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Disconcerting learning on climate sensitivity and the uncertain future of uncertainty

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  • Alexis Hannart
  • Michael Ghil
  • Jean-Louis Dufresne
  • Philippe Naveau

Abstract

How will our estimates of climate uncertainty evolve in the coming years, as new learning is acquired and climate research makes further progress? As a tentative contribution to this question, we argue here that the future path of climate uncertainty may itself be quite uncertain, and that our uncertainty is actually prone to increase even though we learn more about the climate system. We term disconcerting learning this somewhat counter-intuitive process in which improved knowledge generates higher uncertainty. After recalling some definitions, this concept is connected with the related concept of negative learning that was introduced earlier by Oppenheimer et al. (Clim Change 89:155–172, 2008 ). We illustrate disconcerting learning on several real-life examples and characterize mathematically certain general conditions for its occurrence. We show next that these conditions are met in the current state of our knowledge on climate sensitivity, and illustrate this situation based on an energy balance model of climate. We finally discuss the implications of these results on the development of adaptation and mitigation policy. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • 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.
    • Handle: RePEc:spr:climat:v:119:y:2013:i:3:p:585-601
    DOI: 10.1007/s10584-013-0770-z
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    4. Ivan Rudik & Derek Lemoine & Maxwell Rosenthal, 2018. "General Bayesian Learning in Dynamic Stochastic Models: Estimating the Value of Science Policy," 2018 Meeting Papers 369, Society for Economic Dynamics.

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