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Weakening of the Atlantic Meridional Overturning Circulation driven by subarctic freshening since the mid-twentieth century

Nature Geoscience volume 17, pages 1291–1298 (2024)Cite this article

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Abstract

The Atlantic Meridional Overturning Circulation is the main driver of northward heat transport in the Atlantic Ocean today, setting global climate patterns. Whether global warming has affected the strength of this overturning circulation over the past century is still debated: observational studies suggest that there has been persistent weakening since the mid-twentieth century, whereas climate models systematically simulate a stable circulation. Here, using Earth system and eddy-permitting coupled ocean–sea-ice models, we show that a freshening of the subarctic Atlantic Ocean and weakening of the overturning circulation increase the temperature and salinity of the South Atlantic on a decadal timescale through the propagation of Kelvin and Rossby waves. We also show that accounting for upper-end meltwater input in historical simulations significantly improves the data–model agreement on past changes in the Atlantic Meridional Overturning Circulation, yielding a slowdown of 0.46 sverdrups per decade since 1950. Including estimates of subarctic meltwater input for the coming century suggests that this circulation could be 33% weaker than its anthropogenically unperturbed state under 2 °C of global warming, which could be reached over the coming decade. Such a weakening of the overturning circulation would substantially affect the climate and ecosystems.

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Fig. 1: Temperature and salinity fingerprints in the Atlantic Ocean from a weakening of the AMOC.
Fig. 2: Oceanic interhemispheric teleconnection.
Fig. 3: Planetary wave adjustment mechanism.
Fig. 4: Model–data comparison of AMOC indices and measurements.
Fig. 5: AMOC sensitivity to global warming and freshwater input.

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Data availability

Observational SST and sea-ice data were downloaded from the UK Met Office repository (https://www.metoffice.gov.uk/hadobs/hadisst/). Global salinity analyses were obtained from the Met Office EN dataset version 4.2.2.analysis.c14 (https://www.metoffice.gov.uk/hadobs/en4/). Greenland surface mass balance data are freely available from Natural Environment Research Council’s (NERC) Polar Data Centre at https://doi.org/10.5285/77B64C55-7166-4A06-9DEF-2E400398E452. Gridded Greenland surface temperature data are available from the ERA5 public repository (https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5). AMOC estimates from the RAPID array are available at https://rapid.ac.uk/data/data-download. Satellite-based AMOC estimates from ref. 37 are available at https://www.dropbox.com/scl/fi/tmzp6nu400s0fdfmfamq5/MOCproxy_for_figshare_v1.0.mat?rlkey=tg6hyedymohp0111rq5qz3obp&dl=0. Data generated in this study are publicly available via UNSWorks at https://doi.org/10.26190/unsworks/30435 (ref. 64).

Code availability

Computational code for the ACCESS-ESM1.5 and ACCESS-OM2-025 models is available at the ARC COE for Climate Extremes Computational Modelling Systems (https://github.com/coecms/access-esm) and COSIMA (https://github.com/COSIMA/access-om2) Github pages, respectively. Computational codes for the data analyses performed in this study are available upon request from the corresponding author.

References

  1. Weijer, W. et al. Stability of the Atlantic Meridional Overturning Circulation: a review and synthesis. J. Geophys. Res. Oceans 124, 5336–5375 (2019).

    Google Scholar 

  2. Zhang, R. et al. A review of the role of the Atlantic Meridional Overturning Circulation in Atlantic multidecadal variability and associated climate impacts. Rev. Geophys. 57, 316–375 (2019).

    Google Scholar 

  3. Stommel, H. Thermohaline convection with two stable regimes of flow. Tellus 13, 224–230 (1961).

    Google Scholar 

  4. Menviel, L. C., Skinner, L. C., Tarasov, L. & Tzedakis, P. C. An ice–climate oscillatory framework for Dansgaard–Oeschger cycles. Nat. Rev. Earth Environ. 1, 677–693 (2020).

    Google Scholar 

  5. Boers, N. Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation. Nat. Clim. Change 11, 680–688 (2021).

    Google Scholar 

  6. Caesar, L., McCarthy, G. D., Thornalley, D. J. R., Cahill, N. & Rahmstorf, S. Current Atlantic Meridional Overturning Circulation weakest in last millennium. Nat. Geosci. 14, 118–120 (2021).

