This analysis of the multi-model aerosol optical depth (AOD) in eastern China using the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) datasets shows that the global models underestimate the ...This analysis of the multi-model aerosol optical depth (AOD) in eastern China using the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) datasets shows that the global models underestimate the AOD by 33% and 44% in southern and northern China, respectively, and decrease the relative humidity (RH) of the air in the surface layer to 71%–80%, which is less than the RH of 77%–92% in reanalysis meteorological datasets. This indicates that the low biases in the RH partially account for the errors in the AOD. The AOD is recalculated based on the model aerosol concentrations and the reanalysis humidity data. Improving the mean value of the RH increases the multi-model annual mean AOD by 45% in southern China and by 33% in June–August in northern China. This method of improving the AOD is successful in most of the ACCMIP models, but it is unlikely to be successful in GISS-E2-R, in which the plot of its AOD efficiency against RH strongly deviates from the rest of the models. The effect of the improvement in the modeled RH on the AOD depends on the concentration of aerosols. The shape error in the frequency distribution of the RH is likely to be more important than the error in the mean value of the RH, but this requires further research.展开更多
基金jointly supported by the National Key Research and Development Program of China [grant number2016YFE0201400]the Basic Research Program of the State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry,Institute of Atmospheric Physics,Chinese Academy of Sciences [grant number 7-082999]
文摘This analysis of the multi-model aerosol optical depth (AOD) in eastern China using the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) datasets shows that the global models underestimate the AOD by 33% and 44% in southern and northern China, respectively, and decrease the relative humidity (RH) of the air in the surface layer to 71%–80%, which is less than the RH of 77%–92% in reanalysis meteorological datasets. This indicates that the low biases in the RH partially account for the errors in the AOD. The AOD is recalculated based on the model aerosol concentrations and the reanalysis humidity data. Improving the mean value of the RH increases the multi-model annual mean AOD by 45% in southern China and by 33% in June–August in northern China. This method of improving the AOD is successful in most of the ACCMIP models, but it is unlikely to be successful in GISS-E2-R, in which the plot of its AOD efficiency against RH strongly deviates from the rest of the models. The effect of the improvement in the modeled RH on the AOD depends on the concentration of aerosols. The shape error in the frequency distribution of the RH is likely to be more important than the error in the mean value of the RH, but this requires further research.
