Tidal deformation of the Earth’s surface results from the addition of two distinct processes. The first, known as the Solid Earth Tide (SET), corresponds to the deformation of the solid Earth caused by the gravitational attraction of the Moon and the Sun. The second, Ocean Tide Loading (OTL), arises from the redistribution of oceanic mass associated with tides, which imposes a variable load on the seafloor and surrounding crust, thereby inducing additional time-dependent deformation. Monitoring this response is crucial in geodesy for estimating the elastic and mechanical properties of the shallow Earth’s crust, for correcting geodetic measurements, and for constraining ocean tide models. On the other hand, tidal triggering of earthquakes suggests that Earth’s tidal forces influence seismic activity, particularly in the oceanic crust, highlighting the need to measure Earth tides in the deep ocean. However, the seafloor tidal response in the deep ocean remains sparse and poorly constrained due to the logistical challenges associated with continuous deployment of sensors in such an extreme environment. Here, we demonstrate that Distributed Acoustic Sensing (DAS) is able to monitor Earth tides in the deep sea. While DAS is challenged by high instrumental noise and environmental thermal fluctuations at low frequencies (< 0.01 mHz), we achieve a sensitivity on the order of picostrain per second from a submarine cable in the Mediterranean Sea by leveraging a signal processing approach for low-frequency noise suppression and the thermal stability of the deep Mediterranean Sea. While standard noise removal, an essential step in data pre-processing attenuates part of the Earth tide signals, it ultimately improves the continuous monitoring of Earth tides over distances of tens of kilometers, with kilometer-scale spatial resolution. The results from our measurements align closely with theoretical predictions. These findings validate the efficacy of Distributed Acoustic Sensing at extracting sub-nanostrain signals at periods exceeding several hours and demonstrate that DAS can serve as a new tool for seafloor geodesy applications.

Monitoring subsea temperature variations is essential for capturing dynamic physical processes, including mesoscale eddies, wind-driven upwelling and downwelling, internal wave propagation, and turbulent mixing. These phenomena strongly influence nutrient distribution and biological productivity within marine ecosystems. However, traditional in situ measurements often fail to resolve fine-scale thermal fluctuations due to limited sampling density. To address this limitation, Distributed Acoustic Sensing (DAS) offers a transformative solution by leveraging existing fiber-optic infrastructure to enable continuous, high-resolution monitoring of the subsea environment. Nevertheless, low-frequency (LF) DAS signals are influenced by multiple factors, including mechanical cable vibrations and deformation, thermo-optic effects, and optical noise, which complicate their interpretation.Here, we evaluate the potential and limitations of DAS for long-range temperature measurements by characterizing the LF-DAS response to subsea temperature variations and optimizing these signals across timescales from days to seasons. The results show that DAS strain and temperature are highly coherent (>0.5) at frequencies below 10 cycles per day. After denoising, DAS strain variations correlate well with temperature changes ranging from 0.4 to 10 K, although discrepancies between channels emerge at ultra-low frequencies. These signals are likely influenced by optical noise and amplified during rapid temperature changes, but can be mitigated through spatial averaging. With preliminary processing, DAS can resolve temperature fluctuations below 0.1 K, achieving meter-scale spatial resolution and minute-scale temporal resolution. These results demonstrate that DAS provides a powerful approach for observing subsea temperature variability, offering new insights into ocean dynamics through unprecedented spatiotemporal resolution.

