Sensitivities of Arctic Clouds to Climate Change · Arctic clouds and climate Kay and L’Ecuyer...

Sensitivities of Arctic Clouds to Climate Change

Xiyue (Sally) Zhang1

Tapio Schneider1, Kyle Pressel1, Colleen Kaul1, and João Teixeira2

1 Environmental Science and Engineering, California Institute of Technology, Pasadena, CA 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA

CFMIP September 27, 2017 Tokyo, Japan

Arctic clouds and climate

Kay and L’Ecuyer 2013

Observed monthly mean over Arctic Ocean (70-82N, 07/2006–02/2011)

observational uncertainty of CERES-EBAF and are smallerthan the annual mean differences between 2B-FLXHR-LIDAR and CERES-EBAF (Table 3). Yet, because the effectsof sampling are not negligible when considering the differ-ences between 2B-FLXHR-LIDAR and CERES-EBAF,CERES-EBAF observations are reported over the same area(70–82!N) and time period (July 2006 to February 2011)when they are used for evaluating 2B-FLXHR-LIDAR.

3. Results

3.1. Early 21st Century Climatology3.1.1. Average Arctic Ocean Radiative Fluxes From2B-FLXHR-LIDAR[12] The mean annual cycle and geographic distribution

of Arctic radiative fluxes are presented in Figures 1 and 2,respectively. As expected, incoming solar radiation is aprimary driver of monthly and geographic variability inclimatological Arctic Ocean radiative fluxes. Monthly mean

net TOA radiation is positive only in June and Julywhen net shortwave radiation gains exceed net longwaveradiation losses (Figure 1a). As latitude increases, netTOA radiation becomes more negative because net short-wave radiation decreases faster than net longwave radiationincreases (Figures 2a–2c).[13] In all sunlit months, net shortwave radiation is larger

at the TOA than at the surface, a difference that results fromshortwave absorption by the atmosphere (Figure 1). Netlongwave radiation is positive at the surface and negative atthe TOA, a difference that results from longwave absorptionand reemission by the atmosphere (the greenhouse effect).Due to the greenhouse effect and absorbed shortwave radia-tion, Arctic Ocean net surface radiation is positive fromApril to September and is also positive in its annual mean(+29Wm"2 total, +65Wm"2 shortwave, and "36Wm"2

longwave) (Figure 1b). Geographic variations in surface netlongwave radiation track variations in near-surface air tem-perature, water vapor, and liquid cloud cover and exhibitmuch less geographic variability than net surface shortwaveradiation (Figures 2d and 2e). As a result, geographic varia-tions in total net surface radiation are largely dictated bynet surface shortwave radiation (Figure 2f).3.1.2. Average Arctic Ocean Cloud Amount and CloudForcing From 2B-FLXHR-LIDAR[14] We next evaluate temporal and geographic variations

in Arctic Ocean cloud amount and cloud influence onradiative fluxes. As is standard, cloud influence on radiativefluxes is measured using cloud forcing, the differencebetween all-sky and clear-sky fluxes [Ramanathan et al.,1989]. The term “cloud forcing” can be used interchange-ably with cloud radiative effect and with cloud radiativeforcing. In reality, direct observations of cloud forcing arenot available because all estimates of cloud forcing rely ona separate clear-sky radiative transfer calculation (as is thecase for 2B-FLXHR-LIDAR) or a comparison of tempo-rally averaged fluxes in cloudy and clear-sky conditions(as is the case for CERES-EBAF).[15] Figure 3 contains the climatological annual cycle of

total cloud fraction and cloud forcing over the Arctic Ocean.According to CloudSat +CALIPSO observations (Figure 3a),monthly mean Arctic Ocean cloud fractions are within 0.15of the annual mean value (0.68). October is the cloudiestmonth (total cloud fraction= 0.83), while June is the clearestmonth (total cloud fraction= 0.57). When just CloudSat isused to detect Arctic Ocean clouds, only 78% of theCloudSat +CALIPSO clouds are detected. That almost aquarter of Arctic Ocean CloudSat +CALIPSO clouds aredetected by CALIPSO alone provides strong motivation notto rely on just CloudSat-detected clouds to constrain radiativetransfer calculations, as was done in Zygmuntowska et al.[2012]. Monthly variations in Arctic Ocean cloud fractionsfrom CloudSat +CALIPSO are more constant through theannual cycle than cloud fractions derived from surface obser-vations [Eastman and Warren, 2010, Figure 5; Beesley andMoritz, 1999, Figure 1] or MODIS [Kato et al., 2006,Figure 2; Liu et al., 2010].[16] We next evaluate cloud forcing averaged over the

