The Microphysics of Clouds over the Antarctic Peninsula – Part 2

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The Microphysics of Clouds over the Antarctic Peninsula – Part 2:
modelling aspects within Polar WRF
ConstantinoListowski1 andTomLachlan-Cope1
1BritishAntarcticSurvey,NERC,HighCross,MadingleyRd,Cambridge,CB30ET,UK
Correspondenceto:C.Listowski([email protected])
Abstract. The first intercomparisons of cloud microphysics schemes implemented in the Weather Research and Forecasting
(WRF)mesoscaleatmosphericmodel(version3.5.1)areperformedintheAntarcticPeninsulausingthepolarversionofWRF
(Polar WRF) at 5 km resolution, along with comparisons to the British Antarctic Survey’s aircraft measurements (presented
inPart1ofthiswork,Lachlan-Copeetal.,2016).Thisstudyfollowspreviousworkssuggestingthemisrepresentationofthe
5 cloud thermodynamic phase in order to explain large radiative biases derived at the surface in Polar WRF continent-wide,
and in the Polar WRF-based operational forecast model Antarctic Mesoscale Prediction System (AMPS) over the Larsen C
Iceshelf.Severalcloudmicrophysicsschemesareinvestigated:theWRFSingle-Moment5-classscheme(WSM5),theWRF
Double-Moment6-classscheme(WDM6),theMorrisondouble-momentscheme,theThompsonscheme,andtheMilbrandt-
Yau Double-Moment 7-class scheme. WSM5 used in AMPS struggles the most to capture the observed supercooled liquid
10 phasemainlybecauseoftheiricenucleiparameterisationoverestimatingthenumberofactivatedcrystals,whileothermicro-
physicsschemes(butnotWSM5’supgradedversion,WDM6)managemuchbettertodoso.Thebestperformingschemeis
theMorrisonschemeforitsbetteraveragepredictionofoccurrencesofclouds,andcloudphase,aswellasitslowestsurface
radiativebiasovertheLarsenCiceshelfintheinfrared.ThisisimportantforsurfaceenergybudgetconsiderationwithPolar
WRFsincethecloudradiativeeffectismorepronouncedintheinfraredovericysurfaces.However,ourinvestigationshows
15 thatalltheschemesfailatsimulatingthesupercooledliquidmassatsometemperatures(altitudes)whereobservationsshow
evidenceofitspersistence.AnicenucleiparameterisationrelyingonbothtemperatureandaerosolcontentlikeDeMottetal.
(2010) (not currently used in WRF cloud schemes) is in best agreement with the observations, at temperatures and aerosol
concentrationcharacteristicoftheAntarcticPeninsulawheretheprimaryiceproductionoccurs(Part1),comparedtoparame-
terisationonlyrelyingontheatmospherictemperature(usedbytheWRFcloudschemes).Overall,arealisticicemicrophysics
20 implementationisparamounttothecorrectrepresentationofthesupercooledliquidphaseinAntarcticclouds.
1 Introduction
Tropospheric Clouds in Antarctica are amongst the least well observed on Earth due to the remote environment and harsh
conditions that make field observation difficult. As a result of this, no modelling study has ever focused on comparing the
performancesofWRFcloudmicrophysicsschemestoin-situcloudmeasurements.Yet,thisisanecessarysteptoimproveour
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abilitytomodeltheAntarcticatmosphere.Betterunderstandingthemeteorologyisalsocrucialforprovidingreliableforecast
toaircraftorgroundoperationsintheAntarctic.
Much attention has focused on Antarctica’s energy budget in recent years notably due to the West Antarctic Ice Sheet
warming(O’Donnelletal.,2011;Bromwichetal.,2013b),andonlargeicemassloss(gain)recordedinWest(East)Antarctica
5 (HarigandSimons,2015).AssessinghowatmosphericdrivenprocessesaffecttheevolutionAntarctica’sicemassandsurface
energy budget requires to improve our understanding, and modelling of the clouds in that region. Importantly, changes in
microphysicalpropertiesofAntarcticcloudsimpacttheatmospheredynamicsatlowersouthernlatitudesandevenatnorthern
latitudes,sincetheiralteredradiativepropertiesmodifytheNorth-Southtemperaturegradient(Lubinetal.,1998).
