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Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation

Nature Climate Change volume 11, pages 680–688 (2021)Cite this article

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Matters Arising to this article was published on 30 November 2023

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Abstract

The Atlantic Meridional Overturning Circulation (AMOC), a major ocean current system transporting warm surface waters toward the northern Atlantic, has been suggested to exhibit two distinct modes of operation. A collapse from the currently attained strong to the weak mode would have severe impacts on the global climate system and further multi-stable Earth system components. Observations and recently suggested fingerprints of AMOC variability indicate a gradual weakening during the last decades, but estimates of the critical transition point remain uncertain. Here, a robust and general early-warning indicator for forthcoming critical transitions is introduced. Significant early-warning signals are found in eight independent AMOC indices, based on observational sea-surface temperature and salinity data from across the Atlantic Ocean basin. These results reveal spatially consistent empirical evidence that, in the course of the last century, the AMOC may have evolved from relatively stable conditions to a point close to a critical transition.

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Fig. 1: Comparison of robustness of different EWS indicators.
Fig. 2: Spatial trends and EWS in Atlantic SSTs and salinity.
Fig. 3: EWS for SST- and salinity-based AMOC indices.
Fig. 4: EWS for AMOC index SSTSG-GM with modelled fixed point.
Fig. 5: AMOC index SSTSG-GM and EWS as functions of global mean temperature (GMT).
Fig. 6: AMOC strength and SST-based index in CMIP5 models.

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Data availability

The HadISST reanalysis data used here are publicly available at https://www.metoffice.gov.uk/hadobs/hadisst/. The CMIP5 data are publicly available at https://esgf-node.llnl.gov/projects/cmip5/. The grid cells used to define the subpolar gyre region can be downloaded from http://www.pik-potsdam.de/~caesar/AMOC_slowdown/. Ocean salinity data can be obtained from https://www.metoffice.gov.uk/hadobs/en4/.

Code availability

All Python code used for the analysis is available from the author upon request (boers@pik-potsdam.de) or on GitHub at https://github.com/niklasboers/AMOC_EWS.

Change history

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Acknowledgements

The author thanks M. Rypdal for many stimulating discussions, L. Jackson and V. Skiba for helpful comments and A. Robinson for providing the time series of the AMOC strength and SST-based AMOC index from the different CMIP control simulations. N.B. acknowledges funding by the Volkswagen Foundation. This is TiPES contribution #116; the Tipping Points in the Earth System (TiPES) project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 820970.

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  1. Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany

    Niklas Boers

  2. Potsdam Institute of Climate Impact Research, Potsdam, Germany

    Niklas Boers

  3. Department of Mathematics and Global Systems Institute, University of Exeter, Exeter, UK

    Niklas Boers

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Extended data

Extended Data Fig. 1 Mean early-warning indicators for the Atlantic ocean.

a, Corrected restoring rate λ estimated from the HadISST dataset assuming autocorrelated noise. b, Same as (a) but for the EN4 salinity dataset. c, Variance estimated from the HadISST dataset. d, Same as (c) but for the EN4 salinity dataset. e, AC1 estimated from the HadISST dataset. f, Same as (e) but for the EN4 salinity dataset. Note the high values in the northern Atlantic and the subpolar gyre region in particular for λ and AC1.

Extended Data Fig. 2 Trends of early-warning indicators for the Atlantic ocean.

a, Linear trends of the corrected restoring rate λ estimated from the HadISST dataset assuming autocorrelated noise. b, Same as (a) but for the EN4 salinity dataset. c, Linear trends of the variance estimated from the HadISST dataset. d, Same as (c) but for the EN4 salinity dataset. e, Linear trends of the AC1 estimated from the HadISST dataset. f, Same as (e) but for the EN4 salinity dataset. Note the high positive values in the northern Atlantic and the subpolar gyre region in particular for λ and AC1, but also in the southern Atlantic ocean where a salinity pileup has recently been associated with an AMOC slowdown [46].

Extended Data Fig. 3 Same as Fig. 3, but with sliding window size w = 60 yr to estimate EWS.

a, SST-based AMOC indices (thin) together with 50-yr running means (thick). b, Salinity-based AMOC indices (thin) together with 50-yr running means (thick). c, The restoring rate λ of the SST-based AMOC indices, estimated under the assumption of autocorrelated noise. d, The restoring rate λ of the salinity-based AMOC indices, estimated under the assumption of autocorrelated noise. e, Same as (c) but for the variance. f, Same as (d) but for the variance. g, Same as (c) but for the AC1. h, Same as (d) but for the AC1. The dashed lines indicate the linear trends of the three early-warning indicators, with p-values given in the legends. Values for each sliding window are plotted at the centre point of that window. Data for the first and the last w/2 = 30 yr are omitted because no full time windows to estimate the different early-warning indicators are available there.

