Abstract
The Atlantic Meridional Overturning Circulation (AMOC) exhibits two stable states in models of varying complexity. Shifts between alternative AMOC states are thought to have played a role in past abrupt climate changes, but the proximity of the climate system to a threshold for future AMOC collapse is unknown. Generic early warning signals of critical slowing down before AMOC collapse have been found in climate models of low and intermediate complexity. Here we show that early warning signals of AMOC collapse are present in a fully coupled atmosphere-ocean general circulation model, subject to a freshwater hosing experiment. The statistical significance of signals of increasing lag-1 autocorrelation and variance vary with latitude. They give up to 250 years warning before AMOC collapse, after ~550 years of monitoring. Future work is needed to clarify suggested dynamical mechanisms driving critical slowing down as the AMOC collapse is approached.
Highlights
The Atlantic Meridional Overturning Circulation (AMOC) exhibits two stable states in models of varying complexity
The representation of the AMOC in FAMOUS is comparable to a variety of other fully coupled climate models (Figs 1,2), all of which are of a higher spatial resolution, and several of which feature in the CMIP3 and CMIP5 data sets used in the IPCC assessments
Comparison of the transient simulation with the equilibrium runs (Fig. 3) shows that the AMOC was forced fast enough to shift it away from equilibrium, such that it lagged the forcing
Summary
The Atlantic Meridional Overturning Circulation (AMOC) exhibits two stable states in models of varying complexity. Numerical model projections suggest that the AMOC is likely to weaken over the 21st century[14], but the likelihood of an abrupt collapse is very uncertain[15,16], partly because most state-of-theart climate models used for future projections cannot yet correctly simulate past abrupt climate changes[17,18] This has generated interest in the possibility that generic early warning signals could exist before abrupt AMOC transitions[9,19,20,21], which might be diagnosed directly from data. For a low-order dynamical system approaching a threshold where its current state becomes unstable and it transitions to some other state, one can expect to see it become more sluggish in its response to small perturbations[20] This phenomenon of ‘critical slowing down’ (CSD) can be detected in time series as increasing autocorrelation over time, measured by estimating the AR(1) coefficient (see Methods). A value of t 1⁄4 1 implies an indicator is always rising, t 1⁄4 À 1, always decreasing and t 1⁄4 0 having no trend (see Methods)
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