efforts, allow more efficient development of new modeling tools, promote higher standards or best practices, facilitate interpretation of results from different modeling approaches, speed scientific advance, and improve understanding of the various sources of modeling uncertainty.

Decision makers at many levels often desire projections of climate and its future range of variability and extremes localized to individual locations or very fine, subkilometer, scales. While the resolution of global climate models may eventually be this fine, the committee judges that this is not likely to occur within the next 20 years. In the meantime, other statistical or dynamical downscaling techniques will continue to be used to provide finer-scale projections. Although an evaluation of various methods would be useful, this would likely require a study unto itself. Furthermore, as global climate resolutions become finer themselves, statistical downscaling techniques become simpler and more straightforward and the need for more computationally expensive dynamical downscaling may diminish. Thus, the committee focused its attention in this report on improving the fidelity of models at all scales. However, like downscaling itself, using climate models with finer grids does not guarantee a much more certain or reliable climate model projection at local scales. As noted above, the uncertainties in climate models even at local scales derive in large part from global-scale processes such as cloud and carbon-cycle feedbacks, as well as uncertainties in how future human greenhouse gas and aerosol emissions unfold.

Recommendation 3.1: To address the increasing breadth of issues in climate science, the climate modeling community should vigorously pursue a full spectrum of models and evaluation approaches, including further systematic comparisons of the value added by various downscaling approaches as the resolution of climate models increases.

Recommendation 3.2: To support a national linked hierarchy of models, the United States should nurture a common modeling infrastructure and a shared model development process, allowing modeling groups to efficiently share advances while preserving scientific freedom and creativity by fostering model diversity where needed.

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