biological_timeseries_prototype.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Timeseries inside and out of polynyas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import os\n",
    "from glob import glob\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import esmlab\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "from scipy import stats\n",
    "import pop_tools \n",
    "import pandas as pd\n",
    "from statistics import mean\n",
    "from matplotlib import style\n",
    "from scipy import stats\n",
    "import matplotlib.path as mpath"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the CESM2 4p4z tseries files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "case = 'g.e22a06.G1850ECOIAF_JRA_PHYS_DEV.TL319_g17.4p4z.005'\n",
    "path = '/glade/scratch/kristenk/archive/' + case + '/ocn/proc/tseries/month_1'\n",
    "variables = ['spC','coccoC','diatC','zoo1C','zoo2C','zoo3C','zoo4C',\n",
    "             'x_graze_zoo1_zint','x_graze_zoo2_zint','x_graze_zoo3_zint','x_graze_zoo4_zint',\n",
    "             'photoC_TOT_zint', 'photoC_diat_zint', 'photoC_sp_zint', 'photoC_cocco_zint',\n",
    "            'IFRAC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading g.e22a06.G1850ECOIAF_JRA_PHYS_DEV.TL319_g17.4p4z.005\n",
      "spC\n",
      "coccoC\n",
      "diatC\n",
      "zoo1C\n",
      "zoo2C\n",
      "zoo3C\n",
      "zoo4C\n",
      "x_graze_zoo1_zint\n",
      "x_graze_zoo2_zint\n",
      "x_graze_zoo3_zint\n",
      "x_graze_zoo4_zint\n",
      "photoC_TOT_zint\n",
      "photoC_diat_zint\n",
      "photoC_sp_zint\n",
      "photoC_cocco_zint\n",
      "IFRAC\n",
      "CPU times: user 7.87 s, sys: 427 ms, total: 8.3 s\n",
      "Wall time: 16.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ds_list = []\n",
    "print(f'reading {case}')\n",
    "ds = xr.Dataset()\n",
    "\n",
    "for v in variables:  \n",
    "    print(v)\n",
    "    files = sorted(glob(f'{path}/{case}.pop.h.{v}.006201-012212.nc'))       \n",
    "    dsv=xr.open_mfdataset(files, data_vars=\"minimal\", coords='minimal', compat=\"override\", parallel=True, concat_dim=\"time\",\n",
    "                       drop_variables=[\"transport_components\", \"transport_regions\"], decode_times=True)\n",
    "    \n",
    "    ds = xr.merge((ds, dsv))\n",
    "    ds_list.append(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords = {'x':'TLONG','y':'TLAT'}\n",
    "keepthese=['z_t','z_t_150m','time_bound','TAREA','REGION_MASK'] + variables\n",
    "keep_vars = keepthese +list(coords.values())+['dz','KMT']\n",
    "ds = ds.drop([v for v in ds.variables if v not in keep_vars])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### trim latitude to the same length as polynas and fix time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ds.isel(nlat=slice(0,76))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fix time dimension so it's a mean of the timebounds\n",
    "ds[\"time\"] = ds.time_bound.compute().mean(dim=\"d2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid = pop_tools.get_grid('POP_gx1v7')\n",
    "ds_grid = ds_grid.isel(nlat=slice(0,76))\n",
    "lons=ds_grid.TLONG\n",
    "lats=ds_grid.TLAT\n",
    "area=ds_grid.TAREA\n",
    "area_m=ds_grid.TAREA * 1e-4\n",
    "\n",
    "ds['z_t']=ds_grid.z_t\n",
    "depths_m=ds_grid.z_t * 0.01\n",
    "dz=ds_grid.dz\n",
    "dz_m=ds_grid.dz * 0.01\n",
    "tlen=len(ds.time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make the biological variables 2-D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = ds.zoo1C * 10.\n",
    "ds['zoo1C_zint'] = tmp.sum(dim='z_t_150m')\n",
    "tmp = ds.zoo2C * 10.\n",
    "ds['zoo2C_zint'] = tmp.sum(dim='z_t_150m')\n",
    "tmp = ds.zoo3C * 10.\n",
    "ds['zoo3C_zint'] = tmp.sum(dim='z_t_150m')\n",
    "tmp = ds.zoo4C * 10.\n",
    "ds['zoo4C_zint'] = tmp.sum(dim='z_t_150m')\n",
    "\n",
    "ds['zooC_TOT_zint'] = ds['zoo1C_zint'] + ds['zoo2C_zint'] + ds['zoo3C_zint'] + ds['zoo4C_zint']\n",
    "\n",
    "tmp = ds.spC * 10.\n",
    "ds['spC_zint'] = tmp.sum(dim='z_t_150m')\n",
    "tmp = ds.diatC * 10.\n",
    "ds['diatC_zint'] = tmp.sum(dim='z_t_150m')\n",
    "tmp = ds.coccoC * 10.\n",
    "ds['coccoC_zint'] = tmp.sum(dim='z_t_150m')\n",
    "\n",
    "ds['phytoC_TOT_zint'] = ds['spC_zint'] + ds['diatC_zint'] + ds['coccoC_zint']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the 3-D vars now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ds.drop(['spC','coccoC','diatC','zoo1C','zoo2C','zoo3C','zoo4C'])\n",
    "ds  = ds.drop(['z_t','z_t_150m'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### update the variable list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#redefine variable list\n",
    "variables = ['spC_zint'] # 'spC_zint','coccoC_zint','diatC_zint','zoo1C_zint','zoo2C_zint','zoo3C_zint','zoo4C_zint',\n",
    "             #'x_graze_zoo1_zint','x_graze_zoo2_zint','x_graze_zoo3_zint','x_graze_zoo4_zint',\n",
    "             #'photoC_TOT_zint', 'photoC_diat_zint', 'photoC_sp_zint', 'photoC_cocco_zint',\n",
    "            #'IFRAC','phytoC_TOT_zint','zooC_TOT_zint']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get polynya masks\n",
    "\n",
    "#### 0 = not a polynya\n",
    "#### 1 = open water polynya\n",
    "#### 2 = coastal polynya\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# thres_var='aice'\n",
    "# thres = '25%'\n",
    "\n",
    "# thres_var='hi'\n",
    "# thres = '0.8m'\n",
    "\n",
    "thres_var='hi'\n",
    "thres = '0.22m'\n",
    "\n",
    "path = '/glade/p/cesm/pcwg/landrum/' + case + '/ice/polynyas/monthly/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = path + case + '.' + thres_var + '_' + thres + 'thresh.polynya_sh.006201-012212.nc'\n",
    "\n",
    "ds_poly = xr.open_dataset(file, drop_variables=[\"transport_components\", \"transport_regions\"], decode_times=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rename the coords so they match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_poly = ds_poly.rename({'lon': 'nlon','lat': 'nlat'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_poly['time'] = ds.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['polynyas'] = ds_poly['polynyas']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### make a deciyear var for plotting monthly timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "deciyear = np.full((732),np.nan)\n",
    "i=0\n",
    "for year in np.arange(1958,2019,1):\n",
    "    \n",
    "    for month in np.arange(1,13):\n",
    "        \n",
    "        #print(i,year, month,year + month/12. - (1./24.))\n",
    "        deciyear[i] = year + month/12. - (1./24.)\n",
    "        \n",
    "        i = i+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make sure that land is nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds  = ds.where(ds_grid.REGION_MASK>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2afea18806d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEWCAYAAACOv5f1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABP7UlEQVR4nO29e7xkVXnn/f3tOqe7oaEVRA2KsUFRhETREGPGXFRUWqPijKNBJ0qMGfLOa2aSOEmEOJPoGPKSm07eTDQSoxIvQbxFdIabKOqMF2y8gIgICiqCXJRr05dzaj/zx967atWutS9Vp+pUVZ/n+/nsPlV777XWsy+1eq3fetazZGY4juM4G4tk1gY4juM4649X/o7jOBsQr/wdx3E2IF75O47jbEC88nccx9mAeOXvOI6zAfHKf0ZI+klJ90nqzNoWx3E2Hl75rxOSbpT0zOK7mX3PzA4ys+4s7apD0qGSPiJpl6TvSnpZ6fiJkr4p6X5Jn5L0yODYBfl/bsW2T9JVwfHteZr78zyeSQ1150t6mqS0VN6pNXkdLul8STdLMknbI+c8U9KX82v/vqSXVOS1SdIH8+drkp5WOv4Hkr4u6V5JN0j6g3GvMz/+svxZ7JL0L5IOrclrs6R3SLpH0g8lvaZ0/HhJV+RlXSHp+DrbnP0Lr/ydOv4O2Ac8FPh3wFslHQcg6TDgw8B/BQ4FdgLvLxKa2XPy/9wOMrODgM8BHwjy/mfgK8CDgNcBH5T04Bpbms6/OSzPzM6pySsFLgReFDso6VjgfXk5DwCOB66oye9/A78G/DCWHfAK4BBgB/Dbkk6pyavyOvN7/zbg5WTP5H7gLTV5vR44Gngk8HTgDyXtyPPaBHwUeE9u2znAR/P9zkbAzHyb8ga8m6zC2Q3cB/whsB0wYCk/5zLgT8kqyfuAj5FVAO8F7gG+BGwP8jwGuAT4MXAt8JIJ27yVrOJ/TOk6zso/nwZ8rnT+buCYSF7bgS5wZP79McBe4ODgnM8C/0+FLbXnA08DbhrjGpfyZ7C9tP99wBvHyO8m4GkN5/z/wN+OeZ1/BrwvOPao/BkdXJHfD4BnB9/fCJybf352flzB8e8BO2b9e/FtfTZv+a8DZvZysh/W8y1rlf5FxamnkLXqHk72w/488E6ylvU1wJ8ASNpKVvG/D3gI8FLgLUWrvIykt0i6q2K7ssKWxwBdM/tWsO9rQFHGcfn34hp3Ad8Ojoe8Avismd0QpP2Omd1bkXeZNuc/RNKtubTy5vwejctTACRdJekWSe+pk1faIknALwJXB/s+Lun0/GvTdZbv+bfJ/4PO8zpd0sfzz4cADwvPj+R1pZmF8V2upPoZOPsZXvnPF+80s2+b2d3ABcC3zewTZrZKJpk8MT/vecCNZvZOM1s1sy8DHwL+bSxTM/t/zeyBFdvjK2w5CLi7tO9u4OCWx0NeAbxrhLxHteWbZNLM4cAzgJ8B3lSRVxuOIPtP+EVksskBwN+uIb+C15P95t5Z7DCz55nZWfnXNd1zMzvLzJ4XnEvp/HGfn7Mf4pX/fHFr8Hl35Hvxg34k8HNhC55Mk/+JCdpyH7CttG8bcG/L4wBI+oXcrg+2zVvS1cHA7S82nW9mPzSzb5hZmvcu/pD8P0JJvxjkdTXt2E32H/G3zOw+MrnluS3TRpH022T/Cf6Kme2tOG0i9zw4tzi+1ryc/RCv/NePSYZP/T7w6VIL/iAz+w+xkyX9fckT5r4WFeK3gCVJRwf7nkBfsrg6/16UsZVMqirndyrw4bwSJUh7lKSwldnL28yOs/7A7Webzo9gZAOtmNlng7zaShpXMsHnJek3gNOBE83spppTm66zfM+PAjaTPasBzOxO4Jbw/Ehej8+lqILHU31Pnf2NWQ86bJQN+AJwWvB9O8MDvr8ZHP9T4F3B92cC1+efDwa+SyZNLOfbzwKPm7DN55J5n2wFnkomCxyXH3tw/v1FwBbgz4EvlNIfANwFPKPifvxVnvZf5+c9uOH+Rc8nG/D9SbIK/xHAp8ha7nXXtiW/LgMeC2wJjv0GcANwFHAgcB7w7pq8Nuf53UQ2kLqFfCCVrEf2w7bPpuE6jyMb/P/F3Pb3kA/gVuR1FvBpMm+eY8j+M9iRH9uUv0O/k9v/2/n3TbP+rfi2PtvMDdgoG3Ay2aDvXcDvr6Xyz78/FvifwO3Aj4BPAsdP2OZDgX8BduW2v6x0/Jlkevvu3P7tpeMvzSsURfLenqfZTeat9MwGWyrPB15D5rlyP1mv6G+p8IAJ0lh5Kx1/Q35vbyfzcjqkJq8bI/ltz4/dAKyQySzF9vdB2guAP2p7X4CX5c9iF5mr5qHBsT8CLgi+bwbeQfYfxq3Aa0p5PZHMhXU38GXgibP+nfi2flvROnEcx3E2EK75O47jbEC88nccx9mAeOXvOI6zAfHK33EcZwOyNGsD2rBpeatt2fRAFLpeV41Tq2J/BZYnUFWGTePhZiBlJ4bnFp9DeyQsUbZP/RJVOJ/kfiK9I+WyR7y2LIu8PBu+xt6xks2V9yJmU4w8T9MYBrfMmzxvS7JybAm6y5B0obO3/yy0aqjs1FA2q+I+G+IxjzscgOu+8QNA2TW1uazoc7TSe9Eyo5iN06ZVeRUnFc+9eN8Bpdm5Rx/7sNocv/7DW3nEIXfQJWFbssI1dz2ULbd3QfTvvYQVRRS+VcG9vXfXzXeYWV2QwEZOevpW+9GPmwPuXnHl3ovMbMdaypoVC1H5b9n8QJ5y7G+hNM12mGVh0mIUfRkp28IffvE56Xd4igpKadp/aYvzgnKGKpDe8TTLz6x3vsygmydMgh/CcofuQZtJlxNIRLqU2ZGspKibZhXVapqlT9N+XoWdnX5eQ9dU/A2PKa+sEvp5Bef1jhVUXWuYLrjvsXvSs1PCOsKSpH8PrF+RldOa1N8XHpMGf/RJ9jfdlEAK3QM7dJfF7sM67DocNt8FD7hhNbuXqbHpx3tJ9q32/pPuXXP4fnRt4PlbR5AkmMTFl/8xADse/1+ycjcv957D0H8EFtzj4n6nKepa/x6Gzynfwv8kh+5B6V0ceB8KimcYPL+q/3jLx6rf6xa1f+y3JUEnwZYSbGkJ27KEJaJzXzap+cKdb6zN8tF//mb+5iX/yF3drZx04A/4mY/+Dse87R5sKSHdvES6nGDLCd1NCeoaSdeyZ93N7zPwic/91+82G1/Pj37c5fKLfrLxvM7h1x221rJmxUJU/o7jOOuJAWllC3P/wDV/x6nhOY957axNcGaAYaxYt3FrQ77Qz1WSvippZ77vUEmXSLou/3tIcP4Zkq6XdK2kk6Z0iQvU8k/ASPrST+Q4kHV9w//Syt3fWLc5OM+kIihM3oXNk8X02a71zy2X0dvy7n1CJuUUp+QyhyWFLppfW0ewkg7mRS7bW+R6yuX3xiBKJGBpZkuv6x9KZJBdq1l2XnhrBvIZln8GzjXrKwDd7MRy5PAhaaNubMAsH7LI/02FJdbr4nf2dFm+u0tnZROrW5bYdK8NyAEkYEvJ8DMKyw+/B9Jf5+77+4c2L6OV7uB5gRSlFLIqI39/kuw+SsI6QKq+nFdc8xrGRAbkm4YGalQqaqKQ6trIPzCg85exTvtrferTv87Pbb6L767ew5/f/vNs/V4nH9PJ3iGlmUxnSyJdFtYFraQkKyJhsoviTbjl/3QzuyP4fjpwqZmdlYf0Ph14bb6Q0ClkoTweBnxC0mNsCiv+ecvfcRynhGF0rXlbAyeTrZ5G/veFwf5zzWyvZRFqrweevJaCqvDK33EcJ0KKNW7AYZJ2BttpkawMuDhfJ7k4/lAzuwUg//uQfP/DyeJTFdyU75s4CyP7WJKgXEJQmvblhxgpPbkGAo+e8H/q0n97odTRk35qDTLoKLMnkHlUeEqsdsFSUOHt0hnylDGUvRYJYJAuiSSXCqIyxVrJvX4GPF7KSEP31lAm4RRyR0RGGLi3KYE8JKQqj5LCrkBUC72Kkv6+AemnC5bktqRgidh86260/WCSbibBKLVcVkuytzyNyE3lz6Vrss39n4f2rWZ5Dd6YvvTTKWSu3OWwcP8NJDXrggrPsEBmHIUBryjoPdMyUS+ekPxSivd+4PzQ6y1Re+mnnL6wOck8v9rwmw/5NIckB7K8tIe96RIH3GZ9Lzcjl/tSTJAuJ6RLRtJJMilwVDtrMKDbzt/1DjM7oeGcp5rZzZIeAlwi6Zs158Zu1FQcfb3l7zgN7DjudbM2wZkBLVv+jZjZzfnf24CPkMk4t0o6HCD/e1t++k1kYckLjgBuntAlDbAYlb+yLfMZZ8BHumpws/Z71UBj0t8GWoAlX+wB3+uEwJ89yX3EI4PJSeCnD71WjLpBC1jCltTzMx9onRct5dBXvM2AaYxyAza/vt41Ru6vle91RR49cluV+9FT9nUPW+Ll60qDPIrPeV5Ksy1Z7X9euncvya69dDdDshrkA5BkzybzO0/6g49VvR765Q2Unw/Uhr2HcN6EiWwOAlQ6ExTvleVzCCzYX0n4TIL8BwbsK9LHehVVPY3o/uI+ld/nlgzcn+UOttxpSAEP6uwG4G5b4Sc2383Snv47oTTNnnk+2J8uQbpJdDclpJuU9QSWJ1OlGbBi1rg1IWlrsThPvuDRs4GvA+eTLXRE/vej+efzgVMkbZZ0JNkyopdP5KJKLIzs4ziOs14Y1lb2aeKhwEfyBdOWgPeZ2YWSvgScJ+lVZOszvBjAzK6WdB7wDWAVePU0PH0KYxzHcWbKL//KX3D7E5b5YHQh0hmQd1bXnI3ZdxhcSrPY/yPgxIo0ZwJnrr30ehZD9oFBv+qy5BOTI8oyQnmAM5BaoiQRKSPMN5QF8pAGPelnKYGlDiwtwXK22XIH63RyP/sin/7AZD/mi7BOX54YklvKEklZoindm8qBv4rB2soByLq5E+HtKR9L06y7boHEFUg+A9JPmkthNrj17nMgHWXhMLKwGOkBm+geciBX/dXvsfnHq5nPfUpwT8llw5LkUvUOFdeyZYkLrz4TlpLe/IierQMnGgqe33BGpQHa8taWQuoJ04XSUUl2qnz2SfzzkPw3UG6D/FMlpaUgI3+nmy/20MS4rXsf161s49DOrup3LZ9fkXayuE6Z9NqX3tZKNqzcvC0yU6v8JT02n9FWbPdI+t26mW2O4zjzgei22BaZqVX+ZnatmR1vZscDP0O2vupH6M9sOxq4NP/uOGvmWU/904nn+YwTz5p4nhuZk054/axNaEU24KvGbZFZL83/RODbZvZdSScDT8v3n0O2WHVDAJV8Cn0393NOjFFmcqscsqBrSEblYH2vq5mfEPOlDr8XfttFCAoJW86PFREiO8pkn0LuIfOXN/WL6WXXyVxHJMvCBCBIG160Oo+fcsiLopxxfMxhQMaoiyDZKzc8v5dZZF+4P/CFHygz/8FZpwipwMDcg6TwoDKIPmAx4OOu3GOLLoMeNMB9R21j849XsE1LaKWbHa8Kn1EuIyy6KV2bOR2xuR9lyaomn6hnUZFn5P2unMMR+v3HbAqloUK2I3unO/evVNpHInY9aoXrVg7kx+lBfP6+R3Pk5tvZd1Aufy71o8NaR6Qd+uHRp0Dm57/YlXsT61X5nwL8c/55YGZbPvFhiHwm3GkAmzc/cD1sdBzH6ZEueMu+iakP+EraBLwA+MAo6czsbDM7wcxO2LS8dTrGOY7jRCha/q75r43nAF82s1vz71Uz21rR8/6A4clOTekG8ugv/tAjzLc4vzyRppRPz57gfOsIljvYpqXe5BbrdHphHJR7tRBMWiq2MP9MhkiwTj5BpvDoqJi8E/PWGJp4NeoTH/HeDhF7VuXnVvW38O5J00Evq4EJb4M2arW/LxZVohd6oZB4wklspXvb3QTpskg35fe/FKKgvxBQXl6d+0eNR1Yj4XkxL6Vwq5po1kTNezHkGZVE7CmfDwPPOpNoqq/50x/7Aw48dDfX7jucT979OC675WhuWXkg9x0BqwdtYvWgZVa3LrF64BKrB3RIN6knr6kX0oP6ZzAChuiSNG6LzHpY/1L6kg9Uz2xzHMeZG1JT47bITFXzl3Qg8Czgt4LdZxGZ2eY4zsblqDe/ic3bZ21FH0Pss+ZwFIvMVCt/M7sfeFBpX+XMtpqc8q41fQ8CG5RIhuKyFH/DLqjUlwtSgaznQdQjlEYi69sWn4ekjpKkYrH1dlMyL6MUEHkUyDiFF0q2yHouTdBptwYrsclWwd9YhMWI11FbGiWGmNxUfkax+xlLW/Ka6UVHDZWI1PoyTCENVTnaKI+iyuC7dNHO1/PkX38T6eaEdG8nl+vSnj2WJKUyw2sq5d+L/zNsRG9PYs2zhmL3KXY8iN7aZpJfj9JawLVURfuMTYqETDrbW++id9CWvXz5vkey87af5PbrH8S/rD6evYevcP/hm0hW6MX16S2AZJl3V7Kax3qaxJTcwmwgXXBZpwkP7+A4jhNh0Qd0m/DK33Ecp4SZ6E4qVsScshiVvwGp9bvdoccH9CcShd3ionsedn3DcLxpvrZsJ1c8YnF/WtKTYqrWD7b+YiVmxaSl0iXGuuGF3JN7V/QkjpBx4v3FJhxNQPJpnvBVUUgYp6igYpGSofMCkn1d0k2dnifV0DUZfcmw7EHD8D1ZOVAs3yc6m5JMSlrNZTuVJhcFZcUWWmnteZOvs6zo86mYWFWWO0vHa6WfGjtik74G7DKrln7KYdeB7paEZKW6ukkPXeHuXQdw5Y8fxu3XP4gHXpOw94eHkjw0Zc8hCcv3GZ19oG7hYWUk3SyEd7JiaCWL8zRJUm/5O47jbCyyAd/9u3rcv6/OccbkUef+Gds4YNZmODNiIwz4LszVFZKPigk/Je8bYFDyKdKVJwfBcFe5FEK47fqnYZ7l0MQD9pRtK2SJYIWrXtjj0Jupl7ZU/ggxZUe5rtpzGsoaSreWNysSpjh6LChbqZFu6pDs6+YSYem6Q8kHhr1mcg8eSxIu2vn63m5LlIUNLrZyWGMjLvnELqvO86YxXtD4EkSt7FR1rCncdDjhK9gGVoPrlQ/7Dk7qV9ral7Bv1yZuu+tgDvpOwrYbVnjwV1bY9u0Erebyzmru3dM1lnYbS3tSOrtTOntSOntTkpVsmxRdU+O2yHjL33GcmbL9bX81d83QYobv/sxiVP5GMIhX4Q9d9R9+LE3VYFp4SmzQLSirMcJlGNEyMvA7MFDbiyhZHgUe7lUM9HyCcAThoPXQAHdwzb0Ip8mwv/xAmvL8hKoWcwsqB8SrqCujdy+Klmdw/2v8vKMLsQShEJQkyIyLvvQn2Wm3byZZDYrN5wNYh17U0MYeYvhMRxj4LQZXo4uyVA32roWm+QOjpou0/le2Jmz5UU1eqwJL2LfrAA67KWXT3ftQamy7UXQ3JyztSfuhO5Q9A6WQrKa9tbAnPuA7x94+ku5pOgW4xcweU3XCYlT+juM464jBvLf8v21mT6w7QdJX6o575e84jlPCECvzHd7hRWs9ZyEqf2FZZMem86r8zAu5JBL1cmjBkSppotTFHeqKR7roFpEmBggkFoXnUyGzWLEObn4vLME6fdt6qYv5ECHFeb1z+j7rrSSdKumhiqrHVSf9xPKN5ZNY/3mW7tnQoiXlwf+iHGlojkQh+QAceFPC8v1GZ1+aLRAT+rOHg8dF/rGQGTAUcqG8XvKQH36w0Ex43si++kVeaeRzlZ29AofnKhR29E5va4+EUmP3YbD7sE18489+b9jM3dlFp5tTNv24w/J9K5nffpqy5Y59rG5dygZyiyJFf1C/eDbFb2NCZP4Y89vyzxeGX9M583t1juM4M0OkLbaZWScdI+kCSf9T0qMkvUvSXZIul/S4Nnl45e84JX75V/5i1iZsGI4+682zNiGKkbX8m7YZcjbwFuA9wCeBC4FDgDcC/6NNBgsh+/Qod1sruvZW6sKGvv6SsoiMFfnKrCcHmIIyQo+bWJe3yrOm8HLp2Ryclw7ui3p1lMvrdoN5BF3QUj/PwjtotXSjpL55YZRJY7jtUrIpeo3l7EeVJJqeY0miKOff81YqjgWSnMJzYjaGcz5qvGW2fT9l070pSe4/rm7aX7Qln6MxIOHUtQLH8aapiijbhiJcxbjOLy3sHQj30CAXqmss76o+JVkBTKTLYul+6OSSD10j2bPCUhqsBax8ToEFix+lgQw3QeZ8wPdgM/sYgKQ3mtm5+f6PSXpDmwwWq/J3HMdZB4y5X6wlHI1+U+nYpjYZeOXvOI5TwoCV+Y7t83eSDjKz+8zsLcVOSY8GPtEmg7m+uh5G1FOicdJQbE1eamSKkjeIoD8BqyptrNsblhXaG9g0KENoUGLKz4suWhPcB0uVT/oKIi3mksRAZEUzIgJPP9JmcU45ZEZ5EZWy7ZFrHrre4j5UeWIVZVV8j3Xne55RsUlx4bOropd/kH6pf7HJirHljlU6e7vxCKGhlAj9+9c06SohkBTX2KoMo2u2yau8OE7bdJPA4JBr93HZha8dOpSsACuiuwmUwtJu0Erak9WsC8m+YLZdkvTey553zxomIFYz3wu0m9nbKvZfD/xumzwWo/J3HMdZR4z5nuFbIOnhZvaDcdJ65e84OTt++nV0tx0AW5dnbYozB8xzyx9A0k+Tef38/DjpF6byH5BAiklRZU+aunRpIT1AX78IYpCUJRazrItZ5BF2s3uZN8QICuSAqCdPWYqy/gS0XgyfkhwTykVZnJ6gvJIk1JN0Ruzel72UFHpUxK6zvK9iUtKQbFdXNlR7cJgNToqriIlU2DJE4U0UeBWtHryJ5T378jTQ2dPNIoTGrqNqMlHM1nHj5oxCTHasm9Q1ivRTJ/XV2VP6vVjFJDjlqmXSBfZk3j7JStp79sXvAgoPo24/rlPZpgmG9jHTXLf8JT2dzKXz5HHzmGrlL+mBwNuBnyLrSf0GcC3wfmA7cCPwEjO7c5p2OI7jjEI24DvX4R3OB34u1/jHYtr/tf0NcKGZHQM8AbgGOB241MyOBi7NvzuOs5/zqL8ueyTOM5r3SV7vA/5Y0thGTM16SduAXwL+EcDM9pnZXWTdlHPy084BXtgqw7IEYPkCKMVWklAGFkUJPitNs0lQRYwcy/+GoZJ7Wyi9pMPxheq8htpuoR2FfRX5xmSXXjjb1WJiTNq/3oHEJY+ghuuoXAAmdg3hfWraaijKGlrQpiKvoQV0qhbSGSpIWSweqfcLWLpzD+kBm0Fi+Z59JLtX0Uo3u6+9kME2GFo8tCU8XmVHHtZ5JE+f8JWLSYct0kWfY4Un3Mg0pZWwJXHTM+Iiwzf/5PeQgbrQ2SOWd+ehmcvvV/E7LxZAyu9zeM9bLcTU9rKA1NS4zQoz+y3g62QzfMdimv91HQXcDrxT0lckvV3SVuChZnYLQP73IbHEkk6TtFPSzn2rNdMDHcdxpkCXpHFri6ROXg9+PP9+qKRLJF2X/z0kOPcMSddLulbSSVV5mtmfkoV1GItpVv5LwJOAt+Zxp3cxgsRjZmeb2QlmdsKmpa3TstFxHGeIYobvBFv+v0MmexdE5W9JxwKnAMcBO4C3SKocfDCzfxrx0npMc8D3JuAmM/ti/v2DZBd4q6TDzewWSYcDtzVnNSz5DJ8S8WwJvWXMoBeWN4/EkmTnR1dMovAmseoQz1FTS3aWJwCFUkbhgVR45pTPK2wnLvmQFl4dpWMpmayRQtanLnk1hbY1XUMVgW0DVIWvDhklRHGVRFV6ZpVhusvXGPsuw/JJXtrXRSulWM/hOxD1KLHBFcXy9zB7rnXp1kjV76CCoXsVTuQL72spj6j0V+HFJLPs12Wl966GZAUsyTx/OnuC9yr0wgvKVSS/Sck9IZNawF3SEcCvAGcCr8l3nww8Lf98DnAZ8Np8/7lmthe4QdL1wJOBz9fk/3gyB5pefW5mH26ya2qVv5n9UNL3JT3WzK4FTgS+kW+nAmflfz86LRscx3HGwQxW0okJI/8d+EPg4GDfgPwtqZC/Hw58ITjvpnxfFEnvAB4PXE0wkgPMrvLP+Y/AeyVtAr4DvJKsrXGepFcB3wNe3JiLMdTiG2jlwmALJhbVM/g+kDZJUFLhg1x8CEM8hOEQyi3pgqoonqEN5UikRSuxojU1UEa4sEixbmmkdaZid12rqOTvPmBj2YaBQbh4a6uxt1Q1EF1RbuO+uh5AcV5Fy3MgMmVhTj6wGDm5wZ6+L3ov4ihEF+gZChcy6UHXWC8juF8DdsTCeMTyqeqBxUjphx0zQyspj/jEKpdd8IfR07Wad1DDhXHCd2Xgeqxd73KNZLJPq8r/MEk7g+9nm9nZxRdJzwNuM7MrJD2tRX6xl7Xugp9iZse2MbTMVCt/M/sqcELk0InTLNdxHGettJzhe4eZxeq4gqcCL5D0XGALsE3Se6iWv28CHhGkPwK4uSb/z0s61sy+0cbYkPmdwuY4zn7Pcae/edYmRJmUq6eZnWFmR5jZdrKB3E+a2a+RTdI6NT8tlL/PB06RtFnSkcDRwOU1RZxD9h/AtZKulHSVpCvbXOPChHeIrrVLKZxBTG7oRTMsyxaBlGJJdRgAgnJ6g3gVA16xQdDY4iiRQdxouIJySIWBdWQt/rdXRnYv1AW6XWzT0tC5g/cusGlUCSI4vxfVM43dz5JkVqSNhc4ovg9IdDVlB+sYD4T96JTOCRiQrcL3q9jCZ1w1aN67tiAUSC9Kq6Fw4aBiwHKSg5NVMg3Eywnu90AYj4rfTWtbU+stsjLwPFOh1ZSlXSuVSZPVXPIxSJehu7mD9q2i1YqB5/J4PBG5b81MPbzDWUTkbzO7WtJ5ZGOjq8CrzSxyxT3eAbwcuIoR3QoWp/J3HMdZRya9Rq+ZXUbm1YOZ/YgK+dvMziTzDGrD98zs/HHs8crfcZyZsP2tf8VW5jN+TubtM5+2lfimpPcBHwP2Fjtn6uo5FQLPlLIHz4DcEPpc18kYlva7kGG3sbTwSpAAdZJ+GW3XnC1FH62K8FmWTAZsT0vXEftcyEId9SWoBNi7ApuXh7v2hfwT2TdAXWeyXHax0Eb5tJ680EJGGPJuitgRyjmltD2JrizbVMg/BQPhG8q2Vnl3leXIIi+K55gOzz0p2xP1agnyapJeat69wfMCaSa3oU76qYzimlaUkecvsyxJIXF1uyR74km6B0Dn7kyeVJp5/aSbOlink926NB38rVUwaT//BVjGseAAskr/2cE+Yw5cPR3HcRaSScs+k0TSS4GLzeyV4+bhlb/jOE6Jwttnjnkk8AFJy2ThIS4ALjdr3wVamMp/YN3amkk/5Yk1UVLLJJ+e90AXOi31vUKeCcffu4PHBw2P2FuWSvoHsm5zeF45dEOV9BPzBIKsu7x5GXXzRTDCqJJJVbeetRORQaDk0RSTXwa8m/K0kec9tCsymSsLNZD0yxs42D+3N9lrNRK5tY1XV4Wc1XsX20hdTfe8xSSrut9Fv5xCnut75gxJP3W2VEk+4fGE/F3Ln0kKityDpXs6JCu53NPNvX66ZDeuo+x3ZfmEL6z/nIrrqvISmxDzvJiLmZ0FnCXpYOCZZGul/L2ka8iCvV1kZrfW5bEwlb/jOM56YSZW57jyLzCze4GP5FsRGO45wD8BlRFBwSd5OY6zTjzpt948axNGYp7j+Ut6Umwjm0X8KTOrrfhhkVr+4QSZum5e4DXTS1fXVS26qd0uFIviVMUJCs8vPteRRCSDKnmmdxwK+WfAw6eNdNRb0LdTOods8YtcaulFRbTqiKZDNHmRxD6HJAxKDHV5h5PGqp53aWJaGFOnl4eUeYsUk/N6tgTnBdJB5QI4ZW+diBxVlX9f+mHQO6wkzZXXN456hJU/Q600VikLht/Lz6VJfirey6pyYlF1JW5/0rbhrFYh2ZvF9unsg2TFSJdF2sneSymbLKfCzrr4U4Un04RYAM3/r2uOGfCMpgwWp/J3HMdZR+a58jezp681D6/8HcdxSiyKn3/u7fMfyJbMhWwG8dvMrDqeRs5iVP5ZHywut8S6oeFIRtlbJkYv77xf2WVYAurZknf723gY1IXWrbKpkJqKMlp595QKKq4n8FTRandw8Zqiu09+XuHlUl4Qp8r2OqrkrFBiaPKhTkuST+k59uzO9w149vQN6U0GU3Bd5fhMUUuK+xdeT1F2RIoLQ0MP5B9ef+TzkKxV9mjpXUpE/qpb6Kd3TkvvnKqYQ5WeaaXjpTylYFEXYDWyGN/yfZnc09kLS3uNzl5j38Fi07IgEZYkmZda8R4nFd5+aYONYzLPfv4BbwWWgbfk31+e7/vNpoSLUfk7juOsI5nn70L4w/ysmT0h+P5JSV9rk3Ahrs5xHGe9mWdvn4CupEcVXyQdRTTu6TAL0vK34Yk1BTFZp9uiizo0garkmWN5d7P8v79qPHAGziuFt62yoYrYeXVST5lQJivyyF9WSX3PiFJo5SIU8cB1VNlb9qSqurbiXgTST29iVCz/BskHs2wFs/KkrqGYxsK6DHlvDU2+Ct+tJk+kNNhfITf28lcgURXJyt5VLbzXhmgj9zTlUc6vuEexSW1tJJ/iXQ//pspktwRWIrJP4eWztMdY3pXS2ZuSLi+x7+AOn/no6zjphNcHJ4/hObcGFkXzB/4A+JSk75C9ao8kWzGxkQWp/B3HcdYXW4DK38wulXQ08Fiyyv+b+eLvjXjl7zjO3PGcx7wWth0wUxsWYcBXUodsJu92svr8REmY2Zua0k618pd0I3AvmQa1amYnSDoUeD+ZsTcCLzGzO1tlGHqklBnFEyWUTOq8GUIPoIIuwQSsiPSinsZQPemkyYOinLYne0TsjpJmdpTCLPfShN5EoXePhBKVvGaGy6mMVRT7HpNNytJPeKycf2yiWzB5qG9UbIJSKQR3kbaY6FZu2TVdRyhHlSUHaTBkcsVi9UOTt0LZJ5ambEtM8om+u5FJglVhrQckVWueyFVl24DkQ+bxY6CuccSlu7jkc/9lIJkMkn2Z5LPp3lWSfSlLu1b72RbePuXiqjyiJkh2W+e/8ieL47+HOV3J6+lmdkfw/XTgUjM7S9Lp+ffXroMdjuPMkOc85rVc8K0/n7UZLRHdxfD2OcLMHj9OwlnIPicDT8s/n0M2KaG58rdIK