{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b89963d3-4ed7-48ce-a634-84ca826c2826",
   "metadata": {},
   "source": [
    "# Climate, oceans and data\n",
    "\n",
    "Author: [Dr. Chelle Gentemann](https://cgentemann.github.io).\n",
    "\n",
    "This notebook accompanies a lecture for Berkeley's Data 100 that covers the fundamental physical mechanisms behind global warming and analyzes CO2 and ocean temperature data.\n",
    "\n",
    "The original resides in [this github repository](https://github.com/cgentemann/d100_gentemann), this is a copy kept as part of the class materials.\n",
    "\n",
    "Copyright (c) 2021 Chelle Gentemann, [MIT Licensed](https://opensource.org/licenses/MIT)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7cb7e2ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2149/665068690.py:6: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
      "  plt.style.use(\"seaborn-whitegrid\")\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Small style adjustments for more readable plots\n",
    "plt.style.use(\"seaborn-whitegrid\")\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 6)\n",
    "plt.rcParams[\"font.size\"] = 14"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c5b304-9b3f-4949-a852-3d7fbf4c7afd",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "## Calculate E$_{in}$ = E$_{out}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5309d56b-8863-4b8f-b764-83cbcd679356",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temperature = 255.10 K\n"
     ]
    }
   ],
   "source": [
    "# Solve for T (slide 15)\n",
    "Ω = 1372  # W/m2 incoming solar\n",
    "σ = 5.67e-8  # stephan boltzman constant W/m2/K4\n",
    "A = 0.3\n",
    "T = ((Ω * (1 - A)) / (4 * σ)) ** 0.25\n",
    "print(\"Temperature =\", \"%.2f\" % T, \"K\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dec0d4d-4ddf-46a8-9660-d328afca8216",
   "metadata": {
    "tags": []
   },
   "source": [
    "### calculate greenhouse effect\n",
    "- bonus points for greek letters\n",
    "- to get a sigma type '\\sigma' then hit tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d5faeea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "greenhouse effect: 149.98 W m-2\n"
     ]
    }
   ],
   "source": [
    "# standard values\n",
    "Ω = 1372  # W/m2 incoming solar\n",
    "σ = 5.67e-8  # stephan boltzman constant W/m2/K4\n",
    "T = 288  # temperature K\n",
    "E = (T ** 4) * σ - (Ω * (1 - A)) / 4\n",
    "print(\"greenhouse effect:\", \"%.2f\" % E, \"W m-2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ee32a01-c844-4679-9c52-e8dd028e3c0a",
   "metadata": {},
   "source": [
    "## Experiment\n",
    "- Solve for the temperature, so that you can see how changes in albedo and greenhouse effect impact T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa54dddd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temperature = 288.0 K\n"
     ]
    }
   ],
   "source": [
    "# Solve for T, including the greenhouse effect\n",
    "Tnew = (((Ω * (1 - A)) / 4 + E) / σ) ** 0.25\n",
    "print(\"temperature =\", Tnew, \"K\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6909486e-d38a-43cb-a80a-8c7ec4b5726a",
   "metadata": {},
   "source": [
    "### calculate what a change in the greenhouse effect does to temperature\n",
    "- what if you increase E by 10%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d6e136bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temperature = 290.73\n",
      "10% increase in E leads to  2.73  degree K increase in temperature\n",
      "10% increase in E leads to  0.95 % increase in temperature\n"
     ]
    }
   ],
   "source": [
    "Enew = E * 1.1\n",
    "Tnew = (((Ω * (1 - 0.3)) / 4 + Enew) / σ) ** 0.25\n",
    "print(\"temperature =\", \"%.2f\" % Tnew)\n",
    "print(\n",
    "    \"10% increase in E leads to \",\n",
    "    \"%.2f\" % (Tnew - T),\n",
    "    \" degree K increase in temperature\",\n",
    ")\n",
    "print(\n",
    "    \"10% increase in E leads to \",\n",
    "    \"%.2f\" % (100 * ((Tnew - T) / T)),\n",
    "    \"% increase in temperature\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a8f96c11",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>date_index</th>\n",
       "      <th>fraction_date</th>\n",
       "      <th>c02</th>\n",
       "      <th>data_adjusted_season</th>\n",
       "      <th>data_fit</th>\n",
       "      <th>data_adjusted_seasonally_fit</th>\n",
       "      <th>data_filled</th>\n",
       "      <th>data_adjusted_seasonally_filed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>1</td>\n",
       "      <td>21200</td>\n",
       "      <td>1958.0411</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>2</td>\n",
       "      <td>21231</td>\n",
       "      <td>1958.1260</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>21259</td>\n",
       "      <td>1958.2027</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.43</td>\n",
       "      <td>316.19</td>\n",
       "      <td>314.90</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>21290</td>\n",
       "      <td>1958.2877</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "      <td>317.30</td>\n",
       "      <td>314.98</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>21320</td>\n",
       "      <td>1958.3699</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "      <td>317.86</td>\n",
       "      <td>315.06</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  date_index  fraction_date     c02  data_adjusted_season  \\\n",
       "0  1958      1       21200      1958.0411  -99.99                -99.99   \n",
       "1  1958      2       21231      1958.1260  -99.99                -99.99   \n",
       "2  1958      3       21259      1958.2027  315.70                314.43   \n",
       "3  1958      4       21290      1958.2877  317.45                315.16   \n",
       "4  1958      5       21320      1958.3699  317.51                314.71   \n",
       "\n",
       "   data_fit  data_adjusted_seasonally_fit  data_filled  \\\n",
       "0    -99.99                        -99.99       -99.99   \n",
       "1    -99.99                        -99.99       -99.99   \n",
       "2    316.19                        314.90       315.70   \n",
       "3    317.30                        314.98       317.45   \n",
       "4    317.86                        315.06       317.51   \n",
       "\n",
       "   data_adjusted_seasonally_filed  \n",
       "0                          -99.99  \n",
       "1                          -99.99  \n",
       "2                          314.43  \n",
       "3                          315.16  \n",
       "4                          314.71  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in data and print some\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "DATA_DIR = Path.home()/Path('shared/climate-data')\n",
    "\n",
    "monthly_2deg_path = DATA_DIR / \"era5_monthly_2deg_aws_v20210920.nc\"\n",
    "\n",
    "file = DATA_DIR / \"monthly_in_situ_co2_mlo_cleaned.csv\"\n",
    "data = pd.read_csv(file)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de6d38c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the CO2, note the -99 values you see above showing up in the plot\n",
    "plt.plot(data[\"fraction_date\"], data[\"c02\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a032fae7",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>date_index</th>\n",
       "      <th>fraction_date</th>\n",
       "      <th>c02</th>\n",
       "      <th>data_adjusted_season</th>\n",
       "      <th>data_fit</th>\n",
       "      <th>data_adjusted_seasonally_fit</th>\n",
