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北太平洋表层海水 pCO2 分布和潜在环境机制


北太平洋作为碳汇在全球碳循环中发挥着关键作用。然而，由于其范围广阔、影响因素复杂，对该领域二氧化碳浓度时空动态及其影响因素的全面了解仍然难以实现，此前对北太平洋二氧化碳分压的研究也相对匮乏。 。虽然流行的机器学习方法已被广泛应用于预测海洋二氧化碳分压 (pCO2)，但其有限的可解释性阻碍了在阐明潜在机制方面取得实质性进展。本研究引入网格时空神经网络加权回归 (GSTNNWR) 模型来阐明相关环境变量和 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测，表现出值得称赞的性能（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 的定量特征。结果显示，温度对 pCO2 具有显着的正向影响，平均归一化系数为 0.28，叶绿素和盐度对 pCO2 在不同时空位置和温度下的影响存在差异，其平均归一化系数分别为 -0.10 和 - 0.04.我们的研究结果将深入了解北太平洋碳循环的机制和相互作用，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控。