青藏高原极端异常积雪事件对大气的亚季节反馈

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Yumeng Liu, Wenkai Li
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引用次数: 0

摘要

青藏高原的积雪具有明显的亚季节变化特征,在影响大气方面起着至关重要的作用。本研究利用数值实验研究了青藏高原极端异常积雪事件对大气的反馈作用,重点关注局地和非局地大气温度。研究结果表明,由这些事件直接引起的绝热加热会导致青藏高原局部地表能量冷却反应,从而导致局部气温下降。这种冷却效应放大了与青藏高原极端异常积雪事件相关的局地大气温度异常,约占最终局地地表气温异常总量的 50%。此外,青藏高原积雪通过绝热过程对大气温度和环流产生非局部影响。青藏高原下游的大气温度响应在不同高度和地区有所不同,既有冷异常响应,也有暖异常响应。这些变化取决于绝热加热中水平平流和垂直平流的相对贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Subseasonal Feedback of Extreme Anomalous Tibetan Plateau Snow Cover Events on the Atmosphere
Tibetan Plateau snow cover exhibits notable subseasonal variability and plays a crucial role in influencing the atmosphere. This study employs numerical experiments to investigate the atmospheric feedback resulting from extreme anomalous snow cover events on the Tibetan Plateau, with a focus on both local and nonlocal atmospheric temperatures. The findings reveal that diabatic heating, directly induced by these events, leads to a local surface energy cooling response over the Tibetan Plateau, contributing to a reduction in local temperatures. This cooling effect amplifies local atmospheric temperature anomalies associated with extreme anomalous Tibetan Plateau snow cover events, constituting approximately 50% of the total final local surface air temperature anomalies. Furthermore, the Tibetan Plateau snow cover, through adiabatic processes, exerts a nonlocal influence on atmospheric temperature and circulation. The atmospheric temperature responses downstream of the Tibetan Plateau vary at different heights and regions, featuring both cold and warm anomaly responses. These variations depend on the relative contributions of horizontal advection and vertical advection in adiabatic heating.
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来源期刊
Monthly Weather Review
Monthly Weather Review 地学-气象与大气科学
CiteScore
6.40
自引率
12.50%
发文量
186
审稿时长
3-6 weeks
期刊介绍: Monthly Weather Review (MWR) (ISSN: 0027-0644; eISSN: 1520-0493) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies. This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments. MWR also addresses phenomena having seasonal and subseasonal time scales.
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