practice_notebook.ipynb

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   "source": [
    "# Load libraries\n",
    "\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.pyplot as plt\n",
    "import zarr\n",
    "import gcsfs\n",
    "import xesmf as xe\n",
    "from scipy.interpolate import griddata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0c9ca1",
   "metadata": {},
   "source": [
    "# Try accessing a dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e2f0193",
   "metadata": {},
   "source": [
    "#### Please read these first!:\n",
    "More information on datasets here: https://computing-docs.readthedocs.io/en/latest/load_with_catalog.html \n",
    "\n",
    "This link contains more information on what CMIP6 data looks like and what all the labels mean: https://computing-docs.readthedocs.io/en/latest/download_cmip.html\n",
    "\n",
    "Cristi has many model output files within his directory `/data/keeling/a/cristi/a/esm_data`. We're going to be using a lot of CMIP6 data, which is from the Climate Model Intercomparison Project phase 6. There's variable output from many different models under different scenarios within CMIP6. This data is kept in `/data/keeling/a/cristi/a/esm_data/cmip6`.\n",
    "\n",
    "You can type `cd /data/keeling/a/cristi/a/esm_data/cmip6` into your terminal to look at the directory.\n",
    "\n",
    "Once you're in the CMIP6 directory, you'll see all of the CMIP6 models. We'll be using data from any and all of the models, so feel free to pick whichever you'd like! I suggest using CESM2, CanESM5, BCC-CSM2-MR, or GFDL-ESM4 since they've given me the least trouble. \n",
    "\n",
    "Type `cd [modelname]` to go into that directory.\n",
    "\n",
    "Once you're within a model directory, you'll see a bunch of experiments that the model has simulated.\n",
    "\n",
    "The two experiments that we'll use the most are:\n",
    " - piControl - This is a control simulation, with everything kept to preindustrial conditions.\n",
    " - historical - This is a simulation with conditions following those in observations from 1850~2014. \n",
    "\n",
    "We'll be using piControl the most, so type `cd piControl`.\n",
    "\n",
    "Typically you'll be taken to one or two subdirectories with a name like `r1i1p1f1`. Feel free to pick either one. This is the variant number, and we usually pick `r1i1p1f1`. \n",
    "\n",
    "Next, you'll be taken to the actual datasets! The first part of the file name is the variable. The variables we'll be using the most are:\n",
    " - tas - Surface air temperature. This is the air temperature just a couple meters off the ground.\n",
    " - ts - Surface skin temperature. This is the actual temperature of the surface.\n",
    " - rlds, rlut, rsds, hfls, hfss, rsdt, rsut - These are different radiative and heat fluxes. We'll go over this later.\n",
    " \n",
    "Try following the load_with_catalog tutorial linked at the top to load a TAS dataset and then plot the time mean surface skin temperature over the whole globe."
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_id</th>\n",
       "      <th>branch_method</th>\n",
       "      <th>branch_time_in_child</th>\n",
       "      <th>branch_time_in_parent</th>\n",
       "      <th>experiment</th>\n",
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       "      <th>long_name</th>\n",
       "      <th>units</th>\n",
       "      <th>vertical_levels</th>\n",
       "      <th>init_year</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>time_range</th>\n",
       "      <th>path</th>\n",
       "      <th>version</th>\n",
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       "      <td>Surface Upward Latent Heat Flux</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0950-01-16 12:00:00</td>\n",
       "      <td>1099-12-16 12:00:00</td>\n",
       "      <td>0950-01-16 12:00:00-1099-12-16 12:00:00</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>abrupt quadrupling of CO2</td>\n",
       "      <td>abrupt-4xCO2</td>\n",
       "      <td>mon</td>\n",
       "      <td>native atmosphere N96 grid (144x192 latxlon)</td>\n",
       "      <td>gn</td>\n",
       "      <td>CSIRO-ARCCSS</td>\n",
       "      <td>...</td>\n",
       "      <td>surface_upward_sensible_heat_flux</td>\n",
       "      <td>Surface Upward Sensible Heat Flux</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0950-01-16 12:00:00</td>\n",
       "      <td>1099-12-16 12:00:00</td>\n",
       "      <td>0950-01-16 12:00:00-1099-12-16 12:00:00</td>\n",
       "      <td>/data/cristi/a/cristi/esm_data/cmip6/ACCESS-CM...</td>\n",
       "      <td>v0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>standard</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>abrupt quadrupling of CO2</td>\n",
       "      <td>abrupt-4xCO2</td>\n",
       "      <td>mon</td>\n",
       "      <td>native atmosphere N96 grid (144x192 latxlon)</td>\n",
       "      <td>gn</td>\n",
       "      <td>CSIRO-ARCCSS</td>\n",
       "      <td>...</td>\n",
       "      <td>surface_downwelling_longwave_flux_in_air</td>\n",
       "      <td>Surface Downwelling Longwave Radiation</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0950-01-16 12:00:00</td>\n",
       "      <td>1099-12-16 12:00:00</td>\n",
