TY - JOUR

T1 - On the appropriate and inappropriate uses of probability distributions in climate projections and some alternatives

AU - Katzav, Joel

AU - Thompson, Erica L.

AU - Risbey, James

AU - Stainforth, David A.

AU - Bradley, Seamus

AU - Frisch, Mathias

N1 - David A. Stainforth. The U.K. Economic and Social Research Council (ES/R009708/1) Centre for Climate Change. Economics and Policy (CCCEP); The Grantham Research Institute on Climate Change and the Environment. James Risbey. The Decadal Climate Forecast Project at CSIRO.

PY - 2021/11/25

Y1 - 2021/11/25

N2 - 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.

AB - 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.

KW - Climate projection

KW - Deep uncertainty

KW - Possibility theory

KW - Probability

KW - Uncertainty representations

UR - http://www.scopus.com/inward/record.url?scp=85120674204&partnerID=8YFLogxK

U2 - 10.1007/s10584-021-03267-x

DO - 10.1007/s10584-021-03267-x

M3 - Article

AN - SCOPUS:85120674204

VL - 169

JO - CLIMATIC CHANGE

JF - CLIMATIC CHANGE

SN - 0165-0009

IS - 1-2

M1 - 15

ER -