Near-inertial waves (NIWs) are an important source of turbulence for the ocean interior. Mesoscale anticyclonic eddies are known to facilitate their propagation at depth while trapping them. However, in situ observations have so far focused on large (>50 km radius), energetic eddies, whereas most of the ocean is populated by smaller, moderately energetic fine-scale structures. Are these smaller structures efficient to trap NIWs and enhance turbulence? Here, we present in situ observations from the BioSWOT-Med 2023 cruise addressing this issue by surveying a fine-scale frontal area of the North Balearic front in the Mediterranean Sea, assisted by the first high-resolution Sea Surface Height images of the new Surface Water and Ocean Topography (SWOT) satellite mission during its Calibration/Validation phase. We explore how fine scales modulate the evolution of turbulence below the mixed layer after experiencing two consecutive strong wind events. We show that turbulence remains low in the front and its cyclonic side, while being greatly enhanced in the anticyclonic side. The latter side is dominated by a fine-scale anticyclone (12.8 km of radius, Rossby number of 0.5) that trapped NIWs, increasing turbulent dissipation level to several 10(-8) W kg(-1). The NIW-induced vertical kinetic energy flux reach up to 5.1 mW m(-2) below the pycnocline and represent similar to 20% of the wind power input into inertial motions, higher or similar to previous estimations outside and inside mesoscale anticyclones. Future work is needed to investigate whether these results extend to fine scales elsewhere in the world ocean, especially in regions with larger baroclinic Rossby radius of deformation.

The sixth report published in March 2023, from the IPCC 2023, lists several alarming findings about the ocean (Lee et al., 2023). The rise in sea level has accelerated and is now three times faster than it was during the period 1901-1971. The increase in ocean levels and the multiplication of energetic oceanic events represent a real problem for the management of coastal infrastructure. The proximity of human activities near the seaside makes these areas particularly vulnerable to risks. The submersion risks are seriously considered, given the damage that certain storms can have on the coast. Real-time measurement of the offshore wave field makes it possible to improve coastal submergence warning systems and predictions of submersion. Nevertheless, maintaining hydraulic measurement stations is still a challenge since the ocean is a hostile and vast environment that induces high installation costs. Consequently, a significant portion of our oceans remains unmonitored, making it difficult to find effective solutions for managing coastal infrastructures. On the other hand, ocean warming has been faster in the last century than in about 11,000 years (medium). For instance, marine heatwaves will increase in number and intensity, compromising many ecosystems. These marine heat waves can be detected on the surface with satellites. Still, their dynamic can greatly differ from the evolution of marine heat waves at depths where they remain poorly documented.\\ Distributed Acoustic Sensing (DAS) technology is a new photonic method that can convert several tens of kilometer-long seafloor fiber-optic telecommunication cables into dense arrays of strain sensors. With such spatial and temporal resolution, DAS is a new transforming approach for in-situ oceanographic measurements. For a recent DAS experiment performed on seafloor cables along the French Mediterranean coast, we show that it is possible to measure ocean swell fields up to depths of about 100 meters, and track water temperature variability from the coast to the bottom of the Mediterranean sea with mK sensitivity. Because DAS data are acquired at the speed of light from the land termination of the cable, the technology also enables the establishment of effective and rapid submergence warning systems, capable of anticipating the impact of storms or marine heat waves in real-time. Considering the vast network of submarine telecommunications cables and the ability of DAS to operate on fiber optic cables with live traffic, DAS could be easily and rapidly implemented across the globe.

By dissipating energy and generating mixing, internal tides (ITs) are important for the climatological evolution of the ocean. Our understanding of this class of ocean variability is however hindered by the rarity of observations capable of capturing ITs with global coverage. The data provided by the Global Drifter Program (GDP) offer high temporal resolution and quasi-global coverage, thus bringing promising perspectives. However, due to their inherent drifting nature, these instruments provide a distorted view of the IT signal. By theoretically rationalizing this distortion and leveraging a massive synthetic drifter numerical simulation, we propose a global metric converting semi-diurnal IT energy levels from GDP data to levels comparable to Eulerian datasets (two numerical simulations, and a satellite altimetry IT atlas). We find that the simulation with a dedicated focus on IT representation is the one where the converted Lagrangian levels perform best. This supports renewed efforts in the concurrent numerical modeling of ITs/ocean circulation. The substantial deficit of energy in the IT atlas highlights the inability for altimetric estimates to measure incoherent and fine-scale ITs and strongly supports the need to isolate ITs signature in the data collected by the new wide-swath altimetry mission SWOT.

