDHMTLgrVwShjQIFCiA6Ohrffvst4uPjERQUhCpVqmDN\nmjXo0qWL285Tu3ZtXLt2zWadyZMnu5QzILMwc+ZMTJ061WadkiVLUogKQRAuQ6Ie8pJAQUFBinJp\n39qSQc4cRw84BEEQvicpKQnr1q3DunXrfNaH27dvY9iwYRg2bJjP+uAN6L7nG1JTU7Fv3z7s27fP\n6+eeNGmSS6syZCUuX75M3xGCyIR421ONRD3ki27+oyq5l1n7sXX1OEJf9OnTB8uWLfN1NwgnofHT\nLzR2+obGT9/Q+OkXGjt9Q+OnX3wxUUeJ8gDkyZNHtVxahzksLMytxxEEQRAEQRAEQRCEOyBLPYAy\nZcogMDAQjx49UpTfunULAFCuXDm3Hkfok5IlS/q6C4QL0PhlXI4cASZNAi5cAP73PyA1FQgM5H9B\nQUBaWkls3gy0a+frnlpy8CDwzTdAjhxAiRJAp04AfdSU0HdP39D46RcaO31D40c4AlnqwWPgGzdu\njCNHjijKDx8+jKioKKtZSZ09jtAnMTExvu4C4QI0fhmT9HTg1VeBzZuBs2eBZ8+A5GTg6VPg/n3g\n5k0gPj4GPXoAd+74urdKnj0D2rThon7uXGD0aKB8eWD7dveex2gENm4EmjUDihUDVq1yb/uehr57\n+obGT7/Q2OkbGj/CEUjU/z+jRo3Cjz/+iCdPngAAHj9+jHXr1ikSvWzbtg358uXDtm3bHDqOIAiC\nUGf9euDiRfv1njwBxo3zfH8c4dtvgXv3lGUpKUDnzsD+/e45x927QN26QIcOwJ493JOhd2/3TBw8\neMDbTEtzvS3CcW7f5p9/K7l4CYIgCEIzJOr/n5deegkzZszAkCFDMHPmTPTr1w+ffvopunbtqqhn\nNBoVyQ+0HkcQBEEoYQyYOVPeHzmSi+TERODRIyA+HhCT069YAfz5p3zswYPAX395t88SKSnA7Nny\nfs+eQPHifDsxkVvw//vP9fOMGQMcOqQsk7wbTp92vt2UFKBJE279f/FFvu8Jjh/nEwerVgHbtnGv\nA09y4wbw5pvAoEHA8+eePRfAQ0WcISkJqF2bh2sUKQL06wdcvuzevhEEQRBZBwOjVOxexWAwUPZ7\ngiDcztOnwPLlQFQUF2p6YO9eoFEjvh0YCFy9ChQsaFmva1fgp5/4drFiwEcfAb/8wq2cAJ8YGDtW\nrv/kCX+tcmWgenXP9H35cqBPH75doABw5Qpw/TrQsCG3rgNceK9Z4/w59u8H6teX90eM4JMc16/z\n/fLlgVOnAH9/x9uOiwOaNpX3R49WTlK4gw0bgI4dlWVvvQUsWuTe80gcOwa0bcuFPQDMmsUnRdyJ\n0QisXMknmM6c4eEhdetyz4ncubW3o3ZtIiL4eObLZ/24Z8+AtWuBMmWAxo2dew8EQRCEZ/GJ3mOE\nV6FLThCEJxg+nDGAMX9/xo4c8XVv7GM0Mta2Le8zwFi/ftbrXrvGWI4ccl3zv2zZGPvnH97mTz8x\nVrgwL8+enbFLl9zf93//ZaxsWfn8H38sv/bXX3K5wcDY6dPOnSM9nbFateS2OneWz50zp1z+22/O\ntf/++5bX8eefnWvLGg0bqo/X11+79zyMMbZ1K2MhIcrzNG3q3nPs368cE/Hvtdf4508rffqotzNq\nlPVjzp41j16wAAAgAElEQVRlrEIFuW7r1s5/vkTu32fs778Z27ZNH78dBEEQGR1f6D1yvycIjcTF\nxfm6C4QLZPbx27GD/09PB77+2r1tP34sWz+dxWgEFiwAZswATp4E+vcHtmyRX7dlUb14MQ4rVwLW\nVglNS+PW/Fq1gC5duPUU4C7Oy5e71m+RJ0+49b1aNZ6pHwBCQ4EhQ+Q69etz13uAS6/p0507V2ys\n7HYfFMStzgBQtarsIQAA33/vXPs7d1qWvfIKd8XfuJH33RUuXuSeGADg5xeH2rXl14YNcz3nwMGD\n/PO0fz+3/L/8Mrdii+zda1nmLD/8wL1KzEMhJNasAZYu1dZWWhqwaZO8P3q0vP3VV9zrQyQlhedv\nqF2bJ5OU+PVXoEYN4PBhbec1hzHunVG4MPc2eOkl3t433yjrZfbfzswMjZ2+ofEjHMLr0whZHLrk\n+mXPnj2+7gLhApl5/BISGPPzky14uXLxMndw/jxjoaHc6rxhg/PtfPyxdUv7G2/YPlYau/v3GRs7\nlltko6IYi43lfbPWLsBYmTKOWVCtkZrKWKtWlu0vWmRZd/9++XU/P34NHeWFF+Q2PvhA+drevfJr\nefIwlpzsWNv37vHxlPpXrJjl+6pTh7Hp0xlr3pyxQoUYK1iQ12venLEJE/hn4fZt6+eYPFluq169\nPSwhgbGqVeWySpUcvyYS//sfY4GB6uNdvDhjJUvK+5s2OX8eiUWL5OsFMBYUxK/B+fOM9e8vlwcG\nMvbii4xNmWL7+xcXJx9TpAj3yqhXTy7r2VOu+9NPjBUtqnyPQUHK/nTs6Ph7Sk/nXgFq1zB3bsbu\n3JHrZubfzswOjZ2+ofHTL77Qe6QwvQyJeoIg3M3ff1s+mMfGuqftwYPlNlu0cK6NxETGwsPVBUSf\nPoylpDjWXlqaLNRXrFC2FxjI2KBBfGJDKjtwwLl+SxiNyusgucPbclVu0UKu26oVnxTQytWr8rH+\n/lyEi6SnM1aihFxn40bH3s+PP8rHvvACD1Ho3Jmfy9YEidpfqVKMDRjA2KpVXKyePcvHp3Rpuc6P\nP/LzXrrEQyKk8gsXLPtmNDI2fjxjlSsztnmzev+XLVPvS61ajN26xSd+pLK333bs2pjz1VfKc0RF\nKUM6EhIYq1jRsi8xMXyc1Bg5Uq43eDAv+/NPucxgYOzYMcaOH1dO1gF83I8cUU7sZMumFOH2SE5m\nrFs3y3HMn1/ef/NNpy8ZQRBElodEfRaARD1BEO5m8WJLUeGOeOKkJMbCwpSC+dkz1/oXGsotjX5+\njE2c6LoV3WjkMdpvvMHYd98x9vAhL+/b133C7ttvldfW3HKuhijSAC58pfd68CC3zL70EmPx8ZbH\nzp6tnBBQ49135Trdujn2fkTr8sSJcvm1a9x6GxTkuLgX/6ScBpLVNylJPke7dvJr8+db9u333+XX\nS5ZU/3y89ZZcR8ov8Oqr8mdzxw759XLl7F8Po5HndMibl7HXX+eTE48eWU4e1K5tOcHCGI9rj462\nvA4zZ6qfS/Qk2L5dfq19e7m8Z0/lOBUowNinnzL2+LFcv359+fXZs9XfW3o6Yzdu8M/jtm3ci6R5\nc8sJqqQk/rpYvn+//WtHEARBWEKiPgtAol6/kBuUvsnM4zdkiLq4unjRtXbXrLFsc8sWx9pIT1cm\n95o9m4svNTFrDWfGbtcupSByxFIukpjIXc+ltl5/XftEhHkyup49GVuwQGmtbtmSW7ZF6taVX//2\nW/W2//1XrpMjB2NPn2rrk9GotPL/8YdlnatXGXvnHT5R8s033KJ+4wZj584x9sMPXPg3aMBYcLB9\ngT9woHL85s+XX2vXzvLcr76qPP7MGcs6lSvLr+/ezdjz58rXk5KU1/jyZdvXZN8++++jbl3Gnjyx\nfV0vXeITSOIk2LFjcp3kZMamTpVfz5VLGTrxzz/ya9myKa/v3r2W5xQnm6Kj+YTDzp18kuH+fcam\nTVNa39X+hgxRfv46dJBfy5OHh1mI45eQwNtOTHRPWIunePaMX09HQ1MyG5n5vpcVoPHTLyTqswAk\n6vUL/bjqm8w8fg0ayA/iefPK25984lq7rVtbigBHrd5btsjHhoZyC6ijODN2aWk8Flw697Ztjp+X\nMcbmzpXbKFTIsVwFRiNjvXrZF4xTp8rHXLmiFHZqlmGp7agoue6HH2rrk+hBEBLieOiDSHIynxSY\nOJGLwfr1uWVefG/79inH78IF5WSEKMhv3ODvWTx+zhzlOR8/VuYDsDaZIX52v/jC9vuQVo6w9lel\nCmMPHmi7Jikpygz5RYtycbltm3K8AP7ZMKdJE8vzV6+uLqCfPFGuCmEtz4C1v48+smz30iXl6gr8\nc7+HVaxoGUITFMTf61tvMXbihLbr4w3E/A1duvi6N74lM9/3sgI0fvqFRH0WgEQ9QRDuJD1duZSX\nmKDMUbdskf/9zzKeF2AsMtKxvolW59Gjne+PM4weLZ9bTD6mlaQk5cTA3LmOt5GcrHSrlv5E4WQw\ncIszY4zNmiWXW3O9l/j6a6WgM0/IZ+4BsH69UgR26OD4+7FHQgJjS5fy5G2ff65eR1wOcOdOuXzS\nJMvr1Lq18tidO+XXqlWz3o8vvlC28/rr3APBnLQ0pSfGyy/z5HWhoYwFBPAJM1sJAdU4e1bpKaD2\nPapWTT0OftMmy7pLllg/l7Wl8cS/3Lm5+G7enLEaNbinw4oV1tvct089eaKtv4gI5ybsPMEHHyj7\n9s8/vu4RQRBZDRL1WQAS9QRBuBPR8hkerrTEVq3qfLuffSa306CBUgyqJThTQ4ylDwzkVmhvcviw\nUkQ7mg9g3jzRWsldjp3BaOSx4t27c5frNm14QjfRKhsRwa2dYoI5W2KOMT5pUqeOXL9FC36u58+5\n2AsK4mL27l2++oCYMT0sjCdi8wXDhsn9GDOGlyUnK8W19Jc9uzImf8oU+TUpyZwa165ZWpzDwhj7\n5Rdlvd9+U46B+USIs2zfrsxHIf2FhPAJB2vhIOnpysR7+fLZ/tz99Zey/QoV+KQEwFj58jwvgDPe\nGPfu8Vh78/5ny8bfl1rehbFjHT+Puzl/3tJj4fXXfd0rgiCyGiTqswAk6vWLHt2gkpP5w/yECfwh\nNyvj6Pj9+y9jq1dzkexIZmlv8/PP8sNr8+Y83lXaDw52XqS0basUl2KCsxkz5Mze1jJ8372rDAXQ\nklzOGs5+94xGZTz/6tXaj336VCkyzd3A3cHNmzzeXzqHmH0+Z04+lvY4ckRpCe7Vi08aiKLGPPa9\ndGn1WHVPYT5+mzfLfYmO5mWrViknUMqUkfd37JCPFd3qbVmbGeMC75VXLMXnO+/IbucDB8rlw4a5\n7z0zxie/pPh/Pz9+rps37R+3cqXcp0mT7Nf/7jvGhg7lE0fS+xInQlzh7l3GYmP3sOPHufeO+Hty\n7x7/Xkh9DQjgeRfcwfnz/DenZ08+8aIlft9oVF920t9f3UsjK6DH5xZChsZPv5CozwKQqNcvevxx\nFR8OAwO5VUxrQq3MhiPjd/KkUmAZDK7Hp3uKiRMtrZ6iUHQ2WV7x4nIbJ08qE5xJn6eAAL79wguW\nsd/9+sl1S5Vy3srNmGvfPTE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9QTgCiXqC0IjeYpusWeqBrOmCrzZ+5q735mRES70zol7v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\n27dvV58+fbzG4+PjtW/fPhUUFAT1PATOaqDrhDydp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mjb/4ovPef9UqUdQCwIIF5tt4qenUKSAqSowLFhTT1Y0rpRMReQr2aBPZiL0y\ncmP+5MXcyc0d83fpkuXzpUoBgwYpx8WKiR5mZxbZgHhavHcvsGtX/hfZtuSvalWgRg0xfvRI7N1N\n6nHHz54nYf7IFiy0iYiISHrHjlk+P3680quc3+rVE/3hWmO6KNuyZerFQUTkSTh1HJw6TkREJLsv\nvgBGjxbjwYOBNm3E2LgiuCc7dw6oXFmMfXzESuTPPKNuTERErsSp40RERER2MH2iHR0tCmwW2UJE\nBPD882KclgYsWqRuPEREnoCFNkmNvTJyY/7kxdzJzR3zd/y4Mo6OVi8OV7Anf6ZbmQ0YIPrIb992\nXkxkHXf87HkS5o9swUKbiIiIpPbggdiyCxArjxtX2SZFnz5iYTijhQudt484ERFlxx5tsEebiIhI\nZr/+CtStK8ZRUWL/aMruyhVg6FBgxQrl3IkTQPXq6sVEROQK7NEmIiIispHptPFnn1UvDq0rVw74\n8Uega1fl3OLF6sVDROTOWGiT1NgrIzfmT17MndzcLX9ZF0Jzd47m7803lfHixUBGhmPxkPXc7bPn\naZg/sgULbSIiIpIan2jbplUroFgxMb50CfjlF3XjISJyR+zRBnu0iYiIZGUwAEWLAvfvi+MrV4Cy\nZdWNSQbvvgt8840Y9+8PfP21uvEQEeUn9mgTERER2eDvv5Uiu2hRoEwZdeORRY8eynj5cuDJE+fe\nf+dOYNUq4MYN596XiEgWLLRJauyVkRvzJy/mTm7ulL8//lDGVasCOp16sbiKM/LXoIFYHA0AEhOB\n3bsdvmWmf/8baNIE6NwZKFkSeP558z56T+ZOnz1PxPyRLVhoExERkbRMC23un209vR7o0EE5XrfO\nOfc9cwb48EPzc0ePAmPGOOf+RESyYI822KNNREQkq/feA+bOFeMvvxT7RJN1tm4FWrQQ4woVgD//\ndGxGQHo6UL++2NccAJ55RpnWX7GiuD8RkRrYo01ERERkAz7Rtl+jRkBAgBgnJJj/WdojNlYpsgsU\nAH7+WSncExKAR48cuz8RkUxYaJPU2CsjN+ZPXsyd3Nwpf1l7tD2Bs/Ln4yO2+jJav97+eyUmAl98\noRx/9hlQu7Z4Ug6I1eHPnbP//u7CnT57noj5I1uw0CYiIiIp3bolCjwA8PNTFvci67Vrp4wd6dP+\n17+UaeJVqgAffSTGkZHKNadP239/IiLZsEcb7NEmIiKS0a5dQEyMGL/4InDggKrhSCkxEQgJATIy\nxDTvnj2mALfNAAAgAElEQVTFLyxatgTq1gW8vfO+x9WrQKVKytTwH38EunQR42HDgK++EuOxY4FP\nPsmfn4OIKDfs0SYiIiKykidOG3e2oCCgXj0xNhiABQuA8eOBhg3F1lz/+U/e9/jiC6XIrllTbOtl\nxCfaROSpWGiT1NgrIzfmT17MndzcJX+euhCas/P3/vuWzycmAu+8A4waJYpwS44fNy/GJ040X7mc\nhbY5d/nseSrmj2xhxYQgIiIiIu05dUoZe1Kh7WyvvQZER4tfXCQliSn469YB166J70+YIFYN//pr\noGhR5XUGAzB4sJh2DgDNmokvU1kLbYPBsS3EiIhkwR5tsEebiIhIRqVLK8Xg2bNARIS68biThw9F\nAb5pk3KubFlg8mSgcWOgRAlg+XLg9dfF97y8xNPtrL/wMBjE9PQ7d8TxlSviPkRErsQebSIiIiIr\n3L2rFNm+vso2UuQcfn7A2rVi6rjRX38BPXoApUqJP3NjkQ0AAwdanlWg03H6OBF5JhbaJDX2ysiN\n+ZMXcyc3d8jf0aPKuEoV61bHdheuyp+3N/DNN2IV8eBg8+89eaKMixcHRo/O+T4stBXu8NnzZMwf\n2YKFNhEREUln715lXLu2enF4gi5dgN9/B0aMAOrXBwoVEud9fIDwcLFSuWnvdlamhfaZM/kbKxGR\nVrBHG+zRJiIikk2rVsCWLWK8YIHY/5lcIz0dSEtTCu68rFsHvPqqGDdrBmzdmn+xERFZwh5tIiIi\nojykpwO//KIc16+vXiyeyMvL+iIbMH+ibbpSPBGRO2OhTVJjr4zcmD95MXdykz1/J04AycliXLo0\nEBamajguJ1v+KlYUi6cBwNWrYn9uTyVb7sgc80e2YKFNREREUjHtz65fn/sya523N1C9unJ87Jh6\nsRARuQp7tMEebSIiIpm8/jqwbJkYz5ghtpYibXvnHeA//xHjqVOBYcPUjYeIPAt7tImIiIhyYTAA\ne/Yox+zPlsNzzylj063ZiIjcFQttkhp7ZeTG/MmLuZObzPm7dAn4+28xDggAatRQNx41yJg/FtqC\njLkjBfNHtmChTURERNIw/f/cunVF/y9p37PPKr30p04BqanqxkNElN/Yow32aBMREcnilVeATZvE\neOJEYMQIdeMh61WuDJw7J8YHDgAvvqhuPETkOdijTURERJSDxETg55+V427d1IuFbMfp40TkSVho\nk9TYKyM35k9ezJ3cZM3fihXA06diXKcOUKGCuvGoRdb8mRbaR46oF4eaZM0dCcwf2YKFNhEREUlh\nyRJl3L27enGQfZ5/XhnziTYRuTv2aIM92kRERFp39SpQrpzY3kuvF8clS6odFdni2jWgdGkx9vMD\n7t8XuSQiym/s0SYiIiKyYOlSUWQDQOPGLLJlVLIkEBIixg8fitXHiYjcFQttkhp7ZeTG/MmLuVPf\n5cvAjz8qe0rbQrb8PXoETJumHHv6tHHZ8mek0wH16inHGzaoF4taZM0dCcwf2YKFNhERkUROnwbe\neAOoWBF47TXg5ZeB5GS1o8pfs2eLqeIAUKIE8Prr6sZD9mvfXhmvXateHERE+Y092mCPNhERyeH+\nfaB8eeDuXfPzkyYBH32kTkz5LTlZ/FLh9m1x/O9/A++/r25MZL/bt8UvSzIyxBPua9fEMRFRfmKP\nNhEREeVo9+7sRTYAfPml6Hl1R5MnK0V2+fLAO++oGw85JjhYmT5uMADr16sbDxFRfmGhTVJjr4zc\nmD95MXfq2LNHGb/3HhAaKsa3bgFz51p/HxnyZzAA48cD48Yp58aMAXx9VQtJM2TIX25efVUZe9r0\ncdlz5+mYP7IFC20iIiJJmBbaLVsCH3+sHE+Z4l5PtT/+GPjkE+W4Th3gzTfVi4ecx7TQ3rbNvf69\nJSIyYo822KNNRETal5oKFCkCPHkijm/dAgICgPBwZaGwN94AfvhB9L7K7NIloEIFZTuvpk2BVauA\nZ55RNy5ynmrVgD/+EONVq4COHdWNh4jcG3u0iYiIyKL9+5Uiu2pV0evq6yumVxstWgTMmKFOfM60\na5dSZNeqBWzaxCLb3XTooIyXL1cvDiKi/MJCm6TGXhm5MX/yYu5cz3TaeIMGyrhXL+Dtt5XjoUOB\nFi2Ar79WCvOstJ4/05+1fXvAx0e9WLRI6/mzRrduynjdOs+ZPu4OufNkzB/ZgoU2ERGRBEyLz/r1\nzb83cyZQu7YYZ2QAW7cC//wn0Lmz8mRYJjn9UoHcR40aQGSkGKekABs3qhsPEZGzsUcb7NEmIiJt\ne/oUKFYMePBAHCckAGFh5tf8/bfo0c76wGXWLGDAAFdE6Rw3byr7Kvv4APfuAQULqhsT5Y/PPxcr\nyQNAp07AypWqhkNEbow92kRERJTN0aNKkV22rNhPOqvSpYGdO8VCYn36KOeHDQNOnXJNnM6wd68y\nfuklFtnuzHT6+MaNwP376sVCRORsLLRJauyVkRvzJy/mzrV27lTGDRrkvqp4aKjoz65RQxw/eiSe\ndKelKddoOX+cNp43LefPFpGRQHS0GD9+LHq13Z275M5TMX9kCxbaREREGrdlizJu1izv6wsWFCuQ\n+/qK4yNHgNGj8yc2Z8utF53cj+lT7e+/Vy0MIiKnY4822KNNRETa9fAhEBioPJG+ckVMH7dGbCww\nZIgY63Sif7thw3wJ0ymSk4GiRcWCbjodkJQkjsl9Xb4s9kzPyBDHZ84AlSurGxMRuR/2aBMREZGZ\nuDilyK5e3foiGwAGDVKegBsMQM+eQGqq00N0mr17lYKrRg0W2Z4gNBR45RXleO5c9WIhInImFtok\nNfbKyI35kxdz5zqm08ZbtrTttXo98N//iifigHh6+NNP2s3fkiXKuEkT9eLQOq3mz179+yvj+fO1\n/csgR7lb7jwN80e2YKFNRESkYaaFdqtWtr++dGngnXeUY9OF1dSUmAisWQOMHSv6spOTzbd36tlT\nvdjItVq2VLaru3MHWL5c1XCIiJyCPdpgjzYREanj2jUxVbpMGcvfT0gAKlYU40KFRM+yPdtdbdmi\nFOnVqwMnTtgXr7MMHSr6x41/9Xp5AX37At9+K46rVweOH899dXVyL5MmAR9/LMZ16gD79qkbDxG5\nF/ZoExEReYi1a8VTvLJlxX7RM2YAT56YX2P6NLtxY/v3lK5XD/D2FuPffwdu3bLvPs7w55/AtGlK\nkQ0A6elKkQ0AvXuzyPY0ffsCBQqI8a+/ir3jiYhkxkKbpMZeGbkxf/Ji7hxz7ZooLIyLnB08CAwe\nDHTvrhSg6enAzJnKa2ztzzbl7w/Urq0cz5oVZ//NHLRihTIODc2+4JmXl9j3m3Lmjp+/kBCgSxfl\nePZs9WLJT+6YO0/C/JEtvNV647S0NEyePBlXr17FjRs3cO3aNQwaNAjdu3c3uy49PR1Tp07F6dOn\nUbZsWaSnp6NXr16oUqVKrvf/8ccfsWbNGkRHR+PEiRNo0qQJ+vTpk58/EhERUZ4MBtEznZSU/Xsr\nVwLTpwMffAAsXgycPCnO+/uLItwRjRsD8fFifOSIY/dyxI8/KuOxY4FnngE6dlTOtWoFlCzp+rhI\nff37KwviLVoETJki/v0gIpKRaj3aQ4cOxbvvvptZMG/YsAHt27fHtGnTMHjw4Mzr3njjDZQvXx4T\nJkwAANSoUQNVq1bF8lxWyoiLi0PXrl2RkJAAf39/PHr0CBEREZg+fTo6deqU7Xr2aBMRkat89x3Q\nr59yvHo1sGEDMG+eOPb2BhYuFP2qCQni3GefAZ9/7tj77tgBNG0qxlWqAKdPO3Y/e1y4AISHi3GB\nAsDNm+KJ9vvvA7NmifObN9u36BvJz2AQ27oZf8E0axYwYIC6MRGRe1Cj3lOl0E5OTkZISAh69eqF\nOXPmZJ6vVasWzp49i6SkJOj1eixZsgSjRo3C+fPnodeLWe7Dhw9H9erV0atXrxzvHxMTg8qVK+Ob\nb77JPPfRRx/h559/xhELv8ZnoU1ERK6QmAhUrqw8zR40SDzBfvwYaNgQ+O237K8JChIFqqNP9lJT\nRVFrnK5+9apYkdyVpkwBPvxQjNu0ATZuFOOMDGDZMqBYMRbZnm7mTGDgQDHmonhE5CwesxiaXq9H\nqVKlkJycbHa+UqVKuH//Pm7evAkAmDp1Klq3bp1ZZAPAlClTci2yHz9+jPj4eNSoUcPs/LPPPotj\nx44hMTHRiT8JqY29MnJj/uTF3Nnnk0+UIrtCBWDiRDH29RVbGgUHZ3/Nxx87Z/psoUJA3brGozhV\ntvkynTbetasy1uvF1HgW2dZx589fz55A4cJi/PvvwC+/qBuPs7lz7jwB80e2UKXQ9vPzw4ULF7Bo\n0SKz85cvX0ZgYCBCQkJw69YtHDlyBMHBwZgzZw4mTpyIfv36Yfr06bneOyEhAenp6QgKCjI7HxgY\nCAA4c+aMc38YIiIiKxw5AsydqxxPm6YUFABQvjxw+DAwbBhQrpw4V6uWc6fOGqeOA2IPa1c6dQo4\ncECMvb2BV1917fuTHIoUMV8Mz10XRSMi96faYmhZnT59Gvv27cP48eOh1+tx/vx5AMD69euxZcsW\nFC9eHBkZGahWrRrS0tIwfPhwi/dJ+t+jAl9fX7PzxuMkS6vPAOjduzfCwsIAAEWLFsVzzz2HmJgY\nAMpvr3isveOYmBhNxcNj5o/HPLZ03KhRDAYPBgwGcdyqVQzat89+/Z9/xqFtW2Dy5Bhcvw6cPBmH\n/fudF0+5cuIYiMHGjcDmzXEoVCj/f/6XX45Bz54AII5btIhBsWLayQ+PtXX83nsx/9vuLQ7LlgHT\npsWgeHHtxOfosZFW4uGxbcdGWomHx5aPY2NjcfTo0cz6Tg2qLYZmKiMjA82bN0dwcDCWLVsGAIiP\nj0eDBg3w5ptvYsGCBZnXDhw4EAsXLsSNGzfg4+OT7V7G161YscJs4bOtW7eiZcuWWL9+PV555RWz\n17BHm4iI8tNvvynbaxUoAJw4IRYkU0P16spiU0uWAK+/nv/v+eGHoj8bAHx8gP37geeey//3JXnV\nrq2sWfCvfym9/URE9vCYHu2sRowYgYoVK2Lp0qWZ54L/16iW9bcQRYsWxb179/D7779bvFexYsUs\nnn/48GHm68l9ZP3tIsmF+ZMXcyfcvCkWM2vSROwDnMNkK7Ppr927q1dkA0C3boDxyXIuG3g4zS+/\nKEU2IIomFtmO8YTPX//+ynjuXLFgnjvwhNy5M+aPbKF6oR0bG4uAgAB8++230Ol0uHz5MtLS0lCx\nYkUUKlQIGTn8l9Xb2/Ks9/DwcPj4+ODu3btm569duwYAqFy5snN/ACIi8kh37gDR0WLP6507gVu3\ngKlTga1bza9LSgJMfo9sVkCo4bXXlPGmTUCWdUmd7ocflHHLlmKldaK8dOsmVskHxKr7rG+ISDaq\nFtqLFy+GXq/Hp59+mnluzpw58PLyQoECBdCiRQtcuHDB7DW3b99GUFAQqlWrZvGevr6+aNiwIQ4f\nPmx2/tChQ4iKikLx4sWd/4OQaox9GCQn5k9ezJ1YzOz69eznP/rI/Onbf/8LPHokxs89p0whV0uV\nKkB0dAwAsa3YunX5+37x8cp4+HCxwjg5xhM+f4UKAT16KMc//aReLM7kCblzZ8wf2UK1v+62bNmC\n6dOnIzU1FZMmTcr8OnbsGLy8vAAAn376KbZv3565JVdKSgo2bdqEcePGZV6zefNmBAUFYfPmzZn3\nHjJkCJYvX4779+8DAO7du4dVq1Zh9OjRLv4piYhILQYDsGsXcPGi8++dlATExirHw4aJwgAQq4sb\nn2AbDMCcOcp1/ftrY09g06faCxfm3/vcvSu2aAIALy/1f8lAcjHd7i3rTBEiIq1TZdXxxMREdOnS\nBSkpKThg3Ovjfzp06JA5fuGFFzB//nz069cPoaGhuHLlCr744ots+2hnZGRAZ/J/Lq1bt8bkyZMx\nYMAAREdHY//+/Zg0aRK6mm7aSW4hLi6Ov12UGPMnLxlyN3488OmnYg/qw4eB8HDn3furr5Qp11Wr\nir5jX19gwgRxbtQooHNnUXSfPSvOBQSYP6FTU4UKcdDpYmAwAFu2AAkJYl9vZ9u3T/yyARBP8/39\nnf8enkiGz58zxMSIX9CkpwNHj4r2DNknJnpK7twV80e2UKXQDgoKQrKVTWFt2rRBmzZtcvx+69at\ncefOnWzne/fujd69e9sbIhERSc64T/T9+2IxLtMny45ITARmzFCOP/tMFAMffigWbUpMFE/RN24E\n/vhDua5zZ+0UmqVKiX7pn34ShfDcucCkSc5/H9Np4/XqOf/+5N4CAoA6dZR/j7Zvd80q+UREzsBO\nKZIaf6soN+ZPXlrPncEAnDmjHH//PXDjhnPuvX698jQ7KgowTpYqUgR47z3luk2bgB07lONmzZzz\n/s4QExNjtijbvHmiX9vZWGjnD61//pypeXNl7A7Txz0pd+6I+SNbsNAmIiK38/ffwIMHyvHjx8DM\nmc6596VLyvjVV8XTbKNXXlHGGzaIra2MGjd2zvs7yyuvAOXKifHt28DKlc69/5MnYr9sIxbaZI+s\nhbaLt8ElIrIbC22SGvczlBvzJy+t5+706eznZs0yL77t9fffyrhMGfPv1aoFBAaK8Y0bylPiyEig\ndGnH39tZ4uLi4OUF/OMfyjnTvb6d4ehRIDVVjMuXz/5nRfbT+ufPmV56SUwhB4ArV5Q1D2TlSblz\nR8wf2YKFNhERuR3TaeNGd+44Z4Vt00I7a/Hs5SV6n7Nq2tTx980Pb78NeP9vtZa9e4HLl513b04b\nJ2coUMB8Nog7TB8nIs/AQpukxl4ZuTF/8tJ67kyfaIeGKmNn/E96boU2AFhav7NJE8ff15mM+StZ\n0vyXACtWOOf+BgOwerVyzELbubT++XO2Fi2UcX7v+57fPC137ob5I1uw0CYiIrdj+kT7nXeU8Z49\njvd45lVot2xpvle2Tie2KdIq0z21ly93zj3nzgV27xZjnc68z5bIVu3bK+MdO8TK/kREWsdCm6TG\nXhm5MX/y0nruTJ9od+oEFCsmxrduOdbj+fSp+erlJUtmv6Z4cdFXavT880rftlaY5q9DB2X6+P79\nYmsyR5w/DwwbphwPGwZERDh2TzKn9c+fs5UrJ7b5AsSe2mvXqhuPIzwtd+6G+SNbsNAmIiK3kpKi\n9Bp7eQGVKgH16yvf37PH/nvfuKE8EQ8JEf2jlrRtq4y1/jQ3MNA8Rkemj+/YAbRqJXIAANWqAWPH\nOhYfEQB06aKMnb1CPhFRftAZDNwoQafTgX8MRETu4ehR8RQZEE9Sz54FpkwBPvxQnHvrLeC//7Xv\n3gcOiJXFASA6WryXJcnJQK9eYtXxBQuAoCD73s9Vvv8e6NNHjF96CfjtN9tebzAA//yn+crl3t7i\nCfkLLzgtTPJgFy8CFSqIcYECwM2bQNGiqoZERBJRo97jE20iInIrptPGIyPFPxs0UM458kT72jVl\nnNt2XQEBwKpVwMaN2i+yAbEfuPHp/IEDYvq3LY4cMS+yixQRK7yzyCZnCQsDatYU4ydPgPXrVQ2H\niChPLLRJauyVkRvzJy8t5850ITRjof3CC0ChQmKckAD89Zd9985rITRZZM1fsWLm25LNn2/b/fbv\nV8YvvQScOgV062Z/fJQ7LX/+8pPp9PFly9SLwxGemjt3wfyRLVhoExGRWzF9ol2livinj4+ymBJg\n/1Ntdym0LenbVxl//71Y+M1aBw8q427dgFKlnBYWUaauXZXx5s2OL9xHRJSfWGiT1LifodyYP3lp\nOXeWnmgDzpk+7i6FtqX8tW0rFngDxM+5ZYv19zt0SBm/+KJjsVHetPz5y0/h4crCfRkZwJw56sZj\nD0/Nnbtg/sgWLLSJiMitmPYXV66sjE1XHjd9AmsLdym0LSlQQCwUZzRvnnWvS00Ffv9dOTYuREeU\nH95/Xxn/5z/Ao0fqxUJElBsW2iQ19srIjfmTl1Zzl54uVvw2Ml2ILCpKGZ87p2zTZQt3KbRzyl+/\nfsp4/XrretmPHRN/7oCYqv/MM47HR7nT6ufPFV55BShfXowTE+Xr1fbk3LkD5o9swUKbiIjchmmR\nHRAA6E3+litdGihcWIzv3hX/k24rdym0cxIZCbz8shg/fQo0aiS2R8uN6ewAThun/OblBfTvrxz/\n+9/2/dKMiCi/cR9tcB9tIiJ3ceUKEBoqxmXKZH8i+9xz4gksAMTHK0WlNdLSAF9fMdbrxR7Z3t6O\nx6w1P/0EtGmjFC9BQcCuXUC1apav79NHLJ4GANOmAR984JIwyYPdvg2ULSs+gwDw669A7drqxkRE\n2sZ9tImIiBxw/74ytjSF2bRn+9w52+59/boyDglxzyIbAFq1AlavVrZDS0wERozI+XrTJ9rGfY6J\n8lNwMNC9u3I8c6Z6sRAR5YSFNkmNvTJyY/7kpdXc2VJo5zUlOqtr15Sx7NPG88rfq68CW7cqx5s3\nm//8Rg8fAn/8IcY6HRdCcxWtfv5cyXRRtOXLgRs31IvFFsyd3Jg/sgULbSIicht5FdoREcrY1ifa\n7t6fnVW9ekDDhmKcng788EP2a44eFdssAUDVqoC/v+viI89Ws6YyXTwtTaxATkSkJSy0SWrcz1Bu\nzJ+8tJo700I7ICD79x15ou1Ohba1+evbVxl/9132Rafmz1fGnDbuOlr9/Lma6VPtOXPEAn5ax9zJ\njfkjW7DQJiIit2Frj7Yt66K4U6FtrS5dlKfUZ86IRaeMtm4132u7a1fXxkbUtStQvLgY//WXWLSP\niEgrWGiT1NgrIzfmT15azV1ehXZQEFCsmBinpJgXz3m5elUZlyplX3xaYW3+/PyA119Xjo2FdXIy\n8M47yvnOnYF27ZwXH+VOq58/V/P1BTp1Uo5/+UW9WKzF3MmN+SNbsNAmIiK3kVehDdg/fTwhQRmH\nhdkUltRMp48vWABcuCBWIb90SZwLDARmzVInNqK6dZWx6YwLIiK1sdAmqbFXRm7Mn7y0mjtrCm17\nF0S7cEEZV6xoW1xaY0v+6tRR9ht/8kRMJ//6a+X7M2YAJUo4Nz7KnVY/f2rIWmi7eJtcmzF3cmP+\nyBYstImIyG0kJytjZz7RfvRImTqu1wOhofbFJyOdDvjyS+X4yBFl3K4d0KOH62MiMoqIEC0hAJCU\nZPsih0RE+YWFNkmNvTJyY/7kpdXc5dfU8YsXlSdloaGAj49d4WmGrfmrUwd47TXzc0WLipWedTrn\nxUXW0ernTw06nfj302jfPvVisQZzJzfmj2zBQpuIiNxGfk0dd6dp4/aaOBEoUEA5njbNc1ZfJ20z\nnT6u9UKbiDwHC22SGntl5Mb8yUuructrH21AFNrGp7BnzgCnT+d9X3crtO3JX8WK4gl22bLAkCFA\nr17Oj4uso9XPn1pkKrSZO7kxf2QLFtpEROQ2rHmiHRAAtGwpxgYDMG5c3vd1t0LbXn37AleuAF99\nxSnjpB21aom1EwDg99/N/ztARKQWFtokNfbKyI35k5dWc2dNoQ0Ao0cr4yVL8n6q7W6FtlbzR9Zh\n/sz5+wM1aoixwQAcOKBuPLlh7uTG/JEtWGgTEZHbsLbQrlMHaNVKjDMygC++yP2+poV2eLj98RFR\n/jCdPv7bb+rFQURkpDMYtL7jYP7T6XTgHwMRkdwMBrFYV3q6OH70CPD1zfn6334DatcWY51OrCxu\nadsug0FMN3/4UBwnJgKBgU4NnYgcNHs2MGCAGPfpA3z3nbrxEJG2qFHv8Yk2ERG5hdRUpcj29c29\nyAZEX2ejRmJsMAC//GL5ups3lSK7SBGgWDHnxEtEzmM60+TPP9WLg4jIiIU2SY29MnJj/uSlxdxZ\nO23cVP36yvjgQcvXZO3PdodFwLSYP7Ie85edLIU2cyc35o9swUKbiIjcQnKyMra20H7xRWVsbaFN\nRNoTGgp4eYnx1atihgsRkZrYow32aBMRuYNDh5TC+fnngcOH837NX38B5cqJcUAAcPeusk2Q0dix\nwGefifHw4cDkyc6LmYicJzxc+cXYyZNAVJS68RCRdrBHm4iIyE6mU8cDAqx7TZkyQEiIGCcnA+fO\nZb+GK44TyUGW6eNE5BlYaJPU2CsjN+ZPXlrMnT092jpd3tPH3XHquBbzR9Zj/iyTodBm7uTG/JEt\nWGgTEZFbsKfQBvIutM+fV8buUmgTuSMZCm0i8hwstElqMTExaodADmD+5KXF3OVHof3gAfD332Ls\n7Q2UL29/fFqixfyR9Zg/y2QotJk7uTF/ZAsW2kRE5BbsLbRr1lTGR44oe3ED5k+zw8NFsU1E2iRD\noU1EnoOFNkmNvTJyY/7kpcXc2Vtoly4NlColxg8fAmfOKN87e1YZV67sWHxaosX8kfWYP8tMWzsS\nEsx/aaYVzJ3cmD+yBQttIiJyC/YW2kDO08fdtdAmckf+/kCJEmL85InYvo+ISC0stElq7JWRG/Mn\nLy3mLjlZGTtSaB86pIzdtdDWYv7IesxfzrQ+fZy5kxvzR7ZgoU1ERG7Bnn20jUz7tPlEm0heWi+0\nichzsNAmqbFXRm7Mn7y0mDtHpo5nXRDt6VPAYDDv13anQluL+SPrMX8503qhzdzJjfkjW7DQJiIi\nt+BIoV2yJFC2rBinpgKnTgGJicDdu+Kcn5+yYBoRaZdpoW26awARkavpDAaDQe0g1KbT6cA/BiIi\nuVWuDJw7J8anTgGRkba9vmNHYM0aMZ4/X9yvXj1x/PzzwOHDzouViPLH/v1AnTpi/OyzwLFj6sZD\nRNqgRr3HJ9pEROQWHHmiDWTv02Z/NpF8qlRRxmfPanOLLyLyDCy0SWrslZEb8ycvLebO0UI76xZf\n7lxoazF/ZD3mL2dFi4pWEAB49Ai4dEndeLJi7uTG/JEtWGgTEZH0njwRvdUAoNOJnmpbmT7RPnoU\nOHlSOY6IcCw+InId07aR06fVi4OIPBt7tMEebSIi2SUlAUFBYlykiLKIma3Cwiw/Afv1V6B2bbvD\nI32MuLgAACAASURBVCIX6t8fmDNHjL/8Ehg6VN14iEh97NEmIiKygyN7aJsynT5uik+0ieTBJ9pE\npAUstElq7JWRG/MnL63l7t49ZWxPf7aR6fRxoxIlgMBA+++pRVrLH9mG+cudlgtt5k5uzB/ZgoU2\nERFJ7+JFZVymjP33adbM/NjHB/j8c/vvR0Sup+VCm4g8B3u0wR5tIiLZffUVMGyYGL/3HjB7tv33\nWr0a+OMPoFYt4OWX7VtYjYjUk5EB+PsrCyTevq2s4UBEnkmNes/bpe9GRESUD/78UxlXquTYvTp2\nFF9EJCe9XuynffSoOD5zRvzSjIjIlTh1nKTGXhm5MX/y0lruTAvt8HD14pCF1vJHtmH+8lalijLW\n0vRx5k5uzB/ZgoU2ERFJ7/x5ZcxCm4jYp01EamOPNtijTUQks6dPgUKFxD8B4MED9lUTebqlS4Hu\n3cW4XTtg3Tp14yEidXEfbSIiIhtdvqwU2SVLssgmIj7RJiL1sdAmqbFXRm7Mn7y0lDv2Z9tOS/kj\n2zF/eatcWRlfuACkp6sXiynmTm7MH9mChTYREUmNhTYRZVW4MBAcLMbp6cCNG+rGQ0Sehz3aYI82\nEZHMhg8Hpk4V4y++AD79VN14iEgbnn9e2eJr/36gVi114yEi9bBHm4iIyEZ8ok1ElpQtq4yvXFEv\nDiLyTCy0SWrslZEb8ycvLeWOhbbttJQ/sh3zZ51y5ZTxX3+pF4cp5k5uzB/ZgoU2ERFJy2BgoU1E\nlpk+0dZKoU1EnoM92mCPNhGRrG7cEFt6AcAzzwB37wI6nboxEZE2LFgA9Oolxt26ib21icgzsUeb\niIjIBlmfZrPIJiIjPtEmIjWx0CapsVdGbsyfvLSSO04bt49W8kf2Yf6swx5tcjbmj2zBQpuIiKRl\n+j/P5curFwcRaU+ZMsr46lUgI0O9WIjI87BHG+zRJiKS1eDBwIwZYjx1KjBsmLrxEJG2BAUBSUli\n/PffQKlS6sZDROpgjzYREZENrl9XxsZF0YiIjLQ4fZyIPAMLbZIae2XkxvzJSyu5Y6FtH63kj+zD\n/FlPawuiMXdyY/7IFiy0iYhIWiy0iSg3poX2lSvqxUFEnoc92mCPNhGRrIoUAe7fF+Pbt0U/JhGR\n0fjxwCefiPHw4cDkyerGQ0TqYI82ERGRlVJSlCK7QAGgWDF14yEi7dHa1HEi8hwstElq7JWRG/Mn\nLy3k7sYNZVyiBKDn32hW00L+yH7Mn/W0Vmgzd3Jj/sgW/N8SIiKSEvuziSgvpquOs0ebiFyJPdpg\njzYRkYxWrwY6dRLjtm2B9evVjYeItOfhQ8DfX4wLFAAePeLsFyJPxB5tIiIiK5k+0S5VSr04iEi7\n/PyU9RuePAFu3VI3HiLyHCy0SWrslZEb8ycvLeSOU8ftp4X8kf2YP9uEhirjc+fUiwNg7mTH/JEt\nWGgTEZGUWGgTkTWio5Xx/v3qxUFEnoU92mCPNhGRjF59FVi3ToxXrlT6tYmITM2ZA/TvL8adOwMr\nVqgbDxG5Hnu0iYiIrMQn2kRkjbp1lfGvv6oXBxF5FhbaJDX2ysiN+ZOXFnLHQtt+Wsgf2Y/5s021\namJRNAC4elXdbb6YO7kxf2QLFtpERCQdg8G80C5RQr1YiEjbvL2BWrWUYz7VJiJXYI822KNNRCSb\nO3eAwEAxDggA7t9XNx4i0raRI4GJE8V4yBDgq6/UjYeIXIs92kRERFbgtHEisgX7tInI1Vhok9TY\nKyM35k9eaueOhbZj1M4fOYb5s12dOsr48GHg8WN14mDu5Mb8kS1YaBMRkXRYaBORLYoXB8LDxfjx\nY+DoUXXjISL3xx5tsEebiEg206YBQ4eK8cCBwIwZ6sZDRNrXsyewcKEyXrBA3XiIyHXYo01ERGQF\nPtEmIlu99ZYy/uEHYOtW9WIhIvfHQpukxl4ZuTF/8lI7d9euKWNu7WU7tfNHjmH+7NO8OfD668rx\ne+8BKSmujYG5kxvzR7ZgoU1ERNK5dEkZlyunXhxEJJfYWKBYMTG+cAEYMADIyFA3JiJyT+zRBnu0\niYisce+eePpTqpTakQChocCVK2J89iwQEaFuPEQkj+++A/r1U46HDAG+/BLQ6dSLiYjyF3u0iYhI\nk65fBypUAEqXBlasUDeWtDTgr7/EWKcTRTcRkbX69DEvtKdNAz78EHj6VL2YiMj9sNAmqbFXRm7M\nnzwWLwbu3BHjrl3Vzd2lS4Dxl9JlywK+vqqFIi1+9uTG/DlGpwPmzgU6d1bOTZ0KxMSI3QwaNQJq\n1gRq1wZee818TQhHMXdyY/7IFt5qvXFaWhomT56Mq1ev4saNG7h27RoGDRqE7t275/ia3377DaNH\nj8bmzZvzvP+PP/6INWvWIDo6GidOnECTJk3Qp08fZ/4IREQew3SVbwC4fFmdOAAgIUEZV6igXhxE\nJC8vL2DRIuDRI2DjRnEuPl58mfrtN/HfnD17gIIFXR8nEclLtUJ7xIgRePfdd1GlShUAwIYNG9C+\nfXvcvHkTgwcPznZ9SkoKevbsiTJlyuR577i4OAwYMAAJCQnw9/fHo0ePEBERgSJFiqBTp05O/1lI\nPTExMWqHQA5g/uSRdWXeP/+MUSUOgIW2M/CzJzfmzzl8fYF164B//Qv49FMgPd3ydQcPikXT5s1z\nvI+buZMb80e2UGXqeHJyMmbPno1p06Zlnmvbti1efPFFjB49GhkWln+cMmUKwsPDrWpiHzNmDDp2\n7Ah/f38AQMGCBdGjRw+MHTvWeT8EEZEHyfpEe8kSZfq2q7HQJiJn0euBjz8G9u4FevcGRo4E1q4F\nDhwAxo1Trps/H/jmG9XCJCIJqVJo6/V6lCpVCsnJyWbnK1WqhPv37+PmzZtm57du3Yro6GiUsGKz\n1MePHyM+Ph41atQwO//ss8/i2LFjSExMdPwHIM1gr4zcmD95ZC20z52Lw+HD6sTCQttx/OzJjflz\nvjp1RDE9fjzQvj3w4oui6H7rLeWa//s/4OpVx96HuZMb80e2UKXQ9vPzw4ULF7Bo0SKz85cvX0Zg\nYCBCQkIyz925cwe7du1Chw4drHqanZCQgPT0dAQFBZmdDwwMBACcOXPGCT8BEZFnyVpoA+KpthpY\naBORK+h0wJw5QNWq4vjBA2DYMHVjIiJ5qNajndXp06exb98+jB8/Hnq9Uv9PnjwZI0eOtPo+SUlJ\nAADfLMvQGo+N38+qd+/eCAsLAwAULVoUzz33XGYfhvG3VzzW3nFMTIym4uEx8+eux9evi2Mg7n//\njMHGjUDbtq6P5+xZ8f4irjjExan/58NjHvPYfY+//joGjRsDQByWLQPeeScGTZvafz8jrfx8PLbt\n2Egr8fDY8nFsbCyOHj2aWd+pQWdw9c7dFmRkZKB58+YIDg7GsmXLMs+vXLkSQUFBmX9gvXv3xuXL\nl7Fjx44c7xUfH48GDRpgxYoV/8/encfZXPZ/HH+fwci+c5OyK7IVLVIaJVtI0krWyk2rUqnu1N2e\n6KftTmlRWRKFiKxRpI0QIkIUkX3sy8zvj6vje86YGXM451znOuf1fDw8ur7fOXPmw6cv85nr+lxX\n0MZn06dPV/PmzTVx4kRdddVVQZ9j4wBzAHDFnj1SoULH3y9WTMriZ5dRiSU5Wdq/3/RYAkAkdexo\njjmUzO7jHTpId95pjgADEPts1Hsx8e1Jv379VLlyZX300UfH7m3atEnLly8/VmT7negPqFixYpne\n37t3ryQzW434kfGni3AL+XND4LLx8uX9o9nauVPKZO/KiApcNl6hAkX2yeLZcxv5i76BA6XChc34\nwAFp+HCpYUOzW/mRIzl/H3LnNvKHUFj/FmXw4MEqVKiQhg4dKp/Pp/Xr1+vQoUOaPHmyfvnlF3Xr\n1u3Yry+//FIrVqxQt27dNG7cuEzfr0qVKkpOTtbOnTuD7m/atEmSVL169Yj/ngAgnmQstP0zyunp\n0q5d0Y2F/mwANpQtK02ZItWt691LTzc7k19xRfT/LgQQ+6wuHR85cqS2bt2qu++++9i9Rx55RE8+\n+aRy5z6+fbxJkyby+XzZLh2XpGbNmql69ep67bXXjt279dZb9e2332rp0qXHvZ6l4wCQtbFjpeuu\nM+N27aSFC6X16831b79JlStHL5aXX5buvdeMe/Y0GxUBQLSkp0s//SQ9+KA0c6Z3/7bbOP4LiGUJ\ntXR86tSpevnll7V//349//zzx34tXrw40yJbMr3cGc/YnjJlikqUKKEpU6Ycu9enTx99/PHH2r17\ntyRp165d+vTTT/X4449H7jcEAHEqcEb7X/8yvdl+O3ZENxZmtAHY5PNJ550nTZ0qPfGEd/+dd6Sf\nf7YWFoAYZKXQ3rZtmzp06KAff/xRDz/8sB555JFjvzLuFi5J48ePV+vWrTV//nzNnz9frVu31vjx\n4yWZnu20tDT5fL5jr2/ZsqUGDBig3r1768UXX1SPHj30/PPP6zr/lAziBr0ybiN/bsi80J4tKfqF\n9po13phC++Tx7LmN/NmXK5f0+ONS8+bmOi3NHP11ogkzcuc28odQWDneq0SJEkpNTc3x69u1a6d2\n7dpl+rFWrVppRybf6XXt2lVdu3Y92RABAP8I14z2rl1SwYLmG9STxYw2gFgycKA0fboptKdPN33c\nrVrZjgpALLC+GRpwKjLuSg+3kD83/LOXpKTAQjtFUs4L7eHDpRIlpDp1zI69J+PoUWn1au+6SpWT\nex/w7LmO/MWOWrVMf7bf/fdLhw9n/Xpy5zbyh1BQaAMAshWOGe0BA0yhvHx58AZCofj9d69IL1NG\nKl785N4HAMLpv//1TmNYsUIaOtRuPABiA4U2nEavjNvInxtOtUd727bgTYLWrTu5OJYv98Y1apzc\ne8Dg2XMb+YstZcpIjzziXffvL+3cmXm/NrlzG/lDKCi0AQBZSkuTNm/2rsuUCX1G++uvg69///3k\nYvnlF29cs+bJvQcARMK990oVKpjxtm3S6adLycnSNddkv5QcQPyi0IbT6JVxG/mLfdu2mSXfklS0\nqHTaaf4l2ymSclZoz5kTfM2Mtn08e24jf7HntNOk55/3rvftk44ckcaPl8aM8e6TO7eRP4SCQhsA\nkKWMy8al0Ge0MxbazGgDiEc33CC1bXv8/Zdfjn4sAOyj0IbT6JVxG/mLfVkX2rMlnbjQ3rlTWrQo\n+N7JFNrp6cxohxPPntvIX2zy+cwM9u+/mx8M5s1r7n//vfTtt2ZM7txG/hAKCm0AQJYCC+2yZc1/\nQ5nRnjv3+A2BNm+W9u8PLY6NG6XUVDMuWtQr+gEglvh80plnSmefLd10k3efWW0g8VBow2n0yriN\n/MW+rGe0UySduNDOuGzcb/360OLIOJvt84X2+QjGs+c28ueGe+7xxmPHSn/+Se5cR/4QCgptAECW\nMiu0ixb17u3caXYmz0pgoZ0njzcOdUM0+rMBuKZePalxYzM+ckQaNcpuPACii0IbTqNXxm3kL/Zt\n2+aNS5Y0/82dW8qXb7Yksyx89+7MP/fQIemnn7zrFi28cah92vRnhxfPntvInztuucUbf/kluXMd\n+UMoKLQBAFnaudMbB85kFyrkjbNaPr5ypZnFkaSKFaVzz/U+FmqhzYw2ABc1aeKNv/7aOy4RQPyj\n0IbT6JVxG/mLfbt2eeMiRbxx2bIpx8bbt2f+ucuWeeNzzpEqVPCuQ106zox2ePHsuY38uaNyZal8\neTNOTZUKFUqxGg9ODc8eQkGhDQDIUuCMdmChnZOdx7MrtEOZ0d60Sdq61Yzz5zc7+gKAC3y+4Fnt\nL7+0FwuA6KLQhtPolXEb+Yt9gTPagUvHjxyZfWycVaG9dKk3rlXLLB/3C6XQnjTJG59/vpTEv1yn\njGfPbeTPLYGToJ9+OttWGAgDnj2Egm9XAABZympGOyc92hlntM84wzuW688/zWZpOTFunDdu1y5n\nnwMAsSJwRnvJEunwYXuxAIgeX3p6errtIGzz+XzijwEAgqWnmyO5/Jv3HDwoJSebcd++0qBBZvz8\n89JDDwV/7v79UsGC5ugvn0/as8cs+y5f3hTZkvTbb6Z/MTu7d0ulSnlF+dq1wTPjAOCCihW9lTzz\n50sXXWQ1HCDh2Kj3mNEGAGRq716vyM6XzyuyJal4cW+c2Yz2ihXe+dpVqpgiWwp9Q7QvvvCK7Hr1\nKLIBuClw+Th92kBioNCG0+iVcRv5i21ZHe0lSX//PfvYOLNCO+Oycb+cbIh28KD0v/9Jb70ljRzp\n3b/mmhPHjJzh2XMb+XOPt3x8toYONX/PwT08ewhFbtsBAABiU1ZHe0kn7tHOqtAOnJFeuzbzr/va\na2Zpekb0ZwNwVbt2UokS0rZt5u++N96Q7r3XdlQAIokZbTiN8wzdRv5iW3Yz2o0apRwbZ1ZoZ9xx\n3C+wJzurQnvatOPvVa4s1a6ddawIDc+e28ife4oUkf7zH0lKkSQ9/XTw37FwA88eQkGhDQDIVHYz\n2ic6RzurGe3AQvu3347/vPR06ccfj7/ftau3YzkAuKhXL6lSJTPetk164QW78QCIrBwV2gcPHtSk\nSZP0wAMPqFWrVmrQoIHOP/98tWrVSg8++KAmT56sQzk9pwUII3pl3Eb+Ylt2M9q//jr72NhfaB88\nKP3732bTMv9sda5c0llneZ9XpYo3zqzQXrtW2r7djIsVk2bOlMaOlR5++OR/Hzgez57byJ+b8uaV\nOnacfex68GDpjz/sxYPQ8ewhFNn2aP/5558aOHCghg0bpl3/TG3ky5dPxYoVU3p6upYuXaovvvhC\nAwcOVNGiRdW1a1f17dtX5cqVi0rwAIDICbVH+9VXpTffDH5d9ermm0u/8uXNkWGHD0tbtphjvwoW\n9D4eOJvdoIF0+eWn9nsAgFjSpIk0ZYq0YIF04IDUv7/07ru2owIQCVnOaD/22GOqVq2ahg0bpmuv\nvVaffPKJNmzYoL179+qPP/7Qn3/+qf3792v9+vUaM2aM2rVrp3fffVdVqlRR//79o/l7QAKjV8Zt\n5C+2Bc5oZyy0r7oqJeh1aWnSO+8EvyY5WXrwweB7uXIFb4i2Zk3wxzMW2ogMnj23kT93XX55igYM\n8K6HDZN+/tlSMAgZzx5CkWWhPWzYMA0aNEgbN27U22+/rWuuuUann376ca8rX768rr32Wr377rva\nuHGjBg0apPfffz+iQQMAIi9wRjvj0vHcub1Z7fR0adw4c3a2ZGaoly2T/v7b9FZnFLh8PGOh/cMP\n3phCG0A8uvxyqWVLM05Plx56yG48ACIjy0J71apV6tWrl/Lly5fjN8ufP7969+6tlStXhiU44ETo\nlXEb+Ytt2S0dnz17tqpV8647dfLGN94o1awpFS6c+ftmtSFaWppZTul3/vmhx4yc4dlzG/lzlz93\nzz/vbfA4ZYq0erW9mJBzPHsIRZaF9mmnnXbSb3oqnwsAiA3ZbYYmmd5CvwMHvHH37tm/b1Yz2qtW\nSampZly6tOnnBoB4VKeON6stmY0fAcSXE+46/sYbb+jjjz/O9jWjR4/Wmxl3wAGigF4Zt5G/2Jbd\njHZKSoquvlq6+ebg+zVqSBddlP37ZjWjnbE/m+O8Iodnz23kz12BuWva1LtPoe0Gnj2EIttCe9y4\ncbrjjjtUNLOpjADFihVTr169NGHChLAGBwCw50Qz2pL0yitm9tmve/cTF8hZzWjTnw0gkVxxhTf+\n8kvTPgMgfmRbaI8YMUIXXXSRmjVrlu2bNGvWTI0aNWITNEQdvTJuI3+x7UQ92pJUooT00Udmmfdl\nl0k9e574fStV8sbr1klHj5px4Iw2/dmRxbPnNvLnrsDc1aollSplxlu3svu4C3j2EIpsC+3vvvtO\nrVq1ytEbtWjRQt9//31YggIA2JeTGW3JnAu7YYM0e3bw+dpZKVhQKlPGjA8fNp975Ij000/ea+rX\nP6mQAcAZSUnm708/lo8D8SXbQnvLli0qn8PdaMqVK6ctW7aEJSggp+iVcRv5i20n6tE+FRmXj//y\ni7Rvn7k+/XSpbNlTenucAM+e28ifuzLmLnD5+KxZ0Y0FoePZQyiyLbTz58+v3bt35+iNdu/erfz5\n84clKACAXYcPS3v3mnFSkpmFDqeMG6KxbBxAIgostOfMMX/3AogP2RbaVatW1Zw5c3L0Rl9//bWq\nBR6qCkQBvTJuI3+xK/BnrIULm2I70KnmLuOMdsYdxxFZPHtuI3/uypi7ypWlM8804z17JLowYxvP\nHkKRbaHdpk0bTZgwQd988022b/Ltt99q/PjxatOmTViDAwDYEdifnXHZeDgEzmj/+iuFNoDE5PMF\nH/M1erS9WACEly89PT09qw/u2LFDNWvW1IEDB/TCCy+oS5cuyps377GPHzhwQB9++KEefPBB5cuX\nT8uWLVOxYsWiEng4+Xw+ZfPHAAAJZ+FCb0OyunWlRYvC+/6LFknnnmvGBQqYzdAOHjTXf/8tlSwZ\n3q8HALFq5kyv2C5eXNq4UQr4dhtAGNio97Kd0S5WrJgmTJig5ORk/fvf/1bRokVVr149NW7cWOee\ne66KFSumnj17Kjk5WRMmTHCyyAYAHC+7jdDCoW5d6ZxzzHjvXq/IrlSJIhtAYmnSRKpQwYy3b5c+\n+8xuPADCI9tCW5IuuOACLV68WH369FGZMmW0ZMkSzZ07V4sXL1aZMmXUp08fLVmyROezew0soFfG\nbeQvdp3oaK9TzZ3PJ/Xocfx9lo1HB8+e28ifuzLLXVKS1K2bd/3uu9GLB6Hh2UMoTlhoS9K//vUv\nDRo0SOvWrdOuXbu0YcMG7dq1S+vWrdOgQYNUxn8gKgAgLkR6RluSbrlFypMn+B6FNoBE1LWr+QGk\nJE2dKm3YYDUcAGGQbY92oqBHGwCCDR4s9eljxnfdJb3ySmS+zg03SB9/7F3PnCldfnlkvhYAxLIr\nr5RmzDDje++V/u//7MYDxJOY6tFevXr1Sb/pqXwuAMC+aMxoS8cvH/dvwAYAiea227zx4MHS55/b\niwXAqcuy0D7nnHN0++2365dffsnxm/3888/q0aOHzvHvcANEGL0ybiN/sSvSPdp+TZtKF15oxq1b\nR7aoh4dnz23kz13Z5a5DB6llS++6c2eWkMcanj2EIndWHxgzZozuv/9+vf3222rQoIFatmypCy+8\nUFWqVFHx4sWVnp6u7du3a9WqVZo/f74mT56sJUuWqHr16vrkk0+i+XsAAIRZtGa0k5LMcvElS+jP\nBpDYkpKkDz6Q6tWT/vzT7EDevbs0bZrXvw3AHdn2aB85ckQjR47U//73P33//ffZvlGjRo3Uq1cv\n3XDDDcqVK1fYA40kerQBIFj79tK4cWY8ZoyZaQEARN68edJll0lHj5rrqVOlZs3sxgS4zka9l+PN\n0DZs2KCvvvpKy5Yt099//y2fz6dSpUqpVq1auuyyy1SuXLlIxxoxFNoAEOyKK6RZs8x42jSzSQ8A\nIDp69pTeesuM69WTFiwwM94ATk5MF9rxjELbXbNnz1ZKSortMHCSyF/satDAfGMnSd99J11wQfDH\nyZ3byJ/byJ+7cpq7jRulqlWl/fvN9YgR0s03RzY2nBjPnrtiatdxSapUqZI+++yzaMUCAIgRu3d7\n48KF7cUBAImoXDnpvvu860cekfbutRcPgNBlO6OdlJSk4cOH6+Y4/xEaM9oAEKxsWemvv8z4jz+k\n00+3Gw8AJJpdu6QqVaRt28z1nXdKr75qNybAVTE3ow0ASEx79njjQoXsxQEAiapIEemll7zr116T\nvvzSXjwAQkOhDadxnqHbyF9sSksLXqJYoMDxryF3biN/biN/7go1d7fcIrVp41137x78g1BEF88e\nQpHlOdp+W7du1fr163P8hmeeeeYpBQQAsGvfPsm/uipfPsmxExsBIG74fNKbb0pz50o7dkjr1pnl\n4w8/bDsyACdywh7tkN7M59NR/6F/DqFHGwA8f/1lerQlqXRpafNmu/EAQKIbOlS6/XYzLlpUWrvW\n/BdAztio9044o33ppZeqUqVKOXozn893ygEBAOxKTfXGBQvaiwMAYHTtKg0YIK1eLe3caXq3n3zS\ndlQAssOu42JG22WcZ+g28hebfvpJOu88M65bV1q06PjXkDu3kT+3kT93nUruRo6UOnY044IFzax2\nyZLhiw0nxrPnLnYdBwBYx4w2AMSeG26QzjnHjPfskV5/3W48ALLHjLaY0QaAQJMnS1ddZcYtWkhT\nptiNBwBgfPSRdNNNZlytmrRypdkwDUD2mNEGAFgXeHQMM9oAEDuuvloqVMiMV62SFiywGw+ArGVb\naM+aNUtNmzaNVixAyDjP0G3kLzblZOk4uXMb+XMb+XPXqeYuXz6pfXvveuTIU4sHoeHZQyiyLbRT\nUlJUunTpTD82Y8YMdezYUa1atdIDDzygTZs2RSRAAEB0Bc5o+2dOAACxIbCj86OPJAdP1gUSQrY9\n2s8++6wGDhyoFStWBBXcI0aMUOfOnYPWuZcrV04LFy7MsjCPZfRoA4Dn6aelxx4z44cflp591m48\nAADPkSPS6adLW7aY65kzpcsvtxsTEOtirkd71qxZql+/flDxfOTIEd1///1KTk7WsGHDtGLFCg0Y\nMECbNm3Siy++GPGAAQCRxYw2AMSu3LnNDuR+LB8HYlO2hfby5cuPOytuzpw52rJli3r16qXOnTur\nevXq6tu3r6699lp98cUXkYwVOA69Mm4jf7GJHu34R/7cRv7cFa7c+Xcel6RJk6S0tLC8LU6AZw+h\nyLbQ3rp1q8qXLx90b968eZKktm3bBt1PSUnR2rVrwxweACDamNEGgNh24YVSyZJmvHmztHix3XgA\nHC/bQjtPnjzat29f0L0FCxbI5/OpQYMGQffz58+vgwcPhj9CIBsZV1zALeQvNuXkeC9y5zby5zby\n565w5S4pSWrWzLueMiUsb4sT4NlDKLIttKtWraqZM2ceuz58+LDmzp2r2rVrq2CG7742bdqkyTIG\nNwAAIABJREFU4sWLRyZKAEDUBC4dZ0YbAGJTy5bemEIbiD3ZFtrXX3+9Pv30U/3nP//RjBkz1KNH\nD+3YsUPXX3/9ca/95ptvVKNGjYgFCmSGXhm3kb/YlJMZbXLnNvLnNvLnrnDmrnlzyecz4/nzpZ07\nw/bWyALPHkKRbaF93333qWHDhnr22WfVrFkzDR8+XLVq1dI999wT9LpNmzZp2rRpaty4cUSDBQBE\nXk42QwMA2FWqlFS/vhkfPSrNmGE3HgDBsj1HWzLHeU2YMEErV67UGWecoeuuu06nnXZa0GsWL16s\n6dOnq0OHDqpYsWIk440IztEGAE+lStK6dWb8229S5cpWwwEAZKF/f+mpp8y4e3fpnXfsxgPEKhv1\n3gkL7awcOnRIc+bM0Y4dO3TOOefonHPOCXdsUUOhDQCekiWlbdvMePNmqXRpu/EAADI3f7508cVm\nXLas9McfZqM0AMFs1HvZPoqzZs3S3Xffrc2bNwfdX7NmjWrWrKnmzZvrxhtvVJ06ddStW7eIBgpk\nhl4Zt5G/2JST473IndvIn9vIn7vCnbsLLvCO+dq0SeJ/jcji2UMosi20hw4dqi+++EJlypQJut+t\nWzetWbNGTZo0Ub9+/VSvXj29//77GjZsWCRjBQBE2OHDkv+kxqQkKUOnEAAghuTKJd14o3f9wQf2\nYgEQLNul42effbZatGihwYMHH7u3YsUK1axZU02aNDl29NehQ4dUv359lS5dOug4MFewdBwAjB07\nJP9JjUWKsIstAMS6H34wM9uS2cDyr7+kAgXsxgTEmphbOr5hw4bjeq/9SyZuu+22Y/eSk5N10003\nafHixeGPEAAQNTk52gsAEDsaNJDOPtuM9+yRxo+3Gw8AI9tC+/Dhw8qTJ0/QvR9++EGSdLF/54V/\nnH766dq1a1eYwwOyR6+M28hf7Mnp0V7kzm3kz23kz12RyJ3PJ3Xu7F2zfDxyePYQimwL7TJlymj5\n8uVB97799luVLl1aZ555ZtD91NTU4479AgC4JScboQEAYkvHjqbglsx52n/+aTceACcotC+77DK9\n++67WrlypSRp8uTJ+uWXX9SyZcvjXrt06VInz9CG21JSUmyHgFNA/mJPTme0yZ3byJ/byJ+7IpW7\nM8+UmjQx47Q0aeTIiHyZhMezh1BkW2g/+uij2r9/v2rVqqXy5curdevWypMnj+6///6g1x09elSf\nffaZLrrooogGCwCILGa0AcBNgcvH339fYp9fwK5sC+0aNWpozpw5atGihfLnz68mTZpoypQpqlWr\nVtDrZs2apeLFi+vaa6+NaLBARvTKuI38xZ6cboZG7txG/txG/twVydy1by/lz2/Gy5ZJixZF7Esl\nLJ49hCL3iV7QoEEDTZw4MdvXXHnllVq6dGnYggIA2BG4dJwZbQBwR6FC0jXXSCNGmOsPP5TOPddu\nTEAiy/Yc7UTBOdoAYAwcKD3wgBnfd580aJDdeAAAOTdtmtS8uRmXLm02Rct9wmk1IP7F3DnaAIDE\nwow2ALjriiuksmXNeMsW6Ysv7MYDJDIKbTiNXhm3kb/YQ492YiB/biN/7op07nLlkjp18q7feCOi\nXy7h8OwhFBTaAIBjcnq8FwAgNvXs6Z2pPXmytGqV3XiAREWPtujRBgC/m2+WRo0y4+HDpY4d7cYD\nAAhdmzbSpElmfPfd0ssv240HsI0ebQCAVTldOg4AiF133eWN33sveLUSgOig0IbT6JVxG/mLPTnd\nDI3cuY38uY38uStauWvaVDrrLDNOTeUEiXDh2UMoKLQBAMcwow0A7ktKCp7V/u9/pUcekeiUBKKH\nHm3Row0AfmefLa1cacbLl0s1atiNBwBwcg4ckFJSpO++8+7deaf06qvWQgKsoUcbAGAVM9oAEB9O\nO02aOVO66irv3muvSePH24sJSCQU2nAavTJuI3+xhx7txED+3Eb+3BXt3BUoYArr9u29e7fdJv31\nV1TDiBs8ewgFhTYAQJLp3Quc0S5QwF4sAIDwyJ1bevttqXx5c711q9S9O/3aQKTRoy16tAFAkvbt\n84rrvHlNfx8AID7MmiVdcYV3/frrUu/e9uIBookebQCANdu2eeNixezFAQAIv8svl+67z7vu21da\nscJePEC8o9CG0+iVcRv5iy1btnjjMmWyfy25cxv5cxv5c5ft3D3zjFS7thnv3y916iQdOmQ1JKfY\nzh/cQqENAJAkbd7sjU9UaAMA3HPaadKIEVJysrlesEB68km7MQHxih5t0aMNAJL03ntmgxxJuuUW\n6YMP7MYDAIiMQYPM0nFJSkqSvvpKatTIbkxAJNGjDQCwJnBGu3Rpe3EAACKrTx+pSRMzTkszP1zd\nvdtuTEC8sVZoHzp0SE8//bR69eql9u3bq2HDhho1alTIr8nKmDFj1LFjRw0YMEC33HKL3nvvvUj8\nNmAZvTJuI3+xhR7txEH+3Eb+3BUruUtKkt5/Xypa1FyvXSu9+KLdmFwQK/mDG3Lb+sL9+vVTz549\nddZZZ0mSJk2apLZt22rLli265557cvyazMyePVu9e/fW2rVrVbBgQR04cEDVqlVTkSJF1L59+8j/\n5gDAQfRoA0DiOOMM6aWXvJahSZOkp56yGxMQT6z0aKempqp06dLq0qWLhgwZcuz+BRdcoF9//VXb\nt2/X3r17T/iapKTMJ+RTUlJUvXp1vfXWW8fuPfTQQ5o2bZp++umn415PjzYASE2bSjNnmvEXX0jN\nm9uNBwAQWamp5jjHo0fN9d9/SyVL2o0JiISE6dFOSkpS2bJllZqaGnS/atWq2r17t7Zs2ZKj12Tm\n4MGDmjdvnmr7zy74R506dbR48WJtCzwoFgBwDD3aAJBYChWSLrzQu541y14sQLyxUmgXKFBAa9as\n0YgRI4Lur1+/XsWLF1fp0qVz9JrMrF27VkePHlWJEiWC7hcvXlyStHLlyjD+TmAbvTJuI3+xhR7t\nxEH+3Eb+3BWLuWva1Bv7VzUhc7GYP8SumNl1fMWKFZo/f7769u2b5ZLwnLxm+/btkqS8efMG3fdf\n+z8OAPAcPSpt3epdlyplLxYAQPRccYU3njHDXhxAvLG2GVqgtLQ03XHHHerQoYP69et30q+RdGzt\nvc/nC7p/9J/mk4z3/bp27aqKFStKkooWLap69eopJSVFkvfTK65j7zolJSWm4uGa/Ll6XaNGitLS\nJGm2CheW8uSJrfi45pprruPl2i9W4rn44hTlzy/t2zdba9ZIa9emqFKl2Ikv1q79YiUerjO/Hjx4\nsBYtWnSsvrPBymZoGT344IPasWOH3nrrrSwL4Zy8RpKWL1+uWrVqaezYsUE7jI8fP17t27fX119/\nrUaNGgV9DpuhAUh0S5ZIdeuacY0a0vLlduMBAERPq1bSlClmPHSodOutduMBwi1hNkMLNHjwYBUq\nVEhDhw6Vz+fT+vXrdejQoZBf41elShUlJydr586dQfc3bdokSapevXpkfiOwIuNPF+EW8hc7QunP\nlsid68if28ifu2I1dywfz5lYzR9ik9VCe+TIkUpKStJjjz127N6QIUOC+q9z8ppAefPmVePGjbVw\n4cKg+wsWLFDNmjVVisZDADgOZ2gDQOIKLLS//tpeHEA8sbZ0fOrUqerfv7/at28fNI3/9ddf6/PP\nP8/xa6ZMmaJOnTpp+PDhatmy5bF7Xbp00erVq1W4cGHt2rVLlSpV0ptvvqnrrrvuuFhYOg4g0b30\nknT//WZ8993Syy/bjQcAED1Hj0pFikh795rrP/+UypWzGxMQTjbqPSuboW3btk0dOnTQvn379MMP\nPwR9rF27djl+jV9aWlpQ33bLli01YMAA9e7dW3Xr1tV3332n559/PtMiGwDAGdoAkMhy5ZLOO8+b\nzf7xR6ltW7sxAa6zUmiXKFFCqampp/wayRTVO3bsOO5+165d1bVr15MNEY6YPXv2sd0F4R7yFztO\npkeb3LmL/LmN/LkrlnPXoAGF9onEcv4Qe6xvhgYAsI8ebQBIbA0aeOMff7QXBxAvYuJ4L9vo0QaQ\n6OrXl/x7SH73nXTBBXbjAQBE16pVkv9wnpIlzUqnbE7UBZySkMd7AQDso0cbABJblSpmQzRJ2rpV\nWr/ebjyA6yi04TTOM3Qb+YsN6emco51oyJ/byJ+7Yjl3SUlmdZNfhr2IodjOH2IPhTYAJLidO6XD\nh824UCEpXz678QAA7Dj/fG9MnzZwaujRFj3aABLbihVSjRpmXLWq6dMDACSesWMl/2m4V1whzZhh\nNx4gXOjRBgBEHf3ZAADp+J3H09LsxQK4jkIbTqNXxm3kLzb88Yc3LlcuZ59D7txG/txG/twV67mr\nUEEqVcqMd+2SfvrJbjyxJtbzh9hCoQ0ACW7tWm9cqZK9OAAAdvl8UosW3vVnn9mLBXAdPdqiRxtA\nYuvRQ3r3XTN+/XWpd2+78QAA7Ans065Xj1ltxAd6tAEAUbdunTdmRhsAElvz5lJyshkvWsR52sDJ\notCG0+iVcRv5iw0ns3Sc3LmN/LmN/LnLhdwVKiRdfrl3PXGivVhijQv5Q+yg0AaABHbkSPBsRYUK\n9mIBAMSGtm298YQJ9uIAXEaPtujRBpC41q3zZrH/9S9p0yar4QAAYsAff0hnnGHGefJIf/8tFSli\nNybgVNCjDQCIKvqzAQAZlS8vnXeeGR8+LH3yid14ABdRaMNp9Mq4jfzZF9ifXbFizj+P3LmN/LmN\n/LnLpdzdfLM3fvVVicWfbuUP9lFoA0AC4wxtAEBmunWT8uUz40WLpG++sRsP4Bp6tEWPNoDEdcst\n0vDhZjx0qHTrrXbjAQDEjttvN/82SNINN0gffWQ3HuBk0aMNAIiqwB7tUJaOAwDi3513euNPPpE2\nbrQXC+AaCm04jV4Zt5E/+0526Ti5cxv5cxv5c5druatTR2rc2IyPHJEee8xuPLa5lj/YRaENAAnq\n4EFvdiIpyTvKBQAAv/vu88bvviuNHWsvFsAl9GiLHm0AiWnVKql6dTM+4wxp/Xq78QAAYk96utSx\nozRqlLkuWlRavFg680y7cQGhoEcbABA17DgOADgRn0964w1vH4+dO6VLLpG++85qWEDMo9CG0+iV\ncRv5s+tUCm1y5zby5zby5y5Xc1ekiDRihJQrl7nesEG69FLpzTftxhVtruYPdlBoA0CCClwqXqGC\nvTgAALHv4oulCRPM0nFJOnxY+ve/paeeMsvLAQSjR1v0aANITLfeKr3zjhkPGSL17Gk3HgBA7Fu7\nVurQQVq40LvXt6/04ov2YgJOhB5tAEDUbNnijUuVshcHAMAdlSpJc+ZIV17p3Rs4UPr+e3sxAbGI\nQhtOo1fGbeTPrr//9salS4f2ueTObeTPbeTPXfGSu4IFpYkTg4vtMWPsxRMt8ZI/RAeFNgAkqMAZ\n7VALbQBAYsubV7rnHu963Dh6tYFA9GiLHm0AialQIWnPHjPescPb4AYAgJw4cMD8oDY11VwvXizV\nqWM3JiAz9GgDAKJi3z6vyM6TxxzdAgBAKE47TWrVyrseN85eLECsodCG0+iVcRv5syewP7tUKcnn\nC+3zyZ3byJ/byJ+74jF311zjjeO90I7H/CFyKLQBIAGdykZoAAD4tWwpJSeb8eLF0po1duMBYgU9\n2qJHG0DimTxZuuoqM27WTJo61W48AAB3tW4tff65GQ8eHLxJGhAL6NEGAEQFO44DAMIlsE973jx7\ncQCxhEIbTqNXxm3kz55TLbTJndvIn9vIn7viNXeNGnnjefPi95iveM0fIoNCGwASUGChXaqUvTgA\nAO6rVUsqWNCMN26UNmywGw8QC+jRFj3aABJPly7SBx+Y8TvvSN27240HAOC2K6+UZsww41GjpBtv\ntBsPEIgebQBAVNCjDQAIp4sv9sbffGMvDiBWUGjDafTKuI382UOPdmIjf24jf+6K59wFFtrxuiFa\nPOcP4UehDQAJiBltAEA4XXih5POZ8eLF0p49duMBbKNHW/RoA0gs6elSvnzSwYPmes8eqUABuzEB\nANxXu7a0dKkZz5olNWliNx7Ajx5tAEDEpaZ6RXb+/BTZAIDwoE8b8FBow2n0yriN/NkRjmXj5M5t\n5M9t5M9d8Z67wEJ7zhx7cURKvOcP4UWhDQAJhv5sAEAkBC4V/+orae9ee7EAttGjLXq0ASSW8eOl\na64x49atpYkT7cYDAIgftWpJy5aZ8cSJ5t8ZwDZ6tAEAEff33964VCl7cQAA4k+rVt54yhR7cQC2\nUWjDafTKuI382UGPNsif28ifuxIhdy1beuPJk81JF/EiEfKH8KHQBoAEQ482ACBSGjWSChUy43Xr\npJUrrYYDWEOPtujRBpBYbrpJ+ugjM/7wQ6lTJ7vxAADiS/v20rhxZjxokHTffXbjAejRBgBE3ObN\n3pgebQBAuAX2aU+ebC8OwCYKbTiNXhm3kT87NmzwxuXLn9x7kDu3kT+3kT93JUruAvu0v/pK2rPH\nXizhlCj5Q3hQaANAAklLk37/3buuUMFeLACA+HT66VKdOmZ8+LA0c6bdeAAb6NEWPdoAEseff3qz\n2CVLBh/1BQBAuDz8sPT882bcs6c0ZIjdeJDY6NEGAETUunXeuGJFW1EAAOJdPB/zBeQEhTacRq+M\n28hf9IWr0CZ3biN/biN/7kqk3DVsKBUpYsYbNkjLltmNJxwSKX84dRTaAJBAmNEGAERDnjxSs2be\n9ZQp9mIBbKBHW/RoA0gct90mvf22Gb/2mnTHHXbjAQDEr/fek7p3N+OUFOnLL62GgwRGjzYAIKKY\n0QYAREuLFt547lxp1y57sQDRRqENp9Er4zbyF33hOtqL3LmN/LmN/Lkr0XJXtqxUv74ZHzkijR1r\nN55TlWj5w6mh0AaABMEZ2gCAaOvY0RsPG2YtDCDq6NEWPdoAEsPGjdLpp5txiRLS1q124wEAxL/N\nm82/PUePmutVq6SqVe3GhMRDjzYAIGLozwYARFuZMlKrVt71Bx/YiwWIJgptOI1eGbeRv+gKZ6FN\n7txG/txG/tyVqLnr0sUbf/CBaWVyUaLmDyeHQhsAEgQz2gAAG1q3looXN+Pff5dmzLAbDxAN9GiL\nHm0AieH226WhQ8341VelO++0Gw8AIHHcdZf02mtmfOGF0vz5ks9nNyYkDnq0AQARE7jjODPaAIBo\n6ttXSk424+++k8aNsxsPEGkU2nAavTJuI3/RRY82/Mif28ifuxI5dxUqBK+kevhhc7a2SxI5fwgd\nhTYAJADO0AYA2PbII1Lhwmb8669Sv37ubowGnAg92qJHG0D8W7/eK65LlZK2bLEbDwAgMT33nCm4\n/dq0kd56S/rXv+zFhPhHjzYAICJWr/bGVavaiwMAkNjuu09q0cK7njhROv10qVkzacoUe3EB4Uah\nDafRK+M28hc9gYV2tWqn/n7kzm3kz23kz13kTsqbV5o0SXrgAe9eWpo0fbrUqpU5IWPPHnvxZYf8\nIRQU2gCQAFat8sbMaAMAbMqVSxowQBo/XmrcOPhjQ4dK9etLGzfaiQ0IF3q0RY82gPh3zTXmGxpJ\nGjlSuukmu/EAAOC3YYN0//3SmDHevXr1pK++kgoVshcX4gc92gCAiAic0Q7H0nEAAMLljDOk0aOl\nYcPMbLckLVokXXeddPiw1dCAk0ahDafRK+M28hcdaWnSb79511WqnPp7kju3kT+3kT93kbus+XxS\nly5mB3K/qVOlXr2kWFl4Sv4QCgptAIhzGzdKBw6YcYkSUrFiduMBACAr3btL/ft71++8Iz3zjL14\ngJNFj7bo0QYQ3778Urr8cjO+6CJp/ny78QAAkJ30dKlbN+n99717778vde5sLya4jR5tAEDYcYY2\nAMAlPp9ZQt60qXevRw9p1ix7MQGhotCG0+iVcRv5i45IFNrkzm3kz23kz13kLueSk6WxY6Xatc31\nkSNS+/bSsmX2YiJ/CAWF9j/S02NnowUACCfO0AYAuKhIEWnyZKlcOXO9a5fUqpW0aZPduICcoEdb\nZs3+jTemq3Zt6ZFHbEcDAOFVp470889m/N130gUX2I0HAIBQLFokXXqptGePuT7vPGnOHKlgQbtx\nwR02erQptGX+4CXzxzB+vHT11XbjAYBwSU+XChSQ9u8319u2ScWL240JAIBQTZ0qXXWVdPSoub7y\nSunDD6UyZezGBTewGVoM6NRJWrrUdhTIKXpl3Eb+Im/jRq/ILlYsfEU2uXMb+XMb+XMXuTt5zZtL\nQ4Z419OnS9WrS4MHS/v2RScG8odQUGj/o1Il8989e6S2baWtW+3GAwDh4F8yLknVqtmLAwCAU3Xr\nrdLjj3vXu3dLffpI5ctLd98tvfaa9Mkn0p9/2osR8GPpuMxSgp9/TlfDhl7vxyWXSDNmSHnz2o0N\nAE7FY49JTz9txnfdJb3yit14AAA4VdOmSXfeGbzZZ0Znn232JKlZU2rY0PR4+3zRixGxhaXjFtWq\nJQ0f7j2Ac+dK3buzEzkAt82