{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transient CO<sub>2</sub>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import product\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.colors as colors\n",
    "import cmocean\n",
    "\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "os.environ['CESMDATAROOT'] = '/glade/scratch/mclong/inputdata'\n",
    "import pop_tools\n",
    "\n",
    "import utils\n",
    "import data_collections\n",
    "import plot\n",
    "\n",
    "from ncar_jobqueue import NCARCluster\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/glade/u/home/mgrover/miniconda3/envs/cesm2-marbl/lib/python3.7/site-packages/distributed/node.py:164: UserWarning: Port 8787 is already in use.\n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the HTTP server on port 40653 instead\n",
      "  expected, actual\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://10.12.206.57:44211</li>\n",
       "  <li><b>Dashboard: </b><a href='https://jupyterhub.hpc.ucar.edu/stable/user/mgrover/proxy/40007/status' target='_blank'>https://jupyterhub.hpc.ucar.edu/stable/user/mgrover/proxy/40007/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>24</li>\n",
       "  <li><b>Cores: </b>24</li>\n",
       "  <li><b>Memory: </b>558.72 GiB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://10.12.206.57:44211' processes=24 threads=24, memory=558.72 GiB>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster = NCARCluster(memory='10 GB')\n",
    "cluster.scale(10) #adapt(minimum_jobs=0, maximum_jobs=24)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments = [\n",
    "    'historical',\n",
    "    'SSP1-2.6',\n",
    "    'SSP2-4.5',\n",
    "    'SSP3-7.0',\n",
    "    'SSP5-8.5',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{ 'esm_collection': 'data/campaign-cesm2-cmip6-timeseries.json',\n",
       "  'name': 'global_mean_timeseries_ann',\n",
       "  'operator_kwargs': [ { },\n",
       "                       { 'include_ms': False,\n",
       "                         'normalize': False},\n",
       "                       { }],\n",
       "  'operators': [ 'center_time',\n",
       "                 'global_mean',\n",
       "                 'resample_ann'],\n",
       "  'preprocess': None,\n",
       "  'query': { 'experiment': [ 'historical',\n",
       "                             'SSP1-2.6',\n",
       "                             'SSP2-4.5',\n",
       "                             'SSP3-7.0',\n",
       "                             'SSP5-8.5'],\n",
       "             'stream': 'pop.h'}}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = dict(\n",
    "    experiment=experiments,\n",
    "    stream='pop.h',\n",
    ")\n",
    "glb_int = data_collections.global_integral_timeseries_ann(query)\n",
    "glb_avg = data_collections.global_mean_timeseries_ann(query)\n",
    "glb_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ocn.historical.pop.h': <xarray.Dataset>\n",
       " Dimensions:                 (lat_aux_grid: 395, member_id: 11, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, time: 165, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)\n",
       " Coordinates: (12/13)\n",
       "   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04\n",
       "     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8\n",
       "     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19\n",
       "   * member_id               (member_id) int64 1 2 3 4 5 6 7 8 9 10 11\n",
       "   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05\n",
       "   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05\n",
       "     ...                      ...\n",
       "   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05\n",
       "   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05\n",
       "   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05\n",
       "   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0\n",
       "     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0\n",
       "   * time                    (time) int64 1850 1851 1852 1853 ... 2012 2013 2014\n",
       " Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg\n",
       " Data variables: (12/56)\n",
       "     TAREA                   (nlat, nlon) float64 1.125e+13 ... 7.432e+12\n",
       "     sound                   float64 1.5e+05\n",
       "     ocn_ref_salinity        float64 34.7\n",
       "     days_in_norm_year       timedelta64[ns] 365 days\n",