The field experiment C-SWOT-2023 was carried out in North-Western Mediterranean Sea and aimed to support the new-generation SWOT altimeter (NASA/CNES) calibration and validation during its fast sampling phase. The daily overflight of the satellite between Marseille and Menorca was the opportunity to revisit the main aspects of the ocean circulation in the North-Western Mediterranean sub-basin such as the North Current, the Balearic Front or the eddy soup in the winter convection area. The originality of the field experiment lies in the deployment of two research vessels (the R/V Thetys II for IFREMER and the R/V Atalante for the SHOM) that sailed along together to explore statistics of the surface ocean dynamics (vorticity, strain, divergence) that are seldomly accessible in fine scale observations. High resolution transects recording velocities, temperature and salinity in the first four hundred meters under the SWOT swaths were performed in order to disentangle the geostrophic and the ageostrophic part of the circulation. Dozens of drifting buoys have been dropped to assess the lagrangian aspect of the dynamics. A short overview of the 3D observations dataset will be proposed before focusing on the comparison between the dynamics experienced in situ and those observed remotely by SWOT.

By giving a highly resolved 2D view of sea level (down to submesoscales), the recently launched SWOT altimetric satellite is revealing a brand new view on upper ocean dynamics. The signatures of ageostrophic processes (e.g. submesoscale, internal gravity waves) on SWOT SSH is nevertheless expected to complicate the estimation of the upper ocean circulation from SWOT altimetry. An improved knowledge of the relative importance of these signatures is thus required. From April to July 2023, SWOT flew over the Western Mediterranean sea daily. Meanwhile, three different in-situ campaigns of the SWOT-Adac Consortium (C-SWOT-2023, FaSt-SWOT and BIOSWOT-Med) deployed numerous in situ instruments, including drifters, to sample the upper ocean underneath the satellite tracks. We combine here these in-situ observations with wind reanalysis and SWOT sea level data to reconstruct the near-surface horizontal momentum balance. For given observation sources, an original statistical method enables us to not only quantify contributions from the different dynamical terms involved (e.g. inertial acceleration, coriolis acceleration, pressure gradient and wind stress vertical divergence) but also identify different dynamical regimes. This analysis reveals in particular limits of the geostrophic approximation and the dominance of inertial balance. We also present a detailed error budget including SWOT noise and comparisons between analyses with Pre-SWOT L4 gridded and SWOT sea levels.

Improving our understanding and ability to represent surface oceanic dynamics is crucial for the study and forecast of the climate system, as it modulates air-sea interactions and marine ecosystem. The recently launched SWOT altimetric satellite is providing a 2D highly resolved vision of sea level (down to submesoscales) and may thus offer a brand new view on upper ocean circulation.If geostrophy has historically allowed a global estimation of mesoscale and larger ocean surface circulation from classical altimetry, it is jeopardized at the scales resolved by SWOT by contributions from higher frequency processes such as internal tides, near-inertial waves and wind effect.Drifters trajectories, which provide a high frequency 'ground-truth’ estimate of the upper ocean circulation and wind reanalysis products are thus highly complementary to altimetry to reconstruct surface ocean dynamic. The horizontal surface momentum conservation is here reconstructed from historical altimetric data, drifters derived currents (Global Drifter Program) and atmospheric reanalysis products. We will present our ability at closing upper ocean momentum balance globally and quantify contributions from different terms involved (inertial acceleration, coriolis acceleration, pressure gradient and wind stress vertical divergence). This will allow to qualify and map the dominant dynamical balances, revealing the limit of geostrophy and the dominance of inertial balance in some areas. An error budget is also estimated.

