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
[media-caption path="/wp-content/uploads/2020/10/Screen-Shot-2020-10-29-at-1.22.57-PM.png" link="#"]Figure 18. Comparison of Sentinel-1 (S-1) SAR deep-learning predictions of significant wave height Hs and buoy measurements. (b) scatter plot of SAR Hs vs buoy Hs. (d) RMS error of SAR prediction vs. buoy measurements as a function of Hs; error bars show one standard deviation. From Quach et al. (2020).[/media-caption]
Synthetic Aperture Radar (SAR) sensors on satellites measure backscatter from the ocean surface and can be used to estimate wave height at very high spatial resolution (~10 m) relative to satellite altimetry. Two Sentinel-1 satellites of the European Space Agency (ESA) collected SAR measurements of the ocean surface from 2015-2018, together covering the entire globe every six days. Data-driven approaches to predicting significant wave height (Hs) from SAR have either used relatively limited in-situ data sets or used a wave model (e.g. WaveWatch-3) as the “training” data for a deep learning approach. Quach et al.(2020) improve on previous approaches to estimation of Hs from SAR by creating a comprehensive in-situ observational record. They compiled data from the US National Data Buoy Center and Coastal Data Information Program, Canadian Marine Environmental Data Services, the international OceanSITES project, and the OOI. Surface wave data sets from the OOI Irminger Sea, Argentine Basin and Southern Ocean surface buoys were used. The authors note the importance of the Southern Ocean Array, where “many of the largest wave heights are recorded… [from] an under sampled region of the ocean.”
The comprehensive in-situ data set is split into separate training and validation segments. When SAR Hs from training data are compared to altimeter Hs from the validation segment, the deep learning algorithm shows root-mean-square (RMS) error of 0.3 m, a 50% improvement relative to prior approaches. Comparison with the buoy validation segment (Fig. 18) shows RMS error of 0.5 m. The authors attribute the increased error to the larger number of extreme sea states in the observations and the relative paucity of extremes in the training data.
Observational sea state information is critical for understanding surface wave phenomena (generation, propagation and decay), predicting wave amplitudes, and estimating extreme sea states. Thus, the improvement in RMS error using the deep learning technique notable. The availability of in-situ data from extreme environments such as those sampled by the OOI Irminger Sea and Southern Ocean Arrays are key to validation of these new approaches.
The Cascadia Subduction Zone extends from northern California to British Columbia. It has experienced magnitude 9 megathrust events with a reoccurrence rate of every ~500 years over the past 10,000 years  and large earthquakes at intervals of ~ 200-1200 years . The last Cascadia megathrust rupture occurred on January 26, 1700 . When the next event occurs, it is estimated that financial losses would be ~ $60 billion USD with substantial loss of life. Hence, there is significant research focused on understanding seismic processes along this ~ 1100 km subduction zone, the generation of slow earthquakes, and causes of variation in seismicity along strike.
Understanding the factors that control seismic events was/is a major driver in the siting of OOI-RCA core geophysical instrumentation on the southern line of the Regional Cabled Array: the RCA is one of the few places in the world where seismic-focused instrumentation occurs on both the down-going tectonic plate and on the overlying margin. The offshore network is especially valuable in determining earthquake source depths that inform on interpolate dynamics . The central section of the Cascadia Margin is the only area that experiences repeat, measurable shallow crustal earthquakes [1-3]. RCA data flowing from the seismic network at Slope Base and Southern Hydrate Ridge, and from the Cascadia Initiative are providing new insights into factors controlling seismicity along this portion of the margin [1,4] (note because the RCA broadband seismometers are buried, they have lower noise levels at higher frequencies than the Cascadia Initiative instruments ).
Most recently, Morton et al.,  examined data from the Cascadia Initiative  and the RCA. Shallow earthquakes are focused in the area of a subducted seamount [1-3] and another cluster to the north (Fig. 1b and c). Based on earthquake locations, they suggest that subduction of the seamount produces stress heterogeneities, faulting, fracturing of the overriding Siletz terrane (old oceanic crust) (Fig 1b), and fluid movement promoting seismic swarms. Because this area is the most seismically active area along the Cascadia margin, it is an optimal area to examine the impacts of local earthquakes on, for example, gas hydrate deposits and fluid expulsion.