       "     sflux_factor            float64 0.1\n",
       "     HUS                     (nlat, nlon) float64 2.397e+06 ... 1.473e+06\n",
       "     ...                      ...\n",
       "     salt_to_Svppt           float64 1e-09\n",
       "     rho_sw                  float64 1.026\n",
       "     nsurface_u              float64 8.297e+04\n",
       "     FG_CO2                  (time, member_id) float64 ...\n",
       "     ATM_CO2                 (time, member_id) float64 ...\n",
       "     SST                     (time, member_id) float64 ...,\n",
       " 'ocn.SSP5-8.5.pop.h': <xarray.Dataset>\n",
       " Dimensions:                 (lat_aux_grid: 395, member_id: 3, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, time: 86, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)\n",
       " Coordinates: (12/13)\n",
       "   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04\n",
       "     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8\n",
       "     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19\n",
       "   * member_id               (member_id) int64 4 10 11\n",
       "   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05\n",
       "   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05\n",
       "     ...                      ...\n",
       "   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05\n",
       "   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05\n",
       "   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05\n",
       "   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0\n",
       "     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0\n",
       "   * time                    (time) int64 2015 2016 2017 2018 ... 2098 2099 2100\n",
       " Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg\n",
       " Data variables: (12/56)\n",
       "     TAREA                   (nlat, nlon) float64 1.125e+13 ... 7.432e+12\n",
       "     sound                   float64 1.5e+05\n",
       "     ocn_ref_salinity        float64 34.7\n",
       "     days_in_norm_year       timedelta64[ns] 365 days\n",
       "     sflux_factor            float64 0.1\n",
       "     HUS                     (nlat, nlon) float64 2.397e+06 ... 1.473e+06\n",
       "     ...                      ...\n",
       "     salt_to_Svppt           float64 1e-09\n",
       "     rho_sw                  float64 1.026\n",
       "     nsurface_u              float64 8.297e+04\n",
       "     FG_CO2                  (time, member_id) float64 ...\n",
       "     ATM_CO2                 (time, member_id) float64 ...\n",
       "     SST                     (time, member_id) float64 ...,\n",
       " 'ocn.SSP3-7.0.pop.h': <xarray.Dataset>\n",
       " Dimensions:                 (lat_aux_grid: 395, member_id: 3, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, time: 86, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)\n",
       " Coordinates: (12/13)\n",
       "   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04\n",
       "     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8\n",
       "     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19\n",
       "   * member_id               (member_id) int64 4 10 11\n",
       "   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05\n",
       "   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05\n",
       "     ...                      ...\n",
       "   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05\n",
       "   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05\n",
       "   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05\n",
       "   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0\n",
       "     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0\n",
       "   * time                    (time) int64 2015 2016 2017 2018 ... 2098 2099 2100\n",
       " Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg\n",
       " Data variables: (12/56)\n",
       "     TAREA                   (nlat, nlon) float64 1.125e+13 ... 7.432e+12\n",
       "     sound                   float64 1.5e+05\n",
       "     ocn_ref_salinity        float64 34.7\n",
       "     days_in_norm_year       timedelta64[ns] 365 days\n",
       "     sflux_factor            float64 0.1\n",
       "     HUS                     (nlat, nlon) float64 2.397e+06 ... 1.473e+06\n",
       "     ...                      ...\n",
       "     salt_to_Svppt           float64 1e-09\n",
