differ considerably in their simulation of cloud feedbacks. Cloud feedbacks differ substantially between the NCAR and GFDL models, and these very likely account for most of the difference in their respective estimates of climate sensitivity. Even modest changes in cloud parameterizations have been found to affect cloud feedbacks and, hence, sensitivity. The primary difference is that for the NCAR model, there is a strong negative feedback involving low clouds. For the GFDL model, there is a moderate positive feedback involving low clouds, which adds to the positive feedback involving high clouds.
It is important to realize that feedbacks interact. For instance, small changes in the strength of the water vapor feedback may increase or decrease the level of uncertainty due to the cloud feedback. If the water vapor feedback is weak, then the uncertainty due to cloud feedback would be smaller. This interaction must be taken into account when diagnosing feedbacks in models. It is also important to understand that even regional feedbacks can have global-scale impacts. For instance, snow-ice albedo feedbacks are geographically confined to a small percentage of the earth’s surface, but can affect temperature patterns over much of the planet.
So how do we go about resolving these uncertainties? Developing better estimates of climate sensitivity requires a multifaceted approach involving model diagnostics, field measurements of important feedback processes, analysis of global observations, comparisons of simulated and observed climate history, and process modeling. GFDL and NCAR, along with their partners, are active in all of these research areas. Within another six months or so, more comprehensive model intercomparison studies will be under way.
In comparisons of models to observations, the GFDL model does seem to capture some features of seasonal variations in cloud climatology, although the model has a general bias toward too much cloudiness. Interannual variability of cloudiness is similar in both the GFDL and the NCAR models, but the cloud response to warming is quite different. Thus, interannual variability may not be an adequate surrogate for global warming.