Climatic Change

, Volume 110, Issue 1–2, pp 385–401 | Cite as

Extreme climate events in China: IPCC-AR4 model evaluation and projection

  • Zhihong Jiang
  • Jie Song
  • Laurent Li
  • Weilin Chen
  • Zhifu Wang
  • Ji Wang


Observations from 550 surface stations in China during 1961–2000 are used to evaluate the skill of seven global coupled climate models in simulating extreme temperature and precipitation indices. It is found that the models have certain abilities to simulate both the spatial distributions of extreme climate indices and their trends in the observed period. The models’ abilities are higher overall for extreme temperature indices than for extreme precipitation indices. The well-simulated temperature indices are frost days (Fd), heat wave duration index (HWDI) and annual extreme temperature range (ETR). The well-simulated precipitation indices are the fraction of annual precipitation total due to events exceeding the 95th percentile (R95T) and simple daily intensity index (SDII). In a general manner, the multi-model ensemble has the best skill. For the projections of the extreme temperature indices, trends over the twenty-first century and changes at the end of the twenty-first century go into the same direction. Both frost days and annual extreme temperature range show decreasing trends, while growing season length, heat wave duration and warm nights show increasing trends. The increases are especially manifested in the Tibetan Plateau and in Southwest China. For extreme precipitation indices, the end of the twenty-first century is expected to have more frequent and more intense extreme precipitation. This is particularly visible in the middle and lower reaches of the Yangtze River, in the Southeast coastal region, in the west part of Northwest China, and in the Tibetan Plateau. In the meanwhile, accompanying the decrease in the maximum number of consecutive dry days in Northeast and Northwest, drought situation will reduce in these regions.


