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 a few kilometers.

We present a new parametric autocovariance kernel function for characterizing properties of the mesoscale eddy field and the non-phase-locked internal tide from ocean time series records. We demonstrate that the model captures the important spectral properties, namely the spectral roll-off of the mesoscale continuum and the broad spectral “cusps” centered around the tidal forcing frequencies. The spectral cusp model has three main parameters that characterize the non-phase-locked internal tide: the amplitude, a decorrelation timescale and a shape parameter that captures the rate at which the cusp rolls away. Estimation of the third shape parameter is novel. We argue that an integral timescale is the most suitable characteristic timescale and show how it relates to the parametric decorrelation timescale. A key innovation of this work is that we estimate the parameters in the frequency domain using the debiased Whittle likelihood, thus avoiding the computational demands of estimating the autocovariance in the time domain. We apply our spectral parameter estimation technique to output from idealized and realistic numerical experiments of internal tides propagating through a mesoscale eddy field. We robustly demonstrate that both the non-phase-locked amplitude and integral timescale are influenced by the amplitude of the mesoscale flow field. Furthermore, we reveal that the integral timescale is set by global properties of the eddy field, whereas the shape of the spectral cusp is set by its local properties. The semi-diurnal integral timescale, calculated from a 12-month long, realistically forced ocean basin experiment, was 5–7 d and relatively constant in space.

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 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 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 of offshore deployments.

A document by Julián Peláez Quiñones. Click on the document to view its contents.

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.

Two methods for the mapping of ocean surface currents from satellite measurements of sea level and future current vectors are presented and contrasted. Both methods rely on the linear and Gaussian analysis frameworkwith different levels of covariance definitions. The first method separately maps sea level and currents with single-scale covariance functions and leads to estimates of the geostrophic and ageostrophic circulations. The second maps both measurements simultaneously and projects the circulation onto 4 contributions: geostrophic, ageostrophic rotary, ageostrophic divergent and inertial. When compared to the first method, the second mapping moderately improves the resolution of geostrophic currents but significantly improves estimates of the ageostrophic circulation, in particular near-inertial oscillations. This method offers promising perspectives for reconstructions of the ocean surface circulation. Even the hourly dynamics can be reconstructed from measurements made locally every few days because nearby measurements are coherent enough to help fill the gaps. Based on numerical simulation of ocean surface currents, the proposed SKIM mission that combines a nadir altimeter and a Doppler scatterometer with a 300 km wide swath (with a mean revisit time of 3 days) would allow the reconstruction of 50% of the near-inertial variance around an 18 hour period of oscillation.

The future wide-swath satellite altimeters, such as the upcoming Surface Water Ocean Topography (SWOT) mission, will provide instantaneous 2D measurements of sea level down to the spatial scale of O(10 km) for the first time. However, the validity of the geostrophic assumption for estimating surface currents from these instantaneous maps is not known a priori. In this study, we quantify the accuracy of geostrophy for the estimation of surface currents from a knowledge of instantaneous sea level using the hourly snapshots from a tide- and eddy-resolving global numerical simulation. Geostrophic balance is found to be the leading-order balance in frontal regions characterized by large kinetic energy, such as the western boundary currents and the Antarctic Circumpolar Current. Everywhere else, geostrophic approximation ceases to be a useful predictor of ocean velocity, which may result in significant high-frequency contamination of geostrophically computed velocities by fast variability (e.g., inertial and higher). As expected, the validity of geostrophy is shown to improve at low frequencies (typically$<$0.5 cpd). Global estimates of the horizontal momentum budget reveal that the tropical and mid-latitude regions where geostrophic balance fails are dominated by fast variability and turbulent stress divergence terms rather than higher-order geostrophic terms. These findings indicate that the estimation of velocity from geostrophy applied on SWOT instantaneous sea level maps may be challenging away from energetic areas.

Extraction of internal tidal (IT) 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 spatially two-dimensional SSH snapshots, which allows us to treat IT extraction as an image translation problem for the first time. We design and train TITE, a conditional Generative Adversarial Network, which, given a snapshot of raw SSH from an idealized numerical eddying simulation, generates a snapshot of the embedded IT 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 ITs in most individual snapshots considered and reproduces physically meaningful statistical properties. Predictably, TITE’s performance decreases with the intensity of the turbulent flow.

The future Surface Water and Ocean Topography (SWOT) mission will soon provide Sea Surface Height (SSH) measurements resolving scales of a few tens of kilometers. Over a large fraction of the globe, the SSH signal at these scales is essentially a superposition of a component due to balanced motions (BM) and another component due to internal tides (IT). Several oceanographic applications require the separation of these components and their mapping on regular grids. For that purpose, the paper introduces an alternating minimization algorithm that iteratively implements two data assimilation techniques, each specific to the mapping of one component: a quasi-geostrophic model with Back-and-Forth Nudging for BM, and a linear shallow-water model with 4-Dimensional Variational (4DVar) assimilation for IT. The algorithm is tested with Observation System Simulation Experiments (OSSE) where the truth is provided by a primitive-equation ocean model in an idealized configuration simulating a turbulent jet and a mode-one IT. The algorithm reconstructs almost 80\% of the variance of BM and IT, the remaining 20\% being mostly due to dynamics that cannot be described by the simple models used. Importantly, in addition to the reconstruction of stationary IT, the amplitude and phase of nonstationary IT are reconstructed. Although idealized, this study represents a step forward towards the disentanglement of BM and IT signals from real SWOT data.

The Lagrangian and Eulerian near-surface current signatures of a low-mode internal tide propagating through a turbulent jet are compared in an idealized numerical simulation. We estimate and compare internal tides’ stationary and nonstationary velocity amplitudes as well as non-stationarity timescales. We find Lagrangian internal tides total amplitude similar to Eulerian one. Lagrangian velocity are mostly nonstationary and Lagrangian non-stationary timescales are comparable to or smaller than Eulerian ones. This low-bias is proposed to be the result of the deformation of internal tide surface signal along the drift induced by lower frequency surface currents. A model based on the latter hypothesis successfully predicts Lagrangian autocovariance and highlights its dependence to Eulerian autocovariance and to the properties of the internal tides and jet. We address the implications of these results in the context of the Surface Water and Ocean Topography (SWOT) mission, for which the separation of mesoscale balanced flow and internal tides with data at the ocean’s surface was raised.