Generating mechanisms and parameterizations for enhanced turbulence in the wake of a seamount in the path of the Kuroshio are investigated. Full-depth profiles of finescale temperature, salinity, horizontal velocity, and microscale thermal-variance dissipation rate up- and downstream of the ~10-km-wide seamount were measured with EM-APEX profiling floats and ADCP moorings. Energetic turbulent kinetic energy dissipation rates and diapycnal diffusivities above the seamount flanks extend at least 20 km downstream. This extended turbulent wake length is inconsistent with isotropic turbulence, which is expected to decay in less than 100 m based on turbulence decay time of N^-1 ~100 s and the 0.5 m s^-1 Kuroshio flow speed. Thus, the turbulent wake must be maintained by continuous replenishment which might arise from (i) nonlinear instability of a marginally unstable vortex wake, (ii) anisotropic stratified turbulence with expected downstream decay scales of 10–100 km, and/or (iii) lee-wave critical-layer trapping at the base of the Kuroshio. Three turbulence parameterizations operating on different scales, (i) finescale, (ii) large-eddy, and (iii) reduced-shear, are tested. Average ε vertical profiles are well reproduced by all three parameterizations. Vertical wavenumber spectra for shear and strain are saturated over 10–100 m vertical wavelengths comparable to water depth with spectral levels independent of ε and spectral slopes of –1, indicating that the wake flows are strongly nonlinear. In contrast, vertical divergence spectral levels increase with ε.

Over the last twenty years, profiling floats have revolutionized ocean observations with globally distributed Lagrangian arrays performing fixed vertical sampling cycles. Here we investigate adaptive sampling with an array of inter-dependent floats guided by a software package called FlowPilot, which uses all available float measurements to select park depths that provide favorable drifts based on sampling goals. Drift predictions are performed with multiple prediction methods, including methods that use float data (drift velocity, geostrophic velocity calculations) or from external sources like numerical ocean forecast models. A skill-based weight is assigned to each method based on how accurately it predicts recent drifts. With this generalized approach to prediction, disparate methods can be combined numerically to permit multi-method optimization. The emergent skill of FlowPilot is tested and quantified by numerical simulations that minimize dispersion by keeping a grid of floats close to the center of the deployment box.

The Electromagnetic Autonomous Profiling Explorer (EM-APEX) float provides temperature, salinity, pressure, and ocean current measurements. We implemented a passive acoustic hydrophone system for the EM-APEX to measure the ambient sound of the ocean. Two EM-APEX acoustic floats were built, and five deployments were made in Puget Sound off the Washington Coast, and Hawaii. Time-averaged and frequency bin-averaged acoustic spectra were transmitted to the shore station in near real-time via the Iridium connection, and the raw acoustic data were recovered post-deployment for source classification and acoustic environment study using machine learning methods. The results confirmed that ambient noise levels from this platform could be used to derive sea surface processes such as wind speed, and the transient noise from ship traffic and calls of marine mammals can be detected when floats are in depth-holding mode.

Six profiling floats measured water-mass properties (Т, S), horizontal velocities (u, v) and microstructure thermal-variance dissipation rates χT in the upper ~1 km of Iceland and Irminger Basins in the eastern sub-polar North Atlantic from June 2019 to April 2021. The floats drifted into slope boundary currents to travel counterclockwise around the basins. Pairs of velocity profiles half an inertial period apart were collected every 7–14 days. These half-inertial-period pairs are separated into subinertial eddy (sum) and inertial/semidiurnal (difference) motions. Eddy flow speeds are ~O(0.1 m s^-1) in the upper 400 m, diminishing to ~O(0.01 m s^-1) by ~800-m depth. In late summer through early spring, near-inertial motions are energized in the surface layer and permanent pycnocline to at least 800-m depth almost simultaneously (within the 14-day temporal resolution), suggesting rapid transformation of large-horizontal-scale surface-layer inertial oscillations into near-inertial internal waves with high vertical group velocities through interactions with eddy vorticity-gradients (effective β). During the same period, internal-wave vertical shear variance was 2–5 times canonical midlatitude magnitudes and dominantly clockwise-with-depth (downward energy propagation). In late spring and early summer, shear levels are comparable to canonical midlatitude values and dominantly counterclockwise-with-depth (upward energy propagation), particularly over major topographic ridges. Turbulent diapycnal diffusivities K ~O(10^-4 m² s^-1) are an order of magnitude larger than canonical mid-latitude values. Depth-averaged (10–1000 m) diffusivities exhibit factor-of-three month-by-month variability with minima in early August.

