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Verlagsausgabe
DOI: 10.5445/IR/1000083425
Veröffentlicht am 14.06.2018
Originalveröffentlichung
DOI: 10.1007/s10113-018-1338-2
Scopus
Zitationen: 3

Bridging uncertainty concepts across narratives and simulations in environmental scenarios [in press]

Pedde, Simona; Kok, Kasper; Onigkeit, Janina; Brown, Calum; Holman, Ian; Harrison, Paula A.

Abstract:
Uncertainties in our understanding of current and future climate change projections, impacts and vulnerabilities are structured by scientists using scenarios, which are generally in qualitative (narrative) and quantitative (numerical) forms. Although conceptually strong, qualitative and quantitative scenarios have limited complementarity due to the lack of a fundamental bridge between two different concepts of uncertainty: linguistic and epistemic. Epistemic uncertainty is represented by the range of scenarios and linguistic variables within them, while linguistic uncertainty is represented by the translation of those linguistic variables via the fuzzy set approach. Both are therefore incorporated in the models that utilise the final quantifications. The application of this method is demonstrated in a stakeholder-led development of socioeconomic scenarios. The socioeconomic scenarios include several vague elements due to heterogeneous linguistic interpretations of future change on the part of stakeholders. We apply the so-called ‘Centre of Gravity’ (CoG) operator to defuzzify the quantifications of linguistic values provided by stak ... mehr


Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 1436-3798, 1436-378X
URN: urn:nbn:de:swb:90-834251
KITopen ID: 1000083425
HGF-Programm 12.02.02; LK 01
Erschienen in Regional environmental change
Seiten 1-12
Vorab online veröffentlicht am 11.05.2018
Schlagworte Participatory socioeconomic scenarios, Fuzzy sets, Linguistic and epistemic uncertainties, Narratives and environmental models
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