2c:null 2d:["$","title",null,{"children":"Visualising absolute and anomaly temperature. | Atelier de cartographie de Sciences Po | Observable"}] 32:T40f,Plot.plot({ title: "Global surface temperature anomaly, 1850-2024", subtitle: "From pre-industrial period", caption: "Source: Berkeley Earth Land/Ocean Temperature.", style: { background: "#fff7eb" }, marginTop: 40, y: { grid: true, label: "temperature deviation (°C)\n from 1850-1900 average", ticks: 5, tickFormat: (d) => d.toLocaleString(undefined, { minimumFractionDigits: 1 }), tickSize: 4 }, marks: [ // Baseline reference (1850 to 1900) baseline_plot(annual_data, "anomaly_1850_1900"), Plot.ruleX(annual_data, { x: "date", y: "anomaly_1850_1900", stroke: (d) => (d.anomaly_1850_1900 >= 0 ? "coral" : "cadetblue"), strokeWidth: 2 }), Plot.ruleY([0]), // Target 1.5°C and 2°C paris_targets_plot(), Plot.dotY(annual_data, { x: "date", y: "anomaly_1850_1900", r: 2, fill: (d) => (d.anomaly_1850_1900 >= 0 ? "coral" : "cadetblue"), stroke: "white", strokeWidth: 1, tip: true }) ] })33:T8e3,Plot.plot({ title: "Monthly global surface temperature, 1850-2024", caption: "Source: Berkeley Earth Land/Ocean Temperature.", style: { background: "#fff7eb" }, height: 550, x: { tickFormat: (d) => Plot.formatMonth("en")(d - 1), ticks: 12, label: null }, y: { grid: true, label: "↑ temperature (°C)", tickFormat: (d) => d.toLocaleString(undefined, { minimumFractionDigits: 1 }), tickSize: 4 }, color: { legend: true, label: null, type: "threshold", domain: [1900, 1950, 2000, 2015], range: ["#ffe86e", "#ffc656", "#ffa33d", "#ff7a23", "#ff4400"] }, marks: [ Plot.lineY(monthly_data, { x: "month", y: "absolute", stroke: "year", strokeWidth: (d) => (d.year >= 2015 ? 2 : 0.7), curve: "natural", tip: true, sort: { channel: "-stroke" } }), // REFERENCE PERIOD Plot.lineY(monthly_baseline_1850_1900, { x: "month", y: "mean", curve: "natural", strokeWidth: 1.5, _strokeDasharray: 4 }), Plot.text(monthly_baseline_1850_1900, { filter: (d) => d.month === 7, x: "month", y: "mean", dy: 33, text: (d) => "Pre-industrial reference period (1850-1900)", lineWidth: 10, stroke: "#fff7eb", strokeWidth: 5, fill: "black", fontWeight: "bold", textAnchor: "middle" }), // 1.5°C TARGET Plot.lineY(monthly_baseline_1850_1900, { x: "month", y: "target_15", curve: "natural", strokeWidth: 1, strokeDasharray: 4 }), Plot.text(monthly_baseline_1850_1900, { filter: (d) => d.month === 7, x: "month", y: "target_15", text: (d) => "+1.5°C", stroke: "#fff7eb", strokeWidth: 8, fill: "black", textAnchor: "middle" }), // 2°C TARGET Plot.lineY(monthly_baseline_1850_1900, { x: "month", y: "target_20", curve: "natural", strokeWidth: 1, strokeDasharray: 4 }), Plot.text(monthly_baseline_1850_1900, { filter: (d) => d.month === 7, x: "month", y: "target_20", text: (d) => "+2°C", stroke: "#fff7eb", strokeWidth: 8, fill: "black", textAnchor: "middle" }) ] })34:T5f0,Plot.plot({ title: "Monthly global surface temparature anomaly, 1850-2024", subtitle: "From pre-industrial period", caption: "Source: Berkeley Earth Land/Ocean Temperature.", style: { background: "#fff7eb" }, height: 550, marginTop: 40, x: { tickFormat: (d) => Plot.formatMonth("en")(d - 1), ticks: 12, label: null }, y: { grid: true, label: "temperature deviation (°C)\n from 1850-1900 average", ticks: 5, tickFormat: (d) => d.toLocaleString(undefined, { minimumFractionDigits: 1 }), tickSize: 4 }, color: { legend: true, label: null, type: "threshold", domain: [1900, 1950, 2000, 2015], range: ["#ffe86e", "#ffc656", "#ffa33d", "#ff7a23", "#ff4400"] }, marks: [ Plot.tickY(monthly_data, { x: "month", y: "anomaly_1850_1900", stroke: "year", strokeWidth: (d) => (d.year >= 2015 ? 2.5 : 1), curve: "natural", tip: true, sort: { channel: "-stroke" } }), Plot.dotY(monthly_data, { filter: (d) => d.year === 2024, x: "month", y: "anomaly_1850_1900", fill: "year", stroke: "#fff7eb" }), Plot.ruleY([0]), Plot.ruleY([1.5], { strokeWidth: 1, strokeDasharray: 4 }), Plot.ruleY([2], { strokeWidth: 1, strokeDasharray: 4 }) // Plot.tip( // [ // `Since June, 2023 (red dots) is breaking record each month with a large gap.` // ], // { x: 8, y: 1.7, dy: 0, anchor: "bottom-left", fill: "#fff7eb" } // ) ] 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30:{"id":"35ca311cac741615","slug":"visualising-absolute-and-anomaly-temperature","title":"Visualising absolute and anomaly temperature.","thumbnail":"6cadecdb70711f4d2d2d3abb3a248cc32f0b49b691c8c3cbf93a98b1fcde1638","access_level":"public","create_time":"$D2023-10-19T15:12:02.217Z","update_time":"$D2025-01-21T13:44:50.582Z","view_time":null,"first_public_time":"$D2023-10-26T15:35:08.740Z","fork":false,"stars":35,"starred":false,"owner":{"type":"team","id":"5d76df5eab7f8bb7","login":"ateliercartographie","name":"Atelier de cartographie de Sciences Po","avatar_url":"https://avatars-next.observableusercontent.com/fab2304a50866da3a682fdbbdf200ecb01ddfd5fbf74f60756d36188cfd2cf68","tier":"starter_2024"},"version":525,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"b5f87f2a35829281","login":"tombor","name":"Thomas Ansart","avatar_url":"https://avatars-next.observableusercontent.com/b086dcc95470aae08b776435f7604190c606884a2187354d54b5fbae116aa241","tier":null}],"members":[{"type":"team","id":"5d76df5eab7f8bb7","login":"ateliercartographie","name":"Atelier de cartographie de Sciences Po","avatar_url":"https://avatars-next.observableusercontent.com/fab2304a50866da3a682fdbbdf200ecb01ddfd5fbf74f60756d36188cfd2cf68","tier":"starter_2024","role":"editor"},{"type":"individual","id":"b5f87f2a35829281","login":"tombor","name":"Thomas Ansart","avatar_url":"https://avatars-next.observableusercontent.com/b086dcc95470aae08b776435f7604190c606884a2187354d54b5fbae116aa241","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":14,"collection":null,"type":"notebook","body":{"title":"Visualising absolute and anomaly temperature.","cells":[{"id":0,"value":"# Visualising absolute and anomaly temperature.\n## In favor of more useful visual marks.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":332,"value":"When we talk about temperature in a climate change context, we usually see noisy line charts of yearly anomaly from a reference period. This reference period can change among sources. See three examples below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":298,"value":"

\"MET — © MET office. Deviation from 1850-1900 period.

\"NASA\" — © NASA. Deviation from 1951-1980 period.

\"Copernicus\" — © Copernicus. Deviation from 1991-2020 period.