Ocean flows at scales smaller than few hundreds of kilometers display rich dynamics, mainly associated with quasi- geostrophic motions and internal gravity waves. Although both of these processes act on comparable lengthscales, the former, which include meso and submesoscale turbulent flows, are considerably slower than the latter, which take part in the ocean fast variability. Understanding how their effects overlap is crucial for several fundamental and applied questions, including the interpretation and exploitation of new, high-resolution satellite altimetry data, and the characterization of material transport at fine scales. In this study we investigate these points by examining Lagrangian pair-dispersion statistics in a high-resolution global-ocean numerical simulation including high-frequency motions, such as internal gravity waves. In particular, we aim at assessing the sensitivity of the particle relative-dispersion process on ageostrophic, fast fluid motions. For this purpose we select a study area close to Kuroshio Extension, characterized by energetic submesoscales, and focus on the seasonal variability of the Lagrangian dynamics. We find that in winter pair dispersion is predominantly influenced by meso and submesoscale motions, meaning nearly balanced dynamics. The behavior of the different Lagrangian indicators considered agrees in this case with the theoretical predictions, based on the shape of the kinetic energy spectrum, in quasi-geostrophic turbulent flows. Conversely, in summer, when high-frequency motions gain importance and submesoscales are less energetic, the situation is found to be more subtle, and the usual relations between dispersion properties and spectra do not seem to hold. We explain this apparent inconsistency relying on a decomposition of the flow into nearly-balanced motions and internal gravity waves. Through this approach, we show that while the latter contribute to the kinetic energy spectrum at small scales, they do not impact relative dispersion, which is essentially controlled by the nearly-balanced, mainly rotational, flow component at larger scales.

Distributed Acoustic Sensing (DAS) is a photonics technology converting seafloor telecommunications and optical fiber cables into dense arrays of strain sensors, allowing to monitor various oceanic physical processes. Yet, several applications are hindered by the limited knowledge of the transfer function between geophysical variables and DAS measurements. This study investigates the quantitative relationship between surface gravity DAS-recorded wave-generated strain signals along the seafloor and the pressure at a colocated sensor. A remarkable linear correlation is found over various sea conditions allowing us to reliably determine significant wave heights from DAS data. Utilizing linear wave potential theory, we derive an analytical transfer function linking cable deformation and wave kinematic parameters. This transfer function provides a first quantification of the effects related to surface gravity waves and fiber responses. Our results validate DAS's potential for real-time reconstruction of the surface gravity wave spectrum over extended coastal areas. It also enables the estimation of waves hydraulic parameters at depth without the need from offshore deployments.

Abstract Despite intensified efforts to better quantify Internal Tide dynamics over past decades, large uncertainties remain regarding their distribution and lifecycle in the ocean. In particular, internal tide incoherence (loss of time‐regularity) has limited our ability to characterize, understand, and predict internal tides, challenging the exploitation of new‐generation wide‐swath satellite altimeters. Based on a realistic high‐resolution numerical simulation, we quantify the internal tide distribution and incoherence properties in the North Atlantic. We quantify IT incoherence for sea level and surface currents, and for different vertical modes independently. Our results show that typical decorrelation timescale induced by the mesoscale turbulence are rather short—below 25 days for the first vertical mode. It further exhibits a strong dependence of the internal tide incoherence with location, reflecting regions of enhanced eddy activity, and with vertical mode number—higher baroclinic modes being much more incoherent with shorter decorrelation timescale.

In this study, we carried out a novel massive Lagrangian simulation experiment derived from a global 1/48° tide-resolving numerical simulation of the ocean circulation. This first-time twin experiment enables a comparison between Eulerian (fixed-point) and Lagrangian (along-flow) estimates of kinetic energy (KE), and the quantification of systematic differences between both types of estimations. This comparison represents an important step forward for the mapping of upper ocean high-frequency variability from drifter database. Eulerian KE rotary frequency spectra and band-integrated energy levels (e.g., tidal and near-inertial) are considered as references, and compared to Lagrangian estimates. Our analysis reveals that, apart from the near-inertial band, Lagrangian spectra are systematically smoother, e.g., with wider and lower spectral peaks compared to Eulerian counterparts. Consequently, Lagrangian KE levels obtained from spectra band integrations tend to underestimate Eulerian levels on average at low-frequency and tidal bands. This underestimation is more significant in regions characterized by large low-frequency KE. In contrast, Lagrangian and Eulerian near-inertial spectra and energy levels are comparable. Further, better agreements between Lagrangian and Eulerian KE levels are generally found in regions of convergent surface circulation, where Lagrangian particles tend to accumulate. Our results demonstrate that Lagrangian estimates may provide a distorted view of high-frequency variance. To accurately map near-surface velocity climatology at high frequencies (e.g., tidal and near-inertial) from Lagrangian observations of the Global Drifter Program, conversion methods accounting for the Lagrangian bias need to be developed.

