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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. [ABSTRACT FROM AUTHOR]

A deforestation experiment is performed over the western Mediterranean, applying two different RCMs with differing domains and an ensemble technique to obtain a measure of their internal variability. The internal variability is used to assign statistical significance to the results, and also to discuss whether the models are sufficiently free to develop internal mesoscale processes. Considerable internal variability values found for hydrological variables even in autumn and winter seem to support the assumption that the models are free enough to be applied to such a sensitivity study. The combined use of two models, with strongly differing domains, and significance assigned through the use of internal variability should highlight responses to deforestation which are of physical origin and not a result dependent on one particular model. The overall significant response from both RCMs to deforestation is a reduction of evaporation (spring and summer, extending over the whole deforested zone) and a decrease in precipitation (late spring and summer, over some regions). A detailed analysis over subzones shows remarkable agreement between the two models over some of these subzones, showing non-local effects in precipitation response. [ABSTRACT FROM AUTHOR]

Two regional climate models have been applied to the task of generating an ensemble of realizations of the year 1982 with observed boundary conditions in areas covering parts of the Mediterranean countries. These realizations were generated by applying boundary conditions from the ECMWF ERA reanalysis project consecutively, carrying over the soil variables from the regional models from one iteration to the next. Monthly mean fields for six iterations of each model have been used as statistical ensembles in order to investigate the internal variability of the regional model dynamics. This internal variability is a necessary consequence of the non-linear physical feedback mechanisms of the RCM being active. A small value of internal variability will give better statistics for climate sensitivity signals, but will make these results less credible. The internal variability is important for the quantitative assessment of a climate sensitivity signal. With the present choice of models and integration domains the internal variabilities of surface fields and precipitation do reach levels that are less than, but in summer of comparable order of magnitude to, corresponding atmospheric variabilities of an atmospheric general circulation model. [ABSTRACT FROM AUTHOR]