On the appropriate and inappropriate uses of probability distributions in climate projections and some alternatives

Joel Katzav (), Erica L. Thompson (), James Risbey (), David A. Stainforth (), Seamus Bradley () and Mathias Frisch ()
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Joel Katzav: The University of Queensland
Erica L. Thompson: London School of Economics and Political Science
James Risbey: CSIRO Oceans & Atmosphere
David A. Stainforth: Grantham Research Institute On Climate Change and the Environment, London School of Economics and Political Science
Seamus Bradley: University of Leeds
Mathias Frisch: Leibniz University Hannover

Climatic Change, 2021, vol. 169, issue 1, No 15, 20 pages

Abstract: Abstract When do probability distribution functions (PDFs) about future climate misrepresent uncertainty? How can we recognise when such misrepresentation occurs and thus avoid it in reasoning about or communicating our uncertainty? And when we should not use a PDF, what should we do instead? In this paper, we address these three questions. We start by providing a classification of types of uncertainty and using this classification to illustrate when PDFs misrepresent our uncertainty in a way that may adversely affect decisions. We then discuss when it is reasonable and appropriate to use a PDF to reason about or communicate uncertainty about climate. We consider two perspectives on this issue. On one, which we argue is preferable, available theory and evidence in climate science basically exclude using PDFs to represent our uncertainty. On the other, PDFs can legitimately be provided when resting on appropriate expert judgement and recognition of associated risks. Once we have specified the border between appropriate and inappropriate uses of PDFs, we explore alternatives to their use. We briefly describe two formal alternatives, namely imprecise probabilities and possibilistic distribution functions, as well as informal possibilistic alternatives. We suggest that the possibilistic alternatives are preferable.

Keywords: Climate projection; Uncertainty representations; Probability; Deep uncertainty; Possibility theory (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10584-021-03267-x

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