Spatial reconstruction of long-term (2003–2020) sea surface <i>p</i>CO<sub>2</sub> in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis

Wang, Zhixuan; Wang, Guizhi; Guo, Xianghui; Bai, Yan; Xu, Yi; Dai, Minhan

doi:https://doi.org/10.5194/essd-15-1711-2023

Articles | Volume 15, issue 4

https://doi.org/10.5194/essd-15-1711-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-15-1711-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 4

Data description paper

|

17 Apr 2023

Data description paper |

| 17 Apr 2023

Spatial reconstruction of long-term (2003–2020) sea surface pCO₂ in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis

Zhixuan Wang, Guizhi Wang, Xianghui Guo, Yan Bai, Yi Xu, and Minhan Dai

Data sets

Datasets of reconstructed sea surface pCO2 in the South China Sea Zhixuan Wang and Minhan Dai https://doi.org/10.57760/sciencedb.02050

Short summary

We reconstructed monthly sea surface pCO₂ data with a high spatial resolution in the South China Sea (SCS) from 2003 to 2020. We validate our reconstruction with three independent testing datasets and present a new method to assess the uncertainty of the data. The results strongly suggest that our reconstruction effectively captures the main features of the spatiotemporal patterns of pCO₂ in the SCS. Using this dataset, we found that the SCS is overall a weak source of atmospheric CO₂.

Spatial reconstruction of long-term (2003–2020) sea surface pCO2 in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis

Data sets

Spatial reconstruction of long-term (2003–2020) sea surface pCO₂ in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis