ContentslistsavailableatScienceDirect
Journal
of
Hydrology:
Regional
Studies
jou rn a l h om ep a ge :w w w . e l s e v i e r . c o m / l o c a t e / e j r h
Future
changes
in
precipitation
and
water
resources
for
Kanto
Region
in
Japan
after
application
of
pseudo
global
warming
method
and
dynamical
downscaling
Kenji
Taniguchi
FacultyofEnvironmentalDesign,KanazawaUniversity,Kakuma-machi,Kanazawa,920-1192,Japan
a
r
t
i
c
l
e
i
n
f
o
Articlehistory:
Received22December2015
Receivedinrevisedform20October2016 Accepted30October2016
Availableonline9November2016 Keywords:
Precipitation Climatechange Waterresources Dynamicaldownscaling Pseudoglobalwarming
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Studyregion:TheKantoregion,Japan.
Studyfocus:Detailedassessmentofpresentandfutureclimateconditionsandtheireffects onwaterresourcesintheKantoregionofJapanusingamodifiedpseudoglobalwarming dynamicaldownscalingmethodwithanumericalweatherpredictionmodel.
Newhydrologicalinsights:Infutureclimateconditions,resultsonthechangeinannual precipitationarescattered,withsignificantvariationsinmeanannualprecipitationand thestandarddeviationinverylimitedareas.Incontrast,minimumannualprecipitationis foundtodecreaseandyearswithlowrainfalltobemorefrequent.Duringthedriersummer season,theminimumaccumulatedrainfallisexpectedtobecomesmalleracrossawide regioninthefuture.Inaddition,frequencydistributionsoffuturedailyprecipitationshow adecreaseofweakprecipitationandanincreaseofheavyprecipitation.Suchvariations areunfavorableforwaterrechargeandindicatethatwaterresourcesmanagementwill becomeincreasinglydifficultinthefuturebecauseofglobalwarming.Thelowerrainfall conditionsareduetothelowerrelativehumidity,morefrequentstablestratificationsand sub-synopticatmosphericconditionsleadingtohigher-pressureanomaliesaroundJapan. ©2016TheAuthor.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCC BYlicense(http://creativecommons.org/licenses/by/4.0/).
1. Introduction
Globalwarminghasworldwidesignificanceforhumanactivities.Toinvestigatetheeffectsofclimatechangeduetoglobal warming,projectionsusingatmosphere-oceancoupledgeneralcirculationmodels(AOGCMs)provideimportant informa-tion.However,thespatialresolutionofAOGCMsistoocoarsetopermitassessmentoftheimpactsoffutureclimatechange onaspecificregion,basin,orcity.Furthermore,someimportantregionalfeaturesareoftennotresolvedinAOGCMs.Detailed investigationsintotheeffectsofclimatechangedemandregionalclimateinformationwithhigherspatialresolution. There-foredownscalingmethodshavebeenappliedtoAOGCMsoutput(Leungetal.,2003;Wangetal.,2004).Generally,thereare twotypesofdownscalingmethods:statisticalanddynamicaldownscalingmethods.Instatisticaldownscaling,statistical relationshipsbetweenglobalandregionalclimaticvariablesareusedtogeneratehigher-resolutionregionalclimate infor-mation.Dynamicalandphysicaltheoriesofmeteorologyareappliedindynamicaldownscalingusinganumericalmodel (Dickinsonetal.,1989;Giorgi,1990).Thecomputationalloadofstatisticaldownscalingislow,butitisuncertainwhether relationshipsthatholdinthecurrentclimatewouldbeapplicableunderfutureclimaticconditions.Dynamical downscal-ing(DDS)requiresstrongcomputationalcapabilityandthevolumeofthedownscalingoutputisconsiderable.However,
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http://dx.doi.org/10.1016/j.ejrh.2016.10.004
2214-5818/©2016TheAuthor.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/ 4.0/).
universaldynamicalandphysicaltheoriescanbeappliedtobothcurrentandfutureclimaticconditions.Therefore,inthis study,DDSisusedtoproducehigher-resolutionclimateinformation.
ThereareuncertaintiesinAOGCMclimateprojections,whicharecausedbyuncertaintyinfutureemissionscenarios, imperfectinitialandboundaryconditions,incompleteunderstandingofclimatesystems,andmodel imperfections.To reducetheseuncertainties,studieshaveproposedananalysismethodbasedonprojectionsfrommultipleAOGCMs,the so-calledmulti-modelensemblemethod(Collins,2007;Knuttietal.,2010).Suchaconceptisinvaluableinassessmentsof theeffectsofclimatechangeusingaDDSmethod.OneproblemtobeaddressedinDDSoffutureclimateconditionsisAOGCM incompleteness.ThepurposebehindsimulatingpastandcurrentclimatesusingAOGCMsisnottocreateperfect reproduc-tionsbuttoenablethegeneralcharacteristicsoftheclimatetobeestablished.Thefinerclimateinformationgeneratedasa DDSoutputisstronglyinfluencedbyAOGCMbias(Katoetal.,2001).Therefore,itisdifficulttoevaluatethereproducibility ofcurrentclimateconditionsobtainedbyDDSwithaforcingfromanAOGCM.However,climatologicalreanalysisdatacan beusedforDDSforcingofpastandcurrentclimates.ToobtainfutureDDSoutputs,Satoetal.(2007)generatedinitialand boundaryconditionsbycoupling6-hourlyreanalysisdataandclimatologicalmonthlymeananomaliesofglobalwarming extractedfromanAOGCM.Theresultingforcingdata,whichtheycalledthepseudoglobalwarming(PGW)condition,was usedforDDSoffutureclimatetoinvestigateprecipitationinMongolia.DDSwasappliedtothepresentclimateusingboth reanalysisdataandtheAOGCMoutput,anddemonstratedbetterreproducibilityofDDSprecipitationwhenusingreanalysis datathanwhenusingtheAOGCMoutput.ThetwoDDSresultsshowedsimilarvariationsinfutureprecipitation(i.e. decreas-ingandincreasingovernorthernandsouthernMongolia,respectively).Yoshikaneetal.(2012)examinedthereproducibility ofdownscalingresultsforfuturerainyseasons,orJuneclimate,inJapanfromDDSwithPGWconditions.Theyfoundsimilar characteristicsbetweenresultsbasedonPGWconditionsanddirectoutputsfromanAOGCM.Thesestudiesindicatethe potentialofthePGWconditionforgeneratingreliableDDSresultsoffutureclimate.Kawaseetal.(2009)usedDDSwith thePGWconditiontoexamineactivitiesoftheBaiurainband,whichformsduringJuneandJulyaroundJapan.Theyfound asouthwardshiftandanincreaseintheprecipitationfromtherainbandunderfutureclimateconditions.
