| 高分辨率海水表层二氧化碳分压重构——以大西洋为例 |
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| 引用本文： | 王馨仪,吴楚仪,吴森森,陈奕君,杜震洪.高分辨率海水表层二氧化碳分压重构——以大西洋为例[J].海洋学报,2023,45(3):147-158. |
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| 作者姓名： | 王馨仪 吴楚仪 吴森森 陈奕君 杜震洪 |
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| 作者单位： | 1.浙江大学 地球科学学院，浙江 杭州 310027 |
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| 基金项目： | 国家自然科学基金（201300001）。 |
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| 摘 要： | 海洋是自然界中重要的碳汇，海-气二氧化碳通量通常利用大气和海水表层的二氧化碳分压(pCO2)差进行估算。受制于时空分布不均匀的观测样本和预测数据，目前已有海水表层二氧化碳分压的重构结果在空间分辨率上仍有较大可提升空间。为在高空间分辨率下更好地拟合时空变化，基于表层大洋二氧化碳地图(SOCAT)的海水表层二氧化碳逸度(f CO2)数据集和遥感卫星等多源数据，利用XGBoost模型建立了海水表层二氧化碳分压值与海洋物理、生物、光学等要素的非线性关系，并根据样本时空频率构建权重模型，最终重构了2000-2018年大西洋0.041 7°×0.041 7°下月度海水表层二氧化碳分压分布。预测结果的相关系数为0.966，均方根误差为8.087μatm，平均偏差为4.012μatm，与同类重构结果相比，海水表层二氧化碳分压的时空变化趋势一致性强，且在空间分辨率上具有优势。
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| 关 键 词： | 海水表层二氧化碳分压 遥感卫星 高空间分辨率 |
| 收稿时间： | 2022-04-22 |
Reconstruction of sea surface pCO2 with high resolution: A case study of the Atlantic Ocean |
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| Affiliation: | 1.School of Earth Sciences, Zhejiang University, Hangzhou 310027, China2.Zhejiang Provincial Key Laboratory of Geographic Information System, Hangzhou 310030, China3.Department of Geographic and Spatial Information Science, Zhejiang University, Hangzhou 310027, China |
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| 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. |
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