A novel probabilistic methodology for the local assessment of future trends of climatic actions

Climate change could significantly affect climatic actions, so influencing not only existing structures, designed in accordance to the provisions of past Codes, but also updating of structural Codes. In the paper a new probabilistic technique for bias correction and downscaling of climate projections is presented to assess uncertainties in the future trends of climate extremes at the local scale. Referring to the relevant historical period, climate model outputs at relevant weather stations are compared with recorded daily series of maximum and minimum temperatures and water precipitations to derive appropriate monthly error probability density functions. Subsequently, new climate data series are generated by adding to the climate models output a random error term sampled from the monthly error PDFs. An extreme value analysis is finally carried out for each generated series according different time windows to assess how characteristic values (50 years return period) vary over time. The results for snow loads at the investigated weather stations confirm that the proposed methodology is suitable to reproduce recorded past climate extremes, so suggesting that it could be a useful tool to improve the climate change forecasts derived from climate models.