Posts Tagged ‘Science Highlights’
Salinification of the Cold Pool on the New England Shelf
(Adapted from Taenzer et al., 2025)
The continental shelf within the Mid-Atlantic Bight is cooled and mixed vertically in the winter. This relatively cold, fresh water is trapped below the seasonally-warming surface layer, retaining its properties as a subsurface “cold pool” throughout most of the spring and summer. The cold pool is important for regional ecosystems, serving as a cold-water habitat and a nutrient reservoir for the continental shelf. It is known that the cold pool warms and shrinks in volume as a result of advective fluxes and heat exchange with surrounding waters. A recent paper by Taenzer et al. (2025) shows for the first time that the cold pool is also subject to salt fluxes and increases significantly in salinity from April to October.
The Pioneer New England Shelf (NES) inshore moorings (ISSM and PMUI) are positioned shoreward of the shelfbreak front and sample conditions on the outer continental shelf where the cold pool can be identified. The authors extracted data from these two moorings from a quality-controlled data set containing timeseries of hydrographic data (temperature, salinity and pressure) from all of the Pioneer NES moorings on a uniform space-time grid, covering the timeframe from January 2015 through May 2022 (Taenzer et al., 2023). The cold pool study used data from 2 m depth, 7 m depth, and 2 m above the bottom on ISSM and from roughly 28 m to 67 m depth on PMUI.
Seven years of data from the Pioneer ISSM and PMUI moorings were used to create a composite annual cycle, which showed that subsurface salinity on the outer shelf consistently increases in the spring and summer. Evaluating the 67 m depth salinity record, and restricting the time period to when the moorings are in the cold pool, resulted in a salinification estimate of 0.18 PSU/month, or ~1 PSU over the six month period (Figure 34a). It was shown that this salinity change could not be explained by a seasonal change in the frontal position.
Isolating the corresponding cold pool region within the New England Shelf and Slope (NESS) model (Chen and He, 2010), and computing a similar multi-year mean, showed a salinification trend nearly identical to that from the observations (Figure 34b). Using the model, it was possible to define a three-dimensional cold pool volume and estimate terms in the cold pool salinity budget. It was found that cross-frontal fluxes transport salt from offshore to the cold pool at a relatively steady rate throughout the year, and that along-shelf advection contributes little to the salinification process. It was argued that the cold pool exhibits two regimes that result in the seasonal salinification: During the winter, vertical mixing is strong, and the cold pool gets replenished with fresh water from the surface layer, which tends to balance the cross-shelf salt flux. During the spring and summer, surface stratification increases, vertical mixing is inhibited, the cold pool is effectively isolated from surface mixing, and the cross-shelf salt flux results in cold pool salinification.
This project shows the importance of long-duration observations in key locations to isolate phenomena that would not be identifiable from a short-term process study. It is notable that the authors undertook a significant quality control effort and created a merged, depth-time gridded data set that was made publicly available. By combining the observations with a high-resolution regional model, the authors were able to examine the cold pool salinity budget and attribute the observed signals to ocean processes.
[caption id="attachment_36391" align="alignnone" width="402"] Figure 34: The seven-year mean annual cycle of continental shelf cold pool salinity from a) Pioneer Array PMUI salinity at 67m depth, b) NESS model salinity for all waters below 10◦C along 70.875 W. The shaded envelope depicts one standard deviation of interannual variability. The salinification trend is from a linear fit during the stratified season (April-October). From Taenzer et al., 2025.[/caption]
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References:
Chen, K., & He, R. (2010). Numerical investigation of the Middle Atlantic Bight Shelfbreak Frontal circulation using a high-resolution ocean hindcast model. J. Physical Oceanog., 40 (5), 949 – 964. doi:10.1175/2009JPO4262.1
Taenzer, L.L., G.G. Gawarkiewicz and A.J. Plueddemann, (2023). Gridded hydrography and bulk air-sea interactions observed by the Ocean Observatory Initiative (OOI) Coastal Pioneer New England Shelf Mooring Array (2015-2022) [data set], Woods Hole Oceanographic Inst., Open Access server, https://doi.org/10.26025/1912/66379.
Taenzer, L.L., K. Chen, A.J. Plueddemann and G.G. Gawarkiewicz, (2025). Seasonal salinification of the US Northeast Continental Shelf cold cool driven by imbalance between cross-shelf fluxes and vertical mixing. J. Geophys. Res., accepted.
