read_in_4p2z_hist-ssp370-runs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mini-ensemble of CESM2-4p2z historical + ssp370"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import os\n",
    "from glob import glob\n",
    "from collections import OrderedDict\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "import ncar_jobqueue\n",
    "from dask.distributed import Client\n",
    "\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",
    "\n",
    "#import cftime\n",
    "import matplotlib.colors as colors\n",
    "import utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  specify desired variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables = ['photoC_TOT_zint_100m','POC_FLUX_100m','IFRAC']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grab the first ensemble member (002) - this one goes from 1850 to 2014 (hist) and 2015 to 2100 (ssp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get historical part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "case = 'b.e21p4.BHIST.f09_g17.4p2z.002'\n",
    "path = '/glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/' + case + '/ocn/proc/tseries/month_1/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting on  photoC_TOT_zint_100m\n",
      "starting on  POC_FLUX_100m\n",
      "starting on  IFRAC\n"
     ]
    }
   ],
   "source": [
    "ds_hist = xr.Dataset()\n",
    "#ds_fosi_seas = xr.Dataset()\n",
    "\n",
    "for var in variables:\n",
    "    \n",
    "    print('starting on ', var)\n",
    "    ################ FOSI\n",
    "    \n",
    "    files = sorted(glob(f'{path}/{case}.pop.h.{var}.??????-??????.nc'))  \n",
    "    \n",
    "    ds_tmp = xr.open_mfdataset(files, data_vars=\"minimal\", coords='minimal', compat=\"override\", parallel=True, concat_dim=\"time\", combine='nested',\n",
    "                       drop_variables=[\"transport_components\", \"transport_regions\"], decode_times=True)\n",
    "    \n",
    "    \n",
    "    keep_vars=['z_t','z_t_150m','time_bound','dz','TLAT','TLONG','time'] + [var]\n",
    "\n",
    "    ds_tmp = ds_tmp.drop([v for v in ds_tmp.variables if v not in keep_vars])\n",
    "    \n",
    "    ds_hist = xr.merge([ds_hist,ds_tmp],combine_attrs='override')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get future part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "case = 'b.e21p4.BSSP370.f09_g17.4p2z.002'\n",
    "path = '/glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/' + case + '/ocn/proc/tseries/month_1/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting on  photoC_TOT_zint_100m\n",
      "starting on  POC_FLUX_100m\n",
      "starting on  IFRAC\n"
     ]
    }
   ],
   "source": [
    "ds_ssp = xr.Dataset()\n",
    "#ds_fosi_seas = xr.Dataset()\n",
    "\n",
    "for var in variables:\n",
    "    \n",
    "    print('starting on ', var)\n",
    "    ################ FOSI\n",
    "    \n",
    "    files = sorted(glob(f'{path}/{case}.pop.h.{var}.??????-??????.nc'))  \n",
    "    \n",
    "    ds_tmp = xr.open_mfdataset(files, data_vars=\"minimal\", coords='minimal', compat=\"override\", parallel=True, concat_dim=\"time\", combine='nested',\n",
    "                       drop_variables=[\"transport_components\", \"transport_regions\"], decode_times=True)\n",
    "    \n",
    "    \n",
    "    keep_vars=['z_t','z_t_150m','time_bound','dz','TLAT','TLONG','time'] + [var]\n",
    "\n",
    "    ds_tmp = ds_tmp.drop([v for v in ds_tmp.variables if v not in keep_vars])\n",
    "    \n",
    "    ds_ssp = xr.merge([ds_ssp,ds_tmp],combine_attrs='override')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### concat hist and ssp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1 = xr.concat([ds_hist,ds_ssp],dim='time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now grab ensemble member 003"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### historical part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "case = 'b.e21p5.BHIST.f09_g17.4p2z.003'\n",
    "path = '/glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/' + case + '/ocn/proc/tseries/month_1/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting on  photoC_TOT_zint_100m\n",
      "starting on  POC_FLUX_100m\n",
      "starting on  IFRAC\n"
     ]
    }
   ],
   "source": [
    "ds_hist = xr.Dataset()\n",
    "#ds_fosi_seas = xr.Dataset()\n",
    "\n",
    "for var in variables:\n",
    "    \n",
    "    print('starting on ', var)\n",
    "    ################ FOSI\n",
    "    \n",
    "    files = sorted(glob(f'{path}/{case}.pop.h.{var}.??????-??????.nc'))  \n",
    "    \n",
    "    ds_tmp = xr.open_mfdataset(files, data_vars=\"minimal\", coords='minimal', compat=\"override\", parallel=True, concat_dim=\"time\", combine='nested',\n",
    "                       drop_variables=[\"transport_components\", \"transport_regions\"], decode_times=True)\n",
    "    \n",
    "    \n",
    "    keep_vars=['z_t','z_t_150m','time_bound','dz','TLAT','TLONG','time'] + [var]\n",
    "\n",
    "    ds_tmp = ds_tmp.drop([v for v in ds_tmp.variables if v not in keep_vars])\n",
    "    \n",
    "    ds_hist = xr.merge([ds_hist,ds_tmp],combine_attrs='override')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### future part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "case = 'b.e21p5.BSSP370.f09_g17.4p2z.003'\n",
    "path = '/glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/' + case + '/ocn/proc/tseries/month_1/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting on  photoC_TOT_zint_100m\n",
      "starting on  POC_FLUX_100m\n",
      "starting on  IFRAC\n"
     ]
    }
   ],
   "source": [
    "ds_ssp = xr.Dataset()\n",
    "#ds_fosi_seas = xr.Dataset()\n",
    "\n",
    "for var in variables:\n",
    "    \n",
    "    print('starting on ', var)\n",
    "    ################ FOSI\n",
    "    \n",
    "    files = sorted(glob(f'{path}/{case}.pop.h.{var}.??????-??????.nc'))  \n",
    "    \n",
    "    ds_tmp = xr.open_mfdataset(files, data_vars=\"minimal\", coords='minimal', compat=\"override\", parallel=True, concat_dim=\"time\", combine='nested',\n",
    "                       drop_variables=[\"transport_components\", \"transport_regions\"], decode_times=True)\n",
    "    \n",
    "    \n",
    "    keep_vars=['z_t','z_t_150m','time_bound','dz','TLAT','TLONG','time'] + [var]\n",
    "\n",
    "    ds_tmp = ds_tmp.drop([v for v in ds_tmp.variables if v not in keep_vars])\n",
    "    \n",
    "    ds_ssp = xr.merge([ds_ssp,ds_tmp],combine_attrs='override')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### concat hist and ssp for 003"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds2 = xr.concat([ds_hist,ds_ssp],dim='time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fix the time dimension and then trim them so they both start in 1920"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1['time'] = ds1.time_bound.compute().mean(dim=\"d2\")\n",
