全球不同类型气溶胶光学厚度的时空分布特征
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摘要
利用MERRA-2(第2版现代研究与应用再分析)资料分析了1980-2017年全球硫酸盐、黑碳、有机碳、海盐、沙尘及总气溶胶光学厚度的时空分布特征;选取了北美、北非、南非、印度、中国和印度洋6个典型区域研究了硫酸盐、黑碳、有机碳、海盐和沙尘气溶胶对总气溶胶光学厚度的贡献率。结果表明,硫酸盐、黑碳、有机碳、海盐和沙尘气溶胶在全球非均匀分布,并且具有季节变化;全球总气溶胶的光学厚度(Aerosol Optical Depth,AOD)在夏季最大(0.137),春季次之(0.130),冬季最小(0.118);在6个典型区域里,北非地区总气溶胶的光学厚度最大,为0.43;其次是中国的东部地区,为0.41;每个区域其主要气溶胶的类型并不相同,在北美、中国东部及印度中部地区,硫酸盐是主导的气溶胶类型,贡献率分别为66%,63%和42%,在印度洋、南非及北非地区,海盐、有机碳和沙尘分别是最主要的气溶胶类型,贡献率分别为65%,51%和82%;对于黑碳、硫酸盐和总气溶胶,中国东部地区和印度中部地区有较为明显的增长趋势,其中总气溶胶光学厚度的线性增长率分别为0.007 a-1和0.0056 a-1,但在2010年以后,中国东部地区出现明显的下降。
The spatial and temporal distribution of aerosol optical thickness for sulfate,black carbon,organic carbon,sea salt,dust,and total aerosols from 1980 to 2017 was analyzed using MERRA-2;six typical regions were selected to study the contribution of each type of aerosol to total aerosol optical depth.The results show that five types of aerosols are unevenly distributed globally and have seasonal variations;the global total aerosol optical thickness is the largest in summer(0.137),followed by spring(0.130),and the smallest in winter(0.118);in the six typical regions,the largest aerosol optical depth is in North Africa whose value is 0.43,followed by the eastern part of China,which is 0.41;the dominant types of aerosols in each region are different,in North America,Eastern China and Central India,sulfate is the dominant aerosol type with the contribution of 66%,63% and 42% to total AOD,respectively,in the Indian Ocean,South Africa and North Africa,sea salt,organic carbon and dust are the main types of aerosols,respectively,with the contribution of 65%,51% and 82%,respectively.There is a clear growth trend for black carbon,sulfate and total aerosols in Eastern China and central India and the linear trend 0.007 a-1 and 0.0056 a-1 for total aerosol optical depth in Eastern China and central India,respectively,but after 2010 there is a significant decline in Eastern China.
The spatial and temporal distribution of aerosol optical thickness for sulfate,black carbon,organic carbon,sea salt,dust,and total aerosols from 1980 to 2017 was analyzed using MERRA-2;six typical regions were selected to study the contribution of each type of aerosol to total aerosol optical depth.The results show that five types of aerosols are unevenly distributed globally and have seasonal variations;the global total aerosol optical thickness is the largest in summer(0.137),followed by spring(0.130),and the smallest in winter(0.118);in the six typical regions,the largest aerosol optical depth is in North Africa whose value is 0.43,followed by the eastern part of China,which is 0.41;the dominant types of aerosols in each region are different,in North America,Eastern China and Central India,sulfate is the dominant aerosol type with the contribution of 66%,63% and 42% to total AOD,respectively,in the Indian Ocean,South Africa and North Africa,sea salt,organic carbon and dust are the main types of aerosols,respectively,with the contribution of 65%,51% and 82%,respectively.There is a clear growth trend for black carbon,sulfate and total aerosols in Eastern China and central India and the linear trend 0.007 a-1 and 0.0056 a-1 for total aerosol optical depth in Eastern China and central India,respectively,but after 2010 there is a significant decline in Eastern China.
引文
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Dobbie S,Li J,Harvey R,et al,2003.Sea-salt optical properties and GCM forcing at solar wavelengths[J].Atmospheric Research,65(3):211-233.
Dong X Q,2018.Preface to the special issue:Aerosols,clouds,radiation,precipitation,and their interactions[J].Advances in Atmospheric Sciences,35(2),133-134.
Erickson D J,Merrill J T,Duce R A,1986.Seasonal estimates of global atmospheric sea-salt distributions[J].Journal of Geophysical Research Atmospheres,91(D1):1067-1072.
