notebooks/forcing_iron_flux.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating some iron flux forcing plots\n",
    "\n",
    "This notebook reads in the CESM1 historical run (for CMIP5),\n",
    "the ensemble of 11 CESM2 historical runs (for CMIP6),\n",
    "and the two SSP5-8.5 CESM2 historical runs (also for CMIP6).\n",
    "\n",
    "Two time series plots are generated from global annual averages of iron flux.\n",
    "One compares the CESM2 and CESM1 historical runs\n",
    "(with CESM2 showing individual ensemble members as well as the ensemble mean).\n",
    "The other also adds the two SSP5-8.5 runs and their means.\n",
    "\n",
    "Lastly, a surface plot showing the 1995-2014 [ensemble] mean from CESM2 is generated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This notebook uses several python packages\n",
    "\n",
    "The watermark package shows the version number used to help others recreate this environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intake_esm 2020.3.16.2.post4\n",
      "esmlab     2019.4.27.post55\n",
      "cmocean    2.0\n",
      "cartopy    0.17.0\n",
      "numpy      1.18.1\n",
      "cftime     1.0.3.4\n",
      "intake     0.5.4\n",
      "xarray     0.15.0\n",
      "Mike Levy 2020-05-12 \n",
      "\n",
      "compiler   : GCC 7.3.0\n",
      "system     : Linux\n",
      "release    : 3.10.0-693.21.1.el7.x86_64\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 72\n",
      "interpreter: 64bit\n",
      "host name  : casper21\n",
      "Git hash   : 1f304abea5a2b2adda3f8f68561303c74ce633ea\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "\n",
    "import cftime\n",
    "\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import cmocean\n",
    "\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "import esmlab\n",
    "\n",
    "import intake\n",
    "import intake_esm\n",
    "%load_ext watermark\n",
    "%watermark -a \"Mike Levy\" -d -iv -m -g -h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the intake_esm datastores\n",
    "\n",
    "The `intake_esm` package is used to help identify which files belong in each experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Although we do only look at historical and SSP5-8.5 in this notebook, the other SSPs are also available\n",
    "cesm2 = intake.open_esm_datastore('data/campaign-cesm2-cmip6-timeseries.json').search(experiment=['historical', 'SSP1-2.6', 'SSP2-4.5', 'SSP3-7.0', 'SSP5-8.5'], variable='IRON_FLUX')\n",
    "cesm1 = intake.open_esm_datastore('data/glade-cesm1-cmip5-timeseries.json').search(experiment='historical', variable='IRON_FLUX')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>historical</td>\n",
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       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
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       "      <td>historical</td>\n",
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       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
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       "      <th>4</th>\n",
       "      <td>historical</td>\n",
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       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>185001-201412</td>\n",
       "      <td>5</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>691</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.006</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>185001-201412</td>\n",
       "      <td>6</td>\n",
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       "      <td>721</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.007</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
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       "      <td>7</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>751</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.007</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>190001-194912</td>\n",
       "      <td>7</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>751</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.007</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>195001-199912</td>\n",
       "      <td>7</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
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       "      <th>9</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.007</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>200001-201412</td>\n",
       "      <td>7</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>751</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
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       "      <th>10</th>\n",
       "      <td>historical</td>\n",
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       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>185001-189912</td>\n",
       "      <td>8</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>781</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.008</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>190001-194912</td>\n",
       "      <td>8</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>781</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.008</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>195001-199912</td>\n",
       "      <td>8</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>781</td>\n",
       "      <td>piControl</td>\n",
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       "      <td>historical</td>\n",
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       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
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       "      <td>8</td>\n",
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       "      <th>14</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.009</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>185001-189912</td>\n",
       "      <td>9</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>811</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.009</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>190001-194912</td>\n",
       "      <td>9</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>811</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.009</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>195001-199912</td>\n",
       "      <td>9</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>811</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.009</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>200001-201412</td>\n",
       "      <td>9</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>811</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.010</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>185001-189912</td>\n",
