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The ocean circulation is modulated by meandering currents and eddies. Forecasting their evolution is a key target of operational models, but their forecast skill remains limited. We propose a machine learning approach that improves the output of an ocean circulation model by learning and predicting its systematic biases. This method can be applied a priori to any region, and is tested in the Gulf of Mexico, where the Loop Current (LC) and the large anticyclonic eddies that detach from it are major forecasting targets. The LC dynamics are recurrent and lie on a low‐dimensional dynamical attractor. Building upon the information gained analyzing this low dimensional attractor, we improve the representation of sea surface anomalies in model outputs through information from satellite altimeter data using a Sequence‐to‐Sequence model, which is a special class of Recurrent Neural Network. Building upon the HYCOM‐NCODA analysis system, we deliver a correction to the forecast at the observation resolution. For at least 15 days the proposed method learns to forecast the systematic bias in the HYCOM‐NCODA, outperforming persistence, and improving the forecast. This data‐driven approach is fast and can be implemented as an added step to any dynamical hindcasting or forecasting model. It offers an interesting avenue for further developing hybrid modeling tools. In these tools, fundamental physical conservations are preserved through the integration of partial differential equations which obey them. In addition, the method highlights specific deficiencies of the hindcast system that deserve further investigation in the future. Plain Language Summary: Predicting the evolution of ocean circulation using a physically constrained models is critical for weather forecasting and for quantifying nutrient transport and water mass exchanges, but models are far from perfect. We propose a generalized data‐driven method to improve a circulation model performance by identifying and predicting its systematic biases, and we test it in the Gulf of Mexico. We use a recurrent neural network to improve and predict the evolution of sea surface anomalies over 7–15 days using information obtained from satellite altimetry data. The results show that our proposed data‐driven method is fast and accurate and can be easily implemented as an additional step to other dynamic models, providing an interesting avenue for further development of hybrid forecasting tools. Key Points: Machine learning tools are used to improve model predictive skill using observationally based data setsOur method has great potential to extend the current prediction length from about 7 days to 11–15 days without significantly compromising accuracy [ABSTRACT FROM AUTHOR]

The potential predictability of fields at the ocean surface in the northern Gulf of Mexico (GoM) is investigated through five ensembles of regional ocean simulations between 2014 and 2016. The ensembles explore two horizontal resolutions and different representations of the riverine inflow, and focus on the interactions between the Loop Current system (LCS) and the riverine system. The potential predictability of the surface fields is high when simulated by an ocean‐only model forced by appropriate atmospheric forcing and boundary conditions, and the ensembles simulate similar LCS behavior up to 5 months. The ensemble spread provides a mean to quantify the potential predictability. The ensembles confirm that LCS‐riverine interactions are modulated by the LC mesoscale variability. The relationship is two‐ways, with the LCS being influenced by—And not only influencing—The freshwater plume. Whenever the freshwater flux is strong, the northward extension of the LCS is constrained by the intensified salinity fronts. This influence is slightly stronger if the riverine inflow is simulated in an active fashion with a meridional velocity component proportional to the flux. Sea surface temperature (SST) and salinity (SSS) predictability have opposite seasonality in their signal, with the SST (SSS) being more predictable in summer (winter). Partially resolving submesoscale instabilities and improving the realism of the riverine fluxes' representation causes the spread to increase, especially in SST. When increasing resolution, the spread increases also for surface vorticity due to feedbacks between the mesoscale and submesoscale circulations. The intraseasonal and interannual signal in vorticity is however similar among ensembles. Plain Language Summary: In this work we ask the questions: How predictable may be the circulation and the sea surface fields in the northern Gulf of Mexico (GoM), and what is the role of the riverine input? Under the assumption of a nearly perfect model and known atmospheric forcing and oceanic boundary fields, we simulate the GoM circulation through five groups of simulations with similar set‐ups (ensembles) which members differ only in their initial conditions. Each ensemble spans 32 months and we explore two horizontal resolutions and different representations of the riverine inflow. The predictability of the main current in the GoM, the so‐called Loop Current, is high under the conditions of our simulations. The northward extension of the Loop Current, however, is influenced by the riverine input and its representation. This influence is stronger if the riverine inflow is simulated accounting for a meridional velocity component proportional to the freshwater flux. The sea surface temperature (SST) field is more predictable in Summer, and the salinity (SSS) in winter. Increasing model resolution, on the other hand, decreases the predictability due to feedbacks between the mesoscale (several tens of kilometers in scale) and submesoscale (less than five km in scale) circulations. Key Points: The interaction of the Loop Current (LC) system and the river input in the northern Gulf of Mexico is two waysThe interaction depends on model resolution and riverine input configurationSpread of Sea surface temperature and Sea surface salinity show opposite seasonality. Increasing spatial resolution decreases the predictability of surface relative vorticity [ABSTRACT FROM AUTHOR]

