Improving Simulations of Phase Partitioning in Mixed-Phase Clouds in the Community Earth System Model (CESM) with ARM Measurements

 
Poster PDF

Authors

Xiaohong Liu — Texas A&M University
Meng Zhang — Lawrence Livermore National Laboratory
Wang Yong — University of Wyoming
Damao Zhang — Pacific Northwest National Laboratory
Zhien Wang — University of Colorado
Hsi-Yen Ma — Lawrence Livermore National Laboratory
Shaocheng Xie — Lawrence Livermore National Laboratory

Category

High-latitude clouds and aerosols

Description

Mixed-phase clouds consisting of both liquid and ice water occur frequently at high latitudes and in mid-latitude storm track regions. This type of clouds has been shown to play a critical role in the surface energy balance, surface air temperature, and sea ice melting in the Arctic. Cloud phase partitioning between liquid and ice water determines the cloud optical depth of mixed-phase clouds because of distinct optical properties of liquid and ice hydrometeors. The representation and simulation of cloud phase partitioning in state-of-the-art global climate models (GCMs) are associated with large biases. In this study, the phase partitioning in mixed-phase clouds and its seasonal variations simulated from the Community Atmosphere Model version 5 (CAM5) are evaluated against the long-term, ground-based, multi-sensor, remote-sensing retrievals at the ARM North Slope of Alaska (NSA) site as well as global satellite measurements. The modeled mixed-phase clouds show severe underestimations of supercooled liquid fraction (SLF) in all the seasons at the NSA site and on the global scale. To reduce this model bias, we improve the critical processes in CAM5 determining the phase partitioning in mixed-phase clouds by (1) incorporating a physically based ice nucleation parameterization with connection to dust aerosol, (2) improving the treatment of Wegener–Bergeron–Findeisen (WBF) process, (3) changing the phase transition temperatures for detrained cloud water from shallow convection, and (4) running CAM5 in the weather forecast mode (i.e., DOE CAPT) with more realistic meteorological fields. We found that (1) a classical-nucleation-theory-based ice nucleation parameterization in mixed-phase clouds increases the SLF especially at temperatures colder than -20°C, and significantly improves the model agreement with observations in the Arctic; (2) Applying a random number from -6 to 0 for the time-scale exponent parameter of WBF process to account for the small-scale mixing between ice and supercooled liquid water slows down the WBF process and significantly increases the SLF; (3) Changing the transition temperature for detrained cloud water increases the SLF at higher temperatures and improves the SLF over the Southern Ocean; (4) Low SLF biases in some regions can only be improved through the improved circulation under the CAPT framework.