Read MoreSubsurface Temperature Anomalies off Central Oregon during 2014–2021
Brandy T. Cervantes, Melanie R. Fewings, and Craig M. Risien
Cervantes et al. (2024) use water temperature observations from a stationary oceanographic platform located in 80 m water depth off Newport, Oregon to calculate variations from the long term mean temperature at the surface, near surface, and bottom from 1999 to 2021. This site, known as NH-10, was occupied since 1999 successively by an Oregon State University National Oceanographic Partnership Program (OSU NOPP), GLOBEC Long Term Observation Program, Oregon Coastal Ocean Observing System (OrCOOS), NANOOS/CMOP. Since 2015 it has been occupied by the NSF OOI Coastal Endurance Oregon Shelf mooring (CE02SHSM). The temperature observations from these different programs that have not previously been combined into one long time series. Of particular interest are the details of the marine heatwave (MHW) periods of 2014–2016 and 2019– 2020, which had widespread impacts on marine ecosystems. Strong deviations from the mean water temperature observed near the ocean bottom during late 2016 are the largest sustained warm anomalies in the time series. The 2019–2020 period shows warm anomalies in the summer and fall that are only observed near the surface.
They also analyze the local winds during years with and without MHWs and find that spring/summer upwelling favorable, or northerly winds, which are important for bringing cold, nutrient rich water to the surface in coastal regions, interrupt MHW events and can lessen extreme heating during MHWs in coastal waters as illustrated in Figure 33.
The three periods detailed in Figure 33 show warmer daily surface temperatures during the MHW years than the non‐MHW years and several days during 2014–2016 with surface and bottom anomalies greater than 4°C and during 2014–2016 and 2019–2020 with surface anomalies greater than 4°C (Figure 12a). During upwelling favorable winds (negative wind stress), the three periods follow similar patterns with colder surface temperatures typically associated with higher wind stress magnitudes. During downwelling‐favorable winds (positive wind stress), 2014–2016 is substantially warmer at the surface than the other periods at all wind stress values.
[caption id="attachment_36388" align="alignnone" width="526"] Figure 33: 8‐Day low‐pass filtered surface temperature at NH‐10/CE02SHSM for (a) 1999–2000, (d) 2014–2015, and (g) 2019–2020; 8‐day low‐pass filtered along‐shelf surface velocity for (b) 1999–2000, (e) 2014–2015, and (h) 2019–2020; and NDBC 46050 wind stress vectors (thin light lines) and along‐shelf 8‐day wind stress (thick lines) (c) 1999–2000, (f) 2014–2015, and (i) 2019–2020. Events identified as surface marine heatwaves are shaded in gray. The thick black line in panels (a–b), (d–e), and (g–h) is the climatological mean computed over the full NH‐10 time series (Figure 33c), repeated twice, and the thin black lines are the 90th and tenth percentiles.[/caption]
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References:
Cervantes, B. T., Fewings, M. R., & Risien, C. M. (2024). Subsurface temperature anomalies off central Oregon during 2014–2021. Journal of Geophysical Research: Oceans, 129, e2023JC020565. https://doi.org/10.1029/2023JC020565
Read MoreSoundscapes Spanning the Oregon Margin and 300 Miles Offshore
Figure 32: An example of a daily spectrogram generated by the RCA Data Team spectrogram viewer. A Humpback whale song is visible throughout the day at ~40-1000 Hz. A chorus of Fin Whale vocalizations is visible at 20-40 Hz. A weather event is visible at 0100, and a ship passage at 2200.[/caption]
The Regional Cabled Array (RCA) operates six broadband hydrophones that continuously capture soundscapes across the Cascadia Margin (Oregon Shelf and Oregon Offshore – seafloor), near the toe of the margin (Slope Base -seafloor and 200 m water depth) and 300 miles offshore at Axial Seamount (Axial Base – seafloor and 200 m water depth). The hydrophones, operational since 2014, capture signals from 10-64,000 Hz, including vessel traffic, marine mammal vocalization, wind, surf, and seismic events. The RCA broadband acoustic archive currently contains forty years (350,000 hours) of acoustic data in miniSeed format.
