Download Cloud feedback mechanisms and their representation in global

ArticleTitle:
Cloudfeedbackmechanismsandtheirrepresentationinglobalclimate
models
ArticleType:
AdvancedReview
Authors:
PauloCeppi
DepartmentofMeteorology,UniversityofReading,Reading,UnitedKingdom
[email protected]
FlorentBrient
CentreNationaldeRecherchesMétéorologiques,Météo-France/CNRS,Toulouse,France
MarkD.Zelinka
CloudProcessesResearchGroup,LawrenceLivermoreNationalLaboratory,Livermore,United
States
DennisL.Hartmann
DepartmentofAtmosphericSciences,UniversityofWashington,Seattle,UnitedStates
Abstract
Cloudfeedback–thechangeintop-of-atmosphereradiativefluxresultingfromthecloudresponse
to warming – constitutes by far the largest source of uncertainty in the climate response to CO2
forcing simulated by global climate models (GCMs). We review the main mechanisms for cloud
feedbacks, and discuss their representation in climate models and the sources of inter-model
spread. Global-mean cloud feedback in GCMs results from three main effects: (1) rising freetroposphericclouds(apositivelongwaveeffect);(2)decreasingtropicallowcloudamount(apositive
shortwaveeffect);(3)increasinghigh-latitudelowcloudopticaldepth(anegativeshortwaveeffect).
These cloud responses simulated by GCMs are qualitatively supported by theory, high-resolution
modeling, and observations. Rising high clouds are consistent with the Fixed Anvil Temperature
(FAT)hypothesis,wherebyenhancedupper-troposphericradiativecoolingcausesanvilcloudtopsto
remain at a nearly fixed temperature as the atmosphere warms. Tropical low cloud amount
decreasesaredrivenbyadelicatebalancebetweentheeffectsofverticalturbulentfluxes,radiative
cooling, large-scale subsidence, and lower-tropospheric stability on the boundary-layer moisture
budget.High-latitudelowcloudopticaldepthincreasesaredominatedbyphasechangesinmixedphase clouds. The causes of inter-model spread in cloud feedback are discussed, focusing
particularly on the role of unresolved parameterized processes such as cloud microphysics,
turbulence,andconvection.
Graphical/VisualAbstractandCaption
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Spatial distribution of cloud feedback (in W m per K surface warming) predicted by a set of global climate
modelssubjectedtoanabruptincreaseinCO2.RedrawnwithpermissionfromZelinkaetal.(2016).
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INTRODUCTION
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Astheatmospherewarmsundergreenhousegasforcing,globalclimatemodels(GCMs)predictthat
clouds will change, resulting in a radiative feedback by clouds1, 2. While this cloud feedback is
positiveinmostGCMsandhenceactstoamplifyglobalwarming,GCMsdivergesubstantiallyonits
magnitude3. Accurately simulating clouds and their radiative effects has been a long-standing
challengeforclimatemodeling,largelybecausecloudsdependonsmall-scalephysicalprocessesthat
cannotbeexplicitlyrepresentedbycoarseGCMgrids.IntherecentClimateModelIntercomparison
Project phase 5 (CMIP5)4, cloud feedback was by far the largest source of inter-model spread in
equilibrium climate sensitivity, the global-mean surface temperature response to CO2 doubling5-7.
The important role of clouds in determining climate sensitivity in GCMs has been known for
decades8-11, and despite improvements in the representation of cloud processes12, much work
remainstobedonetonarrowtherangeofGCMprojections.
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Despite these persistent difficulties, recent advances in our understanding of the fundamental
mechanisms of cloud feedback have opened exciting new opportunities to improve the
representation of the relevant processes in GCMs. Thanks to increasing computing power,
turbulence-resolving model simulations have offered novel insight into the processes controlling
marinelowcloudcover13-16,ofkeyimportancetoEarth’sradiativebudget17.Clevercombineduseof
model hierarchies and observations has provided new understanding of why high-latitude clouds
brighten18-20,whytropicalanvilcloudsshrinkwithwarming21,andhowcloudsandradiationrespond
tostormtrackshifts22-24,tonameafewexamples.
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Thegoalofthisreviewistosummarizethecurrentunderstandingofcloudfeedbackmechanisms,
andtoevaluatetheirrepresentationincontemporaryGCMs.Althoughtheobservationalsupportfor
GCMcloudresponsesisassessed,wedonotprovideathoroughreviewofobservationalestimates
ofcloudfeedback,nordowediscusspossible“emergentconstraints”25.Thediscussionisorganized
into two main sections. First, we diagnose cloud feedback in GCMs, identifying the cloud property
changes responsible for the radiative response. Second, we interpret these GCM cloud responses,
discussingthephysicalmechanismsatplayandtheabilityofGCMstorepresentthem,andbriefly
reviewing the available observational evidence. Based on this discussion, we conclude with
suggestionsforprogresstowardanimprovedrepresentationofcloudfeedbackinclimatemodels.
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DIAGNOSINGCLOUDFEEDBACKINGLOBALCLIMATEMODELS
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Webeginbydocumentingthemagnitudeandspatialstructureofcloudfeedbackincontemporary
GCMs,andidentifythecloudpropertychangesinvolvedintheradiativeresponse.Althoughclouds
mayrespondtoanyforcingagent,inthisreviewwewillfocusoncloudfeedbacktoCO2forcing,of
highestrelevancetofutureanthropogenicclimatechange.
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Global-meancloudfeedback
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Theglobal-meancloudfeedbackstrength(quantifiedbythefeedbackparameter;Box1)isplottedin
Fig. 1, along with the other feedback processes included in the traditional decomposition. The
feedback parameters are derived from CMIP5 experiments forced with abrupt quadrupling of CO2
concentrations relative to pre-industrial conditions. In the following discussion we quote the
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numbersfromananalysisof28GCMs5(coloredcirclesinFig.1).Twootherstudies(greysymbolsin
Fig.1)showsimilarresults,buttheyincludesmallersubsetsoftheavailablemodels.
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Feedback parameter (W m−2 K−1)
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Caldwell et al. 2016 (28 models)
Vial et al. 2013 (11 models)
Zelinka et al. 2016 (7 models)
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Fig. 1. Strengths of individual global-mean feedbacks and equilibrium climate sensitivity (ECS) for CMIP5
models,derivedfromcoupledexperimentswithabruptquadruplingofCO2concentration.Modelnamesand
feedback values are listed in the Supporting Information, Table S1. Feedback parameter results are from
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Caldwelletal. ,withadditionalcloudfeedbackvaluesfromVialetal. andZelinkaetal. ECSvaluesaretaken
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fromAndrewsetal. ,Forsteretal. ,andFlatoetal. FeedbackparametersarecalculatedasinSodenetal. butaccountingforrapidadjustments;thecloudfeedbackfromZelinkaetal.iscalculatedusingcloud-radiative
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kernels (Box2).Circlesarecoloredaccordingtothetotalfeedbackparameter.ThePlanckfeedback(mean
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valueof-3.15Wm K )isexcludedfromthetotalfeedbackparametershownhere.
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Box1:Climatefeedbacks
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IncreasinggreenhousegasconcentrationscauseapositiveradiativeforcingF(Wm-2),towhichthe
climatesystemrespondsbyincreasingitstemperaturetorestoreradiativebalanceaccordingto
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N denotes the net energy flux imbalance at the top of atmosphere, and ∆T is the global-mean
surfacewarming.Howeffectivelywarmingreestablishesradiativebalanceisquantifiedbythetotal
feedback parameter λ (in W m-2 K-1). For a positive (downward) forcing, warming must induce a
negative(upward)radiativeresponsetorestorebalance,andhenceλ<0.Whenthesystemreaches
anewsteadystate,N=0andthusthefinalamountofwarmingisdeterminedbybothforcingand
feedback,∆T=–F/λ.Amorepositivefeedbackimpliesmorewarming.
