Posts by Amber Coogan
Global Estimate of Turbulent Heat Flux
(Adapted from Wang et al., 2026)
The net heat exchange between atmosphere and ocean is composed of radiative and turbulent components. Radiative components are the net shortwave and longwave fluxes. Turbulent components are the sensible heat flux (SHF) and latent heat flux (LHF), which are an important part of the global atmosphere-ocean heat exchange. The turbulent fluxes can be measured directly, but are often parameterized based on air-sea temperature and humidity differences and the wind stress. These parameterizations are sometimes called semi-empirical bulk formulas, as exemplified by the COARE algorithm (Edson et al., 2013). The bulk formulas for turbulent flux are tuned using available observations, but may not perform well in environmental conditions where validation is lacking. Thus, there is interest in improving global fields of SHF and LHF using other approaches.
A recent study by Wang et al. (2026) used a neural network approach to create high-resolution (0.25 degree) global ocean flux fields. For ground truth, the authors utilized nearly 200 buoys from 13 projects and arrays, including 6 buoys from OOI. The OOI Southern Ocean, Argentine Basin and Irminger Sea Arrays were particularly important for extending the geographic coverage to high latitude where long-term marine meteorological data are scarce (Fig. 3). The analysis period (1993 to 2017) was determined by the availability of the in-situ datasets.
The in-situ meteorological variables were used to compute turbulent heat fluxes using the COARE 3.6 algorithm (Edson et al., 2013). A unique aspect of the processing was to quality-control the fluxes using the expected values for the Bowen ratio, defined as B = SHF/LHF. The quality-controlled, observation-based fluxes were then used to train and validate the neural network model. The model jointly estimated SHF and LHF while constrained by limits to the Bowen ratio based on observations. The model was further trained using multiple “learning data sets”, which included atmospheric reanalysis products, sea surface temperature, salinity, wind and wave products, and satellite-derived sea surface height.
The results were compared to 7 different global flux products. Notable in the comparisons was the consistently low RMS error in Bowen ratio across all ocean basins for the neural network model. Considering temporal trends over the analysis period (Fig. 2), showed that the neural network model had significant positive trends in SHF (different from all but one of the products) and LHF (similar to four of the products). Spatial maps of the trends indicated that the positive trends tend to be at high latitude for SHF and in the tropics and mid-latitudes for LHF.
This study highlights the value of long-term ocean observations, and in particular the importance of meteorological observations at high latitude. The constrained neural network approach showed good results compared to other global flux products. The resulting 0.25 degree, gridded global turbulent flux field is an important complement to existing reanalysis fluxes and has the advantage of more accurate representation of the Bowen ratio. As noted by the authors, improved understanding of turbulent heat fluxes on a global scale is an important step towards achieving global closure of the ocean surface energy budget.
[caption id="attachment_38060" align="alignnone" width="958"]
Figure 1: Locations of 197 buoy sites used in the analysis. Note the relative scarcity of sites in the southern oceans and in the high latitude Atlantic Ocean. From Wang et al., 2026.[/caption]
[caption id="attachment_38061" align="alignnone" width="698"]
Figure 2: Time variability of global-mean sensible (upper) and latent (lower) heat flux for the neural network (BrTHF) and seven other flux products. Trends (right hand panel) that pass a significance test are shown with an asterisk. From Wang et al., 2026.[/caption]
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References:
Wang, Y., R. Tang, M. Liu, L. Huang and Z.-L. Li, 2026. Bowen ratio-constrained global dataset of bulk air-sea turbulent heat fluxes from 1993-2017, Earth Sys. Sci. Data, 18, 3391-3414, https://doi.org/10.5194/essd-18-3391-2026.
Edson, J.B., K. Venkata, R. A. Weller, S.P. Bigorre, A.J. Plueddemann, C. Fairall, S.D. Miller, L. Mahrt, D. Vickers and H. Hersbach. 2013. On the Exchange of Momentum over the Open Ocean, J. Phys. Oceanogr., 43, 1589–1610, https://doi.org/10.1175/jpo-d-12-0173.1.