 Tréhu, A.M., Wilcock, W.S.D., Hilmo, R., Bodin, P., Connolly, J., Roland, E.C., and Braunmiller, R., (2018) The role of the Ocean Observatories Initiative in Monitoring the offshore earthquake activity of the Cascadia Subduction Zone. Oceanography, 31, 104-113.
 Tréhu, A.M., Blakely, R.J., and Williams, M., (2012) Subducted seamounts and recent earthquakes beneath the central Cascadia Forearc. Geology, 40, 103-106.
 Tréhu, A.M., Braunmiller, J., and Davis, E., (2015) Seismicity of the Central Cascadia Continental Margin near 44.5° N: a decadal view. Seismological Research Letters, 86, 819-829.
 Morton, Bilek, S.L., and Rowe, C.A. (2018) Newly detected earthquakes in the Cascadia subduction zone linked to seamount subduction and deformed upper plate. Geology, 46, 943-946.
 Satake, K.Shimazaki, K., Tsuji, Y., and Ueda, K., (1996) Time and size of a giant earthquake in Cascadia inferred from Japanese tsunami records of January 1700. Nature, 379, 246-249.
 Goldfinger, C., Nelson, C.H., Eriksson, E., et al., (2012) Turbidite event history: Methods and implications for Holocene paleoseismicity of the Cascadia Subduction Zone. US Geological Survey Professional Paper (1661-F), 184 pp.
 Toomey, D.R., Allen, R.M., Barclay, A.H., Bell, S.W., Bromirski, P.D. et al., (2014) The Cascadia Initiative: A sea change in seismological studies of subduction zones. Oceanography, 27, 138-150.
[media-caption path="/wp-content/uploads/2020/10/Endurance-Array-Science-Highlight.png" link="#"]Figure 19: Regional T/S variability at the Washington offshore profiling mooring. The end member Pacific Subarctic Upper Water (PSUW) and Pacific Equatorial Water (PEW) masses are indicated on each plot at the left and right respectively. T/S at the mooring is a mixture of PSUW and PEW. The left plot shows the seasonal variability. The right plot shows interannual variability in summer. Interannual variability from 100-250m exceeds seasonal variability. In 2015, T/S at the mooring is closer in character to climatological averages at Vancouver Island, BC while in 2018, T/S at the mooring is similar to that south of Newport, OR. Figure from Risien et al. adapted from Thomson and Krassovski (2010).[/media-caption]
Risien et al. (2020) presented over five years of observations from the OOI Washington offshore profiling mooring. First deployed in 2014, the Washington offshore profiler mooring is on the continental slope about 65 km west of Westport, WA. Its wire Following Profiler samples the water column from 30 m depth down to 500 m, ascending and descending three to four times per day. Traveling at approximately 25 cm/s, the profiler carries physical (temperature, salinity, pressure, and velocity) and biochemical (photosynthetically active radiation, chlorophyll, colored dissolved organic matter fluorescence, optical backscatter, and dissolved oxygen) sensors. The data presented included more than 12,000 profiles. These data were processed using a newly developed Matlab toolbox.
The observations resolve biochemical processes such as carbon export and dissolved oxygen variability in the deep source waters of the Northern California Upwelling System. Within the Northern California Current System, over the slope there is a large-scale north-south variation in temperature and salinity (T/S). Regional T/S variability can be understood as a mixing between warmer, more saline Pacific Equatorial Water (PEW) to the south, and fresher, colder Pacific Subarctic Upper Water (PSUW) to the north. Preliminary results show significant interannual variability of T/S water properties between 100-250 meters. In summer, interannual T/S variability is larger than the mean seasonal cycle (see Fig 19). While summer T/S variability is greatest on the interannual scale, T/S does covary on a seasonal scale with dissolved Oxygen (DO), spiciness and Particulate Organic Carbon (POC). In particular, warmer, more saline water is associated with lower DO in fall and winter.
Risien, C.M., R.A. Desiderio, L.W. Juranek, and J.P. Fram (2020), Sustained, High-Resolution Profiler Observations from the Washington Continental Slope , Abstract [IS43A-05] presented at Ocean Sciences Meeting 2020, San Diego, CA, 17-21 Feb.
