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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 -2 Spatial distribution of cloud feedback (in W m per K surface warming) predicted by a set of global climate modelssubjectedtoanabruptincreaseinCO2.RedrawnwithpermissionfromZelinkaetal.(2016). 1 2 INTRODUCTION 3 4 5 6 7 8 9 10 11 12 13 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. 14 15 16 17 18 19 20 21 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. 22 23 24 25 26 27 28 29 30 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. 31 DIAGNOSINGCLOUDFEEDBACKINGLOBALCLIMATEMODELS 32 33 34 35 Webeginbydocumentingthemagnitudeandspatialstructureofcloudfeedbackincontemporary GCMs,andidentifythecloudpropertychangesinvolvedintheradiativeresponse.Althoughclouds mayrespondtoanyforcingagent,inthisreviewwewillfocusoncloudfeedbacktoCO2forcing,of highestrelevancetofutureanthropogenicclimatechange. 36 Global-meancloudfeedback 37 38 39 40 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 41 42 numbersfromananalysisof28GCMs5(coloredcirclesinFig.1).Twootherstudies(greysymbolsin Fig.1)showsimilarresults,buttheyincludesmallersubsetsoftheavailablemodels. 5 Feedback parameter (W m−2 K−1) ●●●●● 2 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −1 43 ● Caldwell et al. 2016 (28 models) Vial et al. 2013 (11 models) Zelinka et al. 2016 (7 models) ensemble mean WV+LR Albedo Cloud LW Cloud SW Cloud Total Equilibrium climate sensitivity (K) 3 ● ● 4 ● ● ● ● ● ● ● ● 3 2 1 ● ● ● ● ● ● ● ● ECS 44 45 46 47 48 49 50 51 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 5 6 26 Caldwelletal. ,withadditionalcloudfeedbackvaluesfromVialetal. andZelinkaetal. ECSvaluesaretaken 27 28 29 30 fromAndrewsetal. ,Forsteretal. ,andFlatoetal. FeedbackparametersarecalculatedasinSodenetal. butaccountingforrapidadjustments;thecloudfeedbackfromZelinkaetal.iscalculatedusingcloud-radiative 31 kernels (Box2).Circlesarecoloredaccordingtothetotalfeedbackparameter.ThePlanckfeedback(mean -2 -1 valueof-3.15Wm K )isexcludedfromthetotalfeedbackparametershownhere. 52 53 Box1:Climatefeedbacks 54 55 IncreasinggreenhousegasconcentrationscauseapositiveradiativeforcingF(Wm-2),towhichthe climatesystemrespondsbyincreasingitstemperaturetorestoreradiativebalanceaccordingto 56 57 58 59 60 61 62 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. 63 64 65 66 67 68 69 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. 70 71 boththeirgreenhousewarmingandtheirreflectionofsolarradiation,givingrisetoacloudfeedback (Box2),positiveinmostcurrentGCMs. 72 73 74 75 76 77 78 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). 79 80 81 82 83 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). 84 Box2:Cloud-radiativeeffectandcloudfeedback 85 86 87 88 89 90 91 92 93 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. 94 95 96 97 98 99 100 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. 101 102 103 104 105 106 107 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. 108 RapidAdjustments 109 110 111 112 113 114 115 116 117 118 119 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. 120 Decompositionbycloudtype 121 122 123 124 125 126 127 128 129 130 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.) 131 132 133 134 135 136 137 138 139 140 141 142 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. 143 Spatialdistributionofcloudfeedback 144 145 146 147 148 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 149 150 151 152 153 154 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. 1.0 0.5 0.0 (a) Standard decomposition ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −0.5 Feedback parameter (W m−2 K−1) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● LW SW net ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −1.0 1.0 (b) Free−tropospheric clouds (CTP ≤ 680 hPa) ● ● 0.5 ● ● ● ● ● ● ● 0.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −0.5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −1.0 1.0 (c) Low clouds (CTP > 680 hPa) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.5 0.0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −0.5 −1.0 155 Total Amount Altitude OD Residual 156 157 158 159 160 161 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. 26 Resultsarebasedonananalysisof11CMIP3and7CMIP5models ;theCMIP3valuesdonotaccountforrapid adjustments. Model names and total feedback values are listed in Table S2. Redrawn with permission from 26 Zelinkaetal. 162 163 164 165 166 167 168 169 170 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). 171 172 Thepatternsofcloudamountandopticaldepthchangessuggesttheexistenceofdistinctphysical processesindifferentlatituderangesandclimateregimes,asdiscussedinthenextsection. 173 -2 174 175 176 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 26 permissionfromZelinkaetal. 177 178 179 TheresultsinFig.4allowustofurtherrefinetheconclusionsdrawnfromFig.2.Inthemulti-model mean,thecloudfeedbackincurrentGCMsmainlyresultsfrom 180 • globallyrisingfree-troposphericclouds, 181 • decreasinglowcloudamountatlowtomiddlelatitudes,and 182 • increasinglowcloudopticaldepthatmiddletohighlatitudes. 183 Summary 184 185 186 187 188 189 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. 190 191 INTERPRETINGCLOUDPROPERTYCHANGESINGLOBALCLIMATEMODELS 192 193 194 195 Havingdiagnosedtheradiatively-relevantcloudresponsesinGCM,weassessourunderstandingof thephysicalmechanismsinvolvedinthesecloudchanges,anddiscusstheirrepresentationinGCMs. We consider in turn each of the three main effects identified in the previous section, and address thefollowingquestions: 196 197 • What physical mechanisms are involved in the cloud response? To what extent are these mechanismssupportedbytheory,high-resolutionmodeling,andobservations? 198 199 • How well do GCMs represent these mechanisms, and what parameterizations does this dependon? 200 • Whatexplainstheinter-modelspreadincloudresponses? 1.5 Cloud feedback (W m−2 K−1) 1.0 0.5 0.0 Total Amount Altitude Optical Depth −0.5 −1.0 −1.5 −90 201 −50 −30 −15 0 latitude 15 30 50 90 202 203 204 205 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 26 thefeedback,dashedotherwise.RedrawnwithpermissionfromZelinkaetal. 206 207 Cloudaltitude 208 Physicalmechanisms 209 210 211 212 213 214 215 216 217 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. 218 219 220 221 222 223 224 225 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 226 227 228 229 230 231 232 233 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. 234 235 236 237 238 239 240 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. 241 242 243 244 245 246 247 248 249 250 251 252 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. 