Advertisement

Theoretical and Applied Climatology

, Volume 138, Issue 3–4, pp 2007–2020 | Cite as

Spatiotemporal distribution of anthropogenic aerosols in China around 2030

  • Shu LiEmail author
  • Tijian Wang
  • Bingliang Zhuang
  • Min Xie
  • Yong Han
Original Paper
  • 62 Downloads

Abstract

In the context of global warming, the future spatiotemporal distribution of aerosols in China is a common concern of the government and the scientific community. In this study, the regional climate model RegCM4 is used to simulate the spatiotemporal distribution of anthropogenic aerosols including sulfate, black carbon, and organic carbon in China around 2030 under the RCP4.5 and RCP8.5 scenarios and estimate the contributions of climate difference, emission difference, and extra-regional transport difference to the change of anthropogenic aerosol concentration in the study area. The results show that the annual average concentrations of anthropogenic aerosols around 2030 decreased significantly with respect to those around 2010, and the decrease amplitude of black carbon surface concentration is the smallest, especially in the RCP8.5 scenario. The annual averages for sulfate, black carbon, and organic carbon surface concentrations in the central and eastern parts of China will be 8.5, 1.7, and 3.7 μg m−3, respectively, under the RCP4.5 scenario, whereas 10.0, 2.2, and 4.4 μg m−3, respectively, under the RCP8.5 scenario. The surface concentration of sulfate is higher in summer and spring, while lower in winter and autumn. The surface concentrations of black carbon and organic carbon are higher in winter and lower in other seasons. The results of sensitivity experiments demonstrate that the future difference in local emissions between RCP8.5 and RCP4.5 scenarios has the greatest impact on the anthropogenic aerosol concentrations throughout China, while the effects of future climate difference and extra-regional transport difference are much smaller around 2030. For the aerosol column burdens, the effect of future local emission difference between the two scenarios is still dominant, and the effect of extra-regional transport difference becomes very significant during spring and winter for organic carbon and black carbon. The results of this paper suggest that the impacts of future climate difference and extra-regional transport difference between RCP8.5 and RCP4.5 scenarios on anthropogenic aerosols are non-negligible in certain regions and seasons besides the impact of future local emission difference in China around 2030.

Notes

Funding information

This study acquired support from the National Key Basic Research Development Program of China (2014CB441203), the National Natural Science Foundation of China (91544230, 41621005, 41575145), and the National Key Research and Development Plan of China (2016YFC0203303).

