{ "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Global Temperature Predictions\n", "\n", "This section uses global temperature data combined with global $\\text{CO}_2$ concentration and warming data provided by the IPCC to compare a simple model with Global warming estimates laid out in the Special Report on Emission Scenarios(SRES){cite}`SRES`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [ "remove-input" ], "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from dur_utils import colours\n", "from numpy.lib.stride_tricks import sliding_window_view\n", "from scipy.optimize import curve_fit\n", "from scipy import stats\n", "import matplotlib as mpl\n", "mpl.style.use('../matplotlibrc')" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The global Temperature data is taken from [Berkley Earth](http://berkeleyearth.org/data/). The temperature data has missing fields as with the Global $\\text{CO}_2$ data. Further, the date is formatted awkwardly into a fixed width table format with commented-out headers. Finally, the global temperature data is seasonal. Thus some data cleaning and formatting is required.\n", "\n", "When ananlysing the data, a Fourier-based fit could be applied similarly to that performed in the section on Global $\\text{CO}_2$ data. Because this section is not looking for a functional form, an average is more straightforward to implement.\n", "\n", "After cleaning the data of null fields, a moving average can be used to remove the seasonal trends." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [ "remove-input" ], "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Number of Null Values\n", "year 0\n", "month 0\n", "monthly_anomaly 1\n", "monthly_anomaly_unc 3\n", "dt 0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load, Format and clean\n", "path = \"http://berkeleyearth.lbl.gov/auto/Global/Complete_TAVG_complete.txt\"\n", "colnames=['year', 'month', 'monthly_anomaly', 'monthly_anomaly_unc',\n", " 'yearly_anomaly', 'yearly_anomaly_unc', '5yearly_anomaly',\n", " '5yearly_anomaly_unc', '10yearly_anomaly', '10yearly_anomaly_unc',\n", " '20yearly_anomaly', '20yearly_anomaly_unc'\n", " ]\n", "temp_data = pd.read_fwf(path, skiprows=34, names=colnames)\n", "temp_data['dt'] = temp_data['month']/12 + temp_data['year']\n", "#remove the moving averages\n", "temp_data.drop( columns=['yearly_anomaly', 'yearly_anomaly_unc',\n", " '5yearly_anomaly', '5yearly_anomaly_unc', '10yearly_anomaly',\n", " '10yearly_anomaly_unc', '20yearly_anomaly', '20yearly_anomaly_unc'],\n", " inplace=True\n", " )\n", "# Format data \n", "null_sum = (temp_data.isna()).values.sum(axis=0)\n", "path_ml = 'https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_gl.csv' \n", "co2_data = pd.read_csv(path_ml, header=0, comment='#')\n", "\n", "pd.DataFrame(data=null_sum,\n", " index=temp_data.columns,\n", " columns=['Number of Null Values']\n", " )" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "tags": [ "hide-input" ], "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "#Drop NA\n", "temp_data = temp_data.dropna()\n", "WINDOW = 120\n", "# Sliding window weighted average:\n", "slv = sliding_window_view(temp_data, WINDOW, 0)\n", "time_midpoint = np.mean(slv[:,-1,:], axis=1)\n", "win_ave_temp = slv[:,2,:]\n", "win_ave_unc = slv[:,3,:]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "def moving_ave_frame(df:pd.DataFrame, window:int)->pd.DataFrame:\n", " 'Applies a moving average'\n", " slv = sliding_window_view(df, window, axis=0)\n", " moving_averages = np.mean(slv, axis=2)\n", " ma_df = pd.DataFrame(moving_averages,columns=df.keys())\n", " return ma_df\n", "def moving_std_frame(df:pd.DataFrame, window:int)->pd.DataFrame:\n", " 'Applies a moving average'\n", " slv = sliding_window_view(df, window, axis=0)\n", " moving_averages = np.std(slv, axis=2)/np.sqrt(window)\n", " ma_df = pd.DataFrame(moving_averages,columns=df.keys())\n", " return ma_df\n", "\n", "def lb_ub(values, sigma, factor=1):\n", " lb = values - sigma*factor\n", " ub = values + sigma*factor\n", " return lb, ub\n", "\n", "def P1(x, a0, a1):\n", " return a0 + a1*x" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [ "hide-input" ], "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1, figsize = (10, 6))\n", "ave_temp = np.mean(win_ave_temp,axis=1)\n", "ave_unc = np.mean(win_ave_unc, axis=1)\n", "lb, ub = lb_ub(ave_temp, ave_unc)\n", "#Plotting\n", "ax.plot(time_midpoint,\n", " ave_temp,\n", " color=colours.durham.ink\n", " )\n", "ax.fill_between(time_midpoint,\n", " lb,\n", " ub,\n", " color = colours.durham.ink,\n", " alpha=0.2\n", " )\n", "ax.set_xlabel('Window Midpoint (Year)')\n", "ax.set_ylabel('10 Year Moving Average of \\n \\\n", " Land Average Temperature Anomaly $(^{\\circ}C)$'\n", " );" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Predictions\n", "Global temperatures provide an interesting case study for a further introduction to predictive modeling. \n", "\n", "There has already been substantial warming compared to the period from $1850-1900$. This period is set as a pre-industrial baseline{cite}`preindbaseline`. Further temperature rises will continue to increase the occurrence of extreme weather events and myriad other consequences, with an entire [IPCC](https://www.ipcc.ch/sr15/) report dedicated to the consequences of warming over $1.5^\\circ C$. The special report on emission scenarios(SRES){cite}`SRES` produced by the IPCC in 2000, details multiple families of emission scenarios with their associated warming. \n", "\n", "These scenarios are developed using six independent models. Such models use economic driving forces and predict the consequences of changing macro-economic behavior, for example, the percentage of fossil fuel used compared to renewables. \n", "\n", "These highly detailed models are not suitable for this section. Using $\\text{CO}_2$ concentration, a simple model can be formed to determine the change in temperature over the next 30 years.\n", "The three scenarios that will be investigated are approximations of the A1 and B1 storylines described from page 247 onwards in the [SRES](https://www.ipcc.ch/site/assets/uploads/2018/03/emissions_scenarios-1.pdf).\n", "- A1B) Continued growth in emission Rates. This scenario represents a growth-focused world with a balanced mix of energy sources. \n", "- A1T) Net-zero by 2030, no further reductions. This scenario corresponds to a growth-focused world with renewable energy sources.\n", "- B1) Net-zero by 2030, and then reducing total atmospheric carbon at the same rate it is currently produced. Corresponding to a global focus on emissions reduction.\n", "The relationship between $\\text{CO}_2$ concentration used is:\n", "\n", "```{math}\n", "T(C) = T_0 + S \\log_2(C/C_0).\n", "```\n", "Where $C$ is the concentration of $\\text{CO}_2$ and $S$ is a fitted sensitivity factor." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Current Warming trend " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "tags": [ "hide-input" ], "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "trend_data = temp_data[temp_data['dt'] > 1960]\n", "trend_ave = moving_ave_frame(trend_data,WINDOW)\n", "lb, ub = lb_ub(trend_ave['monthly_anomaly'],\n", " trend_ave['monthly_anomaly_unc']\n", " )\n", "trend_ave.to_csv(f'../output_files/temperature_{int(WINDOW/12)}'\\\n", " '_moving_average.csv'\n", " ) \n", "trend_fit, trend_error = curve_fit(P1, trend_ave['dt'],\n", " trend_ave['monthly_anomaly'],\n", " sigma=trend_ave['monthly_anomaly_unc']\n", " )\n", "gradient_lb, gradient_ub = lb_ub(trend_fit[1], trend_error[1,1]) \n", "lb, ub = lb_ub(trend_ave['monthly_anomaly'],\n", " trend_ave['monthly_anomaly_unc']\n", " )\n", "#Plotting\n", "fig, ax = plt.subplots(1,1, figsize=(10, 6))\n", "time = np.linspace(2017.3, 2055, 300)\n", "trend_lb = trend_fit - np.diagonal(trend_error)\n", "trend_ub = trend_fit + np.diagonal(trend_error)\n", "plt.fill_between(trend_ave['dt'], lb, ub, alpha=0.2,\n", " color=colours.durham.ink)\n", "plt.plot(trend_ave['dt'], trend_ave['monthly_anomaly'],\n", " c=colours.durham.ink,\n", " label='Moving Average')\n", "plt.plot(time, P1(time, *trend_fit),\n", " linestyle='--',\n", " color=colours.durham.ink,\n", " label='Linear Trend')\n", "ax.set_xlabel('Window Midpoint (Year)')\n", "ax.set_ylabel(f'{int(WINDOW / 12)} Year Moving Average of \\n' \\\n", " r'Land Average Temperature Anomaly $(^{\\circ}\\textrm{{C}})$'\n", " )\n", "#Warming Targets\n", "x = np.linspace(1960,2055,100)\n", "y = np.ones_like(x)\n", "plt.plot(x, y*1.5, color=colours.durham.red,\n", " label='$1.5^\\circ C$ Warming',\n", " linestyle='--'\n", " )\n", "plt.plot(x, y*2, color=colours.durham.red,\n", " label='$2.0^\\circ C$ Warming',\n", " linestyle='-.'