Atmospheric, Oceanic, and Planetary Physics, Department of Physics, University of Oxford, Oxford OX1 3PU, United Kingdom;
Atmospheric, Oceanic, and Planetary Physics, Department of Physics, University of Oxford, Oxford OX1 3PU, United Kingdom.
Proc Natl Acad Sci U S A. 2021 Dec 7;118(49). doi: 10.1073/pnas.2112087118.
Attribution of extreme weather events has expanded rapidly as a field over the past decade. However, deficiencies in climate model representation of key dynamical drivers of extreme events have led to some concerns over the robustness of climate model-based attribution studies. It has also been suggested that the unconditioned risk-based approach to event attribution may result in false negative results due to dynamical noise overwhelming any climate change signal. The "storyline" attribution framework, in which the impact of climate change on individual drivers of an extreme event is examined, aims to mitigate these concerns. Here we propose a methodology for attribution of extreme weather events using the operational European Centre for Medium-Range Weather Forecasts (ECMWF) medium-range forecast model that successfully predicted the event. The use of a successful forecast ensures not only that the model is able to accurately represent the event in question, but also that the analysis is unequivocally an attribution of this specific event, rather than a mixture of multiple different events that share some characteristic. Since this attribution methodology is conditioned on the component of the event that was predictable at forecast initialization, we show how adjusting the lead time of the forecast can flexibly set the level of conditioning desired. This flexible adjustment of the conditioning allows us to synthesize between a storyline (highly conditioned) and a risk-based (relatively unconditioned) approach. We demonstrate this forecast-based methodology through a partial attribution of the direct radiative effect of increased CO2 concentrations on the exceptional European winter heatwave of February 2019.
归因于极端天气事件的领域在过去十年中迅速扩展。然而，气候模型在极端事件关键动力驱动因素方面的代表性不足，导致人们对基于气候模型的归因研究的稳健性产生了一些担忧。也有人认为，由于动力噪声压倒任何气候变化信号，基于无条件风险的事件归因方法可能会导致假阴性结果。“故事情节”归因框架旨在减轻这些担忧，该框架检查气候变化对极端事件个别驱动因素的影响。在这里，我们提出了一种使用成功预测该事件的业务性欧洲中期天气预报中心（ECMWF）中程预报模型对极端天气事件进行归因的方法。成功预测不仅确保模型能够准确地再现所讨论的事件，而且还确保分析明确是对该特定事件的归因，而不是多个共享某些特征的不同事件的混合。由于这种归因方法取决于在预报初始化时可预测的事件组成部分，因此我们展示了如何调整预报的提前期可以灵活地设置所需的条件水平。这种对条件的灵活调整使我们能够在故事情节（高度条件化）和基于风险（相对非条件化）方法之间进行综合。我们通过对 2019 年 2 月异常欧洲冬季热浪中 CO2 浓度增加对直接辐射效应的部分归因来演示这种基于预报的方法。
