Expert judgement and uncertainty quantification for climate change

445 Managing the risks of climate change requires a consistent and comprehensive approach to quantifying uncertainty and a clear narrative to describe the process. As economist Charles Kolstad noted, such efforts are neither new nor confined to the climate arena: “Uncertainty affects many different kinds of agents in the world — including governments — and there are a whole host of instruments that have already been set up to deal with these uncertainties. We don’t need to eliminate uncertainty — uncertainty is fine as long as it’s quantified”1. Process-based models (PBMs) often form the sole basis for uncertainty quantification of climate projections. Such models incorporate operative physics at scales that are manageable from a computational and data acquisition viewpoint. However, some climate projection uncertainties — variously termed model, structural, deep2 or even wicked — take the scientific community outside its comfort zone. As we discuss below, these uncertainties cannot be tightly constrained with observations; as such, strictly speaking, PBMs cannot be validated. A variety of types of formalized expert judgement (see Box 1), some with greater rigour than others, has played an only limited role in climate-change-related assessments of various physical hazards3 where deep uncertainty prevails4–9. In contrast, it has been a mainstay in other areas of risk analysis since 1975. In the climate change arena, it has proved difficult to reconcile formalized expert judgement with PBMs, as the projections and uncertainty estimates based on each can often be substantially different. It is even more difficult to determine why they differ. Although the Intergovernmental Panel on Climate Change (IPCC) has provided guidance documents on combining multi-model climate projections and on characterizing and communicating uncertainty10–12, IPCC recently declined to provide general guidance for combining distinct lines of evidence arising from, for example, expert judgement and PBMs13. There is, however, a history of successful efforts in other areas. The nuclear sector developed techniques for melding structured expert judgement (SEJ, a type of formalization; see Box 1) with the complex suite of models used to predict the consequences of a nuclear accident14. A problem for risk analysts was that ‘domain experts’ (for example, specialists in atmospheric dispersion as opposed to whole-system modellers) working with high-fidelity Expert judgement and uncertainty quantification for climate change