TheAntarcticPeninsulaischaracterisedbyhighmountainsformingabarriertothedominantwesterlies,andwhichroughly
10 extends across the longitudes 67◦W to 65◦W at the latitude of Rothera station (67.586◦S), with altitudes up to around 2500
metersinsomeplaces.Thismajortopographyfeaturecausessignificantdifferencesbetweeneachsideintermsoftemperatures
(MorrisandVaughan,2003),andprecipitations(KingandTurner,1997),andalsoaerosolsandcloudmicrophysics(ascon-
cludedinPart1ofthiswork,Lachlan-Copeetal.,2016).Significantclimatechangeshavebeenrecentlyobservedacrossthe
Peninsuladuringthelastfewdecades(O’Donnelletal.,2011;Turneretal.,2016).Interestingly,oceanicdrivenmechanisms
15 arethemaincontributortoglaciersmeltinginthePeninsula(Woutersetal.,2015).Inthiscontext,improvingthemodelling
of the different components of the energy budget of the Antarctic Peninsula is required to better understand its climatologi-
cal evolution,and how atmospheric-driven processesact along with ocean-driven processes toimpact Antarctica’s ice mass
balance,andtemperatures.Cloudsareoneoftheleastwellunderstoodoftheatmosphericcomponents(Boucheretal.,2013;
Flatoetal.,2013).
20 Recentstudieshavepointedtowardsantarcticcloudsbeingresponsibleforlargeshortwave(SW)andlongwave(LW)surface
radiativebiases(severaltensofWm 2)inhighresolutionmodelsoverthewholecontinent(Bromwichetal.,2013a),andmore
−
specificallyovertheEasternPeninsula’sLarsenCIceShelf(Kingetal.,2015).Improvedcloudphysicsallowingforrealistic
icesupersaturationsledtolowerthesurfaceenergybudgetbiasesintheRACMO2highresolutionclimatemodel(vanWessem
et al., 2014a). King et al. (2015) compared three mesoscale models simulations over the Larsen C during a summer month,
25 and showedhow theydiffered inthe amountof liquid andice thatwere formedin cloudsand this resultedin comparatively
differentsurfacebiases.Theystronglypointtowardsproblemsinthemodellingofthethermodynamicphaseofcloudswithin
themodels,andmorespecificallytowardsthesupercooledliquidcomponent(liquidmaintainedatT 0 C).Themodellingof
◦
≤
themixed-phasecloudsneedstobeimprovedinmodels,andthemisrepresentation(underestimation)ofsupercooledliquidover
AntarcticacanberelatedtoitspoorrepresentationoverthesurroundingSouthernOceanasawhole(LawsonandGettelman,
30 2014).
A related issue deals with the initiation of the ice phase in clouds, which is driven by the Ice nucleating particles (INPs).
TheyarethesubstratesneededtoactivateicecrystalsgrowtheitherdirectlyfromthevapourcondensingontheINP(deposition
freezing), or from the freezing of supercooled droplets following immersion of, contact with, or condensation on, an INP
(Hoose and Möhler, 2012). In the latter case the INP act as a Cloud Condensation Nuclei (CCN) first to form a droplet.
35 Homogeneous freezing of droplets (ie without the intervention of an INP) can occur at temperatures usually believed to be
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colder than -38 (Hoose and Möhler, 2012) although there are possible significant effects already below -30 (Herbert et al.,
◦ ◦
2015).InaremoteplacelikeAntarcticalittleisknownabouttheexactnatureoftheINPsalthoughstudieshavebeenidentifying
variousplausiblesources.Biologicalsourcesfromthesnowysurface,blowingsnow,sulfateparticlesresultingfromsea-surface
emissions, mineral dust lifted from ice-free regions or brought by winds from continental landmasses at lower latitudes e.g
5 South-America.ManycandidatesarefoundintheliteraturetoexplainthepresenceofINPsinAntarctica(seeBromwichetal.,
2012, for a review). Similar questions arise for INPs in marine air in remote places like in the middle of the Souther Ocean
(Burrowsetal.,2013),whichsurroundstheantarcticcontinent.RegardingCCNs,whichareneededtoactivateclouddroplet
growth,seasaltisknowntobeanefficientsubstrate.Interestingly,itsemissioninpolarregion’sboundarylayerisbelievedto
beenhancedinplaceswherebrine-richsnowcoveringseaicecanbeliftedbythewinds(Yangetal.,2008).
