CouplingIndex_model_convertSMvolumetric.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import libraries \n",
    "import comet as cm \n",
    "import numpy as np \n",
    "import xarray as xr \n",
    "import pickle\n",
    "import pandas as pd\n",
    "import datetime \n",
    "#import regionmask\n",
    "from ComputeLCLpressure_function import ComputeLCLpressure\n",
    "\n",
    "# Plotting utils \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.colors as colors\n",
    "import cartopy\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.util\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set directories and file names \n",
    "dailyDir   = '/Users/mdfowler/Documents/Analysis/Coupling_initial/data/day/'\n",
    "#dailyDir   = '/glade/work/mdfowler/data/CESM2/'\n",
    "middleName = '_day_CESM2_amip_r10i1p1f1_gn_'\n",
    "varNames   = ['hfls','hfss','mrso','mrsos','tas','hurs','ps']\n",
    "\n",
    "# Set time period portion of fileNames \n",
    "# timeName_flx  = ['19500101-19591231','19600101-19691231','19700101-19791231','19800101-19891231', \n",
    "#                  '19900101-19991231','20000101-20091231','20100101-20150101']\n",
    "timeName_flx  = ['19700101-19791231','19800101-19891231', \n",
    "                 '19900101-19991231','20000101-20091231']\n",
    "\n",
    "timeName_soil = ['19500101-19991231','20000101-20150101']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/lib/python3.8/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'hfls' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    }
   ],
   "source": [
    "# Read in test file to get lat/lon \n",
    "fileName = dailyDir+varNames[0]+middleName+timeName_flx[0]+'.nc'\n",
    "testDF   = xr.open_dataset(fileName, decode_times=True)\n",
    "\n",
    "# Get lat and lon \n",
    "lat = testDF.lat.values\n",
    "lon = testDF.lon.values \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/lib/python3.8/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'hfss' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n",
      "<ipython-input-4-1f491abff45f>:10: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.\n",
      "  shDF['time'] = shDF.indexes['time'].to_datetimeindex() # Convert from cf time (non-standard calendar) to datetime\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with  19700101-19791231\n",
      "Done with  19800101-19891231\n",
      "Done with  19900101-19991231\n",
      "Done with  20000101-20091231\n"
     ]
    }
   ],
   "source": [
    "# Read in surface fluxes along\n",
    "\n",
    "for iT in range(len(timeName_flx)):\n",
    "\n",
    "    # --- Compile surface fluxes into singe large arrays in time \n",
    "\n",
    "    # Sensible heat flux\n",
    "    shFile       = dailyDir+varNames[1]+middleName+timeName_flx[iT]+'.nc' # File name\n",
    "    shDF         = xr.open_dataset(shFile,decode_times=True)\n",
    "    shDF['time'] = shDF.indexes['time'].to_datetimeindex() # Convert from cf time (non-standard calendar) to datetime\n",
    "\n",
    "\n",
    "    if iT==0:\n",
    "        shflx_full = shDF\n",
    "    else: \n",
    "        shflx_full = xr.concat([shflx_full, shDF], dim=\"time\")\n",
    "\n",
    "    print('Done with ', timeName_flx[iT])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mdfowler/opt/anaconda3/lib/python3.8/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'mrsos' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n",
      "<ipython-input-7-fdd1827c18d8>:15: RuntimeWarning: Converting a CFTimeIndex with dates from a non-standard calendar, 'noleap', to a pandas.DatetimeIndex, which uses dates from the standard calendar.  This may lead to subtle errors in operations that depend on the length of time between dates.\n",
      "  soil10DF['time'] = soil10DF.indexes['time'].to_datetimeindex()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with  19500101-19991231\n",
      "Done with  20000101-20150101\n"
     ]
    }
   ],
   "source": [
    "# Read in soil moisture\n",
    "\n",
    "for iT in range(len(timeName_soil)):\n",
    "\n",
    "    # --- Compile soil moisture into singe large arrays in time \n",
    "\n",
    "#     # Full column SM \n",
    "#     soilFile = dailyDir+varNames[2]+middleName+timeName_soil[iT]+'.nc' # File name\n",
    "#     soilDF   = xr.open_dataset(soilFile,decode_times=True) \n",
    "#     soilDF['time'] = soilDF.indexes['time'].to_datetimeindex()\n",
    "    \n",
    "    # 10 cm SM \n",
    "    soil10File = dailyDir+varNames[3]+middleName+timeName_soil[iT]+'.nc' # File name\n",
    "    soil10DF   = xr.open_dataset(soil10File,decode_times=True)\n",
    "    soil10DF['time'] = soil10DF.indexes['time'].to_datetimeindex()\n",
    "    \n",
    "    if iT==0:\n",
    "#         soilM_full   = soilDF \n",
    "        soilM10_full = soil10DF\n",
    "    else: \n",
    "#         soilM_full   = xr.concat([soilM_full, soilDF], dim=\"time\")\n",
    "        soilM10_full = xr.concat([soilM10_full, soil10DF], dim=\"time\")\n",
    "\n",
    "    print('Done with ', timeName_soil[iT])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pressure level of LCL (computed separately in Get_LCLpressure.py and saved as pickle file)\n",
    "lclPath = '/Users/mdfowler/Documents/Analysis/Coupling_initial/Coupling_CAM6CLM5/processed_data/LCL-pressure-HeightAboveGround_1970-2014.p'\n",
    "#lclPath = '/glade/work/mdfowler/data/CESM2/LCL-pressure-HeightAboveGround_1970-2014.p'\n",
    "\n",