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The total feedback λ equals the sum of contributions from different feedback processes, each of
whichisassumedtoperturbthetop-of-atmosphereradiativebalancebyagivenamountperdegree
warming. The largest such process involves the increase in emitted longwave radiation following
Planck’slaw(anegativefeedback).Additionalfeedbacksresultfromincreasedlongwaveemissionto
spaceduetoenhancedwarmingaloft(negativelapseratefeedback);increasedgreenhousewarming
bywatervapor(positivewatervaporfeedback);anddecreasingreflectionofsolarradiationassnow
andiceretreat(positivesurfacealbedofeedback).Changesinthephysicalpropertiesofcloudsaffect
N=F+λ∆T.
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boththeirgreenhousewarmingandtheirreflectionofsolarradiation,givingrisetoacloudfeedback
(Box2),positiveinmostcurrentGCMs.
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Themulti-model-meannetcloudfeedbackispositive(0.43Wm-2K-1),suggestingthatonaverage,
clouds cause additional warming. However, models produce a wide range of values, from weakly
negativetostronglypositive(-0.13to1.24Wm-2K-1).Despitethisconsiderableinter-modelspread,
only two models, GISS-E2-H and GISS-E2-R, produce a (weakly) negative global-mean cloud
feedback. In the multi-model mean, this positive cloud feedback is entirely attributable to the
longwave(LW)effectofclouds(0.42Wm-2K-1),whilethemeanshortwave(SW)cloudfeedbackis
essentiallyzero(0.02Wm-2K-1).
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Of all the climate feedback processes, cloud feedback exhibits the largest amount of inter-model
spread,originatingprimarilyfromtheSWeffect3,6,26,32.Theimportantcontributionofcloudstothe
spreadintotalfeedbackparameterandequilibriumclimatesensitivity(ECS)standsoutinFig.1.The
net cloud feedback is strongly correlated with the total feedback parameter (r=0.80) and ECS
(r=0.73).
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Box2:Cloud-radiativeeffectandcloudfeedback
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The radiative impact of clouds is measured as the cloud-radiative effect (CRE), the difference
betweenclear-skyandall-skyradiativefluxatthetopofatmosphere.Cloudsreflectsolarradiation
(negative SW CRE, global-mean effect of -45 W m-2) and reduce outgoing terrestrial radiation
(positive LW CRE, 27 W m-2), with an overall cooling effectestimated at -18 W m-2 (numbers from
Henderson et al.33). CRE is proportional to cloud fraction, but is also determined by cloud altitude
and optical depth. The magnitude of SW CRE increases with cloud optical depth, and to a much
lesserextentwithcloudaltitude.Bycontrast,theLWCREdependsprimarilyoncloudaltitude,which
determines the difference in emission temperature between clear and cloudy skies, but also
increaseswithopticaldepth.
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Asthecloudpropertieschangewithwarming,sodoestheirradiativeeffect.Theresultingradiative
flux response at the top of atmosphere, normalized by the global-mean surface temperature
increase,isknownascloudfeedback.ThisisnotstrictlyequaltothechangeinCREwithwarming,
because the CRE also responds to changes in clear-sky radiation – for example due to changes in
surfacealbedoorwatervapor34.TheCREresponsethusunderestimatescloudfeedbackbyabout0.3
W m-2 on average34, 35. Cloud feedback is therefore the component of CRE change that is due to
changingcloudpropertiesonly.
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VariousmethodsexisttodiagnosecloudfeedbackfromstandardGCMoutput.Thevaluespresented
inthispaperareeitherbasedonCREchangescorrectedfornon-cloudeffects30,orestimateddirectly
from changes in cloud properties, for those GCMs providing appropriate cloud output31. The most
accurateprocedureinvolvesrunningtheGCMradiationcodeoffline–replacinginstantaneouscloud
fieldsfromacontrolclimatologywiththosefromaperturbedclimatology,whilekeepingotherfields
unchanged – to obtain the radiative perturbation due to changes in clouds36, 37. This method is
computationallyexpensiveandtechnicallychallenging,however.
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RapidAdjustments
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Thecloud-radiativechangesthataccompanyCO2-inducedglobalwarmingpartlyresultfromarapid
adjustment of clouds to CO2 forcing and land-surface warming38, 39. Because it is unrelated to the
global-meansurfacetemperatureincrease,thisrapidadjustmentistreatedasaforcingratherthana
feedbackinthecurrentfeedbackanalysisframework40.Animportantimplicationisthatcloudscause
uncertainty in both forcing and feedback. For a quadrupling of CO2 concentration, the estimated
global-mean radiative adjustment due to clouds ranges between 0.3 and 1.1 W m-2, depending on
theanalysismethodandGCMset,andhasbeenascribedmainlytoSWeffects6,41,42.Accountingfor
thisadjustmentreducesthenetandSWcomponentofthecloudfeedback.Wereferthereaderto
Andrewsetal.43andKamaeetal.44forathoroughdiscussionofrapidcloudadjustmentsinGCMs.
Hereafterwefocussolelyonchangesincloudpropertiesthataremediatedbyincreasesinglobalmeantemperature.
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Decompositionbycloudtype
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For models providing output that simulates measurements taken by satellites, the total cloud
feedback can be decomposed into contributions from three relevant cloud properties: cloud
altitude, amount, and optical depth (plus a small residual)45. The multi-model-mean net cloud
feedback can then be understood as the sum of positive contributions from cloud altitude and
amountchanges,andanegativecontributionfromopticaldepthchanges(Fig.2a).Thevariouscloud
properties have distinctly different effects on LW and SW radiation. Increasing cloud altitude
explains most of the positive LW feedback, with minimal effect on SW. By contrast, cloud amount
and optical depth changes have opposing effects on SW and LW radiation, with the SW term
dominating. (Note that 11 of the 18 feedback values in Fig. 2 include the positive effect of rapid
adjustments,yieldingamorepositivemulti-modelmeanSWfeedbackcomparedwithFig.1.)
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ThecloudpropertydecompositioninFig.2acanberefinedbyseparatelyconsideringlow(cloudtop
pressure > 680 hPa) and free-tropospheric clouds (cloud top pressure ≤ 680 hPa), as this more
effectivelyisolatesthefactorscontributingtothenetcloudfeedback26.Thisverticaldecomposition
reveals that the multi-model mean LW feedback is entirely due to rising free-tropospheric clouds
(Fig.2b).Forsuchclouds,amountandopticaldepthchangesdonotcontributetothenetfeedback
becausetheirSWandLWeffectscancelnearlyperfectly.Meanwhile,theSWcloudfeedbackcanbe
ascribed to low cloud amount and optical depth changes (Fig. 2c). Thus, the results in Fig. 2b,c
highlight the three main contributions to the net cloud feedback in current GCMs: rising freetroposphericclouds(apositiveLWeffect),decreasinglowcloudamount(apositiveSWeffect),and
increasinglowcloudopticaldepth(aweaknegativeSWeffect),yieldinganetpositivefeedbackin
the multi-model mean. It is noteworthy that all CMIP5 models agree on the sign of these
contributions.
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Spatialdistributionofcloudfeedback
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The contributions to LW and SW cloud feedback are far from being spatially homogeneous,
reflecting the distribution of cloud regimes (Fig. 3). Although the net cloud feedback is generally
positive, negative values occur over the Southern Ocean poleward of about 50° S, and to a lesser
extentovertheArcticandsmallpartsofthetropicaloceans.Themostpositivevaluesarefoundin
regions of large-scale subsidence, such as regions of low SST in the equatorial Pacific and the
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subtropical oceans. Weak to moderate subsidence regimes cover most of the tropical oceans, and
areassociatedwithshallowmarinecloudssuchasstratocumulusandtradecumulus.InmostGCMs
such clouds decrease in amount17, 46, strongly contributing to the positive low cloud amount
feedback seen in Fig. 2c. This explains the importance of shallow marine clouds for the overall
positive cloud feedback, and their dominant contribution to inter-model spread in net cloud
feedback17.
