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

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 excludes 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.

The missing risks of climate change

“…These challenges are not, however, insurmountable barriers to their inclusion in policymaking or economic valuations. There are opportunities to use imprecise probabilities, formal possibilistic approaches and informal possibilistic approaches 26 such as 'tales of the future', which encapsulate physically realistic and plausible futures focused on the aspects of the system of concern 27,28 .…”

Uncertainty and sensitivity analysis for probabilistic weather and climate risk modelling: an implementation in CLIMADA v.3.1.

“…Overall, we hope that the simplicity of use of the presented unsequa module will motivate modellers to include uncertainty and sensi-80 tivity analysis as a natural part of climate risk modelling. Finally, we caution that numbers even with elaborate error bars and distributions can give a false sense of accuracy (Hinkel and Bisaro, 2016;Katzav et al, 2021) and that modellers should remember to reflect on the wider, non-quantifiable un-85 certainties, unknowns and normative choices of their models.…”

Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0

“…Overall, we hope that the simplicity of use of the presented unsequa module will motivate modellers to include uncertainty and sensitivity analyses as natural parts of climate-risk modelling. Finally, we caution that numbers even with elaborate error bars and distributions can give a false sense of accuracy (Hinkel and Bisaro, 2016;Katzav et al, 2021) and that modellers should remember to reflect on the wider, nonquantifiable uncertainties, unknowns, and normative choices of their models.…”