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 https://github.com/OpenEarthLab/ORCA.

Utilizing long-range dependency, though extensively studied in homogeneous graphs, has not been well investigated on heterogeneous graphs. Addressing this research gap presents two major challenges. The first is to alleviate computational costs while endeavoring to leverage as much effective information as possible in the presence of heterogeneity. The second involves overcoming the well-known over-smoothing issue occurring in various graph neural networks. To this end, we investigate the importance of different meta-paths and introduce an automatic framework for utilizing long-range dependency on heterogeneous graphs, denoted as Long-range Meta-path Search through Progressive Sampling (LMSPS). Specifically, we develop a search space with all meta-paths related to the target node type. By employing a progressive sampling algorithm, LMSPS dynamically shrinks the search space with hop-independent time complexity. Utilizing a sampling evaluation strategy as the guidance, LMSPS conducts a specialized and effective meta-path selection. Subsequently, only effective meta-paths are employed for retraining to reduce costs and overcome the over-smoothing issue. Extensive experiments on various heterogeneous datasets demonstrate that LMSPS discovers effective long-range meta-paths and outperforms the state-of-the-art. Besides, it ranks top-1 on the leaderboards of \texttt{ogbn-mag} in Open Graph Benchmark. Our code is available at https://github.com/JHL-HUST/LDMLP.