Uncertainty in climate change modelling: can global sensitivity analysis be of help?

Barry Anderson (), Emanuele Borgonovo (), Marzio Galeotti and Roberto Roson

Departmental Working Papers from Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano

Abstract: The complexity of integrated assessment models (IAMs) prevents the direct appreciation of the impact of uncertainty on the model predictions. However, for a full understanding and corroboration of model results, analysts might be willing, and ought to identify the model inputs that influence the model results the most (key drivers), appraise the relevance of interactions and the direction of change associated with the simultaneous variation of the model inputs. We show that such information is already contained in the data set produced by Monte Carlo simulations and that it can be extracted without additional calculations. Our discussion is guided by an application of the proposed methodologies to the well-known DICE model of William Nordhaus (2008). A comparison of the proposed methodology to approaches previously applied on the same model shows that robust insights concerning the dependence of future atmospheric temperature, global emissions and current carbon costs and taxes on the model’s exogenous inputs can be obtained. The method avoids the fallacy of a priori deeming the important factors based on sole intuition.

Keywords: OR in Environment; Robustness and Sensitivity; Climate change; Global sensitivity analysis; Integrated Assessment Modelling (search for similar items in EconPapers)
JEL-codes: Q50 Q54 Q56 (search for similar items in EconPapers)
Date: 2012-09-11
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Citations: View citations in EconPapers (1)

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Journal Article: Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help? (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:mil:wpdepa:2012-18

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