Extended Data Fig. 4 Same as Fig. 3, but with sliding window size w = 80 yr to estimate EWS.

a, SST-based AMOC indices (thin) together with 50-yr running means (thick). b, Salinity-based AMOC indices (thin) together with 50-yr running means (thick). c, The restoring rate λ of the SST-based AMOC indices, estimated under the assumption of autocorrelated noise. d, The restoring rate λ of the salinity-based AMOC indices, estimated under the assumption of autocorrelated noise. e, Same as (c) but for the variance. f, Same as (d) but for the variance. g, Same as (c) but for the AC1. h, Same as (d) but for the AC1. The dashed lines indicate the linear trends of the three early-warning indicators, with p-values given in the legends. Values for each sliding window are plotted at the centre point of that window. Data for the first and the last w/2 = 40 yr are omitted because no full time windows to estimate the different early-warning indicators are available there.

Extended Data Fig. 5 Same as Fig. 4 in the main text, but for the remaining six AMOC indices as indicated in the legends.

a, SST-based AMOC indices and fitted fixed point of a conceptual AMOC model. b, Salinity-based AMOC indices and fitted fixed point of a conceptual AMOC model. b,c, The restoring rate λ of the SST-based AMOC indices, estimated under the assumption of autocorrelated noise. d, The restoring rate λ of the salinity-based AMOC indices, estimated under the assumption of autocorrelated noise. e, Same as (c) but for the variance. f, Same as (d) but for the variance. g, Same as (c) but for the AC1. h, Same as (d) but for the AC1. The dashed lines indicate the linear trends of the three early-warning indicators, with p-values given in the legends. Values for each sliding window are plotted at the centre point of that window. Data for the first and the last w/2 = 35 yr are omitted because no full time windows to estimate the different early-warning indicators are available there.

Extended Data Fig. 6 Same as Fig. 5 in the main text, but for all eight AMOC indices SSTSG−GM (a), SSTSG−GM−AMO (c), SSTDIPOOLE (e), SSTSG−NH (g), SNN1 (b), SNN2 (d), SN (f), and SS (h) as indicated in the legends.

In each panel, the respective AMOC index (top) and the corresponding variance (bottom) are shown as functions of the control parameter T.

Extended Data Fig. 7 Same as Extended Data Fig. 5, but with sliding window size w = 60 yr to estimate EWS.

a, SST-based AMOC indices and fitted fixed point of a conceptual AMOC model. b, Salinity-based AMOC indices and fitted fixed point of a conceptual AMOC model. b,c, The restoring rate λ of the SST-based AMOC indices, estimated under the assumption of autocorrelated noise. d, The restoring rate λ of the salinity-based AMOC indices, estimated under the assumption of autocorrelated noise. e, Same as (c) but for the variance. f, Same as (d) but for the variance. g, Same as (c) but for the AC1. h, Same as (d) but for the AC1. The dashed lines indicate the linear trends of the three early-warning indicators, with p-values given in the legends. Values for each sliding window are plotted at the centre point of that window. Data for the first and the last w/2 = 30 yr are omitted because no full time windows to estimate the different early-warning indicators are available there.

Extended Data Fig. 8 Same as Extended Data Fig. 5, but with sliding window size w = 80 yr to estimate EWS.

a, SST-based AMOC indices and fitted fixed point of a conceptual AMOC model. b, Salinity-based AMOC indices and fitted fixed point of a conceptual AMOC model. b,c, The restoring rate λ of the SST-based AMOC indices, estimated under the assumption of autocorrelated noise. d, The restoring rate λ of the salinity-based AMOC indices, estimated under the assumption of autocorrelated noise. e, Same as (c) but for the variance. f, Same as (d) but for the variance. g, Same as (c) but for the AC1. h, Same as (d) but for the AC1. The dashed lines indicate the linear trends of the three early-warning indicators, with p-values given in the legends. Values for each sliding window are plotted at the centre point of that window. Data for the first and the last w/2 = 40 yr are omitted because no full time windows to estimate the different early-warning indicators are available there.

Extended Data Fig. 9 Early-warning signals for the SST-based AMOC index SSTSG−GM.

Same as Fig. 4, but using the ERSST instead of the HadISST dataset.

Extended Data Fig. 10 AMOC strength and SST-based index in CMIP5 models.

Same as Fig. 6, but for the modelled maximum AMOC strength over all ocean depths from 20∘N to 60∘N, instead of at 26∘N.

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Boers, N. Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation. Nat. Clim. Chang. 11, 680–688 (2021). https://doi.org/10.1038/s41558-021-01097-4

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