3.5.4.5. Temporal Resolution (Mean versus Variability)
For the most part, climate changes calculated from climate model experiments
have been mean monthly changes in relevant variables. Techniques for generating
changes in variability emerged in the 1990s (Mearns et al., 1992, 1996,
1997; Wilks, 1992; Semenov and Barrow, 1997). The most common technique involves
manipulation of the parameters of stochastic weather generators to simulate
changes in variability on daily to interannual time scales (e.g., Bates et
al., 1994, 1996). Several studies have found important differences in the
estimated impacts of climate change when effects of variance change were included
(Mearns et al., 1997; Semenov and Barrow, 1997). Combined changes in
mean and variability also are evident in a broad suite of statistical downscaling
methods (Katz and Parlange, 1996; Wilby et al., 1998). Other types of
variance change still are difficult to incorporate, such as possible changes
in the frequency and intensity of El Niño events (Trenberth and Hoar,
1997). However, where ENSO signals are strong, weather generators can be conditioned
on ENSO phases, enabling scenarios of changed ENSO frequency to be generated
stochastically (e.g., Woolhiser et al., 1993). However, climate models
still are not capable of clearly indicating how ENSO events might change in
the future (TAR WGI Chapter 9).
3.5.4.6. Incorporation of Extremes in Scenarios
Whereas changes in both the mean and higher order statistical moments (e.g.,
variance) of time series of climate variables affect the frequency of extremes
based on these variables (e.g., extreme high daily or monthly temperatures;
drought and flood episodes), other types of extremes are based on complex atmospheric
phenomena (e.g., hurricanes). Given the importance of the more complex extremes—such
as hurricanes, tornadoes, and storm surges (see Table
1-1)—it would be desirable to incorporate changes in the frequency
of such phenomena into scenarios. Unfortunately, very little work has been performed
on how to accomplish this, and there is only limited information on how the
frequency, intensity, and spatial characteristics of such phenomena might change
in the future (see Section 3.8.5).
An example of an attempt to incorporate such changes into impact assessments
is the study of McInnes et al. (2000), who developed an empirical/dynamical
model that gives return period versus height for tropical cyclone-related storm
surges for a location on the north Australian coast. The model can accept changes
in tropical cyclone characteristics that may occur as a result of climate change,
such as changes in cyclone intensity. Other methods for incorporating such changes
into quantitative climate scenarios remain to be developed; further advances
in this area of research can be expected over the next few years.
3.5.4.7. Surprises: Low-Probability, High-Impact Events
Several types of rapid, nonlinear response of the climate system to anthropogenic
forcing, sometimes referred to as "surprises," have been suggested.
These include reorganization of the thermohaline circulation, rapid deglaciation,
and fast changes to the carbon cycle (e.g., Stocker and Schmittner, 1997). For
instance, it has been suggested that a sudden collapse of the thermohaline circulation
in the North Atlantic—an event that has not been simulated by any AOGCM
(TAR WGI Chapter 9) but cannot be
ruled out on theoretical grounds (TAR WGI Chapter
7)—could cause major disruptions in regional climate over northwest
Europe. Such a possibility has been used to create synthetic arbitrary climate
scenarios to investigate possible extreme impacts (Alcamo et al., 1994;
Klein Tank and Können, 1997).
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