Cloud-climate feedbacks:what we think we know and why we think

we know it

David Mansbach

14 April 2006

T1<T0

T2<T0

(slightly)

T24

T14

T04

T24

T14

• Clouds, in general and today– Greenhouse effect, albedo effect, and satellite measurements

• Cloud changes in a perturbed climate– Very wide range of scales and processes involved

• Modeling– Parameterize clouds and take GHG-forced runs as a predictions– Parameterizations inspired by physical principles but lead to errors

compared to validation

• Observations– Understand observed cloud changes to variability of specific conditions– Merge observed cloud tendencies with modeled large-scale changes or

conceptually suggested changes

• Trade-offs

Longwave forcing: cloud greenhouse effect and cloud albedo effect

• As a cloud gets thicker, it acts like a blackbody: absorbing at all wavelengths and emitting according to T4

• Higher clouds -- in cold upper atmosphere -- emit less IR/longwave radiation to space, and keep more energy in the Earth system

• Thickness, water/ice distribution, sun angle affect how much cloud reflects sunlight (its albedo)

T1<T0

T2<T0

(slightly)

T24

T14

T04

T24

T14

T2<<T0

Increasing SSTBony et al. 2004/Emanuel 1994

Tropical and extratropical clouds

Bony et al. 2006/Cotton 1990

Area of ascent is small; area of decent is large

Cloud cover is greater in areas of descent, and lower in altitude

Frontal systems form a variety of clouds

Nature strength of storms determines clouds

Annual ERBE Net Radiative Cloud Forcing

from Randall, 2004

So now we just need to decide how clouds will change in the future...

• Define cloud radiative forcing at any point as the difference in outgoing radiation with a cloud present minus that with clear sky• Satellite data such as ERBE show net effect of cloud forcing is dominated by SW effect; CRF ~ -20 W m-2

Changing clouds, changing cloud radiative forcing• Cloud processes operate on some

small scales -- think of a thunderstorm in the distance or wispy clouds overhead

• More condensed water generally means more optically thick clouds -- ie, more absorption and emission of longwave -- and affects refletion

• Shortwave reflectivity also depends on number of droplets -- sunlight will be reflected more if there are many small droplets (also leads to interplay with aerosols!)

• Overall effects of clouds depend on myriad processes -- ie, thermodynamic, microphysical, optical, convective, dynamic

• Many effects can be hypothesized• ie CO2x2 -> more evaporation, ->

more cloud liquid water -> more SW reflectivity -> negative feedback (ie Somerville and Remer 1984)

• cf: CO2x2 -> warmer SST -> breakup of SC, greater areas of deep convection -> positive feedback

from NASA

That’s why we have models• to look at global CRF changes, try using a global model

• although scales of individual clouds might be ~100m or ~1 km, climate model resolution ~100km

• parameterizations link large-scale climate to cloud properties based on observations and theory

– also conserve important properties, such as moisture, energy, etc.

– easier said than done -- larger-scale conditions do not necessarily fully determine actual cloud fields; radiative impacts and feedbacks could be considerable

– GCM-simulated current cloud climatology is often obviously unrealistic

Schmidt et al 2006

CTP

Bony et al. 2006

water vapor clouds aerosols lapse rate w/water vapor

lapse rate total

Different cutting-edge models also don’t agree preciselyAlthough spread is large, modern models predict a positive cloud feedback to global warming, meaning that future cloud forcing is less negative (clouds will not cool the Earth system as much as today)

Concntrating on different models’ cloud response to forcing

SCRF SCRF LCRF LCRF SCRF SCRF LCRF LCRF

Williams et al. 2006

Using observations to inform discussion of clouds in future climate

Using years of satellite and reanalysis data, plot average cloud properties as functions of temperature advection and vertical velocity

•These data are for conditions of SST and lower-tropospheric static stability in “normal”/moderate conditions

•This allows for a sort of empirical partial derivative of various cloud properties

Norris and Iacobellis 2005

• even if GCM clouds are unrealistic, dynamical predictions can be combined with CRF observations

• General inferences from past polar amplification and known storm dynamics, as well as a GCM (Dai et al. 2001), suggest storm track weakening (less extreme vertical velocity) along with warmer SST and little change in vertical stability -> less cloudiness, thinner clouds

• modeled temperature changes would lead to less broad marine stratocumulus and less marine fog -> less SW CRF, positive forcing

for surface temperature advection

for mid-troposphere vertical velocity

Norris and Iacobellis 2005

for stronger storm track

Other observations relevant to midlatitude CRF changes

net SW flux net LW flux net precip flux

weak storms

moderate storms

strong storms

Tselioudis and Rossow, 2006

Implications of observed CRF

tendencies

• a GCM (Carnell and Senior 1998) predicts fewer weak and moderate storms, but more strong storms

• implied additional SW cooling is 0 to 3.5 W m-2 in different areas (fewer clouds, but more reflective)

• implied additional LW warming is 0.1 to 2.2 W m-2 (fewer clouds, but higher)

• overall increase in strength dominates, leads to global cloud COOLING of ~1 W m-2

• analysis of cloud response to circulation and temperature changes is consistent with other study, but choice of modeled circulation changes are different

• if these midlatitude changes were factored into Norris & Iacobellis’s figures, total CRF would still be positive, but less so, because of thermodynamic response

net SW flux net LW flux net precip flux

weak storms

moderate storms

strong storms

Annual ERBE Net Radiative Cloud Forcing

• Global feedbacks of clouds unknown; depends on myriad processes on various scales

• Physical mechanisms can be hypothesized to support SW and LW feedbacks of any sign

• The latest round of models predict positive cloud feedback; some observational analysis shows consistent physical reasoning for this

• Model spread is large; model clouds still have many errors

• How predictable are clouds really?

Tradeoffs