Diagnosis(of(tropical(biases(and(the(MJO(from(patterns(in(

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1 1 Diagnosis(of(tropical(biases(and(the(MJO(from(patterns(in( 2 MERRA s(analysis(tendency(fields( BrianE.Mapes 9 RosenstielSchoolofMarineandAtmosphericSciences,UniversityofMiami 10 Miami,FL mapes@miami.edu 12 Telephone:(305)421N and JulioT.Bacmeister 17 NationalCenterforAtmosphericResearch 18 Boulder,CO SubmittedAugust6, RevisedJanuary4, ForMERRAspecialissueofJ.Climate 28 29

2 2 Abstract 1 2 TheModernEraReanalysisforResearchandApplications(MERRA)is 3 realistic,includingitsmaddennjulianoscillation(mjo)whichtheunderlyingmodel 4 (GEOSN5)lacks.InMERRA sbudgets,analysis'tendencies(ats)makeevolution 5 realisticdespitemodelshortcomings.atsarethenegativeofphysicalprocess 6 errors,ifdynamicaltendenciesareaccurate.patternresemblancebetweenatsand 7 physicaltendenciessuggestwhichprocessesareerroneous.weexaminedpatterns 8 oftropicalatsin4dimensionsandfoundseveralnoteworthyfeatures. 9 TemperatureATprofilesshowthatmoistphysicshaserroneoussharp 10 coolingat700hpa,asignatureofmisplacedmeltingandperhapsexcessive 11 precipitationevaporation.thisexcitesadistinctive(fingerprint)erroneousshort 12 verticalwavelengthtemperaturestructure,perhapsacauseofgeosn5 stoonslow 13 convectivelycoupledwaves.theglobe slargestatof200hpawindstemsfrom 14 overactiveheatingovertheintranamericasseasregioninsummer,withthesame 15 moistphysicsfingerprint.theerroneousheatingproducesabaroclinicvortexthat 16 iscounteredbyatsopposingitstemperatureandmomentumfieldsinathermal 17 windbalancedsense.lackofrestraintinthedeepconvectionschemeisalso 18 indicatedinmjocomposites,wherethewatervaporatisanomalouslypositiveon 19 theleadingedgeindicatingaprematurevaporsink.sincegeosn5lacksanmjo,this 20 diagnosissuggeststhatthetransitionfromshallowtodeepconvection(moistening 21 todrying)iscrucialintherealnworldmjo.thisisnotnews,butitsdiagnosisbyats 22 providesanobjective,repeatablewaytomeasuretheeffectthatcouldbeauseful 23 guideinmodeldevelopment

3 3 1.(Introduction( 1 2 Therearemanywaystolearnfromtheconfrontationofanatmosphere 3 modelwithobservations,inserviceofmodelimprovement.thestudyofinitial 4 tendencies(orerrorsinonentimenstepforecasts,klinkerandsardeshmukh1992)is 5 appealingbecausetheeffectofamodelprocesserrorislocalized.however, 6 initializationshockmaydominatetheresults,makinginterpretationsubtle(e.g. 7 Juddetal.2008).Attheotherextremeoftimescale,thebiasesofunconstrained 8 modelrunscanalsobehardtointerpret,sinceinteractingerrorshavetimeto 9 pervadeallaspectsofthesimulation.inbetweenthefirsttimestepandfreemodel 10 climatologyliestheprocessoferrorgrowthandspread,whichmayyieldlongnterm 11 errorsquitedifferentfromtheoriginalsourceoftheerror(e.g.rodwellandjung ).Atstilllongerleadtimes,othercoupledmodelcomponents(landandocean) 13 canfurtherevolvetheerrors(e.g.songandmapes2012).a seamless suiteofdata 14 assimilationandforecastactivities,withinitializedforecastsexaminedatvarious 15 leadtimes,isarguablythebestwaytoevaluateandimprovemodelsofboth 16 weatherandclimate(jeukenetal.1996,phillipsetal.2004,rodwellandpalmer ,Boyleetal.2008,Andersonetal.2010,Martinetal.2010). 18 Dataassimilationcanalsoteachusaboutnature,notjustaboutmodelerrors. 19 Whilemodelaccuracyishelpfulinensuringtherealismofanalyzedstates,small 20 modelerrorscanactuallybeinformativeiftheyactasspacentime fingerprint 21 patternsthatfacilitateprocessinterpretations.lessonsaboutnatureareprobably 22 cleanestwhenthemodelexertslittleofitsowninfluence,butratheractsonlyasa 23 quantitativeframeworkofgoverningequations.suchisthecasewiththestudyhere 24 ofthemaddennjulianoscillation(mjo,maddenandjulian1994),aprominentform 25 ofvariabilityinnaturethatthefreenrunninggeosn5modellacksalmostcompletely 26 (seefigs.3g,4g,6g,7gofkimetal.2009).thatsamemodel(slightlyupdatedbut 27 stilllackinganmjo)underpinsthenewmodernerareanalysisforresearchand 28 Applications(MERRA,Rieneckeretal.2008,2011). 29

