Climate Dynamics

, Volume 48, Issue 1–2, pp 367–386 | Cite as

A new statistical approach to climate change detection and attribution

  • Aurélien Ribes
  • Francis W. Zwiers
  • Jean-Marc Azaïs
  • Philippe Naveau
Article

Abstract

We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the “models are statistically indistinguishable from the truth” paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951–2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 \(\pm\) 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings (\(-0.01\pm 0.02\) K).

Keywords

Detection Attribution Climate change Optimal fingerprint 

Notes

Acknowledgments

The authors are grateful to the two anonymous referees for their constructive comments, which were of great value in improving the paper. Part of this work has been supported by the Fondation STAE, via the project Chavana, and by the Extremoscope and ANR-DADA projects.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Aurélien Ribes
    • 1
  • Francis W. Zwiers
    • 2
  • Jean-Marc Azaïs
    • 3
  • Philippe Naveau
    • 4
  1. 1.CNRM, Météo France/CNRSToulouseFrance
  2. 2.Pacific Climate Impacts ConsortiumUniversity of VictoriaVictoriaCanada
  3. 3.IMTUniversity of ToulouseToulouse Cedex 9France
  4. 4.Laboratoire des Sciences du Climat et de l’EnvironnementLSCE/IPSL, CEA-CNRSUVSQ, Université Paris-SaclayGif-sur-YvetteFrance

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