       "     rho_sw                  float64 1.026\n",
       "     nsurface_u              float64 8.297e+04\n",
       "     FG_CO2                  (time, member_id) float64 ...\n",
       "     ATM_CO2                 (time, member_id) float64 ...\n",
       "     SST                     (time, member_id) float64 ...,\n",
       " 'ocn.SSP1-2.6.pop.h': <xarray.Dataset>\n",
       " Dimensions:                 (lat_aux_grid: 395, member_id: 3, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, time: 86, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)\n",
       " Coordinates: (12/13)\n",
       "   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04\n",
       "     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8\n",
       "     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19\n",
       "   * member_id               (member_id) int64 4 10 11\n",
       "   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05\n",
       "   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05\n",
       "     ...                      ...\n",
       "   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05\n",
       "   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05\n",
       "   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05\n",
       "   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0\n",
       "     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0\n",
       "   * time                    (time) int64 2015 2016 2017 2018 ... 2098 2099 2100\n",
       " Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg\n",
       " Data variables: (12/56)\n",
       "     TAREA                   (nlat, nlon) float64 1.125e+13 ... 7.432e+12\n",
       "     sound                   float64 1.5e+05\n",
       "     ocn_ref_salinity        float64 34.7\n",
       "     days_in_norm_year       timedelta64[ns] 365 days\n",
       "     sflux_factor            float64 0.1\n",
       "     HUS                     (nlat, nlon) float64 2.397e+06 ... 1.473e+06\n",
       "     ...                      ...\n",
       "     salt_to_Svppt           float64 1e-09\n",
       "     rho_sw                  float64 1.026\n",
       "     nsurface_u              float64 8.297e+04\n",
       "     FG_CO2                  (time, member_id) float64 ...\n",
       "     ATM_CO2                 (time, member_id) float64 ...\n",
       "     SST                     (time, member_id) float64 ...,\n",
       " 'ocn.SSP2-4.5.pop.h': <xarray.Dataset>\n",
       " Dimensions:                 (lat_aux_grid: 395, member_id: 3, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, time: 86, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)\n",
       " Coordinates: (12/13)\n",
       "   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04\n",
       "     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8\n",
       "     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19\n",
       "   * member_id               (member_id) int64 4 10 11\n",
       "   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05\n",
       "   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05\n",
       "     ...                      ...\n",
       "   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05\n",
       "   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05\n",
       "   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05\n",
       "   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0\n",
       "     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0\n",
       "   * time                    (time) int64 2015 2016 2017 2018 ... 2098 2099 2100\n",
       " Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg\n",
       " Data variables: (12/56)\n",
       "     TAREA                   (nlat, nlon) float64 1.125e+13 ... 7.432e+12\n",
       "     sound                   float64 1.5e+05\n",
       "     ocn_ref_salinity        float64 34.7\n",
       "     days_in_norm_year       timedelta64[ns] 365 days\n",
       "     sflux_factor            float64 0.1\n",
       "     HUS                     (nlat, nlon) float64 2.397e+06 ... 1.473e+06\n",
       "     ...                      ...\n",
       "     salt_to_Svppt           float64 1e-09\n",
       "     rho_sw                  float64 1.026\n",
       "     nsurface_u              float64 8.297e+04\n",
       "     FG_CO2                  (time, member_id) float64 ...\n",
       "     ATM_CO2                 (time, member_id) float64 ...\n",
