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Abstract Tropical cyclone (TC) hazards coupled with dense urban development along the coastline have resulted in trillions in US damages over the past several decades, with an increasing trend in losses in recent years. So far, this trend has been driven by increasing coastal development. However, as the climate continues to warm, changing TC climatology may also cause large changes in coastal damages in the future. Approaches to quantifying regional TC risk typically focus on total storm damage. However, it is crucial to understand the spatial footprint of TC damage and ultimately the spatial distribution of TC risk. Here, we quantify the magnitude and spatial pattern of TC risk (in expected annual damage) across the US from wind, storm surge, and rainfall using synthetic TCs, physics-based hazard models, and a county-level statistical damage model trained on historical TC data. We then combine end-of-century TC hazard simulations with US population growth and wealth increase scenarios (under the SSP2 4.5 emission scenario) to investigate the sensitivity of changes in TC risk across the US Atlantic and Gulf coasts. We find that not directly accounting for the effects of rainfall and storm surge results in much lower risk estimates and smaller future increases in risk. TC climatology change and socioeconomic change drive similar magnitude increases in total expected annual damage across the US (roughly 160%), and that their combined effect (633% increase) is much higher. 

Sediment cores from blue holes have emerged as a promising tool for extending the record of long‐term tropical cyclone (TC) activity. However, interpreting this archive is challenging because storm surge depends on many parameters including TC intensity, track, and size. In this study, we use climatological‐hydrodynamic modeling to interpret paleohurricane sediment records between 1851 and 2016 and assess the storm surge risk for Long Island in The Bahamas. As the historical TC data from 1988 to 2016 is too limited to estimate the surge risk for this area, we use historical event attribution in paleorecords paired with synthetic storm modeling to estimate TC parameters that are often lacking in earlier historical records (i.e., the radius of maximum wind for storms before 1988). We then reconstruct storm surges at the sediment site for a longer time period of 1851–2016 (the extent of hurricane Best Track records). The reconstructed surges are used to verify and bias‐correct the climatological‐hydrodynamic modeling results. The analysis reveals a significant risk for Long Island in The Bahamas, with an estimated 500‐year stormtide of around 1.63 ± 0.26 m, slightly exceeding the largest recorded level at site between 1988 and 2015. Finally, we apply the bias‐corrected climatological‐hydrodynamic modeling to quantify the surge risk under two carbon emission scenarios. Due to sea level rise and TC climatology change, the 500‐year stormtide would become 2.69 ± 0.50 and 3.29 ± 0.82 m for SSP2‐4.5 and SSP5‐8.5, respectively by the end of the 21st century. 

Abstract Extreme winds associated with tropical cyclones (TCs) can cause significant loss of life and economic damage globally, highlighting the need for accurate, high‐resolution modeling and forecasting for wind. However, due to their coarse horizontal resolution, most global climate and weather models suffer from chronic underprediction of TC wind speeds, limiting their use for impact analysis and energy modeling. In this study, we introduce a cascading deep learning framework designed to downscale high‐resolution TC wind fields given low‐resolution data. Our approach maps 85 TC events from ERA5 data (0.25° resolution) to high‐resolution (0.05° resolution) observations at 6‐hr intervals. The initial component is a debiasing neural network designed to model accurate wind speed observations using ERA5 data. The second component employs a generative super‐resolution strategy based on a conditional denoising diffusion probabilistic model (DDPM) to enhance the spatial resolution and to produce ensemble estimates. The model is able to accurately model intensity and produce realistic radial profiles and fine‐scale spatial structures of wind fields, with a percentage mean bias of −3.74% compared to the high‐resolution observations. Our downscaling framework enables the prediction of high‐resolution wind fields using widely available low‐resolution and intensity wind data, allowing for the modeling of past events and the assessment of future TC risks. 

Abstract Two tropical cyclones (TCs) that make landfall close together can induce sequential hazards to coastal areas. Here we investigate the change in sequential TC hazards in the historical and future projected climates. We find that the chance of sequential TC hazards has been increasing over the past several decades at many US locations. Under the high (moderate) emission scenario, the chance of hazards from two TCs impacting the same location within 15 days may substantially increase, with the return period decreasing over the century from 10–92 years to ~1–2 (1–3) years along the US East and Gulf coasts, due to sea-level rise and storm climatology change. Climate change can also cause unprecedented compounding of extreme hazards at the regional level. A Katrina-like TC and a Harvey-like TC impacting the United States within 15 days of each other, which is non-existent in the control simulation for over 1,000 years, is projected to have an annual occurrence probability of more than 1% by the end of the century under the high emission scenario. 

Abstract Tropical cyclones (TCs) are drivers of extreme rainfall and surge, but the current and future TC rainfall–surge joint hazard has not been well quantified. Using a physics-based approach to simulate TC rainfall and storm tides, we show drastic increases in the joint hazard from historical to projected future (SSP5–8.5) conditions. The frequency of joint extreme events (exceeding both hazards’ historical 100-year levels) may increase by 7–36-fold in the southern US and 30–195-fold in the Northeast by 2100. This increase in joint hazard is induced by sea-level rise and TC climatology change; the relative contribution of TC climatology change is higher than that of sea-level rise for 96% of the coast, largely due to rainfall increases. Increasing storm intensity and decreasing translation speed are the main TC change factors that cause higher rainfall and storm tides and up to 25% increase in their dependence. 

