Regional climate models downscaling in the Alpine area with Multimodel SuperEnsemble

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 where compared with observations, due to their difficulties in representing the complex orography of the Alps and limitations in their physical parametrization. <br><br> Therefore the aim of this work is reducing these model biases using a specific post processing statistic technique to obtain a more suitable projection of climate change scenarios in the Alpine area. <br><br> For our purposes we use a selection of RCMs runs from the ENSEMBLES project, carefully chosen in order to maximise the variety of leading Global Climate Models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observation for the Greater Alpine Area are extracted from the European dataset E-OBS produced by the project ENSEMBLES with an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (1957–present) were carefully gridded on a 14-km grid over Piedmont Region with an Optimal Interpolation technique. <br><br> 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. <br><br> We propose also the first application to RCMS of a brand new probabilistic Multimodel SuperEnsemble Dressing technique to estimate precipitation fields, already applied successfully to weather forecast models, with careful description of precipitation Probability Density Functions conditioned to the model outputs. This technique reduces the strong precipitation overestimation by RCMs over the alpine chain and reproduces well the monthly behaviour of precipitation in the control period

Impact of climate change and water use policies on hydropower potential in the south-eastern Alpine region

“…However, despite the uncertainty related to the identification of possible trend of annual precipitation, most CMs agree in predicting a tendency of higher precipitation in winter/spring and lower precipitation in summer/autumn (Beniston, 2012a;Cane et al, 2013;Finger et al, 2012;Gobiet et al, 2014), in agreement with the trends detected in instrumental time series (Brunetti et al, 2006(Brunetti et al, , 2009Brugnara et al, 2012). Another problematic issue is related to the seasonal distribution of precipitation: indeed, unimodal (with peak in summer) and bimodal (with peaks in autumn and spring) distributions coexist in the GAR depending on the location and altitude of the investigated station (Brunetti et al, 2006(Brunetti et al, , 2009Beniston, 2006).…”

“…Analyses on instrumental time series from 1800 to 2000 (Brunetti et al, 2006(Brunetti et al, , 2009Brugnara et al, 2012) give evidence of i) opposite trends in different subregions of the GAR (with significative positive trends in the northern parts and less significative negative trends in the southern parts) and ii) alternations of opposite trends depending on the starting time and the time window length selected for the trend analysis. Contradicting trends of future annual precipitation in the GAR emerge also when comparing different CMs projections, as well as alternations of periods characterized by opposite trends are often present within the temporal evolution of some CM runs (e.g., Gobiet et al, 2014;Finger et al, 2012;Cane et al, 2013).…”

A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes

“…They examined many observed climatic teleconnections of South Korean climate and other Asian parameters and noted that the multimodel superensemble based forecast data sets carried the best integrity for these teleconnections for predicting South Korean climate. The regional Environmental Agency of Torino Italy [ Cane et al , ] utilized this method for seasonal forecasts for the Alpine area and extended the current deterministic approach toward a probabilistic application for seasonal rainfall predictions that showed great promise. The following quote from a World Meteorological Organization Workshop in Beijing 2000 by Mylne [] of the UK Met Office speaks for the seasonal climate superensemble: “Results show that the superensemble provides the best deterministic forecast, better than all individual models or the simple ensemble mean of the models.…”