6Z8fC1EWmnRuQUd+mPpUb/obpA+qfanirXmQ4q5Bk29lMreRdFETCItw7zX0s3P6STBvAah5chrUfZ5r1tcpvw90vpXPhg6tH5wXfqBsvPeTRLYFfZiglaooPfMBsJAxAYpq65hwP5Iq7PcMpdl11a16Ews/3KrvM6vv6n3GK5ZHSuv7vcSc5gYpWc9FBIlZemOe4dOTfbB0l5Y3tWls2sVrabQEdq3Srp1C1rptltTemqt/4Vo+V8g6dlmdvGoCadd+RtwsbJf+tvM7GzgoWZ2C4CZ3SLpIbGEkk4DTgPYsjQcF8RxHGdaGAsj+3wB+IikBFghdzYzs8ZKc9r9mqea2ZPIQoy+WtIvNSUoMLOzzewEMzthU2e2Az+O40yGZ/1C23XJZ0zQcarb5oC/Bn4eONDMtpnZwW0qfphyy9/Mbs7/3ibpI8CTgVslHZ63+g8HbmuZWfa3PIgZY8j/eoz/wcNBvPApd1uOqRSDxVW6YZvBWysNdsW6+DEJKOx6V8lKZv0B9DTwme8k8RAPFaEOKu2vHEAfTKOyTFAe7B2wN5DKzLL7U7TOlETz780HSAL/eivmM5SuJ5R+8sHdMALpgARR9+uPSUl1slL4iAqb2qznW343y5QHp2O/g7YLorSt6cprE+eD7auHHTxU/vL9xtLulM6eLslKN3sPg1e+SgYcmiMxpVp4Ebx9gOuAr4+yglfB1Cp/SVuBxMzuzT8/G/hvwPnAqcBZ+d+PTssGx3GccbDFGfC9BbhM0gVkC7kDzNzV86FkWlRRzvvM7EJJXwLOk/Qq4HvAi6dog+M4zljMiazTxA35tinfWjO1yt/MvgM8IbL/R8CJY2UadvvDyJtRA2q8XWJ+2k2M0u0tbI3NE6jLs01UwphXUpg+adGNj117+DfpDM8ziM07GIeyHAP1i+MMeaykw8chl8g6cekHMg+WoLxeCIbBzIBBWUEQX3ymUfYKPH7Ki7eUzqv6XrnAThuvnbJnFMQlyLaN2yoPs7KkNODr34/uqTQlub/vww/k6/Za5uVTJXUNeDaV7klZSptwVM9F8PYxszeMm9Zn+DqO45TI/s+Z/8pf0gnA68hi+vTq8za+/175O46zrvzy8/8y+3BYZ7aGNLAgrp7vJQvuNpczfCdDuMZur2vZ4lrrzhnVw6Hu/JjXRBgtsYq1RiYsTwwqd8PrIjL2utPBPUoSSAMvmp4EUhPds6BqERioDjch9SWZWL6xyUxl2a5XRsS7Knb/8+cii5QbkTdqQwnU7StkozSQt8qBR5u8ddpSJSEWnlHZzlLhsQmAVfmn/d9fnddZObxF7vGjvV2sI7pb+7L0l975Gk582p+hbt/GwsOq51kVyEGVz6FpwuSYLIjmf7uZnT9OwsWp/B3HcdYJQ6SL4e3zJ5LeDlzKoLfPh5sSeuXvOI4TYTEa/rwSOIZsKcfeahPAflL5W6RbN2qfLJRl2ixA0mRLOe+yF0KvrBa2jSIvxcoO05Tln6Z809I9TdNBT5xyvrF8eseseiJRWTaIrY0co87TZ+i4MXTDw/hKxblJMhjFMjwWXm8YgXTAeyYiP4SSVuDtUngWZea3kJFiNEl5UWks7R/r7S8/n8AjLkxTRRtZpWgtBx5ckkG3iy0ts3Tn/Szdtbt3+qWX/RE7jnsdLHcGpJ+e7V1DafmZ5teR5JFZE2Wy8CR1mgUZ8AWeYGY/PU7ChejXOI7jrDvWYps9X5B07DgJF6Pl7zjOfsdzjvp97IDNszajkgVp+f8CcKqkG8g0/yKw237i6mmsfTJHeSJRk9RS7mpW5psMSyPleC51i3TEyh6FIv9R8mryWolN/Krz5AmJ3baeFBIc7A2mVUhFA3naoIxRZWchJw08h4gHULEOc9P6xGFY79g6wjD8LpZtCM5V6Xs0z2BhnSF7yve26jcRk3yCiVJAySsnMnGuKmR6Yyj1YEJZAuECTNq7AhLp1s10bvwhHJAFbLQDN2XHilDSST8rVXl2xZjgJC8D0qFJgHPJjnETuuzjOI5TxsjGE5q2BiRtkXS5pK9JulrSG/L9h0q6RNJ1+d9DgjRnSLpe0rWSTqo10+y7sa3NJXrl7ziOEyGcalC1tWAv8AwzewJwPLBD0lPor2h4NJmb5ukAuX5/CnAcWav+Lfk6vQNI+nJTwU3nLIbsA/1QylWeNVC9H5rj2fTOG3GiSHF+lfxTDnFbxTjST51HTlPehWdP+Xj5zS5PACsIrzu0JyZ5Rb17uoFnUosYTTE7Q3t7NkVCO4dlQX8d5tDOmAdTaH+5vJjMAv2JU+XQzElwb8qrbMWkjTC0duxZVIVibiOPQWAXcdp4XxXXEtLLL4+/1elAmqK031JOdu2FQx4Ad9+L0i7avTe73iTJ/pZXyot6qY1g87hMILs81PJ9+dflfDOqVzQ8GTjXzPYCN0i6niwU/udLWT9O0pU1RQt4QJ1ti1P5O47jrBtqO+B7mKSdwfez8xUL+zllLfcrgEcDf2dmX5RUtaLhw8lW5yq4Kd9X5pgWtnXrDi5O5R9rBdQNpDa1AoYG7hp8yENiLeyqHsBafPjLdiWlZtoog72jlFv2/a/aL2X7ynbFejtVA8GVByPl906v6LEV9762rLy8mP9/bNGTOp/+6LyDYMBTwXsQG9wtWvwDIRgKextsiA7KVgz0xnoBvWsZvoSRqetRFGFZggWCZIYtd9C2g7IefTG3pLA17NVU6SttHRDWQrufzB1mdkJtNmZd4HhJDyQLc/9TNafHLmzIkra6fh2LU/k7juOsF0Yk5PcaszS7S9JlZFp+1YqGNwGPCJIdAdw8UUNyfMDXcRwnilpsDTlID85b/Eg6AHgm8E36KxrC4IqG5wOnSNos6UjgaODyyVzPIAvS8m8/tD5athWST1vJqGBgQZVABqnKZ9RB5Vi6chmxbvBQF7/hOkeV0Mr3ryz/lM8dGoTP/44S+TSWb7gWMQxIDENl9cor/P9LA7cq3dfoYHlkUHXYaAYio8aksCIkQTigPLQYUHB+1eBqLOJpk1QUHh9FQmmSGYdkPoPEYJVsMDc/pp7cE9yDRLAUVEl18lrdQk6TYjJVzuHAObnunwDnmdnHJX2eyIqGZna1pPOAb5DdtVfnstHEWZDK33EcZ52ZjLfPlcATI/srVzQ0szOBM9vkL+nfAH8OPIR+d8TMbFtTWq/8Hcdxyhi0mcQ1B/wF8Hwzu2bUhItT+ddJHpPKt60M0jT1vUoGqZJ7op4bDR4O5TLq7kXdNcb2jSL/DPjD18g/sfkZdZ5AVVTJMaEEVRdyIzMw98PvRu57GA6i5j40hRIoQkJUSTeFh09qg3JVTyIKPJKq5I9wfkSYX52HT9Xzb5J+KucV1NyjIk03sF8lmwv5xwzCAdbGUA1hfTCdSnoaSvMUuHWcih/WofLPta6dwA/M7HmSDgXeD2wHbgReYmZ3TtsOx3GckViM2D47Jb0f+BdGXMxlPbx9fgcI/2eKTmt2HMeZJ2TN2xywDbgfeDbw/Hx7XpuEU235SzoC+BWywYvX5LtPJj6tuZryTS7LC+N6zwyU0dA1rjq3LrzEKBPH2nSfY549ba99lOsLqet+l9drLWwr2xTKMFW2tQ3TUb6OmAdT3cS43jnF8dKxprWhy940MXqTuoK1kJUwMJkrlGcq36dc+qnyLgplq5jkUzVBr446j606muSkXniW4rcrWM09fbq5M8u4E7fK601PAmNS3j5TxcxeOW7aVk9a0lDQ7di+CP8d+EMGVd2Bac1ko9SxMk+TtFPSzn3p7tgpjuM4U6JFRM85GBCWdISkj0i6TdKtkj6UN7obafvffDmoUNW+0KjnAbeZ2RUtyxjAzM42sxPM7IRNyQHjZOE4jjM+1mKbPe8kmxj2MLIYQB/L9zVSK/tI+ok8wwMkPZH+lLZtwIENeT8VeIGk5wJbgG2S3kP1tOZ6Yl3jJi+W8vnlc2MeDLEYLjGSCXU1Y9dV5VERO96mq1w3YamO/PrNUhSbVBOuW1vY0vScCmJxitpSnlxWvr5QbmjyEiub1mbCWVW8maFzgWKxGusOx9upk/p611Ixvydcl7ecru3kxVjsqSp5qI0cVJe2XE6X+Lq7q6t928plx+REGJYeJ8Uk4h5NnwebWVjZv0vS77ZJ2KT5nwT8Oll8iTcF++8F/qguoZmdAZwBIOlpwO+b2a9J+kuy6cxnMTit2XEcZz4w5kLWacEdkn4N+Of8+0uBH7VJWFv5m9k5ZFOTX2RmH1qbjT3OIjKt2XEcZ56YE2+eJn4D+B/Am/Pv/yff10grbx8z+5CkXyFbXWZLsP+/tUx/GZlXT+205ta08ZhpE+q4bsJK0yST8Hhskkko1bTtjg51wQtZpUbuGSfvWOjiMoHkE/4N6UlBRVyW2olVJbqRCVZlqryEYnJfeeJVzIunTQyirlXbFZY1isTQDc4t0oaSlTQ8Ma3terQxyautHDrKdbTxFqrKK7ZoUPG96pmF96JbJX1NWZdZgMrfzL4HvGCctG29ff4e+FXgP5Lp/i8GHjlOgY7jOM5kkHSUpI9Juj33+PmopKPapG3r7fOvzOwVwJ1m9gbg5xmMOe04jrNfsSCTvN4HnEcWPfRhwAfo6/+1tJ3kVTja3y/pYWQDCkeOaOT0qJpgUqatBBN0RWNyRz+7QPYoU+UN1LY7H0sTW90pMySerqnsOvknaZYeinujYt3aIt0kvC6qJsllBdd/h2FPpIFjkdXHmmiaXBZSF2+pSgIp2zSqnBd6+LT9PfTKrnl2beXFquNVaySH5VZ9r3uXyhMLJ42xKOEdZGbvDr6/R9Jvt0nYtvL/eL4gwV8CXya7NW8fyUTHcZxFYj5a9lHyGGkAn5J0OnAumcW/CvzPNnm0HfB9Y/7xQ5I+Dmwxs7tHtHftNLU+mgYy26bvnV4/oFQ+PuALP04Lv4mqQeYRr2vNZZdsGJgH0Lastq26GDWtyIHeSLG/PBjdFAKiHG6iXGZV672gajC7aj5JYVfVsbrjsTkP474PTT3McRjFiaKNPU1lTJA5kXWquIKssi9uzm8Fxwx441CKEk2TvP5NzTHaRI5zHMdZSOa48jezI5W1cH7ezP7POHk0tfyfXy4z/6v8s1f+juPsn8xx5Q9gZqmkvyJzwBmZpklerwSQtAV4EVkM/iLN+t6a2kG90uDkBGSOJsmnKU00HMKY+daHVhhlAYwJE9hQK4HVpY3RZiw2kj60oXYwuiwrVck0oRxUNbegLU1zOMLz2qzH3BTiouW7EJ2/kUYeQNnOKf/GGt+fKTNH3jxNXCzpRcCHzUbTv9oO+P4LcBfZYO+efN9i3BrHcZxxWAxvn9cAW4FVSXuYwhq+R5jZjjUY6DiOs1AsQsvfzA4eN23byv9zkn7azK4at6CJ0OSlMCGGuqNtons25TFBe0b1KqqMyllHk4w2EDlz2AtpTfJWy2uqJffyiXoixW5FTG6piorZyoPF+u9G3fWkBp3AA6lp4ZuybQNRPPvp1vL+Rd+XimtoK/dN8veQZzh6iJORy5hOtpNG0iHA0QyG3vlMU7q2lf8vAL8u6QaydSKLrsXjx7DVcRxnvlkQzV/Sb5ItlXsE8FXgKWRrrTyjKW3byv854xrnOI6zkCxA5U9W8f8s8AUze7qkY4A3tEnYdpLXd9dg3ASwRu+OUaiTQEaSfOqOV01MGWUSVAVDE5lK++vShDTJLNH8miYqrUGCs2TE59nm2cQ8kWLeLNDsZVQTLiN6ryqCUQ7YomTQA2nUNZlLkTFb/yYapMxxf1sjpauYOFgrOdVNQJuw/KsJK1VTYo+Z7ZGEpM1m9k1Jj22TcKoLuDuO4zhT5aY89M6/AJdIuhO4uU1Cr/wdx3FiLIDsY2b/Ov/4ekmfAh4AXNgm7WJU/jZd75lK1tKNXGsXtIWcNPY9KckhreSjNh4VdZFGW695uwaiEwEjpyUVHipDUSUjZYwY/ymaby//3BspTbKyxln4pxTTqLa8trSNqTNKXmvAut2+HFTEappwGcOFzveAr6SfBQ4zswuKfWb2aUkvAH6aLPZPLbOdRuc4jjOvWIttdvwlcE1k/zfyY40sRsvfcRxnvZnjlj/wIDO7sbzTzK6X9KA2GUyt8s/jAX0G2JyX80Ez+5M8DvX7yeIE3Qi8xMzubMywKo5PyCS6qeXypk2bRVlC6tYOHsXmmrg8rfNpCos8avpxJus0ecfEFm0Jk9R4tlR5BY19v4bsKE0EKxfX4llXyj1VMYLC4xCfKBWbvLdW2jzb0N5Ogq2uZp+7XbAEU36/unGPvUlKw2LuvX0OqDm2tU0G05R99gLPMLMnAMcDOyQ9BTgduNTMjgYuzb87juPMDzb3yzh+QtKZ0uD/8JLeAHyyTQZTq/wt477863K+GXAycE6+/xzghdOywXEcZ2zmW/P/z8BRwPWSPpRv1wOPJQv21shUNX9JHbJR50cDf2dmX5T0UDO7BcDMbpH0kFaZFd3GbsUdr5pgNKoU1NTVbVpladS0seNt8xu1W97SG6ZVuqF88oya1satkmmq0sfOr7DHSucqSTLJAAbvaVFGw6S0xklnZZllFAJppSinLDMNhAiPSFBmaXXcpbY2jbreb8+gFu/oGDKelPRCb6vT4YJb38pJB50K3W72PCVIEqxTSjglj5+1IukRwD8BP0H2azvbzP6mTv6WdAbwKrJpgv/JzC4aMs1sF/BSSUcBx+W7rzaz77S1baqVv5l1gePzSQgfkfRTbdNKOg04DWCLWklYjuPsZ5y09RXTWaC9BROSdVaB/2xmX5Z0MHCFpEuAXyeTv8/K1+A9HXitpGOBU8gq9IeRyTuPyevSIfLKvnWFH7Iu3j5mdpeky4AdwK2SDs9b/YcDt1WkORs4G+ABncNs5IG9moiTUepaDm1b7JNilPzG8qcPWtlrsb2450U0yrblhpR7Ow0t/XLrvs5+CxZpUWxhlsZeSv3hpvJbpysCdJZ7GnWD8pOyY1ymUV5xnV248I6zgbzyz8sqnqfCMBjhOzhpJnCJucpRKB33SroGeDiZ/P20/LRzgMuA1+b7zzWzvcANuZTzZLJgbRNlapq/pAfnLX4kHQA8E/gmcD5wan7aqcBHp2WD4zjOWFjm7dO0AYdJ2hlsp1VlKWk78ETgi8CA/A0U8vfDge8HyW7K902cabb8DwfOyXX/BDjPzD4u6fPAeZJeBXwPePEUbXAcxxmPdi3/O8zshKaTJB0EfAj4XTO7R9VSVuxArSWSngD8Yv71s2b2tSZ7YIqVv5ldSfa/XHn/j4ATx8iw+phU7TMe3uS2g0KTHOCcNnW2NnWF647Hrmtg0DT0T08Gj1fZlCTDZbbxRS++xtbRLWyoIhlO2xs4bHqGa10wpO39LcppWqwotjhMlV3TkEFijDvAH8ujm3LhnW8vpS9fn2HBc9EU5a5JuXJKWiar+N9rZh/Od1fJ3zcBjwiSH0FNoDZJvwP8e6DI9z2Szjazv22yy8M7OI7jxJiAq2fuh/+PwDVm9qbgUJX8fT5wiqTNko4kW6Hr8poiXgX8nJn9sZn9MdliLv++zeV5eAfHceaKZ3V+lWQ5r5pGXX50UkzOj/+pwMuBqyR9Nd/3R8BZRORvM7ta0nlkMXpWgVdXefrkiMGVI7rEpaMhFqbyH+q2Dxys8Bgpe7OMIDFUUu7GjtKtbXN+VTdaik/xj/ruV8gjdcT84GPHkwoZLU2h0xnOq0ynE0SkHE2aKJ77kMdPHUUIhXK4g/CdqcqvyRtqLdJKWXKqCrcwjmy5nlSF+BjpGdWcG3o5hdFXi9sWeHRNEjEZ2cfM/jfVlXFU/jazM4EzWxbxTuCLkj6Sf38h8I42CRem8nccx1lP5jmkc4GZvSl3o/8Fsv9kXmlmX2mT1it/x3GcGAtQ+Ut6t5m9HPhyZF8tCznga2k6sPUPlGSQNO1v4fHYFhKmK2/j0Db9KJJPG0qTo2Jb1IZyNzoi+Vx4x9l5tMXg3pW/F2nCrcgvn6Yf3crlllDMxjoKuSR/1gPXX/cu1L0Hk/KmiU1aK6+THHtHi2PlvCZp26i0Kb/md3fh3Zla0dP7h9KmfRkotaHnWnmfxmUCA77rwHHhl9y1/mfaJFzIyt9xHGeq2HxH9ZR0hqR7gcdLuiff7iVzGW01cdYrf8dxnBhz3PI3s//PzA4G/tLMtuXbwWb2IDM7o00eC6L5l7pzpS6/pemgB1B0YYoJdYWbupVr8TyIeU5USD5F7JMdh/xmvS1le2OLfPRknWbJZcCGu4edCnYc8pslT5rM2+bCO85mx6El9+OquETFZLC297owu84bJpwoFdgXjQQ6qaiWbWnrMdbk2dXEtCZEjTlRL7zXF97zzvblWTro+VMwycWcmPvFXAoeI+m5wIU24mo23vJ3HMeJMM+yT8BbgX8HXCfpLEnHtE3olb/jOE6ZNpLPHFT+ZvYJM/t3wJPI1gW4RNLnJL0yDytRyWLIPka82w69Lme0+w5r78KP2lUedUGWOgLJp1iz9ILb/378/MqLfFRN7DIbiLHSk5byhXSG4q8EFMdC+efCO/4h+/vjf+gfi8XM6Xa58J53smPbK/v5ReSAkw46dXBH7zpaPKtw3dzys43IQDFaS0MDto1IWZ6bB3lnjPIq4zEFxy667xzqsNRQWdIpFI5wBvCkJ8HNQeXehnzB9l8jm0n8FeC9ZH7/p9IPGz3EYlT+juM468ikZvhOG0kfBo4B3g08vwgTDbxf0s66tF75O47jRNA8htMY5n+YWXTB9qZQ04tX+ZdH9ytklrrue2VsoBGoyn9Ibmq71mlZgukkefL6YZkL73z7sMdPGDo55vETIz8/5sFTJ/PU2TXOMWj2/KiSCU46oHFSY0Yo/YS09C6a9rtVmTb2Lq1R4hnlWkaKqQSlCXMxO42Ldr+7xjYb+Dwk/WQHRrOpLXOi6TdhZp+U9K/I1gJeCvb/U1Paxav8Hcdx1oEFkX3eDTwK+Cr96J5Gtmh8LYtT+cf+h0+DlkmT31LQahqlBaMkqT6/ZuC5csA51noL/exLfv1mac+nv4pYS3rHA36jNs1+TWzRk1Eppy07GsSoGDAe6g2Mywit/JFb6aPkUbmATGT/GC1zq3huxf5oD2AaLEDlD5wAHGs2ehfQXT0dx3EiLIif/9eBnxgn4eK0/B3HcdaT+ajco0j6GJmFBwPfkHQ5sLc4bmYvaMpjISp/Y7grqESDXcq0oRMzgiw0UHbZT7lqALUkAQ3JP0P2VCyYUo6cOSblgdsh3/ictr7Wi0AxeHjS1ldkO5LIurhtJIOY7NBGQoo96pIUVPU+TEKmmRp1A7cVss7EJJowlEMp74KijCq5aLxy5z68w1+tNYOpVf6SHkE26PATQAqcbWZ/I+lQ4P1ko9M3Ai8xszunZYfjOM6ozLufv5l9WtILgUcDV5nZRaPmMU3NfxX4z2b2OLJFhV8t6VjgdOBSMzsauDT/7jiOM1/Urf8x6bUDRkTSW4DfAx4EvFHSfx01j6m1/POZZrfkn++VdA3wcOBk+lOOzwEuA17bLtP+lO7hrl9NH01Jc/e/zi+/jcdOBQMRR6X+OrfhOrahZ1B+GUUohIlRI1VdtKvRK2yhKF9P1P8/DBdS3lcwqqdKlyGJYqB51TJ8xLrTVIn1Fk0ZtL2NzLIm+acoLyL9jGrHOMxzyx/4JeAJZtaVdCDwWeCNo2SwLpq/pO3AE4EvAg8tpiCb2S2SHlKR5jTgNIAtHLgeZjqO42TM/ySvfWbWBTCz+6XRg0hNvfKXdBDwIeB3zeyetjaa2dnA2QDbkgfN92NwHGciPHv5lNpW/noy5wO+x0i6Mv8s4FH5dwFmZo9vymCqlX8eUvRDwHvN7MP57lslHZ63+g8nW3asHrPBLmekO24V3j5DXkHZzvgiEFX/MUX2V64jG+tCh5O4ShEzoR8101ZWgOl43oRSSM8bhv1P8okRhhA46YCX10s9a6X8rsUmIk5yIZhKO8a4roZJWrXySkwiq6jEL9rz3no7YnnFoni2SbcG5rzyf9xaM5imt4+AfwSuMbM3BYfOJws1elb+t9V6k47jOOuGMdMB3SbM7LtrzWOaLf+nksWXvkrSV/N9f0RW6Z8n6VXA94AXT9EGx3GcsZjzAd81M01vn/9Npj/FOHHyBVZNNul3E3seB2Xvgaooj710FRNz9uUyTS4r7Nj2yvoufYWnxzhRM9fCRpB6qrho97vj3j/h+1MhLYzjVTLghZYm7WIETYsm+6Ny6hri9bSRakZlWlE8o2WtX1GzYCFm+DqO46wn8z7JaxJ45e84jlPGbK4Xc5F0FfG+yXx4+0ySUbrcVRNKKheE6J2wtklPTYuQOPNBLwZQLv9Yt4sSBROSWni4tJQfBmXHNB6Daq3xb0atpGpsX+v11uV78b73jZVu3UI4DxU+m2Jb8ry1ZrAwlb/jOM56Ms+yz7x7+ziO4ywmxuTngEwQSfdSL/tsa8pjv6z8B+KJlDx7et3IvBvbOOHE2W8prx/77E0vAyYsfZTiUUVjUDWFIx+H1rLUeNfaJMMOSDUj3LeLV87l2cunDK3fW5X3tOL6ZJlPL+u1YmYHrzWP/bLydxzHWSvzLPuUyWOkbSm+m9n3mtLMRxANx3GcOUOpNW6zRtILJF0H3AB8mmyNlAvapN0Ylb+lQzFKLtrzXpd8nAEu3ve+/rsS2wIstVZbkAAsjZ9TV+a4W4SRyq6hbSjnga3bbX4ALfOP3t9JYy23Fkh6h6TbJH092HeopEskXZf/PSQ4doak6yVdK+mkhuzfSLZeyrfM7EiyCbT/p41dG6PydxzHGYFskpc1bi15F7CjtC+6qFW+4NUpwHF5mrdI6tTkvWJmPwISSYmZfQo4vo1R+3XlP9QyaNmycTYuF6+cO1qLvoGhNJEWdttexFq3wKiRfgcXr5w7YOeGIW2xtcDMPgP8uLT7ZLLFrMj/vjDYf66Z7TWzG4DrgSfXZH9XHjb/M8B7Jf0N2SqKjezXlb/jOM64tGz5HyZpZ7Cd1jL7gUWtgGJRq4cD3w/OuynfV8XJwG6yJR0vBL4NPL+NAe7t4ziOU6a9pn+HmZ0wwZJj05krLTGzXQCStgEfG6Wgxan8m7qoLSIHFt1Xx5kFoWQyEGG2TNO7XPVbCNOt0c//ku77B75fvHIuz+r8aqs89w+m7s1TtajVTcAjgvOOAG6uykTSbwH/jaz1n5JP8gKOajLAZR/HcZwYZs3b+BSLWsHgolbnA6dI2izpSOBo4PKafH4fOM7MtpvZUWZ2pJk1VvywSC1/x3Gc9cImt4yjpH8GnkY2PnAT8CdULGplZldLOg/4BtnA7auLhdor+DZw/zh27T+Vf3mBlt5uG+rCOk4tdVLMOJ5iFe/l4CnjhUMoZTrCqaO3Wut+R1WS0MR/e+u6mMtkZB8ze2nFoeiiVmZ2JnBmy+zPAD4n6YvA3iCP/9SUcP+p/B3HcSbJYni1vg34JHAVrZ1PM7zyd5yAZyW+pLSToYplV+eMVTN7zTgJ9/sBX5d8nImwlsmBLSZVrWnS1sjmVKzLu4ZJkJd03z+Ux2JLPkxskteU+ZSk0yQdnoeMOFTSoW0STq3lL+kdZKvN3GZmP5XvOxR4P7CdLADRS8zszmnZ4DiOMw5ipPANs+Rl+d8zgn0zd/V8Fy3jWTjOrHlW8mKXfJxBpuvqOSET7cjINltXTzP7jKTtpd0nk7k8QRbP4jLgtZMu26UeZ24ZZ1JXZVbDlU/bd3/AO2dCcsol6Qcmkk+UWcTjmoPKvQpJPwt838x+mH9/BfAi4LvA682sHEtoiPXW/KviWQyR61g7Je1c6XswOY7jTJ/51/zfBuwDkPRLZPMG/gm4Gzi7TQZz6+1jZmeTX8Q2HTq//wU7C4/LPU6MOff26QSt+18FzjazDwEfkvTVNhmsd+VfFc9iMlg63a6n40yDYJ3fURlX4izSLcR/fDMJwT4fmn4NHUlLZrZKNlksjCbaql5fb9mnKp6F4zjO/GDM+4DvPwOflvRRsqBunwWQ9Ggy6aeRabp6to5n4TizYiFavs5smGPVx8zOlHQpcDhwsVnvf6IE+I9t8pimt89I8SwcZ8MTyhsVEtCkPdnmUSa9pPv+ufhPed79/M3sC5F932qbfm4HfB3HcWbKnFf+a2W/qvznsRXjOGOxwZ0XimufWQ/ADLpzrPtMgP2q8nccx5kY3vJ3HMfZgHjl7zjOerGRpZ4YM5N/DJjuGr4zxyt/x3GcIWxGk8vWD6/8Hcdxyhg+4LsIeFfZ2R/w97iamcg/rvk7juNsQLzydxzH2WjMPHbP1FnYyt+7yM6i4u/u+Kyb/GPAfId0XjMLW/k7juNMFW/5O47jbDQ8vIPj7NeMIsHMQ6RJJ+OS9APTfR4G5n7+juM4GxCf4es4jrMBcc1/vnBPCWdWjPrulWUJf3cnS9X9lLT2zM3c28dxHGdD4i1/x3GcjYZh3e6sjZgqM6n8Je0A/gboAG83s7Pqzn/MzxzFJTu9y+wsFi7zLDAbIKRzfJXoKSKpA/wd8BzgWOClko5dbzscx3FqsbR5W2DWvfIHngxcb2bfMbN9wLnAyTOww3EcJ4oBllrjtsjMovJ/OPD94PtN+b4BJJ0maaeknbfffvu6Gec4joOZt/ynQMwPa+i/UDM728xOMLMTHvzgB6+DWY7jOH2s223cFplZDPjeBDwi+H4EcHNdgiuuuOI+SddO1arJcBhwx6yNaIHbOVnczskxCRsfuVYj7uXOiz5hHzysxanzfj8rka2zL6ukJeBbwInAD4AvAS8zs6tr0uw0sxPWycSxcTsni9s5WRbBzkWwcX9h3Vv+ZrYq6beBi8hcPd9RV/E7juM4k2cmfv5m9r+A/zWLsh3HcZzZDPiOw9mzNqAlbudkcTsnyyLYuQg27hesu+bvOI7jzJ5Fafk7juM4E8Qrf8dxnA3I3Ff+knZIulbS9ZJOn7U9IZJulHSVpK9K2pnvO1TSJZKuy/8eMgO73iHpNklfD/ZV2iXpjPz+XivppBna+HpJP8jv51clPXeWNublPkLSpyRdI+lqSb+T75+3+1ll51zdU0lbJF0u6Wu5nW/I98/V/dwQmNncbmSuoN8GjgI2AV8Djp21XYF9NwKHlfb9BXB6/vl04M9nYNcvAU8Cvt5kF1lwva8Bm4Ej8/vdmZGNrwd+P3LuTGzMyz4ceFL++WCyOSrHzuH9rLJzru4p2Qz/g/LPy8AXgafM2/3cCNu8t/wXMQjcycA5+edzgBeutwFm9hngx6XdVXadDJxrZnvN7AbgerL7Pgsbq5iJjQBmdouZfTn/fC9wDVksqnm7n1V2VjErO83M7su/LuebMWf3cyMw75V/qyBwM8SAiyVdIem0fN9DzewWyH6QwENmZt0gVXbN2z3+bUlX5rJQ0fWfCxslbQeeSNZandv7WbIT5uyeSupI+ipwG3CJmc31/dxfmffKv1UQuBnyVDN7EtnaBK+W9EuzNmgM5ukevxV4FHA8cAvw1/n+mdso6SDgQ8Dvmtk9dadG9q2brRE75+6emlnXzI4ni+v1ZEk/VXP6zJ/9/sq8V/4jB4FbT8zs5vzvbcBHyLqjt0o6HCD/e9vsLBygyq65ucdmdmteMaTAP9Dv3s/URknLZBXqe83sw/nuubufMTvn9Z7mtt0FXAbsYA7v5/7OvFf+XwKOlnSkpE3AKcD5M7YJAElbJR1cfAaeDXydzL5T89NOBT46GwuHqLLrfOAUSZslHQkcDVw+A/uKH33Bvya7nzBDGyUJ+EfgGjN7U3Boru5nlZ3zdk8lPVjSA/PPBwDPBL7JnN3PDcGsR5ybNuC5ZJ4L3wZeN2t7AruOIvNC+BpwdWEb8CDgUuC6/O+hM7Dtn8m6+CtkLadX1dkFvC6/v9cCz5mhje8GrgKuJPvRHz5LG/Nyf4FMZrgS+Gq+PXcO72eVnXN1T4HHA1/J7fk68Mf5/rm6nxth8/AOjuM4G5B5l30cx3GcKeCVv+M4zgbEK3/HcZwNiFf+juM4GxCv/B3HcTYgXvk7C4Okd0n6t7O2w3H2B7zydxzH2YB45e/MHZK253Hp/yGP+X5xPhs0POdESV9Rtp7COyRtzvffKOkNkr6cHztmNlfhOPONV/7OvHI08HdmdhxwF/Ci4oCkLcC7gF81s58GloD/EKS9w7KAe28Ffn+9DHacRcIrf2deucHMvpp/vgLYHhx7bH78W/n3c8gWhyn4cEU6x3FyvPJ35pW9wecuWeu+IBbmN5a2nM5xnByv/J1F5JvAdkmPzr+/HPj0DO1xnIXDK39n4TCzPcArgQ9IugpIgb+frVWOs1h4VE/HcZwNiLf8HcdxNiBe+TuO42xAvPJ3HMfZgHjl7ziOswHxyt9xHGcD4pW/4zjOBsQrf8dxnA3I/wWtFtHMNHyIyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.spC_zint.isel(time=100).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### function to adjust POP grid for map making"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_pop_grid(tlon,tlat,field):\n",
    "    nj = tlon.shape[0]\n",
    "    ni = tlon.shape[1]\n",
    "    xL = int(ni/2 - 1)\n",
    "    xR = int(xL + ni)\n",
    "\n",
    "    tlon = np.where(np.greater_equal(tlon,min(tlon[:,0])),tlon-360.,tlon)\n",
    "    lon  = np.concatenate((tlon,tlon+360.),1)\n",
    "    lon = lon[:,xL:xR]\n",
    "\n",
    "    if ni == 320:\n",
    "        lon[367:-3,0] = lon[367:-3,0]+360.\n",
    "    lon = lon - 360.\n",
    "    lon = np.hstack((lon,lon[:,0:1]+360.))\n",
    "    if ni == 320:\n",
    "        lon[367:,-1] = lon[367:,-1] - 360.\n",
    "\n",
    "    #-- trick cartopy into doing the right thing:\n",
    "    #   it gets confused when the cyclic coords are identical\n",
    "    lon[:,0] = lon[:,0]-1e-8\n",
    "    \n",
    "    #-- periodicity\n",
    "    lat  = np.concatenate((tlat,tlat),1)\n",
    "    lat = lat[:,xL:xR]\n",
    "    lat = np.hstack((lat,lat[:,0:1]))\n",
    "\n",
    "    field = np.ma.concatenate((field,field),1)\n",
    "    field = field[:,xL:xR]\n",
    "    field = np.ma.hstack((field,field[:,0:1]))\n",
    "    return lon,lat,field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['TOT_zoo_prod_zint'] = ds.x_graze_zoo1_zint + ds.x_graze_zoo2_zint + ds.x_graze_zoo3_zint + ds.x_graze_zoo4_zint\n",
    "ds['z-ratio'] = (ds.x_graze_zoo3_zint + ds.x_graze_zoo4_zint)/ ds.photoC_TOT_zint\n",
    "\n",
    "variables = variables + ['TOT_zoo_prod_zint','z-ratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".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-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",
       "}\n",
       "\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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  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.Dataset&gt;\n",
       "Dimensions:            (d2: 2, nlat: 76, nlon: 320, time: 732, z_t: 60)\n",
       "Coordinates:\n",
       "    TLAT               (nlat, nlon) float64 dask.array&lt;chunksize=(76, 320), meta=np.ndarray&gt;\n",
       "    TLONG              (nlat, nlon) float64 dask.array&lt;chunksize=(76, 320), meta=np.ndarray&gt;\n",
       "  * time               (time) object 0062-01-16 12:00:00 ... 0122-12-16 12:00:00\n",
       "Dimensions without coordinates: d2, nlat, nlon, z_t\n",
       "Data variables:\n",
       "    KMT                (nlat, nlon) float64 dask.array&lt;chunksize=(76, 320), meta=np.ndarray&gt;\n",
       "    REGION_MASK        (nlat, nlon) float64 dask.array&lt;chunksize=(76, 320), meta=np.ndarray&gt;\n",
       "    TAREA              (nlat, nlon) float64 dask.array&lt;chunksize=(76, 320), meta=np.ndarray&gt;\n",
       "    dz                 (z_t, nlat, nlon) float32 dask.array&lt;chunksize=(60, 76, 320), meta=np.ndarray&gt;\n",
       "    time_bound         (time, d2, nlat, nlon) object dask.array&lt;chunksize=(732, 2, 76, 320), meta=np.ndarray&gt;\n",
       "    x_graze_zoo1_zint  (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    x_graze_zoo2_zint  (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    x_graze_zoo3_zint  (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    x_graze_zoo4_zint  (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    photoC_TOT_zint    (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    photoC_diat_zint   (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    photoC_sp_zint     (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    photoC_cocco_zint  (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    IFRAC              (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    zoo1C_zint         (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    zoo2C_zint         (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    zoo3C_zint         (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    zoo4C_zint         (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    zooC_TOT_zint      (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    spC_zint           (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    diatC_zint         (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    coccoC_zint        (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    phytoC_TOT_zint    (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    polynyas           (time, nlat, nlon) float64 nan nan nan ... 0.0 0.0 0.0\n",
       "    TOT_zoo_prod_zint  (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;\n",
