Compute_Global_NPP_Export.ipynb

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   "cell_type": "code",
   "execution_count": 300,
   "id": "7d7b594d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os, subprocess, tarfile\n",
    "from glob import glob\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import cftime\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "import scipy.stats as stats\n",
    "from scipy.io import loadmat\n",
    "\n",
    "import string\n",
    "alphabet=list(string.ascii_lowercase)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "seconds_per_day=60.*60.*24.\n",
    "seconds_per_year=365.*seconds_per_day\n",
    "carbon_molar_mass=12.011\n",
    "cobalt_n_2_c = 106./16."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "52772d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.colors as colors\n",
    "class MidpointNormalize(colors.Normalize):\n",
    "    def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):\n",
    "        self.midpoint = midpoint\n",
    "        super().__init__(vmin, vmax, clip)\n",
    "\n",
    "    def __call__(self, value, clip=None):\n",
    "        # I'm ignoring masked values and all kinds of edge cases to make a\n",
    "        # simple example...\n",
    "        x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]\n",
    "        return np.ma.masked_array(np.interp(value, x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4137d35",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Read in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "db6d0b0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "case={}\n",
    "\n",
    "#control\n",
    "case['path_root']='/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT'\n",
    "case['machine_target']='gfdl.ncrc4-intel19-prod'\n",
    "case['name']='OM4p5_CORE2_IAF_gzCOBALT-013022_cy5'\n",
    "\n",
    "#cases\n",
    "case['path_root']='/archive/Jessica.Luo/gz_test/MOM6_SIS2_gzCOBALTv2'\n",
    "case['machine_target']='gfdl.ncrc4-intel19-prod'\n",
    "\n",
    "case['name']='OM4p5_CORE2_IAF_gzCOBALT-tun_cy5'\n",
    "case['name']='OM4p5_CORE2_IAF_gzCOBALT-tun_hp_cy5'\n",
    "case['name']='OM4p5_CORE2_IAF_gzCOBALT-hp_cy5'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "08adb31b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "# define years for cycle\n",
    "cycle=1\n",
    "if 'cy1' in case['name']:\n",
    "    cycle=1\n",
    "if 'cy2' in case['name']:\n",
    "    cycle=2\n",
    "if 'cy3' in case['name']:\n",
    "    cycle=3\n",
    "if 'cy4' in case['name']:\n",
    "    cycle=4\n",
    "if 'cy5' in case['name']:\n",
    "    cycle=5\n",
    "    \n",
    "print(cycle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "dac633a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "241 301\n"
     ]
    }
   ],
   "source": [
    "start_date=60*(cycle-1)+1\n",
    "end_date=60*cycle+1\n",
    "print(start_date,end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "a24f9e99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gzCOBALT-013022_cy5 gzCOBALT-013022_cy5\n"
     ]
    }
   ],
   "source": [
    "short_casename=case['name'].replace('OM4p5_CORE2_IAF_','')\n",
    "if short_casename==\"gzCOBALT-060822\":\n",
    "    fig_casename='gzCOBALT-tun_cy1'\n",
    "else:\n",
    "    fig_casename=short_casename\n",
    "print(short_casename, fig_casename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "b640d53f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1948-1952.intpp.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1953-1957.intpp.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1958-1962.intpp.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1963-1967.intpp.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1968-1972.intpp.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1973-1977.intpp.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1978-1982.intpp.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1983-1987.intpp.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1988-1992.intpp.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1993-1997.intpp.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1998-2002.intpp.