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Uncertainty is a feature that pervades discussions on climate change issues. IPCC SAR covered main areas of uncertainties, especially those related to:
Several sections in this report (1.5; 2.2; 7.2; 10.1) review new and complementary perspectives that facilitate a better understanding of the tensions between the limited capacity to predict and the urgent need to act in a situation faced with high stakes of risk.
The implications of uncertainty are global in scale and long-term in their impact; quantitative data for baselines and the consequences of climate change are inadequate for decision making. In recent years, researchers and policymakers have become increasingly concerned about the high levels of inherent uncertainty, and the potentially severe consequences of decisions that have to be made.
Conventional frameworks for decision making on climate change policies presume that relevant aspects of the contextual environment are to some extent predictable; therefore uncertainty can be reduced to provide decision makers with appropriate information within appropriate time frames.
This anticipatory management approach is based on the premise that it is possible to predict and anticipate the consequences of decisions and hence to make a proper decision once all the necessary information is gathered to make a scientific forecast. The prevailing image is that “given enough information and powerful enough computers it is possible to predict with certainty, in a quantitative form, which in turn makes it possible to control natural systems” (Tognetti, 1999).
Anticipatory approaches have successfully managed a wide range of decision problems in which the relative uncertainties are reducible, and the stakes or outcomes associated with the decisions to be made are modest (Kay et al., 1999). A number of uncertainty analysis techniques, such as Monte Carlo sampling, Bayesian methods, and fuzzy set theory, have been designed to perform sensitivity and uncertainty analysis related to the quality and appropriateness of the data used as inputs to models. However, these techniques, suitable for addressing technical uncertainties, ignore those uncertainties that arise from an incomplete analysis of the climate change phenomena, or from numerical approximations used in their mathematical representations (modelling uncertainties), as well as uncertainties that arise from omissions through lack of knowledge (epistemological uncertainties). Current methods thus give decision makers limited information regarding the magnitude and sources of the underlying uncertainties and fail to provide them with straightforward information as input to the decision-making process (Rotmans and de Vries, 1997).
The management of uncertainties is not just an academic issue but an urgent task for climate change policy formulation and action. Various vested interests may inhibit, delay, or distort public debate with the result that “procrastination is as real a policy option as any other, and indeed one that is traditionally favoured in bureaucracies; and inadequate information is the best excuse for delay” (Funtowicz and Ravetz, 1990).
Funtowicz and Ravetz have proposed a highly articulated and operational scheme for dealing with the problems of uncertainty and quality of scientific information in the policy context. By displaying qualifying categories of the information–numeral, unit, spread, assessment, and pedigree (NUSAP)–the NUSAP scheme provides a framework for the inquiry and elicitation required to evaluate information quality. By such means it is possible to convey alternative interpretations of the meaning and quality of crucial quantitative information with greater quality and coherence, and thus reduce distortion of its meaning.
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