       "      <td>10</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>841</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.010</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>190001-194912</td>\n",
       "      <td>10</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>841</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.010</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>195001-199912</td>\n",
       "      <td>10</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>841</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.010</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>200001-201412</td>\n",
       "      <td>10</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>841</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.011</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>185001-189912</td>\n",
       "      <td>11</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>871</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.011</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>190001-194912</td>\n",
       "      <td>11</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>871</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.011</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>195001-199912</td>\n",
       "      <td>11</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>871</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>historical</td>\n",
       "      <td>b.e21.BHIST.f09_g17.CMIP6-historical.011</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>200001-201412</td>\n",
       "      <td>11</td>\n",
       "      <td>/glade/campaign/collections/cmip/CMIP6/timeser...</td>\n",
       "      <td>871</td>\n",
       "      <td>piControl</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SSP1-2.6</td>\n",
       "      <td>b.e21.BSSP126cmip6.f09_g17.CMIP6-SSP1-2.6.101</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>10</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SSP1-2.6</td>\n",
       "      <td>b.e21.BSSP126cmip6.f09_g17.CMIP6-SSP1-2.6.101</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>10</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>SSP1-2.6</td>\n",
       "      <td>b.e21.BSSP126cmip6.f09_g17.CMIP6-SSP1-2.6.102</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>11</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SSP1-2.6</td>\n",
       "      <td>b.e21.BSSP126cmip6.f09_g17.CMIP6-SSP1-2.6.102</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>11</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>SSP1-2.6</td>\n",
       "      <td>b.e21.BSSP126cmip6.f09_g17.CMIP6-SSP1-2.6.103</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>4</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>SSP1-2.6</td>\n",
       "      <td>b.e21.BSSP126cmip6.f09_g17.CMIP6-SSP1-2.6.103</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>4</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SSP2-4.5</td>\n",
       "      <td>b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.101</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>10</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>SSP2-4.5</td>\n",
       "      <td>b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.101</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>10</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>SSP2-4.5</td>\n",
       "      <td>b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.102</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>11</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>SSP2-4.5</td>\n",
       "      <td>b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.102</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>11</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>SSP2-4.5</td>\n",
       "      <td>b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.103</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>4</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>SSP2-4.5</td>\n",
       "      <td>b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.103</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>4</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>SSP3-7.0</td>\n",
       "      <td>b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.101</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>10</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>SSP3-7.0</td>\n",
       "      <td>b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.101</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>10</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>SSP3-7.0</td>\n",
       "      <td>b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.102</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>11</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>SSP3-7.0</td>\n",
       "      <td>b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.102</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>11</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>SSP3-7.0</td>\n",
       "      <td>b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.103</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>4</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>SSP3-7.0</td>\n",
       "      <td>b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.103</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>4</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>SSP5-8.5</td>\n",
       "      <td>b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.101</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>10</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>SSP5-8.5</td>\n",
       "      <td>b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.101</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>10</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>SSP5-8.5</td>\n",
       "      <td>b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.102</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>11</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>SSP5-8.5</td>\n",
       "      <td>b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.102</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>11</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>SSP5-8.5</td>\n",
       "      <td>b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.103</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>201501-206412</td>\n",
       "      <td>4</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>SSP5-8.5</td>\n",
       "      <td>b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.103</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>206501-210012</td>\n",
