Climatic Change

, Volume 113, Issue 2, pp 371–392 | Cite as

Future regional projections of extreme temperatures in Europe: a nonstationary seasonal approach

  • Maria Dolores FríasEmail author
  • Roberto Mínguez
  • Jose Manuel Gutiérrez
  • Fernando J. Méndez


This paper analyzes changes of maximum temperatures in Europe, which are evaluated using two state-of-the-art regional climate models from the EU ENSEMBLES project. Extremes are expressed in terms of return values using a time-dependent generalized extreme value (GEV) model fitted to monthly maxima. Unlike the standard GEV method, this approach allows analyzing return periods at different time scales (monthly, seasonal, annual, etc). The study focuses on the end of the 20th century (1961–2000), used as a calibration/validation period, and assesses the changes projected for the period 2061–2100 considering the A1B emission scenario. The performance of the regional models is evaluated for each season of the calibration period against the high-resolution gridded E-OBS dataset, showing a similar South-North gradient with larger values over the Mediterranean basin. The inter-RCM changes in the bias pattern with respect to the E-OBS are larger than the bias resulting from a change in the boundary conditions from ERA-40 to ECHAM5 20c3m. The maximum temperature response to increased green house gases, as projected by the A1B scenario, is consistent for both RCMs. Under that scenario, results indicate that the increments for extremes (e.g. 40-year return values) will be two or three times higher than those for the mean seasonal temperatures, particularly during Spring and Summer in Southern Europe.


Return Period Generalize Extreme Value Return Level Generalize Extreme Value Distribution Nonstationary Model 
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.



The ENSEMBLES data used in this work was funded by the EU FP6 Integrated Project ENSEMBLES (Contract number 505539) whose support is gratefully acknowledged. We acknowledge the E-OBS data set and the data providers in the ECA&D project ( R. Mínguez is indebted to the Spanish Ministry MICINN for the funding provided within the “Ramon y Cajal” program. This work was partly funded by projects “GRACCIE” (CSD2007-00067, Programa Consolider-Ingenio 2010), “AMVAR” (CTM2010-15009) and EXTREMBLES (CGL2010-21869) from Spanish Ministry MICINN, by project C3E (200800050084091) and ESCENA (200800050084265) from the Spanish Ministry MARM, and by project MARUCA (E17/08) from the Spanish Ministry MF. The authors would like to especially thank the anonymous reviewers who helped to considerably improve the former versions of our manuscript.


