13.4.1.1 Regional modelling
The basic strategy in regional modelling is to rely on the GCM to reproduce
the large-scale circulation of the atmosphere and for the regional model to
simulate sub-GCM scale regional distributions or patterns of climate, such as
precipitation, temperature, and winds, over the region of interest (Giorgi and
Mearns, 1991; McGregor, 1997; Giorgi and Mearns, 1999). The GCM provides the
initial and lateral boundary conditions for driving the regional climate model
(RCM). In general, the spatial resolution of the regional model is on the order
of tens of kiilometres, whereas the GCM scale is an order of magnitude coarser.
Further details on the techniques of regional climate modelling are covered
in Chapter 10, Section 10.5.
13.4.1.2 Statistical downscaling
In statistical downscaling, a cross-scale statistical relationship is developed
between large-scale variables of observed climate such as spatially averaged
500 hPa heights, or measure of vorticity, and local variables such as site-specific
temperature and precipitation (von Storch, 1995; Wilby and Wigley, 1997; Murphy,
1999). These relationships are assumed to remain constant in the climate change
context. Also, it is assumed that the predictors selected (i.e., the large-scale
variables) adequately represent the climate change signal for the predictand
(e.g., local-scale precipitation). The statistical relationship is used in conjunction
with the change in the large-scale variables to determine the future local climate.
Further details of these techniques are provided in Chapter
10, Section 10.6.
13.4.1.3 Applications of the methods to impacts
While the two major techniques described above have been available for about
ten years, and proponents claim use in impact assessments as one of their important
applications, it is only quite recently that scenarios developed using these
techniques have actually been applied in a variety of impact assessments, such
as temperature extremes (Hennessy et al., 1998; Mearns, 1999); water resources
(Hassall and Associates, 1998; Hay et al., 1999; Wang et al., 1999; Wilby et
al., 1999; Stone et al., 2001); agriculture (Mearns et al., 1998, 1999, 2000a,
2001; Brown et al., 2000) and forest fires (Wotton et al., 1998). Prior to the
past couple of years, these techniques were mainly used in pilot studies focused
on increasing the temporal (and spatial) scale of scenarios (e.g., Mearns et
al., 1997; Semenov and Barrow, 1997).
One of the most important aspects of this work is determining whether the high
resolution scenario actually leads to significantly different calculations of
impacts compared to that of the coarser resolution GCM from which the high resolution
scenario was partially derived. This aspect is related to the issue of uncertainty
in climate scenarios (see Section 13.5). We provide examples
of such studies below.
Application of high resolution scenarios produced from a regional model (Giorgi
et al., 1998) over the Central Plains of the USA produced changes in simulated
crop yields that were significantly different from those changes calculated
from a coarser resolution GCM scenario (Mearns et al., 1998; 1999, 2001). For
simulated corn in Iowa, for example, the large-scale (GCM) scenario resulted
in a statistically significant decrease in yield, but the high resolution scenario
produced an insignificant increase (Figure 13.5). Substantial
differences in regional economic impacts based on GCM and RCM scenarios were
also found in a recent integrated assessment of agriculture in the south-eastern
USA (Mearns et al., 2000a,b). Hay et al. (1999), using a regression-based statistical
downscaling technique, developed downscaled scenarios based on the Hadley Centre
Coupled AOGCM (HadCM2) transient runs and applied them to a hydrologic model
in three river basins in the USA. They found that the standard scenario from
the GCM produced changes in surface runoff that were quite different from those
produced from the downscaled scenario (Figure 13.6).
Figure 13.5: Spatial pattern of differences (future climate
minus baseline) in simulated corn yields based on two different climate
change scenarios for the region covering north-west Iowa and surrounding
states (a) coarse spatial resolution GCM scenario (CSIRO); (b) high
spatial resolution region climate model scenario (RegCM) (modified
from Mearns et al., 1999). |
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Figure 13.6: Differences in simulated runoff (m3/s)
based on a statistically downscaled climate scenario and a coarse
resolution GCM scenario (labelled Delta Change) for the Animas River
Basin in Colorado (modified from Hay et al., 1999). The downscaled
range (grey area) is based on twenty ensembles. |
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