Articles | Volume 10, issue 1
Earth Syst. Dynam., 10, 31–43, 2019
https://doi.org/10.5194/esd-10-31-2019
Earth Syst. Dynam., 10, 31–43, 2019
https://doi.org/10.5194/esd-10-31-2019

Research article 07 Jan 2019

Research article | 07 Jan 2019

The effect of univariate bias adjustment on multivariate hazard estimates

Jakob Zscheischler et al.

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Cited articles

Addor, N. and Fischer, E. M.: The influence of natural variability and interpolation errors on bias characterization in RCM simulations, J. Geophys. Res.-Atmos., 120, 10180–10195, https://doi.org/10.1002/2014JD022824, 2015. a, b, c
Bosshard, T., Carambia, M., Goergen, K., Kotlarski, S., Krahe, P., Zappa, M., and Schär, C.: Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections, Water Resour. Res., 49, 1523–1536, https://doi.org/10.1029/2011WR011533, 2013. a
Brando, P. M., Balch, J. K., Nepstad, D. C., Morton, D. C., Putz, F. E., Coe, M. T., Silvério, D., Macedo, M. N., Davidson, E. A., Nóbrega, C. C., Alencar, A., and Soares-Filho, B. S.: Abrupt increases in Amazonian tree mortality due to drought–fire interactions, P. Natl. Acad. Sci. USA, 111, 6347–6352, 2014. a
Bröde, P., Blazejczyk, K., Fiala, D., Havenith, G., Holmér, I., Jendritzky, G., Kuklane, K., and Kampmann, B.: The Universal Thermal Climate Index UTCI Compared to Ergonomics Standards for Assessing the Thermal Environment, Ind. Health, 51, 16–24, https://doi.org/10.2486/indhealth.2012-0098, 2013. a
Cannon, A. J.: Multivariate Bias Correction of Climate Model Output: Matching Marginal Distributions and Intervariable Dependence Structure, J. Climate, 29, 7045–7064, https://doi.org/10.1175/jcli-d-15-0679.1, 2016. a
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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.
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