The North Pacific Ocean plays a pivotal role as a carbon sink within the global carbon cycle. However, a comprehensive understanding of the spatiotemporal dynamics of carbon dioxide concentration and its determinants in this domain remains elusive due to its vast dimensions and the intricacies of influencing factors, with previous research on carbon dioxide partial pressure in the North Pacific Ocean also being relatively scarce. While prevalent machine learning methodologies have been extensively applied to predict the partial pressure of ocean carbon dioxide (pCO2), their limited interpretability has impeded substantial progress in elucidating the underlying mechanisms. This study introduces a gridded spatiotemporal neural network weighted regression (GSTNNWR) model to illuminate temporal and spatial relationships among relevant environmental variables and pCO2. The GSTNNWR model achieves high-precision and high-resolution forecasts of surface pCO2 in the North Pacific Ocean, demonstrating commendable performance (R2 = 0.863 and RMSE=15.123 µatm). Simultaneously, we obtain a quantitative characterization of how various environmental factors influence pCO2 across different temporal and spatial scales. Results show a dominant positive effect of temperature on the pCO2, with an averaged normalized coefficient of 0.28, and variability in the effects of chlorophyll and salinity on the pCO2 at different spatial and temporal locations and temperatures, whose average normalized coefficients are −0.10 and −0.04.The findings of our study will provide insights into the mechanisms and interactions within the North Pacific carbon cycle, contributing to a better understanding of ocean carbon sink formation and the dynamic regulation of the North Pacific carbon cycle.

中文翻译：

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时空加权神经网络揭示了北太平洋表层海水 pCO2 分布和潜在的环境机制


北太平洋在全球碳循环中作为碳汇发挥着关键作用。然而，由于其规模庞大且影响因素错综复杂，对二氧化碳浓度的时空动态及其决定因素的全面理解仍然难以捉摸，以往对北太平洋二氧化碳分压的研究也相对稀少。虽然流行的机器学习方法已被广泛用于预测海洋二氧化碳 （pCO2） 的分压，但其有限的可解释性阻碍了阐明潜在机制的实质性进展。本研究引入了一种网格化时空神经网络加权回归 （GSTNNWR） 模型，以阐明相关环境变量与 pCO2 之间的时间和空间关系。GSTNNWR 模型实现了北太平洋表面 pCO2 的高精度和高分辨率预报，表现出值得称道的性能（R2 = 0.863 和 RMSE=15.123 μatm）。同时，我们获得了各种环境因素如何在不同时间和空间尺度上影响 pCO2 的定量表征。结果表明，温度对 pCO2 的正向影响占主导地位，平均归一化系数为 0.28，在不同空间和时间位置和温度下，叶绿素和盐度对 pCO2 影响的可变性，其平均归一化系数为 -0.10 和 -0.04。 有助于更好地了解海洋碳汇的形成和北太平洋碳循环的动态调节。