Arctic Ocean. Annual mean TOA cloud forcing is negativein 2B-FLXHR-LIDAR ("12Wm"2 total, "31Wm"2

shortwave, and 19Wm"2 longwave), indicating that cloudshave a cooling effect at the TOA. In contrast, annual mean

(a)

(b)

(c)

Figure 3. Monthly mean Arctic Ocean (70–82!N)cloud fractions from CloudSat +CALIPSO and cloud forcingfrom 2B-FLXHR-LIDAR: (a) total cloud fraction fromCloudSat +CALIPSO and CloudSat alone, (b) TOA cloudforcing, and (c) surface cloud forcing. In Figure 3a, annualmean cloud fractions are reported for CloudSat +CALIPSOand CloudSat alone. In Figures 3b and 3c, annual meanvalues are reported for 2B-FLXHR-LIDAR. The figureuses CloudSat, CALIPSO, and 2B-FLXHR-LIDAR dataavailable from July 2006 to February 2011 (Table 1).

KAY AND L’ECUYER: ARCTIC OCEAN CLOUD AND RADIATION CLIMATOLOGY

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Arctic clouds and climate

Kay and L’Ecuyer 2013

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On the annual mean, clouds warm the surface.

Arctic clouds in CMIP5

KARLSSON AND SVENSSON: ARCTIC SEA-ICE ALBEDO IN THE CMIP5 ENSEMBLE

JanFebMar AprMayJun Jul AugSep OctNovDec20

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albe

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BCC−CSM1.1

CanESM2

CCSM4

CESM1(CAM5)

CNRM−CM5

CSIRO−Mk3.6.0

EC−EARTH

GFDL−ESM2M

GISS−E2−R

HadGEM2−ES

INM−CM4

IPSL−CM5A−LR

MIROC−ESM

MIROC5

MPI−ESM−LR

MRI−CGCM3

NorESM1−M

ERA−Interim

CMIP3 median

CMIP5 median

APP−x

CLARA−A1

Figure 1. Climatological seasonal cycles of (a) total cloud cover (%), (b) surface cloud radiative effect (W m–2), and (c)surface albedo over sea-ice covered ocean, as defined in the text, north of 66.7ıN. Colored lines show individual CMIP5models, grey envelope represents the range of the CMIP3 model ensemble [Karlsson and Svensson, 2011], and black dashedand solid lines represent APP-x and ERA-Interim, respectively. Gray solid line represents CLARA-A1 surface albedo.Periods considered are 1980–2004 and 1982–2004 for models and observations, respectively.

cover and surface radiative fluxes are from the ExtendedAVHRR Polar Pathfinder (APP-x) product [Wang and Key,2005]. The surface albedo retrievals used are from APP-x[Key et al., 2001] and from the CM SAF (Climate Monitor-ing Satellite Application Facility project) CLouds, Albedoand RAdiation dataset from AVHRR data [CLARA-A1,Riihelä et al., 2013; Karlsson et al., 2013]. Both albedodata sets have been validated against summer in situ datafrom the 1 year Surface Heat Budget of the Arctic Ocean(SHEBA) ice campaign [Persson et al., 2002] showingsimilar accuracy. Root mean square errors for the SHEBAsummer were 0.08 and 0.07 for CLARA-A1 and APP-x,respectively [Riihelä et al., 2013; Key et al., 2001]. Thealbedos reported by the two data sets are not identi-cally defined. In CLARA-A1, it is an inherent surfacereflectance (independent of atmospheric conditions), whilethe retrieved APP-x surface albedo represents the appar-ent albedo for all-sky conditions. Over snow and sea-ice,in clear-sky conditions, the apparent albedo is expected tobe higher than the inherent albedo [Key, 2002] and cloudyconditions will further increase the apparent albedo[Key et al., 2001].