       "      <th>data_filled</th>\n",
       "      <th>data_adjusted_seasonally_filed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>1</td>\n",
       "      <td>21200</td>\n",
       "      <td>1958.0411</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>2</td>\n",
       "      <td>21231</td>\n",
       "      <td>1958.1260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>21259</td>\n",
       "      <td>1958.2027</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.43</td>\n",
       "      <td>316.19</td>\n",
       "      <td>314.90</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>21290</td>\n",
       "      <td>1958.2877</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "      <td>317.30</td>\n",
       "      <td>314.98</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>21320</td>\n",
       "      <td>1958.3699</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "      <td>317.86</td>\n",
       "      <td>315.06</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  date_index  fraction_date     c02  data_adjusted_season  \\\n",
       "0  1958      1       21200      1958.0411     NaN                   NaN   \n",
       "1  1958      2       21231      1958.1260     NaN                   NaN   \n",
       "2  1958      3       21259      1958.2027  315.70                314.43   \n",
       "3  1958      4       21290      1958.2877  317.45                315.16   \n",
       "4  1958      5       21320      1958.3699  317.51                314.71   \n",
       "\n",
       "   data_fit  data_adjusted_seasonally_fit  data_filled  \\\n",
       "0       NaN                           NaN          NaN   \n",
       "1       NaN                           NaN          NaN   \n",
       "2    316.19                        314.90       315.70   \n",
       "3    317.30                        314.98       317.45   \n",
       "4    317.86                        315.06       317.51   \n",
       "\n",
       "   data_adjusted_seasonally_filed  \n",
       "0                             NaN  \n",
       "1                             NaN  \n",
       "2                          314.43  \n",
       "3                          315.16  \n",
       "4                          314.71  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get rid of missing values that are set to -99.99\n",
    "file = DATA_DIR / \"monthly_in_situ_co2_mlo_cleaned.csv\"\n",
    "data = pd.read_csv(file, na_values=-99.99)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac0665df-4756-4c8a-8727-b6f1cf83539d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data again\n",
    "plt.plot(data[\"fraction_date\"], data[\"c02\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7d9660ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use the CO2 in the equation given in class to calculate the greenhouse effect\n",
    "# then calculate the increase in temperature in deg C rather than K\n",
    "E = 133.26 + 0.044 * data[\"c02\"]\n",
    "# calculate new temperature\n",
    "data[\"Tnew\"] = (((Ω * (1 - 0.3)) / 4 + E) / σ) ** 0.25\n",
    "plt.plot(data[\"fraction_date\"], (data[\"Tnew\"] - 273.15) * 9 / 5 + 32)\n",
    "plt.xlabel(\"Year\"), plt.ylabel(\"Temperature (C)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2ee9e0f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explore the different data provided in the dataset, what are they?\n",
    "# can you guess from the names and explain how they were calulated?\n",
    "# can you re-calculate them? can you re-make the figure in the talk?\n",
    "plt.plot(data[\"fraction_date\"], data[\"data_filled\"], \"r.\")\n",
    "plt.plot(data[\"fraction_date\"], data[\"data_adjusted_seasonally_fit\"])\n",
    "plt.ylabel(\"CO2 fraction in dry air (ppm)\"), plt.xlabel(\"Year\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd32b321",
   "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>year</th>\n",
       "      <th>date_index</th>\n",
       "      <th>fraction_date</th>\n",
       "      <th>c02</th>\n",
       "      <th>data_adjusted_season</th>\n",
       "      <th>data_fit</th>\n",
       "      <th>data_adjusted_seasonally_fit</th>\n",
       "      <th>data_filled</th>\n",
       "      <th>data_adjusted_seasonally_filed</th>\n",
       "      <th>Tnew</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1989.5</td>\n",
       "      <td>32705.25</td>\n",
       "      <td>1989.541075</td>\n",
       "      <td>356.468571</td>\n",
       "      <td>356.421111</td>\n",
       "      <td>356.461270</td>\n",
       "      <td>356.400952</td>\n",
       "      <td>356.468571</td>\n",
       "      <td>356.421111</td>\n",
       "      <td>287.808514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1989.5</td>\n",
       "      <td>32736.25</td>\n",
       "      <td>1989.625925</td>\n",
       "      <td>357.840645</td>\n",
       "      <td>357.139355</td>\n",
       "      <td>357.248889</td>\n",
       "      <td>356.537778</td>\n",
       "      <td>357.240317</td>\n",
       "      <td>356.539841</td>\n",
       "      <td>287.819681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1989.5</td>\n",
       "      <td>32764.50</td>\n",
       "      <td>1989.703250</td>\n",
       "      <td>357.965238</td>\n",
       "      <td>356.544603</td>\n",
       "      <td>357.443906</td>\n",
       "      <td>356.010000</td>\n",
       "      <td>357.383750</td>\n",
       "      <td>355.964375</td>\n",
       "      <td>287.820691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1989.5</td>\n",
       "      <td>32795.50</td>\n",
       "      <td>1989.788175</td>\n",
       "      <td>359.331270</td>\n",
       "      <td>356.773175</td>\n",
       "      <td>358.718906</td>\n",
       "      <td>356.144844</td>\n",
       "      <td>358.745469</td>\n",
       "      <td>356.190000</td>\n",
       "      <td>287.831802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1989.5</td>\n",
       "      <td>32825.50</td>\n",
       "      <td>1989.870325</td>\n",
       "      <td>359.363125</td>\n",
       "      <td>356.253281</td>\n",
       "      <td>359.383750</td>\n",
       "      <td>356.277031</td>\n",
       "      <td>359.363125</td>\n",
       "      <td>356.253281</td>\n",
       "      <td>287.832057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year  date_index  fraction_date         c02  data_adjusted_season  \\\n",
       "month                                                                        \n",
       "1      1989.5    32705.25    1989.541075  356.468571            356.421111   \n",
       "2      1989.5    32736.25    1989.625925  357.840645            357.139355   \n",
       "3      1989.5    32764.50    1989.703250  357.965238            356.544603   \n",
       "4      1989.5    32795.50    1989.788175  359.331270            356.773175   \n",
       "5      1989.5    32825.50    1989.870325  359.363125            356.253281   \n",
       "\n",
       "         data_fit  data_adjusted_seasonally_fit  data_filled  \\\n",
       "month                                                          \n",
       "1      356.461270                    356.400952   356.468571   \n",
       "2      357.248889                    356.537778   357.240317   \n",
       "3      357.443906                    356.010000   357.383750   \n",
       "4      358.718906                    356.144844   358.745469   \n",
       "5      359.383750                    356.277031   359.363125   \n",
       "\n",
       "       data_adjusted_seasonally_filed        Tnew  \n",
       "month                                              \n",
