bcodmo_dataset_885506_v2 eng UTF8 dataset service Adam Shepherd BCO-DMO 508-289-2772 266 Woods Hole Rd. MS#36 Woods Hole MA 02543 USA erddap@bco-dmo.org pointOfContact 2026-04-08 ISO 19115-2 Geographic Information - Metadata Part 2 Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) 2 column row [Distribution of dissolved barium in seawater determined using machine learning] - A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning (The Speed, Signature, and Significance of Barium Transformations in Seawater) 2026-04-08 creation erddap.bco-dmo.org bcodmo_dataset_885506_v2 BCO-DMO info@bco-dmo.org https://www.bco-dmo.org/dataset/885506 information web browser Background Information Background information from the source information originator 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. BCO-DMO info@bco-dmo.org https://www.bco-dmo.org/dataset/885506 information web browser Background Information Background information from the source information pointOfContact earth science atmosphere ocean biosphere biology environment theme BCO-DMO project longitude latitude theme CF The data may be used and redistributed for free but is not intended for legal use, since it may contain inaccuracies. Neither the data Contributor, ERD, NOAA, nor the United States Government, nor any of their employees or contractors, makes any warranty, express or implied, including warranties of merchantability and fitness for a particular purpose, or assumes any legal liability for the accuracy, completeness, or usefulness, of this information. BCO-DMO largerWorkCitation project eng geoscientificInformation 1 -180.0 180.0 -77.5 89.5 [Distribution of dissolved barium in seawater determined using machine learning] - A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning (The Speed, Signature, and Significance of Barium Transformations in Seawater) 2026-04-08 creation BCO-DMO info@bco-dmo.org https://www.bco-dmo.org/dataset/885506 information web browser Background Information Background information from the source information originator 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. ERDDAP tabledap 1 -180.0 180.0 -77.5 89.5 tight ERDDAPtabledapDatasetQueryAndAccess https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_885506_v2 ERDDAP:tabledap ERDDAP-tabledap ERDDAP's tabledap service (a flavor of OPeNDAP) for tabular (sequence) data. Add different extensions (e.g., .html, .graph, .das, .dds) to the base URL for different purposes. download [Distribution of dissolved barium in seawater determined using machine learning] - A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning (The Speed, Signature, and Significance of Barium Transformations in Seawater) 2026-04-08 creation BCO-DMO info@bco-dmo.org https://www.bco-dmo.org/dataset/885506 information web browser Background Information Background information from the source information originator 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. OPeNDAP 1 -180.0 180.0 -77.5 89.5 tight OPeNDAPDatasetQueryAndAccess https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_885506_v2 OPeNDAP:OPeNDAP OPeNDAP An OPeNDAP service for tabular (sequence) data. Add different extensions (e.g., .html, .das, .dds) to the base URL for different purposes. download physicalMeasurement Station int Station Depth_m int Depth_m dBa_nmol_kg float Dba_nmol_kg omega_Ba float Omega_ba Ba_star_nmol_kg float Ba_star_nmol_kg Adam Shepherd BCO-DMO 508-289-2772 266 Woods Hole Rd. MS#36 Woods Hole MA 02543 USA erddap@bco-dmo.org distributor OPeNDAP DAP/2.0 https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_885506_v2.html order Data Subset Form ERDDAP's version of the OPeNDAP .html web page for this dataset. Specify a subset of the dataset and download the data via OPeNDAP or in many different file types. download https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_885506_v2.graph order Make-A-Graph Form ERDDAP's Make-A-Graph .html web page for this dataset. Create an image with a map or graph of a subset of the data. mapDigital This record was created from dataset metadata by ERDDAP Version 2.22