Understanding Top‐of‐Atmosphere Flux Bias in the AeroCom Phase III Models: A Clear‐Sky Perspective. (15th September 2021) - Su, Wenying; Liang, Lusheng; Myhre, Gunnar; Thorsen, Tyler J.; Loeb, Norman G.; Schuster, Gregory L.; Ginoux, Paul; Paulot, Fabien; Neubauer, David; Checa‐Garcia, Ramiro; Matsui, Hitoshi; Tsigaridis, Kostas; et al.
Abstract: 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 60 ° S– 60 ° 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 60 ° S– 60 ° 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. Plain Language Summary: Aerosol optical depths (AOD) from satellite retrievals have been used to evaluate the AeroCom models. However, these evaluations are often non‐conclusive due to uncertainties in the retrievals and the differences among many products. In this study, biases in top‐of‐atmosphere (TOA) reflected SW fluxes are linked to biases in AOD and surface albedo by using their respective radiative kernels. We found that biases in AOD and land surface albedo can explain significant amount of the SW flux biases on both global and regional scales. This study 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. This study clearly demonstrates the deficiency of land surface albedo used by the AeroCom models and usage of observation‐based land surface albedo should be required for all models participating in AeroCom experiments. Key Points: Over ocean, aerosol optical depth (AOD) bias contributes about 25% to the 60°S–60°N mean shortwave (SW) flux bias for the multi‐model mean (MMM) result Over land, AOD and land surface albedo biases contribute about 40% and 30% to the 60°S–60°N mean SW flux bias for the MMM result Observation‐based land surface albedo should be used by all models to accurately calculate the top‐of‐atmosphere (TOA) SW fluxes