Sea ice inhibits the development of wind‐generated surface gravity waves which are the dominant factor in upper ocean mixing and air‐sea fluxes. In turn, sea ice properties are modified by wave action. Understanding the interaction of ice and waves is important for characterizing both air‐sea interactions and sea ice dynamics. Current leading theory attributes wave attenuation primarily to scattering by ice floes. Here we use new in situ wave measurements to show that attenuation is dominated by dissipation with negligible effect by scattering. Time series of wave height in ice exhibit an “on/off” behavior that is consistent with switching between two states of sea ice: a relatively unbroken state associated with strong damping (off), possibly caused by ice flexure, and very weak attenuation (on) across sea ice that has been broken up by wave action.

Wake eddies are frequently created by flow separation where ocean currents encounter abrupt topography in the form of islands or headlands. Most previous work has concentrated on wake eddy generation by either purely oscillatory (usually tidal) currents, or quasi‐steady mean flows. Here we report measurements near the point of flow separation at the northern end of the Palau island chain, where energetic tides and vertically sheared low‐frequency flows are both present. Energetic turbulence measured near the very steeply sloping ocean floor varied cubically with the total flow speed (primarily tidal). The estimated turbulent viscosity suggests a regime of flow separation and eddying wake generation for flows that directly feel this drag. Small‐scale (∼1 km), vertically sheared wake eddies of different vorticity signs were observed with a shipboard survey on both sides of the separation point, and significantly evolved over several tidal periods. The net production and export of vorticity into the wake, expected to sensitively depend on the interplay of tidal and low‐frequency currents, is explored here with a simple conceptual model. Application of the model to a 10‐month mooring record suggests that inclusion of high frequency oscillatory currents may boost the net flux of vorticity into the ocean interior by a depth dependent factor of 2 to 25. Models that do not represent the effect of these high frequency currents may not accurately infer the net momentum or energy losses felt where strong flows encounter steep island or headland topography.

Autonomous underwater vehicles (AUVs) typically rely on underwater sound to communicate with sound channel physics defining communication performance. Current acoustic propagation models incorporate channel information and physics but are computationally expensive to run onboard an AUV. This work explores reduced-order proxies for these models through a machine-learning approach that can use in-situ measurements of observed sound-speed profile (SSP) to quickly provide transmission loss (TL) propagation information for vehicles to optimize communication performance through repositioning behaviors. The vertical structure from a year-long time series of mooring observations from offshore San Diego is decomposed with empirical orthogonal functions to reduce the SSPs to three unique amplitudes that explain 95.6% of the total variance. The acoustic propagation model Bellhop is used to estimate TL for a given SSP and different thresholds of TL are used to represent communications performance. Machine-learning models are trained with binarized Bellhop outputs to indicate locations of improved underwater acoustic communications. Amplitude values, desired TL, vehicle depth, and range are used as inputs into machine-learning models for optimizing TL. The best trained model was a random forest (ensemble) network with a 93.3% dice score. The decision tree had a faster prediction speed, relative to Bellhop, with similar prediction accuracy as the ensemble. This work aims to improve sound awareness of AUVs by generating SSP-informed communication performance maps.

Disposal of industrial and hazardous waste in the ocean was a pervasive global practice in the 20th century. Uncertainty in the quantity, location, and contents of dumped materials underscores ongoing risks to marine ecosystems and human health. This study presents an analysis of a wide-area side-scan sonar survey conducted with autonomous underwater vehicles (AUVs) at a dump site in the San Pedro Basin, California. Previous camera surveys located 60 barrels and other debris. Sediment analysis in the region showed varying concentrations of the insecticidal chemical dichlorodiphenyltrichloroethane (DDT), of which an estimated 350-700 t were discarded in the San Pedro Basin between 1947 and 1961. A lack of primary historical documents specifying DDT acid waste disposal methods has contributed to the ambiguity surrounding whether dumping occurred via bulk discharge or containerized units. Barrels and debris observed during previous surveys were used for ground truth classification algorithms based on size and acoustic intensity characteristics. Image and signal processing techniques identified over 74,000 debris targets within the survey region. Statistical, spectral, and machine learning methods characterize seabed variability and classify bottom-type. These analytical techniques combined with AUV capabilities provide a framework for efficient mapping and characterization of uncharted deep-water disposal sites.

Tropical cyclones (TC) are some of the most intense weather systems on Earth and are responsible for generating hazardous waves on the sea surface that dominate the extreme wave climate in several regions, including the Gulf of Mexico and the U.S. East Coast. Modeling these waves is crucial for engineering applications, yet it is notoriously difficult, due to TC’s compact structure and rapid evolution in space and time relative to other weather systems. To better understand the wave structure under TCs, we use satellite altimeter data paired with TC tracks. We parse the data by TC intensity and forward translation velocity, finding evidence of extended fetch. We use the altimeter data to evaluate operational hindcasts, including the US Army Corps of Engineer’s Wave Information Study, National Oceanic and Atmospheric Administration’s National Centers for Environmental Prediction Production Hindcast, and the Institut français de recherche pour l’exploitation de la mer (Ifremer) hindcast. The Ifremer hindcast (1990–2016) is examined in detail. Near the eye in the TC-centered reference frame, we find a pattern of model underestimation in the left sector and overestimation in the right sector except near the eye where wave height remains underestimated. This pattern holds, albeit modulated, across various intensities, forward translation velocities, and radii of maximum winds; the exceptions being the fastest translating storms where the error pattern shows a trend towards overestimation in all sectors. The error patterns for intense and compact TCs exhibited more severe underestimation, which dominated the region near the TC eye.