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Atmospheric oxygen constraints on Southern Ocean productivity and drivers of carbon uptake

Nature Geoscience volume 19, pages 534–541 (2026) Cite this article

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Abstract

Ocean net primary production fixes dissolved carbon into organic matter while producing O2, driving the biological carbon pump that contributes to ocean CO2 uptake. The Southern Ocean plays a critical role in carbon export, yet its productivity estimates remain highly uncertain due to limited observations. Here we constrain Southern Ocean (south of ~44° S) net primary production by linking Coupled Model Intercomparison Project Phase 6 (CMIP6)-modelled productivity to modelled air–sea O2 fluxes and applying O2 flux estimates derived from airborne O2/N2 observations. We find an annual net primary production of 6.5 ± 1.36 PgC yr−1, substantially higher than most CMIP6 model and satellite-based estimates, but consistent with Argo oxygen-based estimates. We show that CMIP6 models with underestimated productivity exhibit weak summer CO2 uptake, with some also showing excessive summer temperature-driven outgassing. Together, these models produce incorrect seasonal CO2 flux cycles with summer outgassing, whereas observation-based estimates indicate summer uptake. These errors may stem from inadequate model representation of ocean vertical mixing, which affects nutrient supply, stratification and heat redistribution. Our productivity estimates provide quantitative benchmarks that, combined with constraints from airborne CO2 observations and surface ocean pCO2 and temperature observations, reduce uncertainty in estimates of model-projected end-of-century Southern Ocean CO2 uptake by 53%.

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Fig. 1: Airborne observations of δ(O2/N2)* latitude–pressure patterns above the SO and airborne-resolved seasonal air–sea O2 fluxes south of ~44° S.
The alternative text for this image may have been generated using AI.
Fig. 2: Observations and CMIP6 model simulations of seasonal air–sea O2 fluxes and NPP south of ~44° S.
The alternative text for this image may have been generated using AI.
Fig. 3: Emergent constraint on annual SO NPP using the SNO of the non-thermal O2 flux cycle (SNObio).
The alternative text for this image may have been generated using AI.
Fig. 4: Observations and CMIP6 model simulations of seasonal air–sea CO2 fluxes, surface ocean seasonal pCO2 change, and its thermal and non-thermal components.
The alternative text for this image may have been generated using AI.
Fig. 5: Analysis of thermal and non-thermal factors leading to biases in CMIP6 model simulations of SO CO2 uptake.
The alternative text for this image may have been generated using AI.

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Data availability

All HIPPO 10-s merged data are available at https://data.eol.ucar.edu/dataset/112.123 (ref. 70). We use updated HIPPO AO2 data from https://data.eol.ucar.edu/dataset/112.005 (ref. 71), https://data.eol.ucar.edu/dataset/117.082 (ref. 72), https://data.eol.ucar.edu/dataset/121.009 (ref. 73), https://data.eol.ucar.edu/dataset/248.010 (ref. 74) and https://data.eol.ucar.edu/dataset/249.010 (ref. 75). All ORCAS 10-s merge data are available at https://data.eol.ucar.edu/dataset/490.024 (ref. 76). Here, we use updated ORCAS AO2 data from https://data.eol.ucar.edu/dataset/490.015 (ref. 77). All ATom 10-s merge data are available at https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1925 (ref. 78), including the version of AO2 data used here. Ground station O2/N2 measurements from the Scripps O2 Program are available at https://doi.org/10.6075/J0WS8RJR (ref. 53). CMIP6 data are available through the Earth System Grid Federation (ESGF; https://esgf-node.llnl.gov/projects/esgf-llnl/). Surface ocean pCO2 data are downloaded at https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/SPCO2_1982_present_ETH_SOM_FFN.html (ref. 50). SST and sea surface salinity data from EN4 are downloaded at https://www.metoffice.gov.uk/hadobs/en4/. Jena APO inversion data are available at https://www.bgc-jena.mpg.de/CarboScope/?ID=apo. Simulations of APO components from APO-MIP1 are available at https://gdex.ucar.edu/datasets/d010018/ (ref. 79). The data needed to reproduce Figs. 1–5 and Extended Data Figs. 1–7 are available via Zenodo at https://doi.org/10.5281/zenodo.17969932 (ref. 80).