[7] Model output is from the CMIP5, which underlies theforthcoming Intergovernmental Panel on Climate Change’sFifth Assessment Report (IPCC-AR5). We analyze monthlymean output for the present-day period (1980–2004) of thehistorical experiment, a simulation where all known forcingsare applied in fully coupled GCMs. In Table S1 (supportinginformation), the 17 GCMs included in the analysis arelisted. The model selection criteria was that all the relevantvariables for the analysis had to be available in the CMIP5database. One ensemble member from each model hasbeen used.

[8] Since the surface albedos of the GCMs are derivedfrom the surface shortwave fluxes, they represent theapparent all-sky albedo. For summer average sea-ice albedo,

the variations in the area of the sea-ice are also considered.We call this albedo the effective summer albedo:

˛siMJJA =P

SW "i FiPSW #i Fi

(1)

where the sums are taken over the summer months(i = {May, June, July, August}) and F represents thefractional area of the sea-ice.

[9] Sea-ice cover masks for the observational recordsare derived from the National Snow and Ice Data Center’smonthly mean sea-ice concentration from Nimbus-7Scanning Multichannel Microwave Radiometer and DefenseMeteorological Satellite Program Special Sensor MicrowaveImager passive microwave data set [Cavalieri et al., 1996,updated yearly] by bilinearly interpolating the sea-iceconcentrations to the observational grid before applying the> 80% sea-ice concentration threshold.

[10] Besides GCMs and observations, we also includethe European Centre for Medium-Range Weather ForecastsInterim reanalysis data (hereon ERA-Interim) [Dee et al.,2011] in the analysis.

3. Annual Cycles[11] Figures 1a and 1b show the average annual cycles of

total cloud cover and surface cloud radiative effect (SCRE,defined as the difference between net all-sky and net clear-sky radiative fluxes) [Ramanathan et al., 1989] over sea-icecovered ocean in the Arctic region in GCMs, reanalysis, andobservations. Although the CMIP5 model ensemble mediantotal cloud cover (Figure 1a) agree well with the observedannual cycle of the total cloud fraction, the across-modelspread is distressingly large. The spread remains at least aslarge as it was in the CMIP3 model ensemble [Eisenmanet al., 2007; Karlsson and Svensson, 2011]. Most GCMsshow an annual cycle in phase with observations, but four

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Karlsson and Svensson (2013)




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BCC−CSM1.1

CanESM2

CCSM4

CESM1(CAM5)

CNRM−CM5

CSIRO−Mk3.6.0

EC−EARTH

GFDL−ESM2M

GISS−E2−R

HadGEM2−ES

INM−CM4

IPSL−CM5A−LR

MIROC−ESM

MIROC5

MPI−ESM−LR

MRI−CGCM3

NorESM1−M

ERA−Interim

CMIP3 median

CMIP5 median

APP−x

CLARA−A1






˛siMJJA =P

SW "i FiPSW #i Fi

(1)






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BCC−CSM1.1

CanESM2

CCSM4

CESM1(CAM5)

CNRM−CM5

CSIRO−Mk3.6.0

EC−EARTH

GFDL−ESM2M

GISS−E2−R

HadGEM2−ES

INM−CM4

IPSL−CM5A−LR

MIROC−ESM

MIROC5

MPI−ESM−LR

MRI−CGCM3

NorESM1−M

ERA−Interim

CMIP3 median

CMIP5 median

APP−x

CLARA−A1






˛siMJJA =P

SW "i FiPSW #i Fi

(1)






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b)

BCC−CSM1.1

CanESM2

CCSM4

CESM1(CAM5)

CNRM−CM5

CSIRO−Mk3.6.0

EC−EARTH

GFDL−ESM2M

GISS−E2−R

HadGEM2−ES

INM−CM4

IPSL−CM5A−LR

MIROC−ESM

MIROC5

MPI−ESM−LR

MRI−CGCM3

NorESM1−M

ERA−Interim

CMIP3 median

CMIP5 median

APP−x

CLARA−A1






˛siMJJA =P

SW "i FiPSW #i Fi

(1)






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Uncertainties in simulated Arctic clouds in GCMs lead to uncertainties in surface climate, e.g. sea ice thickness.



