全球不同类型气溶胶光学厚度的时空分布特征

张芝娟; 陈斌; 贾瑞; 衣育红

doi:10.7522/j.issn.1000-0534.2019.00002

高原气象 >

2019 , Vol. 38 >Issue 3: 660 - 672

DOI: https://doi.org/10.7522/j.issn.1000-0534.2019.00002

论文

全球不同类型气溶胶光学厚度的时空分布特征

张芝娟 ,
陈斌 ,
贾瑞 ,
衣育红

展开

半干旱气候变化教育部重点实验室, 兰州大学大气科学学院, 甘肃兰州 730000;兰州大学资源环境学院, 甘肃兰州 730000

收稿日期: 2018-08-10

网络出版日期: 2019-06-28

基金资助

国家自然科学基金项目（41375032，41775021，41305026）

收起

Global Spatial and Temporal Distribution of Aerosol Optical Depth for Different Kinds of Aerosols

ZHANG Zhijuan ,
CHEN Bin ,
JIA Rui ,
YI Yuhong

Expand

Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, Gansu, China;College of Earth environment Sciences, Lanzhou University, Lanzhou 730000, Gansu, China

Received date: 2018-08-10

Online published: 2019-06-28

Fold

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。

关键词： 气溶胶; 光学厚度; 时空分布

本文引用格式

张芝娟 , 陈斌 , 贾瑞 , 衣育红 . 全球不同类型气溶胶光学厚度的时空分布特征[J]. 高原气象, 2019 , 38(3) : 660 -672 . DOI: 10.7522/j.issn.1000-0534.2019.00002

Abstract

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.

