Mesoscale and submesoscale features play a critical role in transporting heat and biogeochemical tracers from the surface ocean to depths below the mixed layer, by driving vertical motions across density gradients. In the winter of 2022, strong mesoscale and submesoscale features were observed in the Western Mediterranean Sea during the ONR CALYPSO oceanographic campaign. This multidisciplinary experiment combined multiplatform in-situ observations with high-resolution numerical simulations to observe and predict small-scale ocean variability. In particular, a mesoscale density front associated with a vortex dipole was observed using CALYPSO observations and satellite imagery. A 650m resolution model simulation is used here to understand the evolution of the front and the energy transfer to submesoscale cyclonic eddies. The simulation properly reproduces the intense, narrow, and elongated frontal convergence structure forming a dense cyclonic ridge linked to the vortex dipole. The evolution of the front is characterized by: i) an intensification through frontogenesis, and ii) a decay due to favorable conditions for overturning instabilities during a down-front wind event. These processes enhance vertical motion via an across-front ageostrophic secondary circulation and contribute to the restratifying effect. After a few days, the front decayed and cascaded into smaller-scale structures, forming submesoscale cyclonic eddies (SCEs) at the edges of the front. The formation of SCEs is associated with the frontal decay, as well as centrifugal and gravitational instabilities, which transfer energy from the mesoscale front to the SCEs. The SCE structure reveals a 3D helical-spiral recirculation pattern that transports parcels vertically. Observations of oxygen and chlorophyll confirm the enhancement of the vertical transport of tracers from the surface to the ocean interior. Submesoscale eddy-induced frontogenesis mechanism and instability processes drove subduction along outcropping isopycnals at the periphery of the SCE.

The Regeneron Science Talent Search (STS), the nation’s oldest and most prestigious science and mathematics competition for high school seniors, named Jason Youm, a high school senior who joined MSEAS during summer 2024 as an RSI scholar, among the top 300 scholars out of nearly 2,500 in the competition. Each scholar will receive $2,000, and their schools will also receive $2,000 to use toward STEM-related activities. Jason’s research with MSEAS was on “MSEAS-ParEq for Ocean Acoustic Modeling in the New England Seamount,” and was advised by Marcoul Robin. His Regeneron STS project title was “Average Rényi Entanglement Entropy in Gaussian Boson Sampling.”

This is an extraordinary accomplishment deserving of much celebration, so congrats Jason!

Congratulations to Dr. Bastien Schnitzler on his graduation! Bastien, a visiting student at MSEAS during spring/summer 2024, successfully defended and received his PhD from the National School of Aeronautics and Space (ISAE) and University of Toulouse for his research on “Trajectory Optimization for Long-Range Light Vehicles in Unsteady Flow Fields with Obstacles, Diffuse Hazard and Uncertainty.” We wish all the best to Bastien on plotting his future, hazard-free path!

Machine learning techniques are applied to Lagrangian trajectory reconstructions, which are important in oceanography for providing guidance to search and rescue efforts, forecasting the spread of harmful algal blooms, and tracking pollutants and marine debris. This study evaluates the ability of two types of neural networks for learning ocean trajectories from nearly 250 surface drifters released during the Grand Lagrangian Deployment in the Gulf of Mexico from Jul-Oct 2012. First, simple fully connected neural networks were trained to predict an individual drifter’s trajectory over 24 h and 5 d time windows using only that drifter’s previous velocity time series. These networks, despite having successfully learned modeled trajectories in a previous study, failed to outperform common autoregressive models in any of the tests conducted. This was true even when drifters were pre-sorted into geospatial groups based on past trajectories and different networks were trained on each group to reduce the variability that each network had to learn. In contrast, a more sophisticated social spatio-temporal graph convolutional neural network (STN), originally developed for learning pedestrian trajectories, demonstrated greater potential due to two important features: learning spatial and temporal patterns simultaneously, and sharing information between similarly-behaving drifters to facilitate the prediction of any particular drifter. Position prediction errors averaged around 60 km at day 5, roughly 20 km lower than autoregression, and even better for certain subsets of drifters. The passage of Tropical Cyclone Isaac over the drifter array as a tropical storm and Category 1 hurricane provided a unique opportunity to also explore whether these models would benefit from adding wind as a predictor when making short 24 h predictions. The STNs were found to not benefit from wind on average, though certain subsets of drifters exhibited slightly lower reconstruction errors at hour 24 with the addition of wind.