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1. The Coastal Sea‐Level Response to Wind Stress in the Middle Atlantic Bight

   https://doi.org/10.1029/2024JC021269  

   Lentz, Steven J (August 2024, Journal of Geophysical Research: Oceans)

   Abstract Analysis of 40 years of tide gauge data and reanalysis wind stresses from the Middle Atlantic Bight (MAB) indicate that along‐shelf wind stresses are a dominant driver of coastal dynamic sea level (sea level plus atmospheric pressure) variability at daily to yearly time scales. The sea‐level response to along‐shelf wind stress varies substantially along the coast and is accurately reproduced by a steady, barotropic, depth‐averaged model (Csanady, 1978,https://doi.org/10.1175/1520‐0485(1978)008<0047:tatw>2.0.co;2, Arrested Topographic Wave). The model indicates that the sea‐level response in the MAB depends primarily on the along‐shelf distribution of the along‐shelf wind stress, the Coriolis frequency, the bottom drag coefficient, and the cross‐shelf bottom slope. The along‐shelf wind stress varies along the MAB shelf due primarily to changes in the shelf orientation. The sea‐level response depends on both the local and upstream (in the sense of Kelvin wave propagation) along‐shelf wind stresses. Consequently, sea‐level variability at daily, monthly and yearly time scales along much of the central MAB coast is more strongly driven by upstream winds along the southern New England shelf than by local winds along the central MAB shelf. The residual coastal sea‐level variability, after removing the wind‐driven response and the trend, is roughly uniform along the MAB coast. The along‐coast average of the residual sea level at monthly and yearly time scales is caused by variations in shelf water densities primarily associated with the large annual cycle in water temperature and interannual variations in salinity. 

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2. Turbulent Thermal-Wind-Driven Coastal Upwelling: Current Observations and Dynamics

   https://doi.org/10.1175/JPO-D-22-0063.1  

   Lentz, S. J. (December 2022, Journal of Physical Oceanography)

   Abstract A remarkably consistent Lagrangian upwelling circulation at monthly and longer time scales is observed in a 17-yr time series of current profiles in 12 m of water on the southern New England inner shelf. The upwelling circulation is strongest in summer, with a current magnitude of ∼1 cm s −1 , which flushes the inner shelf in ∼2.5 days. The average winter upwelling circulation is about one-half of the average summer upwelling circulation, but with larger month-to-month variations driven, in part, by cross-shelf wind stresses. The persistent upwelling circulation is not wind-driven; it is driven by a cross-shelf buoyancy force associated with less-dense water near the coast. The cross-shelf density gradient is primarily due to temperature in summer, when strong surface heating warms shallower nearshore water more than deeper offshore water, and to salinity in winter, caused by fresher water near the coast. In the absence of turbulent stresses, the cross-shelf density gradient would be in a geostrophic, thermal-wind balance with the vertical shear in the along-shelf current. However, turbulent stresses over the inner shelf attributable to strong tidal currents and wind stress cause a partial breakdown of the thermal-wind balance that releases the buoyancy force, which drives the observed upwelling circulation. The presence of a cross-shelf density gradient has a profound impact on exchange across this inner shelf. Many inner shelves are characterized by turbulent stresses and cross-shelf density gradients with lighter water near the coast, suggesting turbulent thermal-wind-driven coastal upwelling may be a broadly important cross-shelf exchange mechanism. Significance Statement A remarkably consistent upwelling circulation at monthly time scales is observed in a 17-yr time series of current profiles in shallow water off southern New England. This is not the traditional wind-driven coastal upwelling; instead, it is forced by cross-shelf buoyancy (density) gradients, released by turbulent stresses in shallow water. The persistent upwelling circulation is strongest in summer, when wind and wave forcing are weak, and flushes the inner portion of the continental shelf in a few days. Consequently, this buoyancy-driven coastal upwelling is important for cooling the inner shelf and provides a reliable mechanism for cross-shelf exchange. Many inner shelves are characterized by cross-shelf density gradients and turbulent stresses, suggesting this may be a broadly important cross-shelf exchange mechanism. 

