Biogeochemical Argo - Data Products

GOBAI-O2

Primary Contact of the Product:

Jonathan D. Sharp
  • Affiliation:UW CICOES, NOAA PMEL
  • Country:USA
  • Email:jonathan.sharp@noaa.gov

GOBAI-O2

  • Acronym:GOBAI-O2
  • Product Name:Gridded Ocean Biogeochemistry from Artificial Intelligence - Oxygen
  • Feature Type: Grid
  • Gap-Filling Method: Machine learning
  • Depth Range: 0-2000dbar (58 levels)
  • Temp. resolution: Monthly
  • Update Frequency: Yearly
  • Time Range: 2004-2023
  • Ref. dataset(s): BGC-Argo, GLODAPv2
  • Ancillary dataset(s): Roemmich & Gilson Argo Climatology
  • File Format: NetCDF4

Product Links

  • Variables Scope:
  • Detailed Variables: O2
  • Area of coverage: Global Ocean
  • Spatial Resolution: 1° × 1°
GOBAI-O2 : Gridded Ocean Biogeochemistry from Artificial Intelligence - Oxygen.

A gridded data product of ocean oxygen concentrations at monthly resolution on 58 depth levels from 2004 to 2023. GOBAI-O2 is constructed from two types of machine learning algorithms, trained using Argo O2 data and discrete O2 measurements from the GLODAP database, and applied to temperature and salinity grids from Roemmich and Gilson (2009).

Authors Affiliation
Jonathan D. Sharp
(UW CICOES, NOAA PMEL, USA)
Andrea J. Fassbender
(NOAA PMEL, USA)
Brendan R. Carter
(UW CICOES, NOAA PMEL, USA)
Gregory C. Johnson
(NOAA PMEL, USA)
John P. Dunne
(NOAA GFDL, USA)
Cristina Schultz
(Northeastern University, USA)
How to cite BGC-Argo Product
BGC-Argo Product are freely available without restriction. However, to track uptake and impact, we ask that where BGC-Argo Products are used in a publication or product, an acknowledgement be given. One way to do that is to cite an BGC-Argo Product ... (text to be defined)
  • University of Washington CICOES
  • NOAA PMEL
  • NOAA GOMO

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