Chau, T.-T.-T., Gehlen, M., Metzl, N., and Chevallier, F.: CMEMS-LSCE: a global, 0.25°, monthly reconstruction of the surface ocean carbonate system, Earth Syst. Sci. Data, 16, 121–160, https://doi.org/10.5194/essd-16-121-2024, 2024.
Cheng, L. and Gouretski, V.: IAP Global Ocean Oxygen gridded product (1-degree), CASODC [data set], https://doi.org/10.12157/IOCAS.20231214.006, 2024.
Chen, Z., Siedlecki, S., Long, M., Petrik, C. M., Stock, C. A., and Deutsch, C. A.: Skillful multiyear prediction of marine habitat shifts jointly constrained by ocean temperature and dissolved oxygen, Nat. Commun., 15, 900, https://doi.org/10.1038/s41467-024-45016-5, 2024.
Cocco, V., Joos, F., Steinacher, M., Frölicher, T. L., Bopp, L., Dunne, J., Gehlen, M., Heinze, C., Orr, J., Oschlies, A., Schneider, B., Segschneider, J., and Tjiputra, J.: Oxygen and indicators of stress for marine life in multi-model global warming projections, Biogeosciences, 10, 1849–1868, https://doi.org/10.5194/bg-10-1849-2013, 2013.
Franco, A. C., Hernández-Ayón, J. M., Beier, E., Garçon, V., Maske, H., Paulmier, A., Färber-Lorda, J., Castro, R., and Sosa-Ávalos, R.: Air–sea CO
2 fluxes above the stratified oxygen minimum zone in the coastal region off Mexico, J. Geophys. Res.-Oceans, 119, 2923–2937, https://doi.org/10.1002/2013JC009337, 2014.
Garabaghi, F. H., Benzer, S., and Benzer, R.: Modeling dissolved oxygen concentration using machine learning techniques with dimensionality reduction approach, Environ. Monit. Assess., 195, 879, https://doi.org/10.1007/s10661-023-11492-3, 2023.
Garcia, H., Cruzado, A., Gordon, L., and Escanez, J.: Decadal-scale chemical variability in the subtropical North Atlantic deduced from nutrient and oxygen data, J. Geophys. Res.-Oceans, 103, 2817–2830, https://doi.org/10.1029/97JC03037, 1998.
Garcia, H. E., Wang, Z., Bouchard, C., Cross, S. L., Paver, C. R., Reagan, J. R., Boyer, T. P., Locarnini, R. A., Mishonov, A. V., Baranova, O., Seidov, D., and Dukhovskoy, D.: World Ocean Atlas 2023, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation, edited by: Mishonov, A., NOAA Atlas NESDIS 91, 109 pp., https://doi.org/10.25923/rb67-ns53, 2024.
Gilbert, D., Rabalais, N. N., Díaz, R. J., and Zhang, J.: Evidence for greater oxygen decline rates in the coastal ocean than in the open ocean, Biogeosciences, 7, 2283–2296, https://doi.org/10.5194/bg-7-2283-2010, 2010.
Giomi, F., Barausse, A., Steckbauer, A., Daffonchio, D., Duarte, C. M., and Fusi, M.: Oxygen dynamics in marine productive ecosystems at ecologically relevant scales, Nat. Geosci., 16, 560–566, https://doi.org/10.1038/s41561-023-01217-z, 2023.
Gong, H., Li, C., and Zhou, Y.: Emerging global ocean deoxygenation across the 21st century, Geophys. Res. Lett., 48, e2021GL095370, https://doi.org/10.1029/2021GL095370, 2021.
Gouretski, V., Cheng, L., Du, J., Xing, X., Chai, F., and Tan, Z.: A consistent ocean oxygen profile dataset with new quality control and bias assessment, Earth Syst. Sci. Data, 16, 5503–5530, https://doi.org/10.5194/essd-16-5503-2024, 2024.
Grégoire, M., Oschlies, A., Canfield, D., Castro, C., Ciglenecki, I., Croot, P., Salin, K., Schneider, B., Serret, P., and Slomp, C.: Ocean Oxygen: the role of the Ocean in the oxygen we breathe and the threat of deoxygenation, European Marine Board, Ostend, Belgium, Zenodo, https://doi.org/10.5281/zenodo.7941157, 2023.
Guinehut, S., Dhomps, A.-L., Larnicol, G., and Le Traon, P.-Y.: High resolution 3-D temperature and salinity fields derived from in situ and satellite observations, Ocean Sci., 8, 845–857, https://doi.org/10.5194/os-8-845-2012, 2012.
Hauser, D., Tourain, C., Hermozo, L., Alraddawi, D., Aouf, L., Chapron, B., Dalphinet, A., Delaye, L., Dalila, M., and Dormy, E.: New observations from the SWIM radar on-board CFOSAT: Instrument validation and ocean wave measurement assessment, IEEE T. Geosci. Remote Sens., 59, 5–26, https://doi.org/10.1109/TGRS.2020.2994372, 2020.
