Geomatics’challenge:“ManyclimateimpactmodellersaresimplynotabletohandletheamountofdatageneratedbyGCM-RCMsimulations(Wilcke &Lars,2016).”

Dataformat:NetCDF-4FileswithHDF5NetCDF isasetofSWlibrariesandmachine-independentdataformatsthatsupportthecreation,accessandsharingofarray-orientedscientificdata.UsesHD5Fasdatastoragelayer.

Projection:FalseNorthPoleRotatedGrid

CF-1.4convention

Datavolumeandlarge#offiles:4variables:900GBinformof1200files(eachonecontainsentiredomain,5years– dailydata)

UrbanizationandPopulationBoomInparallelwithclimatechange,wearewitnessingapopulationboomandthevastmajoritywillconcentrateinlargemetropolitanagglomerations.In2007,firsttimeinhistory,theurbanpopulationexceededtheruralone.Therefore,thehumankindbecamepredominantlyurbanbased.Thenumberofruraldwellershasbeengrowingsince1950anditisprojectedtoreachitspeakinnearfuture(UN,2014).In2014,about3.9billionpeople,representing54%ofglobalpopulation,wereurbandwellers.Halfoftheurbanpopulationresidesinsmallagglomerationsuptohalfmillioninhabitants.Roughlyoneofeighturbandwellersisaninhabitantofamegacity(settlementswithpopulationof10millionormore).Thenumberofmegacitiesnearlytripledsince1990.Currently,wehave28megacitiesontheplanet,andtheyarehometo453millionsofpeople.Itisprojectedthatbyyear2030therewillbe41megacitiesontheEarth.Theprojectionssaythatby2050theurbansystemswillbehometo66%ofglobalpopulation,representing6.3billonurbandwellers(UN,2014).

ClimatechangeandcitiesUrbansystemsactasimportanteconomichubsand,assuch,theyareverydemandingonresources.Globally,theenergyconsumptionofurbanagglomerationsisupto80%ofthetotalenergyproduction,whichrepresentsapproximately71–76%ofglobalCO2emissions(UN,2014).Thisillustratestheimportantroleofurbanareasasdriversofglobalwarming.Theurbanizedareasarenotonlymajordriversofclimatechange,butsimultaneouslytheyarehotspotsofvariousdisasterrisks,whichmakethemespeciallyvulnerabletochainreactions(WEF,2015).Climatechangehasmajoreconomicconsequencesinformofreductioninlabourproductivity,disruptionoftransportsystemsandsignificantlossesinenergyproductionanditssupplychains(Confalonierietal.,2007).

CLIMATE CHANGE AND IMPACTSON THE QUALITY OF LIFE IN URBAN SYSTEMS

Marek SmidNOVA IMS, Universidade Nova de Lisboa

1. Confalonieri,U.,Menne,B.,Akhtar,R.,Ebi,K.L.,Hauengue,M.,Kovats,R.S.,...&Woodward,A.(2007).Humanhealth.Climatechange2007:impacts,adaptationandvulnerability:contributionofWorkingGroupIItothefourthassessmentreportoftheIntergovernmentalPanelonClimateChange.

2. Donat,M.G.,Alexander,L.V.,Yang,H.,Durre,I.,Vose,R.,&Caesar,J.(2013).Globalland-baseddatasetsformonitoringclimaticextremes.BulletinoftheAmericanMeteorologicalSociety,94(7),997–1006

3. UnitedNations,DepartmentofEconomicandSocialAffairsPopulationDivision(2014).WorldUrbanizationProspects:The2014Revision,Highlights(ST/ESA/SER.A/352).

4. Wilcke,R.A.,&Bärring,L.(2016).Selectingregionalclimatescenariosforimpactmodellingstudies.EnvironmentalModelling&Software,78,191-201.