    "Plcl    = pickle.load( open( lclPath, \"rb\" ) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Limit time period of soil moisture: \n",
    "dates       = pd.DatetimeIndex(soilM10_full['time'].values)\n",
    "iIncludeSM  = np.where((dates.year>=1979) & (dates.year<=2006))[0]\n",
    "soilM10_sel = np.asarray(soilM10_full.mrsos.values)[iIncludeSM,:,:]\n",
    "\n",
    "# Now limit the time period of everything else \n",
    "dates     = pd.DatetimeIndex(shflx_full['time'].values)\n",
    "iInclude  = np.where((dates.year>=1979) & (dates.year<=2006))[0]\n",
    "dates     = dates[iInclude]\n",
    "\n",
    "SHflx_sel = shflx_full.isel(time=iInclude)\n",
    "Plcl_sel  = Plcl[iInclude,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert soil moisture to volumetric (or at leas try...)\n",
    "soilM10_convert = soilM10_sel / (1000.0*0.1)  # divide by density of water and by depth \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-7bb81a162d53>:4: RuntimeWarning: invalid value encountered in greater\n",
      "  pctGT1 = (len(np.where(soilM10_convert>1)[0])/len(np.where(np.isfinite(soilM10_convert)==True)[0]))*100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of values > 1.0: 0.09 percent\n"
     ]
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at distribution of converted soil moisture. Most values should be between 0 and 1. \n",
    "plt.hist(soilM10_convert.reshape(len(lat)*len(lon)*np.shape(soilM10_convert)[0]))\n",
    "\n",
    "pctGT1 = (len(np.where(soilM10_convert>1)[0])/len(np.where(np.isfinite(soilM10_convert)==True)[0]))*100\n",
    "print('Percent of values > 1.0: %.2f percent' % pctGT1)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute coupling index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make one big xr.Dataset containing all the information we need  \n",
    "dFull = SHflx_sel   # hfss \n",
    "dFull['mrsos'] = (('time','lat','lon'), soilM10_sel)\n",
    "dFull['SMconvert'] = (('time','lat','lon'), soilM10_convert)\n",
    "dFull['Plcl']  = (('time','lat','lon'), Plcl_sel)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 192, lon: 288, nbnd: 2, time: 10220)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2006-12-31\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    hfss       (time, lat, lon) float32 -4.4452367 -4.4452367 ... -22.73284\n",
       "    time_bnds  (time, nbnd) object 1978-12-31 00:00:00 ... 2006-12-31 00:00:00\n",
       "    lat_bnds   (time, lat, nbnd) float64 -90.0 -89.53 -89.53 ... 89.53 90.0\n",
       "    lon_bnds   (time, lon, nbnd) float64 -0.625 0.625 0.625 ... 358.1 359.4\n",
       "    mrsos      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    SMconvert  (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    Plcl       (time, lat, lon) float64 6.456 6.456 6.456 6.456 ... nan nan nan\n",
       "Attributes:\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          no parent\n",
       "    branch_time_in_child:   711385.0\n",
       "    branch_time_in_parent:  0.0\n",
       "    case_id:                1587\n",
       "    cesm_casename:          f.e21.FHIST_BGC.f09_f09_mg17.CMIP6-AMIP.010\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-11-21T11:58:13Z\n",
       "    data_specs_version:     01.00.31\n",
       "    experiment:             An atmosphere only climate simulation using presc...\n",
       "    experiment_id:          amip\n",
       "    external_variables:     areacella\n",
       "    forcing_index:          1\n",
       "    frequency:              day\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.a...\n",
       "    grid:                   native 0.9x1.25 finite volume grid (192x288 latxlon)\n",
       "    grid_label:             gn\n",
       "    initialization_index:   1\n",
       "    institution:            National Center for Atmospheric Research, Climate...\n",
       "    institution_id:         NCAR\n",
       "    license:                CMIP6 model data produced by &lt;The National Center...\n",
       "    mip_era:                CMIP6\n",
       "    model_doi_url:          https://doi.org/10.5065/D67H1H0V\n",
       "    nominal_resolution:     100 km\n",
       "    parent_activity_id:     no parent\n",
       "    parent_experiment_id:   no parent\n",
       "    parent_mip_era:         no parent\n",
       "    parent_source_id:       no parent\n",
       "    parent_time_units:      no parent\n",
       "    parent_variant_label:   no parent\n",
       "    physics_index:          1\n",
       "    product:                model-output\n",
       "    realization_index:      10\n",
       "    realm:                  atmos\n",
       "    source:                 CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite v...\n",
       "    source_id:              CESM2\n",
       "    source_type:            AGCM BGC AER\n",
       "    sub_experiment:         none\n",
       "    sub_experiment_id:      none\n",
       "    table_id:               day\n",
       "    tracking_id:            hdl:21.14100/09a305e9-edf8-4118-b6db-b18d92417e5b\n",
       "    variable_id:            hfss\n",
       "    variant_info:           CMIP6 CESM2 AMIP hindcast (1950-2014) with intera...\n",
