global_mean_surface_temp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global Mean Surface Temperature\n",
    "\n",
    "This notebook uses similar techniques to `ECS_Gregory_method.ipynb`. Please refer to that notebook for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:02:46.466926Z",
     "iopub.status.busy": "2020-10-08T16:02:46.466222Z",
     "iopub.status.idle": "2020-10-08T16:02:49.573131Z",
     "shell.execute_reply": "2020-10-08T16:02:49.572458Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/ipykernel_launcher.py:6: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import dask\n",
    "from dask.diagnostics import progress\n",
    "from tqdm.autonotebook import tqdm \n",
    "import intake\n",
    "import fsspec\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:02:49.578477Z",
     "iopub.status.busy": "2020-10-08T16:02:49.577859Z",
     "iopub.status.idle": "2020-10-08T16:02:55.398136Z",
     "shell.execute_reply": "2020-10-08T16:02:55.397447Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3417: DtypeWarning: Columns (10) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<p><strong>pangeo-cmip6 catalog with 4749 dataset(s) from 294376 asset(s)</strong>:</p> <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>activity_id</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>institution_id</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment_id</th>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_id</th>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table_id</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable_id</th>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grid_label</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zstore</th>\n",
       "      <td>294376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>version</th>\n",
       "      <td>529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = intake.open_esm_datastore(\"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\")\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:02:55.416187Z",
     "iopub.status.busy": "2020-10-08T16:02:55.415485Z",
     "iopub.status.idle": "2020-10-08T16:02:55.426684Z",
     "shell.execute_reply": "2020-10-08T16:02:55.427266Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ssp370',\n",
       " 'esm-ssp585',\n",
       " 'ssp126',\n",
       " 'ssp245',\n",
       " 'ssp585',\n",
       " 'ssp245-GHG',\n",
       " 'ssp245-aer',\n",
       " 'ssp245-nat',\n",
       " 'ssp245-stratO3',\n",
       " 'esm-ssp585-ssp126Lu',\n",
       " 'ssp119',\n",
       " 'ssp434',\n",
       " 'ssp460',\n",
       " 'ssp534-over']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[eid for eid in col.df['experiment_id'].unique() if 'ssp' in eid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:02:55.450724Z",
     "iopub.status.busy": "2020-10-08T16:02:55.433007Z",
     "iopub.status.idle": "2020-10-08T16:02:55.782142Z",
     "shell.execute_reply": "2020-10-08T16:02:55.781419Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        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_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>table_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACCESS-CM2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACCESS-ESM1-5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AWI-CM-1-1-MR</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCC-CSM2-MR</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMS-CSM1-0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CESM2-WACCM</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIESM</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCC-CM2-SR5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CanESM5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EC-Earth3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EC-Earth3-Veg</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGOALS-f3-L</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGOALS-g3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FIO-ESM-2-0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GFDL-CM4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GFDL-ESM4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IITM-ESM</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INM-CM4-8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INM-CM5-0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IPSL-CM6A-LR</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KACE-1-0-G</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIOST-ESM</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIROC6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPI-ESM1-2-HR</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPI-ESM1-2-LR</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRI-ESM2-0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NESM3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NorESM2-LM</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NorESM2-MM</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               experiment_id  variable_id  table_id\n",
       "source_id                                          \n",
       "ACCESS-CM2                 3            1         1\n",
       "ACCESS-ESM1-5              3            1         1\n",
       "AWI-CM-1-1-MR              3            1         1\n",
