Incremental scenarios describe techniques where particular climatic (or related) elements are changed incrementally by plausible though arbitrary amounts (e.g., +1, +2, +3, +4°C change in temperature). Also referred to as synthetic scenarios (IPCC, 1994), they are commonly applied to study the sensitivity of an exposure unit to a wide range of variations in climate, often according to a qualitative interpretation of projections of future regional climate from climate model simulations ("guided sensitivity analysis", see IPCC-TGCIA, 1999). Incremental scenarios facilitate the construction of response surfaces - graphical devices for plotting changes in climate against some measure of impact (for example see Figure 13.9b) which can assist in identifying critical thresholds or discontinuities of response to a changing climate. Other types of scenarios (e.g., based on model outputs) can be superimposed on a response surface and the significance of their impacts readily evaluated (e.g., Fowler, 1999). Most studies have adopted incremental scenarios of constant changes throughout the year (e.g., Terjung et al., 1984; Rosenzweig et al., 1996), but some have introduced seasonal and spatial variations in the changes (e.g., Whetton et al., 1993; Rosenthal et al., 1995) and others have examined arbitrary changes in interannual, within-month and diurnal variability as well as changes in the mean (e.g., Williams et al., 1988; Mearns et al., 1992; Semenov and Porter, 1995; Mearns et al., 1996).
Incremental scenarios provide information on an ordered range of climate changes and can readily be applied in a consistent and replicable way in different studies and regions, allowing for direct intercomparison of results. However, such scenarios do not necessarily present a realistic set of changes that are physically plausible. They are usually adopted for exploring system sensitivity prior to the application of more credible, model-based scenarios (Rosenzweig and Iglesias, 1994; Smith and Hulme, 1998).
Analogue scenarios are constructed by identifying recorded climate regimes which may resemble the future climate in a given region. Both spatial and temporal analogues have been used in constructing climate scenarios.
Spatial analogues are regions which today have a climate analogous to that anticipated in the study region in the future. For example, to project future grass growth, Bergthórsson et al. (1988) used northern Britain as a spatial analogue for the potential future climate over Iceland. Similarly, Kalkstein and Greene (1997) used Atlanta as a spatial analogue of New York in a heat/mortality study for the future. Spatial analogues have also been exploited along altitudinal gradients to project vegetation composition, snow conditions for skiing, and avalanche risk (e.g., Beniston and Price, 1992; Holten and Carey, 1992; Gyalistras et al., 1997). However, the approach is severely restricted by the frequent lack of correspondence between other important features (both climatic and non-climatic) of a study region and its spatial analogue (Arnell et al., 1990). Thus, spatial analogues are seldom applied as scenarios, per se. Rather, they are valuable for validating the extrapolation of impact models by providing information on the response of systems to climatic conditions falling outside the range currently experienced at a study location.
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