Simulated and projected climate extremes in the Tarim River Basin using the regional climate model CCLM

Original Paper


The reduction of uncertainty in simulations and projections of regional climate models is a critical issue for regional climate impact studies, especially in the context of climate extremes. In this study, the regional climate model COSMO-CLM (CCLM) is evaluated in terms of daily precipitation and temperature characteristics, in order to obtain reliable projections of climate extremes for the Tarim River Basin (TRB) in Northwest China. The results show that CCLM can acceptably reproduce the annual cycle of maximum and minimum temperature, as well as the spatial distribution of precipitation pattern. Nonetheless, some systematic biases have been encountered. The equidistant cumulative distribution function matching method has been applied, which led to an efficient reduction of the systematic biases of observed and simulated climate variables. The bias correction has further been applied to climate projections for the period of 2016–2035 under the Representative Concentration Pathway 4.5. The projected indices of climate extremes as calculated from the bias-corrected CCLM projections show that most of the TRB is likely to experience a decrease in daily temperature range, and an increase in minimum temperature as well as consecutive wet days. The total precipitation on very wet days is projected to slightly increase at most stations, while the annual total precipitation will mostly increase in the southwestern parts of the TRB. The findings on the spatial–temporal patterns of these climate extremes will enable decision makers, especially in the water and agricultural sectors, to adapt and be better prepared for future climate impacts in the region.


Climate extremes Bias correction Regional climate model CCLM Tarim River Basin 



This study is financially supported by the National Natural Science Foundation of China (No.: 41330529 and 41101023) and the Key Technology Research and Development Program of the Xinjiang Uygur Autonomous Region (No.:201331104), as well as Fudan Tyndall ‘985-III’ Climate Change Foundation (2013–2014).


