Predicting the Evolution of Shallow Cumulus Clouds With a Lotka-Volterra Like Model

Abstract

In numerical weather prediction and climate models, boundary-layer clouds are controlled by a wide range of subgrid-scale processes. However, understanding the nature of these processes and their role in the evolution of the cloud size distribution as a whole has been elusive. To address this issue, we adopt a novel empirical framework from the field of population dynamics to model the evolution of cloud size statistics by using the shallow cumulus properties obtained from a large-eddy simulation (LES). Our approach involves representing the cloud size distribution and the total cloud area using a revised Lotka-Volterra model and ridge linear model, respectively. The physical interpretation of the total cloud area and coefficients obtained from the optimization of the models reveals three stages probably interpreted by dominant processes: the formation of new clouds, the growth of single clouds, and a steady state with organized transitions involving the growth and decay of multiple clouds. Furthermore, we showcase the potential of this framework to serve as a component of scale-aware parameterizations of shallow-convective clouds in atmospheric models.