adaptation committee ac18/tp/7b · adaptation committee ac18/tp/7b 3 of 39 8. an indispensable...

Technicalpaperondataforadaptationatdifferentspatialandtemporalscales

TableofContents Executivesummary 2 Background 3 Introduction 3 Overviewofdatacategoriestosupportadaptation 4 Provisionofdataforadaptationacrossdifferentspatialscales 12 Opportunitiestofurtherenhancedataprovisionanduse 29 Conclusions 35

Internationalarrangementsforcoordinationandcollaborationontheprovisionofclimatedataandservices 36

AdaptationCommittee AC18/TP/7B 15October2020Version01.0

RecommendedactionbytheAdaptationCommittee

TheAdaptationCommittee(AC),atits18thmeeting,willbeinvitedtotakenoteofthistechnicalpaperwhichwasfinalizedaftertheAC’sinformalstocktakemeetinginAugustandisnowintheprocessofbeingconvertedintoauser‐friendlypublication.

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Executivesummary1. Thedemandfordataforadaptationisgrowinginresponsetodifferentpoliticalandpracticalneeds.ThroughtheParisAgreement,PartiestotheUNFCCChavecalledforastrengtheningofglobalcooperationtoensurethatadaptationactionisbasedonandguidedbythebestavailablescience.Atnationalandassociatedsub‐nationalscales,adaptation,followingtheprocessofformulatingandimplementingnationaladaptationplans(NAPs)orotherplansandstrategies,hasenteredtheplanningandimplementationstage.Thisrequiresincreasinglydiversifiedandspecializeddataandrelateddataproducts.Theobjectiveofthepaperistoprovideanoverviewofthecategoriesofdatathatarerequiredforeffectiveadaptation,theformsinwhichthesedataarecurrentlyprovidedatdifferentscales,remaininggapsandchallengesandopportunitiestoenhancetheprovisionanduseofsuchdata.2. Effectiveadaptationtoclimaterisksfollowsacontinuousanditerativeprocessconsistingofthefollowingstages:(i)assessingclimaterisks,(ii)planningadaptation,(iii)implementingadaptationmeasures,and(iv)monitoringandreviewingsuchmeasures.Implementingthesestagesrequiresdifferentcombinationsofobservational,projected,andhistoricaldataofbothclimateandsocio‐economicprocesses.3. Observationaldatasupportsallstagesoftheadaptationprocess.Itincludesobservationsoftheatmosphere,landandoceanaswellasofsocio‐economicprocesses.Projecteddataismainlyrequiredduringtheplanningstageandisprovidedintheformofforecasts,predictions,andprojections,whichmeettheneedsforshort‐,medium‐,andlong‐termplanning,respectively.Historicaldatacomplementsrecentobservationsandoutlookstoformthebasisofunderstandingclimateprocessesandtheirimpactsatallscalesfromthepastintothefuture.4. Actorsatdifferentspatialscalescontributetotheglobalprovisionofobservational,historicalandprojectedclimateandsocio‐economicdata.NationalMeteorologicalandHydrologicalServices(NMHS)playacentralroleinthisregardandcloselycooperatewithinternationalandregionaldatacentres.Theprovisionofclimatedata,inparticular,guidedbythevisionofglobalsharingandopenaccess,isfacilitatedthroughinternationalcoordination,thesettingofqualitystandardsandcapacitybuildingwhilesocio‐economicdataisprovidedbyamultitudeofsourceswithoutglobalcoordinationundertheclimateregime.Inmostcasesdataisfurtherprocessedintoavarietyofdataproductsthatmeetdifferentuserneedsacrossspatialandtemporalscalesandcanalsoassistinclosinggapsinobservationalcoverage.Local‐levelstakeholdersplayanincreasinglyimportantroleincomplementingorvalidatingtop‐downgeneratedproductswithlocalknowledgeandexperience.5. Importantgapsandchallengesremainwithregardtoallcategoriesofadaptationdata.Insituobservationsystemsarestilllackinginsomeregionsoftheworld,mostcriticallyinregionswherepopulationsareatelevatedrisks,suchasincoastalareas,orwherelocalchangeshaveglobalimpacts,forexamplethemeltingofice‐sheetoutletglaciersanditscontributiontosea‐levelrise.Modelleddataishardlydownscaledtolocallevelswhereitismostlyneeded.Theinterpretationofdataisafflictedwithmajorchallengesthatrelatetorathertechnicalissuesaroundthemeasurementsandmodelsbutalsotodeepuncertaintiesregardingfuturedriversofclimatechange,theresponseoftheatmosphereandtheeffectivenessofadaptationmeasuresinlightofchangingnormsandvalues.Ensuringthequalityofdataisalsobecomingincreasinglydifficultasdemandsforthetimelinessofdatasupplyanditsavailabilityatvariousscalesandforevermorespecificadaptationsituationsareontherise.6. However,therearealsoimportantopportunitiestofurtherenhancedataprovisionanduse.Provisioncouldbeincreasedbyfocusingonthreeaspects:(i)exploitingthebigdatathatisyettobeproducedthroughinnovativesolutionslikemachinelearningandcloudandedgecomputing;(ii)furtherensuringopenaccesstoallexistingdata,includingthroughtherescueofhistoricaldataandtheliftingofrestrictivedatapolicies;and(iii)closingremainingdatagapsthroughlong‐termfundingofinsituobservationalsystemsandinnovativewaysofprovidinginterdisciplinarydataandinformationforlocaladaptationdecision‐making.7. Capacitydevelopmentofbothprovidersandusersofdataincountrieswhereitisneededwouldenhanceboththeprovisionanduptakeofdata.Thiswouldincludetheimprovementofinfrastructurerequiredtotransferlargeamountsofdataaswellasthetrainingofpersonnelforthegeneration,downscaling,storageandmanagementaswellastheinterpretationofhigh‐qualitydatathatisrelevanttolocalcontexts.

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8. Anindispensableaspectofencouragingdatauseistheprovisionofguidanceindealingwithuncertaintythatisanintrinsicpartofdataanditsinterpretation.Therearevariousapproachesandmethodsthatcanhelpinmanaginguncertainty,rangingfromiterativeriskmanagementandparticipatoryapproachestomulti‐criteriaanalysisandrobustdecision‐making.Supportinapplyingthesetoolsshouldbeanimportantcomponentoftheprovisionofclimateservices.9. Climateservicesisaconceptthatisintendedtobuildabridgebetweenscientificallygenerateddatasupplyandthedemandofendusersforinformationthatsupportsadaptationdecision‐makingthroughacloseinteractionofdataproviders,serviceprovidersandendusers.Itisstillayoungfieldwithmostlyuncoordinatedactorsandactivitiesbutbearsgreatpotentialoncefullydeveloped.10. Inconclusion,high‐qualityclimateandsocio‐economicdataareessentialatallstagesoftheadaptationprocessandagreatamountofdataalreadyexistsorisunderwayintheformofbigdata.Theglobaltaskistomaketheinformationfromthisdataavailableinaformandatscalesthatsupportsadaptationdecisionmakingatalllevelsandinallregionsoftheworld.Meanwhile,uncertaintieswillremainaninherentpartofusingdatafordecision‐makingandwillneedtobemanaged,nottriedtoovercome.

Background11. TheAdaptationCommittee(AC),atitsfifteenthmeeting,agreedtoprepareapaperonconnectingshort‐,medium‐andlong‐termadaptationplanningatthenationalandsubnationallevel,includingdata,financialflowsandothers.Thispaperwasincludedinitsflexibleworkplanfor2019‐2021.12. Atitssixteenthmeeting,theACconsideredaconceptnoteforthisnewpaperanddecidedtoreduceittofocusonlyondata,takingintoaccountshort‐medium‐andlong‐termperspectivesandlinkagesofdataneedstovariousstagesintheadaptationprocess.Itrequestedthesecretariattopresentadraftforconsiderationatitsseventeenthmeeting.13. AtitsseventeenthmeetingtheACagreedtorevisethepresenteddraft,takingintoconsiderationcommentsprovidedbyACmembersandobserversduringthemeetingandinter‐sessionally,aswellascommentsprovidedbypertinentorganizationsduringapeerreviewprocess.

Introduction14. Thedemandfordataforadaptationisgrowingduetodifferentpoliticalandpracticalneedsatvariouslevels.15. Atthegloballevel,theParisAgreementincludesseveralprovisionswhichimplicateincreasingneedsforadaptation‐relevantdata.InArticle7oftheAgreement,PartiesacknowledgedthatadaptationactionshouldbebasedonandguidedbythebestavailablescienceandthatPartiesshouldstrengthentheircooperationinthisregard.1Theseprovisionsbuildonthosecontainedinthe2010CancunAdaptationFramework,throughwhichPartieswereinvitedto“[…]strengthendata,informationandknowledgesystems[…]and“improvingclimate‐relatedresearchandsystematicobservationforclimatedatacollection,archiving,analysisandmodellinginordertoprovidedecisionmakersatthenationalandregionallevelswithimprovedclimate‐relateddataandinformation”.2TheyarealsosupportedbyongoingworkunderUNFCCCArticle4,paragraphs1(g)and(h)andArticle5andtheagendaitemonresearchandsystematicobservationundertheSubsidiaryBodyforScientificandTechnologicalAdvice.16. Atthenationalaswellasregionalandsubnationallevels,stakeholdershaverealizedadaptationneedsandhave,todifferentdegrees,enteredintoadaptationplanningandimplementationprocesses.Theprocesstoformulateandimplementnationaladaptationplans(NAPprocess),establishedundertheCancunAdaptationFrameworkoftheUNFCCCin2010,3isincreasinglybeingtakenupbycountries.Theseadaptationprocessesrequiredatainordertobeplanned,implementedandmonitored,andtobescaleduponthebackingofevidenceoftheirefficiencyandeffectiveness.

1Article7,paragraphs5,7(a)and(c)oftheParisAgreement.2Decision1/CP.16,paragraph14(h)and(i).3Decision1/CP.16,paragraphs15‐18.


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17. Consideringthesedevelopments,thepaperprovidesanoverviewofdatarequiredandprovidedforadaptationacrossdifferentscales.Itstartsoffbyintroducingkeycharacteristicsofclimaterisksandderivingdifferentcategoriesofdatarequiredattheiterativestagesoftheadaptationprocess(chapter4).Chapter5describesthesourcesandprocessesthroughwhichthesecategoriesofdataareprovidesacrossdifferentspatialscalesincludingremaininggapsandchallenges.Finally,chapter6describesopportunitiestofurtherenhancedataprovisionanduse.

Overviewofdatacategoriestosupportadaptation18. InitscontributiontotheFifthAssessmentReport(2014),WorkingGroupII(WGII)oftheIntergovernmentalPanelonClimateChange(IPCC)describestwocharacteristicsofclimaterisksthatarekeywhenderivingthecategoriesofdatarequiredtoadequatelysupportadaptationtotheserisks.419. Thefirstcharacteristicisthecompositenatureofclimaterisks:climaterisksexistduetotheinteractionofclimate‐relatedhazards(includingsudden‐onseteventsandtrends)withtheexposureandvulnerabilityofhumanandnaturalsystems.Hazards,exposureandvulnerability,inturn,aredrivenbyclimateandsocio‐economicprocesses.Takingadaptationdecisionsinordertoreduceclimaterisksthusrequiresdataandinformationonbothclimateaswellassocio‐economicaspects.520. Thesecondcharacteristicisthedynamicandcomplexnatureofclimaterisks:climaterisksareevolvingovertimeduetochangesinboththeclimateandsocio‐economicsystems.Inordertoaccountforthesechanges,adaptationtosuchrisksmustbeacontinuous,progressiveanditerativeprocess.6Itisoftendescribedascontainingfourcoreandrevolvingstages:(i)assessingclimaterisks,(ii)planningadaptation,(iii)implementingadaptationmeasures,and(iv)monitoringandreviewingsuchmeasures(figure1).Effectiveimplementationofthesestagesrequiresinformationfromobservationsandprojectionsofclimateandsocio‐economicprocessesaswellasfromexperiencewithpastclimateimpactsandrespectivesocio‐economicresponses.21. Thecomposite,dynamicandcomplexnatureofclimaterisksthusleadstothefollowingcategoriesofdatarequiredtosupportadaptation:observational,projectedandhistoricaldataofclimateandsocio‐economicprocesses.Thisdataneedstobecollectedcontinuouslyandmadeavailableatspatialandtemporalresolutionsadequatetosupporttheadaptationprocessatdifferentspatialscales7andfordifferentplanninghorizons.

