3e:null 3f:["$","title",null,{"children":"Global Climate Temperature Trends and Indicators | Christian Klettner | Observable"}] 45:T411,Plot.plot({ width: 800, color: { legend: true, range: ["lightgray", "#4A1FE0", "#E01F54"] }, y: { label: "Anomaly °C"}, marks: [ Plot.frame(), Plot.ruleY([0]), Plot.line(datatropo.params({ type: type, region: (region===null ? "Globe" : region['ClimateZone']) }). filter(d => d.Type===type). filter(d => d.ClimateZone===region), {x: "Date", y: "TemperatureAnomaly", stroke: d=> d.measure="Actual" }), Plot.line(datatropo.params({ type: type, region: (region===null ? "Globe" : region['ClimateZone']) }). filter(d => d.Type===type). filter(d => d.ClimateZone===region), {x: "Date", y: "trend", stroke: d=> d.measure="Trend", strokeWidth: 3 }), Plot.line(datatropo. filter(d => d.Type==="Total"). filter(d => d.ClimateZone==="Globe"), {x: "Date", y: "trend", stroke: d=> d.measure="Global Land and Ocean Trend", strokeWidth: 3 }) ] })46:[] 43:{"id":"5d9bc4392df0bfa2","slug":null,"title":"Global Climate Temperature Trends and Indicators","thumbnail":"13f0216ceae732f651702ad9a95897dbb0ba333091d35b5bdb4c797df01dc206","access_level":"public","create_time":"$D2023-07-19T00:12:58.986Z","update_time":"$D2024-05-14T10:38:39.366Z","view_time":null,"first_public_time":"$D2023-07-19T00:12:58.986Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"1e6bbbc1fea19b9b","login":"klettsch","name":"Christian Klettner","avatar_url":"https://avatars-next.observableusercontent.com/a2034439cf44a563da383de835ee021f6b6b0fe384e6da3aac36176d6f7b8a0a","tier":"starter_2024","bio":"Risk, finance, economic modelling, forecasting and stress testing","following":false},"version":1407,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"9c99afc3bb345c32","login":"klettsch","name":"Christian Klettner","avatar_url":"https://avatars-next.observableusercontent.com/0d0f4aae093b36282a186ea737feb66dc7f5bb69d65e30cc5a159c34f0a77f47","tier":null,"bio":"Risk, finance, economic modelling, forecasting and stress testing","following":false}],"members":[{"type":"individual","id":"9c99afc3bb345c32","login":"klettsch","name":"Christian Klettner","avatar_url":"https://avatars-next.observableusercontent.com/0d0f4aae093b36282a186ea737feb66dc7f5bb69d65e30cc5a159c34f0a77f47","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Global Climate Temperature Trends and Indicators","cells":[{"id":0,"value":"# Global Climate Temperature Trends and Indicators \n\nMonitors real time (monthly) global temperature data from real sources. Updates each month on the 28th. \n\nSources are: \n - [Surface temperature](https://data.giss.nasa.gov/gistemp);\n - [Southern ocean oscillation index](https://www.cpc.ncep.noaa.gov);\n - [Sea ice extent](https://noaadata.apps.nsidc.org);\n - [Tropospheric temperatures](https://www.nsstc.uah.edu/nsstc/);\n\nTrend estimates derived from a trend-cycle-seasonal decomposition which is updated monthly with each data update.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1096,"value":"toc(\"h2\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1111,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1100,"value":"## Surface temperatures\n\nGISTEMP from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas), ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":17,"value":"Plot.plot({ \n marginLeft: 100, \n width: 800,\n color: { legend: true },\n marks: [\n Plot.frame(),\n Plot.tickX(datasst, {x: \"Date\", fy: \"Region\", stroke: \"AvTemp\", strokeWidth: 5}), \n Plot.tip(datasst, Plot.pointer({ x: \"Date\", fy: \"Region\", title: \"AvTemp\", fill: \"AvTemp\" }))\n ],\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":96,"value":"Plot.plot({ \n fontFamily: \"Lato\",\n width: 800,\n color: { legend: true, range: [ \"#4A1FE0\",\"#E01F54\", \"#B5E01F\" ] },\n y: { label: \"Anomaly (°C)\", domain: [-1, 2]}, \n marks: [ \n Plot.frame(),\n Plot.ruleY([0, 1.5]),\n Plot.line(datasst.filter(d => d.Region===\"Globe\"), \n {x: \"Date\", y: \"AvTemp\", stroke: \"lightgray\"}), \n Plot.line(datasst, {x: \"Date\", y: \"trend\", stroke: \"Region\", strokeWidth: 3 })\n ],\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":402,"value":"datasst.groupby(\"Region\").\n filter(d => d.Date === d3.max(d.Date)).\n derive({Date: d => op.mthformat(d.Date)}).\n select(\"Region\", \"Date\", \"AvTemp\", { trend: \"Trend AvTemp\"}).