Gelaro R,Mccarty W,Suárez M J,et al,2017.The Modern-Era Retrospective Analysis for Research and Applications,Version 2(MERRA-2)[J].Journal of Climate,30(14):5419-5454.
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Holben B N,Eck T F,Slutsker I,et al,2012.AERONET-A federated instrument network and data archive for aerosol characterization[J].Remote Sensing of Environment,66(1):1-16.
Huang J P,Liu J J,Chen B,et al,2015.Detection of anthropogenic dust using CALIPSO lidar measurements[J].Atmospheric Chemistry&Physics,15(7):10163-10198.
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Ma X,vonSalzen K,Li J,2008.Modeling sea salt aerosol and its direct and indirect effects on climate[J].Atmospheric Chemistry and Physics,8(5):1311-1327.DOI:10.5194/acp-8-1311-2008.
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Mukkavilli S K,Prasad A A,Taylor R A,et al,2019.Assessment of atmospheric aerosols from two reanalysis products over Australia[J].Atmospheric Research,215:149-164.
Reale O,Lau K M,Silva A D,et al,2014.Impact of assimilated and interactive aerosol on tropical cyclogenesis[J].Geophysical Research Letters,41(9):3282-3288.
Rienecker M M,Suarez M J,Gelaro R,et al,2011.MERRA:NASA’s modern-era retrospective analysis for research and applications[J].Journal of Climate,24(14):3624-3648.
Roberts D L,Jones A,2004.Climate sensitivity to black carbon aerosol from fossil fuel combustion[J].Journal of Geophysical Research Atmospheres,109:D16202.DOI:10.1029/2004JD004676.
Rosenfeld D,Lohmann U,Raga G B,et al,2008.Flood or drought:how do aerosols affect precipitation?[J].Science,321(5894):1309-1313.
Song Z,Fu D,Z X,et al,2018.Diurnal and seasonal variability of PM 2.5,and AOD in North China plain:Comparison of MERRA-2 products and ground measurements[J].Atmospheric Environment,191:70-78.
Verma S,Boucher O,Upadhyaya H C,et al,2006.Sulfate aerosols forcing:An estimate using a three-dimensional interactive chemistry scheme[J].Atmospheric Environment,40(40):7953-7962.
Wang Z,Zhang H,Shen X,et al,2010.Modeling study of aerosol indirect effects on global climate with an AGCM[J].Advances in Atmospheric Sciences,27(5):1064-1077.
Wu G X,Li Z Q,Fu C B,et al,2016.Advances in studying interactions between aerosols and monsoon in China[J].Science China Earth Sciences,59(1):1-16.
Zhang Q,Streets D G,Carmichael G R,et al,2009.Asian emissions in 2006 for the NASA INTEX-Bmission[J].Atmospheric Chemistry&Physics,9(14):5131-5153.
Zhang Y,Bocquet M,Mallet V,et al,2012.Real-time air quality forecasting,part I:History,techniques,and current status[J].Atmospheric Environment,60(32):632-655.
蔡惠文,2012.Terra时代全球气溶胶光学厚度变化特征研究[D].南京:南京信息工程大学.
崔振雷,2008.中国地区和全球大气气溶胶浓度及光学厚度的数值模拟研究[D].南京:南京信息工程大学.
方炜,2017.广州市气溶胶光学厚度及PM2.5浓度的时空特征及其影响因素[D].广州:中山大学.
郝巨飞,袁雷武,李芷霞,等,2018.激光雷达和微波辐射计对邢台市一次沙尘天气的探测分析[J].高原气象,37(4):1110-1119.DOI:10.7522/j.issn.1000-0534.2018.00009.
华雯丽,韩颖,乔瀚洋,等,2018.敦煌沙尘气溶胶质量浓度垂直特征个例分析[J].高原气象,37(5):1428-1439.DOI:10.7522/j.issn.1000-0534.2018.00017.
冷亮,2011.基于VC++与MATLAB太阳光度计直射数据处理软件设计[J].安徽农业科学,39(22):13600-13602.
李剑东,毛江玉,王维强,2015.大气模式估算的东亚区域人为硫酸盐和黑碳气溶胶辐射强迫及其时间变化特征[J].地球物理学报,58(4),1103-1120.
李晓静,高玲,张兴赢,等,2015.卫星遥感监测全球大气气溶胶光学厚度变化[J].科技导报,33(17):30-40.
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