Thomson, R. E., and Krassovski, M. V. (2010), Poleward reach of the California Undercurrent extension, J. Geophys. Res., 115, C09027, doi:10.1029/2010JC006280.
More Science Highlights are available here: https://oceanobservatories.org/science-highlights/
Two- and 3D-imaging of Axial Seamount, coupled with real-time monitoring of seismicity and seafloor deformation, is providing unprecedented insights into submarine volcanism, the nature of melt transport, and caldera dynamics (Figure 14) [1-15]. Recently acquired 3D imaging of the volcano  and analyses of 1999 and 2002 multichannel seismic data [4-7] have led to the remarkable discovery of a root zone 6 km beneath the volcano [2,5]. Carbotte et al.,  describe a 3-to-5 km wide conduit that is interpreted to be comprised of numerous quasi-horizontal melt lenses spaced 400-500 m apart. The conduit is located beneath a 14-km-long magma reservoir (MMR) that spans the caldera of Axial Seamount and a secondary, smaller magma chamber (SMR) located beneath the eastern flank of the volcano [1,3]. This smaller reservoir presumably Dymond hydrothermal field hosting up to 60 m-tall actively venting chimneys, which was discovered on a 2011 RCA cruise. Seismicity prior to, during and subsequent to the 2015 eruption delineates outward dipping normal faults in the southern half of the caldera that extend from near the seafloor to 3-3.25 km depth [3,8-9]. In contrast, a conjugate set of inward dipping faults in the northern portion of the caldera extend to depths of ~ 2.25 km. The outward dipping ring faults were active during inflation and syn-eruptive deformation [[3,8-9]. Source fissures for the 1998, 2011, and 2015 eruptions are located within ± 1 km of where the MMR roof is shallowest (<1.6 km beneath the seafloor) and skewed toward the eastern caldera wall . In concert, these studies are changing long-held views of magma chamber geometry and the deep-rooted feeder systems in mid-ocean ridge environments [2,5].
 Arnulf, A. F., Harding, A. J., Kent, G. M., Carbotte, S. M., Canales, J. P., and Nedimovic, M. R. (2014) Anatomy of an active submarine volcano. Geology, 42(8), 655–658. https://doi.org/10.1130/G35629.1.
 Arnulf, A.F., Harding, A.J., Saustrup, S., Kell, A.M., Kent, G.M., Carbott, S.M., Canales, J.P., Nedimovic, M.R., Bellucci M., Brandt, S., Cap, A., Eischen, T.E., Goulin, M., Griffiths, M., Lee, M., Lucas, V., Mitchell, S.J., and Oller, B. (2019) Imaging the internal workings of Axial Seamount on the Juan de Fuca Ridge. American Geophysical Union, Fall Meeting 2019, OS51B-1483.
 Arnulf, A.F., Harding, A.J., Kent, G.M., and Wilcock, W.S.D. (2018) Structure, seismicity and accretionary processes at the hot-spot influenced Axial Seamount on the Juan de Fuca Ridge. Journal of Geophysical Research, 10.1029/2017JB015131.
 Carbotte, S. M., Nedimovic, M. R., Canales, J. P., Kent, G. M., Harding, A. J., and Marjanovic, M. (2008) Variable crustal structure along the Juan de Fuca Ridge: Influence of on-axis hot spots and absolute plate motions. Geochemistry, Geophysics, Geosystems, 9, Q08001. doi.org/10.1029/2007GC001922.
 Carbotte, S.M., Arnulf, A.F., Spiegelman, M.W., Harding, A.J., Kent, G.M., Canales, J.P., and Nedimovic, M.R. (2019) Seismic images of a deep melt-mush feeder conduit beneath Axial Volcano. American Geophysical Union, Fall Meeting 2019, OS51B-1484.
 West, M., Menke, W., and Tolstoy, M. (2003) Focused magma supply at the intersection of the Cobb hotspot and the Juan de Fuca ridge. Geophysical Research Letters, 30(14), 1724. https://doi.org/10.1029/2003GL017104.
 West, M., Menke, W., Tolstoy, M., Webb, S., and Sohn, R. (2001). Magma storage beneath Axial volcano on the Juan de Fuca mid-ocean ridge. Nature, 413(6858), 833–836. doi.org/10.1038/35101581.
