Compensating Errors in Cloud Radiative and Physical Properties over the Southern Ocean in the CMIP6 Climate Models

The Southern Ocean is covered by a large amount of clouds with high cloud albedo.However,as reported by previous climate model intercomparison projects,underestimated cloudiness and overestimated absorption of solar radiation(ASR)over the Southern Ocean lead to substantial biases in climate sensitivity.The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models.We employ 10-year satellite observations to evaluate cloud radiative effect(CRE)and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud,radiation,and aerosol.The simulated longwave,shortwave,and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations.Total cloud fraction(CF)is also reasonably simulated in CMIP6,but the comparison of liquid cloud fraction(LCF)reveals marked biases in spatial pattern and seasonal variations.The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macroand micro-physical properties,including liquid water path(LWP),cloud optical depth(COD),and cloud effective radius,as well as aerosol optical depth(AOD).However,the large underestimation of both LWP and cloud effective radius(regional means~20%and 11%,respectively)results in relatively smaller bias in COD,and the impacts of the biases in COD and LCF also cancel out with each other,leaving CRE and ASR reasonably predicted in CMIP6.An error estimation framework is employed,and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE.Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations,while the modeled CRE is too sensitive to LWP and COD.The relationships between cloud effective radius,LWP,and COD are also analyzed to explore the possible uncertainty sources in different models.Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection.