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.

© Springer Netherlands

In order to derive optimal policies for greenhouse gas emissions control, the discounted marginal damages of emissions from different gases must be compared.^The greenhouse warming potential (GWP) index, which is most often used to compare greenhouse gases, is not based on such a damage comparison.^This essay presents assumptions under which ratios of gas-specific discounted marginal damages reduce to ratios of discounted marginal contributions to radiative forcing, where the discount rate is the difference between the discount rate relevant to climate-related damages and the rate of growth of marginal climate-related damages over time.^If there are important gas-specific costs or benefits not tied to radiative forcing, however, such as direct effects of carbon dioxide on plant growth, there is in general no shortcut around explicit comparison of discounted net marginal damages.

© 1995 by the IAEE

Although the global agricultural system will need to provide more food for a growing and wealthier population in decades to come, increasing demands for water and potential impacts of climate change pose threats to food systems. We review the primary threats to agricultural water availability, and model the potential effects of increases in municipal and industrial (M&I) water demands, environmental flow requirements (EFRs) and changing water supplies given climate change. Our models show that, together, these factors cause an 18 per cent reduction in the availability of worldwide water for agriculture by 2050. Meeting EFRs, which can necessitate more than 50 per cent of the mean annual run-off in a basin depending on its hydrograph, presents the single biggest threat to agricultural water availability. Next are increases in M&I demands, which are projected to increase upwards of 200 per cent by 2050 in developing countries with rapidly increasing populations and incomes. Climate change will affect the spatial and temporal distribution of run-off, and thus affect availability from the supply side. The combined effect of these factors can be dramatic in particular hotspots, which include northern Africa, India, China, parts of Europe, the western US and eastern Australia, among others.

© 2010 The Royal Society

This paper is a simple, rigorous, practically-oriented exposition of computable general equilibrium (CGE) modeling. The general algebraic framework of a CGE model is developed from microeconomic fundamentals, and employed to illustrate (i) how a model may be calibrated using the economic data in a social accounting matrix, (ii) how the resulting system of numerical equations may be solved for the equilibrium values of economic variables, and (iii) how perturbing this equilibrium by introducing tax or subsidy distortions facilitates analysis of policies' economy-wide impacts.