       "1                          356.421111  287.808514  \n",
       "2                          356.539841  287.819681  \n",
       "3                          355.964375  287.820691  \n",
       "4                          356.190000  287.831802  \n",
       "5                          356.253281  287.832057  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the annual cycle using groupby\n",
    "annual = data.groupby(data.month).mean()\n",
    "annual.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "75e89711",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the anomaly\n",
    "anomaly = annual - annual.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5f2b116b-dd17-4beb-ae1a-736791468a93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(\"fraction_date\", \"data_filled\", \"r.\", data=data)\n",
    "ax.plot(\"fraction_date\", \"data_adjusted_seasonally_fit\", data=data)\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"CO2 fraction in dry air (ppm)\")\n",
    "ax.set_title(\"Monthly Mean CO2\")\n",
    "ax.grid(False)\n",
    "\n",
    "axin1 = ax.inset_axes([0.1, 0.5, 0.35, 0.35])\n",
    "axin1.plot(anomaly.c02, \"b\")\n",
    "axin1.plot(anomaly.c02, \"r.\")\n",
    "axin1.set_title(\"Seasonal Anomaly\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e5f2c706",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>date_index</th>\n",
       "      <th>fraction_date</th>\n",
       "      <th>c02</th>\n",
       "      <th>data_adjusted_season</th>\n",
       "      <th>data_fit</th>\n",
       "      <th>data_adjusted_seasonally_fit</th>\n",
       "      <th>data_filled</th>\n",
       "      <th>data_adjusted_seasonally_filed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>1</td>\n",
       "      <td>21200</td>\n",
       "      <td>1958.0411</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>2</td>\n",
       "      <td>21231</td>\n",
       "      <td>1958.1260</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>21259</td>\n",
       "      <td>1958.2027</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.43</td>\n",
       "      <td>316.19</td>\n",
       "      <td>314.90</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>21290</td>\n",
       "      <td>1958.2877</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "      <td>317.30</td>\n",
       "      <td>314.98</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>21320</td>\n",
       "      <td>1958.3699</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "      <td>317.86</td>\n",
       "      <td>315.06</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  date_index  fraction_date     c02  data_adjusted_season  \\\n",
       "0  1958      1       21200      1958.0411  -99.99                -99.99   \n",
       "1  1958      2       21231      1958.1260  -99.99                -99.99   \n",
       "2  1958      3       21259      1958.2027  315.70                314.43   \n",
       "3  1958      4       21290      1958.2877  317.45                315.16   \n",
       "4  1958      5       21320      1958.3699  317.51                314.71   \n",
       "\n",
       "   data_fit  data_adjusted_seasonally_fit  data_filled  \\\n",
       "0    -99.99                        -99.99       -99.99   \n",
       "1    -99.99                        -99.99       -99.99   \n",
       "2    316.19                        314.90       315.70   \n",
       "3    317.30                        314.98       317.45   \n",
       "4    317.86                        315.06       317.51   \n",
       "\n",
       "   data_adjusted_seasonally_filed  \n",
       "0                          -99.99  \n",
       "1                          -99.99  \n",
       "2                          314.43  \n",
       "3                          315.16  \n",
       "4                          314.71  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the C02 data from the base file rather than the cleaned up one.\n",
    "file = DATA_DIR / \"monthly_in_situ_co2_mlo.csv\"\n",
    "column_names = [\n",
    "    \"year\",\n",
    "    \"month\",\n",
    "    \"date_index\",\n",
    "    \"fraction_date\",\n",
    "    \"c02\",\n",
    "    \"data_adjusted_season\",\n",
    "    \"data_fit\",\n",
    "    \"data_adjusted_seasonally_fit\",\n",
    "    \"data_filled\",\n",
    "    \"data_adjusted_seasonally_filed\",\n",
    "]\n",
    "data = pd.read_csv(file, header=0, skiprows=56, names=column_names)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cff6af2-ea0e-40c5-9c6c-eafb8c22885d",
   "metadata": {},
   "source": [
    "## using xarray to explore era5 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8396032f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                                                                   (time: 504, latitude: 90, longitude: 180)\n",
       "Coordinates:\n",
       "  * time                                                                                      (time) datetime64[ns] ...\n",
       "  * latitude                                                                                  (latitude) float32 ...\n",
       "  * longitude                                                                                 (longitude) float32 ...\n",
       "Data variables: (12/15)\n",
       "    air_pressure_at_mean_sea_level                                                            (time, latitude, longitude) float32 ...\n",
       "    air_temperature_at_2_metres                                                               (time, latitude, longitude) float32 ...\n",
       "    air_temperature_at_2_metres_1hour_Maximum                                                 (time, latitude, longitude) float32 ...\n",
       "    air_temperature_at_2_metres_1hour_Minimum                                                 (time, latitude, longitude) float32 ...\n",
       "    dew_point_temperature_at_2_metres                                                         (time, latitude, longitude) float32 ...\n",
       "    eastward_wind_at_100_metres                                                               (time, latitude, longitude) float32 ...\n",
       "    ...                                                                                        ...\n",
       "    northward_wind_at_100_metres                                                              (time, latitude, longitude) float32 ...\n",
       "    northward_wind_at_10_metres                                                               (time, latitude, longitude) float32 ...\n",
       "    precipitation_amount_1hour_Accumulation                                                   (time, latitude, longitude) float32 ...\n",
       "    sea_surface_temperature                                                                   (time, latitude, longitude) float32 ...\n",
       "    snow_density                                                                              (time, latitude, longitude) float32 ...\n",
       "    surface_air_pressure                                                                      (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    institution:  ECMWF\n",
       "    source:       Reanalysis\n",
       "    title:        ERA5 forecasts</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-532b2043-ddac-4f5a-846a-d24b5a345726' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-532b2043-ddac-4f5a-846a-d24b5a345726' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 504</li><li><span class='xr-has-index'>latitude</span>: 90</li><li><span class='xr-has-index'>longitude</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2bbdc95c-26df-4294-853b-8f139b1ca5f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2bbdc95c-26df-4294-853b-8f139b1ca5f0' 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'>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'>1979-01-16T11:30:00 ... 2020-12-...</div><input id='attrs-851fff16-8916-4aeb-8e64-34c8e161a9fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-851fff16-8916-4aeb-8e64-34c8e161a9fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82de09ca-d386-4c55-b3cb-cb058d6625da' class='xr-var-data-in' type='checkbox'><label for='data-82de09ca-d386-4c55-b3cb-cb058d6625da' 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;1979-01-16T11:30:00.000000000&#x27;, &#x27;1979-02-14T23:30:00.000000000&#x27;,\n",
       "       &#x27;1979-03-16T11:30:00.000000000&#x27;, ..., &#x27;2020-10-16T11:30:00.000000000&#x27;,\n",
       "       &#x27;2020-11-15T23:30:00.000000000&#x27;, &#x27;2020-12-16T11:30:00.000000000&#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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-88.88 -86.88 ... 87.12 89.12</div><input id='attrs-d15f63c7-637c-48b6-9730-fd35960f98e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d15f63c7-637c-48b6-9730-fd35960f98e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4f97b89-8514-4d74-8f6b-a7d36490f9b6' class='xr-var-data-in' type='checkbox'><label for='data-a4f97b89-8514-4d74-8f6b-a7d36490f9b6' 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>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'><pre>array([-88.875, -86.875, -84.875, -82.875, -80.875, -78.875, -76.875, -74.875,\n",
       "       -72.875, -70.875, -68.875, -66.875, -64.875, -62.875, -60.875, -58.875,\n",
       "       -56.875, -54.875, -52.875, -50.875, -48.875, -46.875, -44.875, -42.875,\n",
       "       -40.875, -38.875, -36.875, -34.875, -32.875, -30.875, -28.875, -26.875,\n",
       "       -24.875, -22.875, -20.875, -18.875, -16.875, -14.875, -12.875, -10.875,\n",
       "        -8.875,  -6.875,  -4.875,  -2.875,  -0.875,   1.125,   3.125,   5.125,\n",
       "         7.125,   9.125,  11.125,  13.125,  15.125,  17.125,  19.125,  21.125,\n",
       "        23.125,  25.125,  27.125,  29.125,  31.125,  33.125,  35.125,  37.125,\n",
       "        39.125,  41.125,  43.125,  45.125,  47.125,  49.125,  51.125,  53.125,\n",
       "        55.125,  57.125,  59.125,  61.125,  63.125,  65.125,  67.125,  69.125,\n",
       "        71.125,  73.125,  75.125,  77.125,  79.125,  81.125,  83.125,  85.125,\n",
       "        87.125,  89.125], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>0.875 2.875 4.875 ... 356.9 358.9</div><input id='attrs-02c0db87-7a13-4209-8270-0c9b8ad9c795' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02c0db87-7a13-4209-8270-0c9b8ad9c795' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-783ee728-0625-449e-8926-e7be17eca1f6' class='xr-var-data-in' type='checkbox'><label for='data-783ee728-0625-449e-8926-e7be17eca1f6' 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>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'><pre>array([  0.875,   2.875,   4.875,   6.875,   8.875,  10.875,  12.875,  14.875,\n",
       "        16.875,  18.875,  20.875,  22.875,  24.875,  26.875,  28.875,  30.875,\n",
       "        32.875,  34.875,  36.875,  38.875,  40.875,  42.875,  44.875,  46.875,\n",
       "        48.875,  50.875,  52.875,  54.875,  56.875,  58.875,  60.875,  62.875,\n",
       "        64.875,  66.875,  68.875,  70.875,  72.875,  74.875,  76.875,  78.875,\n",
       "        80.875,  82.875,  84.875,  86.875,  88.875,  90.875,  92.875,  94.875,\n",
       "        96.875,  98.875, 100.875, 102.875, 104.875, 106.875, 108.875, 110.875,\n",
       "       112.875, 114.875, 116.875, 118.875, 120.875, 122.875, 124.875, 126.875,\n",
       "       128.875, 130.875, 132.875, 134.875, 136.875, 138.875, 140.875, 142.875,\n",
       "       144.875, 146.875, 148.875, 150.875, 152.875, 154.875, 156.875, 158.875,\n",
       "       160.875, 162.875, 164.875, 166.875, 168.875, 170.875, 172.875, 174.875,\n",
       "       176.875, 178.875, 180.875, 182.875, 184.875, 186.875, 188.875, 190.875,\n",
       "       192.875, 194.875, 196.875, 198.875, 200.875, 202.875, 204.875, 206.875,\n",
       "       208.875, 210.875, 212.875, 214.875, 216.875, 218.875, 220.875, 222.875,\n",
       "       224.875, 226.875, 228.875, 230.875, 232.875, 234.875, 236.875, 238.875,\n",
       "       240.875, 242.875, 244.875, 246.875, 248.875, 250.875, 252.875, 254.875,\n",
       "       256.875, 258.875, 260.875, 262.875, 264.875, 266.875, 268.875, 270.875,\n",
       "       272.875, 274.875, 276.875, 278.875, 280.875, 282.875, 284.875, 286.875,\n",
       "       288.875, 290.875, 292.875, 294.875, 296.875, 298.875, 300.875, 302.875,\n",
       "       304.875, 306.875, 308.875, 310.875, 312.875, 314.875, 316.875, 318.875,\n",
       "       320.875, 322.875, 324.875, 326.875, 328.875, 330.875, 332.875, 334.875,\n",
       "       336.875, 338.875, 340.875, 342.875, 344.875, 346.875, 348.875, 350.875,\n",
       "       352.875, 354.875, 356.875, 358.875], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2fb3028f-6faa-4918-93d1-8492e264e164' class='xr-section-summary-in' type='checkbox'  ><label for='section-2fb3028f-6faa-4918-93d1-8492e264e164' class='xr-section-summary' >Data variables: <span>(15)</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>air_pressure_at_mean_sea_level</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e3e22bc3-4bb1-476b-b254-d654ec2cdcf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3e22bc3-4bb1-476b-b254-d654ec2cdcf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-914a022b-df8a-4b17-b03c-448ce61ade19' class='xr-var-data-in' type='checkbox'><label for='data-914a022b-df8a-4b17-b03c-448ce61ade19' 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>Mean sea level pressure</dd><dt><span>nameCDM :</span></dt><dd>Mean_sea_level_pressure_surface</dd><dt><span>nameECMWF :</span></dt><dd>Mean sea level pressure</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>msl</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_temperature_at_2_metres</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5293d010-c647-4ed8-b225-26634a3743bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5293d010-c647-4ed8-b225-26634a3743bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f787e9e-1a7d-4584-a0d5-dbd121bed1ca' class='xr-var-data-in' type='checkbox'><label for='data-0f787e9e-1a7d-4584-a0d5-dbd121bed1ca' 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>2 metre temperature</dd><dt><span>nameCDM :</span></dt><dd>2_metre_temperature_surface</dd><dt><span>nameECMWF :</span></dt><dd>2 metre temperature</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>2t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_temperature_at_2_metres_1hour_Maximum</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-386b95ea-a294-46fa-985a-728deaebaa2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-386b95ea-a294-46fa-985a-728deaebaa2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d6dd330-e5d1-4590-a18c-c0c57df450c2' class='xr-var-data-in' type='checkbox'><label for='data-6d6dd330-e5d1-4590-a18c-c0c57df450c2' 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>Maximum temperature at 2 metres since previous post-processing</dd><dt><span>nameCDM :</span></dt><dd>Maximum_temperature_at_2_metres_since_previous_post-processing_surface_1_Hour_2</dd><dt><span>nameECMWF :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd><dt><span>product_type :</span></dt><dd>forecast</dd><dt><span>shortNameECMWF :</span></dt><dd>mx2t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air_temperature_at_2_metres_1hour_Minimum</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a6881348-ae13-4284-91da-2262f3c2c52f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6881348-ae13-4284-91da-2262f3c2c52f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bee505e2-8a2b-47a5-a2c1-cf4bde1fe90a' class='xr-var-data-in' type='checkbox'><label for='data-bee505e2-8a2b-47a5-a2c1-cf4bde1fe90a' 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>Minimum temperature at 2 metres since previous post-processing</dd><dt><span>nameCDM :</span></dt><dd>Minimum_temperature_at_2_metres_since_previous_post-processing_surface_1_Hour_2</dd><dt><span>nameECMWF :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd><dt><span>product_type :</span></dt><dd>forecast</dd><dt><span>shortNameECMWF :</span></dt><dd>mn2t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dew_point_temperature_at_2_metres</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1ef0d2f7-e9fa-4d58-a3c8-c175175894e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ef0d2f7-e9fa-4d58-a3c8-c175175894e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-357e2c46-8e56-4c30-8bf1-ba3e009cb0f3' class='xr-var-data-in' type='checkbox'><label for='data-357e2c46-8e56-4c30-8bf1-ba3e009cb0f3' 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>2 metre dewpoint temperature</dd><dt><span>nameCDM :</span></dt><dd>2_metre_dewpoint_temperature_surface</dd><dt><span>nameECMWF :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>2d</dd><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eastward_wind_at_100_metres</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1d2ab7f4-81c8-4fe3-92af-dcb50c649ba2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d2ab7f4-81c8-4fe3-92af-dcb50c649ba2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-216bb47d-b55d-491d-94e6-562f279e206f' class='xr-var-data-in' type='checkbox'><label for='data-216bb47d-b55d-491d-94e6-562f279e206f' 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>100 metre U wind component</dd><dt><span>nameCDM :</span></dt><dd>100_metre_U_wind_component_surface</dd><dt><span>nameECMWF :</span></dt><dd>100 metre U wind component</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>100u</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eastward_wind_at_10_metres</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b0cd3734-713e-4568-839c-007b536964e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0cd3734-713e-4568-839c-007b536964e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a5c3a61-0925-4327-b576-4c4382cff665' class='xr-var-data-in' type='checkbox'><label for='data-8a5c3a61-0925-4327-b576-4c4382cff665' 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>10 metre U wind component</dd><dt><span>nameCDM :</span></dt><dd>10_metre_U_wind_component_surface</dd><dt><span>nameECMWF :</span></dt><dd>10 metre U wind component</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>10u</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-af443109-31f9-4237-8872-18e4a01b3fea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af443109-31f9-4237-8872-18e4a01b3fea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51bfdc06-8ca2-4647-acb2-0f10ee2705e0' class='xr-var-data-in' type='checkbox'><label for='data-51bfdc06-8ca2-4647-acb2-0f10ee2705e0' 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>Surface solar radiation downwards</dd><dt><span>nameCDM :</span></dt><dd>Surface_solar_radiation_downwards_surface_1_Hour_Accumulation</dd><dt><span>nameECMWF :</span></dt><dd>Surface solar radiation downwards</dd><dt><span>product_type :</span></dt><dd>forecast</dd><dt><span>shortNameECMWF :</span></dt><dd>ssrd</dd><dt><span>standard_name :</span></dt><dd>integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air</dd><dt><span>units :</span></dt><dd>J m**-2</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lwe_thickness_of_surface_snow_amount</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-da66606a-0f8f-4857-a448-c07b0e2cf8e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da66606a-0f8f-4857-a448-c07b0e2cf8e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5d04c69-f8ff-4007-b9ed-6632f9b94c31' class='xr-var-data-in' type='checkbox'><label for='data-a5d04c69-f8ff-4007-b9ed-6632f9b94c31' 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>Snow depth</dd><dt><span>nameCDM :</span></dt><dd>Snow_depth_surface</dd><dt><span>nameECMWF :</span></dt><dd>Snow depth</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>sd</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>northward_wind_at_100_metres</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5cdba6be-f2c4-4670-bfb5-14ffc0d06ae5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cdba6be-f2c4-4670-bfb5-14ffc0d06ae5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9229968-5bd6-4439-87e1-59e0f4728330' class='xr-var-data-in' type='checkbox'><label for='data-d9229968-5bd6-4439-87e1-59e0f4728330' 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>100 metre V wind component</dd><dt><span>nameCDM :</span></dt><dd>100_metre_V_wind_component_surface</dd><dt><span>nameECMWF :</span></dt><dd>100 metre V wind component</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>100v</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>northward_wind_at_10_metres</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6471303f-b9a0-443a-9ae6-058c3c903bfd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6471303f-b9a0-443a-9ae6-058c3c903bfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06500d4f-0930-4f76-b068-9c21fc88cdac' class='xr-var-data-in' type='checkbox'><label for='data-06500d4f-0930-4f76-b068-9c21fc88cdac' 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>10 metre V wind component</dd><dt><span>nameCDM :</span></dt><dd>10_metre_V_wind_component_surface</dd><dt><span>nameECMWF :</span></dt><dd>10 metre V wind component</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>10v</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>precipitation_amount_1hour_Accumulation</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d12778c-1318-4690-b0fe-aa88efa8f8f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d12778c-1318-4690-b0fe-aa88efa8f8f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7efcaab9-8f27-48fb-9b90-3be55e682043' class='xr-var-data-in' type='checkbox'><label for='data-7efcaab9-8f27-48fb-9b90-3be55e682043' 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>Total precipitation</dd><dt><span>nameCDM :</span></dt><dd>Total_precipitation_1hour_Accumulation</dd><dt><span>nameECMWF :</span></dt><dd>Total precipitation</dd><dt><span>product_type :</span></dt><dd>forecast</dd><dt><span>shortNameECMWF :</span></dt><dd>tp</dd><dt><span>standard_name :</span></dt><dd>precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_surface_temperature</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-653f8761-6bc2-4e18-988a-564999feffb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-653f8761-6bc2-4e18-988a-564999feffb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75ff2d51-52b5-4bdb-a266-7c2003067d58' class='xr-var-data-in' type='checkbox'><label for='data-75ff2d51-52b5-4bdb-a266-7c2003067d58' 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>Sea surface temperature</dd><dt><span>nameCDM :</span></dt><dd>Sea_surface_temperature_surface</dd><dt><span>nameECMWF :</span></dt><dd>Sea surface