Towards process-informed bias correction of climate change simulations

Research output: Contribution to journalArticlepeer-review

Authors

  • Douglas Maraun
  • Theodore G. Shepherd
  • Giuseppe Zappa
  • Daniel Walton
  • José M. Gutiérrez
  • Stefan Hagemann
  • Ingo Richter
  • Pedro M. M. Soares
  • Alex Hall
  • Linda O. Mearns

Colleges, School and Institutes

External organisations

  • Max Planck Institute for Meteorology

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.

Details

Original languageEnglish
Pages (from-to)764–773
JournalNature Climate Change
Volume7
Publication statusPublished - 2 Nov 2017

Keywords

  • Forestry, Water resources, Agriculture, Climate-change impacts, Statistics