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Trends and variability in the ocean carbon sink
Gruber, N.; Bakker, D.C.E.; DeVries, T.; Gregor, L.; Hauck, J.; Landschützer, P.; McKinley, G.A.; Müller, J.D. (2023). Trends and variability in the ocean carbon sink. Nat. Rev. Earth Environ. 4(2): 119-134. https://dx.doi.org/10.1038/s43017-022-00381-x
In: Nature Reviews Earth & Environment. Springer Nature: London. ISSN 2662-138X; e-ISSN 2662-138X
Peer reviewed article  
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Author keywords
    FOSSIL-FUEL CO2;ANTHROPOGENIC CO2;SOUTHERN-OCEAN;GLOBAL OCEAN;ATMOSPHERIC CO2;INTERANNUAL VARIABILITY;DIOXIDE EXCHANGE;INCREASE;FLUXES;SIMULATION
Auteurs  Top 
  • Gruber, N.
  • Bakker, D.C.E.
  • DeVries, T.
  • Gregor, L.
  • Hauck, J.
  • Landschützer, P.
  • McKinley, G.A.
  • Müller, J.D.
Abstract
    The ocean has absorbed 25 ± 2% of the total anthropogenic CO2 emissions from the early 1960s to the late 2010s, with rates more than tripling over this period and with a mean uptake of –2.7 ± 0.3 Pg C year–1 for the period 1990 through 2019. This growth of the ocean sink matches expectations based on the increase in atmospheric CO2, but research has shown that the sink is more variable than long assumed. In this Review, we discuss trends and variations in the ocean carbon sink. The sink stagnated during the 1990s with rates hovering around –2 Pg C year–1, but strengthened again after approximately 2000, taking up around –3 Pg C year–1 for 2010–2019. The most conspicuous changes in uptake occurred in the high latitudes, especially the Southern Ocean. These variations are caused by changes in weather and climate, but a volcanic eruption-induced reduction in the atmospheric CO2 growth rate and the associated global cooling contributed as well. Understanding the variability of the ocean carbon sink is crucial for policy making and projecting its future evolution, especially in the context of the UN Framework Convention on Climate Change stocktaking activities and the deployment of CO2 removal methods. This goal will require a global-level effort to sustain and expand the current observational networks and to better integrate these observations with models.
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