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EvaluationanddownscalingofCMIP5climatesimulationsfortheSoutheastUS
FINALPROJECTMEMORANDUM
August21,2015
1.ADMINISTRATIVEProjecttitle:EvaluationanddownscalingofCMIP5climatesimulationsfortheSoutheastUSAgreement#:G13AC00407Awardrecipients:OregonStateUniversity(OSU):PhilipMote,DavidRuppUniversityofIdaho(UI):JohnAbatzoglouTimeperiodcoveredbyreport:6/15/2014through2/28/2015Actualtotalcost:$30,0002.PUBLICSUMMARYThisprojecthasgeneratedaseriesoffreelyavailabledatasetsthatprovideprojectionsofclimatechangeatappropriatespatialscalesthatcandirectlyaddressspecificmanagementquestions.Theseclimatechangeprojectionsaretheresultof“downscaling”outputfromglobalclimatemodels(GCMs)thatformedthebasisofmanyconclusionsintheIntergovernmentalPanelonClimateChange(IPCC)AssessmentReport5(AR5).Thedatasetsincludeprojectionsofclimatevariablesinadditiontodailytemperatureandprecipitationsuchassurfacewinds,humidityandsolarradiationthatareneededinhydrologicandecologicalmodeling.Twoproducts,oneata4-kmresolution,theotherata6-kmresolution,coverthecontinentalUnitedStateshavebeencompletedandareavailablethroughdataserversincludinghttps://www.northwestknowledge.net/Moreover,anevaluationwasdoneofhowwelltheGCMsreproducethehistoricalclimateofSoutheastUSandsurroundingregion.Thisevaluationcanbeusedasonesourceofinformationwhenauserisfacedwithselectingasmallnumberofclimateprojectionsfromthelargersetofavailableprojectionsforanimpactsassessment.Collectively,theguidanceonthecredibilityofGCMsoverthesoutheasternUSandthedownscaleddatasetsprovidenecessaryinformationanddatatodevelopstrategiesforcopingwithclimatechange.3.TECHNICALSUMMARYDownscalingmethodsareusedtobridgethespatialmismatchandbiasesbetweenoutputfromglobalclimatemodels(GCMs–typicalspatialresolutionisseveraldegreeslatitudebylongitude)andinputrequiredbysecondarymodelingapplications.WeadvancedseveraldetailsofstatisticaldownscalingtofacilitatethatdownscaleddatarepresentthesignalofchangesassimulatedbytheGCMwhileretainingmanyofthepropertiesofthetrainingdatasetstoensurecompatibilityforimpactsmodeling.InadditiontodownscalingGCMs,weevaluatedtheGCMswithrespecttotheirabilitytoreproducetheobserved20thcenturyclimatefortheSoutheastUnitedStates(US)andsurroundings.Asuiteofstatisticsthatcharacterizevariousaspectsoftheregionalclimatewascalculatedfrombothmodelsimulationsandobservation-baseddatasets.Lastly,GCMsmodelswererankedbytheirfidelitytotheobservations.
4.PURPOSEANDOBJECTIVES:GCMDownscalingThefirstobjectiveofthisprojectwastogenerateamorephysicallyconsistentanddetailedsetofprojectedmeteorologicalvariablesthanfoundinexistingdownscaledclimateprojections.WhilepreviousdownscalingeffortssuchasBias-CorrectionStatisticalDisaggregation(BCSD)andBias-CorrectionConstructedAnalogs(BCCA)areundoubtedlyvaluable,theyhavelimitationsordrawbacksthatmaymakethemlessdesirableforparticularuses.Theselimitationsincludearestrictedsetofvariables(typicallyonlytemperatureandprecipitation),inabilitytoutilizedailyGCMoutputandpreserveco-variabilityacrossvariables,andissuesinvolvingthetreatmentofmodelbiases.TheMultivariateAdaptiveConstructedAnalogues(MACA,AbatzoglouandBrown2012),andaugmentationstherefore(HegewischandAbatzoglou,forthcoming)largelyovercomesomeoftheselimitationsandallowforamorecomprehensivesetofdownscaledclimateproducts.However,MACAisnotapanaceafordownscaling,asitcannot‘correct’foraglobalclimatemodel’sdeficiencyinsimulatingspatialpatternsofconvectiveprecipitation,orresolvechangesinclimatethatarisefromatmosphere-surfacefeedbacks.Likewise,resolvingthespatialdetailsofconvectivelydrivenprecipitationischallengingforalldownscalingmethods.GCMEvaluationClimatesimulationsfromglobalclimatemodels(GCMs)areoftenreliedupontoprovideplausiblefutureclimatescenariosinclimatechangeimpactsassessmentsatregionalandlocalscales.Frequently,usersareconstrainedtoselectasubsetofthemanyclimateprojectionsavailablefromalargesuiteofGCMs.Modelfidelityisonecriterionthatmaybeusedtoweanthelargepoolofavailableprojections.OursecondobjectiveoftheprojectwastoaidusersinselectingGCMsimulationsbyevaluatinghowindividualGCMsperformedwithrespecttoreproducingthehistorical20th-centuryclimateofthesoutheastUSA.5.ORGANIZATIONANDAPPROACHGCMDownscalingClimatescenariosfrom20CMIP5GCMswiththerequisitedailydatawerestatisticallydownscaledusingtheMultivariateAdaptiveConstructedAnalogues(MACA,AbatzoglouandBrown2012),1950-2005forhistoricalrunsand2006-2099forRCP4.5and8.5(Table1).OutputsfromtwoGCMsthathad360-dayyearswererescaledtoconformtoa365-dayyearcalendar.Twodownscalingproductswereproduced:macav2livnehandmacav2metdata.First,buildingoffthedownscalingperformedfortheNorthwestClimateScienceCenter(whichused“training”datafromthesurfacegriddedmeteorologicaldatasetofLivnehetal.