253 254 255 256 257 258 259 260 261 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. 262 263 264 265 266 267 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. 268 Box3:FATandthecloudaltitudefeedback 269 270 271 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. 272 273 274 275 276 277 278 279 280 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. 281 282 283 Animportantpointtoavoidlosinginthedetailsisthataslongasthefreetroposphericcloudtops rise under global warming, the altitude feedback is positive. The extent to which cloud top temperatureschangeaffectsonlythemagnitudeofthefeedback,notitssign. 284 285 Representationinglobalclimatemodelsandcausesofinter-modelspread 286 287 288 289 290 291 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 292 293 294 295 296 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. 297 298 299 300 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. 301 302 303 304 305 306 307 308 309 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. 310 311 312 313 314 315 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. 316 Lowcloudamount 317 Physicalmechanisms 318 319 320 321 322 323 324 325 326 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. 327 328 329 330 331 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 332 333 334 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. 335 336 337 338 339 340 341 342 343 344 345 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. 346 347 348 349 350 351 352 353 354 355 356 357 358 359 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. 360 361 362 363 364 365 366 367 368 369 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. 370 Representationinglobalclimatemodelsandsourcesofinter-modelspread 371 372 373 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 374 375 376 377 378 379 380 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. 381 382 383 384 385 386 387 388 389 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. 390 391 392 393 394 395 396 397 398 399 400 401 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. 402 403 404 405 406 407 408 409 410 411 412 413 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. 414 Lowcloudopticaldepth 415 Physicalmechanisms 416 417 418 419 420 421 422 423 424 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. 425 426 427 428 429 430 431 432 433 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. 434 435 436 437 438 439 440 441 442 443 444 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. 445 446 447 448 449 450 451 452 453 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. 454 Representationinglobalclimatemodels 455 456 457 458 459 460 461 462 463 464 465 466 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. 467 468 469 470 471 472 473 474 475 476 477 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. 478 479 480 481 482 483 484 485 486 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. 487 Causesofinter-modelspread 488 489 490 491 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. 492 493 494 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 495 496 497 498 499 500 501 502 503 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. 504 505 506 507 508 509 510 511 512 513 514 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. 515 Otherpossiblecloudfeedbackmechanisms:tropicalandextratropicaldynamics 516 517 518 519 520 521 522 523 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. 524 Convectiveaggregationandthe“iriseffect” 525 526 527 528 529 530 531 532 533 534 535 536 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. 537 538 539 540 541 542 543 544 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. 545 546 547 548 549 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. 550 551 552 553 554 555 556 557 558 559 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. 560 561 562 563 564 565 566 567 568 569 570 571 572 573 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. 574 Shiftsinmidlatitudecirculationwithglobalwarming 575 576 577 578 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. 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 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. 596 597 598 599 600 601 602 603 604 605 606 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. 607 608 CONCLUDINGREMARKS 609 PossiblepathwaystoanimprovedrepresentationofcloudfeedbackinGCMs 610 611 612 613 614 RecentprogressontheproblemofcloudfeedbackhasenabledunprecedentedadvancesinprocesslevelunderstandingofcloudresponsestoCO2forcing.Themaincloudpropertychangesresponsible forradiativefeedbackinGCMs–risinghighclouds,decreasingtropicallowcloudamount,increasing lowcloudopticaldepth–aresupportedtovaryingdegreebytheoreticalreasoning,high-resolution modeling,andobservations. 615 616 617 618 619 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 620 621 cloudfeedbackaffectsclimatesensitivity,atmosphericandoceaniccirculation,andregionalclimate, wemustrelyonglobalmodels. 622 623 624 625 626 627 628 629 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. 630 631 632 633 634 635 636 637 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. 638 639 640 641 642 643 644 645 646 647 648 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. 649 650 651 652 653 654 655 656 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. 657 658 659 660 661 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 662 663 can provide process-based understanding of the relationship between clouds and the large-scale environment,whichcanbeexploitedtoidentifyobservationalconstraintsoncloudfeedback. 664 Currentlimitsofunderstanding 665 666 667 668 669 670 671 672 673 674 675 676 677 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. 678 679 680 681 682 683 684 685 686 687 688 689 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. 690 691 ACKNOWLEDGMENTS 692 693 694 695 696 697 698 699 700 701 702 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 703 704 705 theirmodeloutput.ForCMIPtheU.S.DepartmentofEnergy'sProgramforClimateModelDiagnosis andIntercomparisonprovidescoordinatingsupportandleddevelopmentofsoftwareinfrastructure inpartnershipwiththeGlobalOrganizationforEarthSystemSciencePortals. 706 707 FURTHERREADING 708 709 710 Averyusefulreviewofcloudfeedbacksinhigh-resolutionmodels:BrethertonCS.Insightsintolowlatitude cloud feedbacks from high-resolution models. Philosophical transactions. Series A, Mathematical,physical,andengineeringsciences2015,373:3354-3360 711 712 713 A discussion of cloud phase changes in high-latitude clouds: Storelvmo T, Tan I, Korolev AV. 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