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The need to understand differences among general circulation model projections of CO2-induced climatic change has motivated the present study, which provides an intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. This intercomparison uses sea surface temperature change as a surrogate for climate change. The interpretation of cloud-climate interactions is given special attention. A roughly threefold variation in one measure of global climate sensitivity is found among the 19 models. The important conclusion is that most of this variation is attributable to differences in the models' depiction of cloud feedback, a result that emphasizes the need for improvements in the treatment of clouds in these models if they are ultimately to be used as reliable climate predictors. It is further emphasized that cloud feedback is the consequence of all interacting physical and dynamical processes in a general circulation model. The result of these processes is to produce changes in temperature, moisture distribution, and clouds which are integrated into the radiative response termed cloud feedback. INTRODUCTIONProjected increases in the concentration of atmospheric carbon dioxide and other greenhouse gases are expected to have an important impact on climate. The most comprehensive way to infer future climatic change associated with this perturbation of atmospheric composition is by means of three-dimensional general circulation models (GCMs). Schlesinger and Mitchell [1987] have, however, demonstrated that several existing GCMs simulate climate responses to increasing CO2 that differ considerably. Cess and Potter [1988], following a suggestion by Speltnan and Manabe [1984], indicate that differences in global-mean warming, The global-mean direct radiative forcing G of the surfaceatmosphere system is evaluated by holding all other climate parameters fixed. It is this quantity that induces the ensuing climate change, and physically, it represents a change in the net (solar plus infrared) radiative flux at the top of the atmosphere (TOA). For an increase in the CO2 concentration of the atmosphere, to cite one example, G is the reduction in the emitted TOA infrared flux resulting solely from the CO2 increase, and this reduction results in a heating of the surface-atmosphere system. The response process is the change in climate that is then necessary to restore the TOA radiation balance, such that that is either too warm or too cold, then it will respectively produce a climate sensitivity parameter that is too small or too large, and clearly, the intercomparison simulation had to be designed to eliminate this effect. There was also a practical constraint: the CO2 simulations require large amounts of computer time for equilibration of the rather primitive ocean models that have been used in these numerical experiments.An attractive alternative that eliminated both of the above mentioned difficulties was to adopt +_2øK sea surface temperature ( The perpetual July simulation e...

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Understanding the cause of differences among general circulation model projections of carbon dioxide-induced climatic change is a necessary step toward improving the models. An intercomparison of 14 atmospheric general circulation models, for which sea surface temperature perturbations were used as a surrogate climate change, showed that there was a roughly threefold variation in global climate sensitivity. Most of this variation is attributable to differences in the models' depictions of cloud-climate feedback, a result that emphasizes the need for improvements in the treatment of clouds in these models if they are ultimately to be used as climatic predictors.

Numerical weather prediction methods show promise for improving parameterizations in climate GCMs.C limate simulations performed with general circulation models (GCMs) are widely viewed as the principal scientific basis for developing policies to address potential future global climate change (Houghton et al. 2001). In order to reduce uncertainties in these GCM projections of future climate, there is a compelling need to improve the simulation of processes that produce the present climate. This undertaking demands close attention to systematic errors in GCM simulations.Systematic errors are persistent (average) departures of the model solution from an appropriate observational standard. For example, the GCM sys-AFFILIATIONS:

Six years ago, we compared the climate sensitivity of 19 atmospheric general circulation models and found a roughly threefold variation among the models; most of this variation was attributed to differences in the models' depictions of cloud feedback. In an update of this comparison, current models showed considerably smaller differences in net cloud feedback, with most producing modest values. There are, however, substantial differences in the feedback components, indicating that the models still have physical disagreements

There has been a long history of unexplained anomalous absorption of solar radiation by clouds. Collocated satellite and surface measurements of solar radiation at five geographically diverse locations showed significant solar absorption by clouds, resulting in about 25 watts per square meter more global-mean absorption by the cloudy atmosphere than predicted by theoretical models. It has often been suggested that tropospheric aerosols could increase cloud absorption. But these aerosols are temporally and spatially heterogeneous, whereas the observed cloud absorption is remarkably invariant with respect to season and location. Although its physical cause is unknown, enhanced cloud absorption substantially alters our understanding of the atmosphere's energy budget.

Based upon the need to understand differences between general circulation model projections of climatic change due to increasing atmospheric carbon dioxide, the present study first categorizes reasons for these differences and presents suggestions for the design of future climate model simulations, so that these specific categories may directly be addressed and understood. Following this, and based upon tutorial use of a radiative‐convective model, it is suggested that sea surface temperature perturbations may be used, in conjunction with separation of clear and overcast regions within a model, as a surrogate climatic change for the purpose of understanding and intercomparing atmospheric climate feedback processes. This approach is illustrated through use of the Oregon State University/Lawrence Livermore National Laboratory general circulation model, with particular attention being paid to interpreting cloud/climate interactions within the model.