{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#
Concentration de CO2 dans l'atmosphère depuis 1958
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##
Rémy MARION
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##
Avril 2020
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Préambule" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dès 1958, Charles David Keeling a débuté la mesure précise du taux de CO2 dans l'atmosphère à l'observatoire de Mauna Loa, Hawaii, États-Unis. \n", "Ces mesures, qui continuent aujourd'hui, ont permis de montrer une évolution tout au long de l'année du taux de CO2 dans l'hémisphère Nord. Celle-ci étant provenant du cycle de vie des plantes. \n", "De même, ces données ont montrés une évolution continue du taux de CO2 dans l'atmosphère depuis 1958." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Travail à faire" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le but de l'exercide est de réaliser un document computationnel pour :\n", "* Réaliser un graphique qui montrera une oscillation périodique superposée à une évolution systématique plus lente.\n", "* Séparer ces deux phénomènes. Caractériser l'oscillation périodique et proposer un modèle simple de la contribution lente\n", "* Estimer ses paramètres et tenter une extrapolation jusqu'à 2025 (dans le but de pouvoir valider le modèle par des observations futures).\n", "* Déposer dans FUN le résultat." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Données" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données sont disponibles sur le site Web de l'institut Scripps à l'adresse suivante: \n", "https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html \n", "Cette base de données est mise à jour mensuellement. \n", "Nous travaillerons sur une base locale (copiée sur le serveur *Jupyter* de l'INRIA) téléchargée le 12 avril 2020.\n", "La totalité des documents nécessaires à cette étude seront committés sur le serveur *GitLab* de l'INRIA." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Exploration des données" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous commencerons par analyser le contenu du fichier de données (fichier structuré *CSV*) pour ensuite faire un premier tracé de l'ensemble de la base de données. \n", "Nous utiliserons les libraries *pandas* et *matplotlib* pour *python 3.6*." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Import des librairies\n", "import pandas as pd\n", "import matplotlib as plt" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YrMnDateDateCO2seasonallyfitseasonallyCO2seasonally
0adjustedadjusted fitfilledadjusted filled
1Excel[ppm][ppm][ppm][ppm][ppm][ppm]
2195801212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99
3195802212311958.1260-99.99-99.99-99.99-99.99-99.99-99.99
4195803212591958.2027315.70314.44316.19314.91315.70314.44
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" ], "text/plain": [ " Yr Mn Date Date CO2 seasonally fit \\\n", "0 adjusted \n", "1 Excel [ppm] [ppm] [ppm] \n", "2 1958 01 21200 1958.0411 -99.99 -99.99 -99.99 \n", "3 1958 02 21231 1958.1260 -99.99 -99.99 -99.99 \n", "4 1958 03 21259 1958.2027 315.70 314.44 316.19 \n", "\n", " seasonally CO2 seasonally \n", "0 adjusted fit filled adjusted filled \n", "1 [ppm] [ppm] [ppm] \n", "2 -99.99 -99.99 -99.99 \n", "3 -99.99 -99.99 -99.99 \n", "4 314.91 315.70 314.44 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Chargement de la base de données (CSV)\n", "# Les lignes de commentaires sont ignorées\n", "# Le séparateur de champs utilisé dans la base de données est la virgule (,)\n", "data = pd.read_csv('monthly_in_situ_co2_mlo.csv', sep=',', comment='\"')\n", "data.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les 54 premières colonnes de commentaires n'ont pas été prises en compte (commande `comment='\"'`). \n", "Les 2 premières lignes peuvent être elles aussi effacées car ne comportant pas de donées numériques." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(756, 10)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Effacement des 2 premières lignes\n", "# Affichage des dimensions de la base de données\n", "data = data.drop([0,1])\n", "data.shape" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YrMnDateDateCO2seasonallyfitseasonallyCO2seasonally
