To respond to the climate change issue, governments at various levels must make a range of decisions about the appropriate level and design of greenhouse gas mitigation, preparation for adaptation, and the funding level of research across many related disciplines. Because of the complex and global nature of climate change, these decision makers need support from scientific researchers in order to know the costs, benefits, options, and impacts for their decisions. But, of course, our current understanding of future climate change and the processes that contribute to them are incomplete and fraught with uncertainty. Thus, part of the information needed by decision makers is descriptions of the uncertainty in future costs, benefits, and impacts of potential choices.

Uncertainty is not important merely for computing an expected value or ‘best guess’. In fact, information on variability and on low-probability high-consequence events allows decision makers to account for society’s risk-aversion in their choices. Furthermore, today’s decision is not made once now, but will be continually revised in the future as our understanding evolves. The optimal decision today depends not only on current uncertainty, but our expectation of how it will change and how we will respond in the future. This adaptive decision process will be aided by carefully tracking how uncertainties change with new knowledge. Thus, carefully assessing the risks of future climate change impacts is a critical task as a component of scientific support for decision makers.

The task of providing information about uncertainty can be broadly divided into two steps: (1) quantify the uncertainty in future outcomes, and (2) communicate the quantified uncertainties. Each of these steps entails overcoming significant challenges. The paper by Patt and Schrag (2003) is a contribution to the latter step of communicating uncertainty once quantified. They raise important questions about how people translate between linguistic and numerical descriptions of uncertainty and risk that may have implications for how we communicate future assessments.

In this editorial, however, I would like to comment briefly on the former of the two steps previously mentioned, that of quantifying uncertainty. Regardless of how probabilities are communicated (i.e., whether we reflect risk or probability in our language choice), the question of how we estimate these probabilities/risks remains to be adequately addressed. In the most abstract theoretical sense, the process of uncertainty analysis is straightforward. But the operational realities present empirical, methodological, institutional, and philosophical challenges. Here, I will briefly describe these challenges and suggest some activities that the research community can focus on to improve our ability to measure uncertainty as part of the scientific assessment process.

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