4Field,C.B.etal.2014.ClimateChange2014:Impacts,Adaptation,andVulnerability.PartA:GlobalandSectoralAspects.ContributionofWorkingGroupIItotheFifthAssessmentReportoftheIPCC.Availableathttps://www.ipcc.ch/report/ar5/wg2/.

5Notethatthispaperfocusesprimarilyonclimateandsocio‐economicdataasopposedtoinformation.Climatedata,forexample,isdefinedas“historicalandreal‐timeclimateobservationsalongwithdirectmodeloutputscoveringhistoricalandfutureperiods”(WMO.2014.ImplementationPlanoftheGlobalFrameworkforClimateServices.).However,asrawobservationsandmodeloutputsneedtobeprocessedintoinformationinordertobeusefulforadaptationdecision‐making,thepaperalsotouchesupondataproductsandclimateserviceswhichrepresentafluenttransitionfrompuredatatoclimateinformationandknowledge.

6AsagreedbyPartiestotheUNFCCCindecision5/CP.17,paragraph2.7Scalesrefertoanalyticaldimensionsusedtostudyadaptationandmayincludespatial,temporal,institutional,orjurisdictionalscales.Eachscalecanbecomprisedofmultiplelevels.Withregardtospatialscalestheserangefromlocaltoglobal.Withregardtotemporalscalestheserangefromminutestocenturies.Source:Field,C.B.etal.2014.Technicalsummary.In:ClimateChange2014:Impacts,Adaptation,andVulnerability.PartA:GlobalandSectoralAspects.ContributionofWorkingGroupIItotheFifthAssessmentReportoftheIPCC.Availableathttps://www.ipcc.ch/report/ar5/wg2/.


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Figure1.Theiterativeadaptationprocessincludingfourcorestagesandrespectivedataneeds

Source:AdaptedfromUNFCCC.2019.25YearsofAdaptationundertheUNFCCC.ReportbytheAdaptationCommittee.Availableathttps://unfccc.int/sites/default/files/resource/AC_25%20Years%20of%20Adaptation%20Under%20the%20UNFCCC_2019.pdf.

4.1. Observationaldata22. Observationaldataprovidesevidenceoftherecentorpresent‐daysituationoftheclimateaswellasonenvironmentalandsocio‐economicconditions.Itthushelpstoidentifytheclimatevulnerabilityofaparticularsocialornaturalsystem(assessmentstageoftheadaptationprocess).8Thisdataisalsousedtoestablishclimateandsocio‐economicbaselineswhenmodellingfutureconditionsandasreferenceswhenevaluatingadaptationoptions(planningstage).Duringtheimplementationofadaptationmeasures,observationaldataiscomparedtothepriorprojectionsandhenceservestomonitortheeffectivenessofthemeasures(implementationandmonitoringstage).Observationsthereforebuildthecoreofthedatarequiredtosupporttheadaptationprocess.23. InordertosystematicallyobservechangesintheclimatesystemexpertpanelsoftheGlobalClimateObservingSystem(GCOS)haveidentifiedasetof54EssentialClimateVariables(ECVs)(seefigure2).9Dataonthesearecollectedthroughreal‐timeobservationsoftheatmosphere,landandoceanviainsituorremote‐sensingmeasurements.10

8Notethatthispaperwillfocusondatafortheadaptationofsocio‐economicsystemsandwillnotincludespecificdatarequirementstoassessclimaterisksfornaturalsystemsandtheiradaptationneeds.

9MoreinformationonGCOSandtheECVsisavailableathttps://gcos.wmo.int/.10ForadetailedoverviewofexistingECVdatarecordsseehttps://gcos.wmo.int/en/essential‐climate‐variablesandhttp://climatemonitoring.info/ecvinventory/.

Observationalandhistoricaldataofclimateandsocio‐economicprocesses

Observationalandprojecteddataofclimateandsocio‐economicprocesses

Observationaldataofclimateandsocio‐economicprocesses

Observationaldataofclimateandsocio‐economicprocesses


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Figure2.TheGCOSEssentialClimateVariablestoobservetheatmosphere,landandocean

Source:Availableathttps://gcos.wmo.int/en/essential‐climate‐variables/ecv‐factsheets.24. Examplesofgeneralandsector‐specificobservationalsocio‐economicdataincludethefollowing:11

a) Populationdatathatreflectstotalnumber,distribution,structureandinequalitiesincluding,forexample,totalpopulation,populationdensity,urbanpopulation(includingincoastalcities),ageandgenderstructure,ethnicandreligiousaffiliation;

b) Economicdatathatreflectswealthanditsdistributionincludinggrossdomesticproduct(GDP)percapita,GDPannualgrowthrateandGDPfromclimate‐sensitivesectorslikeagriculture,industryandservices;

c) Landcover/landusedatathatreflectsthedistributionoflanduseincludingtotallandarea,arableandpermanentcropland,permanentpasture,forestandwoodland,otherland;

d) Dataonwaterthatreflectswaterresourcesanduseincludingwaterresourcespercapita,annualwithdrawalfordomestic,industrialandagriculturaluse;

e) Dataonagriculture/foodthatreflectsthesocio‐economicvalueoftheagriculturalsectorincludingirrigatedland,agriculturallabourforce,totallabourforce,stocksofdifferentproductionanimals.

25. Eachadaptationcontextrequiresaparticularcombinationoftheseobservedclimateandsocio‐economicdatatoimplementthedifferentstagesoftheadaptationprocessandmightoftenneedmorespecificdatathatwouldhavetobecollectedfortheindividualadaptationactivity.

11BasedonIPCCTGICA,2007,GeneralGuidelinesontheUseofScenarioDataforClimateImpactandAdaptationAssessment.Availableathttp://www.ipcc‐data.org/guidelines/index.html#generalwhichdisseminatessocio‐economicdataandinformationdescribingthepresent‐daysituationwhichservesasthebasistoderivesocio‐economicscenariosforthefuture.


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4.2. Forecasts,predictionsandprojections26. Whileobservationalclimateandsocio‐economicdataisrequiredtoassesscurrentvulnerabilitiesandtomonitortheimplementationprocess,theplanningoffutureadaptationactivitiesrequiresestimationsofhowtheclimateandsocio‐economicfuturemayunfold.Inordertosupportthefullrangeofadaptationactivitiesacrossalltemporalscales,fromshort‐termimplementationofevacuationplanstolong‐termpolicyplanning,differentkindsofweather,climateandsocio‐economicoutlooksarerequired.Theprovisionoftheseissometimescalledseamlesshydro‐meteorologicalandclimateservices(seefigure3).

Figure3.Seamlesshydrometeorologicalandclimateservices

Source:WMO.ClimateServicesforSupportingClimateChangeAdaptation.SupplementtotheTechnicalGuidelinesfortheNAPProcess.Availableathttps://www4.unfccc.int/sites/NAPC/Documents%20NAP/Supplements/WMO_climate%20change%20services%20for%20climate%20change%20adaptation.pdf.27. Therangeofclimateoutlooksareproducedbyvariousclimatemodelsaroundtheglobeandcanbesummarizedintothefollowingthreemaincategories:12

a) Weatherforecastsmakeuseofenormousquantitiesofinformationontheobservedstateoftheatmosphereandcalculate,usingthelawsofphysics,howthisstatewillevolveduringthenextfewdays.Weatherforecastsareparticularlyrelevantinthecaseofextremeweatherthatrequirestheimplementationofanevacuationplanorothersafetymeasures.Theyprovidethebasisforissuingalerts.

b) Climatepredictionsareoutputsofamodelthatcomputestheevolutionoftargetedparametersfrominitialconditionsuptothefinalstateatseasonal,annualordecadaltimescales.Theyare

12Thedescriptionsarebasedonthefollowingsources:IPCCTGICA,2007,GeneralGuidelinesontheUseofScenarioDataforClimateImpactandAdaptationAssessment.Availableathttp://www.ipcc‐data.org/guidelines/index.html#generalandWMO.2018.GuidetoClimatologicalPractices(WMO‐No.100).Geneva.Availableathttp://www.wmo.int/pages/prog/wcp/ccl/guide/guide_climat_practices.php.


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mostinfluencedbythecurrentconditionsthatareknownthroughobservations(initialconditions)andthephysicalprocessesthatwilldeterminefutureevolutionsofclimatevariability.Theydonottakeintoaccountassumptionsorscenariosofhumaninfluenceontheclimate(forcings).Thisisduetothefactthatprojectedlevelsofglobalmeantemperatureincreaseunderdifferentemissionscenariosonlybegintodivergeafter2040whereastheystayapproximately.thesamebeforethatyear.Theperioduntil2040isthereforecalledthenear‐termeraofcommittedclimatechange.13Duringthisperiod,climateimpactscanonlybeinfluencedbyadaptationmeasures,whichisanimportantinformationfordecisionmakers.Mostpredictionsareprobabilistic,thusconsistingofseveralindividualforecastsfromaclimatemodelstartingwithslightlydifferentinitialconditions(bothatmosphericandoceanic)andgeneratingaset(orensemble)offorecasts.Thisway,uncertaintieslinkedtothepredictionareaccountedfor.Predictionsareproducedatdifferenttimeintervals(e.g.monthlyorseasonally)andareusefulformedium‐tolonger‐termstrategicadaptationplanning,suchasinfrastructureplanningandlandzoning.

c) Climateprojectionsarestatementsaboutthelikelihoodthatsomethingwillhappenseveraldecadestocenturiesinthefuture,ifcertaininfluentialconditionsdevelop(e.g.significantchangesintheboundaryconditionsthroughhumaninfluence,forexampletheincreaseingreenhousegasconcentrations).Thus,climateprojectionsprovideindicationstopolicymakersonhowcertainpolicy‐drivenactionmightinfluencefutureoutcomes.Forthis,scenariosaredevelopedcontainingcertain(plausible)assumptionsaboutfutureclimateandsocio‐economicdevelopmentswhichthenserveastheinitialconditionsinaclimatemodel.Theoutputsofthemodelthenprovideinformationonthelikelyconsequencesofsuchdevelopmentsintermsofclimatechange.Byusingdifferentscenariosanddifferentmodels,eachwithitsownparticularclimatesensitivity,arangeofreasonablepossibilitiesofbothsocietaldevelopmentandclimatebehaviourisaccountedfor.Theselong‐termprojectionsareparticularlyrelevantforclimatenegotiationsandtheirimplicationsfornationaladaptationpolicymaking.

28. Theclimateelementstypicallyforecastedinallcategoriesofclimateoutlooksincludesurfaceairtemperatureandtotalprecipitation,butincreasinglyalsoincludeobjectiveseasonaltosub‐seasonalpredictionsincludingotherparameterssuchasthenumberofdayswithprecipitation,snowfall,thefrequencyoftropicalcyclonesandtheonsetandcessationofmonsoonseasons,whichareneededforincreasinglyspecificadaptationplanning.29. Socio‐economicvariablesarenotaccountedforbyweatherforecastsandclimatepredictions.Assuch,thesemustbeassessedandpredictedseparatelywhenundertakingimpactstudiesandplanningadaptationfortheshort‐tomedium‐term.Suchpredictionscouldincludethoseonpopulationtrends,landusescenarios,orincomedevelopments.Climateprojections,however,doalreadyincludeassumptionsonsocio‐economicdevelopmentsasthesearecontainedintheglobalscenariosthatformtheirbasis.Thesamesocio‐economicassumptionsshouldthenbeappliedforlong‐termclimateimpactstudiesinordertoensureconsistencyinthedataused.14

4.3. Historicaldata30. Historicaldataformspartofobservationaldatabuthasbeenrecordedbyinsituinstrumentsbeforethedigitalrevolution40‐50yearsagoandisthereforestoredonpaperorotherobsoletemedia.Accesstohistoricaldataisnonethelessessentialtounderstandthecontextofchangesinclimateanditsextremes,andtoframethesensitivityofman‐madeandnaturalsystemstoclimatevariabilityandextremesasaguidetofutureadaptation.1513Field,C.B.etal.2014.ClimateChange2014:Impacts,Adaptation,andVulnerability.PartA:GlobalandSectoralAspects.ContributionofWorkingGroupIItotheFifthAssessmentReportoftheIPCC.Availableathttps://www.ipcc.ch/report/ar5/wg2/.14IPCCTaskGrouponDataandScenarioSupportforImpactandClimateAssessment(TGICA).2007.GeneralGuidelinesontheUseofScenarioDataforClimateImpactandAdaptationAssessment.Version2.Availableat:http://www.ipcc‐data.org/guidelines/index.html#general.

15WebsiteoftheInternationalDataRescuePortal(I‐DARE),availableathttps://idare‐portal.org/content/importance‐dare.


