期刊
ATMOSPHERIC RESEARCH
卷 291, 期 -, 页码 -出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.atmosres.2023.106820
关键词
Central Africa; Cloud radiative effect; CMIP6; CERES
This study investigates the seasonal variability of cloud radiative effects (CREs) over Congo Basin using 15-year observations from CERES and MODIS instruments. The study finds a net cloud cooling effect at the top of the atmosphere and at the surface, leading to a net warming effect in the atmosphere. The relationships between CREs and cloud properties are examined, and the evaluation of CMIP6 models is performed.
This study investigates the seasonal variability of the cloud radiative effects (CREs) over Congo Basin (CB) using 15-year observations from Clouds and the Earth's Radiant Energy System (CERES) Energy Budget and Filled (EBAF) Ed4.1 level 3b dataset involving CERES and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board Terra and Aqua satellites. The relationships between CREs and cloud properties such as total cloud fraction (TCF), cloud top height (CTH), cloud top temperature (CTT) and cloud optical thickness (COT) are checked. An evaluation of Coupled Model Intercomparison Project (CMIP) Phase 6 in capturing the seasonal cycle of CREs as well as the magnitudes of the CREs along the seasonal cycle is also performed. This study shows a net cloud cooling effect of -8.4 W/m2 and - 43.9 W/m2 respectively at the top of the atmosphere (TOA) and at the surface, leading to a net warming effect of 35.67 W/m2 in the atmosphere. This value implies a large energy source over the Central Africa (CA) atmospheric column. The associated relationships between CREs and cloud properties show that the shortwave CRE is more sensitive to TCF and optical thickness whereas its longwave counterparts is more sensitive to CTH, CTT and COT at the TOA and in the atmosphere. All of the four CMIP6 models used in this study can capture the spatial pattern of CREs as well as their seasonal cycle but misrepresent intensity of CREs. Results also show that a better-simulated TCF considerably reduces the intensity of the annual mean underestimation in both longwave and shortwave CRE for some CMIP6 models, but not for models with overestimated shortwave CRE.
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