    "ds2['time'] = ds2.time_bound.compute().mean(dim=\"d2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 2172)&gt;\n",
       "array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 1920-01-16 12:00:00 ... 2100-12-16 12:00:00</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2172</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-dcf883e3-0431-473d-8968-64e7888b18d4' class='xr-array-in' type='checkbox' checked><label for='section-dcf883e3-0431-473d-8968-64e7888b18d4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1920-01-16 12:00:00 1920-02-15 00:00:00 ... 2100-12-16 12:00:00</span></div><div class='xr-array-data'><pre>array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-4a08997a-5d8c-4253-a854-da1a410be06e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4a08997a-5d8c-4253-a854-da1a410be06e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1920-01-16 12:00:00 ... 2100-12-...</div><input id='attrs-098b7828-c77e-4d06-ad18-0096b5dfec0a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-098b7828-c77e-4d06-ad18-0096b5dfec0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18c91496-bc89-4e1d-acbf-62c1312d0e66' class='xr-var-data-in' type='checkbox'><label for='data-18c91496-bc89-4e1d-acbf-62c1312d0e66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a53a586-ee28-4d3f-be2d-f5d9b682e91c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5a53a586-ee28-4d3f-be2d-f5d9b682e91c' 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.DataArray 'time' (time: 2172)>\n",
       "array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 1920-01-16 12:00:00 ... 2100-12-16 12:00:00"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.time[840:3012]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 1920-01-16 12:00:00 ... 2100-12-16 12:00:00</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2172</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-00509070-574c-4c4f-b359-7d3f0ca2cf0f' class='xr-array-in' type='checkbox' checked><label for='section-00509070-574c-4c4f-b359-7d3f0ca2cf0f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1920-01-16 12:00:00 1920-02-15 00:00:00 ... 2100-12-16 12:00:00</span></div><div class='xr-array-data'><pre>array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-99e9de3b-ae14-4966-862a-d0dc0f273111' class='xr-section-summary-in' type='checkbox'  checked><label for='section-99e9de3b-ae14-4966-862a-d0dc0f273111' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1920-01-16 12:00:00 ... 2100-12-...</div><input id='attrs-0ae411c9-65b7-41f3-bffe-3c24dc8c2cc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ae411c9-65b7-41f3-bffe-3c24dc8c2cc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef258a93-4420-42a3-8652-272391111d3d' class='xr-var-data-in' type='checkbox'><label for='data-ef258a93-4420-42a3-8652-272391111d3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6c8ca52-c7ae-4f57-80cc-81a7cb4c3126' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b6c8ca52-c7ae-4f57-80cc-81a7cb4c3126' 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.DataArray 'time' (time: 2172)>\n",
       "array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 1920-01-16 12:00:00 ... 2100-12-16 12:00:00"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds2.time[48:2220]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1 = ds1.isel(time=slice(840,3012))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds2 = ds2.isel(time=slice(48,2220))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 2172)&gt;\n",
       "array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 1920-01-16 12:00:00 ... 2100-12-16 12:00:00</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2172</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0c5c779f-7839-4dfb-a10d-29d0f38ae1f7' class='xr-array-in' type='checkbox' checked><label for='section-0c5c779f-7839-4dfb-a10d-29d0f38ae1f7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1920-01-16 12:00:00 1920-02-15 00:00:00 ... 2100-12-16 12:00:00</span></div><div class='xr-array-data'><pre>array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-9324aea6-0b21-41fe-8a16-2fb2df7a370d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9324aea6-0b21-41fe-8a16-2fb2df7a370d' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1920-01-16 12:00:00 ... 2100-12-...</div><input id='attrs-3b66c9c3-63fc-4784-b809-21c403102d31' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b66c9c3-63fc-4784-b809-21c403102d31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-690f6fc5-9cc5-43d8-89be-a2132229c411' class='xr-var-data-in' type='checkbox'><label for='data-690f6fc5-9cc5-43d8-89be-a2132229c411' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b104965d-b3d3-4cf3-bff6-731c6443640c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b104965d-b3d3-4cf3-bff6-731c6443640c' 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.DataArray 'time' (time: 2172)>\n",
       "array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 1920-01-16 12:00:00 ... 2100-12-16 12:00:00"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds2['time'] = ds1.time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now both datasets are the same length, so let's try merging them, creating a 'member_id' dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.concat([ds1,ds2],dim='member_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### clean things up a bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['TLONG'] = ds.TLONG.isel(member_id=0).squeeze()\n",
    "ds['TLAT'] = ds.TLAT.isel(member_id=0).squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (d2: 2, member_id: 2, nlat: 384, nlon: 320, time: 2172, z_t: 60, z_t_150m: 15)\n",
       "Coordinates:\n",
       "  * z_t                   (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05\n",
       "  * z_t_150m              (z_t_150m) float32 500.0 1.5e+03 ... 1.35e+04 1.45e+04\n",
       "    TLONG                 (nlat, nlon) float64 320.6 321.7 322.8 ... 319.4 319.8\n",
       "    TLAT                  (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19\n",
       "  * time                  (time) object 1920-01-16 12:00:00 ... 2100-12-16 12...\n",
       "Dimensions without coordinates: d2, member_id, nlat, nlon\n",
       "Data variables:\n",
       "    dz                    (member_id, time, z_t) float32 dask.array&lt;chunksize=(1, 1140, 60), meta=np.ndarray&gt;\n",
       "    time_bound            (member_id, time, d2) object dask.array&lt;chunksize=(1, 360, 2), meta=np.ndarray&gt;\n",
