Articles | Volume 20, issue 3
https://doi.org/10.5194/acp-20-1607-2020
© Author(s) 2020. 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-20-1607-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Feb 2020
Research article |
| 10 Feb 2020
| 10 Feb 2020
Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Moa K. Sporre
Department of Geosciences, University of Oslo, Oslo, Norway
now at: Department of Physics, Lund University, Lund, Sweden
David Neubauer
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, 8092, Switzerland
Harri Kokkola
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Pekka Kolmonen
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Larisa Sogacheva
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Antti Arola
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Gerrit de Leeuw
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Inger H. H. Karset
Department of Geosciences, University of Oslo, Oslo, Norway
Ari Laaksonen
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Ulrike Lohmann
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, 8092, Switzerland
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13 citations as recorded by crossref.
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- Variations in aerosols and aerosols–cloud interactions in Bangkok using MODIS satellite data during high PM2.5 concentrations O. Pilahome et al. 10.1016/j.asr.2022.12.018
- Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing L. Regayre et al. 10.5194/acp-23-8749-2023
- Nonlinearity of the cloud response postpones climate penalty of mitigating air pollution in polluted regions H. Jia & J. Quaas 10.1038/s41558-023-01775-5
- Classification of weather clusters over the Middle East associated with high atmospheric dust-AODs in West Iran K. Mohammadpour et al. 10.1016/j.atmosres.2021.105682
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- Evaluation of liquid cloud albedo susceptibility in E3SM using coupled eastern North Atlantic surface and satellite retrievals A. Varble et al. 10.5194/acp-23-13523-2023
- Constraining effects of aerosol-cloud interaction by accounting for coupling between cloud and land surface T. Su et al. 10.1126/sciadv.adl5044
- Effects of black carbon mitigation on Arctic climate T. Kühn et al. 10.5194/acp-20-5527-2020
- Assessing the Aerosols, Clouds and Their Relationship Over the Northern Bay of Bengal Using a Global Climate Model P. Sharma et al. 10.1029/2022EA002706
- Exploring Satellite-Derived Relationships between Cloud Droplet Number Concentration and Liquid Water Path Using a Large-Domain Large-Eddy Simulation S. Dipu et al. 10.16993/tellusb.27
- Comparative analysis of cloud properties over drought- and flood-prone regions of western India using machine learning techniques N. Mevada & R. Srivastava 10.2166/wcc.2024.511
- Dual-field-of-view high-spectral-resolution lidar: Simultaneous profiling of aerosol and water cloud to study aerosol–cloud interaction N. Wang et al. 10.1073/pnas.2110756119
13 citations as recorded by crossref.
- Impact of Holuhraun volcano aerosols on clouds in cloud-system-resolving simulations M. Haghighatnasab et al. 10.5194/acp-22-8457-2022
- Variations in aerosols and aerosols–cloud interactions in Bangkok using MODIS satellite data during high PM2.5 concentrations O. Pilahome et al. 10.1016/j.asr.2022.12.018
- Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing L. Regayre et al. 10.5194/acp-23-8749-2023
- Nonlinearity of the cloud response postpones climate penalty of mitigating air pollution in polluted regions H. Jia & J. Quaas 10.1038/s41558-023-01775-5
- Classification of weather clusters over the Middle East associated with high atmospheric dust-AODs in West Iran K. Mohammadpour et al. 10.1016/j.atmosres.2021.105682
- Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right? S. Fiddes et al. 10.5194/acp-22-14603-2022
- Evaluation of liquid cloud albedo susceptibility in E3SM using coupled eastern North Atlantic surface and satellite retrievals A. Varble et al. 10.5194/acp-23-13523-2023
- Constraining effects of aerosol-cloud interaction by accounting for coupling between cloud and land surface T. Su et al. 10.1126/sciadv.adl5044
- Effects of black carbon mitigation on Arctic climate T. Kühn et al. 10.5194/acp-20-5527-2020
- Assessing the Aerosols, Clouds and Their Relationship Over the Northern Bay of Bengal Using a Global Climate Model P. Sharma et al. 10.1029/2022EA002706
- Exploring Satellite-Derived Relationships between Cloud Droplet Number Concentration and Liquid Water Path Using a Large-Domain Large-Eddy Simulation S. Dipu et al. 10.16993/tellusb.27
- Comparative analysis of cloud properties over drought- and flood-prone regions of western India using machine learning techniques N. Mevada & R. Srivastava 10.2166/wcc.2024.511
- Dual-field-of-view high-spectral-resolution lidar: Simultaneous profiling of aerosol and water cloud to study aerosol–cloud interaction N. Wang et al. 10.1073/pnas.2110756119
Latest update: 20 Nov 2024
Short summary
The understanding of cloud processes is based on the quality of the representation of cloud properties. We compared cloud parameters from three models with satellite observations. We report on the performance of each data source, highlighting strengths and deficiencies, which should be considered when deriving the effect of aerosols on cloud properties.
The understanding of cloud processes is based on the quality of the representation of cloud...
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