Science Highlights

Flooding and shoreline erosion are increasing threats to coastal communities. Coastal sea levels are influenced by multiple factors, including mean sea level rise, tides, storm surges, river outflow, and waves. A recent paper by Camargo et al. (2025) focused on the extent to which offshore circulation, in particular the shelfbreak jet on the New England shelf, contributed to sea level variability over the period 2014 – 2022. They found that roughly 30% of sea level variance during storm surges can be attributed to the shelfbreak jet.

The authors used a variety of data sets, including tide gauge stations along the New England coastline from Boston, MA to Atlantic City, NJ, wind stress from the ECMWF ERA-5 reanalysis, and shelfbreak jet transport from the OOI Pioneer Array. The jet transport was estimated using upward-looking ADCP data from the Central Inshore, Central, and Central Offshore moorings. The methodology and results are described in Carmargo et al., 2024 and the jet transport data set is available in Zenodo (Carmago, 2024). The data were de-tided and then band-pass filtered (1-15 days) to isolate the storm-surge component of sea level variability. Coherence between coastal sea level and jet transport for 1-15 day periods was established by Carmago et al. (2024).

The influence of jet transport and two components of wind stress (zonal and meridional) on storm surge was evaluated by means of a linear regression model that determined the regression coefficients for each parameter. The regression was run for each tide station using the previously computed jet transport and the ERA-5 wind stress closest to each station. Overall, the regression model explained about 60% of the storm surge variance, with jet transport and zonal wind stress explaining 28% and 38%, respectively. There was modest along-coast variability in the variance explained by each parameter (Figure 3): Variance explained by zonal wind stress tended to increase from NE to SW along the coast (Fall River to Bridgeport), while variance explained by the jet tended to decrease. The wind stress impact was identified as due to either onshore or downwelling-favorable winds. The jet impact was due to changes in the cross-shelf pressure gradient associated with the geostrophically-balanced currentCase studies for specific synoptic events highlight the complexity of coastal storm surge events and the broad suite of observational tools necessary to determine the influence of different mechanisms. The authors note that simple storm surge models will not capture the influence of offshore currents like the shelfbreak jet, and that such influence is likely to be found elsewhere on the US east and west coast.

___________________

References:

Camargo, C.M.L., C.G. Piecuch and B. Raubenheimer, 2025. Do ocean dynamics contribute to coastal floods? A case study of the shelfbreak jet and coastal sea level along southern New England, Earth’s Future, 13, https://doi.org/10.1029/2025EF006708.

Camargo, C. M. L. (2024). Shelfbreak jet transport from OOI pioneer [Dataset]. https://doi.org/10.5281/zenodo.10814048.

Camargo, C. M. L., Piecuch, C. G., & Raubenheimer, B. (2024). From shelfbreak to shoreline: Coastal sea level and local ocean dynamics in the Northwest Atlantic. Geophysical Research Letters, 51(14), e2024GL109583. https://doi.org/10.1029/2024GL109583 .

(Adapted from Stephens et al., 2025)

Upper ocean carbon budgets are difficult to constrain, and those for the mesopelagic zone come with particular challenges. A recent paper by Stephens et al. (2025) took on the challenge of a comprehensive carbon system budget for the upper mesopelagic zone (100-500 m) based on data from the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) program (Siegel et al., 2016). The 2018 EXPORTS field campaign was conducted at Ocean Station Papa in the Northeast Pacific to take advantage of the relatively modest surface forcing, shallow summer mixed layer, tightly coupled food web, and low mesoscale kinetic energy. Nevertheless, the study found that a steady-state assumption for the carbon system was likely not appropriate.

Measuring organic carbon supply and demand is challenging due to a variety of factors. Supply includes sinking particles, migrating zooplankton and fish, disaggregation, mixing and subduction. Demand comes primarily from bacteria and zooplankton. Measurement methods for each supply and demand term have errors, conversions to rates have uncertainties, and each process being measured may have a unique timescale over which a rate integration makes sense. EXPORTS was notable for increasing the number and variety of measurements available for monitoring the mesopelagic carbon budget. Stephens et al. take advantage of this by combining multiple measurement methods, quantifying errors and applying statistical methods for error analysis. 

