Cloud condensation nuclei phenomenology: predictions based on aerosol chemical and optical properties

Abstract. This study presents a comprehensive phenomenological analysis of cloud condensation nuclei (CCN) and aerosol properties – including activation properties, microphysical characteristics, chemical composition, and optical properties – across nine surface sites in different environments. Aerosol properties vary widely, reflecting the diverse environments, and controlling the CCN activation characteristics. Despite their critical role in aerosol–cloud interactions, CCN observations remain sparse and unevenly distributed, limiting global assessments of activation behavior. To address this gap, this study presents CCN predictive methods based on chemical composition combined with particle number size distribution (PNSD) data, and aerosol optical properties (AOPs). The chemical composition driven predictions are tested using three hygroscopicity schemes. All schemes overpredict the CCN concentrations (median relative bias; MRB = 13 %–15 %), although the two composition-derived CCN concentrations are markedly better predictors than the fixed-κchem assumption (MRB = 24 %). The AOPs-derived CCN prediction is based on two approaches: first, an extended empirical parameterization of Shen et al. (2019) (hereafter S2019) to 13 stations, which reduces bias from −27 % to −8 % and improves CCN agreement; and second, a random forest model that infers Twomey activation parameters (C and k) using both the S2019 variables and all the available AOPs. Including all AOPs reduces MRB from 19 % to 15 % and highlights the role of absorption in predicting CCN activation. These findings demonstrate that both chemical and optical measurements can provide a reasonable estimate of CCN concentrations when direct measurements are unavailable. These results will enable retrospective analyses of long-term aerosol time series to investigate aerosol–cloud interactions.