Masking_ZonalStats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regional time series calculations using shapefiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas\n",
    "import os\n",
    "#intake-esm to load the Pangeo CMIP6 cloud collection\n",
    "import intake\n",
    "import regionmask\n",
    "import cartopy.crs as ccrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accessing CMIP6 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activity_id</th>\n",
       "      <th>institution_id</th>\n",
       "      <th>source_id</th>\n",
       "      <th>experiment_id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>table_id</th>\n",
       "      <th>variable_id</th>\n",
       "      <th>grid_label</th>\n",
       "      <th>zstore</th>\n",
       "      <th>dcpp_init_year</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>hfls</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>hus</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>zg</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>prw</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HighResMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>highresSST-present</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>ps</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20170706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511420</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>abrupt-4xCO2</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>sfcWind</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/abrupt...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511421</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>abrupt-4xCO2</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>uas</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/abrupt...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511422</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>CMCC</td>\n",
       "      <td>CMCC-CM2-HR4</td>\n",
       "      <td>abrupt-4xCO2</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>day</td>\n",
       "      <td>rlut</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/abrupt...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511423</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>day</td>\n",
       "      <td>tasmin</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/ScenarioMIP/AS-RCEC/TaiESM1/s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511424</th>\n",
       "      <td>ScenarioMIP</td>\n",
       "      <td>AS-RCEC</td>\n",
       "      <td>TaiESM1</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>day</td>\n",
       "      <td>tasmax</td>\n",
       "      <td>gn</td>\n",
       "      <td>gs://cmip6/CMIP6/ScenarioMIP/AS-RCEC/TaiESM1/s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20210721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>511425 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "        activity_id institution_id     source_id       experiment_id  \\\n",
       "0        HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present   \n",
       "1        HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present   \n",
       "2        HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present   \n",
       "3        HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present   \n",
       "4        HighResMIP           CMCC  CMCC-CM2-HR4  highresSST-present   \n",
       "...             ...            ...           ...                 ...   \n",
       "511420         CMIP           CMCC  CMCC-CM2-HR4        abrupt-4xCO2   \n",
       "511421         CMIP           CMCC  CMCC-CM2-HR4        abrupt-4xCO2   \n",
       "511422         CMIP           CMCC  CMCC-CM2-HR4        abrupt-4xCO2   \n",
       "511423  ScenarioMIP        AS-RCEC       TaiESM1              ssp585   \n",
       "511424  ScenarioMIP        AS-RCEC       TaiESM1              ssp585   \n",
       "\n",
       "       member_id table_id variable_id grid_label  \\\n",
       "0       r1i1p1f1     Amon        hfls         gn   \n",
       "1       r1i1p1f1     Amon         hus         gn   \n",
       "2       r1i1p1f1     Amon          zg         gn   \n",
       "3       r1i1p1f1     Amon         prw         gn   \n",
       "4       r1i1p1f1     Amon          ps         gn   \n",
       "...          ...      ...         ...        ...   \n",
       "511420  r1i1p1f1     Amon     sfcWind         gn   \n",
       "511421  r1i1p1f1     Amon         uas         gn   \n",
       "511422  r1i1p1f1      day        rlut         gn   \n",
       "511423  r1i1p1f1      day      tasmin         gn   \n",
       "511424  r1i1p1f1      day      tasmax         gn   \n",
       "\n",
       "                                                   zstore  dcpp_init_year  \\\n",
       "0       gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN   \n",
       "1       gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN   \n",
       "2       gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN   \n",
       "3       gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN   \n",
       "4       gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...             NaN   \n",
       "...                                                   ...             ...   \n",
       "511420  gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/abrupt...             NaN   \n",
       "511421  gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/abrupt...             NaN   \n",
       "511422  gs://cmip6/CMIP6/CMIP/CMCC/CMCC-CM2-HR4/abrupt...             NaN   \n",
       "511423  gs://cmip6/CMIP6/ScenarioMIP/AS-RCEC/TaiESM1/s...             NaN   \n",
       "511424  gs://cmip6/CMIP6/ScenarioMIP/AS-RCEC/TaiESM1/s...             NaN   \n",
       "\n",
       "         version  \n",
       "0       20170706  \n",
       "1       20170706  \n",
       "2       20170706  \n",
       "3       20170706  \n",
       "4       20170706  \n",
       "...          ...  \n",
       "511420  20210624  \n",
       "511421  20210624  \n",
       "511422  20210624  \n",
       "511423  20210721  \n",
       "511424  20210721  \n",
       "\n",
       "[511425 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = intake.open_esm_datastore(\"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\")\n",
    "col.df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking models available through Pangeo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ACCESS-CM2', 'ACCESS-ESM1-5', 'AWI-CM-1-1-MR', 'AWI-ESM-1-1-LR',\n",
       "       'BCC-CSM2-HR', 'BCC-CSM2-MR', 'BCC-ESM1', 'CAMS-CSM1-0',\n",
       "       'CAS-ESM2-0', 'CESM1-1-CAM5-CMIP5', 'CESM1-WACCM-SC', 'CESM2',\n",
       "       'CESM2-FV2', 'CESM2-WACCM', 'CESM2-WACCM-FV2', 'CIESM',\n",
       "       'CMCC-CM2-HR4', 'CMCC-CM2-SR5', 'CMCC-CM2-VHR4', 'CMCC-ESM2',\n",
       "       'CNRM-CM6-1', 'CNRM-CM6-1-HR', 'CNRM-ESM2-1', 'CanESM5',\n",
       "       'CanESM5-CanOE', 'E3SM-1-0', 'E3SM-1-1', 'E3SM-1-1-ECA',\n",
       "       'EC-Earth3', 'EC-Earth3-AerChem', 'EC-Earth3-CC', 'EC-Earth3-LR',\n",
       "       'EC-Earth3-Veg', 'EC-Earth3-Veg-LR', 'EC-Earth3P', 'EC-Earth3P-HR',\n",
       "       'EC-Earth3P-VHR', 'ECMWF-IFS-HR', 'ECMWF-IFS-LR', 'FGOALS-f3-H',\n",
       "       'FGOALS-f3-L', 'FGOALS-g3', 'FIO-ESM-2-0', 'GFDL-AM4', 'GFDL-CM4',\n",
       "       'GFDL-CM4C192', 'GFDL-ESM2M', 'GFDL-ESM4', 'GFDL-OM4p5B',\n",
       "       'GISS-E2-1-G', 'GISS-E2-1-G-CC', 'GISS-E2-1-H', 'GISS-E2-2-G',\n",
       "       'HadGEM3-GC31-HM', 'HadGEM3-GC31-LL', 'HadGEM3-GC31-LM',\n",
       "       'HadGEM3-GC31-MM', 'IITM-ESM', 'INM-CM4-8', 'INM-CM5-0',\n",
       "       'INM-CM5-H', 'IPSL-CM5A2-INCA', 'IPSL-CM6A-ATM-HR', 'IPSL-CM6A-LR',\n",
       "       'IPSL-CM6A-LR-INCA', 'KACE-1-0-G', 'KIOST-ESM', 'MCM-UA-1-0',\n",
       "       'MIROC-ES2H', 'MIROC-ES2L', 'MIROC6', 'MPI-ESM-1-2-HAM',\n",
       "       'MPI-ESM1-2-HR', 'MPI-ESM1-2-LR', 'MPI-ESM1-2-XR', 'MRI-AGCM3-2-H',\n",
       "       'MRI-AGCM3-2-S', 'MRI-ESM2-0', 'NESM3', 'NorCPM1', 'NorESM1-F',\n",
       "       'NorESM2-LM', 'NorESM2-MM', 'SAM0-UNICON', 'TaiESM1',\n",
       "       'UKESM1-0-LL', 'ssp585'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(col.df['source_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['r1i1p1f1', 'r3i1p1f1', 'r2i1p1f1', 'r4i1p1f1', 'r6i1p1f1',\n",
       "       'r9i1p1f1', 'r8i1p1f1', 'r7i1p1f1', 'r5i1p1f1', 'r10i1p1f1',\n",
       "       'r12i1p1f1', 'r11i1p1f1', 'r13i1p1f1', 'r14i1p1f1', 'r17i1p1f1',\n",
       "       'r15i1p1f1', 'r16i1p1f1', 'r18i1p1f1', 'r20i1p1f1', 'r19i1p1f1',\n",
       "       'r30i1p1f1', 'r29i1p1f1', 'r28i1p1f1', 'r24i1p1f1', 'r27i1p1f1',\n",
       "       'r26i1p1f1', 'r21i1p1f1', 'r25i1p1f1', 'r23i1p1f1', 'r22i1p1f1'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the different runs available within one specific model\n",
    "col.df.query(\"source_id == 'ACCESS-ESM1-5'\")['member_id'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing sea surface temperature data using ACCESS-ESM 1.5 model\n",
    "Using one model and one variable initially to develop the script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.zstore.dcpp_init_year.version'\n"
     ]
    },
    {
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   "source": [
    "# create a subcollection with certain search criteria\n",
    "cat = col.search(\n",
    "    #Set the variables that you want to search for\n",
    "    variable_id = 'tos',\n",
    "    source_id = 'ACCESS-ESM1-5',\n",
    "    #Eperiments of interest\n",
    "    experiment_id = ['historical', 'ssp585'],\n",
    "    #Native grid\n",
    "    grid_label = 'gn',\n",
    "    #Monthly ocean data\n",
    "    table_id = 'Omon',\n",
    "    member_id = 'r22i1p1f1'\n",
    ")\n",
    "\n",
    "\n",
    "# load all datasets into a python dictionary\n",
    "ddict = cat.to_dataset_dict(\n",
    "    zarr_kwargs={'consolidated':True, 'use_cftime':True}, # recommended for faster reading and better time handling\n",
    "    storage_options={'token': 'anon'}, # needed to access the public CMIP6 data on google\n",
    "    aggregate=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (i: 360, j: 300, time: 1980, bnds: 2, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
       "    latitude            (j, i) float64 dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;\n",
       "    longitude           (j, i) float64 dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;\n",
       "  * time                (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:0...\n",
       "    time_bnds           (time, bnds) object dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    tos                 (time, j, i) float32 dask.array&lt;chunksize=(201, 300, 360), meta=np.ndarray&gt;\n",
       "    vertices_latitude   (j, i, vertices) float64 dask.array&lt;chunksize=(300, 360, 4), meta=np.ndarray&gt;\n",
       "    vertices_longitude  (j, i, vertices) float64 dask.array&lt;chunksize=(300, 360, 4), meta=np.ndarray&gt;\n",
