Investigating the Cloud Base Mass Flux and Its Controlling Factors in Shallow Cumulus: Insights from Realistic Large-Eddy Simulations

Shallow cumulus clouds (SCC) play a vital role in regulating the Earth’s energy and water cycles, yet their accurate representation in numerical weather prediction and climate models remains a significant challenge. This study employs realistic large-eddy simulations (LES) using the ICON model to analyze both instantaneous and lifetime-averaged statistics of SCC observed on three different days during the FESSTVaL campaign. The excess of virtual potential temperature within the cloud is used to categorize the clouds into active and passive states. The estimated cloud mass flux follows the Weibull distribution, with distinct shape parameters for active and passive clouds, reflecting the memory of the random process. The unity shape parameter for passive clouds indicates memorylessness, while a shape parameter less than unity for active clouds highlights the role of convective memory, where past convection influences the current convective state.

Additionally, the mass flux distribution varies significantly across different cases. These differences can be explained in terms of efficiency, which depends on energy partitioning, the Bowen ratio, and large-scale forcing, and is conceptually linked to approximating moist atmospheric convection as a moist heat engine. This further highlights the role of turbulent fluxes and boundary layer dynamics in shaping the efficiency, which governs the estimation of moist static energy under varying environmental conditions. These findings enhance our understanding of SCC dynamics and offer valuable insights for improving cloud parameterizations in weather and climate models. This study underscores the crucial role of realistic numerical simulations in addressing the challenges of atmospheric convection and turbulence, particularly at the gray-zone scale.