The diurnal cycling of submesoscale circulations in vorticity, divergence, and strain is investigated using drifter data collected as part of the Lagrangian Submesoscale Experiment (LASER) experiment, which took place in the northern Gulf of Mexico during winter 2016, and ROMS simulations at different resolutions and degree of realism. The first observational evidence of a submesoscale diurnal cycle is presented. The cycling is detected in the LASER data during periods of weak winds, whereas the signal is obscured during strong wind events. Results from ROMS in the most realistic setup and in sensitivity runs with idealized wind patterns demonstrate that wind bursts disrupt the submesoscale diurnal cycle, independently of the time of day at which they happen. The observed and simulated submesoscale diurnal cycle supports the existence of a shift of approximately 1-3 h between the occurrence of divergence and vorticity maxima, broadly in agreement with theoretical predictions. The amplitude of the modeled signal, on the other hand, always underestimates the observed one, suggesting that even a horizontal resolution of 500mis insufficient to capture the strength of the observed variability in submesoscale circulations. The paper also presents an evaluation of how well the diurnal cycle can be detected as function of the number of Lagrangian particles. If more than 2000 particle triplets are considered, the diurnal cycle is well captured, but for a number of triplets comparable to that of the LASER analysis, the reconstructed diurnal cycling displays high levels of noise both in the model and in the observations. [ABSTRACT FROM AUTHOR]

Deepwater corals are found on hard grounds of the continental shelf and slope of all ocean basins, where they enhance the abundance and biodiversity of invertebrates and fishes. Despite their essential role in deep-sea ecosystems, the knowledge of the factors that promote or impede connectivity among discrete coral communities remains elusive. Logistical challenges prevent a direct quantification of the essential factors affecting connectivity, such as timing of spawning, time spent in the water column, and settlement behavior, as well as the details of the physical environment and its variability. This study argues that an integrated framework including population genetic approaches and physical models of the ocean circulation with larval particle tracking capabilities can enhance our understanding. Genetic approaches allow determination of general time-integrated patterns of dispersal distance and direction in virtually any species, while transport models can refine understanding of the processes behind the observed dispersal patterns. Here, this integrated approach is applied towards investigating the connectivity of a deepwater coral, Callogorgia delta , along the upper continental slope of the northern Gulf of Mexico. The circulation in the basin is simulated by a regional ocean model at 1 km horizontal resolution, which is sufficiently detailed to allow for the generation and evolution of submesoscale eddies and vorticity filaments, and for a reliable representation of major bathymetric features. Building upon data from four sites spanning about 250 km of distance and 400 m of depth, it is concluded that depth differences on scales of tens to at most few hundreds of meters are sufficient to limit C. delta connectivity among sites. This result has important implications in the development of restoration and preservation strategies of deepwater corals in the Gulf of Mexico and calls for carefully accounting for the depth dimension in these efforts. • Deepwater coral connectivity is logistically hard to quantify. • Genetic approaches help determining time-integrated patterns of dispersal distance/direction. • Transport models can refine understanding of the processes behind observed patterns. • An integrated framework (population genetic + modeling) enhances understanding. • In the Gulf of Mexico deepwater coral, C. Delta , shows depth separation due to limited transport. [ABSTRACT FROM AUTHOR]