Researchers recently revised their findings on tropical cyclones, showing that extreme warming scenarios result in a notable increase in the number of shallow storms. This discovery, published in Nature Communications, highlights the growing role of artificial intelligence in detecting trends that classical models struggle to capture.
What the Study Reveals: Precise Results and Key Figures
The revised version of the study indicates that under ocean temperatures several degrees higher, tropical cyclones tend to remain weaker in vertical intensity while increasing their frequency. The authors emphasize that systems classified as 'shallow' – i.e., with central pressure not dropping below 1000 hPa – appear more often in extreme climate simulations. The exact figures of the increase remain to be confirmed; the article indicates that previously published values were overestimated, and new estimates will be published in the near future.
This dynamic was observed primarily in the Atlantic North and Western Pacific basins, where surface waters regularly exceed critical thresholds of 28°C. The study specifies that the incidence of these shallow cyclones could modify local precipitation patterns, bringing heavy rains to areas that historically only experience sporadic thunderstorms. In the absence of definitive numerical data, the authors use the expression 'information not confirmed at this stage' for the exact percentage increases.
Finally, researchers emphasize the importance of prediction uncertainty in these new scenarios. Traditional models, like those of ECMWF, show a wider margin of error when it comes to predicting cyclone depth, while machine learning approaches offer finer resolution of storm vertical structures.