This research seeks to advance knowledge in two important areas of climate modeling. The first involves downscaling of future projected climate information and the second involves meso and cloud scale modeling to improve the cloud and radiation parameterizations in the CCSM CAM (Community Climate System Model (CCSM) Atmospheric Model).
1) Currently, climate projections are accomplished using coupled atmospheric and oceanic general circulation models (GCMs), which produce future climate information on horizontal spatial scales of about 200 km. However, in order to assess the potential impacts of future climates on the environment and on social systems, information is required on much smaller regional scales, i.e., roughly 1 to 2 orders of magnitude smaller that those of the GCMs. The study examines statistical-dynamical methods to downscale general (global) circulation model (GCM) results to regional scales. The NCAR CCSM and PCM (Parallel Climate Model) will provide the global, course scale, simulations. The research will focus on the North American region and downscaled information will be compared to available observations for this region.
2) Collaborative research with the University of Wisconsin will be conducted to examine the details of the mechanisms that control the simulations of clouds in the Wisconsin Cloud Resolving Model (CRM) and the parameterizations in the CCSM CAM. The work will include an examination of the CRM results, including the detailed energy, mass and cloud microphysics budgets. Simplified versions of CCSM will be used; the single column model that replicates the CAM-2 physics, and/or the CAM-2 coupled to a slab ocean model.
This research is important because, if successful, it will enable more credible projections of climate variability and change, which is a requirement for assessing the impacts of climate variability and change on regional spatial scales. Moreover, the CCSM is a model used by over 100 scientists and its improvement will benefit the climate research community at-large.
