read_in_4p2z_hist-ssp370-runs-v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mini-ensemble of CESM2-4p2z historical + ssp370"
   ]
  },
  {
   "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",
    "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": 2,
   "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": 3,
   "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": 4,
   "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": 5,
   "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": 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_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) #, preprocess=preprocess)\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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1 = xr.concat([ds_hist,ds_ssp],dim='time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now grab ensemble members 003, 004, 005, 006"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cases_hist = ['b.e21p5.BHIST.f09_g17.4p2z.003','b.e21p5.BHIST.f09_g17.4p2z.004','b.e21p5.BHIST.f09_g17.4p2z.005','b.e21p5.BHIST.f09_g17.4p2z.006']\n",
    "cases_ssp = ['b.e21p5.BSSP370.f09_g17.4p2z.003','b.e21p5.BSSP370.f09_g17.4p2z.004','b.e21p5.BSSP370.f09_g17.4p2z.005','b.e21p5.BSSP370.f09_g17.4p2z.006']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting on  photoC_TOT_zint_100m\n",
      "path: /glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/b.e21p5.BHIST.f09_g17.4p2z.003/ocn/proc/tseries/month_1/\n",
      "starting on  POC_FLUX_100m\n",
      "path: /glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/b.e21p5.BHIST.f09_g17.4p2z.003/ocn/proc/tseries/month_1/\n",
      "starting on  IFRAC\n",
      "path: /glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/b.e21p5.BHIST.f09_g17.4p2z.003/ocn/proc/tseries/month_1/\n",
      "starting on  photoC_TOT_zint_100m\n",
      "starting on  POC_FLUX_100m\n",
      "starting on  IFRAC\n",
      "starting on  photoC_TOT_zint_100m\n",
      "path: /glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/b.e21p5.BHIST.f09_g17.4p2z.004/ocn/proc/tseries/month_1/\n",
      "starting on  POC_FLUX_100m\n",
      "path: /glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/b.e21p5.BHIST.f09_g17.4p2z.004/ocn/proc/tseries/month_1/\n",
      "starting on  IFRAC\n",
      "path: /glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/b.e21p5.BHIST.f09_g17.4p2z.004/ocn/proc/tseries/month_1/\n",
      "starting on  photoC_TOT_zint_100m\n",
      "starting on  POC_FLUX_100m\n",
      "starting on  IFRAC\n",
      "starting on  photoC_TOT_zint_100m\n",
      "path: /glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/b.e21p5.BHIST.f09_g17.4p2z.005/ocn/proc/tseries/month_1/\n",
      "starting on  POC_FLUX_100m\n",
      "path: /glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/b.e21p5.BHIST.f09_g17.4p2z.005/ocn/proc/tseries/month_1/\n",
      "starting on  IFRAC\n",
      "path: /glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/b.e21p5.BHIST.f09_g17.4p2z.005/ocn/proc/tseries/month_1/\n",
      "starting on  photoC_TOT_zint_100m\n",
      "starting on  POC_FLUX_100m\n",
      "starting on  IFRAC\n",
      "starting on  photoC_TOT_zint_100m\n",
      "path: /glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/b.e21p5.BHIST.f09_g17.4p2z.006/ocn/proc/tseries/month_1/\n",
      "starting on  POC_FLUX_100m\n",
      "path: /glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/b.e21p5.BHIST.f09_g17.4p2z.006/ocn/proc/tseries/month_1/\n",
      "starting on  IFRAC\n",
      "path: /glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/b.e21p5.BHIST.f09_g17.4p2z.006/ocn/proc/tseries/month_1/\n",
      "starting on  photoC_TOT_zint_100m\n",
      "starting on  POC_FLUX_100m\n",
      "starting on  IFRAC\n"
     ]
    }
   ],
   "source": [
    "#ds2 will be the dataset with variables dims nlon,nlat,time,member_id\n",
    "\n",
    "\n",
    "for i in [0,1,2,3]:\n",
    "\n",
    "    ### first grab historical part of this ensemble member\n",
    "    ds_hist = xr.Dataset()\n",
    "\n",
    "    path = '/glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/' + cases_hist[i] + '/ocn/proc/tseries/month_1/'\n",
    "    \n",
    "    for var in variables:\n",
    "    \n",
    "        print('starting on ', var)\n",
    "        print('path:', path)\n",
    "        \n",
    "        ################ FOSI\n",
    "        \n",
    "        files = sorted(glob(f'{path}/{cases_hist[i]}.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) #, preprocess=preprocess)\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')\n",
    "\n",
    "    #### now grab the ssp part of this ensemble member\n",
    "    ds_ssp = xr.Dataset()\n",