5. WorldEconomicForum(WEF).(2015).GlobalRisks2015.10thEdition.

References

METHODOLOGY

Subset:Thisprocedureleadstosubselectofthemodelsfromensembleofsimulations.Theresultingsubsetmaintainsthemodelspreadregardingthevariabilitypresentinentireinputensemble.Themethodenhancesthequalityoftheensemblebyreducingthepossiblebiasintheoriginalfullsetofsimulations.TheothermainadvantageofapplyingsuchamethodologyrepresentsthereductionofthecomputationalcostofallthefollowingoperationsInnutshell,themethodutilizestheindexofinnervariability.Thesubsequentclusteranalysisclassifieseachmemberoftheoriginalensembleintogroupsbasedontheirsimilarity.Infinalstep,thosegroupsaresampledinordertoobtainsubsetofthefullsetofsimulations.

Downscaling:Intheattempttocontributeonassessmentoffutureclimatechangeatcitylevelthisstudyexploresdownscalingtechniques.Fromtwowidefamilies,thestatisticaldownscalingispreferredduetoconstraintsontimeandfinancialresourcesofdynamicalmodels.Amongavailablegroupsofstatisticaldownscalingprocedures,theregressionmethodswereselectedmainlyfortheirabilitytoemploythefullrangeofavailablepredictorvariables.

Climateindices:Furthermore,weproposetocomputeseveralclimaticindicesprovidingtheinformationrelatedextremetemperatureandprecipitationeventsoccurrence,suchasthoseproposedbytheExpertTeamonClimateChangeDetectionandIndices(ETCCDI).Furthermore,weproposetocomputeseveralclimaticindicesprovidingtheinformationrelatedextremetemperatureandprecipitationeventsoccurrence,suchasthoseproposedbytheExpertTeamonClimateChangeDetectionandIndices(ETCCDI).Thoseindicesrepresentthetooltobetterquantifyobservedchangeinclimate,particularlyofitsmoreextremeaspects.Hence,climateindicesallowustobuildaclearpictureoflong-termvariabilityofextremes(Donat etal.,2013).

Context Challenges

Scientificpublications:1. Downscalingclimateprojections:adiscussionoftechniquesforimpactstudiesinurbanareas2. ClimatechangepatternsofextremeprecipitationandtemperaturefromEURO-CORDEXsubset3. ExtremeheatrelatedCCimpactassessmentbasedonstatisticallydownscaledclimateindices

Themainscientificcontributionisthedevelopmentofanewdownscalingclimatemodel,specificallytailoredforurbanenvironment,providingfutureclimatescenarios(withfocusonprojectionsofextremeevents)infine(city-level)spatial-temporalscale.ThismodelwillbedevelopedfullywithopensourcetechnologyandcomplementedwithdocumentationinordertobeintegratedintotheOpenCityToolkit.

Anotherexpectedresultisaninventoryofimportantinfrastructuresandassetsofthetargetareaofthecasestudy(Lisbonmetropolitanarea)informofadetailedharmonizeddatabase.Furthermore,thecataloguedinfrastructuresandassetstogetherwiththeexposuremapswillbepublishedviaaWebGIS platform,thusavailableforallthestakeholdersandgeneralpublic.

Results

Theurbanplannersrepresenttheprimaryusergroupoftheresultsofthisproject.Theforeseendeliverableswillserveasadecisionmakingsupportmaterialinfieldsofurbanplanning,CCimpactadaptationandmitigationstrategies,publichealth,energysectorandpreservationofculturalheritage.Notonlythestakeholdersfromprivatesector(e.g.insurancecompanies,developersorrealestateentrepreneurs),buttheindividualcitizensaswell,willbeabletobenefitfromthisresearch.Atlastbutnotatleast,alsothescientificcommunity,namelytheurbanclimatemodellerscanusethisstudyasabasisfortheirownfurtherresearch.

