Effects of Cloud Parameterization on the Simulation of Climate Changes in the GISS GCM. Part II: Sea Surface Temperature and Cloud Feedbacks

Abstract

The influence of the sea surface temperature distribution on cloud feedbacks is studied by making two sets of doubled CO2 experiments with the Goddard Institute for Space Studies (GISS) GCM at 4° latitude ? 5° longitude resolution. One set uses Q fluxes obtained by prescribing observed sea surface temperatures (MODELII?), and the other set uses Q fluxes obtained by prescribing the simulated sea surface temperature of a coupled ocean?atmosphere model (MODELIIO). The global and annual mean surface air temperature change (?Ts) obtained in MODELII? is reduced from 4.11° to 3.02°C in MODELIIO. This reduced sensitivity, aside from reduced sea ice/snow?albedo feedback, is mainly due to cloud feedback that becomes nearly neutral in MODELIIO. Furthermore, the negative effect on climate sensitivity of anvil clouds of large optical thickness identified by Yao and Del Genio changes its sign in MODELIIO primarily due to sharply reduced increases of cloud water in the tropical upper troposphere. Colder tropical sea surface temperature in MODELIIO results in weaker deep convective activity and a more humid lower atmosphere in the warmer climate relative to MODELII?, which then removes the negative feedback of anvil clouds and sharply reduces the positive feedback of low clouds. However, an overall positive cloud optical thickness feedback is still maintained in MODELIIO. It is suggested that the atmospheric climate sensitivity, partially due to changes in cloud feedbacks, may be significantly different for climate changes associated with different patterns of sea surface temperature change, as for example in warm versus cold paleoclimate epochs. Likewise, the climate sensitivity in coupled atmosphere?ocean models is also likely to be significantly different from the results obtained in Q-flux models due to the different simulations of sea surface temperature patterns in each type of model.