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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 13, issue 8 | Copyright
Nat. Hazards Earth Syst. Sci., 13, 2089-2099, 2013
https://doi.org/10.5194/nhess-13-2089-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 22 Aug 2013

Research article | 22 Aug 2013

Evaluation and projection of daily temperature percentiles from statistical and dynamical downscaling methods

A. Casanueva1, S. Herrera1,2, J. Fernández1, M. D. Frías1, and J. M. Gutiérrez3 A. Casanueva et al.
  • 1Grupo de Meteorología, Dpto. Matemática Aplicada y Ciencias de la Computación, Univ. de Cantabria, Avda. de los Castros, s/n, 39005 Santander, Spain
  • 2Predictia Intelligent Data Solutions, S.L. CDTUC, Avda. de los Castros, s/n, 39005, Santander, Spain
  • 3Grupo de Meteorología, Instituto de Física de Cantabria, CSIC-Univ. de Cantabria, Avda. de los Castros, s/n, 39005 Santander, Spain

Abstract. The study of extreme events has become of great interest in recent years due to their direct impact on society. Extremes are usually evaluated by using extreme indicators, based on order statistics on the tail of the probability distribution function (typically percentiles). In this study, we focus on the tail of the distribution of daily maximum and minimum temperatures. For this purpose, we analyse high (95th) and low (5th) percentiles in daily maximum and minimum temperatures on the Iberian Peninsula, respectively, derived from different downscaling methods (statistical and dynamical). First, we analyse the performance of reanalysis-driven downscaling methods in present climate conditions. The comparison among the different methods is performed in terms of the bias of seasonal percentiles, considering as observations the public gridded data sets E-OBS and Spain02, and obtaining an estimation of both the mean and spatial percentile errors. Secondly, we analyse the increments of future percentile projections under the SRES A1B scenario and compare them with those corresponding to the mean temperature, showing that their relative importance depends on the method, and stressing the need to consider an ensemble of methodologies.

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