A PDF-based hybrid prognostic cloud scheme for general circulation models

A new cloud parameterization based on prognostic equations for the subgrid-scale fluctuations in temperature and total water content is introduced for global climate models. The proposed scheme, called hybrid prognostic cloud (HPC) parameterization, employs simple probability density functions (PDFs) to the horizontal subgrid-scale inhomogeneity, allowing them to vary in shape in response to small-scale processes such as cumulus detrainment and turbulent mixing. Simple tests indicate that the HPC scheme is highly favorable as compared to a diagnostic scheme in terms of the cloud fraction and cloud water content under either uniform or non-uniform forcing. The relevance of the HPC scheme is investigated by implementing it in an atmospheric component model of the climate model MIROC with a coarse resolution of T42. A comparison of the short-term integrations between the T42 model and a global cloud resolving model (GCRM) reveals that the HPC scheme can reproduce, to a certain degree, the subgrid-scale variance and skewness of temperature and total water content simulated in the GCRM. It is also found that the HPC scheme significantly alters the climatological distributions in cloud cover, precipitation, and moisture, which are all improved from the model using a conventional diagnostic cloud scheme.