       "    photoC_TOT_zint_100m  (member_id, time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 360, 384, 320), meta=np.ndarray&gt;\n",
       "    POC_FLUX_100m         (member_id, time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 360, 384, 320), meta=np.ndarray&gt;\n",
       "    IFRAC                 (member_id, time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 360, 384, 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-266f86d2-68f2-4ec9-b959-b18a80d7bdc1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-266f86d2-68f2-4ec9-b959-b18a80d7bdc1' 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>member_id</span>: 2</li><li><span>nlat</span>: 384</li><li><span>nlon</span>: 320</li><li><span class='xr-has-index'>time</span>: 2172</li><li><span class='xr-has-index'>z_t</span>: 60</li><li><span class='xr-has-index'>z_t_150m</span>: 15</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ddead124-3806-4d19-8221-9a5737d131c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ddead124-3806-4d19-8221-9a5737d131c9' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 5.375e+05</div><input id='attrs-16c0ba6d-0590-498e-ad02-fe6efdc48849' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16c0ba6d-0590-498e-ad02-fe6efdc48849' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da8df16a-8a40-4404-8498-c2bb3b01630c' class='xr-var-data-in' type='checkbox'><label for='data-da8df16a-8a40-4404-8498-c2bb3b01630c' 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>depth from surface to midpoint of layer</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>valid_min :</span></dt><dd>500.0</dd><dt><span>valid_max :</span></dt><dd>537500.0</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
       "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
       "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
       "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
       "       2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,\n",
       "       2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,\n",
       "       4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,\n",
       "       6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,\n",
       "       1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,\n",
       "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
       "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
       "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t_150m</span></div><div class='xr-var-dims'>(z_t_150m)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.35e+04 1.45e+04</div><input id='attrs-b1837bc1-6093-4a5d-b2d7-cba5164db568' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1837bc1-6093-4a5d-b2d7-cba5164db568' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc1a82b9-00ce-480b-a163-28185a1fa2f2' class='xr-var-data-in' type='checkbox'><label for='data-fc1a82b9-00ce-480b-a163-28185a1fa2f2' 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>depth from surface to midpoint of layer</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>valid_min :</span></dt><dd>500.0</dd><dt><span>valid_max :</span></dt><dd>14500.0</dd></dl></div><div class='xr-var-data'><pre>array([  500.,  1500.,  2500.,  3500.,  4500.,  5500.,  6500.,  7500.,  8500.,\n",
       "        9500., 10500., 11500., 12500., 13500., 14500.], dtype=float32)</pre></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'>320.6 321.7 322.8 ... 319.4 319.8</div><input id='attrs-76572371-c1ae-41fc-a3f7-642243f73fee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76572371-c1ae-41fc-a3f7-642243f73fee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-078a313b-2032-42f7-86d3-1b88eaabc36b' class='xr-var-data-in' type='checkbox'><label for='data-078a313b-2032-42f7-86d3-1b88eaabc36b' 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'><pre>array([[320.56250892, 321.68750895, 322.81250898, ..., 317.18750883,\n",
       "        318.31250886, 319.43750889],\n",
       "       [320.56250892, 321.68750895, 322.81250898, ..., 317.18750883,\n",
       "        318.31250886, 319.43750889],\n",
       "       [320.56250892, 321.68750895, 322.81250898, ..., 317.18750883,\n",
       "        318.31250886, 319.43750889],\n",
       "       ...,\n",
       "       [320.25133086, 320.75380113, 321.25577325, ..., 318.74424456,\n",
       "        319.24621668, 319.74869143],\n",
       "       [320.23459477, 320.70358949, 321.17207442, ..., 318.82794339,\n",
       "        319.29642832, 319.76542721],\n",
       "       [320.21650899, 320.6493303 , 321.08163473, ..., 318.91838308,\n",
       "        319.3506875 , 319.78351267]])</pre></div></li><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'>-79.22 -79.22 ... 72.19 72.19</div><input id='attrs-30326683-d2b3-4929-a0b3-c212abf12562' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30326683-d2b3-4929-a0b3-c212abf12562' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5d6e76c-088f-4864-ad89-6e6fd83f0fdc' class='xr-var-data-in' type='checkbox'><label for='data-b5d6e76c-088f-4864-ad89-6e6fd83f0fdc' 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'><pre>array([[-79.22052261, -79.22052261, -79.22052261, ..., -79.22052261,\n",
       "        -79.22052261, -79.22052261],\n",
       "       [-78.68630626, -78.68630626, -78.68630626, ..., -78.68630626,\n",
       "        -78.68630626, -78.68630626],\n",
       "       [-78.15208992, -78.15208992, -78.15208992, ..., -78.15208992,\n",
       "        -78.15208992, -78.15208992],\n",
       "       ...,\n",
       "       [ 71.29031715,  71.29408252,  71.30160692, ...,  71.30160692,\n",
       "         71.29408252,  71.29031716],\n",
       "       [ 71.73524335,  71.73881845,  71.74596231, ...,  71.74596231,\n",
       "         71.73881845,  71.73524335],\n",
       "       [ 72.18597561,  72.18933231,  72.19603941, ...,  72.19603941,\n",
       "         72.18933231,  72.18597562]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1920-01-16 12:00:00 ... 2100-12-...</div><input id='attrs-c9af86e4-ab81-4582-b202-0d682f8ae72d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9af86e4-ab81-4582-b202-0d682f8ae72d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cbf17d5-dd84-44eb-95e3-90a1400c7496' class='xr-var-data-in' type='checkbox'><label for='data-0cbf17d5-dd84-44eb-95e3-90a1400c7496' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-94f7dd0a-4574-4ba4-86f5-fa2236fafc2d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-94f7dd0a-4574-4ba4-86f5-fa2236fafc2d' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dz</span></div><div class='xr-var-dims'>(member_id, time, z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1140, 60), meta=np.ndarray&gt;</div><input id='attrs-acbe98d7-d0b0-41a4-b3c0-4f1da0265d49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acbe98d7-d0b0-41a4-b3c0-4f1da0265d49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a74efbc-8521-41b3-9207-3fb07d792996' class='xr-var-data-in' type='checkbox'><label for='data-8a74efbc-8521-41b3-9207-3fb07d792996' 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> 0.99 MiB </td> <td> 267.19 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2, 2172, 60) </td> <td> (1, 1140, 60) </td></tr>\n",
