用生成模型重建海洋微尺度流场,发现锋面是能量级联的关键调控者。
Generative data assimilation highlights fronts as key regulators of ocean energy cascade

- 融合卫星数据与生成式深度学习,重建无间隙千米级表层流场。
- 10公里以上能量向上传递,促进中尺度涡旋季节变化;以下则向消散传递。
- 锋面区跨尺度能量转移效率高出十倍,是未来参数化重点目标。
中尺度涡旋是海洋环流的基础,但其能量如何通过动能级联受亚中尺度运动(几公里尺度)影响仍不明确。高分辨率模拟预测,亚中尺度锋面是级联的关键调节者,既能将能量向下传至耗散,也能向上传递以维持和塑造中尺度涡旋的季节性。然而,由于现有观测和状态估计无法在足够大范围内解析亚中尺度流速,验证这些预测一直困难。本文结合多源卫星观测与生成式深度学习框架,重构出无间隙、千米级表面流场,其动力学特性来自模拟学习。应用于富含涡旋的阿古拉斯洋流系统,结果表明:10公里以上存在向上传递的能量级联,贡献于中尺度涡旋的季节性;10公里以下,锋面辐合驱动能量向下传递至耗散。两种路径均集中于锋面,跨尺度能量转移效率最高可达十倍。尽管范围有限,锋面贡献了大部分区域积分级联,确立其为能量级联的关键调控者,并成为下一代涡旋参数化的关键目标。
原文摘要 · Abstract (English)
Mesoscale eddies are fundamental to the ocean circulation, yet the extent to which submesoscale motions, a few kilometers across, influence mesoscale eddy energetics through a kinetic energy cascade remains uncertain. High-resolution simulations predict that submesoscale fronts are key regulators of the cascade, transferring energy both downscale towards dissipation and upscale to sustain and shape the seasonality of mesoscale eddies. Testing these predictions has remained difficult because existing observations and state estimates cannot resolve submesoscale currents over sufficiently broad domains. Here we map the ocean's submesoscale energy cascade by combining multi-source satellite observations with a generative deep learning framework, reconstructing gap-free, kilometer-scale surface currents with physically plausible dynamics learned from simulations. Applying this to the eddy-rich Agulhas Current system, we find that submesoscales energize the mesoscale through an upscale energy cascade above 10 km, contributing to the seasonality of mesoscale eddies. Below 10 km, convergence at submesoscale fronts drives a downscale cascade towards dissipation. Both upscale and downscale pathways concentrate within fronts, where cross-scale transfer is up to an order of magnitude more efficient. Despite their limited extent, fronts account for a substantial fraction of the domain-integrated cascade, establishing them as key regulators of the cascade and targets for next-generation eddy parameterizations.
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