基于国际大气化学—气候模式比较计划模式数据评估未来气候变化对中国东部气溶胶浓度的影响

刘瑞金; 廖宏; 常文渊; 张天航; 靳少非

doi:10.3878/j.issn.1006-9895.1612.16218

基于国际大气化学—气候模式比较计划模式数据评估未来气候变化对中国东部气溶胶浓度的影响

doi: 10.3878/j.issn.1006-9895.1612.16218

刘瑞金^{1, 2,},
廖宏^3, ,,
常文渊¹,
张天航⁴,
靳少非⁵

1.
中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室, 北京 100029
2.
中国科学院大学, 北京 100049
3.
南京信息工程大学环境科学与工程学院, 南京 210044
4.
中国气象局国家气象中心, 北京 100081
5.
中国科学院东北地理与农业生态研究所湿地生态与环境重点实验室, 长春 130102

基金项目:

国家重点基础研究发展计划（973计划） 2014CB441202

国家自然科学基金项目 41475137

国家自然科学基金项目 91544219

详细信息

作者简介:
刘瑞金, 男, 1984年出生, 博士研究生, 主要从事气溶胶气候效应及气候变化的模拟研究。E-mail:liurj@mail.iap.ac.cn

通讯作者:
廖宏, E-mail:hongliao@mail.iap.ac.cn

中图分类号: P401
计量
- 文章访问数: 1853
- HTML全文浏览量: 2
- PDF下载量: 1743
- 被引次数: 0
出版历程
- 收稿日期: 2016-08-19
- 网络出版日期: 2017-01-11
- 刊出日期: 2017-07-15

Impact of Climate Change on Aerosol Concentrations in Eastern China Based on Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) Datasets

1.
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry(LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
2.
Graduate University of Chinese Academy of Sciences, Beijing 100029
3.
School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044
4.
National Meteorological Center, China Meteorological Administration, Beijing 100081
5.
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102

Funds:

National Basic Research Program of China (973 Program) 2014CB441202

National Natural Science Foundation of China 41475137

National Natural Science Foundation of China 91544219

摘要

摘要: 气候变化引起的地面气溶胶浓度变化与区域空气质量密切相关。本文利用“国际大气化学—气候模式比较计划”（Atmospheric Chemistry and Climate Model Intercomparison Project, ACCMIP）中4个模式的试验数据分析了RCP8.5情景下2000~2100年气候变化对中国气溶胶浓度的影响。结果显示，在人为气溶胶排放固定在2000年、仅考虑气候变化的影响时，2000~2100年气候变化导致中国北部地区（31°N~45°N, 105°E~122°E）硫酸盐、有机碳和黑碳气溶胶分别增加28%、21%和9%，硝酸盐气溶胶在中国东部地区减少30%。气候变化对细颗粒物（PM_2.5）浓度的影响有显著的季节变化特征，冬季PM_2.5浓度在中国东部减少15%，这主要是由硝酸盐气溶胶在冬季的显著减少造成的；夏季PM_2.5浓度在中国北部地区增加16%，而长江以南地区减少为9%，这可能与模式模拟的未来东亚夏季风环流的增强有关。
- ACCMIP (Atmospheric Chemistry and Climate Model Intercomparison Project) /
- 气候变化 /
- 人为气溶胶 /
- PM_2.5
Abstract: Changes in surface layer aerosol concentrations induced by climate change are important for understanding regional air quality. In this study, the impact of climate change on surface-layer aerosol concentrations over East Asia were investigated using multi-model results from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) under the RCP8.5 scenario during 2000-2100. With anthropogenic emissions of aerosols and aerosol precursors kept at year 2000 levels, the annual mean concentrations of surface-layer sulfate, organic carbon, and black carbon over northern China (31°N-45°N, 105°E-122°E) were simulated to increase by 28%, 21%, and 9%, respectively, owing to climate change over 2000-2100. Compared to that in 2000, annual mean surface-layer nitrate concentration in 2100 over eastern China was simulated to decrease by 30% by climate change alone. The climate-induced changes in fine particulate matter (PM_2.5) concentrations were simulated to have large seasonal variation. Due to significant decreases in nitrate concentrations in the winter, wintertime PM_2.5 concentrations over eastern China were simulated to decrease by 15% over 2000-2100. Furthermore, the changes in summertime PM_2.5 concentrations during 2000-2100 were found to have different patterns in northern and southern China; PM_2.5 concentrations in northern China would increase by 16%, while those in southern China would decrease by 9%.
- ACCMIP (Atmospheric Chemistry and Climate Model Intercomparison Project) /
- Climate change /
- Anthropogenic aerosol /
- PM_2.5