Impact

Actions

Theresultingmethodisadjustabletoanyurbansystemandforthewiderangeofclimatechangeinducednaturalhazards.Duetoitslowcomputationalcostandonlymoderaterequirementsintermsofscientificandtechnologicalexpertisethemethod,whenappliedinothermetropolitanareas,mayserveasabasisforcomparisons.Henceallowsforjudgmentsathigher(e.g.stateorregional)scale.Byincorporatingtheothervariablesandindicesmanyotheraspectsoffutureclimatebehaviourscanbeassessed.TheWebGIS platformcanbelinkedtoEarlyWarningSystems(EWSs)ifthosewouldbeavailableintheparticularmunicipality.ThevalueofthetoolcanbeenhancedbydeeperintegrationwithESR08,particularlybyfusionofthethermalandtheairqualityinformation.

ScalingUp

Consortium

ThecontributorsgratefullyacknowledgefundingfromtheEuropeanUnionthroughtheGEO-Cproject(H2020-MSCA-ITN-2014,GrantAgreementNumber642332,

http://www.geo-c.eu/).

Acknowledgements

1stPCs(LVD)1x1km

PCA

Regression- Kriging

Downscaleto1x1km

Evaluationofgoodnessoffitofdownscalingmodel(MODISandmeteorologicaldata(40yearsperiod)

Analysebias;comparePDFs,useTaylorsdiagramstodocumentskillofreproducingthevariabilityofT,Tx,Tn

AnalyzepatternsofmeanT,Tx,Tnforfutureperiods:2041– 2070+2071– 2100(forextendedwarmperiods:May,June,July,August,September)

Spatio-temporalexponentialvariograms(by30years)

LocalDescriptionVariables(LVD)• LatandLong• Elevation,slope,etc.• 6LULC• Distancetocoastandcitycentre,

etc.

EURO-CORDEX12.5km

Meteorologicaldata EURO-CORDEX

• Multi– modelensemble• Res:0.11(approx.12.5km)

Variablesofinterest:

• Near-surfaceairT(2m)• Precipitation• Relativehumidity• Globalradiation

SatellitetemperaturedataMODIS– res1x1km,LandSurfaceTemperature,daily

LandUse/LandCoverdataLand use/LandCover(LULC)classification:

• DerivedfromLANDYNresearchproject• Projectionsfor2020,2030,2040• Resolution:1x1km

ElevationdataSRTM(availableinres:30m,90m,1km)

CorineLandCover:

• Resolution:100m,250m

• Year2012

Dataontheimportantcityassetsandinfrastructures:

buildings,subwaynetwork,railwaynetwork,ports,airports,telecommunicationinfrastructure,waterpipelines,sewagenetwork,electricgrid,gaspipelines,powerplants,industry,culturalheritage,sport&leisurefacilities,etc.

SNIRH ECA&D

E-OBSDatabank-InternationalSurfaceTemperatureInitiativeRef.per.:1961-2000

Impact assessmentDiscussexposureofpopulatedareasandvarious

infrastructures,culturalheritage,geomedicine,etc.

WebGIS platform(includingharmonized

database of cityassessts and

infrastructures)

1 2 3 4 5Sub– Setting

ofClimateMME

Combiningthesubset

Datapre-processing

Index Description Units

HWMId HeatWaveMagnitudeIndexDaily ranking

Intensitycategory:

DTR– Diurinaltemperaturerange Meandifferencebetweendailymaximumanddailyminimumtemperature

°C

Durationcategory:

ECAHWFI (WSDI) Warmspelldaysindexwrt90thpercentileofreferenceperiod

#ofdays

Frequencycategory:

TX90p- Warmdays ShareofdayswhenTmax>90thpercentile

%ofdays

TN90p- Warmnights ShareofdayswhenTmin>90percentile

%ofdays

SUBSET DOWNSCALING

DOWNSCALING

T

tLat.Long.JAN1

DEC31

12x12x365x70x10=1.3x1015

DATA

CLIMATEINDICES

GENERALMETHODOLOGY