    "    \n",
    "    path = '/glade/campaign/cesm/development/bgcwg/projects/CESM2-4p2z-CMIP6-like-run/' + cases_ssp[i] + '/ocn/proc/tseries/month_1/'\n",
    "\n",
    "    for var in variables:\n",
    "        \n",
    "        print('starting on ', var)\n",
    "        ################ FOSI\n",
    "        \n",
    "        files = sorted(glob(f'{path}/{cases_ssp[i]}.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')\n",
    "\n",
    "    ds_tmp = xr.concat([ds_hist,ds_ssp],dim='time')\n",
    "\n",
    "    if i == 0:\n",
    "        ds2 = ds_tmp\n",
    "    else:\n",
    "        ds2 = xr.concat([ds2,ds_tmp],dim='member_id')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fix the time dimension and then trim them so they both start in 1920"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 4, 1, 0, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0)], dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 1920-02-01 00:00:00 ... 2101-01-01 00:00:00\n",
       "Attributes:\n",
       "    long_name:  time\n",
       "    bounds:     time_bound</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-f8d83078-d043-445e-8e32-413eb7a623b6' class='xr-array-in' type='checkbox' checked><label for='section-f8d83078-d043-445e-8e32-413eb7a623b6' 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>1920-02-01 00:00:00 1920-03-01 00:00:00 ... 2101-01-01 00:00:00</span></div><div class='xr-array-data'><pre>array([cftime.DatetimeNoLeap(1920, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 4, 1, 0, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0)], dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-ae36e301-1b20-49c1-b2f1-39946f3b7218' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ae36e301-1b20-49c1-b2f1-39946f3b7218' 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'>1920-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-1cfe7d5f-0259-4125-965a-3058f0de5403' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cfe7d5f-0259-4125-965a-3058f0de5403' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf34a55f-26a7-4c44-9d2f-1255f3df5575' class='xr-var-data-in' type='checkbox'><label for='data-bf34a55f-26a7-4c44-9d2f-1255f3df5575' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bound</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1920, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 4, 1, 0, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c499a1af-2f1c-4d99-a3ba-c7fb35d997e1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c499a1af-2f1c-4d99-a3ba-c7fb35d997e1' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bound</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 2172)>\n",
       "array([cftime.DatetimeNoLeap(1920, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 4, 1, 0, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0)], dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 1920-02-01 00:00:00 ... 2101-01-01 00:00:00\n",
       "Attributes:\n",
       "    long_name:  time\n",
       "    bounds:     time_bound"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds2.time[48:2220]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds2 = ds2.isel(time=slice(48,2220))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 4, 1, 0, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0)], dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 1920-02-01 00:00:00 ... 2101-01-01 00:00:00\n",
       "Attributes:\n",
       "    long_name:  time\n",
       "    bounds:     time_bound</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-6f2419a6-0c88-42cf-ad7d-ba25c3b5bebf' class='xr-array-in' type='checkbox' checked><label for='section-6f2419a6-0c88-42cf-ad7d-ba25c3b5bebf' 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>1920-02-01 00:00:00 1920-03-01 00:00:00 ... 2101-01-01 00:00:00</span></div><div class='xr-array-data'><pre>array([cftime.DatetimeNoLeap(1920, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 4, 1, 0, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0)], dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-d63bc8f7-d7d7-41d7-bc6c-3ee75a10b21d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d63bc8f7-d7d7-41d7-bc6c-3ee75a10b21d' 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'>1920-02-01 00:00:00 ... 2101-01-...</div><input id='attrs-4d16e960-3902-48e1-b693-cdf3dfa267b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d16e960-3902-48e1-b693-cdf3dfa267b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27d664dc-68e0-43f0-90d9-aa93e57c33ef' class='xr-var-data-in' type='checkbox'><label for='data-27d664dc-68e0-43f0-90d9-aa93e57c33ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bound</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1920, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 4, 1, 0, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a07d578-12f0-48e9-912d-b3d831eca100' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a07d578-12f0-48e9-912d-b3d831eca100' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bound</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'time' (time: 2172)>\n",
