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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-4963', Anonymous Referee #1, 19 Nov 2025
  • RC2: 'Comment on egusphere-2025-4963', Anonymous Referee #3, 06 Dec 2025
  • AC1: 'Response to Referee Comments', Inés Zabala, 07 Feb 2026

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Inés Zabala on behalf of the Authors (07 Feb 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (13 Feb 2026) by Imre Salma
RR by Anonymous Referee #1 (17 Feb 2026)
RR by Anonymous Referee #3 (27 Feb 2026)
ED: Publish as is (27 Feb 2026) by Imre Salma
AR by Inés Zabala on behalf of the Authors (03 Mar 2026)  Manuscript 
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Short summary
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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