首个全球海洋生物地球化学季节预报系统，实现多变量长期预测。
BG4Sea: Biogeochemical Seasonal Forecastability via Progressive Information Scaling
- 用垂直压缩+时序传播+物理场调制的模块化架构建模海洋状态
- 在1/4度分辨率下对六类海洋变量实现6个月预报，优于基准方法
- 结构可解释，适合生态管理与碳循环研究者参考
海洋生物地球化学预报对管理海洋生态系统和碳循环日益重要，但全球季节性预报产品远落后于物理海洋学，受限于过程复杂性和数据稀缺。本文提出BG4Sea，据我们所知是首个全球性、数据驱动的多变量海洋生物地球化学状态季节预报系统。该模型采用模块化设计：柱状自编码器将垂直剖面压缩至低维隐空间，隐空间预报器向前推进时间，表面强迫调节器通过特征逐维线性调制（FiLM）注入物理边界信息，水平耦合模块通过交叉注意力引入邻近柱体上下文。模型基于全球海洋再分析数据集BIORYS4（NEMO/PISCES）训练与评估，以1/4度、月分辨率生成六个月预报，涵盖溶解化学、生物及碳池变量，在多数变量和预报时效上优于持续性与气候平均基准。我们将BG4Sea定位为未来更复杂方法的可解释基线，并分析各组件的可预测性贡献及其结构局限。
原文摘要 · Abstract (English)
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.
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