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Predictions of regional climate change for the next few decades are characterized by high uncertainty, but this uncertainty is potentially reducible through investments in climate science.
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Faced with the realities of a changing climate, decision makers in a wide variety of organiza- tions are increasingly seeking quantitative climate predictions. Specifically, they require predictions of the regional and local changes in climate that will impact people, economies, and ecosystems. Such predictions are available (e.g., Solomon et al. 2007) but are subject to considerable uncertainty. Thus, an important issue for these decision makers, and for organizations that fund climate research, is as follows: what is the scope for narrowing the uncertainty through future investments in climate science? Here, we address this question through analysis of twenty-first-century surface air temperature predictions (shown in Fig. 1) in the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset, as used in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4; Solomon et al. 2007). This analysis is subject to some caveats, which we acknowledge and discuss.
PARTITIONING UNCERTAINTY. Uncertainty in climate predictions arises from three distinct sources. The first is the internal variability of the climate system, that is, the natural fluctuations that arise in the absence of any radiative forcing of the planet. Appreciation of these fluctuations is an important matter for decision makers because they have the potential to reverse - for a decade or so- the longer-term trends that are associated with anthropogenic climate change. The second is model uncertainty (also known as response uncertainty): in response to the same radiative forcing, different models simulate somewhat different changes in climate. The third is scenario uncertainty: uncertainty in future emissions of greenhouse gases, for example, causes uncertainty in future radiative forcing and hence climate. The method we use to separate these different sources of uncertainty, using 15 global climate models and three emissions scenarios, is described in appendix A.
The relative importance of the three sources of uncertainty varies with prediction lead time and with spatial and temporal averaging scale (Fig. 2; see also Räisänen 2001). The figure shows that for time horizons of many decades or longer, the dominant sources of uncertainty at regional...