平流层波驱动事件作为平流层突然变暖的替代方案

T. Reichler, M. Jucker
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引用次数: 2

摘要

摘要北平流层极地涡旋强度的自然变化,即所谓的极地涡旋事件,有助于改善冬季气候的亚季节到季节(S2S)预测。过去对极涡事件的研究主要集中在平流层突然变暖事件(SSWs)上,这是一类相对较强的极涡减弱事件。通常,当极地涡旋反转其冬季西风方向时,ssw被定义为。然而,在本研究中,我们使用了另一种定义,即基于平流层下层的加权时间积分上升波活动通量。我们使用平流层解析模式和ERA5再分析的长时间控制模拟,将波活动定义的各个方面与北极上空常见的ssw进行比较。大约一半的波事件与普通的ssw相同。然而,基于波事件而不是使用常见的SSW定义来定义平流层弱极端有几个优点:波活动通量定义用一个标准捕获各种不同的事件类型,检测强SSW和强最终变暖事件,避免对地面影响很小的弱SSW,并可能延长地面响应的预测范围。因此,我们得出结论,波驱动是平流层极涡事件的一个有用的早期指标,它利用平流层的潜力,比普通的ssw更好地产生可预测的地面信号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stratospheric wave driving events as an alternative to sudden stratospheric warmings
Abstract. Natural variations in the strength of the northern stratospheric polar vortex, so-called polar vortex events, help to improve subseasonal-to-seasonal (S2S) predictions of winter climate. Past research on polar vortex events has been largely focused on sudden stratospheric warming events (SSWs), a class of relatively strong weakenings of the polar vortex. Commonly, SSWs are defined when the polar vortex reverses its climatological wintertime westerly wind direction. In this study, however, we use an alternative definition, based on the weighted time-integrated upward wave activity flux at the lower stratosphere. We use a long control simulation with a stratosphere-resolving model and the ERA5 reanalysis to compare various aspects of the wave activity definition with common SSWs over the Arctic. About half of the wave events are identical to common SSWs. However, there exist several advantages for defining stratospheric weak extremes based on wave events rather than using the common SSW definition: the wave activity flux definition captures with one criterion a variety of different event types, detects strong SSWs and strong final warming events, avoids weak SSWs that have little surface impact, and potentially lengthens the prediction horizon of the surface response. We therefore conclude that the wave driving represents a useful early indicator for stratospheric polar vortex events, which exploits the stratospheric potential for creating predictable surface signals better than common SSWs.
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CiteScore
6.40
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