Gulf Stream Species in Cold North Waters Spur Scientific Discovery

In early 2017, something strange was going on in the waters off the coast of Rhode Island.  Fishers were pulling up a very unusual bycatch of Gulf Stream flounder and juvenile Black Sea bass. The Gulf Stream flounder are typically found in warmer waters, and while black sea bass are a very common fish, they are typically not present in cold water in the middle of winter, particularly not their juveniles. The fish coming up in their nets were so unusual for this time of the year, that a member of the Commercial Fisheries Research Foundation (CFRF) sent a photograph to Woods Hole Oceanographic Institution (WHOI) scientist Glen Gawarkiewicz asking what he thought might be causing these tropical fish to end up in cold New England waters in the dead of winter.

[media-caption path="/wp-content/uploads/2021/12/KIMG0396-2.jpg" link="#"]Anna Mercer from CFRF sent this photo of Gulf Stream flounder and juvenile Black Sea Bass to Glen Gawarkiewicz, launching an investigation that resulted in three research papers.[/media-caption]

“One fun thing about my relationship with the fishing community now is if they see unusual things, they send them to me. And then I’ll usually go right to the Pioneer Array website,  look at the data and see if there’s some kind of a story, I can tell about the oceanographic conditions that might have given rise to unusual features, or unusual fish,” said Gawarkiewicz.

This query about an unusual catch prompted a scientific investigation whose results were recently published in the Journal of Geophysical Research: Oceans. The authors, WHOI colleagues Ke Chen, Glen Gawarkiewicz, and Jiayan Yang, identified for the first time the cause and multi-faceted dynamics at play in a subsurface marine heat wave (a high temperature anomaly event), expanding views of contributing factors to such ocean-altering events. They pinpointed the interplay between smaller scale cyclonic eddies and warm water intrusions that created an anomalous marine heat wave.

The research team started with the traditional hypothesis that warm core rings at the shelf break were pushing warm and salty water onto the shelf towards shore, generating a marine heat wave. But  after analyzing the fishing data and oceanographic data from the Pioneer Array, the team was not able to pinpoint the exact process causing the marine heat wave.  Chen took up the challenge and created a numerical model simulating ocean conditions during November 2016-February 2017.

The model, which was at an exceptionally fine scale of 1 kilometer resolution, captured what the team saw from the data. The model revealed warm core rings were not solely responsible for the increase in temperature of water moving into shore, but rather the heated, salty water was being moved by a combination of previously overlooked smaller scale cyclone-like eddies (circular currents of water) in the periphery of the warm core ring. The model also revealed a very persistent wind blowing from west to east in late January 2017 that worked jointly with the cyclonic eddies that had already changed the outer shelf conditions.

The winds brought the warm and salty water from the shelf break to around 50 meters depth for a distance larger than 100 kilometers over five-six days. The intrusion was localized. It moved along the slope,  climbed up the shelf, and moved onshore.

“So this remarkable intrusion was really different from what has been known of the dynamics contributing to onshore intrusions for this region, “explained Chen. “It is a very new and exciting finding that such intrusions can be a combination of smaller scale eddies and wind causing warm water intrusions at a particular location.”  The model shows the complexity of such intrusions that may result from multiple sources and conditions.  “It provides a more complete picture of conditions and how they might ultimately impact what fish are caught and where they are caught,” added Chen.

Gawarkiewicz concluded, “The Pioneer Array has just worked wonders for our relationship with the fishing industry and has also allowed us to see ongoing changes in this ocean region that keep accelerating.  The Pioneer Array just brilliantly combines both the cutting-edge research, like the modeling Ke is doing, with the real dire societal need of identifying and ultimately understanding changing ocean conditions. I only wish that we could have a Pioneer Array, basically in every region of the country.”

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See Boston Globe article on December 28, 2021 for other ways OOI data are contributing to scientific understanding of the changing ocean.

