Local estimates of global climate change: A statistical downscaling approach

For the purposes of estimating local changes in surface climate at selected stations in the central Argentina region, induced by an enhanced CO, conce ntration, projected by general circulation models (GCM), a statistical meth od to derive local scale monthly mean minimum, maximum and mean temperature s from large-scale atmospheric predictors is presented. Empirical relations hips are derived among selected variables from the NCEP re-analyses and loc al data for summer and winter months, tested against an independent set of observed data and subsequently applied to the MADAM and MPI GCM control run s. Finally, the statistical approach is applied to a climate change experim ent performed with the MPI model to construct a local climate change scenar io. The comparison between the estimated versus the observed mean temperature f ields shows good agreement and the temporal evolution of the estimated vari ables is well-captured, though, the estimated temperatures contain less int erannual variability than the observations. For the present day climate simulation, the results from the HADAM and MPI GCMs are used. It is shown that the pattern of estimated temperatures obtai ned using the MPI large-scale predictors matches the observations for summe r months, though minimum and mean temperatures are slightly underestimated in the southeast part of the domain. However, the differences are well with in the range of the observed variability. The possible anthropogenic climate change at the local scale is assessed by applying the statistical method to the results of the perturbed run conduc ted with the MPI model. For summer and winter months, the local temperature increase is smaller for minimum temperature than for maximum temperature f or almost all the stations, yielding an enhanced temperature amplitude in b oth seasons. The temperature amplitude (difference between maximum and mini mum) for summer months was larger than for winter months. The estimated max imum temperature increase is found to be larger for summer months than for winter months for all the stations, while for the minimum, temperature incr eases for summer and winter months are similar. Copyright (C) 1999 Royal Me teorological Society.