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1. An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2020

   https://doi.org/10.5194/essd-12-3653-2020  

   Olsen, Are ; Lange, Nico ; Key, Robert M. ; Tanhua, Toste ; Bittig, Henry C. ; Kozyr, Alex ; Álvarez, Marta ; Azetsu-Scott, Kumiko ; Becker, Susan ; Brown, Peter J. ; et al ( January 2020 , Earth System Science Data)

   null (Ed.)

   Abstract. The Global Ocean Data Analysis Project (GLODAP) is asynthesis effort providing regular compilations of surface-to-bottom oceanbiogeochemical data, with an emphasis on seawater inorganic carbon chemistryand related variables determined through chemical analysis of seawatersamples. GLODAPv2.2020 is an update of the previous version, GLODAPv2.2019.The major changes are data from 106 new cruises added, extension of timecoverage to 2019, and the inclusion of available (also for historicalcruises) discrete fugacity of CO2 (fCO2) values in the mergedproduct files. GLODAPv2.2020 now includes measurements from more than 1.2 million water samples from the global oceans collected on 946 cruises. Thedata for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate,phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12,CFC-113, and CCl4) have undergone extensive quality control with afocus on systematic evaluation of bias. The data are available in twoformats: (i) as submitted by the data originator but updated to WOCEexchange format and (ii) as a merged data product with adjustments appliedto minimize bias. These adjustments were derived by comparing the data fromthe 106 new cruises with the data from the 840 quality-controlled cruises ofthe GLODAPv2.2019 data product using crossover analysis. Comparisons toempirical algorithm estimates provided additional context for adjustmentdecisions; this is new to this version. The adjustments are intended toremove potential biases from errors related to measurement, calibration, anddata-handling practices without removing known or likely time trends orvariations in the variables evaluated. The compiled and adjusted dataproduct is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate,4 µmol kg−1 in dissolved inorganic carbon, 4 µmol kg−1in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % inthe halogenated transient tracers. The other variables included in thecompilation, such as isotopic tracers and discrete fCO2, were notsubjected to bias comparison or adjustments. The original data and their documentation and DOI codes are available at theOcean Carbon Data System of NOAA NCEI(https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2020/, lastaccess: 20 June 2020). This site also provides access to the merged dataproduct, which is provided as a single global file and as four regional ones– the Arctic, Atlantic, Indian, and Pacific oceans –under https://doi.org/10.25921/2c8h-sa89 (Olsen et al., 2020). Thesebias-adjusted product files also include significant ancillary andapproximated data. These were obtained by interpolation of, or calculationfrom, measured data. This living data update documents the GLODAPv2.2020methods and provides a broad overview of the secondary quality controlprocedures and results. 

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2. Coastal Ocean Data Analysis Product in North America (CODAP-NA) – an internally consistent data product for discrete inorganic carbon, oxygen, and nutrients on the North American ocean margins

   https://doi.org/10.5194/essd-13-2777-2021  

   Jiang, Li-Qing ; Feely, Richard A. ; Wanninkhof, Rik ; Greeley, Dana ; Barbero, Leticia ; Alin, Simone ; Carter, Brendan R. ; Pierrot, Denis ; Featherstone, Charles ; Hooper, James ; et al ( January 2021 , Earth System Science Data)

   null (Ed.)

   Abstract. Internally consistent, quality-controlled (QC) data products play animportant role in promoting regional-to-global research efforts tounderstand societal vulnerabilities to ocean acidification (OA). However,there are currently no such data products for the coastal ocean, where mostof the OA-susceptible commercial and recreational fisheries and aquacultureindustries are located. In this collaborative effort, we compiled, quality-controlled, and synthesized 2 decades of discrete measurements ofinorganic carbon system parameters, oxygen, and nutrient chemistry data fromthe North American continental shelves to generate a data product calledthe Coastal Ocean Data Analysis Product in North America (CODAP-NA). Thereare few deep-water (> 1500 m) sampling locations in the currentdata product. As a result, crossover analyses, which rely on comparisonsbetween measurements on different cruises in the stable deep ocean, couldnot form the basis for cruise-to-cruise adjustments. For this reason, carewas taken in the selection of data sets to include in this initial releaseof CODAP-NA, and only data sets from laboratories with known qualityassurance practices were included. New consistency checks and outlierdetections were used to QC the data. Future releases of this CODAP-NAproduct will use this core data product as the basis for cruise-to-cruisecomparisons. We worked closely with the investigators who collected andmeasured these data during the QC process. This version (v2021) of theCODAP-NA is comprised of 3391 oceanographic profiles from 61 researchcruises covering all continental shelves of North America, from Alaska toMexico in the west and from Canada to the Caribbean in the east. Data for 14variables (temperature; salinity; dissolved oxygen content; dissolvedinorganic carbon content; total alkalinity; pH on total scale; carbonateion content; fugacity of carbon dioxide; and substance contents of silicate,phosphate, nitrate, nitrite, nitrate plus nitrite, and ammonium) have beensubjected to extensive QC. CODAP-NA is available as a merged data product(Excel, CSV, MATLAB, and NetCDF; https://doi.org/10.25921/531n-c230,https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0219960.html, last access: 15 May 2021)(Jiang et al., 2021a). The original cruise data have also been updated withdata providers' consent and summarized in a table with links to NOAA'sNational Centers for Environmental Information (NCEI) archives(https://www.ncei.noaa.gov/access/ocean-acidification-data-stewardship-oads/synthesis/NAcruises.html). 

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3. A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning

   https://doi.org/10.26008/1912/bco-dmo.885506.2  

   Horner, Tristan J. ; Mete, Oyku Z. ( January 2023 , Biological and Chemical Oceanography Data Management Office (BCO-DMO))

   We present a spatially and vertically resolved global grid of dissolved barium concentrations ([Ba]) in seawater determined using Gaussian Process Regression machine learning. This model was trained using 4,345 quality-controlled GEOTRACES data from the Arctic, Atlantic, Pacific, and Southern Oceans. Model output was validated by assessing the accuracy of [Ba] simulations in the Indian Ocean, noting that none of the Indian Ocean data were seen by the model during training. We identify a model that can accurate predict [Ba] in the Indian Ocean using seven features: depth, temperature, salinity, as well as dissolved dioxygen, phosphate, nitrate, and silicate concentrations. This model achieves a mean absolute percentage error of 6.0 %, which we assume represents the generalization error. This model was used to simulate [Ba] on a global basis using predictor data from the World Ocean Atlas 2018. The global model of [Ba] is on a 1°x 1° grid with 102 depth levels from 0 to 5,500 m. The dissolved [Ba] output was then used to simulate dissolved Ba* (barium-star), which is the difference between 'observed' and [Ba] predicted from co-located [Si]. Lastly, [Ba] data were combined with temperature, salinity, and pressure data from the World Ocean Atlas to calculate the saturation state of seawater with respect to barite. The model reveals that the volume-weighted mean oceanic [Ba] and and saturation state are 89 nmol/kg and 0.82, respectively. These results imply that the total marine Ba inventory is 122(±7) ×10¹² mol and that the ocean below 1,000 m is at barite equilibrium. 

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4. Changing Hydrographic, Biogeochemical, and Acidification Properties in the Gulf of Maine as Measured by the Gulf of Maine North Atlantic Time Series, GNATS, Between 1998 and 2018

   https://doi.org/10.1029/2022JG006790  

   Balch, William M. ; Drapeau, David T. ; Bowler, Bruce C. ; Record, Nicholas R. ; Bates, Nicholas R. ; Pinkham, Sunny ; Garley, Rebecca ; Mitchell, Catherine ( June 2022 , Journal of Geophysical Research: Biogeosciences)

   The Gulf of Maine North Atlantic Time Series (GNATS) has been run since 1998, across the Gulf of Maine (GoM), between Maine and Nova Scotia. GNATS goals are to provide ocean color satellite validation and to examine change in this coastal ecosystem. We have sampled hydrographical, biological, chemical, biogeochemical, and bio‐optical variables. After 2008, warm water intrusions (likely North Atlantic Slope Water [NASW]) were observed in the eastern GoM at 50–180 m depths. Shallow waters (<50 m) significantly warmed in winter, summer, and fall butcooledduring spring. Surface salinity and density of the GoM also significantly increased over the 20 years. Phytoplankton standing stock and primary production showed highly‐significant decreases during the period. Concentrations of phosphate increased, silicate decreased, residual nitrate [N*; nitrate‐silicate] increased, and the ratio of dissolved inorganic nitrogen:phosphate decreased, suggesting increasing nitrogen limitation. Dissolved organic carbon (DOC) and its optical indices generally increased over two decades, suggesting changes to the DOC cycle. Surface seawater carbonate chemistry showed winter periods where the aragonite saturation (Ω_ar) dropped below 1.6 gulf‐wide due to upward winter mixing of cool, corrosive water. However, associated with increased average GoM temperatures, Ω_arhas significantly increased. These results reinforce the hypothesis that the observed decrease in surface GoM primary production resulted from a switch from Labrador Sea Water to NASW entering the GoM. A multifactor analysis shows that decreasing GoM primary production is most significantly correlated to decreases in chlorophyll and particulate organic carbon plus increases in N* and temperature.

    

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5. Barium in seawater: dissolved distribution, relationship to silicon, and barite saturation state determined using machine learning

   https://doi.org/10.5194/essd-15-4023-2023  

   Mete, Öykü Z. ; Subhas, Adam V. ; Kim, Heather H. ; Dunlea, Ann G. ; Whitmore, Laura M. ; Shiller, Alan M. ; Gilbert, Melissa ; Leavitt, William D. ; Horner, Tristan J. ( January 2023 , Earth System Science Data)

   Abstract. Barium is widely used as a proxy for dissolved silicon and particulateorganic carbon fluxes in seawater. However, these proxy applications arelimited by insufficient knowledge of the dissolved distribution of Ba([Ba]). For example, there is significant spatial variability in thebarium–silicon relationship, and ocean chemistry may influence sedimentaryBa preservation. To help address these issues, we developed 4095 models forpredicting [Ba] using Gaussian process regression machine learning. Thesemodels were trained to predict [Ba] from standard oceanographic observationsusing GEOTRACES data from the Arctic, Atlantic, Pacific, and Southernoceans. Trained models were then validated by comparing predictions againstwithheld [Ba] data from the Indian Ocean. We find that a model trained usingdepth, temperature, and salinity, as well as dissolved dioxygen, phosphate,nitrate, and silicate, can accurately predict [Ba] in the Indian Ocean with amean absolute percentage deviation of 6.0 %. We use this model tosimulate [Ba] on a global basis using these same seven predictors in theWorld Ocean Atlas. The resulting [Ba] distribution constrains the Ba budgetof the ocean to 122(±7) × 1012 mol and revealsoceanographically consistent variability in the barium–silicon relationship. We then calculate the saturation state of seawater with respect to barite. This calculation reveals systematic spatial and vertical variations in marine barite saturation and shows that the ocean below 1000 m is at equilibrium with respect tobarite. We describe a number of possible applications for our model outputs, ranging from use in mechanistic biogeochemical models to paleoproxy calibration. Ourapproach demonstrates the utility of machine learning in accurately simulatingthe distributions of tracers in the sea and provides a framework that couldbe extended to other trace elements. Our model, the data used in training and validation, and global outputs are available in Horner and Mete (2023, https://doi.org/10.26008/1912/bco-dmo.885506.2). 

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