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

Abstract

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 hazard 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 for 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 procedures 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.

Guidelines for Studying Diverse Types of Compound Weather and Climate Events

“…5. Compound event occurrence probabilities and their future changes can be highly uncertain due to combined uncertainties in multiple drivers and associated interplay (Bevacqua, Vousdoukas, Zappa, et al, 2020;Santos et al, 2021;Villalobos-Herrera et al, 2021). In this context, event-based storylines, which explore consequences of high-impact plausible events either or both under present and future climates putting emphasis on plausibility rather than probability, can provide very effective information for improving emergency preparedness to compound events (Sillmann et al, 2021).…”

Attributing Compound Events to Anthropogenic Climate Change

“…The copula equality test can thus be used to evaluate whether the dependence structure between two distributions -for instance between models and observations -is significantly different. It has been applied to evaluate multivariate dependencies in a hydrological modelling context (Vezzoli et al 2017), to assess how well climate models represent the dependence structure between seasonal temperature and precipitation in Germany (Zscheischler and Fischer 2020) and to assess biases in multivariate hazard indicators (Villalobos-Herrera et al 2021).…”

A compound event-oriented framework to tropical fire risk assessment in a changing climate

“…We found that the use of univariate bias adjustment can lead to an over-or underestimation of future climate-related fire risks, despite the overall clear sign of climate-change induced risks, regardless of the bias-correction method employed. In fact, the way how climate models' biases affect the final risks is still a topic of discussion in the scientific community [59] and the way how changes in the dependence of the drivers will affect compound events in a warming world is a challenging task in assessing future climate risk of complex events.…”