  1. Bonsal BR, Zhang XB, Vincent L, Hogg WD (2001) Characteristic of daily and extreme temperature over Canada. J Clim 5(14):1959–1976CrossRefGoogle Scholar
  2. Bordoy R, Burlando P (2013) Bias correction of regional climate model simulations in a region of complex orography. J Appl Meteorol Climatol 52:82–101CrossRefGoogle Scholar
  3. Bucchignani E, Montesarchio M, Cattaneo L, Manzi MP, Mercogliano P (2014) Regional climate modeling over China with COSMO-CLM: performance assessment and climate projections. J Geophys Res 119(21):12151–12170Google Scholar
  4. Christensen JH, Carter TR, Rummukainen M, Amanatidis G (2007) Evaluating the performance and utility of regional, climate models: the PRUDENCE project. Clim Chang 81:1–6CrossRefGoogle Scholar
  5. Dominguez F, Rivera E, Lettenmaier DP, Castro CL (2012) Changes in winter precipitation extremes for the western United States under a warmer climate as simulated by regional climate models. Geophys Res Lett 39:L05803. doi: 10.1029/2011GL050762 CrossRefGoogle Scholar
  6. Donat MG, Alexander LV, Yang H, Durre I, Vose R, Dunn RJH, Willett KM, Aguilar E, Brunet M, Caesar J, Hewitson B, Jack C, Klein Tank AMG, Kruger AC, Marengo J, Peterson TC, Renom M, Oria Rojas C, Rusticucci M, Salinger J, Elrayah AS, Sekele SS, Srivastava AK, Trewin B, Villarroel C, Vincent LA, Zhai P, Zhang X, Kitching S (2013) Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset. J Geophys Res 118(5):2098–2118CrossRefGoogle Scholar
  7. Dosio A, Paruolo P (2011) Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: evaluation on the present climate. J Geophys Res 116:D16106. doi: 10.1029/2011JD015934 CrossRefGoogle Scholar
  8. Easterling DR, Meehl GA, Parmsan C, Changnon SA, Karl TR, Mearns LO (2000) Climate extremes: observations, modeling, and impacts. Science 289:2068–2074CrossRefGoogle Scholar
  9. Feng JM, Wang YL, Fu CB (2011) Simulation of extreme climate events over China with different regional climate models. Atmos Oceanic Sci Lett 4(1):47–56CrossRefGoogle Scholar
  10. Fischer T, Menz C, Su BD, Scholten T (2013) Simulated and projected climate extremes in the Zhujiang River Basin South China, using the regional climate model COSMO-CLM. Int J Climatol 33(14):2988–3001CrossRefGoogle Scholar
  11. Frei C, Christensen JH, Deque M, Jacob D, Jones RG, Vidale PL (2003) Daily precipitation statistics in regional climate models: evaluation and intercomparison for the European Alps. J Geophys Res 108:D34124. doi: 10.1029/2002JD002287 Google Scholar
  12. Frich P, Alexander LV, Della-Marta P, Gleason B, Haylock M, Klein Tank AMG, Peterson T (2002) Observed coherent changes in climatic extremes during 2nd half of the 20th century. Clim Res 19:193–212CrossRefGoogle Scholar
  13. Fu CB, Wang SY, Xiong Z, Gutowski WJ, Lee DK, McGregor JL, Sato Y, Kato H, Kim JW, Suh MS (2005) Regional climate model intercomparison project for Asia. Bull Am meteorol Soc 86:257–266CrossRefGoogle Scholar
  14. Gao XJ, Xu Y, Zhao ZC, Pal JS, Giorgi F (2006) On the role of resolution and topography in the simulation of East Asia precipitation. Theor Appl Climatol 86:173–185CrossRefGoogle Scholar
  15. Gao XJ, Shi Y, Zhang DF, Wu J, Giorgi F, Ji ZM, Wang YG (2012) Uncertainties in monsoon precipitation projections over China: results from two high-resolution RCM simulations. Clim Res 52:213–226CrossRefGoogle Scholar
  16. Gao XJ, Wang ML, Giorgi F (2013) Climate change over China in the 21st century as simulated by BCC_CSM1.1-RegCM4.0. Atmos Oceanic Sci Lett 6(5):381–386CrossRefGoogle Scholar
  17. Gemmer M, Fischer T, Jiang T, Su BD, Liu LL (2011) Trends in precipitation extremes in the Zhujiang River Basin, South China. J Clim 24:750–761CrossRefGoogle Scholar
  18. Giorgi F, Bates GT (1989) The climatological skill of a regional model over complex terrain. Mon Weather Rev 117(11):2325–2347CrossRefGoogle Scholar
  19. Giorgi F, Colin J, Ghassem A (2009) Addressing climate information needs at the regional level: the CORDEX framework. WMO Bull 58(3):175–183Google Scholar
  20. Hagemann S, Chen C, Haerter JO, Heinke J, Gerten D, Piani C (2011) Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models. J Hydrometeorol 12:556–578CrossRefGoogle Scholar
  21. He H, Lu GH, Zhao F (2009) Characteristics of the water vapor transport and its budget over the Tarim River basin. Taylor & Francis Group, London, pp 23–29Google Scholar
  22. Hempel S, Frieler K, Warszawski L, Schewe J, Piontek F (2013) A trend-preserving bias correction the ISI-MIP approach. Earth Syst Dyn 4:219–236CrossRefGoogle Scholar
  23. Horova I, Kolacek J, Zelinka J (2012) Kernel Smoothing in MATLAB: theory and practice of kernel smoothing. World Scientific Publishing Co. Pte. Ltd., Singapore, p 244, ISBN 978-981-4405-48-5Google Scholar
  24. IPCC, 2013: 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., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, pp 1535Google Scholar
  25. Ji ZM, Kang SC (2014) Evaluation of extreme climate events using a regional climate model for China. Int J Climatol. doi: 10.1002/joc.4024 Google Scholar
  26. Karl TR, Knight RW, Plummer N (1995) Trends in high-frequency climate variability in the twentieth century. Nature 377:217–220CrossRefGoogle Scholar