       "Attributes: (12/52)\n",
       "    Conventions:             CF-1.7 CMIP-6.2\n",
       "    activity_id:             CMIP\n",
       "    branch_method:           standard\n",
       "    branch_time_in_child:    0.0\n",
       "    branch_time_in_parent:   175317.0\n",
       "    cmor_version:            3.4.0\n",
       "    ...                      ...\n",
       "    variable_id:             tos\n",
       "    variant_label:           r22i1p1f1\n",
       "    version:                 v20200922\n",
       "    version_id:              v20200922\n",
       "    intake_esm_varname:      None\n",
       "    intake_esm_dataset_key:  CMIP.CSIRO.ACCESS-ESM1-5.historical.r22i1p1f1.Om...</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-7145e374-90b1-4369-87ef-78022e4bdb0a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7145e374-90b1-4369-87ef-78022e4bdb0a' 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'>i</span>: 360</li><li><span class='xr-has-index'>j</span>: 300</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span>bnds</span>: 2</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b4a4e483-02a1-4de6-ae5a-6ada10f16bdb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b4a4e483-02a1-4de6-ae5a-6ada10f16bdb' class='xr-section-summary' >Coordinates: <span>(6)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 355 356 357 358 359</div><input id='attrs-28c33860-3c0f-4b4b-add3-d56b3605e598' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28c33860-3c0f-4b4b-add3-d56b3605e598' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f3bc2ca-82ee-46f8-af60-9ef66540b8e7' class='xr-var-data-in' type='checkbox'><label for='data-3f3bc2ca-82ee-46f8-af60-9ef66540b8e7' 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>long_name :</span></dt><dd>cell index along first dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 357, 358, 359], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 295 296 297 298 299</div><input id='attrs-1d1bc523-987b-40cc-9061-2907aa8905dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d1bc523-987b-40cc-9061-2907aa8905dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53ad6b47-46e6-428d-ae95-86a678715ed0' class='xr-var-data-in' type='checkbox'><label for='data-53ad6b47-46e6-428d-ae95-86a678715ed0' 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>long_name :</span></dt><dd>cell index along second dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 297, 298, 299], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;</div><input id='attrs-1a60b20c-07fc-4e4f-a3ed-e4b5950f4b47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a60b20c-07fc-4e4f-a3ed-e4b5950f4b47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-024173bb-aa33-4668-9a84-a1001aa15388' class='xr-var-data-in' type='checkbox'><label for='data-024173bb-aa33-4668-9a84-a1001aa15388' 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>bounds :</span></dt><dd>vertices_latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Bytes </th><td> 843.75 kiB </td> <td> 843.75 kiB </td></tr>\n",
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       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;</div><input id='attrs-f03d93d7-3694-4fcc-81c4-85a14ba73ced' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f03d93d7-3694-4fcc-81c4-85a14ba73ced' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b79c92fc-5a66-49ef-bbd8-3aee9ea9ed88' class='xr-var-data-in' type='checkbox'><label for='data-b79c92fc-5a66-49ef-bbd8-3aee9ea9ed88' 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>bounds :</span></dt><dd>vertices_longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 843.75 kiB </td> <td> 843.75 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (300, 360) </td> <td> (300, 360) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></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'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-667c542d-27a0-446f-bc65-dc03cfce9934' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-667c542d-27a0-446f-bc65-dc03cfce9934' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bb2dbc0-4ca3-4bfa-a8b9-d1560a373732' class='xr-var-data-in' type='checkbox'><label for='data-3bb2dbc0-4ca3-4bfa-a8b9-d1560a373732' 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>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeProlepticGregorian(1850, 1, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeProlepticGregorian(1850, 2, 15, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeProlepticGregorian(1850, 3, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       ...,\n",
       "       cftime.DatetimeProlepticGregorian(2014, 10, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeProlepticGregorian(2014, 11, 16, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeProlepticGregorian(2014, 12, 16, 12, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-cedb6b4b-2c92-4f98-bf9c-0d8b2513e006' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cedb6b4b-2c92-4f98-bf9c-0d8b2513e006' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ecd78cc-e8b3-4c4f-bee3-35a6f55a23f9' class='xr-var-data-in' type='checkbox'><label for='data-1ecd78cc-e8b3-4c4f-bee3-35a6f55a23f9' 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'><table>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 30.94 kiB </td> <td> 30.94 kiB </td></tr>\n",
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       "    <tr><th> Type </th><td> object </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b061b765-d0f8-4fb9-9e5c-cc736f155015' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b061b765-d0f8-4fb9-9e5c-cc736f155015' class='xr-section-summary' >Data variables: <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>tos</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(201, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-2607be76-b9ce-410c-93ee-7c73e2f59f3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2607be76-b9ce-410c-93ee-7c73e2f59f3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be6b4a27-9ddc-495a-b785-ff4399a22852' class='xr-var-data-in' type='checkbox'><label for='data-be6b4a27-9ddc-495a-b785-ff4399a22852' 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: areacello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Temperature of upper boundary of the liquid ocean, including temperatures below sea-ice and floating ice shelves.</dd><dt><span>history :</span></dt><dd>2020-09-22T09:12:02Z altered by CMOR: replaced missing value flag (-1e+20) with standard missing value (1e+20).</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 815.73 MiB </td> <td> 82.81 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1980, 300, 360) </td> <td> (201, 300, 360) </td></tr>\n",
       "    <tr><th> Count </th><td> 11 Tasks </td><td> 10 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertices_latitude</span></div><div class='xr-var-dims'>(j, i, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360, 4), meta=np.ndarray&gt;</div><input id='attrs-1d7648a4-bf2d-4629-982d-721e89eaac91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d7648a4-bf2d-4629-982d-721e89eaac91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78164c49-d907-40ba-b29f-bcd52a8dc253' class='xr-var-data-in' type='checkbox'><label for='data-78164c49-d907-40ba-b29f-bcd52a8dc253' 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'><table>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertices_longitude</span></div><div class='xr-var-dims'>(j, i, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360, 4), meta=np.ndarray&gt;</div><input id='attrs-208f9620-2000-48c5-bd29-d2c684c292ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-208f9620-2000-48c5-bd29-d2c684c292ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ba63255-c352-42d1-9efa-d01115eab465' class='xr-var-data-in' type='checkbox'><label for='data-3ba63255-c352-42d1-9efa-d01115eab465' 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'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d98eb7be-fb36-44e7-932e-37318e00b83c' class='xr-section-summary-in' type='checkbox'  ><label for='section-d98eb7be-fb36-44e7-932e-37318e00b83c' class='xr-section-summary' >Attributes: <span>(52)</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>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>175317.0</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>creation_date :</span></dt><dd>2020-09-22T09:12:03Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacello</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.CSIRO.ACCESS-ESM1-5.historical.none.r22i1p1f1</dd><dt><span>grid :</span></dt><dd>native atmosphere N96 grid (145x192 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2020-09-22T09:12:03Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CSIRO 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>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/a90d691c-3579-4c3c-ba73-fc8210b83504</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>notes :</span></dt><dd>Exp: ESM-historical; Local ID: HI-26; Variable: tos ([&#x27;sst&#x27;])</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</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>22</dd><dt><span>realm :</span></dt><dd>ocean</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>source :</span></dt><dd>ACCESS-ESM1.5 (2019): \n",
       "aerosol: CLASSIC (v1.0)\n",
       "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
       "atmosChem: none\n",
       "land: CABLE2.4\n",
       "landIce: none\n",
       "ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\n",
       "ocnBgchem: WOMBAT (same grid as ocean)\n",
       "seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>status :</span></dt><dd>2021-03-11;created; by gcs.cmip6.ldeo@gmail.com</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>Omon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(30 April 2019) MD5:5bd755e94c2173cb3050a0f3480f60c4</dd><dt><span>title :</span></dt><dd>ACCESS-ESM1-5 output prepared for CMIP6</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/a90d691c-3579-4c3c-ba73-fc8210b83504</dd><dt><span>variable_id :</span></dt><dd>tos</dd><dt><span>variant_label :</span></dt><dd>r22i1p1f1</dd><dt><span>version :</span></dt><dd>v20200922</dd><dt><span>version_id :</span></dt><dd>v20200922</dd><dt><span>intake_esm_varname :</span></dt><dd>None</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.CSIRO.ACCESS-ESM1-5.historical.r22i1p1f1.Omon.tos.gn.gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r22i1p1f1/Omon/tos/gn/v20200922/.nan.20200922</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (i: 360, j: 300, time: 1980, bnds: 2, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
       "    latitude            (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
       "    longitude           (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
       "  * time                (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:0...\n",