IntheaboveDDSstudies,PGWconditionsweregeneratedfrom6-hourlyreanalysisdataandclimatologicalmonthly meananomaliesinfutureclimateconditions.Inthatmethod,therangeofinter-annualvariationsandthediurnalcycle werethesameasthatforreanalysisdata.InXuandYang(2012),atmosphericconditionsforDDSwerepreparedwith climatologicalmeanconditionsusingreanalysisdataand6-hourlyfutureanomaliesusinganAOGCM.TheDDSresultswith thehigh-frequency(6-hourly)anomalyshowedsimilarcharacteristicstotheoriginalAOGCM,andtheroot-mean-square errorsweresmaller.Inthisstudy,PGWconditionsforDDSwerepreparedwithfuturehigh-frequencyanomalies.Then,a DDSmethodwithhigh-frequency-anomalyPGW(hereafter,HF-PGW)conditionswasappliedtotheKantoregionofJapan usingoutputfromfivedifferentAOGCMsinordertoinvestigatefuturechangesinprecipitation.Herein,focuswasgiven totheToneRiverbasinwhichisthesecondlongestriverinJapanandflowsthroughtheKantoregion.Furthermore,the ToneRiverbasinisthelargestinJapanandprecipitationinthisbasinformstheprimarywaterresourcesfortheTokyo metropolitanarea.In2012and2013,thetotalimpoundedwateramountofeightdamsinthebasinfellto40%ofcapacity, anda10%reductioninwaterintakewasimplemented.Thepotentialimpactsofclimatechangeonfuturewaterresources intheKantoregion,asaddressedinthisstudy,shouldbeseeninthecontext.
Inthenextsection,wedescribethedataandmethodsusedforpreparationoftheHF-PGWconditionsandDDSusinga numericalweatherpredictionmodel.InSections3,DDSresultsforthecurrentclimateareevaluated.Generalfeaturesof futureprecipitationvariabilityanditseffectsonwaterresourcesarediscussedinSection4.Relationshipsbetweenlimited precipitationandatmosphericconditionsarealsoexaminedinSection4.Finally,wepresentadiscussionoftheresultsand ourconclusionsinSection5.
2. Dataandmethods 2.1. Data
2.1.1. Japanese25-yearreanalysis
Japanese25-yearreanalysis(JRA-25)data,developedbytheJapanMeteorologicalAgency(JMA),wereusedfor down-scalingofthepresentclimateconditionsandasthebasestatefortheHF-PGWconditions.Theglobalspectralmodelusedin JRA-25hasaspectralresolutionofT106and40verticallayers,withtheheightofthetoplayersetto0.4hPa.Atmospheric variablesinJRA-25areconvertedfromspectraltolatitude–longitudecoordinateswitharesolutionof1.25◦ ×1.25◦.In JRA-25,surfaceandupperairobservations,andmultiplesatellitedataareassimilatedwithathree-dimensionalvariational method.MoredetailsofJRA-25areprovidedbyOnogietal.(2007).
JRA-25coverstheperiodfromJanuary1979toDecember2004.Thereanalysis productfromtheJMAClimateData AssimilationSystem(JCDAS)hasbeenavailablesinceJanuary2005.JCDASrunsthesamesystemasJRA-25.Thesame qualityandaccuracyareexpectedinJRA25andJCDAS;therefore,bothproductsareconsideredashomogeneousdatasets. UsingJRA-25andJCDAS,weappliedDDStothe2000–2010datatoobtaindetailedoutputsregardingthecurrentclimate conditions.
Table1
CMIP5modeloutputsused.
IPCCID InstituteandCountry Resolution Airtemperatureat2ma Differencebetween presentandfuture(K)
Standarddeviationof annualmeanvalue(K) 1 CNRM-CM5 Meteo-France,CentreNationaledeRecherches
Meteorologique(France)
T127,L31 1.05 0.38
2 GFDL-CM3 GeophysicalFluidDynamicsLaboratory(USA) 2deg×2deg,L48 2.85 0.29 3 GISS-E2-R NASA/GoddardInstituteforSpaceStudies
(USA)
2deg×2.5deg,L40 1.04 0.32
4 MIROC5 CenterforClimateSystemResearch(the UniversityofTokyo),NationalInstitutefor EnvironmentalStudies,andFrontierResearch CenterforGlobalChange(Japan)
T85,L40 1.73 0.38
5 MRI-CGCM3 MeteorologicalResearchInstitute,Japan MeteorologicalAgency(Japan)
T159,L48 1.36 0.31
aTheresultsarecalculatedforthemeanconditionsof1996–2005withthespatiallyaveragedvaluefor125◦E−150◦E,30◦N−45◦N.
2.1.2. CMIP5globalwarmingexperiments
ClimateprojectionsofthefifthphaseoftheClimateModelIntercomparisonProject(CMIP5)wereusedforpreparation ofthePGWconditions.InCMIP5(Tayloretal.,2012),simulationsofclimateprojectionsareconductedaccordingtoseveral greenhousegasemissionscenarios,i.e.,representativeconcentrationpathways(RCPs).Forexample,intheRCP4.5scenario, theradiativeforcingoftheEarthbecomes4.5W/m2 bytheendofthe21stcentury(Tayloretal.,2012).Inthisstudy, projectionsbasedontheRCP4.5scenariowereused.Globalwarmingexperimentshavebeenperformedbymorethan30 AOGCMsdevelopedinvariousresearchinstitutesaroundtheworld.WeselectedfiveAOGCMprojectionsforpreparation oftheHF-PGWconditions,detailsofwhicharepresentedinTable1.Weused6-hourlyAOGCMsoutputstopreparethe HF-PGWconditions.ToruntheWeatherResearchandForecasting(WRF)model,weneedfieldsofatmosphericpressure,air temperature,specifichumidity,andwind.However,onlyalimitednumberofCMIP5AOGCMsprovidea6-hourlyoutputof thesevariables.ThesefiveAOGCMswereselected.Fig.1showsTaylor’sdiagramofairtemperatureandspecifichumidityat differentlevelsforeach3-monthperiod.SpatialcorrelationcoefficientsbetweeneachAOGCMandJRA-25using climatolog-ical3-monthlymeanconditions(1980–2000average)werecalculatedfortheregion120◦E–150◦Eand20◦N–50◦N.Standard deviationsofAOGCMoutputvalueswerenormalizedbydividingvaluesbythoseofJRA-25inordertosimultaneously com-paredifferentvariables.Forthe3-monthperiodofDJF(December,January,February),MAM(March,April,May),andSON (September,October,November),almostallvariablesshowcorrelationcoefficientslargerthan0.9.InJJA(June,July,August), correlationcoefficientsaresmallerthanforotherseasons.Specifichumidityat500hPahasasmallercorrelationcoefficient thanothervariablesinallseasons.However,allcorrelationcoefficientsarehigherthan0.8,showingthatclimatological spatialpatternsofatmosphericconditionsarewellreproducedinthesefiveAOGCMs.Standarddeviationsshowacertain amountofscatter.Variabilitybetweenthemodelandthevariableswaslarge,especiallyinJJA.Inotherseasons,valueswere closeto1.0,meaningthatclimatologicalconditionsintheAOGCMsweresimilartothereanalysis.Theseresultsindicate thatatmosphericconditionsintheAOGCMoutputshadsomequantitativedifferences,butspatialpatternsweresimilarto thereanalysisdata.
2.1.3. Otherdata
Forlandsurfaceconditionsinthenumericalweatherpredictionmodel(distributionofseaice,landseamask,surface elevation,soiltype,andvegetationtype),NCEPFinalOperationalGlobalAnalysisdata(NCEPFNL)wereused.These tempo-rallyconstantdatawerepreparedintheGlobalDataAssimilationSysteminNCEPwithaspatialresolutionof1◦×1◦(NCEP, 2000).