       "      <td>4</td>\n",
       "      <td>/glade/collections/cdg/timeseries-cmip6/b.e21....</td>\n",
       "      <td>2015</td>\n",
       "      <td>historical</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    experiment                                           case component  \\\n",
       "0   historical       b.e21.BHIST.f09_g17.CMIP6-historical.001       ocn   \n",
       "1   historical       b.e21.BHIST.f09_g17.CMIP6-historical.002       ocn   \n",
       "2   historical       b.e21.BHIST.f09_g17.CMIP6-historical.003       ocn   \n",
       "3   historical       b.e21.BHIST.f09_g17.CMIP6-historical.004       ocn   \n",
       "4   historical       b.e21.BHIST.f09_g17.CMIP6-historical.005       ocn   \n",
       "5   historical       b.e21.BHIST.f09_g17.CMIP6-historical.006       ocn   \n",
       "6   historical       b.e21.BHIST.f09_g17.CMIP6-historical.007       ocn   \n",
       "7   historical       b.e21.BHIST.f09_g17.CMIP6-historical.007       ocn   \n",
       "8   historical       b.e21.BHIST.f09_g17.CMIP6-historical.007       ocn   \n",
       "9   historical       b.e21.BHIST.f09_g17.CMIP6-historical.007       ocn   \n",
       "10  historical       b.e21.BHIST.f09_g17.CMIP6-historical.008       ocn   \n",
       "11  historical       b.e21.BHIST.f09_g17.CMIP6-historical.008       ocn   \n",
       "12  historical       b.e21.BHIST.f09_g17.CMIP6-historical.008       ocn   \n",
       "13  historical       b.e21.BHIST.f09_g17.CMIP6-historical.008       ocn   \n",
       "14  historical       b.e21.BHIST.f09_g17.CMIP6-historical.009       ocn   \n",
       "15  historical       b.e21.BHIST.f09_g17.CMIP6-historical.009       ocn   \n",
       "16  historical       b.e21.BHIST.f09_g17.CMIP6-historical.009       ocn   \n",
       "17  historical       b.e21.BHIST.f09_g17.CMIP6-historical.009       ocn   \n",
       "18  historical       b.e21.BHIST.f09_g17.CMIP6-historical.010       ocn   \n",
       "19  historical       b.e21.BHIST.f09_g17.CMIP6-historical.010       ocn   \n",
       "20  historical       b.e21.BHIST.f09_g17.CMIP6-historical.010       ocn   \n",
       "21  historical       b.e21.BHIST.f09_g17.CMIP6-historical.010       ocn   \n",
       "22  historical       b.e21.BHIST.f09_g17.CMIP6-historical.011       ocn   \n",
       "23  historical       b.e21.BHIST.f09_g17.CMIP6-historical.011       ocn   \n",
       "24  historical       b.e21.BHIST.f09_g17.CMIP6-historical.011       ocn   \n",
       "25  historical       b.e21.BHIST.f09_g17.CMIP6-historical.011       ocn   \n",
       "26    SSP1-2.6  b.e21.BSSP126cmip6.f09_g17.CMIP6-SSP1-2.6.101       ocn   \n",
       "27    SSP1-2.6  b.e21.BSSP126cmip6.f09_g17.CMIP6-SSP1-2.6.101       ocn   \n",
       "28    SSP1-2.6  b.e21.BSSP126cmip6.f09_g17.CMIP6-SSP1-2.6.102       ocn   \n",
       "29    SSP1-2.6  b.e21.BSSP126cmip6.f09_g17.CMIP6-SSP1-2.6.102       ocn   \n",
       "30    SSP1-2.6  b.e21.BSSP126cmip6.f09_g17.CMIP6-SSP1-2.6.103       ocn   \n",
       "31    SSP1-2.6  b.e21.BSSP126cmip6.f09_g17.CMIP6-SSP1-2.6.103       ocn   \n",
       "32    SSP2-4.5  b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.101       ocn   \n",
       "33    SSP2-4.5  b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.101       ocn   \n",
       "34    SSP2-4.5  b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.102       ocn   \n",
       "35    SSP2-4.5  b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.102       ocn   \n",
       "36    SSP2-4.5  b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.103       ocn   \n",
       "37    SSP2-4.5  b.e21.BSSP245cmip6.f09_g17.CMIP6-SSP2-4.5.103       ocn   \n",
       "38    SSP3-7.0  b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.101       ocn   \n",
       "39    SSP3-7.0  b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.101       ocn   \n",
       "40    SSP3-7.0  b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.102       ocn   \n",
       "41    SSP3-7.0  b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.102       ocn   \n",
       "42    SSP3-7.0  b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.103       ocn   \n",
       "43    SSP3-7.0  b.e21.BSSP370cmip6.f09_g17.CMIP6-SSP3-7.0.103       ocn   \n",
       "44    SSP5-8.5  b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.101       ocn   \n",
       "45    SSP5-8.5  b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.101       ocn   \n",
       "46    SSP5-8.5  b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.102       ocn   \n",
       "47    SSP5-8.5  b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.102       ocn   \n",
       "48    SSP5-8.5  b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.103       ocn   \n",
       "49    SSP5-8.5  b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.103       ocn   \n",
       "\n",
       "   stream   variable     date_range  member_id  \\\n",
       "0   pop.h  IRON_FLUX  185001-201412          1   \n",
       "1   pop.h  IRON_FLUX  185001-201412          2   \n",
       "2   pop.h  IRON_FLUX  185001-201412          3   \n",
       "3   pop.h  IRON_FLUX  185001-201412          4   \n",
       "4   pop.h  IRON_FLUX  185001-201412          5   \n",
       "5   pop.h  IRON_FLUX  185001-201412          6   \n",
       "6   pop.h  IRON_FLUX  185001-189912          7   \n",
       "7   pop.h  IRON_FLUX  190001-194912          7   \n",
       "8   pop.h  IRON_FLUX  195001-199912          7   \n",
       "9   pop.h  IRON_FLUX  200001-201412          7   \n",
       "10  pop.h  IRON_FLUX  185001-189912          8   \n",
       "11  pop.h  IRON_FLUX  190001-194912          8   \n",
       "12  pop.h  IRON_FLUX  195001-199912          8   \n",
       "13  pop.h  IRON_FLUX  200001-201412          8   \n",
       "14  pop.h  IRON_FLUX  185001-189912          9   \n",
       "15  pop.h  IRON_FLUX  190001-194912          9   \n",
       "16  pop.h  IRON_FLUX  195001-199912          9   \n",
       "17  pop.h  IRON_FLUX  200001-201412          9   \n",
       "18  pop.h  IRON_FLUX  185001-189912         10   \n",
       "19  pop.h  IRON_FLUX  190001-194912         10   \n",
       "20  pop.h  IRON_FLUX  195001-199912         10   \n",
       "21  pop.h  IRON_FLUX  200001-201412         10   \n",
       "22  pop.h  IRON_FLUX  185001-189912         11   \n",
       "23  pop.h  IRON_FLUX  190001-194912         11   \n",
       "24  pop.h  IRON_FLUX  195001-199912         11   \n",
       "25  pop.h  IRON_FLUX  200001-201412         11   \n",
       "26  pop.h  IRON_FLUX  201501-206412         10   \n",
       "27  pop.h  IRON_FLUX  206501-210012         10   \n",
