Journal
CLIMATE RESEARCH
Volume 54, Issue 3, Pages 249-+Publisher
INTER-RESEARCH
DOI: 10.3354/cr01110
Keywords
Climate extremes; Indices; NE Mediterranean; ENSEMBLES; Regional Climate Models; RCMs; ENSEMBLES daily gridded observational dataset; E-OBS
Funding
- EU FP6 Integrated ENSEMBLES [505539]
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In the framework of the EU-funded project ENSEMBLES, an integrated set of high resolution climate model simulations for the present and future climate of Europe has been created, as well as a gridded observational dataset (E-OBS) of daily precipitation and temperature, based on a large number of station data in Europe. This study evaluates the ability of E-OBS to reproduce the observed daily temperature and precipitation data from meteorological stations across the NE Mediterranean sub-region. Intercomparisons are performed for the raw time series data, as well as for corresponding indices of climate extremes. Further, the study assesses the accuracy of several ENSEMBLES regional climate models (RCMs) to reproduce indices of climate extremes in the study region. The analysis reveals that the gridded observational dataset satisfactorily reproduces temperature data and indices in most study sites, with deviations evident at high elevation locations. The precipitation indices, however, are less accurate. E-OBS succeeds in estimating trends in extreme temperature and precipitation. Model evaluations vary largely among sites, models and variables. The inter-model correlations range from 0.50 to 0.80. Positive correlations are found for the 90th percentile of maximum temperature between E-OBS and each RCM; however at certain locations, some models are more skilful than others. The 10th percentile of minimum temperature shows higher correlations in southeastern parts of the study region, while precipitation extremes are generally overestimated. Most of the models reproduce the observed extreme temperature trend patterns, but the diversity in results prevents clear conclusions being drawn regarding the models' performance in replicating extreme precipitation trends.
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