\n", " )\n", "plt.fill_between(time, P1(time, *trend_lb),P1(time, *trend_ub))\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "With a simple linear extrapolation of the Berkeley Earth temperature data, one can see that if the current rate of warming persists, then keeping the Global Temperature anomaly below $1.5^\\circ C$ is unlikely, moving into the future. However, this simple model is just for getting a feeling of the data. By combining this dataset with the Global $\\text{CO}_2$ data, the correlation of these datasets can be examined. Both of the datasets' moving averages are plotted below for a visual comparison. Further, the Pearson correlation co-efficient $(r)$ can be calculated for the datasets. $r$ is given by,\n", "```{math}\n", "r(x,y) = \\frac{\\sum (x_i-\\bar{x})(y_i-\\bar{y})}{\\sqrt{\\sum (x_i-\\bar{x})^2 \\sum (y_i-\\bar{y})^2 }}.\n", "```\n", "Where $\\bar{q}$ indicates the mean of $q$. The $r$ value can be calculated for the global $\\text{CO}_2$ concentration and the temperature by taking both variables evaluated simultaneously in time. \n", "\n", "```{note}\n", "The error bars on the $\\text{CO_2}$ concentration are two times the standard error on the mean. However, the error bars on the temperature is the mean of the uncertainty over the window. This is because the uncertainty corresponds to 95\\% of recorded percentages. \n", "```" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [ "remove-input" ], "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "trend_co2_data = co2_data[co2_data['decimal'] > 1960]\n", "trend_co2_ave = moving_ave_frame(co2_data, WINDOW)\n", "trend_co2_std = moving_std_frame(co2_data, WINDOW)\n", "co2_lb, co2_ub = lb_ub(trend_co2_ave['average'], trend_co2_std['average'], factor=2)\n", "trend_co2_ave.to_csv(f'../output_files/co2_{int(WINDOW/12)}'\\\n", " '_moving_average.csv'\n", " )\n", "trend_co2_std.to_csv(f'../output_files/co2_{int(WINDOW/12)}'\\\n", " '_moving_std.csv'\n", " ) \n", "fig, ax = plt.subplots(1, 1,figsize=(10, 6))\n", "#Temp\n", "ax1 = ax.twinx()\n", "ax.plot(trend_ave['dt'], trend_ave['monthly_anomaly'],\n", " c=colours.durham.ink,\n", " label='Moving Average'\n", " )\n", "ax.fill_between(trend_ave['dt'], lb, ub, alpha=0.2,\n", " color=colours.durham.red\n", " )\n", "\n", "ax.set_ylabel(f'{int(WINDOW / 12)} Year Moving Average of \\n' \\\n", " r'Land Average Temperature Anomaly $(^{\\circ}\\textrm{{C}})$')\n", "ax.set_yticks(np.arange(-.2,1.4, .2), np.round(np.arange(-.2,1.4, .2), 2),\n", " color=colours.durham.red\n", " )\n", "ax.set_xlabel('Window Midpoint (Year)')\n", "#CO2\n", "ax1.plot(trend_co2_ave['decimal'],\n", " trend_co2_ave['average'],\n", " color=colours.durham.ink\n", " )\n", "ax1.fill_between(trend_co2_ave['decimal'], co2_lb, co2_ub,\n", " alpha=0.2,\n", " color=colours.durham.ink\n", " )\n", "ax1.set_ylabel(rf'{int(WINDOW / 12)} Year Moving Average of' \\\n", " r' $\\textrm{{CO}}_2$ (ppm)'\n", " )\n", "\n", "plt.xlim(1985, 2017);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [ "hide-input" ], "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pearson Correlation Coefficient:0.99\n" ] } ], "source": [ "co2_slice = np.where((1985 < trend_co2_ave['decimal']) & \\\n", " (trend_co2_ave['decimal'] < 2016)\n", " )\n", "temp_slice = np.where((1985 < trend_ave['dt']) & \\\n", " (trend_ave['dt'] < 2016)\n", " )\n", "bound_co2 = trend_co2_ave['average'].iloc[co2_slice]\n", "bound_temp = trend_ave['monthly_anomaly'].iloc[temp_slice]\n", "r_coef = stats.pearsonr(bound_co2,np.array(bound_temp)[1:])\n", "print(f'Pearson Correlation Coefficient:{r_coef[0]:.2}');" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "As expected the two quantities are very strongly correlated. The Pearson correlation coefficient in this scenario is not particularly useful. The information it provides is redundant with the above graph. However, with high dimensional data, a correlation matrix can glean insight into the relationship of variables. A short medium article on correlation matrix plots can be found [here](https://towardsdatascience.com/better-heatmaps-and-correlation-matrix-plots-in-python-41445d0f2bec)" ] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3.7.13 ('base')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.13" }, "vscode": { "interpreter": { "hash": "59741db575107ab5410b00365ec93382e14756ed49d68fa4219f7bcb47931d56" } } }, "nbformat": 4, "nbformat_minor": 2 }