Evaluation and improvement of CAMS-derived CCN number concentrations using in-situ measurements
- 1Leipzig University (Leipzig, Germany)
Abstract
Cloud condensation nuclei (CCN) are key 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 evaluation focuses on the representation of temporal variability, the applicability of CAMS-derived Nccn across different environments and pollution regimes and in particular, the sensitivity of CCN to supersaturation. At a mid-range supersaturation of s =0.4 % the CAMS-derived Nccn reproduces monthly and seasonal variability well, with monthly median Pearson correlation coefficients ranging from +0.558 to +0.911, while the representation of day-to-day variability is considerably weaker, with significant daily correlations ranging from +0.121 to +0.818. No systematic dependence of model performance on environment or pollution regime is found for this level of supersaturation. The dataset therefore appears well suited for climatological applications and studies focusing on variability beyond synoptic timescales. The analysis further reveals a bias shift in simulated Nccn with increasing supersaturation. This feature 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 parameterization using a modified power law with an s -dependent k spectrum that modifies the supersaturation dependence of total Nccn without altering aerosol size distributions or species concentrations. This is applied to modeled Nccn which are grouped by environment and pollution regimes yielding different levels of best fitting supersaturations. Overall, this correction substantially reduces the median bias by 30.1 % at 0.1 % supersaturation and by 43.3 % at 1.0 % supersaturation, improving agreement of the modeled Nccn with observations at most sites. These results further enhance the applicability of the dataset for global aerosol–cloud interaction studies, leading the way to an improved version of CAMS-derived Nccn.
Versions
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BibTeX
@article{anders2026evaluation,
title = {{Evaluation and improvement of CAMS-derived CCN number concentrations using in-situ measurements}},
author = {Yannick Emanuel Anders and Karoline Block and Mira Pöhlker and Johannes Quaas},
journal = {Geoscientific Model Development},
year = {2026},
month = nov,
doi = {10.5194/gmd-19-10571-2026},
url = {https://gmd.copernicus.org/articles/19/10571/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Evaluation and improvement of CAMS-derived CCN number concentrations using in-situ measurements
AU - Yannick Emanuel Anders
AU - Karoline Block
AU - Mira Pöhlker
AU - Johannes Quaas
PY - 2026
DA - 2026/11/09
JO - Geoscientific Model Development
DO - 10.5194/gmd-19-10571-2026
UR - https://gmd.copernicus.org/articles/19/10571/2026/
AB - Cloud condensation nuclei (CCN) are key 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 evaluation focuses on the representation of temporal variability, the applicability of CAMS-derived Nccn across different environments and pollution regimes and in particular, the sensitivity of CCN to supersaturation. At a mid-range supersaturation of s =0.4 % the CAMS-derived Nccn reproduces monthly and seasonal variability well, with monthly median Pearson correlation coefficients ranging from +0.558 to +0.911, while the representation of day-to-day variability is considerably weaker, with significant daily correlations ranging from +0.121 to +0.818. No systematic dependence of model performance on environment or pollution regime is found for this level of supersaturation. The dataset therefore appears well suited for climatological applications and studies focusing on variability beyond synoptic timescales. The analysis further reveals a bias shift in simulated Nccn with increasing supersaturation. This feature 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 parameterization using a modified power law with an s -dependent k spectrum that modifies the supersaturation dependence of total Nccn without altering aerosol size distributions or species concentrations. This is applied to modeled Nccn which are grouped by environment and pollution regimes yielding different levels of best fitting supersaturations. Overall, this correction substantially reduces the median bias by 30.1 % at 0.1 % supersaturation and by 43.3 % at 1.0 % supersaturation, improving agreement of the modeled Nccn with observations at most sites. These results further enhance the applicability of the dataset for global aerosol–cloud interaction studies, leading the way to an improved version of CAMS-derived Nccn.
SN - 1991-9603
ER -