Pristine oceans are a significant source of uncertainty in quantifying global cloud condensation nuclei

Choudhury, Goutam; Block, Karoline; Haghighatnasab, Mahnoosh; Quaas, Johannes; Goren, Tom; Tesche, Matthias

doi:https://doi.org/10.5194/acp-25-3841-2025

Articles | Volume 25, issue 6

https://doi.org/10.5194/acp-25-3841-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-25-3841-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 6

Research article

| Highlight paper

|

02 Apr 2025

Research article | Highlight paper |

| 02 Apr 2025

Pristine oceans are a significant source of uncertainty in quantifying global cloud condensation nuclei

Goutam Choudhury, Karoline Block, Mahnoosh Haghighatnasab, Johannes Quaas, Tom Goren, and Matthias Tesche

Supplement

https://doi.org/10.5194/acp-25-3841-2025-supplement

Data sets

CALIPSO Lidar Level 2 Aerosol Profile, V4-20 NASA/LARC/SD/ASDC https://doi.org/10.5067/CALIOP/CALIPSO/LID_L2_05KMAPRO-STANDARD-V4-20

Global multiyear 3D dataset of cloud condensation nuclei derived from spaceborne lidar measurements G. Choudhury and M. Tesche https://doi.org/10.1594/PANGAEA.956215

CAMS global reanalysis (EAC4) monthly averaged fields A. Inness et al. https://ads.atmosphere.copernicus.eu/datasets/cams-global-reanalysis-eac4-monthly?tab=overview

Cloud condensation nuclei (CCN) numbers derived from CAMS reanalysis EAC4 (Version 1) K. Block https://doi.org/10.26050/WDCC/QUAERERE\_CCNCAMS\_v1

Cloud droplet number concentration, calculated from the MODIS (Moderate resolution imaging spectroradiometer) cloud optical properties retrieval and gridded using different sampling strategies E. Gryspeerdt et al. https://doi.org/10.5285/864a46cc65054008857ee5bb772a2a2b

MODIS Atmosphere L3 Monthly Product, NASA MODIS Adaptive Processing System S. Platnick et al. https://doi.org/10.5067/MODIS/MYD08_M3.061

CERES SYN1deg Level 3 Data Product, Version 4.1. NASA Langley Research Center https://doi.org/10.5067/Terra+Aqua/CERES/SYN1deg_L3.004A

Precipitation monthly and daily gridded data from 1979 to present derived from satellite measurement Copernicus Climate Change Service https://doi.org/10.24381/cds.c14d9324

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Executive editor

Measurement datasets of cloud condensation nuclei (CCN) are vital for our understanding of aerosol-cloud interaction and reliable climate modelling. This study analyses and compares the only two global CCN datasets derived from satellite and reanalysis data. These key datasets are found to disagree over pristine oceans in terms of their climatology as well as seasonal and annual variations. Given the importance of CCN as a fundamental property in climate model simulations, further research is needed to reconcile these differences and to produce an observation-based dataset that can be confidently used to evaluate our understanding.