Forthelowerboundaryofthedownscalingsimulations,NOAAOptimumInterpolation1/4◦dailySeaSurface Temper-atureAnalysis(NOAAOISST)datawereused(Reynoldsetal.,2007),whichhaveaspatialresolutionof0.25◦×0.25◦and a1-daytemporalresolution.ThisproductusesAdvancedVeryHighResolutionRadiometer(AVHRR)infraredsatelliteSST data.However,sinceJune2002,AVHRRandAdvancedMicrowaveScanningRadiometerSSTdatahavebeenused.Insitu observationdatafromshipsandbuoysareincludedforlarge-scalebiasadjustmentofthesatelliteproducts.
Thedownscalingresultsforprecipitationinthepresentclimatewerevalidatedwithinsituprecipitationobservations fromtheAutomatedMeteorologicalDataAcquisitionSystem(AMeDAS)andmeteorologicalobservatoriesoftheJMA. 2.2. Methodfordynamicaldownscaling
WRFmodelversion3.4(Skamarocketal.,2008)wasadoptedfordownscalingofpresentandfutureclimateconditions. Atwo-level,two-waynestinggridsystemwasappliedtothedownscaling(Fig.2).Theparentdomain(D01),witha 30-kmresolution,wascenteredontheKantoregion,encompasseingtheJapaneseislands.Thefiner-scaledomainhada6-km horizontalresolution.Bothdomainshad35 verticallayersandthetopofthemodeledatmospherewasat10hPa.The Betts–Miller–Janjicmicrophysics(Janjic,1994,2000)andLinicecumulusparameterizationschemes(Linetal.,1983)were
Fig.1.Taylor’sdiagramofatmospherictemperatureat850,500and250hPa(TA850,TA500andTA250)andspecifichumidityat850and500hPa(HUS850 andHUS500)aroundthestudyarea(D01)fromfiveAOGCMSforDJF,MAM,JJA,andSON.
Fig.2. Studyareafordownscaling.Darkerandlightershadesindicateparentandinnerdomains,respectively.Spatialresolutionsare30kmand6kmfor theparentandinnerdomain,respectively.AreasurroundedbywhitelineintheinnerdomainindicatestheToneRiverBasin.Anopensquareshowninthe right-handsidepanelisatargetareaofanalysis.
Table2
Standarddeviationsofmonthlymeanairtemperaturesat2maboveground,850hPa,and500hPa.Avg.andCorr.indicatetheaveragevaluefor12months andcorrelationcoefficientbetweenJRA25/JCDASandeachAOGCM,respectively.
T2m TA850 TA500
Avg. Corr. Avg. Corr. Avg. Corr.
JRA25/JCDAS 0.69 – 0.99 – 1.06 – CNRM-CM5 0.65 0.49 0.95 0.75 1.07 0.62 GFDL-CM3 0.70 0.50 1.05 0.77 1.19 0.78 GISS-E2-R 0.72 0.16 0.97 0.25 1.05 0.56 MIROC5 0.58 0.48 0.92 0.61 1.06 0.60 MRI-CGCM3 0.64 0.42 0.92 0.47 1.02 0.69
Fig.3.SchematicviewofHF-PGWconditions.
usedtocalculateprecipitationinthemodel.PlanetaryboundarylayerprocesseswerecalculatedusingtheYonseiUniversity
non-local-Kscheme(Hongetal.,2006).Physicalprocessesofthesurfacelayerandlandsurfacewerecalculated using
theMonin–ObukhovwithCarlson–Bolandviscoussublayerscheme(Jiménezetal.,2012)andNoahlandsurfacemodel (ChenandDudhia,2001),respectively.Forlongwaveandshortwaveradiation,therapidradiativetransfermodelwiththe Monte–Carloindependentcolumnapproximationmethodofrandomcloudoverlap(Iaconoetal.,2008)wasused.Anoutline ofthemodelsettingsisgiveninTable2.ThesimulationlengthofWRFwas6monthsand10daysforspin-up.Thesimulation wasre-initializedevery6months.
Fordownscaling ofthepresentclimate,theinitialandboundaryconditionswerepreparedfromJRA-25,NCEP-FNL, andNOAAOISSTdatasets.TheDDStargetperiodwasfrom2000to2010.Hereafter,simulationofthepresentclimate iscalledCTL.Forthefutureclimate,theHF-PGWconditionsweregeneratedasfollows.Firstly,climatologicalmonthly conditions(averagedover2001–2010)werecalculatedbyJRA25andJCDAS(Fig.3b).Secondly,6-hourlyanomalieswere determinedbydifferencesbetweenthe6-hourlyfutureprojectionsandtheclimatologicalmonthlymeanpresentconditions inareproductivehistoricalrunofCMIP5(Fig.3candd).Thepresentconditionsweredefinedas10-yearmonthlymeansfrom 2001to2010(or1996–2005forsomeAOGCMsoutputs).Theperiodtomaketheclimatologicalmeanpresentconditionis differentfromtheDDStargetperiodbyusingJRA-25(2000–2010).Thedifferencebetweentheseperiods(1996–2005and 2000–2010)arenotthoughttobecriticalforthepurposeofthispapertoexamineclimatologicalvariability.Then,modified
globalwarmingconditionswerecalculatedbyaddingthefuture6-hourlyanomaliestothepresentclimatologicalmonthly meanconditionsderivedfromJRA-25/JCDAS(Fig.3e).Thetargetfutureperiodwasfrom2060to2070inthisstudy,and theanomaliesweregeneratedforeach6-hourlystepinthatperiod.Asmentionedintheintroduction,PGWconditionsin paststudieshadthesamerangeofinter-annualvariationsanddiurnalcyclesasthatofthereanalysisdata.However,inthis study,thefutureinter-annualvariationandthediurnalcyclewereincludedinHF-PGWconditions.Therefore,featuresof short-termmeteorologicaleventsinHF-PGWconditions(e.g.,frequenciesandlengthofextremeeventssuchastyphoons) areexpectedtobethesameasinfutureprojectionsbyAOGCMs.Standarddeviationswerecalculatedfortheannualmean airtemperatureat2mfromtheEarth’ssurface(hereafterT2m)inthereanalysisdataandforeachAOGCM.Thiswasdone fortheperiod1996–2005usingthespatiallyaveragedvaluefor125◦E−150◦E,30◦N−45◦N.Thestandarddeviationofthe reanalysisdatawas0.38.ThevaluesofAOGCMsweresmallerthanthatofthereanalysis(Table1)butthedifferenceswere notverysignificant.TherangesofinterannualvariationofT2minAOGCMsarethereforeconsideredcomparabletothat ofthereanalysis.Fig.4showsthestandarddeviationofthemonthlymeanT2mfortheperiodof1996–2005.Reanalysis resultsshowthattheinterannualvariationofthemonthlymeanT2mwasapproximately0.5to0.9(averageis0.69).The rangeoftheinterannualvariationtendstobelargerinwinterthaninsummer.ResultsoftheCMIP5AOGCMoutputswere scatteredwithsomelargerandotherssmallerthanthereanalysisdata,butingeneralcomparabletothesedata.Figs.4b andcarethesameasFig.4abutfortheatmospherictemperaturesat850hPa(TA850)and500hPa(TA500),respectively. InmanyAOGCMs,standarddeviationsofTA850duringthefallseasonweresmallerthanofthereanalysisdata.Inother periods,thesestandarddeviationswerescatteredaroundtheresultsofthereanalysisdata.Seasonalpatternsinthestandard deviationsofAOGCMsweresimilartothoseofthereanalysisdata.SuchcharacteristicsarealsorecognizedinTA500.Table2 showssummaryofthestandarddeviationsofclimatologicalmonthlymeanairtemperature(averageofthetwelvemonths, andcorrelationcoefficientbetweenthereanalysisandeachAOGCM).ResultsofCNRM-CM5andGFDL-CM3showaverage standarddeviationscomparabletothereanalysis,andhighercorrelationcoefficientsthanotherAOGCMs.MIROC5and MRI-CGCM3showsmallerstandarddeviations,orinterannualvariationsaresmallerthanthereanalysis.Correlationcoefficients ofthesetwoAOGCMstendstobesmallerthanCNRM-CM5andGFDL-CM3.AveragestandarddeviationsofGISS-E2-Rare comparabletothereanalysisdata.Butcorrelationcoefficientsaresmallerthanothers,indicatinglowerreproducibilityof seasonalprogression.Doneetal.(2015)appliedasimilarmodification(i.e.modificationwithahigh-frequency-anomaly)of atmosphericconditionsandindicatedthatdifferenceswererecoveredbetweenthevarianceinreanalysisandinanAOGCM usingaregionalmodelsimulation.UsingtheHF-PGWmethod,notonlyclimatologicalmeanconditionsbutalsodeviations canbeinvestigatedfromthedownscalingresultsoffutureclimateconditions.