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Sea

ice

albe

do

c)

b)

BCC−CSM1.1

CanESM2

CCSM4

CESM1(CAM5)

CNRM−CM5

CSIRO−Mk3.6.0

EC−EARTH

GFDL−ESM2M

GISS−E2−R

HadGEM2−ES

INM−CM4

IPSL−CM5A−LR

MIROC−ESM

MIROC5

MPI−ESM−LR

MRI−CGCM3

NorESM1−M

ERA−Interim

CMIP3 median

CMIP5 median

APP−x

CLARA−A1






˛siMJJA =P

SW "i FiPSW #i Fi

(1)






4375

Large-eddy simulations of clouds

BOMEX simulated by PyCLES, courtesy of Kyle Pressel

Pressel et al. (2015)https://github.com/pressel/pycles

https://github.com/pressel/pycles



Pressel et al. (2015)https://github.com/pressel/pyclesKaren LaMonteBiennale Venice 2017




Pressel et al. (2015)https://github.com/pressel/pyclesKaren LaMonteBiennale Venice 2017

• Anelastic code using entropy (s) and total water specific humidity (qt) as prognostic variables

• Written in Python, Cython, and C • Advanced numerics in advection and time stepping • One-moment bulk mixed-phase microphysics (Grabowski 1998;

Kaul et al. 2015) • Condensates are partitioned into liquid and ice depending on

temperature

PyCLES

Confidential manuscript submitted to Journal of Advances in Modeling Earth Systems (JAMES)

Figure 1. Liquid fraction as a function of temperature.482

–20–


Model validation: ISDAC campaign

• LES intercomparison project based on aircraft observations north of Barrow, Alaska in April 2008

• A single-layer mixed-phase cloud deck was present over sea ice

• Minimal surface heat fluxes • No shortwave radiation (polar night) • Light snow

Case description

Zhang et al. (submitted)

Model validation: ISDAC campaign

• LES intercomparison project based on aircraft observations north of Barrow, Alaska in April 2008

• A single-layer mixed-phase cloud deck was present over sea ice

• Minimal surface heat fluxes • No shortwave radiation (polar night) • Light snow

Case description


• A well-mixed BL develops after 8 hours of simulation • Overall agreement in the cloud layer with observations and

intercomparison • Most models underestimate snow amount • More mindful setup in forcing will allow better agreement with

observations

PyCLES results

ISDAC idealized climate change


Figure 3. Schematic of the ISDAC i ✓li and qt initial conditions. The cloud layer is indicated in blue. The

BL with height zi has constant ✓li and qt, where qt is determined by the relative humidity (blue dotted line)

near the surface H0. Relative humidity above the BL Hft does not vary with height.

329

330

331

–18–


Table 2. List of parameters and their range for the sensitivity studies of ISDAC i cases. The baseline case

values are in bold.

207

208

Variable Range

✓li,0 261, 265, 269, 273 K

�✓l

3, 5, 7, 9 K

Hft

50, 60, 70, 80%

crophysical processes, as precipitation (snow) is transported downward and subsequently193

sublimates. The warm temperature also allows rain formation and snow melt.194

• The variations in inversion strength �✓li

allow larger changes in BL height (Figure 4d-195

f). At the same time, free-tropospheric specific humidity increases with inversion strength,196

thus allowing less drying through cloud top entrainment. Despite of it, cloud thickness197

increases with inversion strength, which leads to higher LWP in the cloud layer (Fig-198

ure 5c).199

• Increasing free tropospheric relative humidity Hft

has little impact on the ✓li

(Figure200

4g), but significantly moistens the BL and increases qt

near the surface via microphys-201

ical processes. This is mainly achieved by decreased entrainment drying at the cloud202

top. The free troposphere becomes less dry while the entrainment rate changes little.203

Cloud base height increases slightly with Hft

(Figure 4i).204

When more than one parameter is varied, a combination of changes described above oc-205

curs in the BL. To aid the analysis, we produce the same set of simulations using a MLM.206

5 Mixed-layer model209

5.1 Model description210

In order to analyze the ISDAC i cases, we use a simple mixed-layer model (MLM) with

✓l

, qt

, and zi

as prognostic variables [Lilly, 1968; Bretherton and Wyant, 1997; Gesso et al.,

–11–


ISDAC idealized climate change


Figure 3. Schematic of the ISDAC i ✓li and qt initial conditions. The cloud layer is indicated in blue. The

BL with height zi has constant ✓li and qt, where qt is determined by the relative humidity (blue dotted line)

near the surface H0. Relative humidity above the BL Hft does not vary with height.

