Quantifying risk of a noise-induced AMOC collapse from northern and tropical Atlantic Ocean variability

TL;DR

This study assesses how internal Atlantic Ocean variability influences the risk of noise-induced AMOC collapse, emphasizing the importance of accurately modeling decadal variability for reliable climate projections.

Contribution

It demonstrates that current models underestimate Atlantic variability, which significantly impacts the likelihood of AMOC collapse under natural fluctuations and external forcing.

Findings

Noise-induced collapse is unlikely without external forcing shifts.

Current models underestimate Atlantic variability, increasing collapse risk.

Forcing can temporarily overshoot thresholds, affecting collapse probability.

Abstract

The Atlantic Meridional Overturning Circulation (AMOC) exerts a major influence on global climate. There is much debate about whether the current strong AMOC may collapse as a result of anthropogenic forcing and/or internal variability. Increasing the noise in simple salt-advection models can change the apparent AMOC tipping threshold. However, it's not clear if 'present-day' variability is strong enough to induce a collapse. Here, we investigate how internal variability affects the likelihood of AMOC collapse. We examine internal variability of basin-scale salinities and temperatures in four CMIP6 pre-industrial simulations. We fit this to an empirical, process-based AMOC box model, and find that noise-induced AMOC collapse (defined as a decade in which the mean AMOC strength falls below 5 Sv) is unlikely for pre-industrial CMIP6 variability unless external forcing shifts the AMOC closer to a threshold. However, CMIP6 models seem to underestimate present-day Atlantic Ocean variability, and stronger variability substantially increases the likelihood of noise-induced collapse, especially if forcing brings the AMOC close to a stability threshold. Surprisingly, we find a case where forcing temporarily overshoots a stability threshold but noise decreases the probability of collapse. Accurately modelling internal decadal variability is essential for understanding the increased uncertainty in AMOC projections.