Background.Uncertainty about climate change impacts on forests can hinder mitigation andadaptation actions. Scientific enquiry typically involves assessments of uncertainties, yet differentuncertainty components emerge in different studies. Consequently, inconsistent understanding ofuncertainty among different climate impact studies(from the impact analysis to implementingsolutions)can be an additional reason for delaying action. In this review we(a)expanded existinguncertainty assessment frameworks into one harmonised framework for characterizing uncertainty,(b)used this framework to identify and classify uncertainties in climate change impacts studies onforests, and(c)summarised the uncertainty assessment methods applied in those studies.Methods.We systematically reviewed climate change impact studies published between 1994 and 2016. Weseparated these studies into those generating information about climate change impacts on forestsusing models–‘modelling studies’, and those that used this information to design managementactions—‘decision-making studies’. We classified uncertainty across three dimensions:nature,level,andlocation, which can be further categorised into specific uncertainty types.Results. We found thatdifferent uncertainties prevail in modelling versus decision-making studies. Epistemic uncertainty isthe most common nature of uncertainty covered by both types of studies, whereas ambiguity plays apronounced role only in decision-making studies. Modelling studies equally investigate all levels ofuncertainty, whereas decision-making studies mainly address scenario uncertainty and recognisedignorance. Finally, the main location of uncertainty for both modelling and decision-making studies iswithin the driving forces—representing, e.g. socioeconomic or policy changes. The most frequentlyused methods to assess uncertainty are expert elicitation, sensitivity and scenario analysis, but a fullsuite of methods exists that seems currently underutilized.Discussion & Synthesis.The misalignmentof uncertainty types addressed by modelling and decision-making studies may complicate adaptationactions early in the implementation pathway. Furthermore, these differences can be a potential barrierfor communicating researchfindings to decision-makers.

Inconsistent recognition of uncertainty in studies of climate change impacts on forests

Mairota, Paola
Methodology
;
2019

Abstract

Background.Uncertainty about climate change impacts on forests can hinder mitigation andadaptation actions. Scientific enquiry typically involves assessments of uncertainties, yet differentuncertainty components emerge in different studies. Consequently, inconsistent understanding ofuncertainty among different climate impact studies(from the impact analysis to implementingsolutions)can be an additional reason for delaying action. In this review we(a)expanded existinguncertainty assessment frameworks into one harmonised framework for characterizing uncertainty,(b)used this framework to identify and classify uncertainties in climate change impacts studies onforests, and(c)summarised the uncertainty assessment methods applied in those studies.Methods.We systematically reviewed climate change impact studies published between 1994 and 2016. Weseparated these studies into those generating information about climate change impacts on forestsusing models–‘modelling studies’, and those that used this information to design managementactions—‘decision-making studies’. We classified uncertainty across three dimensions:nature,level,andlocation, which can be further categorised into specific uncertainty types.Results. We found thatdifferent uncertainties prevail in modelling versus decision-making studies. Epistemic uncertainty isthe most common nature of uncertainty covered by both types of studies, whereas ambiguity plays apronounced role only in decision-making studies. Modelling studies equally investigate all levels ofuncertainty, whereas decision-making studies mainly address scenario uncertainty and recognisedignorance. Finally, the main location of uncertainty for both modelling and decision-making studies iswithin the driving forces—representing, e.g. socioeconomic or policy changes. The most frequentlyused methods to assess uncertainty are expert elicitation, sensitivity and scenario analysis, but a fullsuite of methods exists that seems currently underutilized.Discussion & Synthesis.The misalignmentof uncertainty types addressed by modelling and decision-making studies may complicate adaptationactions early in the implementation pathway. Furthermore, these differences can be a potential barrierfor communicating researchfindings to decision-makers.
File in questo prodotto:
File Dimensione Formato  
Petr_2019_Environ._Res._Lett._14_113003.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Documento in Post-print
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 1.02 MB
Formato Adobe PDF
1.02 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/247729
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 4
social impact