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Climate Dynamics

, Volume 52, Issue 5–6, pp 3643–3660 | Cite as

Anthropogenic impacts on recent decadal change in temperature extremes over China: relative roles of greenhouse gases and anthropogenic aerosols

  • Wei ChenEmail author
  • Buwen Dong
Article

Abstract

Observational analysis indicates significant changes in some temperature extremes over China across the mid-1990s. The decadal changes in hot extremes are characterized as a rise in annual hottest day and night temperature (TXx and TNx) and an increase in frequencies of summer days (SU) and tropical night (TR). The decadal changes in cold extremes are distinguished by a rise in annual coldest day and night temperature (TXn and TNn) and a decrease in frequencies of ice days (ID) and frost days (FD). These decadal changes manifest not only over China as a whole, but also over individual climate sub-regions. An atmosphere-ocean-mixed-layer coupled model forced by changes in greenhouse gases (GHG) concentrations and anthropogenic aerosol (AA) emissions realistically reproduces the general spatial patterns and magnitudes of observed changes in both hot and cold extremes across the mid-1990s, suggesting a pronounced role of anthropogenic changes in these observed decadal changes. Separately, changes in GHG forcing lead to rise in TXx, TNx, TXn and TNn, increase in frequencies of SU and TR and decrease in frequencies of ID and FD over China through increased Greenhouse Effect with positive clear sky longwave radiation and play a dominant role in simulated changes of both hot and cold extremes over China. The AA forcing changes tend to cool Southern China and warm Northern China during summer via aerosol-radiation interaction and AA-induced atmosphere-cloud feedback and therefore lead to some weak decrease in hot extremes over Southeastern China and increase over Northern China. Meanwhile, AA changes lead to warming over China during winter through cloud feedbacks related to aerosol induced cooling over tropical Indian Ocean and western tropical Pacific, and also induce changes in cold extremes the same sign as those induced by GHG, but with weak magnitude.

Keywords

Hot temperature extremes Cold temperature extremes China Decadal change The mid-1990s Greenhouse gases Anthropogenic aerosol 

Notes

Acknowledgements

This study is supported by the National Natural Science Foundation of China under Grants 41675078, U1502233, 41320104007, by the Youth Innovation Promotion Association of CAS (No. 2018102) and by the UK-China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. BD is supported by the U.K. National Centre for Atmospheric Science-Climate (NCAS-Climate) at the University of Reading. The authors thank Editor Jian Lu and anonymous reviewers for their constructive comments on the earlier version of the paper.

