Climate Dynamics

, Volume 52, Issue 5–6, pp 3575–3598 | Cite as

Classifications of winter atmospheric circulation patterns: validation of CMIP5 GCMs over Europe and the North Atlantic

  • Jan StryhalEmail author
  • Radan Huth


Winter atmospheric circulation over the Euro-Atlantic domain and three subdomains (British Isles, Central Europe, and Eastern Mediterranean) is validated in outputs of historical runs of 32 global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Eight automated classifications of daily SLP patterns from five reanalysis datasets are produced for each domain in order to analyse the effect of the choices of methods and reference data on results. The results show that the ranking of GCMs fundamentally depends on which classification is used; therefore, only parallel usage of multiple classifications can provide robust rankings of models. Considering all eight classifications, three models (HadGEM2-CC, MIROC4h, and CNRM-CM5) are among the best in simulating the frequency of circulation types (CTs) over all four domains. Regardless the domain, the bias in CT frequency of the worst GCMs is larger than 50% of the frequency in the reference reanalysis dataset. Conversely, the best GCM for each domain differs from the reference reanalysis by about 10–20%, which is nearly the same result as found for the NOAA-CIRES Twentieth Century Reanalysis (version 2). The persistence of circulation is simulated better than the frequency with errors rarely exceeding 15%. The GCMs overestimate the frequency of westerly circulation over all domains (by about 7% over the British Isles, 21% over Central Europe, and almost 70% over the Eastern Mediterranean) and also cyclonic CTs, while easterly and anticyclonic CTs are typically underestimated by 30–40%.


Global climate models Validation Atmospheric circulation Circulation classifications 



The work was funded by the Grant Agency of Charles University, Project No. 188214. We thank all climate-modelling groups for making available their GCM simulations and the PCMDI for enabling access to the data. We acknowledge the following organizations for providing their reanalysis datasets: NOAA/OAR/ESRL PSD, Boulder, Colorado for the NCEP/NCAR reanalysis and the Twentieth Century Reanalysis, version 2; ECMWF for ERA-40 and ERA-20C; and JMA for JRA-55. Thanks are also due to all developers of the COST733 software and namely Dr Andreas Philipp from the Institute of Geography, University of Augsburg, Germany for the many instructions on its usage; the Institute is also acknowledged for maintaining the software and enabling access to it. We are grateful to two reviewers, whose insight helped improve the quality of this paper.


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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Physical Geography and Geoecology, Faculty of ScienceCharles UniversityPrague 2Czech Republic
  2. 2.Institute of Atmospheric PhysicsCzech Academy of SciencesPrague 4Czech Republic

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