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Constraints on radiative forcing and future climate change from observations and climate model ensembles

Author

Listed:
  • Reto Knutti

    (University of Bern)

  • Thomas F. Stocker

    (University of Bern)

  • Fortunat Joos

    (University of Bern)

  • Gian-Kasper Plattner

    (University of Bern)

Abstract
The assessment of uncertainties in global warming projections is often based on expert judgement, because a number of key variables in climate change are poorly quantified. In particular, the sensitivity of climate to changing greenhouse-gas concentrations in the atmosphere and the radiative forcing effects by aerosols are not well constrained, leading to large uncertainties in global warming simulations1. Here we present a Monte Carlo approach to produce probabilistic climate projections, using a climate model of reduced complexity. The uncertainties in the input parameters and in the model itself are taken into account, and past observations of oceanic and atmospheric warming are used to constrain the range of realistic model responses. We obtain a probability density function for the present-day total radiative forcing, giving 1.4 to 2.4 W m-2 for the 5–95 per cent confidence range, narrowing the global-mean indirect aerosol effect to the range of 0 to –1.2 W m-2. Ensemble simulations for two illustrative emission scenarios suggest a 40 per cent probability that global-mean surface temperature increase will exceed the range predicted by the Intergovernmental Panel on Climate Change (IPCC), but only a 5 per cent probability that warming will fall below that range.

Suggested Citation

  • Reto Knutti & Thomas F. Stocker & Fortunat Joos & Gian-Kasper Plattner, 2002. "Constraints on radiative forcing and future climate change from observations and climate model ensembles," Nature, Nature, vol. 416(6882), pages 719-723, April.
    • Handle: RePEc:nat:nature:v:416:y:2002:i:6882:d:10.1038_416719a
    DOI: 10.1038/416719a
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    Citations

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    Cited by:

    1. Stern, David I., 2006. "An atmosphere-ocean time series model of global climate change," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1330-1346, November.
    2. Riddhi Singh & Julianne D Quinn & Patrick M Reed & Klaus Keller, 2018. "Skill (or lack thereof) of data-model fusion techniques to provide an early warning signal for an approaching tipping point," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-21, February.
    3. Adam Daigneault & Steve Newbold, 2009. "Climate Response Uncertainty and the Unexpected Benefits of Greenhouse Gas Emissions Reductions," NCEE Working Paper Series 200806, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Mar 2009.
    4. Gerard Roe & Yoram Bauman, 2013. "Climate sensitivity: should the climate tail wag the policy dog?," Climatic Change, Springer, vol. 117(4), pages 647-662, April.
    5. Socrates Kypreos, 2008. "Stabilizing global temperature change below thresholds: Monte Carlo analyses with MERGE," Computational Management Science, Springer, vol. 5(1), pages 141-170, February.
    6. Ryan Sriver & Nathan Urban & Roman Olson & Klaus Keller, 2012. "Toward a physically plausible upper bound of sea-level rise projections," Climatic Change, Springer, vol. 115(3), pages 893-902, December.
    7. Walter Vergara & Alejandro Deeb & Irene Leino & Akio Kitoh & Marisa Escobar, 2011. "Assessment of the Impacts of Climate Change on Mountain Hydrology : Development of a Methodology through a Case Study in the Andes of Peru," World Bank Publications - Books, The World Bank Group, number 2278.
    8. Katsumasa Tanaka & Thomas Raddatz, 2011. "Correlation between climate sensitivity and aerosol forcing and its implication for the “climate trap”," Climatic Change, Springer, vol. 109(3), pages 815-825, December.
    9. Held, Hermann, 2020. "Cost Risk Analysisː How Robust Is It in View of Weitzman's Dismal Theorem and Undetermined Risk Functions?," WiSo-HH Working Paper Series 55, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
    10. In Chang Hwang & Richard S. J. Tol & Marjan W. Hofkes, 2019. "Active Learning and Optimal Climate Policy," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(4), pages 1237-1264, August.
    11. Salvador Pueyo, 2012. "Solution to the paradox of climate sensitivity," Climatic Change, Springer, vol. 113(2), pages 163-179, July.
    12. Alexis Hannart & Michael Ghil & Jean-Louis Dufresne & Philippe Naveau, 2013. "Disconcerting learning on climate sensitivity and the uncertain future of uncertainty," Climatic Change, Springer, vol. 119(3), pages 585-601, August.
    13. A. Lopez & E. Suckling & F. Otto & A. Lorenz & D. Rowlands & M. Allen, 2015. "Towards a typology for constrained climate model forecasts," Climatic Change, Springer, vol. 132(1), pages 15-29, September.
    14. Stephen Newbold & Adam Daigneault, 2009. "Climate Response Uncertainty and the Benefits of Greenhouse Gas Emissions Reductions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 44(3), pages 351-377, November.
    15. Bahn, Olivier & Edwards, Neil R. & Knutti, Reto & Stocker, Thomas F., 2011. "Energy policies avoiding a tipping point in the climate system," Energy Policy, Elsevier, vol. 39(1), pages 334-348, January.
    16. John Halley & Dimitris Kugiumtzis, 2011. "Nonparametric testing of variability and trend in some climatic records," Climatic Change, Springer, vol. 109(3), pages 549-568, December.

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