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31. Historicalclimatedataisusedtoreconstructpastclimates,providinginsightsintothedriversofclimatechange,facilitatingparameterizationofclimateprocessesinGCMsandtheevaluationoftheperformanceofglobalclimatemodels,andtherebyprovidingabasisforpredictingthefutureclimate.LongtimeseriesofdatahavealsoprovedvitalindemonstratingchangeandattributingthischangetohumanandnaturalcausesinIPCCAssessmentReports.32. Historicaldatamayalsoprovideinformationonthewaypastclimateeventshaveimpactedsocialsystems,andthusonthevulnerabilityandadaptivecapacityofsuchsystems.Thistemporalanaloguetechniqueallowsforthecharacterizationofhowhumansystemsmanageandexperienceclimaterisks,theidentificationofthoseprocessesandconditionswhichdeterminetheefficacy,availability,andsuccessofpastandpresentadaptations,thedevelopmentofagreaterunderstandingofhowsocialandbiophysicalprocessesshapevulnerability,andtheestablishmentofarangeofpossiblesocietalresponsestofutureclimatechange.16Ifcombinedwitheffectiveshort‐termforecasting,thisinformationmightoftensufficetoderiveusefulcopingstrategiesforsimilarfutureevents,thusmakingcostlyclimatepredictionsunnecessary.33. Historicaldatathussupportstheassessment,theplanningandtheimplementationstagesoftheadaptationprocessanditisimportantthatasmuchaspracticallypossibleoftheconsiderableamountofearlyinstrumentaldataontemperature,precipitationandothervariablesberecovered.

4.4. Quantitativeandqualitativedata34. Undereachcategoryofdataforadaptation‐observational,projectedandhistorical–quantitativeandqualitativedatacanbuildusefulcomplements.35. Quantitativedataisgeneratedbyobservationalsystems,statistics,censusesandmodelsand,ifproducedandsharedtransparentlyandaccordingtointernationalstandards,canprovideobjectiveinformationthatisrelativelyeasytocompareintimeandspaceanddespitelinguisticand/orculturaldifferences.Withtheaidoftechnology,hugeamountsofsuchdatacanbegenerated,processedandstoredwithmuchlowerlevelsofinvestmentrequiredintermsoftimeandworkforcecomparedtoqualitativedata.However,boththegenerationandinterpretationofquantitativedatarequirescertainskillsanddataliteracyandtheirapplicationisaccompaniedbyacertainlevelofuncertainty(seesections5.5and6.3).Inaddition,importantgapsremaininquantitativedataregardingitscoverageacrossregionsandscaleswhichcanbeanobstacletoeffectiveadaptationplanning.36. Inthesecases,qualitativedataandinformation,whicharenarrativedescriptionsofpast,presentandfutureconditionsandtheirinteractions,canformanimportantcomplementtoquantitativedata.Itismainlyobtainedthroughexpertandotherstakeholderconsultationsanddialogueusingavarietyofparticipatorymethodssuchassurveys,interviewsandgroupdiscussions.Itcanalsotaketheformofprocess‐basedunderstanding,simulationsanddescriptivemodels.17Particularlyinthecaseofhistoricaldata,itcanbeobtainedviatheanalysisofnewspaperarticlesandothertypesofarchives.37. Alongsidefillinggapsinquantitativedata,qualitativedatahelpsinunderstandingaspectsthatarenotquantifiable,includingexperience,judgements,priorities,emotions,behaviour,worldviewsandnon‐monetaryvalues.Theparticipatorynatureofitsgenerationenablestheinvolvementofthosewithinfluenceovertheadaptationprocessorthoseaffectedbyit,particularlyalsoatlocallevels,andopensupopportunitiesofstimulatingtheirinterest,understandingandbuy‐in.Itsupportsthe“co‐production”ofknowledgeamongexpertsandusers.Finally,qualitativedatacanbesuccessfulinconveyinginformationtonon‐specialistsandmakingquantitativedataeasiertounderstand,includingaddressingissuesofuncertainty.18Thus,althoughmoretime‐consumingandsubjective,theinvolvementofqualitativedatainadaptationplanningprocesses,ifgeneratedskilfully,mayleadtomoreeffectiveandsustainableoutcomes.16Ford,J.D.etal.2010.Casestudyandanaloguemethodologiesinclimatechangevulnerabilityresearch.WileyInterdisciplinaryReviews:ClimateChange,1(3).374–92.17Field,C.B.etal.2014.Technicalsummary.In:ClimateChange2014:Impacts,Adaptation,andVulnerability.PartA:GlobalandSectoralAspects.ContributionofWorkingGroupIItotheFifthAssessmentReportoftheIPCC.Availableathttps://www.ipcc.ch/report/ar5/wg2/.18UNEP.2013.PROVIAGuidanceonAssessingVulnerability,ImpactsandAdaptationtoClimateChange.ConsultationDocument.


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38. Anexampleofusingqualitativedatafortheplanningofadaptationisthe“bottom‐up”approachtoprojectionsforclimateimpactstudies.Thisapproachinvolveslocalresourcemanagersanddecision‐makerswithaccesstolocalknowledgealongsidequantitativeobservationalandhistoricaldatawhenassessinghowclimatechangemightaffectcertainpolicyplansandgoals.Thisinvolvementoflocalexpertiseinevaluatingprojectionsisincreasinglyrecognizedasbeingmoreeffectivethansimplydownscalingglobalmodeloutputs(foramoredetaileddescriptionofthisapproachseebox3).

4.5. Datarequirementsatdifferentspatialscales39. Adaptationplanningandimplementationoccursatvariousspatialscalesincludinglocal,national,andregional.Ateachofthesescales,variouscombinationsofclimateandsocio‐economicdataatdifferentspatialandtemporalresolutionsarerequiredalongtheadaptationprocessandforincreasinglyspecificadaptationcontexts.Thereby,amatchingofspecificresolutionsofdatatospecificlevelsofdecision‐makingisnotpossible.Rather,decision‐makersatdifferentlevelsrequirethedatathatbestdescribesthefactorsandprocessesthatarerelevantfortheirrespectivedecision‐makingcontext.40. Forexample,acitymanagerwhosecityislocatedalongariverthatispronetorecurrentflooding,requireshistorical,observedandprojectedhazardandvulnerabilitydatawhichallowshimtotakedecisionsregardingforexampleurbanplanning,protectiveinfrastructure,resettlements,sectoralriskmanagement,earlywarninganddisasterriskfinancing(seefigure4).Thisdatawouldneedtobeatratherhighresolutiontoreflectthelocaltopographyaswellasthesocialandeconomicparticularitiesofthearea.Thesamedatawouldbeimportantforanationaladaptationplannerwhoisresponsiblefornationaldisasterriskfinancing.However,hewouldonlybeinterestedintheaggregatedvaluesatriskandevaluatethatagainstotherdisasterrisksinthecountryinordertoplantotalamountsandtypesofriskfinancingandtheirallocation.Incasetherivercrossedseveralcountries,thefloodriskplanningwouldatbestbeundertakeninatransboundaryapproachinordertoestimatelocalstreamflowsaswellasthelikelyimpactsofsuchflowsandrespectiveadaptationmeasuresonadjacentlocalitiesinthemostefficientandeffectivemanner.Forthis,theregionallevelwouldrequireregionalclimatedatacombinedwithsocio‐economicdatafromthedifferentlocalitiesalongtheriver.Thisinformationwouldallowforaregionaladaptationapproachorplanthatcouldsubsequentlybeimplementedateachoftheinvolvednationaland/orlocallevels.

Figure2.Exampleofhazardandvulnerabilityanalysesrelatedtofloodriskandaccordingdatarequirementsforadaptationdecision‐making,asfacilitatedbytheGlobalFrameworkforClimateServices

Source:WMO.ClimateServicesforSupportingClimateChangeAdaptation.SupplementtotheTechnicalGuidelinesfortheNAPProcess.Availableathttps://www4.unfccc.int/sites/NAPC/Documents%20NAP/Supplements/WMO_climate%20change%20services%20for%20climate%20change%20adaptation.pdf


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41. Thus,ingeneral,thelocalleveliswherehighresolutionanddisaggregationofdataismostlyneeded.Climatedataonmaximum/minimumtemperatureandprecipitationmayberequiredforspecifictimesofthedayorperiodsoftheyeartoidentifytheassociatedextremes,theirfrequency,intensityandchangingtrends.Itmayalsoberequiredatspecificspatialresolution,e.g.urbanenvironmentswhereincreasingpartsoftheworldpopulationreside,andclimateimpactstakeparticularshapes.Similarly,complementarysocio‐economicdataisneededatthesescalestoallowforspecificimpactandvulnerabilitystudies.Generalpopulationtrendsforthenationallevel,forexample,maymaskimportanttrendsinmigrationatlocalscales,e.g.fromruraltourban,andnationally‐averagedscenariosofpercapitaincomemayobscurelocaldisparitiesbetweenrichandpoorormenandwomen.42. Apartfromtheappropriateresolution,thelocallevelmightrequiredatainadditiontowhatisrequiredattheregionalornationallevels.Thismayinclude,forexample,informationonspecificweatherorair‐qualityvariablessuchasonthefrequencyandintensityoffog,whichisnotincludedinglobalECVobservations.1943. Thenationallevelusuallyoperatesfromamoreholisticviewpoint,tryingtoidentifythevariousclimaterisksacrossthecountryandallocatingrespectiveresourcesbyweighingdifferentadaptationneeds.Adaptationplannersatthislevelthusnotonlyneedtotakeintoaccountgeneralclimateandsocio‐economicpatternsoftheircountryasawhole,butalsoconsiderparticularsectorsorareasatriskfromspecifichazardsortrends,suchasfloods,ElNiñoorsealevelrise.Inaddition,theyneedtoaddressregionalinterestswhichconcern,forexample,transboundaryecosystems.Assuch,theydonotonlyrequiredataatnationalresolution,butalsodatafromlocalandregionalscales(seebox1forthetypesofrequireddataandexistingsourcesfornationaladaptationplanninginSt.Luciaaswellasalistoffurtherspecificdataneeds).Thereby,thelevelofaggregationordisaggregationoftherequireddatadependsonthespecificdecisiontobetaken.44. Adaptationplanningattheregionallevelhastwomainobjectives:preventingunintendedtrans‐nationaleffectsofunilateralmeasuresandincreasingtheefficiencyandeffectivenessofadaptationbysharingresources,informationandexperienceamongnationsandlocatingmeasureswheretheyyieldoptimumbenefit.Inordertomeettheseobjectives,adaptationplanningatthisleveldependsnotonlyonregionalclimatedata,butalsoonclimateaswellassocio‐economicdatafromthenationaltolocallevels,particularlyfromthekeyvulnerablesectors,oftheinvolvedcountries.45. Ingeneral,actorsatanyoftheselevelsthatoperateacrossdifferentscalesandworldregions,e.g.bybeingpartofglobalsupplychains,requiredatafromdifferentgeographicalregionsandscalesinordertoeffectivelyadapttoclimateimpacts.

4.6. Aspecialcase:datarequirementsatthegloballevel46. Thegloballevelisaspecialcaseregardingdatarequirementsforadaptationsinceatthisleveldataisnotrequiredtoplanindividualadaptationmeasuresbuttoreviewthecollectiveprogressonadaptationbyallcountries.47. ThroughArticle7oftheParisAgreement,PartiestotheUNFCCChaveestablishedtheglobalgoalonadaptationandtaskedthemselveswithreviewingoverallprogresstowardsthisgoalaswellastheadequacyandeffectivenessofadaptationandsupportprovidedforadaptationatregularintervalsaspartoftheglobalstocktake.20Thesereviewsmightneedtobesubstantiatedbythedifferentcategoriesandformsofclimateandsocio‐economicdata.Approachesandmethodologiesthatcouldassistinundertakingthesereviewsarecurrentlyunderdevelopment.21

19GCOS,195.StatusoftheGlobalObservingSystemforClimate.WMO,2015.Availableathttps://library.wmo.int/doc_num.php?explnum_id=7213.

20Article7,paragraphs1and14(c)and(d)oftheParisAgreement.21TheAdaptationCommitteehasbeenmandatedtodevelopsuchapproachesandmethodologies.FurtherinformationonthisongoingworkisavailableattheCommittee’swebsite(https://unfccc.int/Adaptation‐Committee)andathttps://unfccc.int/topics/adaptation‐and‐resilience/groups‐committees/adaptation‐committee/joint‐ac‐and‐leg‐mandates‐in‐support‐of‐the‐paris‐agreement.