The RCA Data Team has developed a pipeline that can summarize and visualize a year of hydrophone data in 30 minutes. The spectrograms output (see Figure 32) by this pipeline are now easily accessible through an interactive viewer on the RCA’s Data Dashboard. The spectrogram viewer will make OOI-RCA broadband hydrophone data more searchable and accessible to data users and strengthen QA/QC of RCA acoustic data. Any day of hydrophone data, since 2014, will be viewable in minute/hybrid-millidecade resolution. The pipeline also enables users to convert RCA acoustic data to audio format (FLAC or WAV) in bulk. The spectrogram viewer was developed with input and guidance from the Ocean Data Lab at University of Washington and the Monterey Bay Aquarium Research Institute Soundscape team. It utilizes open source acoustic software tools – ooipy, pypam, and mbari-pbp.
Read MoreBloom Compression Alongside Marine Heatwaves Contemporary with the Oregon Upwelling Season
Black et al. (2024) examine the impacts of marine heatwave (MHW) events on upwelling-driven blooms off the Oregon coast. They combine OOI data from Endurance moorings off Oregon with satellite data and indices of upwelling and MHW presence to determine how MHW’s impact these blooms. Their work focuses on MHWs and coincident events that occurred off Oregon during the summers of 2015–2023. They found the presence of MHW’s limited the offshore extent of phytoplankton blooms. In late summer 2015 and 2019, both documented MHW years, coastal phytoplankton biomass extended on average 6 and 9 km offshore of the shelf break along the Newport Hydrographic Line, respectively. During years not influenced by anomalous warming, coastal biomass extended over 34 km offshore of the shelf break. Reduced biomass also occurs with reduced upwelling transport and nutrient flux during these anomalous warm periods. However, the enhanced front associated with a MHW aids in the compression of phytoplankton closer to shore. Over shorter events, heatwaves propagating far inshore also coincide with reduced chlorophyll a and sea-surface density at select cross-shelf locations, further supporting a physical displacement mechanism. Paired with the physiological impacts on communities, heatwave-reinforced physical confinement of blooms over the inner-shelf may have a measurable effect on the gravitational flux and alongshore transport of particulate organic carbon. Black is a PhD student at Oregon State University and notes that all data used in the paper, including of course OOI data, are open source. They provide details regarding data access methods and intermediate processing steps along with code modules to reproduce the work at https://github.com/IanTBlack/oregon-shelf-mhw.
Black et al. focus much of their analysis on the Oregon Offshore mooring, CE04 (Fig. x). Here they show individual warm events aligned with periods where Chl a was much lower than the time-series average and the climatological mean. The analysis period for 2019 had the lowest average Chl a across all years. From the CE04-derived Chl a climatology, they observed an occurrence of a regular spring bloom (April) and a summer bloom (September). The peak of the summer bloom appears contemporary with the warmest time of year at CE04, and years 2019 and 2023 were the only years that experienced MHWs during this same period. The summer blooms of 2019 and 2023 at CE04 were also noticeably suppressed and difficult to differentiate from surrounding Chl a values.
[caption id="attachment_35688" align="alignnone" width="624"] Figure 28: Ocean Observatories Initiative (OOI) CE04, Coastal Upwelling Transport Index (CUTI), and Biologically Effective Upwelling Transport Index (BEUTI) time series between 2015 and 2023. Daily mean values are in light blue. Red vertical spans indicate potential marine heatwave (MHW) events and gray vertical spans indicate the time between the spring and fall transition dates. A centered 11-d rolling mean was applied to smooth the data (black).[/caption]
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Reference:
I Black, IT, Kavanaugh, MT, Reimers, CE. “Bloom compression alongside marine heatwaves contemporary with the Oregon upwelling season.” Limnology and Oceanography, no. (2024): First published: 16 December 2024, https://doi.org/10.1002/lno.12757
Read MoreBringing Computer VISION into the Classroom Utilizing RCA Imagery and the OOI Jupyter Hub
There is a rapidly growing demand in Earth system science for workforce expertise in machine learning. To increase marine science students understanding of, and ability to use, artificial intelligence tools, Dr. Katie Bigham and School of Oceanography undergraduate student Atticus Carter are teaching an undergraduate class focused on applying “computer vision” (processing imagery with the computer) to marine science problems. The Computer Vision Across Marine Sciences prototype course for UW undergraduates in the Ocean Technology program includes the development of a Jupyter Binder (a notebook collecting multiple Jupyter Notebooks). Currently, an enormous amount of time is required to manually process imagery collected in marine environments, resulting in a major bottleneck for all research utilizing marine imagery. In this course, students gain an understanding of computer vision capabilities, model training and evaluation, and research applications. The course utilizes real-world datasets from the OOI Regional Cabled Array (RCA), including imagery from remotely operated vehicles utilized on RCA cruises and fixed camera imagery on the array, and from other systems, exposing students to diverse marine habitats. For their final projects, students will develop bespoke models utilizing datasets of their choosing, including RCA imagery. Students can employ the OOI Jupyter Hub for additional computational power, facilitating easy access to imagery and necessary resources. In collaboration with a faculty member in the UW School of Education quantitative data are collected on student learning and feedback for course improvement. The open-access course text is actively under development and is accessible at OceanCV.org. The team aims to improve the materials based on student feedback. Carter will present findings and learnings from the first version of the class at ASLO 2025 Aquatic Sciences Meeting in the Building Data Literacy Skills in the Next Generation of Aquatic Scientists session hosted by OOI Data Labs.