References

  1. Alexander LV et al (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res 111:D05109.  https://doi.org/10.1029/2005JD006290 Google Scholar
  2. Arribas A et al (2011) The GloSea4 ensemble prediction system for seasonal forecasting. Mon Weather Rev 139(6):1891–1910CrossRefGoogle Scholar
  3. Barsugli J, Battisti DS (1998) The basic effects of atmosphere–ocean thermal coupling on midlatitude variability. J Atmos Sci 55:477–493.  https://doi.org/10.1175/1520-0469 CrossRefGoogle Scholar
  4. Bellouin N, Rae J, Jones A, Johnson C, Haywood J, Boucher O (2011) Aerosol forcing in the climate model intercomparison project (CMIP5) simulations by HadGEM2-ES and the role of ammonium nitrate. J Geophys Res 116:D20206.  https://doi.org/10.1029/2011JD016074 CrossRefGoogle Scholar
  5. Boé J, Terray L (2014) Land–sea contrast, soil-atmosphere and cloud-temperature interactions: interplays and roles in future summer European climate change. Clim Dyn 42:683–699CrossRefGoogle Scholar
  6. Bollasina MA, Ming Y, Ramaswamy V (2011) Anthropogenic aerosols and the weakening of the South Asian summer monsoon. Science 334(6055):502–505CrossRefGoogle Scholar
  7. Bony S et al (2006) How well do we understand and evaluate climate change feedback processes? J Clim 19(15):3445–3482.  https://doi.org/10.1175/jcli3819.1 CrossRefGoogle Scholar
  8. Christidis N et al (2013) A new HadGEM3-A-based system for attribution of weather and climate-related extreme events. J Clim 26:2756–2783.  https://doi.org/10.1175/JCLI-D-12-00169.1 CrossRefGoogle Scholar
  9. Cubasch U et al (2001) Projections of future climate change. In: Houghton JT et al (eds) Climate change 2001: the scientific basis. Cambridge University Press, Cambridge, pp 525–582Google Scholar
  10. Donat MG, Alexander LV, Yang H, Durre I, Vose R, Caesar J (2013) Global land-based datasets for monitoring climatic extremes. Bull Am Meteorol Soc 94:997–1006.  https://doi.org/10.1175/BAMS-D-12-00109.1 CrossRefGoogle Scholar
  11. Dong BW, Gregory JM, Sutton RT (2009) Understanding land-sea warming contrast in response to increasing greenhouse gases. Part I: transient adjustment. J Clim 22:3079–3097CrossRefGoogle Scholar
  12. Dong BW, Sutton RT, Highwood E, Wilcox L (2015) Preferred response of the East Asian summer monsoon to local and nonlocal anthropogenic sulphur dioxide emissions. Clim Dyn.  https://doi.org/10.1007/s00382-015-2671-5 Google Scholar
  13. Dong BW, Sutton RT, Chen W, Liu XD, Lu RY, Sun Y (2016) Abrupt summer warming and changes in temperature extremes over Northeast Asia since the mid-1990s: drivers and physical processes. Adv Atmos Sci 33(9):1005–1023.  https://doi.org/10.1007/s00376-016-5247-3 CrossRefGoogle Scholar
  14. Dong BW, Sutton RT, Shaffrey L (2017a) Understanding the rapid summer warming and changes in temperature extremes since the mid-1990s over Western Europe. Clim Dyn 48:1537–1554.  https://doi.org/10.1007/s00382-016-3158-8 CrossRefGoogle Scholar
  15. Dong BW, Sutton RT, Shaffrey L, Klingaman NP (2017b) Attribution of forced decadal climate change in coupled and uncoupled ocean-atmosphere model experiments. J Clim.  https://doi.org/10.1175/JCLI-D-16-0578.1 Google Scholar
  16. Dwyer JG, Biasutti M, Sobel AH (2012) Projected changes in the seasonal cycle of surface temperature. J Clim 25(18):6359–6374CrossRefGoogle Scholar
  17. Feichter J, Roeckner E, Lohmann U, Liepert B (2004) Nonlinear aspects of the climate response to greenhouse gas and aerosol forcing. J Clim 17:2384–2398CrossRefGoogle Scholar
  18. Freychet S, Tett S, Wang J, Hegerl G (2017) Summer heat waves over eastern china: dynamical processes and trend attribution. Environ Res Lett 12:1–9.  https://doi.org/10.1088/1748-9326/aa5ba3 CrossRefGoogle Scholar
  19. Hansen J, Sato M, Ruedy R (1997) Radiative forcing and climate response. J Geophys Res 102:6831–6864.  https://doi.org/10.1029/96JD03436 CrossRefGoogle Scholar
  20. He J, Soden B (2016) Does the lack of coupling in SST-forced atmosphere-only models limit their usefulness for climate change studies? J Clim 29:4317–4325.  https://doi.org/10.1175/JCLI-D-14-00597.1 CrossRefGoogle Scholar
  21. Hirons LC, Klingaman NP, Woolnough SJ (2015) MetUM-GOML: a near-globally coupled atmosphere—ocean-mixed-layer model. Geosci Model Dev 8:363–379CrossRefGoogle Scholar