AsetofHF-PGW conditionswaspreparedforatmospheric temperature,wind,pressure reducedtomeansealevel, surfacepressure,specifichumidity,andsurfaceskintemperature.SpatialmeandifferencesofT2mbetweenthepresentand futureconditionsforeachAOGCMswerecalculatedforD01(Table1).Theseclimatologicaldifferencescanaffectthemean conditionsinCTLandineachHF-PGWruns.ForSST,theanomalieswerecalculatedonadailybasisandaddedtothepresent climatologyfromtheNOAAOISSTdataset.Theinitialandboundaryconditionsofthedownscalingwerereinitializedevery 6months.Initialconditionsforsoilmoistureandtemperaturewerenotgivenbyspecifictemporaldata,However,Satoetal. (2007)performeddownscalingexperimentswithdifferentsoilconditions,findingthattheeffectsofinitialsoilconditions onprecipitationwerenotalwaysimportant.
3. Resultsofcontrolrun
Fig.5showsascatterplotofmeanannualprecipitationfromtheAMeDASandCTL.Valuesare2000–2010averagesfor129 observationsitesinD02.AnnualprecipitationinCTLisoverestimated.TheratioofCTLprecipitationtoAMeDASobservations averagedforthetargetsitesis1.39,i.e.CTLannualprecipitationwas,onaverageabout40%greaterthantheobserveddata. ThecorrelationcoefficientbetweenCTLandAMeDASmeanannualprecipitationis0.71,suggestedthatCTLcanreproduce thespatialpatternsofannualrainfallwithreasonableaccuracy.GiorgiandMearns(1999)presentedanoverviewofseveral issueswithregionalclimatemodeling,findingthatmodelsthatarerelativelygoodatforecastinguptoafewdaysahead oftenexhibitlargebiaseswhenruninclimatemode.Inourstudy,downscalingrunswerere-initializedevery6months,so itisexpectedthattheaggregatedbiaseshavecausedtheoverestimation.
ComparisonsofthetemporalvariationsinmonthlyprecipitationbytheAMeDASandCTLatspecificlocationsinthe studyareashowfavorableresults(notshown).ThemeancorrelationcoefficientbetweenAMeDASandCTLmonthly pre-cipitationcalculatedfor129observationsitesduringthe11years(132months)is0.66.Thereisaclearseasonaldifference (rainysummeranddrywinter)intheKantoregion.FromJunethroughSeptember,thereisconsiderablerainfallbrought bytheBaiufrontalsystem,typhoons,andtheautumnrainfront.Incontrast,thereisverylittleprecipitationduringwinter. ThecorrelationcoefficientresultsindicatethatthedownscalingresultsoftheCTLreproducedtheseasonalprecipitation variabilityintheKantoregionrelativelywell.
Fig.4.Standarddeviationofthemonthlymeanairtemperatures.a)attheheightof2mfromtheEarth’ssurface,b)at850hPa,c)at500hPa.Valuesare calculatedfortheperiodof1996–2005.
4. Futurevariationsinprecipitationcharacteristics 4.1. Generalcharacteristicsoffuturevariations
Fig.6showsthefuturevariationsinmeanannualprecipitationforeachHF-PGWrun.Theresultsshowthedifferencesin precipitationduringthestudyperiod(11yearsinbothfutureandpresentclimateconditions)betweeneachHF-PGWrun andtheCTL.InHF-PGW-1,2,and5,thereisatrendforincreasingprecipitationoverawideareaofD02,althoughstatistically significantchange(5%significancelevel)isobservedinaverylimitedareaonly.InHF-PGW-3,adecreasingprecipitation trendisdominantandsignificant.
Fig.7showsthedifferencesinstandarddeviationofannualprecipitationbetweenHF-PGWrunsandtheCTL.In HF-PGW-3thestandarddeviationdecreasesacrossawideareaoftheKantoregion,butsignificantdecreasesarescarce.Incontrast,
Fig.5. ComparisonofmeanannualprecipitationbyJMAobservationsandCTL.Resultsareaveragedover2000–2010.Unitsofxandyaxesaremm.
Fig.6. SpatialdistributionsofthedifferenceinmeanannualprecipitationbetweenHF-PGWrunsandCTL.Unitofthecolorbarismm.Areaswithstatistically significantdifferences(5%significantlevel)areindicatedbyhatching.
theotherfourHF-PGWrunsshowanincreasingtrend.ManyHF-PGWrunsindicateanincreasinginterannualvariationin annualprecipitation,includingareaswithsignificantdifferences.Similarresultsshowingincreasinginterannualvariability inthefutureclimatehavebeenfoundinotherstudies(e.g.,Giorgietal.,2004;Lietal.,2007).However,spatialpatternsof thevariabilitydifferbetweentheHF-PGWruns,andcommoncharacteristicsarenotnecessarilyfoundintheresults.Wu etal.(2016)indicatedeffectsofthearcticseaiceontheEastAsianprecipitation,andsometeleconnectionintheclimate system.Toinvestigatethephysicalmechanismsunderlyingthechangesininterannualvariations,DDSforawiderregionis required.
Formeanannualprecipitation,therearenoclearcommonfeaturesandverylittlesignificantvariationamongtheHF-PGW runs(Fig.6).However,resultsofthestandarddeviationindicatethattheamplitudeofinterannualprecipitationvariability mayincreaseinthefutureclimate.Thoseresultsindicatethataverageprecipitationiscomparablebetweenpresentand futureclimateconditions,butanomaliesinextremecaseswillbecomelargerinthefuture.Inthenextsection,extreme casesoflowrainfallsareexamined.