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Box1.DatarequirementsandsourcesforassessingclimaterisksandvulnerabilitiesunderSaintLucia’sNAPprocess

Requireddata Sources

Socio‐economic 2010PopulationandHousingCensus,CountryPovertyAssessment(CPA)andUNDP’sHumanDevelopmentIndex

Meteorological St.Lucia’sMeteorologicalServicewhichprovides24‐hourmeteorologicalforecastingandobservationsfromtwostationsandrainfallrecordsfrom31secondarystations

Climatechangemodelling ProjectionsfromNGOsandresearchgroupsbasedonGCMsandRCMs,mostcomprehensivelyundertakenbyCARIBSAVEforitsClimateChangeRiskAtlas

Landuse,landcoverandnaturalhazardmappingandassessment

Variousriskassessmentandnationallanduseplans,withspecificassessmentsforlandslideriskandtheagriculturalsector

Informationonnaturalresourcesandtheirmanagement

Naturalresourceinventoriesandassessmentse.g.websiteoftheGovernmentofSt.Lucia’sBiodiversityResources

Sector‐relevantinformation Forexample,AssessmentoftheEconomicImpactofClimateChangeontheAgriculturalSectorsupportedbyECLAC,ImpactAssessmentofClimateRiskstotheTourismSector,CountryDocumentforDisasterRiskReduction

Onthebasisofitsstocktakeandvulnerabilityanalysis,St.Luciahasidentifiedalistofpressingdataandinformationneeds,including,forexample,windhazardinformation,localclimateextremeindices,sealevelrisemodellingandcoastalfloodanderosionmapping,amongothers.FurtherinformationontheseactivitiescanbefoundinSt.Lucia’sNationalAdaptationPlanStocktaking,ClimateRiskandVulnerabilityAssessmentReportavailableathttps://www.climatechange.govt.lc/wp‐content/uploads/2018/04/Saint‐Lucia_Stocktaking‐climate‐report_FINAL.pdf.

Provisionofdataforadaptationacrossdifferentspatialscales48. Inparalleltothegrowingdemandfordatatosupportadaptation,thesupplyhasalsobeenincreasingrapidlyinthepastyears.Observationalsystemshavebeenexpandingandareprovidingdatainnearreal‐time,satellitedataiscontributingawealthofcomplementaryinformationonmanyoftheEssentialClimateVariables,andglobalclimatemodelsproduceevermoredetailedprojectionsofthefutureclimateanditsimpacts.Thisexplosionofglobaldataavailabilityhasbeenmadepossiblethroughincreasedcomputercapacitiesandothertechnologicaldevelopmentsontheonehand,andadvancesinglobalcooperationandcoordinationregardingresearch,systematicobservationandmodellingontheotherhand.Figure5illustratestheexchangeofdataandinformationbetweenthedifferentspatialscales.


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Figure5.DataandinformationexchangeaspartoftheClimateServiceInformationSystem(CSIS)oftheGlobalFrameworkforClimateServices

Source:PresentationbytheWMOontheCSISavailableathttps://gfcs.wmo.int//sites/default/files/Rupa%20Kumar%20Kolli%20CSIS.pdf.

5.1. Observationaldata5.1.1. Observationalclimatedata49. Observationalclimatedataisproducedbytheclimateobservingsystemscomposedofnational,regionalandglobalsub‐systems.Thereby,thenationallevelplaysacentralrole.National‐levelbodiessuchasNationalMeteorologicalandHydrologicalServices(NMHSs),oceanographicinstitutionsandspaceagenciesarethelargestprovidersofclimateobservations.22Furthersourcesmayincludeothernationalagencies,commercialentitiesordevelopmentagencies. 50. Theapproximately190NMHSsaroundtheworldstandoutsincetheyaretheofficialauthoritativesource,andoftenthesinglesource,aswellasguardiansofweatherandclimatedataobtainedthroughnetworksofinsitumeasurementstationsintheirrespectivecountries.Inaddition,theyprovidetheirdataandproductstoregionalandglobaldatacentresforarchivingandfurtherprocessing.AccordingtotheWMOGuidetoClimatologicalPractice23,anNMHSmustbeabletoanticipate,investigateandunderstandtheneedsforclimatologicalinformationamonggovernmentdepartments,researchinstitutionsandacademia,commerce,industryandthegeneralpublic;promoteandmarkettheuseoftheinformation;makeavailableitsexpertisetointerpretthedata;andadviseontheuseofthedata.Thus,throughtheirwork,NMHSsformthebasisofglobalclimatemonitoringandanalysis. 51. Nationalaswellasregionalobservationalsystemsprovidetheirdatatoregionalorspecializedclimatecentres(RCCs)..ThesearecentresofexcellencedesignatedbyWMOtostrengthenthecapacityofWMOMembersinagivenregiontodeliverbetterclimateservicestonationalusers.24Examplesincludethe22GCOS:StatusoftheGlobalObservingSystemforClimate.WMO,2015.Availableathttps://library.wmo.int/doc_num.php?explnum_id=7213.

23WMO.2018.GuidetoClimatologicalPractices(WMO‐No.100).Geneva.Availableathttp://www.wmo.int/pages/prog/wcp/ccl/guide/guide_climat_practices.php.

24https://public.wmo.int/en/our‐mandate/climate/regional‐climate‐centres.


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ClimateServicesCentreoftheSouthernAfricanDevelopmentCommunityinBotswana,theBeijingClimateCentreinChina,theRegionalClimateCentreforWesternSouthAmericainEcuadorortheCaribbeanInstituteforMeteorologyandHydrologyfortheCaribbean.25ThemandatoryfunctionsofRCCsinclude:(i)operationalactivitiesforlong‐rangeforecasting;(ii)operationalactivitiesforclimatemonitoring;(iii)operationaldataservices;and(iv)trainingintheuseofoperationalRCCproductsandservices.RCCsarealsoencouragedtotakeupnon‐operationaldataservicessuchascoordination,trainingandcapacitybuilding,andresearchanddevelopment.52. Intermsofobservationfromspaceover40countriesworldwidehaveinvestedinspace‐basedinfrastructurewiththecapabilitytoprovidesophisticated,continuous,andsustainedobservationsoftheentireplanet,andmoreinvestmentsarecomingfromtheprivatesectorandthroughpublic‐privatepartnerships.Ofthe54ECVs,morethanhalfhaveamajorcontributionfromsatelliteobservations.26Theweb‐basedinventoryofexistingandplannedclimatedatarecordsoftheECVsobservablefromspaceisupdatedannuallyandin2019itcontainedinformationforapproximately1300datasets.2753. Datafromthedifferentobservingsystemsisusuallysubmittedtoglobaldatacentreswhichfurtherprocessthedataandmakeitpubliclyavailable.Suchdatacentresholdbasicarchivesofinsituobservationaldata,forexamplerelatingtoindividualEssentialClimateVariablesorgroupsofthem,andsometimesofsatellitedata.Thescalesforwhichthisdataisavailablerangefromnear‐realtimetomillion‐year‐oldproxyrecordsandfromglobaltosub‐nationalandsector‐specificscales.54. GlobaldatacentresthatcollaboratewithWMOinclude,forexample,theWorldDataCenterforMeteorology,AshevilleintheUS;theWorldDataCenterforMeteorology,ObninskinRussia;theChinaMeteorologicalDataSharingServiceSysteminChinaandtheWorldDataCentreforClimate(WDCC)inGermany.2855. Sub‐nationalsourcesalsocontributetotheglobalprovisionofobservationalclimatedata.Theseincludelocalpublicagencies,theprivatesectororinternationaldevelopmentprojects.Inaddition,real‐timeobservationsontheenvironmentareincreasinglybeingcollectedthroughcitizenscienceandcrowdsourcinginitiatives,aswellasextractedfromsocialmediaandsmartphoneactivities,tocomplementofficialobservationalclimatedata.29 56. Altogether,thedifferentobservingsystemsanddatacentresholdavastarrayofobservationaldata.Inordertomakethedatausefulfortheirendusers,includingadaptationplannersanddecision‐makers,climatologistsatthevariouscentresanalyseandsynthesizethedataintoavarietyofdataproducts.Theseproductsprovideclimateinformationatspatialscalesandfortimeintervalsthatmeettherequirementsofspecificadaptationcontexts.Arangeofstatisticalmethodsisappliedtoarriveattheseproducts,someofwhichalsohelpinclosinggapsinobservationalcoverage.3057. Climatedataproductsmaytakethefollowingformats:31

a) Climateatlases:publicationforparticularregionsortheentireglobewithseveralkindsofvisualizationanddescriptivetext;

25Forafulllistofdesignatedcentresrefertohttps://cpdb.wmo.int/regions/africa/regional_centres.26CEOSandESA,2015,SatelliteEarthObservationsInSupportOfClimateInformationChallenges‐CEOSEarthObservationHandbookforCop21.Availableat.

27StatementReportingonProgressbytheCommitteeonEarthObservationSatellites(CEOS)andtheCoordinationGroupforMeteorologicalSatellites(CGMS)onCoordinatedResponsetoUNFCCCNeedsforGlobalObservations,51stSessionoftheoftheSubsidiaryBodyforScientificandTechnologicalAdvice(SBSTA),2‐9December2019,Madrid,Spain.Availableathttps://www4.unfccc.int/sites/SubmissionsStaging/Documents/201911281201‐‐‐CEOS‐CGMS_Statement_for_SBSTA51_v1.1_20191023.pdf.

28Forafulllistofglobaldatacentresrefertohttps://community.wmo.int/meetings/world‐data‐centres.29SeetheinitiativeISeeChange,incollaborationwithNASA’sOrbitingCarbonObservatoryMission30Adetailedanalysisofthepotentialoftheseproductstofillgapsinglobalobservationsisbeyondthescopeofthispaperandneedstobeaccessedinmorespecificliteraturethatispublished,forexample,bytheGCOSprogramme.31FurtherinformationonclimateproductsisavailableatWMO.2018.GuidetoClimatologicalPractices(WMO‐No.100).Geneva.Availableathttp://www.wmo.int/pages/prog/wcp/ccl/guide/guide_climat_practices.php.


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b) Onlinedatabaseswithsoftwaretoolsthatallowcustomerstoproducestatisticsandvisualizationaccordingtotheirneeds;

c) Climatologicaldataperiodicals:routinely(monthlyorannually,sometimesweeklyorseasonally)publishedbulletinscontainingdatafromaselectionofstationswithinparticularareasoracountryandprovidinginformationone.g.maximumandminimumtemperatureandtotalprecipitationforeachday,temperaturesatfixedhours,togetherwiththeassociatedhumidityvalues;dailymeanwindspeedandprevailingdirection,durationofbrightsunshine,orotherlocallyimportantdata(suchasheating,coolingandgrowingdegree‐days);monthlyaveragesandextremesandotherstatisticaldata,ifavailable;

d) Occasionalpublications:producedastheneedarises,forexampletosupporttheplanningoflarge,long‐terminvestmentsortoexplainunusualevents,suchasextremeweatherortodescribeandupdateanimportantpredictedeventsuchasastrongElNiño;theymayalsobepublishedonlong‐term,continuousandhomogeneousseriesofdata(e.g.ontemperatureandprecipitation)whichareofgreatvalueforcomparativeclimatologicalstudiesandforresearchonclimaticfluctuations,trendsandchanges;historicalclimatologicaldataseriesaresometimessummarizedinyearbooksorannualbulletins;

e) Standardproducts:fillthegapbetweentheclimatedataperiodicalsandthosetailoredtoindividualusersandareproducedcontainingcertaintypesofdata(e.g.degree‐dayproducts)tosatisfytheneedsofvarioususers,particularlyatthelocallevel;cost‐sharingamongusersisanimportantaspectofstandardproducts;

f) Specializedproducts:transformtheobservationsintoavalue‐addedproductforparticularrecipientsbyanalysingthedataandpresentingtheinformationwithafocusonthespecificationsthatwillenabletheusertogainoptimumbenefitfromtheapplicationoftheinformation;theuseoftheproductusuallydictatesthetypesofanalysisandmethodstogeneratetheproduct;

g) Climatemonitoringproducts:summarizedinformationonthecurrentclimateconditions,includinglocalvariations,ofacountry,putincontextoftheregionaland/orglobalclimatesystem,includingtheextremesandtheirimpacts;

h) Indices:characterizefeaturesoftheclimateataparticularstationorforanarea,usuallycombiningseveralelementsintocharacteristicsof,forexample,droughts,continentality,phenologicalplantphases,heatingdegree‐days,large‐scalecirculationpatternsandteleconnections;examplesofindicesincludetheElNiñoSouthernOscillation(ENSO)Index;theNorthAtlanticOscillationIndex;descriptorssuchasthemoistureavailabilityindex,usedforderivingcropplanningstrategies;agrometeorologicalindicessuchasthePalmerDroughtSeverityIndex,aridityindexandleafareaindex,whichareusedfordescribingandmonitoringmoistureavailability;andthemeanmonsoonindex,whichsummarizesareasofdroughtsandfloods;32

58. Thefollowingaredataproductsandstatisticalmethodsthatassistinclosinggapsinobservationalcoverage:

a) Griddeddata:griddedclimatedataproductsarevaluesofsurfaceorupper‐airclimatevariables(forexample,airtemperature,atmosphericmoistureorseasurfacetemperature)orindices(forexample,numberoffrostdays),arrangedonaregulargridwithcoveragerangingfromthelocaltoregionaltoglobal.Spatialresolutionofgriddeddatavariesfromafewsquaremetresinthecaseofsub‐urbandatasetsto200‐300kmasfoundinglobalscaledatasets.Temporalresolutionvariesfromthesub‐hourlytoannualtimescale.Griddeddataisderivedfromoriginalobservations(surfaceorsatellite‐based)usinginterpolationtechniquesorfromtheoutputofnumericalorstatisticalclimatemodels.Theyfilldatagapsthatariseduetoanunevengeographicalandtemporaldistributionofclimateobservations.Griddeddatasetsfacilitatethe

32Theconstructionandevaluationofindicesspecifictoclimatechangedetection,climatevariabilityandclimateextremesisdiscussedinGuidelinesonAnalysisofExtremesinaChangingClimateinSupportofInformedDecisionsforAdaptation(WMO/TD‐No.1500,WCDMP‐No.72).