    CAS  Google Scholar 

  7. Ditlevsen, P. & Ditlevsen, S. Warning of a forthcoming collapse of the Atlantic meridional overturning circulation. Nat. Commun. 14, 4254 (2023).

    CAS  Google Scholar 

  8. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Google Scholar 

  9. Danabasoglu, G. et al. North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part II: Inter-annual to decadal variability. Ocean Model. 97, 65–90 (2016).

    Google Scholar 

  10. Latif, M., Sun, J., Visbeck, M. & Hadi Bordbar, M. Natural variability has dominated Atlantic Meridional Overturning Circulation since 1900. Nat. Clim. Change 12, 455–460 (2022).

    CAS  Google Scholar 

  11. McCarthy, G. D. & Caesar, L. Can we trust projections of AMOC weakening based on climate models that cannot reproduce the past? Phil. Trans. R. Soc. A 381, 20220193 (2023).

    Google Scholar 

  12. Lohmann, J. & Ditlevsen, P. D. Risk of tipping the overturning circulation due to increasing rates of ice melt. Proc. Natl Acad. Sci. USA 118, e2017989118 (2021).

    CAS  Google Scholar 

  13. Rahmstorf, S. et al. Thermohaline circulation hysteresis: a model intercomparison. Geophys. Res. Lett. https://doi.org/10.1029/2005GL023655 (2005).

  14. Box, J. E. & Colgan, W. Greenland ice sheet mass balance reconstruction. Part III: marine ice loss and total mass balance (1840–2010). J. Clim. 26, 6990–7002 (2013).

    Google Scholar 

  15. Bamber, J. L. et al. Land ice freshwater budget of the Arctic and North Atlantic oceans: 1. Data, methods, and results. J. Geophys. Res. Oceans 123, 1827–1837 (2018).

    CAS  Google Scholar 

  16. SIMIP Community Arctic sea ice in CMIP6. Geophys. Res. Lett. 47, e2019GL086749 (2020).

    Google Scholar 

  17. Sévellec, F., Fedorov, A. V. & Liu, W. Arctic sea-ice decline weakens the Atlantic Meridional Overturning Circulation. Nat. Clim. Change 7, 604–610 (2017).

    Google Scholar 

  18. Mehling, O., Bellomo, K., Angeloni, M., Pasquero, C. & von Hardenberg, J. High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity. Clim. Dynam. 61, 1519–1534 (2022).

    Google Scholar 

  19. Zhu, C. & Liu, Z. Weakening Atlantic overturning circulation causes South Atlantic salinity pile-up. Nat. Clim. Change 10, 998–1003 (2020).

    Google Scholar 

  20. Roberts, C. D., Garry, F. K. & Jackson, L. C. A multimodel study of sea surface temperature and subsurface density fingerprints of the Atlantic Meridional Overturning Circulation. J. Clim. 26, 9155–9174 (2013).

    Google Scholar 

  21. Ziehn, T. et al. The Australian Earth System Model: ACCESS-ESM1.5. J. South. Hemisph. Earth Syst. Sci. 70, 193–214 (2020).

    Google Scholar 

  22. Kiss, A. E. et al. ACCESS-OM2 v1.0: a global ocean-sea ice model at three resolutions. Geosci. Model Dev. 13, 401–442 (2020).

    Google Scholar 

  23. Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G. & Saba, V. Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature 556, 191–196 (2018).

    CAS  Google Scholar 

  24. Huguenin, M. F., Holmes, R. M. & England, M. H. Drivers and distribution of global ocean heat uptake over the last half century. Nat. Commun. 13, 4921 (2022).

  25. Marchesiello, P. & Estrade, P. Upwelling limitation by onshore geostrophic flow. J. Mar. Res. 68, 37–62 (2010).

    Google Scholar 

  26. Jing, Z. et al. Geostrophic flows control future changes of oceanic eastern boundary upwelling. Nat. Clim. Change 13, 148–154 (2023).

    Google Scholar 

  27. Huang, R. X., Cane, M. A., Naik, N. & Goodman, P. Global adjustment of the thermocline in response to deepwater formation. Geophys. Res. Lett. 27, 759–762 (2000).

