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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Le réchauffement climatique\n",
"\n",
"**Modélisation Numérique en Physique - S2**\n",
"\n",
"**Miniprojet 3**\n",
"\n",
"**ARGUELLO Camilo**\n",
"\n",
"\n",
"## Tableau\n",
"\n",
"1. Introduction\n",
"2. Réchauffement température\n",
"3. Modèle de réchauffement\n",
"4. Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"On se place dans le cas de l'analyse de rechauffement climatique. Pour cela, il faut se poser quelques questions tels que: Pourquoi l'année dérniere s'était le plus froide dans quelques régions et le plus chaude dans d'autres? Comment on est capables à prédire le changement du climat à long terme? Est-ce on peut considérer qu'on est au cours d'un réchauffement climatique? Si cela est vrai, est-ce que le réchauffement est significatif ? \n",
"\n",
"On va commencer pour mettre en évidence la différence entre la **météo** et le **climat**. \n",
"\n",
"- La **méteo** est ce qu'il fait l'atmosphère à court terme, c'est à dire est un processus chaotique, car une perturbation microscopique peut entrâiner des changements à grande échelle. Cela éxplique pourquoi les conditions météorologiques peuvent changer au cours de quelques jours ou semaines, où on peut avoir des jours plus chauds ou plus froids. [nasa.gov/weather](https://www.nasa.gov/mission_pages/noaa-n/climate/climate_weather.html)\n",
" \n",
"\n",
"- Le **climat** montre la moyenne à long terme du temps sur un certain nombre d'années, et il dépend des variables globales qui donnent l'équilibre énergetique dans l'atmosphère. Par exemple des changements produites par les gas à effet serre. [nasa.gov/climate](https://www.nasa.gov/mission_pages/noaa-n/climate/climate_weather.html)\n",
"\n",
"Dans ce calepin, on va analyser un ensemble des données de température quotidiennes enregistres à la station météorologique de Montélimar depuis juin 1920."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from scipy.optimize import curve_fit\n",
"import datetime\n",
"import math"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
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" j t\n",
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"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# I consider 'j' as julian column & 't' as temperature\n",
"columns = ['j', 't'] \n",
"df = pd.read_csv('montelimar_temperature.dat', sep='\\s+', names=columns ,encoding = 'utf-8')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"## Ref\n",
"# Github Gist - Functions with Julian Date and Modified Julian Date\n",
"# https://gist.github.com/jiffyclub/1294443\n",
"\n",
"def mjd_to_jd(mjd):\n",
" \"\"\"\n",
" Convert Modified Julian Day to Julian Day.\n",
" Parameters\n",
" ----------\n",
" mjd : float\n",
" Modified Julian Day\n",
" Returns\n",
" -------\n",
" jd : float\n",
" Julian Day \n",
" \"\"\"\n",
" return mjd + 2400000.5\n",
"\n",
"def jd_to_date(jd):\n",
" \"\"\"\n",
" Convert Julian Day to date.\n",
" Algorithm from 'Practical Astronomy with your Calculator or Spreadsheet', \n",
" 4th ed., Duffet-Smith and Zwart, 2011.\n",
" Parameters\n",
" ----------\n",
" jd : float\n",
" Julian Day\n",
" Returns\n",
" -------\n",
" str : year-month-day\n",
" \"\"\"\n",
" jd = jd + 0.5\n",
" \n",
" F, I = math.modf(jd)\n",
" I = int(I)\n",
" A = math.trunc((I - 1867216.25)/36524.25)\n",
" if I > 2299160:\n",
" B = I + 1 + A - math.trunc(A / 4.)\n",
" else:\n",
" B = I\n",
" C = B + 1524\n",
" D = math.trunc((C - 122.1) / 365.25)\n",
" E = math.trunc(365.25 * D)\n",
" G = math.trunc((C - E) / 30.6001)\n",
" day = C - E + F - math.trunc(30.6001 * G)\n",
" if G < 13.5:\n",
" month = G - 1\n",
" else:\n",
" month = G - 13\n",
" if month > 2.5:\n",
" year = D - 4716\n",
" else:\n",
" year = D - 4715\n",
" return str(year)+'-%02d'%month+'-%02d'%day"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
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"text/plain": [
" j t date d\n",
"0 22553 22.6 1920-08-17 1\n",
"1 22554 22.6 1920-08-18 2\n",
"2 22555 24.8 1920-08-19 3\n",
"3 22556 17.5 1920-08-20 4\n",
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],
"source": [
"# First, lets add one column to the dataframe to have a calendar date to plot\n",
"global_date = list()\n",
"\n",
"for jd in mjd_to_jd(df['j']):\n",
" date = jd_to_date(jd)\n",
" global_date.append(np.datetime64(date))\n",
"\n",
"df['date'] = np.array(global_date, dtype = 'datetime64')\n",
"\n",
"# Also, for management conditions we will have a list of index as day\n",
"df['d'] = np.arange(1, len(df['t']) + 1)\n",
"\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 - Réchauffement température\n",
"\n",
"Les données sont composées d'une colonne de *Modified Julian Day* et une deuxième de *Température*. La première chose qu'on va montrer c'est si à partir d'une échelle de jours petite, (environ 100 jours), il y aura une tendence dans la temperature, et vérifier si la météo dans une certaine période peut être utilisé pour estimer les changements futurs."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def modlin(x, a, b):\n",
" '''\n",
" Fonction qui représente le modèle linéaire\n",
" y(x; a, b) = a + bx\n",
" '''\n",
" return a + b * x"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def plot_model (x,a,b):\n",
" \"\"\"\n",
" @description Cette fonction faite un modèle linéare avec un plot (modèle + données)\n",
" \"\"\"\n",
" y = (a * x) + b\n",
"\n",
" # Calcul de la somme S\n",
" Si = (df['t'][:N] - y) ** 2\n",
" S = np.sum(Si)\n",
"\n",
" plt.plot(x, df['t'][:N], '+k')\n",
" plt.plot(x, y, '-b', label = 'modèle')\n",
" plt.xlabel('Jours')\n",
" plt.ylabel('°C')\n",
" plt.title('Variation météo dans N=' +str(N)+ ' jours, S = ' + str(np.around(S,2)))\n",
" plt.grid()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"N = 100\n",
"x = df['d'][:N]\n",
"regression_lineal = curve_fit(modlin, x, df['t'][:N], p0 = [0., 10.])\n",
"a = regression_lineal[0][1]\n",
"b = regression_lineal[0][0]\n",
"plot_model(x,a,b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Si on se place dans un période `N=100` on peut rémarquer qu'il aura une tendence liée à la saison annuelle. Cependant, en utilisant la somme de moindres carrées, on peut montrer que les données peuvent varier tellement chaque jour que le comportement futur ne peut être estimé."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"N = 200\n",
"x = df['d'][:N]\n",
"regression_lineal = curve_fit(modlin, x, df['t'][:N], p0 = [0., 10.])\n",
"a = regression_lineal[0][1]\n",
"b = regression_lineal[0][0]\n",
"plot_model(x,a,b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ceci est vérifié lorsqu'un plus grand nombre de jours est utilisé, il peut y avoir une journée très chaude ou très froide, ce qui finalement ne détermine pas l'état global de la température."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"N = 1000\n",
"x = df['d'][:N]\n",
"regression_lineal = curve_fit(modlin, x, df['t'][:N], p0 = [0., 10.])\n",
"a = regression_lineal[0][1]\n",
"b = regression_lineal[0][0]\n",
"plot_model(x,a,b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dans le dernier graphique, nous avons une démonstration d'une température annuelle. Dans laquelle, en raison des saisons, il y a des périodes chaudes et froides. Par conséquent, notre modèle linéaire n'est pas utile. Parce que lorsqu'il peut avoir des hivers un peu plus froids et des étés plus chauds. Nous allons donc faire une estimation moyenne de la température de l'année, et comparer avec l'année suivante. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def quadmodel(x, a, b, c):\n",