 Wilcock, W.S.D., Tolstoy, M., Waldhauser, F., Garcia, C., Tan, Y.J., Bohnenstiehl, D.R., Caplan-Auerbach, J., Dziak, R., Arnulf, A.F., and Mann, M.E. (2016) Seismic constraints on caldera dynamics from the 2015 Axial Seamount eruption. Science, 354, 1395-399; https://doi.org/10.1126 /science.aah5563.
 Wilcock, W.S.D., Dziak, R.P., Tolstoy, M., Chadwick, W.W., Jr., Nooner, S.L., Bohnenstiehl, D.R., Caplan-Auerbach, J., Waldhauser, F., Arnulf, A.F., Baillard, C., Lau, T., Haxel, J.H., Tan, Y.J, Garcia, C., Levy, S., and Mann, M.E. (2018) The recent volcanic history of Axial Seamount: Geophysical insights into past eruption dynamics with an eye toward enhanced observations of future eruptions. Oceanography, 31,(1), 114-123.
 Chadwick, W.W., Jr., Nooner, S.L., and Lau, T.K.A. (2019) Forecasting the next eruption at Axial Seamount based on an inflation-predictable pattern of deformation. American Geophysical Union, Fall Meeting 2019, OS51B-1489.
 Chadwick, W.W., Jr., Paduan, J.B., Clague, D.A., Dreyer, B.M., Merle, S.G. Bobbitt, A.M. Bobbitt, Caress, D.W. Caress, Philip, B.T., Kelley, D.S., and Nooner, S. (2016) Voluminous eruption from a zoned magma body after an increase in supply rate at Axial Seamount. Geophysical Research Letters, 43, 12,063-12,070; https://doi. org/10.1002/2016GL071327.
 Nooner, S.L., and Chadwick, W.W. Jr. (2016) Inflation- predictable behavior and co-eruption deformation at Axial Seamount. Science, 354, 1399-1403; https://doi.org/10.1126/ science.aah4666.
 Nooner, S.L., and Chadwick, W.W. Jr. (2016) Inflation- predictable behavior and co-eruption deformation at Axial Seamount. Science, 354, 1399-1403; https://doi.org/10.1126/ science.aah4666.
 Hefner, W.L., Nooner, S.L., Chadwick, W.W., Jr., and Bohnenstiehl, D.R. (2020) Magmatic deformation models including caldera-ring faulting for the 2015 eruption of Axial Seamount. Journal of Geophysical Research, https://doi:10.1029/2020JB019356.
 Levy, S., Bohnenstiehl, D.R., Sprinkle, R., Boettcher, M.S., Wilcock, W.S.D., Tolstoy, M., and Waldhouser, F. (2018) Mechanics of fault reactivation before, during, and after the 2015 eruption of Axial Seamount. Geology, 46(5), 447-450; https://doi.org/10.1130/G39978.1.
[caption id="attachment_18951" align="aligncenter" width="794"] Figure 22. a) Location of bottom pressure recorders (BPRS) at Axial Seamount and vicinity (Cleft segment not shown in this illustration), including DART buoys and an IODP Corked site (after ). Most of the pressure data for this investigation were from Axial Seamount. b) Source regionals for the tsunamis recorded at Axial with yellow circles indicating earthquake locations and circle size proportional to magnitudes. The thin blue lines mark the leading edge of tsunamis at 2 hr intervals after an earthquake. c) Temporal coverage of the BPR records and recorded tsunamis at Axial and adjacent areas 1986-2018. Magenta lines are BPR recordings from the Cleft Segment, south of Axial on the Juan de Fuca Ridge.[/caption]This study by Fine et al.,  examines a 32 year record of high resolution bottom pressure recorder (BPR) measurements made by cabled instruments installed on Axial Seamount in 2014, and uncabled instruments at Axial, the Cleft Segment of the Juan de Fuca Ridge, DART buoys, and an IODP cored observatory (Hole 1026): most of the measurements in this study are from Axial (Figure 22). A total of 41 tsunamis were documented from 1986-2018 with all events associated with tsunamigenic earthquakes with magnitudes of 7.0 or greater. In contrast to coastal tide gauge observations, open ocean measurements by BPRs are advantageous because of the high signal-to-noise ratio. Based on this study, it is possible to forecast the effect of a tsunami originating from a source near a historical source, not only for Axial, but also for locations along the British Columbia‐Washington‐Oregon coast. These results allow a size-frequency model world-wide. The RCA cabled bottom pressure-tilt instruments, with 20 Hz sampling rates and with resolutions of 2 mm of seawater depth, provide especially high-resolution measurements.
