Preprints
https://doi.org/10.5194/egusphere-2025-2559
https://doi.org/10.5194/egusphere-2025-2559
01 Jul 2025
 | 01 Jul 2025

A process-evaluation of the impact of precipitation on aerosol particle number size distributions in three Earth System Models

Sara M. Blichner, Theodore Khadir, Sini Talvinen, Paulo Artaxo, Liine Heikkinen, Harri Kokkola, Radovan Krejci, Muhammed Irfan, Twan van Noije, Tuukka Petäjä, Christopher Pöhlker, Øyvind Seland, Carl Svenhag, Antti Vartiainen, and Ilona Riipinen

Abstract. Accurately modeling the cloud condensation nuclei (CCN) budget is a key factor in reducing uncertainty in aerosol–cloud interactions in Earth system models. Wet deposition—the removal of particles by precipitation—is a major CCN sink, but rainfall can also trigger a replenishment phase via the formation and growth of new particles, partially offsetting losses.  In this study, we evaluate how three general circulation models represent the size- and time-resolved effects of precipitation on the particle number size distribution (PNSD) and the CCN budget. The evaluation is based on correlations between the PNSD and the precipitation rates along back trajectories from three long-term measurement stations. To better isolate the role of precipitation from confounding factors, we also apply a Machine Learning approach (XGBoost), training a separate regression model for each site and data source using a minimal set of physically relevant predictors.

Our results show that at the two high-latitude stations, the models underestimate CCN replenishment following precipitation, with too weak new particle formation and growth. At ATTO, in contrast, two of the models overestimate this effect, simulating an immediate CCN source after rainfall. Observations also suggest that CCN removal is weaker during colder conditions, a pattern that models struggle to capture—either overestimating or underestimating the precipitation effect, depending on the model. The XGBoost analysis confirms the key findings of the correlation analysis while helping to correct for likely confounding influences, showing promise for disentangling spurious correlations and controlling for unrelated factors in model evaluation.

Competing interests: Some authors are members of the editorial board of ACP.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Sara M. Blichner, Theodore Khadir, Sini Talvinen, Paulo Artaxo, Liine Heikkinen, Harri Kokkola, Radovan Krejci, Muhammed Irfan, Twan van Noije, Tuukka Petäjä, Christopher Pöhlker, Øyvind Seland, Carl Svenhag, Antti Vartiainen, and Ilona Riipinen

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-2559', Ruth Price, 21 Jul 2025
  • RC2: 'Comment on egusphere-2025-2559', Joseph Carton-Kelly & Anthony Jones (co-review team), 01 Aug 2025
Sara M. Blichner, Theodore Khadir, Sini Talvinen, Paulo Artaxo, Liine Heikkinen, Harri Kokkola, Radovan Krejci, Muhammed Irfan, Twan van Noije, Tuukka Petäjä, Christopher Pöhlker, Øyvind Seland, Carl Svenhag, Antti Vartiainen, and Ilona Riipinen
Sara M. Blichner, Theodore Khadir, Sini Talvinen, Paulo Artaxo, Liine Heikkinen, Harri Kokkola, Radovan Krejci, Muhammed Irfan, Twan van Noije, Tuukka Petäjä, Christopher Pöhlker, Øyvind Seland, Carl Svenhag, Antti Vartiainen, and Ilona Riipinen

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Short summary
This study looks at how well climate models capture the impact of rain on particles that help form cloud droplets. Using data from three measurement stations and applying both a correlation analysis and a machine learning approach, we found that models often miss how new particles form after rain and struggle in cold environments. This matters because these particles influence cloud formation and climate.
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