Thomas J. Phillips, Gerald L. Potter, David L. Williamson, Richard T. Cederwall, James S. Boyle, Michael Fiorino, Justin J. Hnilo, Jerry G. Olson, Shaocheng Xie, and J. John Yio
To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This effort demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of frequencies, not just at climate scales. A methodology for developing parameterizations in numerical weather prediction (NWP) models therefore may be applicable, provided this approach is appropriately adapted for climate GCMs. Such an NWP-inspired methodology entails the generation of short-range weather forecasts by a realistically initialized climate model, and the use of high-frequency NWP analyses and observations of parameterized variables to evaluate these forecasts. The efficacy of modified GCM parameterizations also can be tested in such a weather-forecasting framework.
In order to further this approach for improvement of parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). In this article, we elaborate the scientific rationale for CAPT, discuss technical aspects of its implementation in a representative climate GCM, and present results that illustrate the CAPT methodology.