    Google Scholar 

  28. Cessi, P., Bryan, K. & Zhang, R. Global seiching of thermocline waters between the Atlantic and the Indian-Pacific Ocean Basins. Geophys. Res. Lett. https://doi.org/10.1029/2003GL019091 (2004).

  29. Marshall, D. P. & Johnson, H. L. Propagation of meridional circulation anomalies along western and eastern boundaries. J. Phys. Oceanogr. 43, 2699–2717 (2013).

    Google Scholar 

  30. Chelton, D. B. & Schlax, M. G. Global observations of oceanic Rossby waves. Science 272, 234–238 (1996).

    CAS  Google Scholar 

  31. Zhang, R. Latitudinal dependence of Atlantic meridional overturning circulation (AMOC) variations. Geophys. Res. Lett. 37, 16703 (2010).

    Google Scholar 

  32. Zhu, C., Liu, Z., Zhang, S. & Wu, L. Likely accelerated weakening of Atlantic overturning circulation emerges in optimal salinity fingerprint. Nat. Commun. 14, 1245 (2023).

    CAS  Google Scholar 

  33. Clement, A. et al. The Atlantic Multidecadal Oscillation without a role for ocean circulation. Science 350, 320–324 (2015).

    CAS  Google Scholar 

  34. Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. https://doi.org/10.1029/2002JD002670 (2003).

  35. Good, S. A., Martin, M. J. & Rayner, N. A. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res. Oceans 118, 6704–6716 (2013).

    Google Scholar 

  36. Kanzow, T. et al. Seasonal variability of the Atlantic Meridional Overturning Circulation at 26.5°N. J. Clim. 23, 5678–5698 (2010).

    Google Scholar 

  37. Frajka-Williams, E. Estimating the Atlantic overturning at 26°N using satellite altimetry and cable measurements. Geophys. Res. Lett. 42, 3458–3464 (2015).

    Google Scholar 

  38. Moat, W. E. et al. Atlantic Meridional Overturning Circulation Observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) Array at 26N from 2004 to 2022 v2022.1 (NERC EDS British Oceanographic Data Centre NOC, 2023); https://doi.org/10.5285/04c79ece-3186-349a-e063-6c86abc0158c

  39. Hersbach, H. et al. ERA5 Hourly Data on Single Levels from 1940 to Present (Copernicus Climate Change Service Climate Data Store, accessed on 1 October 2023).

  40. Otosaka, I. N. et al. Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020. Earth Syst. Sci. Data 15, 1597–1616 (2023).

    Google Scholar 

  41. Bakker, P. et al. Fate of the Atlantic Meridional Overturning Circulation: strong decline under continued warming and Greenland melting. Geophys. Res. Lett. 43, 12252–12260 (2016).

    Google Scholar 

  42. Weijer, W., Cheng, W., Garuba, O. A., Hu, A. & Nadiga, B. T. CMIP6 models predict significant 21st century decline of the Atlantic Meridional Overturning Circulation. Geophys. Res. Lett. https://doi.org/10.1029/2019GL086075 (2020).

  43. Stroeve, J., Holland, M. M., Meier, W., Scambos, T. & Serreze, M. Arctic sea ice decline: faster than forecast. Geophys. Res. Lett. https://doi.org/10.1029/2007GL029703 (2007).

  44. Kim, Y. H., Min, S. K., Gillett, N. P., Notz, D. & Malinina, E. Observationally-constrained projections of an ice-free Arctic even under a low emission scenario. Nat. Commun. 14, 3139 (2023).

    CAS  Google Scholar 

  45. Devilliers, M. et al. A realistic Greenland ice sheet and surrounding glaciers and ice caps melting in a coupled climate model. Clim. Dynam. 57, 2467–2489 (2021).

    Google Scholar 

  46. Menary, M. B. et al. Aerosol-forced AMOC changes in CMIP6 historical simulations. Geophys. Res. Lett. 47, e2020GL088166 (2020).

    Google Scholar 

  47. Millan, R. et al. Rapid disintegration and weakening of ice shelves in North Greenland. Nat. Commun. 14, 6914 (2023).

    CAS  Google Scholar 

  48. Slater, T. et al. Increased variability in Greenland Ice Sheet runoff from satellite observations. Nat. Commun. 12, 6069 (2021).

    CAS  Google Scholar 

  49. Larocca, L. J. et al. Greenland-wide accelerated retreat of peripheral glaciers in the twenty-first century. Nat. Clim. Change 13, 1324–1328 (2023).