Code availability

The code needed to reproduce Figs. 1–5 and Extended Data Figs. 1–7 is available via Zenodo at https://doi.org/10.5281/zenodo.17969932 (ref. 80).

References

  1. Chavez, F. P., Messié, M. & Pennington, J. T. Marine primary production in relation to climate variability and change. Annu. Rev. Mar. Sci. 3, 227–260 (2011).

    Article  Google Scholar 

  2. Henson, S. A., Sanders, R. & Madsen, E. Global patterns in efficiency of particulate organic carbon export and transfer to the deep ocean. Global Biogeochem. Cycles 26, GB1028 (2012).

    Article  Google Scholar 

  3. Arteaga, L. A., Pahlow, M., Bushinsky, S. M. & Sarmiento, J. L. Nutrient controls on export production in the Southern Ocean. Global Biogeochem. Cycles 33, 942–956 (2019).

    Article  CAS  Google Scholar 

  4. Pan, X. L., Lai, X., Makabe, R., Hirano, D. & Watanabe, Y. W. Spatiotemporal high-resolution mapping of biological production in the Southern Ocean. Commun. Earth Environ. 4, 1–8 (2023).

    Article  Google Scholar 

  5. Falkowski, P. G., Barber, R. T. & Smetacek, V. Biogeochemical controls and feedbacks on ocean primary production. Science 281, 200–206 (1998).

    Article  CAS  Google Scholar 

  6. Gruber, N., Landschützer, P. & Lovenduski, N. S. The variable Southern Ocean carbon sink. Annu. Rev. Mar. Sci. 11, 159–186 (2019).

    Article  Google Scholar 

  7. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  8. Mongwe, N. P., Vichi, M. & Monteiro, P. M. S. The seasonal cycle of pCO2 and CO2 fluxes in the Southern Ocean: diagnosing anomalies in CMIP5 Earth system models. Biogeosciences 15, 2851–2872 (2018).

    Article  CAS  Google Scholar 

  9. Anav, A. et al. Evaluating the land and ocean components of the global carbon cycle in the CMIP5 Earth system models. J. Clim. 26, 6801–6843 (2013).

    Article  Google Scholar 

  10. Kessler, A. & Tjiputra, J. The Southern Ocean as a constraint to reduce uncertainty in future ocean carbon sinks. Earth Syst. Dynam. 7, 295–312 (2016).

    Article  Google Scholar 

  11. Johnson, K. S. & Bif, M. B. Constraint on net primary productivity of the global ocean by Argo oxygen measurements. Nat. Geosci. 14, 769–774 (2021).

    Article  CAS  Google Scholar 

  12. Arrigo, K. R., van Dijken, G. L. & Bushinsky, S. Primary production in the Southern Ocean, 1997–2006. J. Geophys. Res. Oceans 113, C08004 (2008).

    Article  Google Scholar 

  13. Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).

    Article  CAS  Google Scholar 

  14. Nevison, C. et al. Net community production in the Southern Ocean: Insights from comparing atmospheric potential oxygen to satellite ocean color algorithms and ocean models. Geophys. Res. Lett. 45, 10549–10559 (2018).

    Article  CAS  Google Scholar 

  15. Schlitzer, R. Carbon export fluxes in the Southern Ocean: results from inverse modeling and comparison with satellite-based estimates. Deep Sea Res. II 49, 1623–1644 (2002).

    Article  CAS  Google Scholar 

  16. Johnson, R., Strutton, P. G., Wright, S. W., McMinn, A. & Meiners, K. M. Three improved satellite chlorophyll algorithms for the Southern Ocean. J. Geophys. Res. Oceans 118, 3694–3703 (2013).

    Article  CAS  Google Scholar 

  17. Behrenfeld, M. J., Boss, E., Siegel, D. A. & Shea, D. M. Carbon-based ocean productivity and phytoplankton physiology from space. Global Biogeochem. Cycles 19, GB1006 (2005).

