Articles | Volume 17, issue 11
https://doi.org/10.5194/essd-17-6071-2025
https://doi.org/10.5194/essd-17-6071-2025
Data description paper
 | 
12 Nov 2025
Data description paper |  | 12 Nov 2025

A surface ocean pCO2 product with improved representation of interannual variability using a vision transformer-based model

Xueying Zhang, Enhui Liao, Wenfang Lu, Zelun Wu, Guansuo Wang, Xueming Zhu, and Shiyu Liang

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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.
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