Storylines: an alternative approach to representing uncertainty in physical aspects of climate change

TLDR

Climate‑change research increasingly demands actionable information, yet the standard probabilistic ensemble approach to uncertainty has notable limitations. The authors propose a “storyline” approach to represent uncertainty, outlining four motivations: risk awareness, decision‑making, physical partitioning, and plausibility boundaries. A storyline is a physically self‑consistent unfolding of past or plausible future events, assessed without a priori probability and focused on driving factors and plausibility. The study finds that the conventional probabilistic method is limited under deep uncertainty, while storylines effectively link physical and human aspects of climate change.

Abstract

As climate change research becomes increasingly applied, the need for actionable information is growing rapidly. A key aspect of this requirement is the representation of uncertainties. The conventional approach to representing uncertainty in physical aspects of climate change is probabilistic, based on ensembles of climate model simulations. In the face of deep uncertainties, the known limitations of this approach are becoming increasingly apparent. An alternative is thus emerging which may be called a 'storyline' approach. We define a storyline as a physically self-consistent unfolding of past events, or of plausible future events or pathways. No a priori probability of the storyline is assessed; emphasis is placed instead on understanding the driving factors involved, and the plausibility of those factors. We introduce a typology of four reasons for using storylines to represent uncertainty in physical aspects of climate change: (i) improving risk awareness by framing risk in an event-oriented rather than a probabilistic manner, which corresponds more directly to how people perceive and respond to risk; (ii) strengthening decision-making by allowing one to work backward from a particular vulnerability or decision point, combining climate change information with other relevant factors to address compound risk and develop appropriate stress tests; (iii) providing a physical basis for partitioning uncertainty, thereby allowing the use of more credible regional models in a conditioned manner and (iv) exploring the boundaries of plausibility, thereby guarding against false precision and surprise. Storylines also offer a powerful way of linking physical with human aspects of climate change.