Climatic Change

, Volume 132, Issue 3, pp 417–432 | Cite as

Exploring scenario and model uncertainty in cross-sectoral integrated assessment approaches to climate change impacts

  • R. DunfordEmail author
  • P. A. Harrison
  • M. D. A. Rounsevell


In this paper we present an uncertainty analysis of a cross-sectoral, regional-scale, Integrated Assessment Platform (IAP) for the assessment of climate change impacts, vulnerability and adaptation. The IAP couples simplified meta-models for a number of sectors (agriculture, forestry, urban development, biodiversity, flood and water resources management) within a user-friendly interface. Cross-sectoral interactions and feedbacks can be evaluated for a range of future scenarios with the aim of supporting a stakeholder dialogue and mutual learning. We present a method to address uncertainty in: i) future climate and socio-economic scenarios and ii) the interlinked network of meta-models that make up the IAP. A mixed-method approach is taken: formal numerical approaches, modeller interviews and network analysis are combined to provide a holistic uncertainty assessment that considers both quantifiable and un-quantifiable uncertainty. Results demonstrate that the combined quantitative-qualitative approach provides considerable advantages over traditional, validation-based uncertainty assessments. Combined fuzzy-set methods and network analysis methods allow maps of modeller certainty to be explored. The results indicate that validation statistics are not the only factors driving modeller certainty; a large range of other factors including the quality and availability of validation data, the meta-modelling process, inter-modeller trust, derivation methods, and pragmatic factors such as time, resources, skills and experience influence modeller certainty. We conclude that by identifying, classifying and exploring uncertainty in conjunction with the model developers, we can ensure not only that the modelling system itself improves, but that the decisions based on it can draw on the best available information: the projection itself, and a holistic understanding of the uncertainty associated with it.


Model Uncertainty Climate Scenario Climate Sensitivity Uncertainty Assessment Scenario Uncertainty 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research leading to these results has received funding from the European Community’s Seventh Framework Programme ([FP7/2007–2013]) under grant agreement number 244031 (the CLIMSAVE Project: Climate Change Integrated Methodology for Cross-sectoral Adaptation and Vulnerability in Europe). The authors would like to thank all the CLIMSAVE team, in particular the meta-modellers and those involved in the implementation of the IAP.

Supplementary material

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • R. Dunford
    • 1
    • 2
    Email author
  • P. A. Harrison
    • 1
  • M. D. A. Rounsevell
    • 2
  1. 1.Environmental Change InstituteOxford University Centre for the EnvironmentOxfordUK
  2. 2.School of GeoSciencesThe University of EdinburghEdinburghUK

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