b540bNbIXBwAA4dKsmVmxNXiwVLly5q9ZsUL64AOpXz/psstMwf3a\na9KhQ9GNFYmLQjvA1VdLL77oXY8cKfXqJR0+bC8mZI9eGbeRv8g6fFj69lvvOpyFNrlzG/lzG/lz\nF7kLn7x5pXvukX791RwB1r+/1LOn1KRJ5itSV6wwK7vOP19asuTkvib5Qyhy2w4g1tx3n7R6tbfZ\nwptvSsuXS2PGsKshALcsWuRthFahgulhAwAgnuTKJbVsaX757d8vff+9tGyZtHChNHq01x66ZInU\noIH0xhtSjx52YkZioEdbx6/ZP3LE7D4+erT3muLFpWefNZsw5MplIUgACNH//Z/54aEk3XyzNGKE\n3XgAALAhNdVMovXvLx04YO4lJUkTJkitW9uNDdFBj3aMyJ1bGjVKeu45r2d7+3bp3/+WypUz5/Y9\n8oj044/0cAOIXYH92ZdcYi8OAABsKlRIeuABs9KrXj1zLy1NuvFGcw+IBArtLPh8ZvOEadO8o78k\nacsWsxv5c8+ZHo8qVaSXXjI/KUP00SvjNvIXOenpZlNHv3BvhEbu3Eb+3Eb+3EXu7DrrLGnqVKli\nRXO9d6/Upo20eXPOPp/8IRQU2ifQtKnp73j8calw4eM/vnatdP/9Zqa7ShWpalVzVm316lKrVtL7\n75sz/gAgmtas8b5xKFJEOuccu/EAABALSpeWPv/c+77+jz+k669n82OEHz3ayvma/aNHzTevixZJ\nkyaZvo5du078/vnzm/7uu+4y/SAAEGmvvWb+zpGkFi2kKVPsxgMAQCz54gszKeYvAe66S3rlFbsx\nIXJs9GhTaOvk/+APHJA+/NAcCbZq1Ylf37y5meFm93IAkbRzp1lV8/ff5nrQIG9TNAAAYDz3nNl3\nya93b3M2d5489mJCZLAZmmNOO0267TZp5Upp/XpzLNiqVeZ6yRJpwADTC+I3dapZiu4/XgCnjl4Z\nt5G/yHj8ca/IPuMMs5FjuJE7t5E/t5E/d5G72NKvn3Tttd71//5njgnbsSPz15M/hIJCOwx8PvPN\nrL9Hu3p1qXZts7vh4sXmv/7dy5culTp3NjsdAkC4LV0qvf66dz1okGlfAQAAwXw+szr1+uu9ezNn\nShdeKK1YYS8uxAeWjis6SwmGDZO6dfOu+/QxfdunnRbRLwsgwbRoYVbPSFKTJuYbBv8P+gAAwPHS\n06WnnjIrwvyKFJE++0xq3NheXAgferQtidYffJ8+pu/D74wzpCeflG65RcqVK+JfHkCcmzNHSkkx\n46Qks6KmVi2rIQEA4IyxY83K0/37zXXJkqYdtGxZu3Hh1CVUj/ahQ4f09NNPq1evXmrfvr0aNmyo\nUaNGHfe6MWPGqGPHjhowYIBuueUWvffeezl6/5P9vEh68UUz2+S3YYOZ5a5b1xwzgNDRK+M28hc+\n6enSww971507R7bIJnduI39uI3/uInexrUMHae5cb+PirVuDWz7JH0KR29YX7tevn3r27Kmz/tkt\nbNKkSWrbtq22bNmie+65R5L5n7l3795au3atChYsqAMHDqhatWoqUqSI2rdvn+V7n+znRVru3GYJ\nyltvmZnsLVvM/WXLpNatpWeeCd75EABy6vPPpfnzzTg5WXriCavhAADgpPPOk0aONBsYp6dLM2ZI\nL7wQ/MNsICesLB1PTU1V6dKl1aVLFw0ZMuTY/QsuuEC//vqrtm/frqSkJKWkpKh69ep66623jr3m\noYce0rRp0/TTTz9l+f6hfp6NpQSpqdJLL0kDBwbvQj5kiNSzZ1RDAeC49HTzjcGiReb67rull1+2\nGxMAAC57+GHp+ee96/vuk7p0kWbNMtfNmkk1arAPiisSZul4UlKSypYtq9TU1KD7VatW1e7du7Vl\nyxYdPHhQ8+bNU+3atYNeU6dOHS1evFjbtm3L9L1P9vOirVAhs+HCb79Jl1/u3e/VS5owwV5cANwz\nZYpXZOfPLz36qN14AABw3ZNPSg0betcvvWTaPfv0Mb/OOcecNnTvvWbj0UOH7MWK2GSl0C5QoIDW\nrFmjESNGBN1fv369ihcvrtKlS2vt2rU6evSoSpQoEfSa4sWLS5JWrlyZ6Xuf7OfZUrq0NH68dP75\n5jo93TywR4/ajcsV9Mq4jfyFx3PPeeOePc3fK5FG7txG/txG/txF7tyRJ485xaNdu8C7s4Nes2aN\nWUHWtKlUqpQ5Juzzz83384C1Hu2MVqxYofnz5+uZZ55RUlKStm/fLknKmzdv0Ov81/6PZ3Syn9e1\na1dVrFhRklS0aFHVq1dPKf9s3+v/SzFS1wsWzNajj0o9eqRo2zZp3brZeuYZqX//6Hx9rrnm2q3r\nI0ekMWNStHGjVKbMbM2dK0kpypNHathwtmbPjnw8frHw58E1+Uu0a79YiYfrnF8vWrQopuLh+sTX\nn3ySoqeflp58craSkxepTZsUHT0qTZ48+5/dyc3rd++erTFjzL/Pl14qXXPNbNWuLTVbCvbFAAAg\nAElEQVRtaj7+5Zez5fPZ//0kyvXgwYO1aNGiY/WdDTFxvFdaWpquvPJKlSxZUqNHj5YkzZs3T5de\neqnGjh0btIHZ9OnT1bx5c02cOFFXXXXVce91Mp9nY81+Zv7zH7MhmiQ1aqR/vnkGgGBjxpifmmfU\nrZv07rvRjwcAgHi3f7/ZbNR/JO/Bg+ZYzYkTza/ffz/+c/LnlypVkjZuNK9/5hmzchXRlzA92hn1\n69dPlStX1kcffXTsXrFixTJ97d69eyWZWefMnOznxYLevc0yFUmaN0/64Qe78QCITf5+7EA+n/Tg\ng9GPBQCARJAvn1dkS1LevGZDtFdfldauNedt33WXOWXIb98+c7rQjh1m3LevtHx59GOHHdYL7cGD\nB6tQoUIaOnSofD6f1q9fr0OHDqlKlSpKTk7Wzp07g16/adMmSVL16tUzfb+T/bxYUK6cdMMN3vXg\nwfZicYV/mQjcRP5OTmZbTXTrJp19dvRiIHduI39uI3/uInduyyp/Pp9Uu7b0yivSL79It90mZbZi\n+ehRczJIDCykRRRYLbRHjhyppKQkPfbYY8fuDRkyRLly5VLevHnVuHFjLVy4MOhzFixYoJo1a6pU\nqVKZvufJfl6s6NPHG48eLa1ebS8WALHp11+98ZQpZvVLwGmGAADAkqpVzb/Ja9dKGzZICxeaJeZJ\n/1RdM2eajZAR/6z1aE+dOlX9+/dX+/btg9bLf/311/r8888lSVOmTFGXLl20evVqFS5cWLt27VKl\nSpX05ptv6rrrrjv2mk6dOmn48OFq2bJljj8vUKz0aPtdcYV3Rl/nztL779uNB0DsSEuTChSQDhww\n19u3S1l0zAAAgBhx553S66+bccWK5ofm/pZRRJ6Nes9Kob1t2zZVrFhR+/btO+433K5dO3366afH\nrocNG6YZM2aobt26+u6779SsWTPdfvvtxz4+ZcoU3XzzzRo1apRatGiR488LFGuF9rx50iWXmHFS\nkuntiOaSUACxa906s7GKZI4S2bLFajgAACAHtm+XqlUz/5WkL7+U/tkgG1GQMIV2rIm1QluSWrQw\nZ/dJ0o03SqNG2Y0nVs2ePfvYNv5wD/kL3bRpUvPmZnzJJdLXX9uJg9y5jfy5jfy5i9y57VTz17u3\n9MYbZvzgg9ILL4QnLpxYwu46juM9+aQ3Hj1amjTJXiwAYkfgRmgxvLcjAADI4J8uV0nS5Mn24kB0\nMKOt2JzRlqR27aQJE8w4f36zkUKDBnZjAmBXYI/XCy9wpBcAAK7Yu1cqXlw6dMhc//67dOaZdmNK\nFMxoI8ibb3q9mPv2SVddZXYwBJC4AnccP+sse3EAAIDQFCgQ3Jc9ZYq1UBAFFNoxrEwZ8wAWL26u\nt2wxS062bbMbVyzhPEq3kb/QBS4dt1lokzu3kT+3kT93kTu3hSN/rVp5Ywrt+EahHePOOsssH8+b\n11yvXGmWlPuP9gGQOPbtk9avN+NcuaTKle3GAwAAQhNYaM+YIR08aC8WRBY92ordHu1AY8ZIN9wg\n+cO88krpk0+kQoXsxgUgepYskerWNeOqVaVVq+zGAwAAQletmrR6tRlPnSo1a2Y3nkRAjzaydN11\n0sCB3vX06dLll3OGLpBIYmXZOAAAOHmtW3vjjz6yFwcii0LbIX36SP/9r3f9449mdmvcOHsx2Uav\nk9vIX2hi6Wgvcuc28uc28ucucue2cOXvppu88dix0v79YXlbxBgKbYf4fFL//tKQIVLSP5n76y+p\nfXuzSdqkSdLRo3ZjBBA5P//sjZnRBgDATeefb1rAJCk11XwPj/hDj7bc6NHOaPJk6dZbpU2bgu+X\nLy/16GF+nXGGndgARMaZZ0obNpjxwoXSuefajQcAAJyc//5XeuIJM27b1mx+jMixUe9RaMvNQluS\nduyQ7r9feu+94z+WO7f01FPSQw+ZmXAAbvvjD++HZwUKSDt3muccAAC4Z/VqsymaZP49/+svqUQJ\nuzHFMzZDQ0iKFZPefdfsPPzQQ1Lp0t7HjhyRHn5Y6tDBfEMer+h1clu852/rVumRR6QPPzz195o/\n3xtfcIH9IjvecxfvyJ/byJ+7yJ3bwpm/qlWlCy804yNHpJEjw/bWiBEU2nGgalXp+efNktIxY0zf\nh9+nn0oVKphC/K+/7MUIJKL//Ed67jmpc2ez2UlmFi0yx/U1aCA1bCjddpvp18rom2+88cUXRyZe\nAAAQPZ06eeOnnorvybFExNJxubt0PCuHD5sl5a++Gnw/f36pb1/zi/O3gcirVUtatsyMzzxT+uUX\n8xz6paVJtWtLy5cHf95NN0kjRgS3fVx4ofT992Y8aZJ01VWRjR0AAETW3r3S2Web9jBJuusu6ZVX\n7MYUr1g6jrDIk8c8pGPGBO9MvG+f9OSTZgb8f/8zBTmAyDh8WPr1V+96/Xqz8iTQpEnHF9mSNGqU\nNGyYd71/v/TTT971RReFNVQAAGBBgQLSSy9516+/Li1ebC8ehBeFdhzr0MF8Ez9unFSnjnd/yxbp\njjvMTNratfbiCwd6ndwWz/lbter4H2YNGCCtWeNdv/CCN77tNrPE3O/OO6U5c8x4wQLvvc4+OzY2\nS4nn3CUC8uc28ucucue2SOSvQwfpiivMOC3N/PsfRwttExqFdpxLSpLatTNHAb3/fvCRXytXSl26\n8DADkeBfMh7o4EHp5ZfNeO5cr+86Tx7p8cfNSpOzzzb39u2TUlKkZs2k//s/7z0aNoxo2AAAIIp8\nPtPu6d/kdO5cafhwuzEhPOjRVvz1aGdn/37zMD/6qNnhUDIFeOBMGoBT98QT5oxMSapXz2x6Jkll\ny5perKuvNkvHJal7d+mdd8x48WLp0ksz3xBNkoYOlW69NaKhAwCAKHvwQenFF824TBnpq6/MpFil\nSmbPF5waerQRcfnymQe5Tx/vXt++5kxuAOETOKN9xx1SqVJmvGmT9OabXpHt80kPPOC9tm5d04/d\npYtZkZLRpZdGLmYAAGDHY49J5cqZ8ebNZp+ltm1Nq2ebNvRuu4hCO0H17y+VL2/Gf/9tjiFyEb1O\nbovn/C1d6o3r1JGuvda7vvtub9yunbdc3K9KFbMZ2sqVpo/7hhvMBmgDBwZvcGhTPOcuEZA/t5E/\nd5E7t0Uyf4UKBW+MFmjSJLM67sYbgzdaRWyj0E5QBQtKgwd712+8If34o714gHhy8KDZDM2vZk3p\n+uu9a3/bhmTOuM9K1apmBcpHH0nz55tj+wAAQHy6/nrvB/O5c0vnnht81Ofo0eZ7ih49WI3qAnq0\nlVg92oHS06VWraQvvjDXDRpI334r5cplNy7AdT//7O30X7Gi2d3/6FHp9NPNcjC/yy6TmNwAAAB+\nR49KK1aYDYwLF5aWLDHLyj/7zHvNGWeYH+jnzWsvTtfQo42o8u9y6H9If/xReustuzEB8SCwP/uc\nc8x/c+UyR3gEym42GwAAJJ5cucz3DoULm+s6daQJE8xkWNOm5t7jj1Nku4BCO8FVrSr16+dd9+kj\nffyxvXhCRa+T2+I1f5kV2pJ0003euE4dqUWL6MUUbvGau0RB/txG/txF7txmM38XXihNny7NmWM2\nTEXso9CG+vWTqlUz44MHzcZLzz8vpaXZjQtwVVaFdqNG5uiOdu2kUaOC+64AAABOpHFj78xtxDZ6\ntJW4PdqB1q6VWrY0uxz7NWpkzuytUcNeXICLzjrL2xX0xx+l+vXtxgMAAJDIbNR7FNqi0Pbbvl26\n+mpp7lzvXnKy9M47UqdO9uICXLJtm1SypBnnzi3t2iXlz283JgAAgETGZmiwqnhxacYMs7Ohf0nK\noUPSLbdITz5pdimPNfQ6uS0e8xf4g6r69eO3yI7H3CUS8uc28ucucuc28odQUGgjSN68pqheuDC4\nt/Txx815vgCy99VX3rhxY3txAAAAwB6Wjoul41nZtcscRzRjhnfv7belHj3sxQTEuvPPN33ZkjRp\nknTVVXbjAQAASHT0aFtCoZ21w4el664z5/dJUp485miByy6zGxcQi1JTpaJFzY79Pp/Z96BoUdtR\nAQAAJDZ6tBFz8uSRhg+X6tY114cPS9dfL23ebDcuP3pl3BZv+Zs3zzsWr27d+C6y4y13iYb8uY38\nuYvcuY38IRQU2jihggWlzz6TypQx11u2SN26xebmaIBN9GcDAABAYum4JJaO59T06VKzZt51+/ZS\nUpJUrpwpvOvVsxcbEAsuucTMakvS2LHStdfajQcAAAD0aFtDoZ1z998vvfRS5h9r1Eh64QXzXyDR\n7N8vFSli2isk015RurTdmAAAAECPNhzw7LNZz1zPm2dm9Hr0kH75JTpLy+mVcVs85W/6dK/IrlEj\n/ovseMpdIiJ/biN/7iJ3biN/CEVu2wHALXnzmoJiyBApd26pbFlp6lSzTNZfZLz7rvlVqZLZOO3W\nW6WqVe3GDUTaxx9746uvthcHAAAA7GPpuFg6Hg7r10v33CONH5/5x6+4Qrr9dqldOyk5ObqxAZF2\n4ICZwU5NNdcLF0rnnms3JgAAABgsHYezzjxTGjdOmjLFbABVqFDwx2fOlG64wbxuyBDpyBE7cQKR\nMG2aV2RXqcLGgAAAAImOQhth1aKFWUa+das5Eqx1a7Mzud/mzVKvXuaM4VtvlR57zMz+nSx6ZdwW\nL/kLXDZ+3XWSz2cvlmiJl9wlKvLnNvLnLnLnNvKHUFBoIyKSk6U2baSJE6V166QnnjDHgPktXy69\n84709NNS/fpSp05m+TngmgMHzA+V/K6/3l4sAAAAiA30aIse7WjZv98cDfbcc9Levcd/PHduU6Tc\nd58pvoFQ+B/haM8mf/ih1LmzGVepIq1alRgz2gAAAK6gRxtxLV8+6dFHzQz3uHGmVztwd+YjR6SR\nI6UGDbI+qxvIaMECc6Rc4cJSnTrSzp3R+9o7dkh9+3rXN99MkQ0AAAAKbVhQsqTZfbxnT7NL+ezZ\nUuPGwa+5/37p8cdPfBY3vTJuO5X87dxpNthr0MAcJ7dnj7R0qTRqVPjiO5EHH5S2bDHj008PLrrj\nHc+e28if28ifu8id28gfQkGhDesuu0yaM8fMTDZq5N1/8kmpTx8pLc1ebIhN339vdvYO3ITMb+7c\nyH7tP/6QHnhAatpUevtt7/6rr5pZdQAAAIAebdGjHUv27TPHg33xhXeva1dp6FDTww1s2iSdfba0\ne7d374orzBFyklShgmlPiJTLLpO++ir4Xrt2ph0CAAAAsYcebSS8/PmlCRPMEUl+w4ZJN94oHTxo\nLSzEkEce8YrsokXNcXJTppj/dyTp99/NrHMkbNx4fJFdtar02muR+XoAAABwE4U2Yk5ysumz7d7d\nu/fJJ2bjtIy7ldMr47ZQ87dggfnBi9/HH5sVEHnySBdd5N2fNy8s4R1n0iRvfNFF0q+/SitWmP7s\nRMOz5zby5zby5y5y5zbyh1BQaCMm5cpllovfe693b+pUqWVL6dAhe3HBnvR06Z57vOs2baQrr/Su\nA/v7I9WnPXGiN77hBqlaNfP/KgAAABCIHm3Rox3L0tPNpmhPPOHde/JJ6bHHrIUES2bPlpo0MeM8\neaRly0yh6zdtmtS8uRmfe660cGF4v/6+fVKJEtKBA+Z69WpzbjYAAABiGz3aQAY+nznm67//9e49\n9ZS0fLm9mGDHDz94444dg4tsySzlTvrnb7TFi4M3SwuHmTO9IrtmTYpsAAAAZI1CG0549FHpggvM\n+PBhqUcP6ehRemVcF0r+An+4Ur/+8R8vXFiqU8eM09Kkb789tdgyClw23qZNeN/bRTx7biN/biN/\n7iJ3biN/CAWFNpyQK5f0zjtmybBkiqinnrIbE6IrsNCuWTPz1wT2aYdzQ7S0tOCN0Ci0AQAAkB16\ntEWPtkueeCJ4GfmECVLbttbCQZSkp5sZ6z17zPWmTdK//nX860aNkm6+2YybNw8+j/1UfPGF2YhP\nkkqWlP76i03QAAAAXGGj3ssd1a8GnKL//Ef6+mtp1ixz3amT1LOndNZZ5uiv3bulypWlhg2lSpVM\njzfc98cfXpFdrJhUpkzmr/O3F0jS99+bAj0c/w+8/ro37tyZIhsAAADZY+k4nJI7tzR6tFSxorlO\nTZ2tgQOl224zR4H172+K7ypVTMH91FPSn39aDRnZyGmvU8Zl41kVz5UrS8WLm/GOHdJvv51afJK0\ndq30+efe9b//fervGQ/oU3Mb+XMb+XMXuXMb+UMoKLThnJIlpfHjpVKlsn/dunWm8K5QwSwnXrAg\nKuEhAnLSny2ZAjzjrPapGjLEzIxLZjl6xt3OAQAAgIzo0RY92q7audMcubRokfT776aHN39+ackS\naf78zI93attWGjgwNoqlb76RXnlFWrNGevhh6ZprbEcUu26/XRo61Iz/7//M6oWs9O/vbZR3773m\n9SfrwAGpfHlp2zZz/dlnbIQGAADgGhv1HoW2KLTj0YEDZtb7zTeljKt88uSRrr9eatfObHBVoEB0\nY9u+XWrfXpozJ/h+377Sc8+Z5fEIdskl3i7iU6dKzZpl/dpJk7xi+OKLT2338TvukP73PzOuUMEs\nRac/GwAAwC026j2WjsNpWfXKnHaadOON0pdfSgsXmrHf4cPSiBHSddeZDdNGj/aWBkfDa68dX2RL\nZqa9S5foxRELctLrlJ6e86XjknT++d544UKT75PxwQdekS1J/fpRZAeiT81t5M9t5M9d5M5t5A+h\noNBG3Dv33P9v796joir3PoB/92CjQKBcDaXyFt7FU3krQ6yjkhZyOlJeMkXQzrHEsFLTVLyl6bHl\nm5WKElKKJWqeTNDURIk3zdTES6AJ5jrG0UQQ5CIKz/vH887AjgEHucze+v2stVezn32ZPfNbj/Sb\n5yaXfTp8WLaMVvTHHzIJHzbMclfz+rBpU/nr4GBgyJDy/bg44NdfG+Y5tCo/Xw4J+OUX4NYt4NIl\nObEZADg5AS1bVn998+ay9RmQPRtOnqz5M+zZI2ezNwkOVu8TEREREVWHXcfBruP3EiGAY8fk+tvR\n0eoZyZ98UnZLrs+u5KdPA507y9f29jLRt7eXXdi//VaWz58vlzG7FxUXA717A8ePy/3GjeUEZ8XF\ncr9XL+Dgwdvf58UXgfh4+XrVKuuT5NJSYN48GQPTPwkdOwKHDskkn4iIiIj0h13HieqZogCPPgrM\nnQucOiWXBTNJSZGTpdVny7Yp+QNkS7ajI2AwAGPHlpd/8UX9vb/WLVxYnmQDwI0b5Uk2cPtu4yYV\nZx4/fNi6awoL5bj9efPKk+zmzYGtW5lkExEREVHNMNEmXavNWJmmTYGoKPWs1N99BzzwADBihFw7\nueL43lu35AznBw/e+Zjuiol2cHD568BAOWM6IH8AOHHizu6flyefUS8dNCrG7/Rp4P33y4+5u1c+\nv0sX6+5bcZz2pk23745/5QrwzDNyIjWTp5+W32WHDta9572G49T0jfHTN8ZPvxg7fWP8qCaYaNM9\n7403gPfeK98vKpKtys89B7RoAfTpAzz2GNCsmRzv3acPEBAA/Oc/NXuf06dlEg3I7uIVx2Y7OqqX\njappq3ZuLjBnjlyK6i9/kV3RCwtrdg9bKiuTS3iZfth44gk5NvvcOeDtt2UcunQBXn7Zuvs98QTQ\nrp18nZ8vu5JXbBmvqKREro9dsUv6tGmyK/8DD9z5ZyIiIiKiexfHaINjtEmKjZWt2xW7LlenWTO5\nTFeXLuXbAw/I7umWREQAy5fL18OGqVu3ATluPChIvm7TRrbCVnUvk+vX5VrcS5fKZLsiPz/ZQquH\nbs8bNpQn0Y0ayXH01rZeV+XIEZlwl5TI/bAw2YPhz9/pvHnyRwpAHvvwQ+D112v33kRERESkHVxH\n20aYaFNFJ04A69fL5K/iZGmAbFnNyqq+a7bRKMf2Dh4sE+cnnpD3e+218nM2bVJ3HQfkeOTmzYFr\n1+T+jBnAggVVJ9u7dgGjR8sJ1ary8MNAaCgwbtztZ+u2leJi2T37t9/k/rRpwOLFdXPvjz4CJk0q\n3w8NlWurm5bpOnFC9lYwtaT/61/Am2/WzXsTERERkTYw0bYRJtr6lZSUBH9//3q5d2kpkJoqu5Lf\ndx/g5SW7Zicny8nLMjLu7L5PPy27JVtak3nKFPWY8SlTZDfwxo1l0p+TA7RqBaSnA2+9JZ/RpF07\n2TKblQVMnaq+b+PG8vx33qnfWdVrKikpCT/95I+335b77u6yJb9p07q5vxCypTwurrysf39g5EiZ\nXP/P/8jvEpBDApKTuVa2teqz7lH9Y/z0jfHTL8ZO3xg//bJFvteoQd+NSEfs7OR45z976im5xvP3\n38s1mk+elC2jp07J8cDV6dkT2Lat6mRu0SKZ+CUkyP0PPpBbdVq0kMtRvfKK7HYNAK6ucmyzaf3p\nGzfkjN6xsUBiYu27ZdfWnj3y+a5elRORmcyZU3dJNiB7A8TGyh9KYmNl2b59cqvIaJTLvTHJJiIi\nIqK6wBZtsEWb6k5pqRwT/NNPwFdfyVnMT56U5X/5i0wwXV2rv0dxMTB0aPm62tXp0UOO7fbysnyf\nbduAZcvk85g88ogcv1yTsdtffw1ERgL9+snu1dYkpKY1y/PyZPds0/vFxwOjRqlndAdki/ypUzLp\nrWtlZTKxt/SjhZ0dsHKleqk3IiIiIrp7sOu4jTDRpvpUVCTHH7dtK1tWrVFcDKxaBfzwg5x9+8YN\n2XLdtKnssn7+vFyOasWK8mXBqlJWJltzJ00CCgpk2YgRcgz67SZbA+QPBsHB5d3UZ8yQreNVuXVL\nTvq2Zg1w5owss7OTa2AbjTL5LitTX2MwyInbnn329s9TG6dOyRb9pCT52QcOlEurPfxw/b4vERER\nEdkOE20bYaKtXxwrY72KM3sDsoV37ly51JglQsjW55dfrtz6bGkyN5N//ENOOHY77dsDr7+ehM6d\n/fHQQ/KHCNIP1j19Y/z0jfHTL8ZO3xg//bJFvsd1tInuEaNGqbtHL10KdO4sW86zstTn/u//Av7+\nwEsvlSfZhgr/WowZIydt+3MCfvCgOsl2cgK6dq3ccv7EE3LisS5d5ORkTLKJiIiI6G7CFm2wRZvu\nHUVFctmxpCR1uaIAvXvLbtQpKbIbd0WtW8ux4C+8IGcFN+nQQXYTHzRIdi3v2RM4elQeGzxYtog7\nOMhJz86dk+/j6Civs6bbOhERERFRbbHruI0w0aZ7SVmZnGF7+nSZAFenUSPZCh4ZCXh6yjHXgYHl\nS2KZDBgg/7t7t/xvkybA6dMyQSciIiIisiV2HSeqoaQ/N83SbRkMMnk+exb48EPZdfvPs4griuxq\nnpYGfPKJTLIBwMdHri3+r3+pZy3fvbs8yQbkhGnWJNmMn34xdvrG+Okb46dfjJ2+MX5UE0y0ie5R\nrq5yJvLvvpMzm8fEAMOGyQT755+B9estj502GoE335St2yEhlY/7+sqJ1oiIiIiI7lXsOg52HSeq\njdRUObGZi4tcJqtHj/pZC5uIiIiI6E5wjLaNMNEmIiIiIiK6O3GMNlENcayMvjF++sXY6Rvjp2+M\nn34xdvrG+FFNMNEmIiIiIiIiqkPsOg52HSciIiIiIrpbses4ERERERERkc4x0SZd41gZfWP89Iux\n0zfGT98YP/1i7PSN8aOaYKJNREREREREVIc4Rhsco01ERERERHS34hhtIiIiIiIiIp1jok26xrEy\n+sb46Rdjp2+Mn74xfvrF2Okb40c1wUSbiIiIiIiIqA5xjDY4RpuIiIiIiOhuxTHaRERERERERDrH\nRJt0jWNl9I3x0y/GTt8YP31j/PSLsdM3xo9qgok2ERERERERUR3iGG1wjDYREREREdHdimO0iYiI\niIiIiHSOiTbpGsfK6Bvjp1+Mnb4xfvrG+OkXY6dvjB/VBBNtIiIiIiIiojrEMdrgGG0iIiIiIqK7\nFcdoExEREREREemczRPttLQ0BAQEIDk52eLxrVu3YubMmZg9ezYmTpyIlStXWnXf+Ph4jBo1CkuW\nLMHo0aMRExNTl49NGsGxMvrG+OkXY6dvjJ++MX76xdjpG+NHNWGzRPubb75BSEgIVq5ciW+//dZi\nU35CQgKuXLmChQsXYt68efjkk09w7tw5rF69utp7JyUlYeLEiVi9ejWmTp2KNWvWYPbs2di6dWt9\nfRyykZ9//tnWj0C1wPjpF2Onb4yfvjF++sXY6RvjRzVhs0T7ueeeQ0xMDKZMmVLlOdHR0ejTp4+q\nLDQ0FNu3b6/23pGRkfjb3/6G+++/HwDQpEkTjBw5EvPnz6/9g5Om5Obm2voRqBYYP/1i7PSN8dM3\nxk+/GDt9Y/yoJmzedby6QelGoxFvvPEG/vjjD3PZsWPH4OvrW+U1N27cQEpKCrp27aoq79atG44f\nP47s7OzaPzQRERERERFRFWyeaFcnPDwcycnJ6NChA6Kjo5GcnIzExETMmjWrymsyMzNRWloKNzc3\nVbmrqysAID09vV6fmRrW+fPnbf0IVAuMn34xdvrG+Okb46dfjJ2+MX5UI8LGMjMzhaIoYv/+/RaP\nf/fdd6Jp06ZCURTh6ekpUlNTq71fSkqKUBRFbN68WVW+d+9eoSiK2L59e6VrAHDjxo0bN27cuHHj\nxo0bt7t0a2iNoGE5OTmIjo7Gxo0bkZKSgqVLl6Jnz5748ssvERgYaPEa8f9d0RVFUZWXlpZaLK94\nDREREREREVFtabbruBACQ4YMwaRJk/Dss89iwYIF+Pnnn9G9e3eEhISgqKjI4nUuLi4WywsKCgAA\nzZo1q7dnJiIiIiIiItJson38+HEUFRWhV69e5rKOHTtiz549sLOzw6lTpyxe17ZtWxiNxkqzAmZl\nZQEAfHx86u+hiYiIiIiI6J6n2UTbzs4OhYWFlcodHR3RunVreHl5WbyucePG8PPzw9GjR1XlR44c\nQadOneDh4VEvz0tEREREREQEaCDRNo2PLisrU5V37doVbdu2xccff6wqT0xMRH56dGcAABFRSURB\nVO/evdGyZUvzvpubGxITE83nREREYNOmTcjLywMAXLt2DVu3bsWcOXPq86MQERERERERQRE2mgks\nJSUFH330EY4dO4azZ8/C29sbvXr1wqhRozB06FAAck3s9957DxkZGfDw8EBJSQkeeeQRhIeHmyc1\nS0xMxMiRI7Fx40YEBASY779u3Trs2bMHvr6+OHToEAYOHIgJEybY4qMSERERERHRPcRmiXZDiY+P\nx7Zt2+Dr64sTJ07g6aefRkhISL1dR3fmwIEDCAsLQ58+feDu7o5r167hzJkzWLFiBXx9fc3nMZ7a\nkZaWhjfeeAMzZ87EU089Vem4Nd8542k71cWP9VGbSkpKsGTJEly8eBGXLl1CVlYWwsPDMWLECNV5\nrHvaZE38WPe0q7i4GGvXrkVubi6Kiopw7Ngx/PWvf0VERIRqRRvWP+2xJnase/rx448/Ys6cOare\nzIBG616DLyjWgPbt2yfc3d1Ffn6+EEKIoqIi4e3tLbZs2VIv19Gd27dvn2jevLmwt7cX7u7u4qWX\nXhLp6emVzmE8bW/79u1i7NixIjw8XCiKIvbv31/pHGu+c8bTNqyNH+uj9kRERIi0tDTz/vbt24Wi\nKGL58uXmMtY97bI2fqx72hQRESF69OghSkpKhBBCHD58WCiKIhYuXGg+h/VPm6yNHeue9hUUFAgf\nHx/Rv39/VblW695dnWj369dPjB8/XlU2depU0b1793q5ju5cUlKSiI2NrfYcxlNbzp8/X2WiZs13\nznjaVnXxY33Unry8PNGkSRPx6quvqsp79OghmjZtKkpLS4UQrHtaZW38WPe0a/LkyaJ169aisLBQ\nCCHEjRs3hKIoYvDgweZzWP+0yZrYse7pQ2RkpHj22WeFv7+/qlyrdc/mk6HVlxs3biAlJQVdu3ZV\nlXfr1g3Hjx9HdnZ2nV5HtSeqGcXAeGpPVfGy5jtnPG2vuvp2u+OMX8MzGAzw8vJCfn6+qrxdu3bI\ny8vD5cuXWfc0zJr4mbDuadPy5cuRkZEBe3t7AMCvv/4KAHjyyScB8G+flt0udiase9q2e/du+Pr6\nonnz5qpyLde9uzbRzszMRGlpKdzc3FTlrq6uAID09PQ6vY5q78yZM5gxYwYWLVqE4cOHIy4uznyM\n8dQPa75zxlP7WB+1xdHRERkZGdiwYYOq/MKFC3B1dYWnpyfrnoZZEz8T1j19WLZsGQYOHIi33noL\nAP/26cmfY2fCuqddOTk52L9/P4KCgir9IKLlutfoNp9Lt65evQpArqtdkWnfdLyurqPaS09Px+bN\nmwEA+fn5aNeuHRwcHBAUFMR46khNvnPGU7tYH7UvLS0NP/zwAxYuXAiDwcC6pzN/jp8J6562rVix\nAufPn8fvv/+Ozz77DEajEQD/9ulBVbEzYd3TriVLlmDGjBkWj2m57t21LdqmXzsqzgQJAKWlpRbL\na3sd1c6jjz6KTz/91Lzv5OSE/v3749133wXAeOqJNd8546ltrI/aV1ZWhtdeew3Dhg3D9OnTAbDu\n6Yml+AGse3owadIkLFu2DAsWLECHDh2we/duAKx/elBV7ADWPS3bsmULBg0aBCcnJ3NZxe9Ny3Xv\nrk20XVxcLJYXFBQAAJo1a1an11HtODk5wdnZWVXWpEkTnD59Grm5uYynjljznTOe2sb6qH3Tp09H\nmzZt8MUXX5jLWPf0w1L8ANY9PXnsscfQrVs3jBgxAoWFhax/OvLn2AGse1qVlZWF06dPw9/fX1Ve\nsfu4luveXZtot23bFkajEbm5uaryrKwsAICPj0+dXkd3Lj8/Hy1btsSUKVNU5cXFxebXjKd+WPOd\nM57axfqofcuXL4eTkxPWrFkDRVFw4cIFlJSUsO7pRFXxY93Trv/+979o2bIlxowZoyr39vbG1atX\ncerUKdY/jbImdqx72pWQkIBffvkFISEh5m3fvn1IS0tDSEgItm3bhnbt2mm27t21iXbjxo3h5+eH\no0ePqsqPHDmCTp06wcPDo06voztnMBgghED79u1V5enp6Xj88cfRrFkzxlNHrPnOGU/tYn3Utri4\nOBgMBsyaNctctmrVKtjZ2bHu6UB18WPd066LFy8iKyvL3PppkpWVBTs7Ozz44IOsfxplTexY97Qr\nNDQUcXFxiImJMW9t2rRBx44dERMTg6CgIBiNRu3WvdsuAKZjCQkJwsPDQ1y7dk0IIURubq5wcXER\nmzZtUp3j6uoqEhISanQd1a3Zs2eLzMxM8/6PP/4oHBwcxE8//WQuYzy1JSMjQyiKIvbt21fpmLWx\nYjxtp7r4sT5q086dO0XPnj3F4sWLxaJFi8xbxbVgWfe0y5r4se5pU1lZmRgwYIBITU01l128eFEY\njUYxbdo0cxnrn/ZYGzvWPf3w8/MT/fr1U5Vpte4pQtxmMVWdW7duHfbs2QNfX18cOnQIAwcOxIQJ\nE8zHExMTMXLkSGzcuBEBAQFWX0d1q6SkBIsXL0ZOTg5KSkpw6dIlzJo1C76+vqrzGE/bS0lJwUcf\nfYRjx47h7Nmz8Pb2Rq9evTBq1CgMHTrUfJ413znj2fCsiR/ro/ZkZ2ejVatWKCwsrLS0SVBQELZu\n3WreZ93THmvjx7qnXfn5+XjvvfeQl5cHo9GIjIwMPP/88wgLC1Odx/qnPdbEjnVP+7Zt24a1a9fi\n22+/haIoGDBgAMLCwhAUFARAm3Xvrk+0iYiIiIiIiBrSXTtGm4iIiIiIiMgWmGgTERERERER1SEm\n2kRERERERER1iIk2ERERERERUR1iok1ERERERERUh5hoExEREREREdUhJtpERER1bOzYsTAYtP0n\n9vfff4ezszNiY2OtOt/f3x+tW7eu56ey3rBhw9CvXz9bPwYREZFF2v6/ACIiIg0wGAxWb7/99hsU\nRYGiKLZ+7GrNmDEDLVu2xJgxY6y+Rkufad68efj++++xZcsWWz8KERFRJYoQQtj6IYiIiLQsLi5O\ntX/gwAFERUXh1VdfxVNPPaU6FhQUBKPRiLKyMhiNxoZ8TKtlZmbCx8cHH374If75z39adY2/vz8u\nXLiAjIyMen466w0aNAiXL1/GsWPHbP0oREREKo1s/QBERERaN3LkSNV+SUkJoqKi0KdPn0rH9GD1\n6tWws7PT5bNXNHr0aLzyyis4dOgQevXqZevHISIiMmPXcSIiojpmaYy2qezq1asYN24cPDw84ODg\ngMGDByMrKwsAsGLFCjzyyCNo3LgxOnbsiK+//tri/b/88kv07dsXzs7OcHR0RO/evWvUhTo+Ph69\nevVC06ZNKx3LycnB+PHj4e7ujvvvvx/9+/fHkSNHLN5n586deOGFF/Dggw/CaDTCxcUFgwYNwoED\nB1TnDR06FI6OjsjPz690j8OHD8NgMGDBggXmslWrVqF79+5wcHBA48aN0apVK7z44ou4cuWK6tqA\ngAAoioL4+HirPzsREVFDYKJNRERUD6oazxwQEICSkhIsWbIEERER2Lt3LwIDAzF37lxERUVh0qRJ\nWL58OYQQGDZsGE6fPq26/t1338WIESPg6emJpUuXYvny5XBxcUFwcDA++eST2z7XxYsXkZmZabEF\n+ObNmxg0aBBiYmIQHByMFStWwNfXFwEBAbh48WKl8zds2IBbt25h8uTJWLt2LaZNm4Zz587hmWee\nwffff28+b8KECSgqKsLGjRsr3SM6Ohp2dnYYN24cAJlkT5w4ER4eHvjggw+wZs0ahIWF4dKlS/jj\njz9U17q7u6Nt27ZISkq67ecmIiJqUIKIiIhqJCYmRiiKImJjYy0eHzNmjFAUxWLZ1KlTVeXTp08X\niqIIHx8fUVBQYC5PS0sTiqKIN99801x25MgRoSiKmD9/fqX3fOmll4Szs7PIz8+v9tl37dolFEUR\nq1atqnRs9erVQlEUsWTJElV5VFSUUBRFtG7dWlVeVFRU6R45OTmiefPmYvDgweay0tJS8dBDD4me\nPXuqzi0oKBDOzs5iyJAh5rKAgADh5uYmysrKqv0cFc+3t7e36lwiIqKGwhZtIiKiBhQeHq7a79u3\nLwDglVdegYODg7m8ffv2cHd3R1pamrlsw4YNUBQFL7/8Mq5cuaLann/+eeTn5+OHH36o9v1NrcKu\nrq6Vjm3btg1GoxGvv/66qnzcuHEWu5k3adLE/Pr69evIzs7GrVu30KdPHxw6dMh8zGAwYNy4cTh8\n+DBOnjxpLt+8eTPy8/MRGhpqLnNzc8O1a9ewY8cOCCvma3Vzc0NxcTGuX79+23OJiIgaChNtIiKi\nBtSyZUvVvqOjIwDA29u70rkODg4oKioy7//yyy8QQqBNmzbw9PRUbaNHj4aiKLh8+XK172/q0m4p\nic3IyMDDDz8Me3t7VbmdnR3at29f6fxz585h+PDhcHFxgbOzMzw8PODp6Yl///vfyM3NVZ0bGhoK\nOzs7REdHm8uio6PRvHlzBAYGmstmzpyJFi1aIDAwEF5eXvj73/+OVatWIS8vz+LnMX0OLS09RkRE\nxFnHiYiINKCqRLFiQiyEQKNGjbBz584q79OpU6dq38fDwwMAcPXq1Tt4ynLXr1+Hn58fbt68iYiI\nCHTt2hVOTk4wGAxYvHgx9u7dqzrf29sbAQEBWL9+PZYuXYrMzEwkJyfj7bffhp2dnfm8jh074uzZ\ns9i5cycOHDiA/fv3Y+LEiYiMjERSUhI6dOiguu/Vq1dhb29v/sGCiIhIC5hoExER6YSPjw927dqF\nNm3aoFWrVnd0jy5dugAAzp49W+lYmzZtsHfvXhQWFqq6sd+6dQvp6elwcXExl+3duxdZWVmIi4vD\n8OHDVfeZO3euxfeeMGECduzYga+++gpHjx4FAFW3cROj0YjAwEBzS3diYiKGDBmCZcuWYc2aNapz\nz549i86dO1vz0YmIiBoMu44TERHVA0st1LXt3jx69GgAwKxZsyx2/c7Ozr7tPby8vODj42NxLHdQ\nUBBu3ryJjz/+WFX+6aef4tq1a6qymzdvAgBKS0tV5cnJyUhOTrb43kOGDEGLFi2wevVqfPbZZ+jb\nty98fHxU5+Tk5FS6rkePHgCAwsJCVfmVK1eQmZkJf39/i+9HRERkK2zRJiIiqgeWEmFrJveqzuOP\nP47IyEhERkbizJkzCA4OhoeHB7Kzs3Hw4EHs2LEDBQUFt71PcHAwFi9ejCtXrsDd3d1cHhISgqio\nKLzzzjvIyMhAjx49kJqaig0bNqBt27a4deuW+dxnnnkGzZo1Q3h4OM6cOYNWrVohNTUVsbGx6Nq1\nK06cOFHpfU2TopnWzF60aFGlcwYMGABPT0/4+fnBy8sLOTk5WLduHQwGg/mHBpOEhATzMmhERERa\nwhZtIiKiGlIUpdrWaUvHq7umJuWzZ8/GN998AxcXFyxatAhhYWF4//33kZubi8WLF1v1/OPHj4ei\nKJXWtb7vvvuwe/dujB07FvHx8QgPD0dqaip27doFb29v1fO4uLhgz5496NatG5YuXYrJkyfj+PHj\n2L59Ox599NEqP1NYWBgURYGzszOCg4MtPltxcTGWLVuG0NBQREZGwsPDA4mJiQgICFCd+/nnn6N7\n9+7o2bOnVZ+biIiooSiitj+vExERke6EhYXhwIEDSEtLg8HQcL+7Z2Vl4aGHHkJYWBhWrlx5x/c5\ndeoUunXrhvj4eLzwwgt1+IRERES1xxZtIiKie9D8+fNx6dIlfP755w36vlFRUSgtLcWECRNqdZ+5\nc+eib9++TLKJiEiT2KJNRERE9e6LL77AhQsXMG/ePPj5+SEhIcHWj0RERFRvmGgTERFRvTMYDLjv\nvvvQt29frF+/Hl5eXrZ+JCIionrDRJuIiIiIiIioDnGMNhEREREREVEdYqJNREREREREVIeYaBMR\nERERERHVISbaRERERERERHWIiTYRERERERFRHWKiTURERERERFSH/g+FyaB0FY1hMQAAAABJRU5E\nrkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# u,v,temp\n", + "\n", + "plt.subplot(2,2,1)\n", + "data = u\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, levels=numpy.arange(-.10,.11,.01))\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title('u (m/s) for expt = '+expt_name,fontsize=16)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = v\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, levels=numpy.arange(-.10,.11,.01))\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title('v (m/s) for expt = '+expt_name,fontsize=16)\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = temp\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "CS = plt.contourf(time, -zl, field.T,levels=numpy.arange(18,30,1))\n", + "plt.colorbar()\n", + "#C = plt.contour(time, -zl, temp.T, 8,linewidth=.5, colors='black')\n", + "#plt.clabel(C, inline=1, fontsize=10)\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title(r'$\\Theta$ ($\\degree$C) for expt = '+expt_name,fontsize=16)\n", + "plt.xlabel('Time (days)',fontsize=14);\n", + "\n", + "\n", + "plt.subplot(2,2,4)\n", + "data = saln[:,:]\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "CS = plt.contourf(time, -zl, field.T,levels=numpy.arange(36.3,37,.01))\n", + "#CS = plt.contourf(time, -zl, field.T)\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title(r'$S$ ($ppt$) for expt = '+expt_name,fontsize=16);\n", + "plt.xlabel('Time (days)',fontsize=14);\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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ZZ0XH//3vfzOO4wTnVkpGOVtczvletGiRSLbmzZuzyMhIduLECeF4Xl6eW13t27cvq1Gj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\nXuIc/UNEREQBij3fRERERERERD7Gnm8iIpKv6SMuxkZERER+8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"input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/STNP/INPUT b/standalone/examples/single_column_MARBL/STNP/INPUT new file mode 120000 index 00000000..3a2d233e --- /dev/null +++ b/standalone/examples/single_column_MARBL/STNP/INPUT @@ -0,0 +1 @@ +../INPUT \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/STNP/MOM_input b/standalone/examples/single_column_MARBL/STNP/MOM_input new file mode 100644 index 00000000..8172d0d1 --- /dev/null +++ b/standalone/examples/single_column_MARBL/STNP/MOM_input @@ -0,0 +1,401 @@ +! This input file provides the adjustable run-time parameters for version 6 of the Modular Ocean Model (MOM6). +! Where appropriate, parameters use usually given in MKS units. + +! This particular file is for the example in single_column/KPP. + +! This MOM_input file typically contains only the non-default values that are needed to reproduce this example. +! A full list of parameters for this example can be found in the corresponding MOM_parameter_doc.all file +! which is generated by the model at run-time. + +! === module MOM_domains === +REENTRANT_Y = True ! [Boolean] default = False + ! If true, the domain is meridionally reentrant. +NIGLOBAL = 2 ! + ! The total number of thickness grid points in the x-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NJGLOBAL = 2 ! + ! The total number of thickness grid points in the y-direction in the physical + ! domain. With STATIC_MEMORY_ this is set in MOM_memory.h at compile time. +NIHALO = 2 ! default = 4 + ! The number of halo points on each side in the x-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +NJHALO = 2 ! default = 4 + ! The number of halo points on each side in the y-direction. How this is set + ! varies with the calling component and static or dynamic memory configuration. +! LAYOUT = 1, 1 ! + ! The processor layout that was actually used. + +! === module MOM === +SPLIT = False ! [Boolean] default = True + ! Use the split time stepping if true. +USE_REGRIDDING = True ! [Boolean] default = False + ! If True, use the ALE algorithm (regridding/remapping). If False, use the + ! layered isopycnal algorithm. +DT = 3600.0 ! [s] + ! The (baroclinic) dynamics time step. The time-step that is actually used will + ! be an integer fraction of the forcing time-step (DT_FORCING in ocean-only mode + ! or the coupling timestep in coupled mode.) +FRAZIL = True ! [Boolean] default = False + ! If true, water freezes if it gets too cold, and the accumulated heat deficit + ! is returned in the surface state. FRAZIL is only used if + ! ENABLE_THERMODYNAMICS is true. +C_P = 3925.0 ! [J kg-1 K-1] default = 3991.86795711963 + ! The heat capacity of sea water, approximated as a constant. This is only used + ! if ENABLE_THERMODYNAMICS is true. The default value is from the TEOS-10 + ! definition of conservative temperature. +SAVE_INITIAL_CONDS = True ! [Boolean] default = False + ! If true, write the initial conditions to a file given by IC_OUTPUT_FILE. + +! === module MOM_fixed_initialization === +USE_MARBL_TRACERS = True + ! "[Boolean] default = False + ! If true, use the MARBL tracer package." +! === module MARBL_tracers === +MARBL_SETTINGS_FILE = "INPUT/marbl_in" ! default = "marbl_in" +MARBL_TRACERS_IC_FILE = "IC_FILE_COLUMN.nc" ! default = "ecosys_jan_IC_omip_MOM_tx0.66v1_c211008.nc" + ! The file in which the MARBL tracers initial values can be found. +MARBL_FESEDFLUX_FILE = "MARBL_FESEDFLUX_FILE_COLUMN.nc" ! default = "fesedflux_total_reduce_oxic_tx0.66v1.c211020.nc" + ! The file in which the iron sediment flux forcing field can be found. +MARBL_FEVENTFLUX_FILE = "MARBL_FEVENTFLUX_FILE_COLUMN.nc" ! default = "feventflux_5gmol_tx0.66v1.c211020.nc" + ! The file in which the iron vent flux forcing field can be found. + + +! === module MOM_grid_init === +GRID_CONFIG = "cartesian" ! + ! A character string that determines the method for defining the horizontal + ! grid. Current options are: + ! mosaic - read the grid from a mosaic (supergrid) + ! file set by GRID_FILE. + ! cartesian - use a (flat) Cartesian grid. + ! spherical - use a simple spherical grid. + ! mercator - use a Mercator spherical grid. +LENLAT = 1.0 ! [degrees] + ! The latitudinal or y-direction length of the domain. +LENLON = 1.0 ! [degrees] + ! The longitudinal or x-direction length of the domain. +TOPO_CONFIG = "file" ! + ! This specifies how bathymetry is specified: + ! file - read bathymetric information from the file + ! specified by (TOPO_FILE). + ! flat - flat bottom set to MAXIMUM_DEPTH. + ! bowl - an analytically specified bowl-shaped basin + ! ranging between MAXIMUM_DEPTH and MINIMUM_DEPTH. + ! spoon - a similar shape to 'bowl', but with an vertical + ! wall at the southern face. + ! halfpipe - a zonally uniform channel with a half-sine + ! profile in the meridional direction. + ! bbuilder - build topography from list of functions. + ! benchmark - use the benchmark test case topography. + ! Neverworld - use the Neverworld test case topography. + ! DOME - use a slope and channel configuration for the + ! DOME sill-overflow test case. + ! ISOMIP - use a slope and channel configuration for the + ! ISOMIP test case. + ! DOME2D - use a shelf and slope configuration for the + ! DOME2D gravity current/overflow test case. + ! Kelvin - flat but with rotated land mask. + ! seamount - Gaussian bump for spontaneous motion test case. + ! dumbbell - Sloshing channel with reservoirs on both ends. + ! shelfwave - exponential slope for shelfwave test case. + ! Phillips - ACC-like idealized topography used in the Phillips config. + ! dense - Denmark Strait-like dense water formation and overflow. + ! USER - call a user modified routine. +TOPO_FILE = "TOPO_FILE_COLUMN.nc" ! default = "topog.nc" + ! The file from which the bathymetry is read. + +MAXIMUM_DEPTH = 6000.0 + ! "[m] + ! The maximum depth of the ocean." + +MINIMUM_DEPTH = 9.5 + ! "[m] default = 0.0 + ! If MASKING_DEPTH is unspecified, then anything shallower than + ! MINIMUM_DEPTH is assumed to be land and all fluxes are masked out. + ! If MASKING_DEPTH is specified, then all depths shallower than + ! MINIMUM_DEPTH but depper than MASKING_DEPTH are rounded to + ! MINIMUM_DEPTH." + +MASKING_DEPTH = 0.0 + ! "[m] default = -9999.0 + ! The depth below which to mask points as land points, for which all + ! fluxes are zeroed out. MASKING_DEPTH is ignored if negative." +ROTATION = "betaplane" ! default = "2omegasinlat" + ! This specifies how the Coriolis parameter is specified: + ! 2omegasinlat - Use twice the planetary rotation rate + ! times the sine of latitude. + ! betaplane - Use a beta-plane or f-plane. + ! USER - call a user modified routine. + +! === module MOM_verticalGrid === +! Parameters providing information about the vertical grid. +NK =65 ! [nondim] + ! The number of model layers. + +! === module MOM_EOS === + +! === module MOM_tracer_flow_control === + +! === module MOM_coord_initialization === +REGRIDDING_COORDINATE_MODE = "Z*" ! default = "LAYER" + ! Coordinate mode for vertical regridding. Choose among the following + ! possibilities: LAYER - Isopycnal or stacked shallow water layers + ! ZSTAR, Z* - stretched geopotential z* + ! SIGMA_SHELF_ZSTAR - stretched geopotential z* ignoring shelf + ! SIGMA - terrain following coordinates + ! RHO - continuous isopycnal + ! HYCOM1 - HyCOM-like hybrid coordinate + ! SLIGHT - stretched coordinates above continuous isopycnal + ! ADAPTIVE - optimize for smooth neutral density surfaces +ALE_COORDINATE_CONFIG = "FILE:./INPUT/vgrid_65L_20200626.nc,dz" ! default = "UNIFORM" + ! Determines how to specify the coordinate resolution. Valid options are: + ! PARAM - use the vector-parameter ALE_RESOLUTION + ! UNIFORM[:N] - uniformly distributed + ! FILE:string - read from a file. The string specifies + ! the filename and variable name, separated + ! by a comma or space, e.g. FILE:lev.nc,dz + ! or FILE:lev.nc,interfaces=zw + ! WOA09[:N] - the WOA09 vertical grid (approximately) + ! FNC1:string - FNC1:dz_min,H_total,power,precision + ! HYBRID:string - read from a file. The string specifies + ! the filename and two variable names, separated + ! by a comma or space, for sigma-2 and dz. e.g. + ! HYBRID:vgrid.nc,sigma2,dz +!ALE_RESOLUTION = 4*2.0, 2*2.01, 2*2.02, 2.05, 2.07, 2.09, 2.13, 2.18, 2.24, 2.3, 2.4, 2.5, 2.62, 2.78, 2.95, 3.17, 3.42, 3.71, 4.07, 4.48, 4.97, 5.55, 6.23, 7.04, 7.99, 9.11, 10.43, 11.98, 13.8, 15.94, 18.42, 21.32, 24.66, 28.51, 32.91, 37.92, 43.56, 49.87, 56.88, 64.55, 72.91, 81.87, 91.4, 101.39, 111.73, 122.29, 132.93, 143.47, 153.78, 163.7, 173.07, 181.78, 189.76, 196.89, 203.17, 208.58, 213.14, 216.87, 219.87, 222.19, 223.94, 225.18, 226.04, 226.59, 226.9, 227.07, 227.14, 2*227.16, 227.17 ! [m] + ! The distribution of vertical resolution for the target + ! grid used for Eulerian-like coordinates. For example, + ! in z-coordinate mode, the parameter is a list of level + ! thicknesses (in m). In sigma-coordinate mode, the list + ! is of non-dimensional fractions of the water column. + +! === module MOM_state_initialization === +INIT_LAYERS_FROM_Z_FILE = True ! [Boolean] default = False + ! If true, initialize the layer thicknesses, temperatures, and salinities from a + ! Z-space file on a latitude-longitude grid. + +! === module MOM_initialize_layers_from_Z === +TEMP_SALT_Z_INIT_FILE = "IC_FILE_COLUMN.nc" ! default = "temp_salt_z.nc" + ! The name of the z-space input file used to initialize temperatures (T) and + ! salinities (S). If T and S are not in the same file, TEMP_Z_INIT_FILE and + ! SALT_Z_INIT_FILE must be set. +Z_INIT_FILE_PTEMP_VAR = "Temp" ! default = "ptemp" + ! The name of the potential temperature variable in TEMP_Z_INIT_FILE. +Z_INIT_FILE_SALT_VAR = "Salt" ! default = "salt" + ! The name of the salinity variable in SALT_Z_INIT_FILE. +Z_INIT_HOMOGENIZE = True ! [Boolean] default = False + ! If True, then horizontally homogenize the interpolated initial conditions. +Z_INIT_ALE_REMAPPING = True ! [Boolean] default = False + ! If True, then remap straight to model coordinate from file. + +! === module MOM_diag_mediator === + +! === module MOM_lateral_mixing_coeffs === + +! === module MOM_set_visc === +HBBL = 10.0 ! [m] + ! The thickness of a bottom boundary layer with a viscosity of KVBBL if + ! BOTTOMDRAGLAW is not defined, or the thickness over which near-bottom + ! velocities are averaged for the drag law if BOTTOMDRAGLAW is defined but + ! LINEAR_DRAG is not. +CDRAG = 0.0 ! [nondim] default = 0.003 + ! CDRAG is the drag coefficient relating the magnitude of the velocity field to + ! the bottom stress. CDRAG is only used if BOTTOMDRAGLAW is defined. +DRAG_BG_VEL = 0.1 ! [m s-1] default = 0.0 + ! DRAG_BG_VEL is either the assumed bottom velocity (with LINEAR_DRAG) or an + ! unresolved velocity that is combined with the resolved velocity to estimate + ! the velocity magnitude. DRAG_BG_VEL is only used when BOTTOMDRAGLAW is + ! defined. +BBL_THICK_MIN = 0.1 ! [m] default = 0.0 + ! The minimum bottom boundary layer thickness that can be used with + ! BOTTOMDRAGLAW. This might be Kv/(cdrag*drag_bg_vel) to give Kv as the minimum + ! near-bottom viscosity. +KV = 1.0E-04 ! [m2 s-1] + ! The background kinematic viscosity in the interior. The molecular value, ~1e-6 + ! m2 s-1, may be used. + +! === module MOM_thickness_diffuse === + +! === module MOM_dynamics_unsplit === + +! === module MOM_continuity === + +! === module MOM_continuity_PPM === +UPWIND_1ST_CONTINUITY = True ! [Boolean] default = False + ! If true, CONTINUITY_PPM becomes a 1st-order upwind continuity solver. This + ! scheme is highly diffusive but may be useful for debugging or in single-column + ! mode where its minimal stencil is useful. +ETA_TOLERANCE = 1.0E-06 ! [m] default = 3.75E-09 + ! The tolerance for the differences between the barotropic and baroclinic + ! estimates of the sea surface height due to the fluxes through each face. The + ! total tolerance for SSH is 4 times this value. The default is + ! 0.5*NK*ANGSTROM, and this should not be set less than about + ! 10^-15*MAXIMUM_DEPTH. +ETA_TOLERANCE_AUX = 0.001 ! [m] default = 1.0E-06 + ! The tolerance for free-surface height discrepancies between the barotropic + ! solution and the sum of the layer thicknesses when calculating the auxiliary + ! corrected velocities. By default, this is the same as ETA_TOLERANCE, but can + ! be made larger for efficiency. + +! === module MOM_CoriolisAdv === +CORIOLIS_EN_DIS = True ! [Boolean] default = False + ! If true, two estimates of the thickness fluxes are used to estimate the + ! Coriolis term, and the one that dissipates energy relative to the other one is + ! used. +BOUND_CORIOLIS = True ! [Boolean] default = False + ! If true, the Coriolis terms at u-points are bounded by the four estimates of + ! (f+rv)v from the four neighboring v-points, and similarly at v-points. This + ! option is always effectively false with CORIOLIS_EN_DIS defined and + ! CORIOLIS_SCHEME set to SADOURNY75_ENERGY. + +! === module MOM_PressureForce === + +! === module MOM_PressureForce_AFV === + +! === module MOM_hor_visc === +BIHARMONIC = False ! [Boolean] default = True + ! If true, use a biharmonic horizontal viscosity. BIHARMONIC may be used with + ! LAPLACIAN. + +! === module MOM_vert_friction === +HARMONIC_VISC = True ! [Boolean] default = False + ! If true, use the harmonic mean thicknesses for calculating the vertical + ! viscosity. +HMIX_FIXED = 0.01 ! [m] + ! The prescribed depth over which the near-surface viscosity and diffusivity are + ! elevated when the bulk mixed layer is not used. +KVML = 0.0 ! [m2 s-1] default = 1.0E-04 + ! The kinematic viscosity in the mixed layer. A typical value is ~1e-2 m2 s-1. + ! KVML is not used if BULKMIXEDLAYER is true. The default is set by KV. +MAXVEL = 6.0 ! [m s-1] default = 3.0E+08 + ! The maximum velocity allowed before the velocity components are truncated. +VEL_UNDERFLOW = 1.0E-30 ! [m s-1] default = 0.0 + ! A negligibly small velocity magnitude below which velocity components are set + ! to 0. A reasonable value might be 1e-30 m/s, which is less than an Angstrom + ! divided by the age of the universe. + +! === module MOM_diagnostics === + +! === module MOM_diabatic_driver === +! The following parameters are used for diabatic processes. + +! === module MOM_CVMix_KPP === +! This is the MOM wrapper to CVMix:KPP +! See http://cvmix.github.io/ +USE_KPP = True ! [Boolean] default = False + ! If true, turns on the [CVMix] KPP scheme of Large et al., 1994, to calculate + ! diffusivities and non-local transport in the OBL. +KPP% +INTERP_TYPE = "cubic" ! default = "quadratic" + ! Type of interpolation to determine the OBL depth. + ! Allowed types are: linear, quadratic, cubic. +NLT_SHAPE = "PARABOLIC" ! default = "CVMix" + ! MOM6 method to set nonlocal transport profile. Over-rides the result from + ! CVMix. Allowed values are: + ! CVMix - Uses the profiles from CVMix specified by MATCH_TECHNIQUE + ! LINEAR - A linear profile, 1-sigma + ! PARABOLIC - A parablic profile, (1-sigma)^2 + ! CUBIC - A cubic profile, (1-sigma)^2(1+2*sigma) + ! CUBIC_LMD - The original KPP profile +%KPP + +! === module MOM_set_diffusivity === +ML_RADIATION = True ! [Boolean] default = False + ! If true, allow a fraction of TKE available from wind work to penetrate below + ! the base of the mixed layer with a vertical decay scale determined by the + ! minimum of: (1) The depth of the mixed layer, (2) an Ekman length scale. +TKE_DECAY = 10.0 ! [nondim] default = 2.5 + ! The ratio of the natural Ekman depth to the TKE decay scale. +ML_OMEGA_FRAC = 1.0 ! [nondim] default = 0.0 + ! When setting the decay scale for turbulence, use this fraction of the absolute + ! rotation rate blended with the local value of f, as sqrt((1-of)*f^2 + + ! of*4*omega^2). + +! === module MOM_bkgnd_mixing === +! Adding static vertical background mixing coefficients +KD = 2.0E-05 ! [m2 s-1] default = 0.0 + ! The background diapycnal diffusivity of density in the interior. Zero or the + ! molecular value, ~1e-7 m2 s-1, may be used. +KD_MIN = 2.0E-06 ! [m2 s-1] default = 2.0E-07 + ! The minimum diapycnal diffusivity. + +! === module MOM_kappa_shear === +! Parameterization of shear-driven turbulence following Jackson, Hallberg and Legg, JPO 2008 +USE_JACKSON_PARAM = True ! [Boolean] default = False + ! If true, use the Jackson-Hallberg-Legg (JPO 2008) shear mixing + ! parameterization. +MAX_RINO_IT = 25 ! [nondim] default = 50 + ! The maximum number of iterations that may be used to estimate the Richardson + ! number driven mixing. + +! === module MOM_diabatic_aux === +! The following parameters are used for auxiliary diabatic processes. +RECLAIM_FRAZIL = False ! [Boolean] default = True + ! If true, try to use any frazil heat deficit to cool any overlying layers down + ! to the freezing point, thereby avoiding the creation of thin ice when the SST + ! is above the freezing point. +VAR_PEN_SW = True ! [Boolean] default = False + ! If true, use one of the CHL_A schemes specified by OPACITY_SCHEME to determine + ! the e-folding depth of incoming short wave radiation. + +! === module MOM_opacity === +PEN_SW_NBANDS = 3 ! default = 1 + ! The number of bands of penetrating shortwave radiation. + +! === module MOM_tracer_advect === + +! === module MOM_tracer_hor_diff === +KHTR = 600.0 ! [m2 s-1] default = 0.0 + ! The background along-isopycnal tracer diffusivity. + +! === module MOM_sum_output === +MAXTRUNC = 5000 ! [truncations save_interval-1] default = 0 + ! The run will be stopped, and the day set to a very large value if the velocity + ! is truncated more than MAXTRUNC times between energy saves. Set MAXTRUNC to 0 + ! to stop if there is any truncation of velocities. +ENERGYSAVEDAYS = 10.0 ! [days] default = 1.0 + ! The interval in units of TIMEUNIT between saves of the energies of the run and + ! other globally summed diagnostics. + +! === module MOM_surface_forcing === +BUOY_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how buoyancy forcing is specified. Valid + ! options include (file), (zero), (linear), (USER), (BFB) and (NONE). +WIND_CONFIG = "data_override" ! default = "zero" + ! The character string that indicates how wind forcing is specified. Valid + ! options include (file), (2gyre), (1gyre), (gyres), (zero), and (USER). +READ_GUST_2D = False ! [Boolean] default = False + ! If true, use a 2-dimensional gustiness supplied from an input file +GUST_CONST = 0.02 ! [Pa] default = 0.0 + ! The background gustiness in the winds. + +! === module MOM_main (MOM_driver) === +DAYMAX = 1825.0 ! [days] + ! The final time of the whole simulation, in units of TIMEUNIT seconds. This + ! also sets the potential end time of the present run segment if the end time is + ! not set via ocean_solo_nml in input.nml. +RESTART_CONTROL = 0 ! default = 1 + ! An integer whose bits encode which restart files are written. Add 2 (bit 1) + ! for a time-stamped file, and odd (bit 0) for a non-time-stamped file. A + ! non-time-stamped restart file is saved at the end of the run segment for any + ! non-negative value. +RESTINT = 3650.0 ! [days] default = 0.0 + ! The interval between saves of the restart file in units of TIMEUNIT. Use 0 + ! (the default) to not save incremental restart files at all. + +! === module MOM_write_cputime === +MAXCPU = 2.88E+04 ! [wall-clock seconds] default = -1.0 + ! The maximum amount of cpu time per processor for which MOM should run before + ! saving a restart file and quitting with a return value that indicates that a + ! further run is required to complete the simulation. If automatic restarts are + ! not desired, use a negative value for MAXCPU. MAXCPU has units of wall-clock + ! seconds, so the actual CPU time used is larger by a factor of the number of + ! processors used. + +! Debugging parameters set to non-default values +U_TRUNC_FILE = "U_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to zonal + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. +V_TRUNC_FILE = "V_velocity_truncations" ! default = "" + ! The absolute path to a file into which the accelerations leading to meridional + ! velocity truncations are written. Undefine this for efficiency if this + ! diagnostic is not needed. diff --git a/standalone/examples/single_column_MARBL/STNP/MOM_override b/standalone/examples/single_column_MARBL/STNP/MOM_override new file mode 120000 index 00000000..5a830e51 --- /dev/null +++ b/standalone/examples/single_column_MARBL/STNP/MOM_override @@ -0,0 +1 @@ +INPUT/STNP/MOM_override \ No newline at end of file diff --git a/standalone/examples/single_column_MARBL/STNP/MOM_override2 b/standalone/examples/single_column_MARBL/STNP/MOM_override2 new file mode 100644 index 00000000..94a755f0 --- /dev/null +++ b/standalone/examples/single_column_MARBL/STNP/MOM_override2 @@ -0,0 +1,4 @@ +! Generated by run_suite.csh +!