       "    variant_label:          r10i1p1f1</pre><div class='xr-wrap' hidden><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-85e02fbd-0d9c-4022-b7c9-af0c74d7a04a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-85e02fbd-0d9c-4022-b7c9-af0c74d7a04a' 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'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li><li><span class='xr-has-index'>time</span>: 10220</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c3f6ca4a-8dbc-4946-a2e9-b7b29580d58c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c3f6ca4a-8dbc-4946-a2e9-b7b29580d58c' class='xr-section-summary' >Coordinates: <span>(3)</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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-ed138414-2135-46c3-a0f1-dd6b916bdd67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed138414-2135-46c3-a0f1-dd6b916bdd67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9836f2ae-1ec9-4b67-9286-a63268916aac' class='xr-var-data-in' type='checkbox'><label for='data-9836f2ae-1ec9-4b67-9286-a63268916aac' 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>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-2913ab3d-1ef2-42c4-8d19-4390d4c541ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2913ab3d-1ef2-42c4-8d19-4390d4c541ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9655184-5a8d-4448-becd-5f179b872277' class='xr-var-data-in' type='checkbox'><label for='data-a9655184-5a8d-4448-becd-5f179b872277' 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>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2006-12-31</div><input id='attrs-3cd548b9-d020-4d2e-b36e-a97585ad05de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3cd548b9-d020-4d2e-b36e-a97585ad05de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90affeea-58c1-4211-adc3-cea37198e67b' class='xr-var-data-in' type='checkbox'><label for='data-90affeea-58c1-4211-adc3-cea37198e67b' 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([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1979-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-03T00:00:00.000000000&#x27;, ..., &#x27;2006-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2006-12-30T00:00:00.000000000&#x27;, &#x27;2006-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0d3929b-fdf2-4fea-8270-6db9ca211d80' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0d3929b-fdf2-4fea-8270-6db9ca211d80' class='xr-section-summary' >Data variables: <span>(7)</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>hfss</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-4.4452367 -4.4452367 ... -22.73284</div><input id='attrs-1d5d513c-8047-4aeb-85d3-fae9c41aa8a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d5d513c-8047-4aeb-85d3-fae9c41aa8a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a5f4a86-7f1d-4dae-ac15-55b292d9c925' class='xr-var-data-in' type='checkbox'><label for='data-3a5f4a86-7f1d-4dae-ac15-55b292d9c925' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>SHFLX</dd><dt><span>description :</span></dt><dd>The surface sensible heat flux, also called turbulent heat flux, is the exchange of heat between the surface and the air by motion of air.</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>id :</span></dt><dd>hfss</dd><dt><span>long_name :</span></dt><dd>Surface Upward Sensible Heat Flux</dd><dt><span>mipTable :</span></dt><dd>day</dd><dt><span>out_name :</span></dt><dd>hfss</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>prov :</span></dt><dd>day ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>standard_name :</span></dt><dd>surface_upward_sensible_heat_flux</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>Surface Upward Sensible Heat Flux</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>variable_id :</span></dt><dd>hfss</dd></dl></div><div class='xr-var-data'><pre>array([[[ -4.4452367 ,  -4.4452367 ,  -4.4452367 , ...,  -4.4452367 ,\n",
       "          -4.4452367 ,  -4.445237  ],\n",
       "        [ -4.3907404 ,  -4.3641753 ,  -4.136916  , ...,  -4.3532405 ,\n",
       "          -4.3400116 ,  -4.3676286 ],\n",
       "        [ -4.423356  ,  -4.477108  ,  -4.5500526 , ...,  -4.4795413 ,\n",
       "          -4.490702  ,  -4.4607983 ],\n",
       "        ...,\n",
       "        [ -5.1546144 ,  -5.1424227 ,  -4.906066  , ...,  -4.741546  ,\n",
       "          -5.118043  ,  -5.260579  ],\n",
       "        [-18.398436  , -18.407938  , -18.250011  , ..., -18.215559  ,\n",
       "         -18.456263  , -18.297232  ],\n",
       "        [-27.067703  , -27.067736  , -27.067368  , ..., -27.06756   ,\n",
       "         -27.06775   , -27.067677  ]],\n",
       "\n",
       "       [[ -4.0951953 ,  -4.095195  ,  -4.0951953 , ...,  -4.0951953 ,\n",
       "          -4.0951953 ,  -4.0951953 ],\n",
       "        [ -4.2846293 ,  -4.2491775 ,  -4.082181  , ...,  -4.2550306 ,\n",
       "          -4.267214  ,  -4.27716   ],\n",
       "        [ -4.4479213 ,  -4.4766026 ,  -4.560481  , ...,  -4.5048933 ,\n",
       "          -4.5526404 ,  -4.5457196 ],\n",
       "...\n",
       "        [  3.9334471 ,   2.2423446 ,   1.9548287 , ...,   2.7677438 ,\n",
       "           2.8867939 ,   5.2556915 ],\n",
       "        [ -4.407888  ,  -4.7743864 ,  -5.1013584 , ...,  -4.5195894 ,\n",
       "          -4.2026258 ,  -4.366879  ],\n",
       "        [ -7.605556  ,  -7.6050344 ,  -7.6059294 , ...,  -7.605586  ,\n",
       "          -7.605626  ,  -7.6055803 ]],\n",
       "\n",
       "       [[ -5.770412  ,  -5.7703953 ,  -5.7701716 , ...,  -5.770417  ,\n",
       "          -5.770416  ,  -5.7704086 ],\n",
       "        [ -4.9327707 ,  -4.87335   ,  -4.5972056 , ...,  -4.7948937 ,\n",
       "          -4.80155   ,  -4.821063  ],\n",