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.2003-2007.intpp.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1948-1952.epc100.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1953-1957.epc100.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1958-1962.epc100.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1963-1967.epc100.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1968-1972.epc100.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1973-1977.epc100.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1978-1982.epc100.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1983-1987.epc100.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1988-1992.epc100.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1993-1997.epc100.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.1998-2002.epc100.nc', '/archive/Jessica.Luo/gz_test/MOM6_SIS2_COBALT/OM4p5_CORE2_IAF_gzCOBALT-013022_cy5/gfdl.ncrc4-intel19-prod/pp/ocean_cobalt_omip_2d/ts/annual/5yr/ocean_cobalt_omip_2d.2003-2007.epc100.nc']\n"
     ]
    }
   ],
   "source": [
    "# read in files\n",
    "diagType = 'ocean_cobalt_omip_2d'\n",
    "varlist = ['intpp','epc100']\n",
    "\n",
    "prefix = os.path.join(case['path_root'], case['name'], case['machine_target'], 'pp', diagType, 'ts', 'annual', '5yr', diagType)\n",
    "files_a = glob('.'.join([prefix, '*', 'intpp', 'nc']))\n",
    "files_b = glob('.'.join([prefix, '*', 'epc100', 'nc']))\n",
    "files_a.extend(files_b)\n",
    "print(files_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "bade5e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_file=os.path.join(case['path_root'], case['name'], case['machine_target'], 'pp')+'/ocean_annual/ocean_annual.static.nc'\n",
    "ds=xr.open_mfdataset(files_a, combine='by_coords', decode_times=False)\n",
    "grid=xr.open_dataset(grid_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b6c1da",
   "metadata": {},
   "source": [
    "# Compute Global NPP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "d74c789c-74bf-4f9d-b6e6-89d3a7871130",
   "metadata": {},
   "outputs": [
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       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;intpp&#x27; (time: 60, yh: 576, xh: 720)&gt;\n",
       "dask.array&lt;concatenate, shape=(60, 576, 720), dtype=float32, chunksize=(5, 576, 720), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) float64 182.5 547.5 912.5 ... 2.099e+04 2.135e+04 2.172e+04\n",
       "  * xh       (xh) float64 -299.8 -299.2 -298.8 -298.2 ... 58.75 59.25 59.75\n",
       "  * yh       (yh) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89\n",
       "Attributes:\n",
       "    long_name:      Primary Organic Carbon Production by All Types of Phytopl...\n",
       "    units:          mol m-2 s-1\n",
       "    cell_measures:  area: areacello\n",
       "    standard_name:  net_primary_mole_productivity_of_biomass_expressed_as_car...\n",
       "    cell_methods:   area:mean yh:mean xh:mean time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'intpp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span class='xr-has-index'>yh</span>: 576</li><li><span class='xr-has-index'>xh</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a3a372dc-8488-438e-8513-820cb167ecdd' class='xr-array-in' type='checkbox' checked><label for='section-a3a372dc-8488-438e-8513-820cb167ecdd' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(5, 576, 720), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 94.92 MiB </td>\n",
       "                        <td> 7.91 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (60, 576, 720) </td>\n",
       "                        <td> (5, 576, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 36 Tasks </td>\n",
