Generate_IC_Files.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this notebook we read in data needed for MARBL and generate 1D input files. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "\n",
    "import cmocean\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pop_tools\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Site to pull out from global IC / forcing files\n",
    "\n",
    "# site_name = 'ARAB'\n",
    "site_name = 'BATS'\n",
    "# site_name = 'EQPAC'\n",
    "# site_name = 'HOT'\n",
    "# site_name = 'KERFIX'\n",
    "# site_name = 'NABE'\n",
    "# site_name = 'STNP'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create restoring climatology (and grab mix layer depth as well)\n",
    "# This function only needs to be run once to create files read in next cell\n",
    "def generate_restoring_fields_from_CESM_run(cesm_in_dir):\n",
    "    archived_case = 'g.e23b15.TL319_t061.G1850MOMMARBL_JRA.012'\n",
    "    mix_layer_file = f'{cesm_in_dir}/{archived_case}.mlotst_climo.nc'\n",
    "    restoring_file = f'{cesm_in_dir}/{archived_case}.MARBL_tracer_climo.nc'\n",
    "    nyears = 3 # use the last {nyears} of monthly data for climatology\n",
    "\n",
    "    # mix_layer\n",
    "    # file_glob = f'/glade/derecho/scratch/mlevy/archive/{archived_case}/ocn/hist/{archived_case}.mom6.h.native.*.nc'\n",
    "    # # use_cftime -- from https://github.com/pydata/xarray/pull/2759 it suppresses warning about time dimension\n",
    "    # ds_mix = xr.open_mfdataset(file_glob, parallel=False, use_cftime=True)[['mlotst']].isel(time=slice(-nyears*12,None)).groupby(\"time.month\").mean(\"time\")\n",
    "    # ds_mix.to_netcdf(mix_layer_file)\n",
    "\n",
    "    # Tracer Climatologies\n",
    "    tracers = ['PO4', 'NO3', 'SiO3', 'ALK', 'NH4', 'Fe', 'Lig', 'O2', 'DIC', 'DOCr', 'DONr', 'DOPr']#, 'DOC', 'DON', 'DOP']\n",
    "    file_glob = f'/glade/derecho/scratch/mlevy/archive/{archived_case}/ocn/hist/{archived_case}.mom6.h.bgc.z.*.nc'\n",
    "    # use_cftime -- from https://github.com/pydata/xarray/pull/2759 it suppresses warning about time dimension\n",
    "    ds_in = xr.open_mfdataset(file_glob, parallel=False, use_cftime=True)\n",
    "    ds_tracer = ds_in[tracers].isel(time=slice(-nyears*12,None)).groupby(\"time.month\").mean(\"time\")\n",
    "    \n",
    "    ds_vert_grid = pop_tools.get_grid('POP_gx1v7')[['z_t', 'z_w_bot']]\n",
    "    z_t = ds_vert_grid['z_t'].data / 100.\n",
    "    z_w = np.zeros(61)\n",
    "    z_w[1:] = ds_vert_grid['z_w_bot'].data / 100.\n",
    "    ds_tracer = ds_tracer.assign_coords({'z_pop_l': z_t, 'z_pop_i': z_w})\n",
    "    ds_tracer['z_pop_l'].attrs = ds_vert_grid['z_t'].attrs\n",
    "    ds_tracer['z_pop_l'].attrs['units'] = 'm'\n",
    "    ds_tracer['z_pop_l'].attrs['axis'] = 'Z'\n",
    "    ds_tracer['z_pop_i'].attrs = ds_vert_grid['z_w_bot'].attrs\n",
    "    ds_tracer['z_pop_i'].attrs['long_name'] = 'depth from surface to layer interface'\n",
    "    ds_tracer['z_pop_i'].attrs['units'] = 'm'\n",
    "    ds_tracer['z_pop_i'].attrs['axis'] = 'Z'\n",
    "    ds_tracer.to_netcdf(restoring_file)\n",
    "    return ds_tracer\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%%time\n",
    "\n",
    "generate_restoring_fields_from_CESM_run('/glade/work/mlevy/cesm_inputdata/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cesm_in_dir='/glade/work/mlevy/cesm_inputdata/'\n",
    "GRID_FILE             =  xr.open_dataset(f'{cesm_in_dir}/mom6.static.nc') # time invariant output from a CESM run on the tx0.66v1 grid\n",
    "TOPO_FILE             = xr.open_dataset('/glade/p/cesmdata/cseg/inputdata/ocn/mom/tx0.66v1/ocean_topog_200701.nc') # topography for CESM run on the tx0.661v1 grid\n",
    "OLD_IC_FILE           =  xr.open_dataset(f'{cesm_in_dir}/ecosys_jan_IC_omip_MOM_tx0.66v1_c221027.nc') # Combination of WOA and other fields from POP includes BGC tracers\n",
    "IC_FILE               =  xr.open_mfdataset(f'{cesm_in_dir}/MOM_IC*.nc', compat='override', join='override', combine_attrs='override').isel(Time=0).drop('Time') # MOM's reported initial conditions from a CESM run on the tx0.66v1 grid\n",
    "\n",
    "MARBL_FESEDFLUX_FILE  =  xr.open_dataset(f'{cesm_in_dir}/fesedflux_total_reduce_oxic_tx0.66v1.c211109.nc') #From Matt's notebook based on POP output; only has nlon nlat, not actual  lon/lat\n",
    "MARBL_FEVENTFLUX_FILE =  xr.open_dataset(f'{cesm_in_dir}/feventflux_5gmol_tx0.66v1.c211109.nc') \n",
    "\n",
    "#Read in MARBL forcing from A-compset run:\n",
    "MARBL_FORCING_FILE    =  xr.open_dataset('/glade/work/mlevy/codes/MOM6-examples/gen_forcing_on_tx066v1/MARBL_forcing.nc')\n",
    "# Read in MOM6 mix-layer depth climotology\n",
    "MIX_LAYER_FILE        = xr.open_dataset(f'{cesm_in_dir}/g.e23b15.TL319_t061.G1850MOMMARBL_JRA.012.mlotst_climo.nc')\n",
    "RESTORING_FILE        = xr.open_dataset(f'{cesm_in_dir}/g.e23b15.TL319_t061.G1850MOMMARBL_JRA.012.MARBL_tracer_climo.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mask out land / layers below ocean floor, then drop fields without 'Layer' dimension\n",
    "for varname in IC_FILE.data_vars:\n",
    "    if 'Layer' in IC_FILE[varname].dims:\n",
    "        IC_FILE[varname].data = np.where(np.isfinite(GRID_FILE['deptho'].data),\n",
    "                                         IC_FILE[varname].data,\n",
    "                                         np.nan)\n",
    "IC_FILE = IC_FILE.drop([var for var in IC_FILE.data_vars if 'Layer' not in IC_FILE[var].dims and 'Interface' not in IC_FILE[var].dims])\n",
    "IC_FILE = IC_FILE.drop_dims(['latq', 'lonq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(lat, lon) is (31.66067,-64.01870), depth = 4549.0\n",
      "(lat, lon) indices are (313,334)\n"
     ]
    }
   ],
   "source": [
    "true_lat_dict = dict()\n",
    "true_lon_dict = dict()\n",
    "lat_dict = dict()\n",
    "lon_dict = dict()\n",
    "xloc_dict = dict()\n",
    "yloc_dict = dict()\n",
    "\n",
    "lat_array = GRID_FILE['geolat'].copy()\n",
    "lon_array = GRID_FILE['geolon'].copy()\n",
    "# lon_array.data = np.where(lon_array.data < -200, lon_array.data+360., lon_array.data)\n",
    "\n",
    "#print(lat_array)\n",
    "\n",
    "#We want to extract data at a certain lat/lon point.\n",
    "# BATS location is 31.666667N, 64.166667W.\n",
    "true_lat_dict['BATS'] =  31.666667\n",
    "true_lon_dict['BATS'] = -64.166667\n",
    "# STNP location is 50N, 145W\n",
    "true_lat_dict['STNP'] =  50.\n",
    "true_lon_dict['STNP'] = -145.\n",
    "# NABE is region bounded by 46N-48N and 17W-21W\n",
    "true_lat_dict['NABE'] =  47.\n",
    "true_lon_dict['NABE'] = -19.\n",
    "# ARAB (S7) looks to be 15N, 62E (don't see explicit location in Smith 1998)\n",
    "true_lat_dict['ARAB'] =  15.\n",
    "true_lon_dict['ARAB'] = 62.\n",
    "# EQPAC was conducted at 140W, presumably along equator\n",
    "true_lat_dict['EQPAC'] =  0.\n",
    "true_lon_dict['EQPAC'] = -140.\n",
    "# HOT deep water station is 22.75 N, 158W\n",
    "true_lat_dict['HOT'] =  22.75\n",
    "true_lon_dict['HOT'] = -158.\n",
    "# KERFIX is 50.666667S, 69.416667E\n",
    "true_lat_dict['KERFIX'] =  -50.666667\n",
    "true_lon_dict['KERFIX'] = 69.416667\n",
    "\n",
    "# Find nearest point to each site on MOM grid\n",
    "for site_name_loc in true_lat_dict:\n",
    "    abslat = np.abs(lat_array-true_lat_dict[site_name_loc])\n",
    "    abslon = np.abs(lon_array-true_lon_dict[site_name_loc])\n",
    "    c = np.maximum(abslat,abslon, )\n",
    "\n",
    "    ([yloc_dict[site_name_loc]],[xloc_dict[site_name_loc]]) = np.where(c == np.min(c))\n",
    "    lon_dict[site_name_loc] = lon_array.isel(xh=xloc_dict[site_name_loc], yh=yloc_dict[site_name_loc]).data.item()\n",
    "    lat_dict[site_name_loc] = lat_array.isel(xh=xloc_dict[site_name_loc], yh=yloc_dict[site_name_loc]).data.item()\n",
    "\n",
    "# First, find the index of the grid point nearest a specific lat/lon.   \n",
    "try:\n",
    "    lat = lat_dict[site_name]\n",
    "    lon = lon_dict[site_name]\n",
    "    yloc = yloc_dict[site_name]\n",
    "    xloc = xloc_dict[site_name]\n",
    "except:\n",
    "    raise ValueError(f'This notebook is not configured to handle {site_name} yet')\n",
    "\n",
    "depth = TOPO_FILE['depth'].isel(ny=yloc,nx=xloc).item()\n",
    "print(f'(lat, lon) is ({lat:.5f},{lon:.5f}), depth = {depth}')\n",
    "print(f'(lat, lon) indices are ({yloc},{xloc})')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#Finding indices for the Temperature and Salinity data (on a different grid)\n",
    "\n",
    "ilon_TS = list(TS_IC_FILE.lon.values).index(TS_IC_FILE.sel(lon=lon, method='nearest').lon)\n",
    "ilat_TS = list(TS_IC_FILE.lat.values).index(TS_IC_FILE.sel(lat=lat, method='nearest').lat)\n",
    "print(' lon index=',ilon_TS,'\\n','lat index=', ilat_TS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6),dpi=120)\n",
    "\n",
    "plt.contourf(lon_array,lat_array,IC_FILE['Temp'][0,:,:],cmap=cmocean.cm.matter_r,vmin=0,vmax=35,levels=np.linspace(0,35,36),extend='both')\n",
    "plt.colorbar()\n",
    "plt.xlabel('Longitude')\n",
    "plt.ylabel('Latitude')\n",
    "plt.title('Initial Condition SST')\n",
    "# plt.plot(IC_FILE.LON[yloc,xloc],IC_FILE.LAT[yloc,xloc],'*r',markersize=5)\n",
    "#plt.plot(TS_IC_FILE.lon[ilon_TS],TS_IC_FILE.lat[ilat_TS],'*r',markersize=8)\n",
    "for site_name_loc in lat_dict.keys():\n",
    "    plt.plot(lon_dict[site_name_loc],lat_dict[site_name_loc],'*b',markersize=5) # Blue is actual location of site\n",
    "    plt.plot(true_lon_dict[site_name_loc],true_lat_dict[site_name_loc],'*y',markersize=2.5) # Yellow is nearest CESM-MOM grid cell center\n",
    "    plt.text(lon_dict[site_name_loc],lat_dict[site_name_loc]+2.5,site_name_loc,color='b',horizontalalignment='center',fontweight='semibold')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_fill_value(ds):\n",
    "    '''\n",
    "    No _FillValue attribute for coordinates, -1e34 for data variables\n",
    "    '''\n",
    "    for var in ds.coords:\n",
    "        ds[var].encoding['_FillValue'] = None\n",
    "        if 'missing_value' in ds[var].encoding:\n",
    "            del ds[var].encoding['missing_value']\n",
    "\n",
    "    for var in ds.data_vars:\n",
    "        if ds[var].data.dtype in ['float32', 'float64']:\n",
    "            ds[var].encoding['_FillValue'] = -1.e34\n",
    "        if 'missing_value' in ds[var].encoding:\n",
    "            del ds[var].encoding['missing_value']\n",
    "\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'lat' (nlat: 2)>\n",
      "[2 values with dtype=float64]\n",
      "Coordinates:\n",
      "    lat      (nlat) float64 ...\n",
      "    lon      (nlat) float64 ...\n",
      "Dimensions without coordinates: nlat\n",
      "Attributes:\n",
      "    units:    degrees_north\n",
      "<xarray.DataArray 'lon' (nlon: 2)>\n",
      "[2 values with dtype=float64]\n",
      "Coordinates:\n",
      "    lat      (nlon) float64 ...\n",
      "    lon      (nlon) float64 ...\n",
      "Dimensions without coordinates: nlon\n",
      "Attributes:\n",
      "    units:    degrees_east\n"
     ]
    }
   ],
   "source": [
    "IC_FILE_COLUMN               = IC_FILE.isel(lath=[yloc-1,yloc], lonh=[xloc-1,xloc])\n",
    "IC_FILE_COLUMN               = IC_FILE_COLUMN.assign_coords({'lath': lat_array.isel(yh=[yloc-1,yloc], xh=xloc).data,\n",
    "                                                             'lonh': lon_array.isel(yh=yloc, xh=[xloc-1,xloc]).data})\n",
    "\n",
    "TOPO_FILE_COLUMN             = TOPO_FILE.isel(ny=[yloc-1,yloc], nx=[xloc-1,xloc])\n",
    "TOPO_FILE_COLUMN             = TOPO_FILE_COLUMN.assign_coords({'ny': lat_array.isel(yh=[yloc-1,yloc], xh=xloc).data,\n",
    "                                                               'nx': lon_array.isel(yh=yloc, xh=[xloc-1,xloc]).data})\n",
    "\n",
    "MARBL_FESEDFLUX_FILE_COLUMN  = MARBL_FESEDFLUX_FILE.isel(NLAT=[yloc-1,yloc], NLON=[xloc-1,xloc]).rename_dims({'NLON':'lon','NLAT':'lat'})\n",
    "MARBL_FESEDFLUX_FILE_COLUMN  = MARBL_FESEDFLUX_FILE_COLUMN.assign_coords({'lat': lat_array.isel(yh=[yloc-1,yloc], xh=xloc),\n",
    "                                                                          'lon': lon_array.isel(yh=yloc, xh=[xloc-1,xloc])})\n",
    "\n",
    "MARBL_FEVENTFLUX_FILE_COLUMN = MARBL_FEVENTFLUX_FILE.isel(NLAT=[yloc-1,yloc], NLON=[xloc-1,xloc]).rename_dims({'NLON':'lon','NLAT':'lat'})\n",
    "MARBL_FEVENTFLUX_FILE_COLUMN = MARBL_FEVENTFLUX_FILE_COLUMN.assign_coords({'lat': lat_array.isel(yh=[yloc-1,yloc], xh=xloc),\n",