Read MoreA Method for Quantifying Correlation in the Shape of Oceanographic Profile Data
Vertical profiles are widely used to identify physical and biogeochemical features of the water column. Recent studies have shown that oceanographic profiles can be represented as functional data objects, where each profile is treated as a single datum and expressed as a function of pressure. This study applies a recently developed technique, which defines a scalar correlation coefficient for functional data, to the analysis of oceanographic profiles. The method represents each profile using basis functions, whose associated weightings are termed basis coefficients, and quantifies dependence through the variability of these coefficients. An important advantage of this method is that the resulting correlation coefficient reflects similarities in overall profile shape, not just correlations between values at specific depths. Two applications of this method are explored: calculating the correlation coefficient between two different oceanographic variables, and estimating the temporal autocorrelation function of a single variable. Each application is demonstrated using two case study datasets: (1) the Coastal Endurance Washington Offshore Profiler Mooring and (2) biogeochemical-Argo floats. The case study using the Washington Offshore Profiler Mooring demonstrates how the method can be used to identify physical drivers of variability in biogeochemical profile structure. For example, Figure 17 shows temporal autocorrelation functions computed from the full profiles of different variables measured by the Washington Offshore profiler mooring. By computing correlation coefficients and autocorrelation functions directly from full profiles, the authors’ approach complements existing methods and naturally incorporates profile shape.
The method has broad potential applications across oceanographic research. It can be applied to the expanding global collection of profiling observations, as well as to ocean model output, to quantify spatio-temporal variability and coupled physical–biogeochemical dynamics. Potential uses include observing system design, evaluation of sampling strategies, assessment of advective versus local variability, validation of machine learning products, and investigation of ecosystem scale decorrelation timescales. A variety of methodological approaches could be integrated with this technique, such as functional regression, clustering, spatial modelling or time series analyses. Together, these extensions would further enhance the ability to quantify and interpret variability in the rapidly growing volume of oceanographic profiling data.
[caption id="attachment_38053" align="alignnone" width="718"] Temporal auto correlation functions for four oceanographic variables measured as profiles at the Coastal Endurance Washington Offshore Profiler Mooring (CE09OSPM).[/caption]
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Reference:
Taylor, M. and S. Henson, 2026. “A method for quantifying correlation in the shape of oceanographic profile data.” Ocean Sci. 22(2). https://doi.org/10.5194/os-22-1377-2026.
Scaling Up Discovery: A Machine Learning–Assisted Framework for Rapid Annotation of Benthic Megafauna in Marine Imagery
Methane hydrate-based ecosystems are amazingly abundant along the ~1000 km-long Cascadia Margin, from Cape Mendocino in Northern California to the northern tip of Vancouver Island. Here, 3,500 methane bubble streams, clustered into more than 1,300 emission sites, have been documented. These sediment-dominated environments, underlain by tonnes of methane hydrate, host robust and diverse microbial communities and are “hot spots” of megafauna. Yet only three sites have been investigated in detail. One is the Regional Cabled Array (RCA) Southern Hydrate Ridge (SHR) site, designated as essential fish habitat. A suite of instruments operates at SHR to understand how these important and dynamic ecosystems respond as shallow hydrate breaks down in response to seismic events, bottom-water warming, and seasonal to decadal change. Among them is a digital still camera focused on the most active emission site, “Einstein’s Grotto.” For over a decade, the camera has streamed 3 images every half hour, 24/7, to shore.
A critical bottleneck in converting the enormous image archive at SHR into reliable ecological data has been the labor-intensive manual identification and annotation of organisms. In “A Machine Learning Assisted Method for Rapidly Annotating Benthic Megafauna in Large Volumes of Marine Imagery,” Bigham and Carter (2026) show how AI, combined with human expertise, can break this bottleneck. Their iterative workflow pairs model predictions with expert validation, correction of misidentified organisms, and retraining to progressively improve performance.
