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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. 

As the global impact of climate change intensifies, there is an urgent need for equitable and efficient climate adaptation policies. Traditional approaches for allocating public resources for climate adaptation that are based on economic benefit-cost analysis often overlook the resulting distributional inequalities. In this study, we apply equity weightings to mitigate the distributional inequalities in two key building and household level adaptation strategies under changing coastal flood hazards: property buyouts and building retrofit in New York City (NYC). Under a mid-range emissions scenario, we find that unweighted benefit cost ratios applied to residential buildings are higher for richer and non-disadvantaged census tracts in NYC. The integration of income-based equity weights alters this correlation effect, which has the potential to shift investment in mitigation towards poorer and disadvantaged census tracts. This alteration is sensitive to the value of elasticity of marginal utility, the key parameter used to calculate the equity weight. Higher values of elasticity of marginal utility increase benefits for disadvantaged communities but reduce the overall economic benefits from investments, highlighting the trade-offs in incorporating equity into adaptation planning. 

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 This study investigates the occurrence of the Weddell Sea polynya under an idealized climate change scenario by evaluating simulations from climate models of different ocean resolutions. The GFDL-CM2.6 climate model, with roughly 3.8-km horizontal ocean grid spacing in the high latitudes, forms a Weddell Sea polynya at similar time and duration under idealized climate change forcing as under preindustrial forcing. In contrast, all convective models forming phase 5 of the Coupled Model Intercomparison Project (CMIP5) show either a cessation or a slowdown of Weddell Sea polynya events under climate warming. The representation of the Antarctic Slope Current and related Antarctic Slope Front is found to be key in explaining the differences between the two categories of models, with these features being more realistic in CM2.6 than in CMIP5. In CM2.6, the freshwater input driven by sea ice melt and enhanced runoff found under climate warming largely remains on the shelf region since the slope front restricts the lateral spread of the freshwater. In contrast, for most CMIP5 models, open-ocean stratification is enhanced by freshening since the absence of a slope front allows coastal freshwater anomalies to spread into the open ocean. This enhanced freshening contributes to the slowdown the occurrence of Weddell Sea polynyas. Hence, an improved representation of Weddell Sea shelf processes in current climate models is desirable to increase our ability to predict the fate of the Weddell Sea polynyas under climate change.