" return (a * (x ** 2)) + (b * x) + c"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"n = len(df['t'])\n",
"step = 365 # Days of year\n",
"\n",
"i = 0\n",
"j = step\n",
"\n",
"# Global mean will contain the means for each year\n",
"global_mean = list()\n",
"global_mean.append(np.mean(df['t'][i:j]))\n",
"\n",
"# Global max will have the maximum per year\n",
"global_max = list()\n",
"global_max.append(np.max(df['t'][i:j]))\n",
"\n",
"for i in range(n):\n",
" if i == j:\n",
" j += step\n",
" global_mean.append(np.mean(df['t'][i:j]))\n",
" global_max.append(np.max(df['t'][i:j]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Maintenant, si nous projetons les températures moyennes pour chaque année, on peut voir une croissance, que nous allons explorer avec une régression linéaire et quadratique."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"N = len(global_mean)\n",
"xdata = np.arange(0, N)\n",
"ydata = np.array(global_mean)\n",
"\n",
"rg, _ = curve_fit(modlin, xdata, ydata) # Best parameters for linear model\n",
"ymod = modlin(xdata, *rg) # Use the parameters in the linear function\n",
"\n",
"plt.plot(xdata, ydata, 'k+', label='data')\n",
"plt.plot(xdata, ymod, 'b', label='model')\n",
"\n",
"# Plot the distances (red lines)\n",
"for i in range(N - 1):\n",
" plt.plot((xdata[i], xdata[i]), (ydata[i], ymod[i]), '-r', linewidth=.2)\n",
"\n",
"plt.title('Évolution moyénne de la témperature')\n",
"plt.ylabel('°C')\n",
"plt.xlabel('Années')\n",
"plt.grid()\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"N = len(global_mean)\n",
"xdata = np.arange(0, N)\n",
"ydata = np.array(global_mean)\n",
"ymaxdata = np.array(global_max)\n",
"\n",
"rg, _ = curve_fit(quadmodel, xdata, ydata)\n",
"ymod = quadmodel(xdata, *rg)\n",
"\n",
"plt.plot(xdata, ydata, 'k+', label='data')\n",
"plt.plot(xdata, ymod, 'b', label='model')\n",
"plt.plot(N-30, ymod[N-30], 'o', c='yellow', label='30 years ago')\n",
"plt.plot(N-1, ymod[N-1], 'o', c='red',label='present')\n",
"\n",
"for i in range(N - 1):\n",
" plt.plot((xdata[i], xdata[i]), (ydata[i], ymod[i]), '-r', linewidth=.2)\n",
"\n",
"# Temperature difference between last 30 years (mean)\n",
"dif_temp = ymod[N-1] - ymod[N-30] \n",
"\n",
"plt.title('Évolution moyénne de la témperature, diff: ' + str(np.around(dif_temp,2)) + '°C' )\n",
"plt.ylabel('°C')\n",
"plt.xlabel('Années')\n",
"plt.grid()\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On constate une augmentation d'environ $1,52°C$ de la température moyenne au cours des 30 dernières années."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 - Modèle de réchauffement\n",
"\n",
"Nous allons maintenant modéliser la croissance de température, sachant qu'il est donné annuellement par une variation sinusoïdale, avec de périodes froides et chaudes. Pour cela on va utiliser une fonction qui dépend des données du problème, telle que $A$ comme l'amplitude maximale par an et $B$ la croissance donnée par le modèle étudié dans la section précédente. Dans cette exemple la phase $\\phi=0$\n",
"\n",
"$$ T(t) = A \\sin{(\\omega t + \\phi)} + B $$"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Here we want to calculate the average amplitude per year\n",
"# We will have a list of amplitudes (one per year)\n",
"# If i Have let's say 100 years to compare with, we will consider the least square method to calculate the amplitude\n",
"# I consider steps = 10 to have each sinusoidal function with the one amplitude in that period \n",
"\n",
"step = 10\n",
"global_amplitude = list()\n",
"\n",
"i = 0\n",
"j = step\n",
"\n",
"Si = (ydata - ymod) ** 2\n",
"\n",
"# When i = 0 and j = 10\n",
"somme = np.sum(Si[i:j])\n",
"for i in range (step):\n",
" global_amplitude.append(somme)\n",
"\n",
"# For the rest... \n",
"while (i < len(Si)):\n",
" if (i == j):\n",
" j += step\n",
" somme = np.sum(Si[i:j])\n",
" for i in range (step):\n",
