LUMIP_CMIP6_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in and do simple analysis of LUMIP simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import CMIP6 output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ncar/usr/jupyterhub/envs/cmip6-201910a/lib/python3.7/site-packages/intake/source/discovery.py:136: FutureWarning: The drivers ['stac-catalog', 'stac-collection', 'stac-item'] do not specify entry_points and were only discovered via a package scan. This may break in a future release of intake. The packages should be updated.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import intake"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Catalog on cloud has more output (glade not up to date?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#catalog = intake.open_esm_datastore(\"../catalogs/pangeo-cmip6.json\")\n",
    "catalog = intake.open_esm_datastore(\"/glade/collections/cmip/catalog/intake-esm-datastore/catalogs/glade-cmip6.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Search for the experiments/variables, then futher refine by models (source_id) and ensemble members (member_id)\n",
    "Looks like there isn't really any hist-noLu data available so playing around with deforest-globe instead (see cells below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sr_nbp = catalog.search(experiment_id=['historical','hist-noLu'], variable_id='nbp', \n",
    "#                        source_id=['CanESM5', 'CESM2'],\n",
    "#                        member_id = ['r1i1p1f1', 'r2i1p1f1', 'r3i1p1f1'])\n",
    "sr_nbp = catalog.search(experiment_id=['historical','hist-noLu'], variable_id='nbp', member_id=['r1i1p1f1'])\n",
    "sr_nbp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog.search(activity_id='LUMIP', table_id='Lmon')\n",
    "sr_nbp = catalog.search(activity_id='LUMIP', variable_id='nbp', experiment_id=['deforest-globe'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'experiment_id': {'count': 1, 'values': ['deforest-globe']},\n",
      " 'source_id': {'count': 4,\n",
      "               'values': ['CNRM-ESM2-1', 'CESM2', 'CanESM5', 'IPSL-CM6A-LR']},\n",
      " 'table_id': {'count': 1, 'values': ['Lmon']}}\n"
     ]
    }
   ],
   "source": [
    "import pprint \n",
    "uni_dict = sr_nbp.unique(['source_id', 'experiment_id', 'table_id'])\n",
    "pprint.pprint(uni_dict, compact=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'grid_label': {'count': 2, 'values': ['gr', 'gn']}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr_nbp.unique('grid_label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the fixed variables output, only available for historical simulations (gridcell area, landfrac)\n",
    "Can subset the models and ensemble members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Would be nice if you could use the source ids that you retrieved earlier, rather than list\n",
    "# every model separetly, but don't know what the syntax should be.\n",
    "# e.g. something like:\n",
    "# sr_fx_ctrl_vars = catalog.search(table_id = ['fx'], source_id = sr_nbp.source_id, \n",
    "#                                 member_id = ['r1i1p1f1'], \n",
    "#                                experiment_id = 'historical', variable_id = ['areacella', 'sftlf'])\n",
    "\n",
    "sr_fx_ctrl_vars = catalog.search(table_id = ['fx'], source_id = ['CNRM-ESM2-1', 'CESM2', 'CanESM5', 'IPSL-CM6A-LR'], \n",
    "                                 member_id = ['r1i1p1f1'], \n",
    "                                 experiment_id = 'historical', variable_id = ['areacella', 'sftlf'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'grid_label': {'count': 2, 'values': ['gn', 'gr']}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr_fx_ctrl_vars.unique('grid_label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert the searchs to dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "xarray will load netCDF datasets with dask using a single chunk for all arrays.\n",
      "For effective chunking, please provide chunks in cdf_kwargs.\n",
      "For example: cdf_kwargs={'chunks': {'time': 36}}\n",
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n",
      "\n",