  22. Jones C et al (2011) The HadGEM2-ES implementation of CMIP5 centennial simulations. Geophys Model Dev 4:543–570CrossRefGoogle Scholar
  23. Kamae Y, Shiogama H, Watanabe M, Kimoto M (2014) Attributing the increase in Northern Hemisphere hot summers since the late 20th century. Geophys Res Lett 41:5192–5199.  https://doi.org/10.1002/2014GL061062 CrossRefGoogle Scholar
  24. Kim YH, Min SK, Zhang X, Zwiers F, Alexander LV, Donat MG, Tung YS (2015) Attribution of extreme temperature changes during 1951–2010. Clim Dyn 46:1769–1782.  https://doi.org/10.1007/s00382-015-2674-2 CrossRefGoogle Scholar
  25. Lamarque JF et al (2010) Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: Methodology and application. Atmos Chem Phys 10:7017–7039.  https://doi.org/10.5194/acp-10-7017-2010 CrossRefGoogle Scholar
  26. Lamarque JF et al (2011) Global and regional evolution of short-lived radiatively-active gases and aerosols in the representative concentration pathways. Clim Change 109:191–212.  https://doi.org/10.1007/s10584-011-0155-0 CrossRefGoogle Scholar
  27. Li X, Ting M (2016) Understanding the Asian summer monsoon response to greenhouse warming: the relative roles of direct radiative forcing and sea surface temperature change. Clim Dyn 49:1–18Google Scholar
  28. Li CX, Zhao TB, Ying KR (2016a) Effects of anthropogenic aerosols on temperature changes in China during the twentieth century based on CMIP5 models. Theor Appl Climatol.  https://doi.org/10.1007/s00704-015-1527-6 Google Scholar
  29. Li Z et al (2016b) Comparison of two homogenized datasets of daily maximum/mean/minimum temperature in China during 1960–2013. J Meteor Res 30(1):053–066.  https://doi.org/10.1007/s13351-016-5054-x CrossRefGoogle Scholar
  30. Ma SM, Zhou TJ, Stone D, Angelil O, Shiogama H (2017) Attribution of the July-August 2013 heat event in central and eastern China to anthropogenic Greenhouse gas emissions. Environ Res Lett 12:054020CrossRefGoogle Scholar
  31. Ming Y, Ramaswamy V (2009) Nonlinear climate and hydrological responses to aerosol effects. J Clim 22:1329–1339CrossRefGoogle Scholar
  32. Otto FE, Massey, van Oldenborgh GJ, Jones RQ, Allen MR (2012) Reconciling two approaches to attribution of the 2010 Russian heat wave. Geophys Res Lett 39:L04702.  https://doi.org/10.1029/2011GL050422 CrossRefGoogle Scholar
  33. Perkins SE (2015) A review on the scientific understanding of heatwaves–their measurement, driving mechanisms, and changes at the global scale. Atmos Res 164:242–267CrossRefGoogle Scholar
  34. Qu X, Hall A (2007) What controls the strength of snow-albedo feedback? J Clim 20(15):3971–3981.  https://doi.org/10.1175/JCLI4186.1 doiCrossRefGoogle Scholar
  35. Rangwala I, Sinsky E, Miller JR (2016) Variability in projected elevation dependent warming in boreal midlatitude winter in CMIP5 climate models and its potential drivers. Clim Dyn 46:2115CrossRefGoogle Scholar
  36. Rayner NA et al (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407.  https://doi.org/10.1029/2002JD002670 CrossRefGoogle Scholar
  37. Robock A (1983) Ice and snow feedbacks and the latitudinal and seasonal distribution of climate sensitivity. J Atmos Sci 40(4):986–997CrossRefGoogle Scholar
  38. Schaller N et al (2016) Human influence on climate in the 2014 southern England winter floods and their impacts. Nat Clim Change 6:627–634.  https://doi.org/10.1038/nclimate2927 CrossRefGoogle Scholar
  39. Shiogama H, Stone DA, Nagashima T, Nozawa T, Emori S (2012) On the linear additivity of climate forcing-response relationships at global and continental scales. Int J Climatol 33:2542–2550CrossRefGoogle Scholar
  40. Smith DM, Murphy JM (2007) An objective ocean temperature and salinity analysis using covariances from a global climate model. J Geophys Res 112:C02022.  https://doi.org/10.1029/2005JC003172 CrossRefGoogle Scholar
  41. Song F, Zhou T, Qian Y (2014) Responses of East Asian summer monsoon to natural and anthropogenic forcings in the 17 latest CMIP5 models. Geophys Res Lett.  https://doi.org/10.1002/2013GL058705 Google Scholar
  42. Stevens B, Feingold G (2009) Untangling aerosol effects on clouds and precipitation in a buffered system. Nat 461:607–613.  https://doi.org/10.1038/nature08281 CrossRefGoogle Scholar
  43. Stott et al (2016) Attribution of extreme weather and climate-related events. Wiley Interdiscipl Rev Clim Change 7:23–41.  https://doi.org/10.1002/wcc.380 CrossRefGoogle Scholar
  44. Stouffer RJ, Wetherald RT (2007) Changes of variability in response to increasing greenhouse gases. part i: temperature. J Clim 20(21):5455CrossRefGoogle Scholar
  45. Sun Y, Zhang X, Zwiers FW, Song L, Wan H, Hu T, Yin H, Ren G (2014) Rapid increase in the risk of extreme summer heat in Eastern China. Nat Clim Change 4:1082–1085.  https://doi.org/10.1038/nclimate2410 CrossRefGoogle Scholar
  46. Sun Y, Song LC, Yin H, Zhang XB. Stott P, Zhou BT, Hu T (2016) Human Influence on the 2015 extreme high temperature events in western China [in “Explaining Extreme Events of 2015 from a Climate Perspective”]. Bull Am Meteor Soc 97:S5–S9CrossRefGoogle Scholar
  47. Thackeray CW, Fletcher CG (2016) Snow albedo feedback: Current knowledge, importance, outstanding issues and future directions. Prog Phys Geogr 40(3):392–408.  https://doi.org/10.1177/0309133315620999 CrossRefGoogle Scholar
  48. Tian FX, Dong BW, Robson J, Sutton RT (2018) Forced decadal changes in the East Asian summer monsoon: the roles of greenhouse gases and anthropogenic aerosols. Clim Dyn 6:1–17Google Scholar
  49. Vannière BE, Guilyardi G, Madec FJ, Doblas R, Woolnough S (2013) Using seasonal hindcasts to understand the origin of the equatorial cold tongue bias in CGCMs and its impact on ENSO. Clim Dyn 40(3–4):963–981CrossRefGoogle Scholar
  50. Walters DN, Best MJ, Bushell AC, Copsey D, Edwards JM, Falloon PD, Roberts MJ (2011) The met office unified model global atmosphere 3.0/3.1 and JULES global land 3.0/3.1 configurations. Geosci Model Dev 4(4):919CrossRefGoogle Scholar
  51. Wang T, Otterå OH, Gao YG, Wang HJ (2012) The response of the North Pacific Decadal Variability to strong tropical volcanic eruptions. Clim Dyn 39(12):2917–2936CrossRefGoogle Scholar
  52. Wang T et al (2013) Anthropogenic agent implicated as a prime driver of shift in precipitation in eastern China in the late 1970s. Atmos Chem Phy 13(24):12433CrossRefGoogle Scholar
  53. Wei K, Chen W (2011) An abrupt increase in the summer high temperature extreme days across China in the mid-1990s. Adv Atmos Sci 28(5):1023–1029.  https://doi.org/10.1007/s00376-010-0080-6 CrossRefGoogle Scholar
  54. Wen HQ, Zhang X, Xu Y, Wang B (2013) Detecting human influence on extreme temperatures in China. Geophys Res Lett 40:1171–1176.  https://doi.org/10.1002/grl.50285 CrossRefGoogle Scholar
  55. Wilcox LJ, Dong BW, Sutton RT, Highwood EJ (2015) The 2014 Hot, Dry Summer in Northeast Asia [in “Explaining Extreme Events of 2014 from a Climate Perspective”]. Bull Am Meteor Soc 96(12):S105–S110.  https://doi.org/10.1175/BAMS-D-15-00123.1 CrossRefGoogle Scholar
  56. Yang FL et al (2001) Snow-albedo feedback and seasonal climate variability over North America. J Clim 14(22):4245–4248CrossRefGoogle Scholar
  57. Yang Y, Russell LM, Lou S, Lamjiri MA, Liu Y, Singh B, Ghan SJ (2016) Changes in sea salt emissions enhance ENSO variability. J Clim 29:8575–8588.  https://doi.org/10.1175/JCLI-D-16-0237.1 CrossRefGoogle Scholar
  58. Yang Y, Russell LM, Lou S, Liao H, Guo J, Liu Y, Singh B, Ghan SJ (2017) Dust-wind interactions can intensify aerosol pollution over eastern China. Nat Commun 8:15333.  https://doi.org/10.1038/ncomms15333 CrossRefGoogle Scholar
  59. Yao Y, Luo Y, Huang JB (2012) Evaluation and projection of temperature extremes over China based on CMIP5 model. Adv Clim Change Res.  https://doi.org/10.3724/SP.J.1248.2012.00179 Google Scholar
  60. Yin H, Sun Y, Wan H, Zhang XB, Lu CH (2016) Detection of anthropogenic influence on the intensity of extreme temperatures in China. Int J Climatol 37:1229–1237.  https://doi.org/10.1002/joc.4771 CrossRefGoogle Scholar
  61. Zhao TB, Li CX, Zuo ZY (2016) Contributions of anthropogenic and external natural forcings to climate changes over China based on CMIP5 model simulations. Sci China Earth Sci 59:503–517.  https://doi.org/10.1007/s11430-015-5207-2 CrossRefGoogle Scholar
  62. Zhou BT, Xu Y, Wu J, Dong S, Shi Y (2016) Changes in temperature and precipitation extreme indices over China: analysis of a high-resolution grid dataset. Int J Climatol 36:1051–1066CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Department of Meteorology, National Centre for Atmospheric Science-ClimateUniversity of ReadingReadingUK

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