       "                        <td> 12 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "  <text x=\"11.048114\" y=\"127.048114\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.048114,127.048114)\">60</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-aee6a860-7c7f-4bf3-9635-6f795c1f3668' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aee6a860-7c7f-4bf3-9635-6f795c1f3668' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>182.5 547.5 ... 2.135e+04 2.172e+04</div><input id='attrs-8a6576a8-420f-4d2d-aa3b-3b6555295f88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a6576a8-420f-4d2d-aa3b-3b6555295f88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f196fdbd-9118-408c-accf-346f02793524' class='xr-var-data-in' type='checkbox'><label for='data-f196fdbd-9118-408c-accf-346f02793524' 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>time</dd><dt><span>units :</span></dt><dd>days since 1948-01-01 00:00:00</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>calendar :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([  182.5,   547.5,   912.5,  1277.5,  1642.5,  2007.5,  2372.5,  2737.5,\n",
       "        3102.5,  3467.5,  3832.5,  4197.5,  4562.5,  4927.5,  5292.5,  5657.5,\n",
       "        6022.5,  6387.5,  6752.5,  7117.5,  7482.5,  7847.5,  8212.5,  8577.5,\n",
       "        8942.5,  9307.5,  9672.5, 10037.5, 10402.5, 10767.5, 11132.5, 11497.5,\n",
       "       11862.5, 12227.5, 12592.5, 12957.5, 13322.5, 13687.5, 14052.5, 14417.5,\n",
       "       14782.5, 15147.5, 15512.5, 15877.5, 16242.5, 16607.5, 16972.5, 17337.5,\n",
       "       17702.5, 18067.5, 18432.5, 18797.5, 19162.5, 19527.5, 19892.5, 20257.5,\n",
       "       20622.5, 20987.5, 21352.5, 21717.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xh</span></div><div class='xr-var-dims'>(xh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-299.8 -299.2 ... 59.25 59.75</div><input id='attrs-06419e91-61f6-4226-9626-2dd1c2e12e1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06419e91-61f6-4226-9626-2dd1c2e12e1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12f1c6d7-1f38-48bd-82e6-83d7452a0c9d' class='xr-var-data-in' type='checkbox'><label for='data-12f1c6d7-1f38-48bd-82e6-83d7452a0c9d' 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>h point nominal longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-299.75, -299.25, -298.75, ...,   58.75,   59.25,   59.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yh</span></div><div class='xr-var-dims'>(yh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.91 -77.72 ... 89.68 89.89</div><input id='attrs-efa2a326-c674-475a-847c-4b2776661cf3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efa2a326-c674-475a-847c-4b2776661cf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c45cf23c-5482-4780-9461-ccacfdf7a01d' class='xr-var-data-in' type='checkbox'><label for='data-c45cf23c-5482-4780-9461-ccacfdf7a01d' 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>h point nominal latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.907938, -77.723813, -77.539688, ...,  89.472   ,  89.6832  ,\n",
       "        89.8944  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0d4afd3-62f2-4117-aa4f-f5ec24482566' class='xr-section-summary-in' type='checkbox'  ><label for='section-d0d4afd3-62f2-4117-aa4f-f5ec24482566' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8ca620e4-90d6-40f6-b850-20689abca0d2' class='xr-index-data-in' type='checkbox'/><label for='index-8ca620e4-90d6-40f6-b850-20689abca0d2' 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(Float64Index([  182.5,   547.5,   912.5,  1277.5,  1642.5,  2007.5,  2372.5,\n",
       "               2737.5,  3102.5,  3467.5,  3832.5,  4197.5,  4562.5,  4927.5,\n",
       "               5292.5,  5657.5,  6022.5,  6387.5,  6752.5,  7117.5,  7482.5,\n",
       "               7847.5,  8212.5,  8577.5,  8942.5,  9307.5,  9672.5, 10037.5,\n",
       "              10402.5, 10767.5, 11132.5, 11497.5, 11862.5, 12227.5, 12592.5,\n",
       "              12957.5, 13322.5, 13687.5, 14052.5, 14417.5, 14782.5, 15147.5,\n",
       "              15512.5, 15877.5, 16242.5, 16607.5, 16972.5, 17337.5, 17702.5,\n",
       "              18067.5, 18432.5, 18797.5, 19162.5, 19527.5, 19892.5, 20257.5,\n",
       "              20622.5, 20987.5, 21352.5, 21717.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c0b77590-36a7-48ff-92ac-5d62e6b52818' class='xr-index-data-in' type='checkbox'/><label for='index-c0b77590-36a7-48ff-92ac-5d62e6b52818' 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(Float64Index([-299.75, -299.25, -298.75, -298.25, -297.75, -297.25, -296.75,\n",