      "--> There will be 4 group(s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ncar/usr/jupyterhub/envs/cmip6-201910a/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'nbp' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n"
     ]
    }
   ],
   "source": [
    "nbp_ds = sr_nbp.to_dataset_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['LUMIP.CCCma.CanESM5.deforest-globe.Lmon.gn', 'LUMIP.CNRM-CERFACS.CNRM-ESM2-1.deforest-globe.Lmon.gr', 'LUMIP.IPSL.IPSL-CM6A-LR.deforest-globe.Lmon.gr', 'LUMIP.NCAR.CESM2.deforest-globe.Lmon.gn'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbp_ds.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "xarray will load netCDF datasets with dask using a single chunk for all arrays.\n",
      "For effective chunking, please provide chunks in cdf_kwargs.\n",
      "For example: cdf_kwargs={'chunks': {'time': 36}}\n",
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n",
      "\n",
      "--> There will be 3 group(s)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/ncar/usr/jupyterhub/envs/cmip6-201910a/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'areacella' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n",
      "/ncar/usr/jupyterhub/envs/cmip6-201910a/lib/python3.7/site-packages/xarray/conventions.py:494: SerializationWarning: variable 'sftlf' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  use_cftime=use_cftime,\n"
     ]
    }
   ],
   "source": [
    "fx_ds = sr_fx_ctrl_vars.to_dataset_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['CMIP.CCCma.CanESM5.historical.fx.gn', 'CMIP.IPSL.IPSL-CM6A-LR.historical.fx.gr', 'CMIP.NCAR.CESM2.historical.fx.gn'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fx_ds.keys()\n",
    "# note that this couldn't find the CNRM fx files, how would one efficiently go about figuring out where those\n",
    "# data might be avaialable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grab NBP for both models / experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbp_canesm = nbp_ds['LUMIP.CCCma.CanESM5.deforest-globe.Lmon.gn'].nbp\n",
    "nbp_cnrm = nbp_ds['LUMIP.CNRM-CERFACS.CNRM-ESM2-1.deforest-globe.Lmon.gr'].nbp\n",
    "nbp_ipsl = nbp_ds['LUMIP.IPSL.IPSL-CM6A-LR.deforest-globe.Lmon.gr'].nbp\n",
    "nbp_cesm = nbp_ds['LUMIP.NCAR.CESM2.deforest-globe.Lmon.gn'].nbp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'nbp' (member_id: 1, time: 1092, lat: 64, lon: 128)>\n",
       "dask.array<transpose, shape=(1, 1092, 64, 128), dtype=float32, chunksize=(1, 1092, 64, 128), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lon        (lon) float64 0.0 2.812 5.625 8.438 ... 348.8 351.6 354.4 357.2\n",
       "  * member_id  (member_id) <U8 'r1i1p2f1'\n",
       "  * lat        (lat) float64 -87.86 -85.1 -82.31 -79.53 ... 82.31 85.1 87.86\n",
       "  * time       (time) object 1850-01-16 12:00:00 ... 1940-12-16 12:00:00\n",
       "Attributes:\n",
       "    standard_name:  surface_net_downward_mass_flux_of_carbon_dioxide_expresse...\n",
       "    long_name:      Carbon Mass Flux out of Atmosphere Due to Net Biospheric ...\n",
       "    comment:        up is changed to down! CFNB*12.011E-9, LUMIP_table_commen...\n",
       "    units:          kg m-2 s-1\n",
       "    original_name:  CFNB\n",
       "    history:        mltby12em9 2019-05-09T01:46:15Z altered by CMOR: Reordere...\n",
       "    cell_methods:   area: mean where land time: mean\n",
       "    cell_measures:  area: areacella"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nbp_canesm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grab landfrac and cell area for both models (different resolutions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "landfrac_cesm = fx_ds['CMIP.NCAR.CESM2.historical.fx.gn'].sftlf\n",
    "area_cesm = fx_ds['CMIP.NCAR.CESM2.historical.fx.gn'].areacella\n",
    "landfrac_canesm = fx_ds['CMIP.CCCma.CanESM5.historical.fx.gn'].sftlf\n",
    "area_canesm = fx_ds['CMIP.CCCma.CanESM5.historical.fx.gn'].areacella\n",
    "landfrac_ipsl = fx_ds['CMIP.IPSL.IPSL-CM6A-LR.historical.fx.gr'].sftlf\n",
    "area_ipsl = fx_ds['CMIP.IPSL.IPSL-CM6A-LR.historical.fx.gr'].areacella"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make weights for global sum\n",
    "Convert landfrac from % to fraction between 0-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "landUp_cesm = area_cesm * landfrac_cesm / 100\n",
