The climatic scenarios show a strong signal of warming in the Alpine areaalready for the mid-XXI century. The climate simulations, however, even whenobtained with regional climate models (RCMs), are affected by strong errorswhen compared with observations, due both to their difficulties inrepresenting the complex orography of the Alps and to limitations in theirphysical parametrization.Therefore, the aim of this work is to reduce these model biases by using aspecific post processing statistic technique, in order to obtain a moresuitable projection of climate change scenarios in the Alpine area.For our purposes we used a selection of regional climate models (RCMs) runswhich were developed in the framework of the ENSEMBLES project. They werecarefully chosen with the aim to maximise the variety of leading globalclimate models and of the RCMs themselves, calculated on the SRES scenarioA1B. The reference observations for the greater Alpine area were extractedfrom the European dataset E-OBS (produced by the ENSEMBLES project), whichhave an available resolution of 25 km. For the study area of Piedmont dailytemperature and precipitation observations (covering the period from 1957 tothe present) were carefully gridded on a 14 km grid over Piedmont regionthrough the use of an optimal interpolation technique.Hence, we applied the multimodel superensemble technique to temperaturefields, reducing the high biases of RCMs temperature field compared toobservations in the control period.We also proposed the application of a brand new probabilistic multimodelsuperensemble dressing technique, already applied to weather forecast modelssuccessfully, to RCMS: the aim was to estimate precipitation fields, withcareful description of precipitation probability density functionsconditioned to the model outputs. This technique allowed for reducing thestrong precipitation overestimation, arising from the use of RCMs, over theAlpine chain and to reproduce well the monthly behaviour of precipitation inthe control period.
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