notebooks/How_To_Calculate_Greenhouse_Gas_Growth_Rates.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to Calculate Greenhouse Gas Growth Rates using Python\n",
    "\n",
    "***\n",
    "\n",
    "### Overview:\n",
    "\n",
    "This How-To demonstrates how to calculate the growth rate, also known as rate of change, for greenhouse gases. This example uses atmospheric carbon dioxide data from OCO-2 (OCO2_L2_LITE_FP v11.1r) and the time series is computed using Python. The rate of change is computed by taking the difference of the area averaged concentrations.\n",
    "\n",
    "### Example:\n",
    "\n",
    "**Example data:** OCO2 L2 Lite v11.1r product ([OCO2_L2_Lite_FP](https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_11.1r/summary?keywords=OCO2_L2_Lite_FP_11.1r))\n",
    "\n",
    "**Example variable used:** XCO2\n",
    "\n",
    "**Estimated time to complete procedure:** 20min\n",
    "\n",
    "\n",
    "### Prerequisites\n",
    "\n",
    "This notebook was written using Python 3.9, and requires these libraries and files: \n",
    "- [Pandas](https://pandas.pydata.org/)\n",
    "- [Matplotlib](http://matplotlib.org/)\n",
    "- CSV file containing the Methane data, generated in the first procedure step"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to import the required Python libraries. If any of the following import commands fail, check the local Python environment and install any missing packages. These lines will be necessary to run the rest of the cells:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the data and recognize the data as dates and numeric values and calculate the month-to-month growth rates of methane concentration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data\n",
    "fid = nc.Dataset('new.nc')\n",
    "\n",
    "xco2 = fid.variables['xco2'][:]\n",
    "lat = fid.variables['lat'][:]\n",
    "lon = fid.variables['lon'][:]\n",
    "time = fid.variables['time'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1150x650 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ttl = \"XCO2 Annual Growth Rate\\nOCO-2 L2 Lite FP v11.1r\"\n",
    "\n",
    "# Create the figure and axis\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11.5, 6.5)\n",
    "\n",
    "# Plot the dates and calculated XCO2 growth rates\n",
    "ax.plot(time, xco2, linewidth=0.6, color='blue')\n",
    "\n",
    "# Create X-axis labels\n",
    "time = [datetime(2000, 1, 1) + timedelta(days=i*30) for i in range(240)]  # Sample monthly data over 20 years\n",
    "xco2 = [400 + 0.5 * i for i in range(len(time))]  # Sample XCO2 growth data\n",
    "\n",
    "# Format the x-axis to show dates\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%b-%Y\"))  # Format dates as \"Jan-2000\"\n",
    "ax.xaxis.set_major_locator(mdates.MonthLocator(interval=6))  # Major ticks every 6 months\n",
    "\n",
    "# Add axes labels and graph title\n",
    "ax.set(xlabel=\"month\", ylabel=\"ppmv\")\n",
    "ax.set(title=ttl)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "\n",
    "# Optional: Additional customization\n",
    "plt.hlines(0, time[0], time[-1], linestyle='dashed', linewidth=0.6)\n",
    "plt.ylim(min(xco2) - 10, max(xco2) + 10)\n",
    "plt.xlim(time[0], time[-1])\n",
    "plt.xticks(rotation=70)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Save the figure if needed\n",
    "figure1 = plt.figure(1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the month-to-month growth rates calculated in the previous cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m ttl \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAIRS CH4 monthly growth rate at 500 hPa for Northern Hemisphere\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Format the dates and change the image size\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m datefmt \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mDateFormatter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mb-\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      5\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots()\n\u001b[1;32m      6\u001b[0m fig\u001b[38;5;241m.\u001b[39mset_size_inches(\u001b[38;5;241m11.5\u001b[39m, \u001b[38;5;241m6.5\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "ttl = \"AIRS CH4 monthly growth rate at 500 hPa for Northern Hemisphere\"\n",
    "\n",
    "# Format the dates and change the image size\n",
    "datefmt = df.DateFormatter(\"%b-%y\")\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11.5, 6.5)\n",
    "\n",
    "# Plot the dates and calculated CH4 growth rates\n",
    "ax.plot(data['Time'].values, data['Mean AIRS Methane'].values,linewidth=0.6,color='blue')\n",
    "ax.xaxis.set_major_formatter(datefmt)\n",
    "\n",
    "# Add axes labels and graph title\n",
    "ax.set(xlabel=\"month\", ylabel=\"CH4 growth rate (ppb/month)\")\n",
    "ax.set(title=ttl)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "\n",
    "# Add center line and format x and y axis ticks\n",
    "plt.hlines(0,'2003-1-1', '2016-7-1', linestyle='dashed', linewidth=0.6)\n",
    "plt.ylim(-20, 20)\n",
    "plt.xlim('2003-1-1', '2016-7-1')\n",