References

  1. Cai W, Li K, Liao H, Wang H, Wu L (2017) Weather conditions conducive to Beijing severe haze more frequent under climate change. Nat Clim Chang 7:257–262CrossRefGoogle Scholar
  2. Cao JJ, Lee SC, Chow JC, Watson JG, Ho KF, Zhang RJ et al (2007) Spatial and seasonal distributions of carbonaceous aerosols over China. J Geophys Res Atmos 112:D22S11.  https://doi.org/10.1029/2006JD008205 CrossRefGoogle Scholar
  3. Carmichael GR, Adhikary B, Kulkarni S, D’Allura A, Tang Y, Streets D et al (2009) Asian aerosols: current and year 2030 distributions and implications to human health and regional climate change. Environ Sci Technol 43:5811–5817CrossRefGoogle Scholar
  4. Chen H, Wang H (2015) Haze days in North China and the associated atmospheric circulations based on daily visibility data from 1960 to 2012. J Geophys Res Atmos 120:5895–5909CrossRefGoogle Scholar
  5. Cooke WF, Liousse C, Cachier H, Feichter J (1999) Construction of a 1° × 1° degree fossil fuel emission data set for carbonaceous aerosol and implementation and radiative impact in the ECHAM4 model. J Geophys Res 104:22137–22162CrossRefGoogle Scholar
  6. Dee DP, Uppala SM, Simmons AJ et al (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Quart J Roy Meteor Soc 137:553–597.  https://doi.org/10.1002/qj828
  7. Emanuel KA (1991) A scheme for representing cumulus convection in large-scale models. J Atmos Sci 48:2313–2335CrossRefGoogle Scholar
  8. Emanuel KA, Zivkovic-Rothman M (1999) Development and evaluation of a convection scheme for use in climate models. J Atmos Sci 56:1766–1782CrossRefGoogle Scholar
  9. Fiore AM, Naik V, Spracklen DV, Steiner A, Unger N, Prather M, Bergmann D, Cameron-Smith PJ, Cionni I, Collins WJ, Dalsøren S, Eyring V, Folberth GA, Ginoux P, Horowitz LW, Josse B, Lamarque JF, MacKenzie IA, Nagashima T, O’Connor FM, Righi M, Rumbold ST, Shindell DT, Skeie RB, Sudo K, Szopa S, Takemura T, Zeng G (2012) Global air quality and climate. Chem Soc Rev 41:6663–6683CrossRefGoogle Scholar
  10. Giorgi F et al (2012) RegCM4: model description and preliminary tests over multiple CORDEX domains. Climate Research 52:7–29.  https://doi.org/10.3354/Cr01018 CrossRefGoogle Scholar
  11. Holtslag AAM, Debruijn EIF, Pan HL (1990) A high-resolution air-mass transformation model for short-range weather forecasting. Mon Weather Rev 118:1561–1575CrossRefGoogle Scholar
  12. Intergovernmental Panel on Climate Change (IPCC) (2013) Climate change 2013: the physical science basis. Cambridge Univ Press, Cambridge, U KGoogle Scholar
  13. Jiang H, Liao H, Pye HOT, Wu S, Mickley LJ, Seinfeld JH, Zhang XY (2013) Projected effect of 2000–2050 changes in climate and emissions on aerosol levels in China and associated transboundary transport. Atmos Chem Phys 13:7937–7960CrossRefGoogle Scholar
  14. Lei Y, Zhang Q, He KB, Streets DG (2011) Primary anthropogenic aerosol emission trends for China, 1990–2005. Atmos Chem Phys 11:931–954.  https://doi.org/10.5194/acp-11-931-2011 CrossRefGoogle Scholar
  15. Li S, Wang T, Solmon F et al (2016) Impact of aerosols on regional climate in southern and northern China during strong/weak East Asian summer monsoon years. J Geophys Res Atmos 121:4069–4081.  https://doi.org/10.1002/2015JD023892
  16. Li M, Zhang Q, Kurokawa J, Woo JH, He K, Lu Z et al (2017) MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP. Atmos Chem Phys 17:34813–34869Google Scholar
  17. Liu HN, Zhang L (2012) The climate effects of anthropogenic aerosols of different emission scenarios in China. Chin J Geophys 55:1867–1875 (in Chinese)Google Scholar
  18. Lu Z, Zhang Q, Streets DG (2011) Sulfur dioxide and primary carbonaceous aerosol emissions in China and India, 1996–2010. Atmos Chem Phys 11:9839–9864.  https://doi.org/10.5194/acp-11-9839-2011 CrossRefGoogle Scholar
  19. Pal JS, Small EE, Eltahir EAB (2000) Simulation of regional-scale water and energy budgets: representation of subgrid cloud and precipitation processes within RegCM. J Geophys Res Atmos 105:29579–29594CrossRefGoogle Scholar
  20. Pye HOT, Liao H, Wu S, Mickley LJ, Jacob DJ, Henze DK et al (2009) Effect of changes in climate and emissions on future sulfate-nitrate-ammonium aerosol levels in the United States. J Geophys Res Atmos 114(D1):241–246CrossRefGoogle Scholar
  21. Reynolds RW, Rayner NA, Smith TM et al (2002) An improved in situ and satellite SST analysis for climate. J Climate 15:1609–1625.  https://doi.org/10.1175/1520-0442(2002)015 CrossRefGoogle Scholar
  22. Shalaby A, Zakey AS, Tawfik AB, Solmon F, Giorgi F, Stordal F, Sillman S, Zaveri RA, Steiner AL (2012) Implementation and evaluation of online gas-phase chemistry within a regional climate model (RegCM-CHEM4). Geosci Model Dev 5(3):741–760.  https://doi.org/10.5194/gmd-5-741-2012 CrossRefGoogle Scholar
  23. Solmon F, Giorgi F, Liousse C (2006) Aerosol modelling for regional climate studies: application to anthropogenic particles and evaluation over a European/African domain. Tellus Series B-Chem Phys Meteorol 58(1):51–72.  https://doi.org/10.1111/j1600-0889200500155x CrossRefGoogle Scholar
  24. Tai APK, Mickley LJ, Jacob DJ (2012) Impact of 2000–2050 climate change on fine particulate matter (pm25) air quality inferred from a multi-model analysis of meteorological modes. Atmos Chem Phys 12:11329–11337CrossRefGoogle Scholar
  25. Tao M, Chen L, Su L, Tao J (2012) Satellite observation of regional haze pollution over the north china plain. J Geophys Res Atmos 117:D12203.  https://doi.org/10.1029/2012JD017915 CrossRefGoogle Scholar
  26. Yang DD, Zhao SY, Zhang H, Shen XY (2017) Simulation of global distribution of temporal and spatial variation of PM2.5 concentration. China Environ Sci 37:1201–1212 (in Chinese)Google Scholar
  27. Zakey AS, Solmon F, Giorgi F (2006) Implementation and testing of a desert dust module in a regional climate model. Atmos Chem Phys 6:4687–4704CrossRefGoogle Scholar
  28. Zakey AS, Giorgi F, Bi X (2008) Modeling of sea salt in a regional climate model: fluxes and radiative forcing. J Geophys Res 113:D14221.  https://doi.org/10.1029/2007jd009209 CrossRefGoogle Scholar
  29. Zhang XY, Wang YQ, Niu T, Zhang XC, Gong SL, Zhang YM, Sun JY (2012) Atmospheric aerosol compositions in China: spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols. Atmos Chem Phys 12:779–799.  https://doi.org/10.5194/acp-12-779-2012 CrossRefGoogle Scholar
  30. Zhao XJ, Zhao PS, Xu J, Meng W (2013) Analysis of a winter regional haze event and its formation mechanism in the North China plain. Atmos Chem Phys 13:5685–5696CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Atmospheric Sciences, CMA-NJU Joint Laboratory for Climate Prediction Studies, Jiangsu Collaborative Innovation Center for Climate ChangeNanjing UniversityNanjingChina
  2. 2.School of Atmospheric SciencesSun Yat-sen UniversityZhuhaiChina

Personalised recommendations