Meaningful climate science

Abstract

Within the climate science community, useable climate science has been understood as quantitative, usually as a best estimate together with a quantified uncertainty. Physical scientists are trained to produce numbers and to draw general, abstract conclusions. In general, however, people relate much better to stories and to events they have experienced, which are inevitably contingent and particular. Sheila Jasanoff has argued elsewhere that the process of abstraction in climate science “detaches knowledge from meaning”. Perhaps useable climate science is, then, meaningful climate science. We argue here that the development of meaningful climate science can be achieved by adopting a storyline approach to climate variability and change. By ‘storyline’ we mean a physically self-consistent unfolding of past events or of plausible future events or pathways. Storylines represent a combination of qualitative and quantitative information, where the qualitative element represents a packaging or contextualization of the quantitative aspects, which ensures that data can be meaningfully interpreted. Viewed from this perspective, we show that physical climate storylines can be aligned with several well-established vehicles for translation of knowledge between diverse communities: narratives, boundary objects, and data journeys. They can therefore be used as a ‘pidgin language’ to enrich the set of tools available to climate scientists to bring meaning to climate knowledge. “And what is the use of a book”, thought Alice, “without pictures or conversations?” (Lewis Carroll, Alice’s Adventures in Wonderland).

Climate Impact Storylines for Assessing Socio-Economic Responses to Remote Events

“…The concept does not rely on a standardized climate modelling toolset such as CMIP6 (Touzé-Peiffer et al, 2020), but instead combines stakeholder evidence, historic events and a mix of data analysis and model experiment techniques to arrive at evidence based narratives of "unfoldings of events and their hypothetical future counterfactuals" (Shepherd et al, 2018). As such it combines quantitative and qualitative elements (Shepherd and Lloyd, 2021), where the quantitative information gives a meaningful contribution to the risk assessment from complex climate change processes, and the qualitative elements provide insights in relevant pathways of risk transmission. By exploring a range of present-day or future counterfactual conditions in most storylines, crucial climatic elements in the storylines are complemented with a quantification of the underlying uncertainty.…”

“…For this, well-designed physical climate storylines triggered by specific climate events (Shepherd et al, 2018;Lloyd and Shepherd, 2020;Sillmann et al, 2021) offer a helpful framework for analyzing how impacts can be reduced and resilience to climate change can be enhanced. A description of selected historic events that have been experienced by individuals can give more meaningful insights than a quantitative uncertainty assessment across a complex chain of causes and effect (Shepherd and Lloyd, 2021). Event-oriented physical climate storylines (or in brief: climate event storylines) generate insights that can lead to better preparedness, for instance by developing stress-tests conditioned on plausible and verifiable boundary conditions, or by revealing previously unexplored risk propagation pathways or responses to emerging risks Albano et al, 2021).…”

A High‐End Estimate of Sea Level Rise for Practitioners

“…This also implies that users who are less risk‐averse, or have the ability, to iteratively build resilience, can decide to consider the mean values for all components from an IPCC assessment and add the high‐end contribution from Antarctica and Greenland to develop a tailored, but still transparent high‐end estimate. In this way, the high‐end components and how best to sum them encourage discussion between sea level scientists and practitioners and co‐production of the most appropriate SLR scenarios for the respective needs, including the development of storylines (T. G. Shepherd & Lloyd, 2021 ). For a more easily accessible approach, and because both perfect correlation and full independence of all components seem unlikely based on today's understanding, practitioners might simply average the high end estimate projections in this paper between the two to derive a single, high end projection for use in planning, if that is more useful than a range.…”

Climate risk assessment needs urgent improvement

“…Moving forward, emerging science and technology such as causal networks, which embed expert knowledge within Bayesian logic, can enable the transparent inclusion of heterogeneous data sources without sacrificing mathematical rigour 17 . Bayesian networks provide the ability to deal-transparently and including both quantitative and qualitative information that bridges the global and local scales consistently-with the unavoidable uncertainty in climate risk.…”