K Franklin Evans
University of Colorado Boulder
Dept of Atmospheric & Oceanic Sciences
Campus Box 311
Boulder, CO 80309

E-mail: evans@nit.colorado.edu
Phone: 303-492-4994
Fax:

The objective of the proposed work is to evaluate parameterizations in global climate models (GCMs) in a novel way using joint distributions of vertically integrated ice cloud mass and precipitation from instruments on the A-train. In the tropics cloud ice mass (or ice water path, IWP) and precipitation are two observable outputs from the convective cloud process. Therefore, the joint distribution of IWP and precipitation will be a strong constraint on both the convective parameterization, which determines most of the rainfall and the injection of cloud ice into the upper troposphere, and on the cloud microphysical parameterization, which determines the residence time of ice cloud particles. This work will help to improve climate models and hence climate prediction.

The proposed work will:

1. Develop a statistical procedure to use CloudSat IWP retrievals to correct MODIS IWP retrievals over a broad swath outside the CloudSat radar path. The CloudSat IWP retrievals and uncertainty estimates will be evaluated with an existing Monte Carlo Bayesian ice cloud retrieval methodology.
2. Produce joint distributions of cloud IWP and precipitation at spatial scales from 10 km to 300 km for selected tropical oceanic regions and seasons. The cloud IWP will be obtained from the CloudSat-trained corrected MODIS retrievals, and the rain rate from AMSR-E GPROF retrievals. A part of producing these joint distributions will be to assign uncertainties due to the satellite retrieval and sampling errors.
3. Compare the measured joint distributions at the appropriate spatial scales with those from the NCAR CAM GCM, the GFDL AM2 GCM, and the NASA GMAO GEOS-5 model. The cloud IWP and precipitation fields from the GCM will be sampled at the local time corresponding to the A-train overpass time. The differences between the measured and modeled joint distributions of IWP and precipitation will be interpreted using GCM runs having adjusted parameterization values, in order to indicate what aspects of the cloud parameterizations might need refinement.