Impact of a cloud thermodynamic phase parameterization based on CALIPSO observations on climate simulation

Cheng, A., K. Xu, Y. Hu, and S. Kato (2012), Impact of a cloud thermodynamic phase parameterization based on CALIPSO observations on climate simulation, J. Geophys. Res., 117, D09103, doi:10.1029/2011JD017263.
Abstract

This study examines the impact of a new cloud thermodynamic phase parameterization on climate simulation. The new parameterization is based on CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) observations and replaces the default parameterization in the Community Atmosphere Model version 4. It is shown that the application of the new cloud phase parameterization results in a small increase in global-mean liquid water path (LWP) and a small decrease in global-mean ice water path (IWP). Large regional increases in LWP mainly occur in tropical regions such as the western Pacific warm pool and northeastern Indian Ocean and middle latitudes, while large decreases in IWP occur in the midlatitude storm track regions. The increase in zonal-mean cloud water content occurs at temperatures between -15 C and -30 C and cloud fraction increases occur at higher altitudes near the -30 C isotherm. Two other sensitivity experiments that favor more ice-phase clouds also increase cloud fractions at the same altitudes, but decrease cloud water content at slightly lower altitudes. It is found that relative humidity increases at the same altitudes where the cloud fraction increases, caused by radiative cooling that is induced by cloud fraction increases but not changes in cloud water content. This result points to a deficiency in cloud fraction parameterizations that rely solely on ambient humidity without taking cloud water/ice content into account. Zonal-mean cloud albedo forcing is sensitive to LWP in mixed-phase clouds and the comparison with observations suggests that the CALIPSO and default parameterizations perform well in the extratropical regions.

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Research Program
Modeling Analysis and Prediction Program (MAP)