       "              -296.25, -295.75, -295.25,\n",
       "              ...\n",
       "                55.25,   55.75,   56.25,   56.75,   57.25,   57.75,   58.25,\n",
       "                58.75,   59.25,   59.75],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;xh&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d6f4c7c4-01fc-40cb-94ff-219cf5d7fcfa' class='xr-index-data-in' type='checkbox'/><label for='index-d6f4c7c4-01fc-40cb-94ff-219cf5d7fcfa' 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(Float64Index([-77.90793753487047,  -77.7238126046114, -77.53968767435234,\n",
       "              -77.35556274409328,  -77.1714378138342, -76.98731288357514,\n",
       "              -76.80318795331607, -76.61906302305701, -76.43493809279795,\n",
       "              -76.25081316253888,\n",
       "              ...\n",
       "               87.99359944564549,   88.2047995039986,   88.4159995623517,\n",
       "               88.62719962070481,  88.83839967905793,  89.04959973741103,\n",
       "               89.26079979576413,  89.47199985411724,  89.68319991247033,\n",
       "               89.89439997082347],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;yh&#x27;, length=576))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dd173ffd-33d3-4b84-a8cc-04c829379851' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd173ffd-33d3-4b84-a8cc-04c829379851' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Primary Organic Carbon Production by All Types of Phytoplankton</dd><dt><span>units :</span></dt><dd>mol m-2 s-1</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</dd><dt><span>standard_name :</span></dt><dd>net_primary_mole_productivity_of_biomass_expressed_as_carbon_by_phytoplankton</dd><dt><span>cell_methods :</span></dt><dd>area:mean yh:mean xh:mean time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'intpp' (time: 60, yh: 576, xh: 720)>\n",
       "dask.array<concatenate, shape=(60, 576, 720), dtype=float32, chunksize=(5, 576, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) float64 182.5 547.5 912.5 ... 2.099e+04 2.135e+04 2.172e+04\n",
       "  * xh       (xh) float64 -299.8 -299.2 -298.8 -298.2 ... 58.75 59.25 59.75\n",
       "  * yh       (yh) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89\n",
       "Attributes:\n",
       "    long_name:      Primary Organic Carbon Production by All Types of Phytopl...\n",
       "    units:          mol m-2 s-1\n",
       "    cell_measures:  area: areacello\n",
       "    standard_name:  net_primary_mole_productivity_of_biomass_expressed_as_car...\n",
       "    cell_methods:   area:mean yh:mean xh:mean time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.intpp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "d612f2f2-dab0-4e7f-8b97-60452b5fe56d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "net primary production: 5.39e+16 g C / yr\n"
     ]
    }
   ],
   "source": [
    "npp = (ds.intpp * grid.areacello).sum(dim=['yh', 'xh'])\n",
    "npp = npp * seconds_per_year * carbon_molar_mass\n",
    "print('net primary production: ' + str(np.format_float_scientific(npp.mean(dim='time').values,2)) + ' g C / yr')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae36347-873f-44ba-bdb3-811dd683e978",
   "metadata": {},
   "source": [
    "# Compute Global Carbon Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "e942e753-307d-447d-b9cf-3134893fc8a4",
   "metadata": {},
   "outputs": [
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       "  * yh       (yh) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89\n",
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       "    units:          mol m-2 s-1\n",
       "    cell_measures:  area: areacello\n",
       "    standard_name:  sinking_mole_flux_of_particulate_organic_matter_expressed...\n",
       "    cell_methods:   area:mean yh:mean xh:mean time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'epc100'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span class='xr-has-index'>yh</span>: 576</li><li><span class='xr-has-index'>xh</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6b096f25-3e01-4ca5-a7de-321630a0519b' class='xr-array-in' type='checkbox' checked><label for='section-6b096f25-3e01-4ca5-a7de-321630a0519b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(5, 576, 720), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 94.92 MiB </td>\n",