The results highlight how powerful this approach can be. The workflow generated 92,153 annotations across 50,840 images and 10 morphological taxonomic groups in just six weeks. Doing this by hand would have taken approximately 5.9 years. Expert review remained essential for correcting false detections and classification errors, and for recognizing the model’s limits in complex underwater settings.
This study provides a practical, expandable framework for transforming continuous marine imagery into ecological time series that track changes in abundance, community composition, and ecosystem dynamics. With local training and validation, it could serve other habitats, imaging platforms, and long-term observing programs. This work highlights the significant discovery opportunities when RCA’s high-density, continuous data streams are paired with AI and human expertise. It offers a scalable pathway toward more timely, reproducible, and data-rich assessments of deep-sea ecosystems, strengthening our ability to detect ecological change in a rapidly changing ocean.
[caption id="attachment_38050" align="alignnone" width="936"]
Example digital still image from the cabled camera at the Regional Cabled Array methane seep site Einstein’s Grotto, Southern Hydrate Ridge. a) Schools of black cod forage above the sedimented seafloor in this nutrient rich environment. Clam shell chaff collects in depressions. b) Annotated image using a human-in-the-loop workflow built around YOLO. A detector trained on manually annotated imagery ran inference over the full image set and wrote YOLO text files with predicted classes and bounding boxes.[/caption]
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Reference: Bigham, K.T., and Carter, A (2026) A machine learning assisted method for rapidly annotating benthic megafauna in large volumes of marine imagery. Limnology and Oceanography: Methods. doi: 10.1002/lom3.70057.
Read MoreOOI Seeks Community Ambassadors to Highlight Research Using OOI Data
Are you using Ocean Observatories Initiative (OOI) data in your research, teaching, or other projects? OOI wants to hear from you!
The OOI Community Ambassador Program recognizes researchers, educators, and other professionals who use OOI data in their work. By featuring their research and experiences, the program highlights the many ways OOI observations are being used across the ocean science community.
Current Community Ambassadors represent a range of research areas, including coastal ocean circulation, marine ecosystems, ocean acoustics, and data analysis. Their work demonstrates how sustained ocean observations contribute to scientific research, the development of new tools, and educational applications.
The Community Ambassador Program is part of OOI’s broader Ambassador Program, which also includes Data Ambassadors. While Community Ambassadors are researchers and educators who use OOI observations in their work, Data Ambassadors represent the scientific and technical expertise within OOI, including the Principal Investigators and their broader teams.
Share Your OOI Research
OOI is looking to feature additional Community Ambassadors who are using its data in research, education, or other applications. Participants are highlighted through short profiles on the OOI website, providing an opportunity to share their work with the broader ocean science community.
Interested in being featured? Visit the OOI Ambassador Program webpage to learn more, or contact OOI Community Engagement Manager Amber Coogan at amber.coogan@whoi.edu.
Three Moorings Redeployed at OOI’s Coastal Endurance Array
Three moorings have been redeployed at OOI’s Coastal Endurance Array, with data now flowing and additional deployments planned for spring 2027.
Three moorings have been successfully redeployed at the U.S. National Science Foundation Ocean Observatories Initiative’s (OOI) Coastal Endurance Array, marking an important step toward restoring sustained ocean observations off the Pacific Northwest coast.
During the October 3–5 Endurance Array 25 expedition aboard the R/V Sally Ride, the Oregon State University (OSU) team redeployed three of the array’s seven moorings. Data from the redeployed moorings are now flowing to the OOI Data Explorer, with meteorological observations also being shared through NOAA’s National Data Buoy Center (NDBC).
The remaining four moorings are scheduled for redeployment in spring 2027, continuing the phased restoration of the Endurance Array’s observing capabilities.
[caption id="attachment_37997" align="alignnone" width="1226"]
Photo credit: Ed Dever, OSU[/caption]
Read More Endurance Energy drills world’s first subsea geothermal well
Endurance Energy has drilled and completed the world’s first subsea geothermal well, operated a power plant nearly a mile beneath the Pacific from Seattle, and demonstrated net-positive power from an active underwater volcano. Read more from Energy Global.