       "    time_bnds           (time, bnds) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    tos                 (time, j, i) float32 dask.array<chunksize=(201, 300, 360), meta=np.ndarray>\n",
       "    vertices_latitude   (j, i, vertices) float64 dask.array<chunksize=(300, 360, 4), meta=np.ndarray>\n",
       "    vertices_longitude  (j, i, vertices) float64 dask.array<chunksize=(300, 360, 4), meta=np.ndarray>\n",
       "Attributes: (12/52)\n",
       "    Conventions:             CF-1.7 CMIP-6.2\n",
       "    activity_id:             CMIP\n",
       "    branch_method:           standard\n",
       "    branch_time_in_child:    0.0\n",
       "    branch_time_in_parent:   175317.0\n",
       "    cmor_version:            3.4.0\n",
       "    ...                      ...\n",
       "    variable_id:             tos\n",
       "    variant_label:           r22i1p1f1\n",
       "    version:                 v20200922\n",
       "    version_id:              v20200922\n",
       "    intake_esm_varname:      None\n",
       "    intake_esm_dataset_key:  CMIP.CSIRO.ACCESS-ESM1-5.historical.r22i1p1f1.Om..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST = ddict['CMIP.CSIRO.ACCESS-ESM1-5.historical.r22i1p1f1.Omon.tos.gn.gs://cmip6/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r22i1p1f1/Omon/tos/gn/v20200922/.nan.20200922']\n",
    "SST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining function to correct coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def corrcoords(array):\n",
    "    '''\n",
    "    Inputs:\n",
    "    array - Data array on which longitude corrections will be applied.\n",
    "    \n",
    "    Output:\n",
    "    Data array with corrected longitude values.\n",
    "    '''\n",
    "    \n",
    "    #Making a deep copy of original longitude values in the array being corrected\n",
    "    lon = array['longitude'][0].values\n",
    "    \n",
    "    #Values changed from 0-360 to between -180 and +180.\n",
    "    lon[np.where(lon > 180)] = lon[np.where(lon >180)]-360\n",
    "    \n",
    "    #Assigning corrected longitudes and latitude values to i and j, which are index based\n",
    "    array.coords['i'] = lon\n",
    "    array.coords['j'] = array['latitude'][:,0].values\n",
    "    \n",
    "    #Removing latitude and longitude dimensions and renaming i and j dimensions\n",
    "    array = array.drop(('latitude', 'longitude'))\n",
    "    array = array.rename(({'i':'longitude', 'j':'latitude'}))\n",
    "    \n",
    "    #Longitude values must be sorted from smallest to largest prior to plotting\n",
    "    array = array.sortby(array.longitude)\n",
    "    \n",
    "    return array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/lib/python3.9/site-packages/xarray/core/indexing.py:1226: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n"
     ]
    }
   ],
   "source": [
    "SST_SO = corrcoords(SST.tos).sel(latitude = slice(-90, -30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f790baf53a0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SST_SO[0].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading shapefile with regions of interest\n",
    "Using `geopandas` to read shapefile and `regionmask` to create the mask to be applied to the dataset.  \n",
    "For this notebook, a subset of the MEASO regions [https://soki.aq/pages/viewpage.action?pageId=61210912], use to carry out ecosystem assessments in the Southern Ocean, will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "measo = geopandas.read_file('shapefiles/measo_wgs84.shp')\n",
    "\n",
    "#West Pacific regions are removed because they have duplicate names as the boundaries straddle the -180/180 boundary\n",
    "measo_sub = measo.query(\"sector != 'West Pacific'\")\n",
    "\n",
    "#Plotting subset for reference\n",
    "measo_sub.plot(column = 'name', legend = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating an xarray mask of the MEASO regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "}\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",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\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",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\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,\n",
       ".xr-attrs 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",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;region&#x27; (region: 16, lat: 77, lon: 360)&gt;\n",
       "array([[[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "\n",
       "       [[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "\n",
       "       [[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "\n",
       "       [[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "\n",
       "       [[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -77.88 -77.63 -77.38 -77.13 ... -32.5 -31.5 -30.5\n",
       "  * lon      (lon) float64 -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5\n",
       "  * region   (region) int64 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'region'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>region</span>: 16</li><li><span class='xr-has-index'>lat</span>: 77</li><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5a54ec62-dbae-4ca2-bf3e-5feb37a6598f' class='xr-array-in' type='checkbox' checked><label for='section-5a54ec62-dbae-4ca2-bf3e-5feb37a6598f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>False False False False False False ... False False False False False</span></div><div class='xr-array-data'><pre>array([[[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "\n",
       "       [[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "\n",
       "       [[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "\n",
       "       [[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "\n",
       "       [[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]]])</pre></div></div></li><li class='xr-section-item'><input id='section-058b45de-9ea3-4c1d-8790-60326f018cc8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-058b45de-9ea3-4c1d-8790-60326f018cc8' 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'>-77.88 -77.63 ... -31.5 -30.5</div><input id='attrs-777b8368-ba40-4a5a-abaf-d1c9f2a3abc1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-777b8368-ba40-4a5a-abaf-d1c9f2a3abc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b09c686-1d97-490c-acd4-fa975a5ae7b0' class='xr-var-data-in' type='checkbox'><label for='data-7b09c686-1d97-490c-acd4-fa975a5ae7b0' 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([-77.876625, -77.629715, -77.381706, -77.132294, -76.880531, -76.626129,\n",
       "       -76.368141, -76.106285, -75.839645, -75.567932, -75.29026 , -75.006363,\n",
       "       -74.715355, -74.417   , -74.110443, -73.795479, -73.471268, -73.137634,\n",
       "       -72.793785, -72.439552, -72.074196, -71.697586, -71.308998, -70.908363,\n",
       "       -70.494987, -70.068848, -69.629288, -69.176323, -68.709351, -68.228439,\n",
       "       -67.733017, -67.223198, -66.698479, -66.159004, -65.604324, -65.034637,\n",
       "       -64.449539, -63.849281, -63.233509, -62.602528, -61.956032, -61.294376,\n",
       "       -60.617313, -59.92524 , -59.21796 , -58.495926, -57.758991, -57.007656,\n",
       "       -56.241821, -55.462036, -54.668247, -53.861053, -53.040443, -52.207062,\n",
       "       -51.360947, -50.502777, -49.632637, -48.751244, -47.858719, -46.955818,\n",
       "       -46.042698, -45.120152, -44.188366, -43.248169, -42.299767, -41.344021,\n",
       "       -40.381168, -39.41209 , -38.437046, -37.456936, -36.472038, -35.483269,\n",
       "       -34.490921, -33.49593 , -32.498589, -31.499844, -30.5     ])</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'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-c742d1aa-f868-451f-87cc-cbefca276cff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c742d1aa-f868-451f-87cc-cbefca276cff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be95708d-ff4d-43d5-9a0b-c2420b2ca1d7' class='xr-var-data-in' type='checkbox'><label for='data-be95708d-ff4d-43d5-9a0b-c2420b2ca1d7' 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([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>5 6 7 8 9 10 ... 15 16 17 18 19 20</div><input id='attrs-1c0df137-07b4-4655-936b-9307b7318ecf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c0df137-07b4-4655-936b-9307b7318ecf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b66b438-b19d-4a0d-a614-0c060f99eeb1' class='xr-var-data-in' type='checkbox'><label for='data-4b66b438-b19d-4a0d-a614-0c060f99eeb1' 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([ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e16ce97-86c6-4ce9-b16e-6a64b22a7a4a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8e16ce97-86c6-4ce9-b16e-6a64b22a7a4a' 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.DataArray 'region' (region: 16, lat: 77, lon: 360)>\n",
       "array([[[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "\n",
       "       [[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "\n",
       "       [[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "\n",
       "       [[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]],\n",
       "\n",
       "       [[False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        ...,\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False],\n",
       "        [False, False, False, ..., False, False, False]]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -77.88 -77.63 -77.38 -77.13 ... -32.5 -31.5 -30.5\n",
       "  * lon      (lon) float64 -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5\n",
       "  * region   (region) int64 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Defining the extent of the shapefile in lat and lon\n",
    "longitude = SST_SO['longitude'].values\n",
    "latitude = SST_SO['latitude'].values\n",
    "\n",
    "#Creating a 3D mask, which includes regions as a dimension\n",
    "mask3D = regionmask.mask_3D_geopandas(measo_sub, longitude, latitude)\n",
    "mask3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;region&#x27; (region: 16)&gt;\n",
       "array([&#x27;EPA&#x27;, &#x27;EPS&#x27;, &#x27;EPN&#x27;, &#x27;EPT&#x27;, &#x27;AOA&#x27;, &#x27;AOT&#x27;, &#x27;AOS&#x27;, &#x27;AON&#x27;, &#x27;CIA&#x27;, &#x27;CIS&#x27;,\n",
       "       &#x27;CIN&#x27;, &#x27;CIT&#x27;, &#x27;EIA&#x27;, &#x27;EIS&#x27;, &#x27;EIN&#x27;, &#x27;EIT&#x27;], dtype=&#x27;&lt;U3&#x27;)\n",
       "Coordinates:\n",
       "  * region   (region) &lt;U3 &#x27;EPA&#x27; &#x27;EPS&#x27; &#x27;EPN&#x27; &#x27;EPT&#x27; ... &#x27;EIA&#x27; &#x27;EIS&#x27; &#x27;EIN&#x27; &#x27;EIT&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'region'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>region</span>: 16</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3a3ca686-4ab5-4f7d-b0f2-2ce1610d8393' class='xr-array-in' type='checkbox' checked><label for='section-3a3ca686-4ab5-4f7d-b0f2-2ce1610d8393' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>&#x27;EPA&#x27; &#x27;EPS&#x27; &#x27;EPN&#x27; &#x27;EPT&#x27; &#x27;AOA&#x27; &#x27;AOT&#x27; ... &#x27;CIT&#x27; &#x27;EIA&#x27; &#x27;EIS&#x27; &#x27;EIN&#x27; &#x27;EIT&#x27;</span></div><div class='xr-array-data'><pre>array([&#x27;EPA&#x27;, &#x27;EPS&#x27;, &#x27;EPN&#x27;, &#x27;EPT&#x27;, &#x27;AOA&#x27;, &#x27;AOT&#x27;, &#x27;AOS&#x27;, &#x27;AON&#x27;, &#x27;CIA&#x27;, &#x27;CIS&#x27;,\n",