temperature</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>sst</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_density</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-400149bc-6dd9-4df4-ad02-a21b4a3225eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-400149bc-6dd9-4df4-ad02-a21b4a3225eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1da6b5d7-a46a-40c1-b082-869e2c6fa813' class='xr-var-data-in' type='checkbox'><label for='data-1da6b5d7-a46a-40c1-b082-869e2c6fa813' 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>Snow density</dd><dt><span>nameCDM :</span></dt><dd>Snow_density_surface</dd><dt><span>nameECMWF :</span></dt><dd>Snow density</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>rsn</dd><dt><span>standard_name :</span></dt><dd>snow_density</dd><dt><span>units :</span></dt><dd>kg m**-3</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_air_pressure</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3085f035-90f4-48e5-b1cc-e315434427ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3085f035-90f4-48e5-b1cc-e315434427ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc4b9737-e872-41d6-966c-a485f7923345' class='xr-var-data-in' type='checkbox'><label for='data-cc4b9737-e872-41d6-966c-a485f7923345' 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>Surface pressure</dd><dt><span>nameCDM :</span></dt><dd>Surface_pressure_surface</dd><dt><span>nameECMWF :</span></dt><dd>Surface pressure</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>sp</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>[8164800 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-663343c6-2d14-47ad-83f7-40e647aa1a56' class='xr-section-summary-in' type='checkbox'  checked><label for='section-663343c6-2d14-47ad-83f7-40e647aa1a56' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>source :</span></dt><dd>Reanalysis</dd><dt><span>title :</span></dt><dd>ERA5 forecasts</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                                                                   (time: 504, latitude: 90, longitude: 180)\n",
       "Coordinates:\n",
       "  * time                                                                                      (time) datetime64[ns] ...\n",
       "  * latitude                                                                                  (latitude) float32 ...\n",
       "  * longitude                                                                                 (longitude) float32 ...\n",
       "Data variables: (12/15)\n",
       "    air_pressure_at_mean_sea_level                                                            (time, latitude, longitude) float32 ...\n",
       "    air_temperature_at_2_metres                                                               (time, latitude, longitude) float32 ...\n",
       "    air_temperature_at_2_metres_1hour_Maximum                                                 (time, latitude, longitude) float32 ...\n",
       "    air_temperature_at_2_metres_1hour_Minimum                                                 (time, latitude, longitude) float32 ...\n",
       "    dew_point_temperature_at_2_metres                                                         (time, latitude, longitude) float32 ...\n",
       "    eastward_wind_at_100_metres                                                               (time, latitude, longitude) float32 ...\n",
       "    ...                                                                                        ...\n",
       "    northward_wind_at_100_metres                                                              (time, latitude, longitude) float32 ...\n",
       "    northward_wind_at_10_metres                                                               (time, latitude, longitude) float32 ...\n",
       "    precipitation_amount_1hour_Accumulation                                                   (time, latitude, longitude) float32 ...\n",
       "    sea_surface_temperature                                                                   (time, latitude, longitude) float32 ...\n",
       "    snow_density                                                                              (time, latitude, longitude) float32 ...\n",
       "    surface_air_pressure                                                                      (time, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    institution:  ECMWF\n",
       "    source:       Reanalysis\n",
       "    title:        ERA5 forecasts"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "ds = xr.open_dataset(DATA_DIR / \"era5_monthly_2deg_aws_v20210920.nc\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d16853bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Frozen({'time': 504, 'latitude': 90, 'longitude': 180})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.dims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cb87fd71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1979-01-16T11:30:00 ... 2020-12-16T11:30:00\n",
       "  * latitude   (latitude) float32 -88.88 -86.88 -84.88 ... 85.12 87.12 89.12\n",
       "  * longitude  (longitude) float32 0.875 2.875 4.875 6.875 ... 354.9 356.9 358.9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "710c0764",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air_temperature_at_2_metres&#x27; (time: 504, latitude: 90, longitude: 180)&gt;\n",
       "[8164800 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1979-01-16T11:30:00 ... 2020-12-16T11:30:00\n",
       "  * latitude   (latitude) float32 -88.88 -86.88 -84.88 ... 85.12 87.12 89.12\n",
       "  * longitude  (longitude) float32 0.875 2.875 4.875 6.875 ... 354.9 356.9 358.9\n",
       "Attributes:\n",
       "    long_name:       2 metre temperature\n",
       "    nameCDM:         2_metre_temperature_surface\n",
       "    nameECMWF:       2 metre temperature\n",
       "    product_type:    analysis\n",
       "    shortNameECMWF:  2t\n",
       "    standard_name:   air_temperature\n",
       "    units:           K</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air_temperature_at_2_metres'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 504</li><li><span class='xr-has-index'>latitude</span>: 90</li><li><span class='xr-has-index'>longitude</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0353aafa-beab-43d1-9c61-d0c3a0c448be' class='xr-array-in' type='checkbox' checked><label for='section-0353aafa-beab-43d1-9c61-d0c3a0c448be' 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>[8164800 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-5d2171fc-f1c7-47f8-938b-e07942188eee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d2171fc-f1c7-47f8-938b-e07942188eee' 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'>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'>1979-01-16T11:30:00 ... 2020-12-...</div><input id='attrs-23ceb056-8358-408f-8e96-9fb613354c98' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23ceb056-8358-408f-8e96-9fb613354c98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ac3ccfa-9403-4060-a7c7-51a3913a9f85' class='xr-var-data-in' type='checkbox'><label for='data-5ac3ccfa-9403-4060-a7c7-51a3913a9f85' 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;1979-01-16T11:30:00.000000000&#x27;, &#x27;1979-02-14T23:30:00.000000000&#x27;,\n",
       "       &#x27;1979-03-16T11:30:00.000000000&#x27;, ..., &#x27;2020-10-16T11:30:00.000000000&#x27;,\n",