       "        [ -3.6644757 ,  -3.7042058 ,  -3.7252853 , ...,  -3.678804  ,\n",
       "          -3.6548643 ,  -3.6492276 ],\n",
       "        ...,\n",
       "        [-12.986506  , -14.251667  , -14.85304   , ..., -11.949723  ,\n",
       "         -12.2512045 , -11.527158  ],\n",
       "        [-24.450386  , -24.54907   , -24.604088  , ..., -24.148212  ,\n",
       "         -24.353617  , -24.485928  ],\n",
       "        [-22.732845  , -22.732769  , -22.732895  , ..., -22.732843  ,\n",
       "         -22.732866  , -22.73284   ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1978-12-31 00:00:00 ... 2006-12-...</div><input id='attrs-23bc0ea3-601f-44ab-8650-bdf9a14a3476' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23bc0ea3-601f-44ab-8650-bdf9a14a3476' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e057dd50-7b23-4ef4-abb9-8c1dfea59d50' class='xr-var-data-in' type='checkbox'><label for='data-e057dd50-7b23-4ef4-abb9-8c1dfea59d50' 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([[cftime.DatetimeNoLeap(1978, 12, 31, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(1979, 1, 1, 0, 0, 0, 0)],\n",
       "       [cftime.DatetimeNoLeap(1979, 1, 1, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(1979, 1, 2, 0, 0, 0, 0)],\n",
       "       [cftime.DatetimeNoLeap(1979, 1, 2, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(1979, 1, 3, 0, 0, 0, 0)],\n",
       "       ...,\n",
       "       [cftime.DatetimeNoLeap(2006, 12, 28, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(2006, 12, 29, 0, 0, 0, 0)],\n",
       "       [cftime.DatetimeNoLeap(2006, 12, 29, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(2006, 12, 30, 0, 0, 0, 0)],\n",
       "       [cftime.DatetimeNoLeap(2006, 12, 30, 0, 0, 0, 0),\n",
       "        cftime.DatetimeNoLeap(2006, 12, 31, 0, 0, 0, 0)]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(time, lat, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.53 -89.53 ... 89.53 90.0</div><input id='attrs-016fa1ec-eef9-4fd8-8df1-fb1226409c20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-016fa1ec-eef9-4fd8-8df1-fb1226409c20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bf7894b-b3bf-4e3b-97f6-876eaf0c61ac' class='xr-var-data-in' type='checkbox'><label for='data-9bf7894b-b3bf-4e3b-97f6-876eaf0c61ac' 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>array([[[-90.        , -89.52879581],\n",
       "        [-89.52879581, -88.58638743],\n",
       "        [-88.58638743, -87.64397906],\n",
       "        ...,\n",
       "        [ 87.64397906,  88.58638743],\n",
       "        [ 88.58638743,  89.52879581],\n",
       "        [ 89.52879581,  90.        ]],\n",
       "\n",
       "       [[-90.        , -89.52879581],\n",
       "        [-89.52879581, -88.58638743],\n",
       "        [-88.58638743, -87.64397906],\n",
       "        ...,\n",
       "        [ 87.64397906,  88.58638743],\n",
       "        [ 88.58638743,  89.52879581],\n",
       "        [ 89.52879581,  90.        ]],\n",
       "\n",
       "       [[-90.        , -89.52879581],\n",
       "        [-89.52879581, -88.58638743],\n",
       "        [-88.58638743, -87.64397906],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 87.64397906,  88.58638743],\n",
       "        [ 88.58638743,  89.52879581],\n",
       "        [ 89.52879581,  90.        ]],\n",
       "\n",
       "       [[-90.        , -89.52879581],\n",
       "        [-89.52879581, -88.58638743],\n",
       "        [-88.58638743, -87.64397906],\n",
       "        ...,\n",
       "        [ 87.64397906,  88.58638743],\n",
       "        [ 88.58638743,  89.52879581],\n",
       "        [ 89.52879581,  90.        ]],\n",
       "\n",
       "       [[-90.        , -89.52879581],\n",
       "        [-89.52879581, -88.58638743],\n",
       "        [-88.58638743, -87.64397906],\n",
       "        ...,\n",
       "        [ 87.64397906,  88.58638743],\n",
       "        [ 88.58638743,  89.52879581],\n",
       "        [ 89.52879581,  90.        ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(time, lon, nbnd)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.625 0.625 0.625 ... 358.1 359.4</div><input id='attrs-e325de0e-e834-4f78-b937-4cc309c902da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e325de0e-e834-4f78-b937-4cc309c902da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc3c7333-15fd-479b-afa6-8cfcb9d40e8b' class='xr-var-data-in' type='checkbox'><label for='data-cc3c7333-15fd-479b-afa6-8cfcb9d40e8b' 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>array([[[ -0.625,   0.625],\n",
       "        [  0.625,   1.875],\n",
       "        [  1.875,   3.125],\n",
       "        ...,\n",
       "        [355.625, 356.875],\n",
       "        [356.875, 358.125],\n",
       "        [358.125, 359.375]],\n",
       "\n",
       "       [[ -0.625,   0.625],\n",
       "        [  0.625,   1.875],\n",
       "        [  1.875,   3.125],\n",
       "        ...,\n",
       "        [355.625, 356.875],\n",
       "        [356.875, 358.125],\n",
       "        [358.125, 359.375]],\n",
       "\n",
       "       [[ -0.625,   0.625],\n",
       "        [  0.625,   1.875],\n",
       "        [  1.875,   3.125],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [355.625, 356.875],\n",
       "        [356.875, 358.125],\n",
       "        [358.125, 359.375]],\n",
       "\n",
       "       [[ -0.625,   0.625],\n",
       "        [  0.625,   1.875],\n",
       "        [  1.875,   3.125],\n",