10 Inthelastdecades,verylocalizedground-measurementsusingin-situorremotesensingtechniqueshaveallowedtocharac-
terisemicrophysicalpropertiesofclouds(particlephase,particlesize,crystalsshape),howevertheseobservationsaresparse
(Lachlan-Cope,2010).Groundbasedremote-sensingmeasurementsprovidelocalcontinuousmeasurementsmakingitpossible
tolinkcloudspropertiestoprecipitationsoraccumulationevents(Gorodetskayaetal.,2015).
TwoaircraftcampaignsledbytheBritishAntarcticSurvey(BAS)tookplaceduringthesummer2010,and2011,measuring
15 cloud properties on both sides of the Antarctic Peninsula (Lachlan-Cope et al., 2016, hereafter referred to as Part 1). These
twocampaigns,alongwithevidenceformisrepresentationofcloudsovertheeasternsideofthePeninsula(Kingetal.,2015),
motivatedthisfirstattempttocomparesomeoftheexistingcloudmicrophysicsschemesimplementedintheWeatherResearch
andForecastingatmosphericmodel(Skamarocketal.,2009)(WRF)v3.5.1,at5kmresolution,totheairbornemeasurements.
We use the polar version of WRF (Polar WRF, Hines and Bromwich, 2008), which has optimised representation for polar
20 regionsintermsofsurfaceproperties(ice,snow,seaice,andseawater)andprocesses(heattransferbetweenthesurfaceand
theatmosphere).PolarWRFiswidelyusedbytheAntarcticcommunityanditisusedbytheAntarcticMesoscalePrediction
System(AMPS,Powersetal.,2012),whichisanoperationalforecastmodelthatprovidessupportforinternationalAntarctic
efforts.
Insection2wepresentthemodelsettingsalongwiththemicrophysicsschemesusedinthiswork,andexplaintheirmain
25 characteristics.Insection3wediscusssimpleresultsofradiationbiasestoillustratetheimportanceofcloudschemesonthe
Peninsula energy budget. In section 4 we compare modelling results to in-situ measurements already presented in Part 1. In
section5wediscusstheresults,therelevancyofthemicrophysicsschemesaswellassomeimportantaspectstoconsiderin
thefutureforimprovingcloudmicrophysicsschemesandparameterisationsinAntarctica.Insection6themainaspectsofthis
workaresummarized.
30 2 Observations,atmosphericmodel,andthecloudmicrophysicsschemes
2.1 Overviewoftheairborneobservations
Two campaigns of in situ cloud measurements took place on both sides of the Antarctic Peninsula (61–73 W) in February
◦
2010, and January 2011, respectively. The observations were made with the British Antarctic Survey’s instrumented Twin
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Otter aircraft (King et al., 2008) based at Rothera research station (67.586 S, 68.133 W). ERA-Interim reanalysis show an
◦ ◦
intensified northerly flow in 2011 to the west of the Peninsula, expected to bring warmer air. However colder temperatures
were observed in the reanalysis, as well as in the radiosonde ascents made at Rothera (not shown), and from the aircraft
measurements(atendencycorrectlyreproducedinthesimulations,seesection4.4).Thiscanbeexplainedbycolderairbeing
5 pulledfromtheWeddellSea(totheeastofthePeninsula)duringthe2011’scampaign,followingintensificationandeastward
movementoftheAmundsenSealowtothewestofthePeninsula(Part1,theirFigure3).Resultsonaveragecloudproperties
(predominantlystratus,oraltostratus)comparingbothcampaignsandbothsidesofthePeninsulaarepresentedinPart1.The
aircraftwasfittedwithvariousinstrumentsmeasuringnotablytemperature,pressure,humidity,turbulence,radiationaswellas
withaDropletMeasurementTechnologyCloud,Aerosol,andPrecipitatingSpectrometer(CAPS)(Baumgardneretal.,2001).
