Quantitative assessment of supercooled liquid water sensitivity to different aerosol field inputs over the Sichuan Basin

Abstract

Aerosol-cloud interactions profoundly influence the formation and evolution of supercooled liquid water, a key factor in in-flight icing. However, accurately quantifying aerosol emission inventories and their spatiotemporal distributions, as well as simulating supercooled liquid water and predicting in-flight icing, remains a major challenge, particularly in high-aerosol environments such as the Sichuan Basin in China. In this study, the Thompson–Eidhammer aerosol-aware microphysics scheme is applied to a high-aerosol icing event to quantitatively assess the sensitivity of supercooled liquid water properties to different aerosol inputs. Three aerosol configurations are examined: the scheme's default settings representing clean conditions; climatological aerosol values representing polluted conditions; and aerosol fields from the Copernicus Atmosphere Monitoring Service (CAMS), representing near-real-time polluted conditions. The CAMS aerosol mass concentrations are converted to number concentrations using typical densities and size parameters of major East Asian aerosol species. All simulations reproduce the synoptic-scale supercooled liquid water and temperature distribution when compared with ERA5. Relative to clean conditions, polluted-environment simulations produce higher supercooled liquid water content, larger cloud droplet number concentrations, smaller median volume diameters, and longer cloud lifetimes. The experiments also reveal that stronger auto-conversion in clean conditions suppresses supercooled liquid water formation, whereas enhanced riming in polluted environments promotes supercooled liquid water depletion. In situ aircraft observations further indicate that the CAMS-driven experiment performs best in capturing the high supercooled liquid water content and large median volume diameters. These findings underscore the importance of near-real-time aerosol inputs for improving simulations of aerosol-cloud interactions and predicting aircraft-icing environments.