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Wind, wave, and acoustic observations are used to test a scaling for ambient sound levels in the ocean that is based on wind speed and the degree of surface wave development (at a given wind speed). The focus of this study is acoustic frequencies in the range 1-20 kHz, for which sound is generated by the bubbles injected during surface wave breaking. Traditionally, ambient sound spectra in this frequency range are scaled by wind speed alone. In this study, we investigate a secondary dependence on surface wave development. For any given wind-speed, ambient sound levels are separated into conditions in which waves are 1) actively developing or 2) fully developed. Wave development is quantified using the non-dimensional wave height, a metric commonly used to analyze fetch or duration limitations in wave growth. This simple metric is applicable in both coastal and open ocean environments. Use of the wave development metric to scale sound spectra is first motivated with observations from a brief case study near the island of Jan Mayen (Norwegian Sea), then robustly tested with long time-series observations of winds and waves at Ocean Station Papa (North Pacific Ocean). When waves are actively developing, ambient sound levels are elevated 2-3 dB across the 1-20 kHz frequency range. This result is discussed in the context of sound generation during wave breaking and sound attenuation by persistent bubble layers. 

In the past decade, two large marine heatwaves (MHWs) formed in the northeast Pacific near Ocean Station Papa (OSP), one of the oldest oceanic time series stations. Physical, biogeochemical, and biological parameters observed at OSP from 2013 to 2020 are used to assess ocean response and potential impacts on marine life from the 2019 northeast Pacific MHW. The 2019 MHW reached peak surface and subsurface temperature anomalies in the summertime and had both coastal, impacting fisheries, and offshore consequences that could potentially affect multiple trophic levels in the Gulf of Alaska. In the Gulf of Alaska, the 2019 MHW was preceded by calm and stratified upper ocean conditions, which preconditioned the enhanced surface warming in late spring and early summer. The MHW coincided with lower dissolved inorganic carbon and higher pH of surface waters relative to the 2013–2020 period. A spike in the summertime chlorophyll followed by a decrease in surface macronutrients suggests increased productivity in the well‐lit stratified upper ocean during summer 2019. More blue whale calls were recorded at OSP in 2019 compared to the prior year. This study shows how the utility of long‐term, continuous oceanographic data sets and analysis with an interdisciplinary lens is necessary to understand the potential impact of MHWs on marine ecosystems.

 

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 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 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 is an accompaniment to the paper titled Statistics of bubble plumes generated by breaking surface waves, by Derakhti et al, in the Journal of Geophysical Research: Oceans. It includes extensive observations from arrays of freely drifting SWIFT buoys and shipboard systems, enabling concurrent high-resolution measurements of wind, waves, and bubble plumes. This dataset allowed us to examine the dependence of the penetration depth and fractional surface area (e.g., whitecap coverage) of bubble plumes generated by breaking surface waves on various wind and wave parameters over a wide range of sea state conditions in the North Pacific Ocean, including storms with sustained winds up to 22 m s-1 and significant wave heights up to 10 m.  Notably, this study provides the first field evidence of a direct relation between bubble plume penetration depth and whitecap coverage, suggesting that the volume of bubble plumes could be estimated by remote sensing techniques. 

Wave crests of unexpected height and steepness pose a danger to activities at sea, and long-term field measurements provide important clues for understanding the environmental conditions that are conducive to their generation and behavior. We present a novel dataset of high-frequency laser altimeter measurements of the sea surface elevation gathered over a period of 18 years from 2003 to 2020 on an offshore platform in the central North Sea. Our analysis of crest height distributions in the dataset shows that mature, high sea states with high spectral steepness and narrow directional spreading exhibit crest height statistics that significantly deviate from standard second-order models. Conversely, crest heights in developing sea states with similarly high steepness but wide directional spread correspond well to second-order theory adjusted for broad frequency bandwidth. The long-term point time series measurements are complemented with space–time stereo video observations from the same location, collected during five separate storm events during the 2019/20 winter season. An examination of the crest dynamics of the space–time extreme wave crests in the stereo video dataset reveals that the crest speeds exhibit a slowdown localized around the moment of maximum crest elevation, in line with prevailing theory on nonlinear wave group dynamics. Extending on previously published observations focused on breaking crests, our results are consistent for both breaking and nonbreaking extreme crests. We show that wave crest steepness estimated from time series using the linear dispersion relation may overestimate the geometrically measured crest steepness by up to 25% if the crest speed slowdown is not taken into account.

Better understanding of the statistics and dynamical behavior of extreme ocean surface wave crests is crucial for improving the safety of various operations at sea. Our study provides new, long-term field evidence of the combined effects of wave field steepness and directionality on the statistical distributions of crest heights in storm conditions. Moreover, we show that the dynamical characteristics of extreme wave crests are well described by recently identified nonlinear wave group dynamics. This finding has implications, for example, for wave force calculations and the treatment of wave breaking in numerical wave models.