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Understanding Top-of-Atmosphere Flux Bias in the AeroCom Phase III Models: A Clear-Sky Perspective
Journal of Advances in Modeling Earth Systems ( IF 4.4 ) Pub Date : 2021-08-25 , DOI: 10.1029/2021ms002584 Wenying Su 1 , Lusheng Liang 2 , Gunnar Myhre 3 , Tyler J. Thorsen 1 , Norman G. Loeb 1 , Gregory L. Schuster 1 , Paul Ginoux 4 , Fabien Paulot 4 , David Neubauer 5 , Ramiro Checa‐Garcia 6 , Hitoshi Matsui 7 , Kostas Tsigaridis 8, 9 , Ragnhild B. Skeie 3 , Toshihiko Takemura 10 , Susanne E. Bauer 9 , Michael Schulz 11
Journal of Advances in Modeling Earth Systems ( IF 4.4 ) Pub Date : 2021-08-25 , DOI: 10.1029/2021ms002584 Wenying Su 1 , Lusheng Liang 2 , Gunnar Myhre 3 , Tyler J. Thorsen 1 , Norman G. Loeb 1 , Gregory L. Schuster 1 , Paul Ginoux 4 , Fabien Paulot 4 , David Neubauer 5 , Ramiro Checa‐Garcia 6 , Hitoshi Matsui 7 , Kostas Tsigaridis 8, 9 , Ragnhild B. Skeie 3 , Toshihiko Takemura 10 , Susanne E. Bauer 9 , Michael Schulz 11
Affiliation
Biases in aerosol optical depths (AOD) and land surface albedos in the AeroCom models are manifested in the top-of-atmosphere (TOA) clear-sky reflected shortwave (SW) fluxes. Biases in the SW fluxes from AeroCom models are quantitatively related to biases in AOD and land surface albedo by using their radiative kernels. Over ocean, AOD contributes about 25% to the 
S–
N mean SW flux bias for the multi-model mean (MMM) result. Over land, AOD and land surface albedo contribute about 40% and 30%, respectively, to the 
S–
N mean SW flux bias for the MMM result. Furthermore, the spatial patterns of the SW flux biases derived from the radiative kernels are very similar to those between models and CERES observation, with the correlation coefficient of 0.6 over ocean and 0.76 over land for MMM using data of 2010. Satellite data used in this evaluation are derived independently from each other, consistencies in their bias patterns when compared with model simulations suggest that these patterns are robust. This highlights the importance of evaluating related variables in a synergistic manner to provide an unambiguous assessment of the models, as results from single parameter assessments are often confounded by measurement uncertainty. Model biases in land surface albedos can and must be corrected to accurately calculate TOA flux. We also compare the AOD trend from three models with the observation-based counterpart. These models reproduce all notable trends in AOD except the decreasing trend over eastern China and the adjacent oceanic regions due to limitations in the emission data set.
更新日期：2021-09-15
S–N mean SW flux bias for the multi-model mean (MMM) result. Over land, AOD and land surface albedo contribute about 40% and 30%, respectively, to the S–N mean SW flux bias for the MMM result. Furthermore, the spatial patterns of the SW flux biases derived from the radiative kernels are very similar to those between models and CERES observation, with the correlation coefficient of 0.6 over ocean and 0.76 over land for MMM using data of 2010. Satellite data used in this evaluation are derived independently from each other, consistencies in their bias patterns when compared with model simulations suggest that these patterns are robust. This highlights the importance of evaluating related variables in a synergistic manner to provide an unambiguous assessment of the models, as results from single parameter assessments are often confounded by measurement uncertainty. Model biases in land surface albedos can and must be corrected to accurately calculate TOA flux. We also compare the AOD trend from three models with the observation-based counterpart. These models reproduce all notable trends in AOD except the decreasing trend over eastern China and the adjacent oceanic regions due to limitations in the emission data set.
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