Diagnostic analysis of cloud radiative properties

A current dilemma of climate modeling is that general circulation model (GCM) results are extremely sensitive to parameterizations of certain poorly understood physical processes, most notably cloud-radiation interactions. As a result, models with different plausible parameterizations give very different results. Yet, we have no firm basis for knowing which parameterization is more nearly {open_quotes}correct.{close_quotes} It is true that parameterizations are not the only shortcoming of GCMs. Our current ability to create models that will adequately simulate today`s climate and predict its evolution is limited by several factors, not just by one. Some of these factors are technical, such as a lack of computer power. Nevertheless, the most critical need is for an improved physical understanding of the key physical processes. Until these processes are much better understood and until this understanding is incorporated in our models, the model results will always be subject to major uncertainties. Reducing these uncertainties so that we can have confidence in the reliability and accuracy of climate forecasts requires focused research on climate processes. Of the many physical processes involved in climate simulations, feedback from cloud-radiation interactions is currently thought to be the largest single source of uncertainty. For this reason, the Committee on Earthmore » and Environmental Sciences continues to rank the role of clouds as the highest science priority for the U.S. Global Change Research Program. As an example of the importance of cloud-radiation feedbacks, it is noteworthy that the sensitivity of model-simulated climates to changes in atmospheric carbon dioxide concentration has undergone major fluctuations in recent years. The equilibrium global average surface temperature change in response to a carbon dioxide doubling, based on GCM results from models developed in the mid-1970s, was typically between 2{degrees}C and 3{degrees}C.« less