Hollitzer, H. A. L., Patara, L., Terhaar, J., and Oschlies, A.: Competing effects of wind and buoyancy forcing on ocean oxygen trends in recent decades, Nat. Commun., 15, https://doi.org/10.1038/s41467-024-53557-y, 2024.
Huang, S., Shao, J., Chen, Y., Qi, J., Wu, S., Zhang, F., He, X., and Du, Z.: Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniques, Front. Mar. Sci., 10, 1291232, https://doi.org/10.3389/fmars.2023.1291232, 2023.
Humphries, N. E., Fuller, D. W., Schaefer, K. M., and Sims, D. W.: Highly active fish in low oxygen environments: Vertical movements and behavioural responses of bigeye and yellowfin tunas to oxygen minimum zones in the eastern Pacific Ocean, Mar. Biol., 171, 55, https://doi.org/10.1007/s00227-023-04366-2, 2024.
IOC, SCOR, and IAPSO: The International thermodynamic equation of seawater – 2010: calculation and use of thermodynamic properties [includes corrections up to 31st October 2015], Intergovernmental Oceanographic Commission Manuals and Guides, 56, UNESCO, Paris, France, 196 pp., https://doi.org/10.25607/OBP-1338, 2015.
Ito, T., Minobe, S., Long, M. C., and Deutsch, C.: Upper ocean O
2 trends: 1958–2015, Geophys. Res. Lett., 44, 4214–4223, https://doi.org/10.1002/2017GL073613, 2017.
Ito, T., Cervania, A., Cross, K., Ainchwar, S., and Delawalla, S.: Mapping dissolved oxygen concentrations by combining shipboard and Argo observations using machine learning algorithms, J. Geophys. Res.: Mach. Learn. Comput., 1, e2024JH000272, https://doi.org/10.1029/2024JH000272, 2024a.
Ito, T., Garcia, H. E., Wang, Z., Minobe, S., Long, M. C., Cebrian, J., Reagan, J., Boyer, T., Paver, C., Bouchard, C., Takano, Y., Bushinsky, S., Cervania, A., and Deutsch, C. A.: Underestimation of multi-decadal global O
2 loss due to an optimal interpolation method, Biogeosciences, 21, 747–759, https://doi.org/10.5194/bg-21-747-2024, 2024b.
Kolodziejczyk, N., Prigent-Mazella, A., and Gaillard, F.: ISAS temperature, salinity, dissolved oxygen gridded fields, SEANOE [data set], https://doi.org/10.17882/52367, 2023.
Li, C., Huang, J., Ding, L., Liu, X., Yu, H., and Huang, J.: Increasing escape of oxygen from oceans under climate change, Geophys. Res. Lett., 47, e2019GL086345, https://doi.org/10.1029/2019GL086345, 2020.
Liu, G., Yu, X., Zhang, J., Wang, X., Xu, N., and Ali, S.: Reconstruction of the three-dimensional dissolved oxygen and its spatio-temporal variations in the Mediterranean Sea using machine learning, J. Environ. Sci., 157, 710–728, https://doi.org/10.1016/j.jes.2025.01.010, 2025.
Liu, Q., Liu, C., Meng, Q., Su, B., Ye, H., Chen, B., Li, W., Cao, X., Nie, W., and Ma, N.: Machine learning reveals biological activities as the dominant factor in controlling deoxygenation in the South Yellow Sea, Cont. Shelf Res., 283, 105348, https://doi.org/10.1016/j.csr.2024.105348, 2024.
Lu, B., Zhao, Z., Han, L., Gan, X., Zhou, Y., Zhou, L., Fu, L., Wang, X., Zhou, C., and Zhang, J.: Oxygenerator: Reconstructing global ocean deoxygenation over a century with deep learning, arXiv [preprint], https://doi.org/10.48550/arXiv.2405.07233, 2024.
Ma, D., Zhao, F., Zhu, L., Li, X., Wei, J., Chen, X., Hou, L., Li, Y., and Liu, M.: Deep learning reveals hotspots of global oceanic oxygen changes from 2003 to 2020, Int. J. Appl. Earth Obs. Geoinf., 136, 104363, https://doi.org/10.1016/j.jag.2025.104363, 2025.
Mears, C., Lee, T., Ricciardulli, L., Wang, X., and Wentz, F.: Improving the Accuracy of the Cross-Calibrated Multi-Platform (CCMP) Ocean Vector Winds, Remote Sens., 14, 4230, https://doi.org/10.3390/rs14174230, 2022.
Milà, C., Ludwig, M., Pebesma, E., Tonne, C., and Meyer, H.: Random forests with spatial proxies for environmental modelling: opportunities and pitfalls, Geosci. Model Dev., 17, 6007–6033, https://doi.org/10.5194/gmd-17-6007-2024, 2024.