(2013)at1/16th-degreeresolution),weexpandedthedownscalingdomainfromtheNorthwestUStothecontiguousUnitedStatestocreatethemacav2livnehdownscaledproduct.Second,utilizingthe‘training’datafromthegriddedmeteorologicaldatasetofAbatzoglou(2013),thatincludesadditionalvariablessuchasdownwardshortwaveradiationandthesurface,humidity,and10-mwindvelocityatacommon1/24th-degreespatialresolution,wecreatedthemacav2metdataproduct.ThelistofvariablesthatareavailablefromtheseproductsisprovidedinTable2.For thiswork,we augmented the originalMACA downscaling approach to better address some of thebiases inherent in GCMs. The updates included (i) continuous trend preservation of the original GCMsignalusinga31-year,21-daysmoothingwindow,2)useofareducedsetofanalogpatternsbutinclusionof a residual term from the constructed analogs, and 3) joint bias correction of temperature andprecipitation to remove intermodelbiases in temperaturecoincidentwithprecipitation (Hegewischand
Abatzoglou, forthcoming). Thesemodifications resulted in significant improvements in downscaling, asseeninacross-validationstudy.Insummary,MACAwaschosenforthisdownscalingoverothermethodsforthefollowingreasons:•MACAusesdailyoutputfromGCMsandismorereadilyabletocapturechangesinhigher-orderclimatestatistics(e.g.,extremes)thanmethodsthattemporallydisaggregatefrommonthlyprojections.• The spatial downscaling from MACA uses observed spatial patterns rather than using interpolationapproaches.•MACA can be extended tomultiple variables.We downscaled daily temperature, precipitation,windspeed,downwardshortwaveradiationandhumidity.•MACAdownscales someof the variables in sets in order to preserve the dependencies between thevariables. For example, the downscaling of temperature jointly with precipitation has been seen toproducebetterresultsincapturinghistoricalstatisticsofsnowfallandcorrectformodelbiasesspecifictoprecipitatingdaysandthusprecipitationphase.GCMEvaluationRetrospective(i.e.,20thcentury)climatescenariosfrom41CMIP5GCMswereexamined.Wecomparedrelevant20th-centuryobservationswith thesuiteofCMIP5globalmodel resultsaccordingtoasuiteofmetrics designed to determine their suitability for Southeast climate studies following the proceduresoutlinedbyRuppetal.(2013).The metrics listed in Table 1 were calculated from up to 5 observational datasets and all the GCM-simulated datasets of temperature and precipitation. The GCMswere then ranked according to theiroverallfidelitywithrespecttoobservations.6.PROJECTRESULTSGCMDownscalingBetweenthetwodownscalingproductsofmacav2livnehandmacav2metdata,atotalof26terabytesofdownscaleddatawereproduced.Althoughtherearenumerouswaystoanalyzethedata,weprovideacoupleexamplesherethatcanbeexplored infurtherdetail throughourwebpage(seeSec.9).Figure1shows the 20-model mean projected change in Mar-May downward shortwave radiation andprecipitation for years 2070-2099 of experiment RCP 8.5with respect to late 20th century climatology.Figure 2 shows differential rates of warming between the coldest day of thewinter andmeanwintertemperature. This elucidates the additional type of information that can be gleaned from MACAdownscalingthatincorporatesdailyGCMprojections.GCMEvaluationTheprojectgeneratedalargenumberofclimatemetricsperGCMandobservationaldataset.ThesehavebeenpresentedinfiguresthatmaybeusedtocompareamongGCMsortoassesstheabilityoftheCMIP5modelsasawholetofaithfullysimulatetheclimateofthesoutheasternUS.Asanexample,Figure3givesameans of comparing all GCMs and all metrics at once, and thus can be used as an initial means ofidentifyingGCMs that do poorly in a particularmetric, of set ofmetrics, thatmay be of interest for aparticularuse.AdetailedassessmentoftheGCMswithrespecttoeachmetricisprovidedinthetechnicalreport“AnEvaluationofCMIP520thCenturyClimateSimulationsfortheSoutheastUSA”.