    "landUp_canesm = area_canesm * landfrac_canesm / 100\n",
    "landUp_ipsl = area_ipsl * landfrac_ipsl / 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check lat diffs between fx vars and output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "latdiffs = landUp_cesm.lat.values - nbp_cesm.lat.values # MISMATCH\n",
    "latdiffs = landUp_canesm.lat.values - nbp_canesm.lat.values # OK\n",
    "latdiffs = landUp_ipsl.lat.values - nbp_ipsl.lat.values # OK\n",
    "print(latdiffs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CESM needs correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "landUp_cesm_modlat = landUp_cesm # create new landUp\n",
    "landUp_cesm_modlat.lat.values = nbp_cesm.lat.values # just assign landUp lats to match nbp lats\n",
    "#print(landUp_cesm_modlat.lat.values - nbp_esm_cesm.lat.values) # sanity checks\n",
    "#print(landUp_cesm_modlat.lat.values - nbp_ssp_cesm.lat.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weighted annual means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weighted_annual_mean(array):\n",
    "    mon_day  = xr.DataArray(np.array([31,28,31,30,31,30,31,31,30,31,30,31]), dims=['month'])\n",
    "    mon_wgt  = mon_day/mon_day.sum()\n",
    "    return (array.rolling(time=12, center=False) # rolling\n",
    "            .construct(\"month\") # construct the array\n",
    "            .isel(time=slice(11, None, 12)) # slice so that the first element is [1..12], second is [13..24]\n",
    "            .dot(mon_wgt, dims=[\"month\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert  to annual fluxes (kgC/m2/y), from kgC/m2/s "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "spy = 365 * 24 * 3600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbp_cesm_amean = weighted_annual_mean(nbp_cesm) * spy\n",
    "nbp_cesm_amean.attrs['units'] = 'kgC m-2 y-1'\n",
    "nbp_cesm_amean \n",
    "temp = nbp_cesm_amean.values\n",
    "#print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbp_ipsl_amean = weighted_annual_mean(nbp_ipsl) * spy\n",
    "nbp_ipsl_amean.attrs['units'] = 'kgC m-2 y-1'\n",
    "nbp_ipsl_amean \n",
    "temp = nbp_ipsl_amean.values\n",
    "#print(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate global sums\n",
    "Instantaneous and cumulative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (member_id: 1, time: 80)>\n",
       "dask.array<mul, shape=(1, 80), dtype=float64, chunksize=(1, 80), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * member_id  (member_id) <U8 'r1i1p1f1'\n",
       "  * time       (time) object 1850-12-15 12:00:00 ... 1929-12-15 12:00:00\n",
       "Attributes:\n",
       "    units:    Pg C y^-1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_wgt_cesm = nbp_cesm_amean * landUp_cesm_modlat\n",
    "ann_glob_cesm = mean_wgt_cesm.sum(dim=('lat','lon')) * 1e-12 #convert to Pg/y\n",
    "# try without weights\n",
    "ann_glob_cesm = nbp_cesm_amean.sum(dim=('lat','lon')) * 1e-12\n",
    "ann_glob_cesm.attrs['units'] = 'Pg C y^-1'\n",
    "cann_glob_cesm = ann_glob_cesm.cumsum(dim='time')\n",
    "cann_glob_cesm.attrs['units'] = 'Pg C'\n",
    "ann_glob_cesm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_wgt_ipsl = nbp_ipsl_amean * landUp_ipsl\n",
    "ann_glob_ipsl = mean_wgt_ipsl.sum(dim=('lat','lon')) * 1e-12 #convert to Pg/y\n",
    "ann_glob_ipsl = nbp_ipsl_amean.sum(dim=('lat','lon')) * 1e-12\n",
    "ann_glob_ipsl.attrs['units'] = 'Pg C y^-1'\n",
    "cann_glob_ipsl = ann_glob_ipsl.cumsum(dim='time')\n",
    "cann_glob_ipsl.attrs['units'] = 'Pg C'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (member_id: 1, time: 91)>\n",
       "dask.array<nancumsum, shape=(1, 91), dtype=float64, chunksize=(1, 91), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * member_id  (member_id) <U8 'r1i1p2f1'\n",
       "  * time       (time) object 1850-12-16 12:00:00 ... 1940-12-16 12:00:00\n",
       "Attributes:\n",
       "    units:    Pg C"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_wgt_canesm = nbp_canesm_amean * landUp_canesm\n",
    "ann_glob_canesm = mean_wgt_canesm.sum(dim=('lat','lon')) * 1e-12 #convert to Pg/y\n",
    "# try without weights\n",
    "ann_glob_canesm = nbp_canesm_amean.sum(dim=('lat','lon')) * 1e-12\n",