       "       &#x27;CIN&#x27;, &#x27;CIT&#x27;, &#x27;EIA&#x27;, &#x27;EIS&#x27;, &#x27;EIN&#x27;, &#x27;EIT&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-6cf5fd2c-3c50-43e0-aee3-e17d706b5b0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6cf5fd2c-3c50-43e0-aee3-e17d706b5b0c' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;EPA&#x27; &#x27;EPS&#x27; &#x27;EPN&#x27; ... &#x27;EIN&#x27; &#x27;EIT&#x27;</div><input id='attrs-93f9bde2-d163-4b3d-b11f-30071670ddc3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93f9bde2-d163-4b3d-b11f-30071670ddc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c75a647-c2d0-44a4-812d-fc342ce00993' class='xr-var-data-in' type='checkbox'><label for='data-9c75a647-c2d0-44a4-812d-fc342ce00993' 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;EPA&#x27;, &#x27;EPS&#x27;, &#x27;EPN&#x27;, &#x27;EPT&#x27;, &#x27;AOA&#x27;, &#x27;AOT&#x27;, &#x27;AOS&#x27;, &#x27;AON&#x27;, &#x27;CIA&#x27;, &#x27;CIS&#x27;,\n",
       "       &#x27;CIN&#x27;, &#x27;CIT&#x27;, &#x27;EIA&#x27;, &#x27;EIS&#x27;, &#x27;EIN&#x27;, &#x27;EIT&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-463a4698-4a08-4e6b-8eff-b7b4b3c6f752' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-463a4698-4a08-4e6b-8eff-b7b4b3c6f752' 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.DataArray 'region' (region: 16)>\n",
       "array(['EPA', 'EPS', 'EPN', 'EPT', 'AOA', 'AOT', 'AOS', 'AON', 'CIA', 'CIS',\n",
       "       'CIN', 'CIT', 'EIA', 'EIS', 'EIN', 'EIT'], dtype='<U3')\n",
       "Coordinates:\n",
       "  * region   (region) <U3 'EPA' 'EPS' 'EPN' 'EPT' ... 'EIA' 'EIS' 'EIN' 'EIT'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a dictionary with region names\n",
    "names = regionmask.from_geopandas(measo_sub, names = \"name\", name = \"name\")\n",
    "\n",
    "#Creating a new variable with the names of regions\n",
    "reg = []\n",
    "[reg.append(names[i].name) for i in mask3D.region.values]\n",
    "\n",
    "#Replacing numbered regions for names in the mask\n",
    "mask3D['region'] = reg\n",
    "\n",
    "#Checking results\n",
    "mask3D.region"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "# Applying masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataArray' object has no attribute 'mask'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_618/836474588.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# mask3D.mask(SST_SO.longitude, SST_SO.latitude)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m#Does not work\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmask3D\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSST_SO\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/srv/conda/lib/python3.9/site-packages/xarray/core/common.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    237\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m         raise AttributeError(\n\u001b[0m\u001b[1;32m    240\u001b[0m             \u001b[0;34m\"{!r} object has no attribute {!r}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m         )\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataArray' object has no attribute 'mask'"
     ]
    }
   ],
   "source": [
    "#Does not work\n",
    "mask3D.mask(SST_SO.longitude, SST_SO.latitude)\n",
    "#Does not work\n",
    "mask3D.mask(SST_SO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataArray' object has no attribute 'mask'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_618/4192035399.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregionmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask_geopandas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeaso_sub\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlongitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlatitude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSST_SO\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlongitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSST_SO\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatitude\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      3\u001b[0m \u001b[0;31m# regionmask.Regions([measo_sub])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/lib/python3.9/site-packages/xarray/core/common.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    237\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m         raise AttributeError(\n\u001b[0m\u001b[1;32m    240\u001b[0m             \u001b[0;34m\"{!r} object has no attribute {!r}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m         )\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataArray' object has no attribute 'mask'"
     ]
    }
   ],
   "source": [
    "#Does not work\n",
    "x = regionmask.mask_geopandas(measo_sub, longitude, latitude)\n",
    "x.mask(SST_SO.longitude, SST_SO.latitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_dict = {'sst': SST_SO, 'mask': mask3D}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = mask3D.sel(region = 'EPA')\n",
    "y = y.rename({'lon': 'longitude','lat': 'latitude'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = SST_SO[0]*y\n",
    "y = x.drop('lat').drop('lon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fdf4eae89d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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upG5m1ipBcaQ/02d/RmVm1sck91M3M+u6Tp4zcvOLmVleuE3dzCxf3PxiZpYTkigUi70OoyYndTOzVgkK7v1iZtbYQk9stntQsPrc+8XMLDfk8dTNzHLEvV/MzPLFzS82MDzIlw2i7CBeHSehoZHuba8FTupmZi0TuKZuZpYTArmfuplZXggKTurWZ7raBmmWJ6JtSV3SKuALwH5ABVgXER+fb3lO6mZmLVJ7Lz6aAf4sIq6RtBy4WtLFEXHTfApzUjcza5UEber9EhH3Afelj7dKuhk4EHBSNzPrlhZq6islrc88XxcR62qWKa0Gng78ZL5xOambWS509foKtXSidHNErJ27SC0Dvgb8aUQ8Ot/QnNTNzFrW3t4vkoZJEvq5EfH1hZTlpG5m1qo29lOXJOBfgZsj4u8XWl5/XhJlZtbX0itKm5nm9hzg9cDzJV2XTifONzLX1G03Y+PjHv/FrJE2jv0SET8k6fneFk7qZmbz4bFfzMxyQkIeJsDMLC889osNELenm81BuPnFzCwvJKFh3yTDzCwn3PxiZpYrvkepmVletDb2S1f15KtG0lmS7ql19ZSkMyRtlHSrpBf3Ij4zszmp0NzUZb2sqX8sIv42O0PS4cApwBHAAcAlkg6LiHIvAjQzq009SdjN6Lfml5OBCyJiCrhT0kbgWODy3oZlZpYhiEK/pc9EL79qTpe0QdI5kvZK5x0I3J1ZZ1M6bzeSTpW0XtL6hzZv7nSsuVSrP7r7qJs1Q0m7ejNTl3UsqUu6RNINNaaTgX8CnggcRXIbp7+rvqxGUVGr/IhYFxFrI2LtPitXduItmJnV175RGtuqqd8Pkg4jScSPj4inSDoSOCkiPlzvNRFxfJNl/wvw7fTpJmBVZvFBwL3NlGNm1i0BRJ+2qTcb1b8AZwDTABGxgeSE5rxI2j/z9OXADenji4BTJI1KOgQ4FLhyvtsxM+sIaeB7vyyJiCu1a/vQzAK2+1FJR5F84d0F/E+AiLhR0oUkd9GeAd7uni9m1n8EfXqitNmoNkt6Imn7tqRXkLSFz0tEvL7BsrOBs+dbtplZN/Rr80uzSf3twDrgyZLuAe4EXtexqMzM+t0gJ/WIuAM4XtJSoBARWzsblplZH+tRd8VmNEzqkt5ZZz4A7bjztfXW5MQEY+PjvQ7DbPAMaE19efr3ScAxJL1TAF4KXNapoMzM+t1AtqlHxF8CSPp34Ohqs4uks4CvdDw6M7N+JEFxsHu/HAyUMs9LwOq2R2NmNhAGf0CvLwJXSvoGSbfGlwNf6FhU1jNj4+Me/8WsGYOc1CPibEnfBY5LZ705Iq7tXFhmZv1tINvUqyQdDGwGvpGdFxG/6FRgZmZ9S4Pf/PJv7BwtcRw4BLiV5GYWZmaLT5v6qUs6Afg4UAQ+ExEfWUh5zTa/PHVWEEeTjtdiZrb4qC03yZBUBP4ReCHJKLVXSbooIm6ab5nz+v0QEdeQ9Fs3M1uc2jNK47HAxoi4IyJKwAUkd4Cbt2bb1LNXlhaAo4GHFrJhM7NBFRLRfPPLSknrM8/XRcS69HGtu709cyGxNfv7YXnm8QxJG/vXFrJh6x+zhwqoPnbXRrM6AqLmPdlq2hwRa+ssa/pub81qNqnfFBG7XEEq6ZX4qlIzW5SCSgtZvYG23+2t2Tb1M5qcZ2aWewGUo7lpDlcBh0o6RNIIyR3lLprjNQ3NNUrj7wAnAgdK+kRm0QoWducjM7OBFm2oqUfEjKTTge+TdGk8JyJuXEiZczW/3AusB04Crs7M3wr8r4Vs2PpLtV3d7ehmcwug0pbWF4iI7wDfaU9pc4/SeD1wvaRzI8I1czOzVJtyetvN1fxyYUS8CrhW0m7vISKO7FhkZmb9KtpXU2+3uZpf/iT9+5JOB2K956YXs+a1o029Exr2fomI+9KHb4uIn2cn4G2dD8/MrP+0sfdL2zXbpfGFNeb9TjsDMTMbJJVobuq2udrUTyOpka+RtCGzaDnwo04GZmbWryL6t/llrjb184DvAn8NvC8zf2tEPNyxqMzM+lyl1wHUMVeXxi3AFuA1AJL2BcaAZZKW+SYZZrZY9WlFvbk2dUkvlfQz4E7gUuAukhq8mdmik1x8FE1N3dbsidIPA88CbouIQ4AX4DZ1M1vEBr33y3RE/BIoSCpExH8BR3UuLDOz/hbR3NRtzQ69+4ikZcBlwLmSHsQDepnZIhUElT4dKKDZmvrJwATJIF7fA24HXtqpoMzM+lqTtfS+ralHxPbM0893KBYzs4ExkGO/SNpK7cHIBERErOhIVGZmfSwZJqA/s/pc/dSXN1puZrZY9WlOb/pEqZmZpar91PuRk7qZWasCyn06TkCzvV/aTtI7JN0q6UZJH83MP0PSxnTZi3sVn5lZPf18RWlPauqSfpukm+SRETGVjimDpMNJ7qZ9BHAAcImkwyKi3Is4bVf/sM/TeMdD1/c6DLM+EIN5orSDTgM+EhFTABHxYDr/ZOCCdP6dkjYCxwKX9yZMM7PdRcB0L8YAaEKvml8OA46T9BNJl0o6Jp1/IHB3Zr1N6bzdSDpV0npJ6x/avLnD4ZqZ7bQom18kXQLsV2PRmel29yIZJOwY4EJJa0j6v89Wc69ExDpgHcAzjj66P78yc+bA8eYOl7Hxcd/v1HJv0TW/RMTx9Zald1T6eiS3DrlSUgVYSVIzX5VZ9SDg3k7FaGY2H0lNvddR1Nar5pdvAs8HkHQYMAJsBi4CTpE0KukQ4FDgyh7FaGZWW0C5Ek1N3darE6XnAOdIugEoAW9Ma+03SroQuIlkFMi3u+eLmfWboDft5c3oSVKPiBLwujrLzgbO7m5EVssVxz2fic07KJeSqyz2WTnOj5/5W/zGTy5t+Dq3p1veBTDdhVq4pL8hGRG3RDI67psj4pFGr+nZxUdmZgOre80vFwNPiYgjgduAM+Z6gZO6mVmLutWlMSL+PSKqNyS6gqTzSENO6lbX8NgQKhZQUQwvG2bpvktZdsAyrj/pxF6HZtZzLdyjdGX1mpp0OnWem3wL8N25VvKAXmZmLWpxlMbNEbG23sJG1/RExLfSdc4k6Txy7lwbc1I3M2tRRLRtmIBG1/QASHoj8BLgBWkvwYbc/GJ1TU/OUNoxTWk66VW6Y/MEUQ6Kwz5szLrRpi7pBOC9wEkRsaOZ17imbmbWoi7ezu6TwChwsSSAKyLirY1e4KRuAFz1/OOJSlDaNs3Uo1NEOallbNxWoihxYMC2mQpvuvEqwH3RbZELqHShn3pE/Fqrr3FSNzNrUVJT73UUtTmpm5nNg4cJsL4WlaBcKjMzOcNEqUw5golycNu2EgC3by/xifKdgJtezCKCUp/epNRJ3cysRQE9GYGxGU7qZmYtinBStz5X2jbN9PYSk5MzbJkuU5QoVYJPx129Ds2sLzmpm5nlRNCbG2A0w0ndzKxFEVCa8YlS62NRqaDizsv/S5Xg4dKuN51yrxezhNvUzcxyxkl9kSts/2X6IK0Nq5BMAJH5GacCURhKlhWKyeMOueK451MplamUg6GxIUZXFFmycpyxB7bzq+3TnPqrWzq2bYOx8fGe/foZGx/f5Xk1jl7GNEjcpm5mliMRMOOkbmaWH66pL3bFoceaXKLa9JIMpZmopCclq80u2eaZToWUjouucoWlj1/Ksn2XMrJinHJphiet+3pHt72YZZs+2t3cMbu8Zsuf3RxTb3mtsupto1aZc8UyKM0/EXiYADOzvHCbukFhKKl9F4cfq4WHhCIgKjxWZ6+eKG2T+I9ziO2PMrNjguntE+y4/2FmJkvMbJ9gxaoVzEzMEOUKe655HOP77MXw0jGmt0+2bfu2q1q1107U1luZ32i9yYmJ3X5ZVOc3Krve+5ld9qByl0Yzs5xxUjczy4lklEa3qS9qURwmCkPMUCAAAhRBQUKFYnJSNCqgApnGmOra86ahYWJomOJYGRULRKVCpTRDeXqGJfvNEJUKheEhlh24DwAr3vzBBW3P5qeTJwibbXZp5bXNnlhtdZ2BaZIJt6mbmeVGJWDKY78sblEcoYIoV4JIb4NVLIhKBAWUdG9UMTkBk7lNVkH1Smxyu6VJqFRQoUhxfIQlo6NpwUU0OoaGhtHYUgpLV1De8ku2nftBlr32Lxa2UdvFfE5QVg1MzXWR8U0yzMzyxL1fbDqSG9WWK7GzlbwSj9XWqzPL6cFSXWd4aIFVdUCjY0nNfGSMwvI9KSxZTmHZnlSW7JW09Q+NEJUKw1sfRCNjC96eJRbSll2rjEa19nZsqx80usipn7ifuplZzjipm5nlRATM+ETp4lauBJWdrSyPyX7ZRyTrVEd/qwBbp6EoGCmKkentUC6hmRKamdplyF5VZtD0FJUtm4nSJFGapLylOtzvzqaX4v5PpLJ0b6aX7MWWmQLlSGIYGREr9t2DoQ6PN5M3rYx7Mkjb6rV+P2kcARXX1M3M8mJnL7Z+42pZlzRbSy9HUkMvR1IbKCjp+lgsCCozqDyTLIBdR3GsVHapuUd5563oNDRMYXwphSXLKS9bydTSfdhcKvBoqcLETIVyBPusWMLo0uUUVx/V9veeR2Pj44/VJmfXKjtZc85jrbxZ/fbeoxJNTe0g6V2SQtLKudbtSVKX9GVJ16XTXZKuyyw7Q9JGSbdKenEv4jMzayhtfmlmWihJq4AXAr9oZv2eNL9ExKurjyX9HbAlfXw4cApwBHAAcImkwyKiXLOgAVKOXS8kqj6s/oSrtm2XI3ZWxAVDBTFaAJV2oJlSOpSAiKHRagGoMkMUK1Apo+FhYqaEikVUKEChQGHpCgrL9qSwfG9mluzFtukKpXKFX9tnefd2QI50Y6TFVre/WPRLl8dg17tQdtjHgPcA32pm5Z62qUsS8Crg+emsk4ELImIKuFPSRuBY4PIehWhmtruAcvM3yVgpaX3m+bqIWNfMCyWdBNwTEddLzV2z0usTpccBD0TEz9LnBwJXZJZvSuflxux/y+xfZwWgTFJLL0qMFIWmJ9HM5M67IRUKUB1zPSowU0rKjQoMjaDCJFEoJBcSFYrJxUZLl1MZGWeGAuVKhUKTB8igqnfRTq35jWq+9V471zYt71pqL98cEWvrLZR0CbBfjUVnAu8HXtRKZB1L6o0CjYjqz4jXAOdnX1Zj/Zp7TtKpwKkAq1atWkCkZmatSZpf2nMSNCKOrzVf0lOBQ4BqLf0g4BpJx0bE/fXK61hSrxdolaQh4PeAZ2RmbwKyGfog4N465a8D1gE84+ij+7NvUUa2Pb3eIF0CQqKYPh4uisLMFKrMJHdOGh1Phu9Ne9KIpGdMobQDFYdQYYgCUBgZR6UJYmI7VMoUlu9JjC4jhpc81rY/3obhB/pJo1ryQu4E5Np3f+r5HZTSYT86uomInwL7Vp9LugtYGxGbG72ul10ajwduiYhNmXkXAadIGpV0CHAocGVPojMza6CbXRpb0cs29VPYtemFiLhR0oXATcAM8PY89HyBpO28oGTKnvDI1tqrNfRiQahSRuWkt0sMjTLJEA9PzDBTmaEcgQQFRLEAe46OMTa+hOLoNJXyHqh61en0BESFSmEoGbhrdBkAy0cKjExtAZZ2dR90imvTi1uvesR0O2FHxOpm1utZUo+IN9WZfzZwdnejMTNrXkS00vulq3rd+2XRKmj3s8IFiQKR9HYpl6AyQwwvYVpDbJuqsGM6KEcwXY7k5hoSw0UhoFQJiipSUJGhkXGGx0SxPJX0XY8KURgihkYZkRh69H50322wV63z2IPBtXObrds19i72U2+Jk3oPVBP67G6FBSIdmCu90AiI0eWU0195xQJQERRhupycqJmaSZpiZirJ8mrzzXBBjA8NMzQ0Qvac6NCj96N7bqF027UMPa2lnlJmA6EbF4J5QC8zs5zpxUnQZjipd1n1JGm1ll6trCcnRqdRuURhcmtygrQ4ApUyo0NDjI4VOHCv3U9sXnr7ZiZnKgwXRTFtjqnW1CtRZKQIy0YKFKYn0dS2pJa+cQOP/OwXLOnauzbLmXBSNzPLjcAnShe9ojJdGtN5EigCooJmptDMJJqeQqWd7YGa3oGmtlEobYfxJ+1WbkHJPU6ny1ApBOUQw4UCDFUHC0suYCpMPEJh+8NM3XEDW++8m8lfbunOG28jnxy1vuGauhUL2u0EqdIRFqmUKUxt25nYZyaT5eVSkuTLJSrbHoEDdk/qx615XM3t/fS+LUxXgqHqVW8RqFxiye+/a+CaXZzMrR/5RKmZWY70652PnNS7ZKYSFAWjQ8nIDNVmF6KS1NbL01Auwcw0MbmdmJokZqaT+41OTVCZ3NFSF8Sn7r/HrjOW7QGPP6Sdb6krXEu3VnWjn3pEb4YAaIaTupnZPLj5ZZF73PIlPLhlO2OFZCgbRXqnospMcu/RqDw2VaanqZQmiYntlH95P5XSJJXSDL2ss/b6TjNmc+nqMRpBJb1AsN84qXdBtQlh3z129jOf2rYFTe+ASrX5JWl6YWaKoaefAMDERZ+g9KtHKJemexK3mdUWBFHpz7EGndQ7rF6b8OiyPYBd270rd6ynMrH9sfGQNTLGtnseolKaQcUCtfu5mFnXf0kGRNlJ3cwsJ1xTX3Tm02ujsGbtLnctGTvhVA464dT2BTVA3OvF+lo4qedenpNQt3/a5nlfWvv16iS+k7qZWU6Ee7/kk2uUZotVUHFN3QZVr+4Bada33KaeL66hd4b3q81HN+50NFvgNvVcWOxJpxcfHrO59OSYjHA/dTOz3PCJUssL19bNwBcfDbDF3uRSSycSu/ezDZKkTd23sxsoTjLd431tA6ePe78U5l7FbHdj4+NOxraoRaXc1LRQkt4h6VZJN0r66Fzru6bO7jVFtxk3b6FNMf5isIEU3bn4SNJvAycDR0bElKR953qNk3oNTjStme/FSd7P1imdPqEfEVSmu9L75TTgIxExlW73wble4OYXM7OWRSvNLyslrc9MrQy9ehhwnKSfSLpU0jFzvcA1dWubVmpHrqVbu80+pjo9vEUL7eWbI2JtvYWSLgH2q7HoTJIcvRfwLOAY4EJJayKi7g1Sc5HUVSg4SfQJ/x+s2+Y65jpyTLax90tEHF9vmaTTgK+nSfxKSRVgJfBQ3dc0SPgDQ9JDwM87VPxKYHOHyp4vx9Qcx9S8foyrUzE9ISL2WUgBkr5HEl8zNkfECfPczluBAyLiLyQdBvwHcHCjmnouknonSVrf6KdTLzim5jim5vVjXP0YU7dJGgHOAY4CSsC7IuI/G70mF80vZmZ5FBEl4HWtvMa9X8zMcsRJfW7reh1ADY6pOY6pef0YVz/G1Pfcpm5mliOuqZuZ5YiTuplZjjippyS9Mh0FrSJpbWb+akkTkq5Lp09nlj1D0k8lbZT0CUnqRkzpsjPS7d4q6cXdiqlGjGdJuiezf06cK8ZukHRCut2Nkt7XzW3PiuOu9P9xnaT16by9JV0s6Wfp3706HMM5kh6UdENmXt0YuvF/qxNTXx5LAyciPCXnFX4deBLwA2BtZv5q4IY6r7kSeDYg4LvA73QppsOB64FR4BDgdqDYjZhqxHgWSd/Z2fPrxtiF/2Ux3d4aYCSN4/AeHVd3AStnzfso8L708fuA/9PhGJ4LHJ09juvF0K3/W52Y+u5YGsTJNfVURNwcEbc2u76k/YEVEXF5JEfeF4CXdSmmk4ELImIqIu4ENgLHdiOmFtSMsUvbPhbYGBF3RNLP94I0nn5xMvD59PHn6fD/KCIuAx5uMoau/N/qxFRPL4+lgeOk3pxDJF2bjpJ2XDrvQGBTZp1N6bxuOBC4u8a2exXT6ZI2pD+pqz/j68XYDb3c9mwB/LukqzOj8z0+Iu4DSP/OOUZ2B9SLodf7rt+OpYGzqK4obTQaWkR8q87L7iMZa+GXkp4BfFPSESTNG7O13D90njHV23ZbYtptY41Hkfsn4EPpdj4E/B3wlk7F0qRebnu250TEvenNDS6WdEuP4mhWL/ddPx5LA2dRJfVoMBpag9dMAdUB6q+WdDvJGMebgIMyqx4E3NuNmNJtr6qx7bbENFuzMUr6F+Dbc8TYDb3c9i4i4t7074OSvkHSbPCApP0j4r60yWzOGx90QL0YerbvIuKB6uM+OpYGjptf5iBpH0nF9PEa4FDgjvQn61ZJz0p7mLwBqFezbreLgFMkjUo6JI3pyl7ElCaEqpcD1d4MNWPsZCwZVwGHSjokHRDplDSerpK0VNLy6mPgRST75yLgjelqb6R7x01WvRh69n/r02Np8PT6TG2/TCQH0SaSWvkDwPfT+b8P3Ehy9v0a4KWZ16wlOfBuBz5JeoVup2NKl52ZbvdWMj1cOh1TjRi/CPwU2EDy4dt/rhi79P88Ebgt3f6ZPTqm1qTHzfXpMXRmOv9xJEOo/iz9u3eH4zifpBlxOj2e/rBRDN34v9WJqS+PpUGbPEyAmVmOuPnFzCxHnNTNzHLESd3MLEec1M3McsRJ3cwsR5zUreckbetAmSdVR2eU9DJJh8+jjB/MHh3TrN85qVsuRcRFEfGR9OnLSEb6M8s9J3XrG0r8jaQb0jHIX53Of15aa/6qpFsknVsdJ17Siem8HyoZP/7b6fw3SfqkpN8ATgL+Jh2j+4nZGriklZLuSh+PS7ogHVDqy8B4JrYXSbpc0jWSviJpWXf3jllzFtXYL9b3fg84CngasBK4StJl6bKnA0eQjPnxI+A5Sm468c/AcyPiTknnzy4wIn4s6SLg2xHxVQDVv2/IacCOiDhS0pEkVxAjaSXw58DxEbFd0nuBdwIfbMN7NmsrJ3XrJ78JnB8RZZIBpy4FjgEeJRnbZhOApOtIbl6yjWQcnjvT158PnDq70BY8F/gEQERskLQhnf8skuabH6VfCCPA5QvYjlnHOKlbP2l0672pzOMyybE731v1zbCz6XFs1rJa42YIuDgiXjPP7Zl1jdvUrZ9cBrxaUlHSPiQ150aj8d0CrJG0On3+6jrrbQWWZ57fBTwjffyKWdt/LYCkpwBHpvOvIGnu+bV02RJJhzXzhsy6zUnd+sk3SEboux74T+A9EXF/vZUjYgJ4G/A9ST8kGclyS41VLwDend696onA3wKnSfoxSdt91T8By9Jml/eQfqFExEPAm4Dz02VXAE9eyBs16xSP0mgDTdKyiNiW9ob5R+BnEfGxXsdl1iuuqdug+6P0xOmNwB4kvWHMFi3X1M3McsQ1dTOzHHFSNzPLESd1M7MccVI3M8sRJ3Uzsxz5/8wg3xR9i0wKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(SST_SO[0]*y).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other things - May be useful"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting data for the Southern Ocean\n",
    "Calculating mean water temperature per month within the SO (30S to 90S)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;bigthetao&#x27; (time: 120, lev: 20, latitude: 77, longitude: 360)&gt;\n",
       "[66528000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2000-01-16T12:00:00 ... 2009-12-16T12:00:00\n",
       "  * lev        (lev) float64 5.0 15.0 25.0 35.0 45.0 ... 165.0 175.0 185.0 195.0\n",
       "  * latitude   (latitude) float64 -77.88 -77.63 -77.38 ... -32.5 -31.5 -30.5\n",
       "  * longitude  (longitude) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "Attributes:\n",
       "    standard_name:   sea_water_conservative_temperature\n",
       "    long_name:       Sea Water Conservative Temperature\n",
       "    comment:         Sea water conservative temperature (this should be contr...\n",
       "    units:           degC\n",
       "    original_units:  K\n",
       "    history:         2019-11-15T15:50:50Z altered by CMOR: Converted units fr...\n",
       "    cell_methods:    area: mean where sea time: mean\n",
       "    cell_measures:   area: areacello volume: volcello</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'bigthetao'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 120</li><li><span class='xr-has-index'>lev</span>: 20</li><li><span class='xr-has-index'>latitude</span>: 77</li><li><span class='xr-has-index'>longitude</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e3512c45-31d7-40e9-8c06-af994178206d' class='xr-array-in' type='checkbox' checked><label for='section-e3512c45-31d7-40e9-8c06-af994178206d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[66528000 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-941dcb0a-e4f4-4a5a-b79b-8faf10310378' class='xr-section-summary-in' type='checkbox'  checked><label for='section-941dcb0a-e4f4-4a5a-b79b-8faf10310378' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16T12:00:00 ... 2009-12-...</div><input id='attrs-a8d0c603-5886-4c57-830c-4ab1a0c354bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8d0c603-5886-4c57-830c-4ab1a0c354bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-203fd621-3635-40f8-8327-9ec26b16dcd1' class='xr-var-data-in' type='checkbox'><label for='data-203fd621-3635-40f8-8327-9ec26b16dcd1' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T12:00:00.000000000&#x27;, &#x27;2000-02-15T12:00:00.000000000&#x27;,\n",
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       "       &#x27;2008-01-16T12:00:00.000000000&#x27;, &#x27;2008-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2008-03-16T12:00:00.000000000&#x27;, &#x27;2008-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-05-16T12:00:00.000000000&#x27;, &#x27;2008-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2008-07-16T12:00:00.000000000&#x27;, &#x27;2008-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2008-09-16T00:00:00.000000000&#x27;, &#x27;2008-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2008-11-16T00:00:00.000000000&#x27;, &#x27;2008-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2009-01-16T12:00:00.000000000&#x27;, &#x27;2009-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-03-16T12:00:00.000000000&#x27;, &#x27;2009-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-05-16T12:00:00.000000000&#x27;, &#x27;2009-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-07-16T12:00:00.000000000&#x27;, &#x27;2009-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2009-09-16T00:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2009-11-16T00:00:00.000000000&#x27;, &#x27;2009-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 15.0 25.0 ... 175.0 185.0 195.0</div><input id='attrs-dc09d538-4b22-43da-918d-703cf3f8a25b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc09d538-4b22-43da-918d-703cf3f8a25b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fad2a14-4c1d-4539-ba6e-6f2585d904ef' class='xr-var-data-in' type='checkbox'><label for='data-9fad2a14-4c1d-4539-ba6e-6f2585d904ef' 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>bounds :</span></dt><dd>lev_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>ocean depth coordinate</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([  5.,  15.,  25.,  35.,  45.,  55.,  65.,  75.,  85.,  95., 105., 115.,\n",
       "       125., 135., 145., 155., 165., 175., 185., 195.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... -31.5 -30.5</div><input id='attrs-17e066df-d795-4a31-8a8c-bf89e755968c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-17e066df-d795-4a31-8a8c-bf89e755968c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67a13148-666e-4fd7-9875-be9891ad9349' class='xr-var-data-in' type='checkbox'><label for='data-67a13148-666e-4fd7-9875-be9891ad9349' 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([-77.876625, -77.629715, -77.381706, -77.132294, -76.880531, -76.626129,\n",
       "       -76.368141, -76.106285, -75.839645, -75.567932, -75.29026 , -75.006363,\n",
       "       -74.715355, -74.417   , -74.110443, -73.795479, -73.471268, -73.137634,\n",
       "       -72.793785, -72.439552, -72.074196, -71.697586, -71.308998, -70.908363,\n",
       "       -70.494987, -70.068848, -69.629288, -69.176323, -68.709351, -68.228439,\n",
       "       -67.733017, -67.223198, -66.698479, -66.159004, -65.604324, -65.034637,\n",
       "       -64.449539, -63.849281, -63.233509, -62.602528, -61.956032, -61.294376,\n",
       "       -60.617313, -59.92524 , -59.21796 , -58.495926, -57.758991, -57.007656,\n",
       "       -56.241821, -55.462036, -54.668247, -53.861053, -53.040443, -52.207062,\n",
       "       -51.360947, -50.502777, -49.632637, -48.751244, -47.858719, -46.955818,\n",
       "       -46.042698, -45.120152, -44.188366, -43.248169, -42.299767, -41.344021,\n",
       "       -40.381168, -39.41209 , -38.437046, -37.456936, -36.472038, -35.483269,\n",
       "       -34.490921, -33.49593 , -32.498589, -31.499844, -30.5     ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-8cc0bcbe-ed5c-469b-92dc-b98597145a33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8cc0bcbe-ed5c-469b-92dc-b98597145a33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebbea645-bd44-4682-9fc2-7a2433dfd4a8' class='xr-var-data-in' type='checkbox'><label for='data-ebbea645-bd44-4682-9fc2-7a2433dfd4a8' 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([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4fc58289-18d6-4d49-9c47-dd10e05b4743' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4fc58289-18d6-4d49-9c47-dd10e05b4743' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sea_water_conservative_temperature</dd><dt><span>long_name :</span></dt><dd>Sea Water Conservative Temperature</dd><dt><span>comment :</span></dt><dd>Sea water conservative temperature (this should be contributed only for models using conservative temperature as prognostic field)</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>original_units :</span></dt><dd>K</dd><dt><span>history :</span></dt><dd>2019-11-15T15:50:50Z altered by CMOR: Converted units from &#x27;K&#x27; to &#x27;degC&#x27;. 2019-11-15T15:50:50Z altered by CMOR: replaced missing value flag (-1e+20) with standard missing value (1e+20).</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello volume: volcello</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'bigthetao' (time: 120, lev: 20, latitude: 77, longitude: 360)>\n",
