Modeling Performance of SCALE‐AMPS: Simulations of Arctic Mixed‐Phase Clouds Observed During SHEBA
Chia Rui Ong, M. Koike, Tempei Hashino, Hiroaki Miura
Abstract
Abstract The Advanced Microphysics Prediction System (AMPS), which adopts a two‐moment hybrid‐bin habit‐predicting scheme, was previously developed to study cloud microphysical processes that depend on ice habit; however, only one particular atmospheric model, the University of Wisconsin‐Nonhydrostatic Modeling System, has been used to test the AMPS. In this study, AMPS is implemented into the Scalable Computing for Advanced Library and Environment (SCALE) large‐eddy simulation model. The AMPS Eulerian advection scheme for non‐mass characteristic variables of ice particles, such as axis lengths, is refined to minimize numerical artifacts. The resulting SCALE‐AMPS model successfully reproduces features of mixed‐phase clouds observed during the Surface Heat Budget of the Arctic campaign, including liquid water path (LWP), ice particle size distributions, and ice habits, when ice particle number concentrations ( N ice ) are reproduced. Sensitivity studies show that increases in N ice result in reductions of LWP that are generally consistent with previous results. Interestingly, LWP reductions lead to changes in ice habits through increases in cloud temperature due to weaker cloud top radiative cooling. Furthermore, aspect ratios of precipitating particles also change following LWP reductions, because in Bigg's immersion freezing scheme, adopted in this study, the aspect ratios depend on the initial size of the ice particles and freezing rates depend on both temperature and droplet size. Because habits of ice particles affect their growth rates, fall speeds, and collision rates, the results obtained in this study reveal possible feedback processes of Arctic mixed‐phase clouds operating through ice habits.