       "                        <td> 7.91 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (60, 576, 720) </td>\n",
       "                        <td> (5, 576, 720) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 36 Tasks </td>\n",
       "                        <td> 12 Chunks </td>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-f209a4c8-8291-4cb0-affc-b6ed0187e4c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f209a4c8-8291-4cb0-affc-b6ed0187e4c4' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>182.5 547.5 ... 2.135e+04 2.172e+04</div><input id='attrs-45c1928c-1418-40e4-a3a6-238370b8fb0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45c1928c-1418-40e4-a3a6-238370b8fb0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63e967de-9fd2-46c6-b6a7-f29b84d7d0ec' class='xr-var-data-in' type='checkbox'><label for='data-63e967de-9fd2-46c6-b6a7-f29b84d7d0ec' 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>time</dd><dt><span>units :</span></dt><dd>days since 1948-01-01 00:00:00</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>calendar :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([  182.5,   547.5,   912.5,  1277.5,  1642.5,  2007.5,  2372.5,  2737.5,\n",
       "        3102.5,  3467.5,  3832.5,  4197.5,  4562.5,  4927.5,  5292.5,  5657.5,\n",
       "        6022.5,  6387.5,  6752.5,  7117.5,  7482.5,  7847.5,  8212.5,  8577.5,\n",
       "        8942.5,  9307.5,  9672.5, 10037.5, 10402.5, 10767.5, 11132.5, 11497.5,\n",
       "       11862.5, 12227.5, 12592.5, 12957.5, 13322.5, 13687.5, 14052.5, 14417.5,\n",
       "       14782.5, 15147.5, 15512.5, 15877.5, 16242.5, 16607.5, 16972.5, 17337.5,\n",
       "       17702.5, 18067.5, 18432.5, 18797.5, 19162.5, 19527.5, 19892.5, 20257.5,\n",
       "       20622.5, 20987.5, 21352.5, 21717.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xh</span></div><div class='xr-var-dims'>(xh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-299.8 -299.2 ... 59.25 59.75</div><input id='attrs-fa569195-3ccc-4732-b095-9e930ed11cc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa569195-3ccc-4732-b095-9e930ed11cc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03fdc54c-bb33-4b50-8241-0a14f56f201f' class='xr-var-data-in' type='checkbox'><label for='data-03fdc54c-bb33-4b50-8241-0a14f56f201f' 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>h point nominal longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-299.75, -299.25, -298.75, ...,   58.75,   59.25,   59.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yh</span></div><div class='xr-var-dims'>(yh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.91 -77.72 ... 89.68 89.89</div><input id='attrs-a30dcc99-ba8a-4eb3-89ff-6a8e18442bbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a30dcc99-ba8a-4eb3-89ff-6a8e18442bbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9d1bb36-9b01-4c26-a3f0-bd6f29fd4865' class='xr-var-data-in' type='checkbox'><label for='data-c9d1bb36-9b01-4c26-a3f0-bd6f29fd4865' 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>h point nominal latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-77.907938, -77.723813, -77.539688, ...,  89.472   ,  89.6832  ,\n",
       "        89.8944  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-060f191d-8c91-4a8b-ab6b-9d19648510b9' class='xr-section-summary-in' type='checkbox'  ><label for='section-060f191d-8c91-4a8b-ab6b-9d19648510b9' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0aed6d3b-0ed2-407d-8e4b-a5a25daab3cc' class='xr-index-data-in' type='checkbox'/><label for='index-0aed6d3b-0ed2-407d-8e4b-a5a25daab3cc' 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(Float64Index([  182.5,   547.5,   912.5,  1277.5,  1642.5,  2007.5,  2372.5,\n",
       "               2737.5,  3102.5,  3467.5,  3832.5,  4197.5,  4562.5,  4927.5,\n",
       "               5292.5,  5657.5,  6022.5,  6387.5,  6752.5,  7117.5,  7482.5,\n",
       "               7847.5,  8212.5,  8577.5,  8942.5,  9307.5,  9672.5, 10037.5,\n",