    "                                                                           'lon': lon_array.isel(yh=yloc, xh=[xloc-1,xloc])})\n",
    "\n",
    "MARBL_FORCING_temp    = MARBL_FORCING_FILE.isel(nlat=[yloc-1,yloc], nlon=[xloc-1,xloc])\n",
    "# MARBL_FORCING_temp    = MARBL_FORCING_FILE_COLUMN.assign_coords({'lat': lat_array.isel(yh=[yloc-1,yloc], xh=[xloc-1,xloc]),\n",
    "#                                                                  'lon': lon_array.isel(yh=[yloc-1,yloc], xh=[xloc-1,xloc])})\n",
    "\n",
    "print(MARBL_FORCING_temp.lat[:,0])\n",
    "print(MARBL_FORCING_temp.lon[0,:])\n",
    "\n",
    "#Making some modifications to MARBL_FORCING_FILE_COLUMN:\n",
    "ds = MARBL_FORCING_temp\n",
    "\n",
    "ds = ds.rename_dims({'nlat': 'gridlat_t', 'nlon': 'gridlon_t'})\n",
    "ds = ds.assign_coords({'gridlat_t': MARBL_FORCING_temp.lat[:,0], 'gridlon_t': MARBL_FORCING_temp.lon[0,:]})\n",
    "ds.gridlat_t.attrs['units'] = 'degree_north'\n",
    "ds.gridlat_t.attrs['cartesian_axis'] = 'Y'\n",
    "ds.gridlon_t.attrs['units'] = 'degree_east'\n",
    "ds.gridlon_t.attrs['cartesian_axis'] = 'X'\n",
    "\n",
    "#ds.time.encoding['_FillValue'] = 10000000000. \n",
    "ds.time.attrs['units'] = \"days since 1982-01-01 00:00:00\"\n",
    "ds.time.attrs['time_origin'] = \"01-JAN-1982 00:00:00\"\n",
    "ds.time.attrs['calendar'] = 'NOLEAP'\n",
    "ds.time.attrs['cartesian_axis'] = 'T'\n",
    "ds.time.attrs['modulo'] = \" \"\n",
    "ds.time.attrs['modulo_beg'] = \"1982-01-01 00:00:00\"\n",
    "ds.time.attrs['modulo_end'] = \"1987-01-01 00:00:00\"\n",
    "\n",
    "\n",
    "ds.gridlon_t.encoding['_FillValue'] = None\n",
    "ds.gridlat_t.encoding['_FillValue'] = None\n",
    "\n",
    "MARBL_FORCING_FILE_COLUMN = ds;\n",
    "\n",
    "# Create Restoring Datasets\n",
    "mid_month = [15., 44., 73.5, 104., 134.5, 165., 195.5, 226.5, 257., 287.5, 318.5, 349.]\n",
    "RESTORING_FILE_COLUMN = RESTORING_FILE.isel(xh=[xloc-1,xloc], yh=[yloc-1,yloc]).rename({'z_pop_l': 'DEPTH', 'z_pop_i': 'EDGES', 'month': 'time'})\n",
    "RESTORING_FILE_COLUMN.coords['time'] = mid_month\n",
    "RESTORING_FILE_COLUMN['time'].attrs['units'] = \"days since 0001-01-01 00:00:00\"\n",
    "RESTORING_FILE_COLUMN['time'].attrs['calendar'] = 'NOLEAP'\n",
    "RESTORING_FILE_COLUMN['time'].attrs['modulo'] = \" \"\n",
    "RESTORING_FILE_COLUMN['time'].attrs['cartesian_axis'] = 'T'\n",
    "RESTORING_FILE_COLUMN['DEPTH'].attrs['edges'] = 'EDGES'\n",
    "\n",
    "# Restoring time scale is 1 year\n",
    "tau = 1.\n",
    "rtau = 1. / (tau * 365. * 86400.)\n",
    "mld = MIX_LAYER_FILE.isel(yh=yloc, xh=xloc)['mlotst'].max().values\n",
    "\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN = IC_FILE_COLUMN[['lath', 'lonh']].copy()\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN = MARBL_RESTORING_TIMESCALE_FILE_COLUMN.assign_coords({'DEPTH': np.array([0.45*mld, 0.95*mld, 1.05*mld, 1e4]),\n",
    "                                                                                             'EDGES': np.array([0, 0.9*mld, mld, 1.1*mld, 2e4 - 0.55*mld])})\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['DEPTH'].attrs['units'] = 'm'\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['DEPTH'].attrs['long_name'] = 'depth from surface to midpoint of layer'\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['DEPTH'].attrs['positive'] = 'down'\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['DEPTH'].attrs['axis'] = 'Z'\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['DEPTH'].attrs['edges'] = 'EDGES'\n",
    "\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['EDGES'].attrs['units'] = 'm'\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['EDGES'].attrs['long_name'] = 'depth from surface to midpoint of layer'\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['EDGES'].attrs['positive'] = 'down'\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['EDGES'].attrs['axis'] = 'Z'\n",
    "\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['RTAU'] = xr.DataArray(np.zeros((4,2,2)), dims=['DEPTH', 'lath', 'lonh'])\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['RTAU'].data[:,1,1] = [0, 0, rtau, rtau]\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['RTAU'].attrs['long_name'] = 'Inverse Restoring Time Scale'\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN['RTAU'].attrs['units'] = '1/s'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting all values in da to be equal to the value in [1,1] (corresponding to (xloc,yloc))\n",
    "def set_all_equal(da, third_dim_names='DEPTH'):\n",
    "    print(f'Making sure {da.name} values are identical in every column')\n",
    "    if type(third_dim_names) == str:\n",
    "        third_dim_names = [third_dim_names]\n",
    "    is_3d = any([third_dim_name in da.dims for third_dim_name in third_dim_names])\n",
    "    if is_3d:\n",
    "        da.data[:,0,0] = da.data[:,1,1]\n",
    "        da.data[:,1,0] = da.data[:,1,1]\n",
    "        da.data[:,0,1] = da.data[:,1,1]\n",
    "    else:\n",
    "        da.data[0,0] = da.data[1,1]\n",
    "        da.data[1,0] = da.data[1,1]\n",
    "        da.data[0,1] = da.data[1,1]\n",
    "    return da"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "[var for var in ['h', 'u', 'v', 'u2', 'v2', 'CAu', 'CAv', 'diffu', 'diffv', 'age', 'Kd_shear', 'Kv_shear', 'MARBL_PH_3D', 'MARBL_PH_3D_ALT_CO2'] if var in IC_FILE_COLUMN.data_vars]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making sure Temp values are identical in every column\n",
      "Making sure Salt values are identical in every column\n",
      "Making sure h values are identical in every column\n",
      "Making sure age values are identical in every column\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DIC_ALT_CO2 values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure ALK_ALT_CO2 values are identical in every column\n",
      "Making sure DOC values are identical in every column\n",
      "Making sure DON values are identical in every column\n",
      "Making sure DOP values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure zooC values are identical in every column\n",
      "Making sure spChl values are identical in every column\n",
      "Making sure spC values are identical in every column\n",
      "Making sure spP values are identical in every column\n",
      "Making sure spFe values are identical in every column\n",
      "Making sure spCaCO3 values are identical in every column\n",
      "Making sure diatChl values are identical in every column\n",
      "Making sure diatC values are identical in every column\n",
      "Making sure diatP values are identical in every column\n",
      "Making sure diatFe values are identical in every column\n",
      "Making sure diatSi values are identical in every column\n",
      "Making sure diazChl values are identical in every column\n",
      "Making sure diazC values are identical in every column\n",
      "Making sure diazP values are identical in every column\n",
      "Making sure diazFe values are identical in every column\n",
      "Making sure MARBL_PH_3D values are identical in every column\n",
      "Making sure MARBL_PH_3D_ALT_CO2 values are identical in every column\n",
      "Making sure Kd_shear values are identical in every column\n",
      "Making sure Kv_shear values are identical in every column\n",
      "Making sure eta values are identical in every column\n",
      "----\n",
      "Making sure depth values are identical in every column\n",
      "Making sure mask values are identical in every column\n",
      "----\n",
      "Making sure FESEDFLUXIN values are identical in every column\n",
      "Making sure FESEDFLUXIN_oxic values are identical in every column\n",
      "Making sure FESEDFLUXIN_reduce values are identical in every column\n",
      "Making sure KMT values are identical in every column\n",
      "Making sure TAREA values are identical in every column\n",
      "----\n",
      "Making sure FESEDFLUXIN values are identical in every column\n",
      "Making sure KMT values are identical in every column\n",
      "Making sure TAREA values are identical in every column\n",
      "Making sure TLAT values are identical in every column\n",
      "Making sure TLONG values are identical in every column\n",
      "----\n",
      "Making sure ATM_FINE_DUST_FLUX values are identical in every column\n",
      "Making sure ATM_COARSE_DUST_FLUX values are identical in every column\n",
      "Making sure ATM_BC_FLUX values are identical in every column\n",
      "Making sure SEAICE_DUST_FLUX values are identical in every column\n",
      "Making sure SEAICE_BC_FLUX values are identical in every column\n",
      "Making sure U10_SQR values are identical in every column\n",
      "Making sure ICE_FRAC values are identical in every column\n",
      "Making sure LWDN values are identical in every column\n",
      "Making sure SWDN values are identical in every column\n",
      "Making sure NDEP_NHx values are identical in every column\n",
      "Making sure NDEP_NOy values are identical in every column\n",
      "Making sure RAIN values are identical in every column\n",
      "Making sure EVAP values are identical in every column\n",
      "Making sure SENS_HEAT_FLUX values are identical in every column\n",
      "Making sure TAUX values are identical in every column\n",
      "Making sure TAUY values are identical in every column\n",
      "Making sure SNOW values are identical in every column\n",
      "Making sure SST_RESTORE values are identical in every column\n",
      "----\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure PO4 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure NO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure SiO3 values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure ALK values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure NH4 values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Fe values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure Lig values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure O2 values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DIC values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure DOCr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DONr values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "Making sure DOPr values are identical in every column\n",
      "----\n",
      "Making sure RTAU values are identical in every column\n"
     ]
    }
   ],
   "source": [
    "# Also update Layer dimension for IC_FILE_COLUMN\n",
    "for field in IC_FILE_COLUMN.data_vars:\n",
    "    IC_FILE_COLUMN[field] = set_all_equal(IC_FILE_COLUMN[field], ['Layer', 'Interface'])\n",
    "# Compute depth variable for IC_FILE_COLUMN\n",
    "# assign coordinates to match DataSet\n",
    "depth = -0.5*(IC_FILE_COLUMN.eta[:-1,:,:].rename(Interface='Layer').assign_coords(Layer=IC_FILE_COLUMN['Layer'].values) +\n",
    "              IC_FILE_COLUMN.eta[1:,:,:].rename(Interface='Layer').assign_coords(Layer=IC_FILE_COLUMN['Layer'].values)).rename('depth')\n",
    "IC_FILE_COLUMN = IC_FILE_COLUMN.assign_coords({'Layer': depth.isel(lath=0,lonh=0).values})\n",
    "#IC_drop_vars = ['h', 'u', 'v', 'u2', 'v2', 'CAu', 'CAv', 'diffu', 'diffv', 'age', 'Kd_shear', 'Kv_shear', 'MARBL_PH_3D', 'MARBL_PH_3D_ALT_CO2']\n",
    "IC_drop_vars = ['h', 'age', 'Kd_shear', 'Kv_shear', 'MARBL_PH_3D', 'MARBL_PH_3D_ALT_CO2']\n",
    "for var in IC_drop_vars:\n",
    "    IC_FILE_COLUMN = IC_FILE_COLUMN.drop(var)\n",
    "\n",
    "print('----')\n",
    "\n",
    "for field in TOPO_FILE_COLUMN.data_vars:\n",
    "    TOPO_FILE_COLUMN[field] = set_all_equal(TOPO_FILE_COLUMN[field])\n",
    "TOPO_FILE_COLUMN = set_fill_value(TOPO_FILE_COLUMN)\n",
    "\n",
    "print('----')\n",
    "\n",
    "for field in MARBL_FESEDFLUX_FILE_COLUMN.data_vars:\n",
    "    MARBL_FESEDFLUX_FILE_COLUMN[field] = set_all_equal(MARBL_FESEDFLUX_FILE_COLUMN[field])\n",
    "MARBL_FESEDFLUX_FILE_COLUMN = set_fill_value(MARBL_FESEDFLUX_FILE_COLUMN)\n",
    "\n",
    "print('----')\n",
    "\n",
    "for field in MARBL_FEVENTFLUX_FILE_COLUMN.data_vars:\n",
    "    MARBL_FEVENTFLUX_FILE_COLUMN[field] = set_all_equal(MARBL_FEVENTFLUX_FILE_COLUMN[field])\n",
    "MARBL_FEVENTFLUX_FILE_COLUMN = set_fill_value(MARBL_FEVENTFLUX_FILE_COLUMN)\n",
    "\n",
    "print('----')\n",
    "\n",
    "for field in MARBL_FORCING_FILE_COLUMN.data_vars:\n",
    "    MARBL_FORCING_FILE_COLUMN[field] = set_all_equal(MARBL_FORCING_FILE_COLUMN[field], 'time')\n",
