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

TL;DR

This study employs deep learning to reconstruct a global tropical cyclone wind profile dataset from 1981 to 2020, revealing significant increases in major and high-energy TCs, thus enhancing understanding of climate change impacts.

Contribution

The paper introduces a novel deep learning approach to generate an objective, homogenized dataset of TC wind profiles, enabling detailed analysis of trends in TC structure and energy over four decades.

Findings

Major TC proportion increased by ~13% since 1981

High-energy TCs increased by ~25% over 40 years

Mean total energy of high-energy TCs shows an upward trend

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.