Fig.7. SpatialdistributionsofthedifferenceinthestandarddeviationofannualprecipitationbetweenHF-PGWrunsandCTL.Unitofthecolorbarismm. Areaswithstatisticallysignificantdifferences(5%significantlevel)areindicatedbyhatching.
4.2. Variationsinlowrainfallcases
Consideringwaterresources,thecharacteristicsoflowrainfall(e.g.,frequencyofdroughtyearsandlevelsofdrought) areimportant.Theratioofminimumannualprecipitation(AnPRmin)duringthe11yearsintheCTLandHF-PGWrunswere calculatedby:
AnPRmin=min
AnP(k) CTL/PGW AnPCTL (k=1,2,...,11) (1) Here,AnP(k)CTL/PGWistheannualprecipitationintheCTLorPGWruninthek-thyearofthestudyperiodandAnPCTListhe meanannualprecipitationintheCTL.TheAnPRminoftheCTLaveragedfortheKantoregion(138.25E-141E,35N-37.25N, indicatedinFig.2)is0.815(Table4).Theresultindicatesthatatleast81.5%ofthe11-yearmeanannualprecipitationshould beexpected,eveninadryyear.TheAnPRminofHF-PGW-1isalsolargerthan0.8.However,theotherfourHF-PGWrunsshow smallerAnPRmin.InHF-PGW-3and4inparticular,thespatialmeanAnPRminissmallerthan0.75,orannualprecipitationin adryyearfallsbelow75%ofthemeanannualprecipitation.Theseresultsindicatethatwatershortagecausedbyreduced precipitationcouldbecomemoresevereinthefutureclimateconditions.
Thefrequencyofyearswithannualprecipitation<85%ofthemeanannualprecipitationintheCTLwasalsoexamined. ThefrequencyofCTLaveragedfortheKantoregionis0.873(Table4).Theresultindicatesthatannualprecipitationinthe CTLis<85%onlyonceorlessduringthe11years.IntheHF-PGWruns,annualrainfall<85%occursmorethanonce.In HF-PGW-3and4,aclearincreaseisrecognized.Thefrequencyofyearswithanannualprecipitation<75%ofthemeanannual precipitationisalsoexamined.InCTL,annualrainfall<75%oftheaverageisfoundinalimitedarea,andthespatialmean frequencyisverysmall(0.152).HF-PGW-1,2and5alsoproduceverylowfrequenciesofsuchsmallrainfallamount,butthe othertwoHF-PGWrunsshowedmorefrequentyearswithreducedprecipitation(morethanonce).Theseresultsindicate thatlowprecipitationyearswillbecomemorefrequentinthefuturethaninthecurrentclimate.
Fromwintertospring,precipitationintheKantoregionislight,butwaterlevelsinreservoirsareusuallyreplenished byheavyrainfallduringtheBaiuseason(June–July)andduetotyphoonsinthesummer.Therefore,ifprecipitationduring thosetwoseasonsisreduced,theriskofwatershortageswillincrease(infact,watershortageoftenbecomesaproblem duringsummerintheKantoregion).Weevaluatedtheaccumulatedprecipitationfromthebeginningofeachyeartillthe summer.Wedefinedaminimumratiooftheprecipitationforeachyearas:
AcPRmin=min
AcPi
AcPi, CTL, i=182, 243
Here,AcPiistheaccumulationofdailyprecipitationfromJulianday1toiandAcP
i, CTListheaverageaccumulateddaily precipitationforthe11yearsintheCTL.TheperiodofJuliandaysfrom182to243correspondstoJuly1toAugust31ina non-leapyear.TheaverageAcPRminvaluesofthe11yearsintheCTLare0.85–0.95aroundtheKantoregion,andthespatial averagefortheregionis0.905(Table4).Itindicatesthat,eveninanormalyear,thereisaperiodwiththeaccumulated precipitation10%smallerthantheaverageyear.InHF-PGW-2and5,thespatialmeanAcPRminfortheKantoregionare comarabewiththeCTL(0.892and0.901,respectively).InHF-PGW-1,theresultis0.922,indicatingasmallerriskofwater shortagesinthefutureundertheHF-PGW-1scenario.InHF-PGW-3and4,theresultsare0.829and0.857respectively, indicatingthattherisksofwatershortageswillbehigherinthesefutureclimateconditions.TheminimumAcPRminduring the11yearsisalsoexamined.TheresultfortheCTLis0.724,indicatingthattheaccumulatedprecipitationsometimesfalls below73%oftheaveragearoundtheKantoregionundercurrentclimateconditions.ResultofHF-PGW-1is0.748.However, intheotherfourHF-PGWruns,resultsaresmallerthantheCTL.Thesefindingsindicateperiodsinthefutureclimatein whichmuchlessprecipitationwouldaccumulatebythesummer,whichcouldcauseseverewatershortages.
Table5showsthefrequencyofprecipitation>50mm/dayatthesixlocationsshowninFig.6.InHF-PGW-3,the fre-quencyofprecipitation>50mm/dayissmallerthaninCTLatalllocations.Ontheotherhand,suchheavyrainfalloccurs morefrequentlyatalllocationsintheotherfourHF-PGWruns.InHF-PGW-2,themaximumdailyrainfallislargerthan inCTLatalllocations.However,itdoesnotnecessarilyincreasedintheotherHF-PGWruns.Theratioofthefrequency indailyprecipitationinHF-PGWrunstotheCTLarecalculatedforfourdailyprecipitationintensities,0–5,5–50,50–100, and>100mm/day;values>1indicatethatthefrequencywillbehigherinthefuture,andviceversa.Thespatialmeanvalues fortheKantoregionareshowninTable6.Forprecipitationamounts<5mm/day,therearenoclearvariationsbetweenthe currentandfutureclimateconditionssimulatedbyallfiveHF-PGWruns.Resultsforprecipitationof5–50mm/dayshow cleardecreasesforallHF-PGWrunsexceptHF-PGW-1;nonetheless,thefrequencydoesdecreaseoverlargeareasinthat run.For50–100mm/day,resultsvarybyHF-PGWruns.AlthoughtherearedecreasingtrendsinHF-PGW-3and−4,the frequencyincreasesacrossbroadareasintheotherthreeHF-PGWruns.Forprecipitation>100mm/day,HF-PGW-3shows significantdecreasesinfrequency;yetthereareclearincreasesinotherfourHF-PGWruns,Generally,thefrequencyof lowdailyprecipitationandofconsiderabledailyprecipitationwilldecreaseandincrease,respectively,inthefuture.Heavy rainfalloverrelativelyshortperiodsisnotbeneficialforwaterresourcemanagement.Thisisbecausewaterisreleasedfrom reservoirsinadvanceofheavyraintopreventflooddamage,andbecausetheratioofsurfacestreamwaterislarge,making itineffectiveforrecharginggroundwater.Therefore,suchvariationsinthefrequencydistributionofdailyprecipitationare unfavorableforwaterresourcemanagement.
Thedifferenceofmeanannual precipitationbetweenpresentand futureclimateconditionsdidnot showcommon characteristicsamongtheHF-PGWruns.However,interannualvariations(orstandarddeviationsofannualprecipitation, frequenciesofyearswithlowprecipitation,andmagnitudeoflowprecipitation)andchangesindailyprecipitationfrequency indicatethatchallengesanddifficultiesregardingwateruseintheKantoregionwillariseasaresultofclimatechange.
4.3. Atmosphericconditionsandlimitedrainfallinfutureclimateconditions
Intheprevioussections,thefrequencyoflowrainfallyearshasbeenshowntoincreaseinfutureclimateconditions, andthemagnitudeoflowrainfalltoincrease.Toexaminethecausesoflowrainfallunderfutureclimateconditions,we investigatedatmosphericconditionsaroundtheKantoregion.Fig.8ashowsthemeantotalprecipitationintheregion (138.5◦E-140.5◦E,35.5◦N-37.0◦N)forMay–October,whichistheperiodwithmostofannualrainfall.Althoughfuture varia-tionsinannualprecipitationarenotsignificant,meanfutureprecipitationfortheperiodofMay-Octoberissmallerthanat present.However,onlytheresultofHF-PGW-3showastatisticallysignificantdifference.Therangeinstandarddeviations suggestslessrainfallinfuturedryyears.Fig.8alsoshowstheatmosphericconditionsaveragedoverMay–October.Fig.8b depictsthedifferenceinpotentialtemperaturebetween500and850hPa.TheresultsforHF-PGW-4arecomparabletothe CTL,butthevaluesintheotherfourPGWrunsaregreaterthaninCTL.Thoughstatisticallysignificantdifferencesinthe averagevaluesarefoundonlyinHF-PGW-2and−3,therangeofstandarddeviationsindicatespotentiallygreatervaluesin thefuture.Thelargervaluesindicateamorestablestratification,therebyinhibitingthedevelopmentofconvection.Relative humidity(RH)at850hPaislowerinthefutureclimate(statisticallysignificantaveragevaluesarerecognizedonlyin HF-PGW-1and−5)andtherangeofstandarddeviationsshowsevenlowerRHinfuturedrycases.However,RHat500hPadoes notshowcommoncharacteristicsinthefuture.Fig.8eshowsincreasingspecifichumidityinfutureclimateconditionswith allHF-PGWrunsshowingstatisticallysignificantdifferences.Evenindryyears,specifichumidityisgreaterinthefuture. Theseresultsindicatethatfutureatmosphericwarmingisgreaterthantheincreaseinspecifichumidity;therefore,lower RHconditionsareproducedinthefuture.
Table7showsrainfall,RHat850hPa,andthedifferenceofpotentialtemperature(q)between500and850hPaforthe KantoRegion.ValuesaretotalsoraveragesforMay–Octobereachyear.InTable4,lowrainfallisdefinedbyvaluessmaller thantheaverageminus1intheCTL(or1183mm).LowRHandlargearedefinedasvaluessmaller/largerthan74.3% and22.3K,respectively.LowprecipitationyearsoccurmorefrequentlyinHF-PGWruns.LowerRHyearsandlarger yearsarealsomorefrequentinthefuture.In25lowprecipitationyears,16(or64%)areassociatedwithlowerRH,and10 (40%)withlarge,orunstableconditions.Tenlowprecipitationyears(40%)areassociatedwithlowerRHandfour(16%; 2061,2065,2069inHF-PGW-3;2061inHF-PGW-5)withlarge.In16of24(67%)lowerRHcases,lowprecipitationis
Fig.8.a)Meantotalprecipitation,b)thedifferenceinpotentialtemperaturebetween500and850hPa,c)relativehumidityat850hPa,d)relativehumidity at500hPa,ande)specifichumidityat850hPa.PrecipitationisthetotalamountfromMaythroughOctober,andotherresultsareaveragesforthatperiod. AllresultsarespatiallyaveragedovertheKantoRegion(138.5◦E–140.5◦E,35.5◦N–37.0◦N),andtemporallyaveragedoverthe11-yearstudyperiod.Shaded barsindicateresultswithstatisticallysignificantdifferencesfromCTL.Errorbarsindicatethestandarddeviation.
Table3 SettingsofWRF.
Versionofmodel V3.4
Cloudmicrophysics PurdueLinScheme
Cumulusparameterization Betts–Miller–JanjicScheme Long-andshort-waveradiation RRTMGScheme
Landsurfacescheme Monin–ObukhovwithCarlson–Bolandviscoussub-layerscheme
Landmodel NoahLandSurfaceModel
Planetaryboundarylayerscheme YonseiUniversityScheme
Simulationlength 6monthsand10-daysforspin-up
found.Ontheotherhand,8ofthe26(31%)unstableconditions(larger)arerelatedtolowprecipitation.Theseresults
indicatethatlowRHwillhavealargereffectonfrequentlowrainfallyearsandextremelyweakprecipitationaroundthe
KantoRegion.However,5of25lowprecipitationcases(or20%)werenotrelatedtoeitherlowRHorlarge,soamore
detailedinvestigationisnecessaryinordertounderstandthismechanism.
Fig.9showstheresultsofacompositeanalysisofverticalwindspeedat500hPaaveragedforMay-October.Resultsshow thedifferencebetweentheaverageoflowrainfallyearsinTable3 andtheaverageofthe11yearsinCTL.InallthePGW runs,downwardanomaliesarefoundonthesouthcoastoftheJapanIslands.Suchatmosphericcharacteristicsarefound alsointhelowertroposphere,butaremoreclearlyrecognizedaroundthemiddletroposphere.Thedownwardanomalies arestatisticallysignificantinallHF-PGWruns.Downwardanomaliespreventthedevelopmentofconvection.Therefore, atmosphericconditionswithanomalousdownwardwindsareintimatelylinkedtolowprecipitation.Fig.10isthesameas Fig.9butforageopotentialheightat850hPa.AlltheHF-PGWrunsshowstatisticallysignificantpositiveanomaliesaround theJapanmainisland,thusloweratmosphericpressureishigherinthelowrainfallyears.EspeciallyinHF-PGW-1,3,and4,
Fig.9. Compositeofverticalwindspeedat500hPa.Resultsshowthedifferenceinthemeanverticalwindspeedaveragedforlowrainfallyearsineach HF-PGWsimulationandfor11yearsinCTL.Theunitofthecolorbaris103m/s.Areaswithstatisticallysignificantdifferencesareindicatedbyhatching.
Table4
Summaryoflowprecipitationconditions.Resultsareaveragedfor138.25◦E-141.0◦E,35.0◦N-37.35◦N.
AnPRmin Frequencyoflowprecipitationyears(times) AcPRmin
<85% <75% Average Minimum CTL 0.815 0.873 0.152 0.905 0.724 HF-PGW-1 0.824 1.19 0.086 0.922 0.748 HF-PGW-2 0.775 1.82 0.532 0.892 0.714 HF-PGW-3 0.721 4.69 1.20 0.829 0.628 HF-PGW-4 0.729 3.97 1.49 0.875 0.622 HF-PGW-5 0.775 1.50 0.376 0.901 0.695 Table5
Frequencyofheavyrainfall>50mm/dayandthemaximumdailyrainfall.ValueslargerthanthoseofCTLareindicatedbyboldfont.