       "      <td>0950-01-16 12:00:00-1099-12-16 12:00:00</td>\n",
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       "      <td>v0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>abrupt quadrupling of CO2</td>\n",
       "      <td>abrupt-4xCO2</td>\n",
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       "      <td>native atmosphere N96 grid (144x192 latxlon)</td>\n",
       "      <td>gn</td>\n",
       "      <td>CSIRO-ARCCSS</td>\n",
       "      <td>...</td>\n",
       "      <td>surface_upwelling_longwave_flux_in_air</td>\n",
       "      <td>Surface Upwelling Longwave Radiation</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0950-01-16 12:00:00</td>\n",
       "      <td>1099-12-16 12:00:00</td>\n",
       "      <td>0950-01-16 12:00:00-1099-12-16 12:00:00</td>\n",
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       "      <td>v0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CMIP</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>abrupt-4xCO2</td>\n",
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       "      <td>...</td>\n",
       "      <td>toa_outgoing_longwave_flux</td>\n",
       "      <td>TOA Outgoing Longwave Radiation</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0950-01-16 12:00:00</td>\n",
       "      <td>1099-12-16 12:00:00</td>\n",
       "      <td>0950-01-16 12:00:00-1099-12-16 12:00:00</td>\n",
       "      <td>/data/cristi/a/cristi/esm_data/cmip6/ACCESS-CM...</td>\n",
       "      <td>v0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>11609</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>standard</td>\n",
       "      <td>59400.0</td>\n",
       "      <td>59400.0</td>\n",
       "      <td>update of RCP8.5 based on SSP5</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>Native N96 grid; 192 x 144 longitude/latitude</td>\n",
       "      <td>gn</td>\n",
       "      <td>MOHC</td>\n",
       "      <td>...</td>\n",
       "      <td>surface_downwelling_shortwave_flux_in_air</td>\n",
       "      <td>Surface Downwelling Shortwave Radiation</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-16 00:00:00</td>\n",
       "      <td>2100-12-16 00:00:00</td>\n",
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       "      <th>11610</th>\n",
       "      <td>ScenarioMIP</td>\n",
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       "      <td>59400.0</td>\n",
       "      <td>59400.0</td>\n",
       "      <td>update of RCP8.5 based on SSP5</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>Native N96 grid; 192 x 144 longitude/latitude</td>\n",
       "      <td>gn</td>\n",
       "      <td>MOHC</td>\n",
       "      <td>...</td>\n",
       "      <td>toa_incoming_shortwave_flux</td>\n",
       "      <td>TOA Incident Shortwave Radiation</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-16 00:00:00</td>\n",
       "      <td>2100-12-16 00:00:00</td>\n",
       "      <td>2015-01-16 00:00:00-2100-12-16 00:00:00</td>\n",
       "      <td>/data/cristi/a/cristi/esm_data/cmip6/UKESM1-0-...</td>\n",
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       "    <tr>\n",
       "      <th>11611</th>\n",
       "      <td>ScenarioMIP</td>\n",
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       "      <td>59400.0</td>\n",
       "      <td>59400.0</td>\n",
       "      <td>update of RCP8.5 based on SSP5</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>Native N96 grid; 192 x 144 longitude/latitude</td>\n",
       "      <td>gn</td>\n",
       "      <td>MOHC</td>\n",
       "      <td>...</td>\n",
       "      <td>surface_upwelling_shortwave_flux_in_air</td>\n",
       "      <td>Surface Upwelling Shortwave Radiation</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-16 00:00:00</td>\n",
       "      <td>2100-12-16 00:00:00</td>\n",
       "      <td>2015-01-16 00:00:00-2100-12-16 00:00:00</td>\n",
       "      <td>/data/cristi/a/cristi/esm_data/cmip6/UKESM1-0-...</td>\n",
       "      <td>v0</td>\n",
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       "    <tr>\n",
       "      <th>11612</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>standard</td>\n",
       "      <td>59400.0</td>\n",
       "      <td>59400.0</td>\n",
       "      <td>update of RCP8.5 based on SSP5</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>Native N96 grid; 192 x 144 longitude/latitude</td>\n",
       "      <td>gn</td>\n",
       "      <td>MOHC</td>\n",
       "      <td>...</td>\n",
       "      <td>toa_outgoing_shortwave_flux</td>\n",
       "      <td>TOA Outgoing Shortwave Radiation</td>\n",
       "      <td>W m-2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-16 00:00:00</td>\n",
       "      <td>2100-12-16 00:00:00</td>\n",
       "      <td>2015-01-16 00:00:00-2100-12-16 00:00:00</td>\n",
       "      <td>/data/cristi/a/cristi/esm_data/cmip6/UKESM1-0-...</td>\n",
       "      <td>v0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11613</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>standard</td>\n",
       "      <td>59400.0</td>\n",
       "      <td>59400.0</td>\n",
       "      <td>update of RCP8.5 based on SSP5</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "      <td>Native N96 grid; 192 x 144 longitude/latitude</td>\n",
