Cloud condensation nuclei characteristics at the Southern Great Plains site: role of particle size distribution and aerosol hygroscopicity

Abstract

Abstract The activation ability of aerosols as cloud condensation nuclei (CCN) is crucial in climate and hydrological cycle studies, but their properties are not well known. We investigated the long-term measurements of atmospheric aerosol properties, CCN concentrations (NCCN) at supersaturation (SS = 0.1%–1.0%), and hygroscopicity at the Department of Energy’s Southern Great Plains (SGP) site to illustrate the dependence of NCCN on aerosol properties and transport pathways. Cluster analysis was applied to the back trajectories of air masses to investigate their respective source regions. The results showed that aged biomass burning aerosols from Central America were characterized by higher accumulation mode particles (Naccu; median value 805 cm−3) and relatively high aerosol hygroscopicity (κ; median value ∼0.25) values that result in the higher CCN activation and relatively high NCCN (median value 258–1578 cm−3 at a SS of 0.1%–1.0%). Aerosols from the Gulf of Mexico were characterized by higher Naccu (∼35%), and NCCN (230–1721 cm−3 at a SS of 0.1%–1.0%) with the lowest κ (∼0.17). In contrast, relatively high nucleation mode particles (Nnucl; ∼20%) and low NCCN (128–1553 cm−3 at a SS of 0.1%–1.0%) with higher κ (∼0.30) values were observed on the aerosols associated with a westerly wind. The results indicate particle size as the most critical factor influencing the ability of aerosols to activate, whereas the effect of chemical composition was secondary. Our CCN closure analysis suggests that chemical composition and mixing state information are more crucial at lower SS, whereas at higher SS, most particles become activated regardless of their chemical composition and size. This study affirms that soluble organic fraction information is required at higher SS for better NCCN prediction, but both the soluble organics fraction and mixing state are vital to reduce the NCCN prediction uncertainty at lower SS.