    "MARBL_FORCING_FILE_COLUMN = set_fill_value(MARBL_FORCING_FILE_COLUMN)\n",
    "\n",
    "print('----')\n",
    "\n",
    "for field in RESTORING_FILE_COLUMN.data_vars:\n",
    "    for time in range(12):\n",
    "        RESTORING_FILE_COLUMN[field].data[time, :, :, :] = set_all_equal(RESTORING_FILE_COLUMN.isel(time=time)[field]).data\n",
    "RESTORING_FILE_COLUMN = set_fill_value(RESTORING_FILE_COLUMN)\n",
    "\n",
    "print('----')\n",
    "\n",
    "for field in MARBL_RESTORING_TIMESCALE_FILE_COLUMN.data_vars:\n",
    "    MARBL_RESTORING_TIMESCALE_FILE_COLUMN[field] = set_all_equal(MARBL_RESTORING_TIMESCALE_FILE_COLUMN[field])\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN = set_fill_value(MARBL_RESTORING_TIMESCALE_FILE_COLUMN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replacing missing values of PO4 at level 57\n",
      "Replacing missing values of PO4 at level 58\n",
      "Replacing missing values of PO4 at level 59\n",
      "Replacing missing values of NO3 at level 57\n",
      "Replacing missing values of NO3 at level 58\n",
      "Replacing missing values of NO3 at level 59\n",
      "Replacing missing values of SiO3 at level 57\n",
      "Replacing missing values of SiO3 at level 58\n",
      "Replacing missing values of SiO3 at level 59\n",
      "Replacing missing values of ALK at level 57\n",
      "Replacing missing values of ALK at level 58\n",
      "Replacing missing values of ALK at level 59\n",
      "Replacing missing values of NH4 at level 57\n",
      "Replacing missing values of NH4 at level 58\n",
      "Replacing missing values of NH4 at level 59\n",
      "Replacing missing values of Fe at level 57\n",
      "Replacing missing values of Fe at level 58\n",
      "Replacing missing values of Fe at level 59\n",
      "Replacing missing values of Lig at level 57\n",
      "Replacing missing values of Lig at level 58\n",
      "Replacing missing values of Lig at level 59\n",
      "Replacing missing values of O2 at level 57\n",
      "Replacing missing values of O2 at level 58\n",
      "Replacing missing values of O2 at level 59\n",
      "Replacing missing values of DIC at level 57\n",
      "Replacing missing values of DIC at level 58\n",
      "Replacing missing values of DIC at level 59\n",
      "Replacing missing values of DOCr at level 57\n",
      "Replacing missing values of DOCr at level 58\n",
      "Replacing missing values of DOCr at level 59\n",
      "Replacing missing values of DONr at level 57\n",
      "Replacing missing values of DONr at level 58\n",
      "Replacing missing values of DONr at level 59\n",
      "Replacing missing values of DOPr at level 57\n",
      "Replacing missing values of DOPr at level 58\n",
      "Replacing missing values of DOPr at level 59\n"
     ]
    }
   ],
   "source": [
    "# Make sure there are no missin values in RESTORE_FILE_COLUMN\n",
    "for var in RESTORING_FILE_COLUMN.data_vars:\n",
    "    for k in range(len(RESTORING_FILE_COLUMN['DEPTH'].data)):\n",
    "        if np.any(np.isnan(RESTORING_FILE_COLUMN[var].data[:,k,:,:])):\n",
    "            print(f\"Replacing missing values of {var} at level {k}\")\n",
    "            RESTORING_FILE_COLUMN[var].data[:,k,:,:] = np.where(np.isnan(RESTORING_FILE_COLUMN[var].data[:,k,:,:]),\n",
    "                                                                RESTORING_FILE_COLUMN[var].data[:,k-1,:,:],\n",
    "                                                                RESTORING_FILE_COLUMN[var].data[:,k,:,:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set attributes in MARBL_FORCING_FILE_COLUMN for files that violate typical MOM6 sign convention\n",
    "# (most fluxes are positive into the ocean, but MOM6 expects evaporation and sensible heat flux to be positive up)\n",
    "MARBL_FORCING_FILE_COLUMN['SENS_HEAT_FLUX'].data = -MARBL_FORCING_FILE_COLUMN['SENS_HEAT_FLUX'].data\n",
    "MARBL_FORCING_FILE_COLUMN['SENS_HEAT_FLUX'].attrs['standard_name'] = 'surface_upward_sensible_heat_flux'\n",
    "MARBL_FORCING_FILE_COLUMN['EVAP'].data = -MARBL_FORCING_FILE_COLUMN['EVAP'].data\n",
    "MARBL_FORCING_FILE_COLUMN['EVAP'].attrs['standard_name'] = 'surface_upward_latent_heat_flux_due_to_evaporation'"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "## Clean up TS_IC_FILE attributes\n",
    "TS_IC_FILE_COLUMN['lon'].attrs = dict(units = \"degrees_east\", modulo = 360., point_spacing = \"even\", axis = \"X\")\n",
    "TS_IC_FILE_COLUMN['lat'].attrs = dict(units = \"degrees_north\", point_spacing = \"even\", axis = \"Y\")\n",
    "\n",
    "TS_IC_FILE_COLUMN = set_fill_value(TS_IC_FILE_COLUMN)\n",
    "print(TS_IC_FILE_COLUMN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (lath: 2, lonh: 2, Layer: 65, Interface: 66)\n",
       "Coordinates:\n",
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       "  * lonh         (lonh) float32 -64.69 -64.02\n",
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       "Data variables: (12/35)\n",
       "    Temp         (Layer, lath, lonh) float64 dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;\n",
       "    Salt         (Layer, lath, lonh) float64 dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;\n",
       "    PO4          (Layer, lath, lonh) float64 dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;\n",
       "    NO3          (Layer, lath, lonh) float64 dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;\n",
       "    SiO3         (Layer, lath, lonh) float64 dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;\n",
       "    NH4          (Layer, lath, lonh) float64 dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;\n",
       "    ...           ...\n",
       "    diatSi       (Layer, lath, lonh) float64 dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;\n",
       "    diazChl      (Layer, lath, lonh) float64 dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;\n",
       "    diazC        (Layer, lath, lonh) float64 dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;\n",
       "    diazP        (Layer, lath, lonh) float64 dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;\n",
       "    diazFe       (Layer, lath, lonh) float64 dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;\n",
       "    eta          (Interface, lath, lonh) float64 dask.array&lt;chunksize=(66, 2, 2), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    filename:  ./MOM_IC.nc</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6023ee69-9289-4c00-8126-2e95ad9073a5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6023ee69-9289-4c00-8126-2e95ad9073a5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lath</span>: 2</li><li><span class='xr-has-index'>lonh</span>: 2</li><li><span class='xr-has-index'>Layer</span>: 65</li><li><span class='xr-has-index'>Interface</span>: 66</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5de461d4-601d-4977-8101-9486da9cd931' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5de461d4-601d-4977-8101-9486da9cd931' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lath</span></div><div class='xr-var-dims'>(lath)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>31.1 31.66</div><input id='attrs-55b0ddad-b7c3-41c6-962b-7778f5a588e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55b0ddad-b7c3-41c6-962b-7778f5a588e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d95d2ff-c479-45e4-bc42-360ef4651aca' class='xr-var-data-in' type='checkbox'><label for='data-2d95d2ff-c479-45e4-bc42-360ef4651aca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([31.102219, 31.660667], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lonh</span></div><div class='xr-var-dims'>(lonh)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-64.69 -64.02</div><input id='attrs-ba3cbcb9-09e1-400b-9a99-0aeb4333f3a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba3cbcb9-09e1-400b-9a99-0aeb4333f3a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be7f5bcf-96e6-451f-8890-f387b004b7cc' class='xr-var-data-in' type='checkbox'><label for='data-be7f5bcf-96e6-451f-8890-f387b004b7cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-64.685394, -64.0187  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Layer</span></div><div class='xr-var-dims'>(Layer)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.25 3.75 ... 4.549e+03 4.549e+03</div><input id='attrs-1d9858a6-66dc-4323-a59e-29d217f469f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d9858a6-66dc-4323-a59e-29d217f469f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e1476c6-f039-412c-8c06-f2d2eccb803d' class='xr-var-data-in' type='checkbox'><label for='data-5e1476c6-f039-412c-8c06-f2d2eccb803d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.250000e+00, 3.750000e+00, 6.250000e+00, 8.750000e+00, 1.138500e+01,\n",
       "       1.446000e+01, 1.815500e+01, 2.248500e+01, 2.745500e+01, 3.307500e+01,\n",
       "       3.935500e+01, 4.630000e+01, 5.392500e+01, 6.224000e+01, 7.125500e+01,\n",
       "       8.098000e+01, 9.142500e+01, 1.026000e+02, 1.145150e+02, 1.271900e+02,\n",
       "       1.406450e+02, 1.549150e+02, 1.700550e+02, 1.861300e+02, 2.032150e+02,\n",
       "       2.214000e+02, 2.407900e+02, 2.615100e+02, 2.837200e+02, 3.076050e+02,\n",
       "       3.333850e+02, 3.613300e+02, 3.917600e+02, 4.250750e+02, 4.617600e+02,\n",
       "       5.024050e+02, 5.477500e+02, 5.987050e+02, 6.563950e+02, 7.222050e+02,\n",
       "       7.978050e+02, 8.851550e+02, 9.864300e+02, 1.103850e+03, 1.239390e+03,\n",
       "       1.394315e+03, 1.568745e+03, 1.761440e+03, 1.969940e+03, 2.191090e+03,\n",
       "       2.421660e+03, 2.658830e+03, 2.900405e+03, 3.144820e+03, 3.391020e+03,\n",
       "       3.638320e+03, 3.886295e+03, 4.134680e+03, 4.383315e+03, 4.528337e+03,\n",
       "       4.548995e+03, 4.548996e+03, 4.548997e+03, 4.548998e+03, 4.548999e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Interface</span></div><div class='xr-var-dims'>(Interface)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.5 5.0 ... 5.751e+03 6e+03</div><input id='attrs-f9354111-c30e-49ec-9b4b-702bdc3451c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9354111-c30e-49ec-9b4b-702bdc3451c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78a75aa3-7f91-4204-88e6-0b480ba75746' class='xr-var-data-in' type='checkbox'><label for='data-78a75aa3-7f91-4204-88e6-0b480ba75746' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Interface pseudo-depth, -z*</dd><dt><span>units :</span></dt><dd>meter</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array([0.00000e+00, 2.50000e+00, 5.00000e+00, 7.50000e+00, 1.00000e+01,\n",
       "       1.27700e+01, 1.61500e+01, 2.01600e+01, 2.48100e+01, 3.01000e+01,\n",
       "       3.60500e+01, 4.26600e+01, 4.99400e+01, 5.79100e+01, 6.65700e+01,\n",
       "       7.59400e+01, 8.60200e+01, 9.68300e+01, 1.08370e+02, 1.20660e+02,\n",
       "       1.33720e+02, 1.47570e+02, 1.62260e+02, 1.77850e+02, 1.94410e+02,\n",
       "       2.12020e+02, 2.30780e+02, 2.50800e+02, 2.72220e+02, 2.95220e+02,\n",
       "       3.19990e+02, 3.46780e+02, 3.75880e+02, 4.07640e+02, 4.42510e+02,\n",
       "       4.81010e+02, 5.23800e+02, 5.71700e+02, 6.25710e+02, 6.87080e+02,\n",
       "       7.57330e+02, 8.38280e+02, 9.32030e+02, 1.04083e+03, 1.16687e+03,\n",
       "       1.31191e+03, 1.47672e+03, 1.66077e+03, 1.86211e+03, 2.07777e+03,\n",
       "       2.30441e+03, 2.53891e+03, 2.77875e+03, 3.02206e+03, 3.26758e+03,\n",
       "       3.51446e+03, 3.76218e+03, 4.01041e+03, 4.25895e+03, 4.50768e+03,\n",
       "       4.75652e+03, 5.00516e+03, 5.25384e+03, 5.50255e+03, 5.75127e+03,\n",
       "       6.00000e+03])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77b09813-e85b-4142-9e80-b13929befcb5' class='xr-section-summary-in' type='checkbox'  ><label for='section-77b09813-e85b-4142-9e80-b13929befcb5' class='xr-section-summary' >Data variables: <span>(35)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Temp</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-4e8529ee-f651-4476-b27b-713bfa2ec981' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e8529ee-f651-4476-b27b-713bfa2ec981' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e8ec8a7-34a3-45cb-94e1-b8965582f132' class='xr-var-data-in' type='checkbox'><label for='data-8e8ec8a7-34a3-45cb-94e1-b8965582f132' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Potential Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>checksum :</span></dt><dd>523BAC08F016F13F</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