       "array([cftime.DatetimeNoLeap(1920, 2, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 3, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(1920, 4, 1, 0, 0, 0, 0), ...,\n",
       "       cftime.DatetimeNoLeap(2100, 11, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2100, 12, 1, 0, 0, 0, 0),\n",
       "       cftime.DatetimeNoLeap(2101, 1, 1, 0, 0, 0, 0)], dtype=object)\n",
       "Coordinates:\n",
       "  * time     (time) object 1920-02-01 00:00:00 ... 2101-01-01 00:00:00\n",
       "Attributes:\n",
       "    long_name:  time\n",
       "    bounds:     time_bound"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.time[840:3012]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1 = ds1.isel(time=slice(840,3012))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1['time'] = ds1.time_bound.compute().mean(dim=\"d2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds2['time'] = ds1.time"
   ]
  },
  {
   "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-55d65c82-8d50-4c56-8c7f-0623de770f59' class='xr-array-in' type='checkbox' checked><label for='section-55d65c82-8d50-4c56-8c7f-0623de770f59' 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-c2937390-55e5-468f-b317-1a9ec28f38b7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c2937390-55e5-468f-b317-1a9ec28f38b7' 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-e90ac453-24a9-4ad5-9a42-93b897884c95' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e90ac453-24a9-4ad5-9a42-93b897884c95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13608b66-a133-4831-80d5-8564a9d7321e' class='xr-var-data-in' type='checkbox'><label for='data-13608b66-a133-4831-80d5-8564a9d7321e' 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-a9f1355b-e6bf-41a0-97bb-08053c352dbf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a9f1355b-e6bf-41a0-97bb-08053c352dbf' 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"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.concat([ds1,ds2],dim='member_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "Dimensions:               (d2: 2, member_id: 5, 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",
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       "    TLONG                 (member_id, nlat, nlon) float64 320.6 321.7 ... 319.8\n",
       "    TLAT                  (member_id, nlat, nlon) float64 -79.22 ... 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                    (time, z_t, member_id) float32 dask.array&lt;chunksize=(1140, 60, 1), meta=np.ndarray&gt;\n",
       "    time_bound            (time, d2, member_id) object dask.array&lt;chunksize=(360, 2, 1), meta=np.ndarray&gt;\n",
       "    photoC_TOT_zint_100m  (time, nlat, nlon, member_id) float32 dask.array&lt;chunksize=(360, 384, 320, 1), meta=np.ndarray&gt;\n",
       "    POC_FLUX_100m         (time, nlat, nlon, member_id) float32 dask.array&lt;chunksize=(360, 384, 320, 1), meta=np.ndarray&gt;\n",
       "    IFRAC                 (time, nlat, nlon, member_id) float32 dask.array&lt;chunksize=(360, 384, 320, 1), 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-89ffc8fd-52b7-4bd5-801f-450ee39e4ccc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-89ffc8fd-52b7-4bd5-801f-450ee39e4ccc' 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>: 5</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-aca2b62c-c949-4140-abba-7e1c4cb3651b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aca2b62c-c949-4140-abba-7e1c4cb3651b' 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-82683242-7909-47b8-a419-2a2335345a05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82683242-7909-47b8-a419-2a2335345a05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6049bc2-a9b1-4683-99e2-3591987ad7b3' class='xr-var-data-in' type='checkbox'><label for='data-b6049bc2-a9b1-4683-99e2-3591987ad7b3' 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-03038588-b80f-4108-a93b-d0b3da121775' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03038588-b80f-4108-a93b-d0b3da121775' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84ba1b8b-9d8a-4668-a921-332021ee225f' class='xr-var-data-in' type='checkbox'><label for='data-84ba1b8b-9d8a-4668-a921-332021ee225f' 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'>(member_id, 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-43201aca-679b-4805-b8b9-8e71a87bbf65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43201aca-679b-4805-b8b9-8e71a87bbf65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e0b2aad-c2cb-408b-b400-21304dfc3104' class='xr-var-data-in' type='checkbox'><label for='data-0e0b2aad-c2cb-408b-b400-21304dfc3104' 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]],\n",
