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.
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.Read More
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.Read More
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 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
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.
[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"] (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.
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.
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.
 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.orgRead More
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.
In the summer of 2020 the Rutgers University Ocean Data Labs project worked with the Rutgers Research Internships in Ocean Science to support ten undergraduate students in a virtual Research Experiences for Undergraduates program. Two weeks of research methods training and Python coding instruction was followed by six weeks of independent study with a research mentor.
Dr. Rachel Eveleth (Oberlin College) was one of those mentors. Already using some of the Data Labs materials in her undergraduate oceanography course, she saw an opportunity to leverage the extensive OOI data holdings to engage students in cutting edge research on a limited budget during a time when her own field work was curtailed due to the COVID-19 pandemic. Dr. Eveleth advised Alison Thorson from Sarah Lawrence College (NY) and Brianna Velasco form Humboldt State University (CA) on the study of air-sea fluxes of CO2 on the US east and west coast, respectively.
Preliminary results were presented at the 2020 Fall AGU meeting. A poster authored by Thorson and Eveleth (ED037-0035) evaluated pCO2 data from the three Pioneer Array Surface Moorings during 2016 and 2017. They showed that the annual mean CO2 flux across all three sites for the two years was negative, meaning that the continental shelf acts as a sink of atmospheric carbon. The annual average flux was -0.85 to -1.6 mol C/(m2 yr), but the flux varied significantly between mooring sites and between years (Figure 23). Investigation of short-term variability in pCO2 concentration concurrent with satellite imagery of SST and Chlorophyll was consistent with temperature-driven, but biologically damped, changes.[media-caption path="https://oceanobservatories.org/wp-content/uploads/2021/02/Pioneer-for-Science-Highlights.png" link="#"]Figure 24. Hourly (dots) and monthly (lines) average air and water CO2 concentration observed at the Endurance Array Washington Offshore mooring during 2016 and 2017. From Velasco et al. (2020).[/media-caption]
A poster by Velasco, Eveleth and Thorson (ED004-0045) analyzed pCO2 data from the Endurance Array offshore mooring. Three years of nearly continuous data were available during 2016-2018. The seasonal cycle showed that the pCO2 concentration in water was relatively stable and near equilibrium with the air in winter, decreasing in late spring and summer (Figure 24). Short-term minima in summer were as low as 150 uatm. Like the east coast, the mean air-sea CO2 flux was consistently negative, meaning the coastal ocean acts as a carbon sink. The annual means at the Washington Offshore mooring for 2016, 2017 were -1.9 and -2.1 mol C/(m2 yr), respectively. The seasonal cycle appears to be strongly driven by non-thermal factors (on short time scales), presumably upwelling events and algal blooms.
These studies, although preliminary, are among the first to use multi-year records of in-situ CO2 flux from the OOI coastal arrays, and to our knowledge the first to compare such records between the east and west coast. Dr. Eveleth’s team intends to use the rich, complementary data set available from the OOI coastal arrays to investigate the mechanisms controlling variability and role of biological vs physical drivers.
In the summer of 2020, the Rutgers University Ocean Data Labs project worked with the Rutgers Research Internships in Ocean Science to support ten undergraduate students in a virtual Research Experiences for Undergraduates program. Rutgers led two weeks of research methods training and Python coding instruction. This was followed by six weeks of independent study with one of 13 research mentors.
Dr. Tom Connolly (Moss Landing Marine Labs, San Jose State University) advised Andrea Selkow from Austin College, Texas on her study of dissolved oxygen (DO) off the Washington and Oregon coasts using the OOI Endurance Array.
Selkow evaluated DO data from Endurance Array Surface Moorings during 2017 and 2018. She presented this work as a poster at the conclusion of her summer REU. Selkow focused on the question: Are there similarities in the dissolved oxygen concentrations off the coast of Oregon and Washington during a known low oxygen event? She also considered why there might exist differences based on the spatial variability of wind stress forcing, i.e., do the strong Oregon winds cause dissolved oxygen concentrations to be lower at the Oregon mooring compared to the Washington moorings. Finally, she reviewed the data and tried to answer whether the oxygen data were accurate or affected by biofouling.
She used datasets from the OR and WA Inshore Shelf Mooring time-series and WA Shelf Mooring time-series from Endurance Array. Her focus was on the seafloor data because that is where the lowest oxygen concentrations were expected to be observed.
Selkow focused her attention on low DO observed in the summer of 2017. While Barth et al. (2018) presented a report on these data for one event in July 2017, she expanded the analysis to include the Washington shelf and inshore moorings. She plotted time series data and used cruise data to validate these time series. While overall seasonal trends in DO were similar, she found dissolved oxygen is routinely more quickly depleted off the coast of Oregon than Washington during a low oxygen event (Figure 25). She also looked at the cross-shelf variability in DO time series and found dissolved oxygen is more quickly depleted at the shelf mooring than at the inshore shelf mooring. Upwelling is known to drive the low oxygen events and she inferred that the weaker southward winds over the Washington shelf may be why DO decreases at a slower rate off Washington than Oregon.
Barth, J.A., J.P. Fram, E.P. Dever, C.M. Risien, C.E. Wingard, R.W. Collier, and T.D. Kearney. 2018. Warm blobs, low-oxygen events, and an eclipse: The Ocean Observatories Initiative Endurance Array captures them all. Oceanography 31(1):90–97,
Selkow, A. and T. Connelly. Low Dissolved Oxygen off Washington and Oregon Coast Impacted by Upwelling in 2017, Accessed 13 Jan 2021.Read More