Linking cloud microphysics with macrophysics in climate models through entrainment-mixing processes

Cloud microphysics (e.g., liquid water content and droplet concentration) and cloud macrophysics (e.g., cloud fraction) are represented in climate and weather models separately. Furthermore, turbulent entrainment-mixing processes are assumed to be either extreme homogeneous or inhomogeneous, which hardly cover what occurs in real clouds. This study will first introduce an entrainment-mixing parameterization that can cover all the mixing types including the commonly assumed extremes, and discuss its implementation in climate (CESM) and weather (WRF) models. We will then show how the parameterizations of cloud microphysics and macrophysics are connected through entrainment-mixing parameterization, and further examine the influences on model results. Finally we will explore a hybrid framework that integrates machine-learning, observations and multiscale modeling to improve/unify these parameterizations and conduct model evaluation/ parameter calibration.