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Eliciting Information from Experts on the Likelihood of Rapid Climate Change

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  • Nigel W. Arnell
  • Emma L. Tompkins
  • W. Neil Adger

Abstract

The threat of so‐called rapid or abrupt climate change has generated considerable public interest because of its potentially significant impacts. The collapse of the North Atlantic Thermohaline Circulation or the West Antarctic Ice Sheet, for example, would have potentially catastrophic effects on temperatures and sea level, respectively. But how likely are such extreme climatic changes? Is it possible actually to estimate likelihoods? This article reviews the societal demand for the likelihoods of rapid or abrupt climate change, and different methods for estimating likelihoods: past experience, model simulation, or through the elicitation of expert judgments. The article describes a survey to estimate the likelihoods of two characterizations of rapid climate change, and explores the issues associated with such surveys and the value of information produced. The surveys were based on key scientists chosen for their expertise in the climate science of abrupt climate change. Most survey respondents ascribed low likelihoods to rapid climate change, due either to the collapse of the Thermohaline Circulation or increased positive feedbacks. In each case one assessment was an order of magnitude higher than the others. We explore a high rate of refusal to participate in this expert survey: many scientists prefer to rely on output from future climate model simulations.

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  • Nigel W. Arnell & Emma L. Tompkins & W. Neil Adger, 2005. "Eliciting Information from Experts on the Likelihood of Rapid Climate Change," Risk Analysis, John Wiley & Sons, vol. 25(6), pages 1419-1431, December.
    • Handle: RePEc:wly:riskan:v:25:y:2005:i:6:p:1419-1431
    DOI: 10.1111/j.1539-6924.2005.00689.x
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    1. David Anthoff & Francisco Estrada & Richard S. J. Tol, 2016. "Shutting Down the Thermohaline Circulation," American Economic Review, American Economic Association, vol. 106(5), pages 602-606, May.
    2. Mitchell J. Small, 2008. "Methods for Assessing Uncertainty in Fundamental Assumptions and Associated Models for Cancer Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 28(5), pages 1289-1308, October.
    3. Fermín Mallor & Carmen García‐Olaverri & Sagrario Gómez‐Elvira & Pedro Mateo‐Collazas, 2008. "Expert Judgment‐Based Risk Assessment Using Statistical Scenario Analysis: A Case Study—Running the Bulls in Pamplona (Spain)," Risk Analysis, John Wiley & Sons, vol. 28(4), pages 1003-1019, August.
    4. Gregory F. Nemet & Laura Diaz Anadon & Elena Verdolini, 2017. "Quantifying the Effects of Expert Selection and Elicitation Design on Experts’ Confidence in Their Judgments About Future Energy Technologies," Risk Analysis, John Wiley & Sons, vol. 37(2), pages 315-330, February.
    5. Travis William Reynolds & Ann Bostrom & Daniel Read & M. Granger Morgan, 2010. "Now What Do People Know About Global Climate Change? Survey Studies of Educated Laypeople," Risk Analysis, John Wiley & Sons, vol. 30(10), pages 1520-1538, October.
    6. Julia Reis & Julie Shortridge, 2020. "Impact of Uncertainty Parameter Distribution on Robust Decision Making Outcomes for Climate Change Adaptation under Deep Uncertainty," Risk Analysis, John Wiley & Sons, vol. 40(3), pages 494-511, March.
    7. David Anthoff & Francisco Estrada & Richard S. J. Tol, 2016. "Shutting Down the Thermohaline Circulation," American Economic Review, American Economic Association, vol. 106(5), pages 602-06, May.
    8. Rida Laraki & Estelle Varloot, 2021. "Level-strategyproof Belief Aggregation Mechanisms," Papers 2108.04705, arXiv.org, revised Sep 2022.
    9. Thomas Allen & Paolo Prosperi & Bruce Cogill & Martine Padilla & Iuri Peri, 2019. "A Delphi Approach to Develop Sustainable Food System Metrics," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(3), pages 1307-1339, February.

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