Tropical cyclones that rapidly intensify are typically associated with the highest forecast errors and cause a disproportionate amount of human and financial losses. Therefore, it is crucial to understand if, and why, there are observed upward trends in tropical cyclone intensification rates. Here, we utilize two observational datasets to calculate 24-hour wind speed changes over the period 1982-2009. We compare the observed trends to natural variability in bias-corrected, high-resolution, global coupled model experiments that accurately simulate the climatological distribution of tropical cyclone intensification. Both observed datasets show significant increases in tropical cyclone intensification rates in the Atlantic basin that are highly unusual compared to model-based estimates of internal climate variations. Our results suggest a detectable increase of Atlantic intensification rates with a positive contribution from anthropogenic forcing and reveal a need for more reliable data before detecting a robust trend at the global scale.
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http://dx.doi.org/10.1038/s41467-019-08471-z | DOI Listing |
Nat Commun
January 2025
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA.
The upper ocean provides thermal energy to tropical cyclones. However, the impacts of the subsurface ocean on tropical cyclogenesis have been largely overlooked. Here, we show that the subsurface variabilities associated with the variation in the 26 °C isothermal depth have pronounced impacts on tropical cyclogenesis over global oceans.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Key Laboratory of Ocean Observation and Forecasting, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266000, China.
Tropical cyclones (TCs), particularly those that rapidly intensify (RI), pose a significant threat due to the uncertainty in forecasting them. RI TC periods, which intensify by at least 13 m/s within 24 h, remain challenging to forecast accurately. Existing models achieve a probability of detection (POD) of 82.
View Article and Find Full Text PDFPLoS Negl Trop Dis
January 2025
Institute of Exact and Applied Sciences, University of New Caledonia, Nouméa, Province Sud, New Caledonia.
Background: Leptospirosis is a neglected zoonotic disease prevalent worldwide, particularly in tropical regions experiencing frequent rainfall and severe cyclones, which are further aggravated by climate change. This bacterial zoonosis, caused by the Leptospira genus, can be transmitted through contaminated water and soil. The Pacific islands bear a high burden of leptospirosis, making it crucial to identify key factors influencing its distribution.
View Article and Find Full Text PDFEnviron Epidemiol
February 2025
Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Background: Tropical cyclones pose significant health risks and can trigger outbreaks of diarrheal diseases in affected populations. Although the effects of individual hazards, such as rainfall and flooding, on diarrheal diseases are well-documented, the complex multihazard nature of tropical cyclones is less thoroughly explored. To date, no dedicated review comprehensively examines the current evidence and research on the association between tropical cyclones and diarrheal diseases.
View Article and Find Full Text PDFSci Rep
January 2025
College of Ocean and Meteorology & South China Sea Institute of Marine Meteorology, Guangdong Ocean University, 524088, Zhanjiang, Guangdong, China.
Accurate classification of tropical cyclone (TC) tracks is essential for evaluating and mitigating the potential disaster risks associated with TCs. In this study, three commonly used methods (K-means, Fuzzy C-Means, and Self-Organizing Maps) are assessed for clustering historical TC tracks that originated in the South China Sea from 1949 to 2023. The results show that the K-means method performs the best, while the Fuzzy C-Means and Self-Organizing Maps methods are also viable alternatives.
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