       "BCC-CSM2-MR                3            1         1\n",
       "CAMS-CSM1-0                3            1         1\n",
       "CESM2-WACCM                3            1         1\n",
       "CIESM                      3            1         1\n",
       "CMCC-CM2-SR5               3            1         1\n",
       "CanESM5                    3            1         1\n",
       "EC-Earth3                  3            1         1\n",
       "EC-Earth3-Veg              3            1         1\n",
       "FGOALS-f3-L                3            1         1\n",
       "FGOALS-g3                  3            1         1\n",
       "FIO-ESM-2-0                3            1         1\n",
       "GFDL-CM4                   3            1         1\n",
       "GFDL-ESM4                  3            1         1\n",
       "IITM-ESM                   3            1         1\n",
       "INM-CM4-8                  3            1         1\n",
       "INM-CM5-0                  3            1         1\n",
       "IPSL-CM6A-LR               3            1         1\n",
       "KACE-1-0-G                 3            1         1\n",
       "KIOST-ESM                  3            1         1\n",
       "MIROC6                     3            1         1\n",
       "MPI-ESM1-2-HR              3            1         1\n",
       "MPI-ESM1-2-LR              3            1         1\n",
       "MRI-ESM2-0                 3            1         1\n",
       "NESM3                      3            1         1\n",
       "NorESM2-LM                 3            1         1\n",
       "NorESM2-MM                 3            1         1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there is currently a significant amount of data for these runs\n",
    "expts = ['historical', 'ssp245', 'ssp585']\n",
    "\n",
    "query = dict(\n",
    "    experiment_id=expts,\n",
    "    table_id='Amon',                           \n",
    "    variable_id=['tas'],\n",
    "    member_id = 'r1i1p1f1',                     \n",
    ")\n",
    "\n",
    "col_subset = col.search(require_all_on=[\"source_id\"], **query)\n",
    "col_subset.df.groupby(\"source_id\")[\n",
    "    [\"experiment_id\", \"variable_id\", \"table_id\"]\n",
    "].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:02:55.793574Z",
     "iopub.status.busy": "2020-10-08T16:02:55.791617Z",
     "iopub.status.idle": "2020-10-08T16:02:55.812305Z",
     "shell.execute_reply": "2020-10-08T16:02:55.811474Z"
    }
   },
   "outputs": [],
   "source": [
    "def drop_all_bounds(ds):\n",
    "    drop_vars = [vname for vname in ds.coords\n",
    "                 if (('_bounds') in vname ) or ('_bnds') in vname]\n",
    "    return ds.drop(drop_vars)\n",
    "\n",
    "def open_dset(df):\n",
    "    assert len(df) == 1\n",
    "    ds = xr.open_zarr(fsspec.get_mapper(df.zstore.values[0]), consolidated=True)\n",
    "    return drop_all_bounds(ds)\n",
    "\n",
    "def open_delayed(df):\n",
    "    return dask.delayed(open_dset)(df)\n",
    "\n",
    "from collections import defaultdict\n",
    "dsets = defaultdict(dict) \n",
    "\n",
    "for group, df in col_subset.df.groupby(by=['source_id', 'experiment_id']):\n",
    "    dsets[group[0]][group[1]] = open_delayed(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:02:55.818860Z",
     "iopub.status.busy": "2020-10-08T16:02:55.817633Z",
     "iopub.status.idle": "2020-10-08T16:02:59.086428Z",
     "shell.execute_reply": "2020-10-08T16:02:59.085390Z"
    }
   },
   "outputs": [],
   "source": [
    "dsets_ = dask.compute(dict(dsets))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:02:59.093302Z",
     "iopub.status.busy": "2020-10-08T16:02:59.092472Z",
     "iopub.status.idle": "2020-10-08T16:02:59.095683Z",
     "shell.execute_reply": "2020-10-08T16:02:59.094917Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate global means\n",
    "\n",
    "def get_lat_name(ds):\n",
    "    for lat_name in ['lat', 'latitude']:\n",
    "        if lat_name in ds.coords:\n",
    "            return lat_name\n",
    "    raise RuntimeError(\"Couldn't find a latitude coordinate\")\n",
    "\n",
    "def global_mean(ds):\n",
    "    lat = ds[get_lat_name(ds)]\n",
    "    weight = np.cos(np.deg2rad(lat))\n",
    "    weight /= weight.mean()\n",
    "    other_dims = set(ds.dims) - {'time'}\n",
    "    return (ds * weight).mean(other_dims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:02:59.128718Z",
     "iopub.status.busy": "2020-10-08T16:02:59.103446Z",
     "iopub.status.idle": "2020-10-08T16:03:01.635392Z",
     "shell.execute_reply": "2020-10-08T16:03:01.636107Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "model_id": "dc354ae218784c9b8f3d607f4a02b51d",
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       "version_minor": 0
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       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=29.0), HTML(value='')))"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "expt_da = xr.DataArray(expts, dims='experiment_id', name='experiment_id',\n",
    "                       coords={'experiment_id': expts})\n",
    "\n",
    "dsets_aligned = {}\n",
    "\n",
    "for k, v in tqdm(dsets_.items()):\n",
    "    expt_dsets = v.values()\n",
    "    if any([d is None for d in expt_dsets]):\n",
