3.5.4.3. Constructing Change Fields
Because climate model results generally are not sufficiently accurate (in terms
of absolute values) at regional scales to be used directly (Mearns et al.,
1997), mean differences between the control (or current climate) run and the
future climate run usually are calculated and then combined with some baseline
observed climate data set (IPCC, 1994). Conventionally, differences (future
climate minus control) are used for temperature variables, and ratios (future
climate/control) are used for other variables such as precipitation, solar radiation,
relative humidity, and windspeed. Most impact applications consider one or more
fixed time horizon(s) in the future (e.g., the 2020s, the 2050s, and the 2080s
have been chosen as 30-year time windows for storing change fields in the IPCC-DDC).
Some other applications may require time-dependent information on changes, such
as vegetation succession models that simulate transient changes in plant composition
(e.g., VEMAP members, 1995).
3.5.4.4. Spatial Scale of Scenarios
One of the major problems in applying GCM projections to regional impact assessments
is the coarse spatial scale of the gridded estimates—on the order of hundreds
of kilometers—in relation to many of the exposure units being studied (often
at one or two orders of magnitude finer resolution). Concern about this issue
is raised in Chapters 4 and 5. Several
solutions have been adopted to obtain finer resolution information.
3.5.4.4.1. Simple methods
Conventionally, regional "detail" in climate scenarios has been incorporated
by appending changes in climate from the nearest coarse-scale GCM grid box to
the study area (observation point or region) (e.g., Rosenzweig and Parry, 1994)
or by interpolating from GCM grid box resolution to a higher resolution grid
or point location (Leemans and van den Born, 1994; Harrison and Butterfield,
1996).
Three major methods have been developed to produce higher resolution climate
scenarios at the sub-GCM grid scale: regional climate modeling (Giorgi and Mearns,
1991, 1999; McGregor, 1997), statistical downscaling (von Storch et al.,
1993; Rummukainen, 1997; Wilby and Wigley, 1997), and variable- and high-resolution
GCM experiments (Fox-Rabinovitz et al., 1997). All three methods are
presented in Table 3-4 and discussed in detail in
TAR WGI Chapter 10, but we briefly
review here the first two, since they have been most commonly applied to impact
assessments. Both methods are dependent on large-scale circulation variables
from GCMs. Large-scale circulation refers to the general behavior of the atmosphere
at large (i.e., continental) scales.
3.5.4.4.2. Regional climate modeling
The basic strategy with regional models is to rely on the GCM to reproduce
the large-scale circulation of the atmosphere and to use the regional model,
run at a higher resolution, to simulate sub-GCM scale regional distributions
of climate. In numerous experiments with regional models driven by control and
doubled CO2 output from GCMs for regions throughout the world, the
spatial pattern of changed climate—particularly changes in precipitation—simulated
by the regional model departs from the more general pattern over the same region
simulated by the GCM (TAR WGI Chapter
10).
3.5.4.4.3. Statistical methods
Statistical methods are much less computationally demanding than dynamic methods;
they offer an opportunity to produce ensembles of high-resolution climate scenarios
(for reviews, see von Storch, 1995; Wilby and Wigley, 1997). However, these
techniques rely on the (questionable) assumption that observed statistical relationships
will continue to be valid under future radiative forcing—that is, they
are time-invariant (Wilby, 1997).
Although regional modeling and statistical techniques have been available for
at least a decade—their developers claiming use in impact assessments as
one of their important applications—it is only recently that they have
actually provided scenarios for impact assessments (Mearns et al., 1998,
1999, 2001; Sælthun et al., 1998; Hay et al., 1999; Brown
et al., 2000; Whetton et al., 2001). Mearns et al. (1999,
2001) demonstrate that a high-resolution scenario results in agricultural impacts
that differ from those produced with a coarser resolution GCM scenario (discussed
in Chapter 5). Hay et al. (1999) found differences
in runoff calculations, based on a GCM-scenario and a statistically downscaled
scenario.
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