       "    z-ratio            (time, nlat, nlon) float32 dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</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-1b5e97ea-c099-4cc7-a24d-614e007fb8cf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1b5e97ea-c099-4cc7-a24d-614e007fb8cf' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>d2</span>: 2</li><li><span>nlat</span>: 76</li><li><span>nlon</span>: 320</li><li><span class='xr-has-index'>time</span>: 732</li><li><span>z_t</span>: 60</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-00f25c2c-df90-4b0e-936f-270c065e5b5e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00f25c2c-df90-4b0e-936f-270c065e5b5e' 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>TLAT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(76, 320), meta=np.ndarray&gt;</div><input id='attrs-20d010af-68dd-4bbb-a0f5-3c879b239582' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20d010af-68dd-4bbb-a0f5-3c879b239582' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f037a93-3fd4-499f-a84c-2701aa7f0640' class='xr-var-data-in' type='checkbox'><label for='data-8f037a93-3fd4-499f-a84c-2701aa7f0640' 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>array of t-grid latitudes</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 194.56 kB </td> <td> 194.56 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (76, 320) </td> <td> (76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 6 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,42.704069968794954 0.0,42.704069968794954\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"62.704070\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"140.000000\" y=\"21.352035\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.352035)\">76</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLONG</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(76, 320), meta=np.ndarray&gt;</div><input id='attrs-56e1c055-6cdf-4537-8c48-e4a7ec57f4b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56e1c055-6cdf-4537-8c48-e4a7ec57f4b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1827e814-4286-4b66-b776-8767f09c9322' class='xr-var-data-in' type='checkbox'><label for='data-1827e814-4286-4b66-b776-8767f09c9322' 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>array of t-grid longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 194.56 kB </td> <td> 194.56 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (76, 320) </td> <td> (76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 6 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,42.704069968794954 0.0,42.704069968794954\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"62.704070\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"140.000000\" y=\"21.352035\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.352035)\">76</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>object</div><div class='xr-var-preview xr-preview'>0062-01-16 12:00:00 ... 0122-12-...</div><input id='attrs-5cddcf38-a69a-4dab-a813-d6e8a104520a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cddcf38-a69a-4dab-a813-d6e8a104520a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a124104c-fdd7-4eb1-8c0b-a0e7ffdd3772' class='xr-var-data-in' type='checkbox'><label for='data-a124104c-fdd7-4eb1-8c0b-a0e7ffdd3772' 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>boundaries for time-averaging interval</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([cftime.datetime(62, 1, 16, 12, 0, 0, 0, calendar=&#x27;noleap&#x27;),\n",
       "       cftime.datetime(62, 2, 15, 0, 0, 0, 0, calendar=&#x27;noleap&#x27;),\n",
       "       cftime.datetime(62, 3, 16, 12, 0, 0, 0, calendar=&#x27;noleap&#x27;), ...,\n",
       "       cftime.datetime(122, 10, 16, 12, 0, 0, 0, calendar=&#x27;noleap&#x27;),\n",
       "       cftime.datetime(122, 11, 16, 0, 0, 0, 0, calendar=&#x27;noleap&#x27;),\n",
       "       cftime.datetime(122, 12, 16, 12, 0, 0, 0, calendar=&#x27;noleap&#x27;)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e28a1f1c-991d-4d14-bf55-9ca42c463e55' class='xr-section-summary-in' type='checkbox'  ><label for='section-e28a1f1c-991d-4d14-bf55-9ca42c463e55' class='xr-section-summary' >Data variables: <span>(26)</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>KMT</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(76, 320), meta=np.ndarray&gt;</div><input id='attrs-126871cb-df69-4292-bcb6-e57dceeba250' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-126871cb-df69-4292-bcb6-e57dceeba250' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fecc67e5-428f-46a8-a391-fac4fc39177f' class='xr-var-data-in' type='checkbox'><label for='data-fecc67e5-428f-46a8-a391-fac4fc39177f' 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>k Index of Deepest Grid Cell on T Grid</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 194.56 kB </td> <td> 194.56 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (76, 320) </td> <td> (76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 8 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,42.704069968794954 0.0,42.704069968794954\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"62.704070\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"140.000000\" y=\"21.352035\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.352035)\">76</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>REGION_MASK</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(76, 320), meta=np.ndarray&gt;</div><input id='attrs-04d50bc7-4de8-44e8-96de-6be40b4c3310' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04d50bc7-4de8-44e8-96de-6be40b4c3310' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-654a6e30-b15f-47e9-8ffe-e4ad7cfb4c3d' class='xr-var-data-in' type='checkbox'><label for='data-654a6e30-b15f-47e9-8ffe-e4ad7cfb4c3d' 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>basin index number (signed integers)</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 194.56 kB </td> <td> 194.56 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (76, 320) </td> <td> (76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 8 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,42.704069968794954 0.0,42.704069968794954\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"62.704070\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"140.000000\" y=\"21.352035\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.352035)\">76</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TAREA</span></div><div class='xr-var-dims'>(nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(76, 320), meta=np.ndarray&gt;</div><input id='attrs-935ec69e-d874-43ec-8a25-56d04b00632f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-935ec69e-d874-43ec-8a25-56d04b00632f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96175930-c8b8-477e-85fa-da4d9dee7b03' class='xr-var-data-in' type='checkbox'><label for='data-96175930-c8b8-477e-85fa-da4d9dee7b03' 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>area of T cells</dd><dt><span>units :</span></dt><dd>centimeter^2</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 194.56 kB </td> <td> 194.56 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (76, 320) </td> <td> (76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 8 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,42.704069968794954 0.0,42.704069968794954\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"62.704070\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"140.000000\" y=\"21.352035\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.352035)\">76</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dz</span></div><div class='xr-var-dims'>(z_t, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(60, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-de2e357f-1b4b-46d6-982a-e0242ab10604' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de2e357f-1b4b-46d6-982a-e0242ab10604' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a0920c3-9f17-4980-b026-1a232a494b63' class='xr-var-data-in' type='checkbox'><label for='data-1a0920c3-9f17-4980-b026-1a232a494b63' 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>thickness of layer k</dd><dt><span>units :</span></dt><dd>centimeters</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 5.84 MB </td> <td> 5.84 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (60, 76, 320) </td> <td> (60, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 8 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"204\" height=\"117\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"34\" y2=\"24\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"42\" x2=\"34\" y2=\"67\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"34\" y1=\"24\" x2=\"34\" y2=\"67\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 34.54862050990863,24.548620509908634 34.54862050990863,67.25269047870358 10.0,42.704069968794954\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"34\" y1=\"24\" x2=\"154\" y2=\"24\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"34\" y2=\"24\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"154\" y2=\"24\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 130.0,0.0 154.54862050990863,24.548620509908634 34.54862050990863,24.548620509908634\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"34\" y1=\"24\" x2=\"154\" y2=\"24\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"34\" y1=\"67\" x2=\"154\" y2=\"67\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"34\" y1=\"24\" x2=\"34\" y2=\"67\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"154\" y1=\"24\" x2=\"154\" y2=\"67\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"34.54862050990863,24.548620509908634 154.54862050990863,24.548620509908634 154.54862050990863,67.25269047870358 34.54862050990863,67.25269047870358\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"94.548621\" y=\"87.252690\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"174.548621\" y=\"45.900655\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,174.548621,45.900655)\">76</text>\n",