4 4 AnoveltyofMERRAoverearlierreanalysesisthatitsdatasetsinclude 1 comprehensivesetsofbudgettermsforthemodelstatevariables(wind, 2 temperature,moisture,ozone).theseeulerianbudgetsbalanceexactly,by 3 construction:atermontherighthandsideofeachbudgetcalledtheanalysis' 4 tendency(at)guaranteesit.constructionoftheat soccursduringapredictorn 5 correctororforwardnbackwardtimeintegrationthatdrivesthemodelthrougha 6 sequenceofanalyzedatmosphericstates(asdescribedinsection2). 7 InawellNdeveloped,comprehensive,fullNphysicsGCMlikeGEOSN5,model 8 shortcomingsaremostlysecondnorderweaknesseswithinadequateschemes,not 9 wildlyinaccurateormissingprocesses.thishelpsusinterpretats:ifthespacen 10 timepatternsofatsresemblethepatternofactionofoneofthemodel sphysical 11 tendencies,thatsuggetswhichschemeisatfaultnnactinginabouttherightplace 12 withinawellnanalyzedstate,butinaslightlywrongway(schubertandchang ).Alongtheway,lessonscanalsoemergeaboutnature,atleastiftheyare 14 expressiblewithintheframeworkoftheideasandmechanismsembodiedinthe 15 model sformulation.thisstudyisadiagnosisofsomeerrorsofgeosn5viamerra 16 ATs,containingsomelessonsaboutnature. 17 Section2describesMERRAdataandtheinterpretationofanalysis 18 tendencies(ats).section3examinesatpatternsinsamplesandaveragesover 19 variousregionsofspaceandtime.section4containsthesummaryandconclusions (Data(and(methods( a.analysistendencies TheMERRAreanalysisisdescribedinRieneckeretal.(2011).Itusesa 25 versionofthegeosn5atmosphericmodelcalledgeosagcmneros_7_24.itsphysics 26 aresimilartopriorversionsincludingthensippn2model(bacmeisteretal.2006, 27 Leeetal.2008,Mapesetal.2009),andtheGEOSN5modelusedinKimetal.(2009), 28 althoughthereevaporationofprecipitationhasevolvedsomewhat(relevantto 29 discussionsherein).stateestimation(analysis)isdonebya3doptimal 30

5 interpolation(minimizationofasquaredweightederrorbetweenfirstguessfields andobservations),ingestingmanytypesofobservationsbothfromsatellitesandin situmeasurements(rieneckeretal.2008).giventhesequenceofanalyzedstates every6hours,theincrementalanalysisupdate(iau,bloometal.1996)technique buildsatsthatcarrythemodeltrajectoryinphasespacealmostexactlythroughthe analyzedstates.theatsareconstantwithin6nhourmodelintegrationtime windowscenteredonthe6nhourlyanalysistimes.usingtheiausystem,anyversion ofthemodelcanbedriventhroughanysetofstates(includingotherreanalyses,not justmerrastates)nnacapabilitycalledreplay. ThefollowingdiscussionintroducesournotationabouttheATs,asusedin figurecaptionsanddisucssionsbelow.consideranarbitrarystatevariable'z'whose governingequationinnaturecanbewrittenintermsoflargenscaledynamicalplus physicalprocesses(partialtendencies): Z t = Z lsd + Z phys (1) AGCMhasacorrespondinggoverningequationforZ,butdiscretizedintime asindicatedbyδtandwithapproximationsoftherighthandside: ΔZ Δt = dz + dz dt dyn dt param (2) Themodel sdynamicaltendenciesdz/dtdyn'consistofadvectionofresolvedn scalegradientsbyresolvednscaleflows,pluspressuregradientandcoriolisforcesin thecasewherezismomentum.givenanaccuratestateanalysisanditsgradients, dz/dtdyn Żlsd'sincedynamicalcorenumericserrorsarequitesmallonan instantaneousbasis.moreproblematicarethe physical tendenciesdz/dtparam, whichmusttrytorepresentżphys,'includingallunresolvedfluideddyflux divergences(turbulence,convection,gravitywaves),alltrulynonndynamical processeslikeradiationandmoleculareffects(surfaceconduction,phasechanges, radiation),andalltheinteractionsofthesethings,includingpossible 5

6 nondeterminism.ingeosn5,thephysicaltendenciesdz/dtphysarebrokendown accordingtosubroutinepackages:moistprocessesdz/dtmoist'(convection,cloud, precipitation),turbulencedz/dtturb,radiationdz/dtradandgravitywavedrag dz/dtgwd. Inreanalysis(orreplay)mode,themodel sequationsaremodifiedby additionofananalysistendencydz/dtana: 7 ΔZ analyzed 6h = dz + dz + dz dt dyn,6h dt param,6h dt ana (3) 8 whichisperhapsmoreclearlyviewedasadefinitionoftheatfield: 9 dz /dt ana ΔZ analyzed 6h dz /dt dyn,6h dz /dt param,6h (4) IfthelargeNscalestatevariablesareaccuratelyanalyzed,includingtheir spatialgradientsandtimechanges,thenlargenscaledynamicaltendenciesare accurate(dz/dtdyn' 'Żlsdasarguedabove)andΔZanalyzed/Δt' ' Z/ t.withthose assumptions,(3) (1)gives: dz/dtana' ''>(dz/dtparam'>'żphys)' (5) orinotherwords,ats'can'be'interpreted'as'the'negative'of'model'physical'tendency' error(whereerrorisdefinedasmodelminustruth). TheIAUproduces6NhourlypiecewiseNconstantATscenteredontheanalysis times,whichareincludedas4ndimensionaloutputfieldsinthemerradataset. However,itisworthnotingthatanyschemewhichdrawsthemodeltowardan analyzedstatesequencecouldbeusedtodefineatsusefully,evensimplerelaxation ornudging(jung2011). b.mjodefinitions TheMJOresultsdescribedbelowinvolvetwodifferentapproachestodefine MJOphaseasabasisforcomposites.Inthefirst,eachlongitudehasadifferentphase 6

