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Assessment of extreme temperature percentiles by means of Regional Climate Models

The study of extreme events has become of great interest in the recent years due to their direct impact on society. Extremes can be evaluated both by using extreme value theory or deriving extreme indicators, which are usually based on statistics on the tail of the probability distribution. Some of the most commonly used indicators are based on percentiles. In this study we calculate the most extreme percentiles in maximum (90p, 95p and 99p) and minimum (1p, 5p and 10p) temperature derived from the application of dynamical downscaling methods in the Iberian Peninsula. We analyze the regional climate models (RCMs) of the ENSEMBLES (EU-funded) and ESCENA (strategic action of Plan Nacional de I+D+i 2008-2011 funded by the Spanish government) projects.

The dynamical downscaling methods are first tested in present climate with quasi-perfect (reanalysis) boundary conditions in order to know to what extent these approaches properly reproduce the observed percentiles. Then, we analyze the effect of nesting the RCMs to different Global Circulation Models (GCMs) on the 20C3M scenario. The study focuses on the end of the 20th century (1961–2000), used as a reference period, and assesses the changes projected for the period 2031–2050 considering the A1B emission scenario.

The comparison among the different models is obtained in terms of both the annual and seasonal biases. Analyzing the bias patterns, we are able to know whether the inter-RCM variability leads to larger biases than those due to the driving GCM. Moreover, due to the sensitivity of the results to the observational dataset used for the comparison, we consider the new, public, gridded dataset developed for continental Spain and Balearic Islands with 0.2º resolution (Spain02; Herrera et al. 2012).

Herrera, S., Gutierrez, J., Ancell, R., Pons, M., Frias, M., and Fernandez, J.: Development and Analysis of a 50 year high-resolution daily gridded precipitation dataset over Spain (Spain02). International Journal of Climatology 32:74-85 DOI: 10.1002/joc.2256 .

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