Climatic Change

, Volume 119, Issue 3–4, pp 585–601 | Cite as

Disconcerting learning on climate sensitivity and the uncertain future of uncertainty

  • Alexis HannartEmail author
  • Michael Ghil
  • Jean-Louis Dufresne
  • Philippe Naveau


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.


Probability Density Function Prior Distribution True Belief Climate Sensitivity Mitigation Policy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the French Centre National de la Recherche Scientifique (CNRS) and the Argentinean CONICET for their support of this collaboration. MG’s work is partially supported by NSF grant DMS-0934426 and by US Department of Energy grant DE-SC0006694, while PN also acknowledges the ANR-funded AssimilEx and FP7 ACQWA projects. We thank Andres Farall for pointing out relevant references, and we are grateful to the Editor, to James Risbey and to two anonymous reviewers for their suggestions that helped us improve the original manuscript.

Supplementary material

10584_2013_770_MOESM1_ESM.pdf (634 kb)
(PDF 633 KB)


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Alexis Hannart
    • 1
    Email author
  • Michael Ghil
    • 2
    • 3
  • Jean-Louis Dufresne
    • 4
  • Philippe Naveau
    • 5
  1. 1.Institut Franco-Argentin d’Etudes sur le Climat et ses ImpactsCNRS-CONICET-Universidad de Buenos AiresBuenos AiresArgentina
  2. 2.Geosciences DepartmentUCLALos AngelesUSA
  3. 3.Laboratoire de Météorologie DynamiqueEcole Normale SupérieureParisFrance
  4. 4.Laboratoire de Météorologie DynamiqueCNRS-Polytechnique-ENS-UPMCParisFrance
  5. 5.Laboratoire des Sciences du Climat et l’EnvironnementCNRS-CEAGif-sur-YvetteFrance

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