一个巨大的集合说明了破纪录的高温记录是如何持续的

J. Risbey, Damien B Irving, D. Squire, R. Matear, D. Monselesan, M. Pook, N. Ramesh, D. Richardson, C. Tozer
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引用次数: 0

摘要

2021年6月太平洋西北地区创纪录的炎热天气被用来激发对一个非常大的后预报集合中创纪录的极端温度的研究。西北太平洋最热日数与观测到的事件具有相似的大尺度和天气模式。从固定位置的角度来看,最热的集合日对精确定位的天气模式的干燥期的机会顺序非常敏感。因此,这些日子是罕见的,需要非常大的样本(数万年)来捕捉。破纪录高温记录的持久性可以通过天气“噪音”和采样来理解。当一个破纪录的事件由于天气系统在最佳配置中的偶然对齐而发生时,在(非常不可能的)创纪录事件之后的任何小样本年份中,发现另一个偶然极端事件的可能性极低。虽然基线气候变暖可以缩小更正常的极端天气和破纪录的极端天气之间的差距,但这可能需要几十年的时间,具体取决于气候变化的速度。除非模式样本足够大,能够提供足够的天气结果,包括最优天气组合,否则气候模式不太可能捕捉到观测给出的固定地点的破纪录极端情况。这强调了在评估模式和对天气敏感的极端事件的变化时考虑抽样的必要性。特别是,如果气候模型的评估是基于在小样本中不存在极端情况,那么它不一定在代表极端情况方面有缺陷。
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A large ensemble illustration of how record-shattering heat records can endure
The record-shattering hot day in the Pacific Northwest in June 2021 is used to motivate a study of record-shattering temperature extremes in a very large hindcast ensemble. The hottest days in the Pacific Northwest in the large ensemble have similar large scale and synoptic patterns to those associated with the observed event. From the perspective of a fixed location, the hottest ensemble days are acutely sensitive to the chance sequencing of a dry period with a precisely positioned weather pattern. These days are thus rare and require very large samples (tens of thousands of years) to capture. The enduring nature of record-shattering heat records can be understood through this lens of weather ‘noise’ and sampling. When a record-shattering event occurs due to chance alignment of weather systems in the optimal configuration, any small sample of years subsequent to the (very unlikely) record event has an extremely low chance of finding yet another chance extreme. While warming of the baseline climate can narrow the gap between more regular extremes and record-shattering extremes, this can take many decades depending on the pace of climate change. Climate models are unlikely to capture record-shattering extremes at fixed locations given by observations unless the model samples are large enough to provide enough weather outcomes to include the optimal weather alignments. This underscores the need to account for sampling in assessing models and changes in weather-sensitive extremes. In particular, climate models are not necessarily deficient in representing extremes if that assessment is based on their absence in undersize samples.
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