A33P-05
Incorporation of New Convective Ice Microphysics into the NASA GISS GCM and Impacts on Cloud Ice Water Path (IWP) Simulation

Wednesday, 16 December 2015: 14:40
3008 (Moscone West)
Gregory Elsaesser, Columbia University/NASA GISS, Dept. of Applied Physics and Applied Mathematics, New York, NY, United States and Anthony D Del Genio, NASA Goddard Institute for Space Studies, New York, NY, United States
Abstract:
The CMIP5 configurations of the GISS Model-E2 GCM simulated a mid- and high-latitude ice IWP that decreased by ~50% relative to that simulated for CMIP3 (Jiang et al. 2012; JGR). Tropical IWP increased by ~15% in CMIP5. While the tropical IWP was still within the published upper-bounds of IWP uncertainty derived using NASA A-Train satellite observations, it was found that the upper troposphere (~200 mb) ice water content (IWC) exceeded the published upper-bound by a factor of ~2. This was largely driven by IWC in deep-convecting regions of the tropics.

Recent advances in the model-E2 convective parameterization have been found to have a substantial impact on tropical IWC. These advances include the development of both a cold pool parameterization (Del Genio et al. 2015) and new convective ice parameterization. In this presentation, we focus on the new parameterization of convective cloud ice that was developed using data from the NASA TC4 Mission. Ice particle terminal velocity formulations now include information from a number of NASA field campaigns. The new parameterization predicts both an ice water mass weighted-average particle diameter and a particle cross sectional area weighted-average size diameter as a function of temperature and ice water content. By assuming a gamma-distribution functional form for the particle size distribution, these two diameter estimates are all that are needed to explicitly predict the distribution of ice particles as a function of particle diameter.

GCM simulations with the improved convective parameterization yield a ~50% decrease in upper tropospheric IWC, bringing the tropical and global mean IWP climatologies into even closer agreement with the A-Train satellite observation best estimates.