\n view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":893,"value":"Citations: \n\n - GISTEMP Team, 2023 *GISS Surface Temperature Analysis (GISTEMP)*, version 4. NASA Goddard Institute for Space Studies. Dataset accessed ${ d3.timeFormat(\"%d %b %Y\")(Date.now()) } at [https://data.giss.nasa.gov/gistemp/](https://data.giss.nasa.gov/gistemp/).\n - Lenssen, N., G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019, *Improvements in the GISTEMP uncertainty model*, **J. Geophys. Res. Atmos.**, 124, no. 12, 6307-6326, [doi:10.1029/2018JD029522](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018JD029522).\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1249,"value":"### Trend temperatures in major cities\n\nSource: [HadCRUT](https://crudata.uea.ac.uk/cru/data/temperature/)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1255,"value":"viewof cityselect = Inputs.table(datacsst.groupby('city').\n filter(d => d.period===op.max(d.period)).select('city', 'trend', 'tas_mean').objects(), \n { multiple: false, \n rows: 20, \n header: { city: md`*City*`,\n trend: md`*Trend Temperature Anomaly*
(+°C)`, \n tas_mean: md`*Actual Anomaly*
(+°C)` } \n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1251,"value":"Plot.plot({ \n fontFamily: \"Lato\",\n width: 800,\n color: { legend: true, \n range: [ \"#E01F54\", \"#4A1FE0\", \"lightgray\", \"#B5E01F\" ] },\n y: { domain: [-2, 3] },\n marks: [ \n Plot.frame(),\n Plot.lineY(datacsst.params({cityselect: (cityselect===null ? \"Melbourne\" : cityselect['city'])}).\n filter(d => d.city===cityselect), \n {x: \"period\", \n y: d => d.tas_mean > 3 | d.tas_mean < -2 ? null : d.tas_mean, \n stroke: d => d.type=\"Observed\" }), \n Plot.lineY(datacsst.params({cityselect: (cityselect===null ? \"Melbourne\" : cityselect['city'])}).\n filter(d => d.city===cityselect), \n { x: \"period\", y: \"trend\", stroke: d => d.type= `City Trend: ${d.city}`, strokeWidth: 3 }), \n Plot.line(datasst.filter(d => d.Region===\"Globe\"), \n {x: \"Date\", y: \"trend\", stroke: d => d.type=\"Global Trend\", strokeWidth: 3}), \n Plot.ruleY([0, 1.5])]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":279,"value":"## El-Nino La-Nina Index","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1326,"value":"viewof ninomodel = Inputs.select([\"NINO1+2\", \"NINO3\", \"NINO3+4\", \"NINO4\"]) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":169,"value":"Plot.plot({ \n width: 800, \n inset: 10,\n color: {legend: true, \n domain: [ \"Current\", \"La Nina\", \"El Nino\"], \n range: [ \"lightgray\", \"#4A1FE0\",\"#E01F54\"] }, \n y: { label: \"Anomaly (°C)\", domain: [-4, 4]}, \n x: { ticks: [0, 10, 20, 30, 40].map((d) => new Date(\"1980-01-01\").addYears(d)), \n tickFormat: \"%Y\" },\n marks: [ \n Plot.frame(), \n Plot.line(datanino.params({ninomodel: ninomodel}).filter(d => d.NinoModel===ninomodel), \n { x: \"Date\", y: \"ANOM\", stroke: \"lightgray\" }), \n Plot.differenceY(datanino.params({ninomodel: ninomodel}).filter(d => d.NinoModel===ninomodel), \n { x: \"Date\", y: \"trend\", stroke: null, positiveFill: \"#4A1FE0\", negativeFill: \"#E01F54\", \n fillOpacity: 0.8 }), \n Plot.ruleY([-1.5, 0, 1.5])\n ],\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":376,"value":"datanino.groupby('NinoModel').filter(d => d.Date === d3.max(d.Date)).\n derive({Date: d => op.mthformat(d.Date)}).\n select('NinoModel',\"Date\", {ANOM: \"Observed Anomaly\" }, { trend: \"Trend Anomaly\"}).view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1392,"value":"Correlation between the various measures of Nino:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1368,"value":"datanino.\n select('Date', { NinoModel: 'NinoModel2', ANOM: 'ANOM2'}).\n join(datanino.select('Date', 'NinoModel', 'ANOM'), 'Date').\n groupby('NinoModel', 'NinoModel2').\n rollup( { Cor: d => op.corr(d.ANOM, d.ANOM2)}).\n groupby('NinoModel').\n pivot('NinoModel2', 'Cor').view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":310,"value":"## Sea ice extent\n\nLatest observations on norther and southern hemisphere sea ice extent","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":465,"value":"dataice.derive({Hemisphere: d => \"North\"}).\n concat(dataices.derive({Hemisphere: d => \"South\"})).\n groupby(\"Hemisphere\").\n filter(d => d.Date===d3.max(d.Date)).\n derive({Date: d => op.mthformat(d.Date)}). \n select(\"Hemisphere\", \"Date\", { extent: \"Current Extent\" , trend: \"Trend Extent\"}).