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Geomatics’ challenge: “Many climate impact modellers are simply not able to handle the amount of data generated by GCM-RCM simulations (Wilcke & Lars, 2016).” Data format: NetCDF-4 Files with HDF5 NetCDF is a set of SW libraries and machine-independent data formats that support the creation, access and sharing of array-oriented scientific data. Uses HD5F as data storage layer. Projection: False North Pole Rotated Grid CF-1.4 convention Data volume and large # of files: 4 variables: 900 GB in form of 1200 files (each one contains entire domain, 5 years – daily data) Urbanization and Population Boom In parallel with climate change, we are witnessing a population boom and the vast majority will concentrate in large metropolitan agglomerations. In 2007, first time in history, the urban population exceeded the rural one. Therefore, the human kind became predominantly urban based. The number of rural dwellers has been growing since 1950 and it is projected to reach its peak in near future (UN, 2014). In 2014, about 3.9 billion people, representing 54% of global population, were urban dwellers. Half of the urban population resides in small agglomerations up to half million inhabitants. Roughly one of eight urban dwellers is an inhabitant of a megacity (settlements with population of 10 million or more). The number of megacities nearly tripled since 1990. Currently, we have 28 megacities on the planet, and they are home to 453 millions of people. It is projected that by year 2030 there will be 41 megacities on the Earth. The projections say that by 2050 the urban systems will be home to 66% of global population, representing 6.3 billon urban dwellers (UN, 2014). Climate change and cities Urban systems act as important economic hubs and, as such, they are very demanding on resources. Globally, the energy consumption of urban agglomerations is up to 80% of the total energy production, which represents approximately 71–76% of global CO2 emissions (UN, 2014). This illustrates the important role of urban areas as drivers of global warming. The urbanized areas are not only major drivers of climate change, but simultaneously they are hot spots of various disaster risks, which make them especially vulnerable to chain reactions (WEF, 2015). Climate change has major economic consequences in form of reduction in labour productivity, disruption of transport systems and significant losses in energy production and its supply chains (Confalonieri et al., 2007). CLIMATE CHANGE AND IMPACTS ON THE QUALITY OF LIFE IN URBAN SYSTEMS Marek Smid NOVA IMS, Universidade Nova de Lisboa 1. Confalonieri, U., Menne, B., Akhtar, R., Ebi, K. L., Hauengue, M., Kovats, R. S., ... & Woodward, A. (2007). Human health. Climate change 2007: impacts, adaptation and vulnerability: contribution of Working Group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. 2. Donat, M. G., Alexander, L. V., Yang, H., Durre, I., Vose, R., & Caesar, J. (2013). Global land-based datasets for monitoring climatic extremes. Bulletin of the American Meteorological Society, 94(7), 997–1006 3. United Nations, Department of Economic and Social Affairs Population Division (2014). World Urbanization Prospects: The 2014 Revision, Highlights (ST/ESA/SER.A/352). 4. Wilcke, R. A., & Bärring, L. (2016). Selecting regional climate scenarios for impact modelling studies. Environmental Modelling & Software, 78, 191-201. 5. World Economic Forum (WEF). (2015). Global Risks 2015. 10th Edition. References METHODOLOGY Sub set: This procedure leads to sub select of the models from ensemble of simulations. The resulting subset maintains the model spread regarding the variability present in entire input ensemble. The method enhances the quality of the ensemble by reducing the possible bias in the original full set of simulations. The other main advantage of applying such a methodology represents the reduction of the computational cost of all the following operations In nutshell, the method utilizes the index of inner variability. The subsequent cluster analysis classifies each member of the original ensemble into groups based on their similarity. In final step, those groups are sampled in order to obtain subset of the full set of simulations. Downscaling: In the attempt to contribute on assessment of future climate change at city level this study explores downscaling techniques. From two wide families, the statistical downscaling is preferred due to constraints on time and financial resources of dynamical models. Among available groups of statistical downscaling procedures, the regression methods were selected mainly for their ability to employ the full range of available predictor variables. Climate indices: Furthermore, we propose to compute several climatic indices providing the information related extreme temperature and precipitation events occurrence, such as those proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Furthermore, we propose to compute several climatic indices providing the information related extreme temperature and precipitation events occurrence, such as those proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Those indices represent the tool to better quantify observed change in climate, particularly of its more extreme aspects. Hence, climate indices allow us to build a clear picture of long-term variability of extremes (Donat et al., 2013). Context Challenges Scientific publications: 1. Downscaling climate