NASA Ocean Biology Processing Group: Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Level-2 Ocean Color Data, version R2018.8, NASA Ocean Biology Distributed Active Archive Center [data set], https://doi.org/10.5067/ORBVIEW-2/SEAWIFS/L2/OC/2018, 2018.
Oschlies, A.: A committed fourfold increase in ocean oxygen loss, Nat. Commun., 12, https://doi.org/10.1038/s41467-021-22584-4, 2021.
Oschlies, A., Brandt, P., Stramma, L., and Schmidtko, S.: Drivers and mechanisms of ocean deoxygenation, Nat. Geosci., 11, 467–473, https://doi.org/10.1038/s41561-018-0152-2, 2018.
Ping, B., Meng, Y., Su, F., Xue, C., and Li, Z.: Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning, Mar. Environ. Res., 199, 106578, https://doi.org/10.1016/j.marenvres.2024.106578, 2024.
Regier, P. J., Ward, N. D., Myers-Pigg, A. N., Grate, J., Freeman, M. J., and Ghosh, R. N.: Seasonal drivers of dissolved oxygen across a tidal creek–marsh interface revealed by machine learning, Limnol. Oceanogr., 68, 2359–2374, https://doi.org/10.1002/lno.12426, 2023.
Robinson, C.: Microbial respiration, the engine of ocean deoxygenation, Front. Mar. Sci., 5, https://doi.org/10.3389/fmars.2018.00533, 2019.
Salazar, J. J., Garland, L., Ochoa, J., and Pyrcz, M. J.: Fair train-test split in machine learning: Mitigating spatial autocorrelation for improved prediction accuracy, J. Petrol. Sci. Eng., 209, 109885, https://doi.org/10.1016/j.petrol.2021.109885, 2022.
Schmidtko, S., Stramma, L., and Visbeck, M.: Decline in global oceanic oxygen content during the past five decades, Nature, 542, 335–339, https://doi.org/10.1038/nature21399, 2017.
Shao, J., Huang, S., Chen, Y., Qi, J., Wang, Y., Wu, S., Liu, R., and Du, Z.: Satellite-based global sea surface oxygen mapping and interpretation with spatiotemporal machine learning, Environ. Sci. Technol., 58, 498–509, https://doi.org/10.1021/acs.est.3c08833, 2024.
Sharp, J. D., Fassbender, A. J., Carter, B. R., Johnson, G. C., Schultz, C., and Dunne, J. P.: GOBAI-O2: temporally and spatially resolved fields of ocean interior dissolved oxygen over nearly 2 decades, Earth Syst. Sci. Data, 15, 4481–4518, https://doi.org/10.5194/essd-15-4481-2023, 2023.
Szekely, T., Gourrion, J., Pouliquen, S., and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: data description and validation, Ocean Sci., 15, 1601–1614, https://doi.org/10.5194/os-15-1601-2019, 2019.
Szekely, T., Gourrion, J., Pouliquen, S., Reverdin, G., and Merceur, F.: CORA, Coriolis Ocean Dataset for Reanalysis, SEANOE [data set], https://doi.org/10.17882/46219, 2025.
Valera, M., Walter, R. K., Bailey, B. A., and Castillo, J. E.: Machine learning based predictions of dissolved oxygen in a small coastal embayment, J. Mar. Sci. Eng., 8, 1007, https://doi.org/10.3390/jmse8121007, 2020.
Wagstaff, J. and Bean, B.: remap: Regionalized models with spatially smooth predictions, R J., 14, 160–178, https://doi.org/10.32614/RJ-2023-004, 2022.
Wang, Z.: GEOXYGEN_Code, Zenodo [code], https://doi.org/10.5281/zenodo.19852901, 2026.
Wang, Z., Xue, C., and Ping, B.: A reconstructing model based on time–space–depth partitioning for global ocean dissolved oxygen concentration, Remote Sens., 16, 228, https://doi.org/10.3390/rs16020228, 2024.
Wang, Z., Fu, W., Xue, C., and Wang, G.: GEOXYGEN: A global monthly gridded dissolved oxygen product (0.5°
× 0.5°), Zenodo [data set], https://doi.org/10.5281/zenodo.19703198, 2026a.
Xue, C., Wang, Z., Yue, L., and Niu, C.: A global four-dimensional gridded dataset of ocean dissolved oxygen concentration retrieval from Argo profiles, Geosci. Data J., 11, 775–789, https://doi.org/10.1002/gdj3.251, 2024.
Yamaguchi, R., Kouketsu, S., Kosugi, N., and Ishii, M.: Global upper ocean dissolved oxygen budget for constraining the biological carbon pump, Commun. Earth Environ., 5, https://doi.org/10.1038/s43247-024-01886-7, 2024.
Zhou, Y., Gong, H., and Zhou, F.: Responses of Horizontally Expanding Oceanic Oxygen Minimum Zones to Climate Change Based on Observations, Geophys. Res. Lett., 49, https://doi.org/10.1029/2022gl097724, 2022.