#override DAYMAX = 365 +!#override ALE_COORDINATE_CONFIG = "FILE:INPUT/vgrid_cm4_4.nc,dz" ! 4 meter surface +!#override ALE_COORDINATE_CONFIG = "FILE:INPUT/vgrid_cm4.nc,dz" ! 2 meter surface diff --git a/standalone/examples/single_column_MARBL/STNP/RESTART/.gitignore b/standalone/examples/single_column_MARBL/STNP/RESTART/.gitignore new file mode 100644 index 00000000..d6b7ef32 --- /dev/null +++ b/standalone/examples/single_column_MARBL/STNP/RESTART/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/standalone/examples/single_column_MARBL/STNP/data_table b/standalone/examples/single_column_MARBL/STNP/data_table new file mode 100644 index 00000000..887bbe57 --- /dev/null +++ b/standalone/examples/single_column_MARBL/STNP/data_table @@ -0,0 +1,19 @@ +# gridname, fieldname_code, fieldname_file, file_name, ongrid, factor +"OCN" , "lw", "LWDN", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sw", "SWDN", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "rain", "RAIN", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "evap", "EVAP", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "sens", "SENS_HEAT_FLUX", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "taux", "TAUX", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "tauy", "TAUY", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "snow", "SNOW", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 0.0 +"OCN" , "runoff", "", "", .true., 0.0 +"OCN" , "calving", "", "", .true., 0.0 +"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_dust_flux", "SEAICE_DUST_FLUX", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "seaice_bc_flux", "SEAICE_BC_FLUX", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "nhx_dep", "NDEP_NHx", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "noy_dep", "NDEP_NOy", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "u10_sqr", "U10_SQR", "./INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 diff --git a/standalone/examples/single_column_MARBL/STNP/diag_table b/standalone/examples/single_column_MARBL/STNP/diag_table new file mode 100644 index 00000000..8f6227fa --- /dev/null +++ b/standalone/examples/single_column_MARBL/STNP/diag_table @@ -0,0 +1,771 @@ +"MOM Experiment" +1 1 1 0 0 0 +"prog", 1,"days",1,"days","Time" +"prog_z", 1,"days",1,"days","Time" +"surffluxes", 1,"days",1,"days","Time" +"ave_prog", 1,"days",1,"days","Time", +#"mom", 5,"days",1,"days","Time", +#"bt_mom", 5,"days",1,"days","Time", +"visc", 1,"days",1,"days","Time", +#"energy", 5,"days",1,"days","Time", +"marbl", 1,"days",1,"days","Time", +"marbl2", 1,"days",1,"days","Time", + + +#This is the field section of the diag_table. + +# Prognostic Ocean fields: +#========================= + +"ocean_model","u","u","prog","all",.false.,"none",2 +"ocean_model","v","v","prog","all",.false.,"none",2 +"ocean_model","h","h","prog","all",.false.,"none",1 +"ocean_model","e","e","prog","all",.false.,"none",2 +"ocean_model","temp","temp","prog","all",.false.,"none",2 +"ocean_model","salt","salt","prog","all",.false.,"none",2 +#"ocean_model","Rml","Rml","prog","all",.false.,"none",2 +#"ocean_model","h_ML","h_ML","prog","all",.false.,"none",2 + +#"ocean_model","RV","RV","prog","all",.false.,"none",2 +#"ocean_model","PV","PV","prog","all",.false.,"none",2 +#"ocean_model","e_D","e_D","prog","all",.false.,"none",2 + +"ocean_model","u","u","ave_prog","all",.true.,"none",2 +"ocean_model","v","v","ave_prog","all",.true.,"none",2 +#"ocean_model","h","h","ave_prog","all",.true.,"none",1 +"ocean_model","e","e","ave_prog","all",.true.,"none",2 +"ocean_model","temp","temp","ave_prog","all",.true.,"none",2 +"ocean_model","salt","salt","ave_prog","all",.true.,"none",2 +#"ocean_model","Rml","Rml","ave_prog","all",.true.,"none",2 + +# Z level output +#================ +"ocean_model_z","u","u","prog_z","all",.false.,"none",2 +"ocean_model_z","v","v","prog_z","all",.false.,"none",2 +"ocean_model_z","temp","temp","prog_z","all",.false.,"none",2 +"ocean_model_z","salt","salt","prog_z","all",.false.,"none",2 +# Auxilary Tracers: +#================== +#"ocean_model","vintage","vintage","prog","all",.false.,"none",2 +#"ocean_model","age","age","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","prog","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog","all",.false.,"none",2 +"ocean_model","O2","O2","prog","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog","all",.false.,"none",2 +"ocean_model","DON","DON","prog","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog","all",.false.,"none",2 +"ocean_model","spC","spC","prog","all",.false.,"none",2 +"ocean_model","spP","spP","prog","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog","all",.false.,"none",2 +"ocean_model","PO4","PO4","ave_prog","all",.true.,"none",2 +"ocean_model","NO3","NO3","ave_prog","all",.true.,"none",2 +"ocean_model","SiO3","SiO3","ave_prog","all",.true.,"none",2 +"ocean_model","NH4","NH4","ave_prog","all",.true.,"none",2 +"ocean_model","Fe","Fe","ave_prog","all",.true.,"none",2 +"ocean_model","Lig","Lig","ave_prog","all",.true.,"none",2 +"ocean_model","O2","O2","ave_prog","all",.true.,"none",2 +"ocean_model","DIC","DIC","ave_prog","all",.true.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","ALK","ALK","ave_prog","all",.true.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","ave_prog","all",.true.,"none",2 +"ocean_model","DOC","DOC","ave_prog","all",.true.,"none",2 +"ocean_model","DON","DON","ave_prog","all",.true.,"none",2 +"ocean_model","DOP","DOP","ave_prog","all",.true.,"none",2 +"ocean_model","DOPr","DOPr","ave_prog","all",.true.,"none",2 +"ocean_model","DONr","DONr","ave_prog","all",.true.,"none",2 +"ocean_model","DOCr","DOCr","ave_prog","all",.true.,"none",2 +"ocean_model","microzooC","microzooC","ave_prog","all",.true.,"none",2 +"ocean_model","mesozooC","mesozooC","ave_prog","all",.true.,"none",2 +"ocean_model","spChl","spChl","ave_prog","all",.true.,"none",2 +"ocean_model","spC","spC","ave_prog","all",.true.,"none",2 +"ocean_model","spP","spP","ave_prog","all",.true.,"none",2 +"ocean_model","spFe","spFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoChl","coccoChl","ave_prog","all",.true.,"none",2 +"ocean_model","coccoC","coccoC","ave_prog","all",.true.,"none",2 +"ocean_model","coccoP","coccoP","ave_prog","all",.true.,"none",2 +"ocean_model","coccoFe","coccoFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","ave_prog","all",.true.,"none",2 +"ocean_model","diatChl","diatChl","ave_prog","all",.true.,"none",2 +"ocean_model","diatC","diatC","ave_prog","all",.true.,"none",2 +"ocean_model","diatP","diatP","ave_prog","all",.true.,"none",2 +"ocean_model","diatFe","diatFe","ave_prog","all",.true.,"none",2 +"ocean_model","diatSi","diatSi","ave_prog","all",.true.,"none",2 +"ocean_model","diazChl","diazChl","ave_prog","all",.true.,"none",2 +"ocean_model","diazC","diazC","ave_prog","all",.true.,"none",2 +"ocean_model","diazP","diazP","ave_prog","all",.true.,"none",2 +"ocean_model","diazFe","diazFe","ave_prog","all",.true.,"none",2 +"ocean_model","PO4","PO4","prog_z","all",.false.,"none",2 +"ocean_model","NO3","NO3","prog_z","all",.false.,"none",2 +"ocean_model","SiO3","SiO3","prog_z","all",.false.,"none",2 +"ocean_model","NH4","NH4","prog_z","all",.false.,"none",2 +"ocean_model","Fe","Fe","prog_z","all",.false.,"none",2 +"ocean_model","Lig","Lig","prog_z","all",.false.,"none",2 +"ocean_model","O2","O2","prog_z","all",.false.,"none",2 +"ocean_model","DIC","DIC","prog_z","all",.false.,"none",2 +"ocean_model","DIC_ALT_CO2","DIC_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","ALK","ALK","prog_z","all",.false.,"none",2 +"ocean_model","ALK_ALT_CO2","ALK_ALT_CO2","prog_z","all",.false.,"none",2 +"ocean_model","DOC","DOC","prog_z","all",.false.,"none",2 +"ocean_model","DON","DON","prog_z","all",.false.,"none",2 +"ocean_model","DOP","DOP","prog_z","all",.false.,"none",2 +"ocean_model","DOPr","DOPr","prog_z","all",.false.,"none",2 +"ocean_model","DONr","DONr","prog_z","all",.false.,"none",2 +"ocean_model","DOCr","DOCr","prog_z","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog_z","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog_z","all",.false.,"none",2 +"ocean_model","spChl","spChl","prog_z","all",.false.,"none",2 +"ocean_model","spC","spC","prog_z","all",.false.,"none",2 +"ocean_model","spP","spP","prog_z","all",.false.,"none",2 +"ocean_model","spFe","spFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog_z","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog_z","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog_z","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog_z","all",.false.,"none",2 +"ocean_model","diatChl","diatChl","prog_z","all",.false.,"none",2 +"ocean_model","diatC","diatC","prog_z","all",.false.,"none",2 +"ocean_model","diatP","diatP","prog_z","all",.false.,"none",2 +"ocean_model","diatFe","diatFe","prog_z","all",.false.,"none",2 +"ocean_model","diatSi","diatSi","prog_z","all",.false.,"none",2 +"ocean_model","diazChl","diazChl","prog_z","all",.false.,"none",2 +"ocean_model","diazC","diazC","prog_z","all",.false.,"none",2 +"ocean_model","diazP","diazP","prog_z","all",.false.,"none",2 +"ocean_model","diazFe","diazFe","prog_z","all",.false.,"none",2 + + +# Continuity Equation Terms: +#=========================== +#"ocean_model","dhdt","dhdt","cont","all",.true.,"none",2 +"ocean_model","wd","wd","prog","all",.false.,"none",2 +#"ocean_model","uh","uh","cont","all",.true.,"none",2 +#"ocean_model","vh","vh","cont","all",.true.,"none",2 +#"ocean_model","uhGM","uhGM","cont","all",.true.,"none",2 +#"ocean_model","vhGM","vhGM","cont","all",.true.,"none",2 +#"ocean_model","uhbt","uhbt","cont","all",.true.,"none",2 +#"ocean_model","vhbt","vhbt","cont","all",.true.,"none",2 +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","KH_u","KH_u","cont","all",.true.,"none",2 +#"ocean_model","KH_v","KH_v","cont","all",.true.,"none",2 +#"ocean_model","GMwork","GMwork","cont","all",.true.,"none",2 + +# Continuity Equation Terms In Pure Potential Density Coordiantes: +#================================================================= +#"ocean_model","h_rho","h_rho","cont","all",.true.,"none",2 +#"ocean_model","uh_rho","uh_rho","cont","all",.true.,"none",2 +#"ocean_model","vh_rho","vh_rho","cont","all",.true.,"none",2 +#"ocean_model","uhGM_rho","uhGM_rho","cont","all",.true.,"none",2 +#"ocean_model","vhGM_rho","vhGM_rho","cont","all",.true.,"none",2 + +# +# Tracer Fluxes: +#================== +#"ocean_model","T_adx", "T_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_ady", "T_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffx","T_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","T_diffy","T_diffy","ave_prog","all",.true.,"none",2 +#"ocean_model","S_adx", "S_adx", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_ady", "S_ady", "ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffx","S_diffx","ave_prog","all",.true.,"none",2 +#"ocean_model","S_diffy","S_diffy","ave_prog","all",.true.,"none",2 + + +# Momentum Balance Terms: +#======================= +#"ocean_model","dudt","dudt","mom","all",.true.,"none",2 +#"ocean_model","dvdt","dvdt","mom","all",.true.,"none",2 +#"ocean_model","CAu","CAu","mom","all",.true.,"none",2 +#"ocean_model","CAv","CAv","mom","all",.true.,"none",2 +#"ocean_model","PFu","PFu","mom","all",.true.,"none",2 +#"ocean_model","PFv","PFv","mom","all",.true.,"none",2 +#"ocean_model","du_dt_visc","du_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","dv_dt_visc","dv_dt_visc","mom","all",.true.,"none",2 +#"ocean_model","diffu","diffu","mom","all",.true.,"none",2 +#"ocean_model","diffv","diffv","mom","all",.true.,"none",2 +#"ocean_model","dudt_dia","dudt_dia","mom","all",.true.,"none",2 +#"ocean_model","dvdt_dia","dvdt_dia","mom","all",.true.,"none",2 +# Subterms that should not be added to a closed budget. +#"ocean_model","gKEu","gKEu","mom","all",.true.,"none",2 +#"ocean_model","gKEv","gKEv","mom","all",.true.,"none",2 +#"ocean_model","rvxu","rvxu","mom","all",.true.,"none",2 +#"ocean_model","rvxv","rvxv","mom","all",.true.,"none",2 +#"ocean_model","PFu_bc","PFu_bc","mom","all",.true.,"none",2 +#"ocean_model","PFv_bc","PFv_bc","mom","all",.true.,"none",2 + +# Barotropic Momentum Balance Terms: +# (only available with split time stepping.) +#=========================================== +#"ocean_model","PFuBT","PFuBT","bt_mom","all",.true.,"none",2 +#"ocean_model","PFvBT","PFvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CoruBT","CoruBT","bt_mom","all",.true.,"none",2 +#"ocean_model","CorvBT","CorvBT","bt_mom","all",.true.,"none",2 +#"ocean_model","ubtforce","ubtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","vbtforce","vbtforce","bt_mom","all",.true.,"none",2 +#"ocean_model","u_accel_bt","u_accel_bt","bt_mom","all",.true.,"none",2 +#"ocean_model","v_accel_bt","v_accel_bt","bt_mom","all",.true.,"none",2 +# +# Viscosities and diffusivities: +#=============================== +#"ocean_model","Kd_effective","Kd_effective","visc","all",.true.,"none",2 +"ocean_model","Kd_interface","Kd_interface","visc","all",.true.,"none",2 +#"ocean_model","Ahh","Ahh","visc","all",.true.,"none",2 +#"ocean_model","Ahq","Ahq","visc","all",.true.,"none",2 +#"ocean_model","Khh","Khh","visc","all",.true.,"none",2 +#"ocean_model","Khq","Khq","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_u","bbl_thick_u","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_u","kv_bbl_u","visc","all",.true.,"none",2 +#"ocean_model","bbl_thick_v","bbl_thick_v","visc","all",.true.,"none",2 +#"ocean_model","kv_bbl_v","kv_bbl_v","visc","all",.true.,"none",2 +#"ocean_model","av_visc","av_visc","visc","all",.true.,"none",2 +#"ocean_model","au_visc","au_visc","visc","all",.true.,"none",2 +#"ocean_model","FrictWork","FrictWork","visc","all",.true.,"none",2 +"ocean_model","KPP_uStar","KPP_uStar","visc","all",.true.,"none",2 +"ocean_model","KPP_buoyFlux","KPP_buoyFlux","visc","all",.true.,"none",2 +"ocean_model","KPP_QminusSW","KPP_QminusSW","visc","all",.true.,"none",2 +"ocean_model","KPP_netSalt","KPP_netSalt","visc","all",.true.,"none",2 +"ocean_model","KPP_OBLdepth","KPP_OBLdepth","visc","all",.true.,"none",2 +"ocean_model","KPP_sigma","KPP_sigma","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkRi","KPP_BulkRi","visc","all",.true.,"none",2 +"ocean_model","KPP_Ws","KPP_Ws","visc","all",.true.,"none",2 +"ocean_model","KPP_N","KPP_N","visc","all",.true.,"none",2 +"ocean_model","KPP_N2","KPP_N2","visc","all",.true.,"none",2 +"ocean_model","KPP_Vt2","KPP_Vt2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkUz2","KPP_BulkUz2","visc","all",.true.,"none",2 +"ocean_model","KPP_BulkDrho","KPP_BulkDrho","visc","all",.true.,"none",2 +"ocean_model","KPP_Kd_in","KPP_Kd_in","visc","all",.true.,"none",2 +"ocean_model","KPP_Kheat","KPP_Kheat","visc","all",.true.,"none",2 +"ocean_model","KPP_Ksalt","KPP_Ksalt","visc","all",.true.,"none",2 +"ocean_model","KPP_Kv","KPP_Kv","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_heat","KPP_NLtransport_heat","visc","all",.true.,"none",2 +"ocean_model","KPP_NLtransport_salt","KPP_NLtransport_salt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dTdt","KPP_NLT_dTdt","visc","all",.true.,"none",2 +"ocean_model","KPP_NLT_dSdt","KPP_NLT_dSdt","visc","all",.true.,"none",2 +"ocean_model","KPP_Tsurf","KPP_Tsurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Ssurf","KPP_Ssurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Usurf","KPP_Usurf","visc","all",.true.,"none",2 +"ocean_model","KPP_Vsurf","KPP_Vsurf","visc","all",.true.,"none",2 +"ocean_model","MLD_003","MLD_003","visc","all",.true.,"none",2 + +# +# Kinetic Energy Balance Terms: +#============================= +#"ocean_model","KE","KE","energy","all",.true.,"none",2 +#"ocean_model","dKE_dt","dKE_dt","energy","all",.true.,"none",2 +#"ocean_model","PE_to_KE","PE_to_KE","energy","all",.true.,"none",2 +#"ocean_model","KE_Coradv","KE_Coradv","energy","all",.true.,"none",2 +#"ocean_model","KE_adv","KE_adv","energy","all",.true.,"none",2 +#"ocean_model","KE_visc","KE_visc","energy","all",.true.,"none",2 +#"ocean_model","KE_horvisc","KE_horvisc","energy","all",.true.,"none",2 +#"ocean_model","KE_dia","KE_dia","energy","all",.true.,"none",2 + +# Surface Forcing: +#================= +"ocean_model","taux","taux","surffluxes","all",.true.,"none",2 +"ocean_model","tauy","tauy","surffluxes","all",.true.,"none",2 +"ocean_model","ustar","ustar","surffluxes","all",.true.,"none",2 +"ocean_model","PRCmE","PRCmE","surffluxes","all",.true.,"none",2 +"ocean_model","evap","evap","surffluxes","all",.true.,"none",2 +"ocean_model","precip","precip","surffluxes","all",.true.,"none",2 +"ocean_model","fprec","fprec","surffluxes","all",.true.,"none",2 +"ocean_model","lprec","lprec","surffluxes","all",.true.,"none",2 +"ocean_model","net_heat_surface","net_heat_surface","surffluxes","all",.true.,"none",2 +"ocean_model","SW","SW","surffluxes","all",.true.,"none",2 +"ocean_model","SW_pen","SW_pen","surffluxes","all",.true.,"none",2 +"ocean_model","LwLatSens","LwLatSens","surffluxes","all",.true.,"none",2 +#"ocean_model","p_surf","p_surf","surffluxes","all",.true.,"none",2 +#"ocean_model","salt_flux","salt_flux","surffluxes","all",.true.,"none",2 + +# MARBL diagnostics: +#=================== +"ocean_model", "rsdoabsorb", "rsdoabsorb", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_IFRAC", "ECOSYS_IFRAC", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_XKW", "ECOSYS_XKW", "marbl", "all", .false., "none", 2 +"ocean_model", "ECOSYS_ATM_PRESS", "ECOSYS_ATM_PRESS", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_O2", "PV_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_O2", "SCHMIDT_O2", "marbl", "all", .false., "none", 2 +"ocean_model", "O2SAT", "O2SAT", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR", "CO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR", "DCO2STAR", "marbl", "all", .false., "none", 2 +"ocean_model", "pCO2SURF", "pCO2SURF", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2", "DpCO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "PV_CO2", "PV_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "SCHMIDT_CO2", "SCHMIDT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_CO2", "FG_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH", "PH", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_CO2", "ATM_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "CO2STAR_ALT_CO2", "CO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DCO2STAR_ALT_CO2", "DCO2STAR_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pCO2SURF_ALT_CO2", "pCO2SURF_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "DpCO2_ALT_CO2", "DpCO2_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "FG_ALT_CO2", "FG_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "PH_ALT_CO2", "PH_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "ATM_ALT_CO2", "ATM_ALT_CO2", "marbl", "all", .false., "none", 2 +"ocean_model", "IRON_FLUX", "IRON_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "DUST_FLUX", "DUST_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NOx_FLUX", "NOx_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHy_FLUX", "NHy_FLUX", "marbl", "all", .false., "none", 2 +"ocean_model", "NHx_SURFACE_EMIS", "NHx_SURFACE_EMIS", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatcalc", "zsatcalc", "marbl", "all", .false., "none", 2 +"ocean_model", "zsatarag", "zsatarag", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN", "O2_ZMIN", "marbl", "all", .false., "none", 2 +"ocean_model", "O2_ZMIN_DEPTH", "O2_ZMIN_DEPTH", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint", "photoC_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_TOT_zint_100m", "photoC_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint", "photoC_NO3_TOT_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_TOT_zint_100m", "photoC_NO3_TOT_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint", "DOC_prod_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_prod_zint_100m", "DOC_prod_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint", "DOC_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOC_remin_zint_100m", "DOC_remin_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint", "DOCr_remin_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "DOCr_remin_zint_100m", "DOCr_remin_zint_100m", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ctot", "Jint_Ctot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ntot", "Jint_Ntot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Ptot", "Jint_Ptot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Sitot", "Jint_Sitot", "marbl", "all", .false., "none", 2 +#"ocean_model", "Jint_Fetot", "Jint_Fetot", "marbl", "all", .false., "none", 2 +#"ocean_model", "calcToFloor", "calcToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed", "calcToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "calcToSed_ALT_CO2", "calcToSed_ALT_CO2", "marbl", "all", .false., "none", 2 +#"ocean_model", "pocToFloor", "pocToFloor", "marbl", "all", .false., "none", 2 +"ocean_model", "pocToSed", "pocToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "ponToSed", "ponToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "SedDenitrif", "SedDenitrif", "marbl", "all", .false., "none", 2 +#"ocean_model", "OtherRemin", "OtherRemin", "marbl", "all", .false., "none", 2 +"ocean_model", "popToSed", "popToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "bsiToSed", "bsiToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "dustToSed", "dustToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "pfeToSed", "pfeToSed", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_surf", "sp_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_N_lim_Cweight_avg_100m", "sp_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_surf", "sp_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_P_lim_Cweight_avg_100m", "sp_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_surf", "sp_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_Fe_lim_Cweight_avg_100m", "sp_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_surf", "sp_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_light_lim_Cweight_avg_100m", "sp_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint", "photoC_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_sp_zint_100m", "photoC_sp_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_sp_zint", "photoC_NO3_sp_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint", "graze_sp_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint_100m", "graze_sp_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint", "graze_sp_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint_100m", "graze_sp_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint", "graze_sp_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint_100m", "graze_sp_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint", "graze_sp_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint_100m", "graze_sp_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint", "sp_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_zint_100m", "sp_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint", "sp_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_poc_zint_100m", "sp_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint", "sp_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_loss_doc_zint_100m", "sp_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint", "sp_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "sp_agg_zint_100m", "sp_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_surf", "cocco_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_Cweight_avg_100m", "cocco_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_surf", "cocco_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_Cweight_avg_100m", "cocco_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_surf", "cocco_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_Cweight_avg_100m", "cocco_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_surf", "cocco_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_Cweight_avg_100m", "cocco_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint", "photoC_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint_100m", "photoC_cocco_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_cocco_zint", "photoC_NO3_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint", "cocco_CaCO3_form_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint_100m", "cocco_CaCO3_form_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint", "graze_cocco_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint_100m", "graze_cocco_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint", "graze_cocco_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint_100m", "graze_cocco_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint", "graze_cocco_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint_100m", "graze_cocco_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint", "graze_cocco_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint_100m", "graze_cocco_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint", "cocco_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint_100m", "cocco_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint", "cocco_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint_100m", "cocco_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint", "cocco_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint_100m", "cocco_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg_zint", "cocco_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg_zint_100m", "cocco_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_N_lim_surf", "diat_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_N_lim_Cweight_avg_100m", "diat_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_P_lim_surf", "diat_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_P_lim_Cweight_avg_100m", "diat_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Fe_lim_surf", "diat_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_Fe_lim_Cweight_avg_100m", "diat_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_SiO3_lim_surf", "diat_SiO3_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_SiO3_lim_Cweight_avg_100m", "diat_SiO3_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_light_lim_surf", "diat_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_light_lim_Cweight_avg_100m", "diat_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat_zint", "photoC_diat_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diat_zint_100m", "photoC_diat_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diat_zint", "photoC_NO3_diat_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat_zint", "graze_diat_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zint_100m", "graze_diat_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc_zint", "graze_diat_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc_zint_100m", "graze_diat_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc_zint", "graze_diat_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc_zint_100m", "graze_diat_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo_zint", "graze_diat_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo_zint_100m", "graze_diat_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "diat_loss_zint", "diat_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_zint_100m", "diat_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc_zint", "diat_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_poc_zint_100m", "diat_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc_zint", "diat_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_loss_doc_zint_100m", "diat_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_agg_zint", "diat_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diat_agg_zint_100m", "diat_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_N_lim_surf", "diaz_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_N_lim_Cweight_avg_100m", "diaz_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_P_lim_surf", "diaz_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_P_lim_Cweight_avg_100m", "diaz_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Fe_lim_surf", "diaz_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_Fe_lim_Cweight_avg_100m", "diaz_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_light_lim_surf", "diaz_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_light_lim_Cweight_avg_100m", "diaz_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diaz_zint", "photoC_diaz_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_diaz_zint_100m", "photoC_diaz_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_diaz_zint", "photoC_NO3_diaz_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zint", "graze_diaz_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zint_100m", "graze_diaz_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc_zint", "graze_diaz_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc_zint_100m", "graze_diaz_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc_zint", "graze_diaz_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc_zint_100m", "graze_diaz_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zoo_zint", "graze_diaz_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zoo_zint_100m", "graze_diaz_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "diaz_loss_zint", "diaz_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_zint_100m", "diaz_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "diaz_loss_poc_zint", 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+"ocean_model", "graze_microzoo_doc", "graze_microzoo_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_doc", "graze_mesozoo_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_zootot", "graze_microzoo_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_zootot", "graze_mesozoo_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_microzoo", "graze_microzoo_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_mesozoo", "graze_microzoo_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_microzoo", "graze_mesozoo_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_mesozoo", "graze_mesozoo_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_microzoo", "x_graze_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_mesozoo", "x_graze_mesozoo", "marbl2", "all", .false., "none", 2 +#"ocean_model", "PO4_RESTORE_TEND", "PO4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NO3_RESTORE_TEND", "NO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "SiO3_RESTORE_TEND", "SiO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "NH4_RESTORE_TEND", "NH4_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Fe_RESTORE_TEND", "Fe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "Lig_RESTORE_TEND", "Lig_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "O2_RESTORE_TEND", "O2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_RESTORE_TEND", "DIC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DIC_ALT_CO2_RESTORE_TEND", "DIC_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_RESTORE_TEND", "ALK_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "ALK_ALT_CO2_RESTORE_TEND", "ALK_ALT_CO2_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOC_RESTORE_TEND", "DOC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DON_RESTORE_TEND", "DON_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOP_RESTORE_TEND", "DOP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOPr_RESTORE_TEND", "DOPr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DONr_RESTORE_TEND", "DONr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "DOCr_RESTORE_TEND", "DOCr_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "microzooC_RESTORE_TEND", "microzooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "mesozooC_RESTORE_TEND", "mesozooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spChl_RESTORE_TEND", "spChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spC_RESTORE_TEND", "spC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spP_RESTORE_TEND", "spP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "spFe_RESTORE_TEND", "spFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoChl_RESTORE_TEND", "coccoChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoC_RESTORE_TEND", "coccoC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoP_RESTORE_TEND", "coccoP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoFe_RESTORE_TEND", "coccoFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoCaCO3_RESTORE_TEND", "coccoCaCO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatChl_RESTORE_TEND", "diatChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatC_RESTORE_TEND", "diatC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatP_RESTORE_TEND", "diatP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatFe_RESTORE_TEND", "diatFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diatSi_RESTORE_TEND", "diatSi_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazChl_RESTORE_TEND", "diazChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazC_RESTORE_TEND", "diazC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazP_RESTORE_TEND", "diazP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "diazFe_RESTORE_TEND", "diazFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 + +#============================================================================================= +# +#====> This file can be used with diag_manager/v2.0a (or higher) <==== +# +# +# FORMATS FOR FILE ENTRIES (not all input values are used) +# ------------------------ +# +#"file_name", output_freq, "output_units", format, "time_units", "time_long_name", ... +# (opt) new_file_frequecy, (opt) "new_file_freq_units", "new_file_start_date" +# +# +#output_freq: > 0 output frequency in "output_units" +# = 0 output frequency every time step +# =-1 output frequency at end of run +# +#output_units = units used for output frequency +# (years, months, days, minutes, hours, seconds) +# +#time_units = units used to label the time axis +# (days, minutes, hours, seconds) +# +# +# FORMAT FOR FIELD ENTRIES (not all input values are used) +# ------------------------ +# +#"module_name", "field_name", "output_name", "file_name" "time_sampling", time_avg, "other_opts", packing +# +#time_avg = .true. or .false. +# +#packing = 1 double precision +# = 2 float +# = 4 packed 16-bit integers +# = 8 packed 1-byte (not tested?) diff --git a/standalone/examples/single_column_MARBL/STNP/input.nml b/standalone/examples/single_column_MARBL/STNP/input.nml new file mode 100644 index 00000000..01e68431 --- /dev/null +++ b/standalone/examples/single_column_MARBL/STNP/input.nml @@ -0,0 +1,21 @@ + &MOM_input_nml + output_directory = './', + input_filename = 'n' + restart_input_dir = 'INPUT/', + restart_output_dir = 'RESTART/', + parameter_filename = 'MOM_input', + 'MOM_override', + 'MOM_override2' / + + &diag_manager_nml + max_output_fields = 750, + / + + &ocean_solo_nml + calendar = 'NOLEAP' +/ + + &fms_nml + clock_grain = 'LOOP', + domains_stack_size = 710000, + stack_size = 0 / diff --git a/standalone/examples/single_column_MARBL/STNP/single_column_z.ipynb b/standalone/examples/single_column_MARBL/STNP/single_column_z.ipynb new file mode 100644 index 00000000..5ba27191 --- /dev/null +++ b/standalone/examples/single_column_MARBL/STNP/single_column_z.ipynb @@ -0,0 +1,581 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:847c08f9e6b1a07efd1e43efe9fa87aa4bed23875cde3d67a2c275d725f76697" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

