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 2024 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 for GOBAI-O2-v2.1 are detailed in Sharp et al. (2023).

Some updates to the methodology have been introduced for GOBAI-O2-v2.3, which will be described in an upcoming manuscript (Sharp et al., in prep):
Observational O2 data from floats is still adjusted based on a crossover comparison with bottle O2 data, however, the adjustment equation is now a linear fit of the percent difference (Argo - bottle) in oxygen saturation as a function of oxygen saturation.
Model-based experimentation has revealed some spatial and temporal discontinuities in GOBAI-O2 introduced by the Random Forest Regression models. For this reason, GOBAI-O2-v2.3 is based only on feed-forward neural networks.

Rather than basin-specific clusters for algorithm training and application (as in GOBAI-O2-v2.1 and v2.2), clusters are now developed based on unsupervised learning (Gaussian mixture modeling) with temperature, salinity, and depth data.
Algorithm-based uncertainty is now calculated from an ensemble of five model simulation experiments, rather than just one. This provides a more robust estimate of uncertainty in GOBAI-O2.
Due to limited data in the Mediterranean Sea, the neural network for the cluster covering mostly the upper water column in the Mediterranean has been trained and applied without year as a predictor variable.

Data are in netCDF, Figures are in PNG.

Tags: 0yt5yj, algorithm, oceanography
Organization: National Oceanic and Atmospheric Administration, Department of Commerce