ERDDAP > info > bcodmo_dataset_885506_v2

Grid DAP Data	Sub- set	Table DAP Data	Make A Graph	W M S	Source Data Files	Acces- sible	Title	Sum- mary	FGDC, ISO, Metadata	Back- ground Info	RSS	E mail	Institution	Dataset ID
		data	graph		files	public	[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)		F   I   M	background			BCO-DMO	bcodmo_dataset_885506_v2

The Dataset's Variables and Attributes

Row Type	Variable Name	Attribute Name	Data Type	Value
attribute	NC_GLOBAL	cdm_data_type	String	Other
attribute	NC_GLOBAL	Conventions	String	COARDS, CF-1.6, ACDD-1.3
attribute	NC_GLOBAL	creator_email	String	info at bco-dmo.org
attribute	NC_GLOBAL	creator_name	String	BCO-DMO
attribute	NC_GLOBAL	creator_url	String	https://www.bco-dmo.org/
attribute	NC_GLOBAL	doi	String	10.26008/1912/bco-dmo.885506.2
attribute	NC_GLOBAL	Easternmost_Easting	double	359.5
attribute	NC_GLOBAL	geospatial_lat_max	double	89.5
attribute	NC_GLOBAL	geospatial_lat_min	double	-77.5
attribute	NC_GLOBAL	geospatial_lat_units	String	degrees_north
attribute	NC_GLOBAL	geospatial_lon_max	double	359.5
attribute	NC_GLOBAL	geospatial_lon_min	double	0.5
attribute	NC_GLOBAL	geospatial_lon_units	String	degrees_east
attribute	NC_GLOBAL	infoUrl	String	https://www.bco-dmo.org/dataset/885506
attribute	NC_GLOBAL	institution	String	BCO-DMO
attribute	NC_GLOBAL	license	String	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.
attribute	NC_GLOBAL	Northernmost_Northing	double	89.5
attribute	NC_GLOBAL	sourceUrl	String	(local files)
attribute	NC_GLOBAL	Southernmost_Northing	double	-77.5
attribute	NC_GLOBAL	summary	String	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.
attribute	NC_GLOBAL	title	String	[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)
attribute	NC_GLOBAL	Westernmost_Easting	double	0.5
variable	Station		int
attribute	Station	actual_range	int	1, 41088
attribute	Station	long_name	String	Station
attribute	Station	units	String	unitless
variable	longitude		float
attribute	longitude	_CoordinateAxisType	String	Lon
attribute	longitude	actual_range	float	0.5, 359.5
attribute	longitude	axis	String	X
attribute	longitude	ioos_category	String	Location
attribute	longitude	long_name	String	Longitude_degreese
attribute	longitude	standard_name	String	longitude
attribute	longitude	units	String	degrees_east
variable	latitude		float
attribute	latitude	_CoordinateAxisType	String	Lat
attribute	latitude	actual_range	float	-77.5, 89.5
attribute	latitude	axis	String	Y
attribute	latitude	ioos_category	String	Location
attribute	latitude	long_name	String	Latitude_degreesn
attribute	latitude	standard_name	String	latitude
attribute	latitude	units	String	degrees_north
variable	Depth_m		int
attribute	Depth_m	actual_range	int	0, 5500
attribute	Depth_m	long_name	String	Depth_m
attribute	Depth_m	units	String	meters (m)
variable	dBa_nmol_kg		float
attribute	dBa_nmol_kg	long_name	String	Dba_nmol_kg
attribute	dBa_nmol_kg	units	String	nanomoles per kilogram (nmol/kg)
variable	omega_Ba		float
attribute	omega_Ba	long_name	String	Omega_ba
attribute	omega_Ba	units	String	unitless
variable	Ba_star_nmol_kg		float
attribute	Ba_star_nmol_kg	actual_range	float	-27.19973, 27.89195
attribute	Ba_star_nmol_kg	long_name	String	Ba_star_nmol_kg
attribute	Ba_star_nmol_kg	units	String	nanomoles per kilogram (nmol/kg)

The information in the table above is also available in other file formats (.csv, .htmlTable, .itx, .json, .jsonlCSV1, .jsonlCSV, .jsonlKVP, .mat, .nc, .nccsv, .tsv, .xhtml) via a RESTful web service.