Inter-Comparison of Model-Simulated and Satellite-Retrieved Componential Aerosol Optical Depths in China (2016)

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China’s large aerosol emissions have major impacts on global climate change as well as regional air pollution and its associated disease burdens. A detailed understanding of the spatiotemporal patterns of aerosol components is necessary for the calculation of aerosol radiative forcing and the development of effective emission control policy. Model-simulated and satellite-retrieved aerosol components can support climate change research, PM2.5 source appointment and epidemiological studies. This study evaluated the total and componential aerosol optical depth (AOD) from the GEOS-Chem model (GC) and the Global Ozone Chemistry Aerosol Radiation and Transport model (GOCART), and the Multiangle Imaging Spectroradiometer (MISR) from 2006 to 2009 in China. Linear regression analysis between the GC and AErosol RObotic NETwork (AERONET) in China yielded similar correlation coefficients (0.6 daily, 0.71 monthly) but lower slopes (0.41 daily, 0.58 monthly) compared with those in the U.S. This difference was attributed to GC’s underestimation of water-soluble AOD (WAOD) west of the Heihe-Tengchong Line, the dust AOD (DAOD) in the fall and winter, and the soot AOD (SAOD) throughout the year and throughout the country. GOCART exhibits the strongest dust estimation capability among all datasets. However, the GOCART soot distribution in the Northeast and Southeast has significant errors, and its WAOD in the polluted North China Plain (NCP) and the South is underestimated. MISR significantly overestimates the water-soluble aerosol levels in the West, and does not capture the high dust loadings in all seasons and regions, and the SAOD in the NCP. These discrepancies can mainly be attributed to the uncertainties in the emission inventories of both models, the poor performance of GC under China’s high aerosol loading conditions, the omission of certain aerosol tracers in GOCART, and the tendency of MISR to misidentify dust and non-dust mixtures.
Year: 2016
Language: English
In: Atmospheric Environment, 141 : 320-332 p.

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 Record created 2016-09-05, last modified 2016-09-05