CTL HF-PGW-1 HF-PGW-2 HF-PGW-3 HF-PGW-4 HF-PGW-5 Chichibu Freq.(−) 66 85 92 50 68 84 Max.(mm/day) 197 225 399 197 391 238 Hannou Freq.(−) 66 99 91 62 76 90 Max.(mm/day) 263 284 474 253 179 269 Kamiyoshida Freq.(−) 61 85 87 54 68 93 Max.(mm/day) 250 155 340 130 304 217 Mitsumine Freq.(−) 88 114 105 75 108 114 Max.(mm/day) 242 177 493 183 330 277 Saitama Freq.(−) 57 86 85 55 58 85 Max.(mm/day) 232 260 360 239 362 354 Tokorozawa Freq.(−) 64 95 90 61 72 98 Max.(mm/day) 269 261 513 289 215 264
clearanti-cyclonicanomaliesarerecognized.Convectiveactivitycanweakenunderhighpressureanomalyconditions.More
stableandhigher-pressureconditionswillpreventthegenerationanddevelopmentofconvectioninlowrainfallyearsin
theHF-PGWruns.Itisindicatedthatatmosphericdivergencecanbeformedinthelowertroposphereandcausedecreases
Table6
TheratioofthefrequencyoffourdailyprecipitationintensitiesinHF-PGWrunstoCTL.Resultsareaveragedfor138.25◦E-141.0◦E,35.0◦N-37.35◦N.
RatioofthefrequencyofdailyprecipitationinHF-PGWrunstoCTL
0–5mm/day 5–50mm/day 50–100mm/day >100mm/day
HF-PGW-1 1.01 0.96 1.19 1.28
HF-PGW-2 1.04 0.84 1.23 1.87
HF-PGW-3 1.05 0.87 0.92 0.86
HF-PGW-4 1.03 0.90 0.96 1.30
HF-PGW-5 1.03 0.89 1.18 1.69
Fig.10.Compositeofgeopotentialheightat850hPa.Resultsshowthedifferenceofthemeangeopotentialheightaveragedforlowrainfallyearsineach HF-PGWsimulationandfor11yearsinCTL.Theunitofthecolorbarism.Areaswithstatisticallysignificantdifferencesareindicatedbyhatching.
5. Discussionandconclusion
Inthisstudy,aHF-PGWdynamicaldownscalingmethodwithanumericalweatherpredictionmodelwasappliedtothe
KantoregionofJapan,andfuturechangesinprecipitationwereinvestigated.Simulationofthecurrentclimateoverestimated
precipitation,butthespatialdistributionandseasonalprogressionofthesimulatedprecipitationhadsimilarfeaturesto
actualconditions.
ThefiveHF-PGWrunsdidnotrevealcommoncharacteristicsinthevariationsofmeanannualprecipitationinfuture
climateconditions.TherewaslittlesignificantvariationinfutureannualprecipitationaroundtheKantoRegion.Generally,
atmospherictemperatureandspecifichumidityincreasedinthefutureforallHF-PGWruns,butsuchsimilaritieswere
insufficienttoproducesimilarprecipitationvariationsintheHF-PGWruns.Thedifferencesintheincreasedmagnitudes,
spatialdistributions,andotherfactorssuchasseasurfacetemperaturesandpressuredistributionscomplicatemeteorological
processes,leadingtouncertaintyinthemeanannualprecipitation.
Futurevariationsinstandarddeviationsindicateanincreaseintherangeofinterannualvariationinannualprecipitation
aroundtheKantoregion.Undersuchconditions,watershortagesinayearwithlowprecipitationwouldbemoreseverethan
undercurrentclimateconditions,evidentinthefutureminimumannualprecipitationresults.Furthermore,anincreasing
frequencyoflowprecipitationyearswasproducedinmultiplefutureclimatescenarios,whichhighlightstheissueofwater
shortagesunderconditionsofglobalwarming.
IntheKantoregion,thereisaclearseasonaldifferenceinprecipitation(i.e.,rainysummersanddrywinters).Future
variationsinprecipitationaccumulatedbysummertimeindicatethattherewillbeperiodswithverylittleprecipitation,
whichcouldreducedamimpoundment.Inaddition,theHF-PGWrunsshowedadecreasingnumberofweakprecipitation
K. Taniguchi / Journal of Hydrology: Regional Studies 8 (2016) 287–303 Table7
Precipitation(PR),relativehumidityat850hPa(RH),anddifferenceofpotentialtemperature()between500hPaand850hPafortheKantoRegion.ValuesaretotalsoraveragesforMay–Octoberineachyear. Underliningindicatesvaluessmallerthantheaverageminus1inCTLforPRandRH.For,valueslargerthantheaverageplus1inCTLareindicatedbyunderlining.
CTL HF-PGW-1 HF-PGW-2 HF-PGW-3 HF-PGW-4 HF-PGW-5 PR RH PR RH PR RH PR RH PR RHPR RH 2000 1728 77.6 21.6 1272 76.3 22.6 1235 74.7 23.5 835 74.2 22.5 1334 73.6 22.074.9 134422.4 2001 1312 76.2 22.1 1201 73.8 22.1 1208 73.7 22.7 1024 75.6 22.8 1426 78.2 22.175.7 106222.6 2002 1382 75.4 21.5 2144 75.5 21.7 836 73.3 22.9 738 74.6 22.1 889 75.0 21.475.7 109522.3 2003 1111 79.0 22.7 1433 69.8 22.9 1353 76.5 22.7 812 72.3 22.1 1592 75.3 21.876.8 165521.8 2004 1303 75.2 22.2 1411 74.7 21.4 1934 76.5 23.4 1023 74.6 22.0 1095 72.0 21.674.8 126922.3 2005 1513 76.5 22.0 1269 77.3 22.2 1401 74.6 23.3 971 76.1 22.4 1006 71.9 22.273.6 95422.0 2006 1275 77.6 22.1 1312 73.2 22.6 1601 77.1 23.1 1240 75.1 22.6 812 72.8 21.876.3 142222.4 2007 1124 75.2 21.9 900 73.4 22.6 1512 75.3 23.2 736 72.9 21.8 769 76.5 22.274.4 120922.5 2008 1330 76.8 22.0 1157 73.4 22.6 936 73.6 23.0 998 73.4 22.0 1058 75.2 21.872.5 123521.6 2009 1292 71.9 21.2 935 70.7 21.8 1036 73.7 23.7 687 74.5 22.6 1462 73.0 21.771.8 84422.1 2010 1584 75.4 22.0 1780 76.1 22.5 1334 74.8 22.5 1282 70.5 22.3 1642 76.9 22.373.0 119522.1
Fig.11.Climatological6-hourlyvariationinairtemperature2mabovetheEarth’ssurfaceaveragedfor125◦E−150◦E,30◦N−45◦N.Resultsshowthe
departureofthe6-hourlyclimatologicalairtemperaturefromthedailymeanvalueofeachday.
Theresultsofthisstudyindicatethatvariationsinannualprecipitationwillnothaveasignificanteffectonwaterresources,
butthatseasonaldependenceandchangesinprecipitationpatternscouldaggravatefactorsthatmakewatermanagement
moreproblematic.Forfutureplanningandmanagementofwaterresources,suchconditionsshouldbeconsidered.