A novel method for the inference of spatiotemporal decomposition of oceanic surface flow variability is presented and its performance assessed in a synthetic idealized configuration with horizontally divergentless flow. Inference methodology is designed for observations of surface velocity. The ability of networks of surface drifters and moorings to infer the spatiotemporal scales of surface ocean flow variability is quantified. The sensitivity of inference performance for both types of platforms to the number of observations, geometrical configurations, and flow regimes is presented. As drifters simultaneously sample spatial and temporal variability, they are shown to be able to capture both spatial and temporal flow properties even when deployed in isolation. Moorings are particularly adept for the characterization of the flow's temporal variability and may also capture spatial scales provided they are deployed as arrays. In particular, we show that our method correctly identifies whether drifters are preferentially sampling spatial vs. temporal variability. Pending further developments, this method opens novel avenues for the analysis of existing datasets as well as the design of future experimental campaigns targeting the characterization of small-scale (e.g., <100 km) ocean variability.

Abstract The baroclinic component of the sea surface height, referred to as steric height, is governed by geostrophically balanced motions and unbalanced internal waves, and thus is an essential indicator of ocean interior dynamics. Using yearlong measurements from a mooring array, we assess the distribution of upper‐ocean steric height across frequencies and spatial scales of O (1–20 km) in the northeast Atlantic. Temporal decomposition indicates that the two largest contributors to steric height variance are large‐scale atmospheric forcing (32.8%) and mesoscale eddies (34.1%), followed by submesoscale motions (15.2%), semidiurnal internal tides (8%), super‐tidal variability (6.1%) and near‐inertial motions (3.8%). Structure function diagnostics further reveal the seasonality and scale dependence of steric height variance. In winter, steric height is dominated by balanced motions across all resolved scales, whereas in summer, unbalanced internal waves become the leading‐order contributor to steric height at scales of O (1 km).

Temperature is an essential oceanographic variable (EOV) that still today remains coarsely resolved below the surface and near the seafloor. Here, we gather evidence to confirm that Distributed Acoustic Sensing (DAS) technology can convert tens of kilometer-long seafloor fiber-optic telecommunication cables into dense arrays of temperature anomaly sensors having millikelvin (mK) sensitivity, thus allowing to monitor oceanic processes such as internal waves and upwelling with unprecedented detail. Notably, we report high-resolution observations of highly coherent near-inertial and super-inertial internal waves in the NW Mediterranean sea, offshore of Toulon, France, having spatial extents of a few kilometers and producing maximum thermal anomalies of more than 5 K at maximum absolute rates of more than 1 K/h. We validate our observations with in-situ oceanographic sensors and an alternative optical fiber sensing technology. Currently, DAS only provides temperature changes estimates, however practical solutions are outlined to obtain continuous absolute temperature measurements with DAS at the seafloor. Our observations grant key advantages to DAS over established temperature sensors, showing its transformative potential for the description of seafloor temperature fluctuations over an extended range of spatial and temporal scales, as well as for the understanding of the evolution of the ocean in a broad sense (e.g. physical and ecological). Diverse ocean-oriented fields could benefit from the potential applications of this fast-developing technology.