7.ANALYSISANDFINDINGSGCMDownscalingDownscaledclimateprojectionshaveallbeenconvertedtoNetCDFformatusingCFmetadatastandardsto ensure compatibility across platforms.We provide both daily and aggregatedmonthlyNetCDF files,acknowledging the different needs of end users. All datasets have been transferred to the NorthwestKnowledge Network (NKN) including the Regional Approaches to Climate Change (REACCH) subserver.NKNprovidesseveralservicestoaidusersinacquiringthehosteddata.First, NKN provides a data catalog to aid users in finding information about the data, as well as tomanuallydownloadsingledatafiles(orsubsets)fromtheinternetindifferentformats(i.e.ascii,NetCDF).Thedatacatalogsforthe2downscaledproductsare:• http://thredds.northwestknowledge.net:8080/thredds/catalog/NWCSC_INTEGRATED_SCENARIOS_ALL_CLIMATE/macav2livneh/catalog.html• http://reacchpna.org/thredds/reacch_climate_CMIP5_macav2_catalog.htmlSecond,NKNprovidesTHREDDSservicestothehosteddatafiles.THREDDSenablesuserstomoreeasilydownloadspatial/temporalsubsetsof thedata, includingtheuseofOPeNDAPtoextractsubsetsof thedatafromwithintheuser’sfavoritesoftwareprogram(R,MATLAB,Python,IDL,etc.).Lastly, though each of the raw NetCDF files represent only 5 or 10-year time spans of data, NKN hasaggregatedalltheyearlyfilesforeachofthescenarios(historical,rcp45,rcp85)intoasinglepointerfile,whichcanbeusedtoaidusersforaccessingallyearsofthedata.ThroughNKN’sservices,usersareabletodownloadspatialsubsetsofthedata,aswellaseachdailyvariableaggregatedtomonthlyaverages.DatastorageandaccessfortheSoutheastdatasetswouldbedecideduponconsultationwithSECSC.GCMEvaluationTherankingofGMCsisnotastraightforwardendeavor.Anyrankingwillvarywiththeparticularmetric,orsetofmetrics,chosen.Also,insomecases,metricswillbephysicallyrelatedtotheextenttheyprovideredundant information. Finally, we may have low large uncertainties about the accuracy of ourestimationofthemetricitself.Givingconsiderationtothelattertwoissues,werankedthemodelsusingamethodology that accounted for information redundancy and favored themetricswe believedweremore reliable. Using the approach, we found that models from the CCSM/CESM1, CNRM, CMCC,HadGEM2,GISS-E2,andMPI-ESMfamiliesrankedhigherthantheothers(Figure4).Thisoverallranking,however, is provided as a suggestion. Individuals can examine the results presented in the technicalreportandusetheseaguidetoamodelselectionsuitedtotheirparticularneedsandobjectives.9.MANAGEMENTAPPLICATIONSANDPRODUCTSInadditiontodownscalingthedatasets,wehavecreatedawebinterfaceforpotentialdatauserstolearnmore about the downscaling methodology and visualize certain aspects of the datasets athttp://climate.northwestknowledge.net/MACA/Thiswebsiteprovidesseveralvisualizationtools,includingtheabilityforuserstoexaminespatialpatternsof change for the variables that have been downscaled across seasons, variable and scenarios. Thesedecision support toolsareofutilityboth fordirectusersof the climatedatasets, aswell as forgeneraldepictionofprojectionsacrosstheregion.Moreover,thetechnicalreport“AnEvaluationofCMIP520th