[caption id="attachment_35685" align="alignnone" width="640"] Figure 27: Introductory page for Computer Vision Across Marine Sciences Jupyter Binder and example of artificial intelligence-based predictions of animals in imagery collected by the Regional Cabled Array digital still camera at Southern Hydrate Ridge. This work is supported by an NSF OCE Postdoctoral Research Fellowship to Dr. K. Bigham, University of Washington.[/caption]
Read More Irminger Sea Carbon Cycle
The high-latitude North Atlantic, a region with high phytoplankton production in the spring and deep convection in the winter, is of particular importance for the global carbon cycle. The vertical transport of carbon from near the surface into the deep ocean, by combination of biological and physical processes, is known as the biological carbon pump. The carbon pump is particularly active in the Irminger Sea, yet the carbon budget, and its seasonal and interannual variability, are poorly known. A study by Yoder et al. (2024) used carbon system data from multiple observational assets (moorings and CTD casts) of the OOI Irminger Sea Array to assess net community production in the mixed layer and the implications for the biological pump in this region.
Data analysis was challenging, because it involved working with multiple instrument types, gappy records, calibration offsets, and other idiosyncrasies. In addition, data from multiple instruments and observing platforms needed to be combined to produce continuous records. The primary sensors utilized were pH and pCO2. These are difficult sensors to work with, to the extent that a community workshop was convened to develop a “users guide” to best practices for analysis (Palevsky et al., 2023). Yoder et al. were able to quality control, cross-calibrate, and merge data from the OOI surface mooring, flanking moorings, gliders and shipboard CTD casts (Fogaren and Palevsky 2023; Palevsky et al. 2023) to create the first multi‐year time series of the inorganic carbon system for the Irminger Sea mixed layer. This remarkable data set, based on instruments with sample rates of 1-2 hours, provides a seven-year record with near-daily resolution (Figure 28).
The time series results (Figure 3) showed that carbon system variables (dissolved inorganic carbon (DIC), total alkalinity (TA), and partial pressure of CO2 (pCO2)) co-vary through the annual cycle, with minimums in late summer at the end of the productive season and maximums in winter. The summer draw-down of pCO2 indicates that biophysical effects, rather than temperature, are the primary drivers of pCO2 variability. The influence of vertical mixing and primary productivity can be clearly seen in DIC and TA. In the subpolar North Atlantic, shoaling of the mixed layer in spring is generally associated with spring phytoplankton blooms, as indicated by increasing chlorophyll (Chl) concentration. Interestingly, it is found that highest integrated rates of DIC removal from the mixed layer via photosynthesis take place prior to mixed layer shoaling.
After a thorough analysis that included mixed layer budgets of DIC and TA, followed by assessment of gas exchange, physical transport, and the hydrologic cycle, the authors conclude that strong biological drawdown is the primary removal mechanism of inorganic carbon from the mixed layer. Furthermore, they point out the importance of interannual variability in both the drivers of and resulting magnitude of surface carbon cycling. This is primarily due to variability in net community production. Acknowledging the challenges taken on by OOI to maintain an array in the Irminger Sea, the authors note that “collecting observational data is both costly and challenging, however if only 1 year of data is collected or multiple years are averaged together, [carbon system dynamics] … will be misrepresented.”