Ocean water temperature measurements are fundamental to atmospheric and ocean sciences. Obtaining them, however, often comes along with major experimental and logistic challenges. Except for the uppermost ocean surface temperature, which can be measured from satellites, temperature data of the ocean is often poorly sampled or nonexistent, especially in deep-water regions. Although Distributed Acoustic Sensing (DAS) technology has become popular because its high sensitivity to strains and mechanical vibrations, our work focuses on its usage on tens-of-kilometer-long underwater fibre-optic (FO) telecommunication cables to measure temperature anomalies at the seafloor at millikelvin (mK) sensitivity. This is possible because of the lack of dominant strain signals at frequencies less than about ∼1 mHz, as well as the poor coupling of the fibre with these signals while remaining highly sensitive to slow ambient temperature variations that locally affect its optical path length. DAS allows us to observe significant temperature anomalies at the continental shelf and slope of the Mediterranean sea, South of Toulon, France over periods of several days, with variability remaining relatively low at the deep ocean. By means of this approach, oceanic processes such as near-inertial internal waves and upwelling can be monitored at unprecedented detail.Our observations are validated with oceanographic in-situ sensors and alternative Distributed Fibre Optic Sensing (DFOS) technologies established for temperature sensing. We outline key advantages of DAS thermometry over the aforementioned sensors in terms of spatial coverage, sensitivity, versatility and highest attainable frequency. At the current state of the art, DAS can only measure temperature anomalies as opposed to absolute temperature, a drawback that could be compensated via single temperature calibration measurements.

Sub-mesoscale currents are of great interest for oceanographers but unfortunately their observation is a very difficult task. Lagrangian systems can be used to monitor them and require the implementation of an acoustic signal processing chain leading to the localization of each node of that system. In order to help the design of the Lagrangian system prior to sea trials, a simulation framework coupling the results of an oceanographic model with a ray trace software is presented. To illustrate the benefit of this framework, an experimental set-up composed of 5 sources and 20 floats which are drifting for 15 days is analyzed. A dataset of 358,000 frequency responses reflecting sub-mesoscale dynamics is built up. From this dataset, relevant statistics are calculated to define the best transmission parameters of the acoustic sources and to choose the right ranging method. It turns out that using pseudo-random sequences and allocating the spectral resources with Code Division Multiple Access method is a relevant design in our context. Also, it appears that a non-coherent ranging processor gives the best performance.

The Lagrangian and Eulerian surface current signatures of a low-mode internal tide propagating through a turbulent balanced flow are compared in idealized numerical simulations. Lagrangian and Eulerian total (i.e. coherent plus incoherent) tidal amplitudes are found to be similar. Compared to Eulerian diagnostics, the Lagrangian tidal signal is more incoherent with comparable or smaller incoherence timescales and larger incoherent amplitudes. The larger level of incoherence in Lagrangian data is proposed to result from the deformation of Eulerian internal tide signal induced by drifter displacements. Based on the latter hypothesis, a theoretical model successfully predicts Lagrangian autocovariances by relating Lagrangian and Eulerian autocovariances and the properties of the internal tides and jet. These results have implications for the separation of balanced flow and internal tides signals in the sea level data collected by the future Surface Water and Ocean Topography (SWOT) satellite mission.

A proper extraction of internal tidal signals is central to the interpretation of Sea Surface Height (SSH) data. The increased spatial resolution of future wide-swath satellite missions poses a challenge for traditional harmonic analysis, due to prominent and unsteady wave-mean interactions at finer scales. However, the wide swaths will also produce SSH snapshots that are spatially two-dimensional, which allows us to treat tidal extraction as an image translation problem. We design and train a conditional Generative Adversarial Network, which, given a snapshot of raw SSH from an idealized numerical eddying simulation, generates a snapshot of the embedded tidal component. We test it on data whose dynamical regimes are different from the data provided during training. Despite the diversity and complexity of data, it accurately extracts tidal components in most individual snapshots considered and reproduces physically meaningful statistical properties. Predictably, Toronto Internal Tide Emulator's performance decreases with the intensity of the turbulent flow.