       "        ...,\n",
       "        [355.625, 356.875],\n",
       "        [356.875, 358.125],\n",
       "        [358.125, 359.375]],\n",
       "\n",
       "       [[ -0.625,   0.625],\n",
       "        [  0.625,   1.875],\n",
       "        [  1.875,   3.125],\n",
       "        ...,\n",
       "        [355.625, 356.875],\n",
       "        [356.875, 358.125],\n",
       "        [358.125, 359.375]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mrsos</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-460c186e-db6d-4000-aa43-bfc8d79e572a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-460c186e-db6d-4000-aa43-bfc8d79e572a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-beea5eb9-cf9c-422d-9b6e-bfc3f76c4dba' class='xr-var-data-in' type='checkbox'><label for='data-beea5eb9-cf9c-422d-9b6e-bfc3f76c4dba' 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([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SMconvert</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-49bcb662-4808-4f4f-912c-3e3e970f5cb9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49bcb662-4808-4f4f-912c-3e3e970f5cb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3887297c-b875-45f1-98e0-b489b427ece3' class='xr-var-data-in' type='checkbox'><label for='data-3887297c-b875-45f1-98e0-b489b427ece3' 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([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Plcl</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.456 6.456 6.456 ... nan nan nan</div><input id='attrs-d07abc88-8884-4138-98d1-468bdb35642e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d07abc88-8884-4138-98d1-468bdb35642e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdc7e368-e6d8-4322-b7fd-fc36deb044d9' class='xr-var-data-in' type='checkbox'><label for='data-fdc7e368-e6d8-4322-b7fd-fc36deb044d9' 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([[[ 6.45580421,  6.45580421,  6.45580421, ...,  6.45580421,\n",
       "          6.45580421,  6.45580421],\n",
       "        [10.03939886,  9.97277702,  9.10249232, ...,  9.74829738,\n",
       "          9.86457896,  9.94523805],\n",
       "        [ 9.09528343,  9.25644805,  9.54817103, ...,  9.34209555,\n",
       "          9.42265465,  9.38181243],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[ 8.01539597,  8.01539597,  8.01539597, ...,  8.01539597,\n",
       "          8.01539597,  8.01539597],\n",
       "        [11.74963013, 11.63797464, 10.65638617, ..., 11.54013954,\n",
       "         11.63444675, 11.68574926],\n",
       "        [10.84981663, 10.91386084, 11.10370004, ..., 11.40500895,\n",
       "         11.35914391, 11.21759991],\n",
       "...\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[13.11058165, 13.11058165, 13.1107417 , ..., 13.11058165,\n",
       "         13.11058165, 13.11058165],\n",
       "        [15.76821069, 15.68243757, 14.80567583, ..., 15.52101917,\n",
       "         15.61344929, 15.68248595],\n",
       "        [15.72742349, 15.8402331 , 16.1127133 , ..., 16.21932967,\n",
       "         16.20971711, 16.08888163],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3d7387b4-e62d-442b-9632-6cb59f69c36c' class='xr-section-summary-in' type='checkbox'  ><label for='section-3d7387b4-e62d-442b-9632-6cb59f69c36c' class='xr-section-summary' >Attributes: <span>(45)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>no parent</dd><dt><span>branch_time_in_child :</span></dt><dd>711385.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>0.0</dd><dt><span>case_id :</span></dt><dd>1587</dd><dt><span>cesm_casename :</span></dt><dd>f.e21.FHIST_BGC.f09_f09_mg17.CMIP6-AMIP.010</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-11-21T11:58:13Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.31</dd><dt><span>experiment :</span></dt><dd>An atmosphere only climate simulation using prescribed sea surface temperature and sea ice concentrations but with other conditions as in the Historical simulation.</dd><dt><span>experiment_id :</span></dt><dd>amip</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.amip.none.r10i1p1f1</dd><dt><span>grid :</span></dt><dd>native 0.9x1.25 finite volume grid (192x288 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>no parent</dd><dt><span>parent_experiment_id :</span></dt><dd>no parent</dd><dt><span>parent_mip_era :</span></dt><dd>no parent</dd><dt><span>parent_source_id :</span></dt><dd>no parent</dd><dt><span>parent_time_units :</span></dt><dd>no parent</dd><dt><span>parent_variant_label :</span></dt><dd>no parent</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>10</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2</dd><dt><span>source_type :</span></dt><dd>AGCM BGC AER</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>day</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/09a305e9-edf8-4118-b6db-b18d92417e5b</dd><dt><span>variable_id :</span></dt><dd>hfss</dd><dt><span>variant_info :</span></dt><dd>CMIP6 CESM2 AMIP hindcast (1950-2014) with interactive land (CLM5), data ocean, prescribed sea ice, and non-evolving land ice (CISM2.1)</dd><dt><span>variant_label :</span></dt><dd>r10i1p1f1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 192, lon: 288, nbnd: 2, time: 10220)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2006-12-31\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    hfss       (time, lat, lon) float32 -4.4452367 -4.4452367 ... -22.73284\n",
