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Paper • The following article is Open access
Human-caused ocean warming has intensified recent hurricanes
Daniel M Gilford*, Joseph Giguere and Andrew J Pershing
Published 20 November 2024 • © 2024 The Author(s). Published by IOP Publishing Ltd
Environmental Research: Climate, Volume 3, Number 4 Extreme Weather and Climate Event Attribution
Citation Daniel M Gilford et al 2024 Environ. Res.: Climate 3 045019 DOI 10.1088/2752-5295/ad8d02
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Abstract
Understanding how rising global air and sea surface temperatures (SSTs) influence tropical cyclone intensities is crucial for assessing current and future storm risks. Using observations, climate models, and potential intensity theory, this study introduces a novel rapid attribution framework that quantifies the impact of historically-warming North Atlantic SSTs on observed hurricane maximum wind speeds. The attribution framework employs a storyline attribution approach exploring a comprehensive set of counterfactuals scenarios—estimates characterizing historical SST shifts due to human-caused climate change—and considering atmospheric variability. These counterfactual scenarios affect the quantification and significance of attributable changes in hurricane potential and observed actual intensities since pre-industrial. A summary of attributable influences on hurricanes during five recent North Atlantic hurricane seasons (2019–2023) and a case study of Hurricane Ian (2022) reveal that human-driven SST shifts have already driven robust changes in 84% of recent observed hurricane intensities. Hurricanes during the 2019–2023 seasons were 8.3ms-1 faster, on average, than they would have been in a world without climate change. The attribution framework's design and application, highlight the potential for this framework to support climate communication.
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1. Introduction
Rising global mean air temperatures and sea surface temperatures (SSTs) are expected to influence tropical cyclone activity throughout the 21st century (Sobel et al 2016, Collins et al 2019). Hurricane intensity changes, in particular, are important to understand and elucidate because they are a key driver of storm risks and damages in the United States (e.g. Schmidt et al 2009, Nordhaus 2010, Emanuel 2011). In this study we present a novel rapid attribution framework that quantifies how historical increases in North Atlantic SSTs drive attributable changes in observed hurricane intensities.
Theory and numerical modeling indicate human-caused climate change should strengthen hurricane intensities on average (Emanuel 2005, Schiermeier 2008, Sobel et al 2016, Murakami et al 2020). Wehner and Kossin ( 2024) showed that anthropogenic global warming has already increased the likelihoods of extremely intense (
86ms-1) tropical cyclone potential intensities (PI; i.e. the theoretical maximum intensity of a storm given its environment). Higher PI values mean the theoretical maximum speed limits over the Atlantic have increased, permitting individual storms to reach higher wind speeds in today's warmer climate. Observational evidence corroborates this theory: the frequency of major hurricanes (category 3+) has increased since 1979 (Elsner et al 2008, Holland and Bruyère 2014, Kossin et al 2020). But when a real-world storm develops and threatens a coastline, to what extent is its maximum wind speed attributable to human influences?
Connecting these dots is critical for climate communication. Hurricanes—especially landfalling hurricanes with high intensities—can act as 'focusing events' that draw public attention (Birkland 1998, Arnold et al 2021, Silver and Jackson 2023). Increased attention during and in wake of storms creates opportunities for public and private discourse around climate and disaster preparedness (Cody et al 2017, Wong-Parodi and Garfin 2022). Climate change attribution plays an important role in these discussions. Social studies have shown that personal experiences with extreme weather and attribution messaging both have strong potential to influence public perceptions of climate risk and decision-making (Ogunbode et al 2019, Boudet et al 2020, Osaka and Bellamy 2020, Ettinger et al 2021, McClure et al 2022, Thomas-Walters et al 2024, Zanocco et al 2024). Presenting scientifically-sound estimates, and carefully, deliberately conveying methodologies can be effective for attribution-driven climate communication (Osaka and Bellamy 2020, Ettinger et al 2021, van Oldenborgh et al 2021, Thomas-Walters et al 2024).
Rapid attribution systems designed and used to produce these estimates can also help the research community identify which events/conditions are unusual and warrant closer study (Swain et al 2020, Gilford et al 2022). Today, timely attribution studies of extreme temperatures and other weather events are routinely performed with peer-reviewed methods and delivered by World Weather Attribution (e.g. Philip et al 2020), Climate Central (Gilford et al 2022), and others. But key for such messaging is a pre-existing attribution system that can rapidly and reliably estimate climate's influence on particular events.
While formal extreme event attribution science has become mature in recent decades (e.g. National Academies of Sciences 2016, Philip et al 2020), hurricane attribution science is still relatively nascent and challenging (Seneviratne et al 2021). There is strong evidence from hurricane modeling and 'storyline' attribution (see below) that rainfall intensity and accumulation have already increased by ~10% or more for many individual storms (Van Oldenborgh et al 2017, Trenberth et al 2018, Wang et al 2018, Reed et al 2021, Reed and Wehner 2023). Attributable damages from hurricanes have also been explored (Strauss et al 2021, Wehner and Reed 2022). Meanwhile, high-resolution model simulations project that future storm intensities should increase with ongoing global warming, but suggest anthropogenic influences on recent storms are not yet strong enough to be significant (Lackmann 2015, Patricola and Wehner 2018, Wehner et al 2019). In contrast, Pfleiderer et al ( 2022) used a statistical emulator to show that human influences recently elevated Atlantic SSTs, making the 2020 hurricane season anomalously active, despite offsets from interannual variability in atmospheric circulation. They found that 'SSTs over the MDR [main development region] contain relevant information' for estimating attributable hurricane activity in the North Atlantic.
Thus to calculate the influence of human-caused climate change on hurricane intensities, one must first assess how climate change has influenced SSTs. Specifically, it is critical to obtain or determine a 'counterfactual' estimate of what SSTs might be in our modern world without anthropogenic emissions—an approach commonly used in climate attribution science. In this study we use results from a recent comprehensive study of historically attributable ocean temperatures (Giguere et al 2024) to derive a counterfactual scenario of what North Atlantic SSTs would be in the absence of human-caused global warming. We also consider a suite of additional counterfactual scenarios, with a range of physical and statistical assumptions to evaluate attribution uncertainties.
Potential intensity theory (e.g. Emanuel 1987, Bister and Emanuel 1998) makes the connection between SSTs and intensities explicit by assuming tropical cyclones operate like heat engines, tying a storm's potential maximum to the storm's efficiency and fuel availability (i.e. from ocean heat). Changes in the distribution of actual storm lifetime maximum intensities have a strong empirical relationship with changes in potential intensity (e.g. Emanuel 2000, Sobel et al 2016, Gilford et al 2019, Sparks and Toumi 2024, Wehner and Kossin 2024). We leverage this statistical relationship to estimate how a storm's observed actual intensity (AI) might respond to PI changes that are, in turn, driven by attributable changes in ocean temperatures.
In this study we develop an attribution framework to assess how SSTs made warmer by climate change are influencing observed hurricane intensities. The framework is designed to underpin a rapid attribution system for deployment shortly after a hurricane event, with results communicated through trusted messengers to the public. In section 2 we describe the approach, data, models, and theory of the attribution framework. Section 3 shows attribution estimates for SSTs, potential intensity, and AI. Included are two real-world applications of the framework: estimates of SST-driven attributable intensities over the last five North Atlantic hurricane seasons (2019–2023) and a case study of Hurricane Ian (2022). Study implications and limitations are discussed in the context of our existing scientific understanding in section 4. Finally, we summarize our key findings in section 5.
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