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Abstract Circulation in the Gulf of Mexico is dominated by the Loop Current and associated mesoscale eddies. These mesoscale eddies pose a safety risk to offshore energy production and potential dispersal of large-scale pollutants like oil. We use a data-driven, physics-informed, and numerically consistent deep learning–based ocean emulator called OceanNet to generate a 120-day forecast of the sea surface height (SSH) in the eastern Gulf of Mexico. OceanNet uses a new dataset of high-resolution data assimilative ocean reanalysis (1993–2022) as input. This model is trained using years 1993–2018 and evaluated on four eddies during years 2019–21. For comparison, we use a state-of-the-art numerical ocean model to generate a dynamical model prediction initialized every 5 days from 27 April 2019 to 1 April 2020 (during eddies Sverdrup and Thor) using persistent forcing and boundary conditions. The dynamical model takes seven wall-clock days to run, whereas OceanNet runs in minutes. Edges of Loop Current eddies (LCEs) pose the most potent risk to offshore energy operations and pollutant dispersal due to strong water velocities. Therefore, most of the analysis focuses on edge accuracy, quantified by the modified Hausdorff distance. The edge of the LCEs is defined by the 17-cm sea surface height contour, which generally coincides with the strongest water velocity. The OceanNet prediction outperforms both persistence and the dynamical model prediction. Overall, this new ocean emulator provides a promising new approach to generate seasonal forecasts of LCEs and generates large model ensembles efficiently to quantify forecast uncertainty that is long needed by scientists and decision-makers for offshore operations. Significance StatementCirculation in the Gulf of Mexico (GoM) is dominated by the energetic Loop Current and associated mesoscale eddies (typically 150–400 km in diameter). As these eddies propagate westward through the Gulf, they pose a safety risk to offshore energy production and potential large-scale pollutant dispersal. We used ocean model output (1993–2022) to train a data-driven ocean emulator called OceanNet that generates a seasonal (up to 120 day) prediction of sea surface height (SSH) in the eastern GoM. For comparison, a simple dynamical model prediction is also evaluated. OceanNet’s performance is assessed with a focus on edge accuracy, the most potent risk to offshore energy operations and pollutant dispersal. Overall, OceanNet performs well for a seasonal forecast and shows great potential for further development. 

Abstract Studies suggest a strong link between low‐frequency sea level variability in the South Atlantic Bight (SAB) and open ocean dynamics. However, the mechanisms driving this connection remain unclear. By analyzing a high‐resolution, three‐dimensional baroclinic ocean reanalysis, we identify a pathway that links open ocean dynamics to SAB coastal sea level variability through the shelf edge near Cape Hatteras. Gulf Stream meanders in this region induce sea level fluctuations that propagate along the entire SAB shelf. Using an idealized barotropic model, we further demonstrate that topographic waves mediate the propagation of the Gulf Stream signal onto the shelf. Moreover, the Gulf Stream variability is driven by zonal wind stress in the Northwest Atlantic, which is likely modulated by the North Atlantic Oscillation. These findings offer new insights into regional sea level prediction and contribute to broader climate research efforts. 

Abstract Building upon recent advancements in AI‐driven atmospheric emulation, we present a novel framework for AI‐based ocean emulation, downscaling, and bias correction, with a specific focus on high‐resolution modeling of the regional ocean in the Gulf of Mexico. Emulating regional ocean dynamics poses distinct challenges due to intricate bathymetry, complex lateral boundary conditions, and inherent limitations of deep learning models, including instability and the potential for hallucinations. In this study, we introduce a deep learning framework that autoregressively integrates ocean surface variables at 8 km spatial resolution over the Gulf of Mexico, maintaining physical consistency over decadal time scales. Simultaneously, the framework downscales and bias‐corrects the outputs to 4 km resolution using a physics‐informed generative model. Our approach demonstrates short‐term predictive skill comparable to high‐resolution physics‐based simulations, while also accurately capturing long‐term statistical properties, including temporal mean and variability. 

Abstract This study introduces an ensemble learning model for the prediction of significant wave height and average wave period in stations along the U.S. Atlantic coast. The model utilizes the stacking method, combining three base learner models - Lasso regression, support vector machine, and Multi-layer Perceptron - to achieve more precise and robust predictions. To train and evaluate the models, a twenty-year dataset comprising meteorological and wave data was used, enabling forecasts for significant wave height and average wave period at 1, 3, 6, and 12 hour intervals. The data collection involved two NOAA buoy stations situated on the U.S. Atlantic coast. The findings demonstrate that the ensemble learning model constructed through the stacking method yields significantly higher accuracy in predicting significant wave height within the specified time intervals. Moreover, the study investigates the influence of swell waves on forecasting significant wave height and average wave period. Notably, the inclusion of swell waves improves the accuracy of the 12-hour forecast. Consequently, the developed ensemble model effectively estimates both significant wave height and average wave period. The ensemble model outperforms the individual models in forecasting significant wave height and average wave period. This ensemble learning model serves as a viable alternative to conventional coastal models for predicting wave parameters. 

Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention. Existing forecasting approaches, whether physics-based or data-driven, often emphasize temporal patterns while underexploring how multiple interacting factors jointly shape flood dynamics. To address this problem, we conduct a large-scale data-driven analysis of compound flooding in South Florida, a typical area for compound flooding, by integrating tidal conditions, rainfall, groundwater stage, and human water management activities. Our analysis reveals three key findings: (i) models that capture temporal dynamics alone fail to represent multi-factor interactions during compound events; (ii) subsurface saturation, as reflected by groundwater levels, emerges as a dominant predictor of flood severity, often outweighing immediate rainfall intensity in this porous coastal region; and (iii) the spatial state of surrounding monitoring stations within a finite effective radius provides critical causal context for flooding, while extending temporal history yields diminishing returns during extreme events. These findings suggest that compound flooding is governed more by spatially coupled system states than by long-term temporal dependencies, challenging rain-centric and sequence-dominated forecasting paradigms. By framing data-driven models as tools for scientific inquiry rather than prediction alone, this study offers new insights into the mechanisms of compound flooding and informs the design of more physically grounded early-warning systems for coastal environments.