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Fig.2.GlobalmeanLW(red),SW(blue),andnet(black)cloudfeedbacksdecomposedintoamount,altitude,
optical depth (OD) and residual components for (a) all clouds, (b) free-tropospheric clouds only, and (c) low
cloudsonly,definedbycloudtoppressure(CTP).Multi-modelmeanfeedbacksareshownashorizontallines.
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Resultsarebasedonananalysisof11CMIP3and7CMIP5models ;theCMIP3valuesdonotaccountforrapid
adjustments. Model names and total feedback values are listed in Table S2. Redrawn with permission from
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Taking a zonal-mean perspective highlights the meridional dependence of cloud property changes
andtheircontributionstocloudfeedback(Fig.4).Free-troposphericcloudtopsrobustlyriseglobally,
producing a positive cloud altitude LW feedback at all latitudes that peaks in regions of high
climatological free-tropospheric cloud cover (blue curve). The positive cloud amount feedback
(orangecurve),dominatedbytheSWeffectoflowclouds(cf.Fig.2),alsooccursovermostofthe
globe with the exception of the high southern latitudes; by contrast, the effect of optical depth
changes is near zero everywhere except at high southern latitudes, where it is strongly negative
(greencurve).Thisyieldsacomplexmeridionalpatternofnetcloudfeedback(blackcurveinFig.4).
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Thepatternsofcloudamountandopticaldepthchangessuggesttheexistenceofdistinctphysical
processesindifferentlatituderangesandclimateregimes,asdiscussedinthenextsection.
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Fig.3.Spatialdistributionofthemulti-modelmeannetcloudfeedback(inWm perKsurfacewarming)ina
set of 11 CMIP3 and 7 CMIP5 models subjected to an abrupt increase in CO2 (Table S2). Redrawn with
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TheresultsinFig.4allowustofurtherrefinetheconclusionsdrawnfromFig.2.Inthemulti-model
mean,thecloudfeedbackincurrentGCMsmainlyresultsfrom
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• globallyrisingfree-troposphericclouds,
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• decreasinglowcloudamountatlowtomiddlelatitudes,and
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• increasinglowcloudopticaldepthatmiddletohighlatitudes.
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Summary
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Cloud feedback is the main contributor to inter-model spread in climate sensitivity, ranging from
nearzerotostronglypositive(-0.13to1.24Wm-2K-1)incurrentclimatemodels.Itisacombination
of three effects present in nearly all GCMs: rising free-tropospheric clouds (a LW heating effect);
decreasing low cloud amount in tropics to midlatitudes (a SW heating effect); and increasing low
cloudopticaldepthathighlatitudes(aSWcoolingeffect).Lowcloudamountintropicalsubsidence
regionsdominatestheinter-modelspreadincloudfeedback.
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INTERPRETINGCLOUDPROPERTYCHANGESINGLOBALCLIMATEMODELS
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Havingdiagnosedtheradiatively-relevantcloudresponsesinGCM,weassessourunderstandingof
thephysicalmechanismsinvolvedinthesecloudchanges,anddiscusstheirrepresentationinGCMs.
We consider in turn each of the three main effects identified in the previous section, and address
thefollowingquestions:
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What physical mechanisms are involved in the cloud response? To what extent are these
mechanismssupportedbytheory,high-resolutionmodeling,andobservations?
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How well do GCMs represent these mechanisms, and what parameterizations does this
dependon?
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•
Whatexplainstheinter-modelspreadincloudresponses?
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Fig.4.Zonal-,annual-,andmulti-model-meannetcloudfeedbacksinasetof11CMIP3and7CMIP5models
(Table S2), plotted against the sine of latitude, and partitioned into components due to the change in cloud
amount,altitude,andopticaldepth.Curvesaresolidwhere75%ormoreofthemodelsagreeonthesignof
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thefeedback,dashedotherwise.RedrawnwithpermissionfromZelinkaetal. 206
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Cloudaltitude
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Physicalmechanisms
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Owingtothedecreaseoftemperaturewithaltitudeinthetroposphere,highercloudtopsarecolder
andthusemitlessthermalinfraredradiationtospace.Therefore,anincreaseinthealtitudeofcloud
topsimpartsaheatingtotheclimatesystembyreducingoutgoingLWradiation.Fundamentally,the
rise of upper-level cloud tops is firmly grounded in basic theory (the deepening of the well-mixed
troposphereastheplanetwarms),andissupportedbycloud-resolvingmodelingexperimentsandby
observationsofbothinterannualcloudvariabilityandmulti-decadalcloudtrends.Thecombination
of theoretical and observational evidence, along with the fact that all GCMs simulate rising freetroposphericcloudtopsastheplanetwarms,makethepositivecloudaltitudefeedbackoneofthe
mostfundamentalcloudfeedbacks.
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The tropical free troposphere is approximately in radiative-convective equilibrium, where latent
heating in convective updrafts balances radiative cooling, which is itself primarily due to thermal
emission by water vapor47. Because radiative cooling by the water vapor rotation and vibration
bandsfallsoffrapidlywithdecreasingwatervapormixingratiointhetropicaluppertroposphere48,
sotoomustconvectivemassflux.Hence,massdetrainmentfromtropicaldeepconvectionandits
attendantanvilcloudcoveragebothpeaknearthealtitudewhereemissionfromwatervapordrops
offrapidlywithpressure,whichwerefertoasthealtitudeofpeakradiatively-drivenconvergence.
Becauseradiativecoolingbywatervaporiscloselytiedtowatervaporconcentrationandthelatter
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isfundamentallycontrolledbytemperaturethroughtheClausius-Clapeyronequation,thedramatic
decrease in water vapor concentration in the upper troposphere occurs primarily due to the
decreaseoftemperaturewithdecreasingpressure.Thisimpliesthatthelevelthatmarksthepeak
coverage of anvil cloud tops is set by temperature. As isotherms rise with global warming, so too
must tropical anvil cloud tops, leading to a positive cloud altitude feedback. This “fixed anvil
temperature” (FAT) hypothesis49, illustrated schematically in Fig. 5, provides a physical basis for
earlier suggestions that fixed cloud top temperature is a more realistic response to warming than
fixedcloudaltitude50,51.
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Fig. 5. Schematic of the relationship between clear-sky radiative cooling, subsidence warming, radiativelydrivenconvergence,andaltitudeofanvilcloudsinthetropicsinacontrolandwarmclimate,asarticulatedin
the FAT hypothesis. Upon warming, radiative cooling by water vapor increases in the upper troposphere,
which must be balanced by enhanced subsidence in clear-sky regions. This implies that the level of peak
radiatively-drivenconvergenceandtheattendantanvilcloudcoveragemustshiftupward.TCdenotestheanvil
cloudtoptemperatureisotherm.
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Inpractice,tropicalhighcloudsriseslightlylessthantheisothermsinresponsetomodeledglobal
warming,leadingtoaslightwarmingoftheiremissiontemperature–albeitamuchweakerwarming
than occurs at a fixed pressure level (roughly six times smaller)52. This is related to an increase in
upper tropospheric static stability with warming that was not originally anticipated in the FAT
hypothesis.Theproportionatelyhigheranviltemperature(PHAT)hypothesis52allowsforincreasesin
staticstabilitythatcausethelevelofpeakradiatively-drivenconvergencetoshifttoslightlywarmer
temperatures. The upward shift of this level closely tracks the upward shift of anvil clouds under
global warming, and captures their slight warming. The aforementioned upper-tropospheric static
stability increase has been described as a fundamental consequence of the first law of
thermodynamics,whichresultsinstaticstabilityhavinganinverse-pressuredependence21,although
theradiativeeffectofozonehasalsobeenshowntoplayarole53.
