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Tropical cyclones can rapidly intensify under favorable oceanic and atmospheric conditions. This phenomenon is complex and difficult to predict, making it a serious challenge for coastal communities. A key contributing factor to the intensification process is the presence of prolonged high sea surface temperatures, also known as marine heatwaves. However, the extent to which marine heatwaves contribute to the potential of rapid intensification events is not yet fully explored. Here, we conduct a probabilistic analysis to assess how the likelihood of rapid intensification changes during marine heatwaves in the Gulf of Mexico and northwestern Caribbean Sea. Approximately 70% of hurricanes that formed between 1950 and 2022 were influenced by marine heatwaves. Notably, rapid intensification is, on average, 50% more likely during marine heatwaves. As marine heatwaves are on the increase due to climate change, our findings indicate that more frequent rapid intensification events can be expected in the warming climate. 

Compound flooding, caused by the sequence/co-occurrence of flood drivers (i.e. river discharge and elevated sea level ) can lead to devastating consequences for society. Weak and insufficient progress toward sustainable development and disaster risk reduction are likely to exacerbate the catastrophic impacts of these events on vulnerable communities. For this reason, it is indispensable to develop new perspectives on evaluating compound flooding dependence and communicating the associated risks to meet UN Sustainable Development Goals (SDGs) related to climate action, sustainable cities, and sustainable coastal communities. An indispensable first step for studies examining the dependence between these bivariate extremes is plotting the data in the variable space, i.e., visualizing a scatterplot, where each axis represents a variable of interest, then computing a form of correlation between them. This paper introduces the Angles method, based on Euclidean geometry of the so-called “subject space,” for visualizing the dependence structure of compound flooding drivers. Here, we evaluate, for the first time, the utility of this geometric space in computing and visualizing the dependence structure of compound flooding drivers. To assess the effectiveness of this method as a risk communication tool, we conducted a survey with a diverse group of end-users, including academic and non-academic respondents. The survey results provide insights into the perceptions of applicability of the Angles method and highlight its potential as an intuitive alternative to scatterplots in depicting the evolution of dependence in the non-stationary environment. This study emphasizes the importance of innovative visualization techniques in bridging the gap between scientific insights and practical applications, supporting more effective compound flood hazard communication in a warming climate. 

Abstract. Compound flooding, caused by the sequence and/or co-occurrence of flood drivers (i.e., river discharge and elevated sea level), can lead to devastating consequences for society. Weak and insufficient progress toward sustainable development and disaster risk reduction is likely to exacerbate the catastrophic impacts of these events on vulnerable communities. For this reason, it is indispensable to develop new perspectives on evaluating compound-flooding dependence and communicating the associated hazards to meet UN Sustainable Development Goals (SDGs) related to climate action, sustainable cities, and sustainable coastal communities. The first step in examining bivariate dependence is to plot the data in the variable space, i.e., visualizing a scatterplot, where each axis represents a variable of interest, and then computing a form of correlation between them. This paper introduces the Angles method, based on Euclidean geometry of the so-called “subject space”, as a complementary visualization approach specifically designed for communicating the dependence structure of compound-flooding drivers to diverse end-users. Here, we evaluate, for the first time, the utility of this geometric space in computing and visualizing the dependence structure of compound-flooding drivers. To assess the effectiveness of this method as a hazard communication tool, we conducted a survey with a diverse group of end-users, including academic and non-academic respondents. The survey results provide insights into the perceptions regarding the applicability of the Angles method and highlight its potential as an intuitive alternative to scatterplots in depicting the evolution of dependence in the non-stationary environment. This study emphasizes the importance of innovative visualization techniques in bridging the gap between scientific insights and practical applications, supporting more effective compound flood hazard communication. 

As coastal regions face escalating risks from flooding in a changing climate, Nature-based Solutions (NbS) have garnered attention as promising adaptation measures to mitigate the destructive impacts of coastal flooding. However, the challenge of compound flooding, which involves the combined effects of multiple flood drivers, demands a deeper understanding of the efficacy of NbS against this complex phenomenon. This manuscript reviews the literature on process-based modeling of NbS for mitigating compound coastal flooding and identifies knowledge gaps to enhance future research efforts. We used an automated search strategy within the SCOPUS database, followed by a screening process that ultimately resulted in 141 publications assessing the functionality of NbS against coastal flooding. Our review identified a dearth of research (9 %) investigating the performance of NbS against compound flooding scenarios. We examined the challenges and complexities involved in modeling such scenarios, including hydrologic, hydrodynamic, and ecological feedback processes by exploring the studies that used a process-based modeling framework. Key research gaps were identified, such as navigating the complex environment, managing computational costs, and addressing the shortages of experts and data. We outlined potential modeling pathways to improve NbS characterization in the compound flooding framework. Additionally, uncertainties associated with numerical modeling and steps to bridge the research-to-operation gaps were briefly discussed, highlighting the bottlenecks in operational implementation. 

Abstract Prediction of the rapid intensification (RI) of tropical cyclones (TCs) is crucial for improving disaster preparedness against storm hazards. These events can cause extensive damage to coastal areas if occurring close to landfall. Available models struggle to provide accurate RI estimates due to the complexity of underlying physical mechanisms. This study provides new insights into the prediction of a subset of rapidly intensifying TCs influenced by prolonged ocean warming events known as marine heatwaves (MHWs). MHWs could provide sufficient energy to supercharge TCs. Preconditioning by MHW led to RI of recent destructive TCs, Otis (2023), Doksuri (2023), and Ian (2022), with economic losses exceeding $150 billion. Here, we analyze the TC best track and sea surface temperature data from 1981 to 2023 to identify hotspot regions for compound events, where MHWs and RI of tropical cyclones occur concurrently or in succession. Building upon this, we propose an ensemble machine learning model for RI forecasting based on storm and MHW characteristics. This approach is particularly valuable as RI forecast errors are typically largest in favorable environments, such as those created by MHWs. Our study offers insight into predicting MHW TCs, which have been shown to be stronger TCs with potentially higher destructive power. Here, we show that using MHW predictors instead of the conventional method of using sea surface temperature reduces the false alarm rate by 30%. Overall, our findings contribute to coastal hazard risk awareness amidst unprecedented climate warming causing more frequent MHWs.