Articles | Volume 16, issue 23
https://doi.org/10.5194/acp-16-15413-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-16-15413-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
13 Dec 2016
Research article |
| 13 Dec 2016
The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals
Takuro Michibata
CORRESPONDING AUTHOR
Department of Earth System Science and Technology, Kyushu University, Fukuoka, Japan
Research Institute for Applied Mechanics, Kyushu University, Fukuoka, Japan
Kentaroh Suzuki
Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba, Japan
Yousuke Sato
RIKEN Advanced Institute for Computational Science, Hyogo, Japan
Toshihiko Takemura
Research Institute for Applied Mechanics, Kyushu University, Fukuoka, Japan
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- Quantifying cloud adjustments and the radiative forcing due to aerosol–cloud interactions in satellite observations of warm marine clouds A. Douglas & T. L'Ecuyer 10.5194/acp-20-6225-2020
- Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds D. Rosenfeld et al. 10.1126/science.aav0566
- Aerosol effects on clouds are concealed by natural cloud heterogeneity and satellite retrieval errors A. Arola et al. 10.1038/s41467-022-34948-5
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- Interpretation of Aerosol Effects on Precipitation Susceptibility in Warm Clouds Inferred from Satellite Measurements and Model Evaluation over Northeast Asia S. Park & C. Kim 10.1175/JAS-D-20-0293.1
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- Too Frequent and Too Light Arctic Snowfall With Incorrect Precipitation Phase Partitioning in the MIROC6 GCM Y. Imura & T. Michibata 10.1029/2022MS003046
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Discussed (final revised paper)
Latest update: 24 Apr 2025
Short summary
This study identifies a fundamental flaw of a GCM in aerosol–cloud–precipitation interactions. The model predicts a monotonic increase in the LWP in response to increased aerosols, which is in stark contrast to satellite retrievals that show a regional variation in the sign of the LWP response. The model also fails to represent the observed dependency of the LWP response on macrophysical regimes. The model biases are attributed to the autoconversion process, with a lack of buffering mechanisms.
This study identifies a fundamental flaw of a GCM in aerosol–cloud–precipitation...
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