To simulate the variety of cloud types observed in the atmosphere, climate modelers have historically used different representations for different clouds. This approach causes discontinuities as simulated conditions change such that one cloud scheme turns off and another one turns on. A unified cloud parameterization overcomes this limitation by being general enough that it can be used to represent all cloud types. This ensures smooth transitions of the simulated clouds as environmental conditions change. A team of climate modelers from the National Center for Atmospheric Research, the University of Wisconsin–Milwaukee, the University of Washington, and DOE scientists from Pacific Northwest National Laboratory found improvements in simulated clouds when a statistical interface between cloud properties and cloud processes was introduced in a turbulence scheme to accommodate a diversity of overlapping cloud microphysical conditions within model grid cells. This was allowed to extend throughout the lower atmosphere to simulate all clouds. The researchers expect that alternate methods of accounting for unresolved variability, such as quadrature, could reduce the computation cost of sampling the variability.
The authors thank Jun-Ichi Yano and two anonymous reviewers for their comments on this manuscript. The National Center for Atmospheric Research is supported by the US National Science Foundation. NCAR authors were partially supported by National Science Foundation Grant AGS- 0968657. Co-authors from 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) and support by the National Science Foundation under grant AGS-0968640. PNNL staff were 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.