2B.6 Troposphere Cloud Ice: Satellite Measurements, ECMWF and GEOS5 Analyses, and GCM Simulations

Monday, 28 April 2008: 11:30 AM
Palms E (Wyndham Orlando Resort)
Jui-Lin Li, JPL, Pasadena, CA; and D. Waliser, C. P. Woods, J. D. Chern, J. Bacmeister, J. Jiang, D. Genio, R. Rossow, M. Kharitondov, H. Meng, P. Minnis, S. S. Mack, A. M. Tompkins, W. K. Tao, Z. Kuang, D. G. Vane, G. Stephens, and D. L. Wu

Current advanced global climate models (GCMs), still exhibit considerable disagreement in representing mean cloud ice amount and their spatial variability. Global measurements of cloud ice have been difficult to obtain due to complications associated with multi-level clouds, mixed-phases and multiple hydrometer types, the uncertainty in classifying ice particle size and shape for remote retrievals, and the relatively small time and space scales associated with deep convection. Together, these measurement difficulties make it a challenge to characterize and understand the mechanisms of ice cloud formation and dissipation for integrating cloud schemes in GCMs. New global ice water content (IWC) measurements from A-Train CloudSat and the Aura Microwave Limb Sounder (MLS) are used to assess the current status of climate models in simulating upper-tropospheric cloud physical processes. Comparisons are made with European Centre for Medium-Range Weather Forecasts (ECMWF) and NASA GEOS5 analyses, and simulations from several state-of-the-art GCMs, including the Diabatic Acceleration and Rescaling (DARE) GCM and the GSFC finite volume multiscale-modeling framework (fvMMF) GCMs. We will address the compatibility of the IWC between CloudSat and MLS retrievals and that represented by GCMs including types of frozen cloud condensate mass (e.g., cloud ice, snow and graupel). The main focus is to address compatibility of the classification and definition of cloud condensate mass between observations and GCMs to ensure the best use of CloudSat and MLS data for model evaluation. The effort is to evaluate the potential usefulness of this new data set for improving GCMs, particularly their upper level cloud microphysical parameterizations.
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