       "              10402.5, 10767.5, 11132.5, 11497.5, 11862.5, 12227.5, 12592.5,\n",
       "              12957.5, 13322.5, 13687.5, 14052.5, 14417.5, 14782.5, 15147.5,\n",
       "              15512.5, 15877.5, 16242.5, 16607.5, 16972.5, 17337.5, 17702.5,\n",
       "              18067.5, 18432.5, 18797.5, 19162.5, 19527.5, 19892.5, 20257.5,\n",
       "              20622.5, 20987.5, 21352.5, 21717.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9c3d77e7-edb0-4f16-94df-c8a5c0665510' class='xr-index-data-in' type='checkbox'/><label for='index-9c3d77e7-edb0-4f16-94df-c8a5c0665510' 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(Float64Index([-299.75, -299.25, -298.75, -298.25, -297.75, -297.25, -296.75,\n",
       "              -296.25, -295.75, -295.25,\n",
       "              ...\n",
       "                55.25,   55.75,   56.25,   56.75,   57.25,   57.75,   58.25,\n",
       "                58.75,   59.25,   59.75],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;xh&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1c629ad2-caea-4b60-9162-865b3bd2ff60' class='xr-index-data-in' type='checkbox'/><label for='index-1c629ad2-caea-4b60-9162-865b3bd2ff60' 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(Float64Index([-77.90793753487047,  -77.7238126046114, -77.53968767435234,\n",
       "              -77.35556274409328,  -77.1714378138342, -76.98731288357514,\n",
       "              -76.80318795331607, -76.61906302305701, -76.43493809279795,\n",
       "              -76.25081316253888,\n",
       "              ...\n",
       "               87.99359944564549,   88.2047995039986,   88.4159995623517,\n",
       "               88.62719962070481,  88.83839967905793,  89.04959973741103,\n",
       "               89.26079979576413,  89.47199985411724,  89.68319991247033,\n",
       "               89.89439997082347],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;yh&#x27;, length=576))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db17913a-7d00-4c0a-a910-47d49a4798f1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db17913a-7d00-4c0a-a910-47d49a4798f1' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Downward Flux of Particulate Organic Carbon</dd><dt><span>units :</span></dt><dd>mol m-2 s-1</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</dd><dt><span>standard_name :</span></dt><dd>sinking_mole_flux_of_particulate_organic_matter_expressed_as_carbon_in_sea_water</dd><dt><span>cell_methods :</span></dt><dd>area:mean yh:mean xh:mean time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'epc100' (time: 60, yh: 576, xh: 720)>\n",
       "dask.array<concatenate, shape=(60, 576, 720), dtype=float32, chunksize=(5, 576, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) float64 182.5 547.5 912.5 ... 2.099e+04 2.135e+04 2.172e+04\n",
       "  * xh       (xh) float64 -299.8 -299.2 -298.8 -298.2 ... 58.75 59.25 59.75\n",
       "  * yh       (yh) float64 -77.91 -77.72 -77.54 -77.36 ... 89.47 89.68 89.89\n",
       "Attributes:\n",
       "    long_name:      Downward Flux of Particulate Organic Carbon\n",
       "    units:          mol m-2 s-1\n",
       "    cell_measures:  area: areacello\n",
       "    standard_name:  sinking_mole_flux_of_particulate_organic_matter_expressed...\n",
       "    cell_methods:   area:mean yh:mean xh:mean time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.epc100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "29e8bddc-ff0a-4642-bfc9-df0e6603d2c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "carbon export at 100 m: 6.34e+15 g C / yr\n"
     ]
    }
   ],
   "source": [
    "export = (ds.epc100 * grid.areacello).sum(dim=['yh', 'xh'])\n",
    "export = export * seconds_per_year * carbon_molar_mass\n",
    "print('carbon export at 100 m: ' + str(np.format_float_scientific(export.mean(dim='time').values,2)) + ' g C / yr')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca648f8-40d7-4d65-bc13-6aa45e5b40cd",
   "metadata": {},
   "source": [
    "# Pull together into xarray dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "d00077a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "global_npp = xr.DataArray(npp.values, coords={'time':np.arange(start_date,end_date)}, \n",