       "  <text x=\"12.274310\" y=\"74.978380\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.274310,74.978380)\">60</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bound</span></div><div class='xr-var-dims'>(time, d2, nlat, nlon)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 2, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-e11fa192-44cd-4b69-a43b-82110efe71a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e11fa192-44cd-4b69-a43b-82110efe71a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f3045e9-672f-4667-a3f6-530213f4655b' class='xr-var-data-in' type='checkbox'><label for='data-9f3045e9-672f-4667-a3f6-530213f4655b' 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>boundaries for time-averaging interval</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 284.84 MB </td> <td> 284.84 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 2, 76, 320) </td> <td> (732, 2, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 8 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> object </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"497\" height=\"103\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >732</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"190\" y1=\"38\" x2=\"204\" y2=\"53\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"53\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,53.75244566271946 190.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"242\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"204\" y1=\"14\" x2=\"257\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"242\" y1=\"0\" x2=\"257\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"190.0,0.0 242.45901639344262,0.0 257.40761434319705,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"204\" y1=\"14\" x2=\"257\" y2=\"14\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"204\" y1=\"53\" x2=\"257\" y2=\"53\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"53\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"257\" y1=\"14\" x2=\"257\" y2=\"53\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"204.9485979497544,14.948597949754403 257.40761434319705,14.948597949754403 257.40761434319705,53.75244566271946 204.9485979497544,53.75244566271946\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"231.178106\" y=\"73.752446\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"277.407614\" y=\"34.350522\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,277.407614,34.350522)\">76</text>\n",
       "  <text x=\"187.474299\" y=\"66.278147\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,187.474299,66.278147)\">2</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_graze_zoo1_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-b2078bcb-7883-47fe-a42d-6f48124278d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2078bcb-7883-47fe-a42d-6f48124278d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b71b0b67-62bd-4cc9-9c38-4a6e3590e061' class='xr-var-data-in' type='checkbox'><label for='data-b71b0b67-62bd-4cc9-9c38-4a6e3590e061' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Small microzooplankton Grazing Gain Vertical Integral</dd><dt><span>units :</span></dt><dd>mmol/m^3 cm/s</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_graze_zoo2_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-073f5296-b391-43fa-826c-1cc50013affa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-073f5296-b391-43fa-826c-1cc50013affa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b68b0c7d-7d72-4685-aa74-34570923b010' class='xr-var-data-in' type='checkbox'><label for='data-b68b0c7d-7d72-4685-aa74-34570923b010' 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>Microzooplankton Grazing Gain Vertical Integral</dd><dt><span>units :</span></dt><dd>mmol/m^3 cm/s</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_graze_zoo3_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-a8d046bb-caad-471c-926d-2d7e6f64bd4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8d046bb-caad-471c-926d-2d7e6f64bd4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1399f89c-52cb-492b-a5b6-3904e43b0e66' class='xr-var-data-in' type='checkbox'><label for='data-1399f89c-52cb-492b-a5b6-3904e43b0e66' 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>Mesozooplankton Grazing Gain Vertical Integral</dd><dt><span>units :</span></dt><dd>mmol/m^3 cm/s</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_graze_zoo4_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-9824c3a4-d564-4ec1-b50f-679b69a41042' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9824c3a4-d564-4ec1-b50f-679b69a41042' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1995dc31-b3cc-4b35-bbf5-049f6ace98cb' class='xr-var-data-in' type='checkbox'><label for='data-1995dc31-b3cc-4b35-bbf5-049f6ace98cb' 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>Large Mesozooplankton Grazing Gain Vertical Integral</dd><dt><span>units :</span></dt><dd>mmol/m^3 cm/s</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>photoC_TOT_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-b7d9240b-4878-473d-858e-b68d8c67f582' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7d9240b-4878-473d-858e-b68d8c67f582' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8356eb8-202e-4e40-81c7-eec391daac9b' class='xr-var-data-in' type='checkbox'><label for='data-b8356eb8-202e-4e40-81c7-eec391daac9b' 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>Total C Fixation Vertical Integral</dd><dt><span>units :</span></dt><dd>mmol/m^3 cm/s</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>photoC_diat_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-67908a8f-4adf-45fd-8550-f7bf9d8f601a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67908a8f-4adf-45fd-8550-f7bf9d8f601a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a28ef23-ace1-49c7-9958-cde09eb52feb' class='xr-var-data-in' type='checkbox'><label for='data-6a28ef23-ace1-49c7-9958-cde09eb52feb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diatom C Fixation Vertical Integral</dd><dt><span>units :</span></dt><dd>mmol/m^3 cm/s</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>photoC_sp_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-23f8ac1a-b9f7-41e3-bdcf-0641d196a840' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23f8ac1a-b9f7-41e3-bdcf-0641d196a840' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52bc43eb-46ec-44e5-a769-24afb4fe48b9' class='xr-var-data-in' type='checkbox'><label for='data-52bc43eb-46ec-44e5-a769-24afb4fe48b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Small Phyto C Fixation Vertical Integral</dd><dt><span>units :</span></dt><dd>mmol/m^3 cm/s</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>photoC_cocco_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-16522bd3-29a6-4c0d-91ac-099018227bbd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16522bd3-29a6-4c0d-91ac-099018227bbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbe3a0e5-ff5d-4143-8cbb-180117830381' class='xr-var-data-in' type='checkbox'><label for='data-bbe3a0e5-ff5d-4143-8cbb-180117830381' 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>Coccolithophores C Fixation Vertical Integral</dd><dt><span>units :</span></dt><dd>mmol/m^3 cm/s</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>IFRAC</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-03184367-bfbd-486e-b341-a7ec87cd3b23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03184367-bfbd-486e-b341-a7ec87cd3b23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3002ceb4-475e-4763-b507-9c516d52dfa2' class='xr-var-data-in' type='checkbox'><label for='data-3002ceb4-475e-4763-b507-9c516d52dfa2' 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>Ice Fraction from Coupler</dd><dt><span>units :</span></dt><dd>fraction</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zoo1C_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-e7d279d6-ded7-4317-bccd-5fad0f52799e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7d279d6-ded7-4317-bccd-5fad0f52799e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5975d5d-ef22-4926-9b72-59ae28d55824' class='xr-var-data-in' type='checkbox'><label for='data-f5975d5d-ef22-4926-9b72-59ae28d55824' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Small microzooplankton Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 10 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zoo2C_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-10cb42a3-ad47-425b-b694-de327b41b662' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10cb42a3-ad47-425b-b694-de327b41b662' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e25f3408-5557-45d3-9f37-ab08e507336a' class='xr-var-data-in' type='checkbox'><label for='data-e25f3408-5557-45d3-9f37-ab08e507336a' 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>Microzooplankton Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 10 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zoo3C_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-16ae2e28-7eda-46f6-a472-fdfc28757f06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16ae2e28-7eda-46f6-a472-fdfc28757f06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79833804-ed79-4b30-ba82-c76b6958970c' class='xr-var-data-in' type='checkbox'><label for='data-79833804-ed79-4b30-ba82-c76b6958970c' 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>Mesozooplankton Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 10 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zoo4C_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-c1f0c156-5905-4001-9772-7871117714a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1f0c156-5905-4001-9772-7871117714a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83746039-2420-4d26-bd16-d5165917aaff' class='xr-var-data-in' type='checkbox'><label for='data-83746039-2420-4d26-bd16-d5165917aaff' 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>Large Mesozooplankton Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 10 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC_TOT_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-ae1431fa-647c-42d4-aa4a-d582b4aa63df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae1431fa-647c-42d4-aa4a-d582b4aa63df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f33af44-7412-49dc-b5f6-cad8c0e9174d' class='xr-var-data-in' type='checkbox'><label for='data-4f33af44-7412-49dc-b5f6-cad8c0e9174d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Small microzooplankton Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 37 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spC_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-a7512763-2410-427c-8318-95054231940b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7512763-2410-427c-8318-95054231940b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5237f8f-e893-4786-ae14-4939c85e35f9' class='xr-var-data-in' type='checkbox'><label