Research activities

MEOM’s research is currently organized along the following axes :

• Axis 1 – Scale interactions in the ocean
• Axis 2 - Interactions between ocean dynamics and other components of the climate system
• Axis 3 - Data assimilation, interpretation of observations, design of observation networks

Axis 1 – Scale interactions in the ocean

Ocean dynamics is the expression of physical processes in continuous interaction, which cover spatial scales ranging from kms to planetary scale, and temporal scales ranging from seconds to century. In this context, we study the mechanisms of multiscale oceanic variability, with a particular interest in fine-scale structures (intense eddies, fronts, filaments), their predictability, their mutual interactions, the associated energy transfers and their role in climate variability.

Our recent research focuses on the explicit resolution of fine scales in numerical models based on the community code NEMO. This is achieved by carrying out realistic simulations at very high resolution (1/12°-1/60°) including in some cases the coupling with the atmosphere and its boundary layer, the ice pack, the ice caps and marine biogeochemistry (see axis 2), and by comparing the results with observations and process studies.

These simulations produce simulated data sets used for the preparation of the SWOT mission (to be launched in 2023 to observe for the first time ocean dynamics at scales resolved down to 10 km) and the exploitation of altimetry data, once the satellite is flying. Our objectives are to analyze the observed signals and to study energy exchanges between scales (spectra, structure functions) as well as wave-current interactions. These data allow us to develop deterministic or stochastic parameterizations of sub-grid scale effects (kinetic energy dissipation, role of fine scales in convection, etc.) in intermediate resolution models, in particular via machine learning methods (sparse regression, deep learning) fed by simulated data. The predictability of mesoscale and sub-mesoscale flows is studied by means of very high resolution regional ensemble simulations, with a view to contributing to the design of future space missions (see axis 3).

This activity contributes to the long-term development of the NEMO code used as the dynamic core of operational forecast suites of the marine Copernicus CMEMS service, and in the Earth System Models used for climate projections.

Axis 2 - Interactions between ocean dynamics and other components of the climate system

The ocean is a geophysical fluid that interacts with other components of the climate system (atmosphere, cryosphere, continents) and is a key player in major natural cycles (carbon cycle, water cycle). Until recently, the ocean-ice numerical models (NEMO-LIM) implemented in the MEOM team were forced at the air-sea interface by bulk formulas in order to simulate an oceanic variability approximately in phase with the variability of the atmosphere. However, this approach distorts the physics of the real ocean-atmosphere system (atmospheric heat capacity assumed to be infinite, SST feedbacks on the wind neglected). The use of coupled models appears to be more physically coherent, but the oceanic evolution may be out of phase with the observations.

More recently, our investigations focus on the study and modelling of ocean-ice-atmosphere interfaces. Very high resolution coupled simulations (NEMO+LIM 1/36° + WRF 1/12°) are carried out over the North Atlantic and compared to idealized models with two main objectives : (i) to study ocean-atmosphere feedbacks, energetics and dynamics of the surface ocean at the scale of eddies and filaments ; (ii) to improve the forcing of NEMO by coupling with an atmospheric boundary layer model (Albatros), which is itself guided by a dynamical atmosphere and eventually by reanalysis. At high latitudes, these fine-scale air-sea interactions are mediated by the sea ice, whose rheology is represented in a very realistic way by the NeXtSIM model, also coupled to NEMO. The team relies on the NEMO/NeXtSIM/Albatros suite, on observations to study the fine-scale dynamics of the pack ice (forced and coupled), its impact on local air-ice-sea interactions, and their effect on the climate evolution of the ocean (especially the Arctic Ocean).

We are pursuing work in the framework of internal and external collaborations at the IGE on the ocean-ice sheet interface, in particular to simulate and understand the dynamics of flows under ice shelves and their interactions with the ocean, at the scale of the Southern Ocean. Ensemble approaches will allow us to evaluate uncertainties in these interactions, whether they are related to the chaotic character of oceanic variability or to model uncertainties. Other interesting perspectives are opening up on the likely impact of oceanic chaos on related environments (via the signature on heat content in particular), oceanic biogeochemistry and in the longer term on the coupled ocean-atmosphere system.

Axis 3 – Data assimilation, interpretation of observations, design of observation networks

Our research relies on complementarities between numerical approaches (modelling, assimilation, inversion) and observational approaches (spatial and in situ data) : simulations help the interpretation of observed variability and the detection/attribution of long-term trends ; synthetic data (e.g. SWOT) from high-resolution numerical simulations feed OSSEs guiding the optimization of observing systems, analysis and assimilation of future data ; reanalyses and forecasts estimate the past and future 3D state of the ocean variability constrained by observations.

Our methods are based on probabilistic or Bayesian approaches describing the uncertainty of oceanic states (physical and biogeochemical, simulated and observed), in the current context of increasingly complex data flow and the emergence of artificial intelligence methods in oceanography. They are developed in a non-Gaussian multivariate framework, in order to take into account the specific dynamics and the multiscale character of the variability. The long-term objective is to build a more generic framework for solving inverse problems, with the double concern of describing the uncertainty on the produced solutions and controlling the computational costs, in particular via the development of emulators and simplified dynamical models by statistical learning.

These developments aim at several types of applications : (i) ensemble estimation of uncertainties and their temporal evolution, (ii) assimilation of observations into models, in particular for the improvement of CMEMS operational systems ; (iii) reconstruction of surface layer dynamics (in the context of nadir, wide-swath and Doppler radar altimetry, and optical imagery missions) ; (iv) quantification of the intrinsic chaotic and deterministic parts of the observed ocean variability (heat and volume transport, heat content, sea level), (v) interpretation of the observed evolution of key ocean variables based on ensemble simulations, and (vi) detection and attribution of interannual to decadal fluctuations and observed trends.

Our research in data assimilation further targets other application domains (hydrology, glaciology, etc.) in collaboration with the key players in Grenoble.