       "28  pop.h  IRON_FLUX  201501-206412         11   \n",
       "29  pop.h  IRON_FLUX  206501-210012         11   \n",
       "30  pop.h  IRON_FLUX  201501-206412          4   \n",
       "31  pop.h  IRON_FLUX  206501-210012          4   \n",
       "32  pop.h  IRON_FLUX  201501-206412         10   \n",
       "33  pop.h  IRON_FLUX  206501-210012         10   \n",
       "34  pop.h  IRON_FLUX  201501-206412         11   \n",
       "35  pop.h  IRON_FLUX  206501-210012         11   \n",
       "36  pop.h  IRON_FLUX  201501-206412          4   \n",
       "37  pop.h  IRON_FLUX  206501-210012          4   \n",
       "38  pop.h  IRON_FLUX  201501-206412         10   \n",
       "39  pop.h  IRON_FLUX  206501-210012         10   \n",
       "40  pop.h  IRON_FLUX  201501-206412         11   \n",
       "41  pop.h  IRON_FLUX  206501-210012         11   \n",
       "42  pop.h  IRON_FLUX  201501-206412          4   \n",
       "43  pop.h  IRON_FLUX  206501-210012          4   \n",
       "44  pop.h  IRON_FLUX  201501-206412         10   \n",
       "45  pop.h  IRON_FLUX  206501-210012         10   \n",
       "46  pop.h  IRON_FLUX  201501-206412         11   \n",
       "47  pop.h  IRON_FLUX  206501-210012         11   \n",
       "48  pop.h  IRON_FLUX  201501-206412          4   \n",
       "49  pop.h  IRON_FLUX  206501-210012          4   \n",
       "\n",
       "                                                 path  ctrl_branch_year  \\\n",
       "0   /glade/campaign/collections/cmip/CMIP6/timeser...               601   \n",
       "1   /glade/campaign/collections/cmip/CMIP6/timeser...               631   \n",
       "2   /glade/campaign/collections/cmip/CMIP6/timeser...               661   \n",
       "3   /glade/campaign/collections/cmip/CMIP6/timeser...               501   \n",
       "4   /glade/campaign/collections/cmip/CMIP6/timeser...               691   \n",
       "5   /glade/campaign/collections/cmip/CMIP6/timeser...               721   \n",
       "6   /glade/campaign/collections/cmip/CMIP6/timeser...               751   \n",
       "7   /glade/campaign/collections/cmip/CMIP6/timeser...               751   \n",
       "8   /glade/campaign/collections/cmip/CMIP6/timeser...               751   \n",
       "9   /glade/campaign/collections/cmip/CMIP6/timeser...               751   \n",
       "10  /glade/campaign/collections/cmip/CMIP6/timeser...               781   \n",
       "11  /glade/campaign/collections/cmip/CMIP6/timeser...               781   \n",
       "12  /glade/campaign/collections/cmip/CMIP6/timeser...               781   \n",
       "13  /glade/campaign/collections/cmip/CMIP6/timeser...               781   \n",
       "14  /glade/campaign/collections/cmip/CMIP6/timeser...               811   \n",
       "15  /glade/campaign/collections/cmip/CMIP6/timeser...               811   \n",
       "16  /glade/campaign/collections/cmip/CMIP6/timeser...               811   \n",
       "17  /glade/campaign/collections/cmip/CMIP6/timeser...               811   \n",
       "18  /glade/campaign/collections/cmip/CMIP6/timeser...               841   \n",
       "19  /glade/campaign/collections/cmip/CMIP6/timeser...               841   \n",
       "20  /glade/campaign/collections/cmip/CMIP6/timeser...               841   \n",
       "21  /glade/campaign/collections/cmip/CMIP6/timeser...               841   \n",
       "22  /glade/campaign/collections/cmip/CMIP6/timeser...               871   \n",
       "23  /glade/campaign/collections/cmip/CMIP6/timeser...               871   \n",
       "24  /glade/campaign/collections/cmip/CMIP6/timeser...               871   \n",
       "25  /glade/campaign/collections/cmip/CMIP6/timeser...               871   \n",
       "26  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "27  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "28  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "29  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "30  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "31  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "32  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "33  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "34  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "35  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "36  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "37  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "38  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "39  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "40  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "41  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "42  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "43  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "44  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "45  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "46  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "47  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "48  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "49  /glade/collections/cdg/timeseries-cmip6/b.e21....              2015   \n",
       "\n",
       "   ctrl_experiment  ctrl_member_id  \n",
       "0        piControl               1  \n",
       "1        piControl               1  \n",
       "2        piControl               1  \n",
       "3        piControl               1  \n",
       "4        piControl               1  \n",
       "5        piControl               1  \n",
       "6        piControl               1  \n",
       "7        piControl               1  \n",
       "8        piControl               1  \n",
       "9        piControl               1  \n",
       "10       piControl               1  \n",
       "11       piControl               1  \n",
       "12       piControl               1  \n",
       "13       piControl               1  \n",
       "14       piControl               1  \n",
       "15       piControl               1  \n",
       "16       piControl               1  \n",
       "17       piControl               1  \n",
       "18       piControl               1  \n",
       "19       piControl               1  \n",
       "20       piControl               1  \n",
       "21       piControl               1  \n",
       "22       piControl               1  \n",
       "23       piControl               1  \n",
       "24       piControl               1  \n",
       "25       piControl               1  \n",
       "26      historical              10  \n",
       "27      historical              10  \n",
       "28      historical              11  \n",
       "29      historical              11  \n",
       "30      historical               4  \n",
