Evaluation and downscaling of CMIP5 climate simulations for the Southeast US FINAL PROJECT MEMORANDUM August 21, 2015 1. ADMINISTRATIVE Project title: Evaluation and downscaling of CMIP5 climate simulations for the Southeast US Agreement #: G13AC00407 Award recipients: Oregon State University (OSU): Philip Mote, David Rupp University of Idaho (UI): John Abatzoglou Time period covered by report: 6/15/2014 through 2/28/2015 Actual total cost: $30,000 2. PUBLIC SUMMARY This project has generated a series of freely available datasets that provide projections of climate change at appropriate spatial scales that can directly address specific management questions. These climate change projections are the result of “downscaling” output from global climate models (GCMs) that formed the basis of many conclusions in the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 5 (AR5). The datasets include projections of climate variables in addition to daily temperature and precipitation such as surface winds, humidity and solar radiation that are needed in hydrologic and ecological modeling. Two products, one at a 4-km resolution, the other at a 6-km resolution, cover the continental United States have been completed and are available through dataservers including https://www.northwestknowledge.net/ Moreover, an evaluation was done of how well the GCMs reproduce the historical climate of Southeast US and surrounding region. This evaluation can be used as one source of information when a user is faced with selecting a small number of climate projections from the larger set of available projections for an impacts assessment. Collectively, the guidance on the credibility of GCMs over the southeastern US and the downscaled datasets provide necessary information and data to develop strategies for coping with climate change. 3. TECHNICAL SUMMARY Downscaling methods are used to bridge the spatial mismatch and biases between output from global climate models (GCMs – typical spatial resolution is several degrees latitude by longitude) and input required by secondary modeling applications. We advanced several details of statistical downscaling to facilitate that downscaled data represent the signal of changes as simulated by the GCM while retaining many of the properties of the training datasets to ensure compatibility for impacts modeling. In addition to downscaling GCMs, we evaluated the GCMs with respect to their ability to reproduce the observed 20th century climate for the Southeast United States (US) and surroundings. A suite of statistics that characterize various aspects of the regional climate was calculated from both model simulations and observation-based datasets. Lastly, GCMs models were ranked by their fidelity to the observations.
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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.
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.
Figure1:Projected20modelmeanchangein(top)Mar-Maydownwardradiationandin(top)Dec-Febprecipitation for years 2070-2099 of experiment RCP8.5 versus the historical climate experiment for1950-2005fromdownscaledCMIP5climatemodeloutputs.