FutureatmosphericconditionsintheKantoregionappeartohavelowerRHinthelowertroposphereandmore
sta-bleatmosphericstratificationthaninthecurrentclimate.Moreover,lowerrainfallyearswereoftenfoundinassociation
withlowerRHand/oramorestableatmosphericstratification.Inaddition,compositeanalysesshoweddownwardvelocity
anomaliesandhigher-pressureanomaliesaroundJapan.Tounderstandthefrequentformationofsuchsub-synopticscale
atmosphericconditions,larger-scale(or,synopticscale)analysisisnecessary.
InSection2,interannualvariationsintheAOGCMswerecomparedtothereanalysisdata.Here,amplitudesof
high-frequencyperturbationinAOGCMsandthereanalysisarediscussed.Fig.11showstheclimatologicaldiurnalvariation ofT2m,indicatingthedepartureofclimatological6-hourlyT2mfromtheclimatologicaldailymeanvalueforeachday. Therangeofthediurnalvariationisfrom−1.5to2.0inthereanalysis.ResultsofMRI-CGCM3aresimilartothatofthe reanalysis.However,therangesofdiurnalvariationsinotherAOGCMsaresmallerthaninthereanalysis.Thoughsuch smalleramplitudesmaycausesmallerdiurnalvariationsinHF-PGWconditionsthaninthereanalysis,HF-PGWconditions candirectlyaffecttheboundaryconditionsafterthestartofthesimulation.Atthesametime,Doneetal.(2015)indicated thatthedifferenceofperturbationinareanalysisandanAOGCMcouldberecoveredinadownscalingsimulation.Thus,the effectsofthesmalleramplitudediurnalvariationareexpectedtobesmall.However,XuandYang(2012)proposedabias correctionmethodtomodifysuchdifferencesinamplitudebyusingtheamplituderatiobetweentheAOGCMandreanalysis, andshowedbetterresultswithmodificationoftheamplitude.Atthesametime,asstatedinSection2.2,theHF-PGWmethod couldincludefrequenciesandlengthsofextremeeventsprojectedbyAOGCMsintoDDSresults.Therefore,combineduse ofthismodificationmethodandtheHF-PGWmethodisworthinvestigating.Toinvestigatetheappropriatenessofthese methods,itisalsousefultoimplementdownscalingofconditionsbycombiningclimatologicalmonthlyreanalysisdata andhigh-frequencyanomaliesinthepresentclimateresultsofAOGCMs.Amplitudeorinterannual,seasonal,anddiurnal variationscanbedirectlyexaminedbycomparisonbetweenthedownscalingresultoftheoriginalreanalysisdataandthe
high-frequencyAOGCMbasedanomaliesforthepresentconditions.However,largecomputationalcapacityisnecessaryto dothat,anditdiminishesanadvantageofthePGWmethod(onedownscalingresultofpresentclimateisenoughtocompare tofuturedownscalingresultswithmultiplePGWconditions).Thisverificationisrecommendedwhenenoughcomputational capacityisavailable.
Weusedclimateprojectionsunderonlyoneclimatescenario(i.e.,RCP4.5).Foramorecomprehensiveanalysis,other scenariosshouldalsobeused.Inaddition,toimprovethereproducibilityofthedownscalingresultforthecurrentclimate,the frequentre-initializationmethodforanumericalweatherpredictionmodelcouldbeused,asproposedinLoetal.(2008). Asisthecaseforclimatescenarios,thestudyperiodisanimportantfactorintheassessmentofclimatechange.Eleven yearswereselectedinthisstudyforbothcurrentandfutureclimateconditions(2000–2010and2060–2070),sothatmean conditionsinthetwoperiodscouldbecompared.However,theseperiodsmaynotbesufficienttoinvestigatetheeffectsof decadalorinter-decadalvariability.Longerperioddownscaling(forexample,30years)isproposedforexaminingtheeffects ofdecadal/inter-decadalvariationsinglobalwarming.Inaddition,spectralnudginginadynamicaldownscalingisanother powerfultechniqueforimprovingthereproducibilityofcurrentclimateconditions.Usingsuchmethods,thecharacteristics ofHF-PGWconditionswouldbereflectedmoredirectlybythedownscalingresult,andthustheeffectsofglobalwarming seeninHF-PGWconditionscouldbeexaminedmoreaccurately.
WeusedglobalwarmingprojectionsfromfivedifferentAOGCMsforpreparationoftheHF-PGWconditions,becausethe applicationofamulti-modelensembleisindispensableforassessmentofthefutureclimate.Fromatechnicalstandpoint, moreclimatemodeloutputsshouldbeusedforthispreparationinordertoreduceuncertainties.However,theevaluation ofthemodelperformanceandAOGCMselectionarealsoimportant.Knuttietal.(2010)discussedhowtooptimallycombine theoutputsfrommultipleAOGCMsinCMIP3,andtheysuggestedthatconsiderableimprovementcouldbeexpectedwith ensemblesofasmanyasfivemodels.Theynotedthatanensembledegradeswhenpoorlyperformingmodelsareincluded. SomestudieshaveinvestigatedbiasesanddependenceacrossdifferentAOGCMsinCMIP3,andconcludedthattheeffective numberofmodelswasmuchsmallerthantheactualnumberusedforinvestigation(Junetal.,2008;PennellandReichler, 2011).Itwasalsonotedthat,dependingontheapplication,itwouldbeappropriatetoweighttheAOGCMsaccordingto regionsandseasons(Gleckleretal.,2008;Tebaldietal.,2005).Infuturestudies,AOGCMselectionandthedevelopmentof methodstoevaluatemulti-modeldownscalingresultswouldbeanimportantsubjectforresearch.
Inthisstudywealsoaddressedthepossibleeffectsofprecipitationchangesonwaterresources.Theincreaseinthe numberofdayswithheavyrainfallwouldboostthefrequencyoffloods.Therefore,therisksoffloodsintheKantoregion underfutureclimatescenariosshouldberesearched.Hydrologicalmodelsareinvaluableinfloodanalysis,andthehigh spatialandtemporalresolutionofthedownscalingresultspresentedhereinwouldbeveryusefulforsuchanalyses.Changes inclimatealsohaveconsiderableeffectsonecologicalsystems,andthesedownscalingresultswouldbeusefulinapplications includingtheassessmentofsmall-scaleecologicalandbiologicalsystems.
Acknowledgements
TheauthorisgratefulforuseofCMIP5productsarchivedandpublishedbytheProgramforClimateModelDiagnosisand Intercomparison(PCMDI),andtoalltheresearchinstitutescontributingtothisactivity.TheJapanese25-yearreanalysis datawereprovidedbytheJapanMeteorologicalAgency(JMA)andtheCentralResearchInstituteofElectricPowerIndustry (CRIEPI).TheresearchwassupportedthroughCoreResearchforEvolutionalScienceandTechnology(CREST)fundedby theJapanScienceandTechnologyAgency(JST).Theauthoralsogreatlyappreciatesthecommentsandsuggestionsbythe anonymousreviewers,whichgreatlyimprovethequalityofthispaper.
AppendixA. Supplementarydata
Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ejrh.2016.10.004.
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