This project shows the potential for OOI data, with appropriate processing and analysis, to provide unique insights into the ocean carbon system. It is notable that the authors made a substantial effort to calibrate and combine data from multiple instruments and moorings, and to take advantage of ancillary data (e.g. gliders, OOI CTD casts, and non-OOI CTD casts) in their processing. Enabling this type of analysis was a goal in the design of the multi-platform OOI Arrays and shipboard validation protocols.
[caption id="attachment_34983" align="alignnone" width="623"] Time series of dissolved inorganic carbon (DIC), total alkalinity (TA), partial pressure of CO2 (pCO2) temperature, chlorophyll-a (Chl), and mixed layer depth (MLD) in the Irminger Sea mixed layer from 2015-2022. Colors identify annual cycles. From Yoder et al., 2024.[/caption]
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References:
- Fogaren, K. E., Palevsky, H. I. (2023) Bottle-calibrated dissolved oxygen profiles from yearly turn-around cruises for the Ocean Observations Initiative (OOI) Irminger Sea Array 2014 – 2022. Biological and Chemical Oceanography Data Management Office (BCO-DMO). Version Date 2023-07-19 doi:10.26008/1912/bco-dmo.904721.1
- Palevsky, H.I., S. Clayton and 23 co-authors, (2023).OOI Biogeochemical Sensor Data: Best Practices & User Guide Global Ocean Observing System, 1(1.1), 1–135. https://doi.org/10.25607/OBP-1865.2
- Palevsky, H. I., Fogaren, K. E., Nicholson, D. P., Yoder, M. (2023) Supplementary discrete sample measurements of dissolved oxygen, dissolved inorganic carbon, and total alkalinity from Ocean Observatories Initiative (OOI) cruises to the Irminger Sea Array 2018-2019. Biological and Chemical Oceanography Data Management Office (BCO-DMO). Version Date 2023-07-19 doi:10.26008/1912/bco-dmo.904722.1
- Yoder, M. F., Palevsky, H. I., & Fogaren, K. E. (2024). Net community production and inorganic carbon cycling in the central Irminger Sea. J. Geophys. Res., 129, e2024JC021027. https://doi.org/10.1029/2024JC021027
Impact of Ocean Model Resolution on Temperature Inversions in the Northeast Pacific Ocean
Temperature inversions are a local vertical minimum in temperature located at a shallower depth than a local maximum. In the Northeast Pacific, several water masses and multiple mechanisms for transforming or advecting ocean temperature (cold air events, upwelling, river discharge, cross-shelf eddy transport) create favorable conditions for temperature inversions. Modeling these temperature inversions is challenging. Osborne et al. (2023) analyze observations from 2020 and 2021 to characterize inversions in the Northeast Pacific. The data for these observations come largely from OOI Endurance Array gliders accessed through the GTS database. They compare the observed inversions to model results from the U.S. Navy’s Global Ocean Forecast System version 3.1 (GOFS 3.1) and two instances of the Navy Coastal Ocean Model. Temperature inversions are observed to be present in about 45% of profiles with temperature minimums between 50 – 150 m, temperature maximums between 75 – 175 m, and inversion thickness almost entirely less than 40 m. Modeled temperature inversions are present in only about 5% of model-observations comparisons, with weaker, shallower minimums. This is attributed to two primary causes: coarse model resolution at the inversion depth and the assimilation process which low-pass filters temperature, making inversions weaker. Osborn et al. identify additional work to test the impact of vertical grids on improving model performance.
[caption id="attachment_34977" align="alignnone" width="392"] Maps of inversion counts for observed profiles collected during 2020-2021 and analyzed in this work. Profiles have been filtered to be offshore of the 200 m isobath and to only one profile per collection platform per day (e.g., one profile per glider per day). Black line near the coast marks the 200 m isobath. Light gray indicates no profiles collected during the study period.[/caption]
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References:
J. Osborne V, C. M. Amos and G. A. Jacobs, “Impact of Ocean Model Resolution on Temperature Inversions in the Northeast Pacific Ocean,” OCEANS 2023 – MTS/IEEE U.S. Gulf Coast, Biloxi, MS, USA, 2023, pp. 1-8, doi: 10.23919/OCEANS52994. 2023.10337390.
