The boreal summer tropical upper-tropospheric momentum budget involves a balance between eddy and mean meridional fluxes. In winter, however, the eddy flux itself acts to accelerate and decelerate the zonal flow in the Asian and East Pacific regions, respectively. In a zonal mean sense, the residual of these two is then balanced by the mean meridional flux. These features are qualitatively captured by the CMIP6 suite of models in their control runs. With warming, the CMIP6 ensemble shows that the flux budget changes in a quantitative manner, in both the summer and winter seasons. Apart from the mean meridional flux which is affected by the projected weakening of the Hadley Cells, there are significant changes in eddy fluxes too. Notably, stationary wave fluxes are affected in the Asian and East Pacific regions during the summer and winter seasons, respectively. In the wintertime, extratropical wave activity penetrating into the East Pacific almost shuts off due to a weakening of the prevalent westerlies in the warming simulations. Whereas, in summer, eddy fluxes in the Asian region are displaced upward and changes are observed in the upper troposphere and lower stratosphere. Specifically, rotational flow around the Asian summer anticyclone weakens in the upper troposphere while strengthening in the lower stratosphere. Concomitantly, eddy flux convergence over the equatorial Indian Ocean decreases in the former and increases (by a larger amount) in the latter. In fact, strengthening of summertime tropical and subtropical stationary waves in the lower stratosphere with warming is not restricted to the Asian sector but is observed over all longitudes. Effectively, the magnitude of all terms in the upper tropospheric momentum flux budget decreases with warming, and an ensemble mean continues to yield a marginally westward annual and zonal mean equatorial zonal flow.
Comments by William M. Gray (Colorado State University) on the recently published paper in Science by Webster et al., titled "Changes in tropical cyclone number, duration, and intensity in a warming environment." (September 2005, Vol. 309, pp. 1844-1846, www.sciencemag.org)
Advancing Seasonal Prediction of Tropical Cyclone Activity with a Hybrid AI-Physics Climate Model
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Google Research（谷歌研究院）；Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration（美国国家海洋和大气管理局地球流体动力学实验室）
Mathematical Exploration of Earth Gravitational Field Impact on Seasonal Wind Flux in a Tropical Region
Nigeria Institute of Soil Science（尼日利亚土壤科学研究所）；Institute of Biopaleogeography named under Charles R. Darwin（以查尔斯·达尔文命名的生物古地理学研究所）；University of Abuja（阿布贾大学）；Federal University of Lafia（拉菲亚联邦大学）；Sam Higginbottom University of Agriculture, Technology and Sciences（萨姆·希金博特姆农业、技术与科学学院）；University of Port Harcourt（波特哈科特大学）
Variability of North Atlantic hurricanes: seasonal versus individual-event features
热带气旋受到大量气候因子的影响，这些因素转化为复杂的发生模式。热带气旋活动的年度指标的变化已经被深入研究，特别是自20世纪90年代中期北大西洋突然激活以来。我们首先提供了一个快速的概述不同作者以前的工作，这些年度指标的NAtl盆地，在自然变化的现象，存在的趋势，记录的缺点，以及全球变暖的影响一直是有趣的辩论的主题。接下来，我们提出了一种替代方法，该方法不关注季节特征，而是关注单个事件的特征[Corral et al Nature Phys 6，693，2010]。有人认为，个别风暴功率耗散指数（PDI）构成了一个自然的方式来描述每个事件，并进一步，PDI统计产生一个强大的规律，热带气旋的发生在幂律。在这方面，这些分布的拟合方法进行了讨论。作为这项工作的一个重要扩展，我们引入了一个分布函数，模型的整个范围内的PDI密度（不包括在最小值的不完整性的影响），伽玛分布，包括在一个幂律与指数衰减的尾巴。这种衰减的特征尺度，表示的截止参数，提供了非常有价值的信息的有限大小的盆地，通过最大值的PDIs，盆地可以维持。我们使用伽马拟合来评估海表面温度（SST）对极端PDI值发生的影响，我们发现，对于0.49 degC的SST平均差异，这些全流域事件的值增加了约50%。...
Zonal-mean circulation response to reduced air-sea momentum roughness
表层物理学的不确定性对大气环流的影响相对来说还没有被探索。本文用社区大气模式（CAM 3）研究了低流速时纬向平均环流对减小的海气动量粗糙度（$Z_{0m}$）的敏感性。在一个具有指定海面温度的水行星框架中，对$Z_{0m}$减少的响应类似于La Ni $\tilde{\text{n}}$ a减去El Ni $\tilde{\text{n}}$ o对El Ni $\tilde{\text{n}}$ o南方涛动变率的响应，其中：i）中纬度西风带向极移动，一直延伸到地面; ii）副热带下降区向极移动较弱;及iii）哈德利环流减弱，一般亦伴随着热带会聚区及热带地面东风带向极移。机制否认实验表明，这种响应是由热带潜热和感热通量的减少，减少$Z_{0m}$的影响。通过考虑热带能量通量对Hadley环流强度、对流层上部临界层纬度和对流层下部斜压涡动强迫的影响，阐明了环流响应。ITCZ的转变是通过在热带湿静态能量收支分析。环流对减少的$Z_{0m}$的响应延续到更复杂的设置，具有季节性周期，大气-冰-陆地-海洋相互作用的完全复杂性，以及板状海洋下边界条件。因此，表面参数化参数的相对较小的变化可以导致显著的环流响应。
Hierarchical Testing of a Hybrid Machine Learning-Physics Global Atmosphere Model
Pacific Northwest National Laboratory（太平洋西北国家实验室）；Ocean University of China（中国海洋大学）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）
