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Proceedings of the Royal Society. A, Mathematical, physical, and engineering sciences, 2019-05, Vol.475 (2225), p.20190013-20190013
2019

Details

Autor(en) / Beteiligte
Titel
Storyline approach to the construction of regional climate change information
Ist Teil von
  • Proceedings of the Royal Society. A, Mathematical, physical, and engineering sciences, 2019-05, Vol.475 (2225), p.20190013-20190013
Ort / Verlag
England: The Royal Society Publishing
Erscheinungsjahr
2019
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
  • 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.
Sprache
Englisch
Identifikatoren
ISSN: 1364-5021
eISSN: 1471-2946
DOI: 10.1098/rspa.2019.0013
Titel-ID: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6545051
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