Read MoreNew Hints When Axial Might Erupt: Precursor Events Detected Through Machine Learning
A recent paper by Wang et al., “Volcanic precursor revealed by machine learning offers new eruption forecasting capability” [1] describes the characterization of time-dependent spectral features of earthquakes at Axial Seamount prior to the 2015 using unsupervised machine learning (this method applies algorithms to analyze data without humans in the loop). A major finding from this work is the identification of a distinct burst of mixed-frequency earthquake (MEF) signals that rapidly increased 15 hours prior to the start of the eruption, peaked one hour before lavas reached the seafloor, and earthquakes at Axial Seamount prior to the 2015 using unsupervised machine learning (this method applies algorithms to analyze data without humans in the loop). A major finding from this work is the identification of a distinct burst of mixed-frequency earthquake (MEF) signals that rapidly increased 15 hours prior to the start of the eruption, peaked one hour before lavas reached the seafloor, and migrated along pre-existing faults. The earthquakes are thought to reflect brittle failure driven by magma migration and/or degassing of volatiles. The source mantle beneath Axial Seamount contains extremely high CO2 concentrations leading to high concentrations in the melts [3,4]. MEFs were detected for months prior to the eruption, which could result from volatile release associated with inflation of the sills that feed Axial [5]. Importantly, the identification of these signals may help forecasting of the upcoming Axial eruption and may also be applied to other active volcanoes.
The authors utilized a wealth of geophysical data from the past decade to present an integrated view of Axial (Figure 1) including a subset of earthquake data 4 months prior to the eruption (67,767 out of their >240,000 earthquake catalogue from the RCA seismic array [2]), coupled with bottom pressure from BOTPT instruments at the Central Caldera and Eastern Caldera sites, and 3D modeling.
[caption id="attachment_34973" align="alignnone" width="640"]
After Wang et al., 2024 [1] Figures 1 and 4: a) Heatmap of earthquake density at Axial Seamount from Nov 2014 to Dec 2021 [2]. Image highlights mixed‐frequency earthquakes (MFEs – light blue dots) one day before the eruption, the caldera rim (white solid line), the 1.5 km depth contour of the Axial magma chamber (AMC)(dashed white line), eruptive fissures (orange lines), lava flows of the 2015 eruption (yellow lines), the location of the RCA short-period seismometer array (white triangles),broadband seismometer AXCC1 and bottom pressure tilt instruments (MJ03E and MJ03F. Heatmap shows the number of earthquakes in each 25 m × 25 m bins. b) Cartoon summarizing observations. Tidally‐driven earthquakes occur on caldera ring faults, while the MFEs track movement of volatiles and magma prior to the eruption. c) possible mechanisms of the MFEs. ① and ② correspond to crack opening and volatile/magma influx processes.[/caption]___________________
References:
[1] Wang, K., F. Waldhouser, M. Tolstoy, D. Schaff, T. Sawi, W.S.D. Wilcock, and Y.J. Tan (2024) Volcanic precursor revealed by machine learning offers new eruption forecasting capability. Geophyiscal Research Letters, 51 (19) https://doi.org/10.1029/2024GL108631.
[2] Wang, K., F. Waldhouser, D.P. Schaff, M. Tolstoy, W.S.D. Wilcock, and Y.J. Tan (2024) Real-time detection of volcanic unrest and eruption at Axial Seamount using machine learning. Seismological Research Letters, 95, 2651–2662, doi: 10.1785/0220240086.
[3] Helo, C., M.-A. Longpre, N. Shimizu, D.A. Clague and J. Stix. (2011) Explosive eruptions at mid-ocean ridges driven by CO2-rich magmas. Nature Geoscience. 4, 260–263 (2011). https://doi.org/10.1038/ngeo1104.
[4] Dixon, J. E., E. Stolper, E., and J.R. Delaney (1988). Infrared spectroscopic measurements of CO2 and H2O in Juan de Fuca Ridge basaltic glasses. Earth and Planetary Science Letters, 90(1), 87–104. https://doi.org/10.1016/0012‐821x(88)90114‐8.
Read MoreScience Highlights
Researchers from around the globe are using OOI data to identify short-term changes and long-term trends in the changing global ocean.
The Science Highlights included in the report below were compiled from quarterly reports submitted by the Ocean Observatories Initiative to the National Science Foundation from 2020-2022. They represent only a fraction of the scientific findings that are based on OOI data. A complete list of peer-reviewed publications based on OOI data can be found here.
[embed]https://issuu.com/oceanobservatoriesinitiative/docs/ooi_science_highlights_autosaved_.pptx?fr=sNzM0ZjQ2ODUxNTQ[/embed]
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