Data-Anchored Response Spaces for Closed-Loop Neural Components in Non-Differentiable Numerical Simulators

Neural components embedded in atmospheric simulators shape the evolving states that become their future inputs, making accurate offline prediction insufficient for reliable coupled simulation. Model-based policy optimization can account for these downstream effects, but atmospheric applications lack explicit action labels in reference trajectories and are vulnerable to policies exploiting errors in learned dynamics surrogates. We introduce data-anchored response spaces to enable model-based policy optimization in this setting. Using a known dynamical backbone, we recover effective responses from reference state transitions and use them to initialize a response codebook comprising a compact set of prototype temperature and moisture tendencies. The policy predicts nonnegative mixing weights that sum to one, producing continuous convex combinations of these prototypes rather than selecting discrete actions. This representation anchors policy outputs in responses derived from data while retaining continuous control. We alternate policy optimization through differentiable surrogate rollouts with coupled-simulator evaluation and surrogate refinement on aggregated trajectories, screening candidate updates before acceptance. In a 72-hour global atmospheric simulation, the evaluated configuration reduces time-averaged specific-humidity and temperature RMSE by 30.4% and 38.5%, respectively, relative to the offline-trained policy. Corresponding mean absolute biases decrease by 92.9% and 28.4%. Its mean absolute humidity and temperature biases are also 92.1% and 28.7% lower than those of native physics, although RMSE remains higher. These results demonstrate the potential of combining data-anchored response representations with simulator-informed adaptation in atmospheric systems, where learned outputs continually reshape their own deployment conditions.