We employ a statistical model of North Atlantic tropical cyclone (TC) tracks to investigate the relationship between sea-surface temperature (SST) and North American TC landfall rates. The track model is conditioned on summer SST in the tropical North Atlantic being in either the 19 hottest or the 19 coldest years in the period 1950-2005. For each conditioning many synthetic TCs are generated and landfall rates computed. Compared to direct analysis of historical landfall, the track model reduces the sampling error by projecting information from the entire basin onto the coast. There are 46% more TCs in hot years than cold in the model, which is highly significant compared to random sampling and corroborates well documented trends in North Atlantic TC number in recent decades. In the absence of other effects, this difference results in a significant increase in model landfall rates in hot years, uniform along the coast. Hot-cold differences in the geographic distribution of genesis and in TC propagation do not significantly alter the overall landfall-rate difference in the model, and the net landfall rate is 1/4.7 yr in hot years and 1/3.1 yr in cold years. SST influence on genesis site and propagation does modify the geographic distribution of landfall, however. The Yucatan suffers 3 times greater landfall rate in hot years than cold, while the U.S. mid-Atlantic coast exhibits no significant change. Landfall probabilities increase in hot years compared to all years in Florida, the U.S Gulf coast, the Mexican Gulf coast, and Yucatan with at least 95% confidence.
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)
Unexpected limitation of tropical cyclone genesis by subsurface tropical central-north Pacific during El Niño
School of Oceanography, Shanghai Jiao Tong University（上海交通大学海洋学院）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（南方海洋科学与工程广东省实验室（珠海））；State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences（中国科学院南海海洋研究所热带海洋环境国家重点实验室）；Department of Atmospheric Sciences, National Taiwan University（台湾大学大气科学系）；Department of Atmospheric and Oceanic Science, University of Maryland（马里兰大学大气与海洋科学系）；Indian Institute of Technology, Bombay（印度孟买理工学院）
Insights from Ex-Typhoon Halong (2025) -- An Arctic Cyclone of Tropical Origin
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Alaska Fairbanks（阿拉斯加大学费尔班克斯分校）；Naval Research Laboratory（海军研究实验室）；National Science Foundation National Center for Atmospheric Research（美国国家科学基金会国家大气研究中心）
Anomalous Diffusion of Tropical Cyclones Observed in Huge Ensembles of Hindcasts
NVIDIA Corporation（英伟达公司）；National Energy Research Scientific Computing Center (NERSC), LBNL（美国国家能源研究科学计算中心（NERSC）, 劳伦斯伯克利国家实验室）；Criteo AI Lab（Criteo人工智能实验室）；Earth and Environmental Sciences, Lawrence Berkeley National Laboratory (LBNL)（劳伦斯伯克利国家实验室（LBNL）地球与环境科学部）；The School of Environmental Sciences, University of East Anglia（东英吉利大学环境科学学院）；University of California, Berkeley（加州大学伯克利分校）；Department of Earth System Science, University of California, Irvine（加州大学尔湾分校地球系统科学系）
A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)
Lamont-Doherty Earth Observatory, Columbia University（哥伦比亚大学拉蒙特-多赫蒂地球观测站）；School of Complex Adaptive Systems, Arizona State University（亚利桑那州立大学复杂适应系统学院）；Earth and Environmental Engineering, Columbia University（哥伦比亚大学地球与环境工程系）
Potential Paradigm Shift in Hazard Risk Management: AI-Based Weather Forecast for Tropical Cyclone Hazards
Tongji University（同济大学）；Princeton University（普林斯顿大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Wollongong（伍伦贡大学）；Nanjing University（南京大学）；Shanghai Jiao Tong University（上海交通大学）
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）
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%。...
