A New Prognostic Cloud Cover Scheme for Mesoscale Models

Cloud cover is a critical variable in weather/climate models, because of its role in various feedback processes, and dominant contributor to the wide range of climate sensitivity estimates among models. This study develops a new prognostic cloud cover scheme based on the concept of probability density function (PDF). The scheme represents sources and sinks of cloud cover explicitly and links them to physical processes including radiation, microphysics, and cumulus. Each tendency term is derived from a statistical PDF based scheme that consistently estimates cloud cover and cloud water in a grid. Unlike previous formulations, our new scheme includes those heterogeneous forcings as well as the turbulent effects calculated with the aid of a high-order moment model (CLUBB) that achieves closure by using a sophisticated joint PDF of vertical velocity, temperature, and moisture. Given the large variety of PDFs used in the literature, this study will also seek an ensemble approach to parameterize the PDF in terms of climate regimes, and incorporate the new scheme into the regional climate model CWRF to evaluate the performance. Due to the lack comprehensive observations, we will use the UCLA-LES and the RICO campaign data to verify our result.