Inter-comparison of model-simulated and satellite-retrieved componential aerosol optical depths in China
Inter-comparison of model-simulated and satellite-retrieved componential aerosol optical depths in China
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DOI：
10.1016/j.atmosenv.2016.06.075
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发表时间：
2016-09
影响因子：
5
通讯作者：
Shenshen Li;Chao Yu-;Liangfu Chen;J. Tao;H. Letu;Wei Ge;Yidan Si;Yang Liu
中科院分区：
文献类型：
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作者：
Shenshen Li;Chao Yu-;Liangfu Chen;J. Tao;H. Letu;Wei Ge;Yidan Si;Yang Liu
中国大量的气溶胶排放对全球气候变化以及区域空气污染及其相关疾病负担产生了重大影响。详细了解气溶胶组分的时空分布特征对于计算气溶胶辐射强迫和制定有效的排放控制政策是必要的。模式模拟和卫星反演的气溶胶组分可以支持气候变化研究、PM2.5源指定和流行病学研究。利用GEOS-Chem模式（GC）、全球臭氧化学气溶胶辐射和输送模式（GOCART）和多角度成像光谱仪（MISR）对2006 - 2009年中国气溶胶光学厚度（AOD）进行了分析。GC和AERONET之间的线性回归分析得到了相似的相关系数（每天0.6，每月0.71），但坡度较低（每日0.41，每月0.58）与美国相比。这种差异归因于GC低估了黑河-腾冲线以西的水溶性AOD（WAOD），秋季和冬季的沙尘气溶胶光学厚度（DAOD），以及全年和全国范围内的煤烟气溶胶光学厚度（SAOD）。GOCART在所有数据集中表现出最强的尘埃估计能力。然而，GOCART烟尘分布在东北和东南部有很大的误差，其在污染的华北平原（NCP）和南部的WAOD被低估。MISR显着高估了西部地区的水溶性气溶胶水平，并且没有捕捉到所有季节和地区的高粉尘含量以及NCP的SAOD。这些差异主要归因于两种模式的排放清单的不确定性，GC在中国高气溶胶负荷条件下的性能较差，GOCART中某些气溶胶示踪剂的遗漏，以及MISR错误识别灰尘和非灰尘混合物的趋势。
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, PM2.5source 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.