Attribution of extreme climate events to global climate change as a result of anthropogenic greenhouse gas emissions has become increasingly important. Extreme climate events arise at the intersection of natural climate variability and a forced response of the Earth system to anthropogenic greenhouse gas emissions, which may alter the frequency and severity of such events. Accounting for the effects of both natural climate variability and the forced response to anthropogenic climate change is thus central for the attribution. Here, we investigate the reproducibility of probabilistic extreme event attribution results under more explicit representations of natural climate variability. We employ well-established methodologies deployed in statistical Earth System Model emulators to represent natural climate variability as informed from its spatio-temporal covariance structures. Two approaches towards representing natural climate variability are investigated: (1) where natural climate variability is treated as a single component; and (2) where natural climate variability is disentangled into its annual and seasonal components. We showcase our approaches by attributing the 2022 Indo-Pakistani heatwave to human-induced climate change. We find that explicit representation of annual and seasonal natural climate variability increases the overall uncertainty in attribution results considerably compared to established approaches such as the World Weather Attribution Initiative. The increase in likelihood of such an event occurring as a result of global warming differs slightly between the approaches, mainly due to different assessments of the pre-industrial return periods. Our approach that explicitly resolves annual and seasonal natural climate variability indicates a median increase in likelihood by a factor of 41 (95% range: 6-603). We find a robust signal of increased likelihood and intensification of the event with increasing global warming levels across all approaches. Compared to its present likelihood, under 1.5 °C (2 °C) of global near-surface air temperature increase relative to pre-industrial temperatures, the likelihood of the event would be between 2.2 to 2.5 times (8 to 9 times) higher. We note that regardless of the different statistical approaches to represent natural variability, the outcomes on the conducted event attribution are similar, with minor differences mainly in the uncertainty ranges. Possible reasons for differences are evaluated, including limitations of the proposed approach for this type of application, as well as the specific aspects in which it can provide complementary information to established approaches.

中文翻译：

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代表事件归因背景下的自然气候变化：2022 年印巴热浪


将极端气候事件归因于人为温室气体排放导致的全球气候变化变得越来越重要。极端气候事件发生在自然气候变化和地球系统对人为温室气体排放的强制反应的交叉点上，这可能会改变此类事件的频率和严重程度。因此，考虑自然气候变化和对人为气候变化的被迫反应的影响是归因的核心。在这里，我们研究了在更明确的自然气候变化表征下概率极端事件归因结果的再现性。我们采用统计地球系统模型模拟器中部署的成熟方法来表示自然气候变化，如其时空协方差结构所示。研究了两种表示自然气候变率的方法：（1）将自然气候变率视为单个组成部分； (2) 自然气候变化被分解为其年度和季节性组成部分。我们通过将 2022 年印巴热浪归因于人类引起的气候变化来展示我们的方法。我们发现，与世界天气归因倡议等既定方法相比，年度和季节性自然气候变化的明确表示大大增加了归因结果的总体不确定性。由于全球变暖而导致此类事件发生的可能性增加，不同方法之间略有不同，这主要是由于对工业化前重现期的评估不同。 我们的方法明确解决了年度和季节性自然气候变化问题，表明可能性中位数增加了 41 倍（95% 范围：6-603）。我们发现，随着所有方法中全球变暖程度的增加，该事件的可能性和加剧程度都在增加。与目前的可能性相比，如果全球近地表气温相对于工业化前温度上升 1.5 °C (2 °C)，则该事件发生的可能性将增加 2.2 至 2.5 倍（8 至 9 倍）。我们注意到，无论采用不同的统计方法来表示自然变异，所进行的事件归因的结果都是相似的，主要在不确定性范围方面存在细微差别。评估了差异的可能原因，包括针对此类应用提出的方法的局限性，以及它可以为现有方法提供补充信息的具体方面。