7 7 (front,middle,andbackofthemovingmjo scentralrainyoractivearea).inthis 1 caselongitudeaswellastimecanbepooledtomakephasecomposites,andafew 2 monthsofdataaresufficienttofill8n10phasebins.inthesecond,thewholeworld 3 isassignedonephaseofthemjoonagivenday,so phase thuscorrespondstothe 4 longitudeofenhancedconvection(wheelerandhendon2004,wh04).sincethe 5 dependenceonlongitudeisretained,onlytemporalaveragingcontributesto 6 buildingacompositeandmanymoredataareneededtomakeitsmooth.athird 7 definitionusedinrobertsonandroberts(2012)alsoassignsthewholeworlda 8 singlemjophaseonanygivenday.figure3ofrileyetal.(2011)illustratesourtwo 9 methodsofdefiningthemjo. 10 ThefirstapproachisdefinedfromspaceNtimefilteredtropicalNbelt(15SN15N) 11 outgoinglongwaveradiation(olr)observedbysatellite.theolrtimenlongitude 12 sectionwasbandnpassfilteredfor20n100dayperiodsandplanetarywavenumber 13 1N9,toproduceareferencetimeNlongitudesectionOLR (x,t)whichpassesthe 14 eyeballtest(fig.5below)forappropriatelyisolatingtherelevantlowfrequency 15 aspectsinthetimenlongitudesection.ascatterplotofstandardizedolr and 16 standardized (OLR )/ t,witheachlongitudentimebinasitsdatapoints,makesa 17 circularscatterinwhichazimuthindefinesmjophaseandradiusisamplitude(see 18 Fig.3ofRileyetal.2011,Fig.2ofYasunagaandMapes2012).Dividingphaseinto 19 10binsleavesenoughdataineachbinforasmoothcompositewithjustafewMJO 20 cycles(4monthsofdata).however,aspectsofthemjothatdependonlongitudeare 21 sacrificedintheaveraging.insomeplotsbelow,indianandpacificlongitudeswere 22 averagedseparately,althoughthisfunctionsmoreasasignificancecheck 23 (emphasizingthesimilarities)thananestimateoflongitudedependence(the 24 differences).thiswasourfirstapproach,conductedinitiallywith2.5 o Scout 25 versionsofmerraandlaterrepeatedwithfinalmerradata,fortwo120nday 26 periodsofintensemjoactivity:januarynapril1990andnovember February1993.ThefirstperiodwasthehighestpeakinalongNtermindexofMJO 28 varianceforwhichmerradatawereavailablewhentheresearchbegan,whilethe 29 secondwaschosenbecauseitincludedthetogancoarefieldprogram.forthese 30

8 twoperiods,acomprehensivecompositeofall(morethan100)merradataset variableswasmadeandperused.thesimilarityofresultsbetweenthesetwomjon activeperiodsgivesusconfidencethattheconclusionsreportedherearerobust.we focushereonthe1990results,inparttoemphasizethatthespecialobservationsof 1992N3arenotcriticalforthisexercise.Microwaverainfallestimatesoverocean, obtainedfromremotesensingsystems(ssmi.com),wereusedasanindependent checkonmerrarainfall.itisnoteworthythatthe2.5 o Scoutdatagavecomposite resultsalmostidenticaltothe~100xlargerfinalmerradataset. OursecondapproachtoMJOdefinitionusestheRealNtimeMultivariateMJO index(rmm).wheelerandhendon(2004)defineditscomponents(rmm1,rmm2) astheleadingpairofprincipalcomponentsinananalysisofthecombinedvectorof standardizedandlightlyhighnpassfilteredolr,850hpazonalwind,and250hpa zonalwindtimenlongitudesections.inthisrmmbasedmjodefinition,longitude andlatitudedependencesareretained,sincermm1andrmm2aresimplydaily timeseries.wecomposited1979n2005merradatainwh04 soctantsofrmm1n RMM2phasespace.Forthesecompositeswealsorebinnedthedatafrom2/3x½ degreeresolution(540x360arrays,discardingthe361 st latitude,thenorthpole)to 3.3x5degree(108x36arrays).Dataareon42pressurelevels(25levelsinthe tropospherewith25 50hPaspacing).RMMphasesthusrangefrom1N8. 3.(Results:(analysis(tendencies(and(their(interpretation( ThissectionexaminesMERRA stropicalatsindifferentscales,domains,and contextsbasedonthemostoutstandingfindingsofourcomprehensiveexamination. a.(vertical(structure:(a(fulldresolution(sample(of(dt/dt ana ( Figure1showsdT/dtparamanddT/dtanaatasinglegridpointoverthe equatorialwesternpacificinlatedecember1992,theplaceandtimeofthetoga COAREfieldcampaign.MERRA stotalphysicalheating(fig.1a)istopnheavyin profileandintermittentintime,andshowsadistinctivestripeoflowornegative valuesat700hpa,alsoseeninpriorstudiessuchasfig.4ofmapesetal.(2009), 8