       "                        <td> (65, 2, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Salt</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-b59336bb-d89d-4bbc-ab62-10083820f9d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b59336bb-d89d-4bbc-ab62-10083820f9d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdf13b0c-d6dc-4d6e-b9c6-ffa52d74109c' class='xr-var-data-in' type='checkbox'><label for='data-bdf13b0c-d6dc-4d6e-b9c6-ffa52d74109c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>units :</span></dt><dd>PPT</dd><dt><span>checksum :</span></dt><dd>5A6F559BA825B817</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <td> 2.03 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PO4</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-01c8d7bb-96b8-451f-b23d-31928b359a3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01c8d7bb-96b8-451f-b23d-31928b359a3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-109c8627-a120-46af-ab10-095ae2d66f0f' class='xr-var-data-in' type='checkbox'><label for='data-109c8627-a120-46af-ab10-095ae2d66f0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>5F857DE1C2477212</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-2b71a854-7992-4830-b763-0ccb324e6ad2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b71a854-7992-4830-b763-0ccb324e6ad2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9928512c-b650-4889-b896-c339f8053122' class='xr-var-data-in' type='checkbox'><label for='data-9928512c-b650-4889-b896-c339f8053122' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>AE483472AF02F9F0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> (65, 2, 2) </td>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-239ca4c5-68df-4f46-94a3-c06a2f5d9d88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-239ca4c5-68df-4f46-94a3-c06a2f5d9d88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0824dd9a-d555-4083-8e7d-0d98673a53c7' class='xr-var-data-in' type='checkbox'><label for='data-0824dd9a-d555-4083-8e7d-0d98673a53c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Iron Binding Ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>4C819F032BE6B2F9</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-54fadc8d-b443-4ea9-a5a6-8750320c69f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54fadc8d-b443-4ea9-a5a6-8750320c69f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98b1cdd6-d2c9-4c59-9a69-93ddeb599221' class='xr-var-data-in' type='checkbox'><label for='data-98b1cdd6-d2c9-4c59-9a69-93ddeb599221' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dissolved Oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>2016D87097CB7E00</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Shape </th>\n",
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       "                        <th> Dask graph </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC_ALT_CO2</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-b02f226d-0b13-49a7-9d30-7103d0401cb1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b02f226d-0b13-49a7-9d30-7103d0401cb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6db0a34e-3897-4738-9b53-f6a90af45619' class='xr-var-data-in' type='checkbox'><label for='data-6db0a34e-3897-4738-9b53-f6a90af45619' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dissolved Inorganic Carbon, Alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>  661F7A5AAFED68</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-f8fe5cb0-8c32-4014-907c-3c6f9ed931bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8fe5cb0-8c32-4014-907c-3c6f9ed931bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d11dca5c-1d63-43d3-b17e-f795457367cc' class='xr-var-data-in' type='checkbox'><label for='data-d11dca5c-1d63-43d3-b17e-f795457367cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>checksum :</span></dt><dd>60943F90D45096F1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ALK_ALT_CO2</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-31e5efdf-66e5-4e1c-98ce-a05dca17a772' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31e5efdf-66e5-4e1c-98ce-a05dca17a772' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae916c88-d7f0-48b3-823b-43e8e64edf86' class='xr-var-data-in' type='checkbox'><label for='data-ae916c88-d7f0-48b3-823b-43e8e64edf86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Alkalinity, Alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd><dt><span>checksum :</span></dt><dd>60943F90D45096F1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 2.03 kiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <th> Dask graph </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOC</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-dfc6a0b0-026a-4963-8ef4-414483267637' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfc6a0b0-026a-4963-8ef4-414483267637' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15fbef23-25ee-4649-a16e-f43422bf5b9b' class='xr-var-data-in' type='checkbox'><label for='data-15fbef23-25ee-4649-a16e-f43422bf5b9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dissolved Organic Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>CF87FAE37A2E38C5</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DON</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-bc41e108-5e54-49bd-abe9-6c1986f3c49f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc41e108-5e54-49bd-abe9-6c1986f3c49f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dfc6fb8-7400-407b-a61f-e75eb7f95bcd' class='xr-var-data-in' type='checkbox'><label for='data-9dfc6fb8-7400-407b-a61f-e75eb7f95bcd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dissolved Organic Nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>5B3C4A513D96E8F2</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                        <td> 2.03 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-c066f411-67b0-4b4e-a5c9-8f4da66ff535' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c066f411-67b0-4b4e-a5c9-8f4da66ff535' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d35437d-cb0b-4ca0-b8ce-5172713e446d' class='xr-var-data-in' type='checkbox'><label for='data-6d35437d-cb0b-4ca0-b8ce-5172713e446d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dissolved Organic Phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>28DB2040A78F87FA</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOPr</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-eb267bd4-b573-4ad7-a578-0f8784f4256a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb267bd4-b573-4ad7-a578-0f8784f4256a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24557bc6-1590-4dae-b12a-807fb3822750' class='xr-var-data-in' type='checkbox'><label for='data-24557bc6-1590-4dae-b12a-807fb3822750' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Refractory DOP</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd> 87FB1A1C8D7B3E6</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-c175008c-9c13-474a-adad-0d1fe0b38af9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c175008c-9c13-474a-adad-0d1fe0b38af9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bc0abf0-ef2d-419f-b0b7-dfea76b40206' class='xr-var-data-in' type='checkbox'><label for='data-5bc0abf0-ef2d-419f-b0b7-dfea76b40206' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Refractory DON</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>9F3B7871FF2EBE4E</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOCr</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-7c1413dd-2626-4392-8024-7b52030b7254' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c1413dd-2626-4392-8024-7b52030b7254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbc8c56a-dca5-49b3-8b61-b691bee22b17' class='xr-var-data-in' type='checkbox'><label for='data-fbc8c56a-dca5-49b3-8b61-b691bee22b17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Refractory DOC</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>AAAEADA95CEE92BA</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <td> 2.03 kiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-c3bdfeb6-2c34-48f9-86db-ff0d41945a54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3bdfeb6-2c34-48f9-86db-ff0d41945a54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-595fcd3b-75a6-4b3b-9f13-9c79bd68c9fa' class='xr-var-data-in' type='checkbox'><label for='data-595fcd3b-75a6-4b3b-9f13-9c79bd68c9fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Zooplankton Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>4F95F7C9CF3BFA70</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 2.03 kiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-52d18a0c-dbf5-495c-96e8-9ae4257feb2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52d18a0c-dbf5-495c-96e8-9ae4257feb2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b0d92ea-8d67-4a67-b589-9cf25a1e3b22' class='xr-var-data-in' type='checkbox'><label for='data-2b0d92ea-8d67-4a67-b589-9cf25a1e3b22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Small Phyto Chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>checksum :</span></dt><dd>587DBCDAC65889CD</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 2.03 kiB </td>\n",
       "                        <td> 2.03 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                        <td> 2.03 kiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spFe</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-d751ced2-2179-4d88-b5b3-df8154aa0b4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d751ced2-2179-4d88-b5b3-df8154aa0b4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6742b10d-26c6-4673-bf8e-9502c5e3b878' class='xr-var-data-in' type='checkbox'><label for='data-6742b10d-26c6-4673-bf8e-9502c5e3b878' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Small Phyto Iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd> 3A76D225D447C64</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Shape </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spCaCO3</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-ecca9ede-b099-4fe8-8629-2e1587d752b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ecca9ede-b099-4fe8-8629-2e1587d752b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-252bcb78-13b0-40f9-b9e3-166ce5866827' class='xr-var-data-in' type='checkbox'><label for='data-252bcb78-13b0-40f9-b9e3-166ce5866827' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Small Phyto CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>5E66B71007AF15D3</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> 2.03 kiB </td>\n",
       "                        <td> 2.03 kiB </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-fd1f937c-d5bf-4724-99db-4cab301e8d40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd1f937c-d5bf-4724-99db-4cab301e8d40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-644445b0-3ed5-49f3-8b11-619c1be203bf' class='xr-var-data-in' type='checkbox'><label for='data-644445b0-3ed5-49f3-8b11-619c1be203bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diatom Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>BB54B81021BD4A98</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                        <td> 2.03 kiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatP</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-9974dc04-58f0-4419-b7df-c42184cd6e00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9974dc04-58f0-4419-b7df-c42184cd6e00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f573763-67f4-4ba4-88ef-d14908a003d2' class='xr-var-data-in' type='checkbox'><label for='data-3f573763-67f4-4ba4-88ef-d14908a003d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diatom Phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>98A4290E60D2B489</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                        <th> Array </th>\n",
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       "                </thead>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                        <td> 2.03 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatFe</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-a130cf4c-55a1-4adf-85d0-1c3f0dee8bf2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a130cf4c-55a1-4adf-85d0-1c3f0dee8bf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b31187d6-9cf8-472e-be63-a44144bf0497' class='xr-var-data-in' type='checkbox'><label for='data-b31187d6-9cf8-472e-be63-a44144bf0497' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diatom Iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd> 5823B2DB1B9A6A3</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-eb3dc525-d412-4eca-858a-61c9ad5e348b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb3dc525-d412-4eca-858a-61c9ad5e348b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f778957-42b0-4491-9f38-e82db2617652' class='xr-var-data-in' type='checkbox'><label for='data-9f778957-42b0-4491-9f38-e82db2617652' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diatom Silicon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>B8257195210DEEA7</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                        <td> 2.03 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (65, 2, 2) </td>\n",