       "      <td>gn</td>\n",
       "      <td>MOHC</td>\n",
       "      <td>...</td>\n",
       "      <td>air_temperature</td>\n",
       "      <td>Near-Surface Air Temperature</td>\n",
       "      <td>K</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-16 00:00:00</td>\n",
       "      <td>2100-12-16 00:00:00</td>\n",
       "      <td>2015-01-16 00:00:00-2100-12-16 00:00:00</td>\n",
       "      <td>/data/cristi/a/cristi/esm_data/cmip6/UKESM1-0-...</td>\n",
       "      <td>v0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11614 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       activity_id branch_method branch_time_in_child branch_time_in_parent  \\\n",
       "0             CMIP      standard                  0.0                   0.0   \n",
       "1             CMIP      standard                  0.0                   0.0   \n",
       "2             CMIP      standard                  0.0                   0.0   \n",
       "3             CMIP      standard                  0.0                   0.0   \n",
       "4             CMIP      standard                  0.0                   0.0   \n",
       "...            ...           ...                  ...                   ...   \n",
       "11609  ScenarioMIP      standard              59400.0               59400.0   \n",
       "11610  ScenarioMIP      standard              59400.0               59400.0   \n",
       "11611  ScenarioMIP      standard              59400.0               59400.0   \n",
       "11612  ScenarioMIP      standard              59400.0               59400.0   \n",
       "11613  ScenarioMIP      standard              59400.0               59400.0   \n",
       "\n",
       "                           experiment experiment_id frequency  \\\n",
       "0           abrupt quadrupling of CO2  abrupt-4xCO2       mon   \n",
       "1           abrupt quadrupling of CO2  abrupt-4xCO2       mon   \n",
       "2           abrupt quadrupling of CO2  abrupt-4xCO2       mon   \n",
       "3           abrupt quadrupling of CO2  abrupt-4xCO2       mon   \n",
       "4           abrupt quadrupling of CO2  abrupt-4xCO2       mon   \n",
       "...                               ...           ...       ...   \n",
       "11609  update of RCP8.5 based on SSP5        ssp585       mon   \n",
       "11610  update of RCP8.5 based on SSP5        ssp585       mon   \n",
       "11611  update of RCP8.5 based on SSP5        ssp585       mon   \n",
       "11612  update of RCP8.5 based on SSP5        ssp585       mon   \n",
       "11613  update of RCP8.5 based on SSP5        ssp585       mon   \n",
       "\n",
       "                                                grid grid_label  \\\n",
       "0       native atmosphere N96 grid (144x192 latxlon)         gn   \n",
       "1       native atmosphere N96 grid (144x192 latxlon)         gn   \n",
       "2       native atmosphere N96 grid (144x192 latxlon)         gn   \n",
       "3       native atmosphere N96 grid (144x192 latxlon)         gn   \n",
       "4       native atmosphere N96 grid (144x192 latxlon)         gn   \n",
       "...                                              ...        ...   \n",
       "11609  Native N96 grid; 192 x 144 longitude/latitude         gn   \n",
       "11610  Native N96 grid; 192 x 144 longitude/latitude         gn   \n",
       "11611  Native N96 grid; 192 x 144 longitude/latitude         gn   \n",
       "11612  Native N96 grid; 192 x 144 longitude/latitude         gn   \n",
       "11613  Native N96 grid; 192 x 144 longitude/latitude         gn   \n",
       "\n",
       "      institution_id  ...                              standard_name  \\\n",
       "0       CSIRO-ARCCSS  ...            surface_upward_latent_heat_flux   \n",
       "1       CSIRO-ARCCSS  ...          surface_upward_sensible_heat_flux   \n",
       "2       CSIRO-ARCCSS  ...   surface_downwelling_longwave_flux_in_air   \n",
       "3       CSIRO-ARCCSS  ...     surface_upwelling_longwave_flux_in_air   \n",
       "4       CSIRO-ARCCSS  ...                 toa_outgoing_longwave_flux   \n",
       "...              ...  ...                                        ...   \n",
       "11609           MOHC  ...  surface_downwelling_shortwave_flux_in_air   \n",
       "11610           MOHC  ...                toa_incoming_shortwave_flux   \n",
       "11611           MOHC  ...    surface_upwelling_shortwave_flux_in_air   \n",
       "11612           MOHC  ...                toa_outgoing_shortwave_flux   \n",
       "11613           MOHC  ...                            air_temperature   \n",
       "\n",
       "                                     long_name  units vertical_levels  \\\n",
       "0              Surface Upward Latent Heat Flux  W m-2             1.0   \n",
       "1            Surface Upward Sensible Heat Flux  W m-2             1.0   \n",
       "2       Surface Downwelling Longwave Radiation  W m-2             1.0   \n",
       "3         Surface Upwelling Longwave Radiation  W m-2             1.0   \n",
       "4              TOA Outgoing Longwave Radiation  W m-2             1.0   \n",
       "...                                        ...    ...             ...   \n",
