Storyline approach to the construction of regional climate change information
Open accessAbstract
Climate science seeks to make statements of confidence about what has happened, and what will happen (conditional on scenario). The approach is effective for the global, thermodynamic aspects of climate change, but is ineffective when it comes to aspects of climate change related to atmospheric circulation, which are highly uncertain. Yet, atmospheric circulation strongly mediates climate impacts at the regional scale. In this way, the confidence framework, which focuses on avoiding type 1 errors (false alarms), raises the prospect of committing type 2 errors (missed warnings). This has ethical implications. At the regional scale, however, where information on climate change has to be combined with many other factors affecting vulnerability and exposure—most of which are highly uncertain—the societally relevant question is not ‘What will happen?’ but rather ‘What is the impact of particular actions under an uncertain regional climate change?’ This reframing of the question can cut the Gordian knot of regional climate change information, provided one distinguishes between epistemic and aleatoric uncertainties—something that is generally not done in climate projections. It is argued that the storyline approach to climate change—the identification of physically self-consistent, plausible pathways—has the potential to accomplish precisely this.
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Identifying a robust climate change driver in systems subject to other anthropogenic interference requires careful assessment of other socio-economic factors.
“In the case of systems subject to other anthropogenic interference such as the global biosphere, managed systems such as agriculture, or human systems themselves, identifying a robust climate change driver requires careful assessment of other socio-economic factors 41,42 , adding additional levels of complexity 43 .”
From Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies (2021) · cites this paper as [41]
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Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies
Callaghan, M., Schleussner, C.-F., Nath, S., Lejeune, Q., Knutson, T. R., Reichstein, M., Hansen, G., Theokritoff, E., Andrijevic, M., Brecha, R. J., et al. (2021) · Nature Climate Change · doi:10.1038/s41558-021-01168-6 · Cited by 251 · see citing works
- Authors
- Shepherd, T. G.
- Affiliations
- University of Reading (GB)
- Corresponding author
- Shepherd, Theodore G.
- Venue
- Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Publisher
- The Royal Society
- Published
- 2019-05
- Volume
- 475
- Issue
- 2225
- Pages
- 20190013
- ISSN
- 1364-5021 (ISSN-L)
- 1471-2946
- PMC
- PMC6545051
- OpenAlex
- W2945570949
- Cited by
- 331
- References
- 68
- Citations by year
- 2026: 29 · 2025: 30 · 2024: 59 · 2023: 58 · 2022: 68 · 2021: 62 · 2020: 23 · 2019: 2
- FWCI
- 20.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- CentAUR (University of Reading) (submittedVersion)
- Funders
- H2020 European Research Council (ACRCC, 339390)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
- Keywords
- storyline approach, climate change projections, epistemic uncertainty, aleatoric uncertainty, type II error, climate impact assessment, climate ethics, confidence statements
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