10 TheCAPShasaCloudandAerosolSpectrometer(CAS),aCloudImagingProbe(CIP),andaHotwireLiquidWaterContent
(LWC)Sensor.TheCASmeasuresparticlesize(diameter)between0.5and50µm,andtheHotwirewasusedtovalidatethe
supercooledLWCasderivedfromtheCAS,whichcannotdiscriminatebetweenliquidandice.Also,theCASshowedadistinct
peak in the spectra indicative of drop formations. The CIP images particles with sizes between 25 µm and 1.5 mm, with 25
µmpixelresolution.Particlessmallerthan200µminsizecannotbediscriminatebetweencrystalsanddroplets.Theirnumber
15 concentration is very small compared to the CAS, and therefore they were ignored (see also section 4.3.1 of this paper for
theimpactontheLWC).TheicewatercontentwascalculatedusingtheBrownandFrancismass-dimensionparameterisation
(Brown and Francis, 1995). More details on data processing and the derivation of the ice crystal number concentration are
giveninPart1.Finally,theCIPsamplesatarateofalittlelessthan10Ls 1,hencethelowerlimitforthemeasuredcrystal
−
numberconcentrationofalittlemorethan0.1L 1 (thisexplainsthelowerlimitoftheobservations-thegreyshadedarea-
−
20 showninFigure11).
2.2 Modelsettings
PolarWRFv3.5.1wasusedwithadownscalingmethod(Figure1a)wherea45kmresolutiondomaincontainsasmaller15
kmresolutionnest,whichitselfcontainsasmallernestat5kmresolutioncenteredovertheregionswherethe2010and2011
flights took place (Figure 1b). The topography is from Fretwell et al. (2013). The simulation outputs of the 5 km resolution
25 domain were used for the present analysis. The simulation is one-way, in the sense that no information is passed in return
from one domains to its parent domain. Table 1 summarizes the WRF settings used for the main physical processes, except
forthecloudmicrophysicsschemes,whichareaddressedinsection2.3.Kingetal.(2015)(hereafterreferredtoasK15)were
interestedinthesurfaceradiativebiasesontheLarsenCIceShelfontheeasternsideofthePeninsula.Theyusedoutputsfrom
AMPS(builtonPolarWRFv3.0.1forthe2011period).Forconsistencyweusethesameshortwaveandlongwaveradiation
30 schemesaswellasthesameboundarylayerscheme,surfacescheme,andsubsurfaceschemesasK15(Table1).
One of the main differences with K15 is that our simulation is constrained horizontally and vertically, at the boundaries
ofthe45kmresolutiondomain,withERA-InterimreanalysisdatainsteadofGlobalForecastSystemdata(GFS,runbythe
U.S. National Centers for Environmental Prediction). ERA-Interim is the latest global atmospheric reanalysis (Dee et al.,
2011)providedbytheEuropeanCentreforMedium-RangeWeatherForecasts(ECMWF).Thisreanalysisisbasedonarchived
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Figure 1. (a) WRF configurations for the three domains used for all simulations and (b) close-up on the highest resolution domain with
detailedtopographyfromFretwelletal.(2013).Theblacktrianglesindicatetheflighttracksofthe2010campaign,whiletheredcircles
indicatetheflighttracksofthe2011campaign.Theothermarkersindicatestation(circle),andtheAutomaticWeatherStations(AWS)14
(diamond)and15(square)locatedontheLarsenCIceShelf.
observationsfrom1989onward.Itisobtainedthroughdataassimilationintoanatmosphericmodelrunningataresolutionof
0.75 0.75degrees,whichroughlycorrespondsto30kminlongitudeby80kminlatitude,atthelatitudeofRotherastation
×
(67.586 S).
◦
Werantwosetsofsimulations.Thefirstsetspanstheperiod1stFebruary2010to5thofMarch2010,andthesecondset
5 goes from 1st of January 2011 to 11th of February 2011 (the first two days were not included in the analysis as part of the
modelspin-up).Thesetwoperiodscoverthetimeofthetwoaircraftcampaigns(seePart1),includingtheperiodduring2011
whenacampwassetupontheLarsenCIceShelf,closetotheAutomaticWeatherStationfourteen(AWS14,seeFigure1b),
asdescribedinK15.