 

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RCA Recording Swarm of Earthquakes in Real Time

On December 7, 2021 a swarm of earthquakes began on the Blanco Transform Fault, a major plate boundary at the southern end of the Juan de Fuca Plate. The ongoing seismic swarm is being tracked live by the National Science Foundation’s underwater observatory, the Regional Cabled Array (RCA). The RCA is a component of NSF’s Ocean Observatories Initiative and is operated and maintained by the University of Washington. It includes ~900 km of high power and high bandwidth submarine fiber optic cables that stretch from Pacific City, OR out to the most active volcano off the coast “Axial Seamount” that erupted in 1998, 2011 and again in 2015. A second cable heads south along the Cascadia Subduction Zone and turns east along the Cascadia Margin off Newport, OR. Over 150 instruments on the seafloor and on instrumented moorings provide real-time data flow to shore at the speed of light. A suite of seismometers at the summit of Axial Seamount lit up on December 7, 2021 as the seismic swarm began along the Blanco. This live feed was developed by the UW Applied Physics Laboratory.

 

 

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A Case Study for Open Data Collaboration

Recognizing that freely accessible ocean observatory data has the potential to democratize interdisciplinary science for early career researchers, Levine et al. (2020) set out to demonstrate this capability using the Ocean Observatories Initiative.  Publicly available data from the OOI Pioneer Array moorings were used, and members of the OOI Early Career Scientist Community of Practice (OOI-ECS) collaborated in the study.

A case study was constructed to evaluate the impact of strong surface forcing events on surface and subsurface oceanographic conditions over the New England Shelf.  Data from meteorological sensors on the Pioneer surface moorings, along with data from interdisciplinary sensors on the Pioneer profiler moorings, were used.  Strong surface forcing was defined by anomalously low sea level pressure – less than three times the standard deviation of data from May 2015 – August 2018.  Twenty-eight events were identified in the full record.  Eight events in 2018 were selected for further analysis, and two of those were reported in the study (Figure 24).

[media-caption path="https://oceanobservatories.org/wp-content/uploads/2021/07/CGSN-Highlight.png" link="#"]Figure 24. Two surface forcing events (16 and 27 November) identified from the time series of surface forcing at the Pioneer Central surface mooring.  Vertical lines indicate the peak of the anomalous low-pressure events (gray), as well as times 48 h before (red) and after (blue).  (A) sea level pressure, (B) wind speed, (C) air temperature, (D) latent (solid) and sensible (dashed) heat fluxes, (E) sea surface temperature, and (F) surface current speed and direction. [/media-caption]

The impact of surface forcing on subsurface conditions was evaluated using profile data near local noon on the day of the event, as well as 48 hr before and after (Figure 24). Subsurface data revealed a shallow (40-60 m) salinity intrusion prior to the 16 November event, which dissipated during the event, presumably by vertical mixing and concurrent with increases in dissolved oxygen and decreases in colored dissolved organic matter (CDOM). At the onset of the 27 November event, nearly constant temperature, salinity, dissolved oxygen and CDOM to depths of 60 m were seen, suggesting strong vertical mixing.  Data from multiple moorings allowed the investigators to determine that the response to the first event was spatially variable, with indications of slope water of Gulf Stream origin impinging on the shelf. The response to the second event was more spatially-uniform, and was influenced by the advection of colder, fresher and more oxygenated water from the north.

The authors note that the case study shows the potential to address various interdisciplinary oceanographic processes, including across- and along- shelf dynamics, biochemical interactions, and air-sea interactions resulting from strong storms. They also note that long-term coastal datasets with multidisciplinary observations are relatively few, so that the Pioneer Array data allows hypothesis-driven research into topics such as the climatology of the shelfbreak region, seasonal variability of Gulf Stream meanders and warm-core rings, the influence of extreme events on shelf biogeochemical response, and the influence of a warming climate on shelf exchange.

In the context of the OOI-ECS, the authors note that the study was successfully completed using open-source data across institutional and geographic boundaries, within a resource-limited environment.  Interpretation of results required multiple subject matter experts in different disciplines, and the OOI-ECS was seen as well-suited to “team science” using an integrative, collaborative and interdisciplinary approach.

