Article

Climatic Change

, Volume 112, Issue 3, pp 569-583

First online:

Analysis of climate policy targets under uncertainty

  • Mort WebsterAffiliated withJoint Program on the Science and Policy of Global Change, Massachusetts Institute of TechnologyEngineering Systems Division, Massachusetts Institute of Technology Email author 
  • , Andrei P. SokolovAffiliated withJoint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology
  • , John M. ReillyAffiliated withJoint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology
  • , Chris E. ForestAffiliated withDepartment of Meteorology, Pennsylvania State University
  • , Sergey PaltsevAffiliated withJoint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology
  • , Adam SchlosserAffiliated withJoint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology
  • , Chien WangAffiliated withJoint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology
  • , David KicklighterAffiliated withThe Ecosystems Center, Marine Biological Laboratory
  • , Marcus SarofimAffiliated withAAAS Science and Technology Policy Fellow, U.S. Environmental Protection Agency
    • , Jerry MelilloAffiliated withThe Ecosystems Center, Marine Biological Laboratory
    • , Ronald G. PrinnAffiliated withJoint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology
    • , Henry D. JacobyAffiliated withJoint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology

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Abstract

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.