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Improving the Effectiveness of U.S. Climate Modeling
consumption, and the general economy and well-being of societies. Over the United States increasing amounts of variance of temperature and precipitation are successively explained by ENSO, the PDO, and the NAO (Higgins et al, 2000), implying that being able to predict these patterns would explain successively greater amounts of these crucial climatic variables, with obvious social and economic implications. The response of these patterns to the addition of radiatively active constituents to the atmosphere is also an active field of research with the idea gaining currency that global warming intensity and patterns cannot be understood without understanding the changes of these decadal patterns with time.
One of the recommendations of the IPCC Third Assessment Report was that patterns of long-term climate variability should be addressed more completely. This topic arises both in model calculations and in the climate system. In simulations the issue of climate drift in model calculations needs to be clarified in part because it compounds the difficulty of distinguishing signal and noise. With respect to the long-term natural variability in the climate system per se, it is important to understand this variability and to expand the emerging capability of predicting patterns of such organized variability as ENSO. This predictive capability is both a valuable test of model performance and a useful contribution to natural resource and economic management.
The possibility of projecting these patterns was discussed in NRC (1998c), and indications of predictability of the NAO (Rodwell et al, 1999; Saravanan et al, 2000), the PDO (Venzke et al, 2000), and the subtropical Atlantic Dipole (Chang et al, 1998) have been demonstrated. The actual and simulated forecasting of these patterns requires a tremendous amount of computer resources, comparable to ensembles of global warming simulations. We expect the study of decadal variations and the predictability of these variations to continue with the aim of discovering useful future predictability as a guide to long-term planning similar to the case of global warming simulations.