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    • A global monthly climatology of oceanic total dissolved inorganic carbon (DIC): a neural network approach (NCEI Accession 0222469)

    Metadata Updated: December 1, 2023

    This dataset contains global monthly climatology of oceanic total dissolved inorganic carbon (DIC). (DIC) monthly climatology was created from a neural network approach (Broullón et al., 2020). The neural network was trained with GLODAPv2.2019 (Olsen et al., 2019) and LDEOv2016 (Takahashi et al., 2017) data, using as predictor variables position (latitude, longitude and depth), year, temperature, salinity, phosphate, nitrate, silicate and dissolved oxygen. pCO2 from LDEOv2016 and AT from Broullón et al. (2019) were used to compute DIC surface values to increase the surface coverage in the training data. The relations extracted between the predictor variables and DIC 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.

    Access & Use Information

    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 2023-08-30T13:07:36Z
    Metadata Created Date December 4, 2020
    Metadata Updated Date December 1, 2023
    Reference Date(s) December 1, 2020 (publication)
    Frequency Of Update asNeeded

    Metadata Source

    Harvested from NOAA/NESDIS/ncei/accessions

    Other Data Resources

    Graphic Preview

    Preview graphic

    Additional Metadata

    Resource Type Dataset
    Metadata Date 2023-08-30T13:07:36Z
    Metadata Created Date December 4, 2020
    Metadata Updated Date December 1, 2023
    Reference Date(s) December 1, 2020 (publication)
    Responsible Party (Point of Contact)
    Contact Email
    Guid gov.noaa.nodc:0222469
    Access Constraints Cite as: Broullón, Daniel; Pérez, Fiz F.; Velo, Antón; Hoppema, Mario; Olsen, Are; Takahashi, Taro; Key, Robert M.; Tanhua, Toste; Santana-Casiano, J. Magdalena; Kozyr, Alex (2020). A global monthly climatology of oceanic total dissolved inorganic carbon (DIC): a neural network approach (NCEI Accession 0222469). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/ndgj-jp24. 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:0222469
    Graphic Preview Type PNG
    Harvest Object Id 48e7ddb6-5553-49dd-a9e4-30ae81957a6e
    Harvest Source Id c084a438-6f6b-470d-93e0-16aeddb9f513
    Harvest Source Title NOAA/NESDIS/ncei/accessions
    Licence accessLevel: Public
    Lineage
    Metadata Language eng
    Metadata Type geospatial
    Old Spatial {"type": "Polygon", "coordinates": [[[-179.5, -77.5], [179.5, -77.5], [179.5, 89.5], [-179.5, 89.5], [-179.5, -77.5]]]}
    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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