      MOM6 diagnostics for KPP single column test case

      \n", + "\n", + " Results from this notebook: \n", + "1. Basic diagnostics of KPP boundary layer and prognostic fields. \n", + "\n", + "Caveats regarding this notebook:\n", + "1. This notebook is written for MOM6-examples/ocean_only/single_column_z. \n", + "\n", + "Hopes for the use of this notebook: \n", + "1. To provide a starting point to document single column model tests;\n", + "2. To illustrate a self-contained iPython notebook of use for MOM6 analysis. \n", + "\n", + "This iPython notebook was originally developed at NOAA/GFDL, and it is provided freely to the MOM6 community. GFDL scientists developing MOM6 make extensive use of Python for diagnostics. We solicit modifications/fixes that are useful to the MOM6 community." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import matplotlib.pyplot as plt # For plotting\n", + "import netCDF4 # For reading data\n", + "pylab.rcParams['figure.figsize'] = (17.0, 10.0) # Large figures\n", + "\n", + "path = '../single_column_z/'\n", + "deltaz = 'CM4'\n", + "fname_deltaz = '_CM4'\n", + "expt_name = 'single_column_z'\n", + "\n", + "dpi=200" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "visc = netCDF4.Dataset(path+'visc.nc')\n", + "for v in visc.variables: print v,\n", + "\n", + "# recall data layout is (t,z,y,x)\n", + "\n", + "time = visc.variables['Time'][:]\n", + "\n", + "# KPP boundary layer depth as function of time (metre)\n", + "h = visc.variables['KPP_OBLdepth'][:,0,0]\n", + "\n", + "# KPP tracer diffusivity as function of time and depth (m2/sec)\n", + "Kt = visc.variables['KPP_Kheat'][:,:,0,0]\n", + "\n", + "# KPP velocity diffusivity as function of time and depth (m2/sec)\n", + "Ku = visc.variables['KPP_Kv'][:,:,0,0]\n", + "\n", + "# surface (and penetrating) buoyancy flux, as used by [CVmix] KPP (m2/s3)\n", + "KPP_buoyFlux = visc.variables['KPP_buoyFlux'][:,:,0,0]\n", + "\n", + "# Temperature tendency due to non-local transport of heat, as calculated by KPP (K/s)\n", + "KPP_dTdt = visc.variables['KPP_dTdt'][:,:,0,0]\n", + "\n", + "# Saln tendency due to non-local transport of heat, as calculated by KPP (ppt/s)\n", + "KPP_dSdt = visc.variables['KPP_dSdt'][:,:,0,0]\n", + "\n", + "# friction velocity used by KPP (m/s)\n", + "KPP_uStar = visc.variables['KPP_uStar'][:,0,0]\n", + "\n", + "# Squared buoyancy frequency (1/s^2)\n", + "KPP_N2 = visc.variables['KPP_N2'][:,:,0,0]\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "xh yh zi Time nv zl xq yq Kd_interface KPP_uStar KPP_buoyFlux KPP_QminusSW KPP_netSalt KPP_OBLdepth KPP_sigma KPP_BulkRi KPP_Ws KPP_N KPP_N2 KPP_Vt2 KPP_BulkUz2 KPP_BulkDrho KPP_Kd_in KPP_Kheat KPP_Ksalt KPP_Kv KPP_NLtransport_heat KPP_NLtransport_salt KPP_dTdt KPP_dSdt KPP_Tsurf KPP_Ssurf KPP_Usurf KPP_Vsurf MLD_003 average_T1 average_T2 average_DT Time_bounds\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "prog = netCDF4.Dataset(path+'prog.nc')\n", + "for v in prog.variables: print v,\n", + " \n", + "# depth of cell interface \n", + "zi = prog.variables['zi'][:]\n", + "\n", + "# depth of cell center \n", + "zl = prog.variables['zl'][:]\n", + "\n", + "# zonal velocity as function of time and depth\n", + "u = prog.variables['u'][:,:,0,0]\n", + "\n", + "# zonal velocity as function of time and depth\n", + "v = prog.variables['v'][:,:,0,0]\n", + "\n", + "# temperature as function of time and depth \n", + "temp = prog.variables['temp'][:,:,0,0]\n", + "\n", + "\n", + "# saln as function of time and depth \n", + "saln = prog.variables['salt'][:,:,0,0]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "xq yh zl Time xh yq zi u v h e temp salt wd\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# thickness of cells \n", + "dz = zi[1:]-zi[:-1]\n", + "field = zi[1:]+dz[:]\n", + "#depth = np.ma.masked_array(data, mask=[data>400.])\n", + "depth = field \n", + "\n", + "#print dz\n", + "#print dz[1901:2000]\n", + "print zi\n", + "\n", + "figure(1)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "S = plt.plot(dz,-depth,'*',linewidth=3)\n", + "plt.ylim((-400,0))\n", + "plt.xlim((0, 50))\n", + "yticks = [-400, -350, -300, -250, -200, -150, -100, -50, 0]\n", + "ax.set_yticks(yticks)\n", + "#xticks = [0, .1, .2, .3, .4, .5]\n", + "#xticks = [0, .2, .4, .6, .8, 1.0, 1.2]\n", + "#xticks = [0, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260]\n", + "xticks = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]\n", + "ax.set_xticks(xticks)\n", + "#plt.xlim((0, 40)\n", + "ax.set_xticks(xticks)\n", + "plt.grid()\n", + "plt.xlabel('Delta z (m)',fontsize=18)\n", + "plt.ylabel('depth (m)',fontsize=18)\n", + "plt.title(r'$\\Delta z$ for configuration '+deltaz,fontsize=20)\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "[ 0.00000000e+00 2.00000000e+00 4.00000000e+00 6.00000000e+00\n", + " 8.00000000e+00 1.00100000e+01 1.20200000e+01 1.40400000e+01\n", + " 1.60600000e+01 1.81100000e+01 2.01800000e+01 2.22700000e+01\n", + " 2.44000000e+01 2.65800000e+01 2.88200000e+01 3.11200000e+01\n", + " 3.35200000e+01 3.60200000e+01 3.86400000e+01 4.14200000e+01\n", + " 4.43700000e+01 4.75400000e+01 5.09600000e+01 5.46700000e+01\n", + " 5.87400000e+01 6.32200000e+01 6.81900000e+01 7.37400000e+01\n", + " 7.99700000e+01 8.70100000e+01 9.50000000e+01 1.04110000e+02\n", + " 1.14540000e+02 1.26520000e+02 1.40320000e+02 1.56260000e+02\n", + " 1.74680000e+02 1.96000000e+02 2.20660000e+02 2.49170000e+02\n", + " 2.82080000e+02 3.20000000e+02 3.63560000e+02 4.13430000e+02\n", + " 4.70310000e+02 5.34860000e+02 6.07770000e+02 6.89640000e+02\n", + " 7.81040000e+02 8.82430000e+02 9.94160000e+02 1.11645000e+03\n", + " 1.24938000e+03 1.39285000e+03 1.54663000e+03 1.71033000e+03\n", + " 1.88340000e+03 2.06518000e+03 2.25494000e+03 2.45183000e+03\n", + " 2.65500000e+03 2.86358000e+03 3.07672000e+03 3.29359000e+03\n", + " 3.51346000e+03 3.73565000e+03 3.95959000e+03 4.18477000e+03\n", + " 4.41081000e+03 4.63740000e+03 4.86430000e+03 5.09137000e+03\n", + " 5.31851000e+03 5.54567000e+03 5.77283000e+03 6.00000000e+03]\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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89i3fBQUFpe4/U2XPHT16tDp27ChJatGihXr27Om5haXoP6iatL1x4/vatGmn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\nx44d670WAAAAAIC6CnjxXldPPfWUnnrqKY+xfv36yeVyKS0tzT3Wt29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CsrKyNHToUPXp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\nACDIUbwDAAAAABDk/j8pcgXb1INZ2QAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# KPP related fields \n", + "\n", + "plt.subplot(2,2,1)\n", + "data = KPP_buoyFlux\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, 16, levels=numpy.arange(-2e-7, 2e-7, 1e-8))\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-30,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Upper ocn buoy flux $(m^2/s^3)$ for '+expt_name,fontsize=18)\n", + "#plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = log10(sqrt(KPP_N2))\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, 16, levels=numpy.arange(-5,-1,.25))\n", + "#plt.contourf(time, -zi, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$Log_{10}(N)$ $(1/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = KPP_buoyFlux[:,0]\n", + "CS = plt.plot(time,data,'*',linewidth=3)\n", + "plt.xlim((0, 370))\n", + "plt.xlim((0, 370))\n", + "plt.grid()\n", + "plt.title(r'Surface buoyancy flux for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "plt.ylabel(r'Buoyancy flux $(m^2/s^3)$',fontsize=14)\n", + "\n", + "\n", + "plt.subplot(2,2,4)\n", + "CS = plt.plot(time,KPP_uStar,'*',linewidth=3)\n", + "#plt.ylim((-400,0))\n", + "plt.xlim((0, 370))\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "plt.ylabel(r'$u^{*}$ (m/s))',fontsize=14)\n", + "plt.title(r'$u^{*}$ used in KPP for '+expt_name,fontsize=18)\n", + "#ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "#ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "-c:16: RuntimeWarning: invalid value encountered in sqrt\n", + "-c:16: RuntimeWarning: divide by zero encountered in log10\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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C3fbc3FwhSZJITk72aKdfv34iKirKo42yvCt98ccff4g6deqIK664Qpw8ebJMbdnY+GPI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\ney/q16+P+vXrY/DgwTh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RtfMSI17tjIuLU+hj9eqrr2qcrzIU1ZU+9u4NDQ3F4sWL8c03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v7jjz+iQ4cOaNeuHR566CGMGzcObdu2dZmuFI3MvnMEBTVDSsoQBAWFgk9nEXuBzafTatlH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8V1AsbE\nwY18xpigrl27htDQUHz//fe4++67Bfve69ev49tvv8WoUaPw448/apfFscalS5cQHh6OwsJCKJVK\nwdLEGGOMMdO4TsCYOLiRzxjzCCEhIbhx4wbmz5+PjIwMsZPDGGOMMZFwnYAx87iRzxhjjDHGGGOM\nSQRPvMcYY4wxxhhjjEkEN/IZY4wxxhhjjDGJ4EY+Y4wxxhhjjDEmEdzIZ4wxxhhjjDHGJIIb+Ywx\nxhhjjDHGmERwI58xxhhjjDHGGJMIbuQzxhhjjDHGGGMS8f8Z4ivz1EjQ2gAAAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# KPP related fields \n", + "\n", + "plt.subplot(2,2,1)\n", + "data = Kt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, levels=numpy.arange(0,.5,.05))\n", + "plt.colorbar()\n", + "plt.ylim((-300,0))\n", + "plt.xlim((0, 370))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$\\kappa_{\\Theta} (m^2/s)$ for '+expt_name,fontsize=18)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = Ku\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zi, field.T, levels=numpy.arange(0,.5,.05))\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'$\\kappa_u (m^2/s)$ for '+expt_name,fontsize=18)\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = KPP_dTdt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Non-local $\\Theta$ tendency $(K/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n", + "plt.subplot(2,2,4)\n", + "data = KPP_dSdt\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, 16)\n", + "CS = plt.plot(time, -h,'r-',linewidth=1)\n", + "plt.colorbar()\n", + "plt.xlim((0, 370))\n", + "plt.ylim((-300,0))\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=14)\n", + "plt.title(r'Non-local Saln tendency $(ppt/s)$ for '+expt_name,fontsize=18)\n", + "plt.xlabel('Time (days)',fontsize=14)\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 7, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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WAiSwdlW/eqdc/YO54aq+YY0Bi6p14sY2VvzxJSI2pZJ2rlFA5AKeMBqAiBxj\nbw0AZ9qSqx0lPvxzmgARkWqxECBC1Q/Ctq6KW/og7siHaJMigJhjLR1np3N2tAoqd7HB0VV+q2+3\nt4I8kQGvBFjhgVcIiVRBxIfh6lMApbZpcRSAjXNJ7aulvh86cg65Y7G1Uj/f50ks/q6IwPzAa7EQ\n4ABLxQClOkT97oovt1Gg2u+KoX98UycxnP0d8bjfMY4qIHI/ex/inWjLGzEnIE/hdTkBkR0sBIgk\nds6/0Qonh9SvMC0AVE0kqt7O0OH4JDBMbeAUASIVy9XbLhSwiEC+zoVXvbx+/Q1eQSQi8ngsBDjI\n2hSB0dhoPlTfzjz/qm1ZfL4tK5wYMSDnPEQfuGpB3snjrxTxAzqRV3G0CBA+Ic8jbufr9UUM8mke\nnxMQyYiFAAkcuequ1JV6uRIBduhERETSSe0/nSkAiDKzslhARF7OcJHAFRcLeEHCa7EQYIfSH5Ql\nd9jWrryvkK84YKltSaMULJCruspKLSlFVb9X7JSJ3EpsbsBiu2XMCcjT8feKfAELAQqxNFxfyhx7\nw/N1giD5PGZkGrav5DoEROQiYosILDYQuZ5M/bXSowJM2nfFbQqJiEh2LASIUPUDsCML5kld+V+u\nuwQoccWCQw6JKqjhaoHsMbI4QCQvmT74u2PkAfMBokpuyQkk9slqyFvItVgIUIDSt/uTUowwJgdM\n4IlIDC99r0hPT0d0dDQiIyOxePFiq8d9+umn8PPzw5EjR4zbFi1ahMjISERHR+OLL75wRbhERESk\nEOYEFVgIEMuBOfJKD6WvWhBQsvggdUSDNZzLR2pR9b7Tln5nq25zye+0l344d5Xbt29jypQpSE9P\nx/Hjx/HJJ58gOzvb7LiSkhK888476Natm3Hb4cOHsWXLFpw4cQLp6emYNGkS7ty548rwyYM5cyXe\n10fjMScgNbGVF7g8J3AVe7cdVinmBJVYCJCZ2Yf/agWEcUip6PytDAfkPHwisknuTrlqe17Y4QNA\nVlYWoqKiEBISgoCAAIwcORJpaWlmx7366quYPXs2atSoYdyWlpaGUaNGwd/fHyEhIYiKisKhQ4dc\nGT65ioK//3J+kK8+KtChYoSX/q0TEdnDnKASCwEycraj5+rCRO5lawSAvRECil8FYOJu0VEAa6t8\nWVJUVISwsDDj49DQUBQVFZkcc+TIERQXF2PQoEEm24uLixEaGmrzuUS2KFkEsIU5BZFzxPT7UveR\nspgTSBN1hddzAAAgAElEQVTg7gA8Rq7e/kq3KwAkVHTEmzDGBUE5TpEE4I/XT0RuIlcxQMz7HYCC\nNRFu/zChi7Ozv9rjdRamMWk0GpttlJeXY8aMGVi3bp1xm2Dnji3kJUT+LchtHFKwHmMdfn74hDwU\nLI2wuE8NOQoRqVDV90s3XpywlRfoqj1mTmCb7xUClkK2VXp9QfiEPOBp95xbEHTQaPTuOTmRA6xV\n//l77F6hoaEoLCw0Pi4sLDS5GlBSUoKTJ09Cp9MBAM6dO4chQ4bgs88+M3tu9SsJRL7GXesVMCcg\nNbKUF/D32L2YE1Ti1ABLlrr2dI7ckpCIyBUK1li+6qgmnTt3Rk5ODoqLi1FaWorU1FTEx8cb9997\n7704f/48zpw5gzNnzqBbt27Yvn07YmNjMWjQIHz88ce4e/cuioqKkJOTgy5durjx1ZDL+PJ0HJGj\nJNw9YoiISCrmBJVYCFApS8UDFhSIPBPnCrpXcHAwVq1ahbi4OHTo0AHDhw9HTEwMEhMTsWPHDpvP\njY2NxbBhw9C+fXsMHDgQycnJCAwMdFHk5I0cvaIu5nmGY5gPEHkuj8wJDIVPHyiAMieo5HtTA7yQ\nThCgtzPfRVYz4bbpAkREjoiPjzep+ANAUlKSxWMzMzNNHs+ZMwdz5sxRLDYih+XqATRxdxRE5A18\noAhgwJygAgsBLhSxo0D5xfZy9RAEHVrA/nA9YQLQQuFwnOVxFVMiJ1T/feY8QSKyx9k5+WLn1guC\nDppIp06lOOYE5E24fgC5m29NDXBDpUtvYbVKT+KuRX+IyAP4UPWfyBOImVOvhnn3nHpARKR+vlUI\nIKucqbKHT8izmbiwgk9kmVvnCUopArh4AVUibyRX4T1iR4Es7dginLJ/jKPFAOYERNZ55PoB5LVY\nCHAzRzpSVyQBRERE5FnY/xMRkVxYCLBlqXfcOouIiIjUS8pFA4vFApG3AyQisovTCr2GagoB8+bN\nQ2hoKLRaLbRaLXbt2mXct2jRIkRGRiI6OhpffPGFtIar/jLL/Yu9Qt7mXMWZIUmOPJdDoMjXGYYC\nWvpypk2ncUoAeSjFcgIy4473IuYF5Os8Micgr6OauwZoNBrMmDEDM2bMMNl++PBhbNmyBSdOnMC5\nc+fQq1cvfP/99wgKCrLfqCdWtCQUDyJ2FOCccpEQEbEYQB5JkZyAiIhs88TPTuQw1YwIAABBEMy2\npaWlYdSoUfD390dISAiioqJw6NAhN0TnQyR8MGAFksg5/Bsisow5ARH5GuYEJCfVjAgAgBUrVuD9\n999HbGwsli9fjvr166O4uBj9+vUzHhMaGoqioiIrLawFzuuBzwC00SkfsJfx9FshEpHr6PV66PV6\nd4dBPkyWnACoyAtq6RSOlojImx3FvHl6dwdBEnlUIaB///44d858sPuCBQvwzDPPYO7cuQAq5gZO\nnz4dGzZskHiG8cD9OuAHAEMAQO9UvBaJHdrP4bZEpCbVhgPqdDrodDrj46SkJNfGQ17PJTkBUJEX\nAFAkJ1CbmWB+QkQO6Ih583TGR8wJ1MGjCgF79uwRddykSZPQt29fABXV/sLCQuO+oqIihIWF2W9E\nrR3dUgCLHXuqlOFEgqCDRqN37EREJCvD3y7/JsmXuDQn8FHMC4jUhzkByUU1awT89ttvxu8//fRT\nREVFAQAGDRqEjz/+GHfv3kVRURFycnLQpUsXd4VJRERECpM1J7C1+FWuXpWLY1m8hSAREVEVHjUi\nwJYXX3wRx48fx507dxAeHo7//ve/AIDY2FgMGzYM7du3h5+fH5KTkxEYGOjmaD3DOKRgPcbK2uZo\nbMTrWCBrm0RERFIwJyAiInKOagoBKSkpVvfNmTMHc+bMcWE0rjcOKXg9tz/QVqdI2+CHeyKyRoVX\nRMm7+XpOQERE5CzVTA1wGakJrxNrDQinxB8r95V9SwRBZzJf0JW3KKl+Xt4ehchc1b8Nl/ydqHRY\nNJEq5eohTHB3EJUsvce4My8gIlOW8nb+rZAUvlcIEJvUWvqA/4z1wwvWRNg9hoiIiMijzbS9ezQ2\nuiYOIiJSlO8VAhyQlxDOD/hERETkfmJvU0xERGSDatYI8CSbMMbdIXikqkOWiUg5UobMVt3PWw0R\nucBS2L2q7iuYFxApT8oUnur7mBf4No4IICIiIt8mZtog18sgIiIvwkIAERF5vfT0dERHRyMyMhKL\nFy82279y5Up06NAB7du3R6dOnXD48GEAQH5+PmrWrAmtVgutVoupU6e6OnQiIiKSEXOCCpwaYEmu\nXpHb9BERkevdvn0bU6ZMwddff43GjRuje/fuGDBgALRarfGYcePGGTv0HTt2YMaMGdi3bx8AoHXr\n1sjOznZL7OQhqo4GWArAE1b3tzZCYQW4rhERkRXMCSr51IgAOeeoyXU7P643UIm3PSFSluFvzNLt\nhtx5mzClZWVlISoqCiEhIQgICMDIkSORlpZmckydOnWM31+/fh1NmzZ1dZjkYor+jjtxa2GqxLyA\nSFm28oLqx3kL5gSVOCLAHhGVf+OtA4mIyKX0lyq+bCkqKkJYWJjxcWhoKPR6vdlxK1euxJtvvokb\nN27g4MGDxu35+fno2LEjatWqhfnz56Nfv35yhU9khjkFEZFjmBNI41MjAmRnr+Lv4qF5ETsKXHtC\nIiI309UH5rWu/LJEo9GIamvq1Kn48ccf8eabb2LChIoKcLNmzVBcXIyjR49ixYoVGDt2LK5cuSJX\n+KRShg/r/NBOROQ5mBNI45sjAhRaA2A9xuI1LJS9XSIinyW1oLrbfFNoaCgKCwuNjwsLC02uBlQ3\ncuRITJw4EQAQFBSEoKAgAIBWq0W7du2Ql5eHbt26SQyMvIK9/IFrDBERKUtKXsCcwCbfHRHA2wA5\nhfP2iNTN2hzAqtu95W+8c+fOyMnJQXFxMUpLS5Gamor4+HiTY/Lz843fp6WloW3btgCAS5cuoby8\n3HhMTk4OWre2cpmByEcxJyBSP3trCHnL3zhzgkq+OSJAok0Yg9EJG90dhkfg4oZEpDbBwcFYtWoV\n4uLiUF5ejrFjxyImJgaJiYno1KkTEhISsGzZMuzbtw/l5eVo0KAB1q9fDwDIzMxEYmIi/Pz8IAgC\nli9fjoYNG7r5FZEncMu0gBWuPyURkTdhTlDJqUJAaWkpAgMD5YrF83CIHxGRV4iPjzer+CclJRm/\nf/fddy0+709/+hP+9Kc/KRqbt/D6nICIiLwCc4IKTk0NqF27Nk6dOiVXLKrAK+JERETmfDEnMOLt\nAomISGVEjQiYNm2a2QqLgiCgrKwMCxcuxH333QeNRoPly5crEqS3Y3GBiIjUgjmBY9ZjrLtDICIi\nMhJVCFixYgXat2+P++67D4IgQKPRQBAECIKA06dPo1atWqJvxUCOzysUBB00kfaPkVM+IgBBgJ7/\nv0REBOYEnkQQdGiBPOv7FRigEYF85KGF/A0TkccTBB00Gr27wyCZiCoELFiwAO+99x7eeecdPPzw\nw8btgYGB+OCDDxAVFaVYgN4kLyHc3SEQERE5hTmB4wrWRAAT3B0FERGRyDUCXnnlFWzatAlPP/00\nXnnlFZSVlRn3ser/B84PJCIiH8CcgIiISP1ELxbYrVs3HD58GD/99BO6deuG06dPKxmX++Xqre5y\nZp6f3ec+43DTRERELuFzOQEREZGXkXT7wHvvvRcff/wx3nvvPfTq1cvkKoA34wI/NuTqATRxdxRE\nRORivpoTiLYU8k8DeAbACpnbJCIinySpEGAwceJE9OnTB//73/8QEhIid0yKsrXIhRwLYFSsA1Dg\nVBu2CKcA7LB+bseWIXScvYWKiEjd5F6AlLyPt+YEYvaLsR5jMQ4pTrVhSz4iPKYfFrOoMRGpG/MC\n7+FQIQAA2rRpgzZt2sgZi6qIXflfFwfod0to834ngiIiInIDn84JuEYQERGpkKhCQEJCgvH2QFUZ\nFgUy3D5o+/bt8kfobhz6TkREZOTTOQEREZGXEFUISEtLQ/PmzaHT6Sx2/oCPrxTMqwFEROQjmBOg\not+f6e4g3E/s6EgiIvI8ogoBs2bNwvr167F//35MmDAB48ePR2hoqNKxeSQpCwfmJYQjYoX86wVU\nrEPgero4IMMtZyYiIk/BnICIiEj9RN0+cPHixSgsLMRbb72Fb7/9Fq1bt0Z8fDw2b96M0tJSpWMk\nIiIiD+HzOYGN2wtbY/UiAkcUEhGRm4gqBABAQEAAhgwZgs8++wz5+fnQ6XR49dVX0axZM1y/fl3J\nGN2uYE2EqJEAnjpETpjAFT6JiEg+vpwTmKj6Qd5CgcBSXuAJtyQWBB3zAiIiHye6EFDVjRs3cPXq\nVZSUlOCee+6ROyaPJbbz1sUpHAgREZGH8NWcgFfz/8CfAxGRKokuBPz+++9Yu3Yt+vTpg+joaBQU\nFGD9+vX46aefUKdOHSVjJCIiIg/CnMAGB6YOEBERuZqoxQL/9re/ITU1FQ8++CCefvppbN++HfXq\n1VM6No/lCVMANmEMXsNCm8d4wvBDIiLyLswJiIiI1E9UIWDNmjUICwtDs2bNsGvXLqSnpxv3GW4b\nxHsGO2Y9xmIcUtwdBhERkSjMCYiIiNRPVCFg3LhxJvcE9sl7BotkuFKviwPypDyR9yQmIiIVYE4g\nQq4eaKuzuttsxJ6N6QQc3UdEREoQVQhYu3atwmFU2rx5M+bNm4e8vDx8++23iImJMe5btGgRUlJS\n4O/vj2XLlmHAgAEAgPT0dMyaNQtlZWV48skn8fLLL7ssXrueAbC74ttNGGPxEHbyRETKstdPrFy5\nEsnJyRAEAUFBQUhOTkZsbCwA632Pr2JOIA9PmGZo0VIAE9wdBBGRcpgTVHDorgFKio6OxtatW9Gn\nTx+T7YcPH8aWLVtw4sQJpKenY9KkSSgtLcXt27cxZcoUpKen4/jx4/jkk0+QnZ1t8xyO3DLH0GGb\nddwyLQoknJKlGSIiqkZMPzFu3DgcO3YMx48fR2JiImbMmAHAct9z584dd7wMn+SpOYHSBBV8ELd2\ncYOIyJMxJ6gkqhDwySefoEWLFrh27ZrZvitXriA8PBxbt26VJaCIiAg89NBDZtvT0tIwatQo+Pv7\nIyQkBFFRUcjKykJWVhaioqIQEhKCgIAAjBw5EmlpabLEYvTHrXFMigC2bpfzjLyntyYvIdw1JyIi\nUjEx/UTVle6vX7+Opk2bArDc9xw6dMil8Xsan88JHCTbCAAX5RhERN6IOUElUVMDkpOTMWvWLNSt\nW9dsX7169TB79mysWrUKw4YNkz1Ag+LiYvTr18/4ODQ0FEVFRRAEAWFhYSbb9Xq9xTbmzZtX5VE9\nAB2dC8rOaAAxK/tXV/HhvsDxmIiIXECv11t9r3VpHCcAfY7tY4qKikT1EytXrsSbb76JGzdu4ODB\ngwCs9z2+jDkBERFVxZxAnUQVAr777jv8+9//trq/b9++mDNnjuiT9u/fH+fOnTPbvnDhQiQkJIhu\nR6qqnX5Skl6x86jNeozFa+4OgohURafTQafTGR8nJSW5J47oii9jHB+aHyN24bqpU6di6tSp+PDD\nDzFhwgRkZmbKFKV3YU4gTcGaCIRPkLR8MBGRqjAnUCdRhYCbN2+iVq1aVvfXrFkTv//+u+iT7tmz\nR/SxBqGhoSgsLDQ+NlRzysvLTbYXFhaaVHk8gS4O0O92dxREROojfQqU+Yiq6v2HvX5i5MiRmDhx\nosXnVr+S4IuYExARkbtIywuYE9giao2A0NBQHDt2zOr+EydOIDQ0VLagDKrekmjQoEH4+OOPcffu\nXRQVFSEnJwddunRB586dkZOTg+LiYpSWliI1NRXx8fGyxyJZtTl8uriKfz12lWAiIi8lpp/Iz883\nfp+Wloa2bdsCsN73+DLmBDCfGijTwsFERKQs5gSVRI0IeOyxx/Daa6/h0UcfRXBwsMm+mzdvIikp\nCY899pgsAW3duhXTp0/HhQsX8Nhjj0Gr1WLXrl2IjY3FsGHD0L59e/j5+SE5ORmBgYEIDAzEqlWr\nEBcXh/LycowdO9bk9kJEROTbgoODLfYTiYmJ6NSpExISErBs2TLs27cP5eXlaNCgAdavXw8AVvse\nX8acQGHO3r6PiwkSEVnFnKCSqELAnDlzEBMTg44dO2LSpEmIiooCAOTk5GD16tW4fv26pPmAtgwb\nNszqAkNz5syxeJ74+HjJFX9rtwsSBB00Gr35jqUAZlb5vrpcPQDdH/PtpS0QSEREyrLUT1Sdw/ju\nu+9afa61vsdX+VJOIBeOBiQi8hzMCSqIKgQ0btwYBw4cwNSpUzFz5kzj8DyNRoO4uDisWLECTZo0\nUTRQVfijip+XEI6IHRVzUvISwhGxgncBICIi78CcwIWkjA5wdiQBERH5FFGFAABo0aIFPv/8c1y6\ndAk//vgjBEHAgw8+iPr16ysZn3eyNKKAiIhIJZgToGIkYFudy9YHcMmIQ+YnREQ+Q9RigVXl5uai\nffv26Nq1K3Jzc3Hr1i0l4vIshk5+KWxMC1AWhxUSEZGn8cmcQIrqOQM/aBMRkYeQXAgYOHAgfv75\nZ7PvyZTJrS3+WLhnPcbK0vYmjJGlHSIiImf4fE5g60KAlX1KFPZ5sYCIiKSSXAgg8Uw++HMVXyIi\nIiIiIvIALAQQERERqYDJaEMiIiInsBCgAClD9wvWRKhnSB9HNRARETnNpN930WKDREREVbEQ4Chn\nO24rz+f8fyIiInIGRw4QEZE9LARYIAg6ky8Aoj/4V7+6zw/2RERE6mUxJ5BChjsFyLXYMBERkQEL\nAQpxqADwR7Kgi5M3FiIiInIzd946kNMPiIioGhYCXCAvIVz2YXq8OkBERKRi7iwMEBGRz5NcCHjl\nlVdw3333mX1PvoEFCCIiMmBOoDxLCwqrZpFhIiLyWJILAXPmzDF29FW/9yl2hti568My1yMgIiJX\nYk7gerZGGLJYT0REYnFqgBS5eknz7Jyp2ItZJ4Af/ImIiFSA0wCIiMjDsBBARERERKLxQgQRkfqx\nEKAQS8PzCtZE8KoAERERERERuRULAXKr9kF/PcZKr5w/I1849nA+IRERkffhgoJERGSLQ4WAAwcO\nGL+/deuWbMGono31AziMjoiIvBFzAidIWHeIiIhITg4VAtq0aYNDhw4BAC5fvoyZM2fi1KlTsgbm\nSQRB5+4QbNJrNC49n+C9/9VERCQRcwIbLH3QV3iKoK27CiiBOQERkTo5VAj44YcfcOrUKQiCgKZN\nm+If//gHRo4cKXdsquXMcLzqQ/Vd/SGfiMgbpaenIzo6GpGRkVi8eLHZ/iVLliAqKgrt2rVDnz59\ncObMGeM+f39/aLVaaLVaDB061JVhqwJzAtfxpNGFnFpIRGrFnKBCgCNPGjx4MM6ePQtBEFBeXo7z\n588jKChI7th82zMAdlveJaXzLVgTAUyQJyQiIjW6ffs2pkyZgq+//hqNGzdG9+7dMWDAAGi1WuMx\n3bp1w/Tp01GjRg2sXr0aM2bMwNatWwEAtWrVQnZ2trvC93jMCRzgwKgA9udERM5jTlDJoREBkydP\nxhdffAE/Pz/4+fkhMzMTX331ldyxkYfgqAQiUrOsrCxERUUhJCQEAQEBGDlyJNLS0kyO6d27N2rU\nqAEA6NmzJ4qLi90RqioxJyAiIrVgTlDJoULAggUL0K5dOwCARqNBbGws8vPz5YxL9URPD1gK3lKQ\niEhBRUVFCAsLMz4ODQ1FUVGR1eOTk5MxZMgQ4+Nbt26hU6dOiImJQWpqqqKxqhFzAjLBBRCJyIMx\nJ6jk0NQAAHjwwQeN33fq1Ak5OTmyBORVlgKYIH0eXV5COCJ2FDh16oI1EQifkOdUG9W5egEiIiJ7\nDulv4ZDe9kr1GgmjmjZu3IgjR45g3759xm3FxcVo1KgRzpw5g379+qFDhw5o06aNwzF7I+YENuTq\ngbY6p5rgrQCJiOxjTiCNw4WA6gxXA8iUEh/IpZybiEjN7C6OpgPq66o8TlpodkhoaCgKCwuNjwsL\nC02uBhjs3bsXCxYswP79+xEYGGjc3qhRIwBAy5YtMWDAABw5ckS1nb6rMCewQoaiABGRL7OZF+iY\nE0jh0NSA6g4dOgSdTidHU76Bw+aIiFymc+fOyMnJQXFxMUpLS5Gamor4+HiTY7KzszF58mTs2LED\nDRs2NG6/evUqSktLAQAXL17Evn37EBUV5dL41YY5gRUu7Ps96e4CRESehDlBJacKAfn5+Rg1ahS6\nd+/OhYGqkmnOvy5OnnaIiHxZcHAwVq1ahbi4OHTo0AHDhw9HTEwMEhMTsXPnTgDASy+9hBs3bmDE\niBEmtwQ6efIkYmJi0KFDB/Ts2RPTp09H+/bt3flyPBZzApnYKRjwQz4RkeOYE1RyaGrAlStXMH/+\nfPz73/9Gly5dsHPnTjz22GNyx6ZOtob9SSgQ5CWEI2KFc+sEEBFRhfj4eLOKf1JSkvH7PXv2WHxe\njx49cOLECUVjUzvmBCJwJCARkcdgTlBB0oiA0tJSvPXWW2jVqhW2b9+ODz/8EPv370fnzp2Vio8M\nLBQRpC5CSEREJBfmBEREROoluhDwySefoG3btliwYAHmzp2LU6dOYdiwYUrGpm4K3xKw+kKAnEZA\nRESuwpzAhXiLYSIiUoDoqQGrVq1Cy5YtsXnzZtSrV0/JmDySIOgAABqNXvJzpazeX7AmApsmjMFo\nbJR8HiIiIldgTqAD4FhOYCTndAEWC4iISCLRIwK+/PJLTJ8+HUuXLsVPP/2kZExERETkwZgTuAjX\nFiAiIoVIWiMgISEB8+bNQ2ZmJhYtWoQrV64oFRcBwDPSDnfXmgFSRjwQEZF3YE7gelwbiIiI5CL5\n9oEBAQF4+umn8eyzz+K///0vkpOTjfdTlMPmzZsRFRUFf39/HDlyxLg9Pz8fNWvWhFarhVarxdSp\nU437Dh8+DK1Wi6ioKDz33HOyxSILB4frufL2QGI+yNtbg4DFACIi38OcwLMUrIng7QWJiEgUyYUA\ng3vuuQcvvvgiBg0ahHfffRcajUaWgKKjo7F161b06dPHbF/r1q2RnZ2N7OxsrFy50rj9qaeewpo1\na3Dy5EkUFBRg69atssSiCFvD/DgEkIiIVIg5gY/i2gRERKrlcCHAICwsDAsXLjSp1DsjIiICDz30\nkOjjz549i/Lycmi1WgDAE088gbS0NFliISIiIvGYE4jAoj8REXkApwsBBh06dJCrKavy8/PRsWNH\n9OjRAxkZGQCAoqIihIWFGY8JCQlBUVGR4rHYZOjkFersOQyfiIg8GXMCOxzJD5Za+d4V5yYiIq8j\n+vaBcurfvz/OnTtntn3hwoVISEiw+JxmzZqhuLgYdevWRXZ2NgYPHoyTJ09KOu+8efOM3+t0Ouh0\nOknPd5X1GItxSEFeQjiAApvH6uIA/W7XxEVEZKDX66HX690dBnkB5gQicAg+EXkw5gTq5JZCwJ49\neyQ/JygoCEFBQQAArVaLdu3aIS8vD2FhYSgsLDQeV1RUhNDQUIttVO30FVe14u6KDlziHQYcVbAm\nApjgmnMRkeeq/sEpKSnJfcGQqvlETiAj3jmAiDwNcwJ1km1qgBIEQTB+f+nSJZSXlwOoGA6Yk5OD\n1q1bIywsDH5+fsjOzgYAbNy4EYMGDXJLvN6OKxETEZG7MCfwHCxGEBGpn8cVArZu3YqwsDB88803\neOyxxxAfHw8AyMjIQPv27dG+fXskJCRg+fLlaNiwIQDggw8+wIQJExAVFYXmzZtj+PDh7nwJ1kmY\nl8dOloiIfJ1X5wQKYg5BRET2SJoaUF5ejvXr1yMjIwNXrlyBIAjQaDTGf7dv3+50QMOGDcOwYcPM\nto8YMQIjRoyw+JzY2Fhj9V91jMUBXcU/S8Gh90RE5PGYExAREamXpELA9OnT8d577+HRRx/F/fff\nb7JPrnsGkzldHICz7o6CiIioEnMC38Y7GBERqZukQkBKSgpSU1MxZMgQpeLxeIKgs7pPo9Gbb8zV\nA22tP8eWTRiD1+IWWt3vqk5YF+eS0xARkYowJ3AgJyAiIvIQktYICAoKglarVSoWcgPRxQQX3ZWA\niIjUgTmB69nssyWsQ0RERCSpEDBx4kRs2LBBqVjoD5Ku9PMDOhERuQFzAiIiIvWyOzVg2rRpxrl+\nZWVlWLlyJfbs2YOoqCj4+VXUEQwLAy1fvlzZaImIiMhtmBN4PuMdA7j4MBER2WB3RMCJEyeMX6dO\nnULHjh0hCAJOnjxpsu/EiROuiFedcvWSh+yZjAoQcdU/LyFcUvtERL4kPT0d0dHRiIyMxOLFi832\nL1myBFFRUWjXrh369OmDM2fOGPetW7cOUVFRiIqKwvr1610ZtsdhTkBERGrHnKCC3REBer3eBWGQ\nVdaKAEsBzHRlIERE6nT79m1MmTIFX3/9NRo3bozu3btjwIABJvPbu3XrhunTp6NGjRpYvXo1ZsyY\nga1bt+KXX37B66+/jqNHjwIAOnbsiLi4ODRu3NhdL8etmBO4gROLDhMRkSnmBJUkrRFw9uxZlJeX\nm20XBAFnz/L+dnIzDu9zxlLnmyAiUrOsrCxERUUhJCQEAQEBGDlyJNLS0kyO6d27N2rUqAEA6Nmz\nJ4qLiwEAe/bsQXx8POrUqYM6depg4MCB2LNnj8tfgydiTqAyDoxOJCLyNswJKkkqBLRo0QIXLlww\n237x4kW0bNlStqDIMbynLxGRuaKiIoSFhRkfh4aGoqioyOrxycnJxlviFRcXIzQ0VPRzfQlzAvdg\nX09E5DjmBJXsTg0Q49atW/D395ejKZIoLyEcurgC5Lk7ECIiN8jXF6BAb/vqs2FxOzE2btyII0eO\nYN++fc6G5rOYExARkTswJ5BGVCFg2rRpxu/nzJmDWrVqGR+Xl5fj22+/RXR0tPzRkWfgLQqJyE3s\nTpHS/fFlkNTW7JDQ0FAUFhYaHxcWFppcDTDYu3cvFixYgP379yMwMND43KysLJPn9ujRQ8Ir8D7M\nCfhQ0SQAACAASURBVDwY7xRARF7OZl6gA3MCCUQVAqqu/pubm4ugoCDjY39/f0RGRmLWrFnyR+er\nqi0EmJcQDrxcbb+YNoiICJ07d0ZOTg6Ki4vRqFEjpKamIjk52eSY7OxsTJ48Gbt370bDhg2N2x99\n9FEkJSWhpKQEQMVKw3PnznVp/J6GOQEREakVc4JKogoBhlWCx48fj+XLl6Nu3bpKxuTbqq0O7Cm3\nBcxLCJdn8UIiIhcLDg7GqlWrEBcXh/LycowdOxYxMTFITExE586dMXjwYLz00ku4ceMGRowYAQAI\nDw/Htm3b0LRpU/zjH/9A165dAQBz585V7erAcmFO4AbGRf50og7nOgJERJYxJ6gkaY2AtWvXKhQG\nmeHwPiIi2cTHxyM+Pt5kW1JSkvF7W6v+PvXUU3jqqacUi02tmBMQEZEaMSeoYLcQkJCQgI0bN6Ju\n3bpISEiARqOBIAhmx2k0Gmzfvl2RIH1VwZqIymJArh62rgRswhgXRERERL6MOYGbVZs6SERE5Ci7\nhYAGDRoYV1ds0KCB1eOkrMBIMuNifkRE5ALMCYiIiLyD3UJA1aF/HAboYTh9gIiIXIg5gYcyriFA\nREQkjqQ1Avr374++fftCp9OhS5cuCAiQ9HSvJwg6i9s1Gr3Dbdob8r8JY/AaFjrcPhERkSOYE9im\nRE4ghisWCuRihERE6ucn5eCuXbvi888/R79+/VCvXj0MGDAACxcuxMGDB3H37l2lYiQiIiIPw5zA\njTgCgIiInCSpEDB//nx8/fXXuHTpErZt24auXbti165dePjhh3HfffcpFSMpbam7AyAiIrVhTuCl\nWGQgIvIJkgoBBiUlJbhw4QJ+++03nDt3DgEBAejUqZPcsZGHyEsId3cIRETkoZgTEBERqY+kCX1T\npkyBXq9HQUEBunbtCp1Oh/fffx9du3ZFcHCwUjGSHfygTkRErsacwE04io+IiGQgqRCQnJyMhg0b\nYvbs2YiPj0dsbCz8/BwaVEC25OqBtjpJdwVYj7EKBlQF71RARERgTuCVOC2AiMhnSCoEnD59Gnq9\nHnq9Hu+//z6uXbuG3r17G1cNjomJUSpOn1X5Af+cW+Owd/cCIiLyLcwJyCoWFIiIPJ6k0n3r1q3x\nt7/9DRs2bMDZs2fxzTff4P7778fs2bPRuXNnpWL0TSI7UZeNBLCFwxSJiHwOcwIiIiL1kjQioLy8\nHN999x0yMjKg1+tx4MAB3L59G7GxsdDpdAqFSKrAKQNERD6FOYGP40UAIiJVk1QIqFevHm7dumXs\n5F944QX07NkTderUUSo+koIfxomIyEWYExAREamXpEJAamoqevXqxU7eExgWFIST8/clVPQL1kQ4\nfh4iIvIqzAk8GK/WExGRHZIKAQMHDlQqDiIiIlIR5gRERETqxfv8EBEREakBV+MnIiKZSBoRQK5X\nORy/yu0DXZwI8NaBREREKpCrB6BzcxBERKQGHBHgyVj5JyIiIim4PgAREYnAQoCa5eq5gB8RERER\nERFJIrkQUFZWhp9++gnHjh3DmTNnUFZWJmtAM2bMQGRkJCIjIzF48GBcvHjRuG/RokWIjIxEdHQ0\nvvjiC+P29PR0REdHIzIyEosXL5Y1HiIiUj97/cT+/fsRExODwMBAfPrppyb7/P39odVqodVqMXTo\nUFeFrArMCYiISG2YE1QQVQi4ceMG3nvvPTz66KOoU6cOWrduDa1Wi1atWqFOnTp49NFH8d577+H3\n3393OqCEhATk5OTg1KlTaNeuHebPnw8AOHz4MLZs2YITJ04gPT0dkyZNQmlpKW7fvo0pU6YgPT0d\nx48fxyeffILs7Gyn41CT9Rir+MiA9RiraPtEREoR00+Eh4dj3bp1GD16tNnza9WqhezsbGRnZ2Pb\ntm2uCttjMScgI05hJCKVYU5QyW4hYNmyZWjRogXeeecddO3aFR9++CG+++47nD59Gt999x02bdqE\nzp0745133kHz5s2xbNkypwLq27cv/PwqwurZsyeKi4sBAGlpaRg1ahT8/f0REhKCqKgoZGVlISsr\nC1FRUQgJCUFAQABGjhyJtLQ0p2KQmyDoLH5V3Wf4noiI5CWmnwgPD0d0dLSx/yHLmBM4z1pOUD0X\nsJoTiP3wzQ/pRERmmBNUsnvXgIMHD2L37t2IiYmxuD8mJgbDhg3DokWLcPjwYSxcuBAvvviiLMH9\n5z//wahRowAAxcXF6Nevn3FfaGgoioqKIAgCwsLCTLbr9XpZzk9EROpXVFTkVD9x69YtdOrUCeXl\n5Zg9ezb+8pe/KBClOjAnICIiNWNOUMluIaD6vAhbYmNjRR3fv39/nDt3zmz7woULkZCQAABYsGAB\ngoKCMGaMfLeumzdvnvF7nU4HnU4nW9uK4eq/ROSB9Hq9R3zAuqU/hFv6QzaP0Wg0Tp2juLgYjRo1\nwpkzZ9CvXz906NABbdq0capNtWJOQACYmxCRCeYE6swJ7BYClLBnzx6b+9etW4e0tDRkZGQYt4WG\nhqKwsND42FDNKS8vN9leWFhoUuWpqmqn7zN4T2EiUkD1D05JSUmKnMf++icRQPNxVR6vMDuiev9h\nq58AzJOERo0aAQBatmyJAQMG4MiRI6rt9D0RcwIiInVzVU4A2MsLmBNIIbkQUFRUhK+++gpXrlyB\nIAgm+6ZOnep0QOnp6XjjjTewb98+BAcHG7cPGjQIkydPxvPPP49z584hJycHXbp0QVlZGXJycozV\nmdTUVCQnJzsdBxEReYfOnTuL7icEQTDp265evYpatWohMDAQFy9exL59+/DMM8+4KnSPx5yAiIjU\nhDlBJUmFgDVr1mDy5MkICAjAvffea7Zfjk5/2rRpuHPnDvr37w8A6N69O1auXInY2FgMGzYM7du3\nh5+fH5KTkxEYGIjAwECsWrUKcXFxKC8vx9ixY63OXSTH8I4BRKRmwcHBFvuJxMREdOrUCQkJCfj2\n228xfPhwXL58GTt37sS8efNw4sQJnDx5EpMmTYKfnx9u376N6dOno3379u5+SR6BOQEREakNc4JK\nGqF6Cd+Gxo0bY9q0aXjllVfg7++vZFyy02g0ZlcrPJFGozfd0FZX8W+u3riCsMkxbXUIP5VXMUxm\nKSCcMm/H1t0INJF/HHPKekwtkAcAZueQ0gYReTcl3mM1Gg3wX4ltPq2O93pvwJxAeWY5QTXW8gIA\n1vMGWM8LDMfZu4uRod+veo7qbYhph4i8k1LvsZLzAuYENkm6J8Lt27cxfvx41XX43s7+HFoiIiJ5\nMScgIiJSL0mFgFGjRnnc/XiJiIjI9ZgTEBERqZekNQLefvttDB06FBkZGWjbti38/EzrCHPnzpU1\nOPIwvF0QERH9gTkBERGRekkqBPznP//BF198gSZNmuDUqcoJ4YIgQKPRsNN3J4U/pHP6ARERVcWc\ngIiISL0kFQLmz5+Pd999V9W3SSAiIiLnMSfwYLl6d0dAREQeTtIaAWVlZXj88ceVioWIiIhUgjkB\nERGRekkqBIwfPx6bNm1SKhayJFcvvrLPKwBEROQizAk8FHMBIiISQdLUgJs3b2LhwoXYvXs3IiMj\njQsDGeYDLl++XJEgSWG5egA6NwdBRERqwpyAiIhIvSQVAk6dOgWtVovy8nKcPHnSuN3Q6ZObcKFA\nIiJyMeYERERE6iWpEKDX6xUKg4iIiNSEOQEREZF62V0j4KeffpLUoNTjSWU495CIyGcxJyAiIvIO\ndgsB3bt3xxNPPIEvv/wSgiBYPEYQBOzduxdjxoxB9+7dZQ+SbHD2gzk/2BMRkUjMCciI+QMRkarZ\nnRqQm5uLpUuXYuTIkbhz5w5iYmLQvHlz1KlTByUlJTh79iyys7MRGBiISZMmITc31xVxExERkYsx\nJyAiIvIOdkcE1K9fHwsXLkRxcTE2btyIzp074+rVqzh9+jRKSkrQpUsXbNy4EcXFxVi4cCHq16/v\niriJiIjIxZgTEBEReQfRiwXWqFEDCQkJSEhIUDIenycIOpPHGo3e4nYiIiJ3YU7gGpb6fuYFREQk\nB7sjAqrq27cvXnvtNbPtly5dQr9+/WQLioiIiDwbcwIiIiL1knT7wH379uHYsWM4fvw4UlJSULNm\nTQDAnTt3eBshX2ZcMEjnxiCIiMiVmBMQERGpl6QRAQCwd+9e5Ofno1evXvj555+ViIk80VJ3B0BE\nRJ6GOQEREZE6SS4EhIaG4quvvkLr1q3RuXNnfPfdd0rERVLxNj5ERORizAmIiIjUSXIhAABq1qyJ\njz76CH//+9+h0+mQmpoqd1xE/8/encdFUf5xAP/sCogI3kcoKKamsKyCoqiocSmC4m2UYh55522a\nR4oX3ldlauVVHpV5piilAh6geGEKar9SlCNMATMQBWSf3x/jDnuzC7vsLnzfrxcv3Zlnnvnu7Bzf\neeaZGUIIIWaAcoJKiC4+EEKI2StVQwAACAQChIWFYffu3Zg3b54+YyK6ogMyIYRoFBkZCbFYDBcX\nF6xevVpp/Pnz59GuXTtYWlri0KFDcuO+++47iEQiiEQifP/99+UVslmhnIAQQoi5oJyAo9PDAqOi\nolC7dm25YYMHD0br1q1x/fp1vQZGTAg9H4AQYsby8/MxceJEXLx4EQ0bNkTnzp3Rs2dPuLu782Wa\nNm2K7777DuvWye/wMjIysGzZMty8eRMA4ObmhoCAADRs2LBcv4MpopyAEEKIuaGcoJhOPQK8vb1h\naWmpNNzV1RUjRozQW1CEEEKIvsTHx0MkEqFx48awsLBASEgIIiIi5Mo0bdoUYrEYQqH8YfH06dMI\nDAyEra0tbG1t0atXL5w+fbo8wzdZlBMQQggxN5QTFNOpRwAhhBBiUu7FAH/EaCySlpYGR0dH/rOD\ng4PWr7dLT0+Hg4OD3LRpaWmlCJQQQgghBkU5gU6oIYBoj55FQAgxNa29uT+pX5YoFREIBOUWDiGE\nEEKMhHICnVBDACGEENOlh2eUODg4IDU1lf+cmpoqdzVAkWyS4ODggPj4eLlpu3TpUvagCCGEEKK7\nMuYFlBMUK/VbAwghhBBz0KFDByQmJiI9PR2FhYU4cOAAAgMDVZZljIExxn/29/dHZGQkcnJykJOT\ng8jISPj7+5dX6IQQQgjRI8oJilFDACGEkArN2toaW7duRUBAANq2bYuBAweiXbt2CAsLw/HjxwEA\nV69ehaOjIw4ePIjx48dDLBYDAOzt7bFgwQJ4enrC09MTixYtMtunAxNCCCGVHeUExQRMtpmjAhMI\nBDDHryoQxAAAGPNWGqaKtJxsGdlptalfqYzLm//cVV1WmzoIIRWbIfaxAoEAcNaxzrvmua8n5ctc\ncwJAt7xAVU6gOG1JdWsqp6qstvkHIaTiMtQ+Vue8gHICjahHACGEEEIIIYQQUolQQwAhhBBCCCGE\nEFKJUEMAIYQQQgghhBBSiVBDACGEEEIIIYQQUolQQwDRzpsHBRJCCCGEEEIIMW8m1xAwc+ZMuLi4\nwMXFBX369EFWVhYA4OHDh6hWrRrc3d3h7u6OSZMm8dNcv34d7u7uEIlEmDZtmrFCJ4QQQogeUU5A\nCCGEGIbJNQQEBwcjMTERd+7cgaurK5YvX86Pa9GiBRISEpCQkIAtW7bww0eNGoWdO3ciKSkJjx49\nwpEjR4wResVFvQEIIYQYAeUEhBBCiGGYXEOAj48PhEIuLC8vL6Snp2ssn5KSAolEAnd3dwBAaGgo\nIiIiDB4nIYQQQgyLcgJCCCHEMCyMHYAm33zzDd5//33+88OHD+Hm5gYbGxssX74cvr6+SEtLg6Oj\nI1+mcePGSEtLU1nf4sWL+f97e3vD29vbUKETQkiFFhMTg5iYGGOHQSoRygkIIcQ0UU5gnozSENCj\nRw88fvxYafiKFSsQHBwMAAgPD4eVlRWGDRsGAGjUqBHS09NRo0YNJCQkoE+fPkhKStJpvrIHfXPB\nmLdWwwSCGIPHQgghUoonTkuWLDFeMMSsUU6gG23yAsWcgDFvyhMIIQZDOYF5MkpDwOnTpzWO/+67\n7xAREYGoqCh+mJWVFaysrAAA7u7ucHV1xb179+Do6IjU1FS+XFpaGhwcHAwTOCGEEEL0inICQggh\npPyZ3DMCIiMjsWbNGvzyyy+wtrbmh2dnZ0MikQDgugMmJiaiRYsWcHR0hFAoREJCAgBg3759CAoK\nMkrshBBCCNEfygkIIYQQwzC5ZwRMmTIFBQUF6NGjBwCgc+fO2LJlC6KiorB48WIIhUIwxvDFF1+g\nXr16AIBdu3Zh9OjRKCgogJ+fHwYOHGjMr0AIIYQQPaCcgBBCCDEMAWOMGTuI8iAQCFCRv6r03j/p\nfYKy9wKqup9Q3XSayqirT5s6CCEVmyH2sQKBAHDWsc67FXtfT/SjsuUEssMUh5c0naZyqspqm38Q\nQiouQ+1jdc4LKCfQyORuDSCEEEIIIYQQQojhUEMAIYQQQgghhBBSiVBDACGEEEIIIYQQUolQQwAh\nhBBCCCGEEFKJUEMAIYQQQgghhBBSiVBDACGEkAovMjISYrEYLi4uWL16tdL4/Px8hISEQCwWw8vL\nC48ePQLAvaO+WrVqcHd3h7u7OyZNmlTeoRNCCCFEjygn4FgYOwBCCCHEkPLz8zFx4kRcvHgRDRs2\nROfOndGzZ0+4u7vzZTZv3gx7e3v89NNPOHr0KKZOnYpjx44BAFq0aIGEhARjhU8IIYQQPaGcoBj1\nCCCEEFKhxcfHQyQSoXHjxrCwsEBISAgiIiLkypw8eRLDhw8HAPTt2xdxcXH07mFCCCGkgqGcoBj1\nCCCEEGK+XsQAeTEai6SlpcHR0ZH/7ODggJiYGLVlhEIh6tatiydPngDgugK6ubnBxsYGy5cvh6+v\nrz6/ASGEEEL0gXICnVBDACGEEPNV3Zv7k8pcolREIBCUuvpGjRohPT0dNWrUQEJCAvr06YOkpCTU\nqlWr1HUSQgghxAAoJ9AJNQQQQggxXXdjylyFg4MDUlNT+c+pqalyVwOkZVJSUtCgQQNIJBJkZWWh\nfv36EAqFsLKyAgC4u7vD1dUV9+7dQ6dOncocFyGEEEJ0VMa8gHKCYvSMAEIIIRVahw4dkJiYiPT0\ndBQWFuLAgQMIDAyUKxMUFIS9e/cCAI4dO4bOnTtDKBQiOzsbEokEANcdMDExES1atCj370AIIYSQ\nsqOcoBj1CCCEEFKhWVtbY+vWrQgICIBEIsHw4cPRrl07hIWFwcPDA8HBwZg8eTKGDx8OsVgMOzs7\n7N+/HwAQHR2NsLAwCIVCMMbwxRdfoF69ekb+RoQQQggpDcoJiglYRXwEogoCgaBCPu1RSiCIAQAw\n5i33WXaYNtNpKqOuPm3qIIRUbIbYx3L38UXrOJVPhd7XE/2obDmB7DDF4SVNp6mcqrLa5h+EkIrL\nUPtY3fMCygk0oVsDCCGEEEIIIYSQSoQaAgghhBBCCCGEkEqEGgIIIYQQQgghhJBKhBoCCCGEEEII\nIYSQSoQaAgghhBBCCCGEkEqEGgIIIYQQQgghhJBKhBoCCCGEEEIIIYSQSoQaAgghhBBCCCGEkEqE\nGgIIIYQQQgghhJBKRMAYY8YOojwIBAJUkq9KCCHlzhD7WIFAACBax6l8aF9PSkQ5ASGEGI6h9rG6\n5wWUE2hCPQIIIYQQQgghhJBKhBoCCCGEEEIIIYSQSoQaAgghhBBCCCGEkEqEGgIIIYQQQgghhJBK\nhBoCCCGEEEIIIYSQSoQaAgghhBBCCCGEkEqEGgIIIYQQQgghhJBKhBoCCCGEEEIIIYSQSoQaAkxc\nTEyMsUMoNYq9/Jlr3ADFbgzmGjchlZm5brfmGjdAsRuDucYNUOzEfJhcQ8Bnn32Gtm3bwtXVFd27\nd8eDBw/4cVOnToVIJEK7du2QkJDAD//uu+8gEokgEonw/fffGyNsgzHnDZJiL3/mGjdAsRuDucZd\nGpGRkRCLxXBxccHq1auVxufn5yMkJARisRheXl549OgRP27lypVwcXGBWCzGb7/9Vp5hV3qUEygz\n1+3WXOMGKHZjMNe4AYrdHFBOwDG5hoC5c+fi999/R2JiIoYMGYIlS5YAAA4dOoSUlBQkJSVhx44d\nGDVqFAAgIyMDy5YtQ3x8POLj47F06VL8888/xvwKhBBCTEh+fj4mTpyIyMhI3Lp1CwcPHpQ7cQSA\nzZs3w97eHrdv38bs2bMxdepUAMD169dx+PBh3L59G5GRkRg/fjwKCgqM8TUqJcoJCCGE6BPlBMVM\nriHA1taW/39ubi7s7e0BABERERg+fDgAwN3dHa9fv0ZaWhpOnz6NwMBA2NrawtbWFr169cLp06eN\nEjshhBDTEx8fD5FIhMaNG8PCwgIhISGIiIiQK3Py5En+GNO3b1/ExcVBIpEgIiIC77//PqpUqYLG\njRtDJBLhypUrxvgalRLlBIQQQvSJcoJiFsYOQJUFCxZgz549qFatGr9w09PT4ejoyJdxcHBAWloa\n0tPT4eDgoDRcFYFAYNjADUR6BcQcUezlz1zjBih2YzD9uH10Ki174iiVlpamdPxQ7P4oW0YoFKJu\n3bp48uQJ0tPT4evrKzetumMMMQzKCZSZ/narmrnGDVDsxmCucQMUu2FpnxdQTqCZURoCevTogceP\nHysNX7FiBYKDgxEeHo7w8HCsWrUK06dPx65duwAAjLFSz7Ms0xJCCCl/+tpvm/MJX2VAOQEhhBBt\n6GPfTTlBMaM0BGjbTW/o0KHo2bMnAK7FJTU1FZ6engCKW2ocHBwQHx/PT5OamoouXbroP2hCCCFm\nSXr8kEpNTZW7GiAtk5KSggYNGkAikSArKwv169dXmlbxSgIpO8oJCCGElBfKCYqZ3DMCkpOT+f8f\nO3YMYrEYABAUFIR9+/YBAG7cuMHfm+Hn54fIyEjk5OQgJycHkZGR8Pf3N0rshBBCTE+HDh2QmJiI\n9PR0FBYW4sCBAwgMDJQrExQUhL179wLgjj2dO3dGlSpVEBQUhJ9++om/Bz0xMREdO3Y0xteolCgn\nIIQQok+UExQzuWcEzJo1C/fv30dhYSGaNWuG7du3AwAGDRqE6OhoiEQiVK1ale8a2KhRIyxYsIC/\nKrBo0SI0bNjQaPETQggxLdbW1ti6dSsCAgIgkUgwfPhwtGvXDmFhYfDw8EBwcDAmT56M4cOHQywW\nw87ODvv37wcAtG/fHgMGDECbNm0gFArx9ddfw9LS0sjfqPKgnIAQQog+UU4gg1UCp06dYq6urszZ\n2ZmtWrXK2OFo1LRpUyYWi5mbmxvr0KEDY4yxrKws5u/vz8RiMevZsyd79uyZkaPkjBo1ijVo0IC5\nurrywzTFOmXKFObi4sLc3d3ZjRs3jBEyT1XsYWFhrHHjxszNzY25ubmxkydP8uNWrFjBnJ2dmaur\nK/v111+NETJjjLGUlBTWrVs35urqyt555x22evVqxph5LHd1sZv6cn/58iXz8PBgbm5urGXLlmz6\n9OmMMcYePHjAOnXqxFxdXVlISAgrKChgjDH26tUr9t577zFXV1fWpUsX9vDhQ6PErSn2ESNGsGbN\nmvHL/ObNm4wxxiQSicmsL4QYijnlBIyZT15AOUH5o5zAOMw1L6CcgCiq8A0Br169Yk5OTiwtLY0V\nFhYyDw8Pk16RnZycWFZWltywyZMns40bNzLGGNu4cSObOnWqMUJTcv78eXbjxg25A6e6WA8ePMj6\n9evHGGPsxo0brG3btuUfsAxVsS9evJitX79eqey1a9eYh4cHe/36NUtLS2NOTk4sPz+/PMPlPX78\nmN2+fZsxxlhOTg5r2bIlu3nzplksd3Wxm8Nyz8vLY4wxVlhYyDw9PVlUVBTr06cPO3LkCGOMsWnT\nprENGzYwxhhbt24dmzZtGmOMsSNHjrC+ffsaJWYpVbGPHDmSHTp0SKmsKa0vhBiCueUEjJlPXkA5\nQfmjnMA4y50x880LKCcgskzuGQH6ps27Ik0NU3gipuy7LENDQ00m/m7duqF27dpyw9TFqu6dz8ai\nKnZA9dNITemdoQ0bNoSrqysA7pUobdq0QXp6ulksd3WxA6a/3KtVqwYAKCgoQFFRERo0aIDLly+j\nf//+AOSXuap3z6r6fuVFVeyA6mUuG7ux1xdCDMEccwLAPPICygnKH+UExnt/u7nmBZQTEFkVviFA\n1bsiTXklFggE6NGjB9q0aYPNmzcDAJ4+fYq6desCAOrVq4cnT54YM0SN1MWq7p3Ppuarr76Cs7Mz\nQkNDkZ2dDQA6vZe6PD18+BBXr15F165dzW65S2Pv1q0bANNf7hKJBG5ubmjYsCF8fHxQu3Zt1KtX\njx/fuHFjPjZ17541FsXYRSIRAO7d7M7Ozpg8eTLy8/OVYgdMZ30hRF/McR0357zA3I5Nikz92CSL\ncoLyZa55AeUERFaFbwgwt3dFXr58GTdu3MDZs2exa9cunDlzxtgh6Y1ia6Op/TYff/wx7t+/jzt3\n7qB58+aYOnWqsUNSKzc3F4MHD8bnn3+OGjVqaCxrass9NzcXQ4YMweeffw47OzuzWO5CoRA3b95E\nWloazp8/j5iYGGOHpDVVsa9evRr37t3D77//jpcvX2LZsmV8eVNbXwjRJ3NcnytqXmDq+xpzODZJ\nUU5Q/sw1L6CcgMiq8A0B2rwr0pRIu+jUr18fgwcPxtWrV1G/fn1kZmYC4FrXpWVMkbpYVb13U7Zl\n1xTUq1cPAoEAAoEA48ePx9WrVwGojt2Y61BhYSEGDRqEYcOG8V3QzGW5S2MfOnQoH7u5LHcAqFmz\nJnr37o0HDx7wyxuQX67Sd88CkHv3rLFJY798+TK/flhZWeGjjz7SuMxNbTslpCzMLScAzDsvMJdj\nkyrmcmyinMC4zDUvoJyAAJWgIUCbd0Wairy8POTl5QEAXrx4gcjISIhEIrl3We7duxdBQUHGDFMj\ndbGqe+ezKZHtpnXo0CG+u5QpvTOUMYaPPvoILi4umDFjBj/cHJa7uthNfblnZWUhJycHAPDyih4p\negAAIABJREFU5UucPn0abm5u6NSpE44ePQpAeZkrvntWKDTOrlZV7GKxmF/mjDEcPnxYbpmbyvpC\niCGYU04AmH9eYA7HJnVM/dgEUE5grOVurnkB5QRESXk+mdBYTp48yUQiEXN2dmYrVqwwdjhqPXjw\ngLVp04a1bduWtWzZki1cuJAxJv8amB49epjEa4IYY+z9999n9vb2zNLSkjk4OLCdO3dqjPXjjz/m\nX0Fy/fp1I0auHPuOHTtYaGgoa9OmDWvdujULCAhgaWlpfPnw8HDm7OzMRCIRi4yMNFrcFy5cYAKB\ngLVt25Z/zcupU6fMYrmriv3kyZMmv9xv3brF3NzcWNu2bVmrVq3YkiVLGGOaXxM0ZMgQ5urqyjp3\n7sySk5ONErem2H18fFjbtm3ZO++8w0JCQtjz58/5aUxlfSHEUMwlJ2DMvPICygnKH+UExmGueQHl\nBESRgDEjPs6aEEIIIYQQQggh5arC3xpACCGEEEIIIYSQYtQQQAghhBBCCCGEVCLUEEAIIYQQQggh\nhFQi1BBACCGEEEIIIYRUItQQQAghhBBCCCGEVCLUEEAIIYQQQgghhFQi1BBACCGEEEIIIYRUItQQ\nQAghhBBCCCGEVCLUEEAIIYQQQgghhFQi1BBACCGEEEIIIYRUItQQQAghhBBCCCGEVCLUEEAIIYQQ\nQgghhFQi1BBACCGEEEIIIYRUItQQQAghhBBCCCGEVCLUEEAIIYQQQgghhFQi1BBACCGEEEIIIYRU\nItQQQAghhBBCCCGEVCLUEEAIIYQQQgghhFQi1BBACCGEEEIIIYRUIpWuISAmJgZCoRDfffedsUPh\nmWJMxuLk5AQfHx9jh2EwOTk5CA8PN3YYclauXImXL1/qNM2ff/6JgIAA1KhRA0KhEKNGjTJQdLor\n7+3JXLdfc42bEClTXIdNISZTiKG8UM5gmkqTVwCmm1tQXqEdc427MtNbQ4D0xxcKhdi+fbvqmQmF\nCA4O1tcsy0QgEBg7BCXlHdPNmzexePFiPHr0qFznq4lAIDDJ30YfXr9+jfHjx2PMmDEGnU///v11\nKj9q1CiMHTsWjDGtpxk6dCgSEhIQHh6OvXv3YsKECbqGaVDGWI/Mdb0117iJ4dDxvOy0jenu3bsI\nDQ2Fo6MjrKysUL16dbRu3RoffPABoqKiyiUGbVHOUL7KK2fQRnnkFYBp5xaUV2jPXOOujCwMUeni\nxYsRGhoKa2trpXG0cuhHYWEhtm7diqtXr6J58+YQCoV48OABPD09MW7cOFSpUqXEOm7evImlS5fC\n19cXTZs2LYeoS6brQcOcrFmzBsHBwWjYsKHSuMuXL2Pjxo04ffo0nj9/joCAALi4uGDdunV8mcGD\nB+Pw4cOwt7eHj48P9u7dq1TPH3/8gXr16ukU11tvvYXAwECsW7cOs2fPLrH8v//+i+vXr2PmzJmY\nMmWKTvMqD++++y5evnwJCwuD7N4IqVToeG44165dw7vvvotq1aph9OjRcHFxQWFhIZKSknDy5EnU\nr18fvr6+xg6TRzlD+dKUM+jLrVu3kJ6ejsDAQLVlyiOvAEw7t6C8glRUel+jPTw8cO3aNWzatAlz\n587Vd/UEwOPHj9G7d2/4+fnh+++/55MxiUSCTz/9FJ06dcKJEye0PnhU5AOpqcjMzMSBAwdw8+ZN\nleM7deqEn376Ca1bt4aHhwdOnjwpN54xBnd3d/j6+mLChAkQClV35tm7dy+GDx+uc3xDhw6Fh4cH\nxo4di1q1amks+/z5cwCAnZ2dzvMpiUQiQX5+PqpVq1bqOgQCAaysrPQYFSGVEx3PDWvJkiUoKCjA\njRs30KpVK7lxmzZtwj///GOkyDSjnMHwSsoZ9GXBggVo2rSpxoaA8sgrAMPlFpRXEKKe3p8R8N57\n76F9+/ZYvXo1srOzSyyfmZmJjz/+GI6OjqhatSqaNGmCyZMnK027e/duCIVCREdHY926dWjevDms\nra3RqlUrfP/992WOW9s4AKCgoABr1qyBm5sbqlevDhsbG7i7u2Pjxo18mZycHMyZMwceHh6oUaMG\nqlatipYtW2LevHmlum9K6vXr1+jfvz/q16+PNWvWyF2REQqFWLt2LWrXro2+ffvi9evXautZvHgx\nRo8eDQDw8fHhu4HK3o+Vn5+PFStWQCQSoVq1any9igem0vw2qampeO+991CzZk3UrFkTffv2xf37\n91WWNWQcJf2WR44c0dg9ViQSoWXLlirHydq5cycGDBigsUxaWhr+97//wc/PT254VlYWwsLCMHTo\nUEyaNEltIwAAXLhwAe+++26J8SgSCATo06cPdu/erbHcyJEj0axZMwBcEitdb86fP8+X0XWbPnv2\nLJYtW8b/XgcOHFA7/7y8PMyePRvNmjXju9GKRCLMmDGDL6PqHrXSrBsPHz7EoEGDUKNGDdSsWRP9\n+/fHw4cPtb4nVdv1Vlva7HcA3fZlihYvXgyhUIiUlBSlcYrfW7pMo6KisHz5cjg5OaFq1apwd3dH\nbGwsAODMmTPo1KkTrK2t0ahRIyxfvlypXn3v26XrpKo/U7nf1FzQ8ZxjqOP5H3/8gYYNGyo1AkjJ\nNuaXNYayLnPKGTia1pejR4/qJV8AtMsZykoikSA2NrbEXieGziuAknMLyis4FT2vAPSTWxjiGFNR\ncwu99wgQCoVYtWoVevTogfDwcKxfv15t2efPn6NLly549OgRJkyYADc3N9y8eRPbtm1DVFQUrly5\nAltbW7lp5s+fD8YYZsyYgSpVqmDbtm0YOXIkGjZsiICAgFLFrEscBQUFCAgIwLlz59C/f39MnDgR\nFhYWSEhIwOnTp/kdR3p6On7++Wf079+fv4IbExODNWvWICEhAZGRkaWKdffu3bhy5QrOnj2rtszs\n2bMREBCAXbt2YezYsSrLDBo0CI8fP8Y333yDBQsWwNnZGQDQvHlzANytB7169UJ8fDw++ugjzJ49\nG8+ePcP27dvh5eWF8+fPo3379nJ1avvb/Pvvv+jevTuePHmCyZMnw9nZGefPn4efnx/y8vLk6jRk\nHNr8ln379sVbb72FnTt3Kt2nd/nyZdy9excrVqxQ+1tIHTlyROO2AIC/H1T2oBwXF4eYmBgsXLgQ\nlpaWGqePjY1F586dS4xFHT8/P3z22WeYPn262jITJkyAu7s7ZsyYgYEDB2LgwIEAgNatWwMo3Tb9\nySefoEqVKpgxYwaqVq0KFxcXtfMfPXo0fv75Z4wePRpdunQBYwz37t3DpUuXlMqq6ras7bqRlZWF\nbt264dmzZ5gyZQpatWqF2NhYfh0tqUt0adZbTbTd75Rm+WtL3f2Rc+fOhaWlJebOnYtXr15h7dq1\n6NWrF7Zt24ZZs2Zh0qRJGD9+PA4cOIBFixahQYMGGDdunFI9+tq3q7pl5sSJE/jxxx/x1ltv6fal\nKzk6nhv2eO7o6Ijo6GicPHkSQUFBGsvqK4bSLnPKGUpeX44fP66XfAHQLmcoq99//x3Pnz9H9+7d\n1ZYpj7wCUJ9bODs7U17xRmXKKwD95Bb6PMZU2NyC6Ul0dDQTCARs/fr1jDHGevbsyaytrdmjR4/4\nMgKBgAUHB/Of58+fzwQCAfvuu+/k6tq1axcTCARs4cKFSsO6dOnCXr9+zQ9/8uQJs7a2ZgMHDtQp\nTtl56hLH6tWrmUAgYCtXrtQ4n4KCAiaRSJSGh4eHM4FAwK5cuaIxJnW6du3KrKys2P3799nKlStZ\nWloaS01N5f9WrlzJ/vrrL2ZhYcG6du2qsS7p9zt37pzSuA0bNjChUKg0Ljc3lzVv3px5e3sr1aPt\nbzNv3jwmEAjYgQMH5IZLfwcfH59yiUPb31Ia1507d+SGjxkzhllaWrKMjAyN0798+ZJZWlqynJwc\njeVGjBjBatasyYqKiphEImGrV69mO3fu1DiNrIkTJ7KkpCSV4zZt2sQWLlzI1q5dy8LDw9mff/6p\nVObZs2fM2tqaFRUVaZxPcnIyEwgEbMmSJUrjSrNNu7m5sfz8fG2+IrO2tmb9+vXTWEbV9qTrujF7\n9mwmEAjY0aNH5YYvXrxYaR1VNT9d1ltt6LquarP8VcUdFhbGBAKB3H5bqmnTpnLfW1qnl5eX3DoT\nGRnJBAIBs7KyYomJifzwwsJC1qRJE9a+fXuVsZV1367O1atXmY2NDevSpYvW61llR8dzeYY6np89\ne5ZZWVkxgUDAXFxc2OjRo9nWrVvZ3bt39R6DPpY55Qwlry9lzRcY05wzbNmyhc2aNYtNnjyZJSUl\nsbCwMLZs2TL23nvvsezsbK3KHDhwgIWGhjKxWMwaNmzIQkND2bhx41TGUl55BWPqcwvKKziVIa+Q\nrbcsuYWh8wrGKk5uYbCGgISEBCYUCtmHH37Il1FMHJydnVmjRo2U6pJIJMze3p65urryw6Q/6p49\ne5TKt2/fnolEIp3ilF1JdYmjTZs2rEGDBlrt1KQKCwtZdnY2e/r0KYuLi2MCgYBt3rxZY0zq2NnZ\nsUaNGrG0tDQmEAiU/oRCIUtLS2MNGjRgNWvW1FiXpoN6u3btmLu7O8vMzGRPnz6V+5s4cSKzsLBg\nr169kqtH29/G2dmZOTo6KpXNzMxkQqFQbqdgyDi0/S2Tk5OZUChks2bN4ofl5uYyOzs71rdvX43T\nMsbY3bt3S/wtGGOsSZMmLDg4mGVlZbFJkyYxgUDA1qxZU+J0jDGWn5/P/Pz8VI778ssv2bx58/jP\n7u7ubNOmTSrL2tnZsb/++kvjvDQ1BJRmm962bZvG+clq3Lgxa9q0qcpEWUrTAVvbdaN169asadOm\nSmWzs7OV1lFV89NlvdWGtuuqLstfXw0Be/fulSuXm5vLBAIB8/f3V6pj8ODBzMbGRm6YvvbtqqSk\npDB7e3v29ttvs6dPn5a6nsqGjufq6fN4zhhjt2/fZiNHjmQNGzaUO5Z3796d3b9/X28x6GOZU85Q\n8vpS1nyBMfU5Q0pKCtu8eTN78uQJEwgEbNGiRfy4jz/+mI0fP56lpqaWWEaqX79+bPLkyWrjKM+8\ngjH1uQXlFZzKkFcwpp/cwpB5BWMVK7cw2OMv3dzc8MEHH2Dfvn345JNPIBaLlcokJyejW7duSsMF\nAgFcXFxw+fJlpXGOjo5Kw2xtbfHs2TMA3D1PT548kRtvY2ODGjVqqI21pDhkuwf9+eef8PLy0nif\nttSWLVuwbds23LlzBxKJRG6cNF5dFRQUoEqVKrCxscGGDRvwzjvvyI3/888/YWNjA6FQiMLCwlLN\nA+BeafTq1SvUr19f5XiBQIDMzEw0btyYH1bSbyP14MEDlfeb1a1bV2l+hoxD29/SyckJ/v7+2LNn\nD1atWgULCwscOHAAubm5Wr3WJzs7u8SH39y/fx+pqanw9PTE119/jQ0bNiA2NhZfffUVZs2aVWKM\nmrqWnjp1CnXr1sWLFy9QvXp17Nixg+/WqcjOzg7Z2dl8d09dlWab1vaeSQDYuHEjRo4cCRcXF7Ro\n0QJdu3ZFnz590L9/f622SW3XjeTkZJXraO3atdGgQYMS51Oa9VYTbdfV0iz/snJwcJD7XL16dZXD\npePU3c+s7W+jrZycHPTp0wcvX77E2bNndX7qNSlGx3PDHM8BwNXVFbt27QIApKSkIC4uDt9++y2i\no6PRs2dP3Llzh39ImT5i0Pd2JkU5A6es+QKgPmeIj49Hnz59cOvWLdjY2GDhwoX8OBsbGxw7dgw9\nevQosQwAFBUV4dy5c/j222/VxkF5BeUV0rjLO68A9JNbGGJ/V9FyC4O+B2P58uU4ePAgPv30U6Wn\noJeWQM29JOzNU2xTUlLw9ttvy40bOXIkdu7cqfd5arJhwwZ88skn6N27N6ZPn45GjRrBysoKf//9\nNz788EOlg7i2XF1dcefOHdSsWVPtPVf5+fnIzMxEu3btSjUPgFueHh4eWLVqldoyiit/Sb+NOccx\nbtw4DBkyBL/88gsGDhyIHTt2wN7eHr179y5xWsZYieuQ9PkAQUFBGDlyJABgxowZGDVqFA4dOoQh\nQ4ZonP7HH3/Epk2bVI4LDg7GpEmTsH//fojFYqxatQru7u4qywqFQhQVFZXwjfRLl9fxDBkyBL6+\nvjhx4gRiY2Nx5swZ7N69Gx07dkR0dHSJTwU2xLqhrj5d11tToGk9VbfPUjeNrvtNff42RUVFCAkJ\nwb1793DixAm1CSrRHh3P9X88V9SkSRM0adIE77//Pvz8/BAdHY1z586hR48eeovBUPtAUzlWm0Ic\nZckXpPNVFdPgwYMBALt27ULXrl3ljp1JSUmoVq0aBg0aVGIZALh+/XqJzwegvILyCn0oTV6haTpd\n9uH6/m0qYm5h0IYAJycnTJw4EZ9//jnOnTunNL558+ZISkpSGs4YQ1JSklICoA17e3ucOXNGblij\nRo00TqNLHK1atcLvv/+OoqIiVKlSRW2de/bsQatWrXD8+HG54dHR0dp8DbWGDRuGWbNmIS4uDl27\ndlVZJjY2FkVFRSW+7kXTxvTOO+/g6dOnBnmHcfPmzXHnzh2l4ZmZmXjy5IncQ10MGYe2vyUA9OvX\nDw0aNMCOHTsgEokQFxeHuXPnatVaXKtWLeTm5mosExUVhTp16vCNAAD36p158+Zh06ZNGhsC/v33\nX7x48ULtw0omTJgAT09PxMTEYO/evRg8eDCePn2q8r3gubm5Wr3mRx1DbNOK6tatixEjRmDEiBEA\nuAfKrFmzBocPH8awYcPKXD8ANGvWDPfu3VManp2djX/++afEnb++11tt19WyLn9pwvPixQu54QUF\nBXjy5AlatGhRiujL39SpUxEZGYmtW7eiR48exg6nQqDjuf6P55p4enoiOjqav3JljBgUUc6g3fpS\nlnwBKDlnuHDhAnr27Ml/LiwsxMWLF/m3OmhTJioqCq1atVJ7JZryCsorpCivKFYRcwu9vz5Q0Wef\nfYYaNWpgzpw5SuP69++PjIwM7NmzR274nj178M8//5Tq1SlVq1aFr6+v3J/0iebq6BLHsGHDkJmZ\nqfJprrItTIWFhUotXUVFRVi9erXO30nWpEmT4OLiorGeNWvWQCwWY/z48Rrrkj7lU/ruVlkffvgh\nHj16pLbbWFZWlg5Ry+vXrx//BGRZGzZsKNc4NP2WiiwsLDBy5Ej8+uuvWLJkCQDgo48+0mo+Tk5O\n+O+//zS+4ik6Olqpy5ilpSUmTZqES5cu4dq1a2qn/fnnn/Hee+8pDS8sLETbtm0RFhbGP433zJkz\nePnypcrbRnJzc5GbmwsnJyetvpcqhtimpSQSCXJycpSGe3h4AIDS06PLIjg4GKmpqXw3SqkvvvhC\nq+n1vd5qu98p6/KXJtSnT5+WG75161YUFBToFLM6pbkiq4tNmzZh69atmD59eon7QKIbOp4X08fx\n/PTp0yqvlBYUFODXX3+FQCCASCQyaAy6oJxBu5yhLPkCoDlnKCwsRHx8PLy9vflhEREReP36Nd9L\nVJsyUVFRfM5RUFCg9Po1yisor5Ayh7wCoNyitAzaIwDgWtlmz54td5+S1Jw5c/Dzzz9j7NixuHHj\nBsRiMW7duoVt27ahdevWKpMNdcrSBUeXOKZNm4bjx49j7ty5iI+PR48ePWBhYYHbt2/jf//7H06d\nOgWAe9XOsmXL0KtXLwwYMAD5+fnYv39/mbsQVq1aFSdPnkRgYCAWLFiA5cuX8yu/RCLB/PnzkZ6e\njlOnTpX4urmOHTtCKBRixYoVyMrKgrW1Nd5++2107NgR06ZNw+nTpzFhwgT8+uuv8PX1RbVq1ZCW\nloaoqCj+Pa3aUPxt5syZg/3792PEiBG4evUqWrdujQsXLiA6Ohr16tWTK2/IOLT9LaXGjh2LtWvX\n4scff4S3t7fW97tVr14dYrEYt2/fRseOHZXGJyUl4cmTJ3IHbakJEyZg8eLFWL9+PX744QeV9R85\nckQpQQK4+5jy8/PldtIXL17EiBEjVN5/ePv2bYjFYpUt+trS5zat6L///kOTJk3Qr18/eHh4oFat\nWnj48CG2bNmCOnXqIDg4uNR1K64bn376Kfbv349hw4ZhypQpaNmyJeLi4hAVFYV69eqVeMDR53or\nrU+bdbWsy79nz55o2rQp5s2bh6ysLDRt2hRxcXG4cOGC0rZZWrrWoUv5xMREzJo1C2+99Rbc3d2V\nXvfTokULdOrUSaf5k2J0PNfv8XzGjBnIzs5G37594erqCjs7O6Snp+OHH35AUlISRowYwTcEGCoG\nKW2WOeUM2ucMpc0XAM05w40bN5CXl8f3GPj3338xb948bNu2DU2bNtW6TFpaGn+rwRdffKHUg5Ty\nCsorzCmvAHQ7bug6z4qcWxi8IQAAZs6ciS1btuDx48dyw2vUqIHY2FiEhYXh4MGD+Oqrr/DWW29h\n7NixWLJkCf9gCClN94uU5Z4RXeKwtLTEb7/9hvXr12P//v2IiIhAlSpV0Lp1a7lu3YsWLQJjDLt2\n7UJUVBQaN26MkJAQ/oEkJcWkSZMmTXD9+nVs3rwZI0eOhJOTExhjSE5OhpubG65du4aqVauWWI+j\noyN27tyJ1atXY/z48SgsLMTIkSPRsWNHWFhYICIiAlu2bMGePXswZ84cFBYWolGjRujYsaPcd9UU\nv6rfplatWrhw4QJmzpyJr7/+GgDg7e2NqKgo+Pn5yZU3ZBza/pZSzZs3h4+PD6KionRq3Qe4KxrR\n0dFyB/WkpCQsX74cCQkJEAgE2Lt3L27fvo1vvvkGAPC///0P06dPh0QiwYEDB/D8+XOsXLkSbdu2\n5et49OgR6tSpo7StAECdOnUwf/58nDp1CidPnsTz589RvXp1fpkrio6ORv/+/XX6Xor0tU2rUr16\ndYwfPx7R0dH45ZdfkJeXh/r168PPzw9LliyR68Koql5d1o26devi4sWLmDVrFr766isIBAJ4e3vj\nzJkz6Nq1q9I9g4rT67relkTbdbWsy9/Kygq//fYbpk6dinXr1sHKygoBAQGIjo6Gl5eXUvnSPAeg\nrL+NJllZWWCM4Z9//uG7eMoaOXKk2R6sTQUdz/V3PF+3bh0OHTqE2NhY7Nu3D69evULNmjXRtm1b\nzJo1S+8xlHWZU86gfc5QlnwBUJ0zAFyX/3fffRcxMTG4ePEi7t69i7Vr16JPnz46lVm4cCEOHTqE\n9PR0+Pr68g0EAOUVlFcYN69QVUdJVC1vfR1jgIqdWwiYvp9mQUgF1rt3b1y+fBl///23Vo0tUmlp\naRg4cCCuXLmi13hWrFiBdu3aoVevXmWuy8PDA8eOHdP6ibOV0fPnz1G3bl2MGzcOW7ZsMXY4hBBC\nTFRp8wVAfc7Qr18/uLq6Ijw8XO202pTRhPKK8kV5BTEmgz8joLxERkZCLBaXeP88IaX14MED/Prr\nrwgNDdX5oO7g4IDu3bvr7WnbUmfOnJF7IFBpnThxAu+++y4drGW8evVKadimTZsgkUgqzENiKpOS\njhH5+fkICQmBWCyGl5cXHj16xI9buXIlXFxcIBaL8dtvv5VYZ3JyMjp37gyxWIz333+fv3d2y5Yt\naNu2Ldq0aQMPDw9cv36dn+bWrVvo1q0b3N3dIRaLkZ+fb4jFUKlQXkCMpSz5AqA6Z2CMITY2VuOV\nR23KlITyCsOhvMJ0mEJOoG4e2dnZ8PHxgZ2dHaZMmcLX8/LlSwQEBEAkEqFVq1aYOXNm2W+tYBXA\nq1evmJOTE0tLS2OFhYXMw8OD3bhxw9hhkQri8uXLbNeuXax9+/bMxsaGPXr0qFT15OXlsUGDBrH/\n/vtPL3Fdv36dTZ06tcz1/Pfff2zAgAEsLy9PD1FVHP7+/mzMmDHs66+/Ztu2bWNDhgxhAoGAde3a\nlUkkkjLXX1RUxDIyMkr8Kygo0MO3MW9lXVbaHCPWrVvHpk2bxhhj7MiRI6xv376MMcauXbvGPDw8\n2OvXr1laWhpzcnJiBQUFGuvs06cPO3LkCGOMsWnTprENGzYwxhjLycnh5/fLL7+w7t27M8YYe/ny\nJROJROyPP/5gjDH277//sqKiIn0tvkqJ8gJiDPrKFxhTzhlu3brFBAIBe/z4sdpptCmjCeUVhkV5\nhWmQHh9SUlJYamoqa9u2Lfvtt9/kllFYWBgbO3Ysy8jIYD///LNBcgJ1eceLFy/YxYsX2bZt29jk\nyZP5uPPy8tjFixcZY4wVFBSwbt26saNHj5ZpWVSIhoBz586x3r1785/Xrl3Lli1bZsSISEUycuRI\nJhAIWNOmTdnBgwfLVFdGRgabPXu2XuKaMWMGu3r1apnr+eSTT9jff/+th4gqllWrVjGRSMRsbGyY\nlZUVa968OZs9ezbLzc3VS/3JyclMIBCU+Hfu3Dm9zM+clXVZaXOM8PX1ZdeuXWOMcclUvXr1WFFR\nEVuyZAlbt24dX653797swoULaussLCxk9erV44dfvXqV+fn5KcW0f/9+FhISwhjjEoBRo0aVYskQ\ndSgvIMagz3yBseKc4ccff2QuLi5MKBSy4OBgdv36daWy2pQpCeUVhkV5hWmQHh+kywsAAyC3jGSH\nRUdHGyQnUJV3yDYI7dq1S64hQNGsWbPYli1byrQsyuVhgYaWlpYGR0dH/rODgwNiYmLkyhj6tRKk\n4nv06BH/lN2yWrt2rV7q2bhxo17qWbdunV7qqcju37+PtWvX6u2305biayVNGdPzI2fq1KnDv0dd\nG++++y5sbW2VXgelzTFCtoxQKETdunXx5MkT/mFastOmpaWBMaayzqdPn6JevXr88MaNGyMtLY3/\nvGXLFmzYsAEvXrxAXFwcAOCPP/5AQUEBvL29kZWVhZCQEHz22Wdaf2+irKTfnHICYkj6zBcUHT9+\nHMePHy9zGXUoryg/FT2v0HdOAOieF2jKCezt7XHmzBlER0fj5s2bmDFjBl9mxIgR2LRpE2rXrg03\nNzeD5ATq8o6GDRsC0Hyc+vfff3HkyBGcOXNG62WhSoV4RoC2B3TG9YAwq7+wsDCjx0AX7ZwMAAAg\nAElEQVSxm8+fucZNsZt/3Ibw7Nkz7AJ0+pO+MkuWKZ30TZo0CX/99Rc2bNiA0aNHA+DeBx8XF4fD\nhw/jypUrOHnypNLrS4lutPnNjb39mcJ2S3FT7Kb6Z65xU+zcn6E8e/YMewGt/zTlBFWrVoWvry9E\nIhEaN24MX19f/s/GxgbdunWDr68vatWqZbDvUxqvX7/G0KFDMW3aNDRr1qxMdVWIHgEODg5ITU3l\nP6empsq1ykgtwgJcQ3v+sweu859vwh0Z8c1g75kMAHBDAjxwHdsxRuO83ZDATw+Ar0Nx+BhsBwC5\n+bkhgR8vLS87zgPXsREvIMECAFAZi+z8PMA9eGoUdgEAdmEUP11GfDMwz+LpDiNIrp6bcMc1tMdJ\nDEQjFMcvXUbSOAHgb3ArXTIaodlfGUhuYY8EuGEg5B+Etw938Q2S8Tea8WUXtZiPpeCeZCuIB9if\nAuweDvQA0PhPAC1023kserNspHUmoxGa7c3gxoUWz6tEBQXAypVAWhqQnw/8959OcShJSABiYoAh\nQwAHBz7WpXtXAPEAvpT5nvn5QG4uULdumWbZCMloiSi0wRUM/OsUcBlAKAP2CoBOwOEWgUq/kaEk\noxF2YRS/jvO/w943yXmo+t/5MILgjpsAgGb4u3QB7BVwyxkApoFbFvEAPAG8eYZScgt7bh5TMrjx\nnbiyi1rM57eFIBzGGGyX2z6CcBgeuM59p7/efJ9sYBEK5bZj6XdIgBv/Wbq9ANz2Kd0fnPxrEBcD\nuDik2xQA7rd8E690m5Ylnafs/kG63Ur3CbL7PaB4v5SDf3EFh/lhGfHcts08wX9P2XnITqv1tmUC\ntDlGODg4ICUlBQ0aNIBEIkFWVhbq16+vNK20BV8ikaiss0GDBsjMzJQr7/BmHyArJCQEY8eOBcC9\nErZ79+6oU6cOACAoKAg3b95EYGCgfhZAJaTNb+6PCPyI9xEPT2wEdyXIE/EIj10GnAci5vkhDl0Q\nD+7gGZ/nCU+beH56T8SjC7heHdLpFXm+2RHFwxMzsBGeiJebn5R0nLQuaQzoDvh7nZAr+wB/4iIi\n5OqXJY2X/6wibqlwLAUA2OVlQpUcG+5Klj8i+O+7ETP4OmdgI3rjDF9Gdn6eiOfrl/JHBM6gN18W\nAM6gN///BVik9B1kx2vDHxGIz/NEjk09ue/laROvW12RkcCJE1w+8McfwPTpQGlPCAoLgS+/BEQi\nICCgeHjsm2OIl8IxMSMDsLcv3bxkRMAfebiACFxUuY7qumxLyx8RmIGN/Pak6jdXXFekIuCP3ivP\nAucARJbuBFN23ZTKPcOt27b+3Doy1eYLhMcuQ6aXLZrlJSN3bj3AH2B1BfjPHyh4aYt6yOHXL+n6\nlAk71FuZC8xj/LiJWIl9giUItgYuvPSDJ+JRLzZXbluOz/PkY/DrG8HvG2Sdndob8OdilI2v/i/F\nV7ht/TPltm/pd5Rug9J9lCx1+ytA9f4lHEuxAIsQD08+bsX5AuW3PpWVqeQE6uZRknHjxqFly5aY\nOnVqqZeBVIVoCOjQoQMSExORnp6OBg0a4MCBA2rfaSpL8QRX1fiSqJtesWFAtgFA1bSK9dyE+5th\nn5c6Dk2NGJq+t7ZlPsZmeLQofur1QIXxj2EPO5myY1psx3aMkdvVB4UewpjQ7Yh/Mz/VhwH1tmOM\n3EnKLoyCWyj3eVl8OJZ6qptSwaFDwLFjwPjxgJUV8PHHwF9/AS1aAIwBul5RnDoVqFYN+Ppr4NIl\noHZtLIsPx7XQ9vAIvV78Pe/cAaSv6Ll+HVC3A4iMBPz8AEtLtbPMiG+GjHRfnL8cBvtODzGmxXYs\nBbeMpb+l4m+E3FzAwgKwttbt+yk6cQJo3ZpbXgC8EMufVALgf4eg0EMAoLE5YlD8Sb5BrpTNAAgK\nPQSP0OvF60YLAKGq1+lRX+7i/y89+ZYtp7gdyY5b1GI+3JCAu3X+RA7ayzXuqZpXQovi+mXX3cMt\nAuHeorjhQNrwAEBuuJTsPkH2/4r7FMUYZLcVVdOoIy0ju8/UdVs1Jm2OEUFBQdi7dy//uqvOnTuj\nSpUqCAoKwoQJEzB9+nQ8fvwYiYmJ6NixI4qKilTWWaVKFXTq1AlHjx5F//79sXfvXgQFcQ2vycnJ\nfOt9REQEnJ2dAQB+fn5Yv349Xr58CUtLS5w7dw4ff/xx+S6kCqa0eQEARHj5wdNL+QRbMemVikMX\nreqNQxeVJ+6qZHrZoh5ykellq1X5eHiqrVtd3KrKSE9wZE+YNIlDF6XUX6v5ycWqfPKg6qRIW56I\nB2y4eqWxFM9PyxOVggJg1Cju5P+tt4ArV4AVK4A1a0qXExw6BGzfDuTkAOvXA++9BwBY4LUQAIqb\nVRkDRowA9u4Fvv0W+Ogj1fX9+ScgkQCtWmmcbRy64DIKYck6w1NQvMy7IO7NequwPBgDsrPLfGEC\nDx8Ct24BvXsDVarAE/H8dsL9FtqfMMahCzAPwDxdppInPSGOsylu2PPsq7yeZnrZctuSTTzO+veG\nrX8mImz80HvRWUTAk5+/3Ik3PNG7+9nieb0ZNyxAJn5AaZ/iaROPeP8S1nF/rpFAKsKLa5y09c/k\nGxGUvqvC9heHLvzvXfy7l4208QSQb+w0F6aSE6iah1BY3FlfVc+Kzz77DM+fP8eOHTv0siwqREOA\ntbU1tm7dioCAAEgkEgwfPhzt2rVTKidNYmWTdQByJyxS2iTHJZGtt7T1WXl7aj2t9LuV1MABqG7k\nkG28kG7PmuqRnmioOrmQxi51Kn4gbnq6I2NDM2Am5Iafih2IwJmHNcar0s8/o4ZEjJshxTFeQ3tc\nQ3ucih8IxAJa7ZcYA776CvjsM2Agd6rsfekSdzXfzg545x1g61Zg2zagb1+gaVPN9V2+DDx6BDx4\nAEycyCUPa9cCscCp1QNxCgOxVPp1Z8/mEo3Hj4E5c4Bdu5Tre/YMCAoCFi0CFi9WP99YYM6FeITv\nsMQY9+1YNiwcS2cCp958J3SF3LJHcjLg7w8MGwYs1XBaxxiXlGzbBnToAGzaJD/+1Stg6FCuQeHc\nOUAsRsbAZnD5LwkB//yKr5uNB36pDkAmFun3l0i4HhHVqsHb25v/HhmxzYCLMuV0dGoDt5xl1yt1\nvXYUqTuxliV7YgwAVb3r4ILMibd0G8yIb4ZAT+7KuqrGBemw7RjD9xrajjFyZWV7VsjOU7aM4j5N\n1f7N3jNZ6ftIt1FN+0DFXkF8WfM55qs9RoSFhcHDwwPBwcGYPHkyhg8fDrFYDDs7O+zfvx8A0L59\newwYMABt2rSBUCjE119/DUtLS1haWqo97nzxxRcYOnQoFi5cCJFIxN8ru2HDBpw7dw4SiQR169bF\n999/DwCwt7fHJ598gg4dOqCwsBBBQUHo37+/cRZWBaFNXiA92VSVGBsjqVWcp7pGgNreYp3r1vbE\nmj9xtlF94syf1Ghxsq/I29sbF/FC6/I6n7w/fgz88gswTveTTSWHDgEuLsCnnwIAvKtXByZMAJ4+\nBX77DYiPB9LTueP8Bx9orquwkDtuhodz+UOvXkCfPoCNDd9ThHfqFHDtGnDjBuDrC/Tvr/qkfOlS\nIDYW+P13Lk9R48VFWyxcEYuWZ5Ox8+xwFFlVUR+nRALMmAHs3w/88w8g1HD38P37wIIFXB5x4IBy\nXrR+PZc3hIZyPSEACAuL0GnzVSR7O0Hx0Cq3bublyeUE3HVtbrw+rjdLtwXplXLZ7V82DunJbhy6\nwHNevMpGLymu4a647jrerkDVG3JlZBvrZOcjvXKviuwJt+xyUDVeE+l31KYRQNv9S2n2AabCVHIC\ndfMAACcnJ+Tk5KCgoABHjx7F6dOnYWtrixUrVsDZ2Zmve8qUKfxthqVRIRoCACAwMFDrbpRKCa0M\n2dsDyoumeVb1Vn4XrKbyit+tpHJS6k6KVM1buh8q6YSgqnen4vKxQAbU38dyKn6g7st940ZMQ1tM\nctrKx8DXE/umTEqK/FV3WYwBkyYBmZnAixfcSf4b3tu2AXv2AM+fcwfwd97hrp7//Td3C4E6+/Zx\nvQE2b+ZOjBcsADw8uJN42HEn47PSADgAN28Ct28Dhw8DWVmAqyt3FcLKSr7OCxcANzdgxw7g7l1g\n9GjuaoCtLVe3szNQpQpqvHyO+Y/i0K3bBfxyuS/O/uYHzFS+RQa//841fERGAt27A2fPam4ImDcP\niIgAVq/mGi3q1eN6Tkh7L5w7B7RpAwwYwNVz4AAm39+MhQ+W4ZZ9G/j+FQXgTXe4CzL13rkD9OzJ\nXX24eLG4IQDgGgFky5bGReCUl0wfiNji/2ZAvrFLcf2XXc81kTZwuXlz0yveaiT9vyLFbVj26r2q\n8oqNAOrGK9afEd+M+95equNXtX9RJDtPVftMc6HqGLFkyRL+/1WrVsWBAwdUTjt//nzMnz9fqzoB\noFmzZrh06ZLS8C/fJMSqDBs2DMOGDVM7nuhOl7xAlj6umOmLqpN3XRsCpCcZmhoDdLmqV1I96non\neHt7Y7nMLQF6d/YsMH06rIZORYFt8XGUvxos/V137eIawBWPtQBw/Dhw9Ch3zNu3rzj2wYO5Y/Tu\n3dwFgy5duEbs16+BwYPV99bLzOSOs2+/DQQHc3lB587Ad99xFwoUhYcDy5Zxx3wfHy6ekSPlyzDG\n3Xro7s79LVrE9VD47z8uH/DwAGrUAAD03H0WD1aMgGPMBXRbHYuYhd0BqFjHZ87k8orsbK5h4dYt\nLgZVbt3iLiLMmsV9r9BQrqdE587F8Z08CRw8yPV8mDMHb2U9RvDkSDChAD7LLwLxc/gehLzXr4Ex\nY7jca8IEeH/1ldzo+DzPNz09yk5TzxxVjV0bMaO4J4GKbSVetrcA4iH2rg3IrBLq9imlPZlW15tA\ndvtTukVIZrvUtA1rs38x50YAKVPICTTN4+HDhyqHSyQSlcNLq0I8LFBb+rjKr0ibxFhfZRTLqZum\nNPNTt2y0rUur+GNVfI4Fd9KnK8aApCQMvn4QFucLleLhrV8PfPKJ6jouXQJ+/ZVr9T97ljtASwmF\nXPe8qVO5hGD/fu7E+eBBbt6qJCVxJ8rnzxdfIXBy4lr+Z8xAlaLXGHP5W1hLW/cPHwbefx+oWhVo\n1IjrWh8VpVxvTAwwaBDXCODmBixcCCQmclclBgwAatcGfHzw9cHxiG7hg8v9OiNS3AuBsdy95Y55\nKYg91wWCC5Li+SYnc40V33zDNQzIPkyFMeDzz7meDStXcldYYmK4XgkHD3JXK/r3564eANwBPzCQ\nu1py7hywdSum/P0l/MefQd/Rv8DrYSx3dQGANXuJ9q7XuKse/v5c8rBhA9czQnHn1k31YtZJLOTX\nM+lfbPF6mxHfjOtB8obi1XTFdVtu2Jt6VE4vnS+KT/RVddeXkvZmkZ1HRnwzfriqngrq4pP9V3G4\nInM+uSekopJe/dN3o4S2tyYYax5l6omRlAS8fAnnY3+oj+WPP7jGdBVJORjjTqqtrIDvv+dux5PV\nujWwahWXV3z+OXf7YMuWQHS06ngYA4YP5xoATpwozjHmzeMazVNSIIq9g1rxz7nhGRlcA7n0osTA\ngVzPBEUPHgBFRdztjN9+y504HzvGXVhYtIjLJ1xcgPHj4XX0MmJCuiHms65wPXiXr2LAmON45+Sf\n3IcnT7gLDV5eXF7Rs6fyd4qN5eZz5Qp3vP/yS663xNKlXINF377cOIBrUHj1iuvRMHo0sH493g85\njMQhztgZNRw3Q8VcTvVmGdW5nw2XuLvcssrI4J7V9PPP3PfRI2nXeABy983L/l/29gVtG8fi0EVp\nO41DF2R62Wp9a4860t4C2mxT+ty2Nc2zIjQCkGIVpkeAIZR0VV1deV3HyZaRzqs0PRNUTaN2vjJX\n6NXNp6wnCDpNL41H2xwgLQ2wscH9Wk3R7ZcLwGxfvh4poaSI67L2/Dl3IvrmdRy8LVuAyZO5lnBN\n3tzHC8a4Fuvbt7kr4IoWLwbmzuUeBiRr82age3c8POgEu/wciJAEYBN34JZt8Q4J4Q7qsr0X/vuP\na4jYuZPrATBvHvcnKysLuHIF+Yt+xHZPrsv52ZZ+6PU4EsA4jH34LbpkX0Lb578DcOdO5CdNKk42\n2rUDjhzhDsIAcO8esGQJl0xUq8YlBtLGizZtuIaE7t25OKpUAX74geu1UL06d9CfNg0b+32B2424\nZXTCpQ+G9+8P2NkhueAWHv/vLWCRJZdIDRnCdZv86ScuaWjZEofis5BtUwf72g0D4KP5t1Hjgxv7\nYZHyGnsufqi+kELD1E1P9Q2FGnu/vLnirrK3DJS3a1XDAfU9lRR7LKgrJ/dZ2gtAsfGtlGR7OBBC\nSs+c7qPVVXk0MmiUmAgMGACXw3fx+7Diq5pyD0v76SfumThRUYDiK9QuX+bu4f/qK83d4q2suEZ4\ngOsNcOgQd/Ks6Nw5rgu94mv8OnXiTqI9PLBUko+qVfOB3zO5cr16Ffcu6NMHmDKFu9VQtuv9Dz8A\n3t5cLwAfH+5PVmEh1ygSGYmoD7rjqWN9pNs3Qq1Hz2Hz9AUs8ovQbtfvXNkgcDlBhw5c/gJw9W3d\nyvVYkD4/aOFC7jbF58+5XCckhBtuYcE1BrRqBXz4IZc7LFjAXdkXCLjnLbVuDUmLGrg0tSMgEODW\nByJ0GfQ119iQn4+xD28jrUljoGUHLi+ytuYuEPTsCfTvj8+yEgGBAH+5vw3M+lbjs5LUsXn6AoGz\nz+Dx9ro6nflIr5yXZrvVNI2mnjMlTaM0XOHEXFOdsr0ayvIcDm3mRcxDpWoIUJeQl6W+8qDLfEos\n++IFd8KmbhoVJzIaSU801HQ9Vqk0D9pRlJgIuLoi4UULuNS/A6D4nZ64CKAr0PnhJe4hP56e3EFf\n9j6+W7e4E1wNXXWVCATclfmDB5UbAu7e5Q76u3crT2drC1y5AodLl4CWLTG9QwdgaR2uFV7alQ4A\nxo3jrjT89hv316gRd1XCx0e+nKK6dYHAQIwIDMSIN4OaLPTFmhNzgIICfPRoB2Kavwsfu2iAuQFX\nr3IPMZRavJjr/ZCQwD3L4MgRrtukumUjFHKNAR98wN0mcOsWt5wBrlfAvn34wb14Wa9/dxaG1zgG\ntGuHHm2dkGgvBpNte7G05Hpm/PgjkJeHgb3qY9qeNPy0JwRY+SdQs6b6756SAjg6yq9Pz57h86PT\nkCexwT7HYZBcrCLfu6Ar+HVEltKJtKbjo+J42cYAFWR7EPA0lJfdrrS5TaE0PYpKamjMiG8GN0/t\nbhkihJSNPh/kJaVVol9QwP1rVTwNoHwSY9CEXx85AcCd/K5diwYLxqsvc+gQd8J7+jR30iobw/z5\nXHd3TY0AigYN4nKMr76S71UIcN38585VHg5wvQeDglDTwoI76R47ljueSU/GAe7YN3Uqd7Fi8mQu\nv3Bw4BoCfv1VfUyWllzvQTc3DAAwAAAseuNutwN4O/oRGtx5iofdmqBZ9COu/NWr3O0EUr17c8fj\ndu24E/M6dbiHGT9+zF0cUGXoUO7WyeXLudsp+/Thhjs5Af364bJPLv8bp3o25m536NABEAiwNugK\nLll2kX/ifGgo19vi2jV41+mOFewGBm46BtTeyd2aqM6zZ9wFije3Rkj5Lr0At+9vYf+A9/7P3nmH\nSVmea/w3fWZntvfK0lYWWBAFxKCCBVTsFaKS4kli1Gg8RFNMYiESNRpP4lGTnBgTSzR2jYLEiooF\nEBEWZJe6wPa+0/ucP96pO9/MzhbKwve7rr1g56u7MPO97/3ez/3wjYviU/RDhCbnUvkBg2WgYwd6\nTw24fRChnqHzHc1CpMzQOKaEAEhtpT3ZPgc7QyDV8ydbWUxWE8y4cWLl1fksEGpb02+fZMcPk9N3\nvg9p54mHXfqdQz/Rtm0wdSo79lRQ1bEj8npUmcH0ls0we7ZQ32+4QTyUr79e/P3HPxaDgOzswV33\n8svFqvfy5WKSHkoCvvde8cDuJ7KEUavh1OBs9IknxIN21SrxwAphMAh73uLFQl33esWK+e23D3qQ\ntP83FfBqAdxyC7tKJvDo3Bv59oYnYf9l4pqlpZGdzzhDlAdcfLEYbHz4oXAxJKOgQJRT9KeyEvbt\nozdmIHV88AtqL0hwPq1WrCYE+eNlwHe/EoOsCy8UA6IFC4Q4EmLjRvHvO3OmKNsI/VuuWMGrNZcw\nvX4z57a+xUrEgOSEMRup3VvDsvce4kHNrfjWqiOiQAip//cDvR8kRIWYCf9g308pHBsnLCS671TP\nk4BkYaAyMkcbR9LqVmgScdBX7O67Dx5/nOLXzqblhOG3q0vtfvpFrtXUiOfdhg2QOPcuOTabsJUv\nXEj2VT0ovf64Ea7S4xOlATffLCbu1dVCSH7lFfEM6e4WE/LBMHasOMfHH4tJ+l13iUnwuHGwY4eY\n0Caiqkr8ec89YlJcXS2eedHcdps436JFYvyxZg18+mlEfB8E1Yv+m+qnV9G3aRNPrvom1y74p3Ab\nbNggOiSEMJnEgsBjj4nyiCVLRClfIhEAxBjlttvEV3+ee46LlEouCu8L0d1nfxPZEstJJ4kv4Ha+\nCROvFL+LAwdEmUZxcWz5RiAgxlZffy3GUqHfZX09057byuoHFzD7zxvZeVElAJpuD9pOD/r9TsqN\njRw4Ob7NKyRZiY/6vyy1PbpWf7CfLanufzht+kfS56XM0DnmhACQGPyOwMR3JN0BB81p4PeL4JrP\nPmPKhG1sY+rAx6SavJ8iY7v3iof+U0/BjXdy49pHaMipDE/WUubLL2HBAnZYCji7Pl4Z13pdVLdt\nh1OrhfK8cKFoZ3PppUJE6O4WK/CDZfZsUU+/fLkQABYsEKFB3d3w8MOpnWPBAvHv0E+xDm+rrxeC\nQtowU3F+9Sv45jf5y1XP8O7Es/jDa7eIlf+FC+OFhZwcEZB0xhniQdrfMjkYBrOakow//lHUCW7Y\nIAYrjz0Ga9eK1Q6HQ6yoPPqo+H0tWCAGCA4H/P3v3HHTNq4oepFLNr3KSs5H5ffy3p/P5H/H3MSv\n6+/hg3mnsz7npPh8Cqkci+g/T5LYJ3rfZJ8jA9n0R8jGnyqpfs4cjGwVGZkjhUQdAw7mIDfRSuFh\nCSlsbweViln/9yX//nNkgn7IVg8DAVFrP20afPIJuTVdXHLHG7z6t0SqcQK++krUxZtMWItMZO3r\nhfGRzUqXn9w93WLSnp0tcnL27hUhuFdfLcTwJ5+UXr0fiCuuEOL5rl1iYvrjH4vJ9G23SQcS9ken\nE5kFen3889NoFKJ7U5O49+Hwne/AihX0VmTSNq2Qr66p4ZRvflPU9D/+ePz+N9wg/n/85z/SHY1S\nZaTGBLNmCafCBx+IkskPPhCLMXODD95//EOMAd59V+QYrFsnrv3RR3z802+w/ocncuadH7LHUoYv\nXc243+0jZ20vfo0SXblXUghIxaWT7H0yHDEgEalmF8gTdZlUOKbCAgfDQPX+yUL7RmoiP6IlASAU\nc4MBzj6bs0zvHpT7GIg8W6eobbNYKO/Zz3c3/J1Lal8VG1OdCAUC4gEwfz4rfzqRc9w7YjYfZ6lj\n92/HM6V1m1DYFQqxAj53rrC5Wa2iFZ4qSRudRCiV4oF5993CRv/Xv4oJ6oYNkJWV+nmkRIAQ+fnD\nFwFApPXeey///Ntl9Pwqm9J3XoXx44UNUYqcHDGYeuCBof1uRpqMDNFD+c9/Fu6JzEwhttjtwl1R\nXi7qEB98UFgnvV4x+HrySVrvKuKdqgUs6HsHTgkwq2cDGS4zP995Hz6FknnpH8Ze62NiRIHAALER\nCYkKCIx7HYYWjPlJgpDQFN4vJqeFmfs3pHSNZMSVTcjIyBwdmM1w6aWMf68hbtOhmEhobR4x+V60\nCNas4cr/vMQJ/9iM1uoe3Inef1+MLYDsqtn8ZEes62Dm+ZuZv2JtJPPHaBRdAP70J7E4cc01QxfA\nb75ZBPhlZoryvo8/Fs6/G25I/RxGY+LnrlI5fBEAxPjv739nzO+eYwXLOeW+j8Tv4JVXEjsM7rpL\niBSTJg3/+iPB6aeLhZjnnhNjg2uvFZkIjz4Kd94pfu8zZwrxxGQSHRAWLeKcm9/Ek6ahcXYJHWvE\nz5r/djeZn5vJ+tRM5Yf7B7z0uyPSvPDwEC0aTF9Ti8Inwpml3uOygHBscUw6AkYTI+oOsFjEh+JZ\nZ3HWnU/yx9NuSbxvKoP9/tbjREFqUa/nWzsgvxBOO42Lt77G8U1fofc4U7r9MNu3C+V87FjxAGhq\nEm18dDoA5nV+SFlfE8XmFpj8VOyxc+aIr+GwcKEI1JswQYgMCxYM73wHC6Uytt5w9mzxNRpRKIRD\n4LTTROlCVZVo7RRyNlx1Vdwh9QXHoQgEmDhpJ2e/9h+emH0t397wJH+d831O2/0RD0z66fDuyecj\n02Ghjyzp8oCBiHYQpPB+S/pZkMCNcMHXb/CDz/+P029YM7QSBbmcUEYmzMFaJT+UA++4lUmLBS66\nCP0zfyGroZfeykEI2iOAscMmsmbmz4ef/5xxTXaU/gClXzSLfvOp8v77EVt6VZWw5QfbeCncfnLW\n9pLr7oHb+k3mTCZhzR8OaWni+j6feCYdd5z4OhKJHq9oNKJz0Gjl4ovFQsy3vy1cAOvWiXEhCIeJ\nRKjzrgXjGP/uXmyztaTtcVC7eApam4eKzxqFi2RM3CGDwtDtwJGTpIRiAIYaHpjycYEAd11yD0+s\nXUr7lPy48wzmmjJHB7Ij4CBxRCZsm81ilXXePE7ZO/ilyUH/TBKTmzxbp3joX3QRd//nTj4cP48x\nPftId5qTn2vtWqG6g3jghurCNBqRprtjB9xxB8ZZVuaqPmFfdgV2bZqo2TsYTJw4MuFGMqlTXS3a\nDc6eLcIOB/r9KxS8NP1y/vN/Z3NLwx94Yva1nHbjR9y98E5O2bsWtd+T/PiBePllXnzqitQFAKm3\nXCIHwVCQOE9Vxw5ybV1J95GRkUnMUAa9IzWxPySig9kMmZnsPa2CMZ8cGPHrDaZbesEAACAASURB\nVERap0OMCebMgX37qH69nvpFEyhb1zTwwQsXQkeHsINv2BDJ4amqEgsGb73FhLd3k7nJgiXfhNIf\niDgCRprsbPFzyBxaHn1UZAe99lpEBEjACpZTd0EVM57czIxTvmbrhdWsfuAsVv3PAvaeFhWeOFQC\nAW6Z9Bi5O7oG3vcwkdlpJr3XRlqXfcTPLTsJRieyI+BIYiRC+pIN9EOOgPx81H4vGY4+zIbMoSX/\nD5GwEHD++WTX1/Pk7olofW5mHdjA+xPPTHzg7t2RlNwtW0SabYgFC0Q9/L//zZIrKjhl71puveBB\nrtj8IlfKk/Wji+hAowEILAP++yFm3Pot2k0FNGdGAhLrC47j9MIPeOe4hZEJ+sfBP1Od2H/xBbP2\nbxClKozQ/7NEzoL+nw3J3udR26o6dpBrPwiDEtktICNzdBAcF/SMyyZrX98hv3xap12MCfR6eOMN\n9Pfcw1eLlUx9cfvAB3/6qShnKygQCwLpwaTBs84Stf8bNnCOr4lNS2vYfmEV6S1Wpo5WV5yMNBMn\nivLAFPnxlD/BzuX8veEXNJ9YTEApnt11F1Qx5eXt8J1h3Mv+/Zg67JRtaKarKpd19pNGPMxvuFb+\nsh1CYEvrcsSdQ17pPzaRHQEpcESu7g+FkCNAoWB/dgXlvf3U/ySTi+H+DsZ37uK5p5dEhACFAu65\nh6dmfZvaohoR7DfQve/aJZT/PXtErXuIa66Bf/8bTjmFe1f9AoCXay5j8bdeGNY9yxwFKBR8VToj\nRgQAeGH6lVyx+cXIC6cyeDZtIsvZJwIwB1sWcLD4ROQCrHlU1LpWdewQ77lAYEin65+HIjsKZI5W\nUk7pHuaq12EJBExGcFzQW5FB5oEBnHkjzKXf/TfjPmiIrKTPnAmvvUZrTQH52zuTH+zzidyjrVvj\nxwSTJ0NhITQ3o7W4Oe3+T9lx7gSee/mKg+cIkBk95OfTNKskLAIA1F1Yxbg1+0gzD2OlfJPosFPy\nZetw7zBlUvk8WsdJ/ODWvzH+3T0RIaBz5B0BMqOTY8sRMMwV9xETBEL3cRDb9EkScgQAk6eXU9G7\nn23FUyP3chD55qbnuHjra7SmF4kwvCj2ZY+homeAoJa+PtH1oK5OPPTHjYtsmzNH1Ir9/vc8fOM/\n+etJ3ycwUim1MqMeyeC/y6+ge9LxbC+sZtWkRcx95xOe4L8kj//8jydx9dX/ZHfehKiTBuCrr1hf\nPosTGzeyNzf4/zHkLjhFTMjvfPtufn3ub3CuS6FmcKBqnRTfo3m2Tubt+Qi9x0FVxw4UgQAmlxWr\nPj1ynkP5uSMjcwwx6uyxwXHBheXXU7/qJyN66mQrjGkdNmY8tYWuiTlwbux+fRWZZO3vE5+ziVx9\n5qBosW2bGBtEjwkAfvITaGnhzeqNKAIBdpw3cTg/isxRxgqWx76QBbUL3+SOy+/l3X/MY8Lf99Jw\nYxneLE3csTP+sZmi2nbe+n2/fKivvuLArBJKNrYA0q39pv+zFlt+GrsWjo/bJkUqXQtSYczXB+gq\nzmJ8ZwM+tTLOEQCyK+BY5dgSAvoziMnvUeEKsFgiafXl5fGOgIPIZVteRu33UtmzL66Obn92BTOa\nBuhVHnrof/WVCAccE5XoolCInrfAPQt+PZK3LXO0UlHBrFs28Pb/LeSWj/5Arr2LlxdcJoL/+jGp\nvY7xnbtjhIASczMoFKycfB4nNm7kpelXxBxzzva3WPHWL5nWsoXXpl7MJ4fQMmByWwGYeeALXGod\nPWnZ5Nk6I0LAUDlMboAjNIpTRiaOoYgAh104CI0LyssPqSNg8mv1BBSQX98FS2PHBK4MHT6tirQu\nB/a8BB10QmOCrVtFWUFVVez2q68GYAd3jPStyxylvPjMxSz8xfv8cM4TZB0w4zWqaLilIm6/vB1d\n5O7sjj/BV1/x5bXHc/bP3kPhD8R4rjM7+rjoVys58Ymv2HbZpJSFgJHCYHWQtV9JRqOZlumFsiNA\nJoy8bHosYTZHaugqJEoDDhatrZT3HuCdqgWiPU5mZnhTYFnQEdA7gCPAbBbtc1auFG1uEvTmDSyL\n/ZKRScTu345n/MpneXfiWbxZfT7fOvBU3D5Gl5VMp5lSc2xw1WVbXoZ58/h47KmcsfP9uOMefONW\nHj/pezx+0veY2fhFZMNAesAg9AK9x4HK54173eQSQsCCHe+wI7+KTmNefE5Av44fv3vjNpQf+1K/\nuIzMUU70ythJrDtsE/Zk1w2t4A11FW+9f5aw15tMUFFxSIWAiat3U7t4ivim3+LACpbTOyaTzP1J\nMgvMZhEGvG0b7NwZ7wiIOlf0l4xMIpbrfsspv1vL3nljeON/z2HMn5sky+oymi1kNMW+V9K7LbB2\nLXUXTMRckh52BYQ47cW15Ozu4amVSyjZePBKB7QWl+TrBquTzANm8us62f+NMklHQDTVr9czdk1D\nwu2HXcCUGTGOPSHgSK1xPVj39UnUn4fLEdDYSENOJbVFNZF8gCj2Z1cwpmeAtFazGa68UiTDJnjg\ny8gMmpNO4r+WPMGzJ1zFItequM3FZvEwL+lrDr+m9zj4+fv3wS9/ycfjTmVC5y6Ko7ZzCpT2NfGv\nGUtYXzGbExs3DilDIMvewwePzU+4/aHXl/HtL56Me93otgGwaPsqaotr6ErLFTkBCVD5vNy25kHS\nXZbB36SMzDHO4ar5H4mBuN7mFK3vlErIyUHl8iacSIw0mY1m6i4IruJLpO33VmSSnSy80GyGigqY\nOlW0jpPHBTIjgVrNS09fzOc3zkRl9ZG2J37CnN5sJaPZGvPa5b9/FS69FHNpBnUXVlH9en3M9rym\nLvbOq2D3mWNJb7Wi70k+EU/EObe+w/h39khuK9jazvfnxS9ogHiv5+zuJmdPL/tOqUjoCAh9rkx8\naxcVCbqIHAkiwAX61L9kknPsCQHHGtH1/9GOgPLySF3+EEUIhd+P2pdCC7bWVlrTi/i6aLLkA785\no4Q8Wydab5IBiNks+gzPny8/8GVGlMAy2FI8jSmt2wAY27UHWoICgLk55k8Qlvv9WRVw/PF4btPw\nVvW5XLz1NbHxFEhz2dB5XfQYsvmibCYzD3wR3gYwoWMnd6++gwJLW9L7OnXvx8zf/SE6j1MyP6DQ\n2iYpoIUcATMbNwohwJgb20IwRLB1YaZTDLb1HmfidoZHqoAqI3OEcCQ6BgYizeyIjAkUCvrKM4ft\nClC5U3MWmVptNM4uAaNRWghIxRGQkQE//anICKisHOIdy8jEsoLlIlR7WjmmbTYU/gDVn9eFt2c0\nWzC2WVF6/eHXTn5jPVx3HStYzvaLqpj8Wn2MmyC3uRtLSToBlZKW4wvjAgXnrfiYce/tTX5jgQA1\nz39NUW275GZTmy1h5w+D1UHxlnZ6xmZhKTEN6Agw9DrROMV7Wc4NOLqRhYAQ/ayyR9zAN8V7ynD0\nce72VeR3t0eOCxHtCCgtjZncJOKmjx8m09Irue3ira/xp5evH/imWlpoTS/i47GnwkUXxW32K1U0\nZ5RQ1tuY+Byhh/4jj8BNNw18TRmZQbD3N2PIcvaS5e7hrv/cFW5TWNLXjEOtj3mvTOzcSX3BceHv\n/3Ty9dzxznIhICBEg+bMElAo+LpwMpXdDRjcEfV96canuf7TP7HirV/G3kQ/18Cpe0Q/w8IEgkGW\no1fyPRzKCACoLaqRLg3odx4AvdeZcB8ZmWOR0TgAlrrnik8OUPpF/GdFmsUeGRMA5tJ0MpqTO4OK\nNrfFrXZG898THx3YVRAIkN5qxVJsgu99D447Lm6X3ooMsvb1Jf43CI0JLroInn9eOBtkZEaIFSyn\nbWo+GbVWSjc084e5P4V68f8+vdmCT6fG1CaetQq/n+LdLeGcigMnleJO07DkvkhnorymLsylQnRr\nnlFE8ebIc93UauW0+z9l6QX/QmNzx9xHtOMoa18fWY1mTK2xboQQhl4nad0O1E6JkkGrcAq21hRg\ny0sbUAjQ97okzyNz9CELAYeLoQoNAxy35Kt/serx8/jJs7+P3xjtCNDr0SVbgQ/y44//yNhmaZUy\nz9ZJtr1nwHPQ2kpLRjF78sbDihWSuwxYHhB66E+aBMcfP/A1ZWQGg1LJtsIpTKncxnEd9fDhh/Dh\nh5SYm9lUOiOmNGBix0525kUSqD8Zdwp/mXMdt33wACDKApqC7Qp9KjWNWWUxXTHO3Pke/zzhaorM\nyesET9vzEW6VhmJLi+T2LEdvuHQhGpPLSmPw+luLpw5YGhAWAjwSQsCRKIrKyMjEkdAZEAjw/VP/\nwX/NfypuUh3jCAC8ejUqV/IV/fHv7WXSGzsSbs9otqC1DeAU7OnBY1DjNWjgD38QuT/96KvITLi6\nCUTGBEqlKBuUkRlh2moKcG41kFffhVerhl//GhwOtDYP7ZPzSA+WB+Q2d2PPSAu/lwIqJc++egVX\n/fZFdHbxXBVCgBDdesZlk703ssA27v0Gdp85FnteGsYkIX6VH+3Hq1GS3iItBOh7xbXihIJAAK3V\nTV9pOm1T87HnGpJeJ3QuWQg4NpCFgGj6D3hTGQAfYYPkDKcZn0KJRsqyH+0IUKtR+Qe28OXYu1Ek\n6EGe5rGntorY2sqvLipKGt7XmFlGaV9T4h36+mJCBmVkRpo5C6ZS01rLpPY6uO8++MUvKO1r4ovy\nmXGOgF3RrQSBV6Zdypk73wOCjoCMkvC2hpxKKnsamNS2nc/+OIdpLVt4Y8oF5NglUocBjddNkbmF\nSe11rBk/PyIY9CsPSCQEGN02aotr2J9VTk9ajnAESJUGhM7jFAMSg3doNYsyMkczh9LyfzAcCBqH\nFxQKVB5/zOvrOCnOEeBXK1H6/P1PEYOh2wHSQwKUHh8qr3/gCURrK4aiyqThfX1lGWQ0JXEn9PXF\n3LuMzEhzxdQ7KaptJ7+uk1d+fCGsXQsrV2IpNmEuzQi7Z0p3NtM8oTjmWHNpBrunj2XKJ9sByG3u\nwlxiAqCnMpOsBvHcXbzkZc64+yN2nzUWe64BQ3f8mFrh86Pw+Tlu5U52LJpIegJHQEgI6C8UaFwe\n/GolnVW5tB5fhDPbgK7PiSLJe93Q60TjSKH0V2bUc2wKASM1eT/CRACAdJeFvvRMVD6JSX6wXzAA\nKhVqf/KHtdLvI9vRizIg/WFh8DhSchXQ0iKp+EfTlDlAqUJI/ZeROVhMn86F2/6NW6WFH/0ILBa+\nuek5viw7gQJrO8qgcDahc1eMIwCEBT/T2UdF974YRwBAQ3Ylld0NXLH5RbQ+N/884WqaMkpj7PoK\nv1/UEwYC7LxvIv9+4kKePnEpe3PGxjoCosSAhI4At5UN5bOYeYvIJmhNL6KsL3HZTVJHgIyMzKhG\na3HhzNRJDvoNllhHgF+liKl7lsLQ7RCt0STQOMSYQu0aQAhoaYHi4qS7DFimII8JZA42kyaR1dBL\n+bomdp0wHn71K7jpJswl6ZhLTGGhqnRXM00TS+IO/3LB8Zz/7lto7B60Tg+OHAMAPZVZZDf0Ymy3\nUfXWbsyl6dSfNxFHjoG0rshKfeg9e+oDn3HDrL8x4d29rL11TkJHgKFXjMfTW2LfNwarA7dJy79e\nuIz68ybiVytx5KYlPA/IjoBjiWNTCEjGETi5Hwwml5U+Y6b05D36walSoQokdwSEJghKv/TAIM2d\nuiNgICGgOaMksRAQCMSWNcjIHAwuuYSz6/9DXcEk0eby+ecpVrv4x48m0m4q4Lh2Ef4zoXMXO/Nj\nhYCAUsl7E8/knPrV8UJA0BFwwddvcOsFD3L95X8WAX5RQsA//vUdLt/yEpPa69B7nGTbe3ho3jJa\n04ukSwgCATJcZvJsnbz45OUc37QpvMnksmLVmehILyCwDL4qPZ7jm79K+GMPNiNgbNeesCgiIyMj\nzeFsORiNzurGmamXnLwbzbGOgIBKidKXYLk/SFqUEND/59PYxQqi2jnA50MKYwJLsQlTq1WIpFLI\nQoDMwcZgYNeCcYx/v4H9k8rguuvg1FMZU3kqJ425nIJtHYBwBDRKCQFnHU/VW7vIaDLTVZIT7pjV\nW5lFdkMfVat2seussTzx/lJ6K7Ow5xrCtfvlnzdy7ZnPADDh7T0ofH4+/9EsOqtyE2YE6Pqc+JUK\nTnxiM+f9+D+RH8PqxGXSYs9L4x7lbwBoOb6Q4q8SlycKISC157zG7olrpygzejh2hYDDNeFPdt25\nwz99ustCb3rWwI6AFEoDQtblYTsCWlsHVP+bM0ti6rDx++Hll8XfnU5Qq0GrHfhaMjJDpayMD8af\nHgkCnDwZ9u2Db3yDv875Pr969x549FG603Kw6OMHoM+ecBXfXf93xvTsi3MEnLJ3LeM7d/PxuFMB\n6DFkk+XoDQ9yqzp2MGff55y+6wNWVS9i4u272Js7jpaMYsmMAJPLikNjoMuYy+VbXqaqY0fMNqvW\nFP5+T844Mh195FqlcwKyHSLnI1VHwAtPXcncvaNcMZWRGQaHaoI/EtfRWty4MrQoA8T1RI9zBKgV\nKAYQAgzdziSOgKAQMJAjIAUhwKdT48rQkdkZNcH4/HNoDLqbZCFA5hDw1dIa/AoomdgkFgj+9S/4\n619h6VKmPbcV3nuPua99TsPUMXHHfn3yJHRmF5NfqaOzNDf8ujNLj1+lYObfNkVaaAL23EiIX+7O\nbsrWN6GxuSlb38RfP/4O7909D3teGjqzS7I7h6HXSfe4bCat3Enxpsgk32B14jZpYvZtPqE4rnNB\nCLXDg8blS9kRUPP8Ns5d9k5K+8oceRy7QkAUY7obSHPZDvdtjAgmt5U+U6b0JN9mE616IKXSgJAQ\nkDQjIMnkIbAMAv8dEDbAwsKk14pzBOzZA5dfLh728gNf5hBx5j+W870HvxN5IT0dFAru/tePucr6\nBbz6KhWfvR2TdxFYJr7e/PM5lPU1MvPAF3w4bl54+77sMZyy9xP+eeLVeFXiYexTqbHo0sP1+aV9\nTcxo2sT83Wv4YMLp4WNbMoolHQFZzl569VnhLIICS6SdkNFtw6qLCAEBpZJNpTM4oelLyZ95sI6A\nEnMz+baOlPY9kli9ejU1NTVMnjyZ+++/P267y+Vi8eLF1NTUMHfuXPbti4SX3nvvvUyePJmamhre\nfvvtAc+5d+9eTj75ZGpqaliyZAkej5ggPfbYY0yfPp1p06Yxc+ZMNm7cOOA1ZA49Ko+X0h1JMmtG\nETqrG1e6Dp9KEVf/r7e5ImMCwK9SplYakGBMoLEHSwOSTCBWsDyl0gAAS0k6uc1RWSp33gn//Kf4\nu9ks5wbJHHSuPu8ZlA/9D3cb7hUvKJXiPVNWRtr3b4brr6fsx7dzz/l3h49ZwXJWsJx3lBeQ/b1b\nOefn7/Pe1fNjzts7JpOizW1su2xS+DV7rgFDsDQgo8mC1uFlxtO1tE/Jx5WhAyCgVGArMIY7FkSj\n73XRPlm04jS1R+Y0epsTtyl2Ia3pxCJKNkoHEYeyBlIVAtKbLRg7kocPyhy5yEIA8IfXb2FR3arB\nHXSELoilOy30mrKkhQCnE/R68XeVakBHQGilMFFpgMHjGHjy0NsLGg2YTEl3a8osjQ0L3Lo18qcs\nBMgcKubOhVNOiX89Kwt27ID33hOdK6RQq7l28RMsvO5t2jIiq10NOZUA/PnkH8bs3p2WQ66tC6Xf\nR5GllRMbN7Jgxzu8XbUwvE9repGkIyDL0UufIZOmzFJa0wspsEaEAJM71hEA8GXZCZzQmFwIMHgG\nDgtU+P0UWNuThg8eibhcLq6//npWr17Nli1beOmll9i0aVPMPo888gjFxcXU1tZy2223cfPNNwOw\nceNGXnnlFWpra1m9ejXXXXcdHo8n6Tlvvvlmfvazn1FbW0tRURGPPPIIAN/61rfYvHkzW7Zs4c47\n72TZsmUJr+F2x7aRkjl0TPtwK8t+8Mjhvo0ww3EGaC1u3CYtAZUSVT8hQOt0R8YEQEBCLOiPoScF\nR8BAE4jm5pSEAHNpOnlNUZ81W7fCli3BjfK4QOYQoNPBLbdIb7v/fjEuuPHGsO0/jhtu4IWnL2bl\ndefGvNxTmcVXS6fhTteFXxMZAeI5HMofmH/Px9SfF1uKaCkySdb363udtE/Jx1JoxBglBIQyAqJJ\n5ggwBIWAVMMCTW22mGwDmdGFLAQAubauxIPgVDsHHCHCQNgRIFUaEC0EjEBpQJrbHlsa8P77sHt3\n7E779sGYeMtUf1oyikXwWWilISQEbNkiK/8yo4a3/7KQ7fdXxzgGmjJLWfCDt/m6aErMvl1pueTY\nuym0tNGdloNNa+SdqgUxIkL/1oMhshy99BqyuOGyx7j/9J/FrNCbXFZsWmPM/l8XThZtESXIcvTi\nUapTcgTk2LtR+30x+QajgXXr1jFlyhRKS0tRq9UsXryYlStXxuyzatUqli5dCsCFF17Ip59+it/v\nZ+XKlSxZsgSVSkVpaSlTpkxh3bp1Cc/p9Xr5/PPPufjiiwG45pprwtcyRQmiVquV4uBkSOoa69ev\nPxS/GhkJMrrMaB0plL2NAoQjQCuCAPuNC/oLAX61csDSgLRkYYGhjIBgC8I0sx2efz5+xxTHBeaS\ndHKbg5813d1CQJCFAJnRRE4OV17zKu9yXkyXjPeWz+f9O06N2dWeayCtOygENJppPr4QU6uVL743\nI2Y/c1kGmfvjW2saep18fekk/vLpd9FZ3Cg94n1osDpxG2OFgJ6xWRg7bOH3bDT6Xhc+tTJlR4AQ\nAuSuQ6MVWQhADG5HXWJ2AuEh3WWhz5igNKCfI2BIpQFR1w07AuYivv76V3jiidiTNDRAZWXS6wA4\nNQYxeekKPvS3boVZs8RDv7kZcnOTn0BG5khFoeDd4xbEvRwKDAyFC/591nd5YP5tMfs0Z5SQ5rGT\ndUJPzOtZDlEa0JhVzoGs8lhHQDAsMJq29EIKLW2St5fl6KU1vSilz8BCqzjHkeQI+Ax4KOpLisbG\nRsrLy8Pfl5WV0djYmHAfpVJJbm4u7e3tNDU1UVZWFndsU1OT5Dk7OjrIy8sLv15aWhpzrccee4wJ\nEyawbNky7r1X2E0TXUPm8JDRbUXrPDpaZ0U7Avqv9mudnlghYIDSAIXPL1YLE2gFakdsacC4LXvh\nhz8UmT/RpDguiCkN2LYNZsyAXbvA5RLjgqj3mYzMaKJ1eiG2wtjndHRYYEaThS++N4O1t56MpSQ2\nKLtjUi559fHPYH2vE1t+Gj3jsoW7oFOs0uslMgJQKLAWmhKUGDixFJlkIeAY4ZgVAi7b/BL/9fnj\ngJjwphR6NwowuaQzAhR+P3g8kcC9qK4Bp+7+iN+u/EXcuQZ0BHj6OQKsVvjoo9idUlT+QQQG0hQs\nD9i6Fa66SggBa9bAaaeldA4ZmSOFUHZA6Ks/XWm55NqEENCYWcavFq3gi4pZsTspFNQVTGJSe13M\ny5nOPnoNWQC0mwpihID+GQEgSgxCk/j+ZDl7ac0oSskREBITjiRHwMnAsqgvKRSJbJuHgRtuuIFd\nu3bx0EMPce211x7u25EJonL7uOZCsXqd3m1B4zpyhIB1nDTkY0OOgIAy3vYf5wiIKg349rnPorXE\njotCtcOJHAHa4OqiKugI0Fudojww5PADcLuhsxNK4lPW+2MuMUVKA7ZuhZkzYexYWLUKvF4YP37A\nc8jIHEmE8gOi3QEh7LlpGKKEgLoLq3j7vjPj9uuYlEfB9vjwX32fC0eWeD9bC9IwtQeFAImMAABr\nkRFTW3w+mqHXibXImHLXAFObDY3TK+kuOJI5EnKDhnKN++67j6qqKqZOncof//jHYf8ejlkhoLp9\nO7MObIBAgGxHDzrfoRcC/vzideR3tw+84yBI1DVA63OLWqfQgFipRBkIoPD7Ke89wNyGyFJ/msuG\nwu+PCAHJMgKiVxFtNli/HhxRyuC+fSkp/yBqpunpEeUBO3YIIaCuDl54Ac46K6VzyMiMFsKOAHNs\nu8H+bC+ojhMCQqUBIISAfGtUaYBERkDCNoTBc7WkF6eUEVBoacOh1h9SR0DpzMF9SVFWVsaBAwfC\n3x84cCBmNT+0z/79ogzD7/fT1dVFfn5+3LEh50CicxYUFNDZ2Rmzf/Rqf4jFixezYcMGyfvr72CQ\nOfhorW6q39iB0uPD1GMdUAgYSt3+QBP6E9/+ktl/3ph0n1QJ3Z/W4sJt0sZM8kNoXJ640oBQ+8Dy\nzxrJbojYj7VWd9i2nEgI6O8I0NuC44PoBYIDB4QIoFYP+DM4cgyYeoIrlvX1Ip/l9NPh1lvFmOAI\nEvhkZIZLyBGg9PoxdtiwFElna3VU55FXF/sMVvj8aGzucOaArcCIsUNM8g1WBy4JIcBSZJJsRajv\ndWIpTh9ERoAVj149qnICjpTcoMFeY+PGjTzzzDNs2bKFzZs38+abb1JbWzus38UxKwRofB5y7N1B\ne7vrsDgCztj1PgU9IygEfBJ0BEiUBui9zpgHPgoFXqVwBaj93pigvkdfuZHfvnX7wF0D3HZ0Pnek\nrt9mE4mqwcEtMChHgFVrEuewWoVoUVAgQlosFjjxxJTOISNzpNLfHdCdlsOYnn2M6d6XVAioK5hE\nddt28U0wxzBGCEgvGLA0oN1UQL6tQ7Ind5Yj6AhIsTSgrmDSEeUISIVZs2axdetWmpqa8Hg8vPDC\nC5x7bmx406JFi3jmGdG3+fXXX+fkk09GpVKxaNEinn/+ebxeL42NjWzdupXZs2cnPKdKpWLOnDm8\n9tprADzzzDMsWrQIEKsCIVauXEl1dXX42lLXkDn4hCbn4XraHicZh8kRULH9AEUSvb2HExaos3pw\npWtxq7TM8n0Rsy0+LDBSGqD0+iO9wTdsYNmER8g8IL5XJCgNiGQECCHAYAuOOz7+OLLTIMYELpNW\ndDYAkRGQlwe33y7KAuTFAZlRTn93gCPHgKndRs6ubhy5afg1KsnjOiblkl/XGSPI6cwu3Ok6Akoh\njlkLjBiDjgCD1cle09i489gKpR0B+rAjYODSAJXbh87ipnt8dtjNMBo4Yepf3QAAIABJREFUUnKD\nBnuN+vp65syZg16vR6VSMW/ePN54441h/S4GlmSPUkJCwGB7aAMjFgyo9bkHDOwbFIEARrdNsjRA\n73GKyXUUPoXoHKAK+IQQEAgACqa1bOHyLS+F+6knKg0weMWbXud24dLpxQR+4kTYHxVu1tCQ8kPf\npjWKc/T2ipR2gJ/8BM44I6XVAxmZ0cSL065g9V/PQed1ccF/Jf4g315YzXfX/x2yI6/l2zpoyK4E\noFefhcllReN141FrMbnjwwI9ai0WXTo59m66TLF1tSaXlU5j3oClAXMaPmP2/vVsK5rCzMYvku57\npKHX6/nTn/7E2Wefjd/vZ+nSpZxwwgnceeedzJw5kwsuuIAf/ehHLF26lJqaGtLT03n22WcBOPHE\nE7nkkkuYNm0aSqWSv/zlL2g0GjQajeQ5AR5++GGuuuoqfv3rXzNlyhQefPBBAB566CE+/PBD/H4/\nubm5PPXUU0mvIXPoUHnEc87Q7SC92yomyQeZ/hN8tdsbXpEfKbQWF+YSE36VEoVURkDUuMCvUoT3\nUfoC4eRytmwhvc3GGXeLlf1UuwborU6oqhrymMBj1EREztC4oLRUdG+ZMSP5wTIyowx7roHtF1Vx\nc81f+OzmxEKwK1OPK0NHRpOZvnIRpG3ssGPPNYT3seWnhVsIGqwOzLnxwZqJHAE6qwdbXtqAQoDG\n5mbGU1uw5adhy087YnICPvbD2uTNTyRzg9asWZNwn/65QWeccUbMsY2NjQQCAclzJssNGuw1ampq\nuPPOO+nu7kav17Ny5UqOP/74wf2C+nHMzq40Pg/Zjp7wqvfhcARofB5UX/pg4sD7pkKa245To8ej\n1sTZ+eMcAYBXJToHqH1e9F6X+F34szmuo577T/8ZM5o2YXTbEpYGpLmD9UdupxACbDaoqYkE/rlc\nsHdv6o4AXdAREC0EGAzwjW8M4rcgIzM62F40mck//RoAmy5xe80d+VVUde6A4yKvTWqvC7cZDCiV\ndBrz+NsL/8WPLnkEg8eBXZMWd5629EIKrW1xQkCax05XWi6VPQ1J7/e2NQ9wae2r3H7uCs6u/0+K\nP+WRw7nnnhvnArj77kjvZ51OxwsvvCB57O23387tt9+e0jkBxo4dy2effRb3+v/+7/8mvL9E15A5\nNKiiHAGplAYcDNQe34Dt+waLyAjQiSDAfhN4jUTXgJAQIRwBQSGgro4vvzUNfa+T7edPHLhrQHRp\nwJgxosQvRF1dymMCt0mL1hYUZKLHBfKYQOZoRKHgpScvYvXvzsKaoCwgRGdVLrk7usNCQH5dJ51V\nkVBtW4GR6f+spWdsFgark7YxBXHnsBYaKdjWEfe6xu7BXJou3seBACSowCn/vImLbniL5uMLsece\nWiEgY17ibecFv0LcJzFcOZJyg1JFoVBQU1PDsmXLmD9/PgaDYdgiABzDpQFan1s4AuzCEZA0I+Ag\ntQbU+DzDcwT0u690lwWr1sRr1SppR0A/IcCoE50DQt0DSvuaoKmJPn0mv1l4B5d+91UasisjpQH9\nrmfwOPArFOg8wd+dzQYVFREh4MYb4cwzhcU/BSQdATIyRyGhEgHrL0xYf5H8gb8ve4xoITg3Mvie\n1F7H9sLq8PfbC6u5tPYVztr5Lq3pRQSU8R/trelFFFlibcdqnwdlwI9ZnzFgRkCurYs2UwGfVZ4s\nnFQJBEIZmdGIyh0UArodpHdb0B4WIcA7YPu+waK1uLnctBSfShV37v5dA05TzQuXBqiihYD6erZf\nfBz/fH0xX/zghEg5YD80Di9+RaR9oMHmgvLyyJjg88/hySfh6qtTune3UYPWGvx3kMcFMkcx4TIB\nxW/4RdFDkmGC0fRWZpK1L5LhkV/XRUd1ROTvmphD7s5upry0ndymLrpK4jtvWYtMpLfGlwZobcGW\noxpV+HNRirQuB/YcA21TC4L5BqMnI+BIyQ0azDVCx1x//fVs2bKFdevWUVxcHC4xHCrHrBAQKg04\n7I6AgG/EhIZ0lwWLLl10BPANkBEA4c4BIdGgtK8J6uqoK5gU3sWvUCbtGtCrz0LvClqKrdZYIeDl\nl+HRR1MO9Ak7Anp65Ae+jEwQm86ETWsMW2SNLiv51g72ZUdW1c68/n1WVp/HGTvfD5cM9EeqhaDR\nbcOuScOhMQxYHpVr72LBde+wZsLpQrTri+9jLCMzWlEGSwPSuh1kdFtQ+fwovSNYuocoBUgWGKg6\nGI4AixvS04UjYICuAahUKHz+8Ip/tCOgY5KYSPiVijhnQQiN3YMzSx9VGuCAsjLxWeHzwZtvinaC\nxx0neXx/YhwB8rhARiZMT2UW2Q294e/zt3eG36MAtYun8Mzri8ne20vx3jZaK6UdAVLtAzU2D540\nDV69Oml5QFqXna2XV/PSUxfFtD4cDRwpuUGDvQYQFhVaW1t54YUXWLx48bB+F8eUELCwLuIP0fg9\nZLgsFFrb8CsUg8sIGCE0/mE6Avphqg6GhKkkHAFSQoA6WBoQdAS8NacZ6uo4Y0FECAgoFNKlAYEA\nBo+DnrRsdB4XSp9PlAKUlQkhwOcTIX+58Spk3KmC4Wk2rTG+NEBG5hhAqr1gNPuyxzCmR7SVOa69\nnp15E/ErY4OE9uaM5Yxd77M3Nz4UCKQdAWluOzatEadaP2BGQK6tiy6jeD93peVGBD8ZmVHKhLd3\nk9EpBK3ojABTjw2/QnHIXQHRjoDhtAyMRmt1gymUEdDfEdBPCFCrUfoCYVfApGaTeK7v388t44Nt\nqhTJMgK8OLINYUfAGNt+yMyE9HTxXO/pSdkhuILluI1atDbZESBzbJLMFdA7pp8jYHtnjCMAoGds\nFtl7eyna20br2MK4c4iMgHhHgMbuwW1MRQhwiFwChWLUCQHRuUHTp0/n0ksvDecGhcL3fvSjH9Hc\n3ExNTQ0PPPAADz/8MBCb6XPOOeeEM30SnRNEbtD9999PTU0NbW1t3HTTTUO6BsAll1zC9OnTOf/8\n83n00UcpLIz/tx0MoyYj4K677uLxxx8nPz8fgN/+9rdh9ebee+/l6aefRqVS8fvf/56FCxdKnuPB\nN25l2qSzAdB6hco8rmsP7aaCkWkf+AkwN/XdJUsDBnmOaNLtUY4Af6zTQKo0AFVsaQBNTaK/78SJ\nEHzO+xVK6a4BLhcepQab1oje7STNaYe0NDHx7+4WKwDp4l5Sxao1gbVLfuDLyPSjIaeSyu4GNlTM\njisLCLE3ZyzV7XW8PO0yyXOsL5/NL96/l7/MuQ67ToQJGt02IQRo9MnF0ECAHHu3aPEJLL3qaT4p\nKhr+DyYjM0RGYkww/561bLlnGpwWyQjIaLKg9PmwZaaJnACj5KEHBY3bexAcAa4kjgBPnCNA6fKH\nhQCamkS4X1kZaEX7sYBSER4fxN2/3YMjJ+II0NrcYDRGxgXd3ZCdLX2wBG6TVggZXi/Y7WBKXkYl\nI3Os0FOZxQkNm8U3gQD5dZ209xMCzKXpGDtsOIwGbFnx7x1zWQZqp5eqVTvZsSgSVqYNOgI8ejVb\nnTUJ78HQ7cBSkg7Atsur0dg9Q52+HBaOhNygoVzj4+guLCPAqHEEKBQKli1bxqZNm9i0aVP4Fy3V\na9Htlk77zXCZw3/X+IXKPL5rNy3pxQOXBox0TkAggDZUGjBCmBxBR4BSGVcaoPO6pEsD/MHSgKws\naGwUX1E9rxOWBjgcODQGXGodOrcLk8MqHtK5wZXCnp5BPfBBdgTIyCRiX/aYcJjfCU1fUlsc/3Bu\nyKkEhCAgxb9mLGFHfhU//OzP4dfSPHbs2mBpQD9HQEX3Pk7ZIx44RrcNn1KFUyNSiT8dO1celMsc\nVkZiTODK0JFmFnWtoVrYnD29WHLS8eg0hzwwUHUwMgKsHuEIUCrizq1xSQgBvoDoHJCWJp7Fu3fH\njAkCSgUKf0CypaHG4Qk6Arwx1x7quMCnDS4kdHQIZ4FE9omMzLFI75hMshqEIyB7by+eNA2O3NiQ\n4IBKSV95Jq1jpUV7r17Ni89czAU/Wh3zunAEaPEa1HHdU6Y9uzXsCAo7AoDeMVl0VOePyM8mc2gZ\nVZ+qAYmVaalei+vXr5c8PsMZJQT4xAP+uPZ6mjNLDk1GQJSYEFqFDzkCZu9bh32eQeqolElz2kVa\neKLSgH7tA2NKA8aNE/19m5piH/qJSgMcDuzaNGEpdjsxOmwR5X+IQoBVZ5LDAmWOWUIBgv2/ABqy\nhSMA4PRdH/DBhNPjjg8JAKE/48oNFAremnQu01q2hF8KOwLU+riwwCs3v8CNnzwKBMsC0gYu85GR\nOZQMd0zgytBiDAoBoYyAvPpOLDnpuPXaQy4EiK4BwZ8pEODnRQ8x4e3dwzqn1u6BtDT0Kldca0Kt\n0x07LlCrUfiCjgCtVrTq+/zzuDFBotIAtcOLIzuBI2CI4wK3SSsWKOQxgcwxSDhEMOoLxGp+eosF\npcfHuA8a2HN6peTxPWOzwmUBUqUGDfPGYGqzoTNH5kBamztcGqBzRIQAjc3N4qtfRd8jxgrRQoDM\n6GXUlAYAPProozz++OOceOKJPPzww+Tk5CTstSjF/zj74D93AVDf1wxATetWnp65lIX1bw98A8Ow\n7fcnJESo/D4IBPjHv76DwTW8nAK1z4tHpUk9IyC6NGDCBPjqKzERLy0N75KwNMBuDzsC9G4nRufw\nhQCb1gidQUfA1KmDOlZG5mimIaeSC75+gxxbFxM6d7GhfFbcPqHwwESOAIBtRVNiHQFJMgKO66jH\n6Bb1g7n2SD5AiDVr1sT13ZWROZQMd0zwyI5uDjz7IfYdBrxGUX9ZtLWD1d89iymfbD8MQoA3PMk+\n9eVPSG+zYegZ3rhA6fWDRoNfpRAr/VFIlgZ4g0KAWg2VlbB2LcyKfN4ElArpMQFCdOguNIaTyDU2\njxgX5OQMXQgwajA0NQ36OBmZoxmfVoW1yER2Qy/j3m9gzxmVkvv1jM2iJTNxDXlAqaBjUi75dZ00\nzhZjf43dGy4NiHYE5O0Q4epamwdHrggLtEe5EPataeCuNXcN/4eTOaQcUULAggULaG1tjXt9xYoV\n3Hjjjdxxxx2AqA28+eabw0mLqfIzjYHfz78Nu87ICfvX0W3IxqPS8HnFHC7c9u/UTjJCJQIhIUAZ\n8JPmtlPdXkevKXNY96De7sWnVEl3DUiQEaAKRDkCXn9dtAQrLg7vkqw0wK4RjgCd24XJHiwNyMgA\npxNaW8XDfxCE2weq1bL6LyMTJLAM6DkFc/HVXLTtdT4ZOxevShO3n0uj59rFf2N/dkXCc20vqKa6\nbTsKv5+AUhnuGiCVETCpvQ63StQF59i74xwB8+fPZ/78+eHvo2vrZGRGgoM9Jlh6RiV1eZPY99My\nqlbuxJ6tJ63HyfpzZ1L1xS5OdH5JK8MLYhoM0V0DTlr5xYicU+n1g0pFQKWUdgRIlAaEjqGyEp5/\nHi65JLxLqDRACrXDizPbQHYwxEwXDCoMLxB0dw96XOA2yo4AGZloQiv7G8/8N5P+vYPx7zfw7m/m\nS+77xfdnsEmXvNd8++R88r+OCAFamxtP0BGgdUbE0Py6zvB2AEO3E0dO5PNjzPxK7pp/V/h7eUww\nOjiihIB33nknpf2uu+46Tj9dWGMT9XOUwqzLIMNlxq4zovF5aEsvZEP5LJwa/SFvHxh2BAQiqf3D\nReX34VWqxcO83+Rd73VChnTXAJXfJybwmZni4a+JTDICJCgNsIva4jhHgEIhHvS7dw+tNMAWTDCV\nH/oyMhGys1k79hT+99Wb+PaSJxPu9vfZ1yY9jdmQSa8hi/LeA+zPGSNKA3RGyYyA49rr2ZM7DpB2\nBMjIHGwO9pggOiNA7fZhLTSh73Px5VnHs/h3L4dr3Q8VGnckI0AVbF0YDu4bIgqfWN33q+In8FJd\nAxQ+vxAMQo4Auz2mNCBp1wC7J6Y0IOwIyM0VQcRDKPtzmzSiZFEeE8jIxLD18mq+eeXLNM4uoWes\n9PujcXYpexiX9Dztk/Mo3NYR/l5r8+BO0+A1qNFEOwLqu8LbId4RIDM6GTUZAe3t7eG/v/zyy0yZ\nMgUgaa/F/pj1GeGcAI3Pw19Ovo77zvi5CLw7xEKA1ifeXCq/D2XAj1epQu0b3qBD7ffiUwzOERAu\nDVCrYcyYmLIACJYGEIh3IQTDAp0a4QgIZwSAeOjv2jW00gA5I0BGRpKnT1zKuoqTEnYFSJWvCycz\npW0bIEoDQs6emIyA7m7ybZ2R0gBbV7hjgIzMkcBIjAlcGbqYjICOSbk8/9ylWLNNuHUaVK6RC/NN\nBZUn0jVA6ffjUylihACpgL6BCK3u9+8aoAwKDaij1oP6lwaMEeVG0eOCpF0DHEIICP3eYjIC9u0D\ngyFmoSEVZEeAjIw0u88ai7XAyKrfLxCLcEOkY3I+BVFCgMbuwWPU4umXERASAjQ2Dwp/AH2vE0eO\nnBEw2jmiHAHJ+MlPfsKWLVtwu92MGTOGv/3tb0Bsr0WlUhnTa7E/0UKA1udmQ/ksthdNpqq9/uAI\nAUkyBaIzAlR+H26VNr6V4CBR+X14VepBZQSEwwJDNkCPuK/AMlA8BH6lUtoRYLVi0xpxqYQjQBnw\nR1LEi4pg61aoSdx2RAqrNugIcDjkh76MTD+ee+ab4F8Mfxj6Ax9gS/E0pjdv5q3qRYnbB9bX05WW\nE5sRcLjCAucMcv+RcVTLHOGMxJjAmaEj3Sz+j6s8PrwGNVuvnAyAR69JyRGwjpOGNEGXQu3xMcFX\nCYDS58enVaXURSDZ9UOr+wFVbNcArdONW68lZhgvVRoAYUfACpbzf8rvJHQEaK0eHDkGNKGwwFBp\nQGhMMIQ6f7dJKxwBM2YM+lgZmaOZ5brfwu57aFHeNazztE4roGhzG0BYCPTqVMHSgIgQkF/fhT3H\ngNbmRt/rxGPU4lePmvVkmQSMGiHg6aefTrgtUa/F/ph1GWQ6Re2axucRwXog7O3e4QXyDJZQ+0JV\nwIcq4MOl1mFyW4d1zhhHwCC6Bqj8vlgbYBQJSwPa2mgzFQpHgMeFxuuJOAJmzICHHoIbbhjU/du0\nRrBYRMZAvtyGREYmjhFon7W+Yjbf3PQcINoH2rSiNCDNE/Xe37uX2uIaJrd9DQhHQLLsARmZQ81I\njAlcGVqK+oSzQOX24dOowtvcOk3Y4j4S9J+sS03e1R4vBN18Sp8fn0497NIAVQJHgNbpxqPTxAoB\najVKn1/sFxoTqFRQGMlJSNg1IBDA1Gald0wmKpdXtEgOlQbMmAGbN8O0aYO+f7dRA1t3wcKFgz5W\nRuaoZwTGBD2VWajdPjKazLjSdXjSNKBQ4DWo0dkji6TZe3tpmV6I1uYJlgXIboCjgWNKyulfGhAK\nwhrR0oAUwwSlHAEan3h4DpUYR0C/0gCd1zVwacAPfgA33hizS8KwwOZmmjNLhIjiisoIgEjC8CBD\ngaw6UyRMKD19UMfKyMikxobyWczeL9qpGd22cBtQj0pDhkMIpTQ3syO/KuwIKLQK4U9G5mhCZASI\nkhilxx/pWw94dBrUh7w0wBcjBHh1KjGRHyIKfwC/AlAq4xwBGpcXt76fUyJcGhDMCKiogNdeE2JA\nkIRhgT09eA0aHFl61E4fapdPrBaq1TB2rCgPGOSYAIKOgAMHYPLkQR8rIyOTAgoFjbNLKN3QjCbY\nOhDAWmAku61X7GO3o3Z46CvPQGPzYGqzYS00Hsablhkpjj0hwBUpDYh2BIxoaUAKYkCMEBAQIX8+\nZYJJd4qEHQFKZbwjYKCuASqVaCFYXR2zS8L2gS0ttGQUi7ZjbmekawBAqB5zkDZAh8Yg6pzk1oEy\nMgeNhpxKtD43JX1N4dIAFAoOZJVT0btf7NTczK68CRg8DhR+P8Vm8X6XkTmaiM4IUHl8+DSRIZHn\nMGQEaNyeWEeAVjUsR4DS6yegEj9TQKmIcwS49drYA1SiFCFcGqBQwPnnx+ySsH1gSwuWYhNevRq1\n04vW6haTeBDnmTVraKUBwUmJPC6QkTl4HJhdQtn6ZrQ2j3AEAH3lGRTsD2YHtLRgKUnHbdKitblJ\nb7FiKZYX7I4GRk1pwEjQ3xEQFgJUByEscAAxILprgNLvx6dU4VOKFXo3quQHJ0Dt94a7BqSUEdC/\nNKAfgWXw5HOKhI6AlqyzyLN1ovO4MLgcEEoVD6n/g3joB5YBKOAuIwRDn2RkZOIR75XB7694KPiC\nQsGG8lnM2r+BNLcoDQA4kFVOee8BthbXQFMTBzJn4VLpMHgcshAgc1TijOoaoPL48Wv6OwIObdcA\nlccHwWe30h/AG1UaMJQcAoXPj1+tRAX4VUrJjIAYgqUBoU4DUlyvuI5G/1vxG5qbI0KAy4s2uLIY\nzhSfNUvU+g+CFSwHkxvSvo7kFcjIyMQQaic42P1/yR3h15pmlTD3f9axZcmUsPjWV57BxFV7gjs0\nYS5Jx23UoLV5ULZYMZeYRuYHkDmsHFuOAF2UEOCXKA0Yhi1/sER3DVAFfPiUKrwq9YCdA/7y4g+o\n7NoruS1ZaUBKXQMk8CsShAW2tIjSAJUoDTC4HJHzKxTw5pswfXrSn0USoywEyMgcbGqLa5jaulWU\nBmjEUH1/VgXlvcG2a8HSH5vOiNFtk4UAmaOS6PaBKrcvpjTArdce8tIAtccLweetcAQoB3QEjHtv\nL6fdu1Zym9LrD4d5BVT9HQGeBKUBAVGOkGBMgFIpXRrQ0oK5JB2vToXa6UXt8OLVR53jxhvh5z9P\n+rNIYjQKp+II1ELLyMhI01ZTQOHWjhhHQG9FJgUHOsUOzc1YSkx4jFq0Nk/QESALAUcDx9QnayJH\ngE+lJqBQiAlxFNWtXw//ogmcAf0zAnwKIQSoAskHHud9vZJiS4vktgHDAgfqGiBBwtKA5mZa0otF\n2rjbidbjjg0jnDMn4TmTkpEhWwBlZA4yW4umMqVtWzgsECKOAACam2nKLMWmNZJv60Dj89CnzzyM\ndywjM/JECwFKidKA/mGBWQ29aGxuDhbqmIwAHz6dWqzOJ6F8XROFWzsktyl9Afwq0WVEhAUO4AhQ\nqVD4/JHSAMmTKlFIrZlEOwKcXlRuH15d1DkKC2HixKQ/iyTymEBG5qDTV5aBxu4h84BZtOxEOALy\nDwQ/W5qbMZdm4DZq0Ng9pLdYDm9pwLxBfMkk5dgTAqIzApQRNbx/TkC+pZ21j5wyMhf+hDhBINQ1\nQBnwo/L78CuUeNXqODEi5hivm2JLi/QK/SeEswakhICrKgdfGgAiITiuNMDvh7Y2WjOKcKl1aA+4\n0Ta5QauVPMegWLkSZs4c/nlkZGRiCCyLfG0rmsLUlq3hsMDAsighIBAIC302rZEJnbuEG2AYfYpl\nZI5E3EYNWqcHpdcvURqgFun3UZxz27tU/3vHsK+7jpMkX1dFdQ1Q+fx4E2QERJcJZO7vS9jOL9oR\n4FcpYkQFrdPNVH0/l49ajdIXECUEiRwBiboGBGuI/WrhGNA4vDEOiyHzne/A/fcP/zwyMjIxrGB5\n5EvxG9on51G2oTnsCHDkGNC4vOitjmBpgEkIAeGMANkRcDRwbAkBOmlHAMTnBKS7LLHttBIwtaUW\n7RDyBWIyAgL+lEoDyvoaUQYCCQMF1T4vPqVwBMSJBU6J9oFDLQ3o7ISMDNxqHW6VFp3XJUod+p9/\nKEycKE84ZGQOMnUFk5jQuYtMR1+cIyDb0QM6HXadMVYIkJE52lAocKQb0FpcqDz+eEdAv9IAncWN\nxu5JekqtxUVefWfSfRLV+2vc/dsHqlB5k5csZu9LLgSEwwJVsZZ+rdMjOSYI9REfdGlA0BGAQoFP\nq0JrdePVjUAMVWZmTPtCGRmZg0Pb1ALK1jdFAjoVCjrK80R5QHOzCAsMlQY0W2Qh4Cjh2BICErQP\nhHhHgMltRe91oZBafY/isZdv4BsNnw76Xvp3DQgLAUkcAWN69oljEpQPJHME4Eyha4AEkqUBLS1Q\nLCYGbpUWrc+N1jtCjgAZGZmDjkObRmNWGTWttTFCQHXbdq768lkoLQWQhQCZox5bRhp6sysuI0BK\nCNBa3WgcyXN8jlu5iwW/XDOke4luH6joFxaYiKx9fTGW/2iUPj++KEdA9H4ap1tyTKBMoTRAMk8p\n2DUAwKdVobO6R8YRICMjc0hon5JP6RcteIyRRdKO8jzm/+sj2LYNc2k6nmBYoOwIOHo4poSAdlNB\nuL4+un0gIGrdvc7w9yaXFQCdL/lqv9FtG1LHAamMAJ9HlVQIqOgRrb1ScQREhwWmuWxgsSTMCEha\nGoAC5a5+1+vpEV0BEAKK1ucWjgBZCJCRGTX88twV/N+cH7ArbwIAO/Mm8ofTbuHs+v+IYC+CQkDX\n0SEErF69mpqaGiZPnsz9ElZjl8vF4sWLqampYe7cuezbty+87d5772Xy5MnU1NTw9ttvD3jOvXv3\ncvLJJ1NTU8OSJUvweMTn/QMPPMCUKVOYOnUqp512Gnv3xga/ms1mysrKuOmmm0b6x5eRYB0n0VuY\nRXqLNS4jwK3XxmUE6KzuuNf6o7W540oKUkXtiXUEeHUDtA8MBAYsDQgEMwICKkWka0AgQE5rj+SY\nQOELiFDBwZYG9PRgzxXBoz6dGq0sBMjIjCq2XTqJ9T88kdorJ4df+8fya6ioa4Tjj6d1WiFuowZD\njwN9nwt7vvEw3q3MSHFMCQHr7qpiYsdOFH4/ar8vtjQgyhFw35s/I8feDYDB40h6zjSPPTypHwzh\nrgGBoBAQdASo/L6EAYMhR4BkRgCJHQEvPH0lNDRAUVHsAcFMggFLA/oLD1YrmEwElhFbGiALATIy\no4LAMnjhqSv57w//B/PtGQD4b1Vy/5s/44Jtb8ANNxBYJoSAya1fcyCr/DDf8fBwuVxcf/31rF69\nmi1btvDSSy+xadOmmH0eeeQRiouLqa2t5bbbbuPmm28GYOPGjbzyyivU1tayevVqrrvuOjweT9Jz\n3nzzzfzsZz+jtraWoqIiHnnkEQDmzJnDl19+ydatW7nqqqtYtixk0HEwAAAgAElEQVS2F+Svf/1r\n5s2T040OFe9yHo1VJeTu6JZsHxia0Fet3Mn4d/egtbpRO5I/7zV2DypP8lX8RESHBap8fnxaVVxY\nYHRZgaHbgc7mSSgEKHyBqIwAZbhrQNVbu/jhssehqqrfDahTLA2QeN1q5ccm0RXAq1Whs8hCgIzM\naOKnFX/klN9/xnfOeTacHfDwN25l/vMfwVNP8aucB3AbteTVdWEuSSeglMt4jwaOKSGA7GycGj3l\nvQfwKNUxtejhjIBAgJ98+HvGdouVGr3HmehsAKS57eFJ/WCIdgQoA34RFqhMXhpQ0SscAYlKA9T+\noCNAqYwRAjKdffDqqzB+fOwBqXQNUEqUBlgsYBKWoLAjQC4NkJE56rBpjZSam9lWOLpbeq5bt44p\nU6ZQWlqKWq1m8eLFrFy5MmafVatWsXTpUgAuvPBCPv30U/x+PytXrmTJkiWoVCpKS0uZMmUK69at\nS3hOr9fL559/zsUXXwzANddcE77Wqaeeii5Ylz137lyaovqqb9y4kfb2dhYuXHgofiUyQRqrSsnb\n0YXK48Mb3T4wqjRg4tt7GPd+gygNcCbv7KOxe1C5h9Z2UBXjCBClCskcAVn7Raljos4CSq8ffygj\nQBlxBOj7XKy99Btw1139biC10gDJTkJR4wKfTiU7AmRkjkI8Rg2ZzRbap+Qf7luRGSGOLSEA2JFf\nxZS2bTFuAIg4AoxuG2q/j7LeRmBgR4DB4xCT4EES6hoQkxEQLQRIuALKew/gVaoSlgao/FGOgKjS\nAL1HIh8AUioNSOgISBdtQ9xqLVqvXBogI3M0EsoP2FY0uoWAxsZGyssjroaysjIaGxsT7qNUKsnN\nzaW9vZ2mpibKysrijm1qapI8Z0dHB3l5eeHXS0tL467F/7N37vFR1Xfe/5zbzGRyJySACUQUFYiD\ncpWsrqV2lRJFQbuFsgSt1kW6Sm1XypauFdxFHre4bVkqy+6r1grSJbIKWjSPthXxApHSWBLRB6tc\nklBIAgQyk8yc6/PHmTlzzsw5c0kykMv3/XrxInPmdy4ZdM7v9z2fz+cLYPPmzbjrrrsAAKqq4rHH\nHsMzzzzTN78wkTJGIUBUoMaFBer3Y09HEJ6OYGqKgG65x4UAc1ggo2pQklgD8pvOQ+EYsE7WACXa\nNUDjGEMRwAfl+NaBgMkakKBrgFNYoGleEAkLVFxDbopJEIOaSJAgFQIGD30Q6TqwODL8alx7qtG2\nEOCRgyjo7gAAXHbhJIDUrAG9UgRo0YyAZIqAks5WtOaUOGcEqLKtNcAj23QMAHRrgJTYGqDBpn1g\n2BoAhK0BSkj/fagQQBCDioArG35XNk4UjLnUl+LInhb9TyKYftaJ5MUXX8Qf//hHvPPOOwCAZ599\nFlVVVbjsssug2T1tJTJG0zWlGH7kLE5XFEOJsQZE8gA8HUFwkgJ3QEoaFtgbRQAvyXp7XoTbByYJ\nC8xu7UKgJDtBRoAGldf/21c51lAE8EEZkluI3yEVa4BdRoAYngOF5wAKWQMIYlAiZuv/j5+mQsCg\noVeFAEmSIAg2N5N+zJHiq1FxKl4R8N7Ym7BlWzXuW/g8AKD0vD6zNAcIxqFpyO4Da0AkI0BhY8IC\n3wdwY/Rlib8Vp3JHxncECGNYA2IKAW455KwIEBW9ZWGirgGIuembCgEh3g2XLFIhgCAGIQFXNg6P\nmAiNvYRP9uxbrhvMCv+JsKY6fkxZWRmampqM101NTZan+ZExJ06cQElJCVRVxZkzZ1BcXBy3b0Q5\noKqq7TFLSkrQ3t5uGW9WFPz2t7/F2rVrsXfvXuP+uX//frz77rt49tln4ff7IYoicnNz8dRTTyX+\n5fsZA3FO0HLVZSj67CzarxpmUQQcu7YcVzx8DDM2/QGe8yF4LugZQsnCAoUuCXxPCgGaBk5Rre0D\nXRyEBAqEnNYA/CNzoiGAMTCKuX2gSREQUhB0mBOk1jUgZptpTgAAMlkDCGJQQoqAwUevZnfZ2dk4\nfPhwX13LReHz4Vfimtb/Z2kdCACrbl+HL4quwI3HdE1+2fnk1oBIkaAnhQAjLDA2I0BxmGRoGoYH\n2nE6d0RK1gA+HWuA5mwN+N5057BAIBoW6FZCVAggiEHGk1/NQ+PIay/1ZfSa6dOno7GxES0tLZAk\nCTU1NZgzZ45lTFVVFbZu3QoA2LVrFyorK8FxHKqqqrB9+3bIsozm5mY0NjZixowZjsfkOA4zZ87E\nzp07AQBbt25FVVUVAKC+vh4PPfQQXnvtNYt9YOvWrTh+/DiOHj2K9evXY8mSJQOuCAAMzDlBd64X\nYo4L+c0XLAvXY9eW49VNVRj31lFkdQSR19wJAAkX5gDg6qEiwAgYjCkEJFQEtHXBP8JZEcDJ5vaB\nrNE+MJE1gFU0vbCQwBpQpOZbt8UUAnRFQMiSuUAQxMDnn3PWQuFZtE0YnnwwMSBISRHwyCOPxEkr\nNU2Doih46qmnUFhYCIZhsGHDhoxcZF9y1jsMI/yn4xQBgN5e8Kq2zwCYFAEJwgIjRYKedA2I7MNq\nqiUjwCkIMC94AUHBgy7Bm9AaEAkLBABGVaGxbGJrQJKuAWBsrAGdncAYXSosci64FFIEEMSg5P77\n8U/nEwemDgQ8Hg82bdqE2bNnQ1VVVFdXY8qUKXjiiScwbdo0zJ07Fw8//DCqq6vh8/mQm5uLbdu2\nAQCmTp2K+fPnY9KkSWBZFps3b4YgCBAEwfaYALBhwwYsWrQIjz/+OCoqKrB+/XoAwPe//30EAgF8\n7WtfAwCUl5cbBQMz/c3KEMtgmhPcgDp0DctCzumApX0gABwomYbKtg/hPh9Czmm9pTCfJCyQ75bB\n9qBrACuFjxvTPtDpaT8AZLcF0DkyBwXHz1t+n7qwjIZRNKN9oMoxRqhgb60BiO1cZAoKBMIZAZ0i\n/CXUXowgBhVuN376yTKIOTTfHyykVAj4+c9/jkmTJqGwsBCapoFhGGiaBk3TcOTIEXi93n4/cYlw\nLqsQxf42nModGfdeW3YxprT8EQDgDS/ys2RnRYBX7ALQM0WAoEgAw8S1D3RSBJT4W9GWXQyVYR2t\nAYYiAIDM6aGCCtjE1gA1sTXANiE41hoQKQTYFRsIghi45OWhLTfvUl9FnzBnzpw4FcCaNWuMn91u\nN2pqamz3XbVqFVatWpXSMQFg7Nix2LdvX9z2t956K+l13nvvvbj33nuTjruUDKY5AQB0F3ow8tBp\nS0YAAHQU5yO7rQtZHUG4wtkAyRQBPc0I4EQFKsOAjRQCVL0QkCwj4OTUURj2xTnb91nZHBYYDfnj\nQwrEPJtCQCQsMKk1wHlOAACym4fbL0JxD7kYKoIY9JwdN+xSX0KfUFtbixUrVkBRFNx7771YuXKl\n5f1QKIQlS5bg8OHDyMvLw7Zt21BeXg4AWLduHbZs2QKO4/DMM88Y3X6cjnn06FEsWrQIfr8fFRUV\n2LJlCwRB6NE5Ojo68OCDD+LIkSMQRRHPPfccKisre/w5pPQtvXbtWvz3f/83fvazn1l6HAuCgF/+\n8peoqBg4idIdWQXIEQMQ+fhqVltOMca1/9myLZEiwCuFCwE96RoQXjgbGQFJwgKLA21oyym2T/EP\nYygCACgspysNwOuKgB5aA8Am7xrglkP6dZMigCAIYtAzmOYEABAs8MAdkOI87eeL85HdGoDbpIpJ\nlhHg6mFGACepkNwC3GZrQLKwwLYAOkfGWwNuQJ1xjEj7QJVjrNaAEidrgKpnCSSYE8QpAkxzAkBX\nBHjbuygjgCCIfkkoFMKyZcvw3nvvYcSIEaisrMRtt92GyZMnG2M2btyIUaNGYfv27di5cyeWL1+O\nXbt24eDBg3j55ZfR0NCAU6dO4aabbsKRI0egqqrjMZcvX46VK1di3rx5ePTRR7Fx40Z897vfTfsc\ngiDgwQcfxN13341vfOMbUFUVfr+/V59FShkBP/jBD7Bt2zY88MAD+MEPfgDF5D8fSFV/QFcEAIDE\nxlfD23KKURxoR6dbr2x3ePITZgT0ShGgSoDHYyzEjYwAp0KAvw2tOSVQUmkfiHAhQFXAqKp+TLsA\np55aA8yKAE5XBLhkah9IEAQxFBhMcwJAVwQAsIQFAkDnsBx4zocgZwlQGaA73w0+Q10DOEmB6BF6\nkBGQk6BrQIwiIGwN4EJ9bA2wCQt0+0XI1D6QIIh+SF1dHSoqKlBaWgqe57FgwQLs3r3bMub1119H\ndbWePnznnXfigw8+gKqq2L17NxYuXAiO41BaWoqKigrU1dU5HlOWZezfvx/z5s0DACxevNg4V7rn\nOHPmDD766CN84xvfAKC3Os7L651qM+Vv6ZkzZ+LgwYP44osvMHPmTBw5cqRXJ75UnPfkQ2UY24yA\nny/QUzCPF+qyjPbs4QkLAZH3emwN8HjAaXpYoG3XAEDvHPC+bg24a2ZiawCvyvjfeXoFXmX1cW4l\nhBDn1m/esXC6aiCZNSBRIaD1Oy64ZFH/DKgQQBCDDu17l/oKiP7IYJkTALoiAECcNUDlOLDDitA1\nLAtirhtdw73JrQHdco8KAayk6gF+MRkBjoUATUN2WxcWjnzEMUeAlVVcyY3Th7NRRYCQICwwJWtA\ngkLAWjypZwRQ1wCCGJSsxZNYiycv9WX0ikj3nwhlZWVobm52HMOyLIqKitDa2oqWlhZLJ6DIvi0t\nLbbHbGtrs4QDl5aWGudK9xx//vOfUVxcjK9//eu49tprsWTJkoujCIiQn5+P7du34+///u9x0003\nWZ4CDBQ0lsUFd55tIQDF8YWARO0DI9aAnoQFuhQRyMqytA+UWd5xkV/sbwOKbawB70d/5NSoxD9i\nDfBIQQQFG1sAkLI1IC4jwBwM5NKtAVQIIAiCGFoMhjkBEFUExIYFAgCKixHKd6O7wIPAcG/SsECh\nS4oG/6UBJykQs0yFADWsCHBY5LsvhCC7OcDrdVQEmNP/48ICPfYZAbo1IHHXgLiMAJuwQHcnFQII\ngrj47PkCWP276B87BqJyjWEYKIqCAwcOYMWKFWhsbMSwYcPwL//yL706bo+SXB588EHcfPPN2Ldv\nH0pLS3t1AZeCc97CuPaBAGwLARmzBigma0A4I4BhNUdrQIm/FSgph8KeSpgRYBQCwot8QZEQ4h1C\n/CLWAKXn1gC43XArIbCaZm8/IAiCIAY1A31OYCgC7BauxcXoVvzQWAZdw73Ib76Q8FiGNUDT7JV4\nDnCiCtEt6PtpWlgR4JwRkNMaQKAkGx6WTdg+MHJv10ztA7mQop8rlj60BpAigCCIjHGz81uzbgZm\nmV6vsSkGlJWVoampyXjd1NRkeZofGXPixAmUlJRAVVWcOXMGxcXFcftGnuqrqmp7zJKSErS3t1vG\nR572p3OOsrIyjBkzBqWlpZg+fToA4Gtf+1qvCwE9NnBdc801uO+++5Cfn598cD+jw1OQkiKgLac4\ns2GBYWuAuX2gUyFgeKAdGD48aVhgRM4XyQjwyEEE+cSKgIQZAUmsAeA4aGAgckJakx6CIAhi8DCQ\n5wROGQEArIqAYm/SsEChSwKrwfFJvhOspEB26fdRRtXCYYHO1gBvezcCw736PdpRERCV+JvDAoWg\njLGeo/E7cLoCIWEhIIk1ANALKqyqUSGAIIh+yfTp09HY2IiWlhZIkoSampq4DkBVVVXYunUrAGDX\nrl2orKwEx3GoqqrC9u3bIcsympub0djYiBkzZjgek+M4zJw502gVvHXrVlRVVfXoHGVlZRg+fLhh\nxfvtb3+LCRMm9OqzSEkRMHfuXKM9kJmItCLSPujVV1/t1cVcLM55C+0LAUVFUBkmWgjILs58RoCq\ngFVVqAwLjWUcCwG5oU4gN9c+IyBsD+iZNUCyFBDiYFkwWsw1xSQEh3g3VIYFGQMIgiAGP4NtTuCU\nEQAAKClB93kPVJZBoNgLIVlYYPh9TlSMoL5U4CQVssBFF+NJwgLdnSGIua6wr99+jHlBr3IM+JA+\nd+CDMmSPzfQvfKwetQ8cGW3JHGkbSIUAgiD6Ix6PB5s2bcLs2bOhqiqqq6sxZcoUPPHEE5g2bRrm\nzp2Lhx9+GNXV1fD5fMjNzcW2bdsAAFOnTsX8+fMxadIksCyLzZs3QxAECIJge0wA2LBhAxYtWoTH\nH38cFRUVWL9+PQCkfQ4A+MUvfoG/+7u/Q1dXF8rLy/Hiiy/26rNIqRCwe/dujBkzBrNmzbK9+QMD\nx2+hfQ/43+cL7RfcPI+z3mFRa0DOcJR1NMePC+MVu3DBndu7rgH+qCKA0TRdpm9DthgAcnLw4PUc\nDranoAgIP+13KyGMH5nIGtBtKSDEEWMN4BQZEEVLO0KRc0FlKB2YIAYrFBhImBlMc4K1eBK/KNRT\nm52sAUG4DWsAH5SdZf+aBr5bQjDXBU5UIHlTt8txkgJF4KOL8SRhga6AhFCOC2BZjFJLbMewigZw\nZmtAuEgRUvCAe2n8DhFrgKIBrp5bAyKfIxUCCGLwMtADA+fMmROnAlizZo3xs9vtRk1Nje2+q1at\nwqpVq1I6JgCMHTsW+/bti9vek3Ncd911OHDggO0+PSGlQsCKFSvwwgsvYO/evbj//vtx3333WdIM\nBxodWQXID563fe+Hc9bio9Lr8dz0b+K8Jx9XSZ85HscrdaEjq6B31oDOaEYAGOjBfTZkiwEgO9te\nqh8mThGg6ooA86LdukP61oBIQcI8CRJ5KgQQBEEMFQbbnKC7MAuAQ1jgvHlo6DyGwi86cG5sATSW\nASeptotcPihDcXGQPXzanQNYSYXk4qOKgLC0nnV42i8EJEjZgr1UP3LMGEWAOSzQdl5gtgZ4e24N\niLQNpEIAQRBE/yal1dvTTz+NpqYm/OQnP8GBAwcwbtw4zJkzBy+99BIkKf3E/EvNuSwHawCA/6pc\niqCQhQcWPoduIStx1wCxC+c9+frT/TSJdA1gNTWljICckN8oBCRqH2jOCGA1Vb/+RIUATUluDUD0\naU9uyJoODAAhzu0cSEgQBEEMKgbbnCBYoN+/VDtrwOTJOHZzOervuw7HvlQO2cM75gQIXRIkrwBF\n4MCl2TmAExXdmmBRBDiHBbr9IsRsXREAh24NjKIZ93aNi4YKCgkKAT2yBsR2DQhbA2Q3FQIIgiD6\nMyk/xuV5HnfddRd27dqFY8eOYdasWXj88cdx2WWX9bqH4cXmnLcQEptcstctZCXNCOiVIsDttmQE\nyByf2BqQna1X7J0UAaY2gEp4ke+WQ4A7cdeApNYANUYRkJ1tGSLyLvsuDARBEMSgZDDNCRIqAmKQ\nsnjw3fbFDqFb1gsBLi5tRQAnKZAFkyJAURJmBAgBEWK2oC/YU1QEmLsG2M4LwtYARkmza0DAOi8g\nawBBEMTAoEftAwOBAM6fP4/Ozk7kmkLjBgpnvEXOAXomgrwncfvAsDUg0RgnomGBgZQUAYYkP4E1\ngFeiT/bVIGt0DehLa4CgSIDLuugP8W5oGBh+UIIgBhgzL/UFEMkY6HOCUJ4bsotLaeEqe3jHwMCI\nIkBjmB4UAsJhgSwLRlHBpZARIIYzAhwLAaauARqbmjWA60nXAMk6L4goAagQQBAE0b9JWRHQ1dWF\n559/HjfffDN8Ph+OHz+OF154AV988QVyYqTi/Z0tU6vxz1/916TjuoWsxO0Dw9aAXnUN0KIZAQmt\nAaLJGuCQI2BRBIQzAtxyKGEhgFdlcJqq39ztiLEG2BUNRI4UAQRBEEOJwTQn0FgGP/10mSFpT4SU\nJSS2BmTxUFwsONF+ce4EawoLZBUNjKqh3j0FrGzfGtDlDysCElgDzAt6PSxQPxYfcigEhOcBnKik\nZw2QrfMCUgQQBEEMDFJSBHzrW99CTU0NrrrqKjzwwAN49dVXUVBQkOlryxhd7mx0ubOTjkukCHjy\njcfxt396CVunLsa49j+ndf5vfvgcprT8EcifCU79i14IYDkoLGdbCGBUVb8OrzehNcC8SDe3D0xk\nDXDLIcgsB94p4Tmma4BTIUAbIAnRBEEQRO8YbHMCADg3tjClcbKHs1UEFH5xDvdW/Rod5fngRCUt\nRUBeywV8ad37aPRVhAsBKlhNgywkVgScH5Of2BqgaM5hgQ7zAoVn9TaD6VgDqBBAEAQxIEmpEPDc\nc89h9OjRuOyyy/DGG2+gtrbWeC/SNmgg9QxOlURhgVe3HUGO6MfJvMvSVgTcd+B5PHTPf+LXM73g\nXv3YYg2wCwLMkroR5D3wsqwu1Xe46ZutAQrDRa0B2c6KAL0QwDv/hxBjDbArBFBQIEEQxNBhqM4J\nAGdFQH7zBWS3BtAydRSy27vSKgSUv9eEriIv/uPnyzD7mu+AlVQoHAuFd+4a4AqYFAGJMgIidkGz\nIsDJGgC9YMCHEtsFkxUC5EhYIBUCCIIg+jUpFQKWLFli6Qk8kHsGp0OisMAc0Y+vV9fgs+Kr8I36\nX6d13BJ/Kz4qvR7gPgenKWC1cFiggzUgWwzA786BF+hza4BbDkFhE9ysWRaM6d+bV+wVAQRBEMTQ\nYKjOCQBAzuIh2IQFuvwiPr/1Cvx6x9fwzVtfTKtrQHZrAO1XD0Mw26P79EUFKhsuBDiGBZoyAlKy\nBjBgFRWMGs4AEOwDkzWO1cMEe2UNoPaBBEEQA4GUCgHPP/98hi8jyksvvYTVq1fj008/xYEDBzBl\nyhTjvXXr1mHLli3gOA7PPPMMbrvtNgBAbW0tVqxYAUVRcO+992LlypV9ci0dWQUY1nXW9r1sMYBz\n3kKInCttRUBJZytac0oA7hg4NXlGQI7oR8AVtjIksgYo1vaBnBZWBCS1BiT4zyAFa0CId1tyBAiC\nIIjBy1CdEwBAd4EHWWfjHxC4AhLEbBcUN59214Cc1i4ESqL3eE5UoHKJCwFuf/KuAYxiVQQwigYu\nJENx82AdCjVqH1gDIooAKgQQBEH0b3rUNSCT+Hw+vPLKK1i6dKll+8GDB/Hyyy+joaEBp06dwk03\n3YQjR45AVVUsW7YM7733HkaMGIHKykrcdtttmDx5cq+v5VTuSBR0d8AtBRGK6TKQLQYQcGWnXQgQ\nZBG5oU6cyyrUb/iRsECWc2wfGDkXgIRdAyyKgIg1QErcNcCtJCkEpGANEHmXRTVAEARBEH1Bf5oT\nAMD5MfnIb7oQt10ISPrCHEi7EJDdGsBfJo/UX1gKAWxiRUB2kq4BNooAPijrnQ8criWpNSBSQNC0\n6M+UEUAQBDEgSalrwI4dO3D55ZfjwoX4m19HRwfKy8vxyiuv9MkFjR8/HldffXXc9t27d2PhwoXg\nOA6lpaWoqKhAXV0d6urqUFFRgdLSUvA8jwULFmD37t19ci0ay6IlvxRl55vj3ssJ+eF35UDkXXDJ\nqRcChgfa0Z49HBrLGu37IhkBCmMfFpgtBuB3hVOYWdY2RwAIL9LNioBMWAOoawBBEMSQZqjOCQCg\nY0weCk7E/95uv6hL9dGzQoC/xKu/4Dhw4YwAlUsUFigmtwYoWpwigA8pkD3Oxf+k1gCGARgGjLn2\nT4UAgiCIAUlKhYDNmzdjxYoVyMvLi3uvoKAA//RP/4RNmzb1+cWZaWlpQVlZmfG6rKwMzc3NaGlp\nwejRo+O29xUnCseg/NzxuO2xioDR506kdLwSf9gWABgyf3NGgJ3/PyeUmjWAU60ZAaymXjRrAAUG\nEgRBDA2G8pzg/Jh8FBw/H7ddiIT3QffIe9u74TlnnzEUS05rIM4aoLGMrghQnNoHSkmtAbGKAEbV\ndEWA23mBrlsDEigCAL0QoJquK7YQED5+ovMQBEEQl56UrAF/+MMfsHHjRsf3v/zlL2PVqlUpn/TW\nW2/FqVOn4rY/9dRTmDt3bsrHSZfVq1frP+wDcOUsYNyspPscLyzHGJtFfmRxLnM8XIqI/RtmYsZ3\nPkRLQZnNUaKU+FvRmhsuBISf7hsZAb20BpgVASrDRjMCkigC0rIGOIQFkjWAIIYWe/bswZ49ey71\nZRCXgME2J3gX72DMrHKUz7o86T4d5fnIPxFfCHD7TdYAgcNNz+xH0efn8Oa6W5IeM7u1C/6S6D1e\ntwZwCTMCXAERUrKuAYoGhBfjGsuAVTTDGuCEbg1IkBEQvsZEhQCZFAEEMeSgOcHAJKVCQHd3N7xe\nr+P7WVlZ6OrqSvmkb731VspjI5SVlaGpqcl43dzcjNGjR0NVVcv2pqYmy9MAM5Gb/urwa+bf9b+1\n7zmf90TBGIzpiC8ERJL8WU2FRwoiJ+RHTsif9PeIVQRECgGP/7WAR//o3DUg4DZNEhysARZFAGOy\nBjgpAlK1BpiCAAVVsi8EUFggQQwpZs2ahVmzZhmv16xZc+kuJgWSBciFQiEsWbIEhw8fRl5eHrZt\n24by8nIA6YfSHT16FIsWLYLf70dFRQW2bNkCQRCwd+9ePProo2hoaMD//M//4J577gEAKIqCb3/7\n29izZw80TcMtt9yCZ599FiybkmjvojP45gT63z/Ej4z3fovbbffpGJOPAptCgBAQ0V2aC0BfABec\nOA9XZyil38VOEZDPeZIUAkwZASl0DVA5Boyi6taARIqAZNYAIL5zgCSRNYAghjgXc07QfmNOGqOT\nr82GMinNMsrKyvCnP/3J8f2GhgaLRK+vMLckqqqqwvbt2yHLMpqbm9HY2IgZM2Zg+vTpaGxsREtL\nCyRJQk1NDebMmdNn13CicEycIoBXJPCqjBDvhsi54FZEcJoKr5R84hNXCND0jABwCboGhPMIIvsk\nVATEtA9MGBaYqjVATawIiHwOBEEQ/ZFQKIRly5ahtrYWhw4dwo4dO1BfX28Zs3HjRowaNQoNDQ1Y\nsWIFli9fDsAaSldbW4ulS5dCkqSEx1y+fDlWrlyJhoYGjBw50nh6Xl5ejl/96ldYtGiR5dxvv/02\nPv74Y3z66af49NNPcejQIbz99tsX4ZPpGUN5ThAoyYb7Qgh8TAtBV0CClB3NCHB1SRC64+/nsfBB\nGXy3hGB+uGDPcXrrQY7TF+WyGt+uD3q7QjHHlbxrgGENYEI7iUgAACAASURBVFNTBPSRNUBlGWhc\n/yxkEQRBEDopfUvffvvtePLJJxEMBuPe6+7uxpo1a3D77fbV83R55ZVXMHr0aOzfvx+33367cQOf\nOnUq5s+fj0mTJuGrX/0qNm/eDEEQ4PF4sGnTJsyePRvXXXcd7r77bkt7od5yoiA+I8CQ6jMMFJaD\nGk7OzZKS+wHtFAGspgIsC4V1DgtM1xoQaR+YJXX3rTVAleP6D1NYIEEQ/ZlUAuRef/11VFdXAwDu\nvPNOfPDBB1BVNe1QOlmWsX//fsybNw8AsHjxYuNc5eXl8Pl8cU/6S0tLIYoiQqEQuru7IUlSRhbS\nfcVQnhNoLIMLZXlxqgCXX7R0DQAAIaZYYEd2W1gNEEngD4cFgmWhsSxUBtZFN2AEESouLnnXACMs\nUO8aIHRLScICGV0RkK41wDQvUFwcqQEIgiAGAClZA1atWoUpU6bg+uuvx9KlS1FRUQEAaGxsxH/+\n53/C7/en5QdMxPz58zF//nzH67A7z5w5c/q04m+mNbcEwwPtlm0RWwAAgGEgci545FBKhYARnafx\n56Jx+guTNSCRIiC2EGBrDdA08GpUzhexBuSFLgD5+fYXE24fmMwaEFcIcMcrAuxCDgmCIPoDEdl4\nhLKysjgvo3kMy7IoKipCa2srWlpacMstt1j2bW5uhqZptsdsa2vD8OHDje2lpaVJw+omTJiA2267\nDaNGjYKmaXjkkUdwzTXX9OZXzihDeU4AAP6SbHjbuwHTP5ErIFm6BgAAn4IiIOd0AIFik80ibA0w\nFvDhFoKK6em6OZgwadcAwxqgdw1wXwghmO/wcCA8jg/JSa0BCTMC3DwVAgiCIAYAKRUCRowYgfff\nfx/f/va38dhjjxnyPIZhMHv2bPz85z/HyJEjM3qhl4qAKxvZYsCyzbIwB9IqBFx+9hi2TQ7LQiPW\nAFMhwG6Rny0G0OnWvYdOigC98wBjPGmKWAPyu887FwJSsQbEZATYdQ3Ye8XN4DQFfTPtIwiCSJ09\ndfqfRDCRp639lL179+Ltt99GS0sLNE3DrbfeitmzZ+Omm2661Jdmy1CeEwCAlC3AFbC2DXYF7BQB\nyQsBBcc60FFeEN1gVwhQNJhnBkY+QHh8KooALawI8JwPRW0INqh8imGBCdoHdg/LQt23pznvTxAE\nQfQLUioEAMDll1+O119/HWfPnsWf//xnaJqGq666CsOGDcvk9V1y7AoBlnZ+ACROv/mnUgi44uwX\n+Hz4lfqLiCIgSUZAthjAqdyRxj52hQBOVSCzPCIC/Yg1oCDYARQUxI2PHKsv2gf+9ppbnfcnCILo\nBUfHjUr4fvk44N6/i75e8x9/iRsTGyxnFyBXVlaGEydOoKSkBKqq4syZMyguLk47lK6kpATt7e2W\n8XYyf3Nx4oMPPsCcOXOMAL45c+bg/fff77eFAGDozgkAQMx2wRWIyQjwi8biXBH0gnwq1oBhX3Tg\n7JWF0Q0OigDLuSIdA4Dk1oAYRYCnI4hggbMiQONYcL3NCHBxKXVLIAiCIC4taSe5fPLJJ5g0aRJu\nuOEGfPLJJ7YewcGEkyLAsAYAhj8+WSHAJYcw8sIpnCgYo2+wUQTYFQJ4VTaKDU7WAF6VoTBRKV7E\nGlDQ3ZHQGuBSpPSsATZhgQRBEP2ZVALkqqqqsHXrVgDArl27UFlZCY7j0g6l4zgOM2fOxM6dOwEA\nW7duRVVVleVcmqZZgu+uvPJKvPPOO1AUBZIk4Z133sG4ceMy/Kn0DUNtTgAAYrYAIbYQ0ENrwLDP\nz8UXAiQ1YSGAlTWj2BDJFojNEQAAVrFRBHQEEysCOAa86fy2mKwBxnn7aYcLgiAIwpm0v7m/+tWv\n4uTJk3E/D1aMQoBp0hZnDeBdUBg2aSGg/NxxNBeUQeHCC+mYsECZ4/WFdgycqkBlwv9UDtYATlUg\nc6aKPKsrBxJaAwr1yYe5gBAHy4LRElsDCIIg+jNOAXJPPPEEXnvtNQDAww8/jJMnT8Ln8+HHP/4x\nNmzYAKBnoXQbNmzA008/DZ/Ph9OnT+ORRx4BABw4cACjR4/Gjh07sHTpUvh8PgDAPffcg3HjxmH8\n+PGYOHEixo8fb7QW7O8MtTkBYG8NEAKSxRqgskyKioBzOHuFjTUgvLC2KwQwigqNZSz72BYCYhUB\nqpbUGtBdmKX/kMQaEJkTmc9BEARBDCyG9Le39j3nbcy/63/LnACVYeFSRIi8fvOMtQaInAst+aVJ\nCwFXtn+Oz4uujG4Iy/wjigCFse8awGpq9Km9gzUgVhGgMiy8UpduO8jKsr+gq6/Wr+vM584XnYI1\ngCAIor9jFyBn7nPsdrtRU1Nju2+6oXRjx47Fvn374rZPnz7dYieIwLIsNm3alPR3IDLLWjzpuP2H\n+JHx2s4a4I7pGnChNDeljABbRYAom57k2xQCVM3ami82vC8yTtGix2EZMIqGrI4g2q8pcryez/9m\nLMb97mjK1gAqBBAEQQxcSMuVArH2gGwxAL/Lag04XlietBBwxdkvrIWAsMzfyAjg7MMC9SDA9BUB\nw7rO4rwnP9qWKBaGwZ+LrsQIf6vzRdt1DaCbPkEQBDFE0a0BsYoA0WIN6CjPh9CVWBHASgryWjqt\nYYEsa8kIUHhWl/ibYFTNqghwKASYF+kRa4D7fOKuAf+vKtrVyPnCWSoEEARBDAKoEJACsYWAHNGq\nCPjZX38HdWNuSF4IOPMFjhaNjW5IsX1grDXAKSPAHPqnMHohoCPLISgwzE9vfhRB3lkmSNYAgiAI\ngogiZgsWRQCjahC6JEheXRFw9Evl+PChqUkVAflNF9A5Msfaai+VjABFgxprDVBsugnZtA/M6ggi\nWOB8zz/tK9F/cCebF4R/pEIAQRDEgIUKASkQcGUjJ+Q3XsdmBPx35d/jdO4IeKWuhMcpPd+CpnxT\nUnU4LNDICHAoBFgUASlaAxSWQ1HgjK4ISMDPb3oYWf8nQQEjxhogKBLd9AmCIIghi5TtgssfVQTw\n3RIUN2/I9VsrinF4/njwSTIC8lo6cX50nnUjx4GVkmQEpKMIiBQUUlQEgGHwQ/WfgSuuSDjGUARI\nSVoNEgRBEP0WKgSkgN+dY7UGhKxdAwCgy+VNqggoPd+Ck/mXRTekqAiwZASkaQ1IpggA4GwdsDkf\nKQIIgiCIoUwox5oRYO4YEEH28OBExXaBHiGvpROdl1nnEqm0D9QzApIXAhjFbA1gwaagCNB3TDAn\niDkfR4oAgiAGILW1tfD5fJg4cSKefvrpuPdDoRAWLFgAn8+HG2+8EcePHzfeW7duHSZOnAifz4c3\n33wz6TGPHj2KyspK+Hw+LFy4EJIk9egcwWAQ06dPx+TJk3H11Vfju9/9bq8/ByoEpECsNaAg2IFO\nd65lTLeQlbQQcNmFkziZF1MI0KIZAQrrHBZosQZoqbUPLOpKrghISqw1gNoHEgRBEEOY2K4Bno4g\nQrnWQoDGMlDcPPigsz0g72QnLlxmnUvEFgI0jgErWxf5cYoAjtNtADFwMYoARlHDXQMSKAJSwTQv\nIGsAQRADjVAohGXLlqG2thaHDh3Cjh07UF9fbxmzceNGjBo1Cg0NDVixYgWWL18OADh48CBefvll\nNDQ0oLa2FkuXLoUkSQmPuXz5cqxcuRINDQ0YOXIkNm7c2KNzeDwe7N27F/X19Th8+DD27duHt99+\nu1efBRUCUiC2EDDx9GF8MmKCZUw3n6QQoGm47LxNISBWEZCsfSDHgVVtFAGaYs0ICFsDUlIEJMKu\na4Ag9O6YBEEQBDFAic0IKDnchrYJw+PGSVl8QntA7slOXChNXAiwzwhQU7IGMKaMAD0sUIOnI4hg\nQS8LAbFdA2hOQBDEAKKurg4VFRUoLS0Fz/NYsGABdu/ebRnz+uuvo7q6GgBw55134oMPPoCqqti9\nezcWLlwIjuNQWlqKiooK1NXVOR5TlmXs378f8+bNAwAsXrzYOFe65wCArHAnOFEUoSgKRowY0avP\nIu1CwA9+8AMUhvvPm38ezFgKAZqG607+CYdGTbKMiVUEjLzwF3z/91FZSEF3B0K8G13uaLZApBDA\naqpRCLB72p+KNYBX5OgYxHQN6A1kDSAIgiAcGIpzAjHbBcFUCBj5p9M4NSl+MiZlCZbAwC+tfRf5\nbeeN13knO9FppwgwhQU6dQ1QU2gfaM0IYI1rlj29vIdT1wCCIAYwzc3NGD06mtlWVlaG5uZmxzEs\ny6KoqAitra1oaWlBWVlZ3L4tLS22x2xra8Pw4dFCcWlpqXGudM8BAIqi4Prrr8eIESPw5S9/GRMn\nTuzVZ5H2t7e5l7JdX+XBiLkQMKLzNDhVQUt+qWVMbCHgqrbPsOCj7fi3W1YCCNsCzPkAQNQaoCqp\nhwU6WAPiFAFha0CvFQEsCwbUNYAgCIKIZyjOCWKtASMPtaLxb+MnY3IWD8GkCJj0Px/jsltOAsX6\n69yTfmdrQDphgY5dA6ztA71nunqvBgD0azO6Bmg0JyAIot/w/h4F7++JXyeZYZLloPRjOI7DRx99\nhPPnz2P27NnYs2cPZs2a1ePj0bd3CpgLAded/BP+dNl1cWE6sYUAQZUsnQZKz7dYbQFAvDWAs7cG\npNw1wKQIUBkWXqm794oAO2sA3fQJgiCIIYqYbQ0LHPWn0/jtv86KG6dbA6L3dE5SkeUPGq8dwwKD\nKYQFxlgD2CSKAI1l4OqWcaEsL25c2sRaA2hOQBDERaQONzi+x88CvjQr+vrHa34XN6asrAxNTU3G\n66amJsvT/MiYEydOoKSkBKqq4syZMyguLo7bN/JUX1VV22OWlJSgvb3dMj7ytD/dc5jJz8/H7bff\njv379/eqEEAZAQ5o34v++fZfRQsB155qRMMoX9z42EKASxaRI0YLAXFBgYCxqI+EBUqsAJciIhZL\nRgDLglVV/GTno/CYzsepVkVAt5CFL4aNxa5r7+rR728Qaw2gsECCIAhiiLEWTxp/vpO90pDZu7uC\nyGvpxJmriuL2kbIEuLqiBQNOVKKFAE1LKSxQFVj9tQlGiS8EjH/1CMa/dsQyzrxIlzw8VAb44FHn\nCXTKkDWAIIgBzPTp09HY2IiWlhZIkoSamhrMmTPHMqaqqgpbt24FAOzatQuVlZXgOA5VVVXYvn07\nZFlGc3MzGhsbMWPGDMdjchyHmTNnYufOnQCArVu3oqqqqkfnOHPmDDo7OwEA3d3deOutt+Dzxa9J\n04G+vVMhOxvZIb0QUNR1Bq05JXFDkikCRl34C/6SN8q6E8taFAEh3g23HIo7dmxGAKcpuO/A8/jZ\nX38Hx4rGAtCf1JsLAetnPYYff3kFOj29rP7Hdg0gRQBBEAQxlMnONqwBOecC6C70QOXjn6vIMYoA\nVlKR5e9GJ7Lh7hShMYCYG9PKj2UtGQGymwcXiikExLYP5DiUv9+EzpE5+HTu1dFxprBA/6hc/PjE\nd/pGEUBdAwiCGMB4PB5s2rQJs2fPhqqqqK6uxpQpU/DEE09g2rRpmDt3Lh5++GFUV1fD5/MhNzcX\n27ZtAwBMnToV8+fPx6RJk8CyLDZv3gxBECAIgu0xAWDDhg1YtGgRHn/8cVRUVGD9+vUAkPY5Tp48\niSVLlkDTNASDQSxatAi33357rz4L+vZOhexs5IinAAA5IT9O58SHAnW5vHGKgGwxAGgawDDIFgPw\nu20kgBFFAMsiKHgcCwGx1gBelVEQ7DDGxIYFXsjqpSUgAlkDCIK4hNTj+jT3+EtGroMgDLKz4fLr\nhYAsfzfEHJftMMkrWDICeFGBJxBEJ7IhBET7/TgOnKQAbv2eL3t48CGrZdDOGsCFFGR1BC3jzO0D\nAfRNEQAgawBBEAOeOXPmxKkA1qxZY/zsdrtRU1Nju++qVatsM3HsjgkAY8eOxb59++K2p3sOn88X\n1+awt/TIGvD+++8bPweDwQQjBwnZUWtATsgfv6CHjSJAkcBpKjyy/vnEPrEHADAMVIbR5fYchyDv\nMcabiQ0LZDUVgiKhoDtaCIgNC+wzqGsAQRAEkYAhNyfIygInKmAU3fPvWAiI6RrASlFrACertiqC\nWGuA7OHAB62KAFbVoMYUAviQAk+H9UECo2Rokc6yYMLTAioEEARBDFx6VAi45ppr8OGHHwIAzp07\nh8ceewyHDx/u0wvrV5gLAaJzIcArdRmvI17/iD1AUCRIXHyvXYXh9LFha4BdIYDTrBkBnKpAUK2F\ngNiwwD6DCgEEQRBEAobcnIBhIHkFuAISsvzdCDkWAmLCAk0ZAaykQhFs7tmxhQA3Dz4YowhQ1Liu\nAXxIhidGEcDKmkUR0GdQRgBBEMSgoEeFgM8++wyHDx+GpmkYNWoUfvjDH2LBggV9fW39B1MhIDfU\niU53btwQO0UAAGM/QZEgsTaFADZaCAjyDtYA1ZQRwHEQFAmspiE/GO1H7FRo6DUMY8kIEBSJbvoE\nQRCEwZCbEwCQsl0QAiKyOrsh5toXAmLbB3KSCk8grAiQFKhCKooAB2sAZ9qXZcEHZXjOx1gDJAUQ\nMjMvQHhewEkKzQkIgiAGKD369r7jjjtw4sQJaJoGVVXR1tYGl8v+RjgoyMtDfre+6M4J+eF3xSsC\nRM4FXpXBqgrUyOIeKSgCWA4uWQRYFgqn/3Nwimz8DMRbAyLFArMiwKWIELkM/BuQIoAgCIJIwJCb\nEwAI5rnhOR+C4g8mUASYrAGapmcE+PUHBroiwKEQIKkAa8oIiFUEOGQExFoDOFEBMvHvQIoAgiCI\nQUGPFAEPPfQQ3nzzTbAsC5Zl8fbbb+Pdd9/t62vrP4wbh6vaPwPgnBEAhrGoAiKKgEgLQUF1tgYI\nqmRU/+3sAbHWALtCQMYUAXaFgEw8YSAIgiAGJENuTgDg7LhCDP/sbOKwwCwefFgRwCr6wtnICJBU\nqClZA7i4QgCraNBY6z58SI4PC5TUzNyvYwsBNCcgCIIYkPSoELB27Vpce+21AACGYTB16lQcO3as\nL6+rf1FejuGBdmSH/I4ZAYDVHhBRBJitAXZhfoYiIHzTt7MHsJoKhYlaA9yKfSEgI4qAGGsAr5Ai\ngCAIgogy5OYEAFonDEfxJ+0JwwJlkyKAE/XAv4g1gJVVKCmFBfLgbdoHqrHWgJCiZwSY7teslCFF\nAMOACZ+GFAEEQRADlx4VAgDgqquuMn6eNm0aVFVNMHqAw3E4Unw1xrd+ipyQ3zYjAAC6eRtFQNga\nwKuyszVAsRYCPHIQN3/+Dr6z96cA9IwAlU1uDbhoigC66RMEQRAmhtScAECbUQjoRsghI0DK4iF0\nhRUBkr6YjyoC0sgICCsCFn59B1hJcbQGcLJqnA8IFx8yrgjQaE5AEAQxQOlxISCWyNOAwcr1N0/A\nhNOfIDfUaZsRANgrApJlBKgMaykEhHg3PFIQV7V9hqnNBwEkyAgIxlgDbMIIew0VAgiCIIg0Gexz\ngvkT/kkvBHQmtgbEKgKyLBkBNtYAlnW0BkzYdQRCtxxfCAhbAwBY7AEXzRpAcwKCIIgBSZ8UAj78\n8EPMmjWrLw7Vf5kwARNPH4ZX6kKXy2s7pMvljS8EiKZCgM1CvUvw6sWC8BP/oKBbA3hVBqfqE4fY\njIBIhkCcNYDPkAQQJmsAFQIIgiCIBAyVOUHJJ+3wJioEeAWjawAvpqgI8Hrh8ovWsMCwNYCVVTCK\nCkaJVwREigXmwMCMhQWaLINUCCAIghi49KoQcOzYMSxcuBCVlZWDPhgI11yDKS1/RLeQBZW178tr\nFxZoaR9oowg4l1WIHDEQZw3gVVlfdMMmIyCsCIh0MgAybA0wSTwpI4AgiIFIbW0tfD4fJk6ciKef\nfjru/VAohAULFsDn8+HGG2/E8ePHjffWrVuHiRMnwufz4c0330x6zKNHj6KyshI+nw8LFy6EJOn3\ng71792LKlCkQBAH/+7//a4yvr6/HDTfcgEmTJmHChAl44YUXMvERZJwhNScoKoIisBj1+V8Sdg3g\nw4oAVtLvo0ZGgKRCtcsIKCyEOyDFWQMYVQOranoxQNWgcTGFgHCxINJCkFE1PS+As5+v9ApSBBAE\nQQwKelQI6OjowGOPPYbx48fj5MmT+M1vfgPNFFAzKLn8ckw8ddjRFgDEWwMuuHOj1gCHrgHnvIX6\nDzaFgIgiwC4jIMS54qwB1D6QIAginlAohGXLlqG2thaHDh3Cjh07UF9fbxmzceNGjBo1Cg0NDVix\nYgWWL18OADh48CBefvllNDQ0oLa2FkuXLoUkSQmPuXz5cqxcuRINDQ0YOXIkNm7cCAAoLy/Hr371\nKyxatMhy7tzcXNTU1ODQoUP4/e9/jxUrVuDMmTMX4ZPpG4bknADAucsLUH64CWKOfRFejrEGBHNd\nUUWA7GANKLTOCYxCgKLfh1lFs7UGcCEZsovTAwOhZxIorgwUAQBLIYCjQgBBEMSAJa1CgCRJ+MlP\nfoIrr7wSr776Kn79619j7969mD59eqaur/8wZgxGn292DAoE9EKAV+oCoC/Mz3kLo2GBimzbNeBc\nlvWmH+LdhjXArAgwWwNcioS2nOKLExZI1gCCIAY4dXV1qKioQGlpKXiex4IFC7B7927LmNdffx3V\n1dUAgDvvvBMffPABVFXF7t27sXDhQnAch9LSUlRUVKCurs7xmLIsY//+/Zg3bx4AYPHixca5ysvL\n4fP5wLLWW++4ceNQXl4OABg1ahRGjx6N1tbWTH8svWZIzwkAnB+Tj5LmdoRy3bbv6xkBuhqEk1R0\nF2bBY2QEKFDsrAGxhQA3Bz6k6E/egagiINYaIKkIFHsNawAnKvbtCfsChgGoawBBEMSAJ+VCwI4d\nOzBhwgSsXbsWP/rRj3D48GHMnz8/k9fWvxgxAiInOLYOBOIVAeeyCqPWgGSKAFNGgEcKglccMgLC\nk4Mz3iLkBS+ACcv2KSyQIAjCnubmZowePdp4XVZWhubmZscxLMuiqKgIra2taGlpQVlZWdy+LS0t\ntsdsa2vD8OHDje2lpaVx50rEhx9+iK6uLkyYMCHt3/NiMuTnBAA6xuQBQIKwwKg1gBMVhPLdcAUl\nMKoGTlLtMwIKrXOCiCLAKAQoKlhFhRpTCACAruFeIyyQk1T7QkNfQNYAgiCIQUHK396bNm3C2LFj\n8dJLL6GgoCCT19Q/YVk0FYxOuRAgKFLY/5+4a0CHJ/xZppoRYCoYyCwPlyIixHoyFxZIhQCCIPox\njXvO4OM9ZxOOYRgm4fv9hb/85S9YsmTJgMgIGPJzAuiKAMC5ECBbFAEKZDcHMcsFoUty7hpQYJ0T\nxBUCjIwA0yI/Mn/Ic4OPnE+8ONYAKgQQBHGx+QB/lcbo32XsOgYDKX97/+53v8Nrr72G9evX4/77\n78cVV1yRyevql5woGIMQby8BBGwUASZrgNMT+9iMALM1gNPCGQEx1gAAkDgBEidAUCSEBA9cioiA\nK7tPfk8LLGukA0d+D7rpEwRxsfgIkxMPmAVcM8v0es1TcUPKysrQ1NRkvG5qarI8zY+MOXHiBEpK\nSqCqKs6cOYPi4uK4fSPKAVVVbY9ZUlKC9vZ2y3izoiBCbHHiwoULuOOOO7B27VrMmDEj8e/cD6A5\ngVkRYK/Gk7IECF3WhXkw2wOXXwQrO4cF6jtErAE8+FC0EMDYZQRE5gXZgt4yEHrhwbbQ0BeY5gWs\npNCcgCAIYoCSlm5s7ty5WL16Nd5++22sW7cOHR0dyXcaRJwoHJM0I8CsCOjwFKTUNQCAvSJACSsC\nzGGB4XESGy4EqJJx/Ey1DyRFAEEQA5np06ejsbERLS0tkCQJNTU1mDNnjmVMVVUVtm7dCgDYtWsX\nKisrwXEcqqqqsH37dsiyjObmZjQ2NmLGjBmOx+Q4DjNnzsTOnTsBAFu3bkVVVZXlXJqmWcL0RFHE\n/PnzsWTJEtxzzz0Z/jT6jqE+J4goAhJnBES7BigCh6DXDVdAdG4fmJcHlUFyRYBNIUD0CvrCHBlW\nBDAMKQIIgiAGAWl/e/M8jwceeACdnZ34r//6L+Tk5ODOO+/MxLX1O44XlmN0R5Pj+12CN6oIkEX4\n83KMVn+8miQsMDYjwGQNsGQEmBQBIucy2hS6FPHiZQQIGTgPQRBEhvB4PNi0aRNmz54NVVVRXV2N\nKVOm4IknnsC0adMwd+5cPPzww6iurobP50Nubi62bdsGAJg6dSrmz5+PSZMmgWVZbN68GYIgQBAE\n22MCwIYNG7Bo0SI8/vjjqKiowPr16wEABw4cwN13341z587hN7/5DVavXo2GhgbU1NTg3Xffxdmz\nZ/H8888DAH71q19h0qRJl+TzSoehPCc4V57YGiB5rRkBiouD5BbAhZSwh99moc6yCBZ44I3JCOBM\nhQBWie8aEDlfRBHAOmUQ9AWWrgEazQkIgiAGKD0u4+bm5uIf//Ef0dTUhP/4j//oMw/mSy+9hNWr\nV+PTTz/FgQMHjInVsWPHMGHCBIwfPx4AUFlZiWeffRaA3t7pW9/6FkRRxN/8zd/gZz/7WZ9cSyy/\nu+orCQsBFkWAKqHTnWsUAhwVAU7WAFNYoF1GQMQa4FLEhMfvNTHWAF4hRQBBEAOPOXPmxKkA1qxZ\nY/zsdrtRU1Nju++qVauwatWqlI4JAGPHjsW+ffvitk+fPt1iJ4iwePFiLF68OOnv0J8ZinOCruJs\n/Obvvwopy/6eKMVkBKgCC9Ej6F0AnLoGAOguzILXqWtA2BqgcjbWAK8ATgwrAjIcFmjpGuClOQFB\nEMRApNd3idGjR+Opp57CH//4x764Hvh8Przyyiu4+eab494bN24c6uvrUV9fb9zwAeCb3/wmnnvu\nOXz88cc4fvw4XnnllT65llj2XvklvDjVebJmyQiQRb0QoIQLAU5dAxJZA+zaB0YmByxvZAQAZA0g\nCIIgLj1DaU6gsQx+uvlhvZ2eDXKWYFgDOFGBHFYE8CE53DXAXrrfXejpkTXArAggawBBEASRjD4r\nF1933XV9cpzx48fj6quvTnn8iRMnoKoqJk/WA6XMma21ywAAIABJREFUPZsvNnGKAE/6igBzIcAI\nCzRnBJgVAaxwaawBdNMnCIIgEjAU5gR1uCHh+7KHBxeSAS3aLjBiDXAMCwQQTFQIUGwKASZrgJER\nkOmwQCoEEARBDHgG1Lf3sWPHcP3118Pr9eJf//Vfccstt8T1h07Us3n16tXGz7NmzcKsWbPSOr/2\nvfhtzL9Hf45VBPhdOdZCgF3XgKx4a0CO6LeEBXKaYmsNEHnXxbEGwGQNoEIAQRAA9uzZgz179lzq\nyyCGMJd6TvBb3B7+6XbL9h/iRwB0xYDi4nSPf/gJvRLpApDQGmAqBBjWgOjCm1FUa/tAIyyQR9aZ\nLgC6IiCjGQEaFQIIgohCc4KBySX59r711ltx6tSpuO1PPfUU5s6da7vPZZddhpaWFuTl5aG+vh53\n3HEHPv7447TOa77pZ4JYRcAFTx48UhCAc1hgR1a4Z7ApLHB4oN25faC5a0CMIkDkMmQNUMOKAE0D\nryrGNRAEMXSJXTiZ/fYEkQ6DdU4ARO0BrKRCcXEQPS69MJDAw99dmGXMCWIVAYnbB7osYYFkDSAI\n4mJBc4KBySX59n7rrbfS3sflcsHl0he6kydPxrXXXotPP/0Uo0ePjuvxbNez+WLQLWTBK+nV+LiM\nAIcn9jIn4Pt3PI1/83gAmKwBikNGQExYoDkjIFOKgIg1gFMVyCwHvo9CoAiCIAhisM4JgGhgIC/q\nUn09I0DvGiB77Iv3DV+fiBn5XwZwGoqLAyspRgigbUZAuDgvegVwZA0gCIIgUiRDurG+wdxn+ezZ\ns1DDT6aPHTuGxsZGjBs3DqNHjwbLsqivrwcAvPjii3E9my8WsYoAvzsnaUYAAPz4y9+3KALcckhX\nBIS7BnCqQ/tAPto+UFClzCgCTIUAJ1UDQRAEQWSagTYnAAApS4DQJelWABcHyc3rGQEJrAFffGUs\nMG2a/oJhoLh5CF36vZ5V9PaBqnlXU1ggezHCAqkQQBAEMShI69tbVVW88MIL+P3vf4+Ojg5omgaG\nYYy/X3311V5f0CuvvILly5ejvb0dt99+OyZPnow33ngDv//977F69WqwLAtN07BhwwYMHz4cAPDL\nX/4S999/P0RRxFe+8hXcfffdvb6OnmDbNUBO3DUglhDnhkcOglMViyJAYcM3dHPXADbaPtAli5lR\nBDCMkRFAhQCCIAgiAs0JkiNn8eC7wxkB4bBAPiQnDAuMO4abg9uv3+sNRYB5kW/YCLiL0z6QYcCY\n2wdSIYAgCGJAktZdYvny5Vi6dCnOnDmDYcOGoaioyPi7qKioTy5o/vz5aGpqQnd3N06dOoU33ngD\nAPC1r30NjY2NOHToEBoaGnDPPfcY+0ydOhX19fX4+OOPsWHDhj65jp7QJXijigBFgt+VA48cAqOq\n4FUlpUV0UPDAIwUtioCUrAEpFhrSJqwIYFUFb2/6MnLEQN+fgyAIghhw0JwgOaJXzwjgbDICnNoH\nxiJ7eLgihQC7jACOgyywUAXO0j4wo2GBqoa/+sl+THnhEBUCCIIYcNTW1sLn82HixIl4+umn494P\nhUJYsGABfD4fbrzxRhw/ftx4b926dZg4cSJ8Ph/efPPNpMc8evQoKisr4fP5sHDhQkiS1Ofn6Clp\nfXtv2bIFNTU1uOuuu3p94sGIRRGgiAjxboicAK/UBZnlHHsNmwnyujVA5vjEGQFsuBCghsMC5QyF\nBYYLAVlSN6Y3/aHvj08QBEEMSGhOkBw5nBGgL8z1jICsUFfYw5+iIsDDQwiErQF2GQGsXgRQBNbI\nCMhoWCDLglEVjProtP46QA8ICIIYOIRCISxbtgzvvfceRowYgcrKStx2221G21kA2LhxI0aNGoXt\n27dj586dWL58OXbt2oWDBw/i5ZdfRkNDA06dOoWbbroJR44cgaqqjsdcvnw5Vq5ciXnz5uHRRx/F\nxo0b8d3vfrdPz9FT0ioXu1yuXp1ssGPJCFAkiLwLId6NbDFg2zrQjhDvNsICbTMCIl0DOAEi54JL\nznD7QIYBo2nwSEF0ePJxxwOv9f05CIIgiAEHzQmSI2UJujVAUiG7WIhuAVwo3EUgVUWAmzesAUw4\nIyC2EKAILBSX2RqQwbBAhgE0gA/K2PcP0wCTGoMgCKK/U1dXh4qKCpSWloLneSxYsAC7d++2jHn9\n9ddRXV0NALjzzjvxwQcfQFVV7N69GwsXLgTHcSgtLUVFRQXq6uocjynLMvbv34958+YBABYvXmyc\nq6/O0RvSKgQ8+OCD2Lp1a69OOJiJVQRIrIAQ70ZOyJ/yIt3oGqDK9hkBsdYA9eKEBXrkIPzuHOye\neEffn4MgCIIYcNCcIDmSjSKADyl6RkDKigAuag2QdWuAylmtAboigLuoYYF8UMbnt14B9JENhCAI\norcc33MM765+x/hjR3NzM0aPHm28LisrQ3Nzs+MYlmVRVFSE1tZWtLS0WDrRRPZtaWmxPWZbW5uR\nXwMApaWlxrn66hy9Iak14JFHHgETlrQrioJnn30Wb731FioqKsCGF6WRYKBL7cW71NgpAoK8B7mh\nzpQLASKvP+Xn+cQZATLLWzICMhYWGC4EuOUQgryn749PEASRgD9g6qW+BMIEzQnSQ7cGyEbXAFET\nwF9QwElKymGBiouzdA2wtQbwLFSTNSCjYYGRQkBIgeShfACCIPoP5bMuR/msy43X767ZGzeGoTbo\nBkm/wRsaGiwf2PXXXw9N0/Dxxx8b2yI3/aFOl8sLr9gFj9QNdw8VASLngksR4xQBTtaAjIcFRqwB\nchBBgQoBBEEQQxmaE6SHmOOCt70r2jUAAvhgxBrQg0KAQ0ZAxBrAWsICM2cNYDRdESBTIYAgiItM\nHW7o1f5lZWVoamoyXjc1NVmetEfGnDhxAiUlJVBVFWfOnEFxcXHcvpGn+qqq2h6zpKQE7e3tlvGR\np/19dY7ekPQbfM+ePb06wVBC5N14Y8IcLN23GTLLQWNZvRAg+tPKCIgUAiKKAN6cERCeXEWsAZH2\ngYKSYWuAFESId/f98QmCIIgBA80J0qPhbyfiju/8Xxy/cTQUFweJ1TMC0uoa4ObhChcCGEUDo6hx\nXQOUsDWAFyNhgRfHGqC4M3QOgiCIDDF9+nQ0NjaipaUFJSUlqKmpwebNmy1jqqqqsHXrVkybNg27\ndu1CZWUlOI5DVVUVHnroITz66KM4deoUGhsbMWPGDCiKYntMjuMwc+ZM7Ny5E/PmzcPWrVtRVVXV\np+foDWmVck+cOIGysjJD/hdB0zQ0NTVhzJgxvbqYwcBTX1mFnb+cZyz8Q5yuCJC51D5qkXPBLYfA\nK2FFgKY369VMn7nCsHrXANZkDVAyaw3wyEGyBhAEQRAGNCdIzud/MxZitoBrdn+GE5VlkDg9IyCd\nrgGKi4vvGsCZ9mV1W4AisNaMgAxbA4SgTNYAgiAGHB6PB5s2bcLs2bOhqiqqq6sxZcoUPPHEE5g2\nbRrmzp2Lhx9+GNXV1fD5fMjNzcW2bdsA6O1p58+fj0mTJoFlWWzevBmCIEAQBNtjAsCGDRuwaNEi\nPP7446ioqMD69esBoE/P0VPS+ga//PLLcerUKZSUlFi2nzlzBmPHjoWiKL26mMHAgdHTkRvqhMjr\nT+eDgictRYCREaDKEFRZDwpkrDdzlWGjYYERa0CmFAFmawAVAgiCIIgwNCdIAYbB518Ziy89/YGe\nEQAX+JCshwX2ICOAS2ANiM0IyJhs39Q1gKwBBEEMRObMmYM5c+ZYtq1Zs8b42e12o6amxnbfVatW\nYdWqVSkdEwDGjh2Lffv2xW3vy3P0lD4pFweDQXAcycMAAAyDj0dUGE/n080ICHFuuJWQkQ/Aq3LU\nFhBGYblo+8CwNSDjigCJMgIIgiCI5NCcwEprRTEAQBVYvWtAUEmzfSAHVyBiDVDBqhpU1qZrgKV9\n4EUIC6RCAEEQxIAmpW/wRx55xPh51apV8Hq9xmtVVXHgwAH4fL6+v7oBgPY9m42Hr8XJl04ASL8Q\nYFYEAHo3AKN1YBiVYeO6BghKhsICyRpAEARBmKA5QWLW4knrhmvrAezSMwK4SEaAknL7wLiwQEWD\nxsUoAngWisCBuxhhgVQIIAiCGBSk9A3e0NBg/PzJJ5/A5YpK0DmOw8SJE7FixYq+v7qBSkUFpJff\nAhDOCBB71jUA0J/0xyoC4qwBmgaXIqVsP0gLhgEDjRQBBEEQBACaE6TN+PFQWUbvGiDoGQHpKAIU\nF2eEBbKKZt8+0MgIuAhhgdQ1gCAIYlCQ0jd4JCX4vvvuw4YNG5CXl5fJaxr4VFQYfv2IIkBm0w8L\nBOwLAWZrQI7oB6/KRpeCPiesCHArIVIEEARBEDQnSJesLJy9olBXBLgE8GFFQK/CAu26BriiigD+\nIoQF8iGFCgEEQRADmLS+wZ9//vkMXcYgo7ISP735UQCmsMAUFQFKuLuAWw4B0K0BtooANtw+UBZ1\nW0Am1ABAuBCgUftAgiAIwgLNCVJnzz/fhFO+EojHBfDB9MICZXfUGsAoKtjY9oFhRYAqRDMC0lEc\npA3LglU0cCE5c6oDgiAIIuMkLQTMnTsXL774IvLy8jB37lwwDAMt3NLODMMwePXVVzNykQOO3Fw8\ne+M/ADBlBKSxUBc5F7xSFwBdEWCXEWBYA1QJLkU0uhT0OYw+2ciSukkRQBDEgKW2thYrVqyAoii4\n9957sXLlSsv7oVAIS5YsweHDh5GXl4dt27ahvLwcALBu3Tps2bIFHMfhmWeewW233ZbwmEePHsWi\nRYvg9/tRUVGBLVu2QBCEhOc4dOgQ/uEf/gF+vx+yLOMPf/gD3O7+V3ylOUHPqL/3OgCAeCrSPlBN\nKyPA1WVVBKgx7QMVgbNYA7gMWwO4kAxV4KwFCYIgCGJAkfQuVFRUBCa8GCwqKsKwYcNQVFRk+4eI\nJ8SlFxYI6IGB2WIAgB4C6GQNiGQEZCwoMIzKMPBKXZQRQBDEgCQUCmHZsmWora3FoUOHsGPHDtTX\n11vGbNy4EaNGjUJDQwNWrFiB5cuXAwAOHjyIl19+GQ0NDaitrcXSpUshSVLCYy5fvhwrV65EQ0MD\nRo4ciY0bNyY8RzAYxKJFi/CLX/wC9fX1eO+99yAImftO7w00J+gdklsA14OMACGgdwiyzQjg9CKA\nyrPgwu/rYYGZswYIXRLZAgiCIAY4Sb/FzdI/kgGmT4jXwwI73blp7ZMfvIBQODjQzhogs7wRLCgo\nkpFJkAlUhoVX7EK3kJWxcxAEQWSKuro6VFRUoLS0FACwYMEC7N69G5MnTzbGvP766/i3f/s3AMCd\nd96JBx98EKqqYvfu3Vi4cCE4jkNpaSkqKipQV1cHVVVtj+nz+bB///9v797joizT/4F/5sShRU1F\nPDCYllk4gIIHxNRQIQXDNDVckYrSDt88tttqWiqmVv50W83dPJOK7sY3T7UYairWZlIqamhpX08w\nk+YpXUxlmJn798fIIzgHGGROzOf9evGKeeY5XPOE81xzzf1c9z58/vnnAIBRo0ZhypQpmDRpks1j\n5OXloVu3bmjfvj0AoFGjRi47N45iTnBvyv3NPQLkhpqPCDD4K6AqM8Lgp7DeI0BuLgJAJoPh9qgA\nebnJeSMC5HL43WCjQCIib+fQu3hiYiL69OmD+Ph4dOvWDUolLwLVuaUKQFDZdfwW2LjG21R8qL+l\nCqjRrAF+Rr1TRwQImEcE/HZfzV8DEZErXM4vwpX8o3bX0Wq1CAsLkx6r1Wqp4Z21deRyOZo2bYoL\nFy5Ap9Ohb9++VbbVarUQQljd58WLFxEcHCwtDw0NhVartXuM48ePQ6/XIz4+HpcvX0Zqaireeuut\n2p0QF2JO4Dh9gB8UZUbIhKh2REAsCgBA+kBvCFCaRwQYBUTltOB2jwAA5j4B5SbnNguUyaC6UY5y\nFgKIiLyaQ+/isbGx2Lp1K2bNmgWlUokePXogPj6eSYAdFSMCHLo1oKIQoAyAn8GyR4BRdvvWAHml\nWwOc1SwQ5sIDewQQkSdqGh+BpvER0uP/y8yxWKdiKLunMhqN2Lt3L/bv34/AwED069cPnTt3RlJS\nkrtDs4s5gePK/ZVQ3TJAyGU1bhZYpRBQMSKgco+A27MGAJD6BDi7WaDqOm8NICLydg69i8+ePRsA\ncOPGDezduxd79uzBF198gRkzZiAgIAClpaVOCdKb1aZHQEV3/jKlP1Qmyx4BJrl51gC90k8aEeC0\nZoG4UwjgrAFE5GqHEF39StVQq9UoKSmRHpeUlFT5Nr9ineLiYoSEhMBkMuHy5cto1qyZxbYV3+qb\nTCar+wwJCcGlS5eqrK9Wq20eIyQkBK1bt0bv3r3RpEkTAEBycjIOHTrk8YUA5gSOM/cIMMCklNd8\n+kB/c6pm8FdAZrRxa8DtfVVMIejUZoFyOZQ3DTD6c8YAIiJvVqtxY6Wlpbh06RIuXLiA8+fPQ6lU\nokuXLnUdW71Q21kDgDsjAmzeGiBXST0CnDkiQMhkuE9/gyMCiMgrde3aFUVFRdDpdCgvL0dOTo7F\nh+zk5GRkZ2cDALZs2YK4uDgoFAokJyfjk08+gcFggFarRVFREbp162ZznwqFAt27d8fmzZsBANnZ\n2UhOTrZ5DLlcjn79+uHw4cO4efMmDAYD9uzZg/DwcBeeoXvDnKDmzD0CKmYNqNkHaYO1EQF3FQLu\njAgwTyHo1GaBvDWAiKhecOhd/NVXX0V+fj7Onj2L2NhYxMfHY8WKFYiNjUVAAD8kWnNLFYAGZaUO\nzxpQ8V9r0wdKtwbc7hGgNBmcPGvA7VsDOGsAEXmhgIAAfPTRR+jfvz9MJhPS09MRExODGTNmoEuX\nLkhJScHYsWORnp6OyMhINGjQAOvXrwcAdO7cGUOGDEFUVBTkcjmWLl0KlUoFlUpldZ8AsGjRIowc\nORJvv/02NBoN5s+fDwA2j9GyZUv8+c9/RteuXVFeXo7k5GQMHjzYPSfLAcwJHGdQKSEzmqAoEzUf\nEVBRCPBXQG4UkBsFTArLWQMAwKSSQ1FuhNzg5FsDbrJZIBGRt3PoXXzp0qUIDg7GlClTkJSUhM6d\nO0Mud1LFuZ4oU/pDIUwO3xpQLlfCIFdabRa4PmYkdI1CEXa1BCqTuRBgkDvvgmySyc3TB3JEABF5\nqaSkJItRAJmZmdLv/v7+yMmx7C8AAFOnTsXUqVNrtE8AaNu2Lb799luL5faOkZaWhrS0NLuvwdMw\nJ6gFmQzG230CajoioGIIvs0RAV264HTYN+Z1VQrIy03mWQlq2IPAYZw+kIjcqOBGrLtDqDccehc/\nceIE8vPzkZ+fjxUrVuC///0vevXqJXUNrvg2hO441KoTDrXqiNNN2tZ4G73CD4bbhQCV0bJHwIwB\ns6T1/Ax6KI0GGBROLgTw1gAiIqqEOUHtHB36KFoe+rXqh3k7qs4aYKUQ0LMnjmL77XXlUOiNLikE\nlDVk3yAiIm/m0KfHdu3aoV27dhg9ejQA4KeffsK8efMwZcoUGI1GGI1GpwTpzQrVMYj+0yGHttEr\n/GBQKGGUK6yOCKhQcWuAylTu1BEBQibjrQFERFQFc4La+d/sIQ6tf+fWABsjAiqpmD5QUe7EQoBM\nBtXNchgC2CyQiMibOfTp0WQyYf/+/di1axfy8/PxzTffoKysDJ07d0Z8fLyTQvQ9lUcEWJs+sEK5\nQmW+NcDIWwOIiMi1mBO4xt3NAuVGE0wK6x/yjZV6BJiUTpo2Uy6H6gZ7BBAReTuH3sXvv/9+3Lp1\nS7rIT5o0CY899hiCgoKcFZ/XEq/XftsypT8M8upHBOgVfi5tFsjpA4mIqAJzgpqbg1mVHg10aFup\nR4C/AjKjsDsiwOh3p0eAc5sFlsPgz0IAEZE3c+hdPCcnBz179uRF3sn0ykojAqq5NcDPqHd6s0Dp\n1gCOCCAiotuYE7hG5R4BylsG+4WA29MHOrVHgEzGWQOIiOoBh97FBwwY4Kw4qJKKWwNq2iPAJbMG\n6G+wRwAREUmYE7hGRSHA6K+A3+/lkBnt9QiofGsAZw0gIiLbOM+PBypT+sOgqEGPALnrCgH+Rj1H\nBBAREblYxRD88srTBypsjwhwxfSBSr2RhQAiIi/HQoAHkkYEyBRWpw+U1lP6mW8NcPL0gQLmhIM9\nAoiIiFzL6GfOAYz+NqYPrLKuAkoX3BoAmHsWEBGR92IhwANJswYoqukRIHfN9IFlSn+s7vIsrgbe\n77RjEBERkSXjXbMG2O8RIIfc2dMH+vvjWqsGKBoW7pz9ExGRS3BclweSZg2Q1bxHQLncebMG9Bz7\nH5xr2FL6FoCIiIhco+LWgIpZA+RGE0w16BFgVDmpEPDKK1j04q+4dT9vFyQi8mYOFwKMRiPOnj2L\n0tJSNGzYEK1bt4ZCweFhdenuWQNs9QjQK1xza8C5Rq2ctm8iIvJezAmcz+qIAIX1D/lGPxfMGvCH\nP+AWWAQgIvJ2NbpK/P7771i+fDkSEhIQFBSEdu3aITo6Gg899BCCgoKQkJCA5cuX48aNG/cc0Ouv\nv44OHTqgQ4cOePLJJ3H58mXpuXfffRcdOnRAZGQktm/fLi3Py8tDZGQkOnTogPfff/+eY3A3vcIP\nBoV51gB7PQJcNWsAEZG7nCto69APOR9zAteSCgH+iup7BNxuFqgwmGwWC4iIyLorV64gMTERUVFR\n6N+/P65evWp1vdWrV0Oj0UCj0WDNmjXS8gMHDiA6OhoajQYTJkyo0X7Hjx8PjUaDmJgYFBYW1voY\nb731Fjp27IiIiAj07t0bp06dqvb1VnuVWLBgAdq0aYOFCxciNjYW//znP7F//36cOHEC+/fvx/r1\n69G1a1csXLgQrVu3xoIFC6o9qD0pKSkoKirCsWPHEBERgdmzZwMwv+iNGzfihx9+QF5eHl5++WWU\nl5ejrKwMr776KvLy8nDkyBF8+umnVU6iN5J6BNyeNcBuIcBkgMro3B4BREREAHMCd3C0R4DqlgEm\nuczmOkREZN2MGTMwcOBAHDlyBElJSZgxY4bFOufOncM777yDgoICFBQUYNasWbhw4QIAICMjA6tW\nrcLRo0dx9uxZbNq0ye5+N2zYgOLiYhw9ehQrV65ERkZGrY8xZcoUHD58GEVFRRg+fDgyMzOrfb3V\nFgL27t2Lbdu2oaioCHPmzMHgwYMRExODdu3aISYmBkOGDMG7776LoqIibNu2DXv37q3JebapT58+\nkMvNYT322GPQ6XQAgNzcXIwYMQIKhQKhoaHQaDTSydFoNAgNDYVSqURqaipyc3PvKQZ3q+gRIN0a\nILMxzFImg0GuQIDhFgsBRETkdMwJXK9yIcDcI0DAZGP6QJNKAeXNcufdFkBEVI9t3boV6enpAIBR\no0ZZvX7s2LEDSUlJCAoKQlBQEAYMGIDt27ejuLgYJpMJ0dHRFtvb2m9ubq60PDo6GgaDAVqttlbH\nCAoKkmK8fv06WrZsWe3rrfbT44YNG6rdSYXOnTs7tH51li1bhhEjRgAAdDod+vbtKz2nVquh1Woh\nhEBYWFiV5fn5+Vb3N3PmTOn3+Ph4xMfH11msdUmaPlCugJ9eD5Pc9gVdr/DDffobLAQQkUvl5+fb\nfK+l+os5gesJuQxGpRwGfyUU1Y0I8FPA74aBhQAicilPyQmMX30D49ff1Hr7ixcvomnTpgCA4OBg\n6Vv4ynQ6HdRqtfS44vqj0+mqXH9CQ0Oh1Wrt7vfubSrvy9FjAMC0adOwdu1a3Hfffdi3b1+1r9ct\nnx4TExNx/vx5i+Vz585FSkoKAGDOnDnw8/NDWlpanR238kXfk+mV5h4BBrnSbo8AwHx7wH3lN6BX\n+LkwQiLydXd/cKrJEDQia5gTVM/op4AhQAFZtT0C5FDdKHfejAFERFZ4Sk6g6P0YFL0fkx7r5/4/\ni3VsXXPmzJnj1NhsEULU2b7mzJmDOXPm4L333sOkSZOQlZVld32HCwFarRZff/01rl69ahH4//zP\n/9RoHzt27LD7/OrVq5Gbm4tdu3ZJy9RqNUpKSqrEERYWBpPJVGV5SUlJlUqJNypT+N8ZEWBn+kDA\nXAgILL+JG6r7XBghERERcwJXMfgrYPBXQm4QdgsBJpUC/v8t44gAIqq3rn8ZfE/b27vmNGvWDJcu\nXUJwcDAuXryIkJAQi3XUajUKCgqkxyUlJejRo4fN65K9/VZsExsbW2UbR45ReeRAhZEjR+KJJ56o\n9lw4VAhYtWoVXnnlFSiVSjRq1Mji+Zpe9O3Jy8vDvHnzsGfPHgQE3JmeJjk5Ga+88gomTpyI8+fP\no6ioCN26dYPRaERRURF0Oh1CQkKQk5ODpUuX3nMc7nT39IFlCn/b696+NeDXBs1dGCEREfk65gSu\nYx4RoDTPGmAUEDZ6BBj85PC7wR4BRES1kZycjOzsbEycOBHZ2dlITk62WCchIQGZmZkoLS0FYL5O\nTZ8+Hc2bN4dcLkdhYSGio6Oxbt06PPvss3b3W7F82LBhOHjwoNT3pl+/fg4f4/Tp02jb1jx70pYt\nWxAZGVnt63WoEPDmm29i+vTpePPNN502T/C4ceOg1+uRmJgIAIiLi8M