GOBAI-O2: temporally and spatially resolved fields of ocean interior dissolved oxygen over nearly 2 decades
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Published:2023-10-06
Issue:10
Volume:15
Page:4481-4518
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ISSN:1866-3516
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Container-title:Earth System Science Data
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language:en
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Short-container-title:Earth Syst. Sci. Data
Author:
Sharp Jonathan D.ORCID, Fassbender Andrea J.ORCID, Carter Brendan R.ORCID, Johnson Gregory C.ORCID, Schultz Cristina, Dunne John P.
Abstract
Abstract. For about 2 decades, oceanographers have been installing oxygen
sensors on Argo profiling floats to be deployed throughout the world ocean,
with the stated objective of better constraining trends and variability in
the ocean's inventory of oxygen. Until now, measurements from these
Argo-float-mounted oxygen sensors have been mainly used for localized process
studies on air–sea oxygen exchange, upper-ocean primary production,
biological pump efficiency, and oxygen minimum zone dynamics. Here, we
present a new four-dimensional gridded product of ocean interior oxygen,
derived via machine learning algorithms trained on dissolved oxygen
observations from Argo-float-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, which stands for Gridded Ocean Biogeochemistry from Artificial Intelligence
– Oxygen (Sharp et al., 2022; https://doi.org/10.25921/z72m-yz67); it covers
86 % of the global ocean area on a 1∘ × 1∘
(latitude × longitude) grid, spans the years 2004–2022 with a monthly resolution, and
extends from the ocean surface to a depth of 2 km on 58 levels. Two
types of 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 simulated observations from Earth system model output.
Machine learning represents a relatively new method for gap filling ocean
interior biogeochemical observations and should be explored along with
statistical and interpolation-based techniques. GOBAI-O2 is evaluated
through comparisons to the oxygen climatology from the World Ocean Atlas,
the mapped oxygen product from the Global Ocean Data Analysis Project 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 seasonal cycles of
oxygen averaged over different pressure layers, and globally integrated time
series of oxygen. GOBAI-O2 indicates a declining trend in the oxygen inventory in the
upper 2 km of the global ocean of 0.79 ± 0.04 % per decade between 2004 and 2022.
Funder
Global Ocean Monitoring and Observing Program Joint Institute for the Study of the Atmosphere and Ocean
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
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