    Article  Google Scholar 

  18. Westberry, T., Behrenfeld, M. J., Siegel, D. A. & Boss, E. Carbon-based primary productivity modeling with vertically resolved photoacclimation. Global Biogeochem. Cycles 22, GB2024 (2008).

    Article  Google Scholar 

  19. Yamaguchi, R., Kouketsu, S., Kosugi, N. & Ishii, M. Global upper ocean dissolved oxygen budget for constraining the biological carbon pump. Commun. Earth Environ. 5, 1–12 (2024).

    Article  Google Scholar 

  20. Nevison, C. D. et al. Estimating net community production in the Southern Ocean based on atmospheric potential oxygen and satellite ocean color data. Global Biogeochem. Cycles 26, GB1020 (2012).

    Article  Google Scholar 

  21. Bushinsky, S. M., Gray, A. R., Johnson, K. S. & Sarmiento, J. L. Oxygen in the Southern Ocean from Argo floats: determination of processes driving air–sea fluxes. JGR Oceans 122, 8661–8682 (2017).

    Article  CAS  Google Scholar 

  22. Garcia, H. E. & Keeling, R. F. On the global oxygen anomaly and air–sea flux. J. Geophys. Res.Ocean 106, 31155–31166 (2001).

    Article  CAS  Google Scholar 

  23. Najjar, R. G. & Keeling, R. F. Mean annual cycle of the air–sea oxygen flux: a global view. Global Biogeochem. Cycles 14, 573–584 (2000).

    Article  CAS  Google Scholar 

  24. Bushinsky, S. M. et al. Reassessing Southern Ocean air–sea CO2 flux estimates with the addition of biogeochemical float observations. Global Biogeochem. Cycles 33, 1370–1388 (2019).

    Article  CAS  Google Scholar 

  25. Rödenbeck, C., Quéré, C. L., Heimann, M. & Keeling, R. F. Interannual variability in oceanic biogeochemical processes inferred by inversion of atmospheric O2/N2 and CO2 data. Tellus B 60, 685–705 (2008).

    Article  Google Scholar 

  26. Jin, Y. et al. Improved atmospheric constraints on Southern Ocean CO2 exchange. Proc. Natl Acad. Sci. 121, e2309333121 (2024).

    Article  CAS  Google Scholar 

  27. Jin, Y. et al. The Atmospheric Potential Oxygen forward Model Intercomparison Project (APO-MIP1): evaluating simulated atmospheric transport of air–sea gas exchange tracers and APO flux products. Geosci. Model Dev. 18, 5937–5969 (2025).

    Article  Google Scholar 

  28. Jin, Y. et al. A mass-weighted isentropic coordinate for mapping chemical tracers and computing atmospheric inventories. Atmos. Chem. Phys. 21, 217–238 (2021).

    Article  CAS  Google Scholar 

  29. Jin, Y. et al. Seasonal tropospheric distribution and air–sea fluxes of atmospheric potential oxygen from global airborne observations. Global Biogeochem. Cycles 37, e2023GB007827 (2023).

    Article  CAS  Google Scholar 

  30. Wofsy, S. C. HIAPER Pole-to-Pole Observations (HIPPO): fine-grained, global-scale measurements of climatically important atmospheric gases and aerosols. Phil. Trans. R. Soc. A 369, 2073–2086 (2011).

    Article  CAS  Google Scholar 

  31. Stephens, B. B. et al. The O2/N2 ratio and CO2 airborne Southern Ocean study. Bull. Am. Meteorol. Soc. 99, 381–402 (2018).

    Article  Google Scholar 

  32. Thompson, C. et al. The NASA Atmospheric Tomography (ATom) mission: Imaging the chemistry of the global atmosphere. Bull. Am. Meteorol. Soc. 103, E761–E790 (2022).

    Article  Google Scholar 

  33. Bowman, K. W., Cressie, N., Qu, X. & Hall, A. A hierarchical statistical framework for emergent constraints: application to snow-albedo feedback. Geophys. Res. Lett. 45, 13050–13059 (2018).