    Google Scholar 

  50. Tsujino, H. et al. JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do). Ocean Model. 130, 79–139 (2018).

    Google Scholar 

  51. Ziehn, T. et al. CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP esm-piControl. Version 20210316. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.4248 (2019).

  52. Ziehn, T. et al. CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP historical. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.4272 (2019).

  53. Rashid, H. A. et al. Evaluation of climate variability and change in ACCESS historical simulations for CMIP6. J. South. Hemisph. Earth Syst. Sci. 72, 73–92 (2022).

    Google Scholar 

  54. Mackallah, C. et al. ACCESS datasets for CMIP6: methodology and idealised experiments. J. South. Hemisph. Earth Syst. Sci. 72, 93–116 (2022).

    Google Scholar 

  55. Perner, K. et al. An oceanic perspective on Greenland’s recent freshwater discharge since 1850. Sci. Rep. 9, 17680 (2019).

    Google Scholar 

  56. Ziehn, T. et al. CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 C4MIP esm-ssp585. Version 20210318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.4252 (2019).

  57. McDougall, T. J. Potential enthalpy: a conservative oceanic variable for evaluating heat content and heat fluxes. J. Phys. Oceanogr. 33, 945–963 (2003).

    Google Scholar 

  58. Sverdrup, H. U. Wind-driven currents in a baroclinic ocean with application to the equatorial currents of the eastern Pacific. Proc. Natl Acad. Sci. USA 33, 318–326 (1947).

    CAS  Google Scholar 

  59. Stommel, H. The westward intensification of wind-driven ocean currents. Eos 29, 202–206 (1948).

    Google Scholar 

  60. Pontes, G. M., Sen Gupta, A. & Taschetto, A. S. Projected changes to South Atlantic boundary currents and confluence region in the CMIP5 models: the role of wind and deep ocean changes. Environ. Res. Lett. 11, 094013 (2016).

  61. Sen Gupta, A. et al. Future changes to the upper ocean Western Boundary Currents across two generations of climate models. Sci. Rep. 11, 9538 (2021).

    Google Scholar 

  62. Hawkins, E. et al. Observed emergence of the climate change signal: from the familiar to the unknown. Geophys. Res. Lett. 47, e2019GL086259 (2020).

    Google Scholar 

  63. Hawkins, E. & Sutton, R. Time of emergence of climate signals. Geophys. Res. Lett. https://doi.org/10.1029/2011GL050087 (2012).

  64. Pontes, G. M. & Menviel, L. Dataset from: Persistent Atlantic Overturning Circulation weakening driven by subarctic freshening since the mid-twentieth century. UNSWorks https://doi.org/10.26190/UNSWORKS/30435 (2024).

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Acknowledgements

This project was supported by the Australian Research Council (ARC) under grant number SR200100008. We thank the ARC COE for Climate Extremes Computational Modelling Systems for making the ACCESS-ESM1.5 model configurations available (https://github.com/coecms/access-esm) and the Consortium for Ocean-Sea Ice Modelling in Australia (COSIMA; http://www.cosima.org.au) for making the ACCESS-OM2 suite of models available (https://github.com/COSIMA/access-om2). We thank Dr. N. Yeung, Dr. M. Huguenin, Dr. D. Hutchinson and Dr. P. Spence for their assistance in setting up the ACCESS model simulations. Model simulations were undertaken with the assistance of the National Computational Merit Allocation Scheme from the Australian National Computational Infrastructure (NCI).

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Authors and Affiliations

  1. Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

    Gabriel M. Pontes & Laurie Menviel

  2. Australian Centre for Excellence in Antarctic Sciences, University of New South Wales, Sydney, New South Wales, Australia

    Gabriel M. Pontes & Laurie Menviel

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  1. Gabriel M. Pontes
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Contributions

G.M.P. and L.M. designed the study, contributed to the interpretation of the data and commented on and reviewed the manuscript. G.M.P. performed ACCESS-ESM1.5 and ACCESS-OM2-025 water-hosing experiments, including historical and future projections, conducted the analysis, prepared all figures, produced the schematic in Fig. 3 and wrote the original draft.

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Correspondence to Gabriel M. Pontes.

