PNNL

Abstract

Traditional conceptual models underpinning parameterizations of the interaction between convection and the environment have relied on an assumption that slowly varying large-scale environment is in statistical equilibrium with a large number of small and short-lived clouds. Thus they fail to capture non-equilibrium transitions such as the diurnal cycle and formation of meso-scale convective systems as well as observed precipitation statistics and extremes. Informed by analysis of radar observations, cloud-permitting model simulation, theory and machine learning, this work presents a new cloud population dynamics model for characterizing the interactions between convective and stratiform clouds and with ultimate goal of representing these interactions in global climate models. 12 wet seasons of observation of precipitating clouds by a C-band radar at Darwin, Australia are fed into machine learning algorithm to obtain transition functions that close a set of coupled equation relating large-scale forcing, mass flux, convective cell sizes and stratiform areas. Under realistic large-scale forcing, the coupled model shows that, on the one hand, stratiform clouds damp the variability in size and number of convective cells and therefore convective mass flux. On the other, for the same convective area fraction, a larger number of smaller cells is more favorable for the growth of stratiform area than a smaller number of larger cells. These two factors result in a number of convective cells embedded in a large stratiform area reminiscent of mesoscale convective systems.