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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 11
Hydrol. Earth Syst. Sci., 17, 4323–4337, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Precipitation uncertainty and variability: observations, ensemble...

Hydrol. Earth Syst. Sci., 17, 4323–4337, 2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Nov 2013

Research article | 01 Nov 2013

On the importance of observational data properties when assessing regional climate model performance of extreme precipitation

M. A. Sunyer1, H. J. D. Sørup1,2, O. B. Christensen2, H. Madsen3, D. Rosbjerg1, P. S. Mikkelsen1, and K. Arnbjerg-Nielsen1 M. A. Sunyer et al.
  • 1Department of Environmental Engineering, Technical University of Denmark, Lyngby, Denmark
  • 2Danish Climate Centre, Danish Meteorological Institute, Copenhagen, Denmark
  • 3DHI, Hørsholm, Denmark

Abstract. In recent years, there has been an increase in the number of climate studies addressing changes in extreme precipitation. A common step in these studies involves the assessment of the climate model performance. This is often measured by comparing climate model output with observational data. In the majority of such studies the characteristics and uncertainties of the observational data are neglected.

This study addresses the influence of using different observational data sets to assess the climate model performance. Four different data sets covering Denmark using different gauge systems and comprising both networks of point measurements and gridded data sets are considered. Additionally, the influence of using different performance indices and metrics is addressed. A set of indices ranging from mean to extreme precipitation properties is calculated for all the data sets. For each of the observational data sets, the regional climate models (RCMs) are ranked according to their performance using two different metrics. These are based on the error in representing the indices and the spatial pattern.

In comparison to the mean, extreme precipitation indices are highly dependent on the spatial resolution of the observations. The spatial pattern also shows differences between the observational data sets. These differences have a clear impact on the ranking of the climate models, which is highly dependent on the observational data set, the index and the metric used. The results highlight the need to be aware of the properties of observational data chosen in order to avoid overconfident and misleading conclusions with respect to climate model performance.

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