of human activities, would foster the development of more robust and integrated assessments of key impacts of climate change (see Chapter 4). Finally, the usefulness of climate model experiments to decision making would be improved if they could be used to comprehensively assess a wider variety of climate response strategies, including specific GHG emissions-reduction strategies, adaptation strategies, and solar radiation management strategies (see Chapter 15).
Improve regional climate modeling, observations, and assessments. Given the importance of local and regional information to decision makers, and the fact that it might take decades to develop global models with sufficient resolution to resolve local-scale processes, it is essential to continue improving regional climate information, including observations and assessments of regional climate and climate-related changes as well as models that can project interannual, decadal, and multidecadal climate change, including extreme events, at regional to local scales across a range of future global climate change scenarios. Improvements in regional climate observations, modeling, and assessment activities often go hand in hand—for example, local and regional-scale observations are needed to verify regional models or down-scaled estimates of precipitation. Models also require a variety of information, for example the regional climate forcing associated with aerosols and land use change, that is also useful to decision makers for planning climate response strategies and for other reasons (such as monitoring air quality). It will also be important to improve our understanding and ability to model regional climate dynamics, including atmospheric circulation in complex terrain as well as modes of natural climate variability on all time scales, especially how their intensity and geographic patterns may change under different scenarios of global climate change. Several strategies for improving regional climate models are described in this chapter, including statistical and dynamical approaches. As with the development of global climate models, further progress in regional modeling will require expanded computing resources, improvements in data assimilation and parameterization, and both national and international coordination.
Advance understanding of thresholds, abrupt changes, and other climate “surprises.” Some of the largest potential risks associated with future climate change come not from the relatively smooth changes in average climate conditions that are reasonably well understood and resolved in current climate models, but from extreme events, abrupt changes, and surprises that might occur when thresholds in the climate system (or related human or environmental systems) are crossed. While the paleo-climate record indicates that abrupt climate changes have occurred in the past, and we have many examples of extreme events and nonlinear interactions among different components of the human-environment system that have resulted in significant impacts, our ability to predict these kinds of events or even estimate their likelihood