Future North Atlantic tropical cyclone intensities in thermodynamically modified historical environments

Dataset · Tue Oct 15 00:00:00 EDT 2024

DOI:https://doi.org/10.57931/2467518· OSTI ID:2467518

Lalo, Nicholas ^[1]; Xu, Wenwei ^[2]; Yao, Lili ^[2]; Sun, Ning ^[2]; Balaguru, Karthik ^[2]; Rice, Julian ^[2]; Thurber, Travis ^[2]; Yang, Zhaoqing ^[2]; Deb, Minthun ^[2]; Judi, David ^[2]

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1. Pacific Northwest National Laboratory; Pacific Northwest National Laboratory
2. Pacific Northwest National Laboratory

Tropical cyclones (TCs) rank as the deadliest and most financially crippling natural disasters in the United States for the last half-century. It is imperative to assess potential shifts in TC intensity within the paradigm of an evolving climate. In this study, we have modeled the intensities of 620 historical TC events in the North Atlantic Basin using the Risk Analysis Framework for Tropical Cyclones (RAFT)'s deep learning intensity model. By applying a thermodynamic warming signal extrapolated from Global Climate Models, we rerun historical events under eight different future climate scenarios, providing a spectrum of potential TC intensity outcomes. One of the future simulations indicates a staggering 43% increase in the number of major hurricanes, underscoring the critical impact of climate change on TC intensity. Additionally, an interactive dashboard has been created to enable users to explore individual storm simulations and understand the influence of future climate signals on environmental conditions of TC development and resulting TC intensities. This dataset and the user-friendly tool offer invaluable resources for systematic exploration of the discrete effects that changes in the air-sea thermodynamic state have on the intensities of TCs. 

• Research Organization: MultiSector Dynamics - Living, Intuitive, Value-adding, Environment
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• OSTI ID: 2467518
• Country of Publication: United States
• Language: English

Related Subjects

Climate Change
Machine Learning
TGW
Tropical Cyclones