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A recent decline in North Atlantic subtropical mode water formation

A Publisher Correction to this article was published on 22 September 2020

This article has been updated


As a manifestation of mixing dynamics in the upper ocean, interannual and decadal variability of subtropical mode water (STMW) properties in the North Atlantic Ocean provides a valuable insight into ocean–atmosphere interaction in a changing climate. Here, we use hydrographic data from the Bermuda Atlantic Time-Series Study and Hydrostation S sites near Bermuda, as well as various ocean reanalysis products, to evaluate the modern variability of STMW properties. Our study finds an 86–93% loss of STMW thickness at these sites between 2010 and 2018 and a comparable loss throughout the western subtropical gyre, culminating in the weakest STMW pentad on record. We correlate this decline with a reduction in the annual outcropping volume and northward excursions of the formation region, suggesting a gyre-wide signal of weakening STMW generation. The outcropping volume of STMW is anti-correlated with surface ocean heat content, foreshadowing future STMW loss in the face of continued warming.

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Fig. 1: Map of the study zone and hydrographic profile information.
Fig. 2: Time-series plots of STMW properties from February 1955 to September 2019.
Fig. 3: Six-month window-averaged conditions from BATS and Hydrostation S.
Fig. 4: Time-series plots of STMW properties from observational and reanalysis datasets.
Fig. 5: Spatiotemporal variability of STMW thickness in the North Atlantic subtropical gyre.

Data availability

The authors declare that all data supporting the findings of this study can be found in the following online repositories. The BATS CTD data can be found at The Hydrostation S CTD and extended bottle datasets are available at The ECMWF ORAS4 dataset can be accessed at The Met Office Hadley Centre EN4g10 dataset can be accessed at The NAO index (DJFM) data can be accessed at The Argo data can be accessed at The final Argo data used in this study were accessed on 10 October 2019.

Code availability

All custom code used in this study can be found at

Change history

  • 22 September 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


  1. 1.

    Zanna, L., Khatiwala, S., Gregory, J. M., Ison, J. & Heimbach, P. Global reconstruction of historical ocean heat storage and transport. Proc. Natl Acad. Sci. USA 116, 1126–1131 (2019).

    CAS  Google Scholar 

  2. 2.

    Gruber, N. et al. The oceanic sink for anthropogenic CO2 from 1994 to 2007. Science 363, 1193–1199 (2019).

    CAS  Google Scholar 

  3. 3.

    Chen, X. & Tung, K.-K. Varying planetary heat sink led to global-warming slowdown and acceleration. Science 345, 897–904 (2014).

    CAS  Google Scholar 

  4. 4.

    Joyce, T. M. New perspectives on eighteen-degree water formation in the North Atlantic. J. Oceanogr. 68, 45–52 (2012).

    Google Scholar 

  5. 5.

    McCartney, M. S. The subtropical recirculation of mode waters. J. Mar. Res. 40, 427–464 (1982).

    Google Scholar 

  6. 6.

    Maze, G. & Marshall, J. Diagnosing the observed seasonal cycle of Atlantic subtropical mode water using potential vorticity and its attendant theorems. J. Phys. Oceanogr. 41, 1986–1999 (2011).

    Google Scholar 

  7. 7.

    Forget, G., Maze, G., Buckley, M. & Marshall, J. Estimated seasonal cycle of North Atlantic eighteen degree water volume. J. Phys. Oceanogr. 41, 269–286 (2011).

    Google Scholar 

  8. 8.

    Dickson, R. & Brown, J. The production of North Atlantic deep water: sources, rates, and pathways. J. Geophys. Res. 99, 12319–12341 (1994).

    Google Scholar 

  9. 9.

    Dong, S., Hautala, S. L. & Kelly, K. A. Interannual variations in upper-ocean heat content and heat transport convergence in the western North Atlantic. J. Phys. Oceanogr. 37, 2682–2697 (2007).

    Google Scholar 

  10. 10.

    Xu, X., Rhines, P. B. & Chassignet, E. P. Temperature–salinity structure of the North Atlantic circulation and associated heat and freshwater transports. J. Clim. 29, 7723–7742 (2016).

