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Towards process-informed bias correction of climate change simulations

Author

Listed:
  • Douglas Maraun

    (University of Graz, Wegener Center for Climate and Global Change)

  • Theodore G. Shepherd

    (University of Reading)

  • Martin Widmann

    (School of Geography, Earth and Environmental Sciences, University of Birmingham)

  • Giuseppe Zappa

    (University of Reading)

  • Daniel Walton

    (Institute of the Envionment and Sustainability, University of California
    University of California)

  • José M. Gutiérrez

    (Institute of Physics of Cantabria, CSIC - University of Cantabria)

  • Stefan Hagemann

    (Max Planck Institute for Meteorology
    Institute for Coastal Research, Helmholtz Centre Geesthacht)

  • Ingo Richter

    (Japan-Agency for Marine-Earth Science and Technology (JAMSTEC))

  • Pedro M. M. Soares

    (Instituto Dom Luiz, Faculty of Sciences, University of Lisbon)

  • Alex Hall

    (University of California)

  • Linda O. Mearns

    (National Center for Atmospheric Research (NCAR), PO Box 3000)

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.

Suggested Citation

  • Douglas Maraun & Theodore G. Shepherd & Martin Widmann & Giuseppe Zappa & Daniel Walton & José M. Gutiérrez & Stefan Hagemann & Ingo Richter & Pedro M. M. Soares & Alex Hall & Linda O. Mearns, 2017. "Towards process-informed bias correction of climate change simulations," Nature Climate Change, Nature, vol. 7(11), pages 764-773, November.
    • Handle: RePEc:nat:natcli:v:7:y:2017:i:11:d:10.1038_nclimate3418
    DOI: 10.1038/nclimate3418
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    Cited by:

    1. Martin Mäll & Ryota Nakamura & Ülo Suursaar & Tomoya Shibayama, 2020. "Pseudo-climate modelling study on projected changes in extreme extratropical cyclones, storm waves and surges under CMIP5 multi-model ensemble: Baltic Sea perspective," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(1), pages 67-99, May.
    2. L. V. Noto & G. Cipolla & D. Pumo & A. Francipane, 2023. "Climate Change in the Mediterranean Basin (Part II): A Review of Challenges and Uncertainties in Climate Change Modeling and Impact Analyses," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2307-2323, May.
    3. Christine M. Albano & Maureen I. McCarthy & Michael D. Dettinger & Stephanie A. McAfee, 2021. "Techniques for constructing climate scenarios for stress test applications," Climatic Change, Springer, vol. 164(3), pages 1-25, February.
    4. Maike Holthuijzen & Dave Higdon & Brian Beckage & Patrick J. Clemins, 2023. "Novel application of a process convolution approach for calibrating output from numerical models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.
    5. A. Reder & M. Iturbide & S. Herrera & G. Rianna & P. Mercogliano & J. M. Gutiérrez, 2018. "Assessing variations of extreme indices inducing weather-hazards on critical infrastructures over Europe—the INTACT framework," Climatic Change, Springer, vol. 148(1), pages 123-138, May.
    6. R. Manzanas & L. Fiwa & C. Vanya & H. Kanamaru & J. M. Gutiérrez, 2020. "Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitation in Malawi," Climatic Change, Springer, vol. 162(3), pages 1437-1453, October.
    7. Shoupeng Zhu & Fei Ge & Yi Fan & Ling Zhang & Frank Sielmann & Klaus Fraedrich & Xiefei Zhi, 2020. "Conspicuous temperature extremes over Southeast Asia: seasonal variations under 1.5 °C and 2 °C global warming," Climatic Change, Springer, vol. 160(3), pages 343-360, June.
    8. Thi Lan Anh Dinh & Filipe Aires, 2023. "Revisiting the bias correction of climate models for impact studies," Climatic Change, Springer, vol. 176(10), pages 1-30, October.
    9. Jongsung Kim & Myungjin Lee & Heechan Han & Donghyun Kim & Yunghye Bae & Hung Soo Kim, 2022. "Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario," Sustainability, MDPI, vol. 14(8), pages 1-20, April.
    10. Alison Kay, 2022. "Differences in hydrological impacts using regional climate model and nested convection-permitting model data," Climatic Change, Springer, vol. 173(1), pages 1-19, July.
    11. Emanuele Bevacqua & Laura Suarez-Gutierrez & Aglaé Jézéquel & Flavio Lehner & Mathieu Vrac & Pascal Yiou & Jakob Zscheischler, 2023. "Advancing research on compound weather and climate events via large ensemble model simulations," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    12. D. Carvalho & S. C. Pereira & R. Silva & A. Rocha, 2022. "Aridity and desertification in the Mediterranean under EURO-CORDEX future climate change scenarios," Climatic Change, Springer, vol. 174(3), pages 1-24, October.
    13. Guilong Li & Xuebin Zhang & Alex J. Cannon & Trevor Murdock & Steven Sobie & Francis Zwiers & Kevin Anderson & Budong Qian, 2018. "Indices of Canada’s future climate for general and agricultural adaptation applications," Climatic Change, Springer, vol. 148(1), pages 249-263, May.
    14. Alessandro Dosio & Christopher Lennard & Jonathan Spinoni, 2022. "Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations," Climatic Change, Springer, vol. 170(1), pages 1-24, January.
    15. Plaga, Leonie Sara & Bertsch, Valentin, 2023. "Methods for assessing climate uncertainty in energy system models — A systematic literature review," Applied Energy, Elsevier, vol. 331(C).
    16. Rattana Chhin & Chantha Oeurng & Shigeo Yoden, 2020. "Drought projection in the Indochina Region based on the optimal ensemble subset of CMIP5 models," Climatic Change, Springer, vol. 162(2), pages 687-705, September.
    17. Andrew C. Ross & Raymond G. Najjar, 2019. "Evaluation of methods for selecting climate models to simulate future hydrological change," Climatic Change, Springer, vol. 157(3), pages 407-428, December.
    18. Pablo Borges de Amorim & Pedro B. Chaffe, 2019. "Towards a comprehensive characterization of evidence in synthesis assessments: the climate change impacts on the Brazilian water resources," Climatic Change, Springer, vol. 155(1), pages 37-57, July.
    19. Ponnambalam Rameshwaran & Victoria A. Bell & Helen N. Davies & Alison L. Kay, 2021. "How might climate change affect river flows across West Africa?," Climatic Change, Springer, vol. 169(3), pages 1-27, December.
    20. A. Casanueva & J. Bedia & S. Herrera & J. Fernández & J. M. Gutiérrez, 2018. "Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool," Climatic Change, Springer, vol. 147(3), pages 411-425, April.
    21. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).

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