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Cloud-resolving(horizontalgridspacing≤15km)modelsimulationsoftropicalradiative-convective
equilibrium support the theoretical expectation that the distribution of free-tropospheric clouds
shifts upward with surface warming nearly in lockstep with the isotherms, making their emission
temperatureincreaseonlyslightly53-56.Thisresponseisalsoseeninglobalcloud-resolvingmodels5759
.ThisisimportantforconfirmingthattheresponseseeninGCMs21, 52andmesoscalemodels49is
not an artifact of parameterized convection. Furthermore, observed interannual relationships
between cloud top altitude and surface temperature are also in close agreement with theoretical
expectations60-65. Recent analyses of satellite cloud retrievals showed that both tropical and extratropicalhighcloudshaveshiftedupwardovertheperiod1983-200966,67.
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AlthoughFATwasproposedasamechanismfortropicalcloudaltitudefeedback,itispossiblethat
radiative cooling by water vapor also controls the vertical extent of extratropical motions, and
thereby the strength of extratropical cloud altitude feedback (Thompson et al., submitted
manuscript). In any case, the extratropical free tropospheric cloud altitude feedback in GCMs is at
leastaslargeasitscounterpartinthetropics26,despitehavingreceivedmuchlessattentioninthe
literature.
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Box3:FATandthecloudaltitudefeedback
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Cloud tops rising as the surface warms produces a positive feedback: by rising so as to remain at
nearly constant temperature, their emission to space does not increase in concert with emission
fromtheclear-skyregions,inhibitingtheradiativecoolingoftheplanetunderglobalwarming.
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Thefactthatcloudtoptemperatureremainsroughlyfixedmakestheinterpretationofthefeedback
potentiallyconfusing:howcanhighcloudswarmtheplanetiftheiremissiontemperatureremains
nearly unchanged? It is important to recall that feedbacks due to variable X are defined as the
changeinradiationduetothetemperature-mediatedchangeinXholdingallelsefixed68.Inthecase
where X is cloud top altitude, the feedback quantifies the change in radiation due solely to the
change in cloud top altitude, holding the temperature structure of the atmosphere fixed at its
unperturbed state. Thus, increased cloud top altitude causes a LW heating effect because – in the
radiationcalculation–theemissiontemperatureofthecloudtopactuallydecreasesbytheproduct
ofthemean-statelapserateandthechangeinthecloudtopaltitude.
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Animportantpointtoavoidlosinginthedetailsisthataslongasthefreetroposphericcloudtops
rise under global warming, the altitude feedback is positive. The extent to which cloud top
temperatureschangeaffectsonlythemagnitudeofthefeedback,notitssign.
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Representationinglobalclimatemodelsandcausesofinter-modelspread
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Given its solid foundation in well-established physics (radiative-convective equilibrium, ClausiusClapeyronrelation),itisunsurprisingthatallGCMssimulateanearlyisothermalriseinthetopsof
free tropospheric clouds with warming, in excellent agreement with PHAT. The multi-model mean
net free-tropospheric cloud altitude feedback is 0.20 W m-2 K-1, with an inter-model standard
deviationof0.09Wm-2K-1(Fig.1b).Althoughthespreadinthisfeedbackisroughlyhalfaslargeas
that in the low cloud amount feedback, it is still substantial and remains poorly understood. Since
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thealtitudefeedbackisdefinedastheradiativeimpactofrisingcloudtopswhileholdingeverything
elsefixed(Box3),themagnitudeofthisfeedbackatanygivenlocationshouldberelatedto(1)the
change in free-tropospheric cloud top altitude, (2) the decrease in emitted LW radiation per unit
increase of cloud top altitude, and (3) the free-tropospheric cloud fraction. These are discussed in
turnbelow.
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Based on the discussion above, one would expect the magnitude of the upward shift of freetroposphericcloudtops(term1)toberelatedtotheupwardshiftofthelevelofradiatively-driven
convergence. Both of these are dependent on the magnitude of upper tropospheric warming69, 70,
whichvariesappreciablyacrossmodels71,72forreasonsthatremainunclear.
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ThedecreaseinemittedLWradiationperunitincreaseincloudtopaltitudedependsonthemeanstatetemperatureandhumidityprofileoftheatmosphere,andoncloudLWopacity.Totheextent
that inter-model differences in atmospheric thermodynamic structure are small, inter-model
variance in term 2 would arise primarily from differences in the mean state cloud opacity, which
determineswhetheranupwardshiftisaccompaniedbyalargedecreaseinLWflux(forthickclouds)
or a small decrease in LW flux (for thin clouds). Overall, the dependence of LW fluxes on cloud
optical thickness is small, however, because clouds of intermediate to high optical depth are
completelyopaquetoinfraredradiation.Therefore,wedonotexpectcloudopticaldepthbiasesto
dominatethespreadincloudaltitudefeedback.
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Finally,themean-statefree-troposphericcloudfraction(term3)islikelytoexhibitsubstantialintermodel spread. A four-fold difference in the simulated high (cloud top pressure ≤ 440 hPa) cloud
fraction was found among an earlier generation of models73, though this spread has decreased in
CMIP5models12. Furthermore, climate models systematically underestimate the relative frequency
ofoccurrenceoftropicalanvilandextratropicalcirrusregimes74, 75.Takenalone,suchbiaseswould
leadtomodelssystematicallyunderestimatingthecloudaltitudefeedback.
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Lowcloudamount
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Physicalmechanisms
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The low cloud amount feedback in GCMs is dominated by the response of tropical, warm, liquid
cloudslocatedbelowabout3kmtosurfacewarming.Severaltypesofcloudsfulfillthedefinitionof
“low”,differingintheirradiativeeffectsandinthephysicalmechanismsunderlyingtheirformation,
maintenance and response to climate change. So far, most insights into low cloud feedback
mechanisms have been gained from high-resolution models – particularly large-eddy simulations
(LES)thatcanexplicitlyrepresenttheturbulentandconvectiveprocessescriticalforboundary-layer
clouds on scales smaller than one kilometer76. The low cloud amount feedback in GCMs is
determined by the response of the most prevalent boundary-layer cloud types at low latitudes:
stratus,stratocumulus,andcumulusclouds.
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Although they cover a relatively small fraction of Earth, stratus and stratocumulus (StCu) have a
largeSWCRE,sothatevensmallchangesintheircoveragemayhavesignificantregionalandglobal
impacts. StCu cloud coverage is strongly controlled by atmospheric stability and surface fluxes77:
observations suggest a strong relationship between inversion strength at the top of the planetary
boundarylayer(PBL)andcloudamount78, 79.Astrongerinversionresultsinweakermixingwiththe
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dry free troposphere, shallowing the PBL and increasing cloudiness. Since inversion strength will
increase with global warming owing to the stabilization of the free-tropospheric temperature
profile80,onemightexpectlowcloudamounttoincrease,implyinganegativefeedback81.
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However, LES experiments suggest that StCu clouds are sensitive to other factors than inversion
strength, as summarized by Bretherton15. Over subsiding regions, (1) increasing atmospheric
emissivity owing to water vapor feedback will cause more downward LW radiation, decreasing
cloud-top entrainment and thinning the cloud layer (less cloud and hence a positive radiative
feedback); (2) the slowdown of the general circulation will weaken subsidence, raising cloud tops
and thickening the cloud layer (a negative dynamical feedback); (3) a larger vertical gradient of
specific humidity will dry the PBL more efficiently, reducing cloudiness (a positive thermodynamic
feedback). Evidence for these physical mechanisms is usually also found in GCMs82-84 or when
analyzingobservednaturalvariability85-87.Thereal-worldStCufeedbackwillmostlikelyresultfrom
therelativeimportanceoftheseantagonisticprocesses.LESmodelsforcedwithanidealizedclimate
changesuggestareductionofStCucloudswithwarming76.
