Impacts of DEep submEsoscale Processes on the ocEan ciRculation – DEEPER

To better understand what happens in the deepest parts of the ocean, the DEEPER project relied on very advanced numerical simulations, comparable to “virtual oceans.” These simulations make it possible to realistically reproduce ocean behavior, from the large currents that span entire basins down to much finer motions, only a few kilometers wide, which play a key role in ocean mixing.

The challenge was twofold: achieving a resolution fine enough to represent these small-scale motions and the internal waves that propagate at depth, while at the same time covering a domain large enough to represent the entire Atlantic Ocean. To meet this challenge, researchers used the CROCO numerical model and carried out a series of increasingly detailed simulations, with grid sizes ranging from 6 kilometers down to 1 kilometer. These simulations include realistic seafloor topography, wind forcing, exchanges with the atmosphere, and the effects of tides. For the first time, this approach made it possible to study interactions between deep currents and underwater topography at very high resolution across the whole Atlantic basin—an essential step toward understanding abyssal circulation and deep ocean mixing.

To better identify the mechanisms at work, some simulations were specifically designed to isolate the role of internal waves by deliberately suppressing them. Comparing these experiments made it possible to clearly distinguish the contributions of different types of ocean motions.

Creating these “digital oceans” required exceptional computing resources, relying on powerful supercomputers. The simulations produced an unprecedented volume of data, equivalent to several million high-definition movies. To analyze them, dedicated data-processing tools were developed, allowing researchers to visualize the ocean in four dimensions (space and time) and to track the evolution of currents, eddies, and deep-water upwelling.

Finally, even more detailed simulations were carried out over key regions of the Atlantic, such as the Gulf Stream and the Mid-Atlantic Ridge, with resolutions reaching a few hundred meters. These targeted “zoom” simulations helped clarify how small-scale local motions influence ocean mixing and, ultimately, large-scale ocean circulation.

The DEEPER project has led to major advances in our understanding of the deep ocean, a region that remains poorly known yet is essential to the functioning of the climate system. The results show that deep-ocean circulation arises from a close interplay between large-scale currents, seafloor topography, internal waves, and turbulence—that is, the chaotic motions of seawater.

One key finding concerns how water circulates near the ocean floor. The project revealed a common organization across major ocean basins: water tends to flow downslope along underwater slopes, while a slower upward motion occurs above. This mechanism plays a crucial role in deep-water upwelling, the ventilation of abyssal basins, and the connection between the deep ocean and large-scale circulation. It highlights underwater slopes as essential regions for water-mass transformation.

The project also demonstrated the importance of deep-ocean mixing. Interactions between currents and topography generate intense turbulence near the seafloor, particularly around seamounts. This localized but powerful mixing allows deep waters to rise and strongly influences the dispersion of dissolved substances, such as nutrients and certain chemical elements.

These results show that the mechanisms currently used in climate models to represent deep mixing remain incomplete.

DEEPER also highlighted the role of internal waves, generated by tides or wind, which transport energy throughout the ocean interior. These waves interact with currents and the seafloor, fueling mixing processes and dissipating the energy of large-scale ocean motions. The structure of near-bottom layers therefore controls a significant part of the ocean’s energy balance.

The research further improved our understanding of the processes that transport substances released by hydrothermal sources, such as iron, which are essential for marine life. Their dispersion strongly depends on deep currents, eddies, and topography-driven mixing.

Finally, the project revealed the existence and major role of coherent deep eddies, comparable to gigantic rotating “bubbles” of water. These structures can trap and transport water masses and dissolved substances over long distances, contributing substantially to exchanges and variability in the deep ocean.

Taken together, these findings show that small-scale motions in the deep ocean, long overlooked, are in fact essential for understanding ocean circulation, water-mass mixing, and material transport. Properly representing these processes in models is a key step toward improving climate predictions.

The DEEPER project opens new perspectives for better understanding and anticipating the behavior of the deep ocean at scales that remain largely unexplored. Thanks to highly detailed numerical simulations, researchers have confirmed the central role of interactions between turbulence, internal waves, and seafloor topography in shaping deep-ocean circulation.

