Letter

Detecting failure of climate predictions

  • Nature Climate Change volume 6, pages 861864 (2016)
  • doi:10.1038/nclimate3041
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Abstract

The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty1,2. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies3. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055.

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Acknowledgements

The authors acknowledge the support of the NOAA Climate Program Office, Modeling, Analysis, Predictions, and Projections (MAPP) Program as part of the CMIP5 Task Force. J.C.S. was supported by NOAA grant NA10OAR4320142. E.M.-M. was supported by an ARC DECRA Fellowship and by the ARC Centre for Excellence in Environmental Decisions.

Author information

Affiliations

  1. USGS Patuxent Wildlife Research Center, Laurel, Maryland 20708, USA

    • Michael C. Runge
  2. National Snow and Ice Data Center, University of Colorado, Boulder, Colorado 80309, USA

    • Julienne C. Stroeve
    •  & Andrew P. Barrett
  3. University College London, London WC1E 6BT, UK

    • Julienne C. Stroeve
  4. School of Geography, Planning, and Environmental Management, University of Queensland, St Lucia, Queensland 4072, Australia

    • Eve McDonald-Madden

Authors

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Contributions

M.C.R. and E.M.-M. conceived of the methods; J.C.S. and A.P.B. extracted the sea-ice forecasts from the CMIP5 models; M.C.R. analysed the pintail and sea-ice data and prepared the figures; and M.C.R., E.M.-M. and J.C.S. co-wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Michael C. Runge.

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