//vEPdO7cGUOGDEFUVBTk\ncjmWLl0KlUoFlUqFjz76CP3794fJZEJ6ejpiYmKcEpurfN22F34LbIyBP+bCz6DHTWWgzXXLFSoE\nGm6yRwAREbkUcwLX+feHA3CtdcNqZw0wNwtkjwAiotrIzMxEamoqVq1ahRYtWiAnJweAeaaaJUuW\nYPny5WjZsiWmTZsmfYtf8QEdALKysvDCCy9Ar9ejX79+ePrpp+3ud+jQodi9ezc0Gg38/f2lofyt\nWrVy+Bh/+tOfcPLkSZSXl6Nt27ZYsWJFta/XoU+PZWVleP755512wQeAn3/+2eZzU6dOxdSpUy2W\nJyUlISkpyWkxudrFBiHY3aAv+h/fZr41wE6zwHK5+dYAFgKIiMiVmBO4TtHwDmhUfK1m0weyEEBE\nVCtNmjSxeutA586dsXz5culxRkaGNNXf3eucZ2DAAAAgAElEQVRZm7LW1n4BYPHixVaXO3qMjRs3\nWt2PPQ5dKUaMGOH10/B4E6NcUbNmgZw1gIiIXIw5gWsJhcw8faBJwGRnRICKtwYQEVENOPTp8W9/\n+xsGDx6MXbt2ITw8XJrbt8L06dPrNDhfV9EjwCiz/W2LXuGH+8pvoFyhcmFkRETeJS8vD2+88QaM\nRiOee+45TJ48ucrzZWVlePbZZ3Hs2DE0bNgQ69evxwMPPAAAePfdd7F27VooFAosWLBAasBja5+n\nT5/GyJEjcf36dWg0GqxduxYqlapWx/BkzAlcy6iUQ2EwAUJAKKx/0Df4KW7PGuC8URpERFQ/OFQI\nWLZsGbZv344WLVrg2LFj0nIhBGQyGS/6dcyRWQM4IoCIyLqysjK8+uqr+M9//oPmzZsjLi4OTzzx\nBKKjo6V1Fi9ejJYtW+KTTz7B5s2bMX78eGzZsgUHDhzAxo0b8cMPP+D8+fPo2bMnTpw4AZPJZHOf\n48ePx+TJkzF48GBMnDgRixcvxqRJkxw6xvHjx+Hn59nTwjIncC2hkENmNJl/tzkiQA7VzXKYlNaf\nJyIiquDQp8fZs2fjww8/xGuvveaseHyOeN367wCAm0qc+r6aHgEKFRrdusZCABGRDQUFBdBoNAgN\nDQUApKamIjc3t0ohYOvWrZg3bx4AYNCgQRgzZgxMJhNyc3MxYsQIqZOvRqNBQUEBTCaT1X1GRkZi\n3759+PzzzwEAo0aNwpQpUzBp0iSHjvHdd9+hZ8+erjxNDmNO4BxzMKvKfyXKq7hpWAwhl9npEaCA\n6qYBBn+OCCAiIvsc+vRoNBoxaNAgZ8VCd1Mqqx0RUDF9oEHBQgAR+aCD+eYfOyrP5QuY5+3Nz8+3\nuY5cLkfTpk1x4cIF6HQ69O3bt8q2Wq0WQgir+7x48SKCg+/McRwaGgqtVlurY3g65gQuplRCbhQQ\nwvaIAOPtWwP0f+DtgkREZJ9D3WSef/55rF+/3lmx0N0Uimp7BPDWACLyaTHxwOiZd36skMk4TNoZ\nmBO4mEIhzRpgUti7NYCzBhARUfUc+vR48+ZNzJ07F9u2bUOHDh2kxkAV9wMuWrTIKUH6rIAA/EH/\nO3sEEJHv+ubed6FWq1FSUiI9LikpqfJtfsU6xcXFCAkJgclkwuXLl9GsWTOLbSu+1TeZTFb3GRIS\ngkuXLlVZX61W1+oYno45gYv5+0OhNwKwMyJAJYfyJmcNICKi6jl0pTh27Biio6NhMplw9OhR/PDD\nD1V+qI41aoQ/6G/YLwTIVZw1gIjIjq5du6KoqAg6nQ7l5eXIycmxmGc+OTkZ2dnZAIAtW7YgLi4O\nCoUCycnJ+OSTT2AwGKDValFUVIRu3brZ3KdCoUD37t2xefNmAEB2djaSk5NrdQxPx5zAxeRy6P+g\nguqWwX6PAM4aQERENeDQ18h331NJTtaoEQDz7AG26JV+UJqMHBFARGRDQEAAPvroI/Tv3x8mkwnp\n6emIiYnBjBkz0KVLF6SkpGDs2LFIT09HZGQkGjRoIA1579y5M4YMGYKoqCjI5XIsXboUKpUKKpXK\n6j4BYNGiRRg5ciTefvttaDQazJ8/HwAcPoanY07gercaBSCgVG9z+kCjnwIKo+CIACIiqla1nx5P\nnTqFBx98sMY7dHR9sqNhQwCo9tYAACwEEBHZkZSUZDEKIDMzU/rd398fOTk5VredOnUqpk6dWqN9\nAkDbtm3x7bffWiyvzTE8DXMC9ypraJ5S0t70gQBYCCAiompVe6WIi4vDqFGjsHPnTgghrK4jhMCX\nX36JtLQ0xMXF1XmQPuv2iIDqbg0AWAggIiLnY07gXrcaBQCw1yPAPIKQhQAiIqpOtZ8ef/zxR8yf\nPx+pqanQ6/WIiYlB69atERQUhNLSUhQXF6OwsBAqlQovv/wyfvzxR1fE7RtqUAjQK83fDnD6QCIi\ncjbmBO5V1sgfACBsTIRh9GMhgIiIaqbaK0WTJk0wd+5c6HQ6rFu3Dl27dsW1a9dw4sQJlJaWolu3\nbli3bh10Oh3mzp2LJk2auCJu31CDHgEcEUBERK7CnMC9bjXyh0kuA2xMiWmUbg3glJlERGRfjT89\n+vv7IyUlBSkpKc6MhypzoEcAZw0gIiJXYU7gHrcaBdi8LQC40yOAswYQUb31pbsDqD8cGjvWp08f\nzJo1y2L5lStX0Ldv3zoLim5TqfC7331sFkhERB6HOYHr3Wrkb7cQwB4BRERUUw59etyzZw8OHz6M\nI0eOYO3atQgMDAQA6PV6TiPkJNcCGtnvEaC43SOAhQAiInIh5gSuV20hgD0CiIiohhy+Unz55Zc4\nc+YMevbsiV9++cUZMVEl1wIa2e8RwBEBRETkJswJXKusUQBMCnsjAjh9IBER1YzDVwq1Wo2vv/4a\n7dq1Q9euXbF//35nxEW3VTcigIUAIiJyF+YErlXdiAATbw0gIqIaqtWVIjAwEP/617/w0ksvIT4+\nHjk5OXUdF93234CGNbs1gNMHEhGRGzAncJ2yhrw1gIiI6katPz3KZDLMmDEDGo0Gzz33XF3GRJXU\n9NaAimkEiYiIXI05gWvcauQPobD9Id8ozRrAQgAREdnnUCFg165daNy4cZVlw4YNw6OPPooDBw7U\naWBkdi2wmlsDbhcAOCKAiIhciTmB61U/fSBHBBARUc049OkxPj7e6vKIiAhERETURTx0F/YIICKf\n9h93B0C2MCdwvep6BAi5DCa5jIUAIiKqFj89erhPOqXCKLN9a4BeyekDiYiIfMG1sIbYPqeP3XWM\nfgoWAoiIqFr89Ojhvm/dze7z0q0BLAQQERHVayaVAgdGR9tdx6iSsxBARETV4pXCy/HWACIiIqpg\nUnFEABERVY9XCg8nXr/zY23ZvwbfnjVAwVkDiIiI6rM5mFXlx9pyo0rOWQOIiGrhypUrSExMRFRU\nFPr374+rV69aXW/16tXQaDTQaDRYs2aNtPzAgQOIjo6GRqPBhAkTarTf8ePHQ6PRICYmBoWFhbU+\nBgB8+OGH6NixIyIjI/HGG29U+3p5pfB2fuwRQERERGbsEUBEVDszZszAwIEDceTIESQlJWHGjBkW\n65w7dw7vvPMOCgoKUFBQgFmzZuHChQsAgIyMDKxatQpHjx7F2bNnsWnTJrv73bBhA4qLi3H06FGs\nXLkSGRkZtT5Gbm4utm3bhgMHDuCHH37AlClTqn29vFJ4O5V5JIBRbruhIBEREfkGI28NICKqla1b\ntyI9PR0AMGrUKOTm5lqss2PHDiQlJSEoKAhBQUEYMGAAtm/fjuLiYphMJkRHR1tsb2u/ubm50vLo\n6GgYDAZotdpaHWP58uWYPHkylErzl8NNmzat9vXya2Rvp1LBIFcAMtvTCREREZFvMLFZIBH5Km0+\noMuv9eYXL16UPkAHBwdL38JXptPpoFarpcdqtRparRY6nQ5hYWHS8tDQUGi1Wrv7vXubyvty9BjH\njx/Htm3bMG7cOAQGBmLBggXo0aOH3dfLQoC38/PjbQFEREQEgLcGEJEPU8ebfyp8l2mxSmJiIs6f\nP2+xfM6cOc6Lyw4hRJ3sx2QyobS0FIcOHcL333+PoUOH4uzZs5DZ+bKYnyC9nUrFQgAREREB4PSB\nRFTPfXlvm+/YscPmc82aNcOlS5cQHByMixcvIiQkxGIdtVqNgoIC6XFJSQl69OgBtVqNkpISablW\nq5W+vbe134ptYmNjq2xTm2OEhYXh6aefBgB07doVfn5++PXXX9GiRQubr5dXCm+nUnHGACKiWvKG\nDsF6vR4vvfQSoqKiEB4ejg0bNtTlKaB6xqhSwKhi3yAiIkclJycjOzsbAJCdnY3k5GSLdRISEpCX\nl4fS0lKUlpYiLy8PCQkJCAsLg1wul67r69atQ1JSkt39JicnY926dQCAgwcPQqFQIDQ0FP369XP4\nGAMHDsSuXbsAACdOnMCNGzesFjIqYyHA23FEABFRrXlDh+Bp06ahbdu2OHLkCH788Uf07dvXFaeG\nvBR7BBAR1U5mZiZyc3MRFRWFL774ArNmmadpPXDgAMaMGQMAaNmyJaZNm4bY2FjExsZi+vTpaN68\nOQAgKysLL7zwAjQaDVq3bi19Q29rv0OHDkVoaCg0Gg1Gjx6NrKwsAECrVq0cPsbYsWNx6tQpRERE\n4Omnn8bHH38Mudz+tcDjPkG+/vrryMvLAwA8+OCDWL16NZo2bYozZ84gPDwcjz76KAAgLi4O//jH\nPwCY/+eMHj0aer0eCQkJWLhwodvidzn2CCAiqrWtW7fiu+++A2Duvtu9e3eLa0jl7r0ApO69vXv3\nttq9d8iQITb3a6tD8K5du6weIy0tDevXr8eZM2ekeBo3buzUc+JJmBM4ztwjgA2EiYgc1aRJE6u3\nDnTu3BnLly+XHmdkZEiF/LvXqzzSr7r9AsDixYutLnf0GCqVCmvXrrW6L1s8rmSckpKCoqIiHDt2\nDBEREZg9e7b0XLt27VBYWIjCwkLpgg/Y/kbGJ3BEABH5sov5wI8z7/w4urmHdwi+cOECAgICMG7c\nOERERGDQoEFWmxzVV8wJHMfpA4mIqCY87krRp08faRjDY489Bp1OZ3d9e/Mp+oSwMEwfMMvdURAR\nuUezeCB85p0fKxITExEZGWnx89lnn7kw0Dtq2iFYJpPBZDLh9OnTeOKJJ1BUVIQ+ffpU6UVQ3zEn\ncFzB/3SGtmsrd4dBREQezqO/Sl62bBlGjBghPT5z5gw6deqE++67D7Nnz0bfvn2rdEsEqn4j4xP8\n/PBxN8thI0RE9cLX974Lb+4Q3KxZMwQEBEj3AA4bNgxLliy5h7PhvZgT1MxPgx5xdwhEROQF3FII\nsDV/49y5c5GSkgLAPJejn58f0tLSAJibJuh0OjRs2BCFhYV48skncfToUYeOO3PmTOn3+Ph4xMfH\n1/o1EBH5svz8fOTn57s7jHtW0cl34sSJdjsEZ2ZmorS0FACQl5cnNe6p6N4bHR2NdevW4dlnn7W7\n34rlw4YNs+gQbO0YSqUSiYmJ2L17N/r06YOdO3ciPDzcRWfHNZgTEBF5t/qSE/gatxQC7H07A5in\nUMrNzZWmQAAAPz8/+Pn5ATA3WIqIiMBPP/2EsLAwi29kKt9nWVnliz4REdXe3R+cMjMz3RfMPcjM\nzERqaipWrVqFFi1aICcnB4C54dySJUuwfPnyKh2CAVjt3qvX69GvX78qHYKt7Xfo0KHYvXs3NBoN\n/P39rXYIvvsYH374IdLT0zFu3Dg0btzY4WZAno45ARGRd6svOYGv8bhbA/Ly8jBv3jzs2bMHAQEB\n0vIrV67g/vvvh1wux5kzZ1BUVIR27dohODjY5jcyRERE9nhDh+DWrVtjz549Nl9DfcacgIiIyDk8\nrhAwbtw46PV6JCYmArgzJdCuXbswc+ZMyOVyCCGwaNEiBAcHA7D9jQwRERF5L+YEREREzuFxhYCf\nf/7Z6vJhw4Zh2LBhVp+z9Y0MEREReS/mBERERM7hcdMHEhEREREREZHzsBBARERERERE5ENYCCAi\nIiIiIiLyISwEEBEREREREfkQj2sWSERERERERGThx/+6O4J6gyMCiIiIiIiIiHwICwFERERERERE\nPoSFACIiIiIiIiIfwh4BRETkuS5p3R0BERERUb0jE0IIdwfhCjKZDD7yUuuc7K9VH4vXra9jbTkR\n+QZnvMfKZDIAJQ5uFcb3eqoWc4J7Mw3Tpd/nYJbN5609R0T1n7PeY815wTUHtmjE93o7eGsAERER\nERERkQ9hIYCIiIiIiIjIh7AQQERERERERORDWAggIiIiIiIi8iEsBBARERERERH5EBYCiIiIiIiI\niHwICwFERERERETk065cuYLExERERUWhf//+uHr1qtX1Vq9eDY1GA41GgzVr1kjLDxw4gOjoaGg0\nGkyYMKFG+x0/fjw0Gg1iYmJQWFhY62Ps378fMTExiIiIwKBBg1BaWlrt62UhgIiIiIiIiHzajBkz\nMHDgQBw5cgRJSUmYMWOGxTrnzp3DO++8g4KCAhQUFGDWrFm4cOECACAjIwOrVq3C0aNHcfbsWWza\ntMnufjds2IDi4mIcPXoUK1euREZGRq2PkZGRgYULF6KoqAhDhgxBZmZmta+XhQAiIvJZnlT9HzBg\nABo3boyUlJQqxx4xYgQ6dOiARx99FOnp6SgrK6url09ERES3bd26Fenp6QCAUaNGITc312KdHTt2\nICkpCUFBQQgKCsKAAQOwfft2FBcXw2QyITo62mJ7W/vNzc2VlkdHR8NgMECr1dbqGCdPnkSvXr0A\nAAkJCfjss8+qfb0sBBARkc/ylOo/APzlL3/B2rVrLY4/evRoHDt2DD/99BOMRiOWLFnijFNBRETk\n0y5evIimTZsCAIKDg6VrfWU6nQ5qtVp6rFarodVqodPpEBYWJi0PDQ2FVqu1u9+7t6m8L0ePER4e\nji1btgAA/vd//xfFxcXVvl4WAoiIyGd5SvUfAPr27YugoCCL4yckJEi/x8XFQafT1dXLJyIiqke+\nBvBupR9LiYmJiIyMtPipyTfoziCEqJP9rFmzBn/7298QGRmJy5cvw9/fv9ptlHVyZCIiIi/kKdX/\nyvu3pby8HB9//DEWLVpUi1dKRERUH3xezfMRdp/dsWOHzeeaNWuGS5cuITg4GBcvXkRISIjFOmq1\nGgUFBdLjkpIS9OjRA2q1GiUlJdJyrVYrXe9t7bdim9jY2CrbOHKMivxBo9Fg9+7dAIAzZ85g69at\nds8DwBEBRETk1b4F8NdKP5Y8vfovk8lqtN1rr72Gxx9/HI899pgzwiIiIvJpycnJyM7OBgBkZ2cj\nOTnZYp2EhATk5eWhtLQUpaWlyMvLQ0JCAsLCwiCXy6XeP+vWrUNSUpLd/SYnJ2PdunUAgIMHD0Kh\nUCA0NBT9+vWr8TEq9nX58mUA5hxj7ty5GD16dLWvlyMCiIjIg31Zg3Wi7D7r6dX/yqMBbBUFMjMz\ncenSJSxbtszuayUiIqLayczMRGpqKlatWoUWLVogJycHgLkx8JIlS7B8+XK0bNkS06ZNk67j06dP\nR/PmzQEAWVlZeOGFF6DX69GvXz88/fTTdvc7dOhQ7N69GxqNBv7+/sjKygIAtGrVyuFjZGdnY8mS\nJVAqlUhJScFrr71W7euVibq6McHDyWSyOrsHw9fI7vqSTbxufR1ry4nINzjjPdb8oTjLwa0yHIpj\n3LhxeOihhzBx4kR88MEHOH36tMXQ+3PnzqFXr15SBb5Tp07Yu3cvmjdvjqioKKxevRrR0dEYPHgw\nnn32WTz99NM297thwwZkZ2dj06ZNOHjwIDIyMnD48GHpWPn5+ViwYAE+//zO0McVK1YgKysLO3fu\nREBAgIPng6xhTnBvpmG69PsczLL5vLXniKj+c9Z7rDkvyHZgi1F8r7eDIwKIiMhneUr1HwB69eqF\n48eP4/r16wgLC8OqVauQmJiIV199FW3atEFcXJy0j7feestl54iIiIjqH44IoGpxRAARVcdbRwSQ\nb2JOcG84IoCI7OGIAO/AZoFEREREREREPoSFACIiIiIiIiIfwkIAERERERERkQ9hIYCIiIiIiIjI\nh7AQQERERERERORDWAggIiIiIiIi8iEeVwh466230LFjR0RERKB37944deqU9Nz48eOh0WgQExOD\nwsJCafnq1auh0Wig0WiwZs0ad4TtNPn5+e4OodYYu+t5a9wAY3cHb42bfAdzAkve+u/WW+MGGLs7\neGvcAGMn7+FxhYApU6bg8OHDKCoqwvDhw5GZmQkA2LBhA4qLi3H06FGsXLkSGRkZAIBz587hnXfe\nQUFBAQoKCjBr1iz8+uuv7nwJdcqb/0Eydtfz1rgBxu4O3ho3+Q7mBJa89d+tt8YNMHZ38Na4AcZO\n3sPjCgFBQUHS79evX0fLli0BALm5uUhPTwcAREdHw2AwQKvVYseOHUhKSkJQUBCCgoIwYMAA7Nix\nwy2xExERUd1hTkBEROQcSncHYM20adOwdu1aBAYG4rvvvgMA6HQ6hIWFSeuo1WpotVrodDqo1WqL\n5UREROT9mBMQERHVPZkQQrj6oImJiTh//rzF8rlz5yIlJUV6/N577+H48ePIyspC//79MWvWLMTG\nxgIABgwYgJkzZ2L37t2Qy+WYPHmytA1gHk5YmUwmc9bLISIiAHV9OanN+3bjxo1x5cqVOo2DnIs5\nARFR/eOMj5iOvnczJ7DPLSMCajpMb+TIkXjiiScAmKv6JSUl0kVfq9UiLCwMarUaBQUF0jYlJSXo\n0aOHxb7cUO8gIqJ7wPdt38CcgIiIaoLv3XXL43oEnD59Wvp9y5YtiIyMBAAkJydj3bp1AICDBw9C\noVAgNDQU/fr1Q15eHkpLS1FaWoq8vDwkJCS4JXYiIiKqO8wJiIiInMPjegT86U9/wsmTJ1FeXo62\nbdtixYoVAIChQ4di9+7d0Gg08Pf3R1ZWFgCgVatWmDZtmvStwPTp09G8eXO3xU9ERER1gzkBERGR\nkwgf8MUXX4iIiAgRHh4u3nvvPXeHY9cDDzwgIiMjRadOnUTXrl2FEEJcvnxZJCQkiMjISPHEE0+I\n3377zc1RmmVkZIiQkBAREREhLbMX67hx40SHDh1EdHS0OHjwoDtClliLfcaMGSI0NFR06tRJdOrU\nSWzdulV6bu7cuSI8PFxERESIbdu2uSNkIYQQxcXFolevXiIiIkK0b99evP/++0II7zjvtmL39PN+\n8+ZN0aVLF9GpUyfx8MMPi4kTJwohhDh16pTo3r27iIiIEKmpqUKv1wshhLh165Z45plnREREhOjR\no4c4c+aMW+K2F/tzzz0n2rZtK53zQ4cOCSGEMJlMHvP3QuQs3pQTCOE9eQFzAtdjTuAe3poXMCeg\nu9X7QsCtW7dEmzZthFarFeXl5aJLly4e/Yfcpk0bcfny5SrLxo4dKz744AMhhBAffPCBGD9+vDtC\ns/DVV1+JgwcPVrlw2or1008/FU899ZQQQoiDBw+Kjh07uj7gSqzFPnPmTLFgwQKLdffv3y+6dOki\nDAaD0Gq1ok2bNqKsrMyV4UrOnz8vfvjhByGEEKWlpeLhhx8Whw4d8orzbit2bzjvN27cEEIIUV5e\nLmJjY8WuXbvEk08+KTZt2iSEEGLChAnir3/9qxBCiPnz54sJEyYIIYTYtGmTGDRokFtirmAt9uef\nf15s2LDBYl1P+nshcgZvywmE8J68gDmB6zEncM95F8J78wLmBFSZx/UIqGsFBQXQaDQIDQ2FUqlE\namoqcnNz3R2WXeKuRhhbt26V5kseNWqUx8Tfq1cvNG7cuMoyW7HamvPZXazFDlhvQpKbm4sRI0ZI\n96BqNBppCitXa968OSIiIgCY59eOioqCTqfzivNuK3bA8897YGAgAECv18NoNCIkJAT79u3D4MGD\nAVQ955X/XwwaNAh79+51a3Mba7ED1s955djd/fdC5AzemBMA3pEXMCdwPeYE7jnvgPfmBcwJqLJ6\nXwio6CRcwdPnFJbJZEhMTERUVBQWL14MALh48SKaNm0KAAgODsaFCxfcGaJdtmK1Neezp/n73/+O\n8PBwjBo1SppuxFPnpT5z5gy+//579OzZ0+vOe0XsvXr1AuD5591kMqFTp05o3rw5+vTpg8aNGyM4\nOFh6PjQ0VIqt8nuOXC5H06ZN3fpv9u7YNRoNAPPc7OHh4Rg7dizKysosYgc85++FqK5449+4N+cF\n3nZtupunX5sqY07gWt6aFzAnoMrqfSHA2+YK3rdvHw4ePIidO3ciKysLX375pbtDqjN3Vxs97f/N\na6+9hpMnT+LYsWN46KGHMH78eHeHZNP169cxbNgwLFy4EA0bNrS7rqed9+vXr2P48OFYuHAhGjRo\n4BXnXS6X49ChQ9Bqtfjqq6+Qn5/v7pBqzFrs77//Pn766SccPnwYN2/exDvvvCOt72l/L0R1yRv/\nnutrXuDp7zXecG2qwJzA9bw1L2BOQJXV+0JAxVzDFUpKSqpUtzxNxRCdZs2aYdiwYfj+++/RrFkz\nXLp0CYC5ul6xjieyFevd/x+0Wm2Vyq4nCA4Ohkwmg0wmw8svv4zvv/8egPXY3fk3VF5ejqFDhyIt\nLU0aguYt570i9pEjR0qxe8t5B4BGjRph4MCBOHXqlHS+garnVa1Wo7i4GIC58n758mU0a9bMLfFW\nVhH7vn37pL8PPz8/vPjii3bPuaf9OyW6F96WEwDenRd4y7XJGm+5NjEncC9vzQuYExDgA4WArl27\noqioCDqdDuXl5cjJyUFSUpK7w7Lqxo0buHHjBgDg999/R15eHjQaDZKTk5GdnQ0AyM7ORnJysjvD\ntMtWrLbmfPYklYdpbdiwQRoulZycjE8++US6N6qoqAjdunVzS4xCCLz44ovo0KEDJk2aJC33hvNu\nK3ZPP++XL19GaWkpAODmzZvYsWMHOnXqhO7du2Pz5s0ALM95xf+LLVu2IC4uDnK5e95qrcUeGRkp\nnXMhBDZu3FjlnHvK3wuRM3hTTgB4f17gDdcmWzz92gQwJ3DXeffWvIA5AVlwZWdCd9m6davQaDQi\nPDxczJ07193h2HTq1CkRFRUlOnbsKB5++GHx9ttvCyGqTgOTmJjoEdMECSHEiBEjRMuWLYVKpRJq\ntVqsWrXKbqyvvfaaNAXJgQMH3Bi5ZewrV64Uo0aNElFRUeLRRx8V/fv3F1qtVlp/zpw5Ijw8XGg0\nGpGXl+e2uL/++mshk8lEx44dpWlevvjiC68479Zi37p1q8ef9yNHjohOnTqJjh07ikceeURkZmYK\nIexPEzR8+HAREREh4uLixOnTp90St73Y+/TpIzp27Cjat28vUlNTxbVr16RtPOXvhchZvCUnEMK7\n8gLmBK7HnMA9vDUvYE5Ad5MJ4cZ21kRERERERETkUvX+1gAiIiIiIiIiuoOFACIiIiIiIiIfwkIA\nERERERERkQ9hIYCIiIiIiIjIh7AQQLDaTD4AAAdESURBVGTDxx9/jAYNGrjt+FeuXEHz5s1x6tQp\nm+vs378fcrlcmp/W2X799VcEBwdDq9W65HhERESegDmBJeYERN6NhQDySXK53O7PCy+8gD/+8Y84\nffq022KcP38+EhMT8eCDD7othrs1b94cI0eOxOzZs90dChERUZ1gTlA7zAmIvJvS3QEQucP58+el\n3z///HOMGTOmyrLAwED4+/vD39/fHeGhrKwMy5YtwyeffOKW49vz7LPP4vHHH8e8efPQsGFDd4dD\nRER0T5gT1B5zAiLvxREB5JNCQkKkn0aNGlksa9CggcUwwJkzZyIyMhJr1qxBmzZtEBQUhBdeeAHl\n5eX48MMPERYWhqCgIIwdOxZCCGk7vV6PyZMnIywsDAEBAYiKisLmzZvtxrd9+3bo9Xr07du3yvK8\nvDw8+uijCAwMRO/evXHixIkqz1+5cgXDhw9Hy5Yt4efnh4ceegh///vfpefXrFmD4OBg6PX6Ktul\npaXhqaeeAgCcOnUKSUlJaNCgAfz9/dG+fXv885//lNbt0qUL7r//fnz22Wc1OdVEREQejTkBcwIi\nX8RCAJEDzpw5g7y8PHz55Zf497//jQ0bNmDgwIE4duwYvvrqK+Tk5GDFihXIycmRtsnIyMC+ffvw\n6aef4v/+7/8wduxYjBgxAt9++63N43z11VeIiYmBTCaTlh0/fhxPPfUUBg0ahJ9++gl/+ctfMGXK\nlCrr3Lp1C48//jh27dqFn3/+GW+++Sb+/Oc/4/PPPwcAPPPMMzCZTNiyZYu0zbVr17B582aMHj0a\nAPDyyy/DZDJh7969OHXqFD766CM0a9asSnzdunVDfn7+PZ1LIiIib8acwIw5AZF34q0BRA4QQmDl\nypUIDAxEu3btMHDgQOTn52Pbtm2QyWRo27YtevfujZ07dyI1NRUnT57Ev/71L+h0OrRo0QIA8NJL\nL2HHjh1YsWIF4uLirB7nxIkTaN26dZVly5cvxyOPPIJ58+YBAB544AH8+OOPmDx5srROq1atMHbs\nWOnx6NGjkZ+fj5ycHKSkpCAgIABpaWlYtWoVhg8fDgBYv349GjVqhIEDBwIAfvnlF4wYMQKRkZEA\ngNDQUIv4wsLCUFhYWNvTSERE5PWYE5gxJyDyTiwEEDnggQceQGBgoPS4SZMmaN++fZUKfJMmTXDt\n2jUAwMGDByGEwMMPP1xlP3q9Ho8//rjN4/z+++8WF9ujR48iNja2yrLu3btXeVxeXo7MzExs3LgR\n586dg8FggF6vR+/evaV1xowZg5iYGPzyyy9o1aoVVq1aheeeew5yuXmA0Lhx4zB+/Hhs27YN8fHx\nGDRoELp161blOA0bNkRpaanN+ImIiOo75gRmzAmIvBM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+ "text": [ + "" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Boundary layer depth and SST \n", + "\n", + "\n", + "figure(4)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "CS = plt.plot(time, -h,'-',linewidth=3)\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=18)\n", + "plt.ylabel(r'z(m) with $\\Delta z$ ='+deltaz,fontsize=18)\n", + "plt.title(r'KPP boundary layer depth for '+expt_name,fontsize=18)\n", + "plt.xlim((0, 400))\n", + "xticks = [0, 50, 100, 150, 200, 250, 300, 350, 400]\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "plt.ylim((-300, 0))\n", + "yticks = [-300, -250, -200, -150, -100, -50, 0]\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n", + "\n", + "figure(5)\n", + "fig, ax = plt.subplots(figsize=(16, 10), dpi=dpi)\n", + "CS = plt.plot(time, temp[:,0],'-',linewidth=3)\n", + "plt.grid()\n", + "plt.xlabel('Time (days)',fontsize=18)\n", + "plt.ylabel('SST(C)',fontsize=18)\n", + "plt.title(r'SST for '+expt_name,fontsize=18)\n", + "plt.xlim((0, 400))\n", + "xticks = [0, 50, 100, 150, 200, 250, 300, 350, 400]\n", + "ax.set_xticklabels([\"$%.1f$\" % x for x in xticks], fontsize=18);\n", + "plt.ylim((18, 28))\n", + "yticks = [18, 20, 22, 24, 26, 28]\n", + "ax.set_yticklabels([\"$%.1f$\" % y for y in yticks], fontsize=18)\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 8, + "text": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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tt9/Gyy+/jM8//9zpdgiCyDqQ+z1BEIQDvPfee4iLi8Ps2bNN7pG2WLBgAcqVKwc/Pz+E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\nsd/9+sl1S5Vy3srNmGvfPTE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9QTgCiXqC0IjeYpusWeqBrOmCrzZ+5q735mRES70zol7v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\n27dvV58+fbzG4+PjtW/fPhUUFAT1PATOaqDrhDydp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mjb/4ovPef9UqUdQCwIIF5tt4qenUKSAqSowLFhTT1Y0rpRMReQr2aBPZiL0y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\nrkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# u,v,temp\n", + "\n", + "plt.subplot(2,2,1)\n", + "data = u\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, levels=numpy.arange(-.10,.11,.01))\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title('u (m/s) for expt = '+expt_name,fontsize=16)\n", + "\n", + "plt.subplot(2,2,2)\n", + "data = v\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "plt.contourf(time, -zl, field.T, levels=numpy.arange(-.10,.11,.01))\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title('v (m/s) for expt = '+expt_name,fontsize=16)\n", + "\n", + "plt.subplot(2,2,3)\n", + "data = temp\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "CS = plt.contourf(time, -zl, field.T,levels=numpy.arange(18,30,1))\n", + "plt.colorbar()\n", + "#C = plt.contour(time, -zl, temp.T, 8,linewidth=.5, colors='black')\n", + "#plt.clabel(C, inline=1, fontsize=10)\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title(r'$\\Theta$ ($\\degree$C) for expt = '+expt_name,fontsize=16)\n", + "plt.xlabel('Time (days)',fontsize=14);\n", + "\n", + "\n", + "plt.subplot(2,2,4)\n", + "data = saln[:,:]\n", + "field = np.ma.masked_array(data, mask=[data==0.])\n", + "CS = plt.contourf(time, -zl, field.T,levels=numpy.arange(36.3,37,.01))\n", + "#CS = plt.contourf(time, -zl, field.T)\n", + "plt.colorbar()\n", + "plt.ylim((-100,0))\n", + "plt.ylabel('z (m)',fontsize=14)\n", + "plt.title(r'$S$ ($ppt$) for expt = '+expt_name,fontsize=16);\n", + "plt.xlabel('Time (days)',fontsize=14);\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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ZZ0XH//3vfzOO4wTnVkpGOVtczvletGiRSLbmzZuzyMhIduLECeF4Xl6eW13t27cvq1Gj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\nXuIc/UNEREQBij3fRERERERERD7Gnm8iIpKv6SMuxkZERER+8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"input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file From d8a1bfb953ca2b9f58177085ca47fed7a9c33cf6 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Wed, 10 May 2023 15:44:17 -0600 Subject: [PATCH 093/136] Update forcing for each column The python script that was making sure all four elements of the 2x2 arrays making up forcing data was setting the values to the wrong element - it was using [0,0] when the script to subset the global domain down to 2x2 was placing the location we wanted to run on at [1,1] --- .../INPUT/ARAB/IC_FILE_COLUMN.nc | Bin 134712 -> 125094 bytes .../INPUT/ARAB/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 20765 -> 27249 bytes .../ARAB/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 16739 -> 23208 bytes .../INPUT/ARAB/MARBL_FORCING_FILE_COLUMN.nc | Bin 3125511 -> 3125511 bytes .../INPUT/ARAB/TOPO_FILE_COLUMN.nc | Bin 11889 -> 11561 bytes .../INPUT/BATS/IC_FILE_COLUMN.nc | Bin 134712 -> 125094 bytes .../INPUT/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 20765 -> 27249 bytes .../BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 16739 -> 23208 bytes .../INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc | Bin 3125511 -> 3125511 bytes .../INPUT/BATS/TOPO_FILE_COLUMN.nc | Bin 11889 -> 11561 bytes .../INPUT/EQPAC/IC_FILE_COLUMN.nc | Bin 134712 -> 125094 bytes .../EQPAC/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 20765 -> 27249 bytes .../EQPAC/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 16739 -> 23208 bytes .../INPUT/EQPAC/MARBL_FORCING_FILE_COLUMN.nc | Bin 3125511 -> 3125511 bytes .../INPUT/EQPAC/TOPO_FILE_COLUMN.nc | Bin 11889 -> 11561 bytes .../INPUT/HOT/IC_FILE_COLUMN.nc | Bin 134712 -> 125094 bytes .../INPUT/HOT/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 20765 -> 27249 bytes .../INPUT/HOT/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 16739 -> 23208 bytes .../INPUT/HOT/MARBL_FORCING_FILE_COLUMN.nc | Bin 3125511 -> 3125511 bytes .../INPUT/HOT/TOPO_FILE_COLUMN.nc | Bin 11889 -> 11561 bytes .../INPUT/KERFIX/IC_FILE_COLUMN.nc | Bin 134712 -> 125094 bytes .../KERFIX/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 20765 -> 27249 bytes .../KERFIX/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 16739 -> 23208 bytes .../INPUT/KERFIX/MARBL_FORCING_FILE_COLUMN.nc | Bin 3125511 -> 3125511 bytes .../INPUT/KERFIX/TOPO_FILE_COLUMN.nc | Bin 11889 -> 11561 bytes .../INPUT/NABE/IC_FILE_COLUMN.nc | Bin 134712 -> 125094 bytes .../INPUT/NABE/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 20765 -> 27249 bytes .../NABE/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 16739 -> 23208 bytes .../INPUT/NABE/MARBL_FORCING_FILE_COLUMN.nc | Bin 3125511 -> 3125511 bytes .../INPUT/NABE/TOPO_FILE_COLUMN.nc | Bin 11889 -> 11561 bytes .../INPUT/STNP/IC_FILE_COLUMN.nc | Bin 134712 -> 125094 bytes .../INPUT/STNP/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 20765 -> 27249 bytes .../STNP/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 16739 -> 23208 bytes .../INPUT/STNP/MARBL_FORCING_FILE_COLUMN.nc | Bin 3125511 -> 3125511 bytes .../INPUT/STNP/TOPO_FILE_COLUMN.nc | Bin 11889 -> 11561 bytes 35 files changed, 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z#o~7V7;#(PQ@l1_?JaKqt843@D;#^=A16)TTCuHl`#;;(#{Bc!T5HYS{kv_gX3M7C z60vQqzi&k3Kewsn{{5TURl@zctoZu0$~8my5>|u%*fm32Rc*qJYW1Npi#5;6|K?Oo zDweKhY3NYVHfC9$Wn-32S+-!SoUQ}D$>EBB9E|i=*QYD9s092ONRlh&C+2YYqNB4WNnrX zPOQz+VGwJxbZ};EmJTkg&C+2oYqNA1!rCkyhO#zGhhePE(!rIrSvm}7ZIT4u z87zxgn!B@pmKiLIS(g`vVN8sEQ?v1k6`^QGgub0G#|-W@-Kd z>t~t4vY4g$DAvz1gJm&GbB^`1%wSo}(tI@QXPLpWn5Fp`*3UA7Wid#RY&=Uhmfl`PM&RIz-@Qq9sRlHEQ_E0!{r z&MY~Wfh^@LV_5Pmb6F}`o?)qC`IM!arO`BY`z)l(URs$+OI5sbqPE prHbWKmTHzp)7kB_v|=e^>CBR28OT!3GKMA3GMA;2<(cUY{{u8mF8crg literal 0 HcmV?d00001 diff --git a/standalone/examples/single_column/KPP_MARBL/data_table b/standalone/examples/single_column/KPP_MARBL/data_table index 36cb27c2..32cb124c 100644 --- a/standalone/examples/single_column/KPP_MARBL/data_table +++ b/standalone/examples/single_column/KPP_MARBL/data_table @@ -9,3 +9,7 @@ "OCN" , "snow", "snow", "./INPUT/BATS/forcing.nc", .true., 0.0 "OCN" , "runoff", "", "" , .true., 0.0 "OCN" , "calving", "", "" , .true., 0.0 +"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "u10_sqr", "U10_SQR", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 diff --git a/standalone/examples/single_column/KPP_MARBL/diag_table b/standalone/examples/single_column/KPP_MARBL/diag_table index c434a9d6..ef40b3eb 100644 --- a/standalone/examples/single_column/KPP_MARBL/diag_table +++ b/standalone/examples/single_column/KPP_MARBL/diag_table @@ -9,6 +9,7 @@ "visc", 1,"days",1,"days","Time", #"energy", 5,"days",1,"days","Time", "marbl", 1,"days",1,"days","Time", +"marbl2", 1,"days",1,"days","Time", #This is the field section of the diag_table. @@ -64,12 +65,17 @@ "ocean_model","DOPr","DOPr","prog","all",.false.,"none",2 "ocean_model","DONr","DONr","prog","all",.false.,"none",2 "ocean_model","DOCr","DOCr","prog","all",.false.,"none",2 -"ocean_model","zooC","zooC","prog","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog","all",.false.,"none",2 "ocean_model","spChl","spChl","prog","all",.false.,"none",2 "ocean_model","spC","spC","prog","all",.false.,"none",2 "ocean_model","spP","spP","prog","all",.false.,"none",2 "ocean_model","spFe","spFe","prog","all",.false.,"none",2 -"ocean_model","spCaCO3","spCaCO3","prog","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog","all",.false.,"none",2 "ocean_model","diatChl","diatChl","prog","all",.false.,"none",2 "ocean_model","diatC","diatC","prog","all",.false.,"none",2 "ocean_model","diatP","diatP","prog","all",.false.,"none",2 @@ -96,12 +102,17 @@ "ocean_model","DOPr","DOPr","ave_prog","all",.true.,"none",2 "ocean_model","DONr","DONr","ave_prog","all",.true.,"none",2 "ocean_model","DOCr","DOCr","ave_prog","all",.true.,"none",2 -"ocean_model","zooC","zooC","ave_prog","all",.true.,"none",2 +"ocean_model","microzooC","microzooC","ave_prog","all",.true.,"none",2 +"ocean_model","mesozooC","mesozooC","ave_prog","all",.true.,"none",2 "ocean_model","spChl","spChl","ave_prog","all",.true.,"none",2 "ocean_model","spC","spC","ave_prog","all",.true.,"none",2 "ocean_model","spP","spP","ave_prog","all",.true.,"none",2 "ocean_model","spFe","spFe","ave_prog","all",.true.,"none",2 -"ocean_model","spCaCO3","spCaCO3","ave_prog","all",.true.,"none",2 +"ocean_model","coccoChl","coccoChl","ave_prog","all",.true.,"none",2 +"ocean_model","coccoC","coccoC","ave_prog","all",.true.,"none",2 +"ocean_model","coccoP","coccoP","ave_prog","all",.true.,"none",2 +"ocean_model","coccoFe","coccoFe","ave_prog","all",.true.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","ave_prog","all",.true.,"none",2 "ocean_model","diatChl","diatChl","ave_prog","all",.true.,"none",2 "ocean_model","diatC","diatC","ave_prog","all",.true.,"none",2 "ocean_model","diatP","diatP","ave_prog","all",.true.,"none",2 @@ -128,12 +139,17 @@ "ocean_model","DOPr","DOPr","prog_z","all",.false.,"none",2 "ocean_model","DONr","DONr","prog_z","all",.false.,"none",2 "ocean_model","DOCr","DOCr","prog_z","all",.false.,"none",2 -"ocean_model","zooC","zooC","prog_z","all",.false.,"none",2 +"ocean_model","microzooC","microzooC","prog_z","all",.false.,"none",2 +"ocean_model","mesozooC","mesozooC","prog_z","all",.false.,"none",2 "ocean_model","spChl","spChl","prog_z","all",.false.,"none",2 "ocean_model","spC","spC","prog_z","all",.false.,"none",2 "ocean_model","spP","spP","prog_z","all",.false.,"none",2 "ocean_model","spFe","spFe","prog_z","all",.false.,"none",2 -"ocean_model","spCaCO3","spCaCO3","prog_z","all",.false.,"none",2 +"ocean_model","coccoChl","coccoChl","prog_z","all",.false.,"none",2 +"ocean_model","coccoC","coccoC","prog_z","all",.false.,"none",2 +"ocean_model","coccoP","coccoP","prog_z","all",.false.,"none",2 +"ocean_model","coccoFe","coccoFe","prog_z","all",.false.,"none",2 +"ocean_model","coccoCaCO3","coccoCaCO3","prog_z","all",.false.,"none",2 "ocean_model","diatChl","diatChl","prog_z","all",.false.,"none",2 "ocean_model","diatC","diatC","prog_z","all",.false.,"none",2 "ocean_model","diatP","diatP","prog_z","all",.false.,"none",2 @@ -360,16 +376,14 @@ "ocean_model", "photoC_sp_zint", "photoC_sp_zint", "marbl", "all", .false., "none", 2 "ocean_model", "photoC_sp_zint_100m", "photoC_sp_zint_100m", "marbl", "all", .false., "none", 2 "ocean_model", "photoC_NO3_sp_zint", "photoC_NO3_sp_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "sp_CaCO3_form_zint", "sp_CaCO3_form_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "sp_CaCO3_form_zint_100m", "sp_CaCO3_form_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp_zint", "graze_sp_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp_zint_100m", "graze_sp_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp_poc_zint", "graze_sp_poc_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp_poc_zint_100m", "graze_sp_poc_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp_doc_zint", "graze_sp_doc_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp_doc_zint_100m", "graze_sp_doc_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp_zoo_zint", "graze_sp_zoo_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp_zoo_zint_100m", "graze_sp_zoo_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint", "graze_sp_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zint_100m", "graze_sp_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint", "graze_sp_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc_zint_100m", "graze_sp_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint", "graze_sp_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc_zint_100m", "graze_sp_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint", "graze_sp_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zoo_zint_100m", "graze_sp_zoo_zint_100m", "marbl2", "all", .false., "none", 2 "ocean_model", "sp_loss_zint", "sp_loss_zint", "marbl", "all", .false., "none", 2 "ocean_model", "sp_loss_zint_100m", "sp_loss_zint_100m", "marbl", "all", .false., "none", 2 "ocean_model", "sp_loss_poc_zint", "sp_loss_poc_zint", "marbl", "all", .false., "none", 2 @@ -378,6 +392,35 @@ "ocean_model", "sp_loss_doc_zint_100m", "sp_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 "ocean_model", "sp_agg_zint", "sp_agg_zint", "marbl", "all", .false., "none", 2 "ocean_model", "sp_agg_zint_100m", "sp_agg_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_surf", "cocco_N_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_N_lim_Cweight_avg_100m", "cocco_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_surf", "cocco_P_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_P_lim_Cweight_avg_100m", "cocco_P_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_surf", "cocco_Fe_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Fe_lim_Cweight_avg_100m", "cocco_Fe_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_surf", "cocco_light_lim_surf", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_light_lim_Cweight_avg_100m", "cocco_light_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint", "photoC_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco_zint_100m", "photoC_cocco_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_cocco_zint", "photoC_NO3_cocco_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint", "cocco_CaCO3_form_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form_zint_100m", "cocco_CaCO3_form_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint", "graze_cocco_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zint_100m", "graze_cocco_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint", "graze_cocco_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc_zint_100m", "graze_cocco_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint", "graze_cocco_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc_zint_100m", "graze_cocco_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint", "graze_cocco_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zoo_zint_100m", "graze_cocco_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint", "cocco_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_zint_100m", "cocco_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint", "cocco_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc_zint_100m", "cocco_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint", "cocco_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc_zint_100m", "cocco_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg_zint", "cocco_agg_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg_zint_100m", "cocco_agg_zint_100m", "marbl", "all", .false., "none", 2 "ocean_model", "diat_N_lim_surf", "diat_N_lim_surf", "marbl", "all", .false., "none", 2 "ocean_model", "diat_N_lim_Cweight_avg_100m", "diat_N_lim_Cweight_avg_100m", "marbl", "all", .false., "none", 2 "ocean_model", "diat_P_lim_surf", "diat_P_lim_surf", "marbl", "all", .false., "none", 2 @@ -391,14 +434,14 @@ "ocean_model", "photoC_diat_zint", "photoC_diat_zint", "marbl", "all", .false., "none", 2 "ocean_model", "photoC_diat_zint_100m", "photoC_diat_zint_100m", "marbl", "all", .false., "none", 2 "ocean_model", "photoC_NO3_diat_zint", "photoC_NO3_diat_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat_zint", "graze_diat_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat_zint_100m", "graze_diat_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat_poc_zint", "graze_diat_poc_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat_poc_zint_100m", "graze_diat_poc_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat_doc_zint", "graze_diat_doc_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat_doc_zint_100m", "graze_diat_doc_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat_zoo_zint", "graze_diat_zoo_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat_zoo_zint_100m", "graze_diat_zoo_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat_zint", "graze_diat_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zint_100m", "graze_diat_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc_zint", "graze_diat_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc_zint_100m", "graze_diat_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc_zint", "graze_diat_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc_zint_100m", "graze_diat_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo_zint", "graze_diat_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zoo_zint_100m", "graze_diat_zoo_zint_100m", "marbl2", "all", .false., "none", 2 "ocean_model", "diat_loss_zint", "diat_loss_zint", "marbl", "all", .false., "none", 2 "ocean_model", "diat_loss_zint_100m", "diat_loss_zint_100m", "marbl", "all", .false., "none", 2 "ocean_model", "diat_loss_poc_zint", "diat_loss_poc_zint", "marbl", "all", .false., "none", 2 @@ -418,14 +461,14 @@ "ocean_model", "photoC_diaz_zint", "photoC_diaz_zint", "marbl", "all", .false., "none", 2 "ocean_model", "photoC_diaz_zint_100m", "photoC_diaz_zint_100m", "marbl", "all", .false., "none", 2 "ocean_model", "photoC_NO3_diaz_zint", "photoC_NO3_diaz_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz_zint", "graze_diaz_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz_zint_100m", "graze_diaz_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz_poc_zint", "graze_diaz_poc_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz_poc_zint_100m", "graze_diaz_poc_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz_doc_zint", "graze_diaz_doc_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz_doc_zint_100m", "graze_diaz_doc_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz_zoo_zint", "graze_diaz_zoo_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz_zoo_zint_100m", "graze_diaz_zoo_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zint", "graze_diaz_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zint_100m", "graze_diaz_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc_zint", "graze_diaz_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc_zint_100m", "graze_diaz_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc_zint", "graze_diaz_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc_zint_100m", "graze_diaz_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zoo_zint", "graze_diaz_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zoo_zint_100m", "graze_diaz_zoo_zint_100m", "marbl2", "all", .false., "none", 2 "ocean_model", "diaz_loss_zint", "diaz_loss_zint", "marbl", "all", .false., "none", 2 "ocean_model", "diaz_loss_zint_100m", "diaz_loss_zint_100m", "marbl", "all", .false., "none", 2 "ocean_model", "diaz_loss_poc_zint", "diaz_loss_poc_zint", "marbl", "all", .false., "none", 2 @@ -436,24 +479,42 @@ "ocean_model", "diaz_agg_zint_100m", "diaz_agg_zint_100m", "marbl", "all", .false., "none", 2 "ocean_model", "CaCO3_form_zint", "CaCO3_form_zint", "marbl", "all", .false., "none", 2 "ocean_model", "CaCO3_form_zint_100m", "CaCO3_form_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "zoo_loss_zint", "zoo_loss_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "zoo_loss_zint_100m", "zoo_loss_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "zoo_loss_basal_zint", "zoo_loss_basal_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "zoo_loss_basal_zint_100m", "zoo_loss_basal_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "zoo_loss_poc_zint", "zoo_loss_poc_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "zoo_loss_poc_zint_100m", "zoo_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "zoo_loss_doc_zint", "zoo_loss_doc_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "zoo_loss_doc_zint_100m", "zoo_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo_zint", "graze_zoo_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo_zint_100m", "graze_zoo_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo_poc_zint", "graze_zoo_poc_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo_poc_zint_100m", "graze_zoo_poc_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo_doc_zint", "graze_zoo_doc_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo_doc_zint_100m", "graze_zoo_doc_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo_zoo_zint", "graze_zoo_zoo_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo_zoo_zint_100m", "graze_zoo_zoo_zint_100m", "marbl", "all", .false., "none", 2 -"ocean_model", "x_graze_zoo_zint", "x_graze_zoo_zint", "marbl", "all", .false., "none", 2 -"ocean_model", "x_graze_zoo_zint_100m", "x_graze_zoo_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_zint", "microzoo_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_zint", "mesozoo_loss_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_zint_100m", "microzoo_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_zint_100m", "mesozoo_loss_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_basal_zint", "microzoo_loss_basal_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_basal_zint", "mesozoo_loss_basal_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_basal_zint_100m", "microzoo_loss_basal_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_basal_zint_100m", "mesozoo_loss_basal_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_poc_zint", "microzoo_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_poc_zint", "mesozoo_loss_poc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_poc_zint_100m", "microzoo_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_poc_zint_100m", "mesozoo_loss_poc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_doc_zint", "microzoo_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_doc_zint", "mesozoo_loss_doc_zint", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_doc_zint_100m", "microzoo_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_doc_zint_100m", "mesozoo_loss_doc_zint_100m", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_zint", "graze_microzoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_zint", "graze_mesozoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_zint_100m", "graze_microzoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_zint_100m", "graze_mesozoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_poc_zint", "graze_microzoo_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_poc_zint", "graze_mesozoo_poc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_poc_zint_100m", "graze_microzoo_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_poc_zint_100m", "graze_mesozoo_poc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_doc_zint", "graze_microzoo_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_doc_zint", "graze_mesozoo_doc_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_doc_zint_100m", "graze_microzoo_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_doc_zint_100m", "graze_mesozoo_doc_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_zoo_zint", "graze_microzoo_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_zoo_zint", "graze_mesozoo_zoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_zoo_zint_100m", "graze_microzoo_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_zoo_zint_100m", "graze_mesozoo_zoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_microzoo_zint", "x_graze_microzoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_mesozoo_zint", "x_graze_mesozoo_zint", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_microzoo_zint_100m", "x_graze_microzoo_zint_100m", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_mesozoo_zint_100m", "x_graze_mesozoo_zint_100m", "marbl2", "all", .false., "none", 