ESTIMATING DAILY WIND-SPEED UNDER CLIMATE-CHANGE

A semi-empirical downscaling approach is presented to estimate spatial and temporal statistical properties of local daily mean wind speed un der global climate change. The present semi-empirical downscaling meth od consists of two elements. Since general circulation models (GCMs) a re able to reproduce the features of the present atmospheric general c irculation quite correctly, the first element represents the large-sca le circulation of the atmosphere. The second element is a link between local wind speed and large-scale circulation pattern (CP). The linkag e is expressed by a stochastic model conditioned on CP types. Paramete rs of the linkage model are estimated using observed data series; then this model is utilized with GCM-generated CP type data corresponding to a 2 x CO2 scenario. Under the climate of Nebraska the lognormal dis tribution is the best two-parameter distribution to describe daily mea n wind speed. The space-time variability of wind speed is described by a transformed multivariate autoregressive (AR) process, and the linka ge between local wind and large-scale circulation is expressed as a co nditional AR process, i.e. the autoregressive parameters depend on the actual daily CP type. The basic tendency of change under 2 x CO2 clim ate is a considerable increase of wind speed from the beginning of sum mer to the end of winter and a somewhat smaller wind decrease in sprin g. Copyright (C) 1996 Elsevier Science Ltd.