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spatialanalysisofclimatevariablesandthestaticordynamicvisualisationofclimatepatternsandtrends;

b) Reanalysis:processthroughwhichnumericalweatherpredictionisdoneretroactivelybyusingthesamepredictionmodelbutincorporatingamorecompletesetofobservationsthathadnotbeenavailableatthetimeoftheoriginalweatherprediction.Theoutputisonauniformgridwithoutanymissingdata.Theresultisanintegratedhistoricalrecordofthestateoftheatmosphericenvironmentforwhichallthedatahavebeenprocessedinthesamemanner.Thereanalysisvaluesarenot“real”databutestimatesofrealdatabasedonunevenlydistributedobservationaldata.Therearestillchallengesoflocalizingreanalysistospatialandtemporalscalesfinerthanthereanalysisgrid;

c) Reprocessing:methodthroughwhichdatafromdifferenttypesofobservationsordifferentinstrumentsisreprocessedinordertoachievehomogenizationorintercalibration;thismethodbenefitsfromimprovedknowledgeofinstrumentcharacteristicsorbettermethodsofgeneratinggriddeddataproductsfromtherawmeasurements.

59. Insum,greatprogresshasbeenmadethroughtheglobalobservingsystemsanddataproductionmethods,forexample,intermsofglobalcoverageofECVdataavailabilitywherebyinsituandspace‐basedobservingsystemscomplementeachother.335.1.2. Observationalsocio‐economicdata60. Regardingthecollectionandmanagementof,particularlyquantitative,observationaldataofsocio‐economicprocesses,thereiscurrentlynointernationalcoordinationundertheclimateregimecomparabletothatonclimatedata.Incontrast,suchdataiscollectedandheldbyawidevarietyofsourcesatallspatialscalesthatmainlycollectthedataforotherpurposes.Thisseparatecollectionofsocio‐economicandclimatedataoftenmakesanattributionofobservedsocio‐economicchangestoacause,suchasclimatechange,difficult.Aslongasmorecollocatedtimeseriesofclimateobservationsandsocio‐economicparametersarenotavailable,quantitativesocio‐economicdataforvulnerabilityassessmentsandadaptationplanning,implementationandmonitoringmustbecollectedfromtheexistingsourcesforeachindividualadaptationcontextandbecomplementedbyqualitativeinformationthatiscollectedspecificallyfortheadaptationactivity.61. Atthegloballevel,thesourcesforquantitativedataincludeUNorganizations,suchastheDepartmentofEconomicandSocialAffairs(e.g.UNStatisticsDivision,populationdivisionanddatabases34,divisionforeconomicanalysis),theWorldBank,theOrganisationforEconomicCo‐operationandDevelopment(OECD)andspecializedresearchinstitutions,suchastheSocioeconomicDataandApplicationsCenterofNASAandtheInternationalInstituteforAppliedSystemsAnalysis(IIASA)inAustria,tonamejustafew.62. Attheregionallevel,suchdataisprovided,amongothers,byUNregionaleconomiccommissionsorregionaldevelopmentbanks.63. Socio‐economicdataatthenationallevelcanbeobtainedprimarilyfromnationalstatisticalofficesorfromlineministries,civilsocietyorganizations,academiaorprivateinstitutions.Fordataonsmallerspatialscaleswithinacountry,agencies,universitiesorotherarchivesatlocallevelsmaybeconsulted.Socio‐economicmicrodata,includingonindividuals,households,orfirmsmayalsobeobtainedfromsurveysorcompanydetails.Inaddition,qualitativeandanecdotalinformationfromlocalresourcemanagers,policymakersandotherstakeholderscanprovideveryusefulsupplementarymaterials.64. Despitethecurrentabsenceofagloballycoordinatedapproachtocollectingsocio‐economicdataforadaptation,severalinternationaleffortsareunderwaythatmightcontributetoanincreasedsystematizationofthecollectionandprovisionofsuchdata.65. Oneisthe2030AgendaforSustainableDevelopment,which,withits17SustainableDevelopmentGoals,relatedtargetsandthenational,regionalandglobal,indicator‐basedreviewprocesses,willenhance33GCOS,195.StatusoftheGlobalObservingSystemforClimate.WMO,2015.Availableathttps://library.wmo.int/doc_num.php?explnum_id=7213.

34See,forexample,theDemographicExplorerforClimateAdaptation(DECA)oftheUNPopulationFund,availableatnijel.org/un_popclimate/deca.


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thecollectionofsocio‐economicdatainmanyfieldswhicharealsorelevantforadaptationtoclimatechange.35Althoughnationalreviewsarevoluntaryandcountry‐led,UNMemberStateshavedevelopedacommonindicatorframework36andnationalreviewsshouldbeconsistentwiththevoluntarycommonreportingguidelinesproposedbytheUNSecretary‐General,amongotherstofacilitatetheglobalreviewprocess.Inordertoenhancesuchconsistency,theUnitedNationssystem,includingthroughitsRegionalCommissionsandcountryteamsisofferingcoherentsupporttotheconductofnationalreviewsinthecontextoftheSDGs,includingthroughbuildingthecapacityofnationalstatisticaloffices,datasystemsandevaluationbodies.37Assuch,enhancedstandardization,coordinationandcomparabilityofnationallycollectedsocio‐economicdatathatisalsorelevantforclimateadaptationmaybeexpected.3866. Inaddition,theUnitedNationsEconomicCommissionforEurope(UNECE)playsaleadingroleinglobaleffortstoadvancethedevelopmentofofficialstatisticsforclimateanalysisandglobalreporting,includingforadaptation.Inthiscontext,ithasproducedRecommendationsonClimateChange‐RelatedStatisticsin2014(seebox6),39andsince2015hasorganizedanannualExpertForumonClimateChangeStatisticswhichprovidesakeyinternationalplatformtosupportthiswork.4067. Ingeneral,socio‐economicdataisoftenprovidedintheformofspecificproductssuchasstatisticalyearbooks,onlinedatabaseswhichprovidetheopportunityofcustomization,orregion‐specificatlases.Takingintoaccountthedifferenceinthewayclimateimpactsmayaffectdifferentgroupsofsociety,disaggregationofthedataaccordingtosex,age,income,race,ethnicity,migrationstatus,disability,geographiclocationandothernationallyrelevantcriteriaisdesirable.

5.2. Projecteddata68. Usingobservationaldataasabasis,thedifferentspatiallevelsalsointeractinprovidingweatherandclimateforecasts,predictionsandprojectionsfordifferentspatialandtemporalscales.69. Regardinglong‐rangeforecastsandpredictions,theWMOGlobalData‐processingandForecastingSystemproducesseasonalforecastsonamonthlyoratleastquarterlybasisincludingforthefollowingvariables:2mtemperature,precipitation,SeaSurfaceTemperature(SST),MeanSea‐LevelPressure(MSLP),500hPaheight,850hPatemperature.41ThesystemiscomposedofthirteenGlobalProducingCentresforLong‐rangeForecasts(GPCLRFs),42RegionalSpecializedMeteorological(Climate)CentresandNMHSs.Thethreeinteractinsuchawaythatforecasts,andpredictionsgeneratedfromaglobalclimatemodelaredownscaledtotheregionallevel(foralimitedareaanduptoaresolutionofafewkilometres)andfurthertothenationallevel(seefigure5above).Thedownscalingmethodscanbeeitherdynamical(usingthelarger‐scaleinformationfromaGCMtosimulatearegionalclimate)orstatistical(creatingstatisticalrelationshipsbetweenthelarge‐scalevariablesandobservedregionalandlocalvariables),oracombinationofthetwo.NMHSsproducedifferentformsofnationalclimateoutlooks,suchas8–14‐dayprobabilisticoutlooks,monthly,seasonalandannualclimateoutlooksorspecificoutlookssuchasmonthlyandseasonaldroughtoutlooksorweeklyregionalhazardsoutlooksforfoodsecurity.43 70. WMORegionalClimateOutlookForumsplayaparticularlyimportantroleintheprovisionofclimateoutlooksandtheirdistribution.44Theyproduceconsensus‐based,user‐relevantclimateoutlookproductsin35Forthegoals,targetsandglobalindicatorsseehttps://sustainabledevelopment.un.org/content/documents/11803Official‐List‐of‐Proposed‐SDG‐Indicators.pdf.36https://unstats.un.org/sdgs/.37ReportoftheSecretaryGeneralonFollowupandReviewofthe2030Agenda.A/70/684.38UNDG.2017.GuidelinestoSupportCountryReportingontheSustainableDevelopmentGoals.Availableathttps://unsdg.un.org/resources/guidelines‐support‐country‐reporting‐sustainable‐development‐goals.39Availableathttp://www.unece.org/index.php?id=37166.40Furtherinformationontheseactivitiesisavailableathttp://www.unece.org/stats/climate.html.41https://community.wmo.int/global‐producing‐centres‐long‐range‐forecasts.42ForafulllistofGPCLRFsandthedataproductsrequiredfromthemrefertohttps://community.wmo.int/global‐producing‐centres‐long‐range‐forecasts.

43WMO.ClimateServicesforSupportingClimateChangeAdaptation.SupplementtotheTechnicalGuidelinesfortheNAPProcess.Availableathttps://www4.unfccc.int/sites/NAPC/Documents%20NAP/Supplements/WMO_climate%20change%20services%20for%20climate%20change%20adaptation.pdf.

44https://public.wmo.int/en/our‐mandate/climate/regional‐climate‐outlook‐products.


