Preprints
https://doi.org/10.5194/egusphere-2025-4963
https://doi.org/10.5194/egusphere-2025-4963
07 Nov 2025
 | 07 Nov 2025
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

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

Inés Zabala, Juan Andrés Casquero-Vera, Elisabeth Andrews, Andrea Casans, Gerardo Carrillo-Cardenas, Anna Gannet Hallar, and Gloria Titos

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 ten 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: 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 enable retrospective analyses of long-term aerosol time series to investigate aerosol–cloud interactions.

Competing interests: One of the co-authors, Anna Gannet Hallar, is a member of the Editorial Board of Atmospheric Chemistry and Physics. The remaining authors declare that they have no competing interests.

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Inés Zabala, Juan Andrés Casquero-Vera, Elisabeth Andrews, Andrea Casans, Gerardo Carrillo-Cardenas, Anna Gannet Hallar, and Gloria Titos

Status: open (until 19 Dec 2025)

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Inés Zabala, Juan Andrés Casquero-Vera, Elisabeth Andrews, Andrea Casans, Gerardo Carrillo-Cardenas, Anna Gannet Hallar, and Gloria Titos
Inés Zabala, Juan Andrés Casquero-Vera, Elisabeth Andrews, Andrea Casans, Gerardo Carrillo-Cardenas, Anna Gannet Hallar, and Gloria Titos
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Latest update: 07 Nov 2025
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Short summary
This study presents a comprehensive analysis of cloud condensation nuclei (CCN) phenomenology across ten observatories in diverse environments. We evaluate CCN prediction methods based on different schemes of aerosol chemical composition and optical properties. We show that simplified methods provide first-order CCN estimates, but new approaches incorporating optical data can substantially improve CCN coverage and prediction accuracy across regions lacking direct measurements.
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