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Analysis of climate policy targets under uncertainty

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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.

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Notes

  1. The G8 leaders, “recognized the scientific view on the need to keep global temperature rise below 2° above pre-industrial levels.”

  2. Scenarios where there is low growth in emissions can lead to some ensemble members where emissions are below the constraint level, especially for the Level 4 and Level 3 cases. The variation in GWP-weighted emissions is less than 1%.

  3. Both the EPPA and earth system components of the IGSM have been updated since the CCSP study but these emissions scenarios remain of interest as a basis for demonstrating how our current best estimate of the cost of achieving them, and their effectiveness in reducing the risk of serious climate change, compares to the uncertainty in these estimates.

  4. In fact, there is some uncertainty, too small to be seen in the figure, in the initial year of the Level 4 and Level 3 scenarios, because under the low-growth ensemble members the constraint is not binding.

  5. As noted above, the IGSM considers uncertainty in carbon uptake by both ocean and terrestrial vegetation. The IGSM accounts for the effect of nitrogen limitation on terrestrial carbon uptake, this significantly reduces both strength of feedback between climate and carbon cycle and uncertainty in this feedback (Sokolov et al. 2008).

  6. Note that the differences between model results in Clarke et al. (2007) reflect both different emissions paths and different carbon cycle models, while the uncertainty ranges in this study for the stabilization scenarios reflect only climate system uncertainties.

  7. IPCC defines “likely” as having a probability of greater than 66% but less than 90%.

  8. Our estimate of 1.8 W/m2 is derived from the GISS model (Hansen et al., 1988), the radiation code from which is used in the MIT IGSM. The IPCC estimates the change in radiative forcing from preindustrial to present to be 1.6 W/m2, based on their estimates of the forcing from individual GHGs (Forster et al., 2007).

  9. We do not have a case comparable to the 3 W/m2 as that was not in the CCSP scenario design, and presents considerable challenges in simulating as it requires the assumption of energy technologies with net negative emissions.

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Acknowledgements

We thank the three anonymous referees for helpful comments on this manuscript. This analysis and the development of the IGSM model used here was supported by the U.S. Department of Energy (DE-FG02-94ER61937), U.S. Environmental Protection Agency, U.S. National Science Foundation, U.S. National Aeronautics and Space Administration, U.S. National Oceanographic and Atmospheric Administration and the Industry and Foundation Sponsors of the MIT Joint Program on the Science and Policy of Global Change.

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Authors and Affiliations

  1. Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USA

    Mort Webster, Andrei P. Sokolov, John M. Reilly, Sergey Paltsev, Adam Schlosser, Chien Wang, Ronald G. Prinn & Henry D. Jacoby

  2. Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, MA, USA

    Mort Webster

  3. Department of Meteorology, Pennsylvania State University, University Park, PA, USA

    Chris E. Forest

  4. The Ecosystems Center, Marine Biological Laboratory, Woods Hole, MA, USA

    David Kicklighter & Jerry Melillo

  5. AAAS Science and Technology Policy Fellow, U.S. Environmental Protection Agency, Washington DC, USA

    Marcus Sarofim

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  1. Mort Webster
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  2. Andrei P. Sokolov
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  3. John M. Reilly
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  4. Chris E. Forest
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  5. Sergey Paltsev
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  6. Adam Schlosser
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  7. Chien Wang
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  8. David Kicklighter
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  9. Marcus Sarofim
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  10. Jerry Melillo
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  11. Ronald G. Prinn
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  12. Henry D. Jacoby
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Correspondence to Mort Webster.

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Webster, M., Sokolov, A.P., Reilly, J.M. et al. Analysis of climate policy targets under uncertainty. Climatic Change 112, 569–583 (2012). https://doi.org/10.1007/s10584-011-0260-0

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