9 9 Figs.1and3ofHagosetal.(2010),andFig.9case1ofLingandZhang(2011).Moist 1 heatingdt/dtmoistprovidesmostofthistotal(notshown),andfurtherbreakdowns 2 areshownlater.theanalysistendency(fig.1b)consistentlyopposesthestripeof 3 moistcoolingat700hpa,suggestingthatitiserroneous,andalsohasanegative 4 stripeat550hpa(the0 o Clevelinthetropics). 5 BasedontheseATstripes,underinterpretation(5)above,wesuspectedthat 6 themodelwasmeltingitsprecipitationatthewronglevel,near700hpainsteadof 7 550hPainthetropics.Inthemodelcode,freezingandmeltingwerebothassigneda 8 timescaleof5000s,atimechosentoexpressicenucleationdelaysinthefreezing 9 process.formelting,5000sissolong(5kmfallatatypicalsnowfallspeedof1m/s) 10 thatasecondarycleanuplineofcodeisactuallyhandlingmostsnowmelting,atthe 11 firstaltitude(goingdownward)wheretexceeds5c.thusmeltingisindeed 12 occurringtoolowinthemerraversion;newerversionsofgeosn5haveupdated 13 thistreatmentbasedontheresearchpresentedhere.precipitationrenevaporation 14 alsoshowsasharpverticalgradientinthe600n700hparange(asweshallsee 15 belowinfig.6c)andmayalsocontributesomeofthephysicserrordeducedfrom 16 theatverticalstructure.thereevaporationismostlyofnonconvectiverain(not 17 shown),anditcorrespondstoacloudvoidbeneathrainngeneratinguppercloud, 18 suggestingthatitisstrongenoughtodrivearesolvednscaledowndraft(fig.8cof 19 Mapesetal.2009).Reevaporationcoolingwasdeliberatelymadestrongerinorder 20 tolimitconvectionnlowlevelconvergenceinteractionsthatleadtoadoubleitcz 21 biasinthepacific(bacmeisteretal.2006). 22 Verticallythinlocalizedcooling(suchasmelting)drivesgravitywave 23 motionsthataredispersiveaccordingtoverticalwavelength,yieldingwavelike 24 temperatureanomalieswithshortverticalwavelengthsthatextendaboveand 25 belowthelocalizedcooling.thiswavelikeforcedtpatterncanthencoupleto 26 convectivemassfluxprofilesthroughbuoyancysorting,producingthinlayered 27 inflowsandoutflowssometimesseennearbutalsoextendingaboveandbelowthe 28 meltinglevelindopplerradardata(mapesandhouze1995).basedonthisphysics, 29 theerroneousverticaltemperaturewaveimpliedbythe~300hpavertical 30

10 10 wavelengthinfig.1bmaybeaconsequenceofthinnlayerphysicalheatingerrorsin 1 asfewasonelayer.butifmodelmeltingismisplacedintheverticalfrom550hpato 2 700hPa,itimpliesadipoleerrorwithtwicethemagnitudeofmelting:bothtoolittle 3 coolingat550hpaandtoomuchat700hpa.thesameshortnwavelengtht profile 4 inconnectionwithraineventscanbeseeninfig.8fofmapesetal.(2009). 5 ThereisindirectevidenceofashortverticalwavelengthheatingandTerror 6 ingeosn5:convectivelyncoupledwavestravelatabout7m/s(fig.6bofleeetal ),muchslowerthaninobservationsandapproximatelyconsistentwiththe 8 ideaofagravitywaveof~300hpaverticalwavelengthasseeninfig.1bcouplingto 9 convection.mightthealtitudeofmeltingbeimportantinsettingthephasespeedof 10 convectivelycoupledwaves?theanswerdependsonacombinationoftheheating 11 profile sverticalfinestructureandtheconvectionscheme ssensitivityprofiles, 12 beyondthepresentscope. 13 b.(the(strongest(feature(of(u200(at(climatology( MERRA sannualmeanclimatology(1979n2005)ofdu/dtanaat200hpais 16 showninfig.2,intheregionofoneofitsmostintensefeatures,regriddedto3.3x5 17 degreeresolution.thelargestvalueexceedsn3m/sday N1 intheannualmeanover 18 thewesternatlanticatabout25n,andthetimenlatitudeclimatologyinpanelb 19 revealsthatitoccursinsummerwhenseasurfacetemperature(sst)iswarmest.a 20 similarlyntimedpositivevalueisseenat10n15n,suggestingthatthesearepartofa 21 couplet. 22 GEOSN5tendstoproducetoomuchdeepconvectionandrainfalloverthe 23 AtlanticWarmPool(AWP)regioninsummerwhendrivenwithobservedSSTfields 24 (standarddiagnosticsnotshown),likemanycontemporarygcms(biasuttietal ,Misraetal.2009).ThisproblemisindicatedinMERRAbythevertically 26 integratedatforwatervapor,d<qv>/dtana(fig.3).positiveatvaluestheresuggest 27 thatthemodelphysicshasanoveractivemoisturesink(precipitation).wespeculate 28 thattheconvectionschemecuestoostronglyonsurfacenbasedparcelinstability 29

