Articles | Volume 21, issue 6
https://doi.org/10.5194/nhess-21-1867-2021
https://doi.org/10.5194/nhess-21-1867-2021
Research article
 | 
17 Jun 2021
Research article |  | 17 Jun 2021

Towards a compound-event-oriented climate model evaluation: a decomposition of the underlying biases in multivariate fire and heat stress hazards

Roberto Villalobos-Herrera, Emanuele Bevacqua, Andreia F. S. Ribeiro, Graeme Auld, Laura Crocetti, Bilyana Mircheva, Minh Ha, Jakob Zscheischler, and Carlo De Michele

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Cited articles

ASCM – American College of Sports Medicine: Prevention of Thermal Injuries During Distance Running – Position stand, Med. Sci. Sport. Exerc., 16, ix–xiv, 1984. 
Berrisford, P., Kållberg, P., Kobayashi, S., Dee, D., Uppala, S., Simmons, A. J., Poli, P., and Sato, H.: Atmospheric conservation properties in ERA-Interim, Q. J. Roy. Meteor. Soc., 137, 1381–1399, https://doi.org/10.1002/qj.864, 2011. 
Bevacqua, E., Maraun, D., Hobæk Haff, I., Widmann, M., and Vrac, M.: Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy), Hydrol. Earth Syst. Sci., 21, 2701–2723, https://doi.org/10.5194/hess-21-2701-2017, 2017. 
Bevacqua, E., Maraun, D., Vousdoukas, M. I., Voukouvalas, E., Vrac, M., Mentaschi, L., and Widmann, M.: Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change, Science Advances, 5, 9, eaaw5531, https://doi.org/10.1126/sciadv.aaw5531, 2019. 
Bevacqua, E., Vousdoukas, M. I., Shepherd, T. G., and Vrac, M.: Brief communication: The role of using precipitation or river discharge data when assessing global coastal compound flooding, Nat. Hazards Earth Syst. Sci., 20, 1765–1782, https://doi.org/10.5194/nhess-20-1765-2020, 2020a. 
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Climate hazards may be caused by events which have multiple drivers. Here we present a method to break down climate model biases in hazard indicators down to the bias caused by each driving variable. Using simplified fire and heat stress indicators driven by temperature and relative humidity as examples, we show how multivariate indicators may have complex biases and that the relationship between driving variables is a source of bias that must be considered in climate model bias corrections.
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