    Article  Google Scholar 

  34. Hayward, T. L. The shallow oxygen maximum layer and primary production. Deep Sea Res. I 41, 559–574 (1994).

    Article  CAS  Google Scholar 

  35. Manizza, M., Keeling, R. F. & Nevison, C. D. On the processes controlling the seasonal cycles of the air–sea fluxes of O2 and N2O: A modelling study. Tellus B 64, 18429 (2012).

    Article  Google Scholar 

  36. Keeling, R. F. & Shertz, S. R. Seasonal and interannual variations in atmospheric oxygen and implications for the global carbon cycle. Nature 358, 723–727 (1992).

    Article  CAS  Google Scholar 

  37. Williamson, D. B. & Sansom, P. G. How are emergent constraints quantifying uncertainty and what do they leave behind?. Bull. Am. Meteorol. Soc. 100, 2571–2587 (2019).

    Article  Google Scholar 

  38. De Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A. & Iudicone, D. Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology. J. Geophys. Res. 109, C12003 (2004).

    Google Scholar 

  39. Long, M. C. et al. Strong Southern Ocean carbon uptake evident in airborne observations. Science 374, 1275–1280 (2021).

    Article  CAS  Google Scholar 

  40. Rödenbeck, C. et al. Data-based estimates of the ocean carbon sink variability—first results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM). Biogeosciences 12, 7251–7278 (2015).

    Article  Google Scholar 

  41. Friedlingstein, P. et al. Global Carbon Budget 2024. Earth Syst. Sci. Data 17, 965–1039 (2025).

    Article  Google Scholar 

  42. Luo, F., Ying, J., Liu, T. & Chen, D. Origins of Southern Ocean warm sea surface temperature bias in CMIP6 models. npj Clim. Atmos. Sci. 6, 127 (2023).

  43. Mitchell, B. G., Brody, E. A., Holm-Hansen, O., McClain, C. & Bishop, J. Light limitation of phytoplankton biomass and macronutrient utilization in the Southern Ocean. Limnol. Oceanogr. 36, 1662–1677 (1991).

    Article  Google Scholar 

  44. Mears, C., Lee, T., Ricciardulli, L., Wang, X. & Wentz, F. Improving the accuracy of the Cross-Calibrated Multi-Platform (CCMP) ocean vector winds. Remote Sensing 14, 4230 (2022).

    Article  Google Scholar 

  45. Wanninkhof, R. Relationship between wind speed and gas exchange over the ocean revisited. Limnol. Ocean Methods 12, 351–362 (2014).

    Article  Google Scholar 

  46. Terhaar, J. et al. Assessment of global ocean biogeochemistry models for ocean carbon sink estimates in RECCAP2 and recommendations for future studies. J. Adv. Model. Earth Syst. 16, e2023MS003840 (2024).

    Article  Google Scholar 

  47. Pinkerton, M. H. et al. Evidence for the impact of climate change on primary producers in the Southern Ocean. Front. Ecol. Evol. 9, 592027 (2021).

    Article  Google Scholar 

  48. Zhao, H., Manizza, M., Lozier, M. S. & Cassar, N. Greener green and bluer blue: ocean poleward greening over the past two decades. Science 388, 1337–1340 (2025).

    Article  CAS  Google Scholar 

  49. Akima, H. A method of bivariate interpolation and smooth surface fitting for irregularly distributed data points. ACM Trans. Math. Softw. 4, 148–159 (1978).

    Article  Google Scholar 

  50. Jersild, A., Landschützer, P., Gruber, N. & Bakker, D. C. E. An Observation-Based Global Monthly Gridded Sea Surface pCO2 and Air–Sea CO2 Flux Product from 1982 Onward and Its Monthly Climatology (NCEI Accession 0160558) version 8.8 (NOAA National Centers for Environmental Information, 2024).

  51. Takahashi, T. et al. Global sea–air CO2 flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects. Deep Sea Res. II 49, 1601–1622 (2002).

