Articles | Volume 17, issue 11
https://doi.org/10.5194/essd-17-6071-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-6071-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
12 Nov 2025
Data description paper |
| 12 Nov 2025
| 12 Nov 2025
A surface ocean pCO2 product with improved representation of interannual variability using a vision transformer-based model
Xueying Zhang
State Key Laboratory of Submarine Geoscience, School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200240, China
Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education, School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200240, China
Shanghai Key Laboratory of Polar Life and Environment Sciences, School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200240, China
State Key Laboratory of Submarine Geoscience, School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200240, China
Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education, School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200240, China
Shanghai Key Laboratory of Polar Life and Environment Sciences, School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200240, China
Wenfang Lu
School of Marine Sciences, State Key Laboratory of Environmental Adaptability for Industrial Products, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, 519082, China
School of Marine Science and Policy, University of Delaware, Newark, Delaware, 19716, USA
Guansuo Wang
Observation and Research Station of Huaniaoshan East China Sea Ocean-Atmosphere Integrated Ecosystem, Ministry of Natural Resources, Shanghai, 200137, China
East China Sea Forecasting and Hazard Mitigation Center, Ministry of Natural Resources, Shanghai, 200137, China
Xueming Zhu
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, 519082, China
Shiyu Liang
CORRESPONDING AUTHOR
John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, 200240, China
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Short summary
We created a new global dataset that reveals how ocean surface carbon dioxide has changed each month over the past four decades. By applying a deep learning model trained on both observational data and model simulations, we improved the representation of interannual variability and more accurately captured ocean responses to climate events like El Niño. This work supports global efforts to understand the ocean’s role in the carbon cycle and its response to climate change.
We created a new global dataset that reveals how ocean surface carbon dioxide has changed each...
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