  27. Kjellström E, Bärring L, Jacob D, Jones R, Lenderink G, Schär C (2007) Variability in daily maximum and minimum temperatures: recent and future changes over Europe. Clim Chang 81:249–265CrossRefGoogle Scholar
  28. Klein Tank AMG, Peterson TC, Quadir DA, Dorji S, Zou X, Tang H, Santhosh K, Joshi UR, Jaswal AK, Kolli RK, Sikder A, Deshpande NR, Revadekar JV, Yeleuova K, Vandasheva S, Faleyeva M, Gomboluudev P, Budhathoki KP, Hussain A, Afzaal M, Chandrapala L, Anvar H, Amanmurad D, Asanova VS, Jones PD, New MG, Spektorman T (2006) Changes in daily temperature and precipitation extremes in central and south Asia. J Geophys Res 111:D16105. doi: 10.1029/2005JD006316 CrossRefGoogle Scholar
  29. Li HB, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J Geophys Res. doi: 10.1029/2009JD012882 Google Scholar
  30. Li ZL, Li CC, Xu ZX, Zhou X (2014) Frequency analysis of precipitation extremes in Heihe River basin based on generalized Pareto distribution. Stoch Environ Res Risk Assess 28:1709–1721CrossRefGoogle Scholar
  31. Lo JC, Yang Z, Pielke RA Sr (2008) Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J Geophys Res. doi: 10.1029/2007JD009216 Google Scholar
  32. Mannig B, Müller M, Starke E, Merkenschlager C, Mao WY, Zhi XF, Podzum R, Jacob D, Paeth H (2013) Dynamical downscaling of climate change in Central Asia. Glob Planet Chang 110:26–39CrossRefGoogle Scholar
  33. Müller WA, Baehr J, Haak H, Jungclaus JH, Kröger J, Matei D, Notz D, Pohlmann H, von Storch JS, Marotzke J (2012) Forecast skill of multi-year seasonal means in the decadal prediction system of the Max Planck Institute for Meteorology. Geophys Res Lett. doi: 10.1029/2012GL053326 Google Scholar
  34. Rahmstorf S, Coumou D (2011) Increase of extreme events in a warming world. PNAS 108(44):17905–17909CrossRefGoogle Scholar
  35. Rajczak J, Pall P, Schär C (2013) Projections of extreme precipitation events in regional climate simulations for Europe and the Alpine Region. J Geophys Res 118:3610–3626CrossRefGoogle Scholar
  36. Schär C, Vidale PL, Lüthi D, Frei C, Haberli C, Liniger MA, Appenzeller C (2004) The role of increasing temperature variability in European summer heatwaves. Nature 427:332–336CrossRefGoogle Scholar
  37. Schoetter R, Hoffmann P, Rechid D, Schlünzen KH (2012) Evaluation and bias correction of regional climate model results using model evaluation measures. J Appl Meteorol Climatol 51(9):1670–1684CrossRefGoogle Scholar
  38. Siliverstovs B, Ötsch R, Kemfert C, Jaeger CC, Haas A, Kremers H (2010) Climate change and modelling of extreme temperatures in Switzerland. Stoch Environ Res Risk Assess 24:311–326CrossRefGoogle Scholar
  39. Smiatek G, Kunstmann H, Knoche R, Marx A (2009) Precipitation and temperature statistics in high-resolution regional climate models: evaluation for the European Alps. J Geophys Res 114:D19107. doi: 10.1029/2008JD011353 CrossRefGoogle Scholar
  40. Steppeler J, Doms G, Schättler U, Bitzer HW, Gassmann A, Damrath U, Gregoric G (2003) Meso-gamma scale forecasts using the non-hydrostatic model. Meteorol Atmos Phys 82:75–96CrossRefGoogle Scholar
  41. Su BD, Jiang T, Jin WB (2006) Recent trends in observed temperature and precipitation extremes in the Yangtze River basin China. Theor Appl Climatol 83(1–4):139–151CrossRefGoogle Scholar
  42. Tao H, Gemmer M, Bai YG, Su BD, Mao WY (2011) Trends of streamflow in the Tarim River Basin during the past fifty years: human impact or climate change? J Hydrol 400:1–9CrossRefGoogle Scholar
  43. Teutschbein C, Seibert J (2010) Regional climate models for hydrological impact studies at the catchment scale: a review of recent model strategies. Geogr Compass 4(7):834–860CrossRefGoogle Scholar
  44. Wang L, Chen W (2014) Equiratio cumulative distribution function matching as an improvement to the equidistant approach in bias correction of precipitation. Atmos Sci Lett 15:1–6CrossRefGoogle Scholar
  45. Wang DN, Menz C, Simon T, Simmer C, Ohlwein C (2013a) Regional dynamical downscaling with CCLM over East Asia. Meteorol Atmos Phys 212(1–2):39–53CrossRefGoogle Scholar
  46. Wang HJ, Chen YN, Chen ZS (2013b) Spatial distribution and temporal trends of mean precipitation and extremes in the arid region, northwest of China, during 1960–2010. Hydrol Process 27(12):1807–1818CrossRefGoogle Scholar
  47. Wang WG, Shao QX, Yang T, Peng SZ, Yu ZB, Taylor J, Xing WQ, Zhao CP, Sun FC (2013c) Changes in daily temperature and precipitation extremes in the Yellow River Basin, China. Stoch Environ Res Risk Assess 27:401–421CrossRefGoogle Scholar
  48. Watanabe S, Kanae S, Seto S, Yeh PJF, Hirabayashi Y, Oki T (2012) Intercomparison of bias-correction methods for monthly temperature and precipitation simulated by multiple climate models. J Geophys Res. doi: 10.1029/2012JD018192 Google Scholar
  49. Yang T, Wang XY, Zhao CY, Chen X, Yu ZB, Shao QX, Xu CY, Xia J, Wang WG (2011) Changes of climate extremes in a typical arid zone: observations and multimodel ensemble projections. J Geophys Res 116:D19106. doi: 10.1029/2010JD015192 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Environmental Science and EngineeringFudan UniversityShanghaiChina
  2. 2.State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjingChina
  3. 3.Department of GeosciencesEberhard Karls UniversityTübingenGermany

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