gest that precipitation extremes are controlled by different physics than time-mean precipitation. Climate models project more frequent floods and droughts in the 21st century, but, as with regional rainfall trends discussed above, intermodel differences in the magnitudes and regional patterns of model trends are substantial, due to many of the same factors. Drought persistence is another example, involving feedbacks between soil moisture, evapotranspiration, atmospheric and surface temperatures, dust aerosols, cloud condensation nuclei, and interactions between regional and synoptic circulation patterns (i.e., blocking). Simulation of these feedbacks requires multiscale modeling with an interactive and sophisticated treatment of land-surface and boundary-layer processes.

Tropical cyclones are only roughly represented in many climate models, primarily because of low spatial resolution of the tight circulation and sharp gradients found in tropical cyclones. Simulations done with very high (25 km or less) resolution models greatly improve the representation of tropical cyclones, even without including the nonhydrostatic effects that are needed to include the vertical component of velocity in the model’s prognostic variables. Some coupled models are now able to simulate interannual variations in the frequency and intensity of tropical cyclones (e.g., National Centers for Environmental Prediction Climate Forecast System [CFS]), and seasonal forecast skill for upcoming hurricane seasons is improving. Seasonal landfall forecasts may be the next frontier.

In most cases, prognoses of severe weather will have to be statistical in nature (i.e., estimation of the likelihood of extreme events in future decades in a specific region). Statistical likelihoods are of great value for many applications, however, such as water resource management, infrastructure and emergency relief planning, and the insurance industry (Box 1.1). It is arguable whether climate models need to generate the full range of behavior and variability that is seen in the real world in order to extract information on extremes. In some cases, probability distribution functions may be constructed and offer appropriate inferences on extremes (e.g., Hegerl et al., 2004). In a nonstationary climate, however, statistical properties of probability distribution functions for some climate phenomena (e.g., the dispersion or shape of distributions) may evolve relative to the historical climate record.

How Quickly Will Sea Level Rise?

Global eustatic (mean) sea-level rise over the past century has been driven by a combination of thermal expansion of the oceans, melting of mountain glaciers and the Greenland ice sheet, and increased dynamical discharge to the oceans in Greenland



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