Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamics
Adeline Hillier, Jennifer Sleeman, Jay Brett, Caroline Tang, Jenelle Millison, Anand Gnanadesikan
Abstract
This work explores a dynamics-informed Temporal Fusion Transformer (TFT) as a data-driven surrogate for computationally intensive Earth system simulations. Focusing on multivariate time series describing global ocean transport, we demonstrate the surrogate's ability to forecast tip events across thousands of time steps. The data involve up to 21 non-stationary time series in addition to static covariates describing free parameters and initial conditions. Modifications to the architecture and objective function yield a surrogate that anticipates the timing of Atlantic and Pacific collapses to high fidelity and captures the stochastic uncertainty in transition timing across ensemble predictions. The learned surrogate achieves a 465x computational speedup over the numerical simulator while maintaining differentiability with respect to parameters and initial conditions.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.