       "\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",
       "        [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]],\n",
       "\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",
       "        [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'>(member_id, 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-d4c96ad5-40d6-4421-8490-16d819fcc65c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4c96ad5-40d6-4421-8490-16d819fcc65c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa5a0a10-2120-4fbc-99c7-8ab732d1b43c' class='xr-var-data-in' type='checkbox'><label for='data-aa5a0a10-2120-4fbc-99c7-8ab732d1b43c' 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]],\n",
       "\n",
       "       [[-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]],\n",
       "\n",
       "       [[-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-51c87406-e2f5-4cb1-b190-276314bb45c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51c87406-e2f5-4cb1-b190-276314bb45c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d11f7e8-736f-47d4-994a-42a6dd1ced54' class='xr-var-data-in' type='checkbox'><label for='data-6d11f7e8-736f-47d4-994a-42a6dd1ced54' 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-ba667264-2795-48fb-991f-eda33735e728' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba667264-2795-48fb-991f-eda33735e728' 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'>(time, z_t, member_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1140, 60, 1), meta=np.ndarray&gt;</div><input id='attrs-a36a8dc9-3ab3-4d75-a0ab-74436256bb69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a36a8dc9-3ab3-4d75-a0ab-74436256bb69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e00a8298-dd5f-459e-aa33-19fc2afd458f' class='xr-var-data-in' type='checkbox'><label for='data-e00a8298-dd5f-459e-aa33-19fc2afd458f' 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> 2.49 MiB </td> <td> 267.19 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2172, 60, 5) </td> <td> (1140, 60, 1) </td></tr>\n",
       "    <tr><th> Count </th><td> 110 Tasks </td><td> 10 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=\"156\" height=\"151\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "Dimensions:               (d2: 2, member_id: 5, nlat: 384, nlon: 320, time: 2172, z_t: 60, z_t_150m: 15)\n",
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     "execution_count": 18,
     "metadata": {},
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    "ds"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### clean things up a bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['TLONG'] = ds.TLONG.isel(member_id=0).squeeze()\n",
    "ds['TLAT'] = ds.TLAT.isel(member_id=0).squeeze()"
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   "execution_count": 20,
   "metadata": {},
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       ".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, member_id: 5, 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                    (time, z_t, member_id) float32 dask.array&lt;chunksize=(1140, 60, 1), meta=np.ndarray&gt;\n",
       "    time_bound            (time, d2, member_id) object dask.array&lt;chunksize=(360, 2, 1), meta=np.ndarray&gt;\n",
       "    photoC_TOT_zint_100m  (time, nlat, nlon, member_id) float32 dask.array&lt;chunksize=(360, 384, 320, 1), meta=np.ndarray&gt;\n",
       "    POC_FLUX_100m         (time, nlat, nlon, member_id) float32 dask.array&lt;chunksize=(360, 384, 320, 1), meta=np.ndarray&gt;\n",
       "    IFRAC                 (time, nlat, nlon, member_id) float32 dask.array&lt;chunksize=(360, 384, 320, 1), 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-3b19fcc7-98bf-4292-97d4-e55de258466b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3b19fcc7-98bf-4292-97d4-e55de258466b' 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>: 5</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-bef03f34-cc9b-4c0a-97b4-6341a8e0602b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bef03f34-cc9b-4c0a-97b4-6341a8e0602b' 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-7a19090e-a076-4e13-8744-c09b6f76be31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a19090e-a076-4e13-8744-c09b6f76be31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc640400-b57c-4563-a3e9-d99f0c58edb9' class='xr-var-data-in' type='checkbox'><label