       "11609  Surface Downwelling Shortwave Radiation  W m-2             1.0   \n",
       "11610         TOA Incident Shortwave Radiation  W m-2             1.0   \n",
       "11611    Surface Upwelling Shortwave Radiation  W m-2             1.0   \n",
       "11612         TOA Outgoing Shortwave Radiation  W m-2             1.0   \n",
       "11613             Near-Surface Air Temperature      K             1.0   \n",
       "\n",
       "      init_year           start_time             end_time  \\\n",
       "0           NaN  0950-01-16 12:00:00  1099-12-16 12:00:00   \n",
       "1           NaN  0950-01-16 12:00:00  1099-12-16 12:00:00   \n",
       "2           NaN  0950-01-16 12:00:00  1099-12-16 12:00:00   \n",
       "3           NaN  0950-01-16 12:00:00  1099-12-16 12:00:00   \n",
       "4           NaN  0950-01-16 12:00:00  1099-12-16 12:00:00   \n",
       "...         ...                  ...                  ...   \n",
       "11609       NaN  2015-01-16 00:00:00  2100-12-16 00:00:00   \n",
       "11610       NaN  2015-01-16 00:00:00  2100-12-16 00:00:00   \n",
       "11611       NaN  2015-01-16 00:00:00  2100-12-16 00:00:00   \n",
       "11612       NaN  2015-01-16 00:00:00  2100-12-16 00:00:00   \n",
       "11613       NaN  2015-01-16 00:00:00  2100-12-16 00:00:00   \n",
       "\n",
       "                                    time_range  \\\n",
       "0      0950-01-16 12:00:00-1099-12-16 12:00:00   \n",
       "1      0950-01-16 12:00:00-1099-12-16 12:00:00   \n",
       "2      0950-01-16 12:00:00-1099-12-16 12:00:00   \n",
       "3      0950-01-16 12:00:00-1099-12-16 12:00:00   \n",
       "4      0950-01-16 12:00:00-1099-12-16 12:00:00   \n",
       "...                                        ...   \n",
       "11609  2015-01-16 00:00:00-2100-12-16 00:00:00   \n",
       "11610  2015-01-16 00:00:00-2100-12-16 00:00:00   \n",
       "11611  2015-01-16 00:00:00-2100-12-16 00:00:00   \n",
       "11612  2015-01-16 00:00:00-2100-12-16 00:00:00   \n",
       "11613  2015-01-16 00:00:00-2100-12-16 00:00:00   \n",
       "\n",
       "                                                    path version  \n",
       "0      /data/cristi/a/cristi/esm_data/cmip6/ACCESS-CM...      v0  \n",
       "1      /data/cristi/a/cristi/esm_data/cmip6/ACCESS-CM...      v0  \n",
       "2      /data/cristi/a/cristi/esm_data/cmip6/ACCESS-CM...      v0  \n",
       "3      /data/cristi/a/cristi/esm_data/cmip6/ACCESS-CM...      v0  \n",
       "4      /data/cristi/a/cristi/esm_data/cmip6/ACCESS-CM...      v0  \n",
       "...                                                  ...     ...  \n",
       "11609  /data/cristi/a/cristi/esm_data/cmip6/UKESM1-0-...      v0  \n",
       "11610  /data/cristi/a/cristi/esm_data/cmip6/UKESM1-0-...      v0  \n",
       "11611  /data/cristi/a/cristi/esm_data/cmip6/UKESM1-0-...      v0  \n",
       "11612  /data/cristi/a/cristi/esm_data/cmip6/UKESM1-0-...      v0  \n",
       "11613  /data/cristi/a/cristi/esm_data/cmip6/UKESM1-0-...      v0  \n",
       "\n",
       "[11614 rows x 36 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Following the tutorial up above, here's the catalog for the CMIP6 data\n",
    "\n",
    "cat = pd.read_csv('/data/keeling/a/cristi/a/esm_data/cmip6_catalog.csv')\n",
    "cat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe8288a",
   "metadata": {},
   "source": [
    "As an example to load data, try looking at the example below. \n",
    "\n",
    "This example takes rsdt data from CESM2 run at monthly intervals for CMIP6. It also only returns the first dataset that corresponds to what we asked for (head(1))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ca4a6a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/data/cristi/a/cristi/esm_data/cmip6/CESM2/piControl/r1i1p1f1/rsdt_Amon_CESM2_piControl_r1i1p1f1_gn_000101-009912.nc']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_rsdt_control = cat.loc[(cat['variable_id']=='rsdt') &\n",
    "               (cat['activity_id']=='CMIP') &\n",
    "               (cat['frequency']=='mon') & \n",
    "               (cat['source_id']=='CESM2') &\n",
    "               (cat['experiment_id'].str.contains('Control'))].head(1)['path'].to_list()\n",
    "path_rsdt_control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01079658",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/keeling/a/mailes2/miniconda3/envs/paleoproj/lib/python3.10/site-packages/xarray/conventions.py:523: SerializationWarning: variable 'rsdt' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 1188, lat: 192, lon: 288, nbnd: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) object 0001-01-15 12:00:00 ... 0099-12-15 12:00:00\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    rsdt       (time, lat, lon) float32 ...\n",
       "    time_bnds  (time, nbnd) object 0001-01-01 00:00:00 ... 0100-01-01 00:00:00\n",
       "    lat_bnds   (lat, nbnd) float32 -90.0 -89.53 -89.53 ... 89.53 89.53 90.0\n",
       "    lon_bnds   (lon, nbnd) float32 -0.625 0.625 0.625 ... 358.1 358.1 359.4\n",
       "Attributes: (12/45)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    case_id:                3\n",
       "    cesm_casename:          b.e21.B1850.f09_g17.CMIP6-piControl.001\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-01-20T21:36:37Z\n",