2 "ocean_model", "insitu_temp", "insitu_temp", "marbl", "all", .false., "none", 2 "ocean_model", "CO3", "CO3", "marbl", "all", .false., "none", 2 "ocean_model", "HCO3", "HCO3", "marbl", "all", .false., "none", 2 @@ -471,7 +532,7 @@ "ocean_model", "O2_CONSUMPTION", "O2_CONSUMPTION", "marbl", "all", .false., "none", 2 "ocean_model", "AOU", "AOU", "marbl", "all", .false., "none", 2 "ocean_model", "PAR_avg", "PAR_avg", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_auto_TOT", "graze_auto_TOT", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_auto_TOT", "graze_auto_TOT", "marbl2", "all", .false., "none", 2 "ocean_model", "photoC_TOT", "photoC_TOT", "marbl", "all", .false., "none", 2 "ocean_model", "photoC_NO3_TOT", "photoC_NO3_TOT", "marbl", "all", .false., "none", 2 "ocean_model", "DOC_prod", "DOC_prod", "marbl", "all", .false., "none", 2 @@ -542,16 +603,35 @@ "ocean_model", "photoNH4_sp", "photoNH4_sp", "marbl", "all", .false., "none", 2 "ocean_model", "DOP_sp_uptake", "DOP_sp_uptake", "marbl", "all", .false., "none", 2 "ocean_model", "PO4_sp_uptake", "PO4_sp_uptake", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp", "graze_sp", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp_poc", "graze_sp_poc", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp_doc", "graze_sp_doc", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp_zootot", "graze_sp_zootot", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_sp_zoo", "graze_sp_zoo", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_sp", "graze_sp", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_poc", "graze_sp_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_doc", "graze_sp_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_zootot", "graze_sp_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_microzoo", "graze_sp_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_sp_mesozoo", "graze_sp_mesozoo", "marbl2", "all", .false., "none", 2 "ocean_model", "sp_loss", "sp_loss", "marbl", "all", .false., "none", 2 "ocean_model", "sp_loss_poc", "sp_loss_poc", "marbl", "all", .false., "none", 2 "ocean_model", "sp_loss_doc", "sp_loss_doc", "marbl", "all", .false., "none", 2 "ocean_model", "sp_agg", "sp_agg", "marbl", "all", .false., "none", 2 -"ocean_model", "sp_CaCO3_form", "sp_CaCO3_form", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_Qp", "cocco_Qp", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_cocco", "photoC_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "photoC_NO3_cocco", "photoC_NO3_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "photoFe_cocco", "photoFe_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNO3_cocco", "photoNO3_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "photoNH4_cocco", "photoNH4_cocco", "marbl", "all", .false., "none", 2 +"ocean_model", "DOP_cocco_uptake", "DOP_cocco_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "PO4_cocco_uptake", "PO4_cocco_uptake", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_cocco", "graze_cocco", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_poc", "graze_cocco_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_doc", "graze_cocco_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_zootot", "graze_cocco_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_microzoo", "graze_cocco_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_cocco_mesozoo", "graze_cocco_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "cocco_loss", "cocco_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_poc", "cocco_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_loss_doc", "cocco_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_agg", "cocco_agg", "marbl", "all", .false., "none", 2 +"ocean_model", "cocco_CaCO3_form", "cocco_CaCO3_form", "marbl", "all", .false., "none", 2 "ocean_model", "diat_Qp", "diat_Qp", "marbl", "all", .false., "none", 2 "ocean_model", "photoC_diat", "photoC_diat", "marbl", "all", .false., "none", 2 "ocean_model", "photoC_NO3_diat", "photoC_NO3_diat", "marbl", "all", .false., "none", 2 @@ -560,11 +640,12 @@ "ocean_model", "photoNH4_diat", "photoNH4_diat", "marbl", "all", .false., "none", 2 "ocean_model", "DOP_diat_uptake", "DOP_diat_uptake", "marbl", "all", .false., "none", 2 "ocean_model", "PO4_diat_uptake", "PO4_diat_uptake", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat", "graze_diat", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat_poc", "graze_diat_poc", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat_doc", "graze_diat_doc", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat_zootot", "graze_diat_zootot", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diat_zoo", "graze_diat_zoo", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diat", "graze_diat", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_poc", "graze_diat_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_doc", "graze_diat_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_zootot", "graze_diat_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_microzoo", "graze_diat_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diat_mesozoo", "graze_diat_mesozoo", "marbl2", "all", .false., "none", 2 "ocean_model", "diat_loss", "diat_loss", "marbl", "all", .false., "none", 2 "ocean_model", "diat_loss_poc", "diat_loss_poc", "marbl", "all", .false., "none", 2 "ocean_model", "diat_loss_doc", "diat_loss_doc", "marbl", "all", .false., "none", 2 @@ -578,11 +659,12 @@ "ocean_model", "photoNH4_diaz", "photoNH4_diaz", "marbl", "all", .false., "none", 2 "ocean_model", "DOP_diaz_uptake", "DOP_diaz_uptake", "marbl", "all", .false., "none", 2 "ocean_model", "PO4_diaz_uptake", "PO4_diaz_uptake", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz", "graze_diaz", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz_poc", "graze_diaz_poc", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz_doc", "graze_diaz_doc", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz_zootot", "graze_diaz_zootot", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_diaz_zoo", "graze_diaz_zoo", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_diaz", "graze_diaz", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_poc", "graze_diaz_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_doc", "graze_diaz_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_zootot", "graze_diaz_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_microzoo", "graze_diaz_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_diaz_mesozoo", "graze_diaz_mesozoo", "marbl2", "all", .false., "none", 2 "ocean_model", "diaz_loss", "diaz_loss", "marbl", "all", .false., "none", 2 "ocean_model", "diaz_loss_poc", "diaz_loss_poc", "marbl", "all", .false., "none", 2 "ocean_model", "diaz_loss_doc", "diaz_loss_doc", "marbl", "all", .false., "none", 2 @@ -591,16 +673,28 @@ "ocean_model", "bSi_form", "bSi_form", "marbl", "all", .false., "none", 2 "ocean_model", "CaCO3_form", "CaCO3_form", "marbl", "all", .false., "none", 2 "ocean_model", "Nfix", "Nfix", "marbl", "all", .false., "none", 2 -"ocean_model", "zoo_loss", "zoo_loss", "marbl", "all", .false., "none", 2 -"ocean_model", "zoo_loss_basal", "zoo_loss_basal", "marbl", "all", .false., "none", 2 -"ocean_model", "zoo_loss_poc", "zoo_loss_poc", "marbl", "all", .false., "none", 2 -"ocean_model", "zoo_loss_doc", "zoo_loss_doc", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo", "graze_zoo", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo_poc", "graze_zoo_poc", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo_doc", "graze_zoo_doc", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo_zootot", "graze_zoo_zootot", "marbl", "all", .false., "none", 2 -"ocean_model", "graze_zoo_zoo", "graze_zoo_zoo", "marbl", "all", .false., "none", 2 -"ocean_model", "x_graze_zoo", "x_graze_zoo", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss", "microzoo_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss", "mesozoo_loss", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_basal", "microzoo_loss_basal", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_basal", "mesozoo_loss_basal", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_poc", "microzoo_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_poc", "mesozoo_loss_poc", "marbl", "all", .false., "none", 2 +"ocean_model", "microzoo_loss_doc", "microzoo_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "mesozoo_loss_doc", "mesozoo_loss_doc", "marbl", "all", .false., "none", 2 +"ocean_model", "graze_microzoo", "graze_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo", "graze_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_poc", "graze_microzoo_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_poc", "graze_mesozoo_poc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_doc", "graze_microzoo_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_doc", "graze_mesozoo_doc", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_zootot", "graze_microzoo_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_zootot", "graze_mesozoo_zootot", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_microzoo", "graze_microzoo_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_microzoo_mesozoo", "graze_microzoo_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_microzoo", "graze_mesozoo_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "graze_mesozoo_mesozoo", "graze_mesozoo_mesozoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_microzoo", "x_graze_microzoo", "marbl2", "all", .false., "none", 2 +"ocean_model", "x_graze_mesozoo", "x_graze_mesozoo", "marbl2", "all", .false., "none", 2 #"ocean_model", "PO4_RESTORE_TEND", "PO4_RESTORE_TEND", "marbl", "all", .false., "none", 2 #"ocean_model", "NO3_RESTORE_TEND", "NO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 #"ocean_model", "SiO3_RESTORE_TEND", "SiO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 @@ -618,12 +712,17 @@ #"ocean_model", "DOPr_RESTORE_TEND", "DOPr_RESTORE_TEND", "marbl", "all", .false., "none", 2 #"ocean_model", "DONr_RESTORE_TEND", "DONr_RESTORE_TEND", "marbl", "all", .false., "none", 2 #"ocean_model", "DOCr_RESTORE_TEND", "DOCr_RESTORE_TEND", "marbl", "all", .false., "none", 2 -#"ocean_model", "zooC_RESTORE_TEND", "zooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "microzooC_RESTORE_TEND", "microzooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "mesozooC_RESTORE_TEND", "mesozooC_RESTORE_TEND", "marbl", "all", .false., "none", 2 #"ocean_model", "spChl_RESTORE_TEND", "spChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 #"ocean_model", "spC_RESTORE_TEND", "spC_RESTORE_TEND", "marbl", "all", .false., "none", 2 #"ocean_model", "spP_RESTORE_TEND", "spP_RESTORE_TEND", "marbl", "all", .false., "none", 2 #"ocean_model", "spFe_RESTORE_TEND", "spFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 -#"ocean_model", "spCaCO3_RESTORE_TEND", "spCaCO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoChl_RESTORE_TEND", "coccoChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoC_RESTORE_TEND", "coccoC_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoP_RESTORE_TEND", "coccoP_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoFe_RESTORE_TEND", "coccoFe_RESTORE_TEND", "marbl", "all", .false., "none", 2 +#"ocean_model", "coccoCaCO3_RESTORE_TEND", "coccoCaCO3_RESTORE_TEND", "marbl", "all", .false., "none", 2 #"ocean_model", "diatChl_RESTORE_TEND", "diatChl_RESTORE_TEND", "marbl", "all", .false., "none", 2 #"ocean_model", "diatC_RESTORE_TEND", "diatC_RESTORE_TEND", "marbl", "all", .false., "none", 2 #"ocean_model", "diatP_RESTORE_TEND", "diatP_RESTORE_TEND", "marbl", "all", .false., "none", 2 diff --git a/standalone/examples/single_column/KPP_MARBL/marbl_in b/standalone/examples/single_column/KPP_MARBL/marbl_in index e69de29b..f4c8add0 100644 --- a/standalone/examples/single_column/KPP_MARBL/marbl_in +++ b/standalone/examples/single_column/KPP_MARBL/marbl_in @@ -0,0 +1,265 @@ +f_graze_CaCO3_remin = 0.5 +QCaCO3_max = 1.0 + +zoo_mort2_exp = 2.0 + +PFT_defaults = 'user-specified' +autotroph_cnt = 4 +zooplankton_cnt = 2 +max_grazer_prey_cnt = 4 + +! autotrophs +autotroph_settings(1)%Ea = 0.32 +autotroph_settings(1)%Nfixer = .false. +autotroph_settings(1)%PCref_per_day = 4.8 +autotroph_settings(1)%Qp_fixed = 8.5470085470085479e-03 +autotroph_settings(1)%agg_rate_max = 0.5 +autotroph_settings(1)%agg_rate_min = 0.01 +autotroph_settings(1)%alphaPI_per_day = 0.35 +autotroph_settings(1)%exp_calcifier = .false. +autotroph_settings(1)%gQfe_0 = 3.0000000000000001e-05 +autotroph_settings(1)%gQfe_min = 2.5e-06 +autotroph_settings(1)%imp_calcifier = .false. +autotroph_settings(1)%is_carbon_limited = .false. +autotroph_settings(1)%kCO2 = 0 +autotroph_settings(1)%kDOP = 0.3 +autotroph_settings(1)%kFe = 3e-05 +autotroph_settings(1)%kNH4 = 0.01 +autotroph_settings(1)%kNO3 = 0.2 +autotroph_settings(1)%kPO4 = 0.01 +autotroph_settings(1)%kSiO3 = 0 +autotroph_settings(1)%lname = "Small Phyto" +autotroph_settings(1)%loss_poc = 0 +autotroph_settings(1)%loss_thres = 0.01 +autotroph_settings(1)%loss_thres2 = 0 +autotroph_settings(1)%mort2_per_day = 0.01 +autotroph_settings(1)%mort_per_day = 0.1 +autotroph_settings(1)%silicifier = .false. +autotroph_settings(1)%sname = "sp" +autotroph_settings(1)%temp_func_form_opt = "q_10" +autotroph_settings(1)%temp_thres = -10 +autotroph_settings(1)%thetaN_max = 2.5 +autotroph_settings(2)%Ea = 0.32 +autotroph_settings(2)%Nfixer = .false. +autotroph_settings(2)%PCref_per_day = 5.3 +autotroph_settings(2)%Qp_fixed = 8.5470085470085479e-03 +autotroph_settings(2)%agg_rate_max = 0.5 +autotroph_settings(2)%agg_rate_min = 0.02 +autotroph_settings(2)%alphaPI_per_day = 0.39 +autotroph_settings(2)%exp_calcifier = .false. +autotroph_settings(2)%gQfe_0 = 3.0000000000000001e-05 +autotroph_settings(2)%gQfe_min = 2.5e-06 +autotroph_settings(2)%imp_calcifier = .false. +autotroph_settings(2)%is_carbon_limited = .false. +autotroph_settings(2)%kCO2 = 0 +autotroph_settings(2)%kDOP = 0.5 +autotroph_settings(2)%kFe = 5e-05 +autotroph_settings(2)%kNH4 = 0.05 +autotroph_settings(2)%kNO3 = 0.4 +autotroph_settings(2)%kPO4 = 0.05 +autotroph_settings(2)%kSiO3 = 1.6 +autotroph_settings(2)%lname = "Diatom" +autotroph_settings(2)%loss_poc = 0 +autotroph_settings(2)%loss_thres = 0.02 +autotroph_settings(2)%loss_thres2 = 0 +autotroph_settings(2)%mort2_per_day = 0.01 +autotroph_settings(2)%mort_per_day = 0.1 +autotroph_settings(2)%silicifier = .true. +autotroph_settings(2)%sname = "diat" +autotroph_settings(2)%temp_func_form_opt = "q_10" +autotroph_settings(2)%temp_thres = -10 +autotroph_settings(2)%thetaN_max = 4 +autotroph_settings(3)%Ea = 0.32 +autotroph_settings(3)%Nfixer = .true. +autotroph_settings(3)%PCref_per_day = 1.6 +autotroph_settings(3)%Qp_fixed = 2.7350427350427355e-03 +autotroph_settings(3)%agg_rate_max = 0.5 +autotroph_settings(3)%agg_rate_min = 0.01 +autotroph_settings(3)%alphaPI_per_day = 0.31 +autotroph_settings(3)%exp_calcifier = .false. +autotroph_settings(3)%gQfe_0 = 6.0000000000000002e-05 +autotroph_settings(3)%gQfe_min = 2.5e-06 +autotroph_settings(3)%imp_calcifier = .false. +autotroph_settings(3)%is_carbon_limited = .false. +autotroph_settings(3)%kCO2 = 0 +autotroph_settings(3)%kDOP = 0.075 +autotroph_settings(3)%kFe = 4.5e-05 +autotroph_settings(3)%kNH4 = 0.2 +autotroph_settings(3)%kNO3 = 2 +autotroph_settings(3)%kPO4 = 0.015 +autotroph_settings(3)%kSiO3 = 0 +autotroph_settings(3)%lname = "Diazotroph" +autotroph_settings(3)%loss_poc = 0 +autotroph_settings(3)%loss_thres = 0.02 +autotroph_settings(3)%loss_thres2 = 0.001 +autotroph_settings(3)%mort2_per_day = 0.01 +autotroph_settings(3)%mort_per_day = 0.1 +autotroph_settings(3)%silicifier = .false. +autotroph_settings(3)%sname = "diaz" +autotroph_settings(3)%temp_func_form_opt = "q_10" +autotroph_settings(3)%temp_thres = 15 +autotroph_settings(3)%thetaN_max = 2.5 +autotroph_settings(4)%Ea = 0.32 +autotroph_settings(4)%Nfixer = .false. +autotroph_settings(4)%PCref_per_day = 4.7 +autotroph_settings(4)%Qp_fixed = 8.5470085470085479e-03 +autotroph_settings(4)%agg_rate_max = 0.5 +autotroph_settings(4)%agg_rate_min = 0.01 +autotroph_settings(4)%alphaPI_per_day = 0.31 +autotroph_settings(4)%exp_calcifier = .true. +autotroph_settings(4)%gQfe_0 = 3.0000000000000001e-05 +autotroph_settings(4)%gQfe_min = 2.5e-06 +autotroph_settings(4)%imp_calcifier = .false. +autotroph_settings(4)%is_carbon_limited = .true. +autotroph_settings(4)%kCO2 = 1 +autotroph_settings(4)%kDOP = 0.25 +autotroph_settings(4)%kFe = 3.2e-05 +autotroph_settings(4)%kNH4 = 0.01 +autotroph_settings(4)%kNO3 = 0.41 +autotroph_settings(4)%kPO4 = 0.006 +autotroph_settings(4)%kSiO3 = 0 +autotroph_settings(4)%lname = "Coccolithophores" +autotroph_settings(4)%loss_poc = 0 +autotroph_settings(4)%loss_thres = 0.01 +autotroph_settings(4)%loss_thres2 = 0 +autotroph_settings(4)%mort2_per_day = 0.01 +autotroph_settings(4)%mort_per_day = 0.1 +autotroph_settings(4)%silicifier = .false. +autotroph_settings(4)%sname = "cocco" +autotroph_settings(4)%temp_func_form_opt = "power" +autotroph_settings(4)%temp_thres = 0 +autotroph_settings(4)%thetaN_max = 3.8 + + + + + +! zoo1: small zooplankton -- these graze on primarily on sp, a bit on cocco/diaz +zooplankton_settings(1)%sname = 'microzoo' +zooplankton_settings(1)%lname = 'Microzooplankton' +zooplankton_settings(1)%z_mort_0_per_day = 0.1 +zooplankton_settings(1)%loss_thres = 0.075 +zooplankton_settings(1)%z_mort2_0_per_day = 0.0028 +zooplankton_settings(1)%Ea = 0.55 +zooplankton_settings(1)%temp_func_form_opt = "q_10" +zooplankton_settings(1)%basal_respiration_rate_per_day = 0 + +! zoo2: medium/large zooplankton -- these graze on medium size phytoplankton and zoo2 +zooplankton_settings(2)%sname = 'mesozoo' +zooplankton_settings(2)%lname = 'Mesozooplankton' +zooplankton_settings(2)%z_mort_0_per_day = 0.035 +zooplankton_settings(2)%loss_thres = 0.075 +zooplankton_settings(2)%z_mort2_0_per_day = 0.02 +zooplankton_settings(2)%Ea = 0.55 +zooplankton_settings(2)%temp_func_form_opt = "q_10" +zooplankton_settings(2)%basal_respiration_rate_per_day = 0 + +! Microzoo settings + +grazing_relationship_settings(1,1)%sname = 'grz_sp_microzoo' +grazing_relationship_settings(1,1)%lname = 'Grazing of sp by microzoo' +grazing_relationship_settings(1,1)%auto_ind_cnt = 1 +grazing_relationship_settings(1,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(1,1)%grazing_function = 2 +grazing_relationship_settings(1,1)%z_umax_0_per_day =4.1 +grazing_relationship_settings(1,1)%z_grz = 1.4 +grazing_relationship_settings(1,1)%graze_zoo = 0.35 +grazing_relationship_settings(1,1)%graze_poc = 0.01 +grazing_relationship_settings(1,1)%graze_doc = 0.057 +grazing_relationship_settings(1,1)%f_zoo_detr = 0.17 +grazing_relationship_settings(1,1)%auto_ind(1) = 1 + +grazing_relationship_settings(2,1)%sname = 'grz_diaz_microzoo' +grazing_relationship_settings(2,1)%lname = 'Grazing of diaz by microzoo' +grazing_relationship_settings(2,1)%auto_ind_cnt = 1 +grazing_relationship_settings(2,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(2,1)%f_zoo_detr = 0.17 +grazing_relationship_settings(2,1)%graze_doc = 0.057 +grazing_relationship_settings(2,1)%graze_poc = 0.01 +grazing_relationship_settings(2,1)%graze_zoo = 0.35 +grazing_relationship_settings(2,1)%grazing_function = 1 +grazing_relationship_settings(2,1)%z_grz = 1.2 +grazing_relationship_settings(2,1)%z_umax_0_per_day = 1 +grazing_relationship_settings(2,1)%auto_ind(1) = 3 + +grazing_relationship_settings(3,1)%sname = 'grz_cocco_microzoo' +grazing_relationship_settings(3,1)%lname = 'Grazing of cocco by microzoo' +grazing_relationship_settings(3,1)%auto_ind_cnt = 1 +grazing_relationship_settings(3,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(3,1)%grazing_function = 1 +grazing_relationship_settings(3,1)%z_umax_0_per_day = 1.7 +grazing_relationship_settings(3,1)%z_grz = 1.5 +grazing_relationship_settings(3,1)%graze_zoo = 0.25 +grazing_relationship_settings(3,1)%graze_poc = 0.18 +grazing_relationship_settings(3,1)%graze_doc = 0.09 +grazing_relationship_settings(3,1)%f_zoo_detr = 0.27 +grazing_relationship_settings(3,1)%auto_ind(1) = 4 + + +grazing_relationship_settings(4,1)%sname = "null" +grazing_relationship_settings(4,1)%lname = "null" +grazing_relationship_settings(4,1)%auto_ind_cnt = 0 +grazing_relationship_settings(4,1)%zoo_ind_cnt = 0 +grazing_relationship_settings(4,1)%f_zoo_detr = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%graze_doc = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%graze_poc = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%graze_zoo = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%grazing_function = 1 +grazing_relationship_settings(4,1)%z_grz = 0.0000000000000000e+00 +grazing_relationship_settings(4,1)%z_umax_0_per_day = 0.0000000000000000e+00 + +! Mesozoo settings + +grazing_relationship_settings(1,2)%sname = 'grz_diat_mesozoo' +grazing_relationship_settings(1,2)%lname = 'Grazing of diat by mesozoo' +grazing_relationship_settings(1,2)%auto_ind_cnt = 1 +grazing_relationship_settings(1,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(1,2)%grazing_function = 1 +grazing_relationship_settings(1,2)%z_umax_0_per_day = 0.9 +grazing_relationship_settings(1,2)%z_grz = 1.0 +grazing_relationship_settings(1,2)%graze_zoo = 0.25 +grazing_relationship_settings(1,2)%graze_poc = 0.28 +grazing_relationship_settings(1,2)%graze_doc = 0.09 +grazing_relationship_settings(1,2)%f_zoo_detr = 0.40 +grazing_relationship_settings(1,2)%auto_ind(1) = 2 + + +grazing_relationship_settings(2,2)%sname = 'grz_cocco_mesozoo' +grazing_relationship_settings(2,2)%lname = 'Grazing of cocco by mesozoo' +grazing_relationship_settings(2,2)%auto_ind_cnt = 1 +grazing_relationship_settings(2,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(2,2)%grazing_function = 1 +grazing_relationship_settings(2,2)%z_umax_0_per_day = 0.015 +grazing_relationship_settings(2,2)%z_grz = 1.4 +grazing_relationship_settings(2,2)%graze_zoo = 0.25 +grazing_relationship_settings(2,2)%graze_poc = 0.18 +grazing_relationship_settings(2,2)%graze_doc = 0.09 +grazing_relationship_settings(2,2)%f_zoo_detr = 0.27 +grazing_relationship_settings(2,2)%auto_ind(1) = 4 + +grazing_relationship_settings(3,2)%sname = 'grz_diaz_mesozoo' +grazing_relationship_settings(3,2)%lname = 'Grazing of diaz by mesozoo' +grazing_relationship_settings(3,2)%auto_ind_cnt = 1 +grazing_relationship_settings(3,2)%zoo_ind_cnt = 0 +grazing_relationship_settings(3,2)%grazing_function = 1 +grazing_relationship_settings(3,2)%z_umax_0_per_day = 0.5 +grazing_relationship_settings(3,2)%z_grz = 1.2 +grazing_relationship_settings(3,2)%graze_zoo = 0.25 +grazing_relationship_settings(3,2)%graze_poc = 0.18 +grazing_relationship_settings(3,2)%graze_doc = 0.09 +grazing_relationship_settings(3,2)%f_zoo_detr = 0.40 +grazing_relationship_settings(3,2)%auto_ind(1) = 3 + +grazing_relationship_settings(4,2)%sname = 'grz_microzoo_mesozoo' +grazing_relationship_settings(4,2)%lname = 'Predation of microzoo by mesozoo' +grazing_relationship_settings(4,2)%auto_ind_cnt = 0 +grazing_relationship_settings(4,2)%zoo_ind_cnt = 1 +grazing_relationship_settings(4,2)%grazing_function = 1 +grazing_relationship_settings(4,2)%z_umax_0_per_day = 0.2 +grazing_relationship_settings(4,2)%z_grz = 1.3 +grazing_relationship_settings(4,2)%graze_zoo = 0.25 +grazing_relationship_settings(4,2)%graze_poc = 0.28 +grazing_relationship_settings(4,2)%graze_doc = 0.09 +grazing_relationship_settings(4,2)%f_zoo_detr = 0.27 +grazing_relationship_settings(4,2)%zoo_ind(1) = 1 + From 4406a7bccac0f8578bbb7f063c93a99d29ed6f14 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 2 Feb 2023 09:54:28 -0700 Subject: [PATCH 065/136] Move KPP_MARBL INPUT to new directory Instead of overloading INPUT/BATS, created INPUT/CESM_MOM_COLUMNS/BATS; I think INPUT/BATS is back to its original state, but I'll compare against MOM6-examples to make sure --- .../single_column/INPUT/BATS/MOM_override | 3 --- .../BATS/IC_FILE_COLUMN.nc | Bin .../BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin .../BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin .../BATS/MARBL_FORCING_FILE_COLUMN.nc | Bin .../INPUT/CESM_MOM_COLUMNS/BATS/MOM_override | 6 +++++ .../INPUT/CESM_MOM_COLUMNS/BATS/WOA_column.nc | Bin 0 -> 15052 bytes .../INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc | Bin 0 -> 75468 bytes .../CESM_MOM_COLUMNS/BATS/forcing_monthly.nc | Bin 0 -> 3280 bytes .../single_column/KPP_MARBL/MOM_override | 2 +- .../single_column/KPP_MARBL/data_table | 24 +++++++++--------- 11 files changed, 19 insertions(+), 16 deletions(-) rename standalone/examples/single_column/INPUT/{ => CESM_MOM_COLUMNS}/BATS/IC_FILE_COLUMN.nc (100%) rename standalone/examples/single_column/INPUT/{ => CESM_MOM_COLUMNS}/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc (100%) rename standalone/examples/single_column/INPUT/{ => CESM_MOM_COLUMNS}/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc (100%) rename standalone/examples/single_column/INPUT/{ => CESM_MOM_COLUMNS}/BATS/MARBL_FORCING_FILE_COLUMN.nc (100%) create mode 100644 standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override create mode 100644 standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/WOA_column.nc create mode 100644 standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc create mode 100644 standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/forcing_monthly.nc diff --git a/standalone/examples/single_column/INPUT/BATS/MOM_override b/standalone/examples/single_column/INPUT/BATS/MOM_override index aee30788..875579f7 100644 --- a/standalone/examples/single_column/INPUT/BATS/MOM_override +++ b/standalone/examples/single_column/INPUT/BATS/MOM_override @@ -1,6 +1,3 @@ ! Generated by create_data.m F_0 = 7.59943e-05 INPUTDIR = "INPUT/BATS" -CESM_INPUTDIR = "INPUT/BATS" -READ_NDEP = FALSE -READ_RIV_FLUXES = FALSE diff --git a/standalone/examples/single_column/INPUT/BATS/IC_FILE_COLUMN.nc b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column/INPUT/BATS/IC_FILE_COLUMN.nc rename to standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc diff --git a/standalone/examples/single_column/INPUT/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column/INPUT/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc rename to standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc diff --git a/standalone/examples/single_column/INPUT/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column/INPUT/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc rename to standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc diff --git a/standalone/examples/single_column/INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc similarity index 100% rename from standalone/examples/single_column/INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc rename to standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc diff --git a/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override new file mode 100644 index 00000000..1f2b1a2b --- /dev/null +++ b/standalone/examples/single_column/INPUT/CESM_MOM_COLUMNS/BATS/MOM_override @@ -0,0 +1,6 @@ +! 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--- a/standalone/examples/single_column/KPP_MARBL/data_table +++ b/standalone/examples/single_column/KPP_MARBL/data_table @@ -1,15 +1,15 @@ # gridname, fieldname_code, fieldname_file, file_name, ongrid, factor -"OCN" , "lw", "netlwdn_sfc", "./INPUT/BATS/forcing.nc", .true., 1.0 -"OCN" , "sw", "netswdn_sfc", "./INPUT/BATS/forcing.nc", .true., 1.0 -"OCN" , "rain", "precip", "./INPUT/BATS/forcing.nc", .true., 1.0 -"OCN" , "evap", "evap", "./INPUT/BATS/forcing.nc", .true., 1.0 -"OCN" , "sens", "shflx", "./INPUT/BATS/forcing.nc", .true., 1.0 -"OCN" , "taux", "STRESS_X", "./INPUT/BATS/forcing.nc", .true., 1.0 -"OCN" , "tauy", "STRESS_Y", "./INPUT/BATS/forcing.nc", .true., 1.0 -"OCN" , "snow", "snow", "./INPUT/BATS/forcing.nc", .true., 0.0 +"OCN" , "lw", "netlwdn_sfc", "./INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc", .true., 1.0 +"OCN" , "sw", "netswdn_sfc", "./INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc", .true., 1.0 +"OCN" , "rain", "precip", "./INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc", .true., 1.0 +"OCN" , "evap", "evap", "./INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc", .true., 1.0 +"OCN" , "sens", "shflx", "./INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc", .true., 1.0 +"OCN" , "taux", "STRESS_X", "./INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc", .true., 1.0 +"OCN" , "tauy", "STRESS_Y", "./INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc", .true., 1.0 +"OCN" , "snow", "snow", "./INPUT/CESM_MOM_COLUMNS/BATS/forcing.nc", .true., 0.0 "OCN" , "runoff", "", "" , .true., 0.0 "OCN" , "calving", "", "" , .true., 0.0 -"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 -"OCN" , "u10_sqr", "U10_SQR", "./INPUT/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_fine_dust_flux", "ATM_FINE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_coarse_dust_flux", "ATM_COARSE_DUST_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "atm_bc_flux", "ATM_BC_FLUX", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 +"OCN" , "u10_sqr", "U10_SQR", "./INPUT/CESM_MOM_COLUMNS/BATS/MARBL_FORCING_FILE_COLUMN.nc", .true., 1.0 From c646066cf22cef9035bbccc4f5ee7a6624fdf3ab Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Thu, 2 Feb 2023 10:23:10 -0700 Subject: [PATCH 066/136] Add support for standalone cases on macOS Tested on my laptop, using homebrew to install gfortran, netcdf, netcdf-fortran, and openmpi (I needed to make a softlink for libnetcdf.a pointing to libnetcdff.a beause the linker was using -lnetcdff -lnetcdf for some reason; not sure if that'll be a problem for everyone or just me) --- standalone/build/.gitignore | 1 + ...examples.sh => build_examples-cheyenne.sh} | 0 standalone/build/build_examples-darwin.sh | 34 ++++ standalone/templates/homebrew-gnu.mk | 170 ++++++++++++++++++ 4 files changed, 205 insertions(+) rename standalone/build/{build_examples.sh => build_examples-cheyenne.sh} (100%) create mode 100755 standalone/build/build_examples-darwin.sh create mode 100644 standalone/templates/homebrew-gnu.mk diff --git a/standalone/build/.gitignore b/standalone/build/.gitignore index 186801f6..970ab8d2 100644 --- a/standalone/build/.gitignore +++ b/standalone/build/.gitignore @@ -1 +1,2 @@ intel/ +gnu/ diff --git a/standalone/build/build_examples.sh b/standalone/build/build_examples-cheyenne.sh similarity index 100% rename from standalone/build/build_examples.sh rename to standalone/build/build_examples-cheyenne.sh diff --git a/standalone/build/build_examples-darwin.sh b/standalone/build/build_examples-darwin.sh new file mode 100755 index 00000000..992ca126 --- /dev/null +++ b/standalone/build/build_examples-darwin.sh @@ -0,0 +1,34 @@ +#!/bin/bash -e +### For NCAR machines, this script should be run as +### $ qcmd -- ./build_examples.sh + +# Save various paths to use as shortcuts +cd ../.. +INTERFACE_ROOT=`pwd -P` +MKMF_ROOT=${INTERFACE_ROOT}/standalone/mkmf +TEMPLATE_DIR=${INTERFACE_ROOT}/standalone/templates +MOM_ROOT=${INTERFACE_ROOT}/MOM6 +cd ../.. +CESM_ROOT=`pwd -P` +FMS_ROOT=${CESM_ROOT}/libraries/FMS +SHR_ROOT=${CESM_ROOT}/components/cdeps/share + +COMPILER=gnu + +# 1) Build FMS + +# (b) build FMS +cd ${INTERFACE_ROOT}/standalone/build +mkdir -p ${COMPILER}/FMS +cd ${COMPILER}/FMS +${MKMF_ROOT}/list_paths ${FMS_ROOT}/src +${MKMF_ROOT}/mkmf -t ${TEMPLATE_DIR}/homebrew-${COMPILER}.mk -p libfms.a -c "-Duse_libMPI -Duse_netCDF -DSPMD" path_names +make -j2 NETCDF=3 REPRO=1 libfms.a + +# 2) Build MOM6 +cd ${INTERFACE_ROOT}/standalone/build +mkdir -p ${COMPILER}/MOM6 +cd ${COMPILER}/MOM6 +${MKMF_ROOT}/list_paths -l ${MOM_ROOT}/{config_src/infra/FMS2,config_src/memory/dynamic_symmetric,config_src/drivers/solo_driver,pkg/MARBL/src,config_src/external,src/{*,*/*}}/ +${MKMF_ROOT}/mkmf -t ${TEMPLATE_DIR}/homebrew-${COMPILER}.mk -o '-I../FMS' -p MOM6 -l '-L../FMS -lfms' -c '-Duse_libMPI -Duse_netCDF -DSPMD' path_names +make -j2 NETCDF=3 REPRO=1 MOM6 diff --git a/standalone/templates/homebrew-gnu.mk b/standalone/templates/homebrew-gnu.mk new file mode 100644 index 00000000..2b10cb70 --- /dev/null +++ b/standalone/templates/homebrew-gnu.mk @@ -0,0 +1,170 @@ +# template for the GNU fortran compiler +# (assumes MacOS with homebrew installs of gfortran, netcdf, netcdf-fortran, and openmpi) +############ +# commands # +############ +FC = mpif90 +CC = gcc +CXX = g++ +LD = mpif90 $(MAIN_PROGRAM) + +######### +# flags # +######### +DEBUG = +REPRO = +VERBOSE = +OPENMP = + +MAKEFLAGS += --jobs=2 + +FPPFLAGS := + +FFLAGS := -fcray-pointer -fdefault-double-8 -fdefault-real-8 -Waliasing -ffree-line-length-none -fno-range-check +FFLAGS += -I$(shell nf-config --includedir) +FFLAGS_OPT = -O3 +FFLAGS_REPRO = -O2 -fbounds-check +FFLAGS_DEBUG = -O0 -g -W -fbounds-check -fbacktrace -ffpe-trap=invalid,zero,overflow +FFLAGS_OPENMP = -fopenmp +FFLAGS_VERBOSE = + +CFLAGS := -D__IFC +CFLAGS += -I$(shell nc-config --includedir) +CFLAGS_OPT = -O2 +CFLAGS_OPENMP = -fopenmp +CFLAGS_DEBUG = -O0 -g + +# Optional Testing compile flags. Mutually exclusive from DEBUG, REPRO, and OPT +# *_TEST will match the production if no new option(s) is(are) to be tested. +FFLAGS_TEST = -O2 +CFLAGS_TEST = -O2 + +LDFLAGS := +LDFLAGS_OPENMP := -fopenmp +LDFLAGS_VERBOSE := + +ifneq ($(REPRO),) +CFLAGS += $(CFLAGS_REPRO) +FFLAGS += $(FFLAGS_REPRO) +else ifneq ($(DEBUG),) +CFLAGS += $(CFLAGS_DEBUG) +FFLAGS += $(FFLAGS_DEBUG) +else ifneq ($(TEST),) +CFLAGS += $(CFLAGS_TEST) +FFLAGS += $(FFLAGS_TEST) +else +CFLAGS += $(CFLAGS_OPT) +FFLAGS += $(FFLAGS_OPT) +endif + +ifneq ($(OPENMP),) +CFLAGS += $(CFLAGS_OPENMP) +FFLAGS += $(FFLAGS_OPENMP) +LDFLAGS += $(LDFLAGS_OPENMP) +endif + +ifneq ($(VERBOSE),) +CFLAGS += $(CFLAGS_VERBOSE) +FFLAGS += $(FFLAGS_VERBOSE) +LDFLAGS += $(LDFLAGS_VERBOSE) +endif + +ifeq ($(NETCDF),3) + # add the use_LARGEFILE cppdef + ifneq ($(findstring -Duse_netCDF,$(CPPDEFS)),) + CPPDEFS += -Duse_LARGEFILE + endif +endif + +LIBS := $(shell nc-config --flibs) +LDFLAGS += $(LIBS) + +#--------------------------------------------------------------------------- +# you should never need to change any lines below. + +# see the MIPSPro F90 manual for more details on some of the file extensions +# discussed here. +# this makefile template recognizes fortran sourcefiles with extensions +# .f, .f90, .F, .F90. Given a sourcefile ., where is one of +# the above, this provides a number of default actions: + +# make .opt create an optimization report +# make .o create an object file +# make .s create an assembly listing +# make .x create an executable file, assuming standalone +# source +# make .i create a preprocessed file (for .F) +# make .i90 create a preprocessed file (for .F90) + +# The macro TMPFILES is provided to slate files like the above for removal. + +RM = rm -f +SHELL = /bin/csh -f +TMPFILES = .*.m *.B *.L *.i *.i90 *.l *.s *.mod *.opt + +.SUFFIXES: .F .F90 .H .L .T .f .f90 .h .i .i90 .l .o .s .opt .x + +.f.L: + $(FC) $(FFLAGS) -c -listing $*.f +.f.opt: + $(FC) $(FFLAGS) -c -opt_report_level max -opt_report_phase all -opt_report_file $*.opt $*.f +.f.l: + $(FC) $(FFLAGS) -c $(LIST) $*.f +.f.T: + $(FC) $(FFLAGS) -c -cif $*.f +.f.o: + $(FC) $(FFLAGS) -c $*.f +.f.s: + $(FC) $(FFLAGS) -S $*.f +.f.x: + $(FC) $(FFLAGS) -o $*.x $*.f *.o $(LDFLAGS) +.f90.L: + $(FC) $(FFLAGS) -c -listing $*.f90 +.f90.opt: + $(FC) $(FFLAGS) -c -opt_report_level max -opt_report_phase all -opt_report_file $*.opt $*.f90 +.f90.l: + $(FC) $(FFLAGS) -c $(LIST) $*.f90 +.f90.T: + $(FC) $(FFLAGS) -c -cif $*.f90 +.f90.o: + $(FC) $(FFLAGS) -c $*.f90 +.f90.s: + $(FC) $(FFLAGS) -c -S $*.f90 +.f90.x: + $(FC) $(FFLAGS) -o $*.x $*.f90 *.o $(LDFLAGS) +.F.L: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -c -listing $*.F +.F.opt: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -c -opt_report_level max -opt_report_phase all -opt_report_file $*.opt $*.F +.F.l: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -c $(LIST) $*.F +.F.T: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -c -cif $*.F +.F.f: + $(FC) $(CPPDEFS) $(FPPFLAGS) -EP $*.F > $*.f +.F.i: + $(FC) $(CPPDEFS) $(FPPFLAGS) -P $*.F +.F.o: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -c $*.F +.F.s: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -c -S $*.F +.F.x: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -o $*.x $*.F *.o $(LDFLAGS) +.F90.L: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -c -listing $*.F90 +.F90.opt: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -c -opt_report_level max -opt_report_phase all -opt_report_file $*.opt $*.F90 +.F90.l: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -c $(LIST) $*.F90 +.F90.T: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -c -cif $*.F90 +.F90.f90: + $(FC) $(CPPDEFS) $(FPPFLAGS) -EP $*.F90 > $*.f90 +.F90.i90: + $(FC) $(CPPDEFS) $(FPPFLAGS) -P $*.F90 +.F90.o: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -c $*.F90 +.F90.s: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -c -S $*.F90 +.F90.x: + $(FC) $(CPPDEFS) $(FPPFLAGS) $(FFLAGS) -o $*.x $*.F90 *.o $(LDFLAGS) From cafe4cba141ba8a2a33f5bbf39200ce282c8a044 Mon Sep 17 00:00:00 2001 From: Michael Levy Date: Fri, 3 Feb 2023 14:07:59 -0700 Subject: [PATCH 067/136] Update forcings to be uniform across grid cells --- .../CESM_MOM_COLUMNS/BATS/IC_FILE_COLUMN.nc | Bin 181607 -> 181607 bytes .../BATS/MARBL_FESEDFLUX_FILE_COLUMN.nc | Bin 22254 -> 21490 bytes .../BATS/MARBL_FEVENTFLUX_FILE_COLUMN.nc | Bin 13284 -> 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