       "[66528000 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2000-01-16T12:00:00 ... 2009-12-16T12:00:00\n",
       "  * lev        (lev) float64 5.0 15.0 25.0 35.0 45.0 ... 165.0 175.0 185.0 195.0\n",
       "  * latitude   (latitude) float64 -77.88 -77.63 -77.38 ... -32.5 -31.5 -30.5\n",
       "  * longitude  (longitude) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "Attributes:\n",
       "    standard_name:   sea_water_conservative_temperature\n",
       "    long_name:       Sea Water Conservative Temperature\n",
       "    comment:         Sea water conservative temperature (this should be contr...\n",
       "    units:           degC\n",
       "    original_units:  K\n",
       "    history:         2019-11-15T15:50:50Z altered by CMOR: Converted units fr...\n",
       "    cell_methods:    area: mean where sea time: mean\n",
       "    cell_measures:   area: areacello volume: volcello"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sst_upper200_SO = sst.sel(latitude = slice(-90, -30))\n",
    "sst_upper200_SO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting horizontal area of grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = [f[0] for f in zip(df2['filename'][0])]\n",
    "filepath = os.path.join(df2['path'][0], filename[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x14a47b591400>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "area = xr.open_dataarray(filepath, mask_and_scale = True, drop_variables = (\"vertices_latitude\", \"vertices_longitude\"))\n",
    "#Correcting longitude prior to extracting Southern Ocean\n",
    "area = uf.corrlong(area)\n",
    "#Extracting Southern Ocean\n",
    "area = area.sel(latitude = slice(-90, -30))\n",
    "#Checking data\n",
    "area.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting thickness (vertical extent) of grid cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = [f[0] for f in zip(df3['filename'][0])]\n",
    "filepath = os.path.join(df3['path'][0], filename[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;thkcello&#x27; (lev: 20, latitude: 77, longitude: 360)&gt;\n",
       "[554400 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lev        (lev) float64 5.0 15.0 25.0 35.0 45.0 ... 165.0 175.0 185.0 195.0\n",
       "  * latitude   (latitude) float64 -77.88 -77.63 -77.38 ... -32.5 -31.5 -30.5\n",
       "  * longitude  (longitude) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "Attributes:\n",
       "    standard_name:  cell_thickness\n",
       "    long_name:      Ocean Model Cell Thickness\n",
       "    comment:        &#x27;Thickness&#x27; means the vertical extent of a layer. &#x27;Cell&#x27; ...\n",
       "    units:          m\n",
       "    cell_methods:   area: mean\n",
       "    cell_measures:  area: areacello volume: volcello\n",
       "    history:        2019-11-15T14:47:29Z altered by CMOR: replaced missing va...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'thkcello'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lev</span>: 20</li><li><span class='xr-has-index'>latitude</span>: 77</li><li><span class='xr-has-index'>longitude</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4b781450-e25c-4eb8-938a-20962ff7b3dc' class='xr-array-in' type='checkbox' checked><label for='section-4b781450-e25c-4eb8-938a-20962ff7b3dc' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[554400 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-760fe51c-ab4d-401d-afad-4e83d2bd1bcd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-760fe51c-ab4d-401d-afad-4e83d2bd1bcd' 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'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 15.0 25.0 ... 175.0 185.0 195.0</div><input id='attrs-d8f503bf-e168-4c34-8199-0b91a9d35227' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8f503bf-e168-4c34-8199-0b91a9d35227' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4abbdf96-c673-48d0-ab3b-fd04faebe7cf' class='xr-var-data-in' type='checkbox'><label for='data-4abbdf96-c673-48d0-ab3b-fd04faebe7cf' 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>bounds :</span></dt><dd>lev_bnds</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>ocean depth coordinate</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([  5.,  15.,  25.,  35.,  45.,  55.,  65.,  75.,  85.,  95., 105., 115.,\n",
       "       125., 135., 145., 155., 165., 175., 185., 195.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-77.88 -77.63 ... -31.5 -30.5</div><input id='attrs-f1bf8087-346b-4663-b124-35c4272009c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1bf8087-346b-4663-b124-35c4272009c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e79b503-13f0-4c8a-ab37-4df2dd027d26' class='xr-var-data-in' type='checkbox'><label for='data-7e79b503-13f0-4c8a-ab37-4df2dd027d26' 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([-77.876625, -77.629715, -77.381706, -77.132294, -76.880531, -76.626129,\n",
       "       -76.368141, -76.106285, -75.839645, -75.567932, -75.29026 , -75.006363,\n",
       "       -74.715355, -74.417   , -74.110443, -73.795479, -73.471268, -73.137634,\n",
       "       -72.793785, -72.439552, -72.074196, -71.697586, -71.308998, -70.908363,\n",
       "       -70.494987, -70.068848, -69.629288, -69.176323, -68.709351, -68.228439,\n",
       "       -67.733017, -67.223198, -66.698479, -66.159004, -65.604324, -65.034637,\n",
       "       -64.449539, -63.849281, -63.233509, -62.602528, -61.956032, -61.294376,\n",
       "       -60.617313, -59.92524 , -59.21796 , -58.495926, -57.758991, -57.007656,\n",
       "       -56.241821, -55.462036, -54.668247, -53.861053, -53.040443, -52.207062,\n",
       "       -51.360947, -50.502777, -49.632637, -48.751244, -47.858719, -46.955818,\n",
       "       -46.042698, -45.120152, -44.188366, -43.248169, -42.299767, -41.344021,\n",
       "       -40.381168, -39.41209 , -38.437046, -37.456936, -36.472038, -35.483269,\n",
       "       -34.490921, -33.49593 , -32.498589, -31.499844, -30.5     ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-06ca14ff-1fa4-420e-a9ab-04858811a8d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06ca14ff-1fa4-420e-a9ab-04858811a8d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79143647-4492-48a7-8a4a-9ba50e894fe7' class='xr-var-data-in' type='checkbox'><label for='data-79143647-4492-48a7-8a4a-9ba50e894fe7' 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([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7e7fb410-9151-4af7-b67f-f54e55633d9a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e7fb410-9151-4af7-b67f-f54e55633d9a' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>cell_thickness</dd><dt><span>long_name :</span></dt><dd>Ocean Model Cell Thickness</dd><dt><span>comment :</span></dt><dd>&#x27;Thickness&#x27; means the vertical extent of a layer. &#x27;Cell&#x27; refers to a model grid-cell.</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello volume: volcello</dd><dt><span>history :</span></dt><dd>2019-11-15T14:47:29Z altered by CMOR: replaced missing value flag (-1e+20) with standard missing value (1e+20).</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'thkcello' (lev: 20, latitude: 77, longitude: 360)>\n",
       "[554400 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lev        (lev) float64 5.0 15.0 25.0 35.0 45.0 ... 165.0 175.0 185.0 195.0\n",
       "  * latitude   (latitude) float64 -77.88 -77.63 -77.38 ... -32.5 -31.5 -30.5\n",
       "  * longitude  (longitude) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "Attributes:\n",
       "    standard_name:  cell_thickness\n",
       "    long_name:      Ocean Model Cell Thickness\n",
       "    comment:        'Thickness' means the vertical extent of a layer. 'Cell' ...\n",
       "    units:          m\n",
       "    cell_methods:   area: mean\n",
       "    cell_measures:  area: areacello volume: volcello\n",
       "    history:        2019-11-15T14:47:29Z altered by CMOR: replaced missing va..."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height = xr.open_dataset(filepath, mask_and_scale = True)\n",
    "#Correcting longitude prior to extracting Southern Ocean\n",
    "height = uf.corrlong(height)\n",
    "#Extracting Southern Ocean\n",
    "height = height.sel(latitude = slice(-90, -30))\n",
    "#Select only data that is in the upper 200 m - Using lower bound of the depth bins\n",
    "height = height.thkcello[height.lev_bnds[:,1] <= 200]\n",
    "#Checking data\n",
    "height"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating weights from thickness data\n",
    "Dividing the vertical height of each grid cell by the sum of heights for all grid cells in the upper 200 m."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x14a473352e20>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_heights = height.sum('lev')\n",
    "weights_vert_levels = height/sum_heights\n",
    "\n",
    "#Checking data\n",
    "weights_vert_levels[0].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating vertically weighted means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x14a4725bbd00>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sst_upper200mean_SO = (sst_upper200_SO*weights_vert_levels).sum('lev')\n",
    "\n",
    "#Applying land mask to the data\n",
    "sst_upper200mean_SO = sst_upper200mean_SO.where(sum_heights > 0)\n",
    "\n",
    "#Checking first time step\n",
    "sst_upper200mean_SO[0].plot(vmin = -2, vmax = 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading shapefile with MEASO regions\n",
    "MEASO regions being used in the analysis were shared by A. Constable on 2021-06-24."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "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>name</th>\n",
       "      <th>sector</th>\n",
       "      <th>zone</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WPA-O</td>\n",
       "      <td>West Pacific</td>\n",
       "      <td>Antarctic Open</td>\n",
       "      <td>POLYGON ((-125.00000 -65.20000, -125.00000 -65...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WPA-N</td>\n",
       "      <td>West Pacific</td>\n",
       "      <td>Antarctic Shelf</td>\n",
       "      <td>POLYGON ((-125.00000 -72.25443, -125.00000 -72...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WPA-O</td>\n",
       "      <td>West Pacific</td>\n",
       "      <td>Antarctic Open</td>\n",
       "      <td>POLYGON ((-180.00000 -72.36900, -180.00000 -72...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WPS</td>\n",
       "      <td>West Pacific</td>\n",
       "      <td>Subantarctic</td>\n",
