Identifying parametric controls and dependencies in integrated assessment models using global sensitivity analysis

Martha P. Butler*, Patrick M. Reed, Karen Fisher-Vanden, Klaus Keller, Thorsten Wagener

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

49 Citations (Scopus)


Integrated assessment models for climate change (IAMs) couple representations of economic and natural systems to identify and evaluate strategies for managing the effects of global climate change. In this study we subject three policy scenarios from the globally-aggregated Dynamic Integrated model of Climate and the Economy IAM to a comprehensive global sensitivity analysis using Sobol' variance decomposition. We focus on cost metrics representing diversions of economic resources from global world production. Our study illustrates how the sensitivity ranking of model parameters differs for alternative cost metrics, over time, and for different emission control strategies. This study contributes a comprehensive illustration of the negative consequences associated with using a priori expert elicitations to reduce the set of parameters analyzed in IAM uncertainty analysis. The results also provide a strong argument for conducting comprehensive model diagnostics for IAMs that explicitly account for the parameter interactions between the coupled natural and economic system components. (C) 2014 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)10-29
Number of pages20
JournalEnvironmental Modelling and Software
Publication statusPublished - Sep 2014


  • Integrated assessment model
  • Global sensitivity analysis
  • Sobol' method
  • Model diagnostics
  • Climate change


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