Tibetan Plateau Precipitation Index Extreme Index Extreme Climate Event Grow Season Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Alexander LV, Arblaster JM (2009) Assessing trends in observed and modelled climate extremes over Australia in relation to future projections. Int J Climatol 29:417–435CrossRefGoogle Scholar
  2. Cressman GW (1959) An operational objective analysis system. Mon Weather Rev 87:367–374CrossRefGoogle Scholar
  3. Delworth TL, Broccoli AJ, Balaji V, Beesley JA, Cooke WF, Stouffer RJ (2006) GFDL’s CM2 global coupled climate models. Part 1: formulation and simulation characteristics. J Climate 19:643–674CrossRefGoogle Scholar
  4. Easterling DR, Evans J, Groisman PY, Karl TR, Kunkel KE, Ambenje P (2000) Observed variability and trends in extreme climate events a brief review. Bull Am Meteorol Soc 81(3):417–425CrossRefGoogle Scholar
  5. Frich P, Alexander LV, Della-Marta P, Gleason B, Haylock M, Klein T, Peterson T (2002) Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim Res 19:193–212CrossRefGoogle Scholar
  6. Gao X, Zhao Z, Ding Y, Huang RH, Giorgi F (2003) Climate change due to greenhouse effects in China as simulated by a regional climate model part II: climate change. Acta Meteorol Sin 61(1):29–38 (in Chinese)Google Scholar
  7. Gao X, Lin W, Kucharsky F, Zhao Z (2004) Simulation of climate and short-term climate prediction in China by CCM3 driven by observed SST. Chin J Atmos Sci 28(1):78–90 (in Chinese)Google Scholar
  8. Groisman PY, Knight RW, Easterling DR, Karl TR, Hegerl GC, Razuvaev VN (2005) Trends in intense precipitation in the climate record. J Climate 18:1326–1350CrossRefGoogle Scholar
  9. Hasumi H, Emori S, K-1 model developers (2004) K-1 coupled model (MIROC) description. K-1 Tech. Rep. 1 Center for Climate System Research, University of TokyoGoogle Scholar
  10. Jiang D, Wang HJ, Lang X (2005) Evaluation of East Asian climatology as simulated by seven coupled models. Adv Atmos Sci 22(4):479–495 (in Chinese)CrossRefGoogle Scholar
  11. Jiang ZH, Zhang X, Wang J (2008) Projection of climate change in China in the 21st century by IPCC-AR4 Models. Geogr Res 27(4):787–799 (in Chinese)Google Scholar
  12. Jiang ZH, Ding Y, Cai M (2009) Monte Carlo experiments on the sensitivity of future extreme rainfall to climate warming. Acta Meteorol Sin 67(2):272–279 (in Chinese)Google Scholar
  13. Katz RW, Brown BG (1992) Extreme events in a changing climate: variability is more important than averages. Clim Change 21:289–302CrossRefGoogle Scholar
  14. Klein T, Konnen GP (2003) Trends in indices of daily temperature and precipitation extremes in Europe 1946–1999. J Climate 16:3665–3680CrossRefGoogle Scholar
  15. Kunkel KE, Pielke J, Changnon SA (1999) Temporal fluctuations in weather and climate extremes that cause economic and human health impacts: a review. Bull Am Meteorol Soc 80:1077–1098CrossRefGoogle Scholar
  16. Kunkel KE, Easterling DR, Redmond K, Hubbard K (2003) Temporal variations of extreme precipitation events in the United States: 1895–2000. Geophys Res Lett 30. doi: 10.1029/2003GL018052
  17. Liu M, Jiang ZH (2009) Simulation ability evaluation of surface temperature and precipitation by thirteen IPCC AR 4 coupled climate models in China during 1961–2000. J Nanjing Inst Meteorol 32(2):256–268 (in Chinese)Google Scholar
  18. Luo Y, Zhao Z, Xu Y, Gao X, Ding Y (2005) Projections of climate change over China for the 21st century. Acta Meteorol Sin 19:401–406Google Scholar
  19. Marti O, Braconnot P, Bellier J, Benshila R, Bony S, Brockmann P, Cadule P, Caubel A, Denvil S, Dufresne JL, Fairhead L, Filiberti MA, Foujols MA, Fichefet T, Friedlingstein P, Gosse H, Grandpeix JY, Hourdin F, Krinner G, Lévy C, Madec G, Musat I, de Noblet N, Polcher J, Talandier C (2005) The new IPSL climate system model: IPSL-CM4. Tech Rep IPSLCM4, Institute Pierre Simon Laplace, ParisGoogle Scholar
  20. Meehl GA, Karl TR, Easterling DR, Changnon S, Pielke R, Changnon D, Evans J, Groisman P, Knutson TR, Kunke KE, Mearns LO, Parmesan C, Pulwarty R, Root T, Sylves RT, Whetton P, Zwiers F (2000) An introduction to trends in extreme weather and climate events: observations, socioeconomic impacts, terrestrial ecological impacts, and model projections. Bull Am Meteorol Soc 81(3):413–416CrossRefGoogle Scholar
  21. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JB, Stouffer RJ, Taylor KF (2007) The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394CrossRefGoogle Scholar
  22. Nakicenovic N, Alcamo J, Davis J, de Vries B, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung TY, Kram T, Lebre La Rovere E, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Riahi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N, Dadi Z (2000) Special report on emissions scenarios. A special report of working group III for the intergovernmental panel on climate change. Cambridge University Press, Cambridge, p 599Google Scholar
  23. National Intelligence Council (NIC) (2009) China: impact of climate change to 2030—a commissioned research report. Special Report, by Joint Global Change Research Institute and Battelle Memorial Institute, Pacific Northwest Division, NIC 2009-020, April 2009Google Scholar
  24. Piao SL, et al (2010) The impacts of climate change on water resources and agriculture in China. Nature 467:43–51. doi: 10.1038/nature09364 CrossRefGoogle Scholar
  25. Qian WH, Lin X (2005) Regional trends in recent precipitation indices in China. Meteorol Atmos Phys 90(3/4):193–207CrossRefGoogle Scholar
  26. Salas-Mélia D, Chauvin F, Déqué M, Douville H, Gueremy JF, Marquet P, Planton S, Royer JF, Tyteca S (2005) Description and validation of the CNRM-CM3 global coupled model. CNRM working note 103Google Scholar
  27. Song J (2000) On the detection of climate change in the proxy records of dryness/wetness in China. Int J Climatol 20:1003–1015CrossRefGoogle Scholar
  28. Sun Y, Solomon S, Dai A, Portmann RW (2005) How often does it rain? J Climate 19:916–934CrossRefGoogle Scholar
  29. Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA (2006) Going to the extremes: an intercomparison of model-simulated historical and future changes in extreme events. Clim Change 79:185–211CrossRefGoogle Scholar
  30. Volodin EM, Diansky NA (2004) El-Nino reproduction in coupled general circulation model of atmosphere and ocean. Russ Meteorol Hydrol 12:5–14Google Scholar
  31. Wang S, Gong D, Chen Z (1999) Serious climatic disasters of China during the past 100 years. Q J Appl Meteorol 10:43–53 (in Chinese)Google Scholar
  32. Washington WM, Weatherly JW, Meehl GA, Semtner AG Jr, Bettge TW, Craig AP, Strand WG Jr, Arblaster J, Wayland VB, James R, Zhang Y (2000) Parallel climate model (PCM) control and transient simulations. Clim Dyn 16:755–774CrossRefGoogle Scholar
  33. Zhai PM, Pan XH (2003) Trends in temperature extremes during 1951–1999 in China. Geophys Res Lett 30. doi: 10.1029/2003Gl018004
  34. Zhai PM, Sun A, Ren F, Liu X, Gao B, Zhang Q (1999) Changes of climate extremes in China. Clim Change 42:203–218CrossRefGoogle Scholar
  35. Zhai PM, Zhang XB, Pan XH (2005) Trends in total precipitation and frequency of daily precipitation extremes over China. J Climate 18(7):1096–1108CrossRefGoogle Scholar
  36. Zhou T, Yu R (2006) Twentieth century surface air temperature over China and the globe simulated by coupled climate models. J Climate 19(22):5843–5858CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Zhihong Jiang
    • 1
  • Jie Song
    • 1
    • 2
  • Laurent Li
    • 1
    • 3
  • Weilin Chen
    • 1
  • Zhifu Wang
    • 1
  • Ji Wang
    • 1
  1. 1.Key Laboratory of Meteorological Disaster, Ministry of EducationNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Northern Illinois UniversityDeKalbUSA
  3. 3.Laboratoire de Météorologie Dynamique, IPSL, CNRS, UPMCParisFrance

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