# 613.1 Special Seminar: W.-K.Tao

NASA Goddard Space Flight Center

Recently Grabowski (2001) and Khairoutdinov and Randall (2001) have proposed the use of 2D CRMs as a "super parameterization" [or multi-scale modeling framework (MMF)] to represent cloud processes within atmospheric general circulation models (GCMs). In the MMF, a fine-resolution 2D CRM takes the place of the single-column parameterization used in conventional GCMs. A prototype Goddard MMF based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM) is now being developed. The prototype includes the fvGCM run at 2.5° x 2° horizontal resolution with 32 vertical layers from the surface to 1 mb and the 2D (x-z) GCE using 64 horizontal and 32 vertical grid points with 4 km horizontal resolution and a cyclic lateral boundary. The time step for the 2D GCE would be 15 seconds, and the fvGCM-GCE coupling frequency would be 30 minutes (i.e. the fvGCM physical time step). A fvGCM-GCE coupler for this prototype has been successfully developed. Because the vertical coordinate of the fvGCM (a terrain-following floating Lagrangian coordinate) is different from that of the GCE (a z coordinate), vertical interpolations between the two coordinates are needed in the coupler. In interpolating fields from the GCE to fvGCM, an existing fvGCM finite-volume piecewise parabolic mapping (PPM) algorithm is used, which conserves the mass, momentum, and total energy. A new finite-volume PPM algorithm, which conserves the mass, momentum and moist static energy in the z coordinate, is being developed for interpolating fields from the fvGCM to the GCE.
Major differences between two MMFs (i.e., the CSU MMF and the Goddard MMF) will be discussed. Performance will be illustrated and critical issues related to the MMFs will be presented. In addition, multi-dimensional cloud datasets (i.e., a cloud data library) generated by the Goddard MMF will be described. This cloud data library will be provided to the global modeling community to help improve the representation and performance of moist processes in climate models and to improve our understanding of cloud processes globally. Software tools needed to produce cloud statistics and to identify various types of clouds and cloud systems from both high-resolution satellite and model data will also be presented.