       "    ...                     ...\n",
       "    variable_id:            rsdt\n",
       "    variant_info:           CMIP6 CESM2 piControl experiment with CAM6, inter...\n",
       "    variant_label:          r1i1p1f1\n",
       "    branch_time_in_parent:  48545.0\n",
       "    branch_time_in_child:   0.0\n",
       "    branch_method:          standard</pre><div class='xr-wrap' style='display:none'><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-d75b4136-97f9-4873-aebd-51fa10063c68' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d75b4136-97f9-4873-aebd-51fa10063c68' 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'>time</span>: 1188</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ff093d90-93fe-4e47-aace-62e9a966b719' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff093d90-93fe-4e47-aace-62e9a966b719' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-615384c2-2a2d-4b32-bd31-93614af11442' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-615384c2-2a2d-4b32-bd31-93614af11442' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72f3a587-d737-4b2a-95a4-5a6242054229' class='xr-var-data-in' type='checkbox'><label for='data-72f3a587-d737-4b2a-95a4-5a6242054229' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-1e452331-dba7-4c40-96cc-ec9e09ae2095' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e452331-dba7-4c40-96cc-ec9e09ae2095' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c139aa4a-bfcd-41d8-aca9-7b0a60aa219e' class='xr-var-data-in' type='checkbox'><label for='data-c139aa4a-bfcd-41d8-aca9-7b0a60aa219e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0001-01-15 12:00:00 ... 0099-12-...</div><input id='attrs-777aeca3-adab-43f6-8acc-f7d7c55d8718' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-777aeca3-adab-43f6-8acc-f7d7c55d8718' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82943ff5-2712-4fa4-8f63-435112cd771b' class='xr-var-data-in' type='checkbox'><label for='data-82943ff5-2712-4fa4-8f63-435112cd771b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>title :</span></dt><dd>time</dd><dt><span>type :</span></dt><dd>double</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1, 1, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 2, 14, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1, 3, 15, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(99, 10, 15, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(99, 11, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(99, 12, 15, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1eb13432-e4e9-45e5-9ab7-665a37abe55d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1eb13432-e4e9-45e5-9ab7-665a37abe55d' class='xr-section-summary' >Data variables: <span>(4)</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>rsdt</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fb2f3387-0e75-4d44-bad8-942e3313a75b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb2f3387-0e75-4d44-bad8-942e3313a75b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d297472-8948-4976-982d-be32eed06f7a' class='xr-var-data-in' type='checkbox'><label for='data-1d297472-8948-4976-982d-be32eed06f7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>comment :</span></dt><dd>at the top of the atmosphere</dd><dt><span>description :</span></dt><dd>at the top of the atmosphere</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>id :</span></dt><dd>rsdt</dd><dt><span>long_name :</span></dt><dd>TOA Incident Shortwave Radiation</dd><dt><span>mipTable :</span></dt><dd>Amon</dd><dt><span>out_name :</span></dt><dd>rsdt</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>prov :</span></dt><dd>Amon ((isd.003))</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>standard_name :</span></dt><dd>toa_incoming_shortwave_flux</dd><dt><span>time :</span></dt><dd>time</dd><dt><span>time_label :</span></dt><dd>time-mean</dd><dt><span>time_title :</span></dt><dd>Temporal mean</dd><dt><span>title :</span></dt><dd>TOA Incident Shortwave Radiation</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>variable_id :</span></dt><dd>rsdt</dd></dl></div><div class='xr-var-data'><pre>[65691648 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8f481928-48a8-483a-a7ca-53f27b991700' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f481928-48a8-483a-a7ca-53f27b991700' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-133530db-a50f-4b07-bd3d-33cc96079395' class='xr-var-data-in' type='checkbox'><label for='data-133530db-a50f-4b07-bd3d-33cc96079395' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[cftime.DatetimeNoLeap(1, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1, 2, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1, 3, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1, 4, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       ...,\n",
       "       [cftime.DatetimeNoLeap(99, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(99, 11, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(99, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(99, 12, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(99, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(100, 1, 1, 0, 0, 0, 0, has_year_zero=True)]],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, nbnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1c20d91a-c29d-4518-bc9b-d5319c61ad15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c20d91a-c29d-4518-bc9b-d5319c61ad15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f52aeeb-8136-4aa2-adb7-6a9ae18550ce' class='xr-var-data-in' type='checkbox'><label for='data-9f52aeeb-8136-4aa2-adb7-6a9ae18550ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([[-90.     , -89.52879],\n",
