Effects of Entrainment and Mixing Scenarios on the Droplet Size Distribution: Spatiotemporal Variability and Cloud-Lifecycle Dependence

Entrainment and mixing play an important role in shaping the cloud droplet size distribution (DSD). In particular, DSD changes after mixing are often characterized by two idealized scenarios: homogeneous and inhomogeneous mixing that may alter cloud properties like cloud albedo and precipitation development differently. However, it is still unknown how mixing scenarios vary in the cloud lifecycle spatially and temporally as the mixing process is affected by multi-scale turbulent motions, including the scale that usually applied large-eddy simulation (LES) cannot resolve. Thus, in this study, to account for all relevant scales of entrainment and mixing in a shallow cumulus cloud field, we employ a novel modeling framework that combines LES with a Lagrangian cloud model and a stochastic turbulence subgrid-scale model. Mixing scenarios are determined by tracking droplet concentration and liquid water content change during the mixing process. We confirm that after homogeneous mixing, the DSD is broadened toward small cloud droplet sizes. In contrast, only the cloud droplet number concentration decreases with an insignificant change in DSD shape after inhomogeneous mixing. Most interestingly, we find that mixing can narrow the DSD if the original DSD is very broad. Furthermore, we use a cloud tracking algorithm to investigate the variability of mixing scenarios during the cumulus cloud life cycle and within a cumulus cloud field. Mainly, mixing becomes more frequent and inhomogeneous as clouds age. All in all, this study clarifies how DSDs change during mixing and how these mixing scenarios vary throughout the cloud lifecycle.