    Google Scholar 

  11. 11.

    Bates, N. R. Multi-decadal uptake of carbon dioxide into subtropical mode water of the North Atlantic Ocean. Biogeosciences 9, 2649–2659 (2012).

    CAS  Google Scholar 

  12. 12.

    Conway, T. M., Palter, J. B. & Souza, G. F. D. Gulf Stream rings as a source of iron to the North Atlantic subtropical gyre. Nat. Geosci. 11, 594–598 (2018).

    CAS  Google Scholar 

  13. 13.

    Palter, J. B., Lozier, M. S. & Barber, R. T. The effect of advection on the nutrient reservoir in the North Atlantic subtropical gyre. Nature 437, 687–692 (2005).

    CAS  Google Scholar 

  14. 14.

    Carlson, C. A. et al. Dissolved organic carbon export and subsequent remineralization in the mesopelagic and bathypelagic realms of the North Atlantic basin. Deep Sea Res. Pt II 57, 1433–1445 (2010).

    CAS  Google Scholar 

  15. 15.

    Hanawa, K. & Talley, L. D. in Ocean Circulation and Climate 373–386 (Elsevier, 2001).

  16. 16.

    Feucher, C., Maze, G. & Mercier, H. Subtropical mode water and permanent pycnocline properties in the world ocean. J. Geophys. Res. Oceans 124, 1139–1154 (2019).

    Google Scholar 

  17. 17.

    Alfultis, M. A. & Cornillon, P. Annual and interannual changes in the North Atlantic STMW layer properties. J. Phys. Oceanogr. 31, 2066–2086 (2000).

    Google Scholar 

  18. 18.

    Billheimer, S. & Talley, L. D. Near cessation of eighteen degree water renewal in the western North Atlantic in the warm winter of 2011–2012. J. Geophys. Res. Oceans 118, 6838–6853 (2013).

    Google Scholar 

  19. 19.

    Kwon, Y.-o & Riser, S. C. North Atlantic subtropical mode water: a history of ocean–atmosphere interaction 1961–2000. Geophys. Res. Lett. 31, L19307 (2004).

    Google Scholar 

  20. 20.

    Peng, G., Chassignet, E. P., Kwon, Y.-o & Riser, S. C. Investigation of variability of the North Atlantic subtropical mode water using profiling float data and numerical model output. Ocean Model. 13, 65–85 (2006).

    Google Scholar 

  21. 21.

    Joyce, T. M., Deser, C. & Spall, M. The relation between decadal variability of subtropical mode water and the North Atlantic Oscillation. J. Clim. 13, 2550–2569 (2000).

    Google Scholar 

  22. 22.

    Hurrell, J. W. Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation. Science 269, 676–679 (1995).

    CAS  Google Scholar 

  23. 23.

    Somavilla, R., González-Pola, C. & Fernández-Diaz, J. The warmer the ocean surface, the shallower the mixed layer. How much of this is true? J. Geophys. Res. Oceans 122, 7698–7716 (2017).

    CAS  Google Scholar 

  24. 24.

    Sugimoto, S., Hanawa, K., Watanabe, T., Suga, T. & Xie, S.-P Enhanced warming of the subtropical mode water in the North Pacific and North Atlantic. Nat. Clim. Change 7, 656–659 (2017).

    CAS  Google Scholar 

  25. 25.

    Lique, C. & Thomas, M. D. Latitudinal shift of the Atlantic Meridional Overturning Circulation source regions under a warming climate. Nat. Clim. Change 8, 1013–1021 (2018).

    Google Scholar 

  26. 26.

    Xu, L., Xie, S.-P. & Liu, Q. Mode water ventilation and subtropical countercurrent over the North Pacific in CMIP5 simulations and future projections. J. Geophys. Res. 117, C12009 (2012).

    Google Scholar 

  27. 27.

    Worthington, L. V. The 18° water in the Sargasso Sea. Deep Sea Res. 5, 297–305 (1959).

    Google Scholar 

  28. 28.

    Talley, L. D. & Raymer, M. E. Eighteen degree water variability. J. Mar. Res. 40, 757–775 (1982).

