Seasonality in the upper ocean turbulence is interpreted in terms of interior baroclinic instabilities and mixed-layer instabilities • Temporal variability in the geostrophic kinetic energy spectrum can be subdivided into distinct seasonal phases • Both a forward enstrophy transfer and an inverse kinetic energy transfer control the spectral slope in the kinetic energy spectrum Supporting Information:

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.

We present a new method to estimate second-order horizontal velocity structure functions, as well as their Helmholtz decomposition into rotational and divergent components, from sparse data collected along Lagrangian observations. The novelty compared to existing methods is that we allow for anisotropic statistics in the velocity field and also in the collection of the Lagrangian data. Specifically, we assume only stationarity and spatial homogeneity of the data and that the cross-covariance between the rotational and divergent flow components is either zero or a function of the separation distance only. No further assumptions are made and the anisotropy of the underlying flow components can be arbitrarily strong. We demonstrate our new method by testing it against synthetic data and applying it to the LASER data set. We also identify an improved statistical angle-weighting technique that generally increases the accuracy of structure function estimations in the presence of anisotropy.

Since the launch of TOPEX/Poseidon, oceanographers have used the geostrophic assumption to infer sea surface velocity from Sea Surface Height (SSH). However, while an estimated 90% of the ocean's kinetic energy exists in the form of currents in quasigeostrophic balance (hereafter qualified as "balanced"; see Ferrari & Wunsch, 2009), one still must account for "unbalanced" flows such as internal tides, hereafter "ITs", for a refined inference of balanced currents (Fu & Ferrari, 2008). Furthermore, ITs play a crucial role in ocean mixing (Lien & Gregg, 2001; Whalen et al., 2020), and are helpful in detecting ocean temperature changes (Zhao, 2016). Therefore, whether ITs are considered "noise" (e.g., for inferring balanced flows) or "signal" (e.g., for inferring tidally induced mixing), their proper extraction from altimetry data is essential.For decades, IT extraction has been conducted via harmonic analysis (Munk & Hasselmann, 1964), a method that relies on a close phase relationship (or coherence) between ITs and astronomical forcings. Departures from this condition are sometimes referred to as "incoherence" (Ponte & Klein, 2015; Zaron & Rocha, 2018). Current altimetry has a typical spatial resolution of O(100) km (Ballarotta et al., 2019), which is sufficient to retrieve mode-1 and some of the mode-2 IT wavelengths of semidiurnal tides, along with the dominant turbulent balanced