       "    <tr><th> Count </th><td> 34 Tasks </td><td> 4 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "</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'>(member_id, time, d2)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 360, 2), meta=np.ndarray&gt;</div><input id='attrs-12d07d14-82f5-458d-a3e2-2962695c824a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12d07d14-82f5-458d-a3e2-2962695c824a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1123527a-7473-4e53-9704-f0af079f1e1f' class='xr-var-data-in' type='checkbox'><label for='data-1123527a-7473-4e53-9704-f0af079f1e1f' 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></dl></div><div class='xr-var-data'><table>\n",
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       "<td>\n",
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       "    <tr><th> Shape </th><td> (2, 2172, 2) </td> <td> (1, 600, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 136 Tasks </td><td> 12 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> object </td><td> numpy.ndarray </td></tr>\n",
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       "<td>\n",
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       "<xarray.Dataset>\n",
       "Dimensions:               (d2: 2, member_id: 2, nlat: 384, nlon: 320, time: 2172, z_t: 60, z_t_150m: 15)\n",
       "Coordinates:\n",
       "  * z_t                   (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05\n",
       "  * z_t_150m              (z_t_150m) float32 500.0 1.5e+03 ... 1.35e+04 1.45e+04\n",
       "    TLONG                 (nlat, nlon) float64 320.6 321.7 322.8 ... 319.4 319.8\n",
       "    TLAT                  (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19\n",
       "  * time                  (time) object 1920-01-16 12:00:00 ... 2100-12-16 12...\n",
       "Dimensions without coordinates: d2, member_id, nlat, nlon\n",
       "Data variables:\n",
       "    dz                    (member_id, time, z_t) float32 dask.array<chunksize=(1, 1140, 60), meta=np.ndarray>\n",
       "    time_bound            (member_id, time, d2) object dask.array<chunksize=(1, 360, 2), meta=np.ndarray>\n",
       "    photoC_TOT_zint_100m  (member_id, time, nlat, nlon) float32 dask.array<chunksize=(1, 360, 384, 320), meta=np.ndarray>\n",
       "    POC_FLUX_100m         (member_id, time, nlat, nlon) float32 dask.array<chunksize=(1, 360, 384, 320), meta=np.ndarray>\n",
       "    IFRAC                 (member_id, time, nlat, nlon) float32 dask.array<chunksize=(1, 360, 384, 320), meta=np.ndarray>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now spin up a cluster and do some analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ClusterClient():\n",
    "    import dask\n",
    "    from dask_jobqueue import PBSCluster\n",
    "    from dask.distributed import Client\n",
    "    cluster = PBSCluster(\n",
    "        cores=2,\n",
    "        memory='20 GB',\n",
    "        processes=1,\n",
    "        queue='casper',\n",
    "        resource_spec='select=1:ncpus=1:mem=20GB',\n",
    "        project='NCGD0011',\n",
    "        walltime='05:00:00',\n",
    "        interface='ext',)\n",
    "\n",
    "    dask.config.set({\n",
    "        'distributed.dashboard.link':\n",
    "        'https://jupyterhub.hpc.ucar.edu/stable/user/{USER}/proxy/{port}/status'\n",
    "    })\n",
    "    client = Client(cluster)\n",
    "    return cluster, client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://128.117.208.85:45415</li>\n",
       "  <li><b>Dashboard: </b><a href='https://jupyterhub.hpc.ucar.edu/stable/user/kristenk/proxy/39367/status' target='_blank'>https://jupyterhub.hpc.ucar.edu/stable/user/kristenk/proxy/39367/status</a></li>\n",
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       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>0</li>\n",
       "  <li><b>Cores: </b>0</li>\n",
       "  <li><b>Memory: </b>0 B</li>\n",
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       "</td>\n",
       "</tr>\n",
       "</table>"
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       "<Client: 'tcp://128.117.208.85:45415' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster, client = get_ClusterClient()\n",
    "cluster.scale(18) \n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get coords and make a ones array for masking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_grid = pop_tools.get_grid('POP_gx1v7')\n",
    "lats = ds_grid.TLAT\n",
    "lons = ds_grid.TLONG\n",
    "area = ds_grid.TAREA\n",
    "area_m = area * 1.e-4\n",
    "area_km = area * 1.e-10\n",
    "lons_norm = utils.normal_lons(lons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ones = np.full((384,320),1,dtype=int)\n",
    "ones = xr.DataArray(ones,dims=('nlat','nlon'))\n",
    "ones = ones.where(ds_grid.REGION_MASK>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "Dimensions:               (d2: 2, member_id: 2, nlat: 384, nlon: 320, time: 2172, z_t: 60, z_t_150m: 15)\n",
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       "  * z_t                   (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05\n",
       "  * z_t_150m              (z_t_150m) float32 500.0 1.5e+03 ... 1.35e+04 1.45e+04\n",
       "    TLONG                 (nlat, nlon) float64 320.6 321.7 322.8 ... 319.4 319.8\n",
       "    TLAT                  (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19\n",
       "  * time                  (time) object 1920-01-16 12:00:00 ... 2100-12-16 12...\n",
       "Dimensions without coordinates: d2, member_id, nlat, nlon\n",
       "Data variables:\n",
       "    dz                    (member_id, time, z_t) float32 dask.array&lt;chunksize=(1, 1140, 60), meta=np.ndarray&gt;\n",
       "    time_bound            (member_id, time, d2) object dask.array&lt;chunksize=(1, 360, 2), meta=np.ndarray&gt;\n",
       "    photoC_TOT_zint_100m  (member_id, time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 360, 384, 320), meta=np.ndarray&gt;\n",
       "    POC_FLUX_100m         (member_id, time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 360, 384, 320), meta=np.ndarray&gt;\n",
