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Discussion papers | Copyright
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 13 Sep 2018

Research article | 13 Sep 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Dynamics (ESD).

The effect of bias adjustment on impact modeling

Jakob Zscheischler1, Erich M. Fischer1, and Stefan Lange2 Jakob Zscheischler et al.
  • 1Institute for Atmospheric and Climate Science, ETH Zurich, Universitaetstrasse 16, 8092 Zurich, Switzerland
  • 2Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany

Abstract. Bias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model output rarely fulfills. Most currently used statistical bias adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple, potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between model output and observations for many climate related impacts. Here we use two hazard indicators, heat stress and a simple fire risk indicator, as proxies for more sophisticated impact models. We show that univariate bias adjustment methods such as univariate quantile mapping often cannot effectively reduce biases in multivariate hazard estimates. In some cases, it even increases biases. These cases typically occur (i) when hazards depend equally strongly on more than one climatic driver, (ii) when models exhibit biases in the dependence structure of drivers, and (iii) when univariate biases are relatively small. Using a perfect model approach, we further quantify the uncertainty of bias-adjusted hazard indicators due to internal variability and show how inadequate bias adjustment can amplify this uncertainty. Both issues can be addressed successfully with a statistical bias adjustment that corrects the multivariate dependence structure in addition to the marginal distributions of the climate drivers. Our results suggest that currently many modelled climate impacts are associated with substantial uncertainties associated with the choice of bias adjustment. We conclude that, as long as bias adjustment is unavoidable for climate impact assessments, the use of statistical bias adjustment approaches that correct the multivariate dependence structure of drivers is preferable.

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Publications Copernicus
Short summary
Many climate models have biases in different variables throughout the world. Adjusting these biases is necessary for estimating climate impacts. Here we demonstrate that widely used univariate bias adjustment methods do not work well for multivariate impacts. We illustrate this problem using fire risk and heat stress as impact indicators. Using an approach that adjusts not only biases in the individual climate variables but also biases in the correlation between them can resolve these problems.
Many climate models have biases in different variables throughout the world. Adjusting these...