    "                             dims=['time'], name='npp', \n",
    "                             attrs={'name':'Total Global NPP','units':'g C y-1'})\n",
    "\n",
    "global_export = xr.DataArray(export.values, coords={'time':np.arange(start_date,end_date)}, \n",
    "                             dims=['time'], name='expc100', \n",
    "                             attrs={'name':'Total Global Carbon Export at 100 m','units':'g C y-1'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "39377dad",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 60)\n",
       "Coordinates:\n",
       "  * time     (time) int64 241 242 243 244 245 246 ... 295 296 297 298 299 300\n",
       "Data variables:\n",
       "    npp      (time) float32 5.377e+16 5.414e+16 5.523e+16 ... 5.25e+16 5.232e+16\n",
       "    expc100  (time) float32 6.325e+15 6.352e+15 ... 6.174e+15 6.165e+15</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-4d2fd372-9fb3-433a-a6ee-5341d011448b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4d2fd372-9fb3-433a-a6ee-5341d011448b' 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>: 60</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-26df7ff7-617a-4a4c-8f20-9c81cbdca855' class='xr-section-summary-in' type='checkbox'  checked><label for='section-26df7ff7-617a-4a4c-8f20-9c81cbdca855' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>241 242 243 244 ... 297 298 299 300</div><input id='attrs-8f654dff-10a3-4d8d-bd83-10eb0c7dfecb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f654dff-10a3-4d8d-bd83-10eb0c7dfecb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c3fe15a-9ecc-4140-86ea-fba5c546e6bc' class='xr-var-data-in' type='checkbox'><label for='data-7c3fe15a-9ecc-4140-86ea-fba5c546e6bc' 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([241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254,\n",
       "       255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268,\n",
       "       269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282,\n",
       "       283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296,\n",
       "       297, 298, 299, 300])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0b6e212-503d-4079-b729-3d3b4e262939' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a0b6e212-503d-4079-b729-3d3b4e262939' class='xr-section-summary' >Data variables: <span>(2)</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>npp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.377e+16 5.414e+16 ... 5.232e+16</div><input id='attrs-39f1a514-d829-4a69-a0a4-40534d92dbd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39f1a514-d829-4a69-a0a4-40534d92dbd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-222ca38b-e520-425f-a55c-26e3359bad10' class='xr-var-data-in' type='checkbox'><label for='data-222ca38b-e520-425f-a55c-26e3359bad10' 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>name :</span></dt><dd>Total Global NPP</dd><dt><span>units :</span></dt><dd>g C y-1</dd></dl></div><div class='xr-var-data'><pre>array([5.3765217e+16, 5.4138806e+16, 5.5226407e+16, 5.6182364e+16,\n",
       "       5.6694427e+16, 5.6430476e+16, 5.6670960e+16, 5.6516096e+16,\n",
       "       5.7117709e+16, 5.6832562e+16, 5.6695488e+16, 5.6118833e+16,\n",
       "       5.6041498e+16, 5.6439942e+16, 5.6119202e+16, 5.5871945e+16,\n",
       "       5.6203908e+16, 5.5973234e+16, 5.5027100e+16, 5.4527560e+16,\n",
       "       5.4201525e+16, 5.4057335e+16, 5.5206552e+16, 5.5260389e+16,\n",
       "       5.4740277e+16, 5.4630116e+16, 5.4816955e+16, 5.4209746e+16,\n",
       "       5.4629832e+16, 5.3881327e+16, 5.3882877e+16, 5.2999656e+16,\n",
       "       5.2851145e+16, 5.3333959e+16, 5.2602303e+16, 5.2699567e+16,\n",
       "       5.2299860e+16, 5.2122757e+16, 5.2276723e+16, 5.1869951e+16,\n",
       "       5.2200457e+16, 5.2146259e+16, 5.2119012e+16, 5.1930076e+16,\n",
       "       5.2131948e+16, 5.1972570e+16, 5.1809546e+16, 5.1855253e+16,\n",
       "       5.2713710e+16, 5.2212221e+16, 5.1759720e+16, 5.2105122e+16,\n",
       "       5.2215618e+16, 5.2801624e+16, 5.2263267e+16, 5.2320592e+16,\n",