11 11 whichiscloselytiedtowarmsst,andistooinsensitivetothemiddlenleveldryair, 1 animportantsuppressingfactorforconvectionintheawpregion. 2 Anoveractivemoisturesinkimpliesanexcessivemoistheatinginthemodel, 3 whichmustproduceerroneouspotentialvorticity(pv)viaitsverticalderivative. 4 WecandiscernthebalancedwindandthermalstructureofthiserroneousPV 5 vortexinfig.4a,whichshowsacrossnsectionoftheatsoftemperature(colors)and 6 u(contours).cyclonicdu/dtanatorquesat200hpaindicatethatwindobservations 7 opposethemodel stendencytoproduceanexcessiveanticycloneaboveits 8 excessiveheating.meanwhile,anticyclonictorquesnear600hpasuggestthatthe 9 modelhasanerroneouscyclonictendency,consistentwithapvsourceduetoan 10 excessiveverticalheatinggradientdq/dzthere.consistently,dt/dtanaisnegativein 11 between,inthe200n600hpalayer,andpositiveat700hpa,whichalsoindicatesvia 12 thetfielddirectlyanexcessivedt/dtparamheatingdipolewith(minus)theprofileof 13 dt/dtana.fig.4bshowsthatthephysicalheating(opencontours)closelyfollowsthe 14 patternthattheatsareopposing,supportingtheinterpretation(5)ofatsasthe 15 negativeofphysical(mostlymoist)heatingerrors.raineventsinfig.8bofmapeset 16 al.(2009)leaveatemperatureresideofsimilarverticalstructure,sothisbalanced 17 climatologicalvortexoferrorsappearstobeaconvectivelydrivenfeaturewrit 18 large.inthiscase,theheatinghasexcessivemagnitude,anditspeculiarprofile 19 (discussedaboveinsection3a)issimplythefingerprintofmerra smoistheating 20 processesthataidsinattribution. 21 c.(mjo(composites(of(ats( SinceMERRAtracksnatureintime,wecanuseindependentsatellite 24 observationsofthemjoasthebasisforcompositingmerraatsandotherfields. 25 RobertsonandRoberts(2012)concludethat MERRAhasproducedaverycredible 26 pictureofintraseasonalvariabilityasjudgedbycomparisonswithradiativefluxes 27 andprecipitationdatathatareindependentoftheassimilation, andwefindthe 28 sameusingolr(notshown)andssmirainfallestimates(infig.7below), 29 encouragingtheuseofindependentmeasurementsasabasisforcomposites. 30

12 12 Figure5showsthestrongMJOactivityofJanuaryNApril1990,with15NN15S 1 OLR(leftpanelcolors)andOLR filteredtomjofrequenciesandwavenumbers 2 (opencontoursonbothpanels).panelbshowsthephasestripsusedtoconstruct 3 ourcomposites.phase0ismostsuppressed(olr maxima),andphase5ismost 4 active(olr minima),astheantipodesof10equallyspacedazimuthbinsinolr vs. 5 dolr /dtspace.manystudiesuse8phasebins,butwe(followingrileyetal.2011) 6 happenedtochoose10initially,andthatresolutionprovesconvenientbelow. 7 Contouringmakessuchbinningdetailsunimportant,exceptthatplotaxislabelsgo 8 from0n9ratherthan0n7or1n8. 9 SelectedtermsintheMJOcompositetemperaturebudgetareshowninFig NotethatmeanfieldsandMJOvariationsarecombinedinFig.6;thesearenot 11 anomalies.sincethemjoislowfrequencyandcyclical,heatstorageδt/δtistinyas 12 dt/dtdynanddt/dtphysnearlybalance(notshown).moistprocesses(panelc) 13 producemostofthephysicalheating,andagainthesharpverticalgradientfrom N700hPaisseen.Evaporationofprecipitation(paneld)iscenteredat750hPa 15 andprovidesasubstantialpartoftheverticalgradientnear600hpa,whichis 16 sharperthanthatinfig.5ofbacmeisteretal.(2006)astheparameterizationhas 17 changedsincethen.theprofilestructureindt/dtana(paneld)seemstoalignwith 18 themoistheatingprofilefinestructure,andnotwiththatofradiativeheating(not 19 shown),againproviding fingerprint typeevidencethatmoistheatingratherthan 20 cloudyradiationisthedominantprocessdrivingthelayeredstructureoferrors. 21 Moisturebudgetprocesses(Fig.7)arerevealingaboutthemodel s 22 difficultiesinsimulatingthejanuarynapril1990mjos.startingatlowerleft,figure 23 7dshowsthatunassimilatedrainfallestimatesbysatelliteindicateatriplingof 24 rainfallfromphase0tophase5forthecompositemjoasdefinedhere,withthe 25 Indiansector(red)rainierthanthePacific(blue)onaverage.MERRA(landplus 26 ocean)hasapositiverainbiascomparedtossmi(oceanonly)acrossallphases,and 27 alsoasmallercompositemjoamplitude(fig.7c).robertsonandroberts(2012) 28 alsofindthatmerra smjoiscrediblebutabitweakinitsrainfallamplitude, 29 consistentwiththenotionthatthemodellackstheoscillationmechanismsandis 30

