Search for: All records

The process of seeking, sampling, and characterizing deep hydrothermal systems is benefited by the use of autonomous underwater vehicles (AUVs) equipped with in situ sensors. Traditional AUV operations require multiple deployments with manual data analysis by ship-board scientists. Development of advanced autonomous methods that analyze in situ data in real-time and allow the vehicle itself to make decisions would improve the efficiency of operations and enable new frontiers in exploration at hydrothermal systems on Ocean Worlds. Adaptive robotic decision making is facilitated by computational models of hydrothermal systems and selected in situ sensors used to refine and validate these predictions. Improving autonomous missions requires better models, and thus an understanding of how different sensors respond to hydrothermally altered seawater. During cruise AT50-15 (Juan De Fuca Ridge, 2023), we performed surveys of the hydrothermal plumes at the Endeavour Segment with AUV Sentry to investigate the utility of in situ sensors measuring tracers such as oxidation-reduction potential, optical backscatter, methane abundance, conductivity, and temperature, for building working models of plume dynamics. We investigated length scales of under 1 km to 5 km with a focus on reoccupying locations over varying time scales. Persistent deep current data were available through the Ocean Networks Canada mooring array. Using these datasets, we investigate two questions: (1) how reliably and at what length scales can real-time current information be used to predict the location and source of a hydrothermal plume? (2) How does the relative age (hence, biogeochemical maturation) of the hydrothermal plume fluid affect the response of different in situ sensors? These results will be used to inform the development of autonomous plume detection algorithms that use real-time, in situ data with the purpose of improving AUV exploration of hydrothermal plumes on Earth and other Ocean Worlds. 

Deep-sea hydrothermal vents inject dissolved and particulate metals, dissolved gasses, and biological matter into the water column, creating plumes several hundred meters above the seafloor that can be traced thousands of kilometers. To understand the impact of these plumes, rosettes equipped with sample bottles and in situ instruments, e.g., for turbidity, oxidation-reduction potential, and temperature, have been key tools for collecting water column fluid for informative ex situ analysis. However, deploying rosettes strategically in distal (>1km) plume-derived fluids is difficult when plume material is entrained rapidly with background water and transported by complicated bathymetric, internal, and/or tidal currents. This problem is exacerbated when the controlling dynamics are also poorly constrained (e.g., no persistent monitoring, few historical data) and data collected while in the field to estimate or compensate for these dynamics are only available to be analyzed hours or days following an asset deployment. Autonomous underwater vehicles (AUVs) equipped with equivalent in situ instruments to rosettes excel at exploration missions and creating highly-resolved maps at different spatial scales. Utilization of AUVs for hydrothermal plume charting and strategic sampling with rosettes is at a techno-scientific frontier that requires new data transmission and visualization interfaces for supporting real-time evidence-based operational decisions made at sea. We formulated a method for monitoring in situ water properties while an AUV is underway that (1) builds situational awareness of deep fluid mass distributions, (2) allows scientists-in-the-loop to rapidly identify fluid distribution patterns that inform adaptations to AUV missions or deployments of other assets, like rosettes, for targeted sample collection, and (3) supports robust formulation of working hypotheses of plume dynamics for in-field investigation. We will present a description of the method with preliminary results from cruise AT50-15 (Juan de Fuca Ridge, 2023) using AUV Sentry and discuss how supervised autonomy will improve ocean robotics for future science missions. 

Horizontal and vertical wavenumbers (kx, kz) immediately below the Ozmidov wavenumber (N3/ε)1/2 are spectrally distinct from both isotropic turbulence (kx, kz > 1 cpm) and internal waves as described by the Garrett–Munk (GM) model spectrum (kz < 0.1 cpm). A towed CTD chain, augmented with concurrent Electromagnetic Autonomous Profiling Explorer (EM-APEX) profiling float microstructure measurements and shipboard ADCP surveys, are used to characterize 2D wavenumber (kx, kz) spectra of isopycnal slope, vertical strain, and isopycnal salinity gradient on horizontal wavelengths from 50 m to 250 km and vertical wavelengths of 2–48 m. For kz < 0.1 cpm, 2D spectra of isopycnal slope and vertical strain resemble GM. Integrated over the other wavenumber, the isopycnal slope 1D kx spectrum exhibits a roughly +1/3 slope for kx > 3 × 10−3 cpm, and the vertical strain 1D kz spectrum a −1 slope for kz > 0.1 cpm, consistent with previous 1D measurements, numerical simulations, and anisotropic stratified turbulence theory. Isopycnal salinity gradient 1D kx spectra have a +1 slope for kx > 2 × 10−3 cpm, consistent with nonlocal stirring. Turbulent diapycnal diffusivities inferred in the (i) internal wave subrange using a vertical strain-based finescale parameterization are consistent with those inferred from finescale horizonal wavenumber spectra of (ii) isopycnal slope and (iii) isopycnal salinity gradients using Batchelor model spectra. This suggests that horizontal submesoscale and vertical finescale subranges participate in bridging the forward cascade between weakly nonlinear internal waves and isotropic turbulence, as hypothesized by anisotropic turbulence theory. 

Abstract Microstructure observations in the Pacific cold tongue reveal that turbulence often penetrates into the thermocline, producing hundreds of watts per square meter of downward heat transport during nighttime and early morning. However, virtually all observations of this deep-cycle turbulence (DCT) are from 0°, 140°W. Here, a hierarchy of ocean process simulations, including submesoscale-permitting regional models and turbulence-permitting large-eddy simulations (LES) embedded in a regional model, provide insight into mixing and DCT at and beyond 0°, 140°W. A regional hindcast quantifies the spatiotemporal variability of subsurface turbulent heat fluxes throughout the cold tongue from 1999 to 2016. Mean subsurface turbulent fluxes are strongest (∼100 W m −2 ) within 2° of the equator, slightly (∼10 W m −2 ) stronger in the northern than Southern Hemisphere throughout the cold tongue, and correlated with surface heat fluxes ( r 2 = 0.7). The seasonal cycle of the subsurface heat flux, which does not covary with the surface heat flux, ranges from 150 W m −2 near the equator to 30 and 10 W m −2 at 4°N and 4°S, respectively. Aseasonal variability of the subsurface heat flux is logarithmically distributed, covaries spatially with the time-mean flux, and is highlighted in 34-day LES of boreal autumn at 0° and 3°N, 140°W. Intense DCT occurs frequently above the undercurrent at 0° and intermittently at 3°N. Daily mean heat fluxes scale with the bulk vertical shear and the wind stress, which together explain ∼90% of the daily variance across both LES. Observational validation of the scaling at 0°, 140°W is encouraging, but observations beyond 0°, 140°W are needed to facilitate refinement of mixing parameterization in ocean models. Significance Statement This work is a fundamental contribution to a broad community effort to improve global long-range weather and climate forecast models used for seasonal to longer-term prediction. Much of the predictability on seasonal time scales is derived from the slow evolution of the upper eastern equatorial Pacific Ocean as it varies between El Niño and La Niña conditions. This study presents state-of-the-art high-resolution regional numerical simulations of ocean turbulence and mixing in the eastern equatorial Pacific. The results inform future planning for field work as well as future efforts to refine the representation of ocean mixing in global forecast models.