\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":404,"value":"To better understand why it can be an issue to use a line mark with absolute or anomaly temperature, let's try a simple minimalist line chart from [Berkeley Earth Land/Ocean Temperature](https://berkeleyearth.org/data/).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":175,"value":"viewof indicator = Inputs.radio(\n [\"absolute\", \"anomaly_1951_1980\", \"anomaly_1850_1900\"],\n {\n label: \"Variable:\",\n value: \"absolute\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":178,"value":"Plot.plot({\n caption: \"Source: Berkeley Earth Land/Ocean Temperature.\",\n y: {\n label:\n indicator === \"absolute\"\n ? \"absolute temperature (°C)\"\n : indicator === \"anomaly\"\n ? \"deviation from 1951-1980 average\"\n : \"deviation from 1850-1900 average\"\n },\n marks: [\n Plot.lineY(annual_data, {\n x: \"date\",\n y: indicator,\n stroke: \"coral\"\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":407,"value":"🧐 \n**See some differences?** \nApart from the y-axis, nothing visually helps us to understand absolute or anomaly temperature. But we can reinforce both concepts with effective visual mark choices.\n\n**Absolute temperatures** are best to highlight an evolution, a tendency. By using a line mark, we link all values together to easily compare a value to the previous/next one and strengthen horizontal reading. \nAs year-to-year variation can be noisy, a rolling average of 5 years helps us to see the long-term tendency. For transparency, we can suggest short-term variation with a dot mark in the background. \nThen comes the context: climate change and climate targets. What is a +1.5°C or +2°C target in absolute temperature? Which period are these targets referencing? Annotations is an effective solution.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":200,"value":"Plot.plot({\n title: \"Global surface temperature, 1850-2024\",\n subtitle: \"Black curve: 5 years rolling average\",\n caption: \"Source: Berkeley Earth Land/Ocean Temperature.\",\n style: { background: \"#fff7eb\" },\n marginTop: 40,\n y: {\n grid: true,\n label: \"↑ temperature (°C)\",\n ticks: 5,\n tickFormat: (d) =>\n d.toLocaleString(undefined, { minimumFractionDigits: 1 }),\n tickSize: 4\n },\n marks: [\n // Baseline reference (1850 to 1900)\n baseline_plot(annual_data, \"absolute\"),\n\n // Raw values\n Plot.dotY(annual_data, {\n x: \"date\",\n y: \"absolute\",\n r: 2,\n fill: \"antiquewhite\",\n stroke: \"coral\",\n strokeWidth: 1\n }),\n\n // Target 1.5°C and 2°C\n paris_targets_plot(undefined, { baseline: annual_baseline_1850_1900 }),\n\n // 5 years rolling average\n Plot.lineY(\n annual_data,\n Plot.windowY(5, {\n x: \"date\",\n y: \"absolute\",\n curve: \"cardinal\",\n strokeWidth: 1.5,\n tip: true\n })\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":437,"value":"**Anomaly temperatures** are best to highlight a deviation from a reference. Vertical reading should be favored and bar mark excels at this. Again annotations help remind the context.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":261,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":413,"value":"---\nWhen it comes to **monthly data**, seasonality of the data is highlighted with a monthly axis. On contrary years are simplified: group by period with a gradient of colors.\n\nFor **absolute temperature**, parts of the ideas above are reused:\n+ Horizontal reading with line mark\n+ Context with annotations","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":353,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":478,"value":"For **anomaly temperature**, bar mark is drop in favor of a tick mark to allow superposition among years inside a month. Thanks to this choice, we can see that deviation from reference was higher during December to March than June to August. But that was before 2023...","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":355,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"---\n## Data cleaning\n_Berkeley Earth_ is a great source, but their all-in-one text file (metadata + data) are a mess to play with...\n+ extracting reference average (1951-1980)\n+ parsing txt file (skip comment lines, slice lines and columns)\n+ calcul absolute from anomaly and reference average (1951-1980)\n+ calcul pre-industrial anomaly (1850-1900)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":83,"value":"### Annual average","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":87,"value":"annual_baseline_1951_1980 = 14.102","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"annual_baseline_1850_1900 = {\n const filter_data = raw_annual_data\n .filter((d) => d.year >= 1850 && d.year <= 1900)\n .map((d) => d.absolute);\n const mean = d3.mean(filter_data);\n return round(mean, 5);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":102,"value":"raw_annual_data = {\n const text = await FileAttachment(\"Land_and_Ocean_summary@1.txt\").text();\n const columns = [\"year\", \"anomaly\"];\n\n return parse_file(text, 58, -1, columns).map(({ year, anomaly }) => ({\n year,\n date: new Date(`${year}-01-01`),\n absolute: round(annual_baseline_1951_1980 + anomaly, 5),\n anomaly_1951_1980: anomaly\n }));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":113,"value":"annual_data = raw_annual_data.map(({ year, ...d }) => ({\n ...d,\n anomaly_1850_1900: round(\n annual_baseline_1951_1980 + d.anomaly_1951_1980 - annual_baseline_1850_1900,\n 4\n )\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":151,"value":"annual_data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":73,"value":"### Monthly average","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":24,"value":"monthly_baseline_1951_1980 = \"12.23 12.44 13.06 13.97 14.95 15.67 15.95 15.79 15.19 14.26 13.24 12.49\"\n .split(\" \")\n .map((d) => +d)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"monthly_baseline_1850_1900 = aq\n .from(raw_monthly_data)\n .filter((d) => d.year >= 1850 && d.year <= 1900)\n .groupby(\"month\")\n .rollup({ mean: aq.op.mean(\"absolute\") })\n .derive({ mean: (d) => aq.op.round_digits(d.mean, 4) })\n .derive({\n target_15: (d) => aq.op.round_digits(d.mean + 1.5, 4),\n target_20: (d) => aq.op.round_digits(d.mean + 2, 4)\n })\n .objects(\"mean_1850_1900\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"raw_monthly_data = {\n const text = await FileAttachment(\"Land_and_Ocean_complete@1.txt\").text();\n const columns = [\"year\", \"month\", \"anomaly\"];\n\n return parse_file(text, 86, 2186, columns).map(({ anomaly, ...d }) => ({\n ...d,\n absolute: round(monthly_baseline_1951_1980[d.month - 1] + anomaly, 4),\n anomaly_1951_1980: anomaly\n }));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":365,"value":"monthly_data = raw_monthly_data.map((d) => ({\n ...d,\n anomaly_1850_1900: round(\n monthly_baseline_1951_1980[d.month - 1] +\n d.anomaly_1951_1980 -\n monthly_baseline_1850_1900[d.month - 1].mean,\n 4\n )\n}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"monthly_data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":26,"value":"---\n## Helpers","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":181,"value":"paris_targets = [\n { target: 1.5, text: \"+1,5°C (from 1850-1900)\" },\n { target: 2, text: \"+2°C (from 1850-1900)\" }\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":186,"value":"function paris_targets_plot(\n data = paris_targets,\n { baseline = 0, ...options } = {}\n) {\n return Plot.marks(\n Plot.ruleY(\n data.map((d) => baseline + d.target),\n { strokeDasharray: 3 }\n ),\n Plot.text(data, {\n x: (d) => new Date(\"1850-01-01\"),\n y: (d) => baseline + d.target,\n text: \"text\",\n dx: 20,\n stroke: \"#fff7eb\",\n strokeWidth: 8,\n fill: \"black\",\n textAnchor: \"start\",\n ...options\n })\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":188,"value":"function baseline_plot(data, variable, dy = -20) {\n const period = [new Date(\"1850-01-01\"), new Date(\"1900-01-01\")];\n // max value on the period\n const max = d3.max(\n data.filter((d) => d.date >= period[0] && d.date <= period[1]),\n (d) => d[variable]\n );\n\n return Plot.marks(\n Plot.lineY(period, {\n x: (d) => d,\n y: max,\n dy,\n strokeWidth: 1,\n markerStart: \"arrow-reverse\",\n markerEnd: \"arrow\"\n }),\n Plot.text([\"Pre-industrial reference period (1850-1900)\"], {\n x: (d) => new Date(\"1875-01-01\"),\n y: max,\n dy: dy - 12,\n lineWidth: 15,\n fontWeight: \"bold\",\n textAnchor: \"middle\"\n })\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"/**\n * Parse berkeley text file and returns an array of objects.\n * @param {string} text - The text to parse.\n * @param {number} start - The line number to start parsing from.\n * @param {number} [end] - The line number to stop parsing at. If not specified, all lines after `start` will be parsed.\n * @param {string[]} columns - An array of column names to use for the resulting objects.\n * @returns {Object[]} An array of objects, where each object represents a row in the parsed data. The keys of each object are the column names specified in the `columns` parameter, and the values are the corresponding values from the parsed data.\n */\nfunction parse_file(text, start, end = undefined, columns) {\n const data = text.split(\"\\n\").slice(start, end);\n\n return data.map((d) => {\n const values = d\n .split(\" \")\n .filter((value) => value !== \"\")\n .slice(0, columns.length + 1);\n\n const row = {};\n columns.forEach((c, i) => (row[c] = +values[i]));\n\n return row;\n });\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"/**\n * Rounds a value to a given number of significant digits\n * Based on https://stackoverflow.com/a/75954111\n *\n * @param value - The number to round\n * @param significant_digits - The number of significant digits\n * @returns The value rounded to the given number of significant digits\n */\nfunction round(value, significant_digits) {\n if (value === 0) return 0;\n const exponent = Math.floor(Math.log10(Math.abs(value)));\n const nIntegers = exponent + 1;\n const precision = 10 ** -(nIntegers - significant_digits);\n return Math.round(value * precision) / precision;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"{\n aq.addFunction(\"round_digits\", round, { override: true });\n return \"Add round to arquero op functions as op.round_digits\";\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W35","stdlib":"1","titling":"auto"}} 31:"$30:body"