       "                        <td> (65, 2, 2) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 10 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazChl</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-c9230133-7712-4870-aba6-579987f121e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9230133-7712-4870-aba6-579987f121e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4aefdcb-453b-46a6-a089-25c3b81d549e' class='xr-var-data-in' type='checkbox'><label for='data-e4aefdcb-453b-46a6-a089-25c3b81d549e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diazotroph Chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd><dt><span>checksum :</span></dt><dd>D1A6DE7946F8EAC2</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazC</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-a862e587-9c4b-4d9f-b75f-a9b514845bfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a862e587-9c4b-4d9f-b75f-a9b514845bfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7dd06d2-a530-4508-b353-dff8f7cff597' class='xr-var-data-in' type='checkbox'><label for='data-e7dd06d2-a530-4508-b353-dff8f7cff597' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diazotroph Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>69A9DF2800466DC2</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazP</span></div><div class='xr-var-dims'>(Layer, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(65, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-6bc311f6-e968-4a6d-9dec-46d3152627c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bc311f6-e968-4a6d-9dec-46d3152627c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb837b24-1655-4a93-b2db-e4d511b576b9' class='xr-var-data-in' type='checkbox'><label for='data-cb837b24-1655-4a93-b2db-e4d511b576b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diazotroph Phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>checksum :</span></dt><dd>DB771700F908D4BE</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Shape </th>\n",
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       "        <td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.03 kiB </td>\n",
       "                        <td> 2.03 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta</span></div><div class='xr-var-dims'>(Interface, lath, lonh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(66, 2, 2), meta=np.ndarray&gt;</div><input id='attrs-5c02a1b7-e22a-4a64-88cf-d3a1e006835e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c02a1b7-e22a-4a64-88cf-d3a1e006835e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-615c1997-bf75-41a0-8eb4-e88311b388ac' class='xr-var-data-in' type='checkbox'><label for='data-615c1997-bf75-41a0-8eb4-e88311b388ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Interface heights</dd><dt><span>units :</span></dt><dd>meter</dd><dt><span>checksum :</span></dt><dd>6453103806C208A0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.06 kiB </td>\n",
       "                        <td> 2.06 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (66, 2, 2) </td>\n",
       "                        <td> (66, 2, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 8 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "  <text x=\"35.294118\" y=\"86.744680\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,86.744680)\">66</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-5fc8ab1f-a558-4556-ab70-afbae1ec4a25' class='xr-section-summary-in' type='checkbox'  ><label for='section-5fc8ab1f-a558-4556-ab70-afbae1ec4a25' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lath</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-abd5d0bb-85ec-44d8-b09c-23f07554a7de' class='xr-index-data-in' type='checkbox'/><label for='index-abd5d0bb-85ec-44d8-b09c-23f07554a7de' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([31.102219, 31.660667], dtype=&#x27;float32&#x27;, name=&#x27;lath&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lonh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8397463c-4332-40e8-b535-655713f5a8f2' class='xr-index-data-in' type='checkbox'/><label for='index-8397463c-4332-40e8-b535-655713f5a8f2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-64.685394, -64.0187], dtype=&#x27;float32&#x27;, name=&#x27;lonh&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Layer</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0bee7c25-0d1b-40ed-9be5-976d9759c85c' class='xr-index-data-in' type='checkbox'/><label for='index-0bee7c25-0d1b-40ed-9be5-976d9759c85c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1.2499999999990907, 3.7499999999990914,  6.249999999999092,\n",
       "        8.749999999999092, 11.384999999999092, 14.459999999999095,\n",
       "         18.1549999999991, 22.484999999999097,   27.4549999999991,\n",
       "         33.0749999999991,   39.3549999999991,   46.2999999999991,\n",
       "         53.9249999999991,   62.2399999999991,   71.2549999999991,\n",
       "        80.97999999999911,   91.4249999999991, 102.59999999999911,\n",
       "        114.5149999999991, 127.18999999999912, 140.64499999999913,\n",
       "        154.9149999999991,  170.0549999999991, 186.12999999999911,\n",
       "       203.21499999999912, 221.39999999999912,  240.7899999999991,\n",
       "       261.50999999999914,  283.7199999999991,  307.6049999999991,\n",
       "        333.3849999999991, 361.32999999999913, 391.75999999999914,\n",
       "       425.07499999999914, 461.75999999999914,  502.4049999999992,\n",
       "        547.7499999999991,  598.7049999999991,  656.3949999999991,\n",
       "        722.2049999999991,  797.8049999999992,  885.1549999999992,\n",
       "        986.4299999999992, 1103.8499999999992, 1239.3899999999992,\n",
       "       1394.3149999999991,  1568.744999999999, 1761.4399999999991,\n",
       "       1969.9399999999991, 2191.0899999999992,  2421.659999999999,\n",
       "        2658.829999999999,  2900.404999999999,  3144.819999999999,\n",
       "        3391.019999999999, 3638.3199999999993,  3886.294999999999,\n",
       "        4134.679999999999, 4383.3150000000005,          4528.3375,\n",
       "        4548.995499999999,  4548.996499999999, 4548.9974999999995,\n",
       "                4548.9985,          4548.9995],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;Layer&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Interface</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8eb36525-567c-44ef-8068-2bd8d6d2ec68' class='xr-index-data-in' type='checkbox'/><label for='index-8eb36525-567c-44ef-8068-2bd8d6d2ec68' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,                2.5,                5.0,\n",
       "                      7.5,               10.0,              12.77,\n",
       "                    16.15, 20.159999999999997, 24.809999999999995,\n",
       "       30.099999999999994,              36.05,              42.66,\n",
       "                    49.94,              57.91,              66.57,\n",
       "                    75.94,              86.02,              96.83,\n",
       "                   108.37,             120.66,             133.72,\n",
       "                   147.57,             162.26,             177.85,\n",
       "                   194.41, 212.01999999999998, 230.77999999999997,\n",
       "       250.79999999999998, 272.21999999999997, 295.21999999999997,\n",
       "       319.98999999999995,             346.78,             375.88,\n",
       "                   407.64,             442.51,             481.01,\n",
       "                    523.8,  571.6999999999999,  625.7099999999999,\n",
       "        687.0799999999999,  757.3299999999999,             838.28,\n",
       "                   932.03,            1040.83,            1166.87,\n",
       "       1311.9099999999999, 1476.7199999999998, 1660.7699999999998,\n",
       "       1862.1099999999997, 2077.7699999999995, 2304.4099999999994,\n",
       "       2538.9099999999994, 2778.7499999999995, 3022.0599999999995,\n",
       "       3267.5799999999995, 3514.4599999999996, 3762.1799999999994,\n",
       "       4010.4099999999994,            4258.95,  4507.679999999999,\n",
       "       4756.5199999999995,            5005.16,            5253.84,\n",
       "                  5502.55,            5751.27,             6000.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;Interface&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fafdaf28-7303-466b-a319-afdcd72f9e63' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fafdaf28-7303-466b-a319-afdcd72f9e63' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>filename :</span></dt><dd>./MOM_IC.nc</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (lath: 2, lonh: 2, Layer: 65, Interface: 66)\n",
       "Coordinates:\n",
       "  * lath         (lath) float32 31.1 31.66\n",
       "  * lonh         (lonh) float32 -64.69 -64.02\n",
       "  * Layer        (Layer) float64 1.25 3.75 6.25 ... 4.549e+03 4.549e+03\n",
       "  * Interface    (Interface) float64 0.0 2.5 5.0 ... 5.503e+03 5.751e+03 6e+03\n",
       "Data variables: (12/35)\n",
       "    Temp         (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
       "    Salt         (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
       "    PO4          (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
       "    NO3          (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
       "    SiO3         (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
       "    NH4          (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
       "    ...           ...\n",
       "    diatSi       (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
       "    diazChl      (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
       "    diazC        (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
       "    diazP        (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
       "    diazFe       (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
       "    eta          (Interface, lath, lonh) float64 dask.array<chunksize=(66, 2, 2), meta=np.ndarray>\n",
       "Attributes:\n",
       "    filename:  ./MOM_IC.nc"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IC_FILE_COLUMN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:      (lath: 2, lonh: 2, Layer: 65, Interface: 66)\n",
      "Coordinates:\n",
      "  * lath         (lath) float32 31.1 31.66\n",
      "  * lonh         (lonh) float32 -64.69 -64.02\n",
      "  * Layer        (Layer) float64 1.25 3.75 6.25 ... 4.549e+03 4.549e+03\n",
      "  * Interface    (Interface) float64 0.0 2.5 5.0 ... 5.503e+03 5.751e+03 6e+03\n",
      "Data variables: (12/35)\n",
      "    Temp         (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    Salt         (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    PO4          (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    NO3          (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    SiO3         (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    NH4          (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    ...           ...\n",
      "    diatSi       (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    diazChl      (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    diazC        (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    diazP        (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    diazFe       (Layer, lath, lonh) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    eta          (Interface, lath, lonh) float64 dask.array<chunksize=(66, 2, 2), meta=np.ndarray>\n",
      "Attributes:\n",
      "    filename:  ./MOM_IC.nc\n",
      "<xarray.Dataset>\n",
      "Dimensions:      (lat: 2, lon: 2, Layer: 65, Interface: 66)\n",
      "Coordinates:\n",
      "  * lat          (lat) float32 31.1 31.66\n",
      "  * lon          (lon) float32 -64.69 -64.02\n",
      "  * Layer        (Layer) float64 1.25 3.75 6.25 ... 4.549e+03 4.549e+03\n",
      "  * Interface    (Interface) float64 0.0 2.5 5.0 ... 5.503e+03 5.751e+03 6e+03\n",
      "Data variables: (12/40)\n",
      "    Temp         (Layer, lat, lon) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    Salt         (Layer, lat, lon) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    PO4          (Layer, lat, lon) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    NO3          (Layer, lat, lon) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    SiO3         (Layer, lat, lon) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    NH4          (Layer, lat, lon) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    ...           ...\n",
      "    coccoFe      (Layer, lat, lon) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    coccoP       (Layer, lat, lon) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    coccoChl     (Layer, lat, lon) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    coccoCaCO3   (Layer, lat, lon) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    mesozooC     (Layer, lat, lon) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "    microzooC    (Layer, lat, lon) float64 dask.array<chunksize=(65, 2, 2), meta=np.ndarray>\n",
      "Attributes:\n",
      "    filename:  ./MOM_IC.nc\n"
     ]
    }
   ],
   "source": [
    "##Modifications so that we use Kristen's 4p2z model:\n",
    "print(IC_FILE_COLUMN)\n",
    "\n",
    "IC_FILE_COLUMN = IC_FILE_COLUMN.rename({'lath': 'lat', 'lonh': 'lon'})\n",
    "\n",
    "IC_FILE_COLUMN['coccoC'] = IC_FILE_COLUMN['spC'] * 0.07\n",
    "IC_FILE_COLUMN['coccoFe'] = IC_FILE_COLUMN['spFe'] * 0.07\n",
    "IC_FILE_COLUMN['coccoP'] = IC_FILE_COLUMN['spP'] * 0.07\n",
    "IC_FILE_COLUMN['coccoChl'] = IC_FILE_COLUMN['spChl'] * 0.07\n",
    "\n",
    "# and then reduce the sp pools so biomass is conserved:\n",
    "IC_FILE_COLUMN['spC'] = IC_FILE_COLUMN['spC'] * 0.93\n",
    "IC_FILE_COLUMN['spFe'] = IC_FILE_COLUMN['spFe'] * 0.93\n",
    "IC_FILE_COLUMN['spP'] = IC_FILE_COLUMN['spP'] * 0.93\n",
    "IC_FILE_COLUMN['spChl'] = IC_FILE_COLUMN['spChl'] * 0.93\n",
    "\n",
    "#For coccoCaCO3, just set that equal to spCaCO3:\n",
    "IC_FILE_COLUMN['coccoCaCO3'] = IC_FILE_COLUMN['spCaCO3']\n",
    "#and then drop spCaCO3 (it's not a tracer in Kristen's 4p2z version of MARBL)\n",
    "IC_FILE_COLUMN = IC_FILE_COLUMN.drop(['spCaCO3'])\n",
    "\n",
    "IC_FILE_COLUMN['mesozooC'] = IC_FILE_COLUMN['zooC'] * 0.56\n",
    "IC_FILE_COLUMN['microzooC'] = IC_FILE_COLUMN['zooC'] * 0.44\n",
    "#and then drop zooC:\n",
    "IC_FILE_COLUMN = IC_FILE_COLUMN.drop(['zooC'])\n",
    "\n",
    "IC_FILE_COLUMN = set_fill_value(IC_FILE_COLUMN)\n",
    "print(IC_FILE_COLUMN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys = [key for key in IC_FILE_COLUMN.keys() if 'Layer' in IC_FILE_COLUMN[key].dims]\n",
    "len(keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Temp',\n",
       " 'Salt',\n",
       " 'PO4',\n",
       " 'NO3',\n",
       " 'SiO3',\n",
       " 'NH4',\n",
       " 'Fe',\n",
       " 'Lig',\n",
       " 'O2',\n",
       " 'DIC',\n",
       " 'DIC_ALT_CO2',\n",
       " 'ALK',\n",
       " 'ALK_ALT_CO2',\n",
       " 'DOC',\n",
       " 'DON',\n",
       " 'DOP',\n",
       " 'DOPr',\n",
       " 'DONr',\n",
       " 'DOCr',\n",
       " 'spChl',\n",
       " 'spC',\n",
       " 'spP',\n",
       " 'spFe',\n",
       " 'diatChl',\n",
       " 'diatC',\n",
       " 'diatP',\n",
       " 'diatFe',\n",
       " 'diatSi',\n",
       " 'diazChl',\n",
       " 'diazC',\n",
       " 'diazP',\n",
       " 'diazFe',\n",
       " 'coccoC',\n",
       " 'coccoFe',\n",
       " 'coccoP',\n",
       " 'coccoChl',\n",
       " 'coccoCaCO3',\n",
       " 'mesozooC',\n",
       " 'microzooC']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1200 with 39 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=3, ncols=13, figsize=(20, 12), sharey = True, dpi=100)\n",
    "fig.suptitle(f\"{site_name} Input Data for MARBL\", fontsize=18, y=0.95)\n",
    "ymax = 500* (1 + TOPO_FILE_COLUMN['depth'].isel(nx=0,ny=0).values.item() // 500)\n",
    "for key, ax in zip(keys, axs.ravel()):\n",
    "    ax.plot(IC_FILE_COLUMN[key][:,0,0],IC_FILE_COLUMN.Layer,'r',linewidth=1)\n",
    "\n",
    "    ax.set_ylim([ymax, 0])\n",
    "    ax.set_title(key)\n",
    "    ax.grid()\n",
    "    #ax.set_xlabel(IC_FILE_COLUMN[key].units)\n",
    "    ax.ticklabel_format(style='plain', axis='x')\n",
    "    if key in ['spFe','diatFe','diatSi','diazChl','diatP','diazFe','diazP','Fe','coccoFe']:\n",
    "        ax.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
    "\n",
    "#fig.delaxes(axs[-1][-1])\n",
    "#fig.delaxes(axs[-1][-2])\n",
    "fig.tight_layout(rect=[0, 0, 1, 0.95])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1200 with 39 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=3, ncols=13, figsize=(20, 12), sharey = True, dpi=100)\n",
    "fig.suptitle(f\"{site_name} Input Data for MARBL -- Upper 1000 m\", fontsize=18, y=0.95)\n",
    "\n",
    "for key, ax in zip(keys, axs.ravel()):\n",
    "    ax.plot(IC_FILE_COLUMN[key][:,0,0],IC_FILE_COLUMN.Layer,'b',linewidth=1)\n",
    "\n",
    "    ax.set_ylim([1000, 0])\n",
    "    ax.set_title(key)\n",
    "    ax.grid()\n",
    "    #ax.set_xlabel(IC_FILE_COLUMN[key].units)\n",
    "    ax.ticklabel_format(style='plain', axis='x')\n",
    "    if key in ['spFe','diatFe','diatSi','diazChl','diatP','diazFe','diazP','Fe','coccoFe']:\n",
    "        ax.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
    "\n",
    "#fig.delaxes(axs[-1][-1])\n",
    "#fig.delaxes(axs[-1][-2])\n",
    "fig.tight_layout(rect=[0, 0, 1, 0.95])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding SSS restoring to the MARBL_FORCING_FILE_COLUMN:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "initial_salt = np.nanmean(IC_FILE_COLUMN.Salt[0,:])\n",
    "initial_temp = np.nanmean(IC_FILE_COLUMN.Temp[0,:])\n",
    "print(initial_salt, initial_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.62379229854495\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (time: 1825, gridlat_t: 2, gridlon_t: 2, nlat: 2,\n",
       "                           nlon: 2)\n",
       "Coordinates:\n",
       "  * time                  (time) float64 0.5 1.5 2.5 ... 1.824e+03 1.824e+03\n",
       "    lat                   (gridlat_t, gridlon_t) float64 ...\n",
       "    lon                   (gridlat_t, gridlon_t) float64 ...\n",
       "    gridlat_t             (nlat) float64 ...\n",
       "    gridlon_t             (nlon) float64 ...\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables: (12/19)\n",
       "    ATM_FINE_DUST_FLUX    (time, gridlat_t, gridlon_t) float64 5.922e-14 ... ...\n",
       "    ATM_COARSE_DUST_FLUX  (time, gridlat_t, gridlon_t) float64 1.871e-12 ... ...\n",
       "    ATM_BC_FLUX           (time, gridlat_t, gridlon_t) float64 1.299e-13 ... ...\n",
       "    SEAICE_DUST_FLUX      (time, gridlat_t, gridlon_t) float64 0.0 0.0 ... 0.0\n",
       "    SEAICE_BC_FLUX        (time, gridlat_t, gridlon_t) float64 0.0 0.0 ... 0.0\n",
       "    U10_SQR               (time, gridlat_t, gridlon_t) float64 18.83 ... 292.4\n",
       "    ...                    ...\n",
       "    SENS_HEAT_FLUX        (time, gridlat_t, gridlon_t) float64 2.508 ... 202.3\n",
       "    TAUX                  (time, gridlat_t, gridlon_t) float64 -0.01919 ... 0...\n",
       "    TAUY                  (time, gridlat_t, gridlon_t) float64 0.001272 ... -...\n",
       "    SNOW                  (time, gridlat_t, gridlon_t) float64 0.0 0.0 ... 0.0\n",
       "    SST_RESTORE           (time, gridlat_t, gridlon_t) float64 21.19 ... 21.23\n",
       "    SSS_RESTORE           (time, gridlat_t, gridlon_t) float64 36.62 ... 36.62</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8bde0bf5-daa7-4fa2-bb9d-5d3d331e81ea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8bde0bf5-daa7-4fa2-bb9d-5d3d331e81ea' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1825</li><li><span>gridlat_t</span>: 2</li><li><span>gridlon_t</span>: 2</li><li><span>nlat</span>: 2</li><li><span>nlon</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ef8cbb69-20af-463f-86cd-8e715c8ef1d7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ef8cbb69-20af-463f-86cd-8e715c8ef1d7' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 1.824e+03 1.824e+03</div><input id='attrs-f7d8bb86-67b9-40c4-81f9-cff51f1a870a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7d8bb86-67b9-40c4-81f9-cff51f1a870a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c9915f1-31fd-416c-b75d-9196b9b31c5d' class='xr-var-data-in' type='checkbox'><label for='data-0c9915f1-31fd-416c-b75d-9196b9b31c5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>days since 1982-01-01 00:00:00</dd><dt><span>time_origin :</span></dt><dd>01-JAN-1982 00:00:00</dd><dt><span>calendar :</span></dt><dd>NOLEAP</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>modulo :</span></dt><dd> </dd><dt><span>modulo_beg :</span></dt><dd>1982-01-01 00:00:00</dd><dt><span>modulo_end :</span></dt><dd>1987-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([5.0000e-01, 1.5000e+00, 2.5000e+00, ..., 1.8225e+03, 1.8235e+03,\n",
       "       1.8245e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a25dfb75-8b01-49e2-955c-73cd5ba5425d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a25dfb75-8b01-49e2-955c-73cd5ba5425d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2474734c-6d18-4b5d-aedd-e42f281ca9c7' class='xr-var-data-in' type='checkbox'><label for='data-2474734c-6d18-4b5d-aedd-e42f281ca9c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[4 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3f63c8b5-f934-4917-a341-05368c7a1fc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f63c8b5-f934-4917-a341-05368c7a1fc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e11d71c-c8b6-4d32-9cf4-eaa95a7934f9' class='xr-var-data-in' type='checkbox'><label for='data-1e11d71c-c8b6-4d32-9cf4-eaa95a7934f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[4 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gridlat_t</span></div><div class='xr-var-dims'>(nlat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e81700d2-3234-46c5-b56d-19fac8bccc82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e81700d2-3234-46c5-b56d-19fac8bccc82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b18ecef3-5c48-4f18-94a1-d3f8a5392bd5' class='xr-var-data-in' type='checkbox'><label for='data-b18ecef3-5c48-4f18-94a1-d3f8a5392bd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>[2 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gridlon_t</span></div><div class='xr-var-dims'>(nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-502ce293-ca46-41f4-948a-b135f36c9e90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-502ce293-ca46-41f4-948a-b135f36c9e90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7437622-50ff-41a4-989f-a7fc08419f6d' class='xr-var-data-in' type='checkbox'><label for='data-d7437622-50ff-41a4-989f-a7fc08419f6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>[2 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-05dcf227-3877-409d-9c52-1d89f053d911' class='xr-section-summary-in' type='checkbox'  ><label for='section-05dcf227-3877-409d-9c52-1d89f053d911' class='xr-section-summary' >Data variables: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ATM_FINE_DUST_FLUX</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.922e-14 5.922e-14 ... 5.998e-14</div><input id='attrs-a864a663-0090-475b-9d8b-e55e707df708' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a864a663-0090-475b-9d8b-e55e707df708' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca27945a-ee71-4b1d-b7e9-40d2a257d166' class='xr-var-data-in' type='checkbox'><label for='data-ca27945a-ee71-4b1d-b7e9-40d2a257d166' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><pre>array([[[5.922166e-14, 5.922166e-14],\n",
       "        [5.922166e-14, 5.922166e-14]],\n",
       "\n",
       "       [[5.846105e-14, 5.846105e-14],\n",
       "        [5.846105e-14, 5.846105e-14]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[6.074288e-14, 6.074288e-14],\n",
       "        [6.074288e-14, 6.074288e-14]],\n",
       "\n",
       "       [[5.998227e-14, 5.998227e-14],\n",
       "        [5.998227e-14, 5.998227e-14]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ATM_COARSE_DUST_FLUX</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.871e-12 1.871e-12 ... 1.879e-12</div><input id='attrs-e78c32fc-13ed-4ebe-89db-32fe726596ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e78c32fc-13ed-4ebe-89db-32fe726596ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc6a3c23-b949-4a5a-8ae6-29143a91e262' class='xr-var-data-in' type='checkbox'><label for='data-bc6a3c23-b949-4a5a-8ae6-29143a91e262' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><pre>array([[[1.870599e-12, 1.870599e-12],\n",