The authors used observations from multiple sources. Near-surface data came from the PMEL Station Papa surface mooring. Shipboard profile data come from two ships operating during the EXPORTS field campaign as well as the OOI Station Papa cruise in 2018. Additional water column data came from two OOI Slocum gliders, one EXPORTS-operated Seaglider, and BGC Argo floats. The authors examined each carbon supply and demand estimate, calculating an uncertainty and discussing potential limitations (Stephens et al., Table 1). A Monte Carlo approach was used to assess overall uncertainty in supply and demand terms, resulting in the conclusion that supply was insufficient to meet demand (e.g. Fig. 1 below). The error analysis allowed the authors to conclude that the mismatch was not the result of problems in estimating supply or demand, but rather a problem with the assumption that supply and demand would balance within the analysis period. In other words, the system was not in steady state.

This project highlights the complexity of the carbon system in the upper ocean and the broad suite of observational tools necessary to address the carbon budget. The authors make three specific recommendations for improved quantification of the biological carbon pump: including the relevant midwater processes, capturing the range of relevant timescales, and providing redundancy in methodology.

___________________

References:

Stephens, B.M., and 20 co-authors, 2025. An upper-mesopelagic-zone carbon budget for the subarctic North Pacific, Biogeosciences, 22, 3301-3328, https://doi.org/10.5194/bg-22-3301-2025.

Siegel, D.A., K.O. Buesseler, M.J. Behrenfeld, C.R. Benitez-Nelson, E. Boss, M.A. Brzezinski, A. Burd, C.A. Carlson, E.A. D’Asaro, S.C. Doney, M.J. Perry, R.H.R. Stanley and D.K. Steinberg, 2016. Prediction of the Export and Fate of Global Ocean Net Primary Production: The EXPORTS Science Plan, Front. Mar. Sci., 3:22, https://doi.org/10.3389/fmars.2016.00022. 

(Adapted from Kohlman et al., 2024)

Marine Heat Waves (MHW; Hobday et al., 2016) are prolonged periods of extreme ocean sea surface temperature (SST) anomalies. MHWs are typically identified in satellite SST and/or ocean color records; subsurface data and interdisciplinary variables are often lacking. Ocean Station Papa (OSP) provided long-term, interdisciplinary, subsurface data to examine the physical and biochemical characteristics of a MHW in the Northeast Pacific.

MHWs may have multi-faceted causes, as well as impacts on primary production and higher trophic levels. A recent paper by Kohlman et al. (2024) examines the 2019 NE Pacific MHW using gridded satellite SST data and in-situ observations from multiple OSP platforms including the NOAA Pacific Marine Environmental Lab (PMEL) surface mooring, two OOI Flanking Moorings, the Applied Physics Lab (U. Washington) waverider mooring and shipboard samples from OOI and the Department of Fisheries and Oceans, Canada.

The 2019 MHW was identified in satellite SST data, but the Kohlman et al. study also assessed vertical stratification and the subsurface extent of the temperature signal. The PMEL surface mooring provided temperature and salinity (T/S) down to 300 m. The OOI Flanking Moorings extended the T/S data to 1500 m. The resulting composite time series from 2013-2020 is shown in Fig. 1. Both the extended 2013-2015 MHW and the 2019 MHW are identifiable. Subsurface temperature anomalies during 2013-2014 were strongest above the mixed layer depth (MLD). In the winter and spring of 2017, deeper waters (120–300 m) remained anomalously warm. This anomaly persisted into 2018 due to strong stratification from a fresher surface layer. During the 2019 MHW, anomalously warm waters extended down to 1000 m, whereas the 2013-2015 MHW extended only to about 150 m.

The authors used interdisciplinary data available from Station Papa platforms to assess the drivers and impacts of the 2019 MHW. They found that weaker winds and smaller significant wave height prior to the summer of 2019 created favorable pre-conditioning in the form of an unusually shallow winter MLD. During the MHW, they found that dissolved inorganic carbon and pCO2 decreased, while pH increased. Shipboard samples indicated a decrease in nutrients and an increase in primary productivity. Finally, they speculated that the increased productivity may have had an impact on higher trophic levels – more blue whale calls were recorded in 2019 at Station Papa than normal for Aug-Sep.

