Climatic Change

, Volume 81, Supplement 1, pp 31–52

An inter-comparison of regional climate models for Europe: model performance in present-day climate

Authors

    • Max Planck Institute for Meteorology
  • Lars Bärring
    • Rossby CentreSMHI
  • Ole Bøssing Christensen
    • Danish Meteorological Institute
  • Jens Hesselbjerg Christensen
    • Danish Meteorological Institute
  • Manuel de Castro
    • Facultad de Ciencias del Medio AmbienteUniversidad de Castilla-La Mancha
  • Michel Déqué
    • Météo-France CNRM
  • Filippo Giorgi
    • The Abdus Salam International Centre for Theoretical Physics
  • Stefan Hagemann
    • Max Planck Institute for Meteorology
  • Martin Hirschi
    • Institut for Atmospheric and Climate Science ETH
  • Richard Jones
    • Met Office Hadley Centre (Reading Unit), Meteorology BuildingUniversity of Reading
  • Erik Kjellström
    • Rossby CentreSMHI
  • Geert Lenderink
    • Royal Netherlands Meteorological Institute
  • Burkhardt Rockel
    • GKSS Forschungszentrum Geesthacht
  • Enrique Sánchez
    • Facultad de Ciencias del Medio AmbienteUniversidad de Castilla-La Mancha
  • Christoph Schär
    • Institut for Atmospheric and Climate Science ETH
  • Sonia I. Seneviratne
    • Global Modeling and Assimilation Office NASA, Goddard Space Flight Center
  • Samuel Somot
    • Météo-France CNRM
  • Aad van Ulden
    • Royal Netherlands Meteorological Institute
  • Bart van den Hurk
    • Royal Netherlands Meteorological Institute
Article

DOI: 10.1007/s10584-006-9213-4

Cite this article as:
Jacob, D., Bärring, L., Christensen, O.B. et al. Climatic Change (2007) 81: 31. doi:10.1007/s10584-006-9213-4

Abstract

The analysis of possible regional climate changes over Europe as simulated by 10 regional climate models within the context of PRUDENCE requires a careful investigation of possible systematic biases in the models. The purpose of this paper is to identify how the main model systematic biases vary across the different models. Two fundamental aspects of model validation are addressed here: the ability to simulate (1) the long-term (30 or 40 years) mean climate and (2) the inter-annual variability. The analysis concentrates on near-surface air temperature and precipitation over land and focuses mainly on winter and summer. In general, there is a warm bias with respect to the CRU data set in these extreme seasons and a tendency to cold biases in the transition seasons. In winter the typical spread (standard deviation) between the models is 1 K. During summer there is generally a better agreement between observed and simulated values of inter-annual variability although there is a relatively clear signal that the modeled temperature variability is larger than suggested by observations, while precipitation variability is closer to observations. The areas with warm (cold) bias in winter generally exhibit wet (dry) biases, whereas the relationship is the reverse during summer (though much less clear, coupling warm (cold) biases with dry (wet) ones). When comparing the RCMs with their driving GCM, they generally reproduce the large-scale circulation of the GCM though in some cases there are substantial differences between regional biases in surface temperature and precipitation.

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Copyright information

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