Local estimates of global change: a statistical downscaling approach

SOLMAN SILVINA ALICIA; NUÑEZ MARIO NÉSTOR

INTERNATIONAL JOURNAL OF CLIMATOLOGY

JOHN WILEY & SONS LTD

Año: 1999 vol. 19 p. 835 - 861

0899-8418

For the purposes of estimating local changes in surface climate at selected stations in the central Argentina region, induced by enhanced CO2 concentration, projected by general circulation models (GCM), a statistical method to derive local sclae monthly mean minimum, maximum and mean temperatures from large-sclae atmospheric predictors is presented. Empirical relationships are derived among selected variables from the NCEP re-analysis and local data for summer and winter months, tested against an independent set of observed data and subsequently applied to the HADAM and MPI GCM control runs. Finally, the statistical approach is applied to a climate change experiment performed with the MPI model to construct a local climate change scenario. THe comparison between the estimated versus the observed mean temperature fields shows good agreement and the temporal evolution of the estimated variables is well captured, though, the estimated temperatures contain less interannual 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 obtained using the MPI large sclae predictors mathces the observations for summer months, though minimum and mean temperatures are slightly underestimated in the southeast part of the domain. However, differences are well within the range of the observed variability. The possible anthropogenic climate change at local scale is assessed by applying the statistical method to the results of the perturbed run conducted with the MPI model. For summer and wintr months, the local temperature increase is smaller for minimum temperature then for the maximum temperature for almost all the stations, yielding an enhanced temperature amplitude in both seasons. The temperature amplitude (difference between maximum and minimum) for summer months was larger than for winter months. The estimated maximum temperature increase is found to be larger for the summer months than for the winter months for all the stations, while for the minimum, temperature increases for summer and winter months are similar.