       "31      historical               4  \n",
       "32      historical              10  \n",
       "33      historical              10  \n",
       "34      historical              11  \n",
       "35      historical              11  \n",
       "36      historical               4  \n",
       "37      historical               4  \n",
       "38      historical              10  \n",
       "39      historical              10  \n",
       "40      historical              11  \n",
       "41      historical              11  \n",
       "42      historical               4  \n",
       "43      historical               4  \n",
       "44      historical              10  \n",
       "45      historical              10  \n",
       "46      historical              11  \n",
       "47      historical              11  \n",
       "48      historical               4  \n",
       "49      historical               4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cesm2.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment</th>\n",
       "      <th>case</th>\n",
       "      <th>component</th>\n",
       "      <th>stream</th>\n",
       "      <th>variable</th>\n",
       "      <th>date_range</th>\n",
       "      <th>member_id</th>\n",
       "      <th>path</th>\n",
       "      <th>ctrl_branch_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>historical</td>\n",
       "      <td>b40.20th.1deg.bdrd.001</td>\n",
       "      <td>ocn</td>\n",
       "      <td>pop.h</td>\n",
       "      <td>IRON_FLUX</td>\n",
       "      <td>185001-200512</td>\n",
       "      <td>0</td>\n",
       "      <td>/glade/p/cgd/oce/projects/cesm2-marbl/intake-e...</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   experiment                    case component stream   variable  \\\n",
       "0  historical  b40.20th.1deg.bdrd.001       ocn  pop.h  IRON_FLUX   \n",
       "\n",
       "      date_range  member_id  \\\n",
       "0  185001-200512          0   \n",
       "\n",
       "                                                path  ctrl_branch_year  \n",
       "0  /glade/p/cgd/oce/projects/cesm2-marbl/intake-e...               151  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cesm1.df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Produce xarray datasets\n",
    "\n",
    "For this notebook, we will drop all the data except `IRON_FLUX`, `TAREA`, and coordinate data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'component.experiment.stream'\n",
      "                \n",
      "--> There is/are 5 group(s)\n",
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'component.experiment.stream'\n",
      "                \n",
      "--> There is/are 1 group(s)\n",
      "\n",
      "cesm2_hist\n",
      "----\n",
      "<xarray.Dataset>\n",
      "Dimensions:     (d2: 2, member_id: 11, nlat: 384, nlon: 320, time: 1980)\n",
      "Coordinates:\n",
      "    TLONG       (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "    TLAT        (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "  * time        (time) object 1850-02-01 00:00:00 ... 2015-01-01 00:00:00\n",
      "  * member_id   (member_id) int64 1 2 3 4 5 6 7 8 9 10 11\n",
      "Dimensions without coordinates: d2, nlat, nlon\n",
      "Data variables:\n",
      "    time_bound  (time, d2) object dask.array<chunksize=(48, 2), meta=np.ndarray>\n",
      "    TAREA       (time, nlat, nlon) float64 dask.array<chunksize=(1980, 384, 320), meta=np.ndarray>\n",
      "    IRON_FLUX   (member_id, time, nlat, nlon) float32 dask.array<chunksize=(1, 48, 384, 320), meta=np.ndarray>\n",
      "Attributes:\n",
      "    calendar:            All years have exactly  365 days.\n",
      "    intake_esm_varname:  IRON_FLUX\n",
      "    cell_methods:        cell_methods = time: mean ==> the variable values ar...\n",
      "    revision:            $Id: tavg.F90 89644 2018-08-04 14:26:01Z klindsay $\n",
      "    time_period_freq:    month_1\n",
      "    Conventions:         CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-...\n",
      "    model_doi_url:       https://doi.org/10.5065/D67H1H0V\n",
      "    source:              CCSM POP2, the CCSM Ocean Component\n",
      "    contents:            Diagnostic and Prognostic Variables\n",
      "    history:             none\n",
      "    title:               b.e21.BHIST.f09_g17.CMIP6-historical.011\n",
      "\n",
      "cesm2_SSP\n",
      "----\n",
      "<xarray.Dataset>\n",
      "Dimensions:     (d2: 2, member_id: 3, nlat: 384, nlon: 320, time: 1032)\n",
      "Coordinates:\n",
      "    TLONG       (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "    TLAT        (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "  * time        (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00\n",
      "  * member_id   (member_id) int64 4 10 11\n",
      "Dimensions without coordinates: d2, nlat, nlon\n",
      "Data variables:\n",
      "    time_bound  (time, d2) object dask.array<chunksize=(48, 2), meta=np.ndarray>\n",
      "    TAREA       (time, nlat, nlon) float64 dask.array<chunksize=(600, 384, 320), meta=np.ndarray>\n",
      "    IRON_FLUX   (member_id, time, nlat, nlon) float32 dask.array<chunksize=(1, 48, 384, 320), meta=np.ndarray>\n",
      "Attributes:\n",
      "    calendar:            All years have exactly  365 days.\n",
      "    intake_esm_varname:  IRON_FLUX\n",
      "    cell_methods:        cell_methods = time: mean ==> the variable values ar...\n",
      "    revision:            $Id$\n",
      "    time_period_freq:    month_1\n",
      "    Conventions:         CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-...\n",
      "    model_doi_url:       https://doi.org/10.5065/D67H1H0V\n",
      "    source:              CCSM POP2, the CCSM Ocean Component\n",
      "    contents:            Diagnostic and Prognostic Variables\n",
      "    history:             none\n",
      "    title:               b.e21.BSSP585cmip6.f09_g17.CMIP6-SSP5-8.5.102\n",
      "\n",
      "cesm1_hist\n",
      "----\n",
      "<xarray.Dataset>\n",
      "Dimensions:     (d2: 2, member_id: 1, nlat: 384, nlon: 320, time: 1872)\n",
      "Coordinates:\n",
      "    TLONG       (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "    TLAT        (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "  * time        (time) object 1850-02-01 00:00:00 ... 2006-01-01 00:00:00\n",
      "  * member_id   (member_id) int64 0\n",
      "Dimensions without coordinates: d2, nlat, nlon\n",
      "Data variables:\n",
      "    time_bound  (time, d2) object dask.array<chunksize=(48, 2), meta=np.ndarray>\n",
      "    TAREA       (time, nlat, nlon) float64 dask.array<chunksize=(1872, 384, 320), meta=np.ndarray>\n",