329

330

331

–18–


Table 2. List of parameters and their range for the sensitivity studies of ISDAC i cases. The baseline case

values are in bold.

207

208

Variable Range

✓li,0 261, 265, 269, 273 K

�✓l

3, 5, 7, 9 K

Hft

50, 60, 70, 80%

crophysical processes, as precipitation (snow) is transported downward and subsequently193

sublimates. The warm temperature also allows rain formation and snow melt.194

• The variations in inversion strength �✓li

allow larger changes in BL height (Figure 4d-195

f). At the same time, free-tropospheric specific humidity increases with inversion strength,196

thus allowing less drying through cloud top entrainment. Despite of it, cloud thickness197

increases with inversion strength, which leads to higher LWP in the cloud layer (Fig-198

ure 5c).199

• Increasing free tropospheric relative humidity Hft

has little impact on the ✓li

(Figure200

4g), but significantly moistens the BL and increases qt

near the surface via microphys-201

ical processes. This is mainly achieved by decreased entrainment drying at the cloud202

top. The free troposphere becomes less dry while the entrainment rate changes little.203

Cloud base height increases slightly with Hft

(Figure 4i).204

When more than one parameter is varied, a combination of changes described above oc-205

curs in the BL. To aid the analysis, we produce the same set of simulations using a MLM.206

5 Mixed-layer model209

5.1 Model description210

In order to analyze the ISDAC i cases, we use a simple mixed-layer model (MLM) with

✓l

, qt

, and zi

as prognostic variables [Lilly, 1968; Bretherton and Wyant, 1997; Gesso et al.,

–11–


• LWP increases with free tropospheric moisture and temperature • LWP decreases with inversion strength • Mixed-layer theory explains most of the changes

In both LES and mixed-layer model:

Inversion strength (K)Sp

ecifi

c hu

mid

ity

abov

e cl

oud

top

(g/k

g)


Figure 7. Cloud top height (zi, in m) and liquid water path (LWP, in g m�2) in climate change simulations

with LES (a, b) and with MLM (c, d) averaged at the 12th hour. The horizontal axis shows the inversion

strength, and the vertical axis shows the specific humidity right above the cloud top. Hatched regions indicate

regions of parameter space where the LES BL decouples, as determined by BIR > 0.15.

502

503

504

505

Figure 8. Entrainment rate diagnosed from LES and parameterized using Eq. (8). Term 1 and 2 correspond

to the right-hand sides of Eq. (8). The BL-mean buoyancy flux B = 6.34 ⇥ 10�3 is fixed for all cases. Term

1 uses a2 = 2.0. The full right-hand side is fitted linearly to the diagnosed entrainment rates with a slope of

0.97 (R2 = 0.87). Intercepts of the linear regressions are assumed zero.

506

507

508

509

–25–

Inversion strength (K)


Figure 7. Cloud top height (zi, in m) and liquid water path (LWP, in g m�2) in climate change simulations

with LES (a, b) and with MLM (c, d) averaged at the 12th hour. The horizontal axis shows the inversion

strength, and the vertical axis shows the specific humidity right above the cloud top. Hatched regions indicate

regions of parameter space where the LES BL decouples, as determined by BIR > 0.15.

502

503

504

505

Figure 8. Entrainment rate diagnosed from LES and parameterized using Eq. (8). Term 1 and 2 correspond

to the right-hand sides of Eq. (8). The BL-mean buoyancy flux B = 6.34 ⇥ 10�3 is fixed for all cases. Term

1 uses a2 = 2.0. The full right-hand side is fitted linearly to the diagnosed entrainment rates with a slope of

0.97 (R2 = 0.87). Intercepts of the linear regressions are assumed zero.