HTML全文

图 1 中国气象局大气成分观测网站点分布和本文研究区域。三角形代表观测站点位置，研究区域包括中国北部（31°N~45°N, 105°E~122°E）、南部（20°N~31°N, 105°E~122°E）和东部地区（北部+南部，20°N~45°N, 105°E~122°E）

Figure 1. Geographic locations of observational sites (triangles) in China Meteorological Administration Atmosphere Watch Network (CAWNET) and the study domains of northern China (31°N-45°N, 105°E-122°E), southern China (20°N-31°N, 105°E-122°E), and eastern China (20°N-45°N, 105°E-122°E)

下载: 全尺寸图片幻灯片

图 2 多模式平均的当前气候态下不同季节气溶胶地表浓度（单位：μg m^-3）的空间分布：（a）硫酸盐，（b）硝酸盐，（c）铵盐，（d）有机碳，（e）黑碳，（f）PM_2.5

Figure 2. The present-day multi-model and seasonal mean surface-layer concentrations (units: μg m^-3) of aerosols over East Asia: (a) Sulfate, (b) nitrate, (c) ammonium, (d) organic carbon, (e) black carbon, (f) PM_2.5 (fine particulate matter)

下载: 全尺寸图片幻灯片

图 3 （a）模式模拟和（b）MODIS观测的550 nm波长年平均气溶胶光学厚度空间分布。图b中灰色部分表示缺少观测数据

Figure 3. The annual mean aerosol optical thickness at 550 nm wavelength over China from MODIS (Moderate Resolution Imaging Spectroradiometer) and ACCMIP models. The gray areas in Fig. b represent the observation data are missing

下载: 全尺寸图片幻灯片

图 4 模式模拟的（左列）和NCEP再分析资料的（右列）中国地区年平均（a）温度（单位：℃）、（b）绝对湿度（单位：g kg^-1）、（c）降水（单位：mm d^-1）和（d）850 hPa风场（单位：m s^-1）的分布

Figure 4. Distributions of annual mean (a) surface temperature (units: ℃), (b) specific humidity (units: g kg^-1), (c) precipitation (units: mm d^-1), and (d) 850-hPa wind (units: m s^-1) over China from ACCMIP models (left column) and NCEP data (right column)

下载: 全尺寸图片幻灯片

图 5 ACCMIP模式模拟的RCP8.5情景下2000~2100年中国季节平均的（a）温度（单位：℃）、（b）绝对湿度（单位：g kg^-1）、（c）降水（单位：mm d^-1）和（d）850 hPa风场（单位：m s^-1）的变化特征。（a）温度和（b）绝对湿度的变化全部通过了95%信度水平检验（t检验），（c）降水变化图中打点区域表示通过了95%信度水平检验

Figure 5. ACCMIP models-simulated changes in seasonal mean (a) surface temperature (units: ℃), (b) specific humidity (units: g kg^-1), (c) precipitation (units: mm d^-1), and (d) 850-hPa wind (units: m s^-1) over China during 2000-2100 under the RCP8.5 scenario. Projected changes in surface temperature in Fig. a and specific humidity in Fig. b are all statistically significant at the 95% confidence level based on the Student's t test. The dotted areas in Fig. c represent statistically significant changes at the 95% confidence level based on the Student's t test

下载: 全尺寸图片幻灯片

图 6 ACCMIP模式模拟的RCP8.5情景下2000~2100年气候变化导致的中国地区人为气溶胶各季节地表浓度的变化（单位：μg m^-3）：（a）硫酸盐，（b）硝酸盐，（c）铵盐，（d）有机碳，（e）黑碳，（f）PM_2.5。打点区域表示偏差通过了95%信度水平检验（t检验）

Figure 6. ACCMIP models-simulated changes in seasonal mean surface-layer concentrations (units: μg m^-3) of aerosols over China during 2000-2100 induced by the projected climate change under the RCP8.5 scenario: (a) Sulfate, (b) nitrate, (c) ammonium, (d) organic carbon, (e) black carbon, (f) PM_2.5. The dotted areas represent statistically significant values at the 95% confidence level based on the Student's t test

下载: 全尺寸图片幻灯片

图 7 ACCMIP模式模拟的2000~2100年气候变化导致的中国地区人为气溶胶年平均地表浓度的变化（单位：μg m^-3）：（a）硫酸盐，（b）硝酸盐，（c）铵盐，（d）有机碳，（e）黑碳，（f）PM_2.5。打点区域表示偏差通过了95%信度水平检验（t检验）

