Abstract
Nitrous oxide is a potent greenhouse gas and the primary ozone-depleting agent of the 21st century, but marine emissions of nitrous oxide remain difficult to constrain due to their spatiotemporal variability. In the Southern Ocean, where extratropical cyclones create conditions conducive to air-sea gas flux, shipboard measurements are unlikely to capture the full extent of nitrous oxide emissions due to the impracticality of sampling said storms. Here, we use machine learning to derive nitrous oxide observations from biogeochemical Argo floats, revealing that low-pressure storms amplify air-sea gradients and create hotspots of emissions. Taking these low-pressure storms into account, rather than assuming 1 atmosphere (the standard condition outside of storms), increases the net annual Southern Ocean nitrous oxide flux by 88%. Our results imply that the Southern Ocean plays a significant role in the global nitrous oxide cycle, and may be a weaker overall sink of greenhouse gases than previously thought.
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Data availability
The processed GO-SHIP N2O data, BGC-Argo float data with interpolated ERA5 and NCEP-NCAR Reanalysis 1 data, N2O fluxes, and figure data used in this study are available via Zenodo under https://doi.org/10.5281/zenodo.17904981 [https://doi.org/10.5281/zenodo.17904981]71. The original GO-SHIP data were obtained from CLIVAR and Carbon Hydrographic Data Office (CCHDO) [https://doi.org/10.6075/J0CCHNNN]72. The original BGC-Argo float data were obtained from a snapshot of the BGC-Argo dataset from October 9th, 2024 [https://doi.org/10.17882/42182]53. The original NCEP-NCAR Reanalysis 1 data was provided by the NOAA PSL, Boulder, Colorado, USA, from their website at [https://psl.noaa.gov]70. The original ERA5 hourly reanalysis datasets were downloaded from the Copernicus Climate Change Service (C3S) Climate Data Store at [cds.climate.copernicus]57. Figures containing maps were created using Cartopy with coastline data from Natural Earth (naturalearthdata.com, CC0 license).
Code availability
The gasex-python library for air-sea gas exchange calculations is available via Zenodo under https://doi.org/10.5281/zenodo.15132888 [https://doi.org/10.5281/zenodo.15132888]61. Python scripts for Random Forest model training, pN2O prediction from BGC-Argo float data, and air-sea N2O flux calculations are publicly available and archived at Zenodo under https://doi.org/10.5281/zenodo.17905126 [https://doi.org/10.5281/zenodo.17905126]73.
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Acknowledgments
We gratefully acknowledge Channing Prend for many conversations about Southern Ocean physical oceanography and BGC-Argo data. Training data were collected and made publicly available by the U.S. Global Ship-based Hydrographic Investigations Program (U.S. GO-SHIP; https://usgoship.ucsd.edu/) and the programs that contribute to it. BGC-Argo data were assembled or collected and made available by the Global Ocean Biogeochemistry Array (GO-BGC) Project funded by the National Science Foundation (NSF grant #OCE-1946578). Development of the BGC-Argo parquet dataset used in this work was supported by NSF grant #OAC-2311383 to D.P.N. C.L.K. was supported by a Doherty Postdoctoral Scholarship funded by the Woods Hole Oceanographic Institution and a U.S. GO-SHIP Postdoctoral Fellowship funded by the National Science Foundation (NSF grant # OCE-2023545). B.X.C. was supported by NSF grant #OCE-2048518. D.P.N.’s contribution to this work was sponsored by the National Science Foundation’s Global Ocean Biogeochemistry Array (GO-BGC) Project under NSF grant #OCE-1946578 with operational support from NSF grant #OCE-2110258. A.M.M. acknowledges partial support from NSF grants #OCE-2023545 and #OCE-1923387. A.F.E. was supported by the 2022 Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES) Summer Internship Program.
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C.L. Kelly, B.X. Chang, and D.P. Nicholson conceived the study. B.X. Chang collected much of the machine learning training data. C.L. Kelly and B.X. Chang processed, analyzed, and curated the training data. C.L. Kelly, A.F. Emmanuelli, and E.R. Park developed the machine learning models. D.P. Nicholson provided critical insights on BGC-Argo data and air-sea gas exchange. D.P. Nicholson and A.M. Macdonald supervised the work. C.L. Kelly wrote the manuscript with input from all authors.
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Kelly, C.L., Chang, B.X., Emmanuelli, A.F. et al. Low-pressure storms drive nitrous oxide emissions in the Southern Ocean. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68744-2
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DOI: https://doi.org/10.1038/s41467-026-68744-2
