Optimal Transition Paths for AMOC Collapse and Recovery in a Stochastic Box Model

TL;DR

This paper uses Large Deviation Theory to identify the most probable noise-induced pathways for AMOC collapse and recovery in a stochastic ocean model, revealing the physical mechanisms and vulnerability factors involved.

Contribution

It introduces a novel application of Large Deviation Theory to determine optimal transition paths for AMOC in a stochastic model, highlighting the physical processes and vulnerability to freshwater forcing.

Findings

AMOC collapse likely begins with a paradoxical initial strengthening.

Recovery involves a slow, 20-year salinification process.

AMOC is most vulnerable to freshwater forcing in the Atlantic thermocline.

Abstract

There is strong evidence that the present-day Atlantic Meridional Overturning Circulation (AMOC) is in a bi-stable regime and hence it is important to determine probabilities and pathways for noise-induced transitions between its equilibrium states. Here, using Large Deviation Theory (LDT), the most probable transition pathways for the noise-induced collapse and recovery of the AMOC are computed in a stochastic box model of the World Ocean. This allows us to determine the physical mechanisms of noise-induced AMOC transitions. We show that the most likely path of an AMOC collapse starts paradoxically with a strengthening of the AMOC followed by an immediate drop within a couple of years due to a short but relatively strong freshwater pulse. The recovery on the other hand is a slow process, where the North Atlantic needs to be gradually salinified over a course of 20 years. The proposed method provides several benefits, including an estimate of probability ratios of collapse between various freshwater noise scenarios, showing that the AMOC is most vulnerable to freshwater forcing into the Atlantic thermocline region. Moreover, a comparison with a quasi-equilibrium approach reveals the contrasts in behavior of a bifurcation-induced and a noise-induced collapse of the AMOC.