 

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Levine, RM, KE Fogaren, JE Rudzin, CJ Russoniello, DC Soule, and JM Whitaker (2020) Open Data, Collaborative Working Platforms, and Interdisciplinary Collaboration: Building an Early Career Scientist Community of Practice to Leverage Ocean Observatories Initiative Data to Address Critical Questions in Marine Science. Front. Mar. Sci. 7:593512. doi: 10.3389/fmars.2020.593512.

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Jupyter Notebook Produces Quality Flags for pH Data

OOI uses the SAMI2-pH sensor from Sunburst Sensors, LLC to measure seawater pH throughout the different arrays.  Assessing the data quality from this instrument is an involved process as there are multiple parameters produced by the instrument that are then used to calculate the seawater pH.  These measurements are subject to different sources of error, and those errors can propagate through the calculations to create an erroneous seawater pH value. Based upon the vendor documentation and MATLAB code Sunburst provides to convert the raw measurements, OOI data team members have created a set of rules from those different measurements to flag the pH data as either pass, suspect or fail.

The resulting flags can be used to remove failed data from further analysis. They can also be used to help generate annotations for further Human in the Loop (HITL) QC checks of the data to help refine quality metrics for the data. OOI team member, Chris Wingard (OSU), has written up the QC process as a Python Jupyter notebook. This notebook and other example notebooks are freely available to the scientific community via the OOI GitHub site (within the OOI Data Team Python toolbox accessed from https://oceanobservatories.org/community-tools/ ).

In this notebook, Wingard shows how the quality rules can be used to remove bad pH data from a time series, and how they can be used to then create annotations. The impact of using these flags is shown with a set of before and after plots of the seawater pH as a function of temperature.  The quality controlled data can then be used to estimate the seasonal cycle of pH to set climatological quality control flags.

Here an example is shown using data from a pH sensor on the Oregon Inshore Surface Mooring (CE01ISSM) near surface instrument frame (NSIF), deployed at 7 m depth (site depth is 25 m).

[media-caption path="https://oceanobservatories.org/wp-content/uploads/2021/07/EA-Highlight.png" link="#"]Figure 25: pH data from the Oregon Inshore Surface Mooring (CE01ISSM) near surface instrument frame (NSIF).  Good data are shown in black, failed data in red.  Note that simple range tests on the final calculated pH are often not enough to distinguish good from failed data.  The automated QC processing examines intermediate measurements and fails data if intermediate measurements are outside acceptable ranges and propagated to final measurements.[/media-caption] [media-caption path="https://oceanobservatories.org/wp-content/uploads/2021/07/EA-highlight-2.png" link="#"]Figure 26: Good data together with annual cycles (red) constructed with available good data from initial deployment through 2021.  Data which falls outside three standard deviations of the climatology is flagged as suspect.  The climatological tests are used to flag suspect data.  Simple range tests for suspect (cyan) and failed (magenta) data are also shown.  The annual cycle at this site is strongly influenced by annual summer upwelling and winter storms and river plumes.  The summer decrease in pH is consistent with cold, relatively acidic upwelled water high in CO2 (see e.g., Evans et al., 2011)[/media-caption]

 


Evans, W., B. Hales, and P. G. Strutton (2011), Seasonal cycle of surface ocean pCO2on the Oregon shelf,J. Geophys. Res., 116, C05012, doi:10.1029/2010JC006625.

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Seismic Hazards Around the Globe: A visualization tool to bring RCA data into the classroom

[media-caption path="https://oceanobservatories.org/wp-content/uploads/2021/07/RCA-globe.png." link="#"]A snapshot view of seismic events centered on the Ring of Fire showing the 2011 Tahoku magnitude 9.1 earthquake. The history of quakes, until this time, is indicated by the color-coded dots that indicate location and magnitude. Source: Deb Kelley and the Center for Environmental Visualization, University of Washington[/media-caption]

As part of the continuing University of Washington engagement effort, and in preparation for the new National Science Foundation K12 education award focused on bringing OOI data into the classroom, Kelley collaborated with the Center for Environmental Visualization within the School of Oceanography to generate an earthquake exploration tool focused on seismic events within the global oceans from 1970 to present.  We anticipate that one of the curriculum modules developed for the K12 program will be focused on geohazards, with an emphasis on the Cascadia Subduction Zone within the context of the “ring of fire.”   A video of this animation is hosted on interactive oceans and a direct link to the developmental site is provided above.  The animation will be used in a Queens College physical geology class this next year that has 150 students (Dr. Dax Soule).  This effort is also in preparation for completing a similar visualization focused on Axial Seamount and Regional Cabled Array seismic data.