    Article  CAS  Google Scholar 

  52. Good, S. A., Martin, M. J. & Rayner, N. A. EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res. Oceans 118, 6704–6716 (2013).

    Article  Google Scholar 

  53. Keeling, R. F. & Morgan, E. J. Scripps O2 Program Data. UC San Diego Library Digital Collections https://doi.org/10.6075/J0WS8RJRs (2019).

  54. Stephens, B. B. et al. Airborne measurements of oxygen concentration from the surface to the lower stratosphere and pole to pole. Atmos. Meas. Tech. 14, 2543–2574 (2021).

    Article  CAS  Google Scholar 

  55. BentJ. Airborne Oxygen Measurements over the Southern Ocean as an Integrated Constraint of Seasonal Biogeochemical Processes (University of California, San Diego, 2014).

  56. Keeling, R. F., Walker, S. J. & Paplawsky, W. Span Sensitivity of the Scripps Interferometric Oxygen Analyzer (2020).

  57. Keeling, R. F., Manning, A. C., Paplawsky, W. J. & Cox, A. C. On the long-term stability of reference gases for atmospheric O2/N2 and CO2 measurements. Tellus B 59, 3–14 (2007).

    Article  Google Scholar 

  58. Jacobson, A. R. et al. CarbonTracker CT2022. NOAA Global Monitoring Laboratory https://doi.org/10.25925/Z1GJ-3254 (2023).

  59. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorolog. Soc. 146, 1999–2049 (2020).

    Article  Google Scholar 

  60. Kosaka, Y. et al. The JRA-3Q Reanalysis. J. Meteor. Soc. Japan 102, 49–109 (2024).

    Article  Google Scholar 

  61. Tsujino, H. et al. JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do). Ocean Model 130, 79–139 (2018).

    Article  Google Scholar 

  62. Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).

    Article  Google Scholar 

  63. Hamme, R. C. & Emerson, S. R. The solubility of neon, nitrogen and argon in distilled water and seawater. Deep Sea Res. I 51, 1517–1528 (2004).

    Article  CAS  Google Scholar 

  64. Jin, X., Najjar, R. G., Louanchi, F. & Doney, S. C. A modeling study of the seasonal oxygen budget of the global ocean. J. Geophys. Res. 112, C05017 (2007).

    Google Scholar 

  65. Griffies, S. M. et al. OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project. Geosci. Model Dev. 9, 3231–3296 (2016).

    Article  Google Scholar 

  66. Orr, J. C. et al. Biogeochemical protocols and diagnostics for the CMIP6 Ocean Model Intercomparison Project (OMIP). Geosci. Model Dev. 10, 2169–2199 (2017).

    Article  CAS  Google Scholar 

  67. Danabasoglu, G. et al. North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part II: inter-annual to decadal variability. Ocean Model 97, 65–90 (2016).

    Article  Google Scholar 

  68. O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    Article  Google Scholar 

  69. Weatherhead, E. C. et al. Factors affecting the detection of trends: statistical considerations and applications to environmental data. J. Geophys. Res. Atmos. 103, 17149–17161 (1998).

    Article  Google Scholar 

  70. Wofsy, S. C. HIPPO Merged 10-Second Meteorology, Atmospheric Chemistry, and Aerosol Data. Version 1.0 (UCAR/NCAR - Earth Observing Laboratory, 2017); https://doi.org/10.3334/CDIAC/HIPPO_010

  71. Stephens, B. et al. HIPPO-1 Airborne Oxygen Instrument. Version 2.0 (UCAR/NCAR—Earth Observing Laboratory, 2021); https://doi.org/10.5065/D6J38QVV

  72. Stephens, B. et al. HIPPO-2 Airborne Oxygen Instrument. Version 2.0 (UCAR/NCAR—Earth Observing Laboratory, 2021); https://doi.org/10.5065/D65Q4TF0

  73. Stephens, B. et al. HIPPO-3 Airborne Oxygen Instrument. Version 2.0 (UCAR/NCAR—Earth Observing Laboratory, 2021); https://doi.org/10.5065/D67H1GXJ

