Articles | Volume 21, issue 12
https://doi.org/10.5194/acp-21-9809-2021
© Author(s) 2021. 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-21-9809-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jun 2021
Research article |
| 30 Jun 2021
| 30 Jun 2021
Evaluation of the CMIP6 marine subtropical stratocumulus cloud albedo and its controlling factors
Bida Jian
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou,
Gansu, China
Jiming Li
CORRESPONDING AUTHOR
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou,
Gansu, China
Guoyin Wang
Department of Atmospheric and Oceanic Sciences & Institute of
Atmospheric Sciences, Fudan University, Shanghai, China
Yuxin Zhao
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou,
Gansu, China
Yarong Li
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou,
Gansu, China
Jing Wang
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou,
Gansu, China
Min Zhang
Inner Mongolia Institute of Meteorological Sciences, Hohhot, Inner
Mongolia, China
Jianping Huang
Key Laboratory for Semi-Arid Climate Change of the Ministry of
Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou,
Gansu, China
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Short summary
We evaluate the performance of the AMIP6 model in simulating cloud albedo over marine subtropical regions and the impacts of different aerosol types and meteorological factors on the cloud albedo based on multiple satellite datasets and reanalysis data. The results show that AMIP6 demonstrates moderate improvement over AMIP5 in simulating the monthly variation in cloud albedo, and changes in different aerosol types and meteorological factors can explain ~65 % of the changes in the cloud albedo.
We evaluate the performance of the AMIP6 model in simulating cloud albedo over marine...
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