  1. Bretherton CS, Widmann M, Dymnikov VP, Wallace JM, Bladé I (1999) The effective number of spatial degrees of freedom of a time–varying field. J Climate 12:1990–2009CrossRefGoogle Scholar
  2. Brown SJ, Caesar J, Ferro CA (2008) Global changes in extreme daily temperature since 1950. J Geophys Res 113:D05,115CrossRefGoogle Scholar
  3. Christensen J, Hewitson B, Busuioc A, Chen AXG, Held I, Jones R, Kolli R, Kwon WT, Laprise R, Rueda VM, Mearns L, Menndez C, Risnen J, Rinke A, Sarr A, Whetton P (2007) Regional climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  4. Coles S (2001) An introduction to statistical modeling of extremes values. Springer, LondonGoogle Scholar
  5. Cooley D (2009) Extreme value analysis and the study of climate change. A commentary on Wigley 1988. Clim Change 97:77–83CrossRefGoogle Scholar
  6. Fischer E, Schar C (2010) Consistent geographical patterns of changes in high-impact european heatwaves. Nature Geoscience 3:398–403CrossRefGoogle Scholar
  7. Giorgi F, Lionello P (2008) Climate change projections for the mediterranean region. Glob Planet Change 63(2–3, Sp. Iss. SI):90–104. doi: 10.1016/j.gloplacha.2007.09.005 CrossRefGoogle Scholar
  8. Goubanova K, Li L (2007) Extremes in temperature and precipitation around the Mediterranean basin in an ensemble of future climate simulations. Glob Planet Change 57:27–42CrossRefGoogle Scholar
  9. Haylock M, Hofstra N, Klein-Tank A, Klok EJ, Jones P, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophys Res 113:D20,119Google Scholar
  10. Herrera S, Fita L, Fernández J, Gutiérrez JM (2010) Evaluation of the mean and extreme precipitation regimes from the ensembles regional climate multimodel simulations over spain. J Geophys Res 115:D21,117. doi: 10.1029/2010JD013936 CrossRefGoogle Scholar
  11. Hofstra N, New M, McSweeney C (2010) The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data. Clim Dyn 35(5):841–858. doi: 10.1007/s00382-009-0698-1 CrossRefGoogle Scholar
  12. Izaguirre C, Méndez FJ, MenéndezM, Luceño A, Losada IJ (2010) Extreme wave climate variability in southern Europe using satellite data. J Geophys Res 115:C04009. doi: 10.1029/2009JC005802
  13. Kharin VV, Zwiers FW (2005) Estimating extremes in transient climate change simulations. J Climate 18:1156–1173CrossRefGoogle Scholar
  14. Kharin VV, Zwiers FW, Zhang XB (2005) Intercomparison of near-surface temperature and precipitation extremes in AMIP-2 simulations, reanalyses and observations. J Climate 18:5201–5223CrossRefGoogle Scholar
  15. Kioutsioukis I, Melas D, Zerefos C (2010) Statistical assessment of changes in climate extremes over Greece (1955–2002). Int J Climatol 30:1723–1737CrossRefGoogle Scholar
  16. Kjellstrom E, Giorgi F (2010) Introduction. Clim Res 44:117–119CrossRefGoogle Scholar
  17. Kunkel KE, Andsager K, Easterling DR (1999) Long-term trends in extreme precipitation events over the conterminous United States and Canada. J Climate 12:2515–2527CrossRefGoogle Scholar
  18. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007) The wcrp cmip3 multi-model dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394CrossRefGoogle Scholar
  19. Méndez FJ, Menéndez M, Luceño A, Losada IJ (2007) Analyzing monthly extreme sea levels with a time-dependent gev model. J Atmos Ocean Technol 24:894–911CrossRefGoogle Scholar
  20. Menéndez M, Méndez FJ, Izaguirre C, no AL, Losada I (2009) The influence of seasonality on estimating return values of signifcant wave height. Coast Eng 56:211–219CrossRefGoogle Scholar
  21. Mínguez R, Méndez FJ, Izaguirre C, Menéndez M, Losada IJ (2010a) Pseudo-optimal parameter selection of non-stationary generalized extreme value models for environmental variables. Environ Model Softw 25:1592–1607. doi: 10.1016/j.envsoft.2010.05.008 CrossRefGoogle Scholar
  22. Mínguez R, Menéndez M, Méndez FJ, Losada IJ (2010b) Sensitivity analysis of time-dependent generalized extreme value models for ocean climate variables. Adv Water Resour 33:833–845. doi: 10.1016/j.advwatres.2010.05.003 CrossRefGoogle Scholar
  23. Morton ID, Bowers J, Mould G (1997) Estimating return period wave heights and wind speeds using a seasonal point process model. Coast Eng 31:305–326CrossRefGoogle Scholar
  24. Nikulin G, Kjellstrom E, Hansson U, Strandberg G, Ullerstig A (2011) Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of regional climate simulations. Tellus, Ser A Dyn Meteorol Oceanogr 63(1):41–55. doi: 10.1111/j.1600-0870.2010.00466.x Google Scholar
  25. Rust H, Maraun D, Osborn T (2009) Modelling seasonality in extreme precipitation. Eur Phys J 174:99–111Google Scholar
  26. Shär C, Jendrithzky G (2004) The European heat wave of 2003: was it merely a rare meteorological event or a first glimpse of climate change to come? Probably both, is the answer, and the anthropogenic contribution can be quantified. Nature 432:559–560CrossRefGoogle Scholar
  27. Sterl A, Severijns C, Dijkstra H, Hazeleger W, van Oldenborgh GJ, van den Broeke M, Burgers G, van den Hurk B, van Leeuwen PJ, van Velthoven P (2008) When can we expect extremely high surface temperatures? Geophys Res Lett 35:L14,703+. doi: 10.1029/2008GL034071 CrossRefGoogle Scholar
  28. 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 3–4:185–211CrossRefGoogle Scholar
  29. Uppala SM et al (2005) The ERA-40 re-analysis. Q J Royal Meteorol Soc 131:2961–3012CrossRefGoogle Scholar
  30. van der Linden P, Mitchell J (eds) (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UKGoogle Scholar
  31. Zahn M, von Storch H (2010) Decreased frequency of North Atlantic polar lows associated with future climate warming. Nature 467:309–312CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Maria Dolores Frías
    • 1
    Email author
  • Roberto Mínguez
    • 2
  • Jose Manuel Gutiérrez
    • 3
  • Fernando J. Méndez
    • 2
  1. 1.Department of Applied Mathematics and Computer ScienceUniversidad de CantabriaSantanderSpain
  2. 2.Environmental Hydraulics Institute (IH Cantabria)Universidad de CantabriaSantanderSpain
  3. 3.Instituto de Física de CantabriaCSIC-UCSantanderSpain

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