Ocean dynamics plays a crucial role in driving global weather and climate
patterns. Accurate and efficient modeling of ocean dynamics is essential for
improved understanding of complex ocean circulation and processes, for
predicting climate variations and their associated teleconnections, and for
addressing the challenges of climate change. While great efforts have been made
to improve numerical Ocean General Circulation Models (OGCMs), accurate
forecasting of global oceanic variations for multi-year remains to be a
long-standing challenge. Here, we introduce ORCA (Oceanic Reliable foreCAst),
the first data-driven model predicting global ocean circulation from multi-year
to decadal time scales. ORCA accurately simulates the three-dimensional
circulations and dynamics of the global ocean with high physical consistency.
Hindcasts of key oceanic variables demonstrate ORCA's remarkable prediction
skills in predicting ocean variations compared with state-of-the-art numerical
OGCMs and abilities in capturing occurrences of extreme events at the
subsurface ocean and ENSO vertical patterns. These results demonstrate the
potential of data-driven ocean models for providing cheap, efficient, and
accurate global ocean modeling and prediction. Moreover, ORCA stably and
faithfully emulates ocean dynamics at decadal timescales, demonstrating its
potential even for climate projections. The model will be available at
this https URL

海洋动力学的准确和高效建模对于对复杂的海洋环流和过程的深入理解、预测气候变化及其相关的远程联系以及应对气候变化的挑战至关重要。本研究介绍了 ORCA（可靠海洋预测模型），这是首个能够在多年到十年时间尺度上预测全球海洋环流的数据驱动模型。ORCA 能够准确模拟全球海洋的三维环流和动力学，并具有高度的物理一致性。回顾性预测表明，ORCA 与最先进的数值海洋环流模型相比，在预测海洋变化和捕捉亚表层海洋和 ENSO 垂直模式的极端事件发生方面具有出色的预测能力。这些结果展示了数据驱动的海洋模型为提供廉价、高效和准确的全球海洋建模和预测的潜力。此外，ORCA 在十年时间尺度上稳定且忠实地模拟海洋动力学，展示了其在气候预测中的潜力。模型可在此 https 网址获取。

ORCA: 全球海洋模拟器用于多年到十年的预测

ORCA: A Global Ocean Emulator for Multi-year to Decadal Predictions

In climate simulations, small-scale processes shape ocean dynamics but remain
computationally expensive to resolve directly. For this reason, their
contributions are commonly approximated using empirical parameterizations,
which lead to significant errors in long-term projections. In this work, we
develop parameterizations based on Fourier Neural Operators, showcasing their
accuracy and generalizability in comparison to other approaches. Finally, we
discuss the potential and limitations of neural networks operating in the
frequency domain, paving the way for future investigation.

气候模拟中，小尺度过程对海洋动力学产生重要影响但直接解析计算代价高。因此，我们开发了基于 Fourier 神经算子的参数化方法，展示了其在准确性和泛化性方面与其他方法的比较。最后，讨论了在频域操作的神经网络的潜力和限制，为未来的研究打下基础。

使用傅里叶神经算子的强大海洋亚格尺度参数化

Robust Ocean Subgrid-Scale Parameterizations Using Fourier Neural  Operators

Researchers typically resort to numerical methods to understand and predict
ocean dynamics, a key task in mastering environmental phenomena. Such methods
may not be suitable in scenarios where the topographic map is complex,
knowledge about the underlying processes is incomplete, or the application is
time critical. On the other hand, if ocean dynamics are observed, they can be
exploited by recent machine learning methods. In this paper we describe a
data-driven method to predict environmental variables such as current velocity
and sea surface height in the region of Santos-Sao Vicente-Bertioga Estuarine
System in the southeastern coast of Brazil. Our model exploits both temporal
and spatial inductive biases by joining state-of-the-art sequence models (LSTM
and Transformers) and relational models (Graph Neural Networks) in an
end-to-end framework that learns both the temporal features and the spatial
relationship shared among observation sites. We compare our results with the
Santos Operational Forecasting System (SOFS). Experiments show that better
results are attained by our model, while maintaining flexibility and little
domain knowledge dependency.

在本文中，我们使用先进的机器学习方法，包括序列模型（LSTM 和 Transformers）和关系模型（图神经网络），建立了一个在巴西东南海岸 Santos-Sao Vicente-Bertioga 海湾地区可以预测洋流速度和海面高度等环境变量的数据驱动模型，并将我们的结果与 Santos 操作预测系统进行了比较，证明了我们的模型可以在保持一定的灵活性和少量领域知识依赖性的同时实现更好的预测效果。