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.

中文翻译：

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面向复合事件的气候模型评估：分解多元火灾和热应激危害的潜在偏差

气候模型的输出受到偏差的影响，需要检测和调整以模拟气候影响。许多气候灾害和气候相关影响与多种驱动因素之间的相互作用有关，即复合事件。到目前为止，气候模型偏差通常是根据感兴趣的危险进行评估的，目前尚不清楚危险驱动因素依赖性的潜在偏差对整体偏差的贡献程度以及驱动因素中的偏差如何相互作用。在这里，基于 copula 理论，我们开发了一个多元偏差评估框架，该框架允许根据潜在的单变量驱动因素及其统计依赖性来解开风险指标中的偏差。基于这个框架，我们通过考虑两个简化的危害指标来剖析一套全球气候模型中火灾和热应激危害的偏差：湿球温度 (WBGT) 和钱德勒燃烧指数 (CBI)。这两个指数仅依赖于温度和相对湿度。气候模型很好地代表了灾害指标的空间格局。然而，极端条件的表示存在大量偏差，尤其是在 CBI 中（绝对偏差的空间平均值：21 ^∘ C ) 由于相对湿度 (20 ^∘ C )驱动的偏差 。与由温度 (1.9 ^∘ C ) 和相对湿度 (1.4  ^∘ C )驱动的偏差相比，WBGT (1.1  ^∘ C ) 中的偏差很小 ，因为这两个偏差可以相互补偿。在许多地区，也存在与统计相关性相关的偏差（0.85  ^∘ C) 对 WBGT 很重要，这表明应考虑针对依赖于多个驱动因素的危险和影响精心设计的基于物理的多变量偏差调整程序。提出的气候模型偏差的复合事件导向评估很容易适用于其他灾害类型。此外，通过增加我们对气候影响模拟中偏差来源的理解，它可以有助于改进当前和未来的风险评估。