Articles | Volume 26, issue 5
https://doi.org/10.5194/acp-26-3697-2026
https://doi.org/10.5194/acp-26-3697-2026
Research article
 | 
16 Mar 2026
Research article |  | 16 Mar 2026

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

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Cited articles

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This study presents a comprehensive analysis of cloud condensation nuclei (CCN) phenomenology across nine observatories in diverse environments. We evaluate CCN prediction methods based on aerosol chemical composition and optical properties, including empirical and machine learning approaches. While simplified chemical schemes provide first-order estimates, incorporating optical data substantially improves CCN prediction accuracy in regions without direct measurements.
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