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Microphysical-macrophysical interactions in marine stratocumulus

Marine stratocumulus is one of the most important low cloud systems over the remote oceans due to their poor representation in climate models, and there have been insufficient long-term observations to constrain the key processes that govern their behavior. The responses of shallow cloud systems to changes in atmospheric aerosols are major sources of uncertainty that limits the accuracy of predictions of future climate. Aerosols modify the cloud microphysics, which modulates cloud dynamics, entrainment and precipitation, and in turn regulates cloud macrophysical processes. This is referred to as the microphysical-macrophysical interactions. A better understanding of this process helps determine cloud radiative properties that impact climate.聽

This proposal mainly focuses on microphysical-macrophysical interactions of marine stratocumulus with the large scale and the mesoscale. We proposed to use large domain Large-Eddy-Simulation model (LES), observational data from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurements (ARM) program fixed Eastern North Atlantic (ENA) site, and a comprehensive radar simulator in our research. On large scales, stratocumulus is observed to weaken and eventually break up to low-coverage cumulus, during which long-range transport of continental air masses transports aerosols that can impact microphysical processes over remote ocean regions downstream. On mesoscales, stratocumulus is observed to organize in a closed-cellular (dim walls, bright centers) or pockets of open-cellular (bright walls, dim centers) structures, and the impact of drizzle is considered to be critical. Modeling work in two different topics of interest is undergoing: I) role of solar-absorbing aerosols in the transition of stratocumulus to trade cumulus and II) role of drizzle in the mesoscale organization of marine stratocumulus. The first research objective is mainly a modeling study while the second one combines all available tools (LES, ENA observations and forward modeling).