" global_amplitude.append(somme)\n",
" i += 1\n",
"\n",
"yamplitude = np.array(global_amplitude) # Amplitude of each year (with 10 times the value per year)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Modéle de température 1 an')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Model of a Sinus function\n",
"# It represents one year\n",
"start_time = 0\n",
"end_time = 365\n",
"\n",
"t = np.arange(start_time, end_time)\n",
"\n",
"A = 5\n",
"w = 2 * np.pi / 365\n",
"phi = 0\n",
"B = ymod[0]\n",
"\n",
"T = lambda t : A * np.sin(w * t + phi) + B\n",
"\n",
"plt.plot(t,T(t))\n",
"plt.xlabel('Jours')\n",
"plt.ylabel('°C')\n",
"plt.title('Modéle de température 1 an')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# On défine une fonction sinusoidal\n",
"\n",
"start_time = 0\n",
"end_time = 365\n",
"years = 365 * 10 # Frequency every 10 years\n",
" # To prevent having too many sinusoidal functions\n",
"\n",
"i = 0\n",
"j = 0\n",
"w = 2 * np.pi / years\n",
"phi = 0\n",
"\n",
"T = lambda t : A * np.sin(w * t + phi) + B\n",
"\n",
"global_T = list()\n",
"global_t = list()\n",
"\n",
"while (i < len(ydata) - 1):\n",
" t = np.arange(start_time, end_time) # To analyse every year \n",
" A = yamplitude[i]\n",
" B = ymod[i]\n",
" global_t.append(t)\n",
" global_T.append(T(t))\n",
"\n",
" start_time += 365\n",
" end_time += 365 \n",
" i += 1\n",
"\n",
"\n",
"global_T = np.array(global_T).flatten() # Array of Temperatures adjusted to the model\n",
"global_t = np.array(global_t).flatten() # Array of time adjusted to the model\n",
"# I use flatten to avoid having shapes of (100, 365) -> 100 years of 365 days and to have 1 row of days for all years"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#erreurs = 2. * np.ones(global_t.shape) # m/s\n",
"# Représentation graphique des données avec les barres d'erreur\n",
"#plt.errorbar(global_t, global_T, yerr=erreurs, marker = '+', linestyle = '')\n",
"\n",
"y = global_T.copy()\n",
"x = global_t.copy()\n",
"\n",
"plt.plot(x,y)\n",
"\n",
"plt.title('Modèle de variation de température')\n",
"plt.xlabel('Jours')\n",
"plt.ylabel('Modèle en °C')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous pouvons voir qu'on a des variations moyennes entre 16 et 12 °C, cependant nous pouvons vérifier une variation supérieure à 5 °C."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def modlin(t, C):\n",
" \"\"\"\n",
" @description cette fonction est de la forme C * t pour donnéer l'augmentation de la temperature globale\n",
" \"\"\"\n",
" return C * t"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# best value C for the model\n",
"rg, _ = curve_fit(f=modlin, xdata=global_t, ydata=global_T) \n",
"\n",
"linear = modlin(global_t, *rg)\n",
"sinusoidal = global_T + rg[0] * global_t\n",
"\n",
"plt.plot(global_t,sinusoidal)\n",
"plt.title(\"Modelisation de température\")\n",
"plt.xlabel('Ans')\n",
"plt.ylabel('Modèle en °C')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On peut enfin recréer une croissance en la température moyenne pendant 100 ans. À l'aide d'une régression linéaire et des fonctions sinusoïdales."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3- Conclusion\n",
"\n",
"Les résultats montrent qu'il est difficile de prédire la météo, mais l'évolution du climat est prévisible. Même si elle a changé plusieurs fois dans l'histoire de la terre, en raison d'altérations de haut niveau, on peut voir que l'augmentation de température au cours des 30 dernières années est importante, et est liée, comme l'affirment de nombreuses études, à une augmentation des gaz à effet serre."
]
}
],
"metadata": {
"interpreter": {
"hash": "e3e3b631d0578064596570627667cf6d3255b0ded78241c485c6d8d72cb2b64f"
},
"kernelspec": {
"display_name": "Python 3.9.5 ('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": "3.9.5"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}