for='data-cc640400-b57c-4563-a3e9-d99f0c58edb9' 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-0ddb65f7-a90b-4185-a6f0-c88738d80a8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ddb65f7-a90b-4185-a6f0-c88738d80a8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5afca70a-ca83-44f4-bab0-4aaa9e2257ba' class='xr-var-data-in' type='checkbox'><label for='data-5afca70a-ca83-44f4-bab0-4aaa9e2257ba' 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-168fcc56-4e5b-40fc-b38d-cb50350c0082' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-168fcc56-4e5b-40fc-b38d-cb50350c0082' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b261cf6f-07b0-4a7a-ba49-05279215e8de' class='xr-var-data-in' type='checkbox'><label for='data-b261cf6f-07b0-4a7a-ba49-05279215e8de' 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-77499284-7a4e-4aa6-b8a3-fbd1a856ab9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77499284-7a4e-4aa6-b8a3-fbd1a856ab9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffd3626a-131b-49ed-9fe9-5cac6b1e436c' class='xr-var-data-in' type='checkbox'><label for='data-ffd3626a-131b-49ed-9fe9-5cac6b1e436c' 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-d478b080-5a21-41a3-9698-52747fefcbfd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d478b080-5a21-41a3-9698-52747fefcbfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15a6c57d-1018-46bf-adc0-92d9d803defa' class='xr-var-data-in' type='checkbox'><label for='data-15a6c57d-1018-46bf-adc0-92d9d803defa' 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-47180dbe-5790-4ff4-b328-f852d5190a15' class='xr-section-summary-in' type='checkbox'  checked><label for='section-47180dbe-5790-4ff4-b328-f852d5190a15' 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'>(time, z_t, member_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1140, 60, 1), meta=np.ndarray&gt;</div><input id='attrs-21fa5190-03c4-44d7-a417-506a773884e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21fa5190-03c4-44d7-a417-506a773884e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e813f4c-c608-44fa-a22c-159a162879f7' class='xr-var-data-in' type='checkbox'><label for='data-5e813f4c-c608-44fa-a22c-159a162879f7' 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> 2.49 MiB </td> <td> 267.19 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (2172, 60, 5) </td> <td> (1140, 60, 1) </td></tr>\n",
       "    <tr><th> Count </th><td> 110 Tasks </td><td> 10 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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       "<xarray.Dataset>\n",
       "Dimensions:               (d2: 2, member_id: 5, 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",
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       "    POC_FLUX_100m         (time, nlat, nlon, member_id) float32 dask.array<chunksize=(360, 384, 320, 1), meta=np.ndarray>\n",
       "    IFRAC                 (time, nlat, nlon, member_id) float32 dask.array<chunksize=(360, 384, 320, 1), meta=np.ndarray>"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "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"
   ]
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  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://128.117.208.109:36361</li>\n",
       "  <li><b>Dashboard: </b><a href='https://jupyterhub.hpc.ucar.edu/stable/user/kristenk/proxy/8787/status' target='_blank'>https://jupyterhub.hpc.ucar.edu/stable/user/kristenk/proxy/8787/status</a></li>\n",
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    "cluster, client = get_ClusterClient()\n",
    "cluster.scale(18) \n",
    "client"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get coords and make a ones array for masking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "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": 25,
   "metadata": {},
   "outputs": [
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       ".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.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-1063d5c1-afeb-4bc3-8ab7-8d643eb39a8b' class='xr-array-in' type='checkbox' checked><label for='section-1063d5c1-afeb-4bc3-8ab7-8d643eb39a8b' 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-cfd170af-fdda-4376-a88f-6c1a8f522e5c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cfd170af-fdda-4376-a88f-6c1a8f522e5c' 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-fa4cbe18-9bb4-4808-8157-e474413843cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa4cbe18-9bb4-4808-8157-e474413843cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00ffe13b-9921-4059-860e-2dd2a80e8a2b' class='xr-var-data-in' type='checkbox'><label for='data-00ffe13b-9921-4059-860e-2dd2a80e8a2b' 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-7f48d1c4-9d6e-4e8e-90d6-ddd27461a505' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7f48d1c4-9d6e-4e8e-90d6-ddd27461a505' 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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.time"