\n view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":661,"value":"### Northern hemisphere (Arctic)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":318,"value":"Plot.plot({ \n y: { label: \"mill sq.km\"}, \n color: { legend: true, range: [\"lightgray\", \"#4A1FE0\", \"#E01F54\"] },\n width: 800,\n marks: [\n Plot.frame(),\n Plot.line(dataice.orderby(\"Date\"),\n { x: \"Date\", y: \"extent\", stroke: d => d.type=\"Arctic Extent\" }),\n Plot.line(dataice.orderby(\"Date\"), \n { x: \"Date\", y: \"trend\", stroke: d => d.type=\"Arctic Trend Extent\" , strokeWidth: 3})\n ] }); ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":503,"value":"Plot.plot({\n width: 800, \n inset: 10,\n y: { label: \"mill sq.km\" }, \n x: { tickFormat: Plot.formatMonth() },\n color: { legend: true, \n transform: (x) => x===(d3.max(dataice.array(\"year\"))) ? \"This year\" : x===(d3.max(dataice.array(\"year\"))-1) ? \"Last year\" : \"All Previous years\",\n range: [\"lightgray\", \"#E01F54\", \"#4A1FE0\"] }, \n marks: [ \n Plot.frame(),\n Plot.line(dataice, \n { x: \"month\", y: \"extent\", stroke: \"year\" })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":419,"value":"### Southern hemisphere (Antarctic)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":430,"value":"Plot.plot({ \n y: { label: \"mill sq.km\"}, \n width: 800,\n color: { legend: true, range: [\"lightgray\", \"#4A1FE0\", \"#E01F54\"] },\n marks: [\n Plot.frame(),\n Plot.line(dataices, { x: \"Date\", y: \"extent\", stroke: d => d.type=\"Antarctic Extent\" }), \n Plot.line(dataices, { x: \"Date\", y: \"trend\", stroke: d => d.type=\"Antarctic Trend Extent\", \n strokeWidth: 3 }), \n Plot.line(dataice, { x: \"Date\", y: \"trend\", stroke: d => d.type=\"Arctic Trend Extent\" })\n ] } );","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":657,"value":"Plot.plot({\n width: 800,\n y: { label: \"mill sq.km\" }, \n x: { tickFormat: Plot.formatMonth() },\n color: { legend: true, \n transform: (x) => x===(d3.max(dataices.array(\"year\"))) ? \"This year\" : x===(d3.max(dataices.array(\"year\"))-1) ? \"Last year\" : \"All Previous years\",\n range: [\"lightgray\", \"#E01F54\", \"#4A1FE0\"] }, \n marks: [ \n Plot.frame(),\n Plot.line(dataices, \n { x: \"month\", y: \"extent\", stroke: \"year\" })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":690,"value":"## Ocean heat content\n\nChange in ocean heat content measured as the cumulative energy absorbed by the oceans over time (in zettajoules since 1992). ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":674,"value":"Plot.plot({ \n width: 800, \n color: { legend: true, range: [\"#E01F54\", \"#4A1FE0\"] },\n y: { label: \"+zettajoules\" },\n marks: [ Plot.frame(), \n Plot.areaY(dataocean, { \n x: \"Date\", \n y1: d => d.NH - 2*d.NHse, \n y2: d => d.NH + 2*d.NHse, \n fill: d => d.type= \"Northern Hemisphere\" , \n opacity: 0.3 }),\n Plot.line(dataocean, { \n x: \"Date\", y: \"NH\" , \n stroke: d => d.type=\"Northern Hemisphere\" }), \n Plot.areaY(dataocean, { \n x: \"Date\", \n y1: d => d.SH - 2*d.SHse, \n y2: d => d.SH + 2*d.SHse, \n fill: d => d.type= \"Southern Hemisphere\", \n opacity: 0.3 }),\n Plot.line(dataocean, { \n x: \"Date\", y: \"SH\", \n stroke: d => d.type=\"Southern Hemisphere\" }), \n Plot.ruleY([0])\n ]\n}) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":732,"value":"## Tropospheric temperatures\n\nThese are the average of temperatures recorded between 0km and approximately 18km in altitude. Source from the University of Alabama Huntsville. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":750,"value":"viewof type = Inputs.select( [\"Total\", \"Land\", \"Ocean\"], {value: \"Total\", label: \"Measurement over:\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":804,"value":"Latest global temperature anomaly as at ${d3.timeFormat(\"%B %Y\")(maxdate)} for ${type}. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":786,"value":"viewof region = Inputs.table(datatropo.\n params({ type: type }).\n filter(d => d.Type===type).\n groupby(\"ClimateZone\").\n filter(d => d.Date==op.max(d.Date)).select(\"ClimateZone\", \"trend\", \"TemperatureAnomaly\").\n spread().objects(), { \n multiple: false,\n rows: 20, \n header: { ClimateZone: md`*Climate Zone*`,\n trend: md`*Trend Temperature Anomaly*
(+°C)`, \n TemperatureAnomaly: md`*Actual Anomaly*
