climate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "95a7abd0",
   "metadata": {},
   "source": [
    "# Unit I: Climate Change Module\n",
    "\n",
    "## Opening Tutorial: Examining CO2 trends in python\n",
    "\n",
    "- Example from <http://climate.nasa.gov/vital-signs/carbon-dioxide/>\n",
    "- Raw data from <https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_mlo.txt>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ffb87442",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn.objects as so"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fe193020",
   "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>decimal_date</th>\n",
       "      <th>average</th>\n",
       "      <th>smooth</th>\n",
       "      <th>std_days</th>\n",
       "      <th>uncertainty</th>\n",
       "      <th>empty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>1958.2027</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.44</td>\n",
       "      <td>-1</td>\n",
       "      <td>-9.99</td>\n",
       "      <td>-0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>1958.2877</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "      <td>-1</td>\n",
       "      <td>-9.99</td>\n",
       "      <td>-0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>1958.3699</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.69</td>\n",
       "      <td>-1</td>\n",
       "      <td>-9.99</td>\n",
       "      <td>-0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>6</td>\n",
       "      <td>1958.4548</td>\n",
       "      <td>317.27</td>\n",
       "      <td>315.15</td>\n",
       "      <td>-1</td>\n",
       "      <td>-9.99</td>\n",
       "      <td>-0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "      <td>1958.5370</td>\n",
       "      <td>315.87</td>\n",
       "      <td>315.20</td>\n",
       "      <td>-1</td>\n",
       "      <td>-9.99</td>\n",
       "      <td>-0.99</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>2024.2083</td>\n",
       "      <td>425.38</td>\n",
       "      <td>423.92</td>\n",
       "      <td>22</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>2024</td>\n",
       "      <td>4</td>\n",
       "      <td>2024.2917</td>\n",
       "      <td>426.57</td>\n",
       "      <td>424.03</td>\n",
       "      <td>24</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>2024</td>\n",
       "      <td>5</td>\n",
       "      <td>2024.3750</td>\n",
       "      <td>426.90</td>\n",
       "      <td>423.61</td>\n",
       "      <td>29</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>2024</td>\n",
       "      <td>6</td>\n",
       "      <td>2024.4583</td>\n",
       "      <td>426.91</td>\n",
       "      <td>424.44</td>\n",
       "      <td>20</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>2024</td>\n",
       "      <td>7</td>\n",
       "      <td>2024.5417</td>\n",
       "      <td>425.55</td>\n",
       "      <td>425.10</td>\n",
       "      <td>24</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>797 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month  decimal_date  average  smooth  std_days  uncertainty  empty\n",
       "0    1958      3     1958.2027   315.71  314.44        -1        -9.99  -0.99\n",
       "1    1958      4     1958.2877   317.45  315.16        -1        -9.99  -0.99\n",
       "2    1958      5     1958.3699   317.51  314.69        -1        -9.99  -0.99\n",
       "3    1958      6     1958.4548   317.27  315.15        -1        -9.99  -0.99\n",
       "4    1958      7     1958.5370   315.87  315.20        -1        -9.99  -0.99\n",
       "..    ...    ...           ...      ...     ...       ...          ...    ...\n",
       "792  2024      3     2024.2083   425.38  423.92        22         0.99   0.40\n",
       "793  2024      4     2024.2917   426.57  424.03        24         0.98   0.38\n",
       "794  2024      5     2024.3750   426.90  423.61        29         0.76   0.27\n",
       "795  2024      6     2024.4583   426.91  424.44        20         0.65   0.28\n",
       "796  2024      7     2024.5417   425.55  425.10        24         0.69   0.27\n",
       "\n",
       "[797 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['year', 'month', 'decimal_date', 'average', 'smooth', 'std_days', 'uncertainty', 'empty']\n",
    "df = pd.read_csv(\"https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_mlo.txt\", sep=\"\\s+\", comment=\"#\", names= columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6bd88d71-295e-4b56-a4d7-411648566bd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<seaborn._core.plot.Plot at 0x7f52b1b09630>"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "image/png": {
       "height": 378.25,
       "width": 509.15
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    so.Plot(df, x=\"decimal_date\", y=\"average\")\n",
    "    .add(so.Line())\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3b4c933-e400-475f-88e6-4a6b19f3b56f",
   "metadata": {},
   "source": [
    "Which months are the CO2 values at the maximum? Minimum?  Why is this?\n",
    "\n",
    "What rolling average is used in computing the \"trend\" line?  How does the trend depend on the rolling average?\n",
    "\n",
    "------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "988c7c2d-4172-4b21-9f8f-c00e64aeb9b6",
   "metadata": {},
   "source": [
    ":::{tip} Exercise 1\n",
    "\n",
    "# Temperature Data\n",
    "\n",
    "Each of the last years has consecutively set new records on global climate.  In this section we will analyze global mean temperature data.\n",
    "\n",
    "Data from: <http://climate.nasa.gov/vital-signs/global-temperature>\n",
    "\n",
    "## Question 1:\n",
    "\n",
    "Describe the data set to the best of your ability given the documentation provided.  Describe what kind of column each data contains and what units it is measured in.  Then address our three key questions in understanding this data:\n",
    "\n",
    "- How are the measurements made? What is the associated measurement uncertainty?\n",
    "- What is the resolution of the data?\n",
    "- Are their missing values? How should they be handled?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "272157b8-b0aa-402d-a708-d226b777e7b9",