506

507

508

509

–25–

GCM forcing experiment

formation. With LES, we can now simulate

Schneider et al. (2017)

FMS (Frierson et al., 2006; Frierson 2007;

O’Gorman & Schneider, 2008)

PyCLES(Pressel et al. 2015)

• Two-stream, grey radiation at equinox • 1 m slab ocean with closed energy budget • Wide range of climates by changing longwave optical depth • Run to statistical steady-states

Common features of the two models:

GCM forcing framework

Horizontal advection✓@T

@t

◆LES

hadv

=

✓@T

@t

◆GCM

hadv

✓@qt@t

◆LES

hadv

=

✓@qt@t

◆GCM

hadv

Vertical advection✓@qt@t

◆LES

vadv

= �wGCM

✓@qt@z

◆LES

vadv

✓@T

@t

◆LES

vadv

= �wGCM

✓@T

@z

◆LES

vadv

✓@s

@t

◆LES

vadv

= (sv � sd)LES

✓@qt@t

◆LES

vadv

+cpmT

"✓@T

@t

◆LES

vadv

+

✓@T

@t

◆GCM

adiab

#

✓@s

@t

◆LES

hadv

= (sv � sd)LES

✓@qt@t

◆GCM

hadv

+cpmT

✓@T

@t

◆GCM

hadv

Above the convective region, LES total tendencies are equal to GCM total tendencies

See Randall and Cripe (1999), Pressel et al. (2015)

GCM time varying forcing experiment

• GCM outputs of temperature and moisture tendencies at 80N are used to force LES

• Forcing includes horizontal advection (directly applied) and vertical advection (partially resolved by LES)

• Forcing is updated every 6 hours • Above the convective region in GCM, LES tendencies

are set to equal to GCM total tendencies to guarantee consistent free troposphere

(K) (kg/kg)

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x


• GCM outputs of temperature and moisture tendencies at 80N are used to force LES

• Forcing includes horizontal advection (directly applied) and vertical advection (partially resolved by LES)

• Forcing is updated every 6 hours • Above the convective region in GCM, LES tendencies

are set to equal to GCM total tendencies to guarantee consistent free troposphere

(K) (kg/kg)

Preliminary results: • dx = dy = 400 m, stretched vertical grids • x = y = 25.6 km, z = 14.4 km • Simulations are run for 35 days

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x


(kg/kg)(K)

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x

(g/kg) (g/kg)

• Liquid water increases significantly with warming • Liquid layers are not adiabatic: neither potential temperature nor specific humidity is well-mixed • Cloud ice generally decreases with warming

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x


(kg/kg)(K)

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x


(g/kg) (g/kg)

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x


(g/kg) (g/kg)

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x


(g/kg) (g/kg)

• Low cloud fraction decreases with warming, despite of an increase of liquid water • Total cloud fraction remains high for all cases • Instead of shortwave cloud radiative effect, we focus on longwave effect

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x


(g/kg) (g/kg)

• Low cloud fraction decreases with warming, despite of an increase of liquid water • Total cloud fraction remains high for all cases • Instead of shortwave cloud radiative effect, we focus on longwave effect

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x

0.40x0.66x1.00x1.50x2.00x

0_40x 0_66x 1_00x 1_50x 2_00x

∆CRELW TOA

(liquid)- -6.4 0 +5.9 +16.6

• Longwave cloud radiative effect (estimated using liquid water) increases with warming

• The sensitivity is ~0.65 Wm-2K-1 • Cloud ice may play an important role in LW CRE

Estimating LW cloud radiative effect

0.40x0.66x1.00x1.50x2.00x

(g/kg)

Summary

• We use a LES code PyCLES to simulate Arctic clouds • A mixed-layer model is used to explain the boundary layer response in an idealized

setting • An idealized GCM is used to provide large-scale forcing under a wide range of climate • The increased liquid water content with warming leads to a potential positive longwave

cloud radiative feedback

• Future work aims to better distinguish cloud changes due to large-scale circulation from local turbulence-driven changes

• Cloud radiative feedbacks: cloud-top longwave cooling is important for boundary layer clouds

• Comprehensive GCM: more realistic climate states in the polar regions

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GCM time varying forcing results

GCM time varying forcing results

Date post:	25-Jun-2020
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Sensitivities of Arctic Clouds to Climate Change · Arctic clouds and climate Kay and L’Ecuyer...

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