Abstract
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 effe...
Highlights
According to the multi-model results from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), the model biases in the aerosol optical depth (AOD) range from −30% to 20%
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
The model chemistry is unable to reproduce the strong growth of sulfate during haze days in urban regions (Chen et al 2016; Huang et al 2014; Zheng et al 2015), which leads to strong low-AOD in northern China even though the RH bias there is not as high as that in southern China
Summary
According to the multi-model results from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), the model biases in the aerosol optical depth (AOD) range from −30% to 20%. Most of these biases are lower than those in observational AOD datasets from satellites because of the absence of nitrates or secondary organic aerosols (SOAs) in some of the models (Shindell et al 2013) and the low biases in the sulfate concentrations in winter and in the concentration of organic aerosols throughout the year (Chang et al 2018). The effects of the RH on the AOD reported here are not comprehensive because the effect of the RH on aerosol chemistry is not concerned because of the limitations of the ACCMIP dataset, but it is meaningful to illustrate the adverse impacts of the model bias in the RH on the simulation of the AOD
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