Climate Trends of Tropical Cyclone Intensity and Energy Extremes Revealed by Deep Learning

• URL: http://arxiv.org/abs/2402.00362v1
• Date: Thu, 1 Feb 2024 06:02:29 GMT
• Title: Climate Trends of Tropical Cyclone Intensity and Energy Extremes Revealed by Deep Learning
• Authors: Buo-Fu Chen, Boyo Chen, Chun-Min Hsiao, Hsu-Feng Teng, Cheng-Shang Lee, Hung-Chi Kuo
• Abstract summary: We use deep learning to reconstruct past "observations" Major cyclone proportion has increased by 13% in the past four decades. The proportion of extremely high-energy TCs has increased by 25%.
• Score: 4.772176643340364
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Anthropogenic influences have been linked to tropical cyclone (TC) poleward migration, TC extreme precipitation, and an increased proportion of major hurricanes [1, 2, 3, 4]. Understanding past TC trends and variability is critical for projecting future TC impacts on human society considering the changing climate [5]. However, past trends of TC structure/energy remain uncertain due to limited observations; subjective-analyzed and spatiotemporal-heterogeneous "best-track" datasets lead to reduced confidence in the assessed TC repose to climate change [6, 7]. Here, we use deep learning to reconstruct past "observations" and yield an objective global TC wind profile dataset during 1981 to 2020, facilitating a comprehensive examination of TC structure/energy. By training with uniquely labeled data integrating best tracks and numerical model analysis of 2004 to 2018 TCs, our model converts multichannel satellite imagery to a 0-750-km wind profile of axisymmetric surface winds. The model performance is verified to be sufficient for climate studies by comparing it to independent satellite-radar surface winds. Based on the new homogenized dataset, the major TC proportion has increased by ~13% in the past four decades. Moreover, the proportion of extremely high-energy TCs has increased by ~25%, along with an increasing trend (> one standard deviation of the 40-y variability) of the mean total energy of high-energy TCs. Although the warming ocean favors TC intensification, the TC track migration to higher latitudes and altered environments further affect TC structure/energy. This new deep learning method/dataset reveals novel trends regarding TC structure extremes and may help verify simulations/studies regarding TCs in the changing climate.

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