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Shallow cumuli (ShCu) usually denote clouds with tops around 2-3 km localized over weak
subsidenceregionsandhighersurfacetemperature.DespitetheirmoremodestSWCRE,ShCuareof
major importance to global-mean cloud feedback in GCMs because of their widespread presence
acrossthetropics17.YetmechanismsofShCufeedbackinLESarelessrobustthanforStCu.Usually,
LES reduce clouds with warming, with large sensitivity to precipitation (mostly related to
microphysical assumptions). This reduction has been explained by a stronger penetrative
entrainment that deepens and dries the PBL more efficiently13, 88 (closely related to the
thermodynamic feedback seen for StCu), although the strength of this positive feedback may
depend on the choice of prescribed or interactive sea surface temperatures (SSTs)89, 90 and
microphysicsparameterization14.OtherfeedbacksseenforStCumayactonShCubutwithdifferent
relativeimportance14.AlthoughLESresultssuggestapositiveShCufeedback14,aglobalmodelthat
explicitly resolves the crudest form of convection shows the opposite response91. Hence further
workwithahierarchyofmodelconfigurations(LES,globalcloud-resolvingmodel,GCMs)combined
withobservationalanalyseswillbeneededtovalidatetheShCufeedback.
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Recent observational studies of the low cloud response to changes in meteorological conditions
broadlysupporttheStCuandShCufeedbackmechanismsidentifiedinLESexperiments84,87,92.These
studiesshowthatlowcloudsinbothmodelsandobservationsaremostlysensitivetochangesinSST
and inversion strength. Although these two effects would tend to cancel each other, observations
and GCM simulations constrained by observations suggest that SST-mediated low cloud reduction
withwarmingdominates,increasingthelikelihoodofapositivelowcloudfeedbackandhighclimate
sensitivity87, 93-95. Nevertheless, recent ground-based observations of co-variations of ShCu with
meteorological conditions suggest that a majority of GCMs are unlikely to represent the temporal
dynamics of the cloudy boundary-layer96, 97. This may reduce our confidence in GCM-based
constraintsofShCufeedbackwithwarming.
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Representationinglobalclimatemodelsandsourcesofinter-modelspread
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Cloud dynamics depend heavily on small-scale processes such as local turbulent eddies, non-local
convective plumes, microphysics, and radiation. Since the typical horizontal grid size of GCMs is
around 50 km, such processes are not explicitly simulated and need to be parameterized as a
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function of the large-scale environment. GCMs usually represent cloud-related processes through
distinct parameterizations, with separate assumptions for subgrid variability, despite a goal for
unification98,99.PhysicalassumptionsusedinPBLparameterizationsoftenrelatecloudformationto
buoyancy production, stability, and wind shear. Low cloud amount feedbacks are constrained by
how these cloud processes are represented in GCMs and how they respond to climate change
perturbations. Since parameterizations are usually crude, it is not evident that the mechanisms of
lowcloudamountfeedbackinGCMsarerealistic.
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AllCMIP5modelssimulateapositivelowcloudamountfeedback,butwithconsiderablespread(Fig
2c);thisfeedbackisbyfarthelargestcontributortointer-modelvarianceinnetcloudfeedback5,17,
26
.Spreadinlowcloudamountfeedbackcanbetracedbacktodifferencesinparameterizationsused
in atmospheric GCMs92, 100-102, and changes in these parameterizations within individual GCMs also
have clear impacts on the intensity (and sign) of the response102-104. Identifying the low cloud
amountfeedbackmechanismsinGCMsisadifficulttask,however,becausethelowcloudresponse
is sensitive to the competing effects of a variety of unresolved processes. Considering that these
processes are parameterized in diverse and complex ways, it appears unlikely that a single
mechanismcanaccountforthespreadoflowcloudamountfeedbackseeninGCMs.
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Ithasbeenproposedthatconvectiveprocessesplayakeyroleindrivinginter-modelspreadinlow
cloud amount feedback105-110. As the climate warms, convective moisture fluxes strengthen due to
therobustincreaseoftheverticalgradientofspecifichumiditycontrolledbytheClausius-Clapeyron
relationship82.IncreasingconvectivemoisturefluxesbetweenthePBLandthefreetropospherelead
to a relatively drier PBL with decreased cloud amount, suggesting a positive feedback, but the
degreetowhichconvectivemoisturemixingincreasesseemstostronglydependonmodel-specific
parameterizations109. GCMs with stronger present-day convective mixing (and therefore more
positive low cloud amount feedback) have been argued to compare better with observations109,
implying that convective overturning strength could provide an observational constraint on GCM
behavior. However, running GCMs with convection schemes switched off does not narrow the
spread of cloud feedback111, suggesting that non-convective processes may play an important role
too92,104.
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We believe that inter-model spread in low cloud amount feedback does not depend on the
representationofconvection(deepandshallow)alone,butratherontheinterplaybetweenvarious
parameterized processes – particularly convection and turbulence. It has been argued that the
relative importance of parameterized convective drying and turbulent moistening of the PBL
accounts for a large fraction of the inter-model differences in both the mean state, and global
warmingresponseoflowclouds46.InGCMsthatattributealargeweighttoconvectivedryinginthe
present-day climate, the strengthening of moisture transport with warming causes enhanced PBL
ventilation, efficiently reducing low cloud amount109. Conversely if convective drying is less active,
turbulencemoisteninginduceslowcloudshallowingratherthanachangeincloudamount46, 110.In
somemodels,additionalparameterization-dependentmechanismsmaycontributetothelowcloud
feedback,suchascloudamountincreasesbyenhancementofsurfaceturbulence83,112orbychanges
incloudlifetime113.
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Lowcloudopticaldepth
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Physicalmechanisms
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Theprimarycontroloncloudopticaldepthisthevertically-integratedliquidwatercontent,termed
liquid water path (LWP). If other microphysical parameters are held constant, cloud optical depth
scales with LWP within the cloud114. Cloud optical depth is also affected bycloud particle size and
cloudicecontent,buttheiceeffectissmallersinceicecrystalsaretypicallyseveraltimeslargerthan
liquid droplets, and therefore less efficient at scattering sunlight per unit mass115. Consistent with
this, the cloud optical depth change maps well onto the LWP response in global warming
experiments,bothquantitiesincreasingatmiddletohighlatitudesinnearlyallGCMs18, 19, 45, 116, 117.
Understanding the negative cloud optical depth feedback therefore requires explaining why LWP
increaseswithwarming,andwhyitdoessomostlyathighlatitudes.
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Two plausible mechanisms may contribute to LWP increases with warming, and both predict a
preferentialincreaseathigherlatitudesandlowertemperatures.Thefirstmechanismisbasedupon
the assumption that the liquid water content within a cloud is determined by the amount of
condensationinsaturatedrisingparcelsthatfollowamoistadiabatGm,fromthecloudbasetothe
cloud top118-120. This is often referred to as the "adiabatic" cloud water content. Under this
assumption, it may be shown that the change in LWP with temperature is a function of the
temperature derivative of the moist adiabat slope, ¶Gm/¶T. This predicts that the adiabatic cloud
water content always increases with temperature, and increases more strongly at lower
temperaturesinarelativesense118.