       "    IFRAC                 (member_id, time, nlat, nlon) float32 dask.array&lt;chunksize=(1, 360, 384, 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-f3f72cd4-9769-4729-bbb4-0b8ba70026b9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f3f72cd4-9769-4729-bbb4-0b8ba70026b9' 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>member_id</span>: 2</li><li><span>nlat</span>: 384</li><li><span>nlon</span>: 320</li><li><span class='xr-has-index'>time</span>: 2172</li><li><span class='xr-has-index'>z_t</span>: 60</li><li><span class='xr-has-index'>z_t_150m</span>: 15</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-78ab67a7-9bcb-40bc-a737-b250132d1785' class='xr-section-summary-in' type='checkbox'  checked><label for='section-78ab67a7-9bcb-40bc-a737-b250132d1785' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 5.375e+05</div><input id='attrs-5c11c300-1d8e-45bc-b0b5-610c5f6d7086' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c11c300-1d8e-45bc-b0b5-610c5f6d7086' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-204c8730-7155-4bea-ad84-088da541f5b2' class='xr-var-data-in' type='checkbox'><label for='data-204c8730-7155-4bea-ad84-088da541f5b2' 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>depth from surface to midpoint of layer</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>valid_min :</span></dt><dd>500.0</dd><dt><span>valid_max :</span></dt><dd>537500.0</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
       "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
       "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
       "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
       "       2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,\n",
       "       2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,\n",
       "       4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,\n",
       "       6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,\n",
       "       1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,\n",
       "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
       "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
       "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t_150m</span></div><div class='xr-var-dims'>(z_t_150m)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 1.35e+04 1.45e+04</div><input id='attrs-7409b5c1-91b5-4689-ad01-7ed6fda38765' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7409b5c1-91b5-4689-ad01-7ed6fda38765' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d894a01b-0f98-41fc-9402-e350efacac15' class='xr-var-data-in' type='checkbox'><label for='data-d894a01b-0f98-41fc-9402-e350efacac15' 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>depth from surface to midpoint of layer</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>valid_min :</span></dt><dd>500.0</dd><dt><span>valid_max :</span></dt><dd>14500.0</dd></dl></div><div class='xr-var-data'><pre>array([  500.,  1500.,  2500.,  3500.,  4500.,  5500.,  6500.,  7500.,  8500.,\n",
       "        9500., 10500., 11500., 12500., 13500., 14500.], dtype=float32)</pre></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'>320.6 321.7 322.8 ... 319.4 319.8</div><input id='attrs-a684667c-36c1-48cb-8814-9db41e220e57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a684667c-36c1-48cb-8814-9db41e220e57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-158fbd69-62a8-4c26-a488-0e564a5728c2' class='xr-var-data-in' type='checkbox'><label for='data-158fbd69-62a8-4c26-a488-0e564a5728c2' 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'><pre>array([[320.56250892, 321.68750895, 322.81250898, ..., 317.18750883,\n",
       "        318.31250886, 319.43750889],\n",
       "       [320.56250892, 321.68750895, 322.81250898, ..., 317.18750883,\n",
       "        318.31250886, 319.43750889],\n",
       "       [320.56250892, 321.68750895, 322.81250898, ..., 317.18750883,\n",
       "        318.31250886, 319.43750889],\n",
       "       ...,\n",
       "       [320.25133086, 320.75380113, 321.25577325, ..., 318.74424456,\n",
       "        319.24621668, 319.74869143],\n",
       "       [320.23459477, 320.70358949, 321.17207442, ..., 318.82794339,\n",
       "        319.29642832, 319.76542721],\n",
       "       [320.21650899, 320.6493303 , 321.08163473, ..., 318.91838308,\n",
       "        319.3506875 , 319.78351267]])</pre></div></li><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'>-79.22 -79.22 ... 72.19 72.19</div><input id='attrs-ed169bb7-2d4d-4260-9f6e-6d4892261ba7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed169bb7-2d4d-4260-9f6e-6d4892261ba7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-098698b5-00b3-43e5-ae7c-7e9e43740218' class='xr-var-data-in' type='checkbox'><label for='data-098698b5-00b3-43e5-ae7c-7e9e43740218' 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'><pre>array([[-79.22052261, -79.22052261, -79.22052261, ..., -79.22052261,\n",
       "        -79.22052261, -79.22052261],\n",
       "       [-78.68630626, -78.68630626, -78.68630626, ..., -78.68630626,\n",
       "        -78.68630626, -78.68630626],\n",
       "       [-78.15208992, -78.15208992, -78.15208992, ..., -78.15208992,\n",
       "        -78.15208992, -78.15208992],\n",
       "       ...,\n",
       "       [ 71.29031715,  71.29408252,  71.30160692, ...,  71.30160692,\n",
       "         71.29408252,  71.29031716],\n",
       "       [ 71.73524335,  71.73881845,  71.74596231, ...,  71.74596231,\n",
       "         71.73881845,  71.73524335],\n",
       "       [ 72.18597561,  72.18933231,  72.19603941, ...,  72.19603941,\n",
       "         72.18933231,  72.18597562]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1920-01-16 12:00:00 ... 2100-12-...</div><input id='attrs-7f5a4bd8-68d6-4dbf-b8d4-6e1930d69b24' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f5a4bd8-68d6-4dbf-b8d4-6e1930d69b24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7864ee4-170e-4a72-ba75-f7307133f8b3' class='xr-var-data-in' type='checkbox'><label for='data-a7864ee4-170e-4a72-ba75-f7307133f8b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1920, 1, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 16, 12, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 10, 16, 12, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 11, 16, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fbc97cb9-5dcf-4e53-89fc-55066bbbbfe1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fbc97cb9-5dcf-4e53-89fc-55066bbbbfe1' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dz</span></div><div class='xr-var-dims'>(member_id, time, z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1140, 60), meta=np.ndarray&gt;</div><input id='attrs-cd50686d-ae14-42b2-84db-f7bb3558ad1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd50686d-ae14-42b2-84db-f7bb3558ad1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e957cdf7-6c51-4d2d-b091-9106e7831150' class='xr-var-data-in' type='checkbox'><label for='data-e957cdf7-6c51-4d2d-b091-9106e7831150' 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> 0.99 MiB </td> <td> 267.19 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2, 2172, 60) </td> <td> (1, 1140, 60) </td></tr>\n",
