Preprints
https://doi.org/10.5194/essd-2022-308
https://doi.org/10.5194/essd-2022-308
 
20 Sep 2022
20 Sep 2022
Status: this preprint is currently under review for the journal ESSD.

GOBAI-O2: temporally and spatially resolved fields of ocean interior dissolved oxygen over nearly two decades

Jonathan D. Sharp1,2, Andrea J. Fassbender2, Brendan R. Carter1,2, Gregory C. Johnson2, Cristina Schultz3,4, and John P. Dunne3 Jonathan D. Sharp et al.
  • 1Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, Seattle, WA, 98105, United States
  • 2Pacific Marine Environmental Laboratory, National Oceanic and Atmospheric Administration, Seattle, WA, 98115, United States
  • 3Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, NJ, 08540, United States
  • 4Princeton University, Princeton, NJ, 08540, United States

Abstract. Over a decade ago, oceanographers began installing oxygen sensors on Argo floats to be deployed throughout the world ocean with the express objective of better constraining trends and variability in the ocean’s inventory of oxygen. Until now, measurements from these Argo-mounted oxygen sensors have been mainly used for localized process studies on air–sea oxygen exchange, biological pump efficiency, upper ocean primary production, and oxygen minimum zone dynamics. Here we present a four-dimensional gridded product of ocean interior oxygen, derived via machine learning algorithms trained on dissolved oxygen observations from Argo-mounted sensors and discrete measurements from ship-based surveys, and applied to temperature and salinity fields constructed from the global Argo array. The data product is called GOBAI-O2 for Gridded Ocean Biogeochemistry from Artificial Intelligence – Oxygen (Sharp et al., 2022; https://doi.org/10.25921/z72m-yz67; last access: 30 Aug. 2022); it covers 86 % of the global ocean area on a 1° latitude by 1° longitude grid, spans the years 2004–2021 with monthly resolution, and extends from the ocean surface to two kilometers in depth on 58 levels. Two machine learning algorithms — random forest regressions and feed-forward neural networks — are used in the development of GOBAI-O2, and the performance of those algorithms is assessed using real observations and Earth system model output. GOBAI-O2 is evaluated through comparisons to the World Ocean Atlas and to direct observations from large-scale hydrographic research cruises. Finally, potential uses for GOBAI-O2 are demonstrated by presenting average oxygen fields on isobaric and isopycnal surfaces, average oxygen fields across vertical–meridional sections, climatological cycles of oxygen averaged over different pressure intervals, and a globally integrated oxygen inventory time series.

Jonathan D. Sharp et al.

Status: open (until 15 Nov 2022)

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Jonathan D. Sharp et al.

Data sets

GOBAI-O2: A Global Gridded Monthly Dataset of Ocean Interior Dissolved Oxygen Concentrations Based on Shipboard and Autonomous Observations (NCEI Accession 0259304) Sharp, Jonathan D.; Fassbender, Andrea J.; Carter, Brendan R.; Johnson, Gregory C.; Schultz, C.; Dunne, John P. https://doi.org/10.25921/z72m-yz67

Jonathan D. Sharp et al.

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Latest update: 20 Sep 2022
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Short summary
Dissolved oxygen content is a critical metric of ocean health. Recently, expanding fleets of autonomous platforms that measure oxygen in the ocean have produced a wealth of new data. We leverage machine learning to take advantage of this growing global dataset, producing a gridded data product of ocean interior dissolved oxygen at monthly resolution over nearly two decades. This work provides novel information for investigations of spatial, seasonal, and interannual variability of ocean oxygen.