A Method for Adaptive Habit Prediction in Bulk Microphysical Models. Part III: Applications and Studies within a Two-Dimensional Kinematic Model

Abstract

rctic mixed-phase clouds are ubiquitous, and the persistence of supercooled liquid is not well understood. Prior studies of mixed-phase clouds predict a single axis length assuming spherical particles or mass?dimensional relationships derived from in situ data. These methods cannot mechanistically evolve particle shape, leading to inaccuracies in estimates of mixed-phase lifetime. Parts I and II of this study report on the development and parcel model testing of an adaptive habit parameterization that predicts two bulk crystal lengths. The method is implemented into a two-dimensional kinematic model in which the dynamic flow field is prescribed, allowing for sedimentation and separate advection of length mixing ratios.Similar to other studies, results show that mass?dimensional relationships produce large variation of phase, despite similar choice in particle type. Results with evolving ice habit promote phase maintenance in cases where mass?dimensional methods glaciate the layers. Adaptive habit simulations with sedimentation increase cloud lifetime at higher ice concentrations but can also lead to lower liquid amounts. Radiative cooling initially increases ice growth with a subsequent enhanced sedimentation flux, altering cloud-phase partitioning dependent on ice concentration. Surface latent and sensible heat fluxes of 50 W m?2 result in an increase in overall water mass, while compensating fluxes establish sufficient energy and mass amounts for liquid and ice maintenance. These studies provide insight into the fluxes that may be necessary for mixed-phase cloud maintenance.