用AI模拟全球海洋变化，速度超原模型150倍且百年稳定
Samudra: An AI Global Ocean Emulator for Climate
- 基于改进的ConvNeXt UNet架构，模拟海洋全深度关键变量
- 运行百年无偏差，速度比原模型快150倍，能复现海洋结构和年际变化
- 适合气候预测、长期模拟研究者，尤其关注高效高精度海洋建模
人工智能模拟器在预报领域已展现出超越传统数值模拟的潜力。本研究构建了一个高性能的全球海洋模拟器——Samudra，用于模拟前沿气候模型中的海洋分量。该模型对海表高度、水平流速、温度和盐度等关键海洋变量进行全深度模拟，采用改进的ConvNeXt UNet架构，在多深度海洋数据上训练。结果表明，Samudra与真实值无漂移，能准确再现海洋变量的垂直结构及其年际变率，且可稳定运行数百年，计算速度达原海洋模型的150倍。然而，其在同时保持稳定性与正确强迫趋势幅度方面仍存挑战，需进一步优化。
原文摘要 · Abstract (English)
AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulations with skill across a range of spatiotemporal scales, a particularly important goal for the ocean. Our work builds a skillful global emulator of the ocean component of a state-of-the-art climate model. We emulate key ocean variables, sea surface height, horizontal velocities, temperature, and salinity, across their full depth. We use a modified ConvNeXt UNet architecture trained on multi-depth levels of ocean data. We show that the ocean emulator - Samudra - which exhibits no drift relative to the truth, can reproduce the depth structure of ocean variables and their interannual variability. Samudra is stable for centuries and 150 times faster than the original ocean model. Samudra struggles to capture the correct magnitude of the forcing trends and simultaneously remain stable, requiring further work.
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