atmospheric turbulence and forced convection. In return, improved regional-scale climate change forecasts, including, for example, wind, snow, and growing-degree-day forecasts at scales of around 5 km, can feed into climate change impact and adaptation studies for cities, agriculture, tourism development (e.g., ski areas), and renewable energy developments. However, regional projections are more reliable for temperature than for precipitation fields because of the intrinsic scale and complexity of physical processes at play. Improved model fidelity at regional scales is essential to assessment of water resource and agricultural stress and to drought and flood hazards, which are also an element of climate extremes.
Another challenge for global and regional climate models is their representation of patterns or modes of variability, such as ENSO, the Southern Annual Mode, the Arctic and North Atlantic Oscillations, and Pacific decadal variability. Because of their persistence, these ocean-atmosphere patterns strongly influence regional climate variability on time scales of years to decades. If not represented well in models, or if these modes are triggered and sustained at different times in different models, regional climate projections can diverge. Such errors place limits on decadal predictability, particularly on regional scales, and caution is required when interpreting the results from a small number of realizations and/or a small number of models. Work is needed to better understand modes of decadal variability, the underlying ocean-atmosphere feedbacks, and their representation in models.
How Will Climate Extremes Change?
Severe weather events such as tropical cyclones, droughts, floods, and heat waves have tremendous impacts on society, economically, and through loss of life. Extreme events are not predictable years in advance, because most of these reflect an instantaneous state of the weather, with its well-known limitations to prediction. On the other hand, insofar as these events are a function of the mean climate state, statistical probabilities for extreme weather events may be possible to project, which would have great value for decision support and infrastructure design. There are good examples of the ability to extract statistical information on climate extremes from climate models (e.g., Katz, 2010; Kharin and Zwiers, 2005), and experience is growing in the application of advanced statistical methods to assessment of climate hazards and climate change adaptation strategies (Klein-Tank et al., 2009).
For reliable insight from climate models, however, models need to be adept at representing the essential phenomena (e.g., tropical cyclone frequency and strength; tornado development; heavy rain events). Physical arguments and climate models sug-