Stochastic Box Modeling of AMOC: Variability, Thresholds, and Tipping Points

The Atlantic Meridional Overturning Circulation  (AMOC) is a vital climate system component, transporting heat and influencing the stability of regional and global climate patterns. Recent research highlights its susceptibility to abrupt transitions driven by nonlinear feedback and external variability, underscoring the need for a probabilistic understanding of its dynamics.

The proposed framework incorporates stochastic forcing into a nonlinear deterministic box model to simulate climate noise, such as fluctuating freshwater fluxes and wind-driven variability (not necessarily with noise). This modification allows the model to capture a broader spectrum of AMOC behavior, including low-frequency oscillations, stochastic resonance, and regime shifts. The study will focus on the salinity advection feedback mechanism and its interaction with stochastic perturbations to determine probabilistic thresholds for AMOC stability under various climate scenarios.

We incorporate system identification techniques to further refine the stochastic box model used. Specifically,  Langevin Regression is used to identify the stochastic nonlinear models that explain the observe hysteresis of the AMOC. Detailed probabilistic bifurcation diagrams that illustrate the AMOC’s sensitivity to stochastic forcing are obtained, thus facilitating the identification of critical parameters influencing regime transitions, and improving the understanding of the interplay between deterministic dynamics and external variability. The aim of these results are to refine predictive tools for assessing AMOC resilience to anthropogenic and natural climate forcings and provide insights into early warning signals for tipping points.