 Fine, I.V., Thomson, R.E., Chadwick, W.W., Jr., and Fox, C.G., (2020) Toward a universal frequency occurrence distribution for tsunamis: statistical analyses of a 32-year bottom pressure record at Axial Seamount. Geophysical Research Letter, https://doi.org/10.1029/2020GL087372.
A two-year record from moorings in the Irminger Sea allowed researchers (Le Bras et al., 2020) to investigate both deep convection and transport of water masses associated with the Atlantic overturning circulation. Using mooring data from the OOI Irminger Sea Array and the Overturning in the Subpolar North Atlantic (OSNAP) array, the authors were able to identify two types of Irminger Sea Intermediate Water (ISIW) formed by deep convection. Upper ISIW is found near the edge of the Irminger Sea western boundary current, whereas Deep ISIW is formed in the basin interior. Water masses were diagnosed using temperature-salinity properties and the planetary potential vorticity (PPV). Figure 19 shows PPV for three different locations, in the boundary current, at its edge, and in the Irminger Sea gyre. Black lines in the figure indicate the isopycnals that bound upper and deep ISIW as defined by the authors, the red contours enclose water with low PPV (indicative of convection) and the green lines indicate the mixed layer depth.
Seasonal pulses of low PPV water in the boundary current occurring below the mixed layer (Figure 19a) suggest subduction from a non-local source offshore. In contrast, low PPV water in the gyre interior is accompanied by a deep winter mixed layer and appears related to local convection. Further analysis by the authors indicates that waters formed by convection in the interior gyre are entrained into the boundary current within a few months of formation. Importantly, it appears that eddy dynamics are responsible for this transport of ventilated water from the interior to the boundary, and that the upper ISIW in the boundary current is a significant component of the Atlantic overturning circulation.Read More
Endurance Array: Event and Seasonal Scale Variability of Surface Heat and Momentum Fluxes off Oregon and Washington
As part of the NSF-funded Ocean Observatories Initiative (OOI) Coastal and Global Scale Arrays, surface buoy meteorological measurements are made using the Air-Sea Interaction Meteorology (ASIMET) package (Figure 20). These measurements are reported on in Dever, E.P, J.P. Fram, C.M. Risien, R.A. Desiderio, C.E. Wingard (2020), Event and Seasonal Scale Variability of Surface Heat and Momentum Fluxes off Oregon and Washington, Abstract [A144A-2411] presented at Ocean Sciences Meeting 2020, San Diego, CA, 17-21 Feb. Radiative and bulk surface fluxes calculated from these measurements are provided as OOI data products. Both the measurements and the estimated fluxes are available through the OOI Data Portal as are all the metadata required to produce these fluxes (raw data, calibration coefficients, data product specifications, data product algorithms etc.).
On the Endurance Array, ASIMET measurements are made at four locations over the Oregon and Washington shelf and slope. These locations lie within the northern California Current Marine Ecosystem. Here upwelling favorable wind forcing and atmospheric conditions occur in spring and summer months with forcing in other months driven by passing low pressure systems. The timing of both the spring transition to upwelling and the fall transition to storm forcing varies from year to year as does the strength of individual events within each season. Upwelling events are associated with strong net shortwave and latent heat fluxes. Storm events are associated with weak net shortwave fluxes and latent fluxes that vary in strength.
Machine to machine (M2M) calls were used to read in hourly bulk surface fluxes from OOI Endurance Array moorings from their initial deployments in April 2015 through February 2020. OOI data product fluxes are calculated with TOGA-COARE and other community standard algorithms. Monthly averages of OOI Endurance Array flux data compare well with one another. Both the Oregon and Washington shelves are subject to heating on an annually averaged basis. The Oregon Shelf mooring (Figure 21) is typical. Late fall and winter show net fluxes from the ocean to the atmosphere. All other months show heat flux into the ocean due to insolation.