23 Nov 2020

23 Nov 2020

Review status: a revised version of this preprint is currently under review for the journal NHESS.

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

Roberto Villalobos-Herrera1,2, Emanuele Bevacqua3, Andreia F. S. Ribeiro4, Graeme Auld5, Laura Crocetti6,7, Bilyana Mircheva8, Minh Ha9, Jakob Zscheischler10,11,12, and Carlo De Michele13 Roberto Villalobos-Herrera et al.
  • 1School of Engineering, Newcastle University, Newcastle upon Tyne, NE2 1HA, United Kingdom
  • 2Escuela de Ingeniería Civil, Universidad de Costa Rica, Montes de Oca, San José 1150-2060, Costa Rica
  • 3Department of Meteorology, University of Reading, Reading, United Kingdom
  • 4Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
  • 5School of Mathematics, The University of Edinburgh, Edinburgh, United Kingdom
  • 6Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
  • 7Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, Switzerland
  • 8Department of Meteorology and Geophysics, Sofia University, Sofia, Bulgaria
  • 9Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Sorbonne Université, Paris and Guyancourt, France
  • 10Climate and Environmental Physics, University of Bern, Bern, Switzerland
  • 11Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
  • 12Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
  • 13Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy

Abstract. Climate models' outputs are affected by biases that need to be detected and adjusted to model climate impacts. Many climate hazards and climate-related impacts are associated with the interaction between multiple drivers, i.e. by compound events. So far climate model biases are typically assessed based on the hazard of interest, and it is unclear how much a potential bias in the dependence of the hazard drivers contributes to the overall bias and how the biases in the drivers interact. Here, based on copula theory, we develop a multivariate bias assessment framework, which allows for disentangling the biases in hazard indicators in terms of the underlying univariate drivers and their statistical dependence. Based on this framework, we dissect biases in fire and heat stress hazards in a suite of global climate models by considering two simplified hazard indicators, the wet-bulb globe temperature (WBGT) and the Chandler Burning Index (CBI). Both indices solely rely on temperature and relative humidity. The spatial pattern of the hazards indicators is well represented by climate models. However, substantial biases exist in the representation of extreme conditions, especially in the CBI (spatial average of absolute bias: 21 °C) due to the biases driven by relative humidity (20 °C). Biases in WBGT (1.1 °C) are small compared to the biases driven by temperature (1.9 °C) and relative humidity (1.4 °C), as the two biases compensate each other. In many regions, also biases related to the statistical dependence (0.85 °C) are important for WBGT, which indicates that well-designed physically-based multivariate bias adjustment should be considered for hazards and impacts that depend on multiple drivers. The proposed compound event-oriented evaluation of climate model biases is easily applicable to other hazard types. Furthermore, it can contribute to improved present and future risk assessments through increasing our understanding of the biases’ sources in the simulation of climate impacts.

Roberto Villalobos-Herrera et al.

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Status: final response (author comments only)
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Roberto Villalobos-Herrera et al.

Roberto Villalobos-Herrera et al.


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Short summary
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, 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.