A near‐inertial wave stalling and breaking in a critical layer was observed for a week by a pair of autonomous velocity and density profilers on concentric paths in an ocean mesoscale eddy. Profiler observations provide estimates of the eddy's vertical vorticity and shear, quantities needed to test theories of downward near‐inertial wave (NIW) energy flux and loss from inertial wave–eddy interactions. The unique observations of the wave's intrinsic frequency ω_i and vertical wavenumber m provide a novel estimate of the vertical group velocity C_gz from changes in ω_i with respect to m. Shear, strain, energy flux convergence, and parameterized turbulence are all elevated near 140 m depth, near the bottom of the strongest eddy velocities. Our observations are consistent with a downgoing NIW's group velocity decreasing owing to wave–eddy interactions, providing important clues on global energetics of NIW mixing.

The goal of the Waves, Langmuir Cells and the Upper Ocean Boundary Layer Departmental Research Initiative (LC–DRI) is to explore the upper ocean physics necessary to advance our understanding of the fluxes into and across the ocean mixed layer, including surface waves and wave breaking, Langmuir cells, and wave–current interaction. A set of comprehensive observational data was collected during the LC–DRI field experiment from various platforms including autonomous floats, drifter, buoys, and shipboard observations. The field campaign was conducted on the coast of Southern California 21 March – 5 April 2017. The fieldwork, including the event log and instrument deployment, is described in Part I. The inter-calibration between observed CTD data from EM-APEX and MLF floats, SWIFT drifters and R/V Sproul are described in Part II. For the MLF vs. EM-APEX calibration, the average salinity of MLF #82 and #83 top and bottom sensors is used as a reference. The calculated salinity offset for EM-APEX #6667, #6672, and #6678 is ~ 0.004 psu, for EM-APEX #6671 and #6674 is ~0.001 psu, and for EM-APEX #6675 is ~–0.001 psu. For seven SWIFT drifters at 0.2, 0.5, and 1.2 m, the calculated temperature offset varies from –0.1 to 0.1°C and the salinity offset varies from –0.003 to 0.2 psu. The salinity data from SWIFT #16 and #17 at 0.2 m exhibited large offsets, which suggest data bias. The comparison of wave energy measurements between SWIFT drifters and a Datawell Waverider buoy moored at CDIP station 299 are described in Part III. Excluding the periods when the mean separation distance was greater than 30 km (periods 3−1, 3, 5, 6, 8, 12), the root-mean-square error (RMSE) of significant wave height (Hs) is 0.25 ± 0.08 m, the RMSE of integrated wave energy is 0.057 ± 0.029 m², and the average percent error of Hs is ~13%. In general, given the temporal, spatial, and spectral differences in the sampling strategy of SWIFTdrifters and the CDIP buoy, the comparison suggests no significant bias in either dataset.

Observations from two companion field programs—Origins of the Kuroshio and Mindanao Current (OKMC) and Observations of Kuroshio Transport Variability (OKTV)—are used here to examine the Kuroshio's temporal and spatial evolution. Kuroshio strength and velocity structure were measured between June 2012 and November 2014 with pressure-sensor equipped inverted echo sounders (PIESs) and upward-looking acoustic Doppler current profilers (ADCPs) deployed across the current northeast of Luzon, Philippines, and east of Taiwan with an 8 month overlap in the two arrays' deployment periods. The time-mean net (i.e., integrated from the surface to the bottom) absolute transport increases downstream from 7.3 Sv (±4.4 Sv standard error) northeast of Luzon to 13.7 Sv (±3.6 Sv) east of Taiwan. The observed downstream increase is consistent with the return flow predicted by the simple Sverdrup relation and the mean wind stress curl field over the North Pacific (despite the complicated bathymetry and gaps along the North Pacific western boundary). Northeast of Luzon, the Kuroshio—bounded by the 0 m s^–1 isotach—is shallower than 750 dbar, while east of Taiwan areas of positive flow reach to the seafloor (3000 m). Both arrays indicate a deep counterflow beneath the poleward-flowing Kuroshio (–10.3 ±â€‰2.3 Sv by Luzon and –12.5 ±â€‰1.2 Sv east of Taiwan). Time-varying transports and velocities indicate the strong influence at both sections of westward propagating eddies from the ocean interior. Topography associated with the ridges east of Taiwan also influences the mean and time-varying velocity structure there.

