Without such efforts, it is likely that feedback loops will generate unintended consequences. For example, biofuel production is affecting food security, which in turn has impacts on migration, health, land use, and sustainable development. Increased efficiency in energy production can increase rates of energy use. Demographic trends or shifts can overwhelm otherwise effective strategies for mitigation, adaptation, or both. The effects of water projects can vary with different levels of flow and usage. As multiple stressors accelerate, these kinds of feedback relationships across mitigation- and adaptation-related variables are likely to increase in policy importance. Researchers need to ask questions such as: Are there critical thresholds in the ability to adapt? If so, what kinds of response can avoid these thresholds? How might future climate-related changes, especially abrupt ones, affect remaining capacities for climate change response? For instance, might some livelihood systems become more or less feasible? Are certain populations more vulnerable?
Climate modelers are also becoming more interested in issues of vulnerability, impacts, and adaptation, especially as they give more serious attention to more severe climate futures than previously examined. In the emerging effort to develop linked mitigation-adaptation scenarios and models, the community of vulnerability, impacts, and adaptation researchers needs to be a full partner, not just to provide input data as an add-on or afterthought. Nonetheless, the necessary integrated approaches have not yet been developed. Both communities will be challenged and will need to work together to address this research front. Development of improved capabilities for vulnerability, impacts, and adaptation modeling in integrated assessment will depend on underlying research by the vulnerability, impacts, and adaptation research community and more focused vulnerability, impacts, and adaptation models and tools that are interoperable with climate models. Approaches to modeling and data acquisition for climate impacts will, in many cases, be scaled differently than for climate drivers: the impacts work will be more local and regional and will need to scale up. In all likelihood, modeling of climate drivers will require downscaling as well. Innovative approaches will be required to overcome data limitations.
Participants observed that decision makers are coming to accept that climate change is a risk management problem, which implies a need to attend more to events that have low probabilities of occurrence, but can produce dramatic impacts if they do occur. Analytic structures are needed to think about costs and benefits in risk terms, to estimate the sensitivity of cost to mitigation, and to address the variety of climate-driven