       "     SST                     (time, member_id) float64 ...}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsets_int = glb_int.to_dataset_dict('FG_CO2', clobber=False)\n",
    "dsets_avg = glb_avg.to_dataset_dict(['ATM_CO2', 'SST',], prefer_derived=True, clobber=False)\n",
    "\n",
    "assert dsets_int.keys() == dsets_avg.keys()\n",
    "\n",
    "dsets = {}\n",
    "for key in dsets_int.keys():\n",
    "    dsets[key] = xr.merge((dsets_int[key], dsets_avg[key]))\n",
    "\n",
    "dsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "del client\n",
    "del cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdAAAABICAYAAABGH6SaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAACQUlEQVR4nO3arYpVURzG4bV0jqDBKergB1rEokXwTqxzN6YTxihot5jEK/ACjJYJRoOox+ABwTDLYB0/9sss1tmb56l7c3j/6QebU1trBQCY5tzoAQAwRwIKAAEBBYCAgAJAQEABICCgABDYm/JyvXi5lf2DXluGe7D6MHpCV+/PXxs9oav720+jJ3Rzsro3ekJXbfV19ISuLl24OXpCV59/fhs9oZvNZlO222097dmkgJb9g1IPn57JqF305tbj0RO6urt/OHpCV6/eHo2e0M2PG89GT+jq5PrL0RO6enj7yegJXT3/+Hr0hG7W6/Ufn/mECwABAQWAgIACQEBAASAgoAAQEFAACAgoAAQEFAACAgoAAQEFgICAAkBAQAEgIKAAEBBQAAgIKAAEBBQAAgIKAAEBBYCAgAJAQEABICCgABAQUAAICCgABAQUAAICCgABAQWAgIACQEBAASAgoAAQEFAACAgoAAQEFAACAgoAAQEFgICAAkBAQAEgIKAAEBBQAAgIKAAEBBQAAgIKAAEBBYCAgAJAQEABICCgABAQUAAICCgABAQUAAICCgABAQWAgIACQEBAASBQW2v//3Kt30spx/3mDHellPJl9IhOlnxbKe6bO/fN15JvK6WUO621q6c92Jv4Q8ettUdnMGgn1VrfLfW+Jd9Wivvmzn3zteTb/sUnXAAICCgABKYG9EWXFbtjyfct+bZS3Dd37puvJd/2V5P+RAQA/OYTLgAEBBQAAgIKAAEBBYCAgAJA4BeE/FgbbxgqBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "current_palette = sns.color_palette('colorblind', 8)\n",
    "sns.palplot(current_palette)\n",
    "\n",
    "exp_colors = {\n",
    "    'PI': current_palette.as_hex()[-1],\n",
    "    'historical': 'k',\n",
    "    'SSP1-2.6': current_palette.as_hex()[0],\n",
    "    'SSP2-4.5': current_palette.as_hex()[1],\n",
    "    'SSP3-7.0': current_palette.as_hex()[2],\n",
    "    'SSP5-8.5': current_palette.as_hex()[3],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "variable_labels = dict(\n",
    "    year='Year',\n",
    "    FG_CO2='Air-sea CO$_2$ flux [Pg C yr$^{-1}$]',\n",
    "    photoC_TOT_zint_100m='NPP (z > -100m) [Pg C yr$^{-1}$]',\n",
    "    photoC_TOT_zint='NPP [Pg C yr$^{-1}$]',\n",
    "    POC_FLUX_100m='POC flux (100 m) [Pg C yr$^{-1}$]',\n",
    "    ATM_CO2='Atmospheric CO$_2$ [ppm]',\n",
    "    SST='Temperature [°C]',\n",
    "    time='',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = 'FG_CO2'\n",
    "x = 'SST'\n",
    "\n",
    "nrow = 2\n",
    "ncol = 2\n",
    "#fig, axs = plt.subplots(nrow, ncol, figsize=(4*ncol, 3*nrow), constrained_layout=False)\n",
    "\n",
    "fig = plt.figure(figsize=(12, 9))\n",
    "gs = gridspec.GridSpec(\n",
    "    nrows=2, ncols=2, \n",
    "    hspace=0.25,\n",
    "    wspace=0.22,\n",
    ")\n",
    "axs = np.empty((2, 2)).astype(object)\n",
    "for i, j in product(range(2), range(2)):\n",
    "    axs[i, j] = plt.subplot(gs[i, j])\n",
    "\n",
    "\n",
    "\n",
    "X = [['time', 'time'], ['time', 'ATM_CO2'],]\n",
    "Y = [['ATM_CO2', 'SST'], ['FG_CO2', 'FG_CO2'],]\n",
    "\n",
    "for i, j in product(range(2), range(2)):\n",
    "    ax = axs[i, j]\n",
    "    x = X[i][j]\n",
    "    y = Y[i][j]\n",
    "    for exp in experiments:\n",
    "        ds = dsets[f'ocn.{exp}.pop.h']\n",
    "        #for member_id in ds.member_id.values:\n",
    "        #    ax.plot(ds.time, ds[v].sel(member_id=member_id), '-', alpha=0.25, linewidth=1, color=exp_colors[exp])\n",
    "        ax.plot(ds.mean('member_id')[x], ds.mean('member_id')[y], '-', linewidth=2, color=exp_colors[exp], label=exp)\n",
    "        \n",
    "    ax.set_ylabel(variable_labels[y])    \n",
    "    ax.legend()\n",
    "    ax.set_xlabel(variable_labels[x])\n",
    "utils.label_plots(fig, [ax for ax in axs.ravel()], xoff=-0.03, yoff=0.015)   \n",
    "\n",
    "utils.savefig(f'global-timeseries-FG_CO2.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ocn.SSP5-8.5.pop.h: 4.60\n",
      "ocn.SSP3-7.0.pop.h: 3.33\n",
      "ocn.SSP1-2.6.pop.h: 1.72\n",
      "ocn.SSP2-4.5.pop.h: 2.54\n"
     ]
    }
   ],
   "source": [
    "baseline = dsets['ocn.historical.pop.h'].SST.isel(time=slice(0, 10)).mean()\n",
    "for exp, ds in dsets.items():\n",
    "    if 'SSP' in exp:\n",
    "        warming = ds.SST.isel(time=-1).mean() - baseline\n",
    "        print(f'{exp}: {warming.values:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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