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Seafloor moorings measuring pressure and temperature were deployed from April to September 2023 at three sites near the route of the fiber optic telecommunications cable that extends offshore of Oliktok Point, Alaska. The raw data data (1 Hertz (Hz) sampling) are processed for hourly estimates of the ocean surface wave conditions, along with average seawater temperature and average depth. The sites were ice-covered from April to July, then mostly open water in August and September. The data were collected to calibrate proxy wave measurements using Distributed Acoustic Sensing (DAS) from the telecommunications cable. 

High-resolution profiles of vertical velocity obtained from two different surface-following autonomous platforms, Surface Wave Instrument Floats with Tracking (SWIFTs) and a Liquid Robotics SV3 Wave Glider, are used to compute dissipation rate profilesϵ(z) between 0.5 and 5 m depth via the structure function method. The main contribution of this work is to update previous SWIFT methods to account for bias due to surface gravity waves, which are ubiquitous in the near-surface region. We present a technique where the data are prefiltered by removing profiles of wave orbital velocities obtained via empirical orthogonal function (EOF) analysis of the data prior to computing the structure function. Our analysis builds on previous work to remove wave bias in which analytic modifications are made to the structure function model. However, we find the analytic approach less able to resolve the strong vertical gradients inϵ(z) near the surface. The strength of the EOF filtering technique is that it does not require any assumptions about the structure of nonturbulent shear, and does not add any additional degrees of freedom in the least squares fit to the model of the structure function. In comparison to the analytic method,ϵ(z) estimates obtained via empirical filtering have substantially reduced noise and a clearer dependence on near-surface wind speed.

 

This dataset includes vessel-based water-column profile and seabed data collected around Blossom Shoals, a shoal complex offshore of Icy Cape in northwestern Alaska (in the Chukchi Sea). Data were collected from the Research Vessel (R/V) Sikuliaq (offshore) and a companion workboat (inshore). Water-column profile data include salinity, temperature, depth, and turbidity data collected using a RBR Maestro CTD/Tu (conductivity, temperature, depth, turbidity) sensor package. Profile data also include median diameters and volumetric concentrations of suspended particles, where were collected using a Sequoia LISST200X (laser in situ scattering transmissometer). Seabed grab samples were collected from the Sikuliaq using a shipek grab sampler and from the workboat using a hand-operated mini van veen grab sampler. Samplers were bagged and returned chilled to the lab for particle-size analyses in an Escitec Bettersizer S3Plus laser diffraction sensor. Sediments were not treated for organics due to generally low organic contents. Samples contained primarily sand except for a few isolated locations where mud was found. Data were collected in November 2019 during the fall freezeup season when pancake ice were beginning to form. Data were also collected in late September and early October 2020 during a mooring recovery cruise. Single-beam bathymetry data (which were only collected in 2020) were gathered using a commercial fish finder mounted on the workboat and connected to a data logger. 

This dataset contains processed ocean surface gravity wave parameters derived from interrogation of a seafloor fiber with distributed acoustic sensing (DAS). These measurements were taken on a fiber within a cable owned by Quintillion extending off the coast near Oliktok Point, Alaska in November 2021 and August 2022. Processing includes calculation of frequency-dependent, channel-specific correction factors using collocated wave buoy (SWIFT) observations, which is then multiplied by the PSD of raw strain-rate. A depth-attenuation correction is then also applied. Dataset includes the raw strain-rate spectra and the derived wave spectra, as well as bulk wave parameters including significant wave height (Hs), peak wave period (Tp), and energy-weighted wave period (Te). 

This dataset contains processed ocean surface gravity wave parameters derived from interrogation of a seafloor fiber with distributed acoustic sensing (DAS). These measurements were taken on a fiber within a cable owned by Quintillion extending off the coast near Oliktok Point, Alaska in November 2021 and August 2022. Processing includes calculation of frequency-dependent, channel-specific correction factors using collocated wave buoy (SWIFT) observations, which is then multiplied by the PSD of raw strain-rate. A depth-attenuation correction is then also applied. Dataset includes the raw strain-rate spectra and the derived wave spectra, as well as bulk wave parameters including significant wave height (Hs), peak wave period (Tp), and energy-weighted wave period (Te). 

Observations of sea ice and the upper ocean from three moorings in the Beaufort Sea quantify atmosphere–ice–ocean momentum transfer, with a particular focus on the inertial-frequency response. Seasonal variations in the strength of mixed layer (ML) inertial oscillations suggest that sea ice damps momentum transfer from the wind to the ocean, such that the oscillation strength is minimal under sea ice cover. In contrast, the net Ekman transport is unimpacted by the presence of sea ice. The mooring measurements are interpreted with a simplified one-dimensional ice–ocean coupled “slab” model. The model results provide insight into the drivers of the inertial seasonality: namely, that a combination of both sea ice internal stress and ocean ML depth contribute to the seasonal variability of inertial surface currents and inertial sea ice drift, while under-ice roughness does not. Furthermore, the importance of internal stress in damping inertial oscillations is different at each mooring, with a minimal influence at the southernmost mooring (within the seasonal ice zone) and more influence at the northernmost mooring. As the Arctic shifts to a more seasonal sea ice regime, changes in sea ice cover and sea ice internal strength may impact inertial-band ice–ocean coupling and allow for an increase in wind forcing to the ocean.

 

Drifting buoy observations of ocean surface waves in hurricanes are combined with modeled surface wind speeds. The observations include targeted aerial deployments into Hurricane Ian (2022) and opportunistic measurements from the Sofar Ocean Spotter global network in Hurricane Fiona (2022). Analysis focuses on the slope of the waves, as quantified by the spectral mean square slope. At low‐to‐moderate wind speeds (<15 m s⁻¹), slopes increase linearly with wind speed. At higher winds (>15 m s⁻¹), slopes continue to increase, but at a reduced rate. At extreme winds (>30 m s⁻¹), slopes asymptote. The mean square slopes are directly related to the wave spectral shapes, which over the resolved frequency range (0.03–0.5 Hz) are characterized by an equilibrium tail () at moderate winds and a saturation tail () at higher winds. The asymptotic behavior of wave slope as a function of wind speed could contribute to the reduction of surface drag at high wind speeds.