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.