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There is nevertheless a large agreement between
experiments. For a given month, the spatial pattern is very
similar, and in fact the spatial correlation is around 90%.
This means than the spatial warming patterns found are an
inherent characteristic of the domain and the month, not a
feature of the GCM neither the SRES scenario used to
perform the simulation.
In the temporal evolution, the importance of different
scenarios and GCM is found. In the A2 scenario the
warming signal is stronger, and it is reflected in a larger
trend in the Principal Component associated to the first
EOF.
climático sobre la Península Ibérica. Asamblea
Hispano Portuguesa de geodesia y geofísica, 2006.
von Storch, H. and F.W. Zwiers. Statistical Analysis in
Climate Research. Cambridge University Press, 2007.
Zorita, E., J.F. Gonzalez-Rouco, H. von Storch, J.P.
Montavez, and F. Valero. Natural and anthropogenic
modes of surface temperature variations in the last
thousand years. Geophys. Res. Lett., 32(8):755 – 762,
2005.
There is also an important difference in the evolution of
maximum and minimum 2-m temperatures along the XXI
century. The maximum temperatures suffer a higher
warming. This could yield in a an increase of the daily
temperature range.
Finally, the amount of warming has also an annual cycle.
Both the maximum and minimum temperatures have a more
marked trend in summer months. This means than the
climate could become more continental in the future.
5. Conclusions
A study of the warming patterns over the IP under several
scenarios and with different GCM has been performed using
a climate version of MM5 mesoescalar model. An EOF
analysis has been employed in order to reduce the noise in
the warming of 2-m maximum and minimum temperature
and reduce the dimensionality of the problem.
It has been found that the first EOF accounts for the main
warming pattern, and this depends on the month and on the
variable. There is an important annual cycle and an
asymmetry in the behavior of maximum and minimum
temperatures.
The most important result is that these projections are
consistent under different scenarios and using different
GCM coupled to the regional climate model. The only
difference between scenarios is the intensity of these
changes in the IP climate, not the qualitative features.
Finally, the spatial structure of the warming patterns seems
to be related to several physical parameters like
continentality or height over the sea. Further research should
be devoted to fully understand these changes, which could
be related to changes in the global circulation.
As future work, others simulations should be performed
driven by other GCM. Also, other task involve checking
whether these patterns are also present for pre-industrial
climate, as obtained by others authors
6. References
Diffenbaugh, N.S., J.S. Pal, F. Giorgi, X.J. Gao. Heat stress
intensification in the Mediterranean climate change
hotspot. Geophys.Res. Lett., 34(11):53 – 70, 2007.
Giorgi, F. Climate change hot-spots. Geophys. Res. Lett.,
33(8):11217 – 11222, 2006.
IPCC. Climate Change 2007 – The Physical Science Basis.
WMO, 2007.
Montávez, J.P., J. Fernández, J.F. González-Rouco, J. Saenz,
E. Zorita, and F. Valero. Proyecciones de cambio