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Nature Geoscience thanks Katinka Bellomo, Chenyu Zhu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: James Super, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Ocean heat budget.

a Annual mean anomalous ocean heat content evolution in ESM-fw for the full ocean depth (black), top 800 m depth (red) and below 800 m depth (orange). Solid lines indicate 4-member ensemble mean and banding indicates ensemble range. b,e Anomalous heat transport rate, c,f anomalous ocean heat uptake rate (methods; negative values indicate ocean heat loss to the atmosphere) and d,g tendencies in ocean heat content in ESM-fw and OM-fw experiment, respectively. Analyses in b-g were performed on the first 50 years of the respective simulations. Stippling indicates regions where all 4 members agree on the sign of change in the ESM-fw ensemble (P < 0.05; methods). Basemaps in b–g created with Cartopy.

Extended Data Fig. 2 Mean upper-layer (top 300 m) salinity budget.

Left column: Eastern South Atlantic (5°E-20°E; 10°S-30°S). Right column: Western South Atlantic (60°W-30°W; 15°S-35°S). a,b Accumulated salinity anomaly (blue), salinity anomaly due to the convergence in oceanic transports (green; see methods), and due to surface fluxes (yellow). c,d Decomposition of oceanic transports contributing to South Atlantic salinification in the ESM-fw. Contributions due to convergences of horizontal advection (light green), horizontal diffusivity (aqua), vertical advection (red), and vertical diffusivity (magenta). e,f Decomposition of surface forcing fluxes into evaporation (brown line), precipitation (dark blue line) and river runoff (cyan line). g,h SST (red line) and evaporation flux (brown line) anomalies. All solid lines indicate 4-member ensemble mean and banding indicates their ensemble ranges.

Extended Data Fig. 3 Kelvin wave fingerprint.

a Density contour referenced to the 2000 m isobar (sigma2) in OM-control (grey) and OM-fw (orange), zonally averaged between eastern and western North Atlantic Ocean boundaries. b-g Density (left column) and velocity (right column) anomalies in the b-c Gulf Stream (35°N), d-e North Brazil Current (3°N) and f-g Benguela (30°S) currents averaged in the last 10 years of the 100-years long OM-fw simulation.

Extended Data Fig. 4 Changes in the Brazil and Antarctic Circumpolar Currents velocities.

a Brazil Current meridional velocity cross section at 35°S. b Antarctic Circumpolar Current zonal velocity cross section at 20°E. Both panels show results from experiment OM-fw averaged in the last 10 years of the 100-years long OM-fw simulation. Units: m.s−1.

Extended Data Fig. 5 South Atlantic salinity indices.

a SSESA index (10°S-30°S; 5°E-20°E) in ESM-fw (solid) and OM-fw (dashed) simulations. b as in ‘a’ but for the Sswsa index (15°S-35°S; 60°W-30°W). c Anomalous Brazil Current volume transport in ESM-fw (orange solid line) and OM-fw (dashed black line). In all panels solid lines indicate 4-member ensemble mean and dashed lines indicate the single OM-fw simulation. Banding indicates 4-member ensemble range for ESM-fw and one standard deviation range of the control run for OM-fw.

Extended Data Fig. 6 Rossby wave feedback.

a SSTSPG index and b Gulf Stream transport anomaly in idealized freshwater perturbation experiments. Solid lines indicate 4-member ensemble mean in ESM-fw and dashed lines indicate the single OM-fw simulation. Banding indicates 4-member ensemble range for ESM-fw and one standard deviation range of the control run for OM-fw. c Detrended time series of the Gulf Stream and SSTSPG obtained through removing their respective 7-years running average. Time series were smoothed by a 5-years running average. d Hovmoeller plot (longitude vs time in model years) of mixed layer depth anomalies averaged between 20°N-30°N in the North Atlantic. Zonal green line indicates the speed of the first-mode baroclinic Rossby wave in the North between 20°N-30°N. Vertical green line indicates the approximate length of the Rossby feedback.

