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 multidepth 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 remains stable, requiring further work.

翻译：用于预测的AI模拟器已成为超越传统数值预测的强大工具。下一个前沿是构建能够在多种时空尺度上精确进行长期气候模拟的模拟器，这对海洋研究尤为重要。本研究构建了一个对最先进气候模型中海洋分量的高精度全球模拟器。我们模拟了关键海洋变量——海表面高度、水平流速、温度和盐度——在其全深度范围内的变化。我们采用改进的ConvNeXt UNet架构，基于多深度层海洋数据进行训练。研究表明，该海洋模拟器——Samudra——相对于真实数据无漂移现象，能够复现海洋变量的垂向结构及其年际变异性。Samudra可稳定运行数百年，且计算速度比原始海洋模型快150倍。当前Samudra在准确捕捉强迫趋势强度的同时保持稳定运行方面仍存在挑战，需进一步研究改进。