Figure 7. ACCMIP models-simulated changes in annual mean surface-layer concentrations (units: μg m^-3) of aerosols over China during 2000-2100 induced by the projected climate change under the RCP8.5 scenario: (a) Sulfate, (b) nitrate, (c) ammonium, (d) organic carbon, (e) black carbon, (f) PM_2.5. The dotted areas represent statistically significant values at the 95% confidence level based on the Student's t test

下载: 全尺寸图片幻灯片

表 1 所选ACCMIP模式的基本信息

Table 1. Description of the ACCMIP (Atmospheric Chemistry and Climate Model Intercomparison Project) models used in this study

研究中心	模式名称	气溶胶成分	分辨率（经度×纬度）	参考文献
GFDL	GFDL-AM3	SO₄, NO₃, BC, POM, SOA, Dust	2°×2.5°	Donner et al.（2011）
GISS	GISS-E2-R	SO₄, NO₃, BC, POM, SOA, Dust	2°×2.5°	Koch et al.（2006）
UKMO	HadGEM2	SO₄, BC, POM, SOA, Dust	1.24°×1.87°	Collins et al.（2011）
NIES	MIROC-CHEM	SO₄, BC, POM, SOA, Dust	2.8°×2.8°	Watanabe et al.（2011）
注：SO₄、NO₃、NH4、BC、POM、SOA、Dust分别代表硫酸盐、硝酸盐、铵盐、黑碳、一次有机气溶胶、二次有机气溶胶和沙尘。GFDL，GISS，UKMO，NIES分别表示美国Geophysical Fluid Dynamics Laboratory，美国Goddard Institute for Space Studies，英国Met Office，日本National Institute for Environmental Studies。

下载: 导出CSV

表 2 ACCMIP模式模拟的各成分气溶胶地表浓度以及与观测的相对偏差

Table 2. Surface-layer aerosol concentrations from the models and normalized mean biases relative to observations

模式	硫酸盐浓度		硝酸盐浓度		铵盐浓度		有机碳浓度		黑碳浓度
模式	模拟/μg m⁻³	归一化平均偏差	模拟/μg m⁻³	归一化平均偏差	模拟/μg m⁻³	归一化平均偏差	模拟/μg m⁻³	归一化平均偏差	模拟/μg m⁻³	归一化平均偏差
GFDL-AM3	8.07	−67.67%	0.95	−91.49%	4.64	−45.70%	5.94	−75.59%	1.32	−77.87%
GISS-E2-R	5.33	−78.64%	3.09	−72.45%	1.88	−77.99%	7.01	−71.19%	1.53	−74.46%
HadGEM2	5.70	−77.18%					4.78	−80.34%	2.46	−58.88%
MIROC-CHEM	3.69	−85.22%					4.69	−80.74%	1.23	−79.39%
平均	5.70	−77.18%	2.02	−81.97%	3.26	−61.85%	5.60	−76.96%	1.63	−72.65%
注：归一化平均偏差定义为： $\sum\nolimits_{i = 1}^n {\left( {{x_i} - {y_i}} \right)} /\sum\nolimits_{i = 1}^n {{y_i} \times 100\% } $ ，x_i和y_i分别代表不同站点的模拟值和观测值。

下载: 导出CSV

参考文献(37)

