AI 中文总结
该研究对比了AMOC预测中物理状态增强与记忆压缩两种策略，发现二者适用于不同任务，记忆压缩在递归预测中表现更优，物理状态增强对短期预测及海洋状态变化预测更有效。
AI 中文摘要
大西洋经向翻转环流（AMOC）通过简化指数进行监测和模拟，但此类预测会丢失温盐结构，可能需要显式物理状态或观测指数的记忆。我们使用留一模型族验证，对比8个模型族的30条分支一致的CMIP6轨迹中的上述策略。盐度、温度和密度信息可改进20年直接预测，而紧凑标量记忆在所有递归预测时程中排名最高，且产生最低的案例平均Brier评分。匹配消融实验证实，记忆反馈可改进长时程预测。物理状态和近期趋势还能预测超出排放路径的未来海洋状态变化，在5年时最为稳健。SSP5--8.5情景下的NorESM识别出标量压缩的依赖强迫极限，MIROC则呈现负长时程迁移。预解分析解释了为何稳定记忆分量不保证完整学习模型的稳定性。因此，物理增强与记忆压缩适用于不同的AMOC预测任务。
英文摘要
The Atlantic Meridional Overturning Circulation is monitored and emulated through reduced indices, but such projections discard thermohaline structure and may require either explicit physical state or memory of the observed index. We compare these strategies in 30 branch-consistent CMIP6 trajectories from eight model families using leave-one-family-out validation. Salinity, temperature and density information improves direct 20-year forecasts, whereas compact scalar memory is top-ranked at every recursive horizon and yields the lowest case-averaged Brier score. A matched ablation confirms that feedback from memory improves long-horizon prediction. Physical state and recent trends also predict future ocean-state changes beyond the emissions pathway, most robustly at five years. NorESM under SSP5--8.5 identifies a forcing-dependent limit of scalar compression, while MIROC shows negative long-horizon transfer. A resolvent analysis explains why stable memory components do not guarantee stability of the complete learned model. Physical augmentation and memory compression therefore serve different AMOC prediction tasks.