Data derived from satellite-based observations, with buoy-based observations and assimilations, are used to calculate ocean Ekman layer transport and evaluate long-term trends in the Arctic Ocean over the period 1979–2014. The 36 year mean of upwelling (downwelling) is 3.7 ±â€‰2.0 (–4.0 ±â€‰2.2) Sv for the entire Arctic Basin, with ~0.3 Sv net downwelling contributed mostly by the Canadian region. With regard to long-term trends, the annual mean upwelling (downwelling) over the entire Arctic Basin is increasing at a linear rate of 0.92 (–0.98) Sv/decade. The Canada/Alaska coasts and Beaufort and Laptev Seas are regions of greatest Ekman transport intensification. The central Arctic Ocean and Lincoln Sea also have an increasing trend in transport. The Canadian and Eurasian regions each account for about half the total vertical Ekman variations in the Arctic Basin.

The oceanic surface mixed layer heat budget in the central equatorial Indian Ocean is calculated from observations at two mooring sites (0°S 79°E and 1.5°S 79°E) during three active and calm phases of Madden–Julian Oscillation (MJO) events between September 2011 and January 2012. At both mooring locations, the surface mixed layer is generally heated during MJO calm phases. During MJO active phases at both mooring locations, the surface mixed layer is always cooled by the net surface heat flux and also sometimes by the turbulent heat flux at the bottom of the surface mixed layer. The turbulent heat flux at the bottom of the surface mixed layer, however, varies greatly among different MJO active phases and between the two mooring locations. A barrier layer exerts control on the turbulent heat flux at the base of the surface mixed layer; we quantify this barrier layer strength by a "barrier layer potential energy," which depends on the thickness of the barrier layer, the thickness of the surface mixed layer, and the density stratification across the isothermal layer. During one observed MJO active phase, a strong turbulent heat flux into the mixed layer was diagnosed, despite the presence of a 10–20 m thick barrier layer. This was due to the strong shear across the barrier layer driven by the westerly winds, which provided sufficient available kinetic energy to erode the barrier layer. To better simulate and predict net surface heat fluxes and the MJO, models must estimate the oceanic barrier layer potential energy, background shear, stratification, and surface forcing accurately.

Five large-amplitude internal solitary waves (ISWs) propagating westward on the upper continental slope in the northern South China Sea were observed in May–June 2011 with nearly full-depth measurements of velocity, temperature, salinity, and density. As they shoaled, at least three waves reached the convective breaking limit: along-wave current velocity exceeded the wave propagation speed C. Vertical overturns of ~100 m were observed within the wave cores; estimated turbulent kinetic energy was up to 1.5 x 10^-4 W kg^-1. In the cores and at the pycnocline, the gradient Richardson number was mostly <0.25. The maximum ISW vertical displacement was 173 m, 38% of the water depth. The normalized maximum vertical displacement was ~0.4 for three convective breaking ISWs, in agreement with laboratory results for shoaling ISWs. Observed ISWs had greater available potential energy (APE) than kinetic energy (KE). For one of the largest observed ISWs, the total wave energy per unit meter along the wave crest E was 553 MJ m^-1, more than three orders of magnitude greater than that observed on the Oregon Shelf. Pressure work contributed 77% and advection contributed 23% of the energy flux. The energy flux nearly equaled CE. The Dubriel–Jacotin–Long model with and without a background shear predicts neither the observed APE > KE nor the subsurface maximum of the along-wave velocity for shoaling ISWs, but does simulate the total energy and the wave shape. Including the background shear in the model results in the formation of a surface trapped core.

