FN Archimer Export Format
PT J
TI A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage
BT 
AF ZEMSKOVA, Varvara E.
   HE, Tai-Long
   WAN, Zirui
   GRISOUARD, Nicolas
AS 1:1;2:1;3:1;4:1;
FF 1:;2:;3:;4:;
C1 Department of Physics, University of Toronto, Toronto, ON, Canada
C2 UNIV TORONTO, CANADA
IN DOAJ
IF 16.6
TC 6
UR https://archimer.ifremer.fr/doc/00788/90003/95566.pdf
   https://archimer.ifremer.fr/doc/00788/90003/95567.pdf
LA English
DT Article
CR OISO - OCÉAN INDIEN SERVICE D'OBSERVATION
AB 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(circle)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 can estimate concentrations of dissolved inorganic carbon (DIC) in the Southern Ocean up to 4 km depth only using data available at the ocean surface. Our model is fast and computationally inexpensive. We apply it to calculate trends in DIC concentrations over the past three decades and find that DIC decreased in the 1990s and 2000s, but has increased, in particular in the upper ocean since the 2010s. 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. Dissolved carbon concentrations in the ocean interior are computed by a deep-learning model using ocean surface data. In the Southern Ocean, they decreased in the 1990s-2000s and increased since 2010, reducing anthropogenic carbon uptake potential. 
PY 2022
PD JUN
SO Nature Communications
SN 2041-1723
PU Nature Portfolio
VL 13
IS 1
UT 000825090700020
DI 10.1038/s41467-022-31560-5
ID 90003
ER

EF