Climatic Change

, Volume 112, Issue 3, pp 569–583

Analysis of climate policy targets under uncertainty

Authors

    • Joint Program on the Science and Policy of Global ChangeMassachusetts Institute of Technology
    • Engineering Systems DivisionMassachusetts Institute of Technology
  • Andrei P. Sokolov
    • Joint Program on the Science and Policy of Global ChangeMassachusetts Institute of Technology
  • John M. Reilly
    • Joint Program on the Science and Policy of Global ChangeMassachusetts Institute of Technology
  • Chris E. Forest
    • Department of MeteorologyPennsylvania State University
  • Sergey Paltsev
    • Joint Program on the Science and Policy of Global ChangeMassachusetts Institute of Technology
  • Adam Schlosser
    • Joint Program on the Science and Policy of Global ChangeMassachusetts Institute of Technology
  • Chien Wang
    • Joint Program on the Science and Policy of Global ChangeMassachusetts Institute of Technology
  • David Kicklighter
    • The Ecosystems CenterMarine Biological Laboratory
  • Marcus Sarofim
    • AAAS Science and Technology Policy Fellow, U.S. Environmental Protection Agency
  • Jerry Melillo
    • The Ecosystems CenterMarine Biological Laboratory
  • Ronald G. Prinn
    • Joint Program on the Science and Policy of Global ChangeMassachusetts Institute of Technology
  • Henry D. Jacoby
    • Joint Program on the Science and Policy of Global ChangeMassachusetts Institute of Technology
Article

DOI: 10.1007/s10584-011-0260-0

Cite this article as:
Webster, M., Sokolov, A.P., Reilly, J.M. et al. Climatic Change (2012) 112: 569. doi:10.1007/s10584-011-0260-0

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.

Supplementary material

10584_2011_260_MOESM1_ESM.docx (253 kb)
ESM 1(DOCX 252 kb)

Copyright information

© Springer Science+Business Media B.V. 2011