Exploring Meteorological and Biomass Burning Aerosol Influences on Marine Stratocumulus in the Southeast Atlantic using WRF-Chem

Abstract

Biomass burning in southern Africa exists in a complex series of relationships with its adjacent and remote environments. Gas and aerosol emissions can be lofted into the free troposphere and undergo long-range transport in the prevailing easterlies. The same large-scale subsidence that drives continental fires during the dry season also promotes extensive stratocumulus clouds in the subtropical southeast Atlantic Ocean. Biomass burning aerosols often exist as layers lofted above stratocumulus; where clouds and aerosol layers intersect, the aerosols can influence cloud microphysics and radiative properties. Meteorological patterns also modulate aerosol transport and clouds, but there is ongoing debate in the literature regarding whether this ultimately makes a difference or if aerosol effects are dominant. Few efforts have been made to model this system. In this research, the regional weather-chemistry model WRF-Chem is used to simulate the interaction of biomass burning aerosols with stratocumulus clouds in the Southeast Atlantic to explore how clouds respond to aerosols and to synoptic-scale meteorological variability. At present, absorbing aerosols are excluded; their role will be explored separately in future research. Comparing simulations with biomass burning present or absent, we found increased liquid water path (LWP), reduced lower tropospheric stability(LTS), reduced low cloud, and reduced effective cloud droplet radius. Comparing strong with weak offshore flow indicative of synoptic-scale meteorological variability, we found similarly strong but opposite sign response in LWP, LTS, and low cloud amount. Intriguingly, the offshore flow pattern was associated with greater distance between aerosol and cloud layers, reducing the incidence of cloud-aerosol interactions during peak aerosol loading. We conclude that the meteorological variability driving both clouds and aerosols is an important element in this system that adds interesting complications to the interpretation of observational datasets.