  74. Stephens, B. et al. HIPPO-4 Airborne Oxygen Instrument. Version 2.0 (UCAR/NCAR—Earth Observing Laboratory, 2021); https://doi.org/10.5065/D679431D

  75. Stephens, B. et al. HIPPO-5 Airborne Oxygen Instrument. Version 2.0 (UCAR/NCAR—Earth Observing Laboratory, 2021); https://doi.org/10.5065/D6WW7G0D

  76. Stephens, B. ORCAS Merge Products. Version 1.0 (UCAR/NCAR—Earth Observing Laboratory, 2017); https://doi.org/10.5065/D6SB445X

  77. Stephens, B. et al. ORCAS Airborne Oxygen Instrument. Version 2.0 (UCAR/NCAR—Earth Observing Laboratory, 2021); https://doi.org/10.5065/D6N29VC6

  78. Wofsy, S. ATom: Merged Atmospheric Chemistry, Trace Gases, and Aerosols, Version 2 (Version 2.0) (ORNL Distributed Active Archive Center, 2021); https://doi.org/10.3334/ORNLDAAC/1925

  79. Stephens, B. et al. Atmospheric Potential Oxygen Forward Model Intercomparison Project (APO-MIP) (UCAR/NCAR—Research Data Archive, 2025); https://doi.org/10.5065/F3PW-A676

  80. Jin, Y. Data and code for atmospheric oxygen constraints on Southern Ocean productivity and drivers of carbon uptake. Zenodo https://doi.org/10.5281/zenodo.17969932 (2025).

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Acknowledgements

We acknowledge the efforts of the full HIPPO, ORCAS and ATom science teams and the pilots and crew of the NSF NCAR GV and NASA DC-8, as well as the NSF NCAR and NASA project managers, field support staff and logistics experts. Atmospheric O2 measurements on HIPPO were supported by NSF grants ATM-0628519 and ATM-0628388. ORCAS was supported by NSF grants PLR-1501993, PLR-1502301, PLR-1501997 and PLR-1501292. Atmospheric O2 measurements on ATom-1 were supported by NSF grants AGS-1547626 and AGS-1547797. Atmospheric O2 measurements on ATom 2–4 were supported by NSF grants AGS-1623745 and AGS-1623748. The recent atmospheric measurements of the Scripps O2 Program have been supported by NSF through grants OPP-1922922 and OPP-2329254, and by the National Oceanic and Atmospheric Administration (NOAA) via grant NA20OAR4320278. For sharing O3, N2O and H2O measurements, we thank J. Elkins, E. Hintsa and F. Moore for ATom-1 N2O data; R.-S. Gao and R. Spackman for HIPPO O3 data; I. Bourgeois, J. Peischl, T. Ryerson and C. Thompson for ATom O3 data; S. Beaton, M. Diao and M. Zondlo for HIPPO and ORCAS H2O data; and G. Diskin and J. DiGangi for ATom H2O data. We acknowledge atmospheric transport modellers, including J. Vance and M. Long (CAM-SD), Y. Niwa (NICAM) and P. K. Patra (MIROC4-ACTM), for providing atmospheric simulations of APO components that are used in this study. Y.J. acknowledges the Advanced Study Program Postdoctoral Fellowship in the NSF National Center for Atmospheric Research. This material is based upon work supported by the NSF National Center for Atmospheric Research, which is a major facility sponsored by the US National Science Foundation under Cooperative Agreement No. 1852977. M.M. was supported by the National Recovery and Resilience Plan project TeRABIT (Terabit network for Research and Academic Big data in Italy – IR0000022 – PNRR Missione 4, Componente 2, Investimento 3.1 CUP I53C21000370006) in the frame of the European Union – NextGenerationEU funding.

