The IPCC function is to assess the state of our understanding and to judge the confidence with which we can make projections of climate change and its impacts. These tentative projections will aid policymakers in deciding on actions to mitigate or adapt to anthropogenic climate change, which will need to be re-assessed on a regular basis. It is recognized that many remaining uncertainties need to be reduced in each of (many) disciplines, which is why IPCC projections and scenarios are often expressed with upper and lower limits. These ranges are based on the collective judgment of the IPCC authors and the reviewers of each chapter, but it may be appropriate in the future to draw on formal methods from the discipline of decision analysis to achieve more consistency in setting criteria for high and low range limits (McBean et al., 1996; see Raiffa, 1968, for an introduction to decision analysis).
Although the SAR on impacts, adaptation, and mitigation (IPCC, 1996b) explicitly links potentially serious climate change with mitigation and adaptation assessment in its Technical Summary, the body of the report is restricted mostly to describing sensitivity and vulnerability assessments (see also Carter et al., 1994). Although this methodology is appropriate for testing sensitivity and vulnerability of systems, it is poorly suited for planning or policy purposes. IAMs available to SAR authors (e.g., Weyant et al., 1996) generate outcomes that are plausible but typically contain no information on the likelihood of outcomes or much information on confidence in estimates of outcomes, how each result fits into broader ranges of uncertainty, or what the ranges of uncertainty may be for each outcome (see Chapter 1 and Section 2.4 for further discussions of integrated assessment issues). However, several studies since the SAR do use probability distributions (e.g., Morgan and Dowlatabadi, 1996, and citations in Schneider, 1997).
IPCC Working Group I (WGI) in its contribution to the SAR (IPCC, 1996a) uses two different methods or techniques to estimate climate change: scenarios and projections. A scenario is a description of a plausible future without estimation of its likelihood (e.g. the individual IS92a-f emission scenarios or climate scenarios generated by GCMs in which a single emission path is used). Scenarios may contain several sources of uncertainty but generally do not acknowledge them explicitly
Careful reading of the SAR WGI Technical Summary (IPCC, 1996a) reveals that the term projection is used in two senses:
Projections are used instead of predictions to emphasize that they do not represent attempts to forecast the most likely evolution of climate in the future, only possible evolutions (IPCC, 1996a, Section F.1). In the SAR, projection and scenario are used to describe possible future states, with projections used mainly in terms of climate change and sea-level rise. This usage defines climate projection as a single trajectory of a subset of scenarios. When used as input into impact assessments, the same climate projections commonly are referred to as climate scenarios.
|Figure 2-1: Schematic depiction of the relationship between "well-calibrated" scenarios, the wider range of "judged" uncertainty that might be elicited through decision analytic techniques, and the "full" range of uncertainty, which is drawn wider to represent overconfidence in human judgments. M1 to M4 represent scenarios produced by four models (e.g., globally averaged temperature increases from an equilibrium response to doubled CO2 concentrations). This lies within a "full" range of uncertainty that is not fully identified, much less directly quantified by existing theoretical or empirical evidence (modified from Jones, 2000).|
Projected ranges are constructed from two or more scenarios in which one or more sources of uncertainty may be acknowledged. Examples include projections of atmospheric CO2 derived from the IS92a-f emission scenarios (IPCC, 1996a), global temperature ranges (IPCC, 1996a), and regional temperature ranges (CSIRO, 1996). A range of projections will always be more likely to encompass what actually will transpire than a single scenario. Although projected ranges are more likely to occur than single scenarios, they are not full-fledged forecasts because they incorporate only part of the total uncertainty space. The relationship between scenarios and projected ranges as treated in the SAR is shown schematically in Figure 2-1.
A projected range is a quantifiable range of uncertainty situated within a population of possible futures that cannot be fully identified (termed "knowable" and "unknowable" uncertainties by Morgan and Henrion, 1990). The limits of this total range of uncertainty are unknown but may be estimated subjectively (e.g., Morgan and Keith, 1995). Given the finding in the cognitive psychology literature that experts define subjective probability distributions too narrowly because of overconfidence (see Section 18.104.22.168), the inner range represents the "well-calibrated" range of uncertainty. Thus, the wider range of uncertainty represents a "judged" range of uncertainty, based on expert judgmentswhich may not encompass the full range of uncertainty given the possibility of cognitive biases such as overconfidence. Although the general point remains that there is always a much wider uncertainty range than the envelope developed by sets of existing model runs, it also is true that there is no distinct line between "knowable" and "unknowable" uncertainties; instead, it is a continuum. The actual situation depends on how well our knowledge (and lack thereof) has been integrated into assessment models. Moreover, new informationparticularly empirical data, if judged reliable and comprehensiveeventually may narrow the range of uncertainty to well inside the well-calibrated range by falsifying certain outlier values.
If the full range of uncertainty in Figure 2-1 were known, the probability of a particular outcome could be expressed as a forecast (provided we can state the probability). Although there are significant sources of uncertainty that cannot yet be quantified, decision analytic elicitation procedures (Section 2.4) can estimate the full range of uncertainties and conditional probabilities (see Section 2.5.5 for an assessment of the state of the science concerning human judgment). Conditional probabilities may be calculated within a projected range even though the probability of the range itself remains unknown.
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