Cloud-climate feedbacks: what we think we know and why we think we know it David Mansbach 14 April 2006 T 1 <T 0 T 2 <T 0 (slightly) T24T24 T14T14 T04T04 T24T24 T14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  T 4 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) T 1 <T 0 T 2 <T 0 (slightly) T24T24 T14T14 T04T04 T24T24 T14T14 T 2 <<T 0

Increasing SST Bony 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 CO 2 x2 -> more evaporation, -> more cloud liquid water -> more SW reflectivity -> negative feedback (ie Somerville and Remer 1984) cf: CO 2 x2 -> 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 water vaporcloudsaerosolslapse rate w/water vapor lapse rate total Different cutting-edge models also don’t agree precisely Although 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 Williams et al. 2006

Norris + Iacobellis (2 slides) - compositing methods and diff regimes final estimates in ‘most likely’ scenarios 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 fluxnet LW fluxnet 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 fluxnet LW fluxnet 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