2.3 Cloudmicrophysicsschemes
10 WeusedfivedifferentmicrophysicsschemetoassesstheirabilitytomodelrealisticcloudsacrosstheAntarcicPeninsula.None
oftheWRFmicrophysicsschemeshasbeenspecificallydeveloppedforAntarcticcloudsmodelling.Asnoworkhasbeendone
sofaroncomparingmicrophysicsschemesimplementedinPolarWRFwithrespecttotheirperformancesforAntarcticclouds,
weusedthemassuchwithnomodification.Thisappearedtobethemostreasonablefirststepthatcanthenhelpguidefurther
developmentofAntarcticcloudsmicrophysicsmodelling.Generallyspeaking,eachschemeisabulkmicrophysicsparameter-
15 isation(BMP)whereeithermass(Single-Moment–SM–scheme),orbothmassandnumberdensity(Double-Moment–DM
– scheme) of the various types of hydrometeors are independently predicted. In the DM case, the scheme allows for a more
realistic behaviour of clouds. Indeed, predicting both the mass and the number density of hydrometeors allows the average
particlesizetobepredicted,whichinturnallowsthemodellingofallsize-dependentprocesseslikesedimentation,accretion,
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growthtobeimproved(Igeletal.,2015).Allschemeshavenon-precipitable,andprecipitablehydrometeors.Theformer(cloud
droplets,icecrystals)areconsideredashavingzerosedimentationvelocityinthecollectionoraccretionprocesses,incontrast
tothelatter(raindrops,snowcrystals,graupel,orhail)thatactascollectorparticles.Finally,wedidnotuseanymicrophysics
radiusbinmodel(asopposedtotheBMPs).Theypredicttheevolutionofcloudparticleswithingivensizebins,andallowfor
5 thepredictionoftheactualparticlesizedistributions.BinmodelsaremissingfromWRFv3.5.1.However,abinmodelismore
demandingintermsofcomputertime,andBMPsareusedincurrentglobalorregionalatmosphericmodels,andinoperational
forecastmodelslikeAMPS.Table2highlightssomeaspectsofthecloudmicrophysicsschemesinvestigatedinthisstudy.
TheactualdefaultmicrophysicsschemeofWRFisWRFSingle-Moment3(WSM3),whichhasbeendiscardedherebecause
it does not allow for the existence of supercooled liquid droplets. Thus, our default reference scheme is the WRF Single-
10 Moment 5 (WSM5) which allows for mixed-phase clouds formation (Hong et al., 2004). WSM5 is a SM scheme for all the
hydrometeors.ItisusedinAMPS,whichwasshowntostrugglewiththepredictionofthecloudthermodynamicphase(King
etal.,2015).
TheWRFdouble-moment6(WDM6,LimandHong,2010)isanimprovementofWSM5,inwhichdropletsandrainare
bothtreatedwithDMschemes,graupelsareincluded,andalltheicephaseparticlesaretreatedwithaSMscheme.Itisused
15 hereinordertotesttheimprovementofthepredictionofthesupercooledliquidphasethatonecouldexpectfromtheuseofa
moresophisticatedparameterisationfortheliquidphase(DMinsteadofSMasinWSM5).
The Morrison scheme (Morrison et al., 2005; Morrison et al., 2009) is a full DM scheme for all icy hydrometeors, and
rain,andSMforwaterdroplets.TheMorrisonschemerequiresthecouplingtotheWRFChemistrymodule(Peckhametal.,
2011)inordertoactasaDMschemefortheclouddroplets,andsincesuchcouplingwasnotavailableweusedtheMorrison
20 schemeasaSMschemeforthewaterdroplets.ThisschemeisusedintheArcticSystemReanalysis(ASR),whichisbasedon
PolarWRFaswell.ItslightlyimprovedthemodellingofthecloudsinthenorthernpolarsummercomparedtoWSM5at30
kmresolution(Wesslénetal.,2014),andthispaperinvestigatesitsabilitytobetterrepresentthecloudsinthesouthernpolar
region.
The Thompson scheme (Thompson et al., 2008) has a state-of-the art parameterisation of snow, which relies on extensive
25 flights measurements, and it uses a more realistic size dependent density for snow particles. The latter are treated as non-
spherical,andtheirdensitydecreaseswithincreasingsize.Thiswasidentifiedashavingamajorinfluenceontheproductionof
supercooleddropsmainlybecauseofadecreasedefficiencyintherimingprocessresultinginlongerlastingsupercooleddrops
(Thompsonetal.,2008).
Finally, The Milbrandt-Yau scheme (Milbrandt and Yau, 2005a, b) (hereafter designated as Milbrandt) is a full double
30 momentscheme(withtheshapeparametersoftheparticledistributionbeingfixed).Itisusedhereinordertotesttheability
ofafulldoublemomentschemetopredictsupercooleddropsbetterthantheMorrisonortheThompsonschemes.
