为新西兰奥特罗亚开发的基于预报模型的极端天气事件归因系统

Jordis S. Tradowsky, G. Bodeker, Christopher John Noble, D. Stone, G. Rye, Leroy Bird, William Herewini, Sapna Rana, Johannes Rausch, I. Soltanzadeh
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摘要

一个高度自动化的极端天气事件(EWE)归因系统已经开发出来,它使用天气研究与预报数值天气预报模式来模拟当前和工业化前气候条件下的极端天气事件。该系统已应用于新西兰奥特罗阿的两个极端降水事件,目的是量化人为气候变化对这些事件严重程度的影响。当前气候条件下目标事件的预报模拟构成第一情景(ALL)。然后,我们以海洋表面温度、大气温度和特定湿度的三角洲场的形式应用气候变化信号,创建第二个“自然”情景(NAT),该情景旨在表示没有人为干扰气候系统的天气系统。第三个场景旨在测试连贯性,通过应用与自然场景(ALL+)相反的符号来生成。每个场景由22个成员组成,其中包括一个不受随机扰动影响的模拟。三个集合的比较表明:(1)NAT集合发展了一个与观测事件相似的极端事件;(2)严重程度,即受强降水影响的最大强度和/或面积,随着边界条件的自然化而变化;(3)严重程度的变化在三个情景中是一致的,信号在不同的集合成员中是稳健的,即在22个集合成员中的大多数中都有典型的表现。因此,本文提出的归因系统可用于提供有关人为气候变化对特定极端事件严重程度的影响的信息。
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A forecast-model-based extreme weather event attribution system developed for Aotearoa New Zealand
A largely automated extreme weather event (EWE) attribution system has been developed that uses the Weather Research and Forecast numerical weather prediction model to simulate EWEs under current and pre-industrial climate conditions. The system has been applied to two extreme precipitation events in Aotearoa New Zealand with the goal of quantifying the effect of anthropogenic climate change on the severity of these events. The forecast simulation of the target event under current climate conditions constitutes the first scenario (ALL). We then apply a climate change signal in the form of delta fields in sea-surface temperature, atmospheric temperature and specific humidity, creating a second ‘naturalised’ scenario (NAT) which is designed to represent the weather system in the absence of human interference with the climate system. A third scenario, designed to test for coherence, is generated by applying deltas of opposite sign compared to the naturalised scenario (ALL+). Each scenario comprises a 22-member ensemble which includes one simulation that was not subject to stochastic perturbation. Comparison of the three ensembles shows that: (1) the NAT ensemble develops an extreme event which resembles the observed event, (2) the severity, i.e. maximum intensity and/or the size of area affected by heavy precipitation, changes when naturalising the boundary conditions, (3) the change in severity is consistently represented within the three scenarios and the signal is robust across the different ensemble members, i.e. it is typically shown in most of the 22 ensemble members. Thus, the attribution system presented here can be used to provide information about the influence of anthropogenic climate change on the severity of specific extreme events.
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