Measurements of Kuroshio Current velocity at the entrance to Luzon Strait along 18.75°N were made with an array of six moorings during June 2012 to June 2013. Strong positive relative vorticity of the order of the planetary vorticity f was observed on the western flank of the Kuroshio in the upper 150 m. On the eastern flank, the negative vorticity observed was about an order of magnitude smaller than f. Kuroshio transport near its origin is computed from direct measurements for the first time. Kuroshio transport has an annual mean of 15 Sv with a standard deviation of 3 Sv. It is modulated strongly by impinging westward propagating eddies, which are identified by an improved eddy detection method and tracked back to the interior ocean. Eight Kuroshio transport anomalies >5 Sv are identified; seven are explained by the westward propagating eddies. Cyclonic (anticyclonic) eddies decrease (increase) the zonal sea level anomaly (SLA) slope and reduce (enhance) Kuroshio transport. Large transport anomalies of >10 Sv within O(10 days) are associated with the pairs of cyclonic and anticyclonic eddies. The observed Kuroshio transport was strongly correlated with the SLA slope (correlation = 0.9). Analysis of SLA slope data at the entrance to Luzon Strait over the period 1992–2013 reveals a seasonal cycle with a positive anomaly (i.e., an enhanced Kuroshio transport) in winter and spring and a negative anomaly in summer and fall. Eddy induced vorticity near the Kuroshio has a similar seasonal cycle, suggesting that seasonal variation of the Kuroshio transport near its origin is modulated by the seasonal variation of the impinging mesoscale eddies.

An array of three bottom-mounted ADCP moorings was deployed on the prevailing propagation path of strong internal tides for nearly 1 year across the continental slope in the northern South China Sea. These velocity measurements are used to study the intra-annual variability of diurnal and semidiurnal internal tidal energy in the region. A numerical model, the Luzon Strait Ocean Nowcast/Forecast System developed at the U.S. Naval Research Laboratory that covers the northern South China Sea and the Kuroshio, is used to interpret the observed variation of internal tidal energy on the Dongsha slope. Internal tides are generated primarily at the two submarine ridges in the Luzon Strait. At the western ridge generation site, the westward energy flux of the diurnal internal tide is sensitive to the stratification and isopycnal slope associated with the Kuroshio. The horizontal shear at the Kuroshio front does not modify the propagation path of either diurnal or semidiurnal tides because the relative vorticity of the Kuroshio in Luzon Strait is not strong enough to increase the effective inertial frequency to the intrinsic frequency of the internal tides. The variation of internal tidal energy on the continental slope and Dongsha plateau can be attributed to the variation in tidal beam propagation in the northern South China Sea.

Strong semidiurnal internal tides are observed on the continental slope of the East China Sea (ECS) using an array of subsurface moorings and EM-APEX floats. A Princeton Ocean Model (POM) is used to simulate the effects of stratification profiles on the generation and propagation of M2 internal tides; model simulations are compared with observations. On the ECS continental slope northeast of Taiwan, the semidiurnal barotropic tidal current flows nearly perpendicular to the shelf break and continental slope, favoring the generation of internal tides. Both the critical slope analysis and numerical model results suggest multiple generation sites on the continental slope, shelf break and around North MienHua Canyon. Unique high-wavenumber semidiurnal internal tides with a dominant vertical scale of ~100 m are found on the continental slope. The high-wavenumber semidiurnal internal tides appear in a form of spatially coherent shear layers across the ECS slope. They propagate vertically both upward and downward. Patches of strong energy and shear at a typical vertical scale of O(50 m) are present at the intersections of the upward and downward propagating high-wavenumber internal tides. The strong shear of high-wavenumber semidiurnal tides could play an important role in triggering shear instability on the ECS slope. The semidiurnal internal tidal energy flux, primarily in low wavenumbers, on the ECS slope, exhibits strong temporal and spatial variations. The observed depth integrated energy flux is 3.0–10.7 kW m^-1, mostly seaward from the continental shelf/slope. The POM model predicts similar seaward energy fluxes at a slightly weaker magnitude, 1.0–7.2 kW m^-1. The difference may be due to the absence of mesoscale processes in the model, e.g., the Kuroshio Current and eddies, the assumed horizontally uniform stratification profiles, insufficient model resolution for the abrupt canyon bathymetry, and the lack of the other major semidiurnal tidal constituent, S2, in the model. On the ECS slope, the total energy in the internal wave continuum, between 0.3 cph (beyond semidiurnal tidal harmonics) and the buoyancy frequency, is 6-13 times that of the Garrett–Munk model, presumably as a result of the energy cascade from strong inertial waves and internal tides in the region.

