用深度学习模型模拟百年气候，发现海温变化显著影响阻塞天气频次。
Large-Ensemble Simulations Reveal Links Between Atmospheric Blocking Frequency and Sea Surface Temperature Variability
- 用大规模深度学习气候模型模拟1900-2010年气候，过滤大气内部噪声。
- 海温变化导致格陵兰冬季阻塞减少、欧洲冬季阻塞增加。
- 揭示了北大西洋海温与格陵兰阻塞的稳定遥相关关系。
大气阻塞事件引发中纬度持续极端天气，但难以区分海表温度（SST）影响与大气内部混沌变率的作用。我们利用两个计算高效的深度学习全球气候模型，进行百年（1900–2010）大规模集合模拟，成功再现了观测到的阻塞气候态，性能达到或超过传统高分辨率模型及代表性CMIP6模型。对大规模集合平均可滤除大气内部噪声，分离出由海温强迫引起的阻塞变率成分，其与再分析数据的相关性显著高于单个成员。研究识别出格陵兰阻塞频率与北大西洋海温及厄尔尼诺型模式之间的稳健遥相关。此外，海温强迫趋势显示冬季格陵兰阻塞频次一致下降，欧洲则上升。结果表明海温变率对阻塞频次具有显著且可物理解释的影响，并确立深度学习模型大规模集合为分离强迫信号与内部噪声的强大工具。
原文摘要 · Abstract (English)
Atmospheric blocking events drive persistent weather extremes in midlatitudes, but isolating the influence of sea surface temperature (SST) from chaotic internal atmospheric variability on these events remains a challenge. We address this challenge using century-long (1900-2010), large-ensemble simulations with two computationally efficient deep-learning general circulation models. We find these models skillfully reproduce the observed blocking climatology, matching or exceeding the performance of a traditional high-resolution model and representative CMIP6 models. Averaging the large ensembles filters internal atmospheric noise to isolate the SST-forced component of blocking variability, yielding substantially higher correlations with reanalysis than for individual ensemble members. We identify robust teleconnections linking Greenland blocking frequency to North Atlantic SST and El Niño-like patterns. Furthermore, SST-forced trends in blocking frequency show a consistent decline in winter over Greenland, and an increase over Europe. These results demonstrate that SST variability exerts a significant and physically interpretable influence on blocking frequency and establishes large ensembles from deep learning models as a powerful tool for separating forced SST signals from internal noise.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。