Towards Process-Informed Bias Correction of Climate Change Simulations

TitleTowards Process-Informed Bias Correction of Climate Change Simulations
Publication TypeJournal Article
Year of Publication2017
AuthorsMaraun, Douglas, Shepherd Theodore G., Widmann Martin, Zappa Giuseppe, Walton Daniel, Gutiérrez José M., Hagemann Stefan, Richter Ingo, Soares Pedro M. M., Hall Alex, and Mearns Linda O.
JournalNature Climate Change
Volume7
Pages764-773
Date Published11/2017
Abstract / Summary

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.

URLhttp://www.nature.com/articles/nclimate3418
DOI10.1038/nclimate3418
Journal: Nature Climate Change
Year of Publication: 2017
Volume: 7
Pages: 764-773
Date Published: 11/2017

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.

DOI: 10.1038/nclimate3418
Citation:
Maraun, D, TG Shepherd, M Widmann, G Zappa, D Walton, JM Gutiérrez, S Hagemann, et al.  2017.  "Towards Process-Informed Bias Correction of Climate Change Simulations."  Nature Climate Change 7: 764-773.  https://doi.org/10.1038/nclimate3418.