13 13 beingforcedtoundergoitonlybytheobservationsitassimilates.phase2asdefined 1 hereisthepeakofmerra sexcessiverainbias.moistureatsinpanels7a,bconfirm 2 thatmerra smoisturesinkismostexcessiveinphase2,althoughthisisjusta 3 modestdeviationfromatimenmeantropicalmoistureprofilebiasthatprevails 4 acrossallphases,withadistinctiveverticalprofile(fig.7a).geosn5developerscall 5 thisbias themushroom afteritsappearanceinzonalmeancrosssections. 6 Convectivemassflux(Fig.7e)alsoshowsadistinctupperNlevelenhancement 7 inphase2,indicatingthatthemodelisprematureinitsonsetofdeepconvectionin 8 themjo sonsetphase,wellbeforethepeakrainfall(atphase5n6).interestingly, 9 observationscompositedinthesameway(albietfromdifferentyears,inthe 10 Cloudsatera,2006N2009)suggestsomeinterpretationsofthemodel sdifficulties. 11 Figure7fshowsrandomlydrawnsamplesofCloudsatecho'objects(Rileyetal ),eachcenteredonitsappropriatephase(phaseisdefinedcontinuouslyhere, 13 butthexaxisislabeledwith10valuesforconvenience).deepconvectiondoes 14 indeedoccurinphase2,infactwiththehighestechotopaltitudeofthewhole 15 display,butitisisolatedandnarrow,quitedistinctfromthewide,overlapping 16 mesoscaleechoobjectsinthemainactivephases(3n6inthisrandomsamplefrom 17 manymore,oftenweaker,mjoeventsin2006n2009).alsoconsistently,lightning 18 peaksinoffnpeakphasesofthemjo(moritaetal.2006),indicatingintensebut 19 sparseconvectioninconditionslikephase2.basedonalltheseindications,it 20 appearsthattheliftednparcelinstabilitydrivingiusolatedcumulonimbuscloudsis 21 stronginearlymjophases,soperhapsitisunsuprisingthatgoesn5 s 22 parameterizedconvection,whichisbasedonverticallynintegratedbuoyancyor 23 cloudworkfunction(moorthiandsuarez1988)mightoverreacttothatinstability. 24 Hypothesestoimprovethemodelwouldbetoincreaseitssensitivitytodryair,as 25 suggestedalsoaroundfig.3,ortofindsomewaytodrawadistinctionbetween 26 isolatedandorganizeddeepconvection(e.g.mapesandneale2011).merra s 27 prematurelywidespreaddeepconvectiondevelopmentisalsoseenasaphaseshift 28 ofitsmiddleandhighcloudrelatedfieldsrelativetomodisandothersatellite 29 observations(fig.4ofrobertsonandroberts2012). 30

14 14 Byusingmanyyears(1979N2005)insteadofjust4monthsofMERRAdata, 1 wecanaddspatialdetailtothisdiagnosisofmjomoistprocessesandtheirerrorsin 2 GEOSN5.Figure8showsMERRA smjoanomaliesofprecipitationandcolumn 3 integratedd<qv>/dtanacompositedaccordingtothewheelernhendonrmmphase 4 definition,averagedover20sn20n.phase8isrepeatedasphase0forcontouring 5 purposes.positiverainanomalies(green)ofabout1mm/dmoveacrosstheindon 6 Pacificsector(60EN180E)fromphases2N6.Before(eastof)theserainanomaliesare 7 positiveatanomalies(red,~0.1mm/d)thatshowthemodel soveractivephysical 8 moisturesink(prematuredeepconvectionorinadequatesurfaceflux)inthose 9 regionsandtimes.linesatphases2and3delineatewhenthisleadingpositiveat 10 featureisoverthepacific,althoughtheeffectisevenstrongerovertheindianocean 11 inphase8. 12 Figure9showsaspatiallydistributedviewofmoisturebudgettermsfor 13 phases2(top3panels)and3(bottom3panels).merraprecipitationcomposites 14 showthemaximum(redareasinfig.9a,9d)movingfromtheeasternindianto 15 westernpacificoceansduringthesephases.aheadofthepeakrainfallanomalies, 16 overthewholewesternpacific,positived<qv>/dtanaandverticallydistributed 17 dqv/dtanasuggest,onceagain,thatthemodelphysicstendencyhasanegativeerror 18 there(excessivemoisturesinkinprematuredeepconvectionorinadequatesource 19 insurfaceflux).theverticalstructureofdqv/dtanainthebottompanelofeachphase 20 (panelsc,f)isbroadlyconsistentwiththatoffig.7a,andindicatesthatthephysics 21 errorisanexcessivesinkatlowlevelsandsourceatupperlevels,againsuggesting 22 prematuretransitiontodeepconvection.robertsonandroberts(2012)find 23 consistentresultsaboutmerra satstructureacrossthemjo,asdefinedintheir 24 differentway.theyalsoexaminedthedivergenceofthelownlevelwindats,and 25 foundevidenceofconvergentwindtendenciesintheboundarylayerplayingarole 26 inorganizingmjoprecipitation,somomentumphysicserrorsandnotjust 27 thermodynamiconesemphasizedheremaybeapartofgeosn5 spoormjo 28 simulation. 29