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A second mechanism involves phase changes in mixed-phase clouds. Liquid water is commonly
found in clouds at temperatures substantially below freezing, down to about -38°C where
homogeneousfreezingoccurs115,121.Cloudsbetween-38°Cand0°Ccontainingbothliquidwaterand
ice are termed mixed-phase. As the atmosphere warms, the occurrence of liquid water should
increase relative to ice; for a fixed total cloud water path, this would lead to an optically thicker
cloudowingtothesmallereffectiveradiusofdroplets19,115,121.Inaddition,ahigherfractionofliquid
waterisexpectedtodecreasetheoverallprecipitationefficiency,yieldinganincreaseintotalcloud
waterandafurtheropticalthickeningofthecloud19,115,119,121.Reducedprecipitationefficiencymay
alsoincreasecloudlifetime,andhencecloudamount121, 122.Becausethephasechangemechanism
can only operate below freezing, its occurrence in low clouds is restricted to middle and high
latitudes.
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Satelliteandin-situobservationsofhigh-latitudecloudssupportincreasesincloudLWPandoptical
depthwithtemperature18,19,120,andsuggestanegativecloudopticaldepthfeedback20,althoughthis
resultissensitivetotheanalysismethod123.ThepositiveLWPsensitivitytotemperatureisgenerally
restricted to mixed-phase regions and is typically larger than that expected from moist adiabatic
increasesinwatercontentalone18, 19.Thislendsobservationalsupportfortheimportanceofphase
change processes. While the moist adiabatic mechanism should still contribute to LWP increases
withwarming,LESmodelingofwarmboundary-layerclouds(inwhichphasechangeprocessesplay
norole)suggeststhatopticaldepthchangesaresmallrelativetotheeffectsofdryinganddeepening
oftheboundarylayerwithwarming13.
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ThelowcloudopticaldepthfeedbackpredictedbyGCMscanonlybetrustedtotheextentthatthe
driving mechanisms are understood and correctly represented. We therefore ask, how reliably are
thesephysicalmechanismsrepresentedinGCMs?Thefirstmechanisminvolvesthesourceofcloud
water from condensation in saturated updrafts. It results from basic, well-understood
thermodynamics that do not directly rely on physical parameterizations, and should be correctly
implemented in all models. As such, it constitutes a simple and powerful constraint on the cloud
watercontentresponsetowarming,tothepointthatsomeearlystudiesproposedtheglobalcloud
feedback might be negative as a result124-126. Considering this mechanism in isolation ignores
importantcompetingfactorsthataffectthecloudwaterbudget,however,suchastheentrainment
of dry air into the convective updrafts, phase change processes, or precipitation efficiency. The
competition between these various factors may explain why no simple, robust LWP increase with
temperatureisseeninallregionsacrosstheworldinGCMs.
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The second mechanism is primarily related to the liquid water sink through conversion to ice and
precipitation by ice-phase microphysical processes. The representation of cloud microphysics in
state-of-the-art GCMs is mainly prognostic, meaning that rates of change between the different
phases–vapor,liquid,ice,andprecipitation–arecomputed.Ratherthanbeingadirectfunctionof
temperature(asinadiagnosticscheme),therelativeamountsofliquidandicethusdependonthe
efficiencies of the source and sink terms. In GCMs, cloud water production in mixed-phase clouds
occurs mainly in liquid form; subsequent glaciation may occur through a variety of microphysical
processes,particularlytheWegener-Bergeron-Findeisen127mechanism(seeStorelvmoetal.128fora
descriptionandareview).Ice-phasemicrophysicsarethereforemainlyasinkofcloudliquidwater.
Upon warming, this sink should become suppressed, resulting in a larger reservoir of cloud liquid
water19.
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InGCMs,theopticaldepthfeedbackislikelydominatedbymicrophysicalphasechangeprocesses.
Severallinesofevidencesupportthisidea.Asinobservations,lowcloudopticaldepthincreaseswith
warming almost exclusively at high latitudes, and the increase in cloud water content is typically
restrictedtotemperaturesbelowfreezing117,129,130–afindingthatcannotbesatisfactorilyexplained
bytheadiabaticwatercontentmechanism.Imposingatemperatureincreaseonlyintheice-phase
microphysics explains roughly 80% of the total LWP response to warming in two contemporary
GCMsruninaquaplanetconfiguration19.Furthermore,changesintheefficiencyofphaseconversion
processes have dramatic impacts on the cloud water climatology and sensitivity to warming in
GCMs131-133.
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Although GCMs agree on the sign of the cloud optical depth response in mixed-phase clouds, the
magnitude of the change remains highly uncertain. This is in large part because the efficiency of
phasechangeprocessesvarieswidelybetweenmodels,impactingthemeanstateandthesensitivity
towarming116.
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GCMs separately simulate microphysical processes for cloud water resulting from large-scale
(resolved) vertical motions, and convective (unresolved, parameterized) motions. In convection
schemes, microphysical phase conversions are crudely represented, usually as simple, model
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dependentanalyticfunctionsoftemperature.Whiletherepresentationofmicrophysicalprocessesis
much more refined in large-scale microphysics schemes, ice-phase processes remain diversely
represented due to limitations in our understanding, particularly with regard to ice formation
processes134, 135. In models explicitly representing aerosol-cloud interactions, an additional
uncertainty results from poorly constrained ice nuclei concentrations122. For mixed-phase clouds,
perturbingtheparameterizationsofphasetransitionscansignificantlyaffecttheratioofliquidwater
to ice, the overall cloud water budget, and cloud-radiative properties19, 133. Owing to these
uncertainties, the simple constraint that the liquid water fraction must increase with warming is
strongbutmerelyqualitativeinGCMs.
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It is believed that mixed-phase clouds may become glaciated too readily in most GCMs121, 128.
Satelliteretrievalssuggestmodelsunderestimatethesupercooledliquidfractionincoldclouds132,136138
; this may be because models assume too much spatial overlap between ice and supercooled
clouds, overestimating the liquid-to-ice conversion efficiency128. An expected consequence is that
liquidwaterandcloudopticaldepthincreasetoodramaticallywithwarminginGCMs,sincethereis
too much climatological cloud ice in a fractional sense. Comparisons with observations appear to
support that idea18, 20. Such microphysical biases could have powerful implications for the optical
depthfeedback,asmodelswithexcessivecloudicemayoverestimatethephasechangeeffect130,133,
139, 140
. In summary, the current understanding is that the negative cloud optical depth feedback is
likelytoostronginmostGCMs.FurtherworkwithobservationaldataisneededtoconstrainGCMs
andconfirmtheexistenceofanegativeopticaldepthfeedbackintherealworld.
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Otherpossiblecloudfeedbackmechanisms:tropicalandextratropicaldynamics
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While the mechanisms discussed above are mainly linked to the climate system’s thermodynamic
response to CO2 forcing, dynamical changes could have equally important implications for clouds
and radiation. This poses a particular challenge: not only are the cloud responses to a given
dynamical forcing uncertain141, but the future dynamical response is also much more poorly
constrained than the thermodynamic one142. Below we discuss two possible effects of changes in
atmosphericcirculation,oneinvolvingthedegreeofaggregationoftropicalconvection,andanother
basedonextratropicalcirculationshiftswithwarming.Weassesstherelevanceoftheseproposed
feedbackprocessesinGCMsandintherealworld.
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Tropical convective clouds both reduce outgoing LW radiation and reflect solar radiation. These
effectstendtooffseteachother,andoverthebroadexpanseofwarmwatersinthewesternPacific
and Indian Ocean areas these two effects very nearly cancel, so that net cloud radiative effect is
about zero143-145. The net neutrality of tropical cloud radiative effects results from a cancellation
between positive effects of thin anvil clouds and negative effects of the thicker rainy areas of the
cloud146.Thatconvectivecloudstendtoriseinawarmedclimatehasbeendiscussedabove,butitis
alsopossiblethattheopticaldepthorareacoverageofconvectivecloudscouldchangeinawarmed
climate. For high clouds with no net effect on the radiation balance, a change in area coverage
without change in the average radiative properties of the clouds would have little effect on the
energy balance (unless the high clouds are masking bright low clouds). Because the individual LW
andSWeffectsoftropicalconvectivecloudsarelarge,asmallchangeinthebalanceoftheseeffects
couldalsoprovidealargefeedback.
