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

doi:10.1016/j.envsoft.2014.05.001

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

Research output: Contribution to journal › Article › peer-review

73 Scopus citations

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 language	English (US)
Pages (from-to)	10-29
Number of pages	20
Journal	Environmental Modelling and Software
Volume	59
DOIs	https://doi.org/10.1016/j.envsoft.2014.05.001
State	Published - Sep 2014

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

All Science Journal Classification (ASJC) codes

Software
Environmental Engineering
Ecological Modeling

Access to Document

10.1016/j.envsoft.2014.05.001

Cite this

@article{6db1b916bb3043c682b06d7f908df4e9,

title = "Identifying parametric controls and dependencies in integrated assessment models using global sensitivity analysis",

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.",

author = "Butler, \{Martha P.\} and Reed, \{Patrick M.\} and Karen Fisher-Vanden and Klaus Keller and Thorsten Wagener",

note = "Funding Information: This work was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, Integrated Assessment Research Program , Grant No. DE-SC0005171 , with additional support from NSF through the Network for Sustainable Climate Risk Management (SCRiM) under NSF cooperative agreement GEO-1240507 and the Penn State Center for Climate Risk Management. The authors thank William Nordhaus for making the DICE model available, and Alex Libardoni, Chris Forest and Roman Olson for providing their empirical climate sensitivity distributions and advice on their use and interpretation. The DICE model and documentation were accessed on 2/5/2011 from http://nordhaus.econ.yale.edu . Current access to the DICE model is http://www.econ.yale.edu/∼nordhaus/homepage/index.html . The CDICE model code is at https://github.com/mpbutler/CDICE2007 . Sobol' sampling and sensitivity analysis code used in this study are from the MOEA Diagnostic Tool ( http://www.moeaframework.org ). Any opinions, findings, and conclusions expressed in this work are those of the authors, and do not necessarily reflect the views of the National Science Foundation or the U.S. Department of Energy. ",

year = "2014",

month = sep,

doi = "10.1016/j.envsoft.2014.05.001",

language = "English (US)",

volume = "59",

pages = "10--29",

journal = "Environmental Modelling and Software",

issn = "1364-8152",

publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Identifying parametric controls and dependencies in integrated assessment models using global sensitivity analysis

AU - Butler, Martha P.

AU - Reed, Patrick M.

AU - Fisher-Vanden, Karen

AU - Keller, Klaus

AU - Wagener, Thorsten

N1 - Funding Information: This work was supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, Integrated Assessment Research Program , Grant No. DE-SC0005171 , with additional support from NSF through the Network for Sustainable Climate Risk Management (SCRiM) under NSF cooperative agreement GEO-1240507 and the Penn State Center for Climate Risk Management. The authors thank William Nordhaus for making the DICE model available, and Alex Libardoni, Chris Forest and Roman Olson for providing their empirical climate sensitivity distributions and advice on their use and interpretation. The DICE model and documentation were accessed on 2/5/2011 from http://nordhaus.econ.yale.edu . Current access to the DICE model is http://www.econ.yale.edu/∼nordhaus/homepage/index.html . The CDICE model code is at https://github.com/mpbutler/CDICE2007 . Sobol' sampling and sensitivity analysis code used in this study are from the MOEA Diagnostic Tool ( http://www.moeaframework.org ). Any opinions, findings, and conclusions expressed in this work are those of the authors, and do not necessarily reflect the views of the National Science Foundation or the U.S. Department of Energy.

PY - 2014/9

Y1 - 2014/9

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/84901329868

UR - https://www.scopus.com/pages/publications/84901329868#tab=citedBy

U2 - 10.1016/j.envsoft.2014.05.001

DO - 10.1016/j.envsoft.2014.05.001

M3 - Article

AN - SCOPUS:84901329868

SN - 1364-8152

VL - 59

SP - 10

EP - 29

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

ER -

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

Abstract

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this