Articles | Volume 23, issue 18
https://doi.org/10.5194/acp-23-10775-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-10775-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sensitivities of cloud radiative effects to large-scale meteorology and aerosols from global observations
Hendrik Andersen
CORRESPONDING AUTHOR
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Jan Cermak
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Alyson Douglas
Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, UK
Timothy A. Myers
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, USA
Physical Science Laboratory, National Oceanic and Atmospheric Administration, Boulder, USA
Peer Nowack
Institute of Theoretical Informatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Philip Stier
Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, UK
Casey J. Wall
Department of Geosciences, University of Oslo, Oslo, Norway
Sarah Wilson Kemsley
Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, UK
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Total article views: 3,305 (including HTML, PDF, and XML)
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Total article views: 596 (including HTML, PDF, and XML)
Thereof 595 with geography defined
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- Microphysical fingerprints in anvil cloud albedo D. Finney et al. 10.5194/acp-25-10907-2025
- Analysis of ship emission effects on clouds over the southeastern Atlantic using geostationary satellite observations N. Benas et al. 10.5194/acp-25-6957-2025
- A systematic evaluation of high-cloud controlling factors S. Wilson Kemsley et al. 10.5194/acp-24-8295-2024
- Modeling of Secondary Air Pollutants in East Asia: Recent Trends and Future Challenges S. Kim et al. 10.5572/KOSAE.2025.41.3.403
- On the characterization of Cloud occurrence and its impact on solar radiation in Mbour, Senegal M. Dramé et al. 10.1016/j.jastp.2024.106284
- Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties F. Bender et al. 10.16993/tellusb.1868
- Analysis of the cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning Y. Jia et al. 10.5194/acp-24-13025-2024
- Multi-sensor analysis of low-level cloudiness and its controlling factors over the Indian Ocean H. Kumar & S. Tiwari 10.1016/j.asr.2025.06.024
9 citations as recorded by crossref.
- Radiative forcing from the 2020 shipping fuel regulation is large but hard to detect J. Zhang et al. 10.1038/s43247-024-01911-9
- Microphysical fingerprints in anvil cloud albedo D. Finney et al. 10.5194/acp-25-10907-2025
- Analysis of ship emission effects on clouds over the southeastern Atlantic using geostationary satellite observations N. Benas et al. 10.5194/acp-25-6957-2025
- A systematic evaluation of high-cloud controlling factors S. Wilson Kemsley et al. 10.5194/acp-24-8295-2024
- Modeling of Secondary Air Pollutants in East Asia: Recent Trends and Future Challenges S. Kim et al. 10.5572/KOSAE.2025.41.3.403
- On the characterization of Cloud occurrence and its impact on solar radiation in Mbour, Senegal M. Dramé et al. 10.1016/j.jastp.2024.106284
- Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties F. Bender et al. 10.16993/tellusb.1868
- Analysis of the cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning Y. Jia et al. 10.5194/acp-24-13025-2024
- Multi-sensor analysis of low-level cloudiness and its controlling factors over the Indian Ocean H. Kumar & S. Tiwari 10.1016/j.asr.2025.06.024
Latest update: 29 Oct 2025
Short summary
This study uses an observation-based cloud-controlling factor framework to study near-global sensitivities of cloud radiative effects to a large number of meteorological and aerosol controls. We present near-global sensitivity patterns to selected thermodynamic, dynamic, and aerosol factors and discuss the physical mechanisms underlying the derived sensitivities. Our study hopes to guide future analyses aimed at constraining cloud feedbacks and aerosol–cloud interactions.
This study uses an observation-based cloud-controlling factor framework to study near-global...
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