A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage

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Varvara E Zemskova , Tai-Long He , Zirui Wan, Nicolas Grisouard 


Uptake of atmospheric carbon by the ocean, especially at high latitudes, plays an important role in offsetting anthropogenic emissions.
At the surface of the Southern Ocean south of 30 degrees S, the ocean carbon uptake, which had been weakening in 1990s, strengthened in the 2000s.
However, sparseness of in-situ measurements in the ocean interior make it difficult to compute changes in carbon storage below the surface.
Here we develop a machine-learning model, which estimates concentrations of dissolved inorganic carbon (DIC) in the Southern Ocean up to 4 km depth only using data available at the ocean surface.
We applied this model to calculate trends in DIC concentrations over the past three decades and found that DIC decreased in the upper 1 km and increased in deeper layers over this period.
However, the particular circulation dynamics that drove these changes may have differed across zonal sectors of the Southern Ocean.
While the near-surface decrease in DIC concentrations would enhance atmospheric CO2 uptake continuing the previously-found trends, weakened connectivity between surface and deep layers and build-up of DIC in deep waters could reduce the ocean's carbon storage potential.




Atmospheric Sciences, Biogeochemistry, Climate, Earth Sciences, Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics


machine learning, Southern Ocean, deep learning model, ocean carbon, ocean carbon budget, dissolved inorganic carbon, ocean carbon storage, ocean carbon sink, ocean carbon uptake, Argo floats, ocean biogeochemistry, ocean satellite data


Published: 2021-04-21 07:13

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CC BY Attribution 4.0 International

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