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realtimeforthecomingseasoninsectorsofcriticalsocio‐economicsignificancefortheregioninquestion.Forumsbringtogethernational,regionalandinternationalclimateexperts,onanoperationalbasis,toproduceregionalclimateoutlooksbasedonclimatepredictionsfromallparticipants.Bybringingtogethercountrieswithcommonclimatologicalcharacteristics,theForumsensureconsistencyintheaccessto,andinterpretationof,climateinformation.Throughinteractionwithusersinthekeyeconomicsectorsofeachregion,extensionagenciesandpolicymakers,theForumsassessthelikelyimplicationsoftheoutlooksonthemostpertinentsocio‐economicsectorsinthegivenregionandexplorethewaystheseoutlookscouldbeusedbythem.Theyalsooffertrainingworkshopsonseasonalclimatepredictiontostrengthenthecapacityofnationalandregionalclimatescientists.Basedontheneedsofspecificsectors,specialized,sector‐orientedoutlookforums,suchastheMalariaOutlookForums(MALOFs)inAfrica,arebeingheldinconjunctionwiththeregionalforums.Theregionalforumsareusuallyfollowedbynationalforumstodevelopdetailednational‐scaleclimateoutlooksandriskinformation,includingwarningsforcommunicationtodecision‐makersandthepublic(seefigure5above).NHMSsarekeyparticipantsintheseforumsthroughwhichtheyprovidetheirproductsandservicesandenterintoadialoguewithendusers.Localandsectoralstakeholders,inturn,mayusethisplatformtoraiseawarenessfortheirparticulardataneeds. 71. Forecastsandpredictionsofsocio‐economicdatathatarerequiredforshort‐tomedium‐termadaptationplanningatregional,nationalorlocalscalesareusuallyproducedbythoseentitiesthatalsomonitorsocio‐economicdevelopmentsandprovidetherespectiveobservationaldata(seesection5.1.2).AsexamplesatthegloballevelmayservetheUNDepartmentofEconomicandSocialAffairs(UNDESA),whichproducestheannualflagshipreport“WorldEconomicSituationandProspects”,incollaborationwiththeUNConferenceonTradeandDevelopment(UNCTAD)andeachofthefiveUNRegionalCommissions,containingaglobaleconomicoutlookaswellasregionalonesforthedifferentUNgeographicalregionsforthesubsequentyear.45ItspopulationdivisionalsoregularlyupdatestheWorldPopulationProspects46aswellastheWorldUrbanizationProspects,47describingtrendsinpopulationandurbanization,respectively,forallcountriesintheworldaswellasadditionalsub‐regions.TheInternationalMonetaryFund(IMF)publishesusuallytwiceayeartheWorldEconomicOutlookreports,whichpresentIMFstaffeconomists'analysesofglobaleconomicdevelopmentsduringthenearandmediumterm,includingformajorregionsandindividualcountries.48Twiceayear,theOECDproducestheOECDEconomicOutlookwhichisananalysisofthemajoreconomictrendsandprospectsforthenexttwoyears,coveringallOECDmembercountriesaswellasselectednon‐membercountries.49TheOECDalsomakesavailablepopulationprojectionsforeachOECDcountryandselectednon‐OECDcountriesuptotheyear2030.5072. Regardinglong‐termclimateprojectionsthesearealsoproducedbydifferentclimatecentresaroundtheworldusingGlobalClimateModels.GlobalcentresthatprovidesuchprojectionsincludetheHadleyCentreoftheUKMetOffice51ortheEuropeanCopernicusClimateChangeService.52Whileformerlyemissionscenarioswereusedtoarriveatclimateprojections,RepresentativeConcentrationPathways(RCPs)havebeenappliedundertheIPCC’sFifthAssessmentReport(2014).Thedifferentpathwaysdescribepossibleradiativeforcingtrajectoriesresultingfromdifferentcombinationsofeconomic,technological,demographic,policyandinstitutionalfuturestoarriveatfourpre‐definedradiativeforcinglevelsintheyear2100.53Thekeydifferencebetweenthetwoscenarioprocessesisthatwhiletheformerappliedasequentialapproach(developingsocio‐economicstorylinestogenerateemissionscenariosandthenclimatescenarios),theRCPprocessdevelopsemissionsandsocio‐economicscenariosinparallel,buildingonthedifferentRCPs.Inthiscontext,SharedSocio‐EconomicPathways(SSPs)areusedalongsidetheRCPstoanalysethefeedbacksbetweenclimatechangeandsocioeconomicfactors,suchasworld45https://www.un.org/development/desa/dpad/publication/world‐economic‐situation‐and‐prospects‐2019/.46Latestrevision(2019)availableathttps://population.un.org/wpp/.47Latestrevision(2018)availableathttps://population.un.org/wup/.48https://www.imf.org/en/Publications/WEO.49www.oecd‐ilibrary.org/economics/oecd‐economic‐outlook_16097408.50https://stats.oecd.org/Index.aspx?DataSetCode=POPPROJ#.51https://www.metoffice.gov.uk/weather/climate‐change/organisations‐and‐reports/met‐office‐hadley‐centre/index.52https://climate.copernicus.eu/.53FurtherinformationontheRCPapproachisavailableathttps://www.nature.com/articles/nature08823andagooddescriptionofmodelsandscenariosisavailableat:https://www.climatescenarios.org/primer/.


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populationgrowth,economicdevelopmentandtechnologicalprogress.54TheintegrationofRCPsandSSPs,inturn,hasallowedforimprovedclimateimpactstudies,suchasthoseundertakenbytheInter‐SectoralImpactModelIntercomparisonProject(ISIMIP)whichaimstoimproveglobalandregionalriskmanagementbyadvancingknowledgeoftherisksofclimatechangethroughintegratingclimateimpactsacrosssectorsandscalesinamulti‐impactmodelframework.5573. GlobalClimateModelOutputs,basedontheRCPs,needtobedownscaledtoregionalandnationallevelsinordertobeusefulforadaptationplanningatthesescales(seecasestudyinbox2).Theythenneedtobeviewedinconjunctionwithprojectedsocio‐economicdatawhenassessinglocalizedclimaterisksandvulnerabilities.Tothisend,theIPCCDataDistributionCentremakesfreelyavailableanumberofrecentglobaldatasetsofbaselineandscenarioinformationonclimatic,environmentalandsocio‐economicconditions.56

Box2.Sudan’sapproachtodevelopregionalclimateprojections

74. Asillustratedinbox3,thereareusuallytwoapproacheswhenapplyingprojectionsforclimateimpactstudiesandadaptationplanning:a“top‐down”anda“bottom‐up”approach.Thelatterisincreasinglyrecognizedasbeingmorepracticalsinceitusestheknowledgeoflocalexpertsanddecision‐makersontheprevailingvulnerabilityandadaptationcontextsandassesseshowclimatechangemightaffectrelatedpolicyplansandgoals.Infact,bothapproachesarecomplementary,andtheircombinedapplicationallowslocalexpertstoevaluatetheplausibilityandcredibilityofnationalclimateprojectionsthataredownscaled54MoreinformationontheSSPsisavailableathttps://iiasa.ac.at/web/home/research/researchPrograms/Energy/SSP_Scenario_Database.html.

55FurtherinformationontheISIMIPisavailableathttps://www.isimip.org/about/#mission.56http://www.ipcc‐data.org/.Foragooddescriptionofmodelsandscenariosseealso:https://www.climatescenarios.org/primer/.

AtthetimeSudanconducteditsNAPprocess(2011‐2014),noregionalclimateprojectionshadbeenavailable.Inordertoundertakeeffectivevulnerabilityassessmentsandadaptationplanning,theSudanesegovernmentdecidedthatsuchprojectionswererequiredandundertookthefollowingstepsaspartoftheNAPprocess:

1. BuildingtechnicalcapacitybyestablishingacollaborativerelationshipbetweenSudanesemeteorologistsandinternationalexperts,aswellasconveningon‐siteandremotecapacitystrengtheningprogrammes;

2. DefiningcurrentclimatetrendsbysummarizingobservedprecipitationandtemperaturecharacteristicsfromsixstationsoverSudanfortheperiod1961‐2010;

3. ObtainingmostrecentGCMoutputsfromIPCCAR5modelrunsforaregionincludingSudanandforthefourRepresentativeConcentrationPathways(RCPs)usedinAR5;

4. Developingfutureregionalclimaticprojectionsbyapplyingstatisticaldownscalingtechniquestodefinefutureclimateatafinerspatialresolution(12‐km)andcorrelatethesewithhistoricalregionalclimate(usingalsoproxydataifrequired)toarriveatmonthly,gridded(50km)timeseriesofprecipitationandminimumandmaximumtemperaturefortheperiod1950–2100;

5. ProducingmapsandchartsthatillustratehowclimatechangewouldunfoldinSudanatthesixstations(plottedforannualaveragetemperatureandannualtotalprecipitationfortheperiod2006–2100);

6. Addressinguncertaintybycharacterizingthemultiplecontributorstouncertainty,includingfutureclimatedriverssuchasgreenhousegasemissions,choiceofclimatemodels,andchoiceofdownscalingmethod.

FurtherinformationontheseactivitiescanbefoundinSudan’sNationalAdaptationPlan(2016),availableathttps://www4.unfccc.int/sites/NAPC/Pages/national‐adaptation‐plans.aspx


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fromglobalscenarios.ThenewapproachofusingRepresentativeConcentrationPathwaysisassumedtobetteraddressboththetop–downandthebottom‐upmethodologies.

Box3.Top‐downandbottom‐upapproachestoprojectionsforclimateimpactstudies

Ifapplyingprojectionsforclimateimpactstudiesandadaptationplanning,therearegenerallytwopathwaysforcarryingoutscenario‐basedassessments:a“top‐down”or“predict‐then‐act”approachanda“bottom‐up”or“assess‐risk‐of‐policy”approach(seefigurebelow).The“topdown”approachinvolvestheinterpretationanddownscalingofglobal‐scaleclimateandsocio‐economicscenariostolowerlevels.Thesearethenappliedtoassesswhichimpactstheymighthaveonparticularsystemsofinterest,followedbytheformulationofadaptationstrategiesandoptions.The“bottom‐up”approachbuildsscenariosbyaggregatingfromthelocaltohigherscales.Itstartswiththevulnerabilityandadaptationdecision‐makingcontextandthenassesseshowclimatechangemightaffectcertainpolicyplansandgoals.Itusesobservationalorevenhistoricaldataandcloselyinvolveslocalresourcemanagersanddecision‐makerswithaccesstolocalknowledgewhereasthetop‐downapproachpredominantlyappliesprojecteddatatodifferentcontexts.Inthepast,mostvulnerabilityandimpactassessmentshaveappliedthetop‐downapproach,typicallyfordefiningmajorprojectedclimatechangeimpactsandprioritizinginterventions.However,thebottom‐upapproachisincreasinglyrecognisedtobemorepractical.Infact,thesetwoapproachesarecomplementary.Thepoliticalscaleat,andspecificpurposeforwhichanadaptationdecisionistakenwilldeterminehowmuchofa“top‐down”or“bottom‐up”approachisapplied.TheRepresentativeConcentrationPathway(RCP)scenarios,succeedingtheSRESscenariosinIPCCreports,aremostlikelybetterabletoaddressbothapproaches.

Top‐downandbottom‐upapproachestoclimateprojections.Source:IPCCTaskGrouponDataandScenarioSupportforImpactandClimateAssessment(TGICA).2007.GeneralGuidelinesontheUseofScenarioDataforClimateImpactandAdaptationAssessment.Version2.Availableat:http://www.ipcc‐data.org/guidelines/index.html#general.

Furtherinformationontheseapproachesisavailablein:IPCCTaskGrouponDataandScenarioSupportforImpactandClimateAssessment(TGICA).2007.GeneralGuidelinesontheUseofScenarioDataforClimateImpactandAdaptationAssessment.Version2.Availableat:http://www.ipcc‐data.org/guidelines/index.html#generalandinField,C.B.etal.2014.ClimateChange2014:Impacts,


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Adaptation,andVulnerability.PartA:GlobalandSectoralAspects.ContributionofWorkingGroupIItotheFifthAssessmentReportoftheIPCC.Availableathttps://www.ipcc.ch/report/ar5/wg2/.

5.3. Historicaldata75. Asoutlinedinsection4.3,historicaldatacanplayanimportantroleinevaluatingprojectionsandvalidatingtheoutputofglobalandregionalclimatemodels(seebox4).Thestorageandprovisionofhistoricaldataisalsoasharedresponsibilityamongstakeholdersworkingatdifferentspatiallevels.Manyglobaldatacentreshaveavitalstakeinarchivingandredistributinghistoricaldata.Inaddition,theInternationalDataRescuePortal(I‐DARE)ofWMOandtheGlobalFrameworkforClimateServices(GFCS)servesasaglobalcentreofexcellenceinrecoveringclimateheritageandmakingitavailableinthestateofartanddigitalformatforresearch,applicationsandclimateservicesbefore,insomecases,itmightbelostforever.57Itparticularlyfocusesonfillinggaps,andextendingtime‐series,oftheGCOSEssentialClimateVariables.Itprovidesasingle‐entrypointforaccessinginformationonthestatusofhistoricalclimatedatabeingdigitizedorinneedofrecoveryanddigitizationsothatsupportcanbefoundtoacceleratedatarescue.DatarescuealsoremainsahighpriorityoftheWMOCommissionforClimatology,aswellastheGlobalClimateObservingSystem(GCOS)programme(forfurtherinformationontheCommissionandGCOS,seesection5.4).76. MostNMHSsalsoholdhistoricaldata.ManyNMHSscarryoutsignificantdigitizationandqualitycontroloftheirdatarecords,particularlyregardingmonthlydataontemperatureandprecipitation,calledclimatologicalnormal.Otherrecordshaveatleastbeenscanned.Inaddition,historicaldatacaninsomecasesbeobtainedfromothernationalagenciesorextractedfromnewspapers,farmers’diaries,othergovernmentdocumentsandobservatoryreports.77. RegionalClimateCentresareencouragedtotakeupnon‐operationaldataserviceswhichincludedatarescueanddatahomogenization.

