Accessing BCO-DMO data
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BCO-DMO ERDDAP
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| Dataset Title: | [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) 
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| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_885506_v2) |
| Information: | Summary
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| FGDC
| ISO 19115
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Attributes {
s {
Station {
Int32 actual_range 1, 41088;
String long_name "Station";
String units "unitless";
}
longitude {
String _CoordinateAxisType "Lon";
Float32 actual_range 0.5, 359.5;
String axis "X";
String ioos_category "Location";
String long_name "Longitude_degreese";
String standard_name "longitude";
String units "degrees_east";
}
latitude {
String _CoordinateAxisType "Lat";
Float32 actual_range -77.5, 89.5;
String axis "Y";
String ioos_category "Location";
String long_name "Latitude_degreesn";
String standard_name "latitude";
String units "degrees_north";
}
Depth_m {
Int32 actual_range 0, 5500;
String long_name "Depth_m";
String units "meters (m)";
}
dBa_nmol_kg {
String long_name "Dba_nmol_kg";
String units "nanomoles per kilogram (nmol/kg)";
}
omega_Ba {
String long_name "Omega_ba";
String units "unitless";
}
Ba_star_nmol_kg {
Float32 actual_range -27.19973, 27.89195;
String long_name "Ba_star_nmol_kg";
String units "nanomoles per kilogram (nmol/kg)";
}
}
NC_GLOBAL {
String cdm_data_type "Other";
String Conventions "COARDS, CF-1.6, ACDD-1.3";
String creator_email "info@bco-dmo.org";
String creator_name "BCO-DMO";
String creator_url "https://www.bco-dmo.org/";
String doi "10.26008/1912/bco-dmo.885506.2";
Float64 Easternmost_Easting 359.5;
Float64 geospatial_lat_max 89.5;
Float64 geospatial_lat_min -77.5;
String geospatial_lat_units "degrees_north";
Float64 geospatial_lon_max 359.5;
Float64 geospatial_lon_min 0.5;
String geospatial_lon_units "degrees_east";
String history
"2024-11-21T08:35:48Z (local files)
2024-11-21T08:35:48Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_885506_v2.html";
String infoUrl "https://www.bco-dmo.org/dataset/885506";
String institution "BCO-DMO";
String license
"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.";
Float64 Northernmost_Northing 89.5;
String sourceUrl "(local files)";
Float64 Southernmost_Northing -77.5;
String summary "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.";
String title "[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)";
Float64 Westernmost_Easting 0.5;
}
}
Data Access Protocol (DAP) and its
selection constraints.
The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.
Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names,
followed by a collection of
constraints (e.g., variable<value),
each preceded by '&' (which is interpreted as "AND").
For details, see the tabledap Documentation.