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Scientific uncertainty and climate change: Part II. Uncertainty and mitigation

Climatic Change volume 124, pages 39–52 (2014)Cite this article

Abstract

In public debate surrounding climate change, scientific uncertainty is often cited in connection with arguments against mitigative action. This article examines the role of uncertainty about future climate change in determining the likely success or failure of mitigative action. We show by Monte Carlo simulation that greater uncertainty translates into a greater likelihood that mitigation efforts will fail to limit global warming to a target (e.g., 2 °C). The effect of uncertainty can be reduced by limiting greenhouse gas emissions. Taken together with the fact that greater uncertainty also increases the potential damages arising from unabated emissions (Lewandowsky et al. 2014), any appeal to uncertainty implies a stronger, rather than weaker, need to cut greenhouse gas emissions than in the absence of uncertainty.

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Fig. 1
Fig. 2
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Notes

  1. PCS, like all other climate sensitivity measures such as the transient climate response (TCR; the expected increase in global temperatures at the time of doubling of CO2 from pre-industrial levels after a 1 %/year increase), is a ratio between expected warming and forcing. Those ratios have been shown to be constant in a linear climate system if forcings grow exponentially (Raupach 2013), implying that an analysis based on PCS likely generalizes to other measures such as TCR.

  2. For tractability and ease of exposition, all estimates relating to the carbon budget in this article are taken from Raupach et al. (2011). Those estimates are consonant with other reviews (e.g., Rogelj et al. 2011). Short-lived gasses such as hydrofluorocarbons do not contribute to the total budget (Smith et al. 2012).

  3. Pacala and Socolow (2004) and Socolow (2011) considered stabilizationof emissions only (i.e., preventing further growth), rather than the cuts to emissions that are necessary to stay within a finite carbon budget. Their analysis thus underestimates the consequences of delayed mitigation, amplifying the point that delay enhances costs.

  4. Lognormal distributions are often characterized by the parameters of the underlying Gaussian distribution, μ G and σ G . They are related to the equivalent parameters of the lognormal distribution via \(\mu _{L} = \exp {(\mu _{G}+.5{\sigma _{G}^{2}})}\) and \(\sigma _{L} = \exp {(\mu _{G}+.5{\sigma _{G}^{2}})} \times \sqrt {\exp {({\sigma _{G}^{2}})}-1}\). All parameter values characterizing lognormal distributions in this article are provided in terms of μ L and σ L .

  5. Note that this involves an inference from the ordinate to the abscissa in Fig. 2; if the function were inverted to show the budget as a function of a temperature response, which is the more routine formulation of Jensen’s inequality, then the function would be convex. For a detailed discussion of the implications of increasing uncertainty in the presence of convex functions, see Lewandowsky et al. (2014).

  6. This asymmetry is exacerbated by the fact that the area below the mean estimate is even greater in our simulation than can be expected in reality: This arises from the intention to keep mean sensitivity ( μ L ) constant across levels of uncertaintyPCS ( σ L ) for our illustrative purposes. In reality, the lower bound of possible climate sensitivities is quite well known and values below 1.5 °C are considered implausible (Meehl et al. 2007). If our simulated distributions of sensitivity were truncated at that lower bound, the asymmetry of the areas representing uncertaintyTR would be further enhanced.

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Acknowledgments

Preparation of this paper was facilitated by a Discovery Grant from the Australian Research Council, an Australian Professorial Fellowship, a Discovery Outstanding Researcher Award, and a Wolfson Research Merit Award from the Royal Society, to the first author, by a Future Fellowship from the Australian Research Council to Ben Newell, and funding from the Australian Research Council Centre of Excellence in Climate Systems Science. The work was also supported by a Linkage Grant from the Australian Research Council and a grant from the National Climate Change Adaptation Research Facility and the CSIRO Climate Adaptation Flagship. We thank four reviewers for their incisive critique and helpful comments. Correspondence to the first author at the School of Experimental Psychology, University of Bristol, 12A Priory Road, Bristol BS8 1TU, United Kingdom (stephan.lewandowsky@bristol.ac.uk). Personal web page: http://www.cogsciwa.com.

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Authors and Affiliations

  1. University of Western Australia, Perth, WA, Australia

    Stephan Lewandowsky

  2. University of Bristol, Bristol, UK

    Stephan Lewandowsky

  3. CSIRO Marine and Atmospheric Research, Hobart, Tasmania, Australia

    James S. Risbey

  4. Australian National University, Canberra, Australia

    Michael Smithson

  5. University of New South Wales, Sydney, Australia

    Ben R. Newell

Authors
  1. Stephan Lewandowsky
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  2. James S. Risbey
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  3. Michael Smithson
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  4. Ben R. Newell
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Correspondence to Stephan Lewandowsky.

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Part I of this paper is published under doi:10.1007/s10584-014-1082-7.

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Lewandowsky, S., Risbey, J.S., Smithson, M. et al. Scientific uncertainty and climate change: Part II. Uncertainty and mitigation. Climatic Change 124, 39–52 (2014). https://doi.org/10.1007/s10584-014-1083-6

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Keywords

  • Temperature Response
  • Climate Sensitivity
  • Carbon Budget
  • Precautionary Principle
  • Mitigation Effort