2195801212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99
3195802212311958.1260-99.99-99.99-99.99-99.99-99.99-99.99
4195803212591958.2027315.70314.44316.19314.91315.70314.44
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6195805213201958.3699317.51314.71317.86315.06317.51314.71
\n", "
" ], "text/plain": [ " Yr Mn Date Date CO2 seasonally fit \\\n", "2 1958 01 21200 1958.0411 -99.99 -99.99 -99.99 \n", "3 1958 02 21231 1958.1260 -99.99 -99.99 -99.99 \n", "4 1958 03 21259 1958.2027 315.70 314.44 316.19 \n", "5 1958 04 21290 1958.2877 317.45 315.16 317.30 \n", "6 1958 05 21320 1958.3699 317.51 314.71 317.86 \n", "\n", " seasonally CO2 seasonally \n", "2 -99.99 -99.99 -99.99 \n", "3 -99.99 -99.99 -99.99 \n", "4 314.91 315.70 314.44 \n", "5 314.99 317.45 315.16 \n", "6 315.06 317.51 314.71 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Affichage partiel de la base de données \"mise en forme\"\n", "data.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous voyons maintenant que la base de données comporte 10 colonnes et 756 lignes. Les commentaires en tête de fichier brut permettent d'avoir plus de détails sur les informations par colonnes :\n", "* 4 formats de date redondants sur les 4 premières colonnes.\n", "* La colonne 5 comporte les mesures mensuelles du taux de CO2 dans l'atmosphère (*ppm*). Ce sont les mesures brutes.\n", "* La colonne 6 reprend les données de la colonne 5 en les adjustant pour éliminer en grande partie le cycle saisonnier (4-harmonic fit with a linear gain factor).\n", "* La colonne 7 reprend la colonne 6 en ajoutant un lissage par *cubic spline*.\n", "* ...\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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31958.02.021231.01958.1260-99.99-99.99-99.99-99.99-99.99-99.99
41958.03.021259.01958.2027315.70314.44316.19314.91315.70314.44
51958.04.021290.01958.2877317.45315.16317.30314.99317.45315.16
61958.05.021320.01958.3699317.51314.71317.86315.06317.51314.71
71958.06.021351.01958.4548-99.99-99.99317.24315.14317.24315.14
81958.07.021381.01958.5370315.86315.19315.86315.22315.86315.19
91958.08.021412.01958.6219314.93316.19314.00315.29314.93316.19
101958.09.021443.01958.7068313.21316.08312.46315.35313.21316.08
111958.010.021473.01958.7890-99.99-99.99312.44315.40312.44315.40
121958.011.021504.01958.8740313.33315.20313.62315.46313.33315.20
131958.012.021534.01958.9562314.67315.43314.77315.51314.67315.43
141959.01.021565.01959.0411315.58315.54315.62315.57315.58315.54
151959.02.021596.01959.1260316.49315.86316.27315.63316.49315.86
161959.03.021624.01959.2027316.65315.38316.98315.69316.65315.38
171959.04.021655.01959.2877317.72315.42318.09315.77317.72315.42
181959.05.021685.01959.3699318.29315.49318.65315.85318.29315.49
191959.06.021716.01959.4548318.15316.03318.04315.94318.15316.03
201959.07.021746.01959.5370316.54315.86316.67316.03316.54315.86
211959.08.021777.01959.6219314.80316.06314.83316.12314.80316.06
221959.09.021808.01959.7068313.84316.72313.32316.22313.84316.72
231959.010.021838.01959.7890313.33316.32313.33316.30313.33316.32
241959.011.021869.01959.8740314.81316.68314.54316.39314.81316.68
251959.012.021899.01959.9562315.58316.35315.72316.47315.58316.35
261960.01.021930.01960.0410316.43316.39316.61316.56316.43316.39
271960.02.021961.01960.1257316.98316.35317.27316.64316.98316.35
281960.03.021990.01960.2049317.58316.28318.03316.71317.58316.28
291960.04.022021.01960.2896319.03316.70319.14316.79319.03316.70
301960.05.022051.01960.3716320.04317.22319.67316.86320.04317.22
311960.06.022082.01960.4563319.59317.48319.01316.93319.59317.48
.................................