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Sofarmoreattentionhasbeendirectedatoceanicboundarylayerclouds,whosenetCREislarge,
sincetheirsubstantialSWeffectisnotbalancedbytheirrelativelysmallLWeffect.ButsincetheSW
effect of tropical convective clouds is as large as that of boundary-layer clouds in stratocumulus
regimes,asubstantialfeedbackcouldoccuriftherelativeareacoverageofthinanvilsversusrainy
cores with higher albedos changes in a way to disrupt the net radiative neutrality of convective
clouds.Relativelylittlehasbeendoneonthisproblem,sinceglobalclimatemodelsdonotresolveor
explicitly parameterize the physics of convective complexes and their associated meso- and
microscaleprocesses.
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It has been proposed that tropical anvil cloud area should decrease in a warmed climate,possibly
causing a negative LW feedback, but the theoretical and observational basis for this hypothesis
remainscontroversial147-151.TheresponseoftropicalhighcloudamounttowarminginGCMsisvery
sensitive to the particular parameterizations of convection and cloud microphysics that are
employed107,152,asmightbeexpected.
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One basic physical argument for changing the area of tropical high clouds with warming involves
simple energy balance and the dependence of saturation vapor pressure on temperature35. The
basicenergybalanceoftheatmosphereisradiativecoolingbalancedbylatentheating.Convection
mustbringenoughlatentheatupwardtobalanceradiativelosses.Radiativelossesincreaserather
slowly with surface temperature (~1.5% per K), whereas the latent energy in the atmosphere
increasesby~7%perKwarming35,153.Ifoneassumesthatlatentheatingisproportionaltosaturation
vaporpressuretimesconvectivemassflux,itfollowsthatconvectivemassfluxmustdecreaseasthe
planet warms35. If the cloud area decreases with the mass flux, then the high cloud area should
decreasewithwarming.Somesupportforthismechanismisfoundinglobalcloud-resolvingmodel
experiments57.
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Anothermechanismisthetendencyoftropicaldeepconvectiontoaggregateinpartofthedomain,
leavinganotherpartofthedomainwithlittlehighcloudandlowrelativehumidity.Thisisobserved
to happen in radiative-convective equilibrium models in which the mesoscale dynamics of
convectivecloudsisresolved154-156,althoughtherelevanceofthismechanismtorealisticmodelsand
therealworldremainsunclear.Thepresenceofconvectionmoistensthefreetroposphere,andthe
radiative and microphysical effects of this encourage convection to form where it has already
influencedtheenvironment.Awayfromtheconvection,theairisdryandradiativecoolingsupports
subsidencethatsuppressesconvection.Ithasbeenarguedthatsinceself-aggregationoccursathigh
temperatures, global warming may lead to a greater concentration of convection that may reduce
theconvectiveareaandleadtoacloudfeedback21.Sincetropicalconvectionisalsoorganizedbythe
large-scale circulations of the tropics, and the physics of tropical anvil clouds are not wellrepresentedinglobalmodels,theseideasremainatopicofactiveresearch.Basicthermodynamics
make the static stability a function of pressure, which may affect the fractional coverage of high
cloudsinthetropics21,52.
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Shiftsinmidlatitudecirculationwithglobalwarming
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Atmospheric circulation is a key control on cloud structure and radiative properties157. Because
current GCMs predict systematic shifts of subtropical and extratropical circulation toward higher
latitudes as the planet warms158, it has been suggested that midlatitude clouds will shift toward
regionsofreducedinsolation,causinganoverallpositiveSWfeedback3,159.
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Althoughthispolewardshiftofstorm-trackcloudscountsamongtherobustpositivecloudfeedback
mechanismsidentifiedinthefifthIPCCassessmentreport(Fig.7.11inBoucheretal.3),thepictureis
muchlessclearinanalysesofcloud-radiativeresponsestostormtrackshiftsinGCMexperiments.
While some GCMs produce a clear cloud-radiative SW dipole in response to storm track shifts160,
others simulate no clear zonal- or global-mean SW response24, 161-163. In the context of observed
variability, the GCMs with no significant cloud-radiative response to a storm-track shift are clearly
moreconsistentwithobservations22, 24.ThelackofanobservedSWcloudfeedbacktostormtrack
shiftsresultsfromfree-troposphericandboundary-layercloudsrespondingtostormtrackvariability
in opposite ways. As the storms shift poleward, enhanced subsidence in the midlatitudes causes
free-tropospheric drying and cloud amount decreases, resulting in the expected shift of freetropospheric cloudiness. Meanwhile, however, lower-tropospheric stability increases, favoring
enhancedboundary-layercloudinessandmaintainingtheSWCREnearlyunchanged24.Theabilityof
GCMs to reproduce this behavior has been linked to their shallow convection schemes163 and to
their representation of the effect of stability on boundary-layer cloud24. If unforced variability
provides a good analog for the cloud response to forced dynamical changes – thought to be
approximately true in GCMs163 – then the above results suggest little SW radiative impact from
futurejetandstormtrackshifts.
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SinceLWradiationismuchmoresensitivetotheresponseoffree-troposphericcloudsthantolow
cloud changes, storm-track shifts do cause coherent LW cloud-radiative anomalies23. These
anomalies are small in the context of global warming-driven cloud feedback, however23, so that
futureshiftsinmidlatitudecirculationappearunlikelytobeamajorcontributiontoglobal-meanLW
cloud feedback. Given the strong seasonality of LW and SW cloud-radiative anomalies, it remains
possiblethatextratropicalcirculationshiftshavenon-negligibleradiativeimpactsonseasonaltime
scales164, 165.Itisalsopossiblethatcloudsandradiationrespondmorestronglytootheraspectsof
atmospheric circulation than the midlatitude jets and storm tracks; it has been recently proposed
thatmidlatitudecloudchangesaremorestronglytiedtoHadleycellshiftsthantothejet165.Further
observationalandmodelingworkisneededtoconfirmtheserelationshipsandassesstheirrelevance
tocloudfeedback.
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CONCLUDINGREMARKS
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PossiblepathwaystoanimprovedrepresentationofcloudfeedbackinGCMs
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RecentprogressontheproblemofcloudfeedbackhasenabledunprecedentedadvancesinprocesslevelunderstandingofcloudresponsestoCO2forcing.Themaincloudpropertychangesresponsible
forradiativefeedbackinGCMs–risinghighclouds,decreasingtropicallowcloudamount,increasing
lowcloudopticaldepth–aresupportedtovaryingdegreebytheoreticalreasoning,high-resolution
modeling,andobservations.
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Muchoftherecentgainsinunderstandingofradiatively-importanttropicallowcloudchangeshave
been accomplished through the use of limited-area, high-resolution LES models, able to explicitly
represent the critical boundary layer processes unresolved by GCMs. Because limited-area models
must be forced with prescribed climate change conditions, however, such models are unable to
representtheimportantfeedbacksofcloudsontothelarge-scaleclimate.Tofullyunderstandhow
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cloudfeedbackaffectsclimatesensitivity,atmosphericandoceaniccirculation,andregionalclimate,
wemustrelyonglobalmodels.
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Accurately representing clouds and their radiative effects in global models remains a formidable
challenge, however, and GCM spread in cloud feedback has not decreased substantially in recent
decades. Uncertainties in the global warming response of clouds are linked to the difficulty in
representing the complex interactions among the various physical processes at play – radiation,
microphysics, convective and turbulent fluxes, dynamics – through traditional GCM
parameterizations. Owing to sometimes unphysical interactions between individual
parameterizations, cloud feedback mechanisms may differ between GCMs46, 110, and these
mechanismsmayalsobedistinctfromthoseactingintherealworld.
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One approach to circumvent the shortcomings of traditional GCM parameterizations involves
embeddingacloud-resolvingmodelineachGCMgridboxoverpartofthehorizontaldomain166-168.
Such“superparameterized”GCMscanthusexplicitlysimulatesomeoftheconvectivemotionsand
subgrid variability that traditional parameterizations fail to represent accurately, while remaining
computationallyaffordablerelativetoglobalcloud-resolvingmodels.However,superparameterized
GCMsremainunabletoresolvetheboundary-layerprocessescontrollingradiatively-importantlow
clouds–andsimilarlytoglobalcloudresolvingmodels,theyreportdisappointinglylargespreadin
theircloudfeedbackestimates15.
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Arecentfurtherdevelopment,madepossiblebysteadyincreasesincomputingpower,involvesthe
useofLESratherthancloud-resolvingmodelsasasubstituteforGCMparameterizations16, 169.First
results suggest encouraging improvements in the representation of boundary-layer clouds (C.
Bretherton,pers.comm.).SuperparameterizationwithLEScombinesaspectsofthemodelhierarchy
intoasinglemodel,makingitpossibletorepresentboththesmall-scaleprocessesandtheirimpact
onthelargescales.Analysesofsuperparameterizedmodelexperimentscouldalsobeusedtodesign
more realistic parameterizations to improve boundary-layer characteristics, cloud variability, and
thus cloud feedback in traditional GCMs. An important caveat, however, is that current LES
superparameterizationsarerelativelycoarseandmaynotrepresentprocessessuchasentrainment
well,sothatfurtherincreasesincomputingpowermaybenecessarytofullyexploitthepossibilities
ofLESsuperparameterization.
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Irrespectiveoffutureincreasesinspatialresolution,GCMswillcontinuerequiringparameterization
oftheimportantmicrophysicalprocessesofliquiddropletandicecrystalformation.Asdiscussedin
this review, microphysical processes constitute a major source of uncertainty in future cloud
responses, particularly with regard to mixed-phase cloud radiative properties19 and precipitation
efficiencyinconvectiveclouds107.Thetreatmentofcloud-aerosolinteractionsalsoremainsdeficient
in current parameterizations170. Improving the parameterization of microphysical processes must
therefore remain a priority for future work; this will involve a combined use of laboratory
experiments171,andsatelliteandin-situobservationsofcloudphase119,138.
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Although the main focus of this paper has been on the representation of clouds in GCMs,
observational analyses will remain crucial to advance our understanding of cloud feedback, in
conjunction with process-resolving modeling and global modeling. On the one hand, reliable
observations of clouds and their environment at both local and global scales are indispensable to
testandimproveprocess-resolvingmodelsandGCMparameterizations.Ontheotherhand,models
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can provide process-based understanding of the relationship between clouds and the large-scale
environment,whichcanbeexploitedtoidentifyobservationalconstraintsoncloudfeedback.
664
Currentlimitsofunderstanding
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We conclude this review by highlighting two problems which we regard as key limitations in our
understandingofhowcloudfeedbackimpactstheclimatesystem’sresponsetoexternalforcing.The
firstproblemrelatestotherelevanceofcloudfeedbacktofutureatmosphericcirculationchanges,
which control climate change impacts at regional scales142. The circulation response is driven by
changesindiabaticheating,towhichtheradiativeeffectsofcloudsareanimportantcontribution.
Hence cloud feedbacks must affect the dynamical response to warming, but the dynamical
implicationsofcloudfeedbackarejustbeginningtobequantifiedandunderstood.Recentworkhas
shownthatcloudfeedbackshavelargeimpactsontheforceddynamicalresponsetowarmingand
particularly the shift of the jets and storm tracks22, 161, 172, 173. Thus the cloud response to warming
appearsasoneofthekeyuncertaintiesforfuturecirculationchanges.Substantialresearchefforts
are currently underway to improve our understanding of cloud-circulation interactions at various
scalesandtheirimplicationsforclimatesensitivity,aproblemidentifiedasoneofthecurrent“grand
challenges”ofclimatescience173-175.
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Our second point concerns the problem of time dependence of cloud feedback. The traditional
feedbackanalysisframeworkisbasedonthesimplifyingassumptionthatfeedbackprocessesscale
with global-mean surface temperature, independent of the spatial pattern of warming. However,
recentresearchshowsthattheglobalfeedbackparameterdoesdependuponthepatternofsurface
warming,whichitselfchangesovertimeinCO2-forcedexperiments7,176-178.Inparticular,mostCMIP5
models subjected to an abrupt quadrupling of CO2 concentrations indicate that the SW cloud
feedbackparameterincreasesafterabouttwodecades,andthisisadirectconsequenceofchanges
intheSSTwarmingpattern179.SincefuturepatternsofSSTincreaseareuncertaininGCMs,andmay
differfromthoseobservedinthehistoricalrecord,thisintroducesanadditionaluncertaintyinthe
magnitude of global-mean cloud feedback and our ability to constrain it using observations180, 181.
Therefore, further work is necessary to understand what determines the spatial patterns of SST
increase,andhowthesepatternsinfluencecloudpropertiesatregionalandglobalscales.
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ACKNOWLEDGMENTS
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The authors thank two anonymous reviewers for their very insightful and constructive comments.
We are also grateful to Chris Bretherton, Jonathan Gregory, Tapio Schneider, Bjorn Stevens, and
Mark Webb for helpful discussions. PC acknowledges support from the ERC Advanced Grant
“ACRCC”.DLHwassupportedbytheRegionalandGlobalClimateModelingProgramoftheOfficeof
ScienceoftheU.S.DepartmentofEnergy (DE-SC0012580).TheeffortofMDZwassupportedbythe
Regional and Global Climate Modeling Program of the Office of Science of the U.S. Department of
Energy(DOE)andbytheNASANewInvestigatorProgram(NNH14AX83I)andwasperformedunder
the auspices of the DOE by Lawrence Livermore National Laboratory under contract DE-AC5207NA27344. IM ReleaseLLNL-JRNL-707398. We also acknowledge the World Climate Research
Program's Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the
climatemodelinggroups(listedintheSupportingInformation)forproducingandmakingavailable
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theirmodeloutput.ForCMIPtheU.S.DepartmentofEnergy'sProgramforClimateModelDiagnosis
andIntercomparisonprovidescoordinatingsupportandleddevelopmentofsoftwareinfrastructure
inpartnershipwiththeGlobalOrganizationforEarthSystemSciencePortals.
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FURTHERREADING
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Averyusefulreviewofcloudfeedbacksinhigh-resolutionmodels:BrethertonCS.Insightsintolowlatitude cloud feedbacks from high-resolution models. Philosophical transactions. Series A,
Mathematical,physical,andengineeringsciences2015,373:3354-3360
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A discussion of cloud phase changes in high-latitude clouds: Storelvmo T, Tan I, Korolev AV. Cloud
Phase Changes Induced by CO2 Warming – a Powerful yet Poorly Constrained Cloud-Climate
Feedback.CurrentClimateChangeReports2015,1:288-296
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Areviewoninteractionsbetweendynamicsandcloudsintheextratropics,includingadiscussionof
the roles of storm-track shifts in future cloud feedbacks: Ceppi P, Hartmann DL. Connections
Between Clouds, Radiation, and Midlatitude Dynamics: a Review. Current Climate Change Reports
2015,1:94-102
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