Extrapolation from historical data cannot reliably predict the time of a potential AMOC collapse

TL;DR

This paper critically evaluates a recent statistical approach estimating the timing of an AMOC collapse, revealing that its predictions are highly sensitive to various uncertainties and may extend far beyond initial estimates.

Contribution

The paper systematically analyzes the robustness of the DD23 method, highlighting key uncertainties and demonstrating their significant impact on collapse timing predictions.

Findings

Estimated collapse time is highly sensitive to model assumptions.

Uncertainties can extend predicted collapse centuries beyond initial estimates.

Robustness of the original prediction is substantially compromised by multiple uncertainties.

Abstract

Ditlevsen and Ditlevsen [Nature Communications, 2023] (DD23 hereafter) propose a statistical framework to estimate the timing of a potential collapse of the Atlantic Meridional Overturning Circulation (AMOC) based on extrapolating information from observed sea-surface temperature (SST) variability. By fitting a stochastic one-dimensional fold-bifurcation model to an SST-based fingerprint of the AMOC using Maximum Likelihood Estimation (MLE), they conclude that a collapse is most likely to occur in the middle of the 21st century, with a reported 95% confidence interval covering the time span from 2037 to 2109. Given the profound implications of such a claim for both climate and society, it is essential to thoroughly test the robustness of this result, to critically assess the underlying assumptions and uncertainties, and to estimate the extent to which the reported confidence interval reflects the true limits of current knowledge. Here we examine the sensitivity of DD23's results and argue that four types of uncertainty are insufficiently explored in their analysis: (i) structural uncertainty associated with the assumed low-order bifurcation model, (ii) statistical uncertainty in their model fit, (iii) uncertainty in the representativeness of SST-based fingerprints as proxies for the high-dimensional AMOC dynamics, and (iv) uncertainty in the underlying data, arising from non-stationary observational coverage and dataset preprocessing. Using synthetic experiments and a systematic analysis of alternative fingerprints and observational products, we show that the tipping times estimated by DD23 are highly sensitive to the uncertainties listed above, and extend several millennia into the future when these uncertainties are thoroughly propagated.