GOBAI-O2: A Global Gridded Monthly Dataset of Ocean Interior Dissolved Oxygen Concentrations Based on Shipboard and Autonomous Observations (NCEI Accession 0259304)

This dataset contains a global gridded data product of observation-based ocean interior dissolved oxygen concentrations. The data product is called GOBAI-O2 for Gridded Ocean Biogeochemistry from Artificial Intelligence - Oxygen. The dissolved oxygen fields were constructed by training machine learning algorithms with observations from shipboard analyses and autonomous profiling floats, then applying those trained algorithms to global gridded fields of temperature and salinity. Those temperature and salinity fields were calculated from a long-term mean field and monthly anomaly fields constructed from the global array of Argo floats (Roemmich and Gilson, 2009), and are presented alongside GOBAI-O2 for easy analysis. Also presented are uncertainty fields for dissolved oxygen, which were calculated by combining three separate sources of uncertainty as described in Sharp et al. (2023), see the Documentation.

The scope and resolution of GOBAI-O2 are as follows: geographically, from -179.5 to 179.5 degrees longitude and -64.5 to 79.5 degrees latitude at 1-degree resolution; with respect to pressure, from 2.5 to 1975 decibars on 58 levels that become incrementally further spaced; and temporally, from January 2004 to December 2023 at monthly resolution. The algorithms used to produce GOBAI-O2 have been validated using real observations and synthetic data from model output, and the data product itself has been compared against the World Ocean Atlas and selected discrete measurements. Results of these validation and comparison exercises are detailed in Sharp et al. (2023).

Data are in netCDF, Figures are in PNG.