Volume 39, Issue 9
VALUE SPECIAL ISSUE ARTICLE

Observational uncertainty and regional climate model evaluation: A pan‐European perspective

Sven Kotlarski

Corresponding Author

E-mail address: sven.kotlarski@meteoswiss.ch

Federal Office of Meteorology and Climatology MeteoSwiss, Zurich‐Airport, Switzerland

Correspondence

Sven Kotlarski, Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, CH‐8058 Zurich‐Airport, Switzerland.

Email: sven.kotlarski@meteoswiss.ch

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Péter Szabó

Hungarian Meteorological Service, Budapest, Hungary

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Sixto Herrera

Meteorology Group, Departamento de Matemática Aplicada y Ciencias de la Computación, Universidad de Cantabria, Spain

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Olle Räty

University of Helsinki, Finland

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Klaus Keuler

Brandenburg University of Technology, Cottbus, Germany

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Pedro M. Soares

Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Portugal

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Rita M. Cardoso

Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Portugal

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Thomas Bosshard

Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

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Christian Pagé

UMR CNRS 5318 CECI – CERFACS, Toulouse, France

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Fredrik Boberg

Danish Meteorological Institute, Copenhagen, Denmark

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José M. Gutiérrez

Meteorology Group, Instituto de Física de Cantabria, CSIC‐Universidad de Cantabria, Spain

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Francesco A. Isotta

Federal Office of Meteorology and Climatology MeteoSwiss, Zurich‐Airport, Switzerland

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Adam Jaczewski

Institute of Meteorology and Water Management, National Research Institute, Warsaw, Poland

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Frank Kreienkamp

Deutscher Wetterdienst, Offenbach, Germany

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Mark A. Liniger

Federal Office of Meteorology and Climatology MeteoSwiss, Zurich‐Airport, Switzerland

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Cristian Lussana

Norwegian Meteorological Institute, Oslo, Norway

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Krystyna Pianko‐Kluczyńska

Institute of Meteorology and Water Management, National Research Institute, Warsaw, Poland

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First published: 10 September 2017
Citations: 36
Funding information Vilho, Yrjö and Kalle Väisälä Foundation of the Finnish Academy of Science and Letters

Abstract

The influence of uncertainties in gridded observational reference data on regional climate model (RCM) evaluation is quantified on a pan‐European scale. Three different reference data sets are considered: the coarse‐resolved E‐OBS data set, a compilation of regional high‐resolution gridded products (HR) and the European‐scale MESAN reanalysis. Five high‐resolution ERA‐Interim‐driven RCM experiments of the EURO‐CORDEX initiative are evaluated against each of these references over eight European sub‐regions and considering a range of performance metrics for mean daily temperature and daily precipitation. The spatial scale of the evaluation is 0.22°, that is, the grid spacing of the coarsest data set in the exercise (E‐OBS). While the three reference grids agree on the overall mean climatology, differences can be pronounced over individual regions. These differences partly translate into RCM evaluation uncertainty. For most cases observational uncertainty is smaller than RCM uncertainty. Nevertheless, for individual sub‐regions and performance metrics observational uncertainty can dominate. This is especially true for precipitation and for metrics targeting the wet‐day frequency, the pattern correlation and the distributional similarity. In some cases the spatially averaged mean bias can also be considerably affected. An illustrative ranking exercise highlights the overall effect of observational uncertainty on RCM ranking. Over individual sub‐domains, the choice of a specific reference can modify RCM ranks by up to four levels (out of five RCMs). For most cases, however, RCM ranks are stable irrespective of the reference. These results provide a twofold picture: model uncertainty dominates for most regions and for most performance metrics considered, and observational uncertainty plays a minor role. For individual cases, however, observational uncertainty can be pronounced and needs to be definitely taken into account. Results can, to some extent, also depend on the treatment of precipitation undercatch in the observational reference.

Number of times cited according to CrossRef: 36

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