Collaborative Research: Understanding the Drivers of Cloud and Precipitation Variability via Ensembles of Large-eddy Simulations and Detailed Cloud Observations

Stratocumulus clouds are ubiquitous over large areas of Earth's oceans. Despite their relatively consistent structure compared to other cloud structures, like thunderstorm complexes, there is significant small-scale spatial variability in stratocumulus clouds that impacts further cloud and precipitation development. This project will use an emerging modeling technique and existing cloud observations to identify the sources of spatial variability at the cloud microphysics level. The research team will then identify the best methods to simulate these sources of variability. The broader societal impact of the project would be to improve modeling of clouds which affect Earth’s radiation balance. There is also a substantial educational aspect to the project, enhancing the training of the next generation of atmospheric scientists. The primary objectives of this project are to understand the microphysical sources of spatial variability in low-level stratocumulus clouds, how this spatial variability influences the temporal evolution of the mean cloud and precipitation processes, and how the design of microphysics parameterizations influences the ability to simulate the observed spatial structure of these cloud and precipitation fields. The research team will run Large Eddy Simulations (LES) of drizzling stratocumulus clouds using Cloud Model 1 (CM1) based on observed cases with Lagrangian microphysics. Lagrangian particle-based methods employ particles that are representative of a multiplicity of millions to billions of identical hydrometeors, which get around problems introduced in traditional bulk and bin microphysics schemes. The project will focus on four primary potential sources of microphysical variability resulting in rain: condensation, stochastic collisions, turbulence-enhanced collision kernels, and giant aerosol particles. The results of the simulations and comparison with observations will then be used to train the Bayesian Observationally-constrained Statistical-physical Scheme (BOSS), with the introduction of a stochastic component. Simulations will be run in a purely non-stochastic mode and a fully stochastic mode to address the hypothesis that the non-stochastic BOSS will not be able to simulate the spatial variability whereas the stochastic BOSS will. Further simulations will address the importance of the identified microphysical variability as well as whether BOSS can be run in a single-category mode with increased predictability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.