The data sets used for this effort include a map centered on the Pacific Ocean that shows the distribution of earthquakes of magnitude ≥6 in the U.S. Geological Survey catalog from 1970 through 2021.  The topographic dataset is licensed under Creative Commons CC BY-4.0.  The data were formatted to match the JSON format recommended for use of global visualization using the ‘Cesium’ interactive virtual earth viewer promoted within its 3D geospatial visualization for the web toolset.  The Cesium JavaScript API was utilized to implement algorithms for procedural color determination based on magnitude and hypocenter point radius animation based on the date-time of the earthquake event.  The resultant animation is highly interactive, allowing the user to choose a 3D global view or a flat view, and viewing speeds of 1-8 times.  In addition, the field of view can be changed to move to a specific area of interest and includes zoom capabilities.  A sliding time bar allows the user to focus in on particular items of interest.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Bottom Boundary Layer O2 Fluxes During Winter on the Oregon Shelf

[caption id="attachment_21037" align="aligncenter" width="640"] Fig. 1 Time series of physical conditions during the February 26–27, 2018 deployment (EC D1) at the mid-shelf site. (a) Wind vectors (15-min averages) measured at the OOI Shelf Surface Mooring (CE02SHSM), (b) wave properties (hourly averages) measured at the OOI Shelf Surface Mooring, (c and d) other near-bottom ADV parameters (15-min averages). Both the winds and ADV velocities are portrayed in earth coordinates (eastward is to the right along the horizontal axis and northward is positive along the vertical axis). ADV, Acoustic Doppler Velocimeter; EC D, eddy covariance deployment[/caption]

The oceanic bottom boundary layer (BBL) is the portion of the water column close to the seafloor where water motions and properties are influenced significantly by the seabed. This study (Reimers & Fogaren, 2021) reported in the Journal of Geophysical Research examines conditions in the BBL in winter on the Oregon shelf. Dynamic rates of sediment oxygen consumption (explicitly oxygen fluxes) are derived from high-frequency, near-seafloor measurements made at water depths of 30 and 80 meters. The strong back-and-forth motions of waves, which in winter form sand ripples, pump oxygen into surface sediments, and contribute to the generation of turbulence in the BBL, were found to have primed the seabed for higher oxygen uptake rates than observed previously in summer.

Since oxygen is used primarily in biological reactions that also consume organic matter, the winter rates of oxygen utilization indicate that sources of organic matter are retained in, or introduced to, the BBL throughout the year. These findings counter former descriptions of this ecosystem as one where organic matter is largely transported off the shelf during winter. This new understanding highlights the importance of adding variable rates of local seafloor oxygen consumption and organic carbon retention, with circulation and stratification conditions, into model predictions of the seasonal cycle of oxygen.

Supporting observations, which give environmental context for the benthic eddy covariance (EC) oxygen flux measurements, include data from instruments contained in OOI’s Endurance Array Benthic Experiment Package and Shelf Surface Moorings. Specifically, velocity profile time-series are drawn from records of a 300-kHz Velocity Profiler (Teledyne RDI-Workhorse Monitor), near-seabed water properties from CTD (SBE 16plusV2) and oxygen (Aanderaa-Optode 4831) sensors, winds from the surface buoy’s bulk meteorological package, and surface-wave data products from a directional wave sensor (AXYS Technologies) (see e.g., Fig 1 above).