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Authors and Affiliations

  1. Earth Observing Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA

    Yuming Jin & Britton B. Stephens

  2. Advanced Study Program, NSF National Center for Atmospheric Research, Boulder, CO, USA

    Yuming Jin

  3. [C]Worthy, Boulder, CO, USA

    Matthew C. Long

  4. National Institute of Oceanography and Applied Geophysics – OGS, Trieste, Italy

    Manfredi Manizza

  5. Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA

    Nicole S. Lovenduski

  6. Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO, USA

    Nicole S. Lovenduski & Cynthia Nevison

  7. Geosciences Research Division, Scripps Institution of Oceanography, University of California, San Diego, San Diego, CA, USA

    Eric J. Morgan & Ralph F. Keeling

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  1. Yuming Jin
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Contributions

Y.J. conceived of the project, conducted the analysis, generated the figures and wrote the paper. B.S. and M.L. contributed to the organization of the paper. B.S., E.M., and R.K. provided O2 measurements. M.M., N.L., and C.N. contributed to the analysis of model simulations. All authors contributed to reviewing and editing the text.

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Correspondence to Yuming Jin.

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Nature Geoscience thanks Sara Mikaloff-Fletcher and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: James Super, in collaboration with the Nature Geoscience team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Location of station measurements from the Scripps O2 program stations with station codes and inlet elevations in meters above sea level.

We only use data from stations marked in red to derive the cubic spline trend. Figure adapted from ref. 27 under a Creative Commons licence CC BY 4.0.

Extended Data Fig. 2 Cross sections of airborne δ(O2/N2)* observations of 12 campaigns.

a, HIPPO-1 (mean date: 2009-01-18). b, ORCAS-1 (2016-01-20). c, ORCAS-2 (2016-02-06). d, ATom-2 (2017-02-09). e, ORCAS-3 (2016-02-24). f, HIPPO-3 (2010-04-05). g, ATom-4 (2018-05-07). h, HIPPO-4 (2011-06-28). i, ATom-1 (2016-08-11). j, HIPPO-5 (2011-08-29). k, ATom-3 (2017-10-12). l, HIPPO-2 (2009-11-10). Colors show the observed δ(O2/N2)* averaged into 2.5° latitude and 50-mbar pressure boxes with campaign averages (75-25°S, 1000-300 mbar) removed. Panels are ordered by the mean date of the year of each campaign. For boxes with fewer than 7 observations, we extrapolated using the Akima function (ref. 49) only for visualization purposes in the figure. Gray contour lines show the observed θe. Numbers of 10-s measurements within each latitude-pressure box are shown in Supplementary Fig. S1.

Extended Data Fig. 3 Flux components used to correct the observed surface seasonal δ(O2/N2)* flux cycle to derive the seasonal non-thermal air-sea O2 flux cycle.

(a–c) Monthly average air-sea thermal-driven O2 flux, air-sea N2 flux, and air-sea Ar flux calculated using ocean heat flux from four reanalyses, averaged from 2009 to 2018. (d) Sum of monthly average (2009 to 2018) air-land O2 flux and O2 fossil fuel burning consumption calculated using monthly average posterior land CO2 flux and prior fossil fuel CO2 emission from CarbonTracker 2022, converted to O2 flux. By comparison, the net \({{\rm{F}}}_{{{\rm{O}}}_{2}}^{{\rm{ocn}}}\) has a seasonal cycle amplitude of 5.0 Tmol day−1. The correction is presented in Supplementary Eq. S3.

Extended Data Fig. 4 Emergent constraint on growing season (October to March) Southern Ocean (SO) net community production (NCP) using the seasonal net outgassing of the non-thermal O2 flux cycle (SNObio).

We use the variable ‘fbddtdic’ as a proxy for NCP, defined as the rate of change of DIC due to biological activity, which is available in only 8 of the 22 models and experiments analyzed here. The emergent constraint method is identical to that reported in Fig. 3. The red point with error bars denotes the mean and standard deviation of constrained NCP (1.5 ± 0.20 PgC yr−1) and SNObio derived using HEC (Methods). We focus specifically on growing season (October to March) NCP because SNObio measures growing season net O2 outgassing that directly relates to biological activities. During winter, negative NCP from respiration and limited photosynthesis does not correlate with SNObio. We note that previous studies suggest that the model variable ‘epc100’ (Extended Data Fig. 5e), which represents the export production at 100-m, can serve as a proxy for NCP, but significant uncertainty arises from temporal variations in organic carbon storage across models (refs. 14,20). Therefore, we do not use ‘epc100’ here.

