Revealing the Statistics of Extreme Events Hidden in Short Weather Forecast Data
Revealing the Statistics of Extreme Events Hidden in Short Weather Forecast Data
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DOI：
10.1029/2023av000881
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发表时间：
2022-06
期刊：
影响因子：
8.4
通讯作者：
J. Finkel;E. Gerber;D. Abbot;J. Weare
中科院分区：
文献类型：
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作者：
J. Finkel;E. Gerber;D. Abbot;J. Weare
极端天气事件具有重大后果，主导气候对社会的影响。虽然高分辨率天气模型可以在天气时间尺度上预测许多类型的极端事件，但长期气候风险评估是一个完全不同的问题。一个世纪一次的事件平均需要100年的模拟时间才能出现一次，远远超过了天气预报模型的典型积分长度。因此，这项任务留给了更便宜，但不太准确，低分辨率或统计模型。但是，天气模型的输出还有未开发的潜力：尽管持续时间很短，但天气预报集合每周都会制作多次。积分是以独立的扰动启动的，使它们随着时间的推移而分散开，并广泛地采样相空间。总的来说，这些集成加起来有数千年的数据。我们建立了从这些短期天气模拟中提取气候信息的方法。使用欧洲中期天气预报中心在次季节到季节（S2S）数据库中存档的集合后报，我们描述了具有数百年重现时间的突然平流层变暖（SSW）事件。其他方法，包括基本的计数策略和马尔可夫状态建模之间的一致结果。通过仔细地将轨迹组合在一起，我们获得了SSW频率及其季节分布的估计值，这些估计值与中等罕见事件的再分析估计值一致，但具有更严格的不确定性界限，并且可以扩展到历史上尚未观察到的前所未有的严重事件。这些方法具有评估整个气候系统极端事件的潜力，超出了平流层极端事件的例子。
Extreme weather events have significant consequences, dominating the impact of climate on society. While high‐resolution weather models can forecast many types of extreme events on synoptic timescales, long‐term climatological risk assessment is an altogether different problem. A once‐in‐a‐century event takes, on average, 100 years of simulation time to appear just once, far beyond the typical integration length of a weather forecast model. Therefore, this task is left to cheaper, but less accurate, low‐resolution or statistical models. But there is untapped potential in weather model output: despite being short in duration, weather forecast ensembles are produced multiple times a week. Integrations are launched with independent perturbations, causing them to spread apart over time and broadly sample phase space. Collectively, these integrations add up to thousands of years of data. We establish methods to extract climatological information from these short weather simulations. Using ensemble hindcasts by the European Center for Medium‐range Weather Forecasting archived in the subseasonal‐to‐seasonal (S2S) database, we characterize sudden stratospheric warming (SSW) events with multi‐centennial return times. Consistent results are found between alternative methods, including basic counting strategies and Markov state modeling. By carefully combining trajectories together, we obtain estimates of SSW frequencies and their seasonal distributions that are consistent with reanalysis‐derived estimates for moderately rare events, but with much tighter uncertainty bounds, and which can be extended to events of unprecedented severity that have not yet been observed historically. These methods hold potential for assessing extreme events throughout the climate system, beyond this example of stratospheric extremes.