Regime identification and control of extremes in the nonautonomous Lorenz model with chaos and intransitivity

Adaptive chaos control has been studied extensively for autonomous systems. For real-world, nonautonomous systems, such as planetary weather, observations of the system state in response to seasonally and diurnally varying forcing are available only at discrete times and locations. We consider a stochastic representation of such systems to account for observational and process noise, as a building block for adaptive control, and develop and test control strategies in an idealized low-dimensional setting. We present the first example of finite-time adaptive chaos control for a seasonally forced and noise-perturbed Lorenz-84 model. We consider two strategies for triggering control: (1) Stochastic: based on transition probabilities for the latent states of a nonhomogeneous hidden Markov model (NHMM); and (2) Dynamical: using local Lyapunov exponents (LLE). The NHMM triggers coincide with strongly positive LLE regimes, confirming their dynamical interpretability. While both approaches enable selective, small energy interventions and achieve significant suppression of extreme excursions, the NHMM and related state-space methods may generalize to interpretable stochastic control models for meteorological applications.