Climatic Change

, Volume 81, Supplement 1, pp 309–327 | Cite as

Gradient in the climate change signal of European discharge predicted by a multi-model ensemble

  • Stefan HagemannEmail author
  • Daniela Jacob


In order to perform hydrological studies on the PRUDENCE regional climate model (RCM) simulations, a special focus was put on the discharge from large river catchments located in northern and central Europe. The discharge was simulated with a simplified land surface (SL) scheme and the Hydrological Discharge (HD) model. The daily fields of precipitation, 2 m temperature and evapotranspiration from the RCM simulations were used as forcing. Therefore the total catchment water balances are constrained by the hydrological cycle of the different RCMs. The validation of the simulated hydrological cycle from the control simulations shows that the multi-model ensemble mean is closer to the observations than each of the models, especially if different catchments and hydrological variables are considered. Therefore, the multi-model ensemble mean can be used to largely reduce the uncertainty that is introduced by a single RCM. This also provides more confidence in the future projections for the multi-model ensemble means. The scenario simulations predict a gradient in the climate change signal over Northern and Central Europe. Common features are the overall warming and the general increase of evapotranspiration. But while in the northern parts the warming will enhance the hydrological cycle leading to an increased discharge, the large warming, especially in the summer, will slow down the hydrological cycle caused by a drying in the central parts of Europe which is accompanied by a reduction of discharge. The comparison of the changes predicted by the multi-model ensemble mean to the changes predicted by the driving GCM indicates that the RCMs can compensate problems that a driving GCM may have with local scale processes or parameterizations.


