Analysis of climate policy targets under uncertainty

• Published in: Climatic change Vol. 112; no. 3-4; pp. 569 - 583
• Main Authors: Webster, Mort, Sokolov, Andrei P., Reilly, John M., Forest, Chris E., Paltsev, Sergey, Schlosser, Adam, Wang, Chien, Kicklighter, David, Sarofim, Marcus, Melillo, Jerry, Prinn, Ronald G., Jacoby, Henry D.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2012; Springer; Springer Nature B.V
• Subjects: Atmospheric Sciences; Climate; Climate change; Climate Change/Climate Change Impacts; Climate policy; Climate science; Climatology. Bioclimatology. Climate change; Computer simulation; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; economic systems; Emission analysis; Emissions; Emissions control; Environmental policy; Exact sciences and technology; External geophysics; General circulation models; Greenhouse effect; Greenhouse gases; Meteorology; Monte Carlo methods; Monte Carlo simulation; policy analysis; Reduction; Risk assessment; Risk management; Science programs; temperature; Uncertainty; United States; United States; Climate Policy; Stabilization Scenario; Emission Path; Supplemental Online Material; Emission Constraint; sustainable development; Mitigation; stabilization; Monte Carlo analysis; climate warming; Climate policy; Emissions reduction; uncertainties; climate change; greenhouse gas
• DOI: 10.1007/s10584-011-0260-0
• ISSN: 0165-0009

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.