CenturyClimateSimulationsfortheSoutheastUSA”willbeavailableonwebsiteoncereporthasobtainedOFRcitation.Wearecurrentlyworkingonadataportaltoaidusersindownloadingspatialsubsetsofthedownscaleddailydata,aswellasaggregationsofeachvariabletomonthlyvalues,informatssuchascsv.10.OUTREACHWehavecontinuedtoupdateourwebpagetoprovidevisualizations,guidanceanddata.PresentationsKatherineHegewisch,JohnAbatzoglou,DavidRupp,PhilMote."StatisticallydownscaledclimatedatausingtheMultivariateAdaptiveConstructedAnalogsapproach"5thannualPacificNorthwestClimateScienceConference(PNWCSC),Sept,2014SeattlePublicationsHegewisch,K.C.,Abatzoglou,J.T.,‘AnimprovedMultivariateAdaptiveConstructedAnalogs(MACA)StatisticalDownscalingMethod’,inpreparation.Rupp,D.E.,2015,An Evaluation of CMIP5 20th Century Climate Simulations for the Southeast USA,USGSOpenFileReportXXXXXReferencesRupp,D.E.,J.T.Abatzoglou,K.C.Hegewisch,andP.W.Mote(2013),EvaluationofCMIP520thcenturyclimatesimulationsforthePacificNorthwestUSA,J.Geophys.Res.Atmos.,118,10,884–10,906,doi:10.1002/jgrd.50843.
Table1.AvailabledownscaledCMIP5globalclimatesimulationsusingMACA,1950-2099,RCP4.5andRCP8.5
BCC-CSM1-1
BCC-CSM1-1-M
BNU-ESM
CanESM2
CCSM4
CNRM-CM5
CSIRO-Mk3-6-0
GFDL-ESM2G
GFDL-ESM2M
HadGEM2-CC
HadGEM2-ES
INMCM4
IPSL-CM5A-LR
IPSL-CM5A-MR
IPSL-CM5B-LR
MIROC5
MIROC-ESM
MIROC-ESM-CHEM
MRI-CGCM3
NorESM1-M
Table2.Downscaledvariables
Variable Abbreviation Height
Temperature,maximum tasmax 2m
Temperature,minimum tasmin 2m
Precipitationrate pr Surface
Relativehumidity,maximum rhsmax 2m
Relativehumidity,minimum rhsmin 2m
Specifichumidity huss 2m
Wind,speed was 10m
Wind,eastward vas 10m
Wind,eastward vas 10m
Downwellingsolarradiation rsds Surface
Table 3. Definitions of global climate performance metrics, the confidence in the metrics for modelranking,andobservationaldatasetsusedbymetric.
Metrica Confidencecategory
Description Observationdatasets
Mean-TMean-P
HighestHighest
Mean annual temperature (T) andprecipitation(P),1960-1999
CRU,PRISM,UDelaware,ERA40d,NCEPd
DTR-MMMc Highest Meandiurnaltemperaturerange,1950-1999 CRUe,PRISMe,NCEP
SeasonAmp-TSeasonAmp-P
HighestHigher
Mean amplitude of seasonal cycle as thedifference between warmest and coldestmonth (T), orwettest and driestmonth (P).Monthly precipitation calculated aspercentageofmeanannualtotal,1960-1999.
CRU,PRISM,UDelaware,ERA40d,NCEPd
SpaceCor-MMM-Tb,cSpaceCor-MMM-Pb,c
HighestHigher
Correlation of simulated with observed themeanspatialpattern,1960-1999.
ERA40,NCEPe
SpaceSD-MMM-Tb,cSpaceSD-MMM-Pb,c
HighestHigher
Standard deviation of the mean spatialpattern, 1960-1999. All standarddeviationsarenormalizedby the standarddeviationoftheobservedpattern.
ERA40,NCEPe
TimeVar.1-TTimeVar.8-T
LowerLowest
Variance of temperature calculated atfrequencies (time periods of aggregation)rangingforN=1and8years,1901-1999.
CRU,PRISM,UDelaware
TimeCV.1-PTimeCV.8-P
LowerLowest
Coefficient of variation (CV) of precipitationcalculated at frequencies (time periods ofaggregation) ranging forN = 1 and 8 wateryears,1902-1999.
CRU,PRISM,UDelaware
TimeVar-MMM-Tc Lower Variance of seasonal mean temperature,1901-1999.
CRU,PRISM,UDelaware
TimeCV-MMM-Pc Lower Coefficient of variation of seasonal meanprecipitation,1901-1999.