   ]
  },
  {
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1920 8 11 5\n",
      "1921 20 23 17\n",
      "1922 32 35 29\n",
      "1923 44 47 41\n",
      "1924 56 59 53\n",
      "1925 68 71 65\n",
      "1926 80 83 77\n",
      "1927 92 95 89\n",
      "1928 104 107 101\n",
      "1929 116 119 113\n",
      "1930 128 131 125\n",
      "1931 140 143 137\n",
      "1932 152 155 149\n",
      "1933 164 167 161\n",
      "1934 176 179 173\n",
      "1935 188 191 185\n",
      "1936 200 203 197\n",
      "1937 212 215 209\n",
      "1938 224 227 221\n",
      "1939 236 239 233\n",
      "1940 248 251 245\n",
      "1941 260 263 257\n",
      "1942 272 275 269\n",
      "1943 284 287 281\n",
      "1944 296 299 293\n",
      "1945 308 311 305\n",
      "1946 320 323 317\n",
      "1947 332 335 329\n",
      "1948 344 347 341\n",
      "1949 356 359 353\n",
      "1950 368 371 365\n",
      "1951 380 383 377\n",
      "1952 392 395 389\n",
      "1953 404 407 401\n",
      "1954 416 419 413\n",
      "1955 428 431 425\n",
      "1956 440 443 437\n",
      "1957 452 455 449\n",
      "1958 464 467 461\n",
      "1959 476 479 473\n",
      "1960 488 491 485\n",
      "1961 500 503 497\n",
      "1962 512 515 509\n",
      "1963 524 527 521\n",
      "1964 536 539 533\n",
      "1965 548 551 545\n",
      "1966 560 563 557\n",
      "1967 572 575 569\n",
      "1968 584 587 581\n",
      "1969 596 599 593\n",
      "1970 608 611 605\n",
      "1971 620 623 617\n",
      "1972 632 635 629\n",
      "1973 644 647 641\n",
      "1974 656 659 653\n",
      "1975 668 671 665\n",
      "1976 680 683 677\n",
      "1977 692 695 689\n",
      "1978 704 707 701\n",
      "1979 716 719 713\n",
      "1980 728 731 725\n",
      "1981 740 743 737\n",
      "1982 752 755 749\n",
      "1983 764 767 761\n",
      "1984 776 779 773\n",
      "1985 788 791 785\n",
      "1986 800 803 797\n",
      "1987 812 815 809\n",
      "1988 824 827 821\n",
      "1989 836 839 833\n",
      "1990 848 851 845\n",
      "1991 860 863 857\n",
      "1992 872 875 869\n",
      "1993 884 887 881\n",
      "1994 896 899 893\n",
      "1995 908 911 905\n",
      "1996 920 923 917\n",
      "1997 932 935 929\n",
      "1998 944 947 941\n",
      "1999 956 959 953\n",
      "2000 968 971 965\n",
      "2001 980 983 977\n",
      "2002 992 995 989\n",
      "2003 1004 1007 1001\n",
      "2004 1016 1019 1013\n",
      "2005 1028 1031 1025\n",
      "2006 1040 1043 1037\n",
      "2007 1052 1055 1049\n",
      "2008 1064 1067 1061\n",
      "2009 1076 1079 1073\n",
      "2010 1088 1091 1085\n",
      "2011 1100 1103 1097\n",
      "2012 1112 1115 1109\n",
      "2013 1124 1127 1121\n",
      "2014 1136 1139 1133\n",
      "2015 1148 1151 1145\n",
      "2016 1160 1163 1157\n",
      "2017 1172 1175 1169\n",
      "2018 1184 1187 1181\n",
      "2019 1196 1199 1193\n",
      "2020 1208 1211 1205\n",
      "2021 1220 1223 1217\n",
      "2022 1232 1235 1229\n",
      "2023 1244 1247 1241\n",
      "2024 1256 1259 1253\n",
      "2025 1268 1271 1265\n",
      "2026 1280 1283 1277\n",
      "2027 1292 1295 1289\n",
      "2028 1304 1307 1301\n",
      "2029 1316 1319 1313\n",
      "2030 1328 1331 1325\n",
      "2031 1340 1343 1337\n",
      "2032 1352 1355 1349\n",
      "2033 1364 1367 1361\n",
      "2034 1376 1379 1373\n",
      "2035 1388 1391 1385\n",
      "2036 1400 1403 1397\n",
      "2037 1412 1415 1409\n",
      "2038 1424 1427 1421\n",
      "2039 1436 1439 1433\n",
      "2040 1448 1451 1445\n",
      "2041 1460 1463 1457\n",
      "2042 1472 1475 1469\n",
      "2043 1484 1487 1481\n",
      "2044 1496 1499 1493\n",
      "2045 1508 1511 1505\n",
      "2046 1520 1523 1517\n",
      "2047 1532 1535 1529\n",
      "2048 1544 1547 1541\n",
      "2049 1556 1559 1553\n",
      "2050 1568 1571 1565\n",
      "2051 1580 1583 1577\n",
      "2052 1592 1595 1589\n",
      "2053 1604 1607 1601\n",
      "2054 1616 1619 1613\n",
      "2055 1628 1631 1625\n",
      "2056 1640 1643 1637\n",
      "2057 1652 1655 1649\n",
      "2058 1664 1667 1661\n",
      "2059 1676 1679 1673\n",
      "2060 1688 1691 1685\n",
      "2061 1700 1703 1697\n",
      "2062 1712 1715 1709\n",
      "2063 1724 1727 1721\n",
      "2064 1736 1739 1733\n",