Read MoreConnect with OOI at AGU 2026
The U.S. National Science Foundation Ocean Observatories Initiative (OOI) will be represented at the 2026 American Geophysical Union (AGU) Annual Meeting, taking place December 7–11 in San Francisco, California. The meeting will bring together researchers and ocean observing professionals to share scientific discoveries, explore emerging technologies, and discuss the future of sustained ocean observations.
Several sessions and community events will highlight advances in ocean observing and opportunities for collaboration.
OOI-Related Scientific Sessions
Long-Term Ocean Observatories as Drivers of Sensor and Data Science Innovation
This session will highlight advances in ocean observing technologies and data systems, including innovations in instrumentation, data processing, and long-term monitoring. Presentations will explore how sustained ocean observations support scientific discovery and drive improvements in observing capabilities.
AI and Machine Learning for Sustained Ocean Observing: Progress and the Road Ahead
This session will explore how artificial intelligence (AI) and machine learning (ML) are advancing the analysis, interpretation, and application of data from long-term ocean observing systems. Topics include automated data processing, quality control, and emerging approaches to extracting scientific insights from large observational datasets.
OOIFB Town Hall
The OOI Facilities Board (OOIFB) will host a Town Hall focused on emerging seafloor sensing technologies, future scientific opportunities, and lessons learned from OOI’s experience supporting sustained ocean observations. The discussion will provide an opportunity for community engagement around advances in ocean observing infrastructure and technology.
Community Forum on the Future of Ocean Observing
Ahead of AGU, the OOIFB is also organizing a Community Forum on the Future of Ocean Observing, planned for December 4–5 in San Francisco. Building on discussions held during the Ocean Observing Exchanges and community input submitted in response to NSF’s recent Dear Colleague Letter, the forum will continue conversations about future ocean observing priorities and capabilities.
Additional details, including session schedules and opportunities to participate, will be shared as they become available.
Read MoreScientists, fishermen want feds to keep funding deep-ocean observation system
New England scientists and fishermen are urging the Trump administration to continue funding the Ocean Observatories Initiative — a network of buoys and gliders that collect data from remote parts of the ocean. The National Science Foundation began quietly dismantling the deep-ocean observation system last summer, until the effort was blocked by the U.S. Senate. Read the full story.
Read MoreNSF Funding Opportunities Relevant to the OOI Community
The U.S. National Science Foundation (NSF) recently announced two funding opportunities that may be of interest to researchers working with ocean observing technologies, infrastructure, and data.
NSF X-Labs: AI for Physical Systems
NSF X-Labs is inviting proposals from interdisciplinary teams developing early-stage technologies that enable new forms of artificial intelligence (AI) integration in physical systems. Areas of interest include advanced sensors, robotics, embodied systems, human-robot interfaces, and cyber-physical systems.
The opportunity may be relevant to researchers exploring applications of AI in ocean observing, autonomous systems, sensing technologies, and other tools that support ocean science and scientific discovery.
Learn more about NSF X-Labs: AI for Physical Systems →
NSF 26-512: Unlocking Dataset Value for AI-Enabled Scientific Discovery (AI Datasets)
The AI Datasets program supports efforts to increase the scientific value of existing datasets by making them more accessible, easier to integrate, and better prepared for AI-driven and automated analysis.
The program includes opportunities to develop data pipelines, improve metadata and feature extraction, integrate multiple datasets, and augment or harmonize existing datasets. These goals may be particularly relevant to researchers interested in using long-term ocean observing datasets for AI-enabled scientific discovery.
NSF anticipates Planning grants of up to $200,000, Impact awards of up to $2 million, and Flagship awards of up to $5 million. The next full proposal deadline is November 4, 2026.
Learn more about NSF 26-512: AI Datasets →
Researchers interested in these opportunities should review the full NSF announcements and solicitation for eligibility, requirements, deadlines, and application guidance.