    "plt.xticks(['2003-1-1', '2016-7-1'],rotation=70)\n",
    "plt.yticks([-20, -10, 0, 10, 20])\n",
    "ax.xaxis.set_major_locator(df.MonthLocator(interval=6))\n",
    "\n",
    "figure1 = plt.figure(1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the figure as a PNG:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "figure1.savefig('AIRS_CH4_mon_500hpa.png', format='png', dpi=360)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 1.** AIRS methane month-to-month growth rate at 500 hPa.\n",
    "\n",
    "*** \n",
    "\n",
    "- Yearly growth rates can be calculated by taking the monthly methane concentrations for a given month, and subtracting the methane concentration from the same month the year before (Figure 2).\n",
    "\n",
    "    1. Example: the yearly growth rate for January 2005 would be the methane concentration for January 2005 minus January 2004 over the change in time. This will produce units of ppb/year. (method from Rigby et al. 2008, Geophysical Research Letters; Simpson et al. 2006,Geophysical Research Letters)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the data and recognize the data as dates and numeric values and calculate the yearly growth rates of methane concentration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data\n",
    "data = pd.read_csv(\"g4.areaAvgTimeSeries.AIRX3STM_006_CH4_VMR_A.500hPa.20020901-20160930.180W_90S_180E_90N.csv\", skiprows=9, usecols=[0,1], names=['Time','Mean AIRS Methane'])\n",
    "\n",
    "# Read the times as dates and the methane concentrations as numeric values\n",
    "data['Time'] = pd.to_datetime(data['Time'])\n",
    "data['Mean AIRS Methane'] = pd.to_numeric(data['Mean AIRS Methane'])\n",
    "\n",
    "# Restrict the range and calculate the month-to-month growth rates\n",
    "dateRange = (data['Time'] >= '2004-1-1') & (data['Time'] <= '2016-7-1') # The range of dates being calculated\n",
    "calcRange = (data['Time'] >= '2003-1-1') & (data['Time'] <= '2015-7-1') # The range including the previous year for calculations\n",
    "range1data = data.loc[dateRange]\n",
    "range2data = data.loc[calcRange]\n",
    "valuesRange1 = range1data['Mean AIRS Methane'].values\n",
    "valuesRange2 = range2data['Mean AIRS Methane'].values\n",
    "subtractedMonthly = valuesRange1\n",
    "subtractedMonthly = [x-y for x, y in zip(valuesRange1, valuesRange2)]   \n",
    "data = data.loc[dateRange]\n",
    "data['Mean AIRS Methane'] = subtractedMonthly"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the yearly growth rates calculated in the previous cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ttl = \"AIRS CH4 yearly growth rate at 500hPa for Northern Hemisphere\"\n",
    "\n",
    "# Format the dates and change the image size\n",
    "datefmt = df.DateFormatter(\"%b-%y\")\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11.5, 6.5)\n",
    "\n",
    "# Plot the dates and calculated CH4 growth rates\n",
    "ax.plot(data['Time'].values, data['Mean AIRS Methane'].values,linewidth=0.6,color='blue')\n",
    "ax.xaxis.set_major_formatter(datefmt)\n",
    "\n",
    "# Add axes labels and graph title\n",
    "ax.set(xlabel=\"month\", ylabel=\"CH4 growth rate (ppb/year)\")\n",
    "ax.set(title=ttl)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "\n",
    "# Add center line and format x and y axis ticks\n",
    "plt.hlines(0,'2004-1-1', '2016-7-1', linestyle='dashed', linewidth=0.6)\n",
    "plt.ylim(-20, 20)\n",
    "plt.xlim('2004-1-1', '2016-7-1')\n",
    "plt.xticks(rotation=70)\n",
    "plt.yticks([-20, -10, 0, 10, 20])\n",
    "ax.xaxis.set_major_locator(df.MonthLocator(interval=6))\n",
    "\n",
    "figure2 = plt.figure(1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the figure as a PNG:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "figure2.savefig('AIRS_CH4_yr_500hpa.png', format='png', dpi=360)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 2.** AIRS methane yearly growth rate at 500hPa.\n",
    "\n",
    "***\n",
    "\n",
    "- Seasonal growth rates can be calculated using the \"Time Series: Seasonal\" service to compute area averaged concentrations for each season. Season-to-season or yearly seasonal growth rates could then be computed as described above."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"1\">THE SUBJECT FILE IS PROVIDED \"AS IS\" WITHOUT ANY WARRANTY OF ANY KIND, EITHER EXPRESSED, IMPLIED, OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTY THAT THE SUBJECT FILE WILL CONFORM TO SPECIFICATIONS, ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR FREEDOM FROM INFRINGEMENT, ANY WARRANTY THAT THE SUBJECT FILE WILL BE ERROR FREE, OR ANY WARRANTY THAT DOCUMENTATION, IF PROVIDED, WILL CONFORM TO THE SUBJECT FILE. THIS AGREEMENT DOES NOT, IN ANY MANNER, CONSTITUTE AN ENDORSEMENT BY GOVERNMENT AGENCY OR ANY PRIOR RECIPIENT OF ANY RESULTS, RESULTING DESIGNS, HARDWARE, SOFTWARE PRODUCTS OR ANY OTHER APPLICATIONS RESULTING FROM USE OF THE SUBJECT FILE. FURTHER, GOVERNMENT AGENCY DISCLAIMS ALL WARRANTIES AND LIABILITIES REGARDING THIRD-PARTY SOFTWARE, IF PRESENT IN THE SUBJECT FILE, AND DISTRIBUTES IT \"AS IS.\""
   ]
  }
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