7282018.07.043296.02018.5370408.90408.08409.43408.65408.90408.08
7292018.08.043327.02018.6219407.10408.63407.33408.91407.10408.63
7302018.09.043358.02018.7068405.59409.09405.66409.18405.59409.09
7312018.010.043388.02018.7890405.99409.62405.83409.44405.99409.62
7322018.011.043419.02018.8740408.12410.39407.47409.72408.12410.39
7332018.012.043449.02018.9562409.23410.16409.07409.97409.23410.16
7342019.01.043480.02019.0411410.92410.87410.29410.23410.92410.87
7352019.02.043511.02019.1260411.66410.90411.24410.47411.66410.90
7362019.03.043539.02019.2027412.00410.45412.25410.68412.00410.45
7372019.04.043570.02019.2877413.52410.72413.73410.91413.52410.72
7382019.05.043600.02019.3699414.83411.42414.54411.13414.83411.42
7392019.06.043631.02019.4548413.96411.38413.91411.35413.96411.38
7402019.07.043661.02019.5370411.85411.03412.36411.57411.85411.03
7412019.08.043692.02019.6219410.08411.62410.23411.81410.08411.62
7422019.09.043723.02019.7068408.55412.06408.52412.05408.55412.06
7432019.010.043753.02019.7890408.43412.07408.67412.29408.43412.07
7442019.011.043784.02019.8740410.28412.56410.29412.54410.28412.56
7452019.012.043814.02019.9562411.85412.78411.88412.79411.85412.78
7462020.01.043845.02020.0410413.37413.33413.11413.05413.37413.33
7472020.02.043876.02020.1257-99.99-99.99-99.99-99.99-99.99-99.99
7482020.03.043905.02020.2049-99.99-99.99-99.99-99.99-99.99-99.99
7492020.04.043936.02020.2896-99.99-99.99-99.99-99.99-99.99-99.99
7502020.05.043966.02020.3716-99.99-99.99-99.99-99.99-99.99-99.99
7512020.06.043997.02020.4563-99.99-99.99-99.99-99.99-99.99-99.99
7522020.07.044027.02020.5383-99.99-99.99-99.99-99.99-99.99-99.99
7532020.08.044058.02020.6230-99.99-99.99-99.99-99.99-99.99-99.99
7542020.09.044089.02020.7077-99.99-99.99-99.99-99.99-99.99-99.99
7552020.010.044119.02020.7896-99.99-99.99-99.99-99.99-99.99-99.99
7562020.011.044150.02020.8743-99.99-99.99-99.99-99.99-99.99-99.99
7572020.012.044180.02020.9563-99.99-99.99-99.99-99.99-99.99-99.99
\n", "

756 rows × 10 columns

\n", "
" ], "text/plain": [ " Yr Mn Date Date CO2 seasonally fit \\\n", "2 1958.0 1.0 21200.0 1958.0411 -99.99 -99.99 -99.99 \n", "3 1958.0 2.0 21231.0 1958.1260 -99.99 -99.99 -99.99 \n", "4 1958.0 3.0 21259.0 1958.2027 315.70 314.44 316.19 \n", "5 1958.0 4.0 21290.0 1958.2877 317.45 315.16 317.30 \n", "6 1958.0 5.0 21320.0 1958.3699 317.51 314.71 317.86 \n", "7 1958.0 6.0 21351.0 1958.4548 -99.99 -99.99 317.24 \n", "8 1958.0 7.0 21381.0 1958.5370 315.86 315.19 315.86 \n", "9 1958.0 8.0 21412.0 1958.6219 314.93 316.19 314.00 \n", "10 1958.0 9.0 21443.0 1958.7068 313.21 316.08 312.46 \n", "11 1958.0 10.0 21473.0 1958.7890 -99.99 -99.99 312.44 \n", "12 1958.0 11.0 21504.0 1958.8740 313.33 315.20 313.62 \n", "13 1958.0 12.0 21534.0 1958.9562 314.67 315.43 314.77 \n", "14 1959.0 1.0 21565.0 1959.0411 315.58 315.54 315.62 \n", "15 1959.0 2.0 21596.0 1959.1260 316.49 315.86 316.27 \n", "16 1959.0 3.0 21624.0 1959.2027 316.65 315.38 316.98 \n", "17 1959.0 4.0 21655.0 1959.2877 317.72 315.42 318.09 \n", "18 1959.0 5.0 21685.0 1959.3699 318.29 315.49 318.65 \n", "19 1959.0 6.0 21716.0 1959.4548 318.15 316.03 318.04 \n", "20 1959.0 7.0 21746.0 1959.5370 316.54 315.86 316.67 \n", "21 1959.0 8.0 21777.0 1959.6219 314.80 316.06 314.83 \n", "22 1959.0 9.0 21808.0 1959.7068 313.84 316.72 313.32 \n", "23 1959.0 10.0 21838.0 1959.7890 313.33 316.32 313.33 \n", "24 1959.0 11.0 21869.0 1959.8740 314.81 316.68 314.54 \n", "25 1959.0 12.0 21899.0 1959.9562 315.58 316.35 315.72 \n", "26 1960.0 1.0 21930.0 1960.0410 316.43 316.39 316.61 \n", "27 1960.0 2.0 21961.0 1960.1257 316.98 316.35 317.27 \n", "28 1960.0 3.0 21990.0 1960.2049 317.58 316.28 318.03 \n", "29 1960.0 4.0 22021.0 1960.2896 319.03 316.70 319.14 \n", "30 1960.0 5.0 22051.0 1960.3716 320.04 317.22 319.67 \n", "31 1960.0 6.0 22082.0 1960.4563 319.59 317.48 319.01 \n", ".. ... ... ... ... ... ... ... \n", "728 2018.0 7.0 43296.0 2018.5370 408.90 408.08 409.43 \n", "729 2018.0 8.0 43327.0 2018.6219 407.10 408.63 407.33 \n", "730 2018.0 9.0 43358.0 2018.7068 405.59 409.09 405.66 \n", "731 2018.0 10.0 43388.0 2018.7890 405.99 409.62 405.83 \n", "732 2018.0 11.0 43419.0 2018.8740 408.12 410.39 407.47 \n", "733 2018.0 12.0 43449.0 2018.9562 409.23 410.16 409.07 \n", "734 2019.0 1.0 43480.0 2019.0411 410.92 410.87 410.29 \n", "735 2019.0 2.0 43511.0 2019.1260 411.66 410.90 411.24 \n", "736 2019.0 3.0 43539.0 2019.2027 412.00 410.45 412.25 \n", "737 2019.0 4.0 43570.0 2019.2877 413.52 410.72 413.73 \n", "738 2019.0 5.0 43600.0 2019.3699 414.83 411.42 414.54 \n", "739 2019.0 6.0 43631.0 2019.4548 413.96 411.38 413.91 \n", "740 2019.0 7.0 43661.0 2019.5370 411.85 411.03 412.36 \n", "741 2019.0 8.0 43692.0 2019.6219 410.08 411.62 410.23 \n", "742 2019.0 9.0 43723.0 2019.7068 408.55 412.06 408.52 \n", "743 2019.0 10.0 43753.0 2019.7890 408.43 412.07 408.67 \n", "744 2019.0 11.0 43784.0 2019.8740 410.28 412.56 410.29 \n", "745 2019.0 12.0 43814.0 2019.9562 411.85 412.78 411.88 \n", "746 2020.0 1.0 43845.0 2020.0410 413.37 413.33 413.11 \n", "747 2020.0 2.0 43876.0 2020.1257 -99.99 -99.99 -99.99 \n", "748 2020.0 3.0 43905.0 2020.2049 -99.99 -99.99 -99.99 \n", "749 2020.0 4.0 43936.0 2020.2896 -99.99 -99.99 -99.99 \n", "750 2020.0 5.0 43966.0 2020.3716 -99.99 -99.99 -99.99 \n", "751 2020.0 6.0 43997.0 2020.4563 -99.99 -99.99 -99.99 \n", "752 2020.0 7.0 44027.0 2020.5383 -99.99 -99.99 -99.99 \n", "753 2020.0 8.0 44058.0 2020.6230 -99.99 -99.99 -99.99 \n", "754 2020.0 9.0 44089.0 2020.7077 -99.99 -99.99 -99.99 \n", "755 2020.0 10.0 44119.0 2020.7896 -99.99 -99.99 -99.99 \n", "756 2020.0 11.0 44150.0 2020.8743 -99.99 -99.99 -99.99 \n", "757 2020.0 12.0 44180.0 2020.9563 -99.99 -99.99 -99.99 \n", "\n", " seasonally CO2 seasonally \n", "2 -99.99 -99.99 -99.99 \n", "3 -99.99 -99.99 -99.99 \n", "4 314.91 315.70 314.44 \n", "5 314.99 317.45 315.16 \n", "6 315.06 317.51 314.71 \n", "7 315.14 317.24 315.14 \n", "8 315.22 315.86 315.19 \n", "9 315.29 314.93 316.19 \n", "10 315.35 313.21 316.08 \n", "11 315.40 312.44 315.40 \n", "12 315.46 313.33 315.20 \n", "13 315.51 314.67 315.43 \n", "14 315.57 315.58 315.54 \n", "15 315.63 316.49 315.86 \n", "16 315.69 316.65 315.38 \n", "17 315.77 317.72 315.42 \n", "18 315.85 318.29 315.49 \n", "19 315.94 318.15 316.03 \n", "20 316.03 316.54 315.86 \n", "21 316.12 314.80 316.06 \n", "22 316.22 313.84 316.72 \n", "23 316.30 313.33 316.32 \n", "24 316.39 314.81 316.68 \n", "25 316.47 315.58 316.35 \n", "26 316.56 316.43 316.39 \n", "27 316.64 316.98 316.35 \n", "28 316.71 317.58 316.28 \n", "29 316.79 319.03 316.70 \n", "30 316.86 320.04 317.22 \n", "31 316.93 319.59 317.48 \n", ".. ... ... ... \n", "728 408.65 408.90 408.08 \n", "729 408.91 407.10 408.63 \n", "730 409.18 405.59 409.09 \n", "731 409.44 405.99 409.62 \n", "732 409.72 408.12 410.39 \n", "733 409.97 409.23 410.16 \n", "734 410.23 410.92 410.87 \n", "735 410.47 411.66 410.90 \n", "736 410.68 412.00 410.45 \n", "737 410.91 413.52 410.72 \n", "738 411.13 414.83 411.42 \n", "739 411.35 413.96 411.38 \n", "740 411.57 411.85 411.03 \n", "741 411.81 410.08 411.62 \n", "742 412.05 408.55 412.06 \n", "743 412.29 408.43 412.07 \n", "744 412.54 410.28 412.56 \n", "745 412.79 411.85 412.78 \n", "746 413.05 413.37 413.33 \n", "747 -99.99 -99.99 -99.99 \n", "748 -99.99 -99.99 -99.99 \n", "749 -99.99 -99.99 -99.99 \n", "750 -99.99 -99.99 -99.99 \n", "751 -99.99 -99.99 -99.99 \n", "752 -99.99 -99.99 -99.99 \n", "753 -99.99 -99.99 -99.99 \n", "754 -99.99 -99.99 -99.99 \n", "755 -99.99 -99.99 -99.99 \n", "756 -99.99 -99.99 -99.99 \n", "757 -99.99 -99.99 -99.99 \n", "\n", "[756 rows x 10 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Graphs des données de la base brutes\n", "data.astype('float')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }