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Detecting spatial differences in thermal stress across China

  • Zhang Jie 
  • Lai Wenli Email author
  • Zhizhong Zhao
  • Hongrui Wang
Original Paper

Abstract

In this study, we investigate spatial differences in thermal comfort conditions using the net effective temperature (NET) considering the synthetic effects of air temperature, relative humidity, and wind speed. Using a daily-scale dataset of maximum air temperature (Tmax), relative humidity, and wind speed from 518 stations during 1960–2016 across China, we analyze the influence of different climate conditions on NET or Tmax at three different levels of hot conditions (35 °C < Tmax < 37 °C, NETTmax35 for HT35 cases; 37 °C < Tmax < 40 °C, NETTmax37 for HT37 cases; NET = 27 °C, NET27 cases). In HT35 (HT37) cases, NETTmax35 (NETTmax37) can reach up to 32 °C (34 °C) in southern China and also can be less than 29 °C (31 °C) in western Northwest China. In NET27 cases as the threshold for the thermal sensation of very hot, Tmax should be over 33 °C in western Northwest China and was less than 30.5 °C in southern China, by contrast. With global warming, there is an increasing trend in the number of extreme hot days in most part of China, but a decreasing trend is detected in the part of Jianghuai region, partly due to the decreasing trend in Tmax.

Notes

Acknowledgments

We wish to thank the editors and three reviewers for their invaluable comments and constructive suggestions to improve the quality of the manuscript.

Funding information

This work is financially supported by Hainan Provincial Natural Science Foundation of China (SQ2019QNJJ0030) and Geography of Key Disciplines in Hainan Province (2017CXTD006).

References

  1. Aghakouchak A, Cheng L, Mazdiyasni O, Farahmand A (2015) Global warming and changes in risk of concurrent climate extremes: insights from the 2014 California drought. Geophys Res Lett 41(24):8847–8852.  https://doi.org/10.1002/2014GL062308 CrossRefGoogle Scholar
  2. Aynsley R, Melbourne W, Vickery B (1977) Architectural aerodynamics. Applied Science Publisher Ltd, LondonGoogle Scholar
  3. Barriopedro D, Fischer EM, Luterbacher J, Trigo RM, García-Herrera R (2011) The hot summer of 2010: redrawing the temperature record map of Europe. Science 332(6026):220–224.  https://doi.org/10.1126/science.1201224 CrossRefGoogle Scholar
  4. Blazejczyk K, Epstein Y, Jendritzky G, Staiger H, Tinz B (2012) Comparison of UTCI to selected thermal indices. Int J Biometeorol 56(3):515–535.  https://doi.org/10.1007/s00484-011-0453-2 CrossRefGoogle Scholar
  5. Diffenbaugh N, Pal J, Giorgi F, Gao X (2007) Heat stress intensification in the Mediterranean climate change hotspot. Geophys Res Let 34(11):224–238.  https://doi.org/10.1029/2007GL030000 CrossRefGoogle Scholar
  6. Diffenbaugh N, Swain D, Touma D (2015) Anthropogenic warming has increased drought risk in California. Proc Natl Acad Sci U S A 112(13):3931–3936.  https://doi.org/10.1073/pnas.1422385112 CrossRefGoogle Scholar
  7. Ding T, Ke Z (2015) Characteristics and changes of regional wet and dry heat wave events in China during 1960-2013. Theor Appl Climatol 122(3–4):651–665.  https://doi.org/10.1007/s00704-014-1322-9 CrossRefGoogle Scholar
  8. Epstein Y, Moran DS (2006) Thermal comfort and the heat stress indices. Ind Health 44:388–398.  https://doi.org/10.2486/indhealth.44.388 CrossRefGoogle Scholar
  9. Fischer E, Oleson K, Lawrence D (2012) Contrasting urban and rural heat stress responses to climate change. Geophys Res Lett 39:L03705.  https://doi.org/10.1029/2011GL050576 Google Scholar
  10. Flach E (1981) Human bioclimatology. In: Landsberg H (ed) World survey of climatology, vol 3. General climatology. Elsevier, Amsterdam, Oxford and New York, pp 1–187Google Scholar
  11. Freitas C, Grigorieva E (2015) A comprehensive catalogue and classification of human thermal climate indices. Int J Biometeorol 59(1):109–120.  https://doi.org/10.1007/s00484-014-0819-3 CrossRefGoogle Scholar
  12. Gao J, Sun Y, Liu Q, Zhou M, Lu Y, Li L (2015) Impact of extreme high temperature on mortality and regional level definition of heat wave: a multi-city study in China. Sci Total Environ 505:535–544.  https://doi.org/10.1016/j.scitotenv.2014.10.028 CrossRefGoogle Scholar
  13. Guerreiro S, Dawson R, Kilsby C, Lewis E, Ford A (2018) Future changes in heat-waves, droughts and floods in 571 European cities. Environ Res Lett 13(3):034009.  https://doi.org/10.1088/1748-9326/aaaad3 CrossRefGoogle Scholar
  14. Hamed K, Rao A (1998) A modified Mann-Kendall trend test for autocorrelated data. J Hydrol 204:182–196.  https://doi.org/10.1016/S0022-1694(97)00125-X CrossRefGoogle Scholar
  15. He L, Cleverly J, Wang B, Jin N, Mi C, Liu D, Yu Q (2018) Multi-model ensemble projections of future extreme heat stress on rice across southern China. Theor Appl Climatol 133(3–4):1107–1118.  https://doi.org/10.1007/s00704-017-2240-4 CrossRefGoogle Scholar
  16. Hentschel G (1987) A human biometeorology classification of climate for large and local scales. In: Proceeding of WMO/HMO/UNEP symposium on climate and human health, Leningrad, vol I, WCPA-No. 1. WMO, GenevaGoogle Scholar
  17. Houghton F, Yaglo C (1923) Determining equal comfort lines. J Am Soc Heat Vent Eng 29:165–176Google Scholar
  18. Kalkstein L, Valimont K (1986) An evaluation of summer discomfort in the United States using a relative climatological index. Bull Am Meteorol Soc 67:842–848.  https://doi.org/10.1175/1520-0477(1986)0672.0.CO;2 CrossRefGoogle Scholar
  19. Kendall M (1975) Rank correlation methods. Oxford Univ. Press, New YorkGoogle Scholar
  20. Landsberg HE (1972) The assessment of human bioclimate: a limited review of physical parameters. World Meteorological Organization, Technical Note No. 123. WMO No. 331. WMO, Geneva, pp 36Google Scholar
  21. Li PW, Chan ST (2000) Application of a weather stress index for alerting the public to stressful weather in Hong Kong. Meteorol Appl 7(4):369–375.  https://doi.org/10.1017/S1350482700001602 CrossRefGoogle Scholar
  22. Lin C, Yang K, Qin J, Fu R (2013) Observed coherent trends of surface and upper-air wind speed over China since. J Clim 26:2891–2903.  https://doi.org/10.1175/JCLI-D-12-00093.1 CrossRefGoogle Scholar
  23. Mann H (1945) Non-parametric test against trend. Econometrika 13:245–259.  https://doi.org/10.2307/1907187 CrossRefGoogle Scholar
  24. Missenard F (1933) Température effective d’une atmosphere Généralisationtempérature résultante d’unmilieu. In Encyclopédie Industrielleet Commerciale, Etude physiologique et technique de la ventilation. Librerie del’Enseignement Technique, ParisGoogle Scholar
  25. Mitchell D, Heaviside C, Schaller N, Allen M, Ebi K, Fischer E, Gasparrini A, Harrington L, Kharin V, Shiogama H, Sillmann J, Sippel S, Vardoulakis S (2018) Extreme heat-related mortality avoided under Paris agreement goals. Nat Clim Chang 8(7):551–553.  https://doi.org/10.1038/s41558-018-0210-1 CrossRefGoogle Scholar
  26. Perkins S, Fischer E (2013) The usefulness of different realizations for the model evaluation of regional trends in heat waves. Geophys Res Lett 40(21):5793–5797.  https://doi.org/10.1002/2013GL057833 CrossRefGoogle Scholar
  27. Shin J, Heo J, Jeong C, Lee T (2014) Meta-heuristic maximum likelihood parameter estimation of the mixture normal distribution for hydro-meteorological variables. Stoch Env Res Risk A 28(2):347–358.  https://doi.org/10.1007/s00477-013-0753-7 CrossRefGoogle Scholar
  28. Smoyer K, Rainham D, Hewko J (2000) Heat-stress-related mortality in five cities in southern Ontario: 1980-1996. Int J Biometeorol 44(4):190–197.  https://doi.org/10.1007/s004840000070 CrossRefGoogle Scholar
  29. Spagnolo J, Dear R (2003) A human thermal climatology of subtropical Sydney. Int J Climatol 23:1383–1395.  https://doi.org/10.1002/joc.939 CrossRefGoogle Scholar
  30. Sun B, Wang H (2017) A trend towards a stable warm and windless state of the surface weather conditions in northern and northeastern China during 1961-2014. Adv Atmos Sci 34:713–726CrossRefGoogle Scholar
  31. Toy S, Aytaç A, Kántor N (2016) Human biometeorological analysis of the thermal conditions of the hot Turkish city of Şanliurfa. Theor Appl Climatol 131(1–2):611–623.  https://doi.org/10.1007/s00704-016-1995-3 Google Scholar
  32. Wang Z, Fang W, Liao Y (2013) Assessment of physical vulnerability to agricultural drought in China. Nat Hazards 67(2):645–657.  https://doi.org/10.1007/s11069-013-0594-1 CrossRefGoogle Scholar
  33. World Health Organization (2003) The health impacts of 2003 summer heat waves, briefing notes for the delegations of the fifty-third session of the WHO (World Health Organization) regional committee for Europe. Switzerland, GenevaGoogle Scholar
  34. Wu J, Gao X, Giorgid F, Chen D (2017) Changes of effective temperature and cold/hot days in late decades over China based on a high resolution gridded observation dataset. Int J Climatol 37(1):788–800.  https://doi.org/10.1002/joc.5038 CrossRefGoogle Scholar
  35. You Q, Jiang Z, Kong L, Wu Z, Bao Y, Kang S, Pepin N (2017) A comparison of heat wave climatologies and trends in China based on multiple definitions. Clim Dyn 48(11–12):3975–3989.  https://doi.org/10.1007/s00382-016-3315-0 CrossRefGoogle Scholar
  36. Zeng Y, Dong L (2015) Thermal human biometeorological conditions and subjective thermal sensation in pedestrian streets in Chengdu, China. Int J Biometeorol 59(1):99–108.  https://doi.org/10.1007/s00484-014-0883-8 CrossRefGoogle Scholar
  37. Zhang Q, Singh VP, Li JF, Chen XH (2011) Analysis of the periods of maximum consecutive wet days in China. J Geophys Res Atmos 116:D23106.  https://doi.org/10.1002/joc.939 CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Zhang Jie 
    • 1
    • 2
  • Lai Wenli 
    • 1
    Email author
  • Zhizhong Zhao
    • 1
  • Hongrui Wang
    • 3
  1. 1.College of Geography and Environmental ScienceHainan Normal UniversityHaikouChina
  2. 2.Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Science and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  3. 3.College of Water SciencesBeijing Normal UniversityBeijingChina

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