Lagrangian investigation of the precipitation efficiency of convective clouds

The efficiencies of a cloud to convert entrained vapor or cloud condensate to surface precipitation are crucial to vertical profiles of latent heating and moistening and - through cloud-radiative feedbacks - to Earth's energy budget. To study these efficiencies we design a novel framework to track Lagrangian particles representative of H2O molecules within Eulerian simulations. A Monte Carlo approach is used to facilitate the transition of particles between water classes (liquid water, rain, graupel, etc.) and – same as rain drops – particles may fall out with respect to dry air.

First, we apply this framework to study the efficiency of individual cumulus clouds. We find that ordinary cumulus congestus clouds entrain only about one third of all entrained vapor through cloud base. However, this fraction of vapor contributes about half of the surface rainfall and its efficiency is thus larger than for the remaining fraction of laterally entraining water vapor. This study explains the low overall efficiency of a cumulus cloud (10%) by the strongly decreasing efficiency of the entrained vapor with entrainment height. In a second study, we extend this framework to test the hypothesis that global warming leads to increased precipitation efficiencies and thus to reduced detrainment of condensate aloft.