BG4Sea：通过渐进信息缩放实现生物地球化学季节可预测性

中文摘要 AI 辅助

海洋生物地球化学预测对于管理海洋生态系统和碳循环愈发重要，但全球季节性预测产品因相关过程复杂和数据稀缺，远远落后于物理海洋学。我们引入了BG4Sea，据我们所知，它是首个用于生成海洋生物地球化学状态多变量季节性预测的全球数据驱动系统。BG4Sea是一种模块化架构，具有将垂直列压缩到低维潜在空间的列自动编码器、在时间上向前传播此表示的潜在预测器、通过特征线性调制注入物理边界信息的表面强迫调节器，以及通过交叉注意力纳入相邻列上下文的水平耦合模块。该模型在全球海洋再分析BIORYS4（NEMO/PISCES）上进行训练和评估，以1/4度、每月分辨率生成六个月的溶解化学、生物学和碳库变量预测，在大多数变量和提前期上优于持续性和气候学。我们将BG4Sea定位为未来更具表现力方法的可解释基线，并讨论了每个组件的可预测性归因以及模型的结构局限性。

英文摘要

Marine biogeochemical forecasting is increasingly important for managing marine ecosystems and the carbon cycle, yet global, seasonal forecast products lag far behind physical oceanography, held back by the complexity of the processes involved and by data scarcity. We introduce BG4Sea, which to our knowledge is the first global, data-driven system to produce multivariate seasonal forecasts of the marine biogeochemical state. BG4Sea is a modular architecture with a column autoencoder that compresses the vertical column into a low-dimensional latent space, a latent forecaster propagates this representation forward in time, a surface-forcing conditioner that injects physical boundary information via Feature-wise Linear Modulation (FiLM), and a horizontal-coupling module that incorporates neighboring-column context through cross-attention. The model is trained and evaluated on the global ocean reanalysis BIORYS4 (NEMO/PISCES), and produces six-month forecasts at 1/4 degree, monthly resolution for dissolved chemistry, biology, and carbon-pool variables, outperforming persistence and climatology across most variables and lead times. We position BG4Sea as an interpretable baseline for future, more expressive approaches, and discuss predictability attribution to each component, alongside the model's structural limitations.

发表机构

• Mercator Océan International（墨卡托海洋国际组织）
• LIP6, Sorbonne Université（巴黎第六大学信息学实验室，索邦大学）
• INRIA (ARCHES)（法国国家信息与自动化研究所（ARCHES团队））

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