Learning Editable Predictive States for Ocean World Modeling

Accurately modeling the coupled evolution of the ocean carbon–physics system and its accompanying events is a long-standing challenge in climate science. Conventional data-driven methods often rely on the regression capacity of neural networks to map past surface fields to future ones without a state into which a physical change could be written, or model individual events in isolation, neglecting the information that sparse event records carry about the continuous fields. In this paper, we propose the State-Perturbation Joint-Embedding Predictive Architecture (SP-JEPA), a world model that represents the environment by a compact state and advances this state autonomously. Central to our method is a basis-anchored state: each frame combines the coefficients of a fixed spatial basis with a learned residual representation, so that a perturbation written into the coefficients changes the decoded field exactly by the prescribed pattern, and a single predictor propagates both the natural and the perturbed futures without reading future observations. Historical fields and source-grounded event observations jointly initialize this state, from which future fields and events are read out. We evaluate natural prediction on three decades of global monthly surface carbon–physics fields with six event families, and response prediction in a controlled transport–exchange environment that provides the true future before and after each perturbation. On the validation splits, SP-JEPA reaches a normalized root mean squared error of 0.131, level with the best field forecaster and 18% below the best compared latent world model, and fusing historical events lowers event-prediction error by 9–11% against absent or mismatched event inputs without increasing field error. On mechanisms unseen during training, its rollout reproduces the mean response of each pulse and reaches a response skill of 0.575 and a latent counterfactual skill of 0.206, whereas compared latent world models score below zero.