       "       [-89.52879, -88.58639],\n",
       "       [-88.58639, -87.64398],\n",
       "       ...,\n",
       "       [ 87.64398,  88.58639],\n",
       "       [ 88.58639,  89.52879],\n",
       "       [ 89.52879,  90.     ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, nbnd)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-01a5aa9d-322a-4897-98ba-85e2d998f03b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01a5aa9d-322a-4897-98ba-85e2d998f03b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef8e7501-f9a7-4db7-a6a0-b3d538bbfc77' class='xr-var-data-in' type='checkbox'><label for='data-ef8e7501-f9a7-4db7-a6a0-b3d538bbfc77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([[ -0.625,   0.625],\n",
       "       [  0.625,   1.875],\n",
       "       [  1.875,   3.125],\n",
       "       ...,\n",
       "       [355.625, 356.875],\n",
       "       [356.875, 358.125],\n",
       "       [358.125, 359.375]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f7a41f9-30ef-42b6-9fa9-c237fd5a91cd' class='xr-section-summary-in' type='checkbox'  ><label for='section-0f7a41f9-30ef-42b6-9fa9-c237fd5a91cd' class='xr-section-summary' >Attributes: <span>(45)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>case_id :</span></dt><dd>3</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.B1850.f09_g17.CMIP6-piControl.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-01-20T21:36:37Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>pre-industrial control</dd><dt><span>experiment_id :</span></dt><dd>piControl</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.piControl.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native 0.9x1.25 finite volume grid (192x288 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl-spinup</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC AER</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/05d3ccf2-420a-4311-8283-01b922604ccf</dd><dt><span>variable_id :</span></dt><dd>rsdt</dd><dt><span>variant_info :</span></dt><dd>CMIP6 CESM2 piControl experiment with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>branch_time_in_parent :</span></dt><dd>48545.0</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_method :</span></dt><dd>standard</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 1188, lat: 192, lon: 288, nbnd: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * time       (time) object 0001-01-15 12:00:00 ... 0099-12-15 12:00:00\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    rsdt       (time, lat, lon) float32 ...\n",
       "    time_bnds  (time, nbnd) object ...\n",
       "    lat_bnds   (lat, nbnd) float32 ...\n",
       "    lon_bnds   (lon, nbnd) float32 ...\n",
       "Attributes: (12/45)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    case_id:                3\n",
       "    cesm_casename:          b.e21.B1850.f09_g17.CMIP6-piControl.001\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-01-20T21:36:37Z\n",
       "    ...                     ...\n",
       "    variable_id:            rsdt\n",
       "    variant_info:           CMIP6 CESM2 piControl experiment with CAM6, inter...\n",
       "    variant_label:          r1i1p1f1\n",
       "    branch_time_in_parent:  48545.0\n",
       "    branch_time_in_child:   0.0\n",
       "    branch_method:          standard"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset(path_rsdt_control[0])\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b26787cf",
   "metadata": {},
   "source": [
    "# Skin Temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b09828a",
   "metadata": {},
   "source": [
    "Try doing something similar for skin temperature! Then, take a mean over time in order to get mean surface temperature over the whole globe, and then plot the corresponding map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de2effbb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e819339e",
   "metadata": {},
   "source": [
    "# TOA Radiation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d6daa1f",
   "metadata": {},
   "source": [
    "After reading through the notes of atmospheric radiation, try doing the same for TOA radiation! \n",
    "\n",
    "You will need to:\n",
    " - Pick out a model\n",
    " - Select the appropriate variables for that model\n",
    " - Add or subtract these variables from each other accordingly\n",
    " \n",
    "And then you can continue the same process as you did for surface temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c2a02f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fb13b3f2",
   "metadata": {},
   "source": [
    "# Surface Fluxes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "854227a4",
   "metadata": {},
   "source": [