15 15 Finally,inlightoftheIndianOceansignatureinFig.8,weconsideracase 1 studyofspecialinterest:octobernnovember2011,duringthedynamofield 2 campaignonmjoinitiation.figure10showsmerra srainrate,precipitablewater, 3 andd<qv>/dtanaforthis61ndayperiod.twomainintraseasonalrainpeaksareseen 4 inlateoctoberandlatenovember,inassociationwithprecipitablewaterpeaks.the 5 watervaporatisindeedatitsmostpositivewhenthermmphaseis8(dotted 6 lines),consistentwiththecompositeresultsoffig.8nnahopefulindicatorthatthese 7 2monthsofDYNAMONsampledMJOactivitymayhavesomegenerality. 8 4.(Summary(and(conclusions( 9 10 WehaveexaminedMERRA sanalysistendencyfieldsinheight,latitude, 11 longitude,andtime(season,mjophase,andinstantaneous).besidesshowingthe 12 model smeanbiases,whichcouldbeknownbysubtractingaveragedfreerunning 13 simulationoutputfromaveragedobservations,atsshowpatternsofmodelerror 14 withinrealisticandrealnworldnsynchronizedsequencesofatmosphericstates.from 15 thesepatternsand(5)afewfindingsemergedaboutmerra sunderlyingmodel 16 versioncalledgeosagcmneros_7_ Themodelhaserroneousshortverticalwavelengthsinitsvertical 18 heatingprofile(fig.1b).theseerrorscomemainlyfrommoist 19 heating(fig.6b),whichhasasteepverticalgradientatmidlevels 20 duetodelayedmeltingofprecipitationtoofarbelowthe0clevelas 21 wellasconcentrationoftheevaporationofprecipitationatabout mb(Fig.6c) Themodelproducesexcessivedeepconvection(moistheatingand 24 drying)overtheatlanticwarmpoolinsummer(fig.3),whereit 25 drivesananomalousbaroclinicvortex(fig.4);andontheleading 26 edgeofthemjo(figs.6,8,9),whereinstabilityislarge(e.g. 27 lightningandtallnarrowcloudsareobserved).thewellnknown 28 insensitivityofconvectionschemestodryness(derbyshireetal )maybethecommonculpritinbotherrors. 30

16 16 Wecanfurtherdeduce,fromthefactthatGEOSN5lackstheMJOentirelyin 1 freerunningmode(kimetal.2009),thatthemoistureatdiagnosispointsto 2 shallowntondeepconvectiontransitionasalikelykeyprocessintherealnworldmjo. 3 Thisisnotanewidea(KemballNCookandWeare2001;KikuchiandTakayabu2004; 4 Kiladisetal.2005;Masunagaetal.2006;BenedictandRandall2007;Zhangand 5 Song2009;Masunaga2009;Katsumataetal.2009),buthavingarepeatable, 6 objectivemethodtomeasuretheeffectinamathematicalconfrontationofmodel 7 withobservationscouldbeuniquelyhelpfulforturningthisfamiliarnotionintoa 8 pathtowardwellcalibratedmodelimprovements. 9 ThetacticofreplayingexistinganalysesusingIAUisveryappealing,in 10 comparisontoexpensiverawndataassimilation,buttheuseofevensimplerlinear 11 relaxationtoestimateatsmaysuffice(jung2011).wehopesuchactivitiesbecome 12 moreroutinelyavailableinthefuture,bothtoimprovemodelsandtoadvance 13 understandingofimperfectlysimulatedbutwellanalyzedphenomena. 14 Acknowledgements( Helpful'suggestions'by'several'seminar'audiences'and'three'reviewers'are'gratefully' 17 acknowledged.'this'material'is'based'on'work'supported'by'nasa'grant'nnx06ad74g,' 18 Office'of'Science'(BER),'U.'S.'Department'of'Energy'grant'DEMSC ,'and'National' 19 Science'Foundation'Grant'No.' 'JTB'acknowledges'support'from'GMAO'and' 20 NASA s'map'program.'the'olr'image'in'fig.'5a'was'provided'by'the'noaa/esrl' 21 Physical'Sciences'Division,'Boulder'Colorado,'from'their'Web'site'at' Kiladis.' 24

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20 Figurecaptions Fig.1:TimeNheightsectionsof(a)MERRA stotalphysicalheating(k/d)and(b) temperatureanalysistendency(k/d)forthegridpointat ,n1.875 correspondingtothetogacoareintensivefluxarrayareaintheequatorial westernpacific,15n31december1992. Fig.2:MERRA sdu/dtana(unitsms N2 )atthe200hpalevel,regriddedto3.3 o x5 o.a) Annualmean.b)ClimatologicalannualcycleasatimeNlatitudesectionfortheboxed regionin(a).largestvaluesareabout4m/sperday. Fig.3:Verticallyintegratedwatervaporanalysistendencyd<qv>/dtana(mm/d)in theintranamericasseasregioninborealsummer. Fig.4:LatitudeNpressure(0N40N,1000N100hPa)sectionoftheJulyclimatological meanover60wn90wofdt/dtana(colors,unitskd N1 ),overlaidwithcontoursof:(a) du/dtana(m/sd N1 )and(b)dt/dtphys(unitskd N1 ).Dottedcontoursindicatenegative values. Fig.5:LongitudeNtimesections(withtimerunningdownward,15SN15N)showing thejanuarynapril1990mjoactivity.(a)satellitenobservedoutgoinglongwave Radiation(OLR,colors)andMJONfilteredOLR (contours,10wm N2 intervals).(b) Localphaseindex(0N9,colors)oftheMJO,whereveritsamplitudeexceeds1 standarddeviation.olr contoursarerepeatedforreference.phaseisazimuthand amplitudeisradiusinascatterplotoftheseolr dataandtheirtimederivative, bothstandardized.magentalinein(b)separatesindianfrompacificdatafor separatesubncomposites(redandbluelinesinfig.7). Fig.6:MJOphasecompositesoftheindicatedtemperaturetendencies(unitsK/day). Phaseincreasesfromrighttoleft,sothattheseshouldbeviewedassimilartoeastN westsectionsratherthantimeseries. 20