       "      <td>POLYGON ((-125.00000 -56.80000, -125.00000 -57...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WPN</td>\n",
       "      <td>West Pacific</td>\n",
       "      <td>Northern</td>\n",
       "      <td>POLYGON ((-125.00000 -40.00000, -125.00000 -40...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>WPT</td>\n",
       "      <td>West Pacific</td>\n",
       "      <td>Temperate</td>\n",
       "      <td>POLYGON ((-125.00000 -30.00000, -125.00000 -30...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>EPA-N</td>\n",
       "      <td>East Pacific</td>\n",
       "      <td>Antarctic Shelf</td>\n",
       "      <td>POLYGON ((-125.00000 -75.00184, -125.00000 -75...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EPA-O</td>\n",
       "      <td>East Pacific</td>\n",
       "      <td>Antarctic Open</td>\n",
       "      <td>POLYGON ((-125.00000 -72.25443, -125.00000 -72...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EPS</td>\n",
       "      <td>East Pacific</td>\n",
       "      <td>Subantarctic</td>\n",
       "      <td>POLYGON ((-125.00000 -65.20000, -125.00000 -65...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EPN</td>\n",
       "      <td>East Pacific</td>\n",
       "      <td>Northern</td>\n",
       "      <td>POLYGON ((-125.00000 -56.80000, -125.00000 -56...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EPT</td>\n",
       "      <td>East Pacific</td>\n",
       "      <td>Temperate</td>\n",
       "      <td>POLYGON ((-125.00000 -40.00000, -125.00000 -39...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AOA-N</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Antarctic Shelf</td>\n",
       "      <td>POLYGON ((-57.02029 -63.34214, -57.01404 -63.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AOA-O</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Antarctic Open</td>\n",
       "      <td>POLYGON ((-57.15097 -61.21343, -57.16815 -61.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>AOT</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Temperate</td>\n",
       "      <td>POLYGON ((-62.79726 -55.70000, -62.94048 -55.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>AOS</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Subantarctic</td>\n",
       "      <td>POLYGON ((-59.00000 -59.40791, -59.14064 -59.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>AON</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Northern</td>\n",
       "      <td>POLYGON ((30.00000 -41.70000, 30.00000 -41.900...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CIA-N</td>\n",
       "      <td>Central Indian</td>\n",
       "      <td>Antarctic Shelf</td>\n",
       "      <td>POLYGON ((30.00000 -70.56253, 30.00000 -70.451...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CIA-O</td>\n",
       "      <td>Central Indian</td>\n",
       "      <td>Antarctic Open</td>\n",
       "      <td>POLYGON ((30.00000 -68.91757, 30.00000 -68.856...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CIS</td>\n",
       "      <td>Central Indian</td>\n",
       "      <td>Subantarctic</td>\n",
       "      <td>POLYGON ((30.00000 -56.50000, 30.00000 -56.300...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CIN</td>\n",
       "      <td>Central Indian</td>\n",
       "      <td>Northern</td>\n",
       "      <td>POLYGON ((30.00000 -47.90000, 30.00000 -47.700...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CIT</td>\n",
       "      <td>Central Indian</td>\n",
       "      <td>Temperate</td>\n",
       "      <td>POLYGON ((30.00000 -41.70000, 30.00000 -41.501...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>EIA-N</td>\n",
       "      <td>East Indian</td>\n",
       "      <td>Antarctic Shelf</td>\n",
       "      <td>POLYGON ((115.00000 -67.52494, 115.00000 -67.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>EIA-O</td>\n",
       "      <td>East Indian</td>\n",
       "      <td>Antarctic Open</td>\n",
       "      <td>POLYGON ((115.00000 -64.23531, 115.00000 -64.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>EIS</td>\n",
       "      <td>East Indian</td>\n",
       "      <td>Subantarctic</td>\n",
       "      <td>POLYGON ((115.00000 -59.50000, 115.00000 -59.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>EIN</td>\n",
       "      <td>East Indian</td>\n",
       "      <td>Northern</td>\n",
       "      <td>POLYGON ((115.00000 -48.60000, 115.00000 -48.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>EIT</td>\n",
       "      <td>East Indian</td>\n",
       "      <td>Temperate</td>\n",
       "      <td>POLYGON ((115.00000 -40.10000, 115.00000 -39.9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>WPA-N</td>\n",
       "      <td>West Pacific</td>\n",
       "      <td>Antarctic Shelf</td>\n",
       "      <td>POLYGON ((169.49154 -71.74412, 169.61905 -71.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>WPA-O</td>\n",
       "      <td>West Pacific</td>\n",
       "      <td>Antarctic Open</td>\n",
       "      <td>POLYGON ((170.00000 -70.63687, 170.00000 -70.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>WPA-N</td>\n",
       "      <td>West Pacific</td>\n",
       "      <td>Antarctic Shelf</td>\n",
       "      <td>POLYGON ((180.00000 -70.78000, 180.00000 -70.8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WPS</td>\n",
       "      <td>West Pacific</td>\n",
       "      <td>Subantarctic</td>\n",
       "      <td>POLYGON ((170.00000 -62.20000, 170.00000 -62.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>WPN</td>\n",
       "      <td>West Pacific</td>\n",
       "      <td>Northern</td>\n",
       "      <td>POLYGON ((170.00000 -56.80000, 170.00000 -56.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>WPT</td>\n",
       "      <td>West Pacific</td>\n",
       "      <td>Temperate</td>\n",
       "      <td>POLYGON ((170.00000 -47.50000, 170.00000 -47.3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name          sector             zone  \\\n",
       "0   WPA-O    West Pacific   Antarctic Open   \n",
       "1   WPA-N    West Pacific  Antarctic Shelf   \n",
       "2   WPA-O    West Pacific   Antarctic Open   \n",
       "3     WPS    West Pacific     Subantarctic   \n",
       "4     WPN    West Pacific         Northern   \n",
       "5     WPT    West Pacific        Temperate   \n",
       "6   EPA-N    East Pacific  Antarctic Shelf   \n",
       "7   EPA-O    East Pacific   Antarctic Open   \n",
       "8     EPS    East Pacific     Subantarctic   \n",
       "9     EPN    East Pacific         Northern   \n",
       "10    EPT    East Pacific        Temperate   \n",
       "11  AOA-N        Atlantic  Antarctic Shelf   \n",
       "12  AOA-O        Atlantic   Antarctic Open   \n",
       "13    AOT        Atlantic        Temperate   \n",
       "14    AOS        Atlantic     Subantarctic   \n",
       "15    AON        Atlantic         Northern   \n",
       "16  CIA-N  Central Indian  Antarctic Shelf   \n",
       "17  CIA-O  Central Indian   Antarctic Open   \n",
       "18    CIS  Central Indian     Subantarctic   \n",
       "19    CIN  Central Indian         Northern   \n",
       "20    CIT  Central Indian        Temperate   \n",
       "21  EIA-N     East Indian  Antarctic Shelf   \n",
       "22  EIA-O     East Indian   Antarctic Open   \n",
       "23    EIS     East Indian     Subantarctic   \n",
       "24    EIN     East Indian         Northern   \n",
       "25    EIT     East Indian        Temperate   \n",
       "26  WPA-N    West Pacific  Antarctic Shelf   \n",
       "27  WPA-O    West Pacific   Antarctic Open   \n",
       "28  WPA-N    West Pacific  Antarctic Shelf   \n",
       "29    WPS    West Pacific     Subantarctic   \n",
       "30    WPN    West Pacific         Northern   \n",
       "31    WPT    West Pacific        Temperate   \n",
       "\n",
       "                                             geometry  \n",
       "0   POLYGON ((-125.00000 -65.20000, -125.00000 -65...  \n",
       "1   POLYGON ((-125.00000 -72.25443, -125.00000 -72...  \n",
       "2   POLYGON ((-180.00000 -72.36900, -180.00000 -72...  \n",
       "3   POLYGON ((-125.00000 -56.80000, -125.00000 -57...  \n",
       "4   POLYGON ((-125.00000 -40.00000, -125.00000 -40...  \n",
       "5   POLYGON ((-125.00000 -30.00000, -125.00000 -30...  \n",
       "6   POLYGON ((-125.00000 -75.00184, -125.00000 -75...  \n",
       "7   POLYGON ((-125.00000 -72.25443, -125.00000 -72...  \n",
       "8   POLYGON ((-125.00000 -65.20000, -125.00000 -65...  \n",
       "9   POLYGON ((-125.00000 -56.80000, -125.00000 -56...  \n",
       "10  POLYGON ((-125.00000 -40.00000, -125.00000 -39...  \n",
       "11  POLYGON ((-57.02029 -63.34214, -57.01404 -63.3...  \n",
       "12  POLYGON ((-57.15097 -61.21343, -57.16815 -61.1...  \n",
       "13  POLYGON ((-62.79726 -55.70000, -62.94048 -55.5...  \n",
       "14  POLYGON ((-59.00000 -59.40791, -59.14064 -59.2...  \n",
       "15  POLYGON ((30.00000 -41.70000, 30.00000 -41.900...  \n",
       "16  POLYGON ((30.00000 -70.56253, 30.00000 -70.451...  \n",
       "17  POLYGON ((30.00000 -68.91757, 30.00000 -68.856...  \n",
       "18  POLYGON ((30.00000 -56.50000, 30.00000 -56.300...  \n",
       "19  POLYGON ((30.00000 -47.90000, 30.00000 -47.700...  \n",
       "20  POLYGON ((30.00000 -41.70000, 30.00000 -41.501...  \n",
       "21  POLYGON ((115.00000 -67.52494, 115.00000 -67.4...  \n",
       "22  POLYGON ((115.00000 -64.23531, 115.00000 -64.0...  \n",
       "23  POLYGON ((115.00000 -59.50000, 115.00000 -59.3...  \n",
       "24  POLYGON ((115.00000 -48.60000, 115.00000 -48.4...  \n",
       "25  POLYGON ((115.00000 -40.10000, 115.00000 -39.9...  \n",
       "26  POLYGON ((169.49154 -71.74412, 169.61905 -71.7...  \n",
       "27  POLYGON ((170.00000 -70.63687, 170.00000 -70.6...  \n",
       "28  POLYGON ((180.00000 -70.78000, 180.00000 -70.8...  \n",
       "29  POLYGON ((170.00000 -62.20000, 170.00000 -62.0...  \n",
       "30  POLYGON ((170.00000 -56.80000, 170.00000 -56.6...  \n",
       "31  POLYGON ((170.00000 -47.50000, 170.00000 -47.3...  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Note that the full file path is being used.\n",
    "regions = geopandas.read_file(r'/g/data/v45/la6889/GIS_DataBase/Shapefiles/MEASO_shapes_withShelf/MEASO_shapes_withShelf.shp')\n",
    "regions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking MEASO regions and data are correctly aligned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'time = 2000-01-16T12:00:00'}, xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot shapefile over the raster to ensure they both have the same CRS and cover the same area\n",
    "#Initialise figure\n",
    "fig, ax = plt.subplots(figsize = (12, 15))\n",
    "\n",
    "#Plot just one timestep of the data array\n",
    "sst_upper200mean_SO[0].plot(ax = ax, add_colorbar = False)\n",
    "\n",
    "#Plot shapefile over the top and show each sector in different colour\n",