This project shows that the characteristics of MHWs are complex. Sustained, multi-disciplinary, subsurface observations are needed to unravel the drivers, pre-conditioning, characteristics, and impacts of these events. Station Papa, among the longest sustained ocean time series sites, is uniquely suited due to the task due to the collaborative observing effort at the site.

___________________

References:

Hobday, A.J., Alexander, L.V., Perkins, S.E., Smale, D.A., Straub, S.C., Oliver, E.C.J., et al. (2016). A hierarchical approach to defining marine heatwaves. Prog. Oceanog., 141(0079–6611), 227–238. https://doi.org/10.1016/j.pocean.2015.12.014.

Kohlman, C., Cronin, M.F., Dziak, R., Mellinger, D.K., Sutton, A., Galbraith, M., et al. (2024). The 2019 marine heatwave at Ocean Station Papa: A multi‐disciplinary assessment of ocean conditions and impacts on marine ecosystems. J. Geophys. Res., 129, e2023JC020167. https://doi.org/10.1029/2023JC020167.

(Adapted from Stopa et al., 2024)

There is interest in using satellite-based Synthetic Aperture Radar (SAR) imagery to assess phenomena related to atmospheric boundary layer stratification over the ocean. Obtaining such information with high spatial and temporal resolution would advance boundary layer research and would be beneficial to the development of offshore wind energy. However, monitoring marine boundary layer structure remotely is a challenge. A recent paper by Stopa et al. (2024) shows that it is possible to utilize SAR imagery, in conjunction with in-situ meteorology, to characterize boundary layer stratification.

The Central Surface Mooring (CNSM) of the OOI Pioneer New England Shelf Array is located near a region of offshore wind development where SAR imagery is also available. The authors use wind speed, air temperature, and sea surface temperature from the CNSM buoy to estimate the bulk Richardson number (Ri), a measure of marine atmospheric boundary layer stability. OOI quality control flags were used to identify good data, and suspect data were replaced with data from redundant sensors. CNSM data from 2014 – 2021 were used in the analysis.

SAR data are commonly used to provide wind speed estimates, but the images also contain rich information about coherent structures in the atmosphere, and those structures correspond to boundary layer stratification regimes (Stopa et al., 2022). The Stopa et al., (2024) study used European Space Agency Sentinel-1 C-band SAR wave mode (S1 WV) imagery spanning the region of the NES Pioneer Array. SAR Images in 20 x 20 km regions were visually examined and classified based on observed “signatures” of atmospheric phenomena. Of particular interest for this study were signatures indicative of waves, rolls/streaks and convection (Table 1).

The relationship of SAR image classes to wind speed and atmospheric stability was examined by plotting the classified data set vs Ri and U10 (Figure 1). U10 shows a change in slope near neutral stratification (Ri = 0) with indications of a Ri-U10 relationship at higher and lower Ri. Of interest is the appearance of Ri boundaries denoting transitions between unstable (Ri < -0.01), neutral, and stable (Ri > 0.01) regimes. Rolls, streaks and convection dominate the unstable regime, while waves dominate the stable regime. In other words, classification of the SAR images provides information about atmospheric stratification.

This project shows the power of combining remote sensing with long-term, in-situ meteorological measurements to gain insights that neither could provide alone. The authors note that when satellite data are available, the SAR-based determination of boundary layer structure can be used over broad areas and long times, and is more efficient than direct measurements such as buoy-based LIDAR.

Table 1: Atmospheric signatures in SAR imagery

___________________

References:

Stopa, J.E., C. Wang, D. Vandemark, R.C. Foster, A. Mouche, and B. Chapron, (2022). Automated Global Classification of Surface Layer Stratification Using High-Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar,” Geophysical Research Letters 49(12), e2022GL098686.

Stopa, J.E., D. Vandemark, R. Foster, M. Emond, A. Mouche, and B. Chapron (2024). Characterizing the Atmospheric Boundary Layer for Offshore Wind Energy Using Synthetic Aperture Radar Imagery. Wind Energy, 27:1340–1352, https://doi.org/10.1002/we.2933.