       "        [1.870599e-12, 1.870599e-12]],\n",
       "\n",
       "       [[1.862687e-12, 1.862687e-12],\n",
       "        [1.862687e-12, 1.862687e-12]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[1.886423e-12, 1.886423e-12],\n",
       "        [1.886423e-12, 1.886423e-12]],\n",
       "\n",
       "       [[1.878511e-12, 1.878511e-12],\n",
       "        [1.878511e-12, 1.878511e-12]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ATM_BC_FLUX</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.299e-13 1.299e-13 ... 1.3e-13</div><input id='attrs-4b8c25b6-1392-4e99-ba24-6b14e2480c3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b8c25b6-1392-4e99-ba24-6b14e2480c3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d5ec572-4640-4875-9b00-447a92aff00a' class='xr-var-data-in' type='checkbox'><label for='data-5d5ec572-4640-4875-9b00-447a92aff00a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><pre>array([[[1.298954e-13, 1.298954e-13],\n",
       "        [1.298954e-13, 1.298954e-13]],\n",
       "\n",
       "       [[1.298185e-13, 1.298185e-13],\n",
       "        [1.298185e-13, 1.298185e-13]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[1.300493e-13, 1.300493e-13],\n",
       "        [1.300493e-13, 1.300493e-13]],\n",
       "\n",
       "       [[1.299724e-13, 1.299724e-13],\n",
       "        [1.299724e-13, 1.299724e-13]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SEAICE_DUST_FLUX</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-f339e1da-37c5-4c6f-9425-44160a550afa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f339e1da-37c5-4c6f-9425-44160a550afa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d25dbdb8-d49a-41fe-9f18-ed78055f039b' class='xr-var-data-in' type='checkbox'><label for='data-d25dbdb8-d49a-41fe-9f18-ed78055f039b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SEAICE_BC_FLUX</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-d87b6888-932e-429b-a823-d84d96e990c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d87b6888-932e-429b-a823-d84d96e990c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6366aafc-aeee-4c7d-b6fe-86b629d7b6a6' class='xr-var-data-in' type='checkbox'><label for='data-6366aafc-aeee-4c7d-b6fe-86b629d7b6a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U10_SQR</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.83 18.83 18.83 ... 292.4 292.4</div><input id='attrs-10cfe3d9-7579-49e2-891a-81f387ff174c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10cfe3d9-7579-49e2-891a-81f387ff174c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b4b11b8-a9b0-4aca-8a27-39c24a4f268c' class='xr-var-data-in' type='checkbox'><label for='data-6b4b11b8-a9b0-4aca-8a27-39c24a4f268c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m2 s-2</dd></dl></div><div class='xr-var-data'><pre>array([[[ 18.829673,  18.829673],\n",
       "        [ 18.829673,  18.829673]],\n",
       "\n",
       "       [[ 57.108193,  57.108193],\n",
       "        [ 57.108193,  57.108193]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[101.914806, 101.914806],\n",
       "        [101.914806, 101.914806]],\n",
       "\n",
       "       [[292.37482 , 292.37482 ],\n",
       "        [292.37482 , 292.37482 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ICE_FRAC</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-bcfa8197-f96a-442c-bb2f-aeb185e4bdf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcfa8197-f96a-442c-bb2f-aeb185e4bdf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb22d940-9488-4aef-9563-f964828da2fb' class='xr-var-data-in' type='checkbox'><label for='data-fb22d940-9488-4aef-9563-f964828da2fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWDN</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-65.03 -65.03 ... -84.75 -84.75</div><input id='attrs-23b66020-db37-4faf-9947-93b593db37db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23b66020-db37-4faf-9947-93b593db37db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22d5ef3e-797b-4104-a1dc-60e2480aa997' class='xr-var-data-in' type='checkbox'><label for='data-22d5ef3e-797b-4104-a1dc-60e2480aa997' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><pre>array([[[-65.033045, -65.033045],\n",
       "        [-65.033045, -65.033045]],\n",
       "\n",
       "       [[-46.875777, -46.875777],\n",
       "        [-46.875777, -46.875777]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[-61.591884, -61.591884],\n",
       "        [-61.591884, -61.591884]],\n",
       "\n",
       "       [[-84.74894 , -84.74894 ],\n",
       "        [-84.74894 , -84.74894 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWDN</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>110.6 110.6 110.6 ... 90.18 90.18</div><input id='attrs-425bc030-2ba9-40f2-b9ee-b4dc363ebb83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-425bc030-2ba9-40f2-b9ee-b4dc363ebb83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-effd9322-b86f-463a-af12-53c4b3bb9e62' class='xr-var-data-in' type='checkbox'><label for='data-effd9322-b86f-463a-af12-53c4b3bb9e62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><pre>array([[[110.561942, 110.561942],\n",
       "        [110.561942, 110.561942]],\n",
       "\n",
       "       [[ 71.580101,  71.580101],\n",
       "        [ 71.580101,  71.580101]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 36.211915,  36.211915],\n",
       "        [ 36.211915,  36.211915]],\n",
       "\n",
       "       [[ 90.182149,  90.182149],\n",
       "        [ 90.182149,  90.182149]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NDEP_NHx</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.5e-13 6.5e-13 ... 6.522e-13</div><input id='attrs-3dbab33b-99e3-4d31-9f16-441727a4ee2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3dbab33b-99e3-4d31-9f16-441727a4ee2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0efd285c-220d-406f-8c0c-18d2370e4574' class='xr-var-data-in' type='checkbox'><label for='data-0efd285c-220d-406f-8c0c-18d2370e4574' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg(N)/m2/sec</dd></dl></div><div class='xr-var-data'><pre>array([[[6.499976e-13, 6.499976e-13],\n",
       "        [6.499976e-13, 6.499976e-13]],\n",
       "\n",
       "       [[6.477776e-13, 6.477776e-13],\n",
       "        [6.477776e-13, 6.477776e-13]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[6.544376e-13, 6.544376e-13],\n",
       "        [6.544376e-13, 6.544376e-13]],\n",
       "\n",
       "       [[6.522176e-13, 6.522176e-13],\n",
       "        [6.522176e-13, 6.522176e-13]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NDEP_NOy</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.973e-13 2.973e-13 ... 2.979e-13</div><input id='attrs-600dfe76-a3ae-4def-b2f3-584ab8a9ef25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-600dfe76-a3ae-4def-b2f3-584ab8a9ef25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a24563a6-08b1-4d4d-9b88-d41b06a37acf' class='xr-var-data-in' type='checkbox'><label for='data-a24563a6-08b1-4d4d-9b88-d41b06a37acf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg(N)/m2/sec</dd></dl></div><div class='xr-var-data'><pre>array([[[2.972804e-13, 2.972804e-13],\n",
       "        [2.972804e-13, 2.972804e-13]],\n",
       "\n",
       "       [[2.966289e-13, 2.966289e-13],\n",
       "        [2.966289e-13, 2.966289e-13]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[2.985835e-13, 2.985835e-13],\n",
       "        [2.985835e-13, 2.985835e-13]],\n",
       "\n",
       "       [[2.979320e-13, 2.979320e-13],\n",
       "        [2.979320e-13, 2.979320e-13]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RAIN</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.157e-08 5.157e-08 ... 1.863e-05</div><input id='attrs-da1ad13e-b00d-47bd-abf7-ca857c01876e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da1ad13e-b00d-47bd-abf7-ca857c01876e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-225690e8-eb32-4301-9289-09954763c1b6' class='xr-var-data-in' type='checkbox'><label for='data-225690e8-eb32-4301-9289-09954763c1b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><pre>array([[[5.156630e-08, 5.156630e-08],\n",
       "        [5.156630e-08, 5.156630e-08]],\n",
       "\n",
       "       [[1.700978e-04, 1.700978e-04],\n",
       "        [1.700978e-04, 1.700978e-04]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[1.785260e-04, 1.785260e-04],\n",
       "        [1.785260e-04, 1.785260e-04]],\n",
       "\n",
       "       [[1.863421e-05, 1.863421e-05],\n",
       "        [1.863421e-05, 1.863421e-05]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EVAP</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.85e-05 2.85e-05 ... 0.0002337</div><input id='attrs-3a820212-fa5b-440a-a427-55836e3eda1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a820212-fa5b-440a-a427-55836e3eda1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0f7f935-24a6-4a2f-ab8b-0e77dbeb7f39' class='xr-var-data-in' type='checkbox'><label for='data-d0f7f935-24a6-4a2f-ab8b-0e77dbeb7f39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>standard_name :</span></dt><dd>surface_upward_latent_heat_flux_due_to_evaporation</dd></dl></div><div class='xr-var-data'><pre>array([[[2.85017661e-05, 2.85017661e-05],\n",
       "        [2.85017661e-05, 2.85017661e-05]],\n",
       "\n",
       "       [[1.69508103e-05, 1.69508103e-05],\n",
       "        [1.69508103e-05, 1.69508103e-05]],\n",
       "\n",
       "       [[1.43460114e-05, 1.43460114e-05],\n",
       "        [1.43460114e-05, 1.43460114e-05]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[1.03747482e-04, 1.03747482e-04],\n",
       "        [1.03747482e-04, 1.03747482e-04]],\n",
       "\n",
       "       [[8.41137530e-05, 8.41137530e-05],\n",
       "        [8.41137530e-05, 8.41137530e-05]],\n",
       "\n",
       "       [[2.33679459e-04, 2.33679459e-04],\n",
       "        [2.33679459e-04, 2.33679459e-04]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SENS_HEAT_FLUX</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.508 2.508 2.508 ... 202.3 202.3</div><input id='attrs-515598b3-15fc-4b50-8652-5978535798dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-515598b3-15fc-4b50-8652-5978535798dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b80b5db-91e2-43b1-b009-0f4d462ef86e' class='xr-var-data-in' type='checkbox'><label for='data-2b80b5db-91e2-43b1-b009-0f4d462ef86e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>standard_name :</span></dt><dd>surface_upward_sensible_heat_flux</dd></dl></div><div class='xr-var-data'><pre>array([[[ 2.50765775e+00,  2.50765775e+00],\n",
       "        [ 2.50765775e+00,  2.50765775e+00]],\n",
       "\n",
       "       [[-1.11135703e-01, -1.11135703e-01],\n",
       "        [-1.11135703e-01, -1.11135703e-01]],\n",
       "\n",
       "       [[-1.53386349e+00, -1.53386349e+00],\n",
       "        [-1.53386349e+00, -1.53386349e+00]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 5.27514605e+01,  5.27514605e+01],\n",
       "        [ 5.27514605e+01,  5.27514605e+01]],\n",
       "\n",
       "       [[ 3.90356615e+01,  3.90356615e+01],\n",
       "        [ 3.90356615e+01,  3.90356615e+01]],\n",
       "\n",
       "       [[ 2.02326229e+02,  2.02326229e+02],\n",
       "        [ 2.02326229e+02,  2.02326229e+02]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TAUX</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.01919 -0.01919 ... 0.5439 0.5439</div><input id='attrs-2bd38a1d-b7e0-41ce-b95e-3c75c27ef801' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bd38a1d-b7e0-41ce-b95e-3c75c27ef801' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-584f5311-dab9-4d64-8cf0-431dcd03c33b' class='xr-var-data-in' type='checkbox'><label for='data-584f5311-dab9-4d64-8cf0-431dcd03c33b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.019193, -0.019193],\n",
       "        [-0.019193, -0.019193]],\n",
       "\n",
       "       [[-0.004461, -0.004461],\n",
       "        [-0.004461, -0.004461]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 0.092036,  0.092036],\n",
       "        [ 0.092036,  0.092036]],\n",
       "\n",
       "       [[ 0.543879,  0.543879],\n",