Key words： Aerosol; aerosol optical dept; spatial and temporal

参考文献

[1]Bellouin N, Quaas J, Morcrette J J, et al, 2013.Estimates of aerosol radiative forcing from the MACC re-analysis[J].Atmospheric Chemistry & Physics, 13(4):2045-2062.
[2]Bocquet M, Elbern H, Eskes H, et al, 2015.Data assimilation in atmospheric chemistry models:current status and future prospects for coupled chemistry meteorology models[J].Atmospheric Chemistry & Physics, 15(10):5325-5358.
[3]Buchard V, Silva A M D, Colarco P R, et al, 2015.Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis[J].Atmospheric Chemistry & Physics Discussions, 15(23):5743-5760.
[4]Buchard V, Silva A M D, Randles C A, et al, 2016.Evaluation of the surface PM2.5, in version 1 of the NASA MERRA aerosol reanalysis over the United States[J].Atmospheric Environment, 125:100-111.
[5]Cao G L, Zhang X Y, Gong S L, et al, 2011.Emission inventories of primary particles and pollutant gases for China[J].Science Bulletin, 56(8):781-788.
[6]Cao J J, Chow J C, 2013.Recent advances for aerosol and environment study in Asia[J].Particuology, 11(1):3-4.
[7]Chow J C, 1995.Measurement methods to determine compliance with ambient air quality standards for suspended particles[J].Air Repair, 45(5):320-382.
[8]Chung S H, Seinfeld J H, 2002.Global distribution and climate forcing of carbonaceous aerosols[J].Journal of Geophysical Research Atmospheres, 107(D19):14-33.
[9]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.
[10]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.
[11]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.
[12]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.
[13]Giordano L, Brunner D, Flemming J, et al, 2015.Assessment of the MACC reanalysis and its influence as chemical boundary conditions for regional air quality modeling in AQMEⅡ-2[J].Atmospheric Environment, 115(3):371-388.
[14]Heidinger A K, Foster M J, Walther A, et al, 2014.The pathfinder atmospheres-extended AVHRR climate dataset[J].Bulletin of the American Meteorological Society, 95(6):909-922.
[15]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.
[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.
[17]Inness A, Baier F, Benedetti A, et al, 2013.The MACC reanalysis:an 8 yr data set of atmospheric composition[J].Atmospheric Chemistry & Physics, 13(8):4073-4109.
[18]IPCC, 2013.Clouds and Aerosols: The Physical Science Basis[C]//Boucher O, Randall D, Artaxo P, et al, eds.Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.Cambridge: Cambridge University Press.
[19]Kahn R A, Gaitley B J, Martonchik J V, et al, 2005.Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth validation based on 2 years of coincident Aerosol Robotic Network (AERONET) observations[J].Journal of Geophysical Research Atmospheres, 110:D10S04.DOI:10.1029/2004JD004706.
[20]Kessner A L, Wang J, Levy R C, et al, 2013.Remote sensing of surface visibility from space:A look at the United States East Coast[J].Atmospheric Environment, 81(2):136-147.
[21]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.
[22]McCarty W, Coy L, GelaroR, et al, 2016.MERRA-2 input observations: Summary and assessment[R/OL].NASA TM-2016-104606, Vol.46, NASA Global Modeling and Assimilation Office.[2018-08-08].https: //ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20160014544.pdf.
[23]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.
[24]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.
[25]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.
[26]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.
[27]Rosenfeld D, Lohmann U, Raga G B, et al, 2008.Flood or drought:how do aerosols affect precipitation?[J].Science, 321(5894):1309-1313.
[28]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.
[29]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.
[30]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.
[31]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.
[32]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.
[33]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.
[34]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.
[35]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.
[36]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.
[37]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.
[38]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.
[39]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.
[40]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.
[41]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.
[42]刘建慧, 赵天良, 韩永翔, 等, 2013.全球沙尘气溶胶源汇分布及其变化特征的模拟分析[J].中国环境科学, 33(10):1741-1750.
[43]刘状, 孙曦亮, 刘丹, 等.2018.2001-2017年北方省份气溶胶光学厚度的时空特征[J].环境科学学报, 38(8):3177-3184.
[44]史莹莹, 张镭, 田鹏飞, 等, 2018.黄土高原半干旱区沙尘气溶胶光学和微物理特性[J].高原气象, 37 (1):286-295.DOI:10.7522/j.issn.1000-0534.2017.00024.
[45]孙雨辰, 2014.基于星载激光雷达的全球气溶胶光学特性研究[D].青岛: 中国海洋大学.
[46]王东东, 朱彬, 江志红, 等, 2014.硫酸盐气溶胶直接辐射效应对东亚副热带季风进程的影响[J].大气科学, 38 (5):897-908.DOI:10.3878/j.issn.1006-9895.1403.13193.
[47]王戎, 2013.黑炭的全球排放和大气迁移及其暴露风险和辐射强迫评估[D].北京: 北京大学.
[48]吴丹, 左芬, 夏俊荣, 等, 2016.中国大气气溶胶中有机碳和元素碳的污染特征综述[J].环境科学与技术, 39(增刊):23-32.
[49]吴涧, 罗燕, 王卫国, 2005.东亚地区人为硫酸盐气溶胶辐射气候效应不同模拟方法的对比[J].云南大学学报(自然科学版), 27(4):323-331.
[50]熊洁, 赵天良, 韩永翔, 等, 2013.1995-2004年东亚沙尘气溶胶的模拟源汇分布及垂直结构[J].中国环境科学, 33(6):961-968.
[51]宿兴涛, 王汉杰, 2009.中国黑碳气溶胶分布特征与辐射强迫的模拟研究[J].大气科学学报, 32(6):798-806.
[52]宿兴涛, 王汉杰, 周林, 2010.中国有机碳气溶胶时空分布与辐射强迫的模拟研究[J].热带气象学报, 26(6):765-772.
[53]杨杰, 王永前, 杨世琦, 等, 2018.基于FY3C/MERSI资料分析重庆市气溶胶光学厚度分布[J].重庆师范大学学报(自然科学版), 35(6):49-55.
[54]杨志峰, 车慧正, 张小曳, 等, 2008.北京地区气溶胶光学特性及其与大气可吸入颗粒物的关系[C]//湖北: 鄂港澳城市群气候与环境研讨会.
[55]俞海洋, 张杰, 李婷, 等, 2018.2000-2013年北京及周边地区大气气溶胶光学厚度时空变化特征及气象影响因素分析[J].气象科学, 2018(4):512-522.
[56]张华, 马井会, 郑有飞, 2008.黑碳气溶胶辐射强迫全球分布的模拟研究[J].大气科学, 32(5):1147-1158.
[57]张亮林, 潘竟虎, 张大弘, 2018.基于MODIS数据的中国气溶胶光学厚度时空分布特征[J].环境科学学报, 38(11):4431-4439.
[58]张明明, 刘振波, 葛云健, 2014.江苏省大气气溶胶光学厚度时空分布研究[J].长江流域资源与环境, 23(12):1775-1782.
[59]张小曳, 2014.中国不同区域大气气溶胶化学成分浓度、组成与来源特征[J].气象学报, 72(6):1108-1117.
[60]张洋, 刘志红, 于明洋, 等, 2014.四川省气溶胶光学厚度时空分布特征[J].四川环境, 33(3):48-53.
[61]张颖, 王体健, 庄炳亮, 等, 2014.东亚海盐气溶胶时空分布及其直接气候效应研究[J].高原气象, 33(6):1551-1561.DOI:10.7522/j.issn.1000-0534.2013.00106.

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract

参考文献