Extended Data Fig. 7 Comparison of SST-based AMOC indices.

a Comparison of four SST-based indices in observations (HadISST). SSTDIPOLE (black)20: North Atlantic (70°W-30°E; 45°N-80°N) minus South Atlantic (70°W-30°E; 40°S-0) SSTs. SSTDIPOLE-SWSA due to the Rossby wave propagation (pink): subpolar North Atlantic (60°W-10°W; 50°N-70°N) minus southwestern South Atlantic SSTs (70°W-30°W; 45°S-10°S). SSTDIPOLE-SESA due to Kelvin wave propagation (green): subpolar North Atlantic (60°W-10°W; 50°N-70°N) minus eastern South Atlantic (5°E-20°E; 30°S-10°S). SSTSPG-PA (blue): averaged SST anomalies from Nov-May in the subpolar gyre (as per ref. 23). In all indices twice the global mean temperature SST anomaly has been subtracted to remove the global warming signal7. The signal-to-noise ratio (R) is indicated for each index in observations, computed as the ratio between the mean change (values from 2000-2023 minus 1900-1950) and the standard deviation from 1900-1950. b-c Comparison of simulated and observed b SSTDIPOLE and c SSTDIPOLE-SESA. Red lines indicate the observed indices. Grey shading indicates the 10-90 percentile range of a 40-member ensemble of the ACCESS-ESM1.5 hist and ssp585 simulations. Thin grey, blue and purple lines show a single member of each ensemble as a reference for the model’s internal variability. Light blue shading indicates 8-member ensemble range of hist-fw simulations and purple shading indicates 8-member ensemble range of ssp585-fw simulations. The same comparison for SSTSPG-PA and SSTDIPOLE-SWSA is found in Fig. 4 of the main paper, where SSTDIPOLE-SWSA is referred to as the ‘modified SSTDIPOLE index’ (SSTDIPOLE*).

Extended Data Fig. 8 Statistical analyses of observation-based AMOC indices.

a Observation weights in the EN4 dataset for the eastern and southwestern South Atlantic. b 5-year running average of the SSESA and SSWSA indices. The grey lines indicate the period excluded from the current analysis due to nearly zero weights attributed to observations in the EN4 dataset. Solid black line indicates linear trend of the SSWSA index from 1995 to 2022 (−0.13 psu.dec−1; p = 2e-6). The dashed black line indicates the linear trend of the SSESA index from 1975 to 2022 (−0.013 psu.dec−1; p = 3e-6). c SST-based indices calculated from the HadISST dataset. Blue line indicates SSTSPG-noPA (as per ref. 23), which does not account for polar amplification, showing no significant trend (p = 0.06). Green line indicates the SSTSPG-PA index corrected by polar amplification7 (trend: 0.1 K.dec−1; p = 3e-5). Magenta line indicate the SSTDIPOLE* index (trend: 0.22 K.dec−1; p = 5e-11). Thin lines represent annual mean values and thicker lines their respective 5-year moving average. Dashed lines indicate linear trends from 1950 to 2023. Bandings indicate two standard deviations range (equivalent to the 95% CI) of the reference period (1900 to 1950) for each index, according to their respective line colours. Vertical magenta line indicates the time-of-emergence (ToE) of the AMOC weakening signal for the SSTDIPOLE* index, defined as the year at which the time series emerges from the two standard deviations envelop with no returning62,63 In b-c, statistical significance of the trends is evaluated through a two-sided Mann-Kendall test with no adjustments.

Extended Data Fig. 9 Observational and modelled salinity trends.

Salinity trends from 2000-2022 in a observations, b 8-member hist-fw ensemble and c 40-member hist ensemble. Stippling indicates where 7 members agree on the sign of the trend for hist-fw simulations and where at least 70% (31 members) of simulations agree in hist simulations. Basemaps created with Cartopy.

Extended Data Fig. 10 Global warming trajectory.

Time series of surface (2 m) global mean temperature anomaly referenced to the 1850-1900 period. Black line indicates observed HadCRUT5 temperature anomaly. Grey line indicates a single member of hist simulations between 1900-2014 and ssp585 simulations between 2015-2100. Blue and purple lines indicate a single member of hist-fw and ssp585-fw simulations, respectively. Shading indicates standard error for HadCRUT5, one standard deviation of a 40-member ensemble of hist and ssp585 simulations, and 8-member ensemble range of hist-fw and ssp585-fw simulations.

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Pontes, G.M., Menviel, L. Weakening of the Atlantic Meridional Overturning Circulation driven by subarctic freshening since the mid-twentieth century. Nat. Geosci. 17, 1291–1298 (2024). https://doi.org/10.1038/s41561-024-01568-1

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