Parameterizing Deep Convection using the Assumed Probability Density Function Method
While deep convective clouds are an integral part of the climate system, explicitly resolving them in decades-long climate simulations is not yet computationally feasible. Thus, they must be parameterized. Researchers, from the University of Wisconsin and the Department of Energy’s Pacific Northwest National Laboratory, developed a parameterization for deep convection using a probability density function (PDF) parameterization and tested it using single-column simulations. The PDF parameterization predicts the PDF of subgrid variability of turbulence, clouds, and hydrometeors. The team interfaced that variability to a prognostic microphysics scheme using a Monte Carlo sampling method.
They used a PDF parameterization to simulate tropical deep convection, the transition from shallow to deep convection over land, and midlatitude deep convection. They compared the parameterized single-column simulations with 3-D reference simulations and found satisfactory agreement except when the convective forcing is weak.
The research also used the same PDF parameterization to simulate shallow cumulus and stratocumulus layers. They concluded that the PDF method is sufficiently general to adequately simulate these five deep, shallow, and stratiform cloud cases with a single equation set. Their findings offer a future possibility, with further refinements at coarse time step and grid spacing, of parameterizing all cloud types in a large-scale model in a unified way.
Coauthors from the University of Wisconsin– Milwaukee acknowledge support by the Office of Science, US Department of Energy, under grants DE-SC0008668 (BER) and DE-SC0008323 (Scientific Discoveries through Advanced Computing, SciDAC). P. Rasch was supported by SciDAC, and M. Wang was supported by SciDAC and the DOE Atmospheric System Research (ASR) Program. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC06-76RLO 1830. The authors would like to thank Mikhail Ovchinnikov and Steven Ghan for helpful discussions. In addition, the authors would like to thank the two anonymous reviewers who provided helpful comments which improved the original manuscript.