    "ann_glob_canesm.attrs['units'] = 'Pg C y^-1'\n",
    "cann_glob_canesm = ann_glob_canesm.cumsum(dim='time')\n",
    "cann_glob_canesm.attrs['units'] = 'Pg C'\n",
    "cann_glob_canesm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the timeseries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Way to calculate ensemble means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (member_id: 1, time: 80)>\n",
       "dask.array<mul, shape=(1, 80), dtype=float64, chunksize=(1, 80), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * member_id  (member_id) <U8 'r1i1p1f1'\n",
       "  * time       (time) object 1850-12-15 12:00:00 ... 1929-12-15 12:00:00\n",
       "Attributes:\n",
       "    units:    Pg C y^-1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann_glob_cesm.mean(dim=\"member_id\")\n",
    "ann_glob_canesm.mean(dim=\"member_id\")\n",
    "#ann_glob_ipsl.mean(dim=\"member_id\")\n",
    "ann_glob_cesm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantaneous global sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Ask, out of a 1x1 grid, the first axes.\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Plot times as x-variable and temperatures as y-variable\n",
    "ax.plot(ann_glob_cesm.coords['time.year'], ann_glob_cesm.mean(dim=\"member_id\"), label='CESM2')\n",
    "ax.plot(ann_glob_canesm.coords['time.year'], ann_glob_canesm.mean(dim=\"member_id\"), label='CanESM')\n",
    "ax.plot(ann_glob_ipsl.coords['time.year'], ann_glob_ipsl.mean(dim=\"member_id\"), label='IPSL')\n",
    "# Add some labels to the plot\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('NBP'+' (' + ann_glob_cesm.attrs['units'] +')')\n",
    "ax.set_title('Deforest-Globe: Projected Changes in Annual Total NBP', fontdict={'size':16})\n",
    "ax.legend(loc='upper left');\n",
    "#plt.savefig('cesm_canesm_ssp585_esm585_nbp.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cummulative global sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cann_glob_canesm_' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-51-c41fb58be661>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Plot times as x-variable and temperatures as y-variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcann_glob_cesm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'time.year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcann_glob_cesm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"member_id\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'CESM2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcann_glob_canesm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'time.year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcann_glob_canesm_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"member_id\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'CanESM'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcann_glob_ipsl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'time.year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcann_glob_ipsl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"member_id\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'IPSL'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'cann_glob_canesm_' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Ask, out of a 1x1 grid, the first axes.\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Plot times as x-variable and temperatures as y-variable\n",
    "ax.plot(cann_glob_cesm.coords['time.year'], cann_glob_cesm.mean(dim=\"member_id\"), label='CESM2')\n",
    "ax.plot(cann_glob_canesm.coords['time.year'], cann_glob_canesm_.mean(dim=\"member_id\"), label='CanESM')\n",
    "ax.plot(cann_glob_ipsl.coords['time.year'], cann_glob_ipsl.mean(dim=\"member_id\"), label='IPSL')\n",
    "\n",
    "# Add some labels to the plot\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('NBP'+' (' + ann_glob_cesm.attrs['units'] +')')\n",
    "ax.set_title('Projected Changes in Cumulated Total NBP', fontdict={'size':16})\n",
    "ax.legend(loc='upper left');\n",
    "#plt.savefig('cesm_canesm_ssp585_esm585_nbp_cumulative.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "CMIP6 2019.10a",
   "language": "python",
   "name": "cmip6-201910a"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}