Box4.Usinghistoricaldatatoassessthevalidityofregionalclimatemodeloutputs

57https://idare‐portal.org/.

TheEUresearchproject“Bottom‐UpClimateAdaptationStrategiesTowardsaSustainableEurope”(BASE),whichwascarriedoutbetween2012‐2016undertheEU’s7thResearchFrameworkProgramme,aimedatmakingexperientialandscientificinformationonadaptationmeaningful,transferableandeasilyaccessibletodecision‐makersatalllevels.AnimportantcomponentoftheprojectwasthedevelopmentofseveralcasestudiesacrossEuropewhichweredesignedtogatherinterdisciplinaryinsightsfromthelocalleveloncosts,benefits,effectiveness,challengesandopportunitiesofadaptationacrosssectors.Fiveofthesecasestudies(locatedinAveiroCoast,CascaisandAlentejo(Spain),CopenhagenandPrague)providedinsightsintotheworkingofbaselineclimatechangeassessments,impactsandadaptationscenarios.Forthisassessmentthecasestudiesusedsocio‐economic(SSP2andSSP5)andtheassociatedclimate(RCP4.5andRCP8.5)scenarios(upto2100),consideringtemperatureandprecipitation,downscaledtotworegions‐theIberianPeninsulaandNorthernEurope‐throughahighresolutionRegionalClimateModel(RCM).ThehorizontalresolutionofthisRCMisabout15km.Inordertoevaluatetheabilityoftheregionalmodeltoreproducetheobservedrecentclimateintermsof2‐metertemperatureandprecipitation,themodeloutputswerecomparedtoobservationsinthecasestudyareas.ObservationaldatafortemperaturewasobtainedfromtheClimaticResearchUnitattheUniversityofEastAnglia1whichprovidesinformationonmonth‐by‐monthvariationinclimateoverthelastcenturyorsoviatime‐seriesdatasets.Thesearebasedonanarchiveofmonthlymeantemperatureprovidedbymorethan4000weatherstationsdistributedaroundtheworldandcalculatedonhigh‐resolution


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5.4. Enablingdataprovisionthroughinternationalcoordination,qualitystandardsandcapacity‐building

78. Thegenerationandsharingofincreasinglylargeamountsofadaptation‐relevantdatabyadiversityofactorsacrossscalesandculturalbackgroundsareonlybeingmadepossiblethrougharangeofinternationaleffortsregardingcoordination,qualitystandardsandcapacity‐building,particularlywithregardtoclimatedata.5.4.1. Internationalcoordination79. Amajorcontributiontotheglobalprovisionofclimatedataandderivedproductsareinternationalarrangementsforcoordinationandcollaboration.Thesearrangementshavebeenestablishedundertheunderstandingthatnosinglenationisabletogeneratethedataandinformationrequiredtounderstandtheglobalclimatesystem.Incontrast,itistothebenefitofallcountriesandincreasinglydiversifieduserneedstoconsolidatethemyriadoflocal,nationalandinternationalobservingsystemsintoaglobalobservingsystemandtojoinforcesinimprovingclimatescenarios,modelsandprojections.Thisrequirescoordinationintermsofinteroperabilityofthesystemsandcompatibilityofclimatedataandproducts.Theinternationalarrangementspromotethefreeandopenexchangeofclimate‐relevantdataandsetstandardsfordatacollection,archivingandexchange.Theyalsomaintainpubliclyavailabledatabasesandpromotefurtherresearchandcollaborationinareaswheretheyidentifygapsorseeopportunities(forexample,seebox5tolearnmoreabouttheopportunitytoexploitbigdata).Finally,theyprovidefinancialandtechnicalsupporttodevelopingcountriesinordertoenhancetheirdatamanagementcapacity.80. Thearrangementscanbegroupedintothosethatprimarilycontributetotheglobalobservationofclimatevariables,thosethatstimulateandcoordinatefurtherscienceinthisregard,andthosethatassessthemeaningofobserveddataforfurtherpolicymaking.Figure6depictstherelationshipbetweentheseareasofworkaswellastheirmainrepresentativearrangement.Ingeneral,thearrangementsaremanifoldandoftenoverlapintermsofmembership,sponsorsorjointprogrammes.TheWorldMeteorologicalOrganization(WMO)standsoutasakeydriverofmanyofthesearrangementsandthecoordinatorofthedatacentresthatareactiveinclimateanalysis,monitoringandprediction(seesections5.1‐5.3above).

(0.5x0.5degree)grids.ThelatesttimeseriesgeneratedbytheClimaticResearchUnitcoverstheperiod1901–2017.Observationaldataforprecipitationwasobtainedfromthedailygriddedobservationaldatasetforprecipitation,temperatureandsealevelpressureinEurope(E‐OBS),2whichisaveryhigh‐resolutiondatasetwitha0.22°regularlatitude‐longituderesolution.Theprecipitationobservationaldatasetcoveredtheperiod1850‐2005.Theoutcomeoftheassessmentwasthattheregionalmodelwasabletocapturethemainfeaturesoftheobservedspatialpatternsoftemperatureandprecipitationinbothregions(IberianPeninsulaandNorthernEurope).1https://catalogue.ceda.ac.uk/uuid/3f8944800cc48e1cbc29a5ee12d8542d.2https://www.ecad.eu/download/ensembles/download.php.ForfurtherinformationontheBASEcasestudiesrefertohttps://baseadaptation.eu/sites/default/files/BASE_Deliverable_5_1_0.pdf


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Figure6.Therelationshipbetweenobservations,science,assessmentandpolicymakingandthemainrepresentativearrangementsoftheseareasofwork

Source:GCOSPosteronitsnewimplementationplananditsrelationshipwiththeUNFCCCParisAgreement.Availableathttps://unfccc.int/files/science/workstreams/research/application/pdf/part1_gcos_richter_poster.pdf.

81. Thefollowingisabriefintroductiontosomeofthemainarrangementsregardingclimateobservation,scienceandassessment.Furtherinformationoneachofthemisavailableintheannex.5.4.1.1. Observations82. TheGlobalClimateObservingSystem(GCOS)programme,establishedin1992,promotesthetakingofneededobservationsbynationalorinternationalorganizationsfortheirowninterestsaswellasforcommongoals(e.g.undertheUNFCCC).TheGCOSprogrammedoesnotdirectlymakeobservationsnorgeneratedataproducts.Incontrast,itsoverarchingaimistoensurethattheobservationsandinformationneededtoaddressclimate‐relatedissues(e.g.data,climateservicesandclimateindicators)areobtainedandmadeavailabletoallpotentialusers.83. TheWorkingGrouponClimateoftheCommitteeonEarthObservationSatellites(CEOS)/CoordinationGroupforMeteorologicalSatellites(CGMS),establishedin2010,isthecentre‐pieceofGEOS’contributiontoclimatechangemonitoring.Itcoordinatesandencouragescollaborationactivitiesbetweentheworld’smajorspaceagenciesintheareaofclimatemonitoring,particularlyregardingtheimplementationandexploitationofEssentialClimateVariable(ECV)time‐series.84. TheGrouponEarthObservation(GEO)establishedin2005,isapartnershipofmorethan110nationalgovernmentsandmorethan110participatingorganizationsthatimprovestheavailability,accessanduseofEarthobservationstosupportclimateaction,disasterriskreduction,andsustainabledevelopment.Inadditiontoover60WorkProgrammes,58activitiesandinitiativesthataddressglobalneeds,coordinationandknowledgegaps,theGEOcommunityiscreatingaGlobalEarthObservationSystemofSystems(GEOSS).Thisistobetterintegrateobservingsystemsandsharedatabyconnectingexisting

582020‐2022GEOWorkProgramme:http://www.earthobservations.org/geoss_wp.php.


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infrastructuresandusingcommonstandards,whichhasalreadymademorethan400millionopenEarthobservationdataandinformationresourcesaccessible.59.85. TheCopernicusClimateChangeService(C3S),establishedin2014,isacomponentoftheCopernicusEarthObservationProgrammeoftheEuropeanUnionandprovidesauthoritativeinformationaboutthepast,presentandfutureclimateinEuropeandtherestoftheWorld.Itoffersfreeandopenaccesstoclimatedataandinformationonimpactsonarangeoftopicsandsectoralareasaswellastoolstotransformdataintorelevantproductsbasedonbestavailablesciencethatsupportadaptationandmitigationpolicies.5.4.1.2. Science86. TheWorldClimateResearchProgramme(WCRP)establishedin1980,aimsatdeterminingthepredictabilityoftheclimateandtheeffectofhumanactivitiesonit.Throughthepast40years,ithasimplementedalargenumberofmajorresearchandCoupledModelIntercomparisonProjectsthroughwhichithasaddressedfrontierscientificquestionsrelatedtothecoupledclimatesystemwhichwouldhavebeentoolargeandtoocomplextobetackledbyasinglenation,agencyorscientificdiscipline.Throughinternationalsciencecoordinationandpartnership,theWCRPcontributestoadvancingtheunderstandingofthemulti‐scaledynamicinteractionsbetweennaturalandsocialsystemsthataffectclimate.87. TheWorldAdaptationScienceProgramme(WASP),whichsucceededtheGlobalProgrammeofResearchonClimateChangeVulnerability,ImpactsandAdaptations(PROVIA)in2018,facilitatestheinformationexchangeandusageamongststakeholderstoadvancethescience‐policy‐practiceinterfaceinthecontextofadaptation.ItfocusesprimarilyontheprovisionofclimatescienceandpolicyproductsandservicestosupporttheUNFCCC,theIntergovernmentalPanelonClimateChange(IPCC)andtheGreenClimateFund.TheWASPjoinsforcestoidentifyknowledgegapsandleading‐edgeresearchprioritiesandmobilizesandcoordinatesthebroaderadaptationcommunitythroughregularactivitieslikeitsbiennialinternationalconferences.88. TheWorldClimateServicesProgramme(WCSP)establishedin2011,contributestoimprovingtheavailabilityandaccesstoreliabledata,advancementoftheknowledgeintheareaofclimatedatamanagementandclimateanalysis,definitionofthetechnicalandscientificstandards,anddevelopmentofactivitiestosupportthemincountries.5.4.1.3. Assessment89. TheIntergovernmentalPanelonClimateChange(IPCC)establishedin1988,istheUNbodyforassessingthesciencerelatedtoclimatechange.Itdoesnotconductitsownresearchbutdeterminesthestateofknowledgeonclimatechange,itsimpactsandfuturerisks,andoptionsforadaptationandmitigationthroughitsassessmentreports.ThenextAssessmentReport,expectedin2021andsubsequentyears,willcontainchaptersthatfocusonregionalaspectsofclimatechange,includingonregionalimpactandriskassessmentsandregionalprojections,butalsoonthemostrecentscientificknowledgeonimpacts,risksandadaptationstrategies,bothattheglobalandregionalscales.TheIPCChasestablishedaDataDistributionCentre(DDC)whichprovidesclimate,socio‐economicandenvironmentaldata,bothfromthepastandalsoinscenariosprojectedtothefuture.90. Manyoftheglobalarrangementshavecreatedregionalsub‐programmesorinitiativesthatfocusontheparticularregionalneeds.Forexample,theGrouponEarthObservationhasfourregionalinitiativeswhichprovideregionaldataresourcesandpromotecollaborationandcoordinationamongtheGEOmembersoftheparticularregion.TheWorldClimateResearchProgrammehasestablishedaparticularframeworktoevaluateregionalclimatemodelperformancethroughasetofexperimentsaimingatproducingregionalclimateprojectionsthroughclimatedownscaling.60

59GEOSSPortal:https://www.geoportal.org/?f:dataSource=dab.60https://www.cordex.org/about/what‐is‐regional‐downscaling/.