    "Try doing the same for the surface fluxes! Use the \"Radiation introduction\" slideshow as a reference and read the pdf on latent heat flux and sensible heat flux to get an understanding of what's happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e78474b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dc166dea",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6bf65d",
   "metadata": {},
   "source": [
    "You'll be doing a regression over time of two different variables at the same location! The following steps should help guide you through this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42cf364e",
   "metadata": {},
   "source": [
    "### Step 1: Load your skin temperature and TOA Radiation datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72cb47fd",
   "metadata": {},
   "source": [
    "You've already done this, so this should be pretty simple. I'd suggest assigning TOA to a variable (e.g. TOA = ds_rsdt.rsdt - ds_rsut.rsut - ds_rlut.rlut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a095dcad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "63940149",
   "metadata": {},
   "source": [
    "### Step 2: Pick a location on Earth"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8c7a6e8",
   "metadata": {},
   "source": [
    "You'll want to pick a latitude and longitude to do this regression at. In the following example, I'll use a location at the equator (lat=0) with longitude=180.\n",
    "\n",
    "TOA_equator = TOA.sel(lat=0, method='nearest').isel(lon=180, method='nearest')\n",
    "\n",
    "sel picks a value from the data array based on the label. So, it picks a location at latitude 0 (On the equator) and longitude 180 (Somewhere in the Pacific Ocean).\n",
    "\n",
    "method='nearest' picks the closest location we have to that longitude or latitude. So, if we don't have latitude 0 but have latitude 0.5 instead, it'll pick lat=0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b62aa55e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f71a03ef",
   "metadata": {},
   "source": [
    "### Step 3: Get the anomalies from both datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a202ba6f",
   "metadata": {},
   "source": [
    "In order to get an understanding of what anomalies and climatologies are, I'd suggest skimming over this webpage: https://cds.climate.copernicus.eu/toolbox/doc/how-to/13_how_to_calculate_climatologies_and_anomalies/13_how_to_calculate_climatologies_and_anomalies.html\n",
    "\n",
    "In short, climatologies are the averages of variables over different time periods, while anomalies are the differences between the actual values and these averages. We're calculating monthly anomalies, so we want values that are the difference between our monthly values and what the normal average value for that month is.\n",
    "\n",
    "For example, if February this year was around 50 degrees on average, but Februaries are typically 40 degrees, that means the February anomaly this year was 10 degrees.\n",
    "\n",
    "Calculate the monthly climatologies using groupby means on months.\n",
    "\n",
    "climatology_sst = sst.groupby('time.month').mean('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22293a0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4cdf3477",
   "metadata": {},
   "source": [
    "Then get anomalies by subtracting our actual values by the climatologies:\n",
    "\n",
    "sst_anomalies = sst.groupby('time.month') - climatology_sst\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e575a05f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7616d72e",
   "metadata": {},
   "source": [
    "### Step 4: Do the regression!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2926a447",
   "metadata": {},
   "source": [
    "Now we can do the regression! I'm not sure how to use LinearRegression exactly, but I suggest using fit(sst, TOA). You should get the regression coefficient. Let me know if this works, and we can talk out a solution if it doesn't (or use np.polyfit).\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6f1f955",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4ae09a1b",
   "metadata": {},
   "source": [
    "### Step 5: Do the same with SST and Surface Radiation (If everything works out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc297704",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6931a22a",
   "metadata": {},
   "source": [
    "### Step 6: Iterate over every point on the globe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a0a0c83",
   "metadata": {},
   "source": [
    "You'll want to start by looking at the latitude and longitude arrays. You can access them using code similar to below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "afd6307c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-90.        , -89.05759162, -88.11518325, -87.17277487,\n",
       "       -86.23036649, -85.28795812, -84.34554974, -83.40314136,\n",
       "       -82.46073298, -81.51832461, -80.57591623, -79.63350785,\n",
       "       -78.69109948, -77.7486911 , -76.80628272, -75.86387435,\n",
       "       -74.92146597, -73.97905759, -73.03664921, -72.09424084,\n",
       "       -71.15183246, -70.20942408, -69.26701571, -68.32460733,\n",
       "       -67.38219895, -66.43979058, -65.4973822 , -64.55497382,\n",
       "       -63.61256545, -62.67015707, -61.72774869, -60.78534031,\n",