      "2065 1748 1751 1745\n",
      "2066 1760 1763 1757\n",
      "2067 1772 1775 1769\n",
      "2068 1784 1787 1781\n",
      "2069 1796 1799 1793\n",
      "2070 1808 1811 1805\n",
      "2071 1820 1823 1817\n",
      "2072 1832 1835 1829\n",
      "2073 1844 1847 1841\n",
      "2074 1856 1859 1853\n",
      "2075 1868 1871 1865\n",
      "2076 1880 1883 1877\n",
      "2077 1892 1895 1889\n",
      "2078 1904 1907 1901\n",
      "2079 1916 1919 1913\n",
      "2080 1928 1931 1925\n",
      "2081 1940 1943 1937\n",
      "2082 1952 1955 1949\n",
      "2083 1964 1967 1961\n",
      "2084 1976 1979 1973\n",
      "2085 1988 1991 1985\n",
      "2086 2000 2003 1997\n",
      "2087 2012 2015 2009\n",
      "2088 2024 2027 2021\n",
      "2089 2036 2039 2033\n",
      "2090 2048 2051 2045\n",
      "2091 2060 2063 2057\n",
      "2092 2072 2075 2069\n",
      "2093 2084 2087 2081\n",
      "2094 2096 2099 2093\n",
      "2095 2108 2111 2105\n",
      "2096 2120 2123 2117\n",
      "2097 2132 2135 2129\n",
      "2098 2144 2147 2141\n",
      "2099 2156 2159 2153\n"
     ]
    }
   ],
   "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+1920,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",
    "# ### slice off the first timeslice (if you are using a recent version of xarray)!!!!\n",
    "# ds_djf = ds_djf.isel(time=slice(1,181))\n",
    "# ds_son = ds_son.isel(time=slice(1,181))\n",
    "# ds_jja = ds_jja.isel(time=slice(1,181))\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": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x15199b20aed0>"
      ]
     },
     "execution_count": 27,
     "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_son.IFRAC.isel(time=0).mean(dim='member_id').plot()"
   ]
  },
  {
   "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": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x15199b088e90>"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "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",
    "ts_siz_jja = 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'))\n",
    "\n",
    "    ### SON\n",
    "    masked_var = ds_jja[var].where(SIZ_mask==1)\n",
    "    ts_siz_jja[var] = (masked_var * masked_area).sum(dim=('nlon','nlat')) / masked_area.sum(dim=('nlon','nlat'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_siz_djf['time'] = np.arange(1920,2100,1)\n",
    "ts_siz_son['time'] = np.arange(1920,2100,1)\n",
    "ts_siz_jja['time'] = np.arange(1920,2100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_siz_djf = ts_siz_djf.load()\n",
    "ts_siz_son = ts_siz_son.load()\n",
    "ts_siz_jja = ts_siz_jja.load()"
   ]
  },
  {
   "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 NPP SON', loc='left')\n",
    "\n",
    "for mem in np.arange(0,5,1):\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": 37,
   "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(0,5,1):\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(0,5,1):\n",
    "    ax.plot(ts_siz_djf['time'].isel(time=slice(0,179)), ts_siz_djf['IFRAC'].isel(member_id=mem).isel(time=slice(0,179)),linewidth=0.5,color='gray')\n",
    "    \n",
    "ax.plot(ts_siz_djf['time'].isel(time=slice(0,179)), ts_siz_djf['IFRAC'].mean(dim='member_id').isel(time=slice(0,179)),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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a change map from 1920s to 2090s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1 = ds.isel(time=slice(0,120)).mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds2 = ds.isel(time=slice(2040,2160)).mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; ()&gt;\n",
       "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-efa9a7bf-3cfa-4978-931c-a776cad6524e' class='xr-array-in' type='checkbox' checked><label for='section-efa9a7bf-3cfa-4978-931c-a776cad6524e' 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-43bfb654-78dd-49d5-8c83-774be9c01b5e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43bfb654-78dd-49d5-8c83-774be9c01b5e' 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-2da318a0-46d4-4eaf-a31a-3e131ead681c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2da318a0-46d4-4eaf-a31a-3e131ead681c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0be80dc4-0c19-49de-a3b2-315e366d2596' class='xr-var-data-in' type='checkbox'><label for='data-0be80dc4-0c19-49de-a3b2-315e366d2596' 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-ef864c7b-6b1a-42e8-bbf2-d2099e5b786a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ef864c7b-6b1a-42e8-bbf2-d2099e5b786a' 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' ()>\n",
       "array(2090)\n",
       "Coordinates:\n",
