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Towards process-informed bias correction of climate change simulations

Nature Climate Change volume 7, pages 764–773 (2017)Cite this article

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Abstract

Biases in climate model simulations introduce biases in subsequent impact simulations. Therefore, bias correction methods are operationally used to post-process regional climate projections. However, many problems have been identified, and some researchers question the very basis of the approach. Here we demonstrate that a typical cross-validation is unable to identify improper use of bias correction. Several examples show the limited ability of bias correction to correct and to downscale variability, and demonstrate that bias correction can cause implausible climate change signals. Bias correction cannot overcome major model errors, and naive application might result in ill-informed adaptation decisions. We conclude with a list of recommendations and suggestions for future research to reduce, post-process, and cope with climate model biases.

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Figure 1: Cross-validation problem.
Figure 2: Unrealistic dry spell lengths.
Figure 3: Non-representative model output.
Figure 4: Missing temperature inversions.
Figure 5: Large-scale circulation problems.
Figure 6: Implausible sub-grid climate change signal.

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Acknowledgements

Many of the ideas laid out in this paper have been developed during discussions within the EU COST Action ES1102 ‘VALUE’, in particular during the workshop ‘Global climate model biases: causes, consequences and correctability’ held at the Max Planck Institute for Meteorology, Hamburg, October 2014.

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Author notes

  1. Stefan Hagemann

    Present address: Institute for Coastal Research, Helmholtz Centre Geesthacht, 21502, Geesthacht, Germany

Authors and Affiliations

  1. University of Graz, Wegener Center for Climate and Global Change, Brandhofgasse 5, 8010, Graz, Austria

    Douglas Maraun

  2. Department of Meteorology, University of Reading, PO Box 243, Reading, RG6 6BB, UK

    Theodore G. Shepherd & Giuseppe Zappa

  3. School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK

    Martin Widmann

  4. Institute of the Envionment and Sustainability, University of California, Los Angeles, California, 90095, USA

    Daniel Walton

  5. Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California, 90095, USA

    Daniel Walton & Alex Hall

  6. Institute of Physics of Cantabria, CSIC - University of Cantabria, Avenida de los Castros, s/n, 39005, Santander, Spain

    José M. Gutiérrez

  7. Max Planck Institute for Meteorology, Bundestrasse 53, 20146, Hamburg, Germany

    Stefan Hagemann

  8. Japan-Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25, Showa-machi, Kanazawa-ku, Yokohama, 236-0001, Japan

    Ingo Richter

  9. Instituto Dom Luiz, Faculty of Sciences, University of Lisbon, 1749-016, Lisbon, Portugal

    Pedro M. M. Soares

  10. National Center for Atmospheric Research (NCAR), PO Box 3000, Boulder, Colorado, 80307, USA

    Linda O. Mearns

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Contributions

The paper is a result of a workshop organized by D.M. and S.H. D.M. wrote the first draft of the manuscript with inputs from all authors. D.M., G.Z., J.M.G. and D.W. contributed analyses underlying the figures. All authors discussed the content of the manuscript.

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Correspondence to Douglas Maraun.

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Maraun, D., Shepherd, T., Widmann, M. et al. Towards process-informed bias correction of climate change simulations. Nature Clim Change 7, 764–773 (2017). https://doi.org/10.1038/nclimate3418

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