Regional Climate Model Global Precipitation Climatology Project Prudence Project Danube Catchment Global Precipitation Climatology Project Data 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bergström S (1992) The HBV model – its structure and applications. Swedish Meteorological and Hydrological Institute, Rep. 4, Norrköping, SwedenGoogle Scholar
  2. Buonomo E, Jones RG, Huntingford C, Hannaford J (2006) The robustness of high resolution predictions of changes in extreme precipitation for Europe. QJR Meteorol Soc (in press)Google Scholar
  3. Christensen JH, Christensen OB (2007) A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Clim Change, doi:10.1007/s10584-006-9210-7 (this issue)
  4. Clapp RB, Hornberger GM (1978) Empirical Equations for some soil hydraulic properties. Water Resour Res 14:601–604CrossRefGoogle Scholar
  5. Déqué M, Jones RG, Wild M, Giorgi F, Christensen JH, Hassell DC, Vidale PL, Rockel B, Jacob D, Kjellström E, de Castro M, Kucharski F, van den Hurk B (2005) Global high resolution versus Limited Area Model scenarios over Europe: results from the PRUDENCE project. Clim Dyn 25:653–670CrossRefGoogle Scholar
  6. Déqué M, Rowell D, Schär C, Giorgi F, Christensen JH, Rockel B, Jacob D, Kjellstrom E, de Castro M, van den Hurk B (2007) An intercomparison of regional climate models for Europe: assessing uncertainties in model projections. Clim Change, doi:10.1007/s10584-006-9228-x (this issue)
  7. Gibson JK, Kållberg P, Uppala S, Hernandez A, Nomura A, Serrano E (1997) Era description. ECMWF Re-Anal Proj Rep Ser 1, Reading, UKGoogle Scholar
  8. Graham LP, Hagemann S, Jaun S, Beniston M (2007) On interpreting hydrological change from regional climate models. Clim Change, doi:10.1007/s10584-006-9217-0 (this issue)
  9. Hagemann S, Dümenil Gates L (2001) Validation of the hydrological cycle of ECMWF and NCEP reanalyses using the MPI hydrological discharge model. J Geophys Res 106:1503–1510CrossRefGoogle Scholar
  10. Hagemann S, Dümenil Gates L (2003) Improving a subgrid runoff parameterization scheme for climate models by the use of high resolution data derived from satellite observations. Clim Dyn 21:349–359CrossRefGoogle Scholar
  11. Hagemann S, Machenhauer B, Jones R, Christensen OB, Déqué M, Jacob D, Vidale PL (2004) Evaluation of water and energy budgets in regional climate models applied over Europe. Clim Dyn 23:547–567CrossRefGoogle Scholar
  12. Huffman GJ, Adler RF, Arkin A, Chang A, Ferraro R, Gruber A, Janowiak J, Joyce RJ, McNab A, Rudolf B, Schneider U, Xie P (1997) The Global Precipitation Climatology Project (GPCP) combined precipitation data set. Bull Am Meteorol Soc 78:5–20CrossRefGoogle Scholar
  13. Jacob D, Bärring L, Christensen OB, Christensen JH, de Castro M, Déqué M, Giorgi F, Hagemann S, Hirschi M, Jones RG, Kjellström E, Lenderink G, Rockel B, Sánchez E, Schär C, Seneviratne SI, Somot S, van Ulden A, van den Hurk B (2007) An inter-comparison of regional climate models for Europe: design of the experiments and model performance. Clim Change, doi:10.1007/s10584-006-9213-4 (this issue)
  14. Jones RG, Murphy JM, Noguer M (1995) Simulation of climate change over Europe using a nested regional climate model. I: assessment of control climate, including sensitivity to location of lateral boundaries. QJR Meteorol Soc 121:1413–1449Google Scholar
  15. Jones RG, Noguer M, Hassell DC, Hudson D, Wilson SS, Jenkins GJ, Mitchell JFB (2004) Generating high resolution climate change scenarios using PRECIS. Met Office Hadley Centre, Exeter, UK, p 35Google Scholar
  16. Latif M, Roeckner E, Botzet M, Esch M, Haak H, Hagemann S, Jungclaus J, Legutke S, Marsland S, Mikolajewicz U (2003) Reconstructing, monitoring, and predicting decadal-scale changes in the North Atlantic thermohaline circulation with sea surface temperature. J Climate 17:1605–1613CrossRefGoogle Scholar
  17. Lenderink G, van den Hurk BJJM, van Ulden AP, van Meijgaard E (2007) Summertime inter-annual temperature variability in an ensemble of regional model simulations: analysis of the surface energy budget. Clim Change, doi:10.1007/s10584-006-9229-9 (this issue)
  18. Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grübler A, Jung TY, Kram T, La Rovere EL, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Raihi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N, Dadi Z (2000) IPCC special report on emissions scenarios. Cambridge University Press, Cambridge, UKGoogle Scholar
  19. Räisänen J, Hansson U, Ullerstig A (2002) First GCM-driven RCAO runs of recent and future climate. In: SWECLIM Newsletter 12:16–21Google Scholar
  20. Räisänen J, Hansson U, Ullerstig A, Döscher R, Graham LP, Jones C, Meier HEM, Samuelsson P, Willén U (2004) European climate in the late twenty-first century: regional simulations with two driving global models and two forcing scenarios. Clim Dyn 22:13–31CrossRefGoogle Scholar
  21. Rowell DP (2005) A scenario of European climate change for the late 21st century: seasonal means and interannual variability. Clim Dyn, doi:10.1007/s00382-005-0068-6
  22. Rudolf B, Rubel F (2005) Global precipitation. In: Hantel M (ed) Observed global climate, Chap. 11. Landolt–Boernstein: numerical data and functional relationships in science and technology – new series, Group 5:Geophysics, vol. 6, Springer, Berlin Heidelberg New York, p 567Google Scholar
  23. van den Hurk B, Hirschi M, Schär C, Lenderink G, van Meijgaard E, van Ulden A, Rockel B, Hagemann S, Graham P, Kjellström E, Jones R (2004) Soil control on runoff response to climate change in regional climate model simulations. J Climate 18:3536–3551CrossRefGoogle Scholar
  24. Xie P, Arkin P (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates and numerical model outputs. Bull Am Meteorol Soc 78:2539–2558CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, B.V. 2007

Authors and Affiliations

  1. 1.Max Planck Institute for MeteorologyHamburgGermany

Personalised recommendations