(+°C)`} });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":738,"value":"$45","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":808,"value":"maxdate = datatropo.rollup({maxdate: d => op.max(d.Date)}).get('maxdate');","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1020,"value":"## Global sea level rise anomaly\n\nLaboratory for Satellite Altimetry / Sea Level Rise, detrended. \n\nSource: [NOAA NEDIS Star](https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/LSA_SLR_timeseries_global.php)\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1054,"value":"viewof sealregion = Inputs.table(\n dataseal.groupby('Region').\n filter(d => d.Date===op.max(d.Date)).select('Region', 'trend' ).objects(), { \n multiple: false,\n rows: 20, \n header: { Region: md`*Ocean/Sea*`,\n trend: md`*Trend Sea Level Anomaly*
(+mm)`} \n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1030,"value":"Plot.plot({\n width: 800,\n color: { legend: true, range: [\"lightgray\", \"#4A1FE0\", \"#E01F54\"] }, \n y: { label: \"Change (mm)\" },\n marks: [\n Plot.frame(),\n Plot.ruleY([0]),\n Plot.lineY(dataseal.\n params({ region: (sealregion===null ? \"Globe\" : sealregion['Region']) }).\n filter( d=> d.Region===region), \n {x: \"Date\", y: \"Sealevel\" , stroke: d => d.type=\"Anomaly\" }),\n Plot.lineY(dataseal.\n params({ region: (sealregion===null ? \"Globe\" : sealregion['Region']) }).\n filter( d=> d.Region===region), \n {x: \"Date\", y: \"trend\", stroke: d => d.type=\"Trend\" , strokeWidth: 3 }), \n Plot.lineY(dataseal.filter( d=> d.Region===\"Globe\"), \n {x: \"Date\", y: \"trend\", stroke: d => d.type=\"Global Trend\" , strokeWidth: 3 })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":327,"value":"# Other","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":196,"value":"import { aq, dt, op } from '@uwdata/arquero'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1090,"value":"import {toc} from \"@mbostock/toc\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":410,"value":"mthformat = d3.timeFormat(\"%B %Y\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1157,"value":"Date.prototype.addYears = function(years) { \n var date = new Date(this.valueOf());\n date.setFullYear(date.getFullYear() + years);\n return date;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":415,"value":"aq.addFunction(\"mthformat\", mthformat); ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":267,"value":"datanino = aq.fromCSV(await fetch(\"https://raw.githubusercontent.com/kletts/climate/master/data/nino.csv\").\n then(res => res.text()));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":298,"value":"datasst = aq.fromCSV(await fetch(\"https://raw.githubusercontent.com/kletts/climate/master/data/sst.csv\").\n then(res => res.text()));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":313,"value":"dataice = aq.fromCSV(await fetch(\"https://raw.githubusercontent.com/kletts/climate/master/data/ice.csv\").\n then(res => res.text())).\n derive({ month: d => op.month(d.Date), \n year: d => op.year(d.Date)});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":424,"value":"dataices = aq.fromCSV(await fetch(\"https://raw.githubusercontent.com/kletts/climate/master/data/ices.csv\").then(res => res.text())).\n derive({ month: d => op.month(d.Date), \n year: d => op.year(d.Date)});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":669,"value":"dataocean = aq.fromCSV(await fetch(\"https://raw.githubusercontent.com/kletts/climate/master/data/ocean.csv\").\n then(res => res.text()));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":734,"value":"datatropo = aq.fromCSV(await fetch(\"https://raw.githubusercontent.com/kletts/climate/master/data/tropo.csv\").\n then(res => res.text()));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1008,"value":"dataseal = aq.fromCSV(await fetch(\"https://raw.githubusercontent.com/kletts/climate/master/data/seal.csv\").\n then(res => res.text()));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1246,"value":"datacsst = aq.fromCSV(await fetch(\"https://raw.githubusercontent.com/kletts/climate/master/data/csst.csv\").\n then(res => res.text())).filter(d => d.period > op.datetime(1880,1,1));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":695,"value":"

\n $\"Creative$
This work is licensed under a Creative Commons Attribution 4.0 International License.\n