Translating Climate-Change Probabilities into Impact Risks - Overcoming the Impact- Model Bottleneck

Projections of climate change in response to increasing greenhouse-gas concentrations are uncertain and likely to remain so for the foreseeable future. As more projections become available for analysts, we are increasingly able to characterize the probabilities of obtaining various levels of climate change in current projections. However, the probabilities of most interest in impact assessments are not the probabilities of climate changes, but rather the probabilities (or risks) of various levels and kinds of climate-change impact. These risks can be difficult to estimate even if the climate-change probabilities are well known. The difficulty arises because, frequently, impact models and assessments are computationally demanding or time consuming of hands-on, human expert analyses, so that severe limits are placed on the numbers of climate- change scenarios from which detailed impacts can be assessed. Estimation of risks of various impacts is generally difficult with the few resulting examples. However, real-world examples from the water-resources sector will be used to show that, by applying several different "derived distributions" approaches for estimating the risks of various impacts from known climate-change probabilities to just a few impact-model simulations, risks can be estimated along with indications of how accurate are the impact-risk estimates. The prospects for a priori selection of a few climate-change scenarios (from a larger ensemble of available projections) that will allow the best, most economical estimates of impact risks will be explored with a simple but real-world example.