OceanMind: Heterogeneity-Aware Spatiotemporal Learning for Sea Surface Temperature Forecasting

Abstract

Accurate ocean temperature forecasting underpins fishery management, maritime safety, and climate early warning, yet its central difficulty lies in pronounced spatiotemporal heterogeneity that challenges uniform modeling: the same periodic forcing can elicit region-dependent responses across latitudes, and the same spatial proximity can reflect different physical relationships across oceanic regimes. Spectral analysis of multidecadal daily observations from three Chinese sea areas reveals that the dominant periodic structure varies by three orders of magnitude across latitudes, accompanied by a sevenfold contraction in seasonal amplitude. Motivated by these observations, we propose OceanMind, a framework that equips each stage of representation learning with explicit regime awareness. Along the temporal dimension, the model adapts its candidate frequency set to the spectral structure of the target domain and learns station-specific amplitude and phase to produce spatially differentiated periodic encodings. Along the spatial dimension, it learns a node-to-region projection and performs differentiated graph convolution in the compressed region space, while a complementary topology-free routing pathway models teleconnection-inspired remote associations beyond local graph topology. On three long-term daily sea-area benchmarks spanning 7.5°N to 41°N, OceanMind reduces mean MSE by 8%–14% over the strongest baseline across all forecast horizons while requiring one to several orders of magnitude less training time and memory than its closest competitors; an additional Himawari-8 clear-sky satellite benchmark further supports its cross-product robustness. The model-learned regional projection patterns and period importance weights are consistent with oceanographic priors, suggesting that heterogeneity-aware design improves accuracy while providing post-hoc interpretability. The source code is publicly available at https://github.com/OvOYu/OceanMind