       "    <tr><th> Count </th><td> 34 Tasks </td><td> 4 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",
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       "  <text x=\"7.474299\" y=\"147.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,147.474299)\">2</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'>(member_id, time, d2)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 360, 2), meta=np.ndarray&gt;</div><input id='attrs-37d50925-f666-4e3f-a5eb-8b578cab7105' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37d50925-f666-4e3f-a5eb-8b578cab7105' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db4ede0a-6a61-4a3a-8bcb-a0d28698232a' class='xr-var-data-in' type='checkbox'><label for='data-db4ede0a-6a61-4a3a-8bcb-a0d28698232a' 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></dl></div><div class='xr-var-data'><table>\n",
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       "<td>\n",
       "<table>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 67.88 kiB </td> <td> 9.38 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2, 2172, 2) </td> <td> (1, 600, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 136 Tasks </td><td> 12 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> object </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "<td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-00a9695a-c850-4894-b5dc-94dcc8e7512f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-00a9695a-c850-4894-b5dc-94dcc8e7512f' 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, member_id: 2, nlat: 384, nlon: 320, time: 2172, z_t: 60, z_t_150m: 15)\n",
       "Coordinates:\n",
       "  * z_t                   (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05\n",
       "  * z_t_150m              (z_t_150m) float32 500.0 1.5e+03 ... 1.35e+04 1.45e+04\n",
       "    TLONG                 (nlat, nlon) float64 320.6 321.7 322.8 ... 319.4 319.8\n",
       "    TLAT                  (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19\n",
       "  * time                  (time) object 1920-01-16 12:00:00 ... 2100-12-16 12...\n",
       "Dimensions without coordinates: d2, member_id, nlat, nlon\n",
       "Data variables:\n",
       "    dz                    (member_id, time, z_t) float32 dask.array<chunksize=(1, 1140, 60), meta=np.ndarray>\n",
       "    time_bound            (member_id, time, d2) object dask.array<chunksize=(1, 360, 2), meta=np.ndarray>\n",
       "    photoC_TOT_zint_100m  (member_id, time, nlat, nlon) float32 dask.array<chunksize=(1, 360, 384, 320), meta=np.ndarray>\n",
       "    POC_FLUX_100m         (member_id, time, nlat, nlon) float32 dask.array<chunksize=(1, 360, 384, 320), meta=np.ndarray>\n",
       "    IFRAC                 (member_id, time, nlat, nlon) float32 dask.array<chunksize=(1, 360, 384, 320), meta=np.ndarray>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a SIZ mask using seasonal average JJA (I'll make DJF and SON too, just in case)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_djf = xr.Dataset()\n",
    "ds_jja = xr.Dataset() # for calculating SIZ mask, whether or not the wintertime ifrac makes a big difference for the following growing season\n",
    "ds_son = xr.Dataset()\n",
    "\n",
    "sp = 8 # sept index , rearing period (rp)\n",
    "i = 5 # june index for jja averages\n",
    "s = 11 #december index for djf averages, summer (s)\n",
    "\n",
    "for year in np.arange(0,180,1):\n",
    "        \n",
    "        #print(year,sp,s,i)\n",
    "    \n",
    "        \n",
    "        ### JJA\n",
    "        jja = ds.isel(time=slice(i,i+3)).mean(dim='time')\n",
    "\n",
    "        ds_jja = xr.concat([ds_jja,jja],dim='time')\n",
    "\n",
    "        i = i + 12\n",
    "        \n",
    "                \n",
    "        ### SON\n",
    "        son = ds.isel(time=slice(sp,sp+3)).mean(dim='time')\n",
    "\n",
    "        ds_son = xr.concat([ds_son,son],dim='time')\n",
    "\n",
    "        sp = sp + 12\n",
    "        \n",
    "                        \n",
    "        ### DJF\n",
    "        djf = ds.isel(time=slice(s,s+3)).mean(dim='time')\n",
    "\n",
    "        ds_djf = xr.concat([ds_djf,djf],dim='time')\n",
    "\n",
    "        s = s + 12\n",
    "\n",
    "\n",
    "ds_djf['time'] = np.arange(1920,2100,1)\n",
    "ds_son['time'] = np.arange(1920,2100,1)\n",
    "ds_jja['time'] = np.arange(1920,2100,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's make some timeseries figure, just use SIZ as as example mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### define Antarctic SIZ as JJA ifrac > 0.85 over the mean over the first 10 years of the run (1920-1929)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "SIZ_mask = ones.where(ds_jja.IFRAC.isel(time=slice(0,10)).mean(dim=('time','member_id')) > 0.85).where(lats<0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x14922bdaf790>"
      ]
     },
     "execution_count": 32,
     "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": [
    "SIZ_mask.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "photoC_TOT_zint_100m\n",
      "POC_FLUX_100m\n",
      "IFRAC\n"
     ]
    }
   ],
   "source": [
    "ts_siz_djf = xr.Dataset()\n",
    "ts_siz_son = xr.Dataset()\n",
    "\n",
    "\n",
    "for var in variables:\n",
    "    \n",
    "    print(var)\n",
    "\n",
    "    masked_area = area.where(SIZ_mask==1)\n",
    "\n",
    "    ### DJF\n",
    "    masked_var = ds_djf[var].where(SIZ_mask==1)\n",
    "    ts_siz_djf[var] = (masked_var * masked_area).sum(dim=('nlon','nlat')) / masked_area.sum(dim=('nlon','nlat'))\n",
    "\n",
    "    ### SON\n",
    "    masked_var = ds_son[var].where(SIZ_mask==1)\n",
    "    ts_siz_son[var] = (masked_var * masked_area).sum(dim=('nlon','nlat')) / masked_area.sum(dim=('nlon','nlat'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_siz_djf['time'] = np.arange(1920,2100,1)\n",
    "ts_siz_son['time'] = np.arange(1920,2100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,10))\n",
    "\n",
    "ax = fig.add_subplot(2,1,1)\n",
    "ax.set_title('a) SIZ NPP SON', loc='left')\n",
    "\n",
    "for mem in np.arange(0,2,1):\n",
