Certified Adversarial Transferability Bounds for Artificial Intelligence Cyclone Track Emulators Under Multi-Model Ensemble Disagreement: Attack Success Guarantees and Evacuation Order Safety

Artificial intelligence cyclone track emulators are increasingly used to generate evacuation triggers, yet their adversarial robustness is typically assessed only against the emulator that was attacked. The framework combines a Lipschitz continuity condition on the emulator mapping with a disagreement radius derived from ensemble spread, yielding a certified upper bound on the probability that a perturbation crafted for a source emulator succeeds against a target emulator. The bound is expressed as a function of the perturbation magnitude, the spectral norm of the emulator Jacobian, and the pairwise disagreement between ensemble members. A case study using NASA POWER monthly reanalysis for Lagos, Nigeria (2000–2023, 312 monthly observations) illustrates the calibration of the disagreement radius; the observed mean temperature was 26.414 °C and the warming trend was 0.1553 °C per decade. The framework yields an evacuation-order safety condition that translates the certified bound into a maximum permissible perturbation magnitude for a given false-alarm tolerance. The contribution is a formal, assumption-explicit guarantee that links adversarial transferability to ensemble disagreement and to the operational safety of evacuation decisions.