Multivariate stochastic downscaling models for generating precipitation and temperature scenarios of climate change based on atmospheric circulation

Abstract

The spatial resolution of General Circulation Models (GCMs) is too coarse to represent regional climate changes at the scales required for environmental impact assessment. Therefore, downscaling of precipitation and temperature has to be carried out from the GCM grids to smaller scales of a few square kilometres. Daily precipitation and temperature are modelled as stochastic processes coupled to atmospheric circulation. Precipitation is linked to circulation patterns (CPs) using conditional model parameters. Temperature is modelled using a simple autoregressive model conditioned on atmospheric circulation and local daily precipitation. The models use an automated objective classification of daily atmospheric circulation patterns based on optimized fuzzy rules. Both temperature and precipitation are downscaled to several locations taking into account the CP dependent spatial correlation. The models were applied to the Mesochora medium-sized mountainous catchment in Central Greece for validation using observed precipitation and temperature and observed classified geo-potential heights (at 700 hPa). GCM scenarios of the ECHAM4 model for 1xCO2 and 2xCO2 cases were used to make climate change predictions (by using classified GCM geopotential heights). Simulated values agree fairly well with historical data. Most of the GCM results (incl. mean daily values, renewal process probabilities, spell lengths) under the 2xCO2 case reflect a somewhat wetter and a more variable precipitation regime over the Mesochora catchment with significantly increased daily mean temperatures.