    "    ax.plot(ts_siz_son['time'], ts_siz_son['photoC_TOT_zint_100m'].isel(member_id=mem)*86400.*0.01,linewidth=0.5,color='gray')\n",
    "    \n",
    "ax.plot(ts_siz_son['time'], ts_siz_son['photoC_TOT_zint_100m'].mean(dim='member_id')*86400.*0.01,linewidth=1,color='black')\n",
    "        \n",
    "ax.set(ylabel='NPP (mmol/m2/d)',xlabel='Year');\n",
    "ax.xaxis.grid(True, which='major',color='lavender')\n",
    "ax.yaxis.grid(False, which='major',color='lavender');\n",
    "#############################3\n",
    "ax = fig.add_subplot(2,1,2)\n",
    "ax.set_title('b) SIZ NPP DJF', loc='left')\n",
    "\n",
    "for mem in np.arange(0,2,1):\n",
    "    ax.plot(ts_siz_djf['time'], ts_siz_djf['photoC_TOT_zint_100m'].isel(member_id=mem)*86400.*0.01,linewidth=0.5,color='gray')\n",
    "    \n",
    "ax.plot(ts_siz_djf['time'], ts_siz_djf['photoC_TOT_zint_100m'].mean(dim='member_id')*86400.*0.01,linewidth=1,color='black')\n",
    "        \n",
    "ax.set(ylabel='NPP (mmol/m2/d)',xlabel='Year');\n",
    "ax.xaxis.grid(True, which='major',color='lavender')\n",
    "ax.yaxis.grid(False, which='major',color='lavender');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,10))\n",
    "\n",
    "ax = fig.add_subplot(2,1,1)\n",
    "ax.set_title('a) SIZ IFRAC SON', loc='left')\n",
    "\n",
    "for mem in np.arange(0,2,1):\n",
    "    ax.plot(ts_siz_son['time'], ts_siz_son['IFRAC'].isel(member_id=mem),linewidth=0.5,color='gray')\n",
    "    \n",
    "ax.plot(ts_siz_son['time'], ts_siz_son['IFRAC'].mean(dim='member_id'),linewidth=1,color='black')\n",
    "        \n",
    "ax.set(ylabel='Ice fraction',xlabel='Year');\n",
    "ax.xaxis.grid(True, which='major',color='lavender')\n",
    "ax.yaxis.grid(False, which='major',color='lavender');\n",
    "################\n",
    "ax = fig.add_subplot(2,1,2)\n",
    "ax.set_title('b) SIZ IFRAC DJF', loc='left')\n",
    "\n",
    "for mem in np.arange(0,2,1):\n",
    "    ax.plot(ts_siz_djf['time'], ts_siz_djf['IFRAC'].isel(member_id=mem),linewidth=0.5,color='gray')\n",
    "    \n",
    "ax.plot(ts_siz_djf['time'], ts_siz_djf['IFRAC'].mean(dim='member_id'),linewidth=1,color='black')\n",
    "        \n",
    "ax.set(ylabel='Ice fraction',xlabel='Year');\n",
    "ax.xaxis.grid(True, which='major',color='lavender')\n",
    "ax.yaxis.grid(False, which='major',color='lavender');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DJF sea ice appears to be drifting a lot after 1975, and NPP responds by increasing... hmmmmm\n",
    "### Or maybe SST is drifting warming and sea ice and NPP are responding... hmmmm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a change map from 1920s to 2090s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1 = ds.isel(time=slice(0,120)).mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds2 = ds.isel(time=slice(2040,2160)).mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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       "array(2090)\n",
       "Coordinates:\n",
       "    time     int64 2090</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-75eee514-4634-478f-becd-fdbe7ef706cf' class='xr-array-in' type='checkbox' checked><label for='section-75eee514-4634-478f-becd-fdbe7ef706cf' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2090</span></div><div class='xr-array-data'><pre>array(2090)</pre></div></div></li><li class='xr-section-item'><input id='section-827e63d7-8a94-4148-8c1d-08949880928d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-827e63d7-8a94-4148-8c1d-08949880928d' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2090</div><input id='attrs-bb728c36-d6c9-49dd-b1d0-22bce2c21a03' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb728c36-d6c9-49dd-b1d0-22bce2c21a03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae3a2a06-fd72-4c84-ac8c-b7028a164f83' class='xr-var-data-in' type='checkbox'><label for='data-ae3a2a06-fd72-4c84-ac8c-b7028a164f83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(2090)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dcfa80b0-43b6-404e-a4b6-5bcfa93a9f99' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dcfa80b0-43b6-404e-a4b6-5bcfa93a9f99' 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>"
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      "text/plain": [
       "<xarray.DataArray 'time' ()>\n",
       "array(2090)\n",
       "Coordinates:\n",
       "    time     int64 2090"
      ]
     },
     "execution_count": 39,
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   ],
   "source": [
    "ds_jja.time[170]"
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  {
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   "source": [
    "ifrac_jja_1920s = ds_jja.IFRAC.isel(time=slice(0,10)).mean(dim='time')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 41,
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   "outputs": [],
   "source": [
    "ifrac_jja_2090s = ds_jja.IFRAC.isel(time=slice(170,180)).mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_diff = ds2 - ds1"
   ]
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  {
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   "execution_count": 43,
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   "outputs": [],
   "source": [
    "ds_diff = ds_diff.load()"
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  {
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   "execution_count": 44,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "############### \n",
    "ax = fig.add_subplot(1,1,1, projection=ccrs.SouthPolarStereo())\n",
    "ax.set_extent([-180, 180, -90, -30], ccrs.PlateCarree())\n",
    "ax.set_title('${\\Delta}$ NPP', loc='left')\n",
    "\n",
    "\n",
    "lon,lat,field = utils.adjust_pop_grid(lons, lats, ds_diff.photoC_TOT_zint_100m.mean(dim='member_id') *86400.*0.01)\n",
    "\n",
    "pc = ax.pcolormesh(lon,lat,field,\n",
    "                    transform=ccrs.PlateCarree(),cmap='bwr',vmin=-20,vmax=20)\n",
    "\n",
    "#### add SIZ contour 1920s\n",
    "ice_lev = [0.85]\n",
    "lon,lat,field = utils.adjust_pop_grid(lons, lats, ifrac_jja_1920s.mean(dim='member_id'))\n",
    "cs = ax.contour(lon,lat,field, colors='blue',\n",
    "                levels = ice_lev,\n",
    "                linewidths = 1,\n",
    "                transform=ccrs.PlateCarree())\n",
    "\n",
    "#### add SIZ contour 2090s\n",
    "ice_lev = [0.85]\n",
    "lon,lat,field = utils.adjust_pop_grid(lons, lats, ifrac_jja_2090s.mean(dim='member_id'))\n",
    "cs = ax.contour(lon,lat,field, colors='crimson',\n",
    "                levels = ice_lev,\n",
    "                linewidths = 1,\n",
    "                transform=ccrs.PlateCarree())\n",
