All values in billion tonnes of carbon per year (GtC/yr), for the globe. For values in billion tonnes of carbon dioxide per year (GtCO₂/yr), multiply the numbers below by 3.664.
1 billion tonnes C = 1 petagram of carbon (10^15 gC) = 1 gigatonne C = 3.664 billion tonnes of CO₂
Methods: The ocean sink (uncertainty of ±0.4 GtC/yr on average) is estimated from the average of several global ocean biogeochemistry models that reproduce the observed mean ocean sink of the 1990s. Cite as: Friedlingstein et al (2020; see summary tab)
Note: the data products include a pre-industrial steady state source of CO₂ from rivers (of about 0.61 GtC/yr) and therefore are not directly comparable with the ocean model results Cite individual estimates as:

Model results used to compute the annual values:

• CESM_ETH Doney, S. C., Lima, I., Feely, R. A., Glover, D. M., Lindsay, K., Mahowald, N., Moore, J. K., and Wanninkhof, R.: Mech- anisms governing interannual variability in upper-ocean inor- ganic carbon system and air–sea CO₂ fluxes: Physical cli- mate and atmospheric dust, Deep-Sea Res. Pt. II, 56, 640–655, https://doi.org/10.1016/j.dsr2.2008.12.006, 2009.
• CSIRO Law, R. M., Ziehn, T., Matear, R. J., Lenton, A., Chamberlain, M. A., Stevens, L. E., Wang, Y.-P., Srbinovsky, J., Bi, D., Yan, H., and Vohralik, P. F.: The carbon cycle in the Australian Commu- nity Climate and Earth System Simulator (ACCESS-ESM1) – Part 1: Model description and pre-industrial simulation, Geosci. Model Dev., 10, 2567–2590, https://doi.org/10.5194/gmd-10- 2567-2017, 2017
• FESOM-1.4-REcoM2 Hauck, J., Zeising, M., Le Quéré, C., Gruber, N., Bakker, D. C. E., Bopp, L., et al. (2020). Consistency and Challenges in the Ocean Carbon Sink Estimate for the Global Carbon Budget. Frontiers in Marine Science, 7. https://doi.org/10.3389/fmars.2020.571720
• MPIOM-HAMOCC6 Paulsen, H., Ilyina, T., Six, K. D. and Stemmler, I.: Incorporating a prognostic representation of marine nitrogen fixers into the global ocean biogeochemical model HAMOCC, J. Adv. Model. Earth Syst., 9(1), 438–464, doi:10.1002/2016MS000737, 2017.
• NEMO3.6-PISCESv2-gas (CNRM) Berthet, S., Séférian, R., Bricaud, C., Chevallier, M., Voldoire, A., & Ethé, C. ( 2019). Evaluation of an online grid‐coarsening algorithm in a global eddy‐admitting ocean biogeochemical model. Journal of Advances in Modeling Earth Systems, 11, 1759– 1783. https://doi.org/10.1029/2019MS001644
• NEMO-PlankTOM5 Buitenhuis, E. T., Hashioka, T., Le Quéré, C. (2013) Combined constraints on global ocean primary production using observations and models Global Biogeochemical Cycles 27. pp. 847-858 doi:10.1002/gbc.20074
• MICOM-HAMOCC (NorESM-OCv1.2) Schwinger, J., Goris, N., Tjiputra, J. F., Kriest, I., Bentsen, M., Bethke, I., Ilicak, M., Assmann, K. M., and Heinze, C.: Evaluation of NorESM-OC (versions 1 and 1.2), the ocean carbon-cycle stand-alone configuration of the Norwegian Earth System Model (NorESM1), Geosci. Model Dev., 9, 2589-2622, 2016.
• MOM6-COBALT (Princeton) Liao, E., Resplandy, L., Liu, J. and Bowman, K. W.: Amplification of the Ocean Carbon Sink During El Niños: Role of Poleward Ekman Transport and Influence on Atmospheric CO 2, Global Biogeochem. Cycles, 34(9), doi:10.1029/2020GB006574, 2020.
• NEMO-PISCES (IPSL) Aumont, O., Ethé, Tagliabue, A., Bopp, L., & Gehlen, M. (2015). PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies.
• Data products used to evaluate the results:
• Landschützer Landschützer, P., Gruber, N., and Bakker, D. C. E.: Decadal variations and trends of the global ocean carbon sink, Global Biogeochem. Cy., 30, 1396–1417, https://doi.org/10.1002/2015GB005359, 2016
• Rödenbeck Rödenbeck, C., Bakker, D. C. E., Metzl, N., Olsen, A., Sabine, C., Cassar, N., Reum, F., Keeling, R. F., and Heimann, M.: Interannual sea–air CO₂ flux variability from an observation-driven ocean mixed-layer scheme, Biogeosciences, 11, 4599-4613, 2014.
• CMEMS Denvil-Sommer, A., Gehlen, M., Vrac, M., and Mejia, C.: LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean pCO₂ over the global ocean, Geosci. Model Dev., 12, 2091–2105, https://doi.org/10.5194/gmd-12-2091-2019, 2019.
• CSIR-ML6 Gregor, L., Lebehot, A. D., Kok, S. and Scheel Monteiro, P. M.: A comparative assessment of the uncertainties of global surface ocean CO₂ estimates using a machine-learning ensemble (CSIR-ML6 version 2019a)-Have we hit the wall?, Geosci. Model Dev., 12(12), 5113–5136, doi:10.5194/gmd-12-5113-2019, 2019.
• Watson et al. Watson, A. J., Schuster, U., Shutler, J. D., Holding, T., Ashton, I. G. C., Landschützer, P., Woolf, D. K. and Goddijn-Murphy, L.: Revised estimates of ocean-atmosphere CO₂ flux are consistent with ocean carbon inventory, Nat. Commun., 11(1), 1–6, doi:10.1038/s41467-020-18203-3, 2020.