Cloud condensation nuclei concentrations derived from the CAMS reanalysis - CCN dataset introduction and evaluation

Sep 15, 2026

Cloud condensation nuclei (CCN) number concentrations underpin aerosol–cloud interactions (ACI), but global assessment remains difficult due to sparse in situ observations and challenges in satellite-based retrievals. To address this, a new CCN dataset is introduced, derived from CAMSRA aerosol mass mixing ratios within a diagnostic model using CAMSRA properties and a simplified kappa–Köhler framework. The dataset provides 3-D global CCN fields across supersaturations and species, spanning 2003–2021 with daily frequency. An evaluation with ground measurements shows improved agreement versus using AOD alone, while the CAMSRA-derived CCN is generally high biased.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages28

Reading Time1 h 11 min

AccessFree to download

Introduction

Aerosol–cloud interactions and CCN challenges

Observational constraints and satellite proxies

Why AOD is insufficient for CCN estimation

Earth Syst. Sci. Data, 16, 443–470, 2024 [https://doi.org/10.5194/essd-16-443-2024](https://doi.org/10.5194/essd-16-443-2024)[ ](https://doi.org/10.5194/essd-16-443-2024)© Author(s) 2024 . This work is distributed under the Creative Commons Attribution 4 .0 License. Cloud condensation nuclei concentrations derived from the CAMS reanalysis Karoline Block 1 , Mahnoosh Haghighatnasab 1,a, Daniel G. Partridge2 , Philip Stier3 , and Johannes Quaas 1 1Leipzig Institute for Meteorology, Faculty of Physics and Earth Sciences, University of Leipzig, Leipzig, Germany 2Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Exeter, United Kingdom 3Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, United Kingdom anow at: Deutscher Wetterdienst, Offenbach, Germany Correspondence: Karoline Block ([karoline.block@uni-leipzig.de](karoline.block@uni-leipzig.de)) Received: 15 May 2023 – Discussion started: 17 July 2023 Revised: 31 October 2023 – Accepted: 16 November 2023 – Published: 19 January 2024 Abstract. Determining number concentrations of cloud condensation nuclei (CCN) is one of the ﬁrst steps in the chain in analysis of cloud droplet formation, the direct microphysical link between aerosols and cloud droplets, and a process key for aerosol–cloud interactions (ACI) . However, due to sparse coverage of in situ measurements and difﬁculties associated with retrievals from satellites, a global exploration of their magnitude, source as well as temporal and spatial distribution cannot be easily obtained. Thus, a better representation of CCN numbers is one of the goals for quantifying ACI processes and achieving uncertainty-reduced estimates of their associated radiative forcing. Here, we introduce a new CCN dataset which is derived based on aerosol mass mixing ratios from the latest Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) in a diagnostic model that uses CAMSRA aerosol properties and a simpliﬁed kappa-Köhler framework suitable for global models. The emitted aerosols in CAMSRA are not only based on input from emission inventories using aerosol observations, they also have a strong tie to satellite-retrieved aerosol optical depth (AOD) as this is assimilated as a constraining factor in thereanalysis. Furthermore, the reanalysis interpolates for cases of poor or missing retrievals and thus allows for a full spatiotemporal quantiﬁcation of CCN numbers. The derived CCN dataset captures the general trend and spatial and temporal distribution of total CCN number concentrations and CCN from different aerosol species. A brief evaluation with ground-based in situ measurements demonstrates the improvement of the modelled CCN over the sole use of AOD as a proxy for CCN as the overall correlation coefﬁcient improved from 0.37 to 0.71 . However, we ﬁnd the modelled CCN from CAMSRAto be generally high biased and ﬁnd a particular erroneous overestimation at one heavily polluted site which emphasises the need for further validation. The CCN dataset ([https://doi.org/10.26050/WDCC/QUAERERE_CCNCAMS_v1](https://doi.org/10.26050/WDCC/QUAERERE_CCNCAMS_v1), Block, 2023), which is now freely available to users, features 3-D CCN number concentrations of global coverage for various supersaturations and aerosol species covering the years 2003–2021 with daily frequency. This dataset is one of its kind as it offers lots of opportunities to be used for evaluation in models and in ACI studies. Published by Copernicus Publications. 444 K. Block et al. : CAMS CCN 1 Introduction Processes of aerosol–cloud interactions (ACI) are still associated with large uncertainties in their contribution to climate forcing (Forster et al., 2021) . One of the reasons for this isan insufﬁcient determination of cloud condensation nuclei (CCN) number concentrations which are a key element for ACI processes (Seinfeld et al., 2016) . A globa

FAQs

Why is determining CCN number concentrations challenging globally?

Sparse in situ measurements and retrieval difficulties from satellites limit reliable global coverage, source attribution, and temporal–spatial distribution.

How was the new CCN dataset derived from CAMS reanalysis?

It uses CAMSRA aerosol mass mixing ratios in a diagnostic model that incorporates CAMSRA aerosol properties and a simplified kappa–Köhler framework for global modeling.

What does the evaluation show compared with using AOD as a proxy?

Ground-based in situ evaluation indicates improved CCN representation over AOD-only estimates, with the overall correlation increasing from 0.37 to 0.71, though a generally high bias remains.

Sep 15, 2026

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