Very large subaqueous sand dunes were discovered on the upper continental slope of the northern South China Sea. The dunes were observed along a single 40 km long transect southeast of 21.93N, 117.53E on the upper continental slope in water depths of 160 m to 600 m. The sand dunes are composed of fine to medium sand, with amplitudes exceeding 16 m and crest-to-crest wavelengths exceeding 350 m. The dunes' apparent formation mechanism is the world's largest observed internal solitary waves which generate from tidal forcing on the Luzon Ridge on the east side of the South China Sea, propagate west across the deep basin with amplitudes regularly exceeding 100 m, and dissipate extremely large amounts of energy via turbulent interaction with the continental slope, suspending and redistributing the bottom sediment. While subaqueous dunes are found in many locations throughout the world's oceans and coastal zones, these particular dunes appear to be unique for two principal reasons: their location on the upper continental slope (away from the influence of shallow-water tidal forcing, deep basin bottom currents and topographically-amplified canyon flows), and their distinctive formation mechanism (approximately 60 episodic, extremely energetic, large amplitude events each lunar cycle).

In passive sonar systems, knowledge of low-frequency shipping noise is significant for target detection performance. However, an accurate model for the shipping noise structure is difficult to obtain, because of the varying distributions of ships and complicated underwater environment. This work characterizes low-frequency distant shipping noise observed in deep water environments as a function of receiver depth and vertical arrival structure placed below the conjugate depth.

Distant shipping noise is examined using a Monte Carlo simulation based on statistics derived from the Historical Temporal Shipping (HITS) database. Source levels and source depths of ships are assigned depending on the ship classification. The complex pressure field radiated from each interferer is computed using a normal mode propagation model, and the predicted values are summed coherently at each receiver location. Parameters for the ocean channel are chosen in agreement with the experimental observations, and sensitivity to exact parameters of the bottom sediment is explored. The depth dependence of the simulated shipping noise is in agreement with published experimental measurements. A Vertical Line Array (VLA) is used to produce vertical beams that isolate the surface interference from nearby targets. Simulation results quantifying the beamformer output as a function of ocean environment, receiver aperture, and frequency are presented for both conventional and adaptive beamformers.

The results suggest a favorable detection performance of a target in the presence of distant shipping interferers and wind noise, by adaptive beamforming using diagonal loading with white noise gain constraint techniques.

Results from a rain and gas exchange experiment (Bio2 RainX III) at the Biosphere 2 Center demonstrate that turbulence controls the enhancement of the air-sea gas transfer rate (or velocity) k during rainfall, even though profiles of the turbulent dissipation rate E are strongly influenced by near-surface stratification. The gas transfer rate scales with E1/2 for a range of rain rates with broad drop size distributions. The hydrodynamic measurements elucidate the mechanisms responsible for the rain-enhanced k results using SF₆ tracer evasion and active controlled flux technique. High-resolution k and turbulence results highlight the causal relationship between rainfall, turbulence, stratification, and air-sea gas exchange. Profiles of beneath the air-sea interface during rainfall, measured for the first time during a gas exchange experiment, yielded discrete values as high as 10^-2 W kg^-1. Stratification modifies and traps the turbulence near the surface, affecting the enhancement of the transfer velocity and also diminishing the vertical mixing of mass transported to the air-water interface. Although the kinetic energy flux is an integral measure of the turbulent input to the system during rain events, E is the most robust response to all the modifications and transformations to the turbulent state that follows. The Craig-Banner turbulence model, modified for rain instead of breaking wave turbulence, successfully predicts the near-surface dissipation profile at the onset of the rain event before stratification plays a dominant role. This result is important for predictive modeling of k as it allows inferring the surface value of E fundamental to gas transfer.

Wind and rain generated ambient sound from the ocean surface represents the background baseline of ocean noise. Understanding these ambient sounds under different conditions will facilitate other scientific studies. For example, measurement of the processes producing the sound, assessment of sonar performance, and helping to understand the influence of anthropogenic generated noise on marine mammals. About 90 buoy-months of ocean ambient sound data have been collected using Acoustic Rain Gauges in different open-ocean locations in the Tropical Pacific Ocean. Distinct ambient sound spectra for various rainfall rates and wind speeds are identified through a series of discrimination processes. Five divisions of the sound spectra associated with different sound generating mechanisms can be predicted using wind speed and rainfall rate as input variables. The ambient sound data collected from the Intertropical Convergence Zone are used to construct the prediction algorithms, and are tested on the data from the Western Pacific Warm Pool. This physically based semi-empirical model predicts the ambient sound spectra (0.5–50 kHz) at rainfall rates from 2–200 mm/h and wind speeds from 2 to 14 m/s.