    "\n",
    "ax.coastlines('10m',linewidth=1)\n",
    "fig.colorbar(pc,extend='both', label='${\\Delta}$ NPP (mmol/m2/d)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a couple climatologies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_clim1 = ds.isel(time=slice(0,120)).groupby('time.month').mean('time')\n",
    "ds_clim2 = ds.isel(time=slice(2040,2160)).groupby('time.month').mean('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_clim1 = ds_clim1.load()\n",
    "ds_clim2 = ds_clim2.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "photoC_TOT_zint_100m\n",
      "POC_FLUX_100m\n",
      "IFRAC\n"
     ]
    }
   ],
   "source": [
    "ts_siz_clim1920s = xr.Dataset()\n",
    "ts_siz_clim2090s = xr.Dataset()\n",
    "\n",
    "\n",
    "for var in variables:\n",
    "    \n",
    "    print(var)\n",
    "\n",
    "    masked_area = area.where(SIZ_mask==1)\n",
    "\n",
    "    ### 1920s\n",
    "    masked_var = ds_clim1[var].where(SIZ_mask==1)\n",
    "    ts_siz_clim1920s[var] = (masked_var * masked_area).sum(dim=('nlon','nlat')) / masked_area.sum(dim=('nlon','nlat'))\n",
    "\n",
    "    ### 2090s\n",
    "    masked_var = ds_clim2[var].where(SIZ_mask==1)\n",
    "    ts_siz_clim2090s[var] = (masked_var * masked_area).sum(dim=('nlon','nlat')) / masked_area.sum(dim=('nlon','nlat'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (member_id: 2, month: 12)\n",
       "Coordinates:\n",
       "  * month                 (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Dimensions without coordinates: member_id\n",
       "Data variables:\n",
       "    photoC_TOT_zint_100m  (month, member_id) float64 dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;\n",
       "    POC_FLUX_100m         (month, member_id) float64 dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;\n",
       "    IFRAC                 (month, member_id) float64 dask.array&lt;chunksize=(12, 2), 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-bc5748ae-92f6-42fe-98e2-11e1157b19d3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bc5748ae-92f6-42fe-98e2-11e1157b19d3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>member_id</span>: 2</li><li><span class='xr-has-index'>month</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a4b4b1e3-35b7-4a40-9902-eaeaf813a4dc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a4b4b1e3-35b7-4a40-9902-eaeaf813a4dc' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-1484ce14-e6b6-4794-a7ae-308dc616a3fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1484ce14-e6b6-4794-a7ae-308dc616a3fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7e93f25-635d-4dc1-9664-035f109bdc76' class='xr-var-data-in' type='checkbox'><label for='data-a7e93f25-635d-4dc1-9664-035f109bdc76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2f04c0cb-d9b7-4e3c-b733-a8ff11f9f943' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f04c0cb-d9b7-4e3c-b733-a8ff11f9f943' class='xr-section-summary' >Data variables: <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>photoC_TOT_zint_100m</span></div><div class='xr-var-dims'>(month, member_id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;</div><input id='attrs-72548c25-6101-4138-8b93-595246f590a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72548c25-6101-4138-8b93-595246f590a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d992599c-a6c8-4102-b657-87b841458cff' class='xr-var-data-in' type='checkbox'><label for='data-d992599c-a6c8-4102-b657-87b841458cff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Count </th><td> 34179 Tasks </td><td> 1 Chunks </td></tr>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (member_id: 2, month: 12)\n",
       "Coordinates:\n",
       "  * month                 (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Dimensions without coordinates: member_id\n",
       "Data variables:\n",
       "    photoC_TOT_zint_100m  (month, member_id) float64 dask.array<chunksize=(12, 2), meta=np.ndarray>\n",
       "    POC_FLUX_100m         (month, member_id) float64 dask.array<chunksize=(12, 2), meta=np.ndarray>\n",
       "    IFRAC                 (month, member_id) float64 dask.array<chunksize=(12, 2), meta=np.ndarray>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_siz_clim2090s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_siz_clim1920s = xr.concat([ts_siz_clim1920s,ts_siz_clim1920s.isel(month=slice(0,6))],dim='month')\n",
    "ts_siz_clim2090s = xr.concat([ts_siz_clim2090s,ts_siz_clim2090s.isel(month=slice(0,6))],dim='month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "xvals = [7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,18]\n",
    "\n",
    "xlabels = ['Jul','Aug','Sep','Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "convert=0.01*86400\n",
    "\n",
    "ax1 = fig.add_subplot(1,1,1)\n",
    "ax1.set_title('a) NPP',fontsize=12,loc='left')\n",
    "ax1.set_ylabel('NPP (mmol C m$^{-2}$ d$^{-1}$)')\n",
    "ax1.set_xlabel('Month')\n",
    "#ax1.set_ylim(20,180)\n",
    "ax1.xaxis.grid(True, which='major',color='lavender')\n",
    "ax1.yaxis.grid(False, which='major',color='lavender')\n",
    "#plot 1920\n",
    "ax1.plot(xvals,ts_siz_clim1920s['photoC_TOT_zint_100m'].mean(dim='member_id').isel(month=slice(6,18))*convert, \n",
    "         color='mediumturquoise', label='1920s', lw=1.5)\n",
    "error = ts_siz_clim1920s['photoC_TOT_zint_100m'].std(dim='member_id').isel(month=slice(6,18))*convert\n",
    "ax1.fill_between(xvals, ts_siz_clim1920s['photoC_TOT_zint_100m'].mean(dim='member_id').isel(month=slice(6,18))*convert-error, \n",
    "                 ts_siz_clim1920s['photoC_TOT_zint_100m'].mean(dim='member_id').isel(month=slice(6,18))*convert+error,color='paleturquoise')\n",
    "\n",
    "\n",
    "#plot 2090s\n",
    "ax1.plot(xvals,ts_siz_clim2090s['photoC_TOT_zint_100m'].mean(dim='member_id').isel(month=slice(6,18))*convert, \n",
    "         color='red', label='2090s', lw=1.5)\n",
    "error = ts_siz_clim2090s['photoC_TOT_zint_100m'].std(dim='member_id').isel(month=slice(6,18))*convert\n",
    "ax1.fill_between(xvals, ts_siz_clim2090s['photoC_TOT_zint_100m'].mean(dim='member_id').isel(month=slice(6,18))*convert-error, \n",
    "                 ts_siz_clim2090s['photoC_TOT_zint_100m'].mean(dim='member_id').isel(month=slice(6,18))*convert+error,color='pink')\n",
    "\n",
    "plt.xticks(xvals, xlabels, rotation='vertical')\n",
    "ax1.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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:miniconda-analysis5]",
   "language": "python",
   "name": "conda-env-miniconda-analysis5-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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}