Determining whether the Atlantic Meridional Overturning Circulation (AMOC)'s transport is in decline is challenging due to the short duration of continuous observations. To estimate how many years are needed to detect a decline, we conduct a simulation study using synthetic data that mimics an AMOC time series. The time series' characteristics are reproduced using the trend, variance, and autocorrelation coefficient of the AMOC strength at 26.5°,N from 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) models under the RCP8.5 future scenario, and from RAPID observations (2004‐‐2018). Our results suggest that the 14‐year RAPID length has just entered the lower limits of the trend’s `detection window’ based on synthetic data generated using CMIP5 trends and variability (14‐‐42 years; median = 24 years), but twice the length is required for detectability based on RAPID variability (29‐‐67 years; median = 43 years). The annual RAPID trend is currently not statistically significant (‐0.11 Sv yr‐1, p>0.05).

Plain Language Summary

There are ongoing discussions in the scientific community about whether the Atlantic Meridional Overturning Circulation transport is slowing down. This is of interest due to the importance of this circulation in transporting heat from the tropics to the northern latitudes. A consensus about its decline is hard to reach due to the limited direct observational data available; with the longest continuous data being 14 years long from 2004 to 2018. We therefore conduct a simulation experiment to examine how many years of data are required to detect a decline in the circulation. We create simulations of the North Atlantic transport based on statistical properties from 20 general circulation models with future climate change projections (until 2100) and from the RAPID array observations (since 2004). Our results demonstrate that the length of data we currently have from observations has just entered the `detection window’ of 14—42 years (based on model simulations). However, the RAPID observations do not currently exhibit a statistically significant trend.