      "    IRON_FLUX   (member_id, time, nlat, nlon) float32 dask.array<chunksize=(1, 48, 384, 320), meta=np.ndarray>\n",
      "Attributes:\n",
      "    title:                     b40.20th.1deg.bdrd.001\n",
      "    history:                   Sat Dec 17 19:11:26 2011: /usr/local/bin/ncrca...\n",
      "    conventions:               CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netc...\n",
      "    contents:                  Diagnostic and Prognostic Variables\n",
      "    source:                    CCSM POP2, the CCSM Ocean Component\n",
      "    revision:                  $Id: tavg.F90 23423 2010-05-28 16:23:16Z njn01 $\n",
      "    calendar:                  All years have exactly  365 days.\n",
      "    start_time:                This dataset was created on 2010-05-31 at 15:1...\n",
      "    cell_methods:              cell_methods = time: mean ==> the variable val...\n",
      "    nsteps_total:              750\n",
      "    tavg_sum:                  2592000.0\n",
      "    tavg_sum_qflux:            2592000.0\n",
      "    NCO:                       4.0.5\n",
      "    nco_openmp_thread_number:  1\n",
      "    intake_esm_varname:        IRON_FLUX\n"
     ]
    }
   ],
   "source": [
    "dq = dict()\n",
    "datasets = dict()\n",
    "\n",
    "# CESM2 Datasets\n",
    "dq['cesm2'] = cesm2.to_dataset_dict(cdf_kwargs={'chunks':{'time': 48}})\n",
    "datasets['cesm2_hist'] = dq['cesm2']['ocn.historical.pop.h']\n",
    "datasets['cesm2_SSP'] = dq['cesm2']['ocn.SSP5-8.5.pop.h']\n",
    "\n",
    "# CESM1 Datasets\n",
    "dq['cesm1'] = cesm1.to_dataset_dict(cdf_kwargs={'chunks':{'time': 48}})\n",
    "datasets['cesm1_hist'] = dq['cesm1']['ocn.historical.pop.h']\n",
    "\n",
    "keep_vars = ['TAREA', 'TLONG', 'TLAT', 'IRON_FLUX', 'time', 'time_bound', 'member_id']\n",
    "for exp in datasets:\n",
    "    datasets[exp] = datasets[exp].drop([v for v in datasets[exp].variables if v not in keep_vars])\n",
    "    print(f'\\n{exp}\\n----\\n{datasets[exp]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute annual means\n",
    "\n",
    "For now this uses `esmlab`, but it may switch to `time_axes` in the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "cesm2_hist\n",
      "----\n",
      "<xarray.Dataset>\n",
      "Dimensions:     (d2: 2, member_id: 11, nlat: 384, nlon: 320, time: 165)\n",
      "Coordinates:\n",
      "  * member_id   (member_id) int64 1 2 3 4 5 6 7 8 9 10 11\n",
      "  * time        (time) object 1850-07-01 17:05:00 ... 2014-07-01 17:00:00\n",
      "    TLONG       (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "    TLAT        (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "Dimensions without coordinates: d2, nlat, nlon\n",
      "Data variables:\n",
      "    time_bound  (d2, time) object 1850-01-01 02:00:00 ... 2015-01-01 00:00:00\n",
      "    TAREA       (time, nlat, nlon) float64 dask.array<chunksize=(1, 384, 320), meta=np.ndarray>\n",
      "    IRON_FLUX   (member_id, time, nlat, nlon) float64 dask.array<chunksize=(1, 1, 384, 320), meta=np.ndarray>\n",
      "Attributes:\n",
      "    history:  \\n2020-05-12 13:27:41.148780 esmlab.resample(<DATASET>, freq=\"a...\n",
      "\n",
      "cesm2_SSP\n",
      "----\n",
      "<xarray.Dataset>\n",
      "Dimensions:     (d2: 2, member_id: 3, nlat: 384, nlon: 320, time: 86)\n",
      "Coordinates:\n",
      "  * member_id   (member_id) int64 4 10 11\n",
      "  * time        (time) object 2015-07-01 17:05:00 ... 2100-07-01 17:00:00\n",
      "    TLONG       (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "    TLAT        (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "Dimensions without coordinates: d2, nlat, nlon\n",
      "Data variables:\n",
      "    time_bound  (d2, time) object 2015-01-01 02:00:00 ... 2101-01-01 00:00:00\n",
      "    TAREA       (time, nlat, nlon) float64 dask.array<chunksize=(1, 384, 320), meta=np.ndarray>\n",
      "    IRON_FLUX   (member_id, time, nlat, nlon) float64 dask.array<chunksize=(1, 1, 384, 320), meta=np.ndarray>\n",
      "Attributes:\n",
      "    history:  \\n2020-05-12 13:27:43.904137 esmlab.resample(<DATASET>, freq=\"a...\n",
      "\n",
      "cesm1_hist\n",
      "----\n",
      "<xarray.Dataset>\n",
      "Dimensions:     (d2: 2, member_id: 1, nlat: 384, nlon: 320, time: 156)\n",
      "Coordinates:\n",
      "  * member_id   (member_id) int64 0\n",
      "  * time        (time) object 1850-07-01 18:02:30 ... 2005-07-01 17:00:00\n",
      "    TLONG       (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "    TLAT        (nlat, nlon) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "Dimensions without coordinates: d2, nlat, nlon\n",
      "Data variables:\n",
      "    time_bound  (d2, time) object 1850-01-02 01:00:00 ... 2006-01-01 00:00:00\n",
      "    TAREA       (time, nlat, nlon) float64 dask.array<chunksize=(1, 384, 320), meta=np.ndarray>\n",
      "    IRON_FLUX   (member_id, time, nlat, nlon) float64 dask.array<chunksize=(1, 1, 384, 320), meta=np.ndarray>\n",
      "Attributes:\n",
      "    history:  \\n2020-05-12 13:27:49.378800 esmlab.resample(<DATASET>, freq=\"a...\n",
      "CPU times: user 11.4 s, sys: 109 ms, total: 11.5 s\n",
      "Wall time: 13.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "datasets_ann = dict()\n",
    "for exp in datasets:\n",
    "    datasets_ann[exp] = esmlab.resample(datasets[exp], freq='ann')\n",
    "    print(f'\\n{exp}\\n----\\n{datasets_ann[exp]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute global means of iron flux\n",
    "\n",
    "Using the previously computed annual means, compute global means (weighted by area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/work/mlevy/miniconda3/envs/cesm2-marbl/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "cesm2_hist\n",
      "----\n",
      "<xarray.DataArray (member_id: 11, time: 165)>\n",
      "array([[9.2300802 , 8.26259593, 8.22607918, ..., 8.87160024, 8.82485228,\n",
      "        8.11518775],\n",
      "       [8.32685797, 7.26389874, 8.52567667, ..., 8.69017733, 9.25795746,\n",
      "        8.37191988],\n",
      "       [8.1106605 , 8.38003757, 8.23106228, ..., 7.89537625, 8.13547233,\n",
      "        8.61778304],\n",
      "       ...,\n",
      "       [7.61386383, 7.59802859, 8.1020224 , ..., 8.35342317, 8.31701001,\n",
      "        7.52157364],\n",
      "       [8.81659523, 7.76057121, 8.21183624, ..., 8.43592903, 7.45949937,\n",
      "        9.3636691 ],\n",
      "       [8.24144115, 9.43141237, 9.26289287, ..., 8.58407975, 7.95796275,\n",
      "        8.55384729]])\n",
      "Coordinates:\n",
