A global monthly climatology of total alkalinity (AT): a neural network approach (NCEI Accession 0222470)

Metadata Updated: December 4, 2020

This NCEI accession contains global monthly climatology of oceanic total alkalinity (AT). Total alkalinity (AT) monthly climatology was created from a neural network approach (Broullón et al., 2019). The neural network was trained with GLODAPv2.2019 data (Olsen et al., 2019) using as predictor variables position (latitude, longitude and depth), temperature, salinity, phosphate, nitrate, silicate and dissolved oxygen. The relations extracted between these predictor variables and AT were used to obtain the climatology passing through the network global monthly climatologies of the predictor variables: temperature and salinity fields of the World Ocean Atlas version 2013 (WOA13), filtered WOA13 oxygen (fifth-order one-dimensional median filter through the depth dimension; see Broullón et al., 2019 for details) and nutrients computed using CANYON-B (Bittig et al., 2018) over the three previous fields. The obtained climatology has a 1ºx1º spatial resolution and 102 depth levels between 0 and 5500 m, with a monthly resolution from 0 to 1500 m and an annual resolution from 1550 to 5500m.

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License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Date 2020-12-02T03:22:48Z
Metadata Created Date December 4, 2020
Metadata Updated Date December 4, 2020
Reference Date(s) December 1, 2020 (publication)
Frequency Of Update asNeeded

Metadata Source

Harvested from NOAA/NESDIS/ncei/accessions

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Additional Metadata

Resource Type Dataset
Metadata Date 2020-12-02T03:22:48Z
Metadata Created Date December 4, 2020
Metadata Updated Date December 4, 2020
Reference Date(s) December 1, 2020 (publication)
Responsible Party (Point of Contact)
Contact Email
Guid gov.noaa.nodc:0222470
Access Constraints Cite as: Broull\u00c3\u00b3n, Daniel; P\u00c3\u00a9rez, Fiz F.; Velo, Ant\u00c3\u00b3n; Hoppema, Mario; Olsen, Are; Takahashi, Taro; Key, Robert M.; Tanhua, Toste; Gonz\u00c3\u00a1lez D\u00c3\u00a1vila, Melchor; Jeansson, Emil; Kozyr, Alex; van Heuven, Steven M. A. C. (2020). A global monthly climatology of total alkalinity (AT): a neural network approach (NCEI Accession 0222470). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0222470. Accessed [date]., Use liability: NOAA and NCEI cannot provide any warranty as to the accuracy, reliability, or completeness of furnished data. Users assume responsibility to determine the usability of these data. The user is responsible for the results of any application of this data for other than its intended purpose.
Bbox East Long 179.5
Bbox North Lat 89.5
Bbox South Lat -77.5
Bbox West Long -179.5
Coupled Resource
Frequency Of Update asNeeded
Graphic Preview Description Preview graphic
Graphic Preview File https://www.ncei.noaa.gov/access/metadata/landing-page/bin/gfx?id=gov.noaa.nodc:0222470
Graphic Preview Type PNG
Licence accessLevel: Public
Metadata Language eng
Metadata Type geospatial
Progress completed
Spatial Data Service Type
Spatial Reference System
Spatial Harvester True
Temporal Extent Begin 1957-01-01
Temporal Extent End 2018-12-31

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