Table3detailsthewaythecloudschemestreattheinitiationofthecloudicephase,andthecloudliquid.Theinitiationofthe
icephaseisthemostcomplexpart,anditreliesonINPparameterisations.TheydiagnosethenumberofINPs,hencethenumber
ofactivatedcrystals,accountingforthevariousfreezingmodesdescribedintheintroduction.TheINPparameterisationsrely
35 ontheatmospherictemperatureonly.Theyareusedinvariouswaysbythedifferentcloudmicrophysicsschemesasillustrated
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in Table 3. This complex issue will be further addressed in section 5.2 where we discuss those parameterisations. They deal
withprimaryiceproduction(dropletsorvapourconvertedtoicethroughinteractionwithINPs),asopposedtosecondaryice
processes,whichresultfromtheinteractionofalreadyformedcrystalswithothercrystalsorwithsupercooleddroplets.The
latter will be further addressed in section 5.2. Finally, the liquid phase relies on a fixed number of droplets or a predicted
5 numberofactivatedCCN(hencenumberofdrops),dependingonthecloudscheme.Theliquidphaseisformedaftertheice
microphysics is computed provided there is still an excess of vapour compared to equilibrium (ie if S >1, where S is the
w w
saturationratiowithrespecttoliquidwater).
3 Preliminaries:resultsinradiationbiases
Large biases in both surface downward shortwave (SW, solar flux) and longwave (LW) radiation were reported east of the
10 PeninsulaovertheLarsenCIceShelfbyK15.Theauthorscomparedthesummertimesurfaceenergybudgetassimulatedfor
January2011bythreemesoscalemodels:AMPS,TheUKMetOfficeUnifiedModel(UM)(seeWilsonandBallard,1999,for
thecloudscheme),andtheRegionalAtmosphericClimateMOdel(RACMO2)version2.3(seevanWessemetal.,2014a,for
thecloudscheme).AfieldcampwasestablishedclosetoAWS14(seeFigure1b)whereradiosondeascentsallowedthewater
vapour column density to be calculated. AWS14 is fitted with SW and LW radiometers. K15 showed that all models mostly
15 overestimatedSWradiationbyseveraltensofWm−2 (positivebias)whiletheyunderestimatedLWradiation(negativebias).
Theypointedtowardsthelackofsimulatedcloudsthatblockedtheincomingshortwavesolarradiation,andemittedthermal
radiationbacktothesurface.TheonlyexceptionwasnotedfortheUMmodelwhichhadseveraltensofWm 2 ofnegative
−
bias in SW suggesting an overestimation of the cloud cover. The AMPS model had very little if no liquid cloud forming at
all during this period over AWS14, explaining the very large biases, especially in SW to which small droplets are the most
20 responsive.Iceclouds,however,weresimulated,andK15pointedtowardsamisrepresentationoftheactualphaseoftheclouds
toexplainthebiasesosberved.
FollowingK15,Table4showsaveragebiasesofdailyaveragedSWandLWdownwardsurfacefluxes.Theywerederived
bysubtractingtheobservedvaluetothemodelledvalue.Threesiteswereselected:BritishAntarcticSurvey’sRotherastation,
on the western side of the Antarctic Peninsula, and two Automatic Weather stations - AWS14 and AWS15 - on the Eastern
25 side of the Peninsula on the Larsen C ice shelf (see Figure 1b). Both AWS are about 70 km apart on the ice shelf. Table 4
alsoindicateswhetherthedifferencebetweenWSM5(usedinAMPS)andtheotherschemesisstatisticallysignificant(witha
Studentt-test).
3.1 TheparticularcaseofAWS14inJanuary2011
WefirstcompareresultsobtainedbyK15withtheAMPSmodelovertheperiod8January2011to8February2011atAWS14
30 (seetheirTable3),toourresultsobtainedwiththeWSM5schemeoverthesameperiod(Table4,fourthcolumnofresults).
Their computed biases are 56 W m 2 and -10 W m 2 in SW and LW, respectively ). Ours are 53 W m 2 and -20 W m 2,
− − − −
respectively.DiscrepanciesinbiasescanresultfromthecombinationofdifferentsettingsintheAMPS(forcing,numberof
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verticallevels,domainsboundaries).However,wedoobtainthesameordersofmagnitudeandsamesignsofbiasesasK15,
consistent with a lack of clouds. A striking result is that the Morrison scheme reduces the biases in both SW and LW in a
statistically significant way at the 99% level, while the Milbrandt, and the Thompson schemes reduce it significantly in LW
only.
5 Figure 2 (bottom) shows the cloud liquid mass integrated over the entire atmospheric column (kg m−2) for the different
simulationsasafunctionoftime,inthemodelgridboxcorrespondingtotheAWS14locationin2011.Figure2(top)showsthe
simulated column density of water vapour compared to the radiosonde ascent measurements from the field camp at AWS14
(presentedinK15,andplottedintheirFigure7).Themodelledwatervapourisconsistentwithobservationsobtainedbetween
day8andday32andthesimulationsdoagreebetweenthemexceptfortheThompsonschemebetweenday15andday20,
10 which better matches observations at that time. The Morrison scheme produces twice to four times more liquid clouds than
theWSM5scheme(Figure2bottom).TheMilbrandt,andThompsonschemesgiveintermediateamountsbetweenWSM5and
Morrison,andWDM6issimilartoWSM5.ThelargeramountofliquidcloudssimulatedwiththeMorrisonschemecompared
toWSM5isconsistentwithitssmallerSWandLWbiasesatAWS14in2011(Table4)andinlinelinewithK15’sconclusion
thatthephaseofthecloudsisresponsiblefortheirSWandLWbiases.TheThompsonscheme,andtheMilbrandtschemesdo
15 havelowerSWbiasesthanWSM5aswell,howevertheimprovementissmallerthanwiththeMorrisonscheme,andlessor
notstatisticallysignificant.ButitstillissignificantforLWradiation.
The total ice mass is similar from one scheme to another, as shown by Figure 3 (top). However an important difference
ariseswhenconsideringthecloudicecrystalsmassonly(iethepristineice–ignoringthemainprecipitableparticleslikesnow
and graupel particles); WSM5 and WDM6 simulate three to four times more ice crystals mass than the other schemes. The
20 Milbrandtschemeonlyformsoccasionallyasmuchicemassaroundthe19th,the25thandthe30thofJanuary.Graupelsare
mainlyabsentexceptfortheMilbrandtschemeswhichtendstoformlowamountsaround0.05kgm 2 onrareoccasions(not
−
shown).
Overall,themaindifferenceinthecloudmicrophysicsbetweenthevarioussimulationsatAWS14istheabilityofthecloud
schemestosustainsupercooledliquiddrops,whichinturnexplainsdifferencesinSWandLWbias.Anotherdifferenceliesin
25 thedistributionofthemasswithintheicephasebetweencloudicecrystalsandsnowparticles.
3.2 Generalresultsinradiationbiases
FortheeasternsideofthePeninsula(AWS14andAWS15),thebiasesshowninTable4(rightpart)demonstratestheimportance
of the choice of the microphysicsscheme for the surfaceenergy budget of theLarsen C in PolarWRF. Similar biases (sign,
andorderofmagnitude)areobservedatagivenyearandforagivenscheme,betweenAWS14andAWS15.Thisisconsistent
30 with the stations being 70 km apart from each other on the ice shelf, which consists of a relatively flat surface covered with
snow,andwherethelargescaleinfluencesarelikelytobesimilarinabsenceofsignificantlocalvariationsinthetopographyor
thenatureofthesurface.AremarkableresultisthattheLWbiasissignificantlyreducedduringbothperiodsofinterestonthe
LarsenCiceshelfusingtheMorrison,Thompson,orMildbrandtschemes,comparedtoWSM5(orWDM6–notshown)as
canbeseenfromthelowerrightpartofTable4.However,theThompson,andtheMilbrandtschemesstillhaveanegativeLW
8
Atmos.Chem.Phys.Discuss.,doi:10.5194/acp-2016-1135,2017
ManuscriptunderreviewforjournalAtmos.Chem.Phys.
Published:19January2017
c Author(s)2017.CC-BY3.0License.
(cid:13)
Figure 2. (Top) Time series of column density of water vapour (kg m−2) for the different simulations computed in the model gridbox
correspondingtotheAWS14location,in2011,alongwiththeradiosondemeasurementsfromthefieldcamp(describedinK15,seetheir
Figure7).(Bottom)Timeseriesofthecolumndensityofthecloudliquid(kgm−2)forthedifferentsimulations.
bias,whileMorrison’sisslightlypositiveandgivesonaveragethesmallestLWbiasatbothAWSstationsforbothyears.The
standarddeviationofdaily-averagedmeasurementsremainshighbutstatisticaltestshowthatthedifferencesbetweenWSM5
andthethreeotherschemesaresignificant,mainlyatthe99%levelin2011,andmainlyatthe95%levelin2010.TheSWbias
issignificantlyreducedonlywiththeMorrisonschemein2011.Howevernoimprovementoccursin2010fortheSWbias.The
5 Milbrandt, and the Thompson scheme’s SW biases are slightly lower in 2011 with differences to WSM5 that are significant
atthe90%level.In2010alltheschemeshavealargenegativeSWbiaswiththelargestamplitudeattributedtotheMorrison
scheme.
For the western side of the Peninsula (Rothera station), SW biases are always positive, and LW biases always negative,
whatever the cloud scheme or the year considered (left part of Table 4). All simulations consistently show this imbalance
10 suggesting no improvement in cloud simulation. Besides, no statistically significant difference is observed between WSM5,
andtheotherschemes.Notethatalmostnocloudliquidwater(notshown)issimulatedaboveRothera(asopposedtoAWS14,
Figure 2), whatever the cloud scheme used, in line with the persistent large SW and LW biases. Ice and snow (graupel),
however,areformedinsimilaramountstotheonesshowninFigure3.Overall(Table4)demonstratesthehighsensitivityof
thesimulateddownwardradiationfluxestothemicrophysicsused.
15 Amajorissueinassessingtheperformancesofthecloudmicrophysicsschemesbyinvestigatingradiationbiasesisthatit
does suppose that the appropriate information is passed on from the cloud scheme to the radiative scheme. This aspect can
explaintheapparentparadoxofthesignificantimprovementoftheLWbiastotheeastofthePeninsulawiththreeschemes,
9
Atmos.Chem.Phys.Discuss.,doi:10.5194/acp-2016-1135,2017
ManuscriptunderreviewforjournalAtmos.Chem.Phys.
Published:19January2017
c Author(s)2017.CC-BY3.0License.
(cid:13)
Figure3.(Top)SameasFigure2forthetotalicephase(ice,snow,andgraupelparticles).(Bottom)SameasFigure2forcloudiceparticles
only.
whilenoconcomitantSWbiasimprovementisbeingobserved.TheDiscrepanciesinSWandLWbiasesimprovementswill
befurtherdiscussedinsection5.Radiativeschemesthemselvesalsorequirecarefulexaminationastheyalsorelyonvarious
assumptions, and simplified geometry to retrieve SW and LW fluxes. The radiative schemes that we used was chosen for
consistencywithK15,inordertocomparetheirconclusions(usingAMPS)toours(usingPolarWRF).Wedonotintendhere
5 toinvestigatetheradiativeschemesimplementedinWRF.Forfurtherassessmentofcloudmicrophysicsschemesperformances
andbehavioursatamuchwiderscale,wenowcomparethesimulationsoutputstoeachotherandtothecloudmicrophysics
propertiesasmeasuredduringtheBASaircraftcampaignsthattookplaceovertheAntarcticPeninsula(presentedinPart1).
4 Results:simulatedcloudsascomparedtoobservations
4.1 GeneraltrendsforsimulatedcloudsacrossthePeninsula
10 ThetopographyoftheAntarcticPeninsula(Figure1)makesitinterestingtofocusonzonaldistributionoflatitudinalaverages
fortheLiquidWaterContent(LWC,ingkg 1),andtheSolidWaterContent(SWC,gkg 1).SWCcomprisesice,snowand
− −
graupelmass.ItisdifferentfromtheIceWaterContent(IWC),whichconsistsonlyinthemassofthecloudicecrystals.LWC
andSWCwererespectivelyaveragedbetweenlatitudes65.5 Sand68.5 Sandaltitudesbelow4500m.Thisgeographicalarea
◦ ◦
includestheregionwherebothflightcampaignstookplaceinsummer2010,and2011(Figure1b).
10