[1]	Allen R J, Landuyt W, Rumbold S T. 2015. An increase in aerosol burden and radiative effects in a warmer world[J]. Nat. Climate Change, 6 (3):269-274, doi: 10.1038/nclimate2827.
[2]	Avise J, Chen J, Lamb B, et al. 2009. Attribution of projected changes in summertime US ozone and PM_2.5 concentrations to global changes[J]. Atmos. Chem. Phys., 9 (4):1111-1124, doi: 10.5194/acp-9-1111-2009.
[3]	Chang W Y, Liao H, Xin J Y, et al. 2015. Uncertainties in anthropogenic aerosol concentrations and direct radiative forcing induced by emission inventories in eastern China[J]. Atmos. Res., 166:129-140, doi: 10.1016/j.atmosres.2015.06.021.
[4]	Christopher S A, Kliche D V, Chou J, et al. 1996. First estimates of the radiative forcing of aerosols generated from biomass burning using satellite data[J]. J. Geophys. Res., 101 (D16):21265-21273, doi: 10.1029/96JD02161.
[5]	Colette A, Bessagnet B, Vautard R, et al. 2013. European atmosphere in 2050, a regional air quality and climate perspective under CMIP5 scenarios[J]. Atmos. Chem. Phys., 13 (15):7451-7471, doi: 10.5194/acp-13-7451-2013.
[6]	Collins W J, Bellouin N, Doutriaux-Boucher M, et al. 2011. Development and evaluation of an earth-system model-HadGEM2[J]. Geosci. Model Dev., 4 (4):1051-1075, doi: 10.5194/GMD-4-1051-2011.
[7]	Dawson J P, Adams P J, Pandis S N. 2007. Sensitivity of ozone to summertime climate in the eastern USA:A modeling case study[J]. Atmos. Environ., 41 (7):1494-1511, doi: 10.1016/j.atmosenv.2006.10.033.
[8]	Donner L J, Wyman B L, Hemler R S, et al. 2011. The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3[J]. J. Climate, 24 (13):3484-3519, doi: 10.1175/2011JCLI3955.1.
[9]	Fang Y Y, Fiore A M, Horowitz L W, et al. 2011. The impacts of changing transport and precipitation on pollutant distributions in a future climate[J]. J. Geophys. Res., 116 (D18), doi: 10.1029/2011JD015642.
[10]	Fu Y, Tai A P K, Liao H. 2016. Impacts of historical climate and land cover changes on fine particulate matter (PM_2.5) air quality in East Asia between 1980 and 2010[J]. Atmos. Chem. Phys., 16 (16):10369-10383, doi: 10.5194/acp-16-10369-2016.
[11]	Gao Y, Zhao C, Liu X, et al. 2014. WRF-Chem simulations of aerosols and anthropogenic aerosol radiative forcing in East Asia[J]. Atmos. Environ., 92 (0):250-266, doi: 10.1016/j.atmosenv.2014.04.038.
[12]	He H, Wang Y S, Ma Q X, et al. 2014. Mineral dust and NOx promote the conversion of SO₂ to sulfate in heavy pollution days[J]. Sci. Rep., 4:4172, doi: 10.1038/srep04172.
[13]	Hedegaard G B, Christensen J H, Brandt J. 2013. The relative importance of impacts from climate change vs. emissions change on air pollution levels in the 21st century[J]. Atmos. Chem. Phys., 13 (7):3569-3585, doi: 10.5194/acp-13-3569-2013.
[14]	Huang X, Song Y, Zhao C, et al. 2014. Pathways of sulfate enhancement by natural and anthropogenic mineral aerosols in China[J]. J. Geophys. Res., 119 (24):14165-14179, doi: 10.1002/2014JD022301.
[15]	Jacob D J, Winner D A. 2009. Effect of climate change on air quality[J]. Atmos. Environ., 43 (1):51-63, doi: 10.1016/j.atmosenv.2008.09.051.
[16]	Jiang H, Liao H, Pye H O T, et al. 2013. Projected effect of 2000-2050 changes in climate and emissions on aerosol levels in China and associated transboundary transport[J]. Atmos. Chem. Phys., 13 (16):7937-7960, doi: 10.5194/acp-13-7937-2013.
[17]	Kalnay E, Kanamitsu M, Kistler R, et al. 1996. The NCEP/NCAR 40-year reanalysis project[J]. Bull. Amer. Meteor. Soc., 77 (3):437-471, doi:10.1175/1520-0477(1996)077 < 0437:TNYRP > 2.0.CO; 2.
[18]	Koch D, Schmidt G A, Field C V. 2006. Sulfur, sea salt, and radionuclide aerosols in GISS ModelE[J]. J. Geophys. Res., 111 (D6):D06206, doi: 10.1029/2004JD005550.
[19]	Lamarque J F, Bond T C, Eyring V, et al. 2010. Historical (1850-2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols:Methodology and application[J]. Atmos. Chem. Phys., 10 (15):7017-7039, doi: 10.5194/acp-10-7017-2010.
[20]	Liao H, Chen W T, Seinfeld J H. 2006. Role of climate change in global predictions of future tropospheric ozone and aerosols[J]. J. Geophys. Res., 111 (D12), doi: 10.1029/2005jd006852.
