Constructing efficient score functions for rare event simulation in high-dimensional ocean-climate models

TL;DR

This paper introduces a data-driven score function for rare event simulation in high-dimensional climate models, improving transition probability estimates in complex ocean circulation systems.

Contribution

It proposes a novel reduced-space projection method for constructing score functions, enhancing the efficiency of rare event simulations in high-dimensional climate models.

Findings

Score function outperforms existing methods in accuracy.

Transition probabilities estimated with low variance.

Effective in small noise and transient forcing scenarios.

Abstract

Calculating transition probabilities between different states of multistable climate tipping systems is computationally challenging in high-dimensional models. Targeted algorithms, such as the Trajectory-Adaptive Multilevel Splitting (TAMS) method, require an adequate score function to be successful, i.e., to provide an estimate of a transition probability with an acceptable variance when only a relatively small ensemble of model trajectories can be computed. Here, we present a data-driven method to derive a score function based on projecting the model dynamics in a reduced state space. Using a spatially two-dimensional partial differential equation model of the Atlantic Meridional Overturning Circulation, we show that this score function performs better than currently available ones. Using the new score function, transition probabilities can be determined with low variance, even in the case of small noise amplitudes. Besides purely noise-induced transitions, we also consider the scenario of combined stochastic and time-dependent deterministic forcing, presenting a strategy to efficiently simulate AMOC tipping events in global ocean and climate models subject to transient climate change.