Articles | Volume 10, issue 1
https://doi.org/10.5194/esd-10-31-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-10-31-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jan 2019
Research article |
| 07 Jan 2019
| 07 Jan 2019
The effect of univariate bias adjustment on multivariate hazard estimates
Jakob Zscheischler
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, ETH Zurich, Universitaetstrasse 16, 8092 Zurich, Switzerland
Climate and Environmental Physics, University of Bern, Sidlerstrasse 5, 3012 Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, 3012 Bern, Switzerland
Erich M. Fischer
Institute for Atmospheric and Climate Science, ETH Zurich, Universitaetstrasse 16, 8092 Zurich, Switzerland
Stefan Lange
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
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- Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods W. Zhang et al. 10.3389/feart.2021.673495
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Latest update: 14 Dec 2024
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...
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