CRU,PRISM,UDelaware
Trend-TTrend-P
LowerLowest
Linear trend of annual temperature andprecipitation,1901-1999.
CRU,PRISM,UDelaware
ENSO-TENSO-P
LowerLowest
Correlation of winter temperature andprecipitationwithNiño3.4index,1901-1999.
CRU,PRISM,UDelaware
Hurst-THurst-P
LowestLowest
Hurst exponent using monthly differenceanomalies (T) or fractional anomalies (P),1901-1999.
CRU,PRISM,UDelaware
aUnlessotherwisenoted,metricsareaverageoverSoutheastUS.bExpandeddomain:115°W–50°W,15°N–55°N.cMMMistheseasondesignation:DJF,MAM,JJA,andSON.dTemperatureonlyusedinranking,notprecipitation.eNotusedinranking.
Figure1:Projected20modelmeanchangein(top)Mar-Maydownwardradiationandin(top)Dec-Febprecipitation for years 2070-2099 of experiment RCP8.5 versus the historical climate experiment for1950-2005fromdownscaledCMIP5climatemodeloutputs.
Figure2:Projected20modelmeanchange(indegreesC)in(top)coldestminimumtemperature(TMIN)and(bottom)averageminimumtemperature(TMIN)eachwinter(Dec-Feb)foryears2040-2069ofexperimentRCP8.5versusthehistoricalclimateexperimentformodelyears1971-2000.
Figure3:RelativeerroroftheensemblemeanofeachmetricforeachCMIP5GCM.Modelsareorderedfromleast(left)tomost(right)totalrelativeerror,wheretotalrelativeerroristhesumofrelativeerrorsfromallmetrics.
DTR.SONDTR.JJA
DTR.MAMDTR.DJF
SpaceSD.SON−PSpaceSD.JJA−P
SpaceSD.MAM−PSpaceSD.DJF−PSpaceSD.SON−TSpaceSD.JJA−T
SpaceSD.MAM−TSpaceSD.DJF−TSpaceCor.SON−PSpaceCor.JJA−P
SpaceCor.MAM−PSpaceCor.DJF−PSpaceCor.SON−TSpaceCor.JJA−T
SpaceCor.MAM−TSpaceCor.DJF−TTimeCV.SON−PTimeCV.JJA−P
TimeCV.MAM−PTimeCV.DJF−PTimeVar.SON−TTimeVar.JJA−T
TimeVar.MAM−TTimeVar.DJF−TTimeCV.8yr−PTimeVar.8yr−TTimeCV.1yr−PTimeVar.1yr−T
Hurst−PHurst−TENSO−PENSO−TTrend−PTrend−T
SeasonAmp−PSeasonAmp−T
Mean−PMean−T
CESM
1−CA
M5
CNRM
−CM5−2
MPI−ESM
−LR
CNRM
−CM5
CMCC
−CMS
CESM
1−FAST
CHEM
CMCC
−CM
GISS−E2−H
−CC
CCSM
4GISS−E2−R
−CC
CESM
1−BG
CHa
dGEM
2−ES
MIROC5
MPI−ESM
−MR
CSIRO−M
k3−6−0
EC−EAR
THGISS−E2−H
BNU−
ESM
CESM
1−WAC
CMGISS−E2−R
GFD
L−ES
M2G
FIO−ESM
CanESM
2Ha
dGEM
2−AO
GFD
L−CM
3IPSL−C
M5A−M
RHa
dGEM
2−CC
HadC
M3
NorESM
1−M
IPSL−C
M5A−LR
MRI−C
GCM
3bcc−csm1−1
IPSL−C
M5B−LR
FGOALS−g2
GFD
L−ES
M2M
CMCC
−CES
Minmcm
4MIROC−
ESM−C
HEM
MIROC−
ESM
FGOALS−s2
bcc−csm1−1−m
0.0
0.2
0.4
0.6
0.8
1.0
Figure4:41CMIP5GCMsrankedaccordingtonormalizederrorscorefromEOFanalysisofperformancemetrics.Rankingisbasedonthefirst5principalcomponents(filledbluecircles).Theopensymbolsshowthemodels’errorscoresusingthefirst2,4,andall22principalcomponents(PCs).RelativeerroroftheensemblemeanofeachmetricforeachCMIP5GCM.Modelsareorderedfromleast(left)tomost(right)totalrelativeerror,wheretotalrelativeerroristhesumofrelativeerrorsfromallmetrics.