       "    time_bnds  (time, nbnd) object 1978-12-31 00:00:00 ... 2006-12-31 00:00:00\n",
       "    lat_bnds   (time, lat, nbnd) float64 -90.0 -89.53 -89.53 ... 89.53 90.0\n",
       "    lon_bnds   (time, lon, nbnd) float64 -0.625 0.625 0.625 ... 358.1 359.4\n",
       "    mrsos      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    SMconvert  (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    Plcl       (time, lat, lon) float64 6.456 6.456 6.456 6.456 ... nan nan nan\n",
       "Attributes:\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          no parent\n",
       "    branch_time_in_child:   711385.0\n",
       "    branch_time_in_parent:  0.0\n",
       "    case_id:                1587\n",
       "    cesm_casename:          f.e21.FHIST_BGC.f09_f09_mg17.CMIP6-AMIP.010\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-11-21T11:58:13Z\n",
       "    data_specs_version:     01.00.31\n",
       "    experiment:             An atmosphere only climate simulation using presc...\n",
       "    experiment_id:          amip\n",
       "    external_variables:     areacella\n",
       "    forcing_index:          1\n",
       "    frequency:              day\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.a...\n",
       "    grid:                   native 0.9x1.25 finite volume grid (192x288 latxlon)\n",
       "    grid_label:             gn\n",
       "    initialization_index:   1\n",
       "    institution:            National Center for Atmospheric Research, Climate...\n",
       "    institution_id:         NCAR\n",
       "    license:                CMIP6 model data produced by <The National Center...\n",
       "    mip_era:                CMIP6\n",
       "    model_doi_url:          https://doi.org/10.5065/D67H1H0V\n",
       "    nominal_resolution:     100 km\n",
       "    parent_activity_id:     no parent\n",
       "    parent_experiment_id:   no parent\n",
       "    parent_mip_era:         no parent\n",
       "    parent_source_id:       no parent\n",
       "    parent_time_units:      no parent\n",
       "    parent_variant_label:   no parent\n",
       "    physics_index:          1\n",
       "    product:                model-output\n",
       "    realization_index:      10\n",
       "    realm:                  atmos\n",
       "    source:                 CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite v...\n",
       "    source_id:              CESM2\n",
       "    source_type:            AGCM BGC AER\n",
       "    sub_experiment:         none\n",
       "    sub_experiment_id:      none\n",
       "    table_id:               day\n",
       "    tracking_id:            hdl:21.14100/09a305e9-edf8-4118-b6db-b18d92417e5b\n",
       "    variable_id:            hfss\n",
       "    variant_info:           CMIP6 CESM2 AMIP hindcast (1950-2014) with intera...\n",
       "    variant_label:          r10i1p1f1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dFull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute CI \n",
    "terra_coupling = cm.metrics.CouplingIndex()\n",
    "terra_coupling = terra_coupling.compute(dFull, xname='mrsos',yname='hfss',averaging='season')\n",
    "\n",
    "terra_coupling2 = cm.metrics.CouplingIndex()\n",
    "terra_coupling2 = terra_coupling2.compute(dFull, xname='SMconvert',yname='hfss',averaging='season')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save out file that does this with volumetric soil moisture instead \n",
    "terra_coupling2.to_netcdf('/Users/mdfowler/Documents/Analysis/Coupling_initial/Coupling_CAM6CLM5/processed_data/TerraLeg_coupling_lhf_vs_VolumetricSoilm_1979-2006_CESM2.nc')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at differences in CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean difference in CI: 0.001\n"
     ]
    }
   ],
   "source": [
    "diffCI = terra_coupling.mrsos_hfss_CI.values[1,:,:] - terra_coupling2.SMconvert_hfss_CI.values[1,:,:]\n",
    "print('Mean difference in CI: %.3f' % np.nanmean(diffCI))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(diffCI.reshape(len(lat)*len(lon)))\n",
    "plt.title('CESM2 daily terrestrial CI\\n Difference between using mass vs. volumetric WC')\n",
    "plt.xlabel('Difference in terrestrial CI [W/m2]')\n",
    "plt.ylabel('Bin Count')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_cmap(colors, position=None, bit=False):\n",
    "    '''\n",
    "    make_cmap takes a list of tuples which contain RGB values. The RGB\n",
    "    values may either be in 8-bit [0 to 255] (in which bit must be set to\n",
    "    True when called) or arithmetic [0 to 1] (default). make_cmap returns\n",
    "    a cmap with equally spaced colors.\n",
    "    Arrange your tuples so that the first color is the lowest value for the\n",
    "    colorbar and the last is the highest.\n",
    "    position contains values from 0 to 1 to dictate the location of each color.\n",
    "    '''\n",
    "    \n",
    "    import matplotlib as mpl\n",
    "    import numpy as np\n",
    "    \n",
    "    bit_rgb = np.linspace(0,1,256)\n",
    "    if position == None:\n",
    "        position = np.linspace(0,1,len(colors))\n",
    "    else:\n",
    "        if len(position) != len(colors):\n",
    "            sys.exit(\"position length must be the same as colors\")\n",
    "        elif position[0] != 0 or position[-1] != 1:\n",
    "            sys.exit(\"position must start with 0 and end with 1\")\n",
    "    \n",
    "    if bit:\n",
    "        for i in range(len(colors)):\n",
    "            colors[i] = (bit_rgb[colors[i][0]],\n",
    "                         bit_rgb[colors[i][1]],\n",
    "                         bit_rgb[colors[i][2]])\n",
    "    \n",
    "    cdict = {'red':[], 'green':[], 'blue':[]}\n",
    "    for pos, color in zip(position, colors):\n",
    "        cdict['red'].append((pos, color[0], color[0]))\n",
    "        cdict['green'].append((pos, color[1], color[1]))\n",
    "        cdict['blue'].append((pos, color[2], color[2]))\n",
    "\n",
    "    cmap = mpl.colors.LinearSegmentedColormap('my_colormap',cdict,256)\n",
    "    return cmap\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create a list of RGB tuples for terrestrial leg (SM, SHFLX)\n",
    "colorsList_SMvSHF = [(124,135,181), \n",
    "                     (107,109,161),\n",
    "                     (51,82,120),\n",
    "                     (49,114,127),\n",
    "                     (97,181,89),\n",
    "                     (200,218,102),\n",
    "                     (255,242,116),\n",
    "                     (238,164,58)] # This example uses the 8-bit RGB\n",
    "\n",
    "my_cmap_SMvSHF = make_cmap(colorsList_SMvSHF, bit=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-0eb02aa99a99>:18: RuntimeWarning: invalid value encountered in less\n",
      "  colorsTerraLeg_volm[np.where(TerraLeg_JJA_volm_cyclic<-20)]                                 = 1\n",
      "<ipython-input-20-0eb02aa99a99>:19: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-20) & (TerraLeg_JJA_volm_cyclic<=-15))]  = 2\n",
      "<ipython-input-20-0eb02aa99a99>:19: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-20) & (TerraLeg_JJA_volm_cyclic<=-15))]  = 2\n",
      "<ipython-input-20-0eb02aa99a99>:20: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-15) & (TerraLeg_JJA_volm_cyclic<=-10))]  = 3\n",
      "<ipython-input-20-0eb02aa99a99>:20: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-15) & (TerraLeg_JJA_volm_cyclic<=-10))]  = 3\n",
      "<ipython-input-20-0eb02aa99a99>:21: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-10) & (TerraLeg_JJA_volm_cyclic<=-5))]   = 4\n",
      "<ipython-input-20-0eb02aa99a99>:21: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-10) & (TerraLeg_JJA_volm_cyclic<=-5))]   = 4\n",
      "<ipython-input-20-0eb02aa99a99>:22: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-5) & (TerraLeg_JJA_volm_cyclic<=-2))]    = 5\n",
      "<ipython-input-20-0eb02aa99a99>:22: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-5) & (TerraLeg_JJA_volm_cyclic<=-2))]    = 5\n",
      "<ipython-input-20-0eb02aa99a99>:23: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-2) & (TerraLeg_JJA_volm_cyclic<=-1))]    = 6\n",
      "<ipython-input-20-0eb02aa99a99>:23: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-2) & (TerraLeg_JJA_volm_cyclic<=-1))]    = 6\n",
      "<ipython-input-20-0eb02aa99a99>:24: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-1) & (TerraLeg_JJA_volm_cyclic<=0))]     = 7\n",
      "<ipython-input-20-0eb02aa99a99>:24: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-1) & (TerraLeg_JJA_volm_cyclic<=0))]     = 7\n",
      "<ipython-input-20-0eb02aa99a99>:25: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_volm[np.where(TerraLeg_JJA_volm_cyclic>0)]                                   = 8\n",
      "<ipython-input-20-0eb02aa99a99>:31: RuntimeWarning: invalid value encountered in less\n",
      "  colorsTerraLeg_mass[np.where(TerraLeg_JJA_mass_cyclic<-20)]                                 = 1\n",
      "<ipython-input-20-0eb02aa99a99>:32: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-20) & (TerraLeg_JJA_mass_cyclic<=-15))]  = 2\n",
      "<ipython-input-20-0eb02aa99a99>:32: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-20) & (TerraLeg_JJA_mass_cyclic<=-15))]  = 2\n",
      "<ipython-input-20-0eb02aa99a99>:33: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-15) & (TerraLeg_JJA_mass_cyclic<=-10))]  = 3\n",
      "<ipython-input-20-0eb02aa99a99>:33: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-15) & (TerraLeg_JJA_mass_cyclic<=-10))]  = 3\n",
      "<ipython-input-20-0eb02aa99a99>:34: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-10) & (TerraLeg_JJA_mass_cyclic<=-5))]   = 4\n",
      "<ipython-input-20-0eb02aa99a99>:34: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-10) & (TerraLeg_JJA_mass_cyclic<=-5))]   = 4\n",
      "<ipython-input-20-0eb02aa99a99>:35: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-5) & (TerraLeg_JJA_mass_cyclic<=-2))]    = 5\n",
      "<ipython-input-20-0eb02aa99a99>:35: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-5) & (TerraLeg_JJA_mass_cyclic<=-2))]    = 5\n",
      "<ipython-input-20-0eb02aa99a99>:36: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-2) & (TerraLeg_JJA_mass_cyclic<=-1))]    = 6\n",
      "<ipython-input-20-0eb02aa99a99>:36: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-2) & (TerraLeg_JJA_mass_cyclic<=-1))]    = 6\n",
      "<ipython-input-20-0eb02aa99a99>:37: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-1) & (TerraLeg_JJA_mass_cyclic<=0))]     = 7\n",
      "<ipython-input-20-0eb02aa99a99>:37: RuntimeWarning: invalid value encountered in less_equal\n",
      "  colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-1) & (TerraLeg_JJA_mass_cyclic<=0))]     = 7\n",
      "<ipython-input-20-0eb02aa99a99>:38: RuntimeWarning: invalid value encountered in greater\n",
