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ABSTRACT

A current dilemma of climate modeling is that model results are strongly sensitive to the treatment of certain poorly-understood physical processes, especially cloud-radiation interactions. Thus, different models with alternative plausible parameterizations often give widely varying results. Yet, we typically have had little basis for estimating which parameterization is more realistic. Of the many physical processes involved in climate simulations, feedbacks due to cloud-radiation interactions are thought to be the largest single source of uncertainty. In fact, most of the global differences in results between leading climate models, as measured by their sensitivity to greenhouse gases, can be traced to different model treatments of cloud-radiation interactions.

Using a modern atmospheric general circulation model (the National Center for Atmospheric Research Community Climate Model: CCM2), we have investigated the effects on climate sensitivity of several different cloud-radiation parameterizations. At the same time, we have validated these parameterizations directly with observations from field experiments. In addition to the original cloud-radiation scheme of CCM2, we tested four parameterizations incorporating prognostic cloud water: one version with prescribed cloud radiative properties and three other versions with interactive cloud radiative properties. Comparisons with measurements suggest that schemes with explicit cloud water budgets and interactive radiative properties are potentially capable of matching observational data closely.