Reimers, C. E., & Fogaren, K. E. (2021). Bottom boundary layer oxygen fluxes during winter on the Oregon shelf. Journal of Geophysical Research: Oceans, 126, e2020JC016828. https://doi.org/10.1029/2020JC016828

 

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Assimilative Model Assessment of Pioneer Array Data

[caption id="attachment_21009" align="alignnone" width="974"] Fig. 1. Color contours of sea surface salinity from the model for three nested grids denoted a) G1, b) G2 (black square in (a)) and c) G3 (red square in (a), (b)). An indicator of frontal position (the 34.5 isohaline) is shown as a black contour. Cross-shelf exchange parameters are computed for an along-shelf section (thick black line). The Pioneer Array assets are shown in the G3 figure.[/caption]

Among the detailed analyses undertaken in this two-part study was quantification of the impact of observations on the reduction of RMS error for estimates of the volume transport across an along-front transect (Fig. 1). Temperature and salinity data from moorings and gliders were impactful for the larger grids (G1, G2). As the grid resolution was increased (G3), submesoscale motions were resolved and velocity data from the moorings became more important for reduction of error variance. An analysis of the sensitivity of shelf-slope exchange indices (e.g. volume transport) to removal of an observation, compared to the direct impact of the observation, showed that the majority of observed variables (e.g., SST, SSH, T, S, U, V) were “synergistic” – providing value to the assimilation through their connection with other variables as represented in the model dynamics. For the highest resolution estimates (G3 grid), the Pioneer Array observing assets were more impactful than other observations (e.g., remote sensing, NDBC and IOOS buoys) in reducing uncertainty, with velocity data being the major contributor. This is not a complete surprise, since the Pioneer Array was “tuned” to these scales. Still, it is gratifying to see that the impact on model fidelity is quantifiable.

The two-part study undertaken by Levin et al. provides a wealth of additional information about the performance of assimilative models as well as the utility of in-situ observations for modeling and prediction. As the authors state, they have “just begun to scratch the surface” of approaches that can be applied to the assessment of model performance as well as the management of observing systems.

Levin J., H.G. Arango, B. Laughlin, E. Hunter, J. Wilkin, and A.M. Moore, 2020. Observation impacts on the Mid-Atlantic Bight front and cross-shelf transport in 4D-Var ocean state estimates: Part I – Multiplatform analysis,Ocean Modeling, 156, 101721, 1-17, doi 10.1016/j.ocemod.2020.101721.

Levin J., H.G. Arango, B. Laughlin, E. Hunter, J. Wilkin, and A.M. Moore, 2021. Observation impacts on the Mid-Atlantic Bight front and cross-shelf transport in 4D-Var ocean state estimates: Part II – The Pioneer Array, Ocean Modeling, 157, 101731, 1-17, doi 10.1016/j.ocemod.2020.101731.

 

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A Bountiful Sea of Data: Making Echosounder Data More Useful

[media-caption path="https://oceanobservatories.org/wp-content/uploads/2021/03/Screen-Shot-2021-03-30-at-5.51.41-PM.png" link="#"]Researchers used echosounder data from the Oregon Offshore site of the Coastal Endurance Array to develop a new methodology that makes it easier to extract dominant patterns and trends.[/media-caption]The ocean is like a underwater cocktail party. Imagine, as a researcher, trying to follow a story someone is telling while other loud conversations are in the background of a recording. This phenomenon, known as the “Cocktail Party Problem,” has been studied since the 1950s (Cherry, 1953; McDermott, 2009). Oceanographers face this challenge in sorting through ocean acoustics data, with its mixture of echoes from acoustic signals sent out to probe the ocean.

Oceanographer Wu-Jung Lee and data scientist Valentina Staneva, at the University of Washington, teamed up to tackle the challenge in a multidisciplinary approach to analyze the vast amounts of data generated by echosounders on Ocean Observatories Initiative (OOI) arrays. Their findings were published in The Journal of the Acoustical Society of America, where they proposed a new methodology that uses machine learning to parse out noisy outliers from rich echosounder datasets and to summarize large volumes of data in a compact and efficient way.