      "  * member_id  (member_id) int64 1 2 3 4 5 6 7 8 9 10 11\n",
      "  * time       (time) object 1850-07-01 17:05:00 ... 2014-07-01 17:00:00\n",
      "Attributes:\n",
      "    units:    Gmol Fe yr$^{-1}$\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/work/mlevy/miniconda3/envs/cesm2-marbl/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "cesm2_SSP\n",
      "----\n",
      "<xarray.DataArray (member_id: 3, time: 86)>\n",
      "array([[10.22805508,  8.07126159,  8.42423748,  9.53026894,  8.96596682,\n",
      "         7.87456704,  8.05911961,  8.39973985,  8.2185051 ,  8.35692582,\n",
      "         7.9219196 ,  8.09448407,  8.21922391,  9.30006426,  8.05286319,\n",
      "         9.62420621,  7.46010944,  9.09675503,  7.37964331,  7.96551272,\n",
      "         8.11395265,  6.82960765,  7.8279804 ,  8.68639238,  8.36813152,\n",
      "         8.43904025,  9.35449393,  8.5621786 ,  8.70406142,  7.69985527,\n",
      "         8.07615765,  8.33324254,  8.55744512,  8.1119493 ,  8.26540959,\n",
      "         9.58081788,  8.29033156,  8.7090888 ,  8.54071348,  8.30150652,\n",
      "         7.62698479,  7.86682735,  8.07491285,  8.70214786,  8.15530609,\n",
      "         8.02146699,  9.50680127,  8.54499751,  9.69574161,  7.9400122 ,\n",
      "         8.9360078 ,  8.20633117,  8.54389429,  9.82211979,  7.42882285,\n",
      "         7.61934511,  8.20460957,  9.07413856,  8.39658346,  8.42063676,\n",
      "         8.49186805,  9.79010818,  8.36446876,  8.21392783,  8.11889465,\n",
      "         8.04891672,  9.1053068 ,  8.64560507,  8.95344122,  7.39846687,\n",
      "         8.12876375,  7.5027715 ,  7.66678657,  7.92380261,  7.77698694,\n",
      "         8.68447357,  9.37835993,  9.20374368,  7.55406972,  8.82387277,\n",
      "         7.26170902,  8.21472267,  8.72167128,  7.97100102, 10.45812759,\n",
      "         9.38192417],\n",
      "       [ 8.36863893,  8.58807523,  8.21369682,  8.76038786,  7.40252477,\n",
      "         9.00636411,  9.74172032,  8.45273045,  7.71742598,  8.93276679,\n",
      "         7.72392889,  9.00959565,  8.93904889,  8.21512114,  7.8743599 ,\n",
      "         7.89675896,  7.8306714 ,  9.02724416,  8.31184073,  9.56281354,\n",
      "         8.00936725,  8.24915854,  8.18819084,  8.02410417,  7.5720012 ,\n",
      "         9.3379229 ,  8.47454996,  7.56024216,  8.59183876,  8.183745  ,\n",
      "         8.86787665,  7.93073749,  7.72618634,  8.13466068,  9.26076485,\n",
      "         8.93309898,  7.84882681,  9.60575195,  9.42714499,  8.62553436,\n",
      "         9.51865401,  8.05317427,  8.34542829,  7.91753886,  8.20537895,\n",
      "         7.39750401,  7.99648788,  8.16018168,  8.6428573 ,  7.92851826,\n",
      "         9.55376868,  8.98727807,  8.54383856,  8.60239026,  8.14189171,\n",
      "         8.21085059,  7.86561573,  9.02708737,  9.71095218,  9.13087182,\n",
      "         8.19408256, 10.19882877,  9.77740443,  8.2054613 ,  8.48933723,\n",
      "         8.64833317,  8.01182447,  9.97901826,  8.33711908,  8.42918423,\n",
      "        10.15567819,  9.33020127,  8.31879518, 10.44608561,  9.69252457,\n",
      "         8.12903309,  8.95898524,  7.64638864,  8.06261485,  7.77198097,\n",
      "         8.76629818,  9.09131323,  9.18278438,  8.82093119,  8.43461979,\n",
      "         9.14603393],\n",
      "       [ 8.07288181,  9.37816145,  8.59710145,  7.77270193,  8.45354312,\n",
      "         7.86585664,  8.78299085,  7.08590122,  8.36031326, 10.44870156,\n",
      "         8.61861192,  8.57451863,  8.39925184,  8.58412576,  9.8827684 ,\n",
      "         7.36273621,  7.98860503,  9.20333197,  8.90971585,  9.73153244,\n",
      "         8.59664041, 10.01350108,  7.75183172,  8.04703758,  9.1032045 ,\n",
      "         8.60162122,  8.12094893,  8.8575927 ,  8.93547313,  8.49775182,\n",
      "         7.77594809,  7.76799487,  9.6339022 ,  9.68416061,  7.46392464,\n",
      "         7.62485755,  8.90024468,  7.95167149,  7.77696726,  8.56327805,\n",
      "         9.62856837,  9.98272633,  7.91402309,  8.07426781,  9.0750147 ,\n",
      "         9.13789574,  8.46834954,  8.11720353,  9.39536639,  9.13653476,\n",
      "         8.89430924,  7.96328707,  9.13590026,  8.43182606,  8.86288487,\n",
      "         9.41290356,  8.82167467,  9.91634635,  8.04964329,  7.68018512,\n",
      "        10.07512167,  8.52554342,  9.01945024,  8.97580907,  8.02765283,\n",
      "         8.29807611,  8.95567147,  8.3193287 ,  8.33350877,  8.0338137 ,\n",
      "         8.14601258,  8.93980399,  9.30996481,  8.87070389,  8.87057347,\n",
      "         8.95596611,  8.71033402,  9.82966597,  8.95676412,  9.2869962 ,\n",
      "         7.94933221,  8.64042484,  8.70362097,  8.94554511,  8.73149459,\n",
      "         9.34640141]])\n",
      "Coordinates:\n",
      "  * member_id  (member_id) int64 4 10 11\n",
      "  * time       (time) object 2015-07-01 17:05:00 ... 2100-07-01 17:00:00\n",
      "Attributes:\n",
      "    units:    Gmol Fe yr$^{-1}$\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/work/mlevy/miniconda3/envs/cesm2-marbl/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "cesm1_hist\n",
      "----\n",
      "<xarray.DataArray (member_id: 1, time: 156)>\n",
      "array([[6.61582808, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312, 6.61378312, 6.61378312, 6.61378312, 6.61378312,\n",
      "        6.61378312]])\n",
      "Coordinates:\n",
      "  * member_id  (member_id) int64 0\n",
      "  * time       (time) object 1850-07-01 18:02:30 ... 2005-07-01 17:00:00\n",
      "Attributes:\n",
      "    units:    Gmol Fe yr$^{-1}$\n",
      "CPU times: user 2min 27s, sys: 2min 10s, total: 4min 37s\n",
      "Wall time: 6min 37s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "iron_flux_glb = dict()\n",
    "for exp in datasets_ann:\n",
    "    iron_flux_glb[exp] = esmlab.weighted_sum(datasets_ann[exp].IRON_FLUX, dim=['nlat', 'nlon'], weights=datasets_ann[exp].isel(time=0).TAREA)\n",
    "    iron_flux_glb[exp] = iron_flux_glb[exp] * 1.0e-16 * 365.0 * 86400.0 # mmolFe/m^2/s to GmolFe/y\n",
    "    iron_flux_glb[exp].attrs['units'] = 'Gmol Fe yr$^{-1}$'\n",
    "    iron_flux_glb[exp] = iron_flux_glb[exp].compute()\n",
    "    print(f'\\n{exp}\\n----\\n{iron_flux_glb[exp]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Series plots\n",
    "\n",
    "Plot just the historical period and then include the future as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_name = f'timeseries.global.historical.IRON_FLUX.pdf'\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "for i in iron_flux_glb['cesm2_hist'].member_id.values:\n",