       "    time     int64 2090"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_jja.time[170]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ifrac_jja_1920s = ds_jja.IFRAC.isel(time=slice(0,10)).mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ifrac_jja_2090s = ds_jja.IFRAC.isel(time=slice(170,180)).mean(dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_diff = ds2 - ds1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_diff = ds_diff.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 46,
   "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": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_clim1 = ds_clim1.load()\n",
    "ds_clim2 = ds_clim2.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 49,
   "metadata": {},
   "outputs": [
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       "}\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",
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (member_id: 5, 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, 5), meta=np.ndarray&gt;\n",
       "    POC_FLUX_100m         (month, member_id) float64 dask.array&lt;chunksize=(12, 5), meta=np.ndarray&gt;\n",
       "    IFRAC                 (month, member_id) float64 dask.array&lt;chunksize=(12, 5), 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-48cf5719-1d9b-468b-94ed-d92fff5e1651' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-48cf5719-1d9b-468b-94ed-d92fff5e1651' 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>: 5</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-40c88adc-75bf-4c45-81a2-811719b9f093' class='xr-section-summary-in' type='checkbox'  checked><label for='section-40c88adc-75bf-4c45-81a2-811719b9f093' 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-4b5a179e-5d76-496f-be61-34cadd495272' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b5a179e-5d76-496f-be61-34cadd495272' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebc7ed63-4afd-4999-9cc8-9f0bd20d7a1a' class='xr-var-data-in' type='checkbox'><label for='data-ebc7ed63-4afd-4999-9cc8-9f0bd20d7a1a' 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-a0941761-747f-4a7e-b41c-72bc21c42f25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a0941761-747f-4a7e-b41c-72bc21c42f25' 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, 5), meta=np.ndarray&gt;</div><input id='attrs-83bb9da0-9f6f-46d2-bfc4-87e7fd0ec389' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83bb9da0-9f6f-46d2-bfc4-87e7fd0ec389' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-049a0d3c-9733-43b3-a630-cb3b6455c73c' class='xr-var-data-in' type='checkbox'><label for='data-049a0d3c-9733-43b3-a630-cb3b6455c73c' 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",
       "<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> 480 B </td> <td> 480 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (12, 5) </td> <td> (12, 5) </td></tr>\n",
       "    <tr><th> Count </th><td> 85509 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "<td>\n",
       "<svg width=\"100\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"50\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"50\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
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       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 50.0,0.0 50.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"25.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5</text>\n",
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       "</td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>POC_FLUX_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, 5), meta=np.ndarray&gt;</div><input id='attrs-15721124-b970-4ca0-83a7-bd958cd91031' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15721124-b970-4ca0-83a7-bd958cd91031' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da70d111-47c1-42ad-9426-23d729fff2e3' class='xr-var-data-in' type='checkbox'><label for='data-da70d111-47c1-42ad-9426-23d729fff2e3' 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> Shape </th><td> (12, 5) </td> <td> (12, 5) </td></tr>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (member_id: 5, 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, 5), meta=np.ndarray>\n",
       "    POC_FLUX_100m         (month, member_id) float64 dask.array<chunksize=(12, 5), meta=np.ndarray>\n",
       "    IFRAC                 (month, member_id) float64 dask.array<chunksize=(12, 5), meta=np.ndarray>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_siz_clim2090s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 51,
   "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": 52,
   "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
}