Abstract Tropical cyclones (TCs) that undergo rapid intensification (RI) before landfall are notoriously difficult to predict and have caused tremendous damage to coastal regions in the United States. Using downscaled synthetic TCs and physics‐based models for storm tide and rain, we investigate the hazards posed by TCs that rapidly intensify before landfall under both historical and future mid‐emissions climate scenarios. In the downscaled synthetic data, the percentage of TCs experiencing RI is estimated to rise across a significant portion of the North Atlantic basin. Notably, future climate warming causes large increases in the probability of RI within 24 hr of landfall. Also, our analysis shows that RI events induce notably higher rainfall hazard levels than non‐RI events with equivalent TC intensities. As a result, RI events dominate increases in 100‐year rainfall and storm tide levels under climate change for most of the US coastline. 

Abstract Accurate delineation of compound flood hazard requires joint simulation of rainfall‐runoff and storm surges within high‐resolution flood models, which may be computationally expensive. There is a need for supplementing physical models with efficient, probabilistic methodologies for compound flood hazard assessment that can be applied under a range of climate and environment conditions. Here we propose an extension to the joint probability optimal sampling method (JPM‐OS), which has been widely used for storm surge assessment, and apply it for rainfall‐surge compound hazard assessment under climate change at the catchment‐scale. We utilize thousands of synthetic tropical cyclones (TCs) and physics‐based models to characterize storm surge and rainfall hazards at the coast. Then we implement a Bayesian quadrature optimization approach (JPM‐OS‐BQ) to select a small number (∼100) of storms, which are simulated within a high‐resolution flood model to characterize the compound flood hazard. We show that the limited JPM‐OS‐BQ simulations can capture historical flood return levels within 0.25 m compared to a high‐fidelity Monte Carlo approach. We find that the combined impact of 2100 sea‐level rise (SLR) and TC climatology changes on flood hazard change in the Cape Fear Estuary, NC will increase the 100‐year flood extent by 27% and increase inundation volume by 62%. Moreover, we show that probabilistic incorporation of SLR in the JPM‐OS‐BQ framework leads to different 100‐year flood maps compared to using a single mean SLR projection. Our framework can be applied to catchments across the United States Atlantic and Gulf coasts under a variety of climate and environment scenarios. 

Abstract The hazards induced by tropical cyclones (TCs), for example, high winds, extreme precipitation, and storm tides, are closely related to the TC surface wind field. Parametric models for TC surface wind distribution have been widely used for hazards and risk analysis due to their simplicity and efficiency in application. Here we revisit the parametric modeling of TC wind fields, including the symmetrical and asymmetrical components, and its applications in storm tide modeling in the North Atlantic. The asymmetrical wind field has been related to TC motion and vertical wind shear; however, we find that a simple and empirical background‐wind model, based solely on a rotation and scaling of the TC motion vector, can largely capture the observed surface wind asymmetry. The implicit inclusion of the wind shear effect can be understood with the climatological relationship between the general TC motion and wind shear directions during hurricane seasons. For the symmetric wind field, the widely used Holland wind profile is chosen as a benchmark model, and we find that a physics‐based complete wind profile model connecting the inner core and outer region performs superiorly compared to a wind analysis data set. When used as wind forcing for storm tide simulations, the physics‐based complete wind profile integrated with the background‐wind asymmetry model can reproduce the observed storm tides with lower errors than the often‐used Holland model coupled with a translation‐speed‐based method. 

Abstract Tropical cyclones (TCs) are one of the greatest threats to coastal communities along the US Atlantic and Gulf coasts due to their extreme wind, rainfall and storm surge. Analyzing historical TC climatology and modeling TC hazards can provide valuable insight to planners and decision makers. However, detailed TC size information is typically only available from 1988 onward, preventing accurate wind, rainfall, and storm surge modeling for TCs occurring earlier in the historical record. To overcome temporally limited TC size data, we develop a database of size estimates that are based on reanalysis data and a physics‐based model. Specifically, we utilize ERA5 reanalysis data to estimate the TC outer size, and a physics‐based TC wind model to estimate the radius of maximum wind. We evaluate our TC size estimates using two high‐resolution wind data sets as well as Best Track information for a wide variety of TCs. Using the estimated size information plus the TC track and intensity, we reconstruct historical storm tides from 1950 to 2020 using a basin‐scale hydrodynamic model and show that our reconstructions agree well with observed peak storm tide and storm surge. Finally, we demonstrate that incorporating an expanded set of historical modeled storm tides beginning in 1950 can enhance our understanding of US coastal hazard. Our newly developed database of TC sizes and associated storm tides/surges can aid in understanding North Atlantic TC climatology and modeling TC wind, storm surge, and rainfall hazard along the US Atlantic and Gulf coasts.