for='data-a5237f8f-e893-4786-ae14-4939c85e35f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Small Phyto Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 10 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatC_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-c74e2a36-2eac-4bb3-921b-a6d6b090538c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c74e2a36-2eac-4bb3-921b-a6d6b090538c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d598d2a-e2f5-4348-96a1-cc5548cc19c5' class='xr-var-data-in' type='checkbox'><label for='data-0d598d2a-e2f5-4348-96a1-cc5548cc19c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Diatom Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 10 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>coccoC_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-f6392bc8-3744-4ca9-9665-feafdaaeb221' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6392bc8-3744-4ca9-9665-feafdaaeb221' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90a44499-7f18-48ee-beba-3713f11e5a68' class='xr-var-data-in' type='checkbox'><label for='data-90a44499-7f18-48ee-beba-3713f11e5a68' 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>Coccolithophores Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 10 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phytoC_TOT_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-348153a2-6155-439b-ba71-93c120be0e06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-348153a2-6155-439b-ba71-93c120be0e06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21eefee0-1c08-4cb2-b4f3-578a71f2ed43' class='xr-var-data-in' type='checkbox'><label for='data-21eefee0-1c08-4cb2-b4f3-578a71f2ed43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Small Phyto Carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd><dt><span>grid_loc :</span></dt><dd>3114</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 28 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polynyas</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.0 0.0 0.0 0.0</div><input id='attrs-0953bd89-0b4f-47ce-ab75-079f15d88bd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0953bd89-0b4f-47ce-ab75-079f15d88bd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b61b2150-8579-48fe-8f05-9597bc9e2976' class='xr-var-data-in' type='checkbox'><label for='data-b61b2150-8579-48fe-8f05-9597bc9e2976' 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>Polynya grid cells with &lt; 0.22m hi and bounded by hi &gt; 0.22m and/or land. Open ocean polynyas=1, coastal polynyas=2</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [ 2.,  2.,  2., ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [ 0.,  0.,  0., ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [ 0.,  2.,  2., ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TOT_zoo_prod_zint</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-0ab1d49f-840d-42ea-8d59-11886ce39c8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ab1d49f-840d-42ea-8d59-11886ce39c8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae70210a-5ac1-4a26-849d-10a3ee19e82c' class='xr-var-data-in' type='checkbox'><label for='data-ae70210a-5ac1-4a26-849d-10a3ee19e82c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Small microzooplankton Grazing Gain Vertical Integral</dd><dt><span>units :</span></dt><dd>mmol/m^3 cm/s</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 20 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z-ratio</span></div><div class='xr-var-dims'>(time, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(732, 76, 320), meta=np.ndarray&gt;</div><input id='attrs-22239c52-7638-4b57-828f-a9e0773463b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22239c52-7638-4b57-828f-a9e0773463b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65a64d05-c0f4-411c-af9f-a44a49a1fd6b' class='xr-var-data-in' type='checkbox'><label for='data-65a64d05-c0f4-411c-af9f-a44a49a1fd6b' 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>Mesozooplankton Grazing Gain Vertical Integral</dd><dt><span>units :</span></dt><dd>mmol/m^3 cm/s</dd><dt><span>grid_loc :</span></dt><dd>2110</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 71.21 MB </td> <td> 71.21 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (732, 76, 320) </td> <td> (732, 76, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 15 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"183\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,109.3920830070827 10.0,38.80384771296506\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"62\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"62\" y1=\"0\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 62.45901639344263,0.0 133.04725168756028,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"133\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"109\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"133\" y1=\"70\" x2=\"133\" y2=\"109\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 133.04725168756028,70.58823529411765 133.04725168756028,109.3920830070827 80.58823529411765,109.3920830070827\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"106.817743\" y=\"129.392083\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"153.047252\" y=\"89.990159\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,153.047252,89.990159)\">76</text>\n",
       "  <text x=\"35.294118\" y=\"94.097965\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.097965)\">732</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d191de27-ced8-4536-9982-c54689eda80d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d191de27-ced8-4536-9982-c54689eda80d' 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:            (d2: 2, nlat: 76, nlon: 320, time: 732, z_t: 60)\n",
       "Coordinates:\n",
       "    TLAT               (nlat, nlon) float64 dask.array<chunksize=(76, 320), meta=np.ndarray>\n",
       "    TLONG              (nlat, nlon) float64 dask.array<chunksize=(76, 320), meta=np.ndarray>\n",
       "  * time               (time) object 0062-01-16 12:00:00 ... 0122-12-16 12:00:00\n",
       "Dimensions without coordinates: d2, nlat, nlon, z_t\n",
       "Data variables:\n",
       "    KMT                (nlat, nlon) float64 dask.array<chunksize=(76, 320), meta=np.ndarray>\n",
       "    REGION_MASK        (nlat, nlon) float64 dask.array<chunksize=(76, 320), meta=np.ndarray>\n",
       "    TAREA              (nlat, nlon) float64 dask.array<chunksize=(76, 320), meta=np.ndarray>\n",
       "    dz                 (z_t, nlat, nlon) float32 dask.array<chunksize=(60, 76, 320), meta=np.ndarray>\n",
       "    time_bound         (time, d2, nlat, nlon) object dask.array<chunksize=(732, 2, 76, 320), meta=np.ndarray>\n",
       "    x_graze_zoo1_zint  (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    x_graze_zoo2_zint  (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    x_graze_zoo3_zint  (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    x_graze_zoo4_zint  (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    photoC_TOT_zint    (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    photoC_diat_zint   (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    photoC_sp_zint     (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    photoC_cocco_zint  (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    IFRAC              (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    zoo1C_zint         (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    zoo2C_zint         (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    zoo3C_zint         (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    zoo4C_zint         (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    zooC_TOT_zint      (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    spC_zint           (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    diatC_zint         (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    coccoC_zint        (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    phytoC_TOT_zint    (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    polynyas           (time, nlat, nlon) float64 nan nan nan ... 0.0 0.0 0.0\n",
       "    TOT_zoo_prod_zint  (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>\n",
       "    z-ratio            (time, nlat, nlon) float32 dask.array<chunksize=(732, 76, 320), meta=np.ndarray>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "lon, lat, field = adjust_pop_grid(lons, lats, ds.TOT_zoo_prod_zint.isel(time=384) * 864.)\n",
    "lon, lat, ice = adjust_pop_grid(lons, lats, ds.IFRAC.isel(time=384))\n",
    "lon, lat, polycoast = adjust_pop_grid(lons, lats, ds.polynyas.isel(time=384))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,5))\n",
    "#----------\n",
    "ax = fig.add_subplot(1,1,1, projection=ccrs.SouthPolarStereo())\n",
    "ax.set_title('zooplankton production, December 2009')\n",
    "ax.set_extent([-180, 180, -90, -40], ccrs.PlateCarree())\n",
    "\n",
    "#################### make it a circle\n",
    "theta = np.linspace(0, 2*np.pi, 100)\n",
    "center, radius = [0.5, 0.5], 0.5\n",
    "verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n",
    "circle = mpath.Path(verts * radius + center)\n",
    "\n",
    "ax.set_boundary(circle, transform=ax.transAxes)\n",
    "#################### end circle\n",
    "ax.coastlines('10m',linewidth=0.5)\n",
    "pc=ax.pcolormesh(lon, lat, field,\n",
    "                 vmin=0, vmax=20, \n",
    "                 cmap='YlGnBu_r',\n",
    "                 transform=ccrs.PlateCarree())\n",
    "\n",
    "pci=ax.contour(lon, lat, ice, \n",
    "                 levels=[15], \n",
    "                 colors='white',\n",
    "                 transform=ccrs.PlateCarree())\n",
    "\n",