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Box5.TheopportunityofexploitingbigdataandthedatarevolutionundertheSustainableDevelopmentGoal(SDG)process

BigDatareferstoacollectionofverylargeandcomplexdatasets,bothstructuredandunstructured,whicharedifficulttoprocessusingtraditionaldatasoftwareapplicationsormanagementtools.1Thelarge‐scaleavailabilityofbigdatafromdiversesourcesandnewtechnologiesprovidegrandopportunitiestoservemany,notfew,whichisanimportantcomponentofthe“datarevolution”whichhasbeencalledforbytheSecretaryGeneraltomeetthedatademandsundertheSustainableDevelopmentGoalprocess.2BigdataisalsopartofoneofthefiveproposedlighthouseactivitiesoftheWorldClimateResearchProgramme(WCRP)whichiscalled“DigitalEarths”.TheWCRPlighthouseactivitiesidentifythekeyscientificoutcomesthatarerequiredfromWCRPtoensureclimatescienceismeetingsocietalneedsforrobustandactionableregionaltolocalclimateinformation.3Therearevariousinnovativesolutionstoharnessbigdata,particularlyclimatedata,andmakeitusefulforapplicationalongtheadaptationprocess.Someoftheseincludethefollowing:Cloudcomputingreferstoanon‐demandavailabilityofcomputersystemresources,especiallydatastorageandcomputingpowerwithoutdirectactivemanagementbytheuser.Thecloudcomputingmodelisbecomingthedominantmodeofworkformostmediumandlarge‐scaleEarthobservationapplications.Machinelearningaremethodsthatmakepredictionsandclassificationsbasedonpatternsandrelationsintheinputdata.MachinelearningmethodshaveemergedasthebestwaytoclassifyEarthobservationimagesforprovidinge.g.landinformation.Datacubesareorganisedcollectionsofremotesensingimagescoveringageographicalarea,inregulartemporalintervals.Datacubesarethusanefficientwaytoexploresatelliteimagearchivesspanningyearsorevendecades.Mostsuccessfulapplicationsoflarge‐scalelandclassificationusingmachinelearningrelyondatacubes.TheSecretary‐General’sIndependentExpertAdvisoryGroupontheDataRevolutionproposed“aprogrammeforexperimentingwithhowtraditionalandnewdatasources(includingbigdata)canbebroughttogetherforbetterandfasterdataonsustainabledevelopment,developingnewinfrastructuresfordatadevelopmentandsharing(suchasa’worldstatisticscloud’),andsupportinginnovationsthatimprovethequalityandreducethecostsofproducingpublicdata.”2Severaltransformativeactionswouldbeneededtomovethedatarevolutionforwardwhicharealsorelevantwithintheclimateregime.Theseinclude:

Improvinghowdataareproducedandused; Closingdatagapstopreventdiscrimination; Buildingcapacityanddataliteracyin“smalldata”(ortraditionaldata)and“bigdata”

analytics; Modernizingsystemsofdatacollection; Liberatingdatatopromotetransparencyandaccountability; Fosteringcollectionofperceptiondatafrompeopleandcitizenempowerment,includingthe

righttounderstandandreviewhowtaxpayermoneyorpublicsectorfinancesarespent; Protectingdatarights;and Developingnewtargetsandindicators.


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5.4.2. Qualitystandards91. Astheglobalcooperationondatagenerationanduserequirestheopensharingofdatabetweenthemanycomponentsystemsandinstitutions,standardsettinghasbecomeindispensabletoensurethehighestqualityofthedataaswellastoenabletheinteroperabilityofsystemsandthecompatibilityofdataandproducts.Thisultimatelyallowstheenduserstohaveconfidenceinthedataandtoadequatelydealwiththeuncertaintiesnaturallyassociatedwiththem.92. TheWMOhasadoptedarangeofglobalstandards,technicalregulationsandsupplementaryguidesforcarryingoutobservationsandprovidingtherequiredmetadatainordertomakeobservationstransparentandreduceuncertainties.Theprincipalareasofstandardizationinclude:(i)instrumentsandmethodsofobservationacrossallcomponents,includingsurface‐basedandspace‐basedelements(observationsandtheirmetadata);(ii)WMOInformationSystem(WIS)exchangeaswellasdiscovery,accessandretrievalservices;and(iii)datamanagement(dataprocessing,qualitycontrol,monitoringandarchiving).61Fromtimetotime,WMOpublishestheGuidetoClimatologicalPractices,62whichdescribes,inaconvenientform,thepractices,proceduresandspecificationsthatWMOMembersareexpectedtofollowwhendevelopingclimateservices.MembercountriesandorganizationsoftheWMO,intheirowninterest,havecommittedtoadheretothesestandardsandtofollowtheguidelineswhenestablishingandmaintainingobservingsystemsandgenerating,archivingandsharingdata.TheglobalobservationoftheGCOSEssentialClimateVariablesisalsoqualitycontrolledaccordingtothesestandards.93. TheIPCCDataDistributionCentre(DDC)providestechnicalguidelinesontheselectionanduseofdifferenttypesofclimateandsocio‐economicdataandscenariosinresearchaswellasclimateimpactandadaptationassessmentswiththeaimofimprovingconsistencyinthisregard.635.4.3. Capacity‐building94. Thegenerationofdatainaccordancewiththequalitystandardsaswellastheirinterpretationanduserequirecapacityonthepartofdataproducersaswellasendusers.Thiscapacityisoftenlacking,particularlyindevelopingandleastdevelopedcountries.Manyoftheinternationalarrangementsthereforeoffercapacitydevelopmentprogrammes.Theseinclude,forexample:

a) TheWMOEducationandTrainingProgrammewithafocusoncapacity‐buildingnecessaryforwell‐functioningmeteorological,hydrologicalandclimateservices;64

b) TheGCOSCooperationMechanisminvolvesfocusedcapacity‐buildingandimprovementofinfrastructureinleastdevelopedcountriesandsmallislanddevelopingStatesinordertosupportcriticalnetworks.Insomecases,thisprogrammealsoincludesfundingofoperatingexpenses.65

c) GEO’sflagships,initiativesandregionalGEOsfocusprimarilyoninstitutionalstrengthening,throughonlineandlocaltraining,webinarsandothermechanisms.Thegoalistohelpsharenew

61WMOIntegratedGlobalObservingSystem(WIGOS)website.Availableathttps://public.wmo.int/en/resources/bulletin/wmo‐integrated‐global‐observing‐system‐wigos.

62Availableathttps://public.wmo.int/en/resources/library/guide‐climatological‐practices‐wmo‐100.63IPCCTaskGrouponDataandScenarioSupportforImpactandClimateAssessment(TGICA).2007.GeneralGuidelinesontheUseofScenarioDataforClimateImpactandAdaptationAssessment.Version2.Availableat:http://www.ipcc‐data.org/guidelines/index.html#general.

64https://public.wmo.int/en/programmes/education‐and‐training‐programme.65GCOS,195.StatusoftheGlobalObservingSystemforClimate.WMO,2015.Availableathttps://library.wmo.int/doc_num.php?explnum_id=7213.

1Provost,F.andT.Fawcett.2013.DataScienceforBusiness.2UN.2014.WorldthatCounts:MobilisingtheDataRevolutionforSustainableDevelopment,aReportbytheSecretary‐General’sIndependentExpertAdvisoryGroupontheDataRevolution.3WorldClimateResearchProgramme.2020.WCRPHigh‐levelScienceQuestionsandFlagshipWorkshop,24—26February2020,Hamburg,Germany.


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knowledge,skillsandinsightstoassistdevelopedanddevelopingcountriesandregionstomakefulluseofEarthobservationsforresearch,policydevelopment,decisionmakingandaction.66

d) TheIPCCTaskGrouponDataandScenarioSupportforImpactandClimateAnalysis(TGICA)contributestobuildingcapacityintheuseofdataandscenariosforclimate‐relatedresearchindevelopingandtransition‐economyregionsandcountries.ItdoesthisthroughthedataandguidanceprovidedviatheDataDistributionCentre,byconveningexpertmeetingsonanas‐neededbasis,andbymaintainingandupdatingagloballistofnetworksforoutreach.67

5.5. Remaininggapsandchallenges95. Despitegreatprogressininternationalcollaborationandtechnologiesfordatageneration,someimportantgapsandchallengesremainregardingtheprovisionanduseofdataandrelatedproducts.Theseinclude:685.5.1. Observationaldata

a) Coverageofinsituobservationsystemsisinsufficientinsomeregionsoftheworld,particularlyinAfrica,Asia,andSouthAmerica.69Thesegapsaremostcriticalinareaswherepopulationsareatelevatedrisks(e.g.smallislands,coastalareas)andwherelocalchangeshaveglobalimpacts(e.g.meltingofice‐sheetoutletglaciersanditscontributiontosea‐levelrise),butalsoinleastdevelopedcountriesandremoteareassuchasthesouthernoceanandmountainousregions;

b) Satellite‐baseddataproductscanhelpinincreasingobservationalcoverage,butarenotalwaysavailableinthespatialresolutionrequired,i.e.oftenthespatialresolutionistoocoarse;

c) Downscaling,reanalysisandgriddeddatatechniquesassistinclosingsomeofthegapsregardingsatellite‐basedobservations.However,theyneedinsitustationsforreferencepurposesatminimumdensityandwithminimumtimeseriesofdatawhich,insomeregions,arenotavailable;

d) Challengesrelatedtotheinterpretationofobservationaldataareattributedto,forexample,measurementuncertainties,differencesinthelengthoftimeseries,insufficientdensityofnetworks,unreliableaccesstometadataandinsufficientsamplesofextremeevents.Thesechallengesleadtouncertaintieswhenusingsuchdata;

e) Observationsofsocio‐economicvariablesareonlyavailableindependentfromclimateobservationsandoftenlackrequiredqualityandcontinuityduetomajordeficienciesinnationalstatisticalsystems.70Longandcollocatedtimeseriesofclimateobservationsandsocio‐economicparameterswouldberequiredtoidentifyrisksandattributechanges.

5.5.2. Forecast,predictionsandprojectionsa) OutputsofGCMsareoftennotdownscaledtospatialscalesrelevantforadaptationplanningatthe

locallevels(seefigure7);71b) Downscalingthroughmulti‐sectorimpactmodelsthatwouldenablemulti‐sector/multi‐purpose

adaptationmeasures(e.g.modellingimpactsonfoodsecurityoroncoastalareas)islacking;c) Deficienciesinlocalcapacitythatrelatetoinfrastructuralproblems,e.g.limitedorprohibitively

expensivebandwidththatmakesdatatransfersextremelyproblematicand/oralimitednumberoftrainedpersonnelareonereasonforthelackindownscaleddata;

66https://www.earthobservations.org/cb.php.67IPCCTGICAwebsitehttps://archive.ipcc.ch/activities/tgica.shtml.68ThesummaryofthegapsandchallengesismainlybasedonGCOS,195.StatusoftheGlobalObservingSystemforClimate.WMO,2015.Availableathttps://library.wmo.int/doc_num.php?explnum_id=7213.

69GCOS‐200.TheGlobalObservingSystemforClimate:ImplementationNeeds.WMO,2016.Availableathttps://library.wmo.int/doc_num.php?explnum_id=3417.70UNDG.2017.GuidelinestoSupportCountryReportingontheSustainableDevelopmentGoals.Availableathttps://unsdg.un.org/resources/guidelines‐support‐country‐reporting‐sustainable‐development‐goals.71Notethatthereare,however,severalglobalinitiativesthatarefocusingonadvancingthescienceandapplicationofregionalclimatedownscaling,e.g.CORDEXoftheWorldClimateResearchProgramme.


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d) Top‐downandbottom‐upapproachestoclimateprojectionsforclimateimpactstudiesandadaptationplanningarenotyetbeingsufficientlyintegrated;

e) Majorchallengesremainwithregardtotheinterpretationofprojectionsbytheenduserwhichinclude:inter‐modelprojectionscatters,theunforcedvariabilityoftheclimatesystemthatneedstobeaccountedfor,individualmodeldiscrimination(weighting)withinmulti‐modelensembles,theoptimalsizesofmodel(super‐)ensembles,theaddedvalueofdifferentdownscalingtechniques(dynamical,statistical)aswellasrelateduncertainties,andthedifferentcredibilityoftheclimatevariables(characteristics)projectedbythemodels.Modelprojections,ifusedimproperly,maybemisleadingforclimateriskandimpactassessments;

f) Projectionsarealsoassociatedwithevenlargeror“deep”uncertaintieswhichrelatetofutureclimatedrivers,theresponseoftheatmospheretothemconsideringitschaoticnature,andtheeffectivenessofadaptationanddevelopmentmeasuresintermsofreducingvulnerability,consideringthatdifferentnormsandvaluesareatplaythatmightevenchangewithinandacrossgenerations.

Figure7.Theglobal‐to‐regionalknowledgegap(R.Sutton,NCAS.U.Reading,July2018)

Source:WorldClimateResearchProgramme.2020.WCRPHigh‐levelScienceQuestionsandFlagshipWorkshop,24—26February2020,Hamburg,Germany.

5.5.3. Historicaldataa) Recovereddatasetsofearlyyearobservationsonlycoversparseareasoftheglobeandreanalysis

canonlyinpartbeusedtomakeupforremaininggaps;72b) Digitizationhasmostlybeenundertakenon