      "  colorsTerraLeg_mass[np.where(TerraLeg_JJA_mass_cyclic>0)]                                   = 8\n"
     ]
    }
   ],
   "source": [
    "# --- Define array of colors for maps --- #\n",
    "\n",
    "# Isolate seasonal arrays (seasons go: DJF,JJA,MAM,SON)\n",
    "TerraLeg_JJA_mass = np.squeeze(np.asarray(terra_coupling.mrsos_hfss_CI.values[1,:,:]))\n",
    "TerraLeg_JJA_volm = np.squeeze(np.asarray(terra_coupling2.SMconvert_hfss_CI.values[1,:,:]))\n",
    "\n",
    "# Make cyclic first \n",
    "TerraLeg_JJA_volm_cyclic, lon_cyclic  = cartopy.util.add_cyclic_point(TerraLeg_JJA_volm, lon)\n",
    "TerraLeg_JJA_mass_cyclic, lon_cyclic = cartopy.util.add_cyclic_point(TerraLeg_JJA_mass, lon)\n",
    "\n",
    "# Now get color coded arrays \n",
    "colorsTerraLeg_volm = np.full([len(lat),len(lon)+1],np.nan)\n",
    "colorsTerraLeg_mass = np.full([len(lat),len(lon)+1],np.nan)\n",
    "\n",
    "# ----- CESM 1: volumetric water content ------\n",
    "\n",
    "# Terra leg  \n",
    "colorsTerraLeg_volm[np.where(TerraLeg_JJA_volm_cyclic<-20)]                                 = 1\n",
    "colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-20) & (TerraLeg_JJA_volm_cyclic<=-15))]  = 2\n",
    "colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-15) & (TerraLeg_JJA_volm_cyclic<=-10))]  = 3\n",
    "colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-10) & (TerraLeg_JJA_volm_cyclic<=-5))]   = 4\n",
    "colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-5) & (TerraLeg_JJA_volm_cyclic<=-2))]    = 5\n",
    "colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-2) & (TerraLeg_JJA_volm_cyclic<=-1))]    = 6\n",
    "colorsTerraLeg_volm[np.where((TerraLeg_JJA_volm_cyclic>-1) & (TerraLeg_JJA_volm_cyclic<=0))]     = 7\n",
    "colorsTerraLeg_volm[np.where(TerraLeg_JJA_volm_cyclic>0)]                                   = 8\n",
    "\n",
    "\n",
    "# ----- CESM 1: mass water content ------\n",
    "\n",
    "# Terra leg  \n",
    "colorsTerraLeg_mass[np.where(TerraLeg_JJA_mass_cyclic<-20)]                                 = 1\n",
    "colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-20) & (TerraLeg_JJA_mass_cyclic<=-15))]  = 2\n",
    "colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-15) & (TerraLeg_JJA_mass_cyclic<=-10))]  = 3\n",
    "colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-10) & (TerraLeg_JJA_mass_cyclic<=-5))]   = 4\n",
    "colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-5) & (TerraLeg_JJA_mass_cyclic<=-2))]    = 5\n",
    "colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-2) & (TerraLeg_JJA_mass_cyclic<=-1))]    = 6\n",
    "colorsTerraLeg_mass[np.where((TerraLeg_JJA_mass_cyclic>-1) & (TerraLeg_JJA_mass_cyclic<=0))]     = 7\n",
    "colorsTerraLeg_mass[np.where(TerraLeg_JJA_mass_cyclic>0)]                                   = 8\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7feb19415b20>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Set up figure \n",
    "fig, axs = plt.subplots(1,2, figsize=(17, 8), \n",
    "                        facecolor='w', edgecolor='k',\n",
    "                        subplot_kw={'projection':ccrs.PlateCarree()})\n",
    "#Adjust space between subplots \n",
    "fig.subplots_adjust(hspace = -0.1,wspace = 0.1)\n",
    "#Put axs handles in array, axs \n",
    "axs = axs.ravel()\n",
    "\n",
    "# Zoom in on NH\n",
    "# axs[0].set_extent([-180,-100,40,90], crs=ccrs.PlateCarree())  \n",
    "# axs[1].set_extent([-180,-100,40,90], crs=ccrs.PlateCarree())  \n",
    "# axs[2].set_extent([-180,-100,40,90], crs=ccrs.PlateCarree())  \n",
    "\n",
    "axs[0].set_extent([-180,180,-60,90], crs=ccrs.PlateCarree())  \n",
    "axs[1].set_extent([-180,180,-60,90], crs=ccrs.PlateCarree())  \n",
    "\n",
    "# ----- Plot mass soil moisture CI ------ \n",
    "bounds = [1,2,3,4,5,6,7,8,9]\n",
    "norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)\n",
    "# Contour plots \n",
    "contour_plot = axs[0].pcolormesh(lon_cyclic,lat,colorsTerraLeg_mass,norm=norm,\n",
    "                             cmap=my_cmap_SMvSHF,\n",
    "                             vmin=1,vmax=9)\n",
    "\n",
    "# Add colorbar \n",
    "ax_position = axs[0].get_position()\n",
    "cbar_ax = fig.add_axes([ax_position.x0+0.2, ax_position.y0-0.05, ax_position.width, 0.03])\n",
    "cbar = plt.colorbar(contour_plot, orientation='horizontal',ticks=range(13),cax=cbar_ax)\n",
    "cbar.set_label('Coupling Index (SM, SHF) [W/m2]',fontsize=12)\n",
    "cbar.ax.set_xticklabels(['<-20','-20','-15','-10','-5','-2','-1','0','>0'],fontsize=12)  # vertically oriented colorbar\n",
    "\n",
    "# Additional options \n",
    "axs[0].set_title('CESM2 Daily Terrestrial CI \\n Soil moisture in mass form (kg/m2)',fontsize=16)\n",
    "axs[0].coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "axs[0].add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n",
    "\n",
    "\n",
    "# ----- Plot volumetric soil moisture CI ------ \n",
    "# Contour plots \n",
    "contour_plot = axs[1].pcolormesh(lon_cyclic,lat,colorsTerraLeg_volm,norm=norm, \n",
    "                             cmap=my_cmap_SMvSHF,\n",
    "                             vmin=1,vmax=9)\n",
    "\n",
    "# Additional options \n",
    "axs[1].set_title('CESM2 Daily Terrestrial CI \\n Soil moisture in volumetric form',fontsize=16)\n",
    "axs[1].coastlines(linewidth=0.5,edgecolor='0.25')\n",
    "axs[1].add_feature(cfeature.BORDERS,linewidth=0.5,edgecolor='0.25')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}