    "pci=ax.contour(lon, lat, polycoast, \n",
    "                 levels=[2], \n",
    "                 colors='red',\n",
    "                 transform=ccrs.PlateCarree())\n",
    "cbar1 = fig.colorbar(pc, ax=ax,extend='max',label='Zooplankton production (mmol m$^{-2}$ d$^{-1}$)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,5))\n",
    "#----------\n",
    "ax = fig.add_subplot(1,1,1, projection=ccrs.SouthPolarStereo())\n",
    "ax.set_title('Polynyas, December 2009')\n",
    "ax.set_extent([-180, 180, -90, -40], ccrs.PlateCarree())\n",
    "\n",
    "#################### make it a circle\n",
    "theta = np.linspace(0, 2*np.pi, 100)\n",
    "center, radius = [0.5, 0.5], 0.5\n",
    "verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n",
    "circle = mpath.Path(verts * radius + center)\n",
    "\n",
    "ax.set_boundary(circle, transform=ax.transAxes)\n",
    "#################### end circle\n",
    "ax.coastlines('10m',linewidth=0.5)\n",
    "pc=ax.pcolormesh(lon, lat, polycoast,\n",
    "                 vmin=0, vmax=2, \n",
    "                 cmap='Set1',\n",
    "                 transform=ccrs.PlateCarree())\n",
    "\n",
    "cbar1 = fig.colorbar(pc, ax=ax,label='Polynya field')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x2afea0b81fd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['zoo3C_zint'].isel(time=384).where(ds['polynyas'].isel(time=384)>=1.).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make monthly timeseries of coastal polynyas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "### redefine variable list for testing purposes\n",
    "variables = ['zoo3C_zint'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 23.9 s, sys: 3.28 s, total: 27.2 s\n",
      "Wall time: 54.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ts_coastal_polys = xr.Dataset({v: ds[v] for v in ['time']})\n",
    "\n",
    "for var in variables:\n",
    "\n",
    "    masked = ds[var].where(ds['polynyas']==2.)\n",
    "\n",
    "    ts_coastal_polys[var] = esmlab.weighted_mean(masked, weights=area_m, dim=['nlat', 'nlon']).compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make monthly timeseries of open water polynyas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 23.7 s, sys: 3.22 s, total: 26.9 s\n",
      "Wall time: 53.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ts_open_polys = xr.Dataset({v: ds[v] for v in ['time']})\n",
    "\n",
    "for var in variables:\n",
    "\n",
    "    masked = ds[var].where(ds['polynyas']==1.)\n",
    "\n",
    "    ts_open_polys[var] = esmlab.weighted_mean(masked, weights=area_m, dim=['nlat', 'nlon']).compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make monthly timeseries of non-polynyas in SIZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 24.2 s, sys: 3.33 s, total: 27.5 s\n",
      "Wall time: 55.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ts_nonpolys = xr.Dataset({v: ds[v] for v in ['time']})\n",
    "\n",
    "for var in variables:\n",
    "\n",
    "    masked = ds[var].where(ds['IFRAC']>0.15).where(ds['polynyas']==0)\n",
    "\n",
    "    ts_nonpolys[var] = esmlab.weighted_mean(masked, weights=area_m, dim=['nlat', 'nlon']).compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([         nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  12.13825321,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  12.40086746,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  61.4547562 ,\n",
       "                nan,          nan,          nan,          nan,\n",
       "        33.66036224,          nan,          nan,          nan,\n",
       "                nan,          nan,  45.48077774,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,   6.02238941,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "        26.14493893,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "       102.34359741,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  30.61855579,\n",
       "                nan,          nan,          nan,  82.80538177,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  15.54280392,\n",
       "        17.02207249,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,   7.93970013,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "        22.17500166,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "        17.03864765,          nan,          nan,  57.74887159,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  18.19963104,\n",
       "        10.40874988,          nan,  18.2644825 , 124.71365356,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  12.27392344,\n",
       "        14.88494205,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "        33.93745804,          nan,   5.47271776,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "       115.82361603,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  59.900254  ,\n",
       "       107.25863647,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  36.57846832,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "         7.23817158,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  57.2192859 ,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "        12.52335199,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,   4.99818087,          nan,\n",
       "         9.91974164,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "       105.96967316,          nan,          nan,          nan,\n",
       "                nan,          nan,   7.05407095,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,   8.99430463,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  24.545552  ,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,   7.25908678,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,   8.46177083,\n",
       "         9.6056035 ,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  28.31571007,\n",
       "                nan,          nan,          nan,          nan,\n",
       "       101.32455444, 120.70747375,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "         8.95258134,          nan,          nan,  54.00273503,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "         8.00503465,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  22.75641832,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "        22.9564743 ,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,  31.76251602,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "         9.05276315,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "        49.22655487,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,  10.62718947,\n",
       "                nan,  36.54645156,  58.83362579,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan,\n",
       "       196.51104736,          nan,          nan,          nan,\n",
       "                nan,          nan,          nan,          nan])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_open_polys.zoo3C_zint.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#style.use('ggplot')\n",
    "fig, ax = plt.subplots(nrows=1,ncols=1,figsize=(15,10))\n",
    "#fig.suptitle('Southern Ocean, ${\\sigma}_{\\Theta}$ 26 to 27 surface outcrop')\n",
    "\n",
    "ax.set_ylabel('mesozooplankton (mmol m$^{-2}$)')\n",
    "ax.plot(deciyear, \n",
    "         ts_nonpolys['zoo3C_zint'], \n",
    "         color='gray', label='Not in polynyas',linewidth=4)\n",
    "ax.scatter(deciyear, \n",
    "         ts_open_polys['zoo3C_zint'], \n",
    "         color='lightgreen', label='Open ocean polynyas')\n",
    "ax.plot(deciyear, \n",
    "         ts_coastal_polys['zoo3C_zint'], \n",
    "         color='blue', label='Coastal polynyas',linewidth=1)\n",
    "\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make timeseries of annual means for coastal polynyas and non-polynas\n",
    "#### (there aren't really enough open ocean polynyas for making annual means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 39.2 ms, sys: 2.63 ms, total: 41.9 ms\n",
      "Wall time: 279 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ts_nonpolys_annual = ts_nonpolys.resample({'time':'A'}).mean(dim='time').compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 34.3 ms, sys: 0 ns, total: 34.3 ms\n",
      "Wall time: 59.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ts_coastal_polys_annual = ts_coastal_polys.resample({'time':'A'}).mean(dim='time').compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = np.arange(1958,2019,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#style.use('ggplot')\n",
    "fig, ax = plt.subplots(nrows=1,ncols=1,figsize=(15,10))\n",
    "#fig.suptitle('Southern Ocean, ${\\sigma}_{\\Theta}$ 26 to 27 surface outcrop')\n",
    "\n",
    "ax.set_ylabel('mesozooplankton (mmol m$^{-2}$)')\n",
    "ax.plot(years, \n",
    "         ts_nonpolys_annual['zoo3C_zint'], \n",
    "         color='gray', label='Not in polynyas',linewidth=1)\n",
    "ax.plot(years, \n",
    "         ts_coastal_polys_annual['zoo3C_zint'], \n",
    "         color='blue', label='Coastal polynyas',linewidth=1)\n",
    "\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot with :\n",
    "thres_var='hi'\n",
    "thres = '0.8m'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#style.use('ggplot')\n",
    "fig, ax = plt.subplots(nrows=1,ncols=1,figsize=(15,10))\n",
    "#fig.suptitle('Southern Ocean, ${\\sigma}_{\\Theta}$ 26 to 27 surface outcrop')\n",
    "\n",
    "ax.set_ylabel('mesozooplankton (mmol m$^{-2}$)')\n",
    "ax.plot(years, \n",
    "         ts_nonpolys_annual['zoo3C_zint'], \n",
    "         color='gray', label='Not in polynyas',linewidth=1)\n",
    "ax.plot(years, \n",
    "         ts_coastal_polys_annual['zoo3C_zint'], \n",
    "         color='blue', label='Coastal polynyas',linewidth=1)\n",
    "\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:miniconda-analysis4]",
   "language": "python",
   "name": "conda-env-miniconda-analysis4-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}