       "       &#x27;2020-11-15T23:30:00.000000000&#x27;, &#x27;2020-12-16T11:30:00.000000000&#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'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-88.88 -86.88 ... 87.12 89.12</div><input id='attrs-ed4ac63c-9d1e-4c28-a45b-1f397e426e3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed4ac63c-9d1e-4c28-a45b-1f397e426e3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-781a0fbb-5f00-41eb-95e7-54b61fd1ad6c' class='xr-var-data-in' type='checkbox'><label for='data-781a0fbb-5f00-41eb-95e7-54b61fd1ad6c' 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>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'><pre>array([-88.875, -86.875, -84.875, -82.875, -80.875, -78.875, -76.875, -74.875,\n",
       "       -72.875, -70.875, -68.875, -66.875, -64.875, -62.875, -60.875, -58.875,\n",
       "       -56.875, -54.875, -52.875, -50.875, -48.875, -46.875, -44.875, -42.875,\n",
       "       -40.875, -38.875, -36.875, -34.875, -32.875, -30.875, -28.875, -26.875,\n",
       "       -24.875, -22.875, -20.875, -18.875, -16.875, -14.875, -12.875, -10.875,\n",
       "        -8.875,  -6.875,  -4.875,  -2.875,  -0.875,   1.125,   3.125,   5.125,\n",
       "         7.125,   9.125,  11.125,  13.125,  15.125,  17.125,  19.125,  21.125,\n",
       "        23.125,  25.125,  27.125,  29.125,  31.125,  33.125,  35.125,  37.125,\n",
       "        39.125,  41.125,  43.125,  45.125,  47.125,  49.125,  51.125,  53.125,\n",
       "        55.125,  57.125,  59.125,  61.125,  63.125,  65.125,  67.125,  69.125,\n",
       "        71.125,  73.125,  75.125,  77.125,  79.125,  81.125,  83.125,  85.125,\n",
       "        87.125,  89.125], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>0.875 2.875 4.875 ... 356.9 358.9</div><input id='attrs-994d3244-6df4-4e3d-8d00-02c065e550df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-994d3244-6df4-4e3d-8d00-02c065e550df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04602087-5236-4f34-a255-da35403e930b' class='xr-var-data-in' type='checkbox'><label for='data-04602087-5236-4f34-a255-da35403e930b' 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>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'><pre>array([  0.875,   2.875,   4.875,   6.875,   8.875,  10.875,  12.875,  14.875,\n",
       "        16.875,  18.875,  20.875,  22.875,  24.875,  26.875,  28.875,  30.875,\n",
       "        32.875,  34.875,  36.875,  38.875,  40.875,  42.875,  44.875,  46.875,\n",
       "        48.875,  50.875,  52.875,  54.875,  56.875,  58.875,  60.875,  62.875,\n",
       "        64.875,  66.875,  68.875,  70.875,  72.875,  74.875,  76.875,  78.875,\n",
       "        80.875,  82.875,  84.875,  86.875,  88.875,  90.875,  92.875,  94.875,\n",
       "        96.875,  98.875, 100.875, 102.875, 104.875, 106.875, 108.875, 110.875,\n",
       "       112.875, 114.875, 116.875, 118.875, 120.875, 122.875, 124.875, 126.875,\n",
       "       128.875, 130.875, 132.875, 134.875, 136.875, 138.875, 140.875, 142.875,\n",
       "       144.875, 146.875, 148.875, 150.875, 152.875, 154.875, 156.875, 158.875,\n",
       "       160.875, 162.875, 164.875, 166.875, 168.875, 170.875, 172.875, 174.875,\n",
       "       176.875, 178.875, 180.875, 182.875, 184.875, 186.875, 188.875, 190.875,\n",
       "       192.875, 194.875, 196.875, 198.875, 200.875, 202.875, 204.875, 206.875,\n",
       "       208.875, 210.875, 212.875, 214.875, 216.875, 218.875, 220.875, 222.875,\n",
       "       224.875, 226.875, 228.875, 230.875, 232.875, 234.875, 236.875, 238.875,\n",
       "       240.875, 242.875, 244.875, 246.875, 248.875, 250.875, 252.875, 254.875,\n",
       "       256.875, 258.875, 260.875, 262.875, 264.875, 266.875, 268.875, 270.875,\n",
       "       272.875, 274.875, 276.875, 278.875, 280.875, 282.875, 284.875, 286.875,\n",
       "       288.875, 290.875, 292.875, 294.875, 296.875, 298.875, 300.875, 302.875,\n",
       "       304.875, 306.875, 308.875, 310.875, 312.875, 314.875, 316.875, 318.875,\n",
       "       320.875, 322.875, 324.875, 326.875, 328.875, 330.875, 332.875, 334.875,\n",
       "       336.875, 338.875, 340.875, 342.875, 344.875, 346.875, 348.875, 350.875,\n",
       "       352.875, 354.875, 356.875, 358.875], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5458c65a-087b-47a6-bfa1-658af6c586ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5458c65a-087b-47a6-bfa1-658af6c586ac' 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>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>nameCDM :</span></dt><dd>2_metre_temperature_surface</dd><dt><span>nameECMWF :</span></dt><dd>2 metre temperature</dd><dt><span>product_type :</span></dt><dd>analysis</dd><dt><span>shortNameECMWF :</span></dt><dd>2t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'air_temperature_at_2_metres' (time: 504, latitude: 90, longitude: 180)>\n",
       "[8164800 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1979-01-16T11:30:00 ... 2020-12-16T11:30:00\n",
       "  * latitude   (latitude) float32 -88.88 -86.88 -84.88 ... 85.12 87.12 89.12\n",
       "  * longitude  (longitude) float32 0.875 2.875 4.875 6.875 ... 354.9 356.9 358.9\n",
       "Attributes:\n",
       "    long_name:       2 metre temperature\n",
       "    nameCDM:         2_metre_temperature_surface\n",
       "    nameECMWF:       2 metre temperature\n",
       "    product_type:    analysis\n",
       "    shortNameECMWF:  2t\n",
       "    standard_name:   air_temperature\n",
       "    units:           K"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# two ways to print out the data for temperature at 2m\n",
    "ds[\"air_temperature_at_2_metres\"]\n",
    "ds.air_temperature_at_2_metres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "51384d7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "280.93103\n",
      "280.93103\n",
      "[280.93103]\n",
      "37.125\n",
      "238.875\n"
     ]
    }
   ],
   "source": [
    "# different ways to access the data using index, isel, sel\n",
    "temp = ds.air_temperature_at_2_metres\n",
    "print(temp[0, 63, 119].data)\n",
    "print(temp.isel(time=0, latitude=63, longitude=119).data)\n",
    "print(temp.sel(time=\"1979-01\", latitude=37.125, longitude=238.875).data)\n",
    "print(temp.latitude[63].data)\n",
    "print(temp.longitude[119].data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49a30ab5-6bed-4a83-a2a6-2e3dcc6b07be",
   "metadata": {},
   "source": [
    "## .isel versus .sel\n",
    "- .isel is endpoint EXclusive\n",
    "- .sel is endpoint INclusive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bde07f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = ds.air_temperature_at_2_metres\n",
    "point1 = temp.isel(time=0, latitude=63, longitude=119)\n",
    "point2 = temp.sel(time=\"1979-01\", latitude=37.125, longitude=238.875)\n",
    "area1 = temp.isel(time=0, latitude=slice(63, 65), longitude=slice(119, 125))\n",
    "area2 = temp.sel(\n",
    "    time=\"1979-01\",\n",
    "    latitude=slice(temp.latitude[63], temp.latitude[65]),\n",
    "    longitude=slice(temp.longitude[119], temp.longitude[125]),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e63546ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "area1 uses isel\n",
      "[37.125 39.125]\n",
      "[238.875 240.875 242.875 244.875 246.875 248.875]\n"
     ]
    }
   ],
   "source": [
    "print(\"area1 uses isel\")\n",
    "# print(area1.dims)\n",
    "print(area1.latitude.data)\n",
    "print(area1.longitude.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4d4f772b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "area2 uses sel\n",
      "[37.125 39.125 41.125]\n",
      "[238.875 240.875 242.875 244.875 246.875 248.875 250.875]\n"
     ]
    }
   ],
   "source": [
    "print(\"area2 uses sel\")\n",
    "# print(area2.dims)\n",
    "print(area2.latitude.data)\n",