Extended Data Fig. 5 CMIP6 model simulations of selected physical and biogeochemical variables.

We show observations and CMIP6 simulations or diagnoses of (a) SST, (b) SSS, (c) MLD, (d) gas-exchange velocity. We also show CMIP6 simulations of (e) 100-m export production, and (f–h) nitrogen, light, and iron limitation of diatom integrated over 100-m from 90°S to ~44°S (defined by 0-30 × 1016 Mθe). For (a) to (d), we compare model simulations with observations (black lines). We use observations of SST and SSS from EN4 (ref. 52), density MLD from de Boyer Montégut et al. (ref. 38) (climatological average from 1963 to 2008), gas exchange velocity calculated from CCMP (ref. 44) wind fields and the method of Wanninkhof (ref. 45). CMIP6 model simulations of MLD are calculated using the same density criteria, defined as the depth at which the potential density exceeds the potential density at 10 meters depth by 0.03 kg m−3. The gas exchange velocity for models is calculated using simulated surface wind speed with the same method as for observation. Note that OMIP models are driven by prescribed wind speed but these wind speeds are not available as model output. We use CMIP6 simulations from coupled runs (solid lines), OMIP-1 (dashed lines), and OMIP-2 (dotted lines), with each model represented by a distinct color (see Supplementary Table S2 for model details). Model seasonal cycles are monthly averages from 2005–2014 (coupled runs and OMIP-2) or 2005–2009 (OMIP-1). Observations are calculated as 2005 to 2014 averages.

Extended Data Fig. 6 Skill-based model weighting reduces uncertainty in end-of-century CO2 uptake projections.

Annual mean SO CO2 flux (negative values indicate uptake) from 10 CMIP6 models during historical (2000-2014) and SSP3-7.0 (2015-2100) periods. The thick black line shows the skill-weighted ensemble mean, where weights are based on model skill in reproducing observed NPP, annual CO2 uptake, and thermal-driven pCO2 changes during 2005-2014 (shown in Fig. 5), with the narrow dark gray shading indicating weighted 1σ uncertainty (Methods). The thick gray line shows the unweighted ensemble mean annual CO2 uptake, with the wide light gray shading indicating unweighted 1σ standard deviation across all models shown here. Models maintain near-constant offsets throughout the century, indicating that present-day biases persist systematically into future projections. The skill-weighted 2081-2100 mean is 1.60 ± 0.16 PgC yr−1, compared to an unweighted ensemble mean of 1.54 ± 0.34 PgC yr−1, reducing uncertainty by 53%.

Extended Data Fig. 7 CMIP6 models show different trends in net primary production (NPP) and the seasonal net outgassing of the non-thermal O2 flux cycle (SNObio) over the Southern Ocean (SO) under the SSP3-7.0 scenario.

(a–j) Within-model relationships between annual SNObio and NPP for ten CMIP6 models over 2015-2100. Points are colored by year (darker = earlier, lighter = later), showing the temporal evolution of both variables. Dashed lines show fitted linear relationships using the ordinary least square fit method, with slopes (PgC Tmol−1) and standard errors reported for each model. (k) Comparison of SNObio trends (x-axis, Tmol century−1) versus NPP trends (y-axis, PgC century−1) across models. The black points and error bars represent the mean and standard deviation of trends calculated using generalized least squares regression with AR(1) correlation structure to account for temporal autocorrelation. Models show diverse future trajectories, with most models exhibiting positive SNObio and NPP trends and only two models showing negative or near-zero trends. Large spreads in projected trends and SNObio-NPP coupling across CMIP6 models highlight substantial uncertainty in Earth system model representations of SO biogeochemical responses to climate change.

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Jin, Y., Stephens, B.B., Long, M.C. et al. Atmospheric oxygen constraints on Southern Ocean productivity and drivers of carbon uptake. Nat. Geosci. 19, 534–541 (2026). https://doi.org/10.1038/s41561-026-01944-z

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