Analysis of climate policy targets under uncertainty

Mort Webster, Andrei P. Sokolov, John M. Reilly, Chris E. Forest, Sergey Paltsev, Adam Schlosser, Chien Wang, David Kicklighter, Marcus Sarofim, Jerry Melillo, Ronald G. Prinn, Henry D. Jacoby

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


Although policymaking in response to the climate change threat is essentially a challenge of risk management, most studies of the relation of emissions targets to desired climate outcomes are either deterministic or subject to a limited representation of the underlying uncertainties. Monte Carlo simulation, applied to the MIT Integrated Global System Model (an integrated economic and earth system model of intermediate complexity), is used to analyze the uncertain outcomes that flow from a set of century-scale emissions paths developed originally for a study by the U. S. Climate Change Science Program. The resulting uncertainty in temperature change and other impacts under these targets is used to illustrate three insights not obtainable from deterministic analyses: that the reduction of extreme temperature changes under emissions constraints is greater than the reduction in the median reduction; that the incremental gain from tighter constraints is not linear and depends on the target to be avoided; and that comparing median results across models can greatly understate the uncertainty in any single model.

Original languageEnglish (US)
Pages (from-to)569-583
Number of pages15
JournalClimatic Change
Issue number3-4
StatePublished - Jun 2012

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Atmospheric Science


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