Abstract
The global ocean carbon dioxide flux (air-sea) has shown a slow upward trend. Based on more than 160,000 quality-controlled measurements of surface ocean carbon dioxide fugacity from 2000 to 2020, a satellite-based ocean–atmosphere carbon dioxide fugacity (fCO2) retrieval algorithm was developed using machine learning methods. A comparative analysis was conducted among various machine learning methods, including XGBoost, random forest, light gradient boosting machine, feedforward neural network, convolutional neural network, and backpropagation neural network. Based on the best performance, the random forest algorithm was selected for model construction. Independent in situ validation showed that the model achieved a low root mean square error (RMSE = 14.35 µatm), a low mean absolute percentage error (MAPE = 2.61%), and a high coefficient of determination (R² = 0.86). The distribution of global air-sea carbon dioxide fugacity from 2000 to 2020 was reconstructed at a resolution of0.25° × 0.25°, and the air–sea carbon dioxide flux (FCO2) of the global ocean during the period of 2000–2020 was further estimated at a resolution of 0.25°×0.25°. During the period of 2000–2020, the global ocean CO2 uptake increased from 1.443 PgC/year in 2000 to 1.894 PgC/year in 2020, and the air-sea carbon dioxide flux in the entire study area increased by 31.2% over the 20 years. These comprehensive oceanic carbon sink datasets and new insights will support future research on ocean carbon sequestration and its climate regulation potential.
Similar content being viewed by others
Acknowledgements
We thank all contributors to the data products offered by Copernicus Maritime Services, which is the basis of this work. The surface ocean CO2 Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS), and the Integrated Marine Biogeochemistry and Ecosystem Research program (IMBER), to deliver a uniformly quality controlled surface ocean CO2 database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.
Funding
This research was funded by the National Natural Science Foundation of China (grant numbers 42074028).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Ji, Y., Wu, H., Liu, X. et al. Satellite estimation of global air sea CO2 flux from 2000 to 2020. Sci Rep (2026). https://doi.org/10.1038/s41598-026-51215-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-51215-5
