Large-Ensemble Simulations Reveal Links Between Atmospheric Blocking Frequency and Sea Surface Temperature Variability
Department of Atmospheric and Climate Science, University of Washington（大气与气候科学系，华盛顿大学）；Department of Geosciences, Princeton University（地球科学系，普林斯顿大学）；High Meadows Environmental Institute, Princeton University（高梅斯环境研究所，普林斯顿大学）
Zilu Meng, Gregory J. Hakim, Wenchang Yang, Gabriel A. Vecchi
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.
Elimination of spectral blocking by ensuring rotation-free property of discretised pressure gradient within a spectral semi-implicit semi-Lagrangian global atmospheric model
Zonal asymmetries control the response of atmospheric blocking to Arctic warming in an aquaplanet experiment
University of Trento（特伦托大学）；Istituto Universitario Superiore di Pavia（帕维亚高等大学研究所）；University of Exeter（埃克塞特大学）；Center Agriculture Food Environment, University of Trento（特伦托大学农业食品环境中心）
Atmospheric rivers and winter sea ice drive recent reversal in Antarctic ice mass loss
University of British Columbia（不列颠哥伦比亚大学）；Danish Meteorological Institute（丹麦气象局）；University of Canterbury（坎特伯雷大学）；University of Groningen（格罗宁根大学）；Royal Dutch Meteorological Institute（荷兰皇家气象研究所）
Marine Heatwaves in the Arabian Sea: Drivers and Impacts on Atmospheric Circulation and Extreme Precipitation
University of Colorado, Boulder（科罗拉多大学博尔德分校）；Texas A&M University–Corpus Christi（德克萨斯农工大学科珀斯克里斯蒂分校）；Scripps Institution of Oceanography, University of California, San Diego（加州大学圣地亚哥分校斯克里普斯海洋研究所）；Florida State University（佛罗里达州立大学）
Air-Sea Interactions on Titan: Lake Evaporation, Atmospheric Circulation, and Cloud Formation
泰坦丰富的湖泊和海洋通过一种通常被称为空气-海洋相互作用的过程与大气交换甲烷蒸汽和能量。这种湍流交换过程的研究与大气中尺度模式耦合到一个平板模式表示下垫面湖泊。通过几十个二维数值模拟，研究了湖泊尺度、有效湖混合层深度、背景风速、气湖温差和大气湿度对海气相互作用过程的影响。一般的，准稳定的解决方案是一个非常弱的背景羽流环流驱动的浮力蒸发的甲烷与一个强大的相反的热直接（海风）环流驱动的湖泊和温暖的内陆空气之间的热对比的非线性叠加。具体的解决方案取决于所选择的大气和湖泊属性参数的值，但这两个环流的叠加的一般解决方案是持久的。与前人的分析工作相一致，感热通量和潜热通量趋向于相反和相等的值，使得它们的比值Bowen比在大多数（但不是全部）准稳态解中接近-1.0。重要的是，在几乎所有的情况下，通量的绝对星等的趋势非常小的值，这样的平衡解决方案也几乎是一个平凡的解决方案，其中海气能量交换约3 W m-2或less. In所有情况下，一个凉爽，潮湿，静态稳定的浅海层，近平静的风和小湍流通量交换与一个较冷的底层湖泊产生的模型。湖泊的温度、空气的海洋属性和海风的强度取决于初始条件，在较小程度上取决于边界条件。
Wintertime Cross-correlational Structures between Sea Surface Temperature Anomaly and Atmospheric-and-Oceanic Fields in the East/Japan Sea Under Arctic Oscillation
