This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
Climate depends strongly on processes related to clouds. Despite this, in current generation climate models, cloud-related processes typically interact with each other only through grid box means and not directly with each other on the small scales. For instance, it would be desirable to let the full variability in small-scale cloud structure directly drive precipitation processes and let precipitation in turn directly alter the small-scale cloud structure. The lack of such an interface leads to degraded simulations of a variety of cloud related processes.
To address this need, the project will develop a physics coupler based on the assumed probability density function (PDF) method. The assumed PDF method produces grid box averages of precipitation rates by upscaling local process rates. In the past, the averaging over the small-scale variability has been performed by either of two methods: (1) analytic integration, or (2) Latin hypercube sampling, which is a type of Monte-Carlo integration. Both methods suffer drawbacks. The investigator will develop a new approach to reduce noise while permitting the use of sophisticated precipitation schemes. The technique will be implemented in a climate model and used to simulate clouds, and may lead, in future work, to a generalized physics coupler that will improve many facets of climate simulations.
Broader impacts of this work include partnership with an undergraduate institution in real-world software engineering, broad training of graduate students and teaching, and disseminating research in the community and benefiting society. Although the research is intended to have immediate application to precipitation processes in climate models, the techniques may be applied in the future to other model types, such as numerical weather prediction models, and other physical phenomena, such as atmospheric chemistry.