[21]	Liao Hong, Chang Wenyuan, Yang Yang. 2015. Climatic effects of air pollutants over China:A review[J]. Adv. Atmos. Sci., 32 (1):115-139, doi: 10.1007/s00376-014-0013-x.
[22]	Liu X, Easter R C, Ghan S J, et al. 2012. Toward a minimal representation of aerosols in climate models:Description and evaluation in the Community Atmosphere Model CAM5[J]. Geosci. Model Dev., 5 (3):709-739, doi: 10.5194/gmd-5-709-2012.
[23]	Mahowald N. 2011. Aerosol indirect effect on biogeochemical cycles and climate[J]. Science, 334 (6057):794-796, doi: 10.1126/science.1207374.
[24]	Myhre G, Shindell D, Bréon F M, et al. 2013. Anthropogenic and natural radiative forcing[M]//Climate Change 2013:The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker T F, Qin D, Plattner G K, et al., Eds. Cambridge, UK and New York, USA:Cambridge University Press, doi:10.1017/CBO9781107415324.018.
[25]	Pope Ⅲ C A, Dockery D W. 2006. Health effects of fine particulate air pollution:Lines that connect[J]. Journal of the Air & Waste Management Association, 56 (6):709-742, doi: 10.1080/10473289.2006.10464485.
[26]	Racherla P N, Adams P J. 2006. Sensitivity of global tropospheric ozone and fine particulate matter concentrations to climate change[J]. J. Geophys. Res., 111 (D24):D24103, doi: 10.1029/2005JD006939.
[27]	Seo K H, Ok J, Son J H, et al. 2013. Assessing future changes in the East Asian summer monsoon using CMIP5 coupled models[J]. J. Climate, 26 (19):7662-7675, doi: 10.1175/JCLI-D-12-00694.1.
[28]	石广玉, 王标, 张华, 等. 2008.大气气溶胶的辐射与气候效应[J].大气科学, 32 (4):826-840. doi: 10.3878/j.issn.1006-9895.2008.04.11 Shi Guangyu, Wang Biao, Zhang Hua, et al. 2008. The radiative and climatic effects of atmospheric aerosols[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 32 (4):826-840, doi: 10.3878/j.issn.1006-9895.2008.04.11.
[29]	Shindell D T, Faluvegi G, Rotstayn L, et al. 2015. Spatial patterns of radiative forcing and surface temperature response[J]. J. Geophys. Res., 120 (11):5385-5403, doi: 10.1002/2014JD022752.
[30]	Tai A P K, Mickley L J, Jacob D J. 2012. Impact of 2000-2050 climate change on fine particulate matter (PM_2.5) air quality inferred from a multi-model analysis of meteorological modes[J]. Atmos. Chem. Phys., 12 (23):11329-11337, doi: 10.5194/acp-12-11329-2012.
[31]	Val Martin M, Heald C L, Lamarque J F, et al. 2015. How emissions, climate, and land use change will impact mid-century air quality over the United States:A focus on effects at national parks[J]. Atmos. Chem. Phys., 15 (5):2805-2823, doi: 10.5194/acp-15-2805-2015.
[32]	von Schneidemesser, E, Monks, P S, Allan J D, et al. 2015. Chemistry and the linkages between air quality and climate change[J]. Chem. Rev., 115 (10):3856-3897, doi: 10.1021/acs.chemrev.5b00089.
[33]	Watanabe S, Hajima T, Sudo K, et al. 2011. MIROC-ESM 2010:Model description and basic results of CMIP5-20c3m experiments[J]. Geosci. Model Dev., 4 (4):845-872, doi: 10.5194/gmd-4-845-2011.
[34]	Winker D M, Tackett J L, Getzewich B J, et al. 2013. The global 3-D distribution of tropospheric aerosols as characterized by CALIOP[J]. Atmos. Chem. Phys., 13 (6):3345-3361, doi: 10.5194/acp-13-3345-2013.
[35]	谢祖欣, 韩志伟. 2014.基于排放清单的中国地区人为排放源的年际变化[J].中国科学院大学学报, 31 (3):289-296. doi: 10.7523/j.issn.2095-6134.2014.03.001 Xie Zuxin, Han Zhiwei. 2014. Investigation of interannual variations in anthropogenic emission in China based on several emission inventories[J]. Journal of University of Chinese Academy of Sciences, 31 (3):289-296, doi: 10.7523/j.issn.2095-6134.2014.03.001.
[36]	Zhang X Y, Wang Y Q, Niu T, et al. 2012. Atmospheric aerosol compositions in China:Spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols[J]. Atmos. Chem. Phys., 12 (2):779-799, doi: 10.5194/ACP-12-779-2012.
[37]	Zhu J L, Liao H, Li J P. 2012. Increases in aerosol concentrations over eastern China due to the decadal scale weakening of the East Asian summer monsoon[J]. Geophys. Res. Lett., 39 (9):L09809, doi: 10.1029/2012gl051428.