chapter - Atmospheric and Oceanic Science

13.2.3. Statistical downscaling
The most sophisticated downscaling techniques calculate changes at a subgrid
level according to climatic statistics or great scale circulation parameters.
Some approaches use statistical relations between the large scale surface climate
and local climate or between data at higher levels of the atmosphere and the local
surface climate. When these methods are applied on GCM's daily level data, it is
possible to obtain daily climatic models for specific areas or locations. Statistical
downscaling is far less demanding computationally than other methods such as the
dynamic downscaling through numeric models. Notwithstanding, they require a
great deal of observational data to establish the statistical relations for current climate
and are based on the assumption that the observed statistical relationships will
continue to hold, even with different climate forcings in the future, that will be
invariant with time. Another problem with daily downscaling is that in many
regions, GCMs do not reproduce adequately interdiurnal variations.
13.2.4. High resolution experiments
Regional climatic scenarios
Another method of obtaining more localized estimations of future climate is
performing experiments with numeric models over the region of interest. This can
be done in different ways:
– Performing experiments with higher resolution GCMs but for different “temporal
cuts” for a limited amount of years,
– Running a GCM with variably resolution over the planet in such a way that the
maximum resolution be over the interest region
– By using a different model, but with higher resolution over a limited area (LAM),
using the GCM outputs as boundary conditions for the LAM, that is to say
through the nesting of an LAM in a GCM.
These methods for obtaining estimations at sub-grid level can reach to a 20
km resolution, and differently from GCM, are capable of taking into account important
local forcings such as coverage of soil and topography. Besides, even though
they have the advantage of having better physical basis than the statistical downscaling,
they present a high computational demand. However, it must we mentioned
that the LAM use does not solve all of the problems of the GCM scenarios. This is
in part because they keep non perfect physical parameterizations and in part
because they transfer errors from GCMs such as the underestimation of the interdiurnal
variability in certain regions.
In the present analysis, the method described in 13.2.1 is used to discuss the
model results over the La Plata basin region and South America, and the method
13.2.4 will be used in experiments mentioned in section 13.5.
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