       "        [ 0.543879,  0.543879]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TAUY</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001272 0.001272 ... -0.1587</div><input id='attrs-308c38e2-9d29-4bf3-84b0-598ed77fec4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-308c38e2-9d29-4bf3-84b0-598ed77fec4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e1d0fa0-b83a-4026-b382-8de8a16b297a' class='xr-var-data-in' type='checkbox'><label for='data-9e1d0fa0-b83a-4026-b382-8de8a16b297a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.001272,  0.001272],\n",
       "        [ 0.001272,  0.001272]],\n",
       "\n",
       "       [[ 0.072049,  0.072049],\n",
       "        [ 0.072049,  0.072049]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 0.04226 ,  0.04226 ],\n",
       "        [ 0.04226 ,  0.04226 ]],\n",
       "\n",
       "       [[-0.158692, -0.158692],\n",
       "        [-0.158692, -0.158692]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SNOW</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-6eba9e37-13bc-44da-a1e8-b0a94694447c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6eba9e37-13bc-44da-a1e8-b0a94694447c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41427d50-b4d9-49ec-ac0c-edef1b25c5fc' class='xr-var-data-in' type='checkbox'><label for='data-41427d50-b4d9-49ec-ac0c-edef1b25c5fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]],\n",
       "\n",
       "       [[0., 0.],\n",
       "        [0., 0.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SST_RESTORE</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>21.19 21.19 21.19 ... 21.23 21.23</div><input id='attrs-41bcd4b4-01c6-4dee-a97f-3dbfad5a87fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41bcd4b4-01c6-4dee-a97f-3dbfad5a87fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9902f544-aa44-422d-a887-65c226646a02' class='xr-var-data-in' type='checkbox'><label for='data-9902f544-aa44-422d-a887-65c226646a02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degC</dd></dl></div><div class='xr-var-data'><pre>array([[[21.191335, 21.191335],\n",
       "        [21.191335, 21.191335]],\n",
       "\n",
       "       [[21.153325, 21.153325],\n",
       "        [21.153325, 21.153325]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[21.267357, 21.267357],\n",
       "        [21.267357, 21.267357]],\n",
       "\n",
       "       [[21.229346, 21.229346],\n",
       "        [21.229346, 21.229346]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSS_RESTORE</span></div><div class='xr-var-dims'>(time, gridlat_t, gridlon_t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>36.62 36.62 36.62 ... 36.62 36.62</div><input id='attrs-41de8bf4-2fb1-45fa-8035-a0a1aece41a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41de8bf4-2fb1-45fa-8035-a0a1aece41a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8c5db60-011a-41bc-817e-ef60ae33e2f7' class='xr-var-data-in' type='checkbox'><label for='data-c8c5db60-011a-41bc-817e-ef60ae33e2f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>PPT</dd></dl></div><div class='xr-var-data'><pre>array([[[36.6237923, 36.6237923],\n",
       "        [36.6237923, 36.6237923]],\n",
       "\n",
       "       [[36.6237923, 36.6237923],\n",
       "        [36.6237923, 36.6237923]],\n",
       "\n",
       "       [[36.6237923, 36.6237923],\n",
       "        [36.6237923, 36.6237923]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[36.6237923, 36.6237923],\n",
       "        [36.6237923, 36.6237923]],\n",
       "\n",
       "       [[36.6237923, 36.6237923],\n",
       "        [36.6237923, 36.6237923]],\n",
       "\n",
       "       [[36.6237923, 36.6237923],\n",
       "        [36.6237923, 36.6237923]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bc6d0db6-4d04-46d5-b3ab-7be036337b34' class='xr-section-summary-in' type='checkbox'  ><label for='section-bc6d0db6-4d04-46d5-b3ab-7be036337b34' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f56b8259-a95d-4b94-bfbc-567e9bdd7e69' class='xr-index-data-in' type='checkbox'/><label for='index-f56b8259-a95d-4b94-bfbc-567e9bdd7e69' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.5,    1.5,    2.5,    3.5,    4.5,    5.5,    6.5,    7.5,    8.5,\n",
       "          9.5,\n",
       "       ...\n",
       "       1815.5, 1816.5, 1817.5, 1818.5, 1819.5, 1820.5, 1821.5, 1822.5, 1823.5,\n",
       "       1824.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=1825))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2caa5e1a-edd2-46b0-ba35-ab90508ff942' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2caa5e1a-edd2-46b0-ba35-ab90508ff942' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (time: 1825, gridlat_t: 2, gridlon_t: 2, nlat: 2,\n",
       "                           nlon: 2)\n",
       "Coordinates:\n",
       "  * time                  (time) float64 0.5 1.5 2.5 ... 1.824e+03 1.824e+03\n",
       "    lat                   (gridlat_t, gridlon_t) float64 ...\n",
       "    lon                   (gridlat_t, gridlon_t) float64 ...\n",
       "    gridlat_t             (nlat) float64 ...\n",
       "    gridlon_t             (nlon) float64 ...\n",
       "Dimensions without coordinates: nlat, nlon\n",
       "Data variables: (12/19)\n",
       "    ATM_FINE_DUST_FLUX    (time, gridlat_t, gridlon_t) float64 5.922e-14 ... ...\n",
       "    ATM_COARSE_DUST_FLUX  (time, gridlat_t, gridlon_t) float64 1.871e-12 ... ...\n",
       "    ATM_BC_FLUX           (time, gridlat_t, gridlon_t) float64 1.299e-13 ... ...\n",
       "    SEAICE_DUST_FLUX      (time, gridlat_t, gridlon_t) float64 0.0 0.0 ... 0.0\n",
       "    SEAICE_BC_FLUX        (time, gridlat_t, gridlon_t) float64 0.0 0.0 ... 0.0\n",
       "    U10_SQR               (time, gridlat_t, gridlon_t) float64 18.83 ... 292.4\n",
       "    ...                    ...\n",
       "    SENS_HEAT_FLUX        (time, gridlat_t, gridlon_t) float64 2.508 ... 202.3\n",
       "    TAUX                  (time, gridlat_t, gridlon_t) float64 -0.01919 ... 0...\n",
       "    TAUY                  (time, gridlat_t, gridlon_t) float64 0.001272 ... -...\n",
       "    SNOW                  (time, gridlat_t, gridlon_t) float64 0.0 0.0 ... 0.0\n",
       "    SST_RESTORE           (time, gridlat_t, gridlon_t) float64 21.19 ... 21.23\n",
       "    SSS_RESTORE           (time, gridlat_t, gridlon_t) float64 36.62 ... 36.62"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MARBL_FORCING_FILE_COLUMN['SST_RESTORE'] = xr.zeros_like(MARBL_FORCING_FILE_COLUMN.SWDN)+initial_temp\n",
    "# MARBL_FORCING_FILE_COLUMN['SST_RESTORE'].attrs = MARBL_FORCING_FILE_COLUMN.SWDN.attrs\n",
    "# MARBL_FORCING_FILE_COLUMN['SST_RESTORE'].attrs['units'] = IC_FILE_COLUMN.Temp.attrs['units']\n",
    "# MARBL_FORCING_FILE_COLUMN['SST_RESTORE'].encoding = IC_FILE_COLUMN.Temp.encoding\n",
    "initial_salt = np.nanmean(IC_FILE_COLUMN.Salt[0,:])\n",
    "print(initial_salt)\n",
    "MARBL_FORCING_FILE_COLUMN['SSS_RESTORE'] = xr.zeros_like(MARBL_FORCING_FILE_COLUMN.SWDN)+initial_salt\n",
    "MARBL_FORCING_FILE_COLUMN['SSS_RESTORE'].attrs = MARBL_FORCING_FILE_COLUMN.SWDN.attrs\n",
    "MARBL_FORCING_FILE_COLUMN['SSS_RESTORE'].attrs['units'] = IC_FILE_COLUMN.Salt.attrs['units']\n",
    "MARBL_FORCING_FILE_COLUMN['SSS_RESTORE'].encoding = IC_FILE_COLUMN.Salt.encoding\n",
    "\n",
    "MARBL_FORCING_FILE_COLUMN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finally save the MARBL column input files within the proper input directory in single_column_MARBL example directory\n",
    "\n",
    "Also write `MOM_override`"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "print(f'({lat:5f}, {lon:5f})')\n",
    "print(f'({lat_dict[site_name]:5f}, {lon_dict[site_name]:5f})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "baseline\n",
      "IC_FILE_COLUMN.nc\n",
      "MARBL_FESEDFLUX_FILE_COLUMN.nc\n",
      "MARBL_FEVENTFLUX_FILE_COLUMN.nc\n",
      "MARBL_FORCING_FILE_COLUMN.nc\n",
      "MARBL_RESTORING_FILE_COLUMN.nc\n",
      "MARBL_RESTORING_TIMESCALE_FILE_COLUMN.nc\n",
      "MOM_override\n",
      "newer_baseline\n",
      "TOPO_FILE_COLUMN.nc\n"
     ]
    }
   ],
   "source": [
    "#%cd /glade/u/home/eyankovsky/MOM6-examples/ocean_only/single_column/INPUT/BATS/\n",
    "USER = os.environ['USER']\n",
    "if  USER == 'eyankovsky':\n",
    "    out_dir = f'/glade/u/home/eyankovsky/CESM/cesm2_3_alpha12b+mom6_marbl/components/mom/standalone/examples/single_column_MARBL/INPUT/{site_name}'\n",
    "elif USER == 'mlevy':\n",
    "    out_dir = f'/glade/work/mlevy/codes/CESM/cesm2_3_beta15+mom6_marbl/components/mom/standalone/examples/single_column_MARBL/INPUT/{site_name}'\n",
    "else:\n",
    "    print(f'Can not find output directory for {USER}, writing netcdf in current dir')\n",
    "    out_dir = f'./{site_name}'\n",
    "\n",
    "# remove existing copies of output files\n",
    "for file in['IC_FILE_COLUMN.nc', 'MARBL_FESEDFLUX_FILE_COLUMN.nc', 'MARBL_FEVENTFLUX_FILE_COLUMN.nc', 'MARBL_FORCING_FILE_COLUMN.nc', 'MARBL_RESTORING_FILE_COLUMN.nc', 'MARBL_RESTORING_TIMESCALE_FILE_COLUMN.nc', 'TOPO_FILE_COLUMN.nc', 'MOM_override']:\n",
    "    full_path = f'{out_dir}/{file}'\n",
    "    try:\n",
    "        os.remove(full_path)\n",
    "    except FileNotFoundError:\n",
    "        print(f'{full_path} does not exist')\n",
    "\n",
    "IC_FILE_COLUMN.drop('eta').to_netcdf(f'{out_dir}/IC_FILE_COLUMN.nc', 'w')\n",
    "\n",
    "TOPO_FILE_COLUMN.to_netcdf(f'{out_dir}/TOPO_FILE_COLUMN.nc', 'w')\n",
    "\n",
    "MARBL_FESEDFLUX_FILE_COLUMN.to_netcdf(f'{out_dir}/MARBL_FESEDFLUX_FILE_COLUMN.nc','w')\n",
    "MARBL_FEVENTFLUX_FILE_COLUMN.to_netcdf(f'{out_dir}/MARBL_FEVENTFLUX_FILE_COLUMN.nc','w')\n",
    "\n",
    "MARBL_FORCING_FILE_COLUMN.to_netcdf(f'{out_dir}/MARBL_FORCING_FILE_COLUMN.nc','w',unlimited_dims='time')\n",
    "RESTORING_FILE_COLUMN.to_netcdf(f'{out_dir}/MARBL_RESTORING_FILE_COLUMN.nc', 'w',unlimited_dims='time')\n",
    "MARBL_RESTORING_TIMESCALE_FILE_COLUMN.to_netcdf(f'{out_dir}/MARBL_RESTORING_TIMESCALE_FILE_COLUMN.nc', 'w')\n",
    "\n",
    "# Generate MOM_override\n",
    "# F_0 is Coriolis parameter, which is 2*sin(lat)*Omega, where Omega is rotation rate of Earth in radians / sec\n",
    "Omega = (2.*np.pi / 86400.) # Earth completes a full rotation (2pi) per day (86400 s)\n",
    "with open(f'{out_dir}/MOM_override', 'w') as f:\n",
    "    f.write('! Generated by Generate_IC_Files.ipynb\\n')\n",
    "    f.write(f'F_0 = {2*np.sin(lat_dict[site_name]*np.pi/180.)*Omega:.5e}\\n')\n",
    "    f.write(f'INPUTDIR = \"INPUT/{site_name}\"\\n')\n",
    "    f.write(f'CESM_INPUTDIR = \"INPUT/{site_name}\"\\n')\n",
    "    f.write(f'SOUTHLAT = {lat:.5f}\\n')\n",
    "    f.write(f'WESTLON = {lon:.5f}\\n')\n",
    "    f.write('CHL_FROM_FILE = FALSE\\n')\n",
    "    f.write('READ_RIV_FLUXES = FALSE\\n')\n",
    "\n",
    "_ = os.system(f'ls {out_dir}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_IC_FILE = xr.open_dataset(f'{out_dir}/IC_FILE_COLUMN.nc',decode_times=False)\n",
    "#testing_ICs = xr.open_dataset('ICs.nc')\n",
    "testing_IC_FILE.Temp[:,1,1].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#(1/1.5854895991882293e-08)/86400/365"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Restoring timescale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# restoring = xr.open_dataset('/glade/p/cesmdata/cseg/inputdata/ocn/pop/gx1v6/forcing/ecosys_restore_inv_tau_POP_gx1v6_20170125.nc')\n",
    "\n",
    "# #print(restoring.RESTORE_INV_TAU )\n",
    "\n",
    "# print('max restoring inverse timescale in years is:',(1/(np.nanmax(restoring.RESTORE_INV_TAU)))/86400/365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# plt.plot(restoring.depth,restoring.RESTORE_INV_TAU[:,100,100])\n",
    "\n",
    "# plt.plot(restoring.depth,restoring.RESTORE_INV_TAU[:,100,100]*np.tanh(restoring.depth/500))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# restoring['RESTORE_INV_TAU'] = restoring.RESTORE_INV_TAU*np.tanh(restoring.depth/500)\n",
    "# restoring['RESTORE_INV_TAU_MARGINAL_SEA_ONLY'] = restoring.RESTORE_INV_TAU_MARGINAL_SEA_ONLY*np.tanh(restoring.depth/500)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %cd /glade/u/home/eyankovsky/MOM6-examples/ocean_only/single_column/KPP_MARBL/INPUT/BATS/\n",
    "# %rm restoring_3D.nc\n",
    "\n",
    "# restoring.to_netcdf('restoring_3D.nc','w')\n",
    "# test = xr.open_dataset('restoring_3D.nc')\n",
    "# print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(test.depth,test.RESTORE_INV_TAU[:,50,50])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "IC_FILE_COLUMN.TEMP[:,0,0].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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