13.5.2.3 Risk assessment approaches

Uncertainty analysis is required to perform quantitative risk or decision analysis (see Toth and Mwandosya (2001) for discussion of decision analysis). By itself, scenario analysis is not equivalent to uncertainty analysis because not all possible scenarios are necessarily treated and, especially, because probabilities are not attached to each scenario (see Morgan and Henrion (1990) for a general treatment of uncertainty analysis; see Katz (2000) for a more recent overview focusing on climate change). Recognising this limitation, a few recent studies (Jones, 2000b; New and Hulme, 2000) have attempted to modify climate scenario analysis, grouping a range of scenarios together and attaching a probability to the resultant classes. Such an approach can be viewed as a first step in bridging the gap between scenario and uncertainty analysis. Single climate scenarios, by definition, are limited to plausibility with no degree of likelihood attached. Since risk analysis requires that probabilities be attached to each climate scenario, subjective probabilities can be applied to the input parameters that determine the climate outcomes (e.g., emissions scenarios, the climate sensitivity, regional climate response patterns), thus allowing distributions of outcomes to be formally quantified.

In formal risk analysis, the extremes of the probability distribution should encompass the full range of possible outcomes, although in climate change studies this remains hard to achieve. The ranges for global warming and sea level rise from the IPCC WGI Second Assessment Report (IPCC, 1996) (hereafter SAR), for example, deliberately did not encompass the full range of possible outcomes and made no reference to probability distributions. As a consequence, the bulk of impact assessments have treated these IPCC ranges as having a uniform probability, i.e., acting as if no information is available about what changes are more likely than others. As pointed out by Titus and Narayanan (1996), Jones (1998, 2000a), and Parkinson and Young (1998), however, where several sources of uncertainty are combined, the resulting probability distribution is not uniform but is a function of the component probability distributions and the relationship between the component elements. For example, descriptions of regional changes in temperature and rainfall over Australia constructed from regional response patterns have been used in a number of hydrological studies where the extreme outcomes have been considered as likely as outcomes in the centre of the range (e.g., Chiew et al., 1995; Schreider et al., 1996; Whetton, 1998). However, when the two component ranges - global warming and normalised local temperature and rainfall change -are randomly sampled and then multiplied together, they offer a distinctly non-uniform distribution (see Figure 13.9a). Further refinements of these approaches for quantifying the risk of climate change are needed (New and Hulme, 2000).

This approach to portraying uncertainty has potentially useful applications when combined with climate impact sensitivity response surfaces (see Section 13.2.1; see also Chapter 3 of TAR WG II (Carter and La Rovere, 2001)). The superimposed response surfaces allow the calculation of probabilities for exceeding particular impact thresholds (Figure 13.9b). Another method of assessing risk using quantified probability distributions is through a series of linked models such as those used for calculating sea level rise (Titus and Narayanan, 1996) and economic damage due to sea level rise (Yohe and Schlesinger, 1998), for quantifying climate uncertainty (Visser et al., 2000), and in integrated assessments (Morgan and Dowlatabadi, 1996). Efforts to make explicit probabilistic forecasts of the climate response to a given emissions scenario for the near future have been made using the current observed climate trajectory to constrain the “forecasts” from several GCMs (Allen et al., 2000). More details on this technique are given in Section 12.4.3.3.

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