       "       5.2109833e+16, 5.2290170e+16, 5.2501839e+16, 5.2324607e+16],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>expc100</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>6.325e+15 6.352e+15 ... 6.165e+15</div><input id='attrs-42569761-7f3b-4d7c-93a7-14195561bb6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42569761-7f3b-4d7c-93a7-14195561bb6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da054551-55dd-4144-a584-185caeb0ece3' class='xr-var-data-in' type='checkbox'><label for='data-da054551-55dd-4144-a584-185caeb0ece3' 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>name :</span></dt><dd>Total Global Carbon Export at 100 m</dd><dt><span>units :</span></dt><dd>g C y-1</dd></dl></div><div class='xr-var-data'><pre>array([6.3249390e+15, 6.3521858e+15, 6.4873312e+15, 6.5421887e+15,\n",
       "       6.6527417e+15, 6.5948010e+15, 6.6153878e+15, 6.5944799e+15,\n",
       "       6.6781056e+15, 6.6130460e+15, 6.6243192e+15, 6.5916173e+15,\n",
       "       6.5728129e+15, 6.6657297e+15, 6.6083677e+15, 6.5036113e+15,\n",
       "       6.5619278e+15, 6.5314287e+15, 6.4456647e+15, 6.4218689e+15,\n",
       "       6.3802362e+15, 6.3178615e+15, 6.5081876e+15, 6.5240478e+15,\n",
       "       6.4166022e+15, 6.4274116e+15, 6.4679829e+15, 6.3981145e+15,\n",
       "       6.3930116e+15, 6.3051639e+15, 6.3378755e+15, 6.2280156e+15,\n",
       "       6.2237152e+15, 6.2720492e+15, 6.1501172e+15, 6.1532601e+15,\n",
       "       6.2140355e+15, 6.1533401e+15, 6.1719217e+15, 6.1207225e+15,\n",
       "       6.2197757e+15, 6.1840410e+15, 6.1401062e+15, 6.1139998e+15,\n",
       "       6.1146054e+15, 6.1599097e+15, 6.1588108e+15, 6.1729106e+15,\n",
       "       6.2370414e+15, 6.1393905e+15, 6.1029724e+15, 6.1979970e+15,\n",
       "       6.2118735e+15, 6.2155881e+15, 6.1821872e+15, 6.1882597e+15,\n",
       "       6.1494048e+15, 6.1695987e+15, 6.1735012e+15, 6.1650562e+15],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-26e0218e-670c-4b5e-a95c-0ba703bd10c4' class='xr-section-summary-in' type='checkbox'  ><label for='section-26e0218e-670c-4b5e-a95c-0ba703bd10c4' 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-39f1cbeb-caf5-46fc-a273-33685a3477c1' class='xr-index-data-in' type='checkbox'/><label for='index-39f1cbeb-caf5-46fc-a273-33685a3477c1' 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(Int64Index([241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253,\n",
       "            254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266,\n",
       "            267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279,\n",
       "            280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292,\n",
       "            293, 294, 295, 296, 297, 298, 299, 300],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e337aa5-2555-4d64-8932-29d569cb52b5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6e337aa5-2555-4d64-8932-29d569cb52b5' 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: 60)\n",
       "Coordinates:\n",
       "  * time     (time) int64 241 242 243 244 245 246 ... 295 296 297 298 299 300\n",
       "Data variables:\n",
       "    npp      (time) float32 5.377e+16 5.414e+16 5.523e+16 ... 5.25e+16 5.232e+16\n",
       "    expc100  (time) float32 6.325e+15 6.352e+15 ... 6.174e+15 6.165e+15"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_npp_export = global_npp.to_dataset()\n",
    "global_npp_export['expc100'] = global_export\n",
    "\n",
    "\n",
    "global_npp_export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "88685692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing to: data/global_npp_export.gzCOBALT-013022_cy5.nc\n"
     ]
    }
   ],
   "source": [
    "file_out='data/global_npp_export.'+fig_casename+'.nc'\n",
    "\n",
    "if not os.path.exists(file_out):\n",
    "    global_npp_export.to_netcdf(path=file_out, mode='w')\n",
    "    print('writing to: '+file_out)\n",
    "else:\n",
    "    print(file_out + ' exists')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34e768be-fe4c-4ee4-a037-89c2f122bb06",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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