DOWNSCALING GCM INFORMATION TO REGIONAL SCALES - A NONPARAMETRIC MULTIVARIATE REGRESSION APPROACH

The multivariate adaptive regression splines (MARS) model was used to capture the observed relationships between sea level pressure (SLP) anomalies over the Euro-Atlantic sector and the winter time (December-February) monthly rainfall at eight sites in Portugal; possible anthropogenic changes of the rainfall in a perturbed future climate were then estimated by using both the observed SLP-rainfall relationships, described by MARS models, and the GCM simulated SLP, taken from the output of the Hadley Centre Transient Climate Change Experiment (UKTR). Also, principal component analysis was carried out to reduce the dimensionality of the SLP data, and to assess the ability of the GCM in simulating the large-scale circulation; the first six principal components were retained as predictors in the MARS model. The MARS model were built up by using the data for 1946-1991 as the training set and that for 1901-1943 as the testing set, showing satisfactory prediction skills. It is concluded that the UKTR control simulation successfully reproduced main features of the large-scale circulation, but the observed relationship between SLP and the regional rainfall was not well preserved. With respect to the 54-year experiment of perturbed run, the MARS estimation of rainfall and the relevant direct GCM rainfall output possess similar multi-annual variations; however, there are substantial differences regarding details; the change in the area mean of winter time mean monthly rainfall in Portugal estimated by MARS (indirect GCM output) is about -12.7 mm per 54-year, and the relevant direct GCM output is -16.9 mm/54-year. This reduction tendency is consistent with previously reported findings respecting rainfall in the Iberian Peninsula, which were based on the MPI (Max-Planck Institute for Meteorology) transient simulations.