       "       -59.84293194, -58.90052356, -57.95811518, -57.01570681,\n",
       "       -56.07329843, -55.13089005, -54.18848168, -53.2460733 ,\n",
       "       -52.30366492, -51.36125654, -50.41884817, -49.47643979,\n",
       "       -48.53403141, -47.59162304, -46.64921466, -45.70680628,\n",
       "       -44.76439791, -43.82198953, -42.87958115, -41.93717277,\n",
       "       -40.9947644 , -40.05235602, -39.10994764, -38.16753927,\n",
       "       -37.22513089, -36.28272251, -35.34031414, -34.39790576,\n",
       "       -33.45549738, -32.51308901, -31.57068063, -30.62827225,\n",
       "       -29.68586387, -28.7434555 , -27.80104712, -26.85863874,\n",
       "       -25.91623037, -24.97382199, -24.03141361, -23.08900524,\n",
       "       -22.14659686, -21.20418848, -20.2617801 , -19.31937173,\n",
       "       -18.37696335, -17.43455497, -16.4921466 , -15.54973822,\n",
       "       -14.60732984, -13.66492147, -12.72251309, -11.78010471,\n",
       "       -10.83769634,  -9.89528796,  -8.95287958,  -8.0104712 ,\n",
       "        -7.06806283,  -6.12565445,  -5.18324607,  -4.2408377 ,\n",
       "        -3.29842932,  -2.35602094,  -1.41361257,  -0.47120419,\n",
       "         0.47120419,   1.41361257,   2.35602094,   3.29842932,\n",
       "         4.2408377 ,   5.18324607,   6.12565445,   7.06806283,\n",
       "         8.0104712 ,   8.95287958,   9.89528796,  10.83769634,\n",
       "        11.78010471,  12.72251309,  13.66492147,  14.60732984,\n",
       "        15.54973822,  16.4921466 ,  17.43455497,  18.37696335,\n",
       "        19.31937173,  20.2617801 ,  21.20418848,  22.14659686,\n",
       "        23.08900524,  24.03141361,  24.97382199,  25.91623037,\n",
       "        26.85863874,  27.80104712,  28.7434555 ,  29.68586387,\n",
       "        30.62827225,  31.57068063,  32.51308901,  33.45549738,\n",
       "        34.39790576,  35.34031414,  36.28272251,  37.22513089,\n",
       "        38.16753927,  39.10994764,  40.05235602,  40.9947644 ,\n",
       "        41.93717277,  42.87958115,  43.82198953,  44.76439791,\n",
       "        45.70680628,  46.64921466,  47.59162304,  48.53403141,\n",
       "        49.47643979,  50.41884817,  51.36125654,  52.30366492,\n",
       "        53.2460733 ,  54.18848168,  55.13089005,  56.07329843,\n",
       "        57.01570681,  57.95811518,  58.90052356,  59.84293194,\n",
       "        60.78534031,  61.72774869,  62.67015707,  63.61256545,\n",
       "        64.55497382,  65.4973822 ,  66.43979058,  67.38219895,\n",
       "        68.32460733,  69.26701571,  70.20942408,  71.15183246,\n",
       "        72.09424084,  73.03664921,  73.97905759,  74.92146597,\n",
       "        75.86387435,  76.80628272,  77.7486911 ,  78.69109948,\n",
       "        79.63350785,  80.57591623,  81.51832461,  82.46073298,\n",
       "        83.40314136,  84.34554974,  85.28795812,  86.23036649,\n",
       "        87.17277487,  88.11518325,  89.05759162,  90.        ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.coords['lat'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b44d3ba4",
   "metadata": {},
   "source": [
    "But before you start your regressions, you'll want to create an array that will hold all your regression coefficients:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a79a565",
   "metadata": {},
   "outputs": [],
   "source": [
    "TS_TOAfit = xr.DataArray(np.zeros(({length of your lat array},{length of your lon array})), dims=['lat','lon'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c374e18",
   "metadata": {},
   "source": [
    "This will create an array of zeroes with the same dimensions as your SST and radiation datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8771b1e5",
   "metadata": {},
   "source": [
    "Now that you have somewhere to store your coefficients, you'll want to iterate over both latitude and longitude to get a regression coefficient over every point on earth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a8801d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_index = 0 # You'll want to create an index for latitude so you can index your coefficients in the TS_TOAfit array\n",
    "for lat in (insert your latitude array!):\n",
    "    lon_index = 0\n",
    "    for lon in (insert your longitude array!):\n",
    "        TS_TOAfit[lat_index, lon_index] = # Insert your regression code!\n",
    "        lon_index += 1 \n",
    "    lat_index += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24c07b6",
   "metadata": {},
   "source": [
    "After you're done, you'll want to set the array's coordinates so it knows where the latitude and longitudes are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d690353d",
   "metadata": {},
   "outputs": [],
   "source": [
    "TS_TOAfit.coords['lon'] = {insert one of your rad or TS datasets}['lon'].values\n",
    "TS_TOAfit.coords['lat'] = {insert one of your rad or TS datasets}['lat'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5674dfbb",
   "metadata": {},
   "source": [
    "Try plotting your coefficients on a map!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "670cb44d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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