The variability of sea surface temperatures (SSTs) is crucial in climate dynamics, influencing marine ecosystems and human activities. This study leverages graph neural networks (GNNs), specifically a GraphSAGE model, to forecast SSTs and their anomalies (SSTAs), focusing on the global scale structure of climatological data. We introduce an improved graph construction technique for SST teleconnection representation. Our results highlight the GraphSAGE model's capability in 1-month-ahead global SST and SSTA forecasting, with SST predictions spanning up to 2 years with a recursive approach. Notably, regions with persistent currents exhibited enhanced SSTA predictability, contrasting with equatorial and Antarctic areas. Our GNN outperformed both the persistence model and traditional methods. Additionally, we offer an SST and SSTA graph data set based on ERA5 and a graph generation tool. This GNN case study has shown how the GraphSAGE can be used in SST and SSTA forecasting, and will provide a foundation for further refinements such as adjusting graph construction, optimizing imbalanced regression techniques for extreme SSTAs, and integrating GNNs with other temporal pattern learning methods to improve long-term predictions.

中文翻译：

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利用图形表示的力量进行气候预测：预测全球月平均海面温度和异常

海面温度（SST）的变化对于气候动态至关重要，影响海洋生态系统和人类活动。本研究利用图神经网络（GNN），特别是 GraphSAGE 模型，来预测海表温度及其异常（SSTA），重点关注气候数据的全球尺度结构。我们引入了一种改进的 SST 遥相关表示图构造技术。我们的结果凸显了 GraphSAGE 模型在提前 1 个月的全球海表温度和海温异常预测方面的能力，采用递归方法，海表温度预测的跨度可达 2 年。值得注意的是，与赤道和南极地区相比，具有持续洋流的地区表现出增强的海温异常可预测性。我们的 GNN 的性能优于持久性模型和传统方法。此外，我们还提供基于 ERA5 的 SST 和 SSTA 图数据集以及图生成工具。该 GNN 案例研究展示了 GraphSAGE 如何用于海温和海温异常预测，并将为进一步细化奠定基础，例如调整图构造、优化极端海温异常的不平衡回归技术以及将 GNN 与其他时间模式学习方法集成以改善长期预测。