This new methodology will help researchers use data from long time series and extract dominant patterns and trends in sonar echoes to allow for better interpretation of what is happening in the water column.

The ocean is highly dynamic and complex at the Oregon Offshore site of the OOI Coastal Endurance Array, where echosounder data from a cabled sonar were used in this paper.  At this site, zooplankton migrate on a diurnal basis from a few hundred meters to the surface, wind-stress curl and offshore eddies interact with the coastal circulation, and a subsurface undercurrent moves poleward. The echosounder data offer opportunities to better understand the animals’ response to immediate environmental conditions and long-term trends. During the total eclipse of the Sun in August 2017, for example, echosounders captured the zooplankton’s reaction to the suddenly dimmed sunlight by moving upwards as if it was dusk time for them to swim toward the surface to feed (Barth et al, 2018).

Open access of echosounder datasets from the OOI arrays offers researchers the potential to study trends that occur over extended stretches of time or space. But commonly these rich datasets are underused because they require significant processing to parse out what is important from what is not.

Echosounders work by sending out pulses of sound waves that bounce off objects. Based on how long it takes for the reflected echo to come back to the sensor, researchers can determine the distance of the object. That data can be visualized as an echogram, an image similar to an ultrasound image of an unborn baby.

But unlike an ultrasound of a baby, when an undersea acoustic sensor records a signal, it may be a combination of signals from different sources. For example, the signal might be echoes bouncing off zooplankton or schools of fish.

[caption id="attachment_20566" align="alignleft" width="350"]Map with location and depths of the Endurance Array and pictures of transducers and profilers on the mooring platform (A) Data used in this work were collected by a three-frequency echosounder installed on a Regional Cabled Array Shallow Profiler mooring hosting an underwater platform (200 m water depth) and profiler science pod located at the Oregon Offshore site of the OOI Coastal Endurance Array (red triangle). The symbols indicate the locations of all OOI echosounders installed along the coast of Oregon and Washington. (B) The transducers are integrated into the mooring platform (from left to right: 120, 200, and 38 kHz). The platform also hosts an instrumented profiler that traverses the water column above the echosounder from ~ 200 m to ~ 5m beneath the ocean’s surface. (Image credit: UW/NSF-OOI/WHOI-V15).[/caption]

“When the scatterers are of different size, they will reflect the sound at different frequencies with different strengths,” said Lee. “So, by looking at how strong an echo is at different frequencies, you will get an idea of the range of sizes that you are seeing in your echogram.”

Current echogram analysis commonly requires human judgement and physics-based models to separate the sources and obtain useful summary statistics. But for large volumes of data that span months or even years, that analysis can be too much for a person or small group of researchers to handle. Lee and Staneva’s new methodology utilizes machine learning algorithms to do this inspection automatically.

“Instead of having millions of pixels that you don’t know how to interpret, machine learning  reduces the dataset to a few patterns that are easier to analyze,” said Staneva.

Machine learning ensures that the analysis will be data-driven and standardized, thus reducing the human bias and replicability challenges inherently present in manual approaches.

“That’s the really powerful part of this type of methodology,” said Lee. “To be able to go from the data-driven direction and say, what can we learn from this dataset if we do not know what may have happened in a particular location or time period.”

Lee and Staneva hope that by making the echosounder data and analytical methods open access, it will improve the democratization of data and make it more usable for everybody, even those who do not live by the ocean.

In the future, they plan to continue working together and use their new methodology to analyze the over 1000 days of echosounder data from the OOI Endurance Coastal and Regional Cabled Array region.

 

 

References

Lee, W-J and Staneva, V (2021).Compact representation of temporal processes in echosounder time series via matrix decomposition. Special Issue on Machine Learning in Acoustics. The Journal of the Acoustical Society of America.

Barth JA, Fram JP, et al. (2018). Warm Blobs, Low-Oxygen Events, and an Eclipse: The Ocean Observatories Initiative Endurance Array Captures Them All.Oceanography, Vol 31.

McDermott, J (2009). The Cocktail Party Problem.Current Biology, Vol 19, Issue 22.

Cherry EC (1953). Some Experiments on the Recognition of Speech, with One and Two Ears.The Journal of the Acoustical Society of America. Vol. 25, No.5.

 

 

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PI Cabled Instrument Provides Real-Time Sonar Measurements of Hydrothermal Plume Emissions

[media-caption path="https://oceanobservatories.org/wp-content/uploads/2021/02/RCA-FOR-SCIENCE-HIGHLIGHTS.png" link="#"]Figure 26. a) Location of the COVIS sonar and RCA infrastructure in the ASHES Hydrothermal Field. Also shown are locations of the active ~ 4 m tall hydrothermal edifices ‘Mushroom’ and ‘Inferno’. c) The COVIS sonar in 2019 (Credit: Rutgers/UW/NSF-OOI/WHOI). The tower is 4.2 m tall and hosts a modified Reson 7125 SeaBat multibeam sonar mounted on a tri-axial rotator. The system was built by the UW Applied Physics Laboratory. d) Selected time-series images from COVIS showing bending of the plume eastward, e) a nearly vertical plume, and f) southward bending of the plume (after Fig. 7 Xu et al., 2020).[/media-caption]

The Cabled Observatory Vent Imaging Sonar (COVIS) was installed on the OOI RCA in the ASHES hydrothermal field (Fig. 26 a-c) at the summit of Axial Seamount in 2018, resulting in the first long-term, quantitative monitoring of plume emissions (Xu et al., 2020). The sonar provides 3-dimensional backscatter images of buoyant plumes above the actively venting ‘Inferno’ and ‘Mushroom’ edifices, and two-dimensional maps of diffuse flow at temporal frequencies of 15 and 2 minutes, respectively. Sonar data coupled with in-situ thermal measurements document significant changes in plume variations (Fig. 26 d-f) and modeling results indicate a heat flux of 10 MW for the Inferno plume (Xu et al., 2020). COVIS will provide key data to the community investigating the impacts of eruptions on hydrothermal flow at this highly active volcano.

[1] Xu, G., Bemis, K., Jackson, D., and Ivakin, A., (2020) Acoustic and in-situ observations of deep seafloor hydrothermal discharge: OOI Cabled Array ASHES vent field case study. Earth and Space Science. Note: This project was funded by the National Science Foundation through an award to PI Dr. K. Bemis, Rutgers University – “Collaborative Research: Heat flow mapping and quantification at ASHES hydrothermal vent field using an observatory imaging sonar (#1736702). COVIS data are available through oceanobservatories.org

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Endurance Oregon Shelf Data Provides Insights into Bottom Boundary Layer Oxygen Fluxes

In February 2021 JGR Oceans article, Clare E. Reimers (Oregon State University) and Kristen Fogaren (Boston College) used data from the Endurance Array Oregon Shelf to advance understanding of how the benthic boundary layer on the Oregon Shelf in winter depends on surface-wave mixing and interactions with the seafloor.

The oceanic bottom boundary layer (BBL) is the portion of the water column close to the seafloor where water motions and properties are influenced significantly by the seabed. This study examines conditions in the BBL in winter on the Oregon shelf. Dynamic rates of sediment oxygen consumption (explicitly oxygen fluxes) are derived from high-frequency, near-seafloor measurements made at water depths of 30 and 80 m. The strong back-and-forth motions of waves, which in winter form sand ripples, pump oxygen into surface sediments, and contribute to the generation of turbulence in the BBL, were found to have primed the seabed for higher oxygen uptake rates than observed previously, in summer. Since oxygen is used primarily in biological reactions that also consume organic matter, the winter rates of oxygen utilization indicate that sources of organic matter are retained in, or introduced to, the BBL throughout the year. These findings counter former descriptions of this ecosystem as one where organic matter is largely transported off the shelf during winter. This new understanding highlights the importance of adding variable rates of local seafloor oxygen consumption and organic carbon retention, with circulation and stratification conditions, into model predictions of the seasonal cycle of oxygen.

The rest of the article can be accessed here.

 

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