Towards process-informed bias correction of climate change simulations

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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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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