Articles | Volume 17, issue 5
Hydrol. Earth Syst. Sci., 17, 2017–2028, 2013

Special issue: Precipitation uncertainty and variability: observations, ensemble...

Hydrol. Earth Syst. Sci., 17, 2017–2028, 2013

Research article 29 May 2013

Research article | 29 May 2013

Regional climate models downscaling in the Alpine area with multimodel superensemble

D. Cane1, S. Barbarino1, L. A. Renier1,2, and C. Ronchi1 D. Cane et al.
  • 1Regional Agency for Environmental Protection – Arpa Piemonte, Torino, Italy
  • 2IPLA Spa, Torino, Italy

Abstract. The climatic scenarios show a strong signal of warming in the Alpine area already for the mid-XXI century. The climate simulations, however, even when obtained with regional climate models (RCMs), are affected by strong errors when compared with observations, due both to their difficulties in representing the complex orography of the Alps and to limitations in their physical parametrization.

Therefore, the aim of this work is to reduce these model biases by using a specific post processing statistic technique, in order to obtain a more suitable projection of climate change scenarios in the Alpine area.

For our purposes we used a selection of regional climate models (RCMs) runs which were developed in the framework of the ENSEMBLES project. They were carefully chosen with the aim to maximise the variety of leading global climate models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observations for the greater Alpine area were extracted from the European dataset E-OBS (produced by the ENSEMBLES project), which have an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (covering the period from 1957 to the present) were carefully gridded on a 14 km grid over Piedmont region through the use of an optimal interpolation technique.

Hence, we applied the multimodel superensemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period.

We also proposed the application of a brand new probabilistic multimodel superensemble dressing technique, already applied to weather forecast models successfully, to RCMS: the aim was to estimate precipitation fields, with careful description of precipitation probability density functions conditioned to the model outputs. This technique allowed for reducing the strong precipitation overestimation, arising from the use of RCMs, over the Alpine chain and to reproduce well the monthly behaviour of precipitation in the control period.