    "        print(f\"Missing experiment for {k}\")\n",
    "        continue\n",
    "    \n",
    "    for ds in expt_dsets:\n",
    "        ds.coords['year'] = ds.time.dt.year\n",
    "        \n",
    "    # workaround for\n",
    "    # https://github.com/pydata/xarray/issues/2237#issuecomment-620961663\n",
    "    dsets_ann_mean = [v[expt].pipe(global_mean)\n",
    "                             .swap_dims({'time': 'year'})\n",
    "                             .drop('time')\n",
    "                             .coarsen(year=12).mean()\n",
    "                      for expt in expts]\n",
    "    \n",
    "    # align everything with the 4xCO2 experiment\n",
    "    dsets_aligned[k] = xr.concat(dsets_ann_mean, join='outer',\n",
    "                                 dim=expt_da)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
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    },
    {
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    },
    {
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    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "[####################################### ] | 99% Completed |  1min 30.8s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "[########################################] | 100% Completed |  1min 30.9s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "with progress.ProgressBar():\n",
    "    dsets_aligned_ = dask.compute(dsets_aligned)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:04:32.765241Z",
     "iopub.status.busy": "2020-10-08T16:04:32.763973Z",
     "iopub.status.idle": "2020-10-08T16:04:32.823543Z",
     "shell.execute_reply": "2020-10-08T16:04:32.822834Z"
    }
   },
   "outputs": [
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       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
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       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:        (experiment_id: 3, source_id: 29, year: 451)\n",
       "Coordinates:\n",
       "  * source_id      (source_id) &lt;U13 &#x27;ACCESS-CM2&#x27; ... &#x27;NorESM2-MM&#x27;\n",
       "  * year           (year) float64 1.85e+03 1.851e+03 ... 2.299e+03 2.3e+03\n",
       "  * experiment_id  (experiment_id) &lt;U10 &#x27;historical&#x27; &#x27;ssp245&#x27; &#x27;ssp585&#x27;\n",
       "Data variables:\n",
       "    tas            (source_id, experiment_id, year) float64 287.0 287.0 ... nan</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-faa90c65-dfc8-4cc4-819e-d0d55cc82f37' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-faa90c65-dfc8-4cc4-819e-d0d55cc82f37' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>experiment_id</span>: 3</li><li><span class='xr-has-index'>source_id</span>: 29</li><li><span class='xr-has-index'>year</span>: 451</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-125cbada-881e-4572-bbfc-61b8de77caa0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-125cbada-881e-4572-bbfc-61b8de77caa0' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>source_id</span></div><div class='xr-var-dims'>(source_id)</div><div class='xr-var-dtype'>&lt;U13</div><div class='xr-var-preview xr-preview'>&#x27;ACCESS-CM2&#x27; ... &#x27;NorESM2-MM&#x27;</div><input id='attrs-b91daa28-5405-4cfa-9402-b5f5581c21b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b91daa28-5405-4cfa-9402-b5f5581c21b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d372478-f0c6-4d56-99a6-a89b3adaa672' class='xr-var-data-in' type='checkbox'><label for='data-1d372478-f0c6-4d56-99a6-a89b3adaa672' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;ACCESS-CM2&#x27;, &#x27;ACCESS-ESM1-5&#x27;, &#x27;AWI-CM-1-1-MR&#x27;, &#x27;BCC-CSM2-MR&#x27;,\n",
       "       &#x27;CAMS-CSM1-0&#x27;, &#x27;CESM2-WACCM&#x27;, &#x27;CIESM&#x27;, &#x27;CMCC-CM2-SR5&#x27;, &#x27;CanESM5&#x27;,\n",
       "       &#x27;EC-Earth3&#x27;, &#x27;EC-Earth3-Veg&#x27;, &#x27;FGOALS-f3-L&#x27;, &#x27;FGOALS-g3&#x27;, &#x27;FIO-ESM-2-0&#x27;,\n",
       "       &#x27;GFDL-CM4&#x27;, &#x27;GFDL-ESM4&#x27;, &#x27;IITM-ESM&#x27;, &#x27;INM-CM4-8&#x27;, &#x27;INM-CM5-0&#x27;,\n",
       "       &#x27;IPSL-CM6A-LR&#x27;, &#x27;KACE-1-0-G&#x27;, &#x27;KIOST-ESM&#x27;, &#x27;MIROC6&#x27;, &#x27;MPI-ESM1-2-HR&#x27;,\n",
       "       &#x27;MPI-ESM1-2-LR&#x27;, &#x27;MRI-ESM2-0&#x27;, &#x27;NESM3&#x27;, &#x27;NorESM2-LM&#x27;, &#x27;NorESM2-MM&#x27;],\n",
       "      dtype=&#x27;&lt;U13&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.85e+03 1.851e+03 ... 2.3e+03</div><input id='attrs-b86e36b0-7f6d-4c3f-8431-63c729e35ad1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b86e36b0-7f6d-4c3f-8431-63c729e35ad1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50f37e64-051a-4314-874a-a8661d402b89' class='xr-var-data-in' type='checkbox'><label for='data-50f37e64-051a-4314-874a-a8661d402b89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1850., 1851., 1852., ..., 2298., 2299., 2300.