projections: a discussion of techniques for impact studies in urban areas 2. Climate change patterns of extreme precipitation and temperature from EURO-CORDEX subset 3. Extreme heat related CC impact assessment based on statistically downscaled climate indices The main scientific contribution is the development of a new downscaling climate model, specifically tailored for urban environment, providing future climate scenarios (with focus on projections of extreme events) in fine (city-level) spatial-temporal scale. This model will be developed fully with open source technology and complemented with documentation in order to be integrated into the Open City Toolkit. Another expected result is an inventory of important infrastructures and assets of the target area of the case study (Lisbon metropolitan area) in form of a detailed harmonized database. Furthermore, the catalogued infrastructures and assets together with the exposure maps will be published via a WebGIS platform, thus available for all the stakeholders and general public. Results The urban planners represent the primary user group of the results of this project. The foreseen deliverables will serve as a decision making support material in fields of urban planning, CC impact adaptation and mitigation strategies, public health, energy sector and preservation of cultural heritage. Not only the stakeholders from private sector (e.g. insurance companies, developers or real estate entrepreneurs), but the individual citizens as well, will be able to benefit from this research. At last but not at least, also the scientific community, namely the urban climate modellers can use this study as a basis for their own further research. Impact Actions The resulting method is adjustable to any urban system and for the wide range of climate change induced natural hazards. Due to its low computational cost and only moderate requirements in terms of scientific and technological expertise the method, when applied in other metropolitan areas, may serve as a basis for comparisons. Hence allows for judgments at higher (e.g. state or regional) scale. By incorporating the other variables and indices many other aspects of future climate behaviours can be assessed. The WebGIS platform can be linked to Early Warning Systems (EWSs) if those would be available in the particular municipality. The value of the tool can be enhanced by deeper integration with ESR08, particularly by fusion of the thermal and the air quality information. Scaling Up Consortium The contributors gratefully acknowledge funding from the European Union through the GEO-C project (H2020-MSCA-ITN-2014, Grant Agreement Number 642332, http://www.geo-c.eu/). Acknowledgements 1st PCs (LVD) 1x1 km PCA Regression - Kriging Downscale to 1x1 km Evaluation of goodness of fit of downscaling model (MODIS and meteorological data (40 years period) Analyse bias; compare PDFs, use Taylors diagrams to document skill of reproducing the variability of T, Tx, Tn Analyze patterns of mean T, Tx, Tn for future periods: 2041 – 2070 + 2071 – 2100 (for extended warm periods: May, June, July, August, September) Spatio-temporal exponential variograms (by 30 years) Local Description Variables (LVD) • Lat and Long • Elevation, slope, etc. • 6 LULC • Distance to coast and city centre, etc. EURO-CORDEX 12.5 km Meteorological data EURO-CORDEX • Multi – model ensemble • Res: 0.11 (approx. 12.5 km) Variables of interest: • Near-surface air T (2m) • Precipitation • Relative humidity • Global radiation Satellite temperature data MODIS – res 1x1 km, Land Surface Temperature, daily Land Use / Land Cover data Land use / Land Cover (LULC) classification: • Derived from LANDYN research project • Projections for 2020, 2030, 2040 • Resolution: 1x1 km Elevation data SRTM (available in res: 30 m, 90 m, 1 km) Corine Land Cover: • Resolution: 100m, 250 m • Year 2012 Data on the important city assets and infrastructures: buildings, subway network, railway network, ports, airports, telecommunication infrastructure, water pipelines, sewage network, electric grid, gas pipelines, power plants, industry, cultural heritage, sport & leisure facilities, etc. SNIRH ECA&D E-OBS Databank - International Surface Temperature Initiative Ref. per.: 1961-2000 Impact assessment Discuss exposure of populated areas and various infrastructures, cultural heritage, geomedicine, etc. WebGIS platform (including harmonized database of city assessts and infrastructures) 1 2 3 4 5 Sub – Setting of Climate MME Combining the subset Data pre-processing Index Description Units HWMId Heat Wave Magnitude Index Daily ranking Intensity category: DTR – Diurinal temperature range Mean difference between daily maximum and daily minimum temperature °C Duration category: ECAHWFI (WSDI) Warm spell days index wrt 90 th percentile of reference period # of days Frequency category: TX90p - Warm days Share of days when Tmax > 90 th percentile % of days TN90p - Warm nights Share of days when Tmin > 90 percentile % of days SUB SET DOWNSCALING DOWNSCALING T t Lat. Long. JAN 1 DEC 31 12 x 12 x 365 x 70 x 10 = 1.3 x 10 15 DATA CLIMATE INDICES GENERAL METHODOLOGY

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Page 1: CLIMATE CHANGE AND IMPACTS ON THE QUALITY …geo-c.eu/data/posters/POSTER_ESR09.pdfGroup II to the fourth assessment report of the Intergovernmental Panel on ... Among available groups

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

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