Read MoreOOI Plankton Imagery and Co-Located Environmental Data Now Ready for Analysis
Since April 2024, the OOI has collected plankton imagery with an Imaging Flow CytoBot (IFCB) on a mooring in the Pioneer Mid-Atlantic Bight (MAB) array. The IFCB is deployed at 7 m depth on the Central Surface Mooring (CNSM), which is located approximately 45 km east of Nags Head, NC in about 30 m of water. The CNSM measures a variety of meteorological and oceanographic variables. A companion mooring at the Central Site contains an ADCP and a wave-powered profiler operating from 2 to 22 m depth. Other moorings and gliders within the Pioneer MAB Array make multidisciplinary measurements from the air-sea interface to the sea floor.
Initially deployed in the spring of 2024, OOI now has 18 months of moored IFCB data, spanning April 2024-April 2025 and November 2025-April 2026. The data are freely available and can be accessed and visualized in several ways. For help choosing a data access point that best fits your data needs, please reference the Quick Access Links post in the IFCB Community forum on the OOI’s Discourse site.
The IFCB dashboard is the primary access point for exploring recovered data, allowing display of images from recovered moored and shipboard data. The dashboard also provides geographical coordinates and allows users to filter samples by relevant tags. The tag for the Central Surface Mooring (“site_cp10cnsm”) can be used to browse a combined shipboard and moored dataset with over 7 million images (Fig. 1).
[caption id="attachment_37880" align="alignnone" width="624"]
Figure 1: OOI IFCB Dashboard timeline tagged with “site_cp10cnsm” to show both shipboard and moored data from April 2024 to April 2026.[/caption]
During deployment, the OOI Data Explorer shows selected parameters from the IFCB header file transmitted via satellite telemetry. The moored time series can be viewed individually to change the time period and data binning, see deployment dates, and read annotations. They can also be compared with time series data from other Pioneer MAB instruments.
Even after recovery, Data Explorer can be useful for exploring events of interest. For example, Winter Storm Gianna affected the Pioneer MAB around Feb 1, 2026. Gianna’s passage was associated with a rapid pressure drop and watermass changes, with a drop of about 1 PSU in salinity (Fig 2, upper). At the same time, there was an increase in IFCB images (more specifically, regions of interest, or ROIs) per ml of water. Evaluating imagery from the OOI IFCB Dashboard (Fig. 2, lower) shows a change in the assemblages of plankton and particles before and after the peak of storm activity.
Data Explorer views and IFCB Dashboard mosaics can help researchers identify which full resolution datasets they might want to access. The raw IFCB data files can be downloaded from the OOI’s Raw Data Archive or used directly within OOI’s JupyterHub from either the mooring or the cruise data. Resources for navigating and using the Raw Data Archive can be found on this page.
Researchers can join OOI’s IFCB community of practice and use the OOI Discourse forum, which serves as an entry point to both OOI data and the global IFCB community. After creating an account, users can post to the IFCB Community on Discourse to share methods and results, troubleshoot data issues, and connect with colleagues. Answers to common questions about working with IFCB data may be found in the McLane IFCB Users Google Group; information about accessing those resources is available in this post. As a complement to the OOI and McLane forums, the OOI Facilities Board (OOIFB) is sponsoring a four-part series of IFCB webinars in 2026. More information about the series can be found at https://ooifb.org/meetings/2026-ifcb-webinar-series/ including recordings of past webinars underneath the “Archived Webinars” heading.
[caption id="attachment_37889" align="alignnone" width="921"]
Figure 2: Upper panel: Data comparison chart from OOI Data Explorer showing ROIs per ml (green line with dots), practical salinity (orange line), and atmospheric pressure (purple line) before and after the eye of the nor’easter Winter Storm Gianna passed over CNSM on Feb. 1, 2026. Lower panels: Mosaics of images from the OOI IFCB Dashboard on Feb. 1, 2026 at 00:25 UTC (left) and 15:25 UTC (right).[/caption]
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