   "metadata": {},
   "source": [
    "## Question 2:\n",
    "\n",
    "Construct the necessary python code to import and prepare for manipulation the following data set: <http://climate.nasa.gov/system/internal_resources/details/original/647_Global_Temperature_Data_File.txt>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7873df03-7ed5-4584-875e-eabf44585437",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4d099fa6-715a-4060-b421-f5a864099f31",
   "metadata": {},
   "source": [
    "## Question 3:\n",
    "\n",
    "Plot the trend in global mean temperatures over time.  Describe what you see in the plot and how you interpret the patterns you observe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40a6c4a8-6906-4fc6-ad9e-59fbaa0143bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "86c4172f-aa23-42b2-9f0a-5e16ad8c58be",
   "metadata": {},
   "source": [
    "# Exercise II: Melting Ice Sheets?\n",
    "\n",
    "- Data description: <http://climate.nasa.gov/vital-signs/land-ice/>\n",
    "- Raw data file: <http://climate.nasa.gov/system/internal_resources/details/original/499_GRN_ANT_mass_changes.csv>\n",
    "\n",
    "## Question 1:\n",
    "\n",
    "- Describe the data set: what are the columns and units? Where do the numbers come from? \n",
    "- What is the uncertainty in measurment? Resolution of the data? Interpretation of missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6c63893-c9e8-414c-acae-6f7273f92f20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4d834348-fe0a-4a46-8268-315ed4f5fc30",
   "metadata": {},
   "source": [
    "## Question 2:\n",
    "\n",
    "Construct the necessary code to import this data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13733788-c895-473e-8835-9332a53d5f84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c8d6b472-3a25-4615-aa52-55c07332eb07",
   "metadata": {},
   "source": [
    "## Question 3:\n",
    "\n",
    "Plot the data and describe the trends you observe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a58da9a5-ee01-4630-8163-dba57845daac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6993f0d2-6a3e-45b8-b534-b6e2ca45eeb9",
   "metadata": {},
   "source": [
    "# Exercise III: Rising Sea Levels?\n",
    "\n",
    "- Data description: <http://climate.nasa.gov/vital-signs/sea-level/>\n",
    "- Raw data file: <http://climate.nasa.gov/system/internal_resources/details/original/121_Global_Sea_Level_Data_File.txt>\n",
    "\n",
    "\n",
    "## Question 1:\n",
    "\n",
    "- Describe the data set: what are the columns and units? \n",
    "- Where do these data come from? \n",
    "- What is the uncertainty in measurment? Resolution of the data? Interpretation of missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a1b5a0a-2ec7-4f1c-ab5c-0784007ff8ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "84db7554-2d25-4b3f-bd12-b93d9f29c363",
   "metadata": {},
   "source": [
    "## Question 2:\n",
    "\n",
    "Construct the necessary R code to import this data set as a tidy `Table` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88f4827e-03e1-419a-89f0-ce6c37c98254",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b4546c03-54f0-4eb6-bb21-b21d33fd7033",
   "metadata": {},
   "source": [
    "## Question 3:\n",
    "\n",
    "Plot the data and describe the trends you observe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69d6310e-1c33-44ac-b5b5-86dbc29469ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "007d0632-d2c4-4e69-9d32-1d61b2f94c2b",
   "metadata": {},
   "source": [
    "# Exercise IV: Arctic Sea Ice?\n",
    "\n",
    "- <http://nsidc.org/data/G02135>\n",
    "- <https://noaadata.apps.nsidc.org/NOAA/G02135/north/monthly/data/>\n",
    "\n",
    "## Question 1:\n",
    "\n",
    "- Describe the data set: what are the columns and units? \n",
    "- Where do these data come from? \n",
    "- What is the uncertainty in measurement? Resolution of the data? Interpretation of missing values?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0eae2a4-0ef2-4ae4-9e20-a5f1b639815d",
   "metadata": {},
   "source": [
    "## Question 2:\n",
    "\n",
    "Construct the necessary code to import this data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c24e790a-1708-4be3-b8d8-75967ffaa2d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f9bba193-74ff-4309-9628-7d4f27065c34",
   "metadata": {},
   "source": [
    "## Question 3:\n",
    "\n",
    "Plot the data and describe the trends you observe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d610dce-5f61-4991-8681-cc4b4548f6e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8baebf62-4472-4fd0-9277-ae355ff60a21",
   "metadata": {},
   "source": [
    "# Exercise V: Longer term trends in CO2 Records\n",
    "\n",
    "\n",
    "The data we analyzed in the unit introduction included CO2 records dating back only as far as the measurements at the Manua Loa observatory.  To put these values into geological perspective requires looking back much farther than humans have been monitoring atmosopheric CO2 levels.  To do this, we need another approach.\n",
    "\n",
    "\n",
    "[Ice core data](http://cdiac.ornl.gov/trends/co2/ice_core_co2.html):\n",
    "\n",
    "Vostok Core, back to 400,000 yrs before present day \n",
    "\n",
    "- Description of data set: <http://cdiac.esd.ornl.gov/trends/co2/vostok.html>\n",
    "- Data source: <http://cdiac.ornl.gov/ftp/trends/co2/vostok.icecore.co2>\n",
    "\n",
    "## Questions / Tasks:\n",
    "\n",
    "- Describe the data set: what are the columns and units? Where do the numbers come from? \n",
    "- What is the uncertainty in measurment? Resolution of the data? Interpretation of missing values?\n",
    "- Read in and prepare data for analysis.\n",
    "- Reverse the ordering to create a chronological record.  \n",
    "- Plot data\n",
    "- Consider various smoothing windowed averages of the data. \n",
    "- Join this series to Mauna Loa data\n",
    "- Plot joined data\n",
    "- Describe your conclusions"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "b30b72c0-3a90-4f3d-befa-305599504997",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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