Preprints
https://doi.org/10.5194/egusphere-2026-1404
https://doi.org/10.5194/egusphere-2026-1404
30 Mar 2026
 | 30 Mar 2026
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Evaluation and improvement of CAMS-derived CCN number concentrations using in-situ measurements

Yannick Emanuel Anders, Karoline Block, Mira Pöhlker, and Johannes Quaas

Abstract. Cloud condensation nuclei (CCN) are essential components of aerosol-cloud interactions (ACI). Thus, a precise knowledge about their number concentrations (Nccn) is crucial for climate models and ACI studies. This study presents a comprehensive evaluation of the recently published CAMS-derived total Nccn using direct observations from 25 ground-based sites. The analysis specifically focuses on the temporal variability, the applicability of CAMS-derived Nccn across different environments and pollution regimes and in particular, the sensitivity of CCN to supersaturation. For the latter aspect, a bias shift is identified in simulated Nccn that correlates to the ratio of the two dominant CCN species, likely reflecting assumptions in the underlying size distributions and/or emissions fractions. To address this issue, we developed an observation-based parametrization that is applied to CAMS-derived total Nccn without modifying aerosol size distributions or species concentrations. This approach substantially reduces biases leading the way to an improved version of CAMS-derived Nccn.

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Yannick Emanuel Anders, Karoline Block, Mira Pöhlker, and Johannes Quaas

Status: open (until 25 May 2026)

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Yannick Emanuel Anders, Karoline Block, Mira Pöhlker, and Johannes Quaas
Yannick Emanuel Anders, Karoline Block, Mira Pöhlker, and Johannes Quaas

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Short summary
Particles in the atmosphere can trigger the formation of cloud droplets, affecting cloud properties and climate. This study evaluates a new global dataset of these particles with measurements from 25 sites around the world. The variability in time and space and their conditional formation behaviour is analysed. The authors identify systematic biases and introduce a simple correction based on observations that greatly improves the dataset’s accuracy.
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