    "regions.plot(ax = ax, column = 'name', legend = True, alpha = 0.5, edgecolor = 'black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'time = 2000-01-16T12:00:00, spatial_ref = 0'}, xlabel='longitude [degrees_east]', ylabel='latitude [degrees_north]'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reg = regions.query('name == \"CIS\"')\n",
    "# reg.plot()\n",
    "fig, ax = plt.subplots(figsize = (5, 20))\n",
    "sst_upper200mean_SO.rio.clip(reg.geometry, reg.crs, drop = True, invert = False, all_touched = True)[0].plot(ax = ax, add_colorbar = False)\n",
    "regions.geometry.boundary.plot(ax = ax, color = None, edgecolor = 'black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting data per MEASO region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting names of regions from shapefile\n",
    "regionNames = sorted(set(regions.name.values))\n",
    "\n",
    "#Subsetting shapefiles into regions - Initialise dictionary that will contain sector limits\n",
    "MEASOregions = {}\n",
    "\n",
    "#Saving each sector as an entry in the dictionary\n",
    "for i in regionNames:\n",
    "    MEASOregions[f\"{i}\"] = regions.query(f'name == \"{i}\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Subsetting shapefiles into sectors - Initialise dictionary that will contain sector limits\n",
    "clipped_varInt = {}\n",
    "clipped_area = {}\n",
    "\n",
    "#Saving each sector as an entry in the dictionary\n",
    "for i in regionNames:\n",
    "    clipped_varInt[f\"{i}\"] = uf.clipDataArray(sst_upper200mean_SO, shp = MEASOregions[i])\n",
    "    clipped_area[f\"{i}\"] = uf.clipDataArray(area, shp = MEASOregions[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'time = 2000-01-16T12:00:00, spatial_ref = 0'}, xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (5, 20))\n",
    "\n",
    "#Plot just one timestep of the data array\n",
    "sst_upper200mean_SO.sel(longitude = slice(-80, 30), latitude = slice(-78, -60))[0].plot(ax = ax, add_colorbar = False)\n",
    "\n",
    "#Plot shapefile over the top and show each sector in different colour\n",
    "regions.geometry.boundary.plot(ax = ax, color = None, edgecolor = 'black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'time = 2000-01-16T12:00:00, lev = 5.0, bin_dept...'}, xlabel='i', ylabel='j'>"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot shapefile over the raster to ensure they both have the same CRS and cover the same area\n",
    "#Initialise figure\n",
    "fig, ax = plt.subplots(figsize = (12, 15))\n",
    "\n",
    "#Plot just one timestep of the data array\n",
    "latsSO.plot(ax = ax, add_colorbar = False)\n",
    "\n",
    "#Plot shapefile over the top and show each sector in different colour\n",
    "regions.plot(ax = ax, column = 'name', legend = True, alpha = 0.5, edgecolor = 'black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking results of extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'time = 2000-01-16T12:00:00, spatial_ref = 0'}, xlabel='longitude [degrees_east]', ylabel='latitude [degrees_north]'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "clipped_varInt[\"CIS\"][0].plot(ax = ax, vmin = -1.85, vmax = -1.55)\n",
    "regions.geometry.boundary.plot(ax = ax, color = None, edgecolor = 'black', alpha = 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating weights from horizontal area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'time = 2000-01-16T12:00:00, spatial_ref = 0'}, xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sst_upper200mean_SO[0].sel(longitude = slice(30, 120), latitude = slice(-70, -45)).plot(ax = ax, vmin = -1.85, vmax = -1.55)\n",
    "regions.geometry.boundary.plot(ax = ax, color = None, edgecolor = 'black', alpha = 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating weighted means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting a list of the years included in the data\n",
    "years = sst_upper200_SO.indexes['time'].year\n",
    "\n",
    "#Loop through all sectors\n",
    "for region in regionNames:\n",
    "    #Slice data per year and calculate seasonal weighted means\n",
    "    for yr in years:\n",
    "        x = uf.weightedMeans(clipped_varInt[region].sel(time = str(yr)), clipped_area[region], meanby = 'month')\n",
    "        #Add time dimension containing the year for which calculations are done\n",
    "        x = uf.addTimeYear(x, yr, by = 'month')\n",
    "        #Save netcdf file\n",
    "        x.to_netcdf('YearlyMonthlyMeans_' + region + 'region_2000-2009.nc')\n",
    "    #Remove unused variables\n",
    "    del x\n",
    "\n",
    "#Remove unused variables\n",
    "del years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (month: 12)&gt;\n",
       "array([6.122052, 6.522734, 6.63957 , 6.382081, 6.104219, 5.707814, 5.344544,\n",
       "       4.997611, 4.780879, 4.824975, 5.062714, 5.429814], dtype=float32)\n",
       "Coordinates:\n",
       "    spatial_ref  int64 0\n",
       "  * month        (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time         (month) object &#x27;2009&#x27; &#x27;2009&#x27; &#x27;2009&#x27; ... &#x27;2009&#x27; &#x27;2009&#x27; &#x27;2009&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-96c91aad-6a8a-4e94-a19d-4a667230a0a2' class='xr-array-in' type='checkbox' checked><label for='section-96c91aad-6a8a-4e94-a19d-4a667230a0a2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>6.122 6.523 6.64 6.382 6.104 5.708 5.345 4.998 4.781 4.825 5.063 5.43</span></div><div class='xr-array-data'><pre>array([6.122052, 6.522734, 6.63957 , 6.382081, 6.104219, 5.707814, 5.344544,\n",
       "       4.997611, 4.780879, 4.824975, 5.062714, 5.429814], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-9997174b-2248-4e74-8841-94028e1e0c01' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9997174b-2248-4e74-8841-94028e1e0c01' 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>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6139c257-f47a-4339-a22d-b0e8eaa1f5f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6139c257-f47a-4339-a22d-b0e8eaa1f5f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ebe0420-7e77-4233-9c8f-0d3410aa328a' class='xr-var-data-in' type='checkbox'><label for='data-0ebe0420-7e77-4233-9c8f-0d3410aa328a' 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>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>-180.0 1.0 0.0 -65.98050946455736 0.0 0.7523702474740835</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-498feb06-3f77-463a-be4f-07d2700ff07b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-498feb06-3f77-463a-be4f-07d2700ff07b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba4d78d1-01fa-4f4c-9dd0-8c4ba0936fa9' class='xr-var-data-in' type='checkbox'><label for='data-ba4d78d1-01fa-4f4c-9dd0-8c4ba0936fa9' 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([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4a52a21a-b2b6-4f42-971e-9c613f1b422a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a52a21a-b2b6-4f42-971e-9c613f1b422a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e22daede-1b8c-4854-bb60-e9c800385be0' class='xr-var-data-in' type='checkbox'><label for='data-e22daede-1b8c-4854-bb60-e9c800385be0' 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;2009&#x27;, &#x27;2009&#x27;, &#x27;2009&#x27;, &#x27;2009&#x27;, &#x27;2009&#x27;, &#x27;2009&#x27;, &#x27;2009&#x27;, &#x27;2009&#x27;, &#x27;2009&#x27;,\n",
       "       &#x27;2009&#x27;, &#x27;2009&#x27;, &#x27;2009&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e41802cc-d128-461d-bfce-e3f566ebff8e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e41802cc-d128-461d-bfce-e3f566ebff8e' 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.DataArray (month: 12)>\n",
       "array([6.122052, 6.522734, 6.63957 , 6.382081, 6.104219, 5.707814, 5.344544,\n",
       "       4.997611, 4.780879, 4.824975, 5.062714, 5.429814], dtype=float32)\n",
       "Coordinates:\n",
       "    spatial_ref  int64 ...\n",
       "  * month        (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time         (month) object ..."
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.open_dataarray('YearlyMonthlyMeans_WPNregion_2000-2009.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Conventions': 'CF-1.7 CMIP-6.2',\n",
       " 'activity_id': 'CMIP',\n",
       " 'branch_method': 'standard',\n",
       " 'branch_time_in_child': 0.0,\n",
       " 'branch_time_in_parent': 21915.0,\n",
       " 'creation_date': '2019-11-15T14:47:31Z',\n",
       " 'data_specs_version': '01.00.30',\n",
       " 'experiment': 'all-forcing simulation of the recent past',\n",
       " 'experiment_id': 'historical',\n",
       " 'external_variables': 'areacello volcello',\n",
       " 'forcing_index': 1,\n",
       " 'frequency': 'fx',\n",
       " 'further_info_url': 'https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r1i1p1f1',\n",
       " 'grid': 'native atmosphere N96 grid (145x192 latxlon)',\n",
       " 'grid_label': 'gn',\n",
       " 'history': '2019-11-15T14:47:31Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.',\n",
       " 'initialization_index': 1,\n",
       " 'institution': 'Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia',\n",
       " 'institution_id': 'CSIRO',\n",
       " 'mip_era': 'CMIP6',\n",
       " 'nominal_resolution': '250 km',\n",
       " 'notes': \"Exp: ESM-historical; Local ID: HI-05; Variable: thkcello (['dht'])\",\n",
       " 'parent_activity_id': 'CMIP',\n",
       " 'parent_experiment_id': 'piControl',\n",
       " 'parent_mip_era': 'CMIP6',\n",
       " 'parent_source_id': 'ACCESS-ESM1-5',\n",
       " 'parent_time_units': 'days since 0101-1-1',\n",
       " 'parent_variant_label': 'r1i1p1f1',\n",
       " 'physics_index': 1,\n",
       " 'product': 'model-output',\n",
       " 'realization_index': 1,\n",
       " 'realm': 'ocean',\n",
       " 'run_variant': 'forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)',\n",
       " 'source': 'ACCESS-ESM1.5 (2019): \\naerosol: CLASSIC (v1.0)\\natmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\\natmosChem: none\\nland: CABLE2.4\\nlandIce: none\\nocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\\nocnBgchem: WOMBAT (same grid as ocean)\\nseaIce: CICE4.1 (same grid as ocean)',\n",
       " 'source_id': 'ACCESS-ESM1-5',\n",
       " 'source_type': 'AOGCM',\n",
       " 'sub_experiment': 'none',\n",
       " 'sub_experiment_id': 'none',\n",
       " 'table_id': 'Ofx',\n",
       " 'table_info': 'Creation Date:(30 April 2019) MD5:e14f55f257cceafb2523e41244962371',\n",
       " 'title': 'ACCESS-ESM1-5 output prepared for CMIP6',\n",
       " 'tracking_id': 'hdl:21.14100/238d0fd0-40cd-40a6-8148-f56c12669260',\n",
       " 'variable_id': 'thkcello',\n",
       " 'variant_label': 'r1i1p1f1',\n",
       " 'version': 'v20191115',\n",
       " 'cmor_version': '3.4.0',\n",
       " 'license': 'CMIP6 model data produced by CSIRO 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.'}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
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   "source": [
    "x = xr.open_dataset(filepath)\n",
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