Meng Jun, Fan Jingfang, Bhatt Uma S, Kurths Jürgen
School of Science, Beijing University of Posts and Telecommunications, 100876, Beijing, China.
School of Systems Science/Institute of Nonequilibrium Systems, Beijing Normal University, 100875, Beijing, China.
Nat Commun. 2023 Oct 18;14(1):6574. doi: 10.1038/s41467-023-42351-x.
The Arctic's rapid sea ice decline may influence global weather patterns, making the understanding of Arctic weather variability (WV) vital for accurate weather forecasting and analyzing extreme weather events. Quantifying this WV and its impacts under human-induced climate change remains a challenge. Here we develop a complexity-based approach and discover a strong statistical correlation between intraseasonal WV in the Arctic and the Arctic Oscillation. Our findings highlight an increased variability in daily Arctic sea ice, attributed to its decline accelerated by global warming. This weather instability can influence broader regional patterns via atmospheric teleconnections, elevating risks to human activities and weather forecast predictability. Our analyses reveal these teleconnections and a positive feedback loop between Arctic and global weather instabilities, offering insights into how Arctic changes affect global weather. This framework bridges complexity science, Arctic WV, and its widespread implications.
北极海冰的迅速减少可能会影响全球天气模式,因此了解北极天气变化对于准确的天气预报和分析极端天气事件至关重要。在人为引起的气候变化背景下,量化这种天气变化及其影响仍然是一项挑战。在此,我们开发了一种基于复杂性的方法,并发现北极季节内天气变化与北极涛动之间存在很强的统计相关性。我们的研究结果突出了北极每日海冰变化的增加,这归因于全球变暖加速了其减少。这种天气不稳定可通过大气遥相关影响更广泛的区域模式,增加人类活动风险并影响天气预报的可预测性。我们的分析揭示了这些遥相关以及北极和全球天气不稳定之间的正反馈循环,为北极变化如何影响全球天气提供了见解。该框架将复杂性科学、北极天气变化及其广泛影响联系起来。