    "    ax.plot(iron_flux_glb['cesm2_hist'].time, iron_flux_glb['cesm2_hist'].sel(member_id=i), linewidth=0.5)\n",
    "\n",
    "ax.plot(iron_flux_glb['cesm2_hist'].time, iron_flux_glb['cesm2_hist'].mean('member_id'), color='k', linewidth=1.5)    \n",
    "\n",
    "ax.plot(iron_flux_glb['cesm1_hist'].time, iron_flux_glb['cesm1_hist'].isel(member_id=0), linestyle='--', color='k', linewidth=1.5)    \n",
    "\n",
    "ax.set_ylabel('Iron deposition [Gmol Fe yr$^{-1}$]');\n",
    "\n",
    "ax.text(cftime.DatetimeNoLeap(1990, 1, 1), iron_flux_glb['cesm2_hist'].sel(time='1990').max('member_id')*1.05, 'CESM2',color='k');\n",
    "\n",
    "ax.text(cftime.DatetimeNoLeap(1965, 1, 1), iron_flux_glb['cesm1_hist'].sel(time='1960')*1.02, 'CESM1',color='k');\n",
    "\n",
    "#plt.savefig(plot_name,dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray (member_id: 11, time: 251)>\n",
      "array([[9.2300802 , 8.26259593, 8.22607918, ...,        nan,        nan,\n",
      "               nan],\n",
      "       [8.32685797, 7.26389874, 8.52567667, ...,        nan,        nan,\n",
      "               nan],\n",
      "       [8.1106605 , 8.38003757, 8.23106228, ...,        nan,        nan,\n",
      "               nan],\n",
      "       ...,\n",
      "       [7.61386383, 7.59802859, 8.1020224 , ...,        nan,        nan,\n",
      "               nan],\n",
      "       [8.81659523, 7.76057121, 8.21183624, ..., 8.82093119, 8.43461979,\n",
      "        9.14603393],\n",
      "       [8.24144115, 9.43141237, 9.26289287, ..., 8.94554511, 8.73149459,\n",
      "        9.34640141]])\n",
      "Coordinates:\n",
      "  * member_id  (member_id) int64 1 2 3 4 5 6 7 8 9 10 11\n",
      "  * time       (time) object 1850-07-01 17:05:00 ... 2100-07-01 17:00:00\n",
      "Attributes:\n",
      "    units:    Gmol Fe yr$^{-1}$\n"
     ]
    }
   ],
   "source": [
    "iron_flux_glb_combine = xr.concat([iron_flux_glb['cesm2_hist'], iron_flux_glb['cesm2_SSP']], dim='time', join='outer')\n",
    "print(iron_flux_glb_combine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_name = f'timeseries.global.historical-SSP585.IRON_FLUX.pdf'\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "for i in iron_flux_glb_combine.member_id.values:\n",
    "    ax.plot(iron_flux_glb_combine.time, iron_flux_glb_combine.sel(member_id=i), linewidth=0.5)\n",
    "\n",
    "ax.plot(iron_flux_glb_combine.time, iron_flux_glb_combine.mean('member_id'), color='k', linewidth=1.5)    \n",
    "\n",
    "ax.plot(iron_flux_glb['cesm1_hist'].time, iron_flux_glb['cesm1_hist'].isel(member_id=0), linestyle='--', color='k', linewidth=1.5)    \n",
    "\n",
    "ax.set_ylabel('Iron deposition [Gmol Fe yr$^{-1}$]');\n",
    "\n",
    "ax.text(cftime.DatetimeNoLeap(1990, 1, 1), iron_flux_glb['cesm2_hist'].sel(time='1990').max('member_id')*1.075, 'CESM2\\n(historical)',color='k');\n",
    "ax.text(cftime.DatetimeNoLeap(2067, 1, 1), iron_flux_glb_combine.sel(time='2067').max('member_id')*1.04, 'CESM2\\n(SSP8.5)',color='k');\n",
    "\n",
    "ax.text(cftime.DatetimeNoLeap(1965, 1, 1), iron_flux_glb['cesm1_hist'].sel(time='1960')*1.02, 'CESM1',color='k');\n",
    "\n",
    "#plt.savefig(plot_name,dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spatial Plot\n",
    "\n",
    "The ensemble mean averaged between 1995 and 2014 from the CESM2 historical run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/work/mlevy/miniconda3/envs/cesm2-marbl/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min value: 1.937666894447467e-08\n",
      "Max value: 0.0010497160800971874\n",
      "CPU times: user 15.3 s, sys: 3.52 s, total: 18.8 s\n",
      "Wall time: 20.1 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.DataArray &#x27;IRON_FLUX&#x27; (nlat: 384, nlon: 320)&gt;\n",
       "array([[           nan,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [4.01281948e-08, 3.76392364e-08, 3.66805627e-08, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       ...,\n",
       "       [           nan,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan]])\n",
       "Coordinates:\n",
       "    TLONG    (nlat, nlon) float64 320.6 321.7 322.8 323.9 ... 318.9 319.4 319.8\n",
       "    TLAT     (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 72.2 72.19 72.19\n",
       "Dimensions without coordinates: nlat, nlon</pre>"
      ],
      "text/plain": [
       "<xarray.DataArray 'IRON_FLUX' (nlat: 384, nlon: 320)>\n",
       "array([[           nan,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [4.01281948e-08, 3.76392364e-08, 3.66805627e-08, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       ...,\n",
       "       [           nan,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan],\n",
       "       [           nan,            nan,            nan, ...,\n",
       "                   nan,            nan,            nan]])\n",
       "Coordinates:\n",
       "    TLONG    (nlat, nlon) float64 320.6 321.7 322.8 323.9 ... 318.9 319.4 319.8\n",
       "    TLAT     (nlat, nlon) float64 -79.22 -79.22 -79.22 ... 72.2 72.19 72.19\n",
       "Dimensions without coordinates: nlat, nlon"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "mmolm2s_to_molm2yr = 1e-3 * 86400. * 365.\n",
    "iron_flux_map = datasets_ann['cesm2_hist'].IRON_FLUX.sel(time=slice('1995', '2014')).mean(['time', 'member_id'])\n",
    "iron_flux_map = iron_flux_map.compute() * mmolm2s_to_molm2yr\n",
    "\n",
    "print(f'Min value: {iron_flux_map.min().values}')\n",
    "print(f'Max value: {iron_flux_map.max().values}')\n",
    "iron_flux_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/work/mlevy/miniconda3/envs/cesm2-marbl/lib/python3.7/site-packages/matplotlib/colors.py:1171: RuntimeWarning: invalid value encountered in less_equal\n",
      "  mask |= resdat <= 0\n"
     ]
    },
    {
     "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": [
    "fig = plt.figure()\n",
    "ax = plt.subplot(1, 1, 1, projection=ccrs.Robinson(central_longitude=305.0))\n",
    "\n",
    "\n",
    "pc = ax.pcolormesh(iron_flux_map.TLONG, iron_flux_map.TLAT, iron_flux_map,\n",
    "                   norm=colors.LogNorm(vmin=1e-6, vmax=1e-3),\n",
    "                   cmap=cmocean.cm.matter,\n",
    "                   transform=ccrs.PlateCarree())\n",
    "\n",
    "# ax.add_feature(cartopy.feature.NaturalEarthFeature('physical','land','110m',\n",
    "#                                                    edgecolor='face',\n",
    "#                                                    facecolor='lightgray'))\n",
    "ax.set_global() \n",
    "ax.coastlines(linewidth=0.5)\n",
    "cb = plt.colorbar(pc, shrink=0.6)\n",
    "cb.ax.set_title('mol m$^{-2}$ yr$^{-1}$');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}