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

Martha Peirce Butler, Patrick M. Reed, Karen Ann Fisher-Vanden, Klaus Keller, Thorsten Wagener

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)10-29
Number of pages20
JournalEnvironmental Modelling and Software
Volume59
DOIs
StatePublished - Jan 1 2014

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Sensitivity analysis
sensitivity analysis
Climate change
climate change
Economics
Uncertainty analysis
Emission control
uncertainty analysis
emission control
economic system
global model
economics
cost
ranking
global climate
Costs
decomposition
Decomposition
climate
resource

All Science Journal Classification (ASJC) codes

  • Software
  • Environmental Engineering
  • Ecological Modeling

Cite this

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