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Science always involves uncertainties. These arise at each step of the scientific method: in the development of models or hypotheses, in measurements and in analyses and interpretation of scientific assumptions. Climate science is not different in this regard from other areas of science. The complexity of the climate system and the large range of processes involved bring particular challenges because, for example, gaps in direct measurements of the past can be filled only by reconstructions using proxy data.

Because the Earth’s climate system is characterized by multiple spatial and temporal scales, uncertainties do not usually reduce at a single, predictable rate: for example, new observations may reduce the uncertainties surrounding short-timescale processes quite rapidly, while longer timescale processes may require very long observational baselines before much progress can be made. Characterization of the interaction between processes, as quantified by models, can be improved by model development, or can shed light on new areas in which uncertainty is greater than previously thought. The fact that there is only a single realization of the climate, rather than a range of different climates from which to draw, can matter significantly for certain lines of enquiry, most notably for the detection and attribution of causes of climate change and for the evaluation of projections of future states.

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