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3. Interannual and Seasonal Along-Shelf Current Variability and Dynamics: Seventeen Years of Observations from the Southern New England Inner Shelf

   https://doi.org/10.1175/JPO-D-22-0064.1  

   Lentz, Steven J. (December 2022, Journal of Physical Oceanography)

   Abstract The characteristics and dynamics of depth-average along-shelf currents at monthly and longer time scales are examined using 17 years of observations from the Martha’s Vineyard Coastal Observatory on the southern New England inner shelf. Monthly averages of the depth-averaged along-shelf current are almost always westward, with the largest interannual variability in winter. There is a consistent annual cycle with westward currents of 5 cm s −1 in summer decreasing to 1–2 cm s −1 in winter. Both the annual cycle and interannual variability in the depth-average along-shelf current are predominantly driven by the along-shelf wind stress. In the absence of wind forcing, there is a westward flow of ∼5 cm s −1 throughout the year. At monthly time scales, the depth-average along-shelf momentum balance is primarily between the wind stress, surface gravity wave–enhanced bottom stress, and an opposing pressure gradient that sets up along the southern New England shelf in response to the wind. Surface gravity wave enhancement of bottom stress is substantial over the inner shelf and is essential to accurately estimating the bottom stress variation across the inner shelf. Significance Statement Seventeen years of observations from the Martha’s Vineyard Coastal Observatory on the inner continental shelf of southern New England reveal that the depth-average along-shelf current is almost always westward and stronger in summer than in winter. Both the annual cycle and variations around the annual cycle are primarily driven by the along-shelf wind stress. The wind stress is opposed by a pressure gradient that sets up along the southern New England shelf and a surface gravity wave–enhanced bottom stress. The surface gravity wave enhancement of bottom stress is substantial in less than 30 m of water and is essential in determining the variation of the along-shelf current across the inner shelf. 

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4. Observations of the Subsurface Structure of TKE Dissipation Rates Near the Martha's Vineyard Coastal Observatory

   https://doi.org/10.1029/2025JC023220  

   Cifuentes‐Lorenzen, A; O’Donnell, J; Zappa, C J; Ullman, D; Shin, Y; Hogan, L (May 2026, Journal of Geophysical Research: Oceans)

   Abstract We present observations of subsurface turbulent kinetic energy (TKE) dissipation rates under moderate‐to‐strong wind forcing on the western North Atlantic shelf. Estimates derived using the structure function method show a clear enhancement relative to law‐of‐the‐wall expectations in the upper water column, with convergence toward rigid‐wall scaling at greater depths. This near‐surface enhancement is consistent with wind energy input into the wave field, suggesting that wave breaking is the primary driver. The data reveal a two‐layer structure: an approximately constant TKE dissipation rate near surface, followed by a region where dissipation decays with depth as . This profile aligns with previous observations by Terray et al. (1996),https://doi.org/10.1175/1520‐0485(1996)026<0792:EOKEDU>2.0.CO;2and Sutherland and Melville (2015),https://doi.org/10.1175/jpo‐d‐14‐0133.1. The constant dissipation layer is confined to the upper portion of the water column, extending to depths less than approximately one‐third of the significant wave height. Below this layer, dissipation remains elevated relative to background levels, with the enhancement extending to depths of 5–6 times the significant wave height. We propose that this deeper extent defines the wave‐affected layer driven by breaking and transport processes, while the approximately constant dissipation region marks the breaking layer dynamically constrained by wave breaking. Therefore, under open ocean conditions, the temporal and spatial scales of the breaking layer should be representative of the higher‐frequency waves that dominate wave breaking. 

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5. Using Explainable AI and Transfer Learning to Understand and Predict the Maintenance of Atlantic Blocking With Limited Observational Data

   https://doi.org/10.1029/2024JH000243  

   Zhang, Huan; Finkel, Justin; Abbot, Dorian S; Gerber, Edwin P; Weare, Jonathan (December 2024, Journal of Geophysical Research: Machine Learning and Computation)

   Abstract Blocking events are an important cause of extreme weather, especially long‐lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and diagnose model deficiencies. We demonstrate this approach on an idealized quasigeostrophic (QG) model developed by Marshall and Molteni (1993),https://doi.org/10.1175/1520‐0469(1993)050<1792:taduop>2.0.co;2. We train a convolutional neural network (CNN), and subsequently, build a sparse predictive model for the persistence of Atlantic blocking, conditioned on an initial high‐pressure anomaly. Shapley Additive ExPlanation (SHAP) analysis reveals that high‐pressure anomalies in the American Southeast and North Atlantic, separated by a trough over Atlantic Canada, contribute significantly to prediction of sustained blocking events in the Atlantic region. This agrees with previous work that identified precursors in the same regions via wave train analysis. When we apply the same CNN to blockings in the ERA5 atmospheric reanalysis, there is insufficient data to accurately predict persistent blocks. We partially overcome this limitation by pre‐training the CNN on the plentiful data of the Marshall‐Molteni model, and then using Transfer learning (TL) to achieve better predictions than direct training. SHAP analysis before and after TL allows a comparison between the predictive features in the reanalysis and the QG model, quantifying dynamical biases in the idealized model. This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data. 

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