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Expert judgement and uncertainty quantification for climate change

Abstract

Expert judgement is an unavoidable element of the process-based numerical models used for climate change projections, and the statistical approaches used to characterize uncertainty across model ensembles. Here, we highlight the need for formalized approaches to unifying numerical modelling with expert judgement in order to facilitate characterization of uncertainty in a reproducible, consistent and transparent fashion. As an example, we use probabilistic inversion, a well-established technique used in many other applications outside of climate change, to fuse two recent analyses of twenty-first century Antarctic ice loss. Probabilistic inversion is but one of many possible approaches to formalizing the role of expert judgement, and the Antarctic ice sheet is only one possible climate-related application. We recommend indicators or signposts that characterize successful science-based uncertainty quantification.

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Figure 1: The effect of probabilistic inversion on SLR probability distributions.
Figure 2: Scatter plots of 10,000 randomly selected samples from before (grey) and after (black) the probabilistic inversion.
Figure 3

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Acknowledgements

We thank J. Hall (Oxford University, UK), K. Keller (Pennsylvania State University, USA), R. Kopp (Rutgers University, USA), J. Rougier (University of Bristol, UK), D. Sexton (Met Office Hadley Centre, UK) and C. Tebaldi (National Center for Atmospheric Research, USA) for either helpful comments on an earlier draft of the manuscript, or useful discussions of the issues raised, or both.

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M.O., C.M.L. and R.M.C. designed the research, conducted analysis of data and results, and contributed to writing, editing and revision. C.M.L. and R.M.C. performed statistical modelling.

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Correspondence to Michael Oppenheimer.

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Oppenheimer, M., Little, C. & Cooke, R. Expert judgement and uncertainty quantification for climate change. Nature Clim Change 6, 445–451 (2016). https://doi.org/10.1038/nclimate2959

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