Reconstruction of sea surface <i>p</i>CO<sub>2</sub> with high resolution: A case study of the Atlantic Ocean

Wang Xinyi; Wu Chuyi; Wu Sensen; Chen Yijun; Du Zhenhong

doi:10.12284/hyxb2023048

Volume 45 Issue 3

Feb. 2023

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Wang Xinyi，Wu Chuyi，Wu Sensen, et al. Reconstruction of sea surface pCO2 with high resolution: A case study of the Atlantic Ocean[J]. Haiyang Xuebao，2023, 45(3)：147–158 doi: 10.12284/hyxb2023048

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Wang Xinyi，Wu Chuyi，Wu Sensen, et al. Reconstruction of sea surface pCO₂ with high resolution: A case study of the Atlantic Ocean[J]. Haiyang Xuebao，2023, 45(3)：147–158 doi: 10.12284/hyxb2023048

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Reconstruction of sea surface pCO₂ with high resolution: A case study of the Atlantic Ocean

doi: 10.12284/hyxb2023048

Wang Xinyi^{1, 2, 3
,},
Wu Chuyi^{1, 2, 3},
Wu Sensen^{1, 2, 3},
Chen Yijun^{1, 2, 3},
Du Zhenhong^{1, 2, 3
,
,}

1.
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
2.
Zhejiang Provincial Key Laboratory of Geographic Information System, Hangzhou 310030, China
3.
Department of Geographic and Spatial Information Science, Zhejiang University, Hangzhou 310027, China

Received Date: 2022-04-22
Rev Recd Date: 2022-10-12

Available Online: 2022-10-25

Publish Date: 2023-02-01

Abstract

Abstract

Ocean is an important carbon sink in nature. The sea-air carbon dioxide flux is usually estimated by the difference of partial pressure of carbon dioxide (pCO₂) between the atmosphere and the sea surface. Due to the imbalance of observation data on temporal and spatial distribution and datasets used for prediction, there is still large room for improvement in spatial resolution for present reconstruction of pCO₂ on sea surface. In order to fit the temporal and spatial variability under high spatial resolution better, based on the sea surface fugacity of carbon dioxide (fCO₂) observations of the Surface Ocean CO₂ Atlas (SOCAT) and other multi-source data including remote sensing data, the nonlinear relationship between sea surface pCO₂ and physical, biological, optical factors was established by a XGBoost model and a weight model was built based on spatiotemporal frequency of samples. A 0.041 7°×0.041 7° monthly sea surface pCO₂ dataset in Atlantic from 2000 to 2018 was finally constructed with correlation coefficient of 0.966, mean squared error of 8.087 μatm and mean error of 4.012 μatm on prediction dataset. The reconstruction is highly consistent to other similar reconstruction results on temporal and spatial trend and also gains advantage in spatial resolution.
- sea surface carbon dioxide partial pressure,
- remote sensing,
- high spatial resolution

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References(22)

References

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