Quantifying statistical uncertainty in the attribution of human influence on severe weather
Quantifying statistical uncertainty in the attribution of human influence on severe weather
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DOI：
10.1016/j.wace.2018.01.002
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发表时间：
2018-06-01
影响因子：
8
通讯作者：
Wehner, Michael F.
中科院分区：
文献类型：
--
作者：
Paciorek, Christopher J.;Stone, Daithi A.;Wehner, Michael F.
气候变化背景下的事件归因试图了解人为温室气体排放对极端天气事件的作用，无论是特定事件还是事件类别。一种常见的事件归因方法是使用气候模型在事实（真实世界）和反事实（可能没有人为温室气体排放的世界）情景下的输出来估计两种情景下相关事件的概率。事件归因然后通过两个概率的比率来量化。虽然这一方法在过去15年中已被多次采用，但用于根据气候模型集合估计风险比的统计技术并未利用统计文献中现有的整套方法，在某些情况下以非标准方式使用和解释了自助法。我们提出了一个精确的频率统计框架，量化抽样的不确定性对估计的风险比的影响，提出使用的统计方法，是新的事件归因，并评估各种方法，使用统计模拟。我们的结论是，现有的统计方法尚未在使用的事件归因有几个优势，广泛使用的自举，包括更好的统计性能在重复样本和鲁棒性小的估计概率。使用这些方法的软件可以通过可用于R或Python的climextRemes包获得。虽然我们专注于频率统计方法，但贝叶斯方法在考虑采样不确定性以外的不确定性来源时可能特别有用。
Event attribution in the context of climate change seeks to understand the role of anthropogenic greenhouse gas emissions on extreme weather events, either specific events or classes of events. A common approach to event attribution uses climate model output under factual (real-world) and counterfactual (world that might have been without anthropogenic greenhouse gas emissions) scenarios to estimate the probabilities of the event of interest under the two scenarios. Event attribution is then quantified by the ratio of the two probabilities. While this approach has been applied many times in the last 15 years, the statistical techniques used to estimate the risk ratio based on climate model ensembles have not drawn on the full set of methods available in the statistical literature and have in some cases used and interpreted the bootstrap method in non-standard ways. We present a precise frequentist statistical framework for quantifying the effect of sampling uncertainty on estimation of the risk ratio, propose the use of statistical methods that are new to event attribution, and evaluate a variety of methods using statistical simulations. We conclude that existing statistical methods not yet in use for event attribution have several advantages over the widely-used bootstrap, including better statistical performance in repeated samples and robustness to small estimated probabilities. Software for using the methods is available through the climextRemes package available for R or Python. While we focus on frequentist statistical methods, Bayesian methods are likely to be particularly useful when considering sources of uncertainty beyond sampling uncertainty.