DC-Ocean: Deep Latent Compression for Global High-Resolution Ocean Forecasting

High-resolution global ocean states expose a bottleneck in data-driven forecasting: effective long-horizon rollouts require a compact, decodable latent state that reduces I/O and stabilizes error growth. While learned compression has matured for natural RGB images, its assumptions do not transfer to ocean modeling: ocean states are multi-variable and multi-depth, their grids are orders of magnitude larger, and fixed land--sea boundaries create sharp discontinuities that make aggressive downsampling error-prone. We present DC-Ocean, the first deep latent compression architecture for multi-variable, multi-depth global $1/12^\circ$ ocean states, built on an autoencoder that couples convolutional feature extraction with transformer-based global context modeling to retain both coastal sharpness and basin-scale structure. To explicitly address coastline-induced artifacts, DC-Ocean integrates boundary-aware gated convolutions together with a spherical geodesic front propagation interpolation scheme for stable behavior near land masks. Across key ocean variables, DC-Ocean achieves higher reconstruction fidelity than strong autoencoder baselines adapted from the natural-image domain at matched downsampling factors. Beyond reconstruction, we perform autoregressive forecasting directly in latent space and decode predictions back to the physical domain. DC-Ocean matches short-term accuracy of full-resolution forecasting while achieving lower RMSE and slower error growth at medium and long lead times. Together, these results demonstrate that DC-Ocean provides a practical framework for stable and extended high-resolution sub-daily ocean forecasting.