Does the Frequency of Rapid Intensification Vary with Climate?


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Rapid intensification (RI) of tropical cyclones, defined as an increase in maximum sustained winds of at least 30 knots within a 24-hour period, presents a special challenge in forecasting, and some recent analyses have suggested it is becoming more frequent. Prior work showed that RI is correlated with various large-scale environmental conditions, including wind shear, sea surface temperatures, ocean heat content, and potential intensity. These factors are commonly used as predictors in operational forecasts of tropical cyclone intensity, such as the Statistical Hurricane Intensity Prediction Scheme (SHIPS). The long-term trend of the RI ratio can be partially explained by these environmental parameters, utilizing both observational data and model simulations. However, it remains uncertain how tropical cyclone RI activities will change in a future climate, particularly under much hotter conditions than the present.

This research utilizes the International Best Track Archive for Climate Stewardship (IBTrACS) dataset from the satellite era (1980-present), as well as downscaled tropical cyclones from simulations representing very hot climates (8x and 32x CO2 compared to preindustrial levels) and very cold climates (Last Glacier Maximum). Additionally, we use tracked tropical cyclones in high-resolution Community Earth System Model (CESM) simulations. These simulations are capable of simulating a realistic distribution of tropical cyclone intensities. CESM is used to simulate both historical and future climates throughout the 21st century. These datasets cover a wide range of climates, allowing one to study the frequency of RI in different climates.

To investigate the connections between the RI ratio and thermodynamic parameters, as well as other relevant variables in different climates, we develop a novel machine-learning-based model. This approach allows for a more accurate representation of the complex relationships between the RI ratio and environmental factors compared to traditional methods. The resulting model is then employed to predict the trends in tropical cyclone RI during the late 21st century based on the outputs of CESM. This analysis aims to enhance our understanding of the risks associated with tropical cyclones in the context of climate change.

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