Realism of Lagrangian Large Eddy Simulations Driven by Renalysis Meteorology: Tracking a Pocket of Open Cells Under a Biomass Burning Aerosol Layer. (25th November 2021)

• Record Type: Journal Article
• Authors: Kazil, Jan; Christensen, Matthew W.; Abel, Steven J.; Yamaguchi, Takanobu; Feingold, Graham
• Is Part Of: Journal of advances in modeling earth systems. Volume 13:Number 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-25
• Subjects: Lagrangian large eddy simulations -- boundary layer clouds -- stratocumulus transitions -- mesoscale organization; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2021MS002664
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: An approach to drive Lagrangian large eddy simulation (LES) of boundary layer clouds with reanalysis data is presented and evaluated using satellite (Spinning Enhanced Visible and Infrared Imager, SEVIRI) and aircraft (Cloud‐Aerosol‐Radiation Interactions and Forcing, CLARIFY) measurements. The simulations follow trajectories of the boundary layer flow. They track the formation and evolution of a pocket of open cells (POC) underneath a biomass burning aerosol layer in the free troposphere. The simulations reproduce the evolution of observed stratocumulus cloud morphology, cloud optical depth, and cloud drop effective radius, and capture the timing of the cloud state transition from closed to open cells seen in the satellite imagery on the three considered trajectories. They reproduce a biomass burning aerosol layer identified by the in‐situ aircraft measurements above the inversion of the POC. Entrainment of aerosol from the biomass burning layer into the POC is limited to the extent of having no impact on cloud‐ or boundary layer properties, in agreement with the CLARIFY observations. The two‐moment bin microphysics scheme used in the simulations reproduces the in‐situ cloud microphysical properties reasonably well. A two‐moment bulk microphysics scheme reproduces the satellite observations in the non‐precipitating closed‐cell state, but overestimates liquid water path and cloud optical depth in the precipitating open‐cell state due to insufficient surface precipitation. A boundary layer cold and dry bias occurring in LES can be counteracted by reducing the grid aspect ratio and by tightening the large scale wind speed nudging towards the surface. Plain Language Summary: We developed and tested an approach to represent clouds with greater accuracy in computer simulations. In this approach, a global model provides meteorological input at its coarse resolution to a high resolution model. The global model is a good representation of the atmosphere at its resolution because it ingests observations. The high resolution model represents clouds on much smaller areas than a global model, but is able to represent processes that the global model cannot. The high resolution model follows clouds so that their evolution can be studied. We compare the clouds simulated by the high resolution model with satellite imagery, satellite measurements, and measurements that were taken on an aircraft. We show that the simulated clouds agree well with the observations as the clouds evolve from one cloud type to another. The high resolution model also simulates aerosol, small particles existing in air from which cloud droplets form. The simulated aerosol also agrees well with the observations. This work thus establishes that the approach we developed can realistically represent clouds and their evolution, and provides the basis for the application of the approach in scientific research. Key Points: An approach to drive Lagrangian large eddy simulation of boundary layer clouds with reanalysis data is presented and evaluated The approach realistically simulates the considered cloud state transition, based on evaluation with satellite and in‐situ data A reduced grid aspect ratio and large scale wind speed nudging time scale towards the surface counteract a boundary layer cold and dry bias