    "print(area2.longitude.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2941b58f",
   "metadata": {},
   "outputs": [],
   "source": [
    "point = ds.sel(time=\"1979-01\", latitude=37.125, longitude=238.875)\n",
    "area = ds.sel(\n",
    "    time=\"1979-01\",\n",
    "    latitude=slice(temp.latitude[63], temp.latitude[65]),\n",
    "    longitude=slice(temp.longitude[119], temp.longitude[125]),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5dbbccf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.air_temperature_at_2_metres.sel(time=\"1979-01\").plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a885bd08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.air_temperature_at_2_metres.sel(latitude=37.125, longitude=238.875).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45e79271",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds.air_temperature_at_2_metres.mean(\"time\").plot()\n",
    "mean_map = ds.mean(\"time\")  # takes the mean across all variables in ds\n",
    "mean_map.air_temperature_at_2_metres.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "702649a8-4474-411e-bb64-87fdb697235f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate a map of the mean values across all time\n",
    "ave = ds.mean((\"latitude\", \"longitude\"))\n",
    "ave.air_temperature_at_2_metres.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ae20beb-b74d-4871-90bd-b2e42086f153",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate a map of the mean values across all time\n",
    "ave = ds.mean(\"time\")\n",
    "ave.air_temperature_at_2_metres.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f90faf4-fea6-4d73-aa29-f8b8cfbd6071",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate a map of the mean values across all time\n",
    "ave = ds.mean((\"latitude\", \"longitude\"))\n",
    "ave.air_temperature_at_2_metres.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "052c3032-a6a8-4775-abf0-71106310c28c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ave = ds.mean(keep_attrs=True)\n",
    "ave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bf848ed-b5ee-445f-8277-e6b025829fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = np.cos(np.deg2rad(ds.latitude))\n",
    "weights.name = \"weights\"\n",
    "weights.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82a49ba8-7545-4ab4-9ac4-0299cba231df",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_weighted = ds.weighted(weights)\n",
    "weighted_mean = ds_weighted.mean()\n",
    "weighted_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ff32c49-5745-4ccd-91bc-4e1e2f767b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_weighted = ds.weighted(weights)\n",
    "weighted_mean = ds_weighted.mean((\"latitude\", \"longitude\"))\n",
    "weighted_mean.air_temperature_at_2_metres.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38de9743-2512-4ef2-ace7-1996f5f6c3b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the annual cycle\n",
    "annual_cycle = weighted_mean.groupby(\"time.month\").mean()\n",
    "annual_cycle.air_temperature_at_2_metres.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dea0b2da-aa1e-4100-a4b4-963e28808d51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the annual cycle at a point\n",
    "weighted_trend = (\n",
    "    weighted_mean.groupby(\"time.month\") - annual_cycle + annual_cycle.mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f335d09-ed1a-48ca-a381-bf44d25db091",
   "metadata": {},
   "outputs": [],
   "source": [
    "weighted_mean.air_temperature_at_2_metres.plot()\n",
    "weighted_trend.air_temperature_at_2_metres.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f63e93b-30df-4907-be1b-957234321b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.arange(284, 291, 0.25)\n",
    "xr.plot.hist(\n",
    "    weighted_mean.air_temperature_at_2_metres.sel(time=slice(\"1980\", \"2000\")),\n",
    "    bins=bins,\n",
    "    density=True,\n",
    "    alpha=0.9,\n",
    "    color=\"b\",\n",
    ")\n",
    "xr.plot.hist(\n",
    "    weighted_mean.air_temperature_at_2_metres.sel(time=slice(\"2000\", \"2020\")),\n",
    "    bins=bins,\n",
    "    density=True,\n",
    "    alpha=0.85,\n",
    "    color=\"r\",\n",
    ")\n",
    "plt.ylabel(\"Probability Density (/K)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6122444-d527-4423-a38a-4d67fcfc7d3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pfit = ds.air_temperature_at_2_metres.polyfit(\"time\", 1)\n",
    "pfit.polyfit_coefficients[0] *= 3.154000000101e16  # go from nanosecond to year\n",
    "pfit.polyfit_coefficients[0].plot(cbar_kwargs={\"label\": \"trend deg/yr\"});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "163ea9e9-dd42-4dd5-881f-91ada55663ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.timedelta64(1, \"Y\")\n",
    "# /np.timedelta64(1,'s').data\n",
    "pfit.polyfit_coefficients[0, 80, 10] * 3.154000000101e16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffc3089d-238b-41c5-b1b0-3b080e072238",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs\n",
    "\n",
    "p = pfit.polyfit_coefficients[0].plot(\n",
    "    subplot_kws=dict(projection=ccrs.Orthographic(0, 90), facecolor=\"gray\"),\n",
    "    transform=ccrs.PlateCarree(central_longitude=0),\n",
    "    cbar_kwargs={\"label\": \"trend deg/yr\"},\n",
    ")\n",
    "\n",
    "p.axes.coastlines();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd64d0e-7310-488e-ade6-874f9eab1219",
   "metadata": {},
   "source": [
    "- Subset all variables to just the Arctic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dd3e29e-74c4-4d07-9c14-47d0d407467e",
   "metadata": {},
   "outputs": [],
   "source": [
    "arctic_ds_weighted = ds.sel(latitude=slice(70,90)).weighted(weights)\n",
    "arctic_weighted_mean = arctic_ds_weighted.mean((\"latitude\", \"longitude\"))\n",
    "arctic_annual_cycle = arctic_weighted_mean.groupby(\"time.month\").mean()\n",
    "arctic_weighted_trend = (\n",
    "    arctic_weighted_mean.groupby(\"time.month\") - arctic_annual_cycle + arctic_annual_cycle.mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b2374ee-143f-40e8-a331-a86ebb448ab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "arctic_weighted_mean.air_temperature_at_2_metres.plot()\n",
    "arctic_weighted_trend.air_temperature_at_2_metres.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "853a6ff3-ea7e-43aa-afc7-e655fb6453d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_trend(x, y):\n",
    "    pf = np.polyfit(x, y, 1)\n",
    "    return pf[0]\n",
    "\n",
    "x = arctic_weighted_mean.time.dt.year\n",
    "y = arctic_weighted_mean.air_temperature_at_2_metres\n",
    "arctic_trend = linear_trend(x, y)\n",
    "x = weighted_mean.time.dt.year\n",
    "y = weighted_mean.air_temperature_at_2_metres\n",
    "trend = linear_trend(x, y)\n",
    "print('linear trend: global=',trend,'arctic', arctic_trend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ac309e6-1003-4397-a86f-f90ae504c2c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.arange(245, 280, 1)\n",
    "xr.plot.hist(\n",
    "    arctic_weighted_mean.air_temperature_at_2_metres.sel(time=slice(\"1980\", \"2000\")),\n",
    "    bins=bins,\n",
    "    density=True,\n",
    "    alpha=0.9,\n",
    "    color=\"b\",\n",
    ")\n",
    "xr.plot.hist(\n",
    "    arctic_weighted_mean.air_temperature_at_2_metres.sel(time=slice(\"2000\", \"2020\")),\n",
    "    bins=bins,\n",
    "    density=True,\n",
    "    alpha=0.85,\n",
    "    color=\"r\",\n",
    ")\n",
    "plt.ylabel(\"Probability Density (/K)\");"
   ]
  }
 ],
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