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Original Research Papers

Exploiting an ensemble of regional climate models to provide robust estimates of projected changes in monthly temperature and precipitation probability distribution functions

Authors:

Francisco J. Tapiador ,

Institute of Environmental Sciences University of Castilla-La Mancha (UCLM), Avda. Carlos III s/n. 45071 Toledo, ES
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Enrique Sánchez,

Institute of Environmental Sciences University of Castilla-La Mancha (UCLM), Avda. Carlos III s/n. 45071 Toledo, ES
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Raquel Romera

Institute of Environmental Sciences University of Castilla-La Mancha (UCLM), Avda. Carlos III s/n. 45071 Toledo, ES
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Abstract

Regional climate models (RCMs) are dynamical downscaling tools aimed to improve the modelling of local physical processes. Ensembles of RCMs are widely used to improve the coarse-grain estimates of global climate models (GCMs) since the use of several RCMs helps to palliate uncertainties arising from different dynamical cores and numerical schemes methods. In this paper, we analyse the differences and similarities in the climate change response for an ensemble of heterogeneous RCMs forced by one GCM (HadAM3H), and one emissions scenario (IPCC’s SRES-A2 scenario). As a difference with previous approaches using PRUDENCE database, the statistical description of climate characteristics is made through the spatial and temporal aggregation of the RCMs outputs into probability distribution functions (PDF) of monthly values. This procedure is a complementary approach to conventional seasonal analyses. Our results provide new, stronger evidence on expected marked regional differences in Europe in the A2 scenario in terms of precipitation and temperature changes. While we found an overall increase in the mean temperature and extreme values, we also found mixed regional differences for precipitation.

How to Cite: Tapiador, F.J., Sánchez, E. and Romera, R., 2009. Exploiting an ensemble of regional climate models to provide robust estimates of projected changes in monthly temperature and precipitation probability distribution functions. Tellus A: Dynamic Meteorology and Oceanography, 61(1), pp.57–71. DOI: http://doi.org/10.1111/j.1600-0870.2007.00374.x
  Published on 01 Jan 2009
 Accepted on 24 Jul 2008            Submitted on 28 Mar 2008

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