Towards process-informed bias correction of climate change simulations

Douglas Maraun (), Theodore G. Shepherd, Martin Widmann, Giuseppe Zappa, Daniel Walton, José M. Gutiérrez, Stefan Hagemann, Ingo Richter, Pedro M. M. Soares, Alex Hall and Linda O. Mearns
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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
José M. Gutiérrez: Institute of Physics of Cantabria, CSIC - University of Cantabria
Stefan Hagemann: Max Planck Institute for Meteorology
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

Nature Climate Change, 2017, vol. 7, issue 11, 764-773

Abstract: 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.

Date: 2017
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DOI: 10.1038/nclimate3418

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