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>experiment_id</span></div><div class='xr-var-dims'>(experiment_id)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;historical&#x27; &#x27;ssp245&#x27; &#x27;ssp585&#x27;</div><input id='attrs-0f3ed0ff-8118-4370-aeee-47f28ef01e89' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f3ed0ff-8118-4370-aeee-47f28ef01e89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1ca3a53-a621-4a5f-a14a-aadda0cd249a' class='xr-var-data-in' type='checkbox'><label for='data-d1ca3a53-a621-4a5f-a14a-aadda0cd249a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;historical&#x27;, &#x27;ssp245&#x27;, &#x27;ssp585&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-52efd800-d9b8-4ad8-b772-f8856ebef49d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-52efd800-d9b8-4ad8-b772-f8856ebef49d' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(source_id, experiment_id, year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>287.0 287.0 287.2 ... nan nan nan</div><input id='attrs-616cf572-22c7-42c5-8b37-84d91c3708e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-616cf572-22c7-42c5-8b37-84d91c3708e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2795513-7ea2-40c1-9363-7f5fa528c344' class='xr-var-data-in' type='checkbox'><label for='data-e2795513-7ea2-40c1-9363-7f5fa528c344' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[287.00749364, 287.01698452, 287.17454216, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan]],\n",
       "\n",
       "       [[287.78735509, 287.66659835, 287.60427076, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan]],\n",
       "\n",
       "       [[         nan, 286.82136175, 286.83013242, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan]],\n",
       "...\n",
       "       [[286.75054527, 286.8976191 , 286.81241996, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan]],\n",
       "\n",
       "       [[287.58305201, 287.80131659, 287.70510569, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan]],\n",
       "\n",
       "       [[287.14579909, 287.16514246, 287.06030532, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan],\n",
       "        [         nan,          nan,          nan, ...,          nan,\n",
       "                  nan,          nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ad990185-b250-4531-ba4e-ead68ee49a03' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ad990185-b250-4531-ba4e-ead68ee49a03' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:        (experiment_id: 3, source_id: 29, year: 451)\n",
       "Coordinates:\n",
       "  * source_id      (source_id) <U13 'ACCESS-CM2' ... 'NorESM2-MM'\n",
       "  * year           (year) float64 1.85e+03 1.851e+03 ... 2.299e+03 2.3e+03\n",
       "  * experiment_id  (experiment_id) <U10 'historical' 'ssp245' 'ssp585'\n",
       "Data variables:\n",
       "    tas            (source_id, experiment_id, year) float64 287.0 287.0 ... nan"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_ids = list(dsets_aligned_.keys())\n",
    "source_da = xr.DataArray(source_ids, dims='source_id', name='source_id',\n",
    "                         coords={'source_id': source_ids})\n",
    "\n",
    "big_ds = xr.concat([ds.reset_coords(drop=True)\n",
    "                    for ds in dsets_aligned_.values()],\n",
    "                    dim=source_da)\n",
    "\n",
    "big_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:04:32.829971Z",
     "iopub.status.busy": "2020-10-08T16:04:32.828634Z",
     "iopub.status.idle": "2020-10-08T16:04:32.851415Z",
     "shell.execute_reply": "2020-10-08T16:04:32.850677Z"
    }
   },
   "outputs": [
    {
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       "      <td>286.803313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experiment_id   source_id    year         tas\n",
       "0    historical  ACCESS-CM2  1900.0  287.019917\n",
       "1    historical  ACCESS-CM2  1901.0  286.966182\n",
       "2    historical  ACCESS-CM2  1902.0  286.994328\n",
       "3    historical  ACCESS-CM2  1903.0  286.797043\n",
       "4    historical  ACCESS-CM2  1904.0  286.803313"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = big_ds.sel(year=slice(1900, 2100)).to_dataframe().reset_index()\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:04:32.858065Z",
     "iopub.status.busy": "2020-10-08T16:04:32.856378Z",
     "iopub.status.idle": "2020-10-08T16:04:33.605563Z",
     "shell.execute_reply": "2020-10-08T16:04:33.604840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7faa8e635d50>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 801.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 365,
       "width": 809
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(data=df_all,\n",
    "            x=\"year\", y=\"tas\", hue='experiment_id',\n",
    "            kind=\"line\", ci=\"sd\", aspect=2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-08T16:04:33.611072Z",
     "iopub.status.busy": "2020-10-08T16:04:33.610311Z",
     "iopub.status.idle": "2020-10-08T16:04:33.615958Z",
     "shell.execute_reply": "2020-10-08T16:04:33.616520Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['historical', 'ssp245', 'ssp585'], dtype=object)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# why are there axis legends for 9, 10 - doesn't make sense\n",
    "df_all.experiment_id.unique()"
   ]
  }
 ],
 "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": "3.7.8"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "03320b0767b3436b85a2a412816d776b": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "1.2.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "1.2.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
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