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The effect of experiment conditioning on estimates of human influence on extreme weather
Weather and Climate Extremes ( IF 8 ) Pub Date : 2022-03-06 , DOI: 10.1016/j.wace.2022.100427
Dáithí A. Stone 1 , Suzanne M. Rosier 1 , Leroy Bird 2 , Luke J. Harrington 3 , Sapna Rana 4 , Stephen Stuart 1 , Sam M. Dean 1
Affiliation  

Many methods and climate/weather modelling tools have been used over the past decade for assessment of the role of anthropogenic emissions in recent specific weather events (“event attribution”). Differences in the methods and models often correspond to differences in the characterisation, or conditioning, of an observed extreme event within a model, and this might be expected to affect any attribution statement. In practice, however, it may not always be feasible or practical to use the most appropriate method for the question at hand, or to use multiple methods so as to arrive at a generic conclusion. This is especially true given the growing interest in making rapid assessments of extreme events within operational forecast centres. How transferable are conclusions across methods, hence allowing the substitution of one method or modelling tool for another? In this paper we investigate differences in event attribution conclusions across a wide range of experiment designs, running from free-running simulations of atmosphere–ocean climate models, through to weather forecasts constrained to reproduce the nature of the event quite closely. Across a number of recent extreme weather events over Aotearoa New Zealand, we find no systematic differences in conclusions across the various experiment setups. This surprising result offers hope that attribution statements may be transferable across methods, because errors that arise when transferring results across methods are overshadowed by other errors and uncertainties given current technology.



中文翻译:

实验条件对估计人类对极端天气影响的影响

在过去十年中,许多方法和气候/天气建模工具已被用于评估人为排放在近期特定天气事件(“事件归因”)中的作用。方法和模型的差异通常对应于模型内观察到的极端事件的特征或条件的差异,这可能会影响任何归因陈述。然而,在实践中,对手头的问题使用最合适的方法或使用多种方法来得出一个通用的结论可能并不总是可行或实用的。鉴于对在业务预报中心内对极端事件进行快速评估的兴趣日益浓厚,这一点尤其正确。结论在方法之间的可转移性,因此允许用一种方法或建模工具代替另一种?在本文中,我们研究了各种实验设计中事件归因结论的差异,从大气-海洋气候模型的自由运行模拟到非常接近地再现事件性质的天气预报。在新西兰 Aotearoa 最近发生的一些极端天气事件中,我们发现各种实验设置的结论没有系统性差异。这一令人惊讶的结果为归因陈述可以跨方法转移提供了希望,因为在跨方法转移结果时出现的错误被当前技术下的其他错误和不确定性所掩盖。

更新日期:2022-03-06
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