21 21 1 Fig.7:(a)N(e):MJOphasecompositesfromJFMA1990.Panel(a)showsdqv/dtana, 2 and(b)showsitsverticalintegral.panel(c)issurfaceraindifference(merra 3 minusssmi),while(d)isthessmiestimate(overoceanonly).redandbluecurves 4 usedatawestandeastoflongitude110e,respectively(seefig.3),whileblackis 5 averagedoveralllongitudes.panel(e)ismerra sconvectionschememassflux, 6 while(f)depictsarandomsampleofcloudsatnobserved echoobjects from2006n ,centeredontheirMJOphaseasdefinedinthesameway,withagrayNtoNred 8 radarreflectivityscale(asinrileyetal.2011) Fig.8:20SN20NaveragesofRMMNbased(WheelerandHendon2004)MJOcomposite 11 anomaliesofmerra sd<qv>/dtana(bluenredshadingandn0.1and0.1contours)and 12 precipitation(opencontours,greenispositiveandorangeisnegativewithcontours ,1,1.5).Unitsaremm/dforbothquantities Fig.9:MapsandcrossNsectionsofmoisturebudgettermsinRMMphases2and3, 16 whenthemjoprecipitationanomaly(redinpanelsaandd)ismovingfromthe 17 IndianoceantothewesternPacific.Positivehumidityanalysistendencies(red 18 areas,d<qv>/dtanainb,eanddqv/dtanainc,f)suggestwheremodelphysicsmakes 19 excessivedrying Fig.10:DailytimeseriesovertheIndianOcean(50N100E,10SN10N)duringthe 22 DYNAMOfieldcampaignofMERRA s(a)rainfallrate,(b)columnwatervapor(or 23 precipitablewaterpw),and(c)d<qv>/dtanawith5ndayboxcarsmoothing(heavy 24 line).dottedlinesin(c)aretimeswhenthermmindexphaseis8(c.f.fig.8)

22 (a) (b) Fig. 1: Time-height sections of (a) MERRA s total physical heating (K/d) and (b) temperature analysis tendency (K/d) for the grid point at , corresponding to the TOGA COARE Intensive Flux Array area in the equatorial western Pacific, December 1992.

23 (a) (b) J F M A M J J A S O N D Fig. 2. MERRA s du/dtana (units m s-2) at the 200 hpa level, regridded to 3.3o x 5o. a) Annual mean. b) Climatological annual cycle as a time-latitude section for the boxed region in (a). Largest values are about 4 m/s per day.

24 Fig. 3: July climatology of vertically integrated water vapor analysis tendency d<q v >/dt ana (mm/d) in the Intra-Americas Seas region.

25 (a) (b) Fig. 4: Latitude-pressure (0-40N, hpa) section of the July climatological mean over 60W-90W of dt/dt ana (colors, units K d -1 ), overlaid with contours of: (a) du/dt ana (m/s d -1 ) and (b) dt/dt phys (units K d -1 ). Dotted contours are negative values.

26 (a) (b) Fig. 5: Longitude-time sections (with time running downward, 15S-15N) showing the January-April 1990 MJO activity. (a) Satellite-observed Outgoing Longwave Radiation (OLR, colors) and MJO-filtered OLR (contours, 10 W m -2 intervals). (b) Local phase index (0-9, colors) of the MJO, wherever its amplitude exceeds 1 standard deviation. OLR contours are repeated for reference. Phase is azimuth and amplitude is radius in a scatter plot of these OLR data and their time derivative, both standardized. Magenta line in (b) separates Indian from Pacific data for separate sub-composites (red and blue lines in Fig. 7).

27 a) dt/dt phys c) REEVAP b) dt/dt moist d) dt/dt ana Fig. 6: MJO phase composites of the indicated temperature tendencies (units K/day). Phase increases from right to left, so that these should be viewed as similar to east-west sections rather than time series.

28 a) dq v /dt ana b) d<q v >/dt ana c) MERRA SSMI rain d) SSMI rain e) MERRA convective mass flux f) Cloudsat echo objects in 10 MJO phases 15 z 10 (km) MJO phase Fig. 7: (a)-(e): MJO phase composites from JFMA Panel (a) shows dq v /dt ana, and (b) shows its vertical integral. Panel (c) is surface rain difference (MERRA minus SSMI), while (d) is the SSMI estimate (over ocean only). Red and blue curves use data west and east of longitude 110E, respectively (see Fig. 3), while black is averaged over all longitudes. Panel (e) is MERRA s convection scheme mass flux, while (f) depicts a random sample of Cloudsat-observed echo objects from , centered on their MJO phase as defined in the same way, with a gray-tored radar reflectivity scale (as in Riley et al. 2011).

29 Fig. 8: 20S-20N averages of RMM-based (Wheeler and Hendon 2004) MJO composite anomalies of MERRA s d<q v >/dt ana (blue-red shading and -0.1 and 0.1 contours) and precipitation (open contours, green is positive and orange is negative with contours 0.5, 1, 1.5). Units are mm/d for both quantities.

30 Fig. 9: Maps and cross-sections of moisture budget terms in RMM phases 2 and 3, when the MJO precipitation anomaly (red in panels a and d) is moving from the Indian ocean to the western Pacific. Positive humidity analysis tendencies (red areas, d<q v >/dt ana in b,e and dq v /dt ana in c,f) suggest where model physics makes excessive drying. (a) (b) (c) (d) (e) (f)

31 Fig. 10: Daily time series over the Indian Ocean (50-100E, 10S-10N) during the DYNAMO field campaign of MERRA s (a) rainfall rate, (b) Column water vapor (or precipitable water PW), and (c) d<q v >/dt ana with 5-day boxcar smoothing (heavy line). Dotted lines in (c) are times when the RMM index phase is 8 (compare Fig. 8).