These studies have also highlighted the limitations of current modeling tools. To fully capture energy exchanges and the fine-scale interactions between waves and turbulence, even more advanced simulations will be required. Such approaches will make it possible to investigate internal waves and small-scale ocean motions with an unprecedented level of detail, paving the way toward a more complete understanding of deep-ocean dynamics.

The project also emphasizes the importance of abyssal mixing in the global redistribution of heat, carbon, and nutrients. These processes directly influence the climate system and the large-scale ocean circulation. They also control the transport of natural and human-derived substances—such as chemical elements, pollutants, and plastic debris—down to the deepest parts of the ocean.

The exploitation of unprecedented volumes of ocean data now makes it possible to analyze the deep ocean in all its dimensions, both in space and time. The use of artificial intelligence opens up new opportunities, including the automatic identification of regions of intense mixing, the prediction of the dispersion of dissolved substances or plastics, and the development of a true “digital twin” of the deep ocean capable of simulating and anticipating its evolution almost in real time.

These advances offer concrete opportunities to improve climate models, better represent deep-ocean circulation and water-mass transformations, and provide valuable tools for more sustainable ocean management. In particular, they will contribute to improved monitoring of carbon, nutrients, and plastics, and to better anticipation of the impacts of climate change and pollution on deep-sea ecosystems.

The meridional overturning circulation controls the fluxes of heat and carbon in the ocean on long time scales. Dense waters are sinking to the abyss at high latitudes, and are uplifted on their way south to come back to the surface and close the circulation. The classical view is that upwelling of abyssal waters is driven by turbulent mixing associated with breaking internal waves in the ocean interior. But this has been contradicted by observations, and a new paradigm has emerged in the recent years. Instead, the deep branch of the overturning circulation is shaped by highly localized turbulent processes that generate mixing close to the bottom of the ocean and drive upwelling of water masses over sloping topography. However, these small-scale turbulent processes are not well understood nor parameterized, which limits the accuracy of ocean models and the predictive skills of climate models. Theoretical and process studies are now beginning to highlight the role played by submesoscale (< 30 km) processes in the ocean bottom layer. But we still miss a clear picture of their phenomenology and impacts on the large scale circulation.

The goals of the DEEPER project are (1) to quantify the impacts of deep-sea submesoscale processes and internal waves on mixing and water mass transformations, (2) to explore ways of parameterizing these impacts using the latest advances in machine learning, i.e. applying deep learning to the deep ocean.

A hierarchy of numerical simulations will be built to characterise submesoscale processes and their interaction with the internal wave field in the deep ocean using cutting-edge realistic modelling with the ROMS/CROCO model. This problem requires simulations that resolve submesoscale processes (1-30 km), and a large enough domain to generate realistic levels of internal waves and allow to evaluate impacts on the large scale circulation. This will be the first time a terrain-following model — particularly advantageous to study flow-topography interactions — will be used at sub-kilometer resolution over a domain as large as the full Atlantic Ocean.

We will quantify how submesoscale processes and internal waves modify the large-scale budgets of energy and buoyancy, and impact mixing and water-mass transformations. We will characterize the impact of boundary potential vorticity (PV) fluxes related to diabatic processes on the mean large-scale PV budget and associated circulation. We will then characterise processes responsible for mixing in the different regions of the Atlantic Ocean: breaking of internal tides, near-inertial waves, or lee waves; formation of hydraulic jumps; or small scale instability such as gravitational, centrifugal or symmetrical instability. We will also investigate processes driving the restratification of the bottom layer such as submesoscale baroclinic instability, or deep frontogenesis. We will select some critical locations, where we observe a significant impact on water-mass transformations, and investigate the processes at play in more detail by means of very-high resolution simulations, using the non-hydrostatic version of CROCO, and idealized process studies. This downscaling will allow to check the sensitivity of the processes to the model resolution and the validity of the hydrostatic hypothesis.
Finally, we will take advantage of machine learning methods to parameterise these processes for coarser simulations. We will train a deep neural network using submesoscale resolving simulations from this project to predict mixing and eddy buoyancy fluxes for mesoscale-resolving and coarser simulations. This parameterization will then be tested over other geographical regions where similar outputs are available, and applied to different numerical models (in particular the eNATL60 simulation using the NEMO model). The long-term objective will be to be able to implement it in a global climate simulation to assess impacts on the overturning circulation over long time scales.