    Google Scholar 

  29. 29.

    Jenkins, W. J. On the climate of a subtropical ocean gyre: decadal time scale variations in water mass renewal in the Sargasso Sea. J. Mar. Res. 40, 265–290 (1982).

    Google Scholar 

  30. 30.

    Deser, C., Phillips, A. S. & Alexander, M. A. Twentieth century tropical sea surface temperature trends revisited. Geophys. Res. Lett. 37, L10701 (2010).

    Google Scholar 

  31. 31.

    Wu, L. et al. Enhanced warming over the global subtropical western boundary currents. Nat. Clim. Change 2, 161–166 (2012).

    CAS  Google Scholar 

  32. 32.

    Argo Argo Float Data and Metadata from Global Data Assembly Centre (Argo GDAC) (SEANOE, 2019);

  33. 33.

    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 

  34. 34.

    Balmaseda, M. A., Mogensen, K. & Weaver, A. T. Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart. J. R. Meteorol. Soc. 139, 1132–1161 (2013).

    Google Scholar 

  35. 35.

    Taylor, B. A. H. & Stephens, J. A. The North Atlantic Oscillation and the latitude of the Gulf Stream. Tellus 50A, 134–142 (1998).

    Google Scholar 

  36. 36.

    Häkkenen, S. & Rhines, P. B. Decline of subpolar North Atlantic circulation during the 1990s. Science 304, 555–559 (2004).

    Google Scholar 

  37. 37.

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

    CAS  Google Scholar 

  38. 38.

    Thornalley, D. J. R. et al. Atlantic overturning during the past 150 years. Nature 556, 227–230 (2018).

    CAS  Google Scholar 

  39. 39.

    Seidov, D., Mishonov, A., Reagan, J. & Parsons, R. Eddy-resolving in situ ocean climatologies of temperature and salinity in the Northwest Atlantic Ocean. J. Geophys. Res. Oceans 124, 41–58 (2019).

    Google Scholar 

  40. 40.

    Seidov, D., Mishonov, A., Reagan, J. & Parsons, R. Multidecadal variability and climate shift in the North Atlantic Ocean. Geophys. Res. Lett. 44, 4985–4993 (2017).

    Google Scholar 

  41. 41.

    Maze, G., Forget, G., Buckley, M., Marshall, J. & Cerovecki, I. Using transformation and formation maps to study the role of air–sea heat fluxes in North Atlantic eighteen degree water formation. J. Phys. Oceanogr. 39, 1818–1835 (2009).

    Google Scholar 

  42. 42.

    Xu, X., Rhines, P. B. & Chassignet, E. P. On mapping the diapycnal water mass transformation of the upper North Atlantic Ocean. J. Phys. Oceanogr. 48, 2233–2258 (2018).

    Google Scholar 

  43. 43.

    Schlunegger, S. et al. Emergence of anthropogenic signals in the ocean carbon cycle. Nat. Clim. Change 9, 719–725 (2019).

    CAS  Google Scholar 

  44. 44.

    Capotondi, A., Alexander, M. A., Bond, N. A., Curchitser, E. N. & Scott, J. D. Enhanced upper ocean stratification with climate change in the CMIP3 models. J. Geophys. Res. 117, C04031 (2012).

    Google Scholar 

  45. 45.

    Hazeleger, W. & Drijfhout, S. S. Mode water variability in a model of the subtropical gyre: response to anomalous forcing. J. Phys. Oceanogr. 28, 266–288 (1998).

    Google Scholar 

  46. 46.

    Billheimer, S. & Talley, L. D. Annual cycle and destruction of eighteen degree water. J. Geophys. Res. Oceans 121, 6604–6617 (2016).

    Google Scholar 

  47. 47.

    Qiu, B., Chen, S. & Hacker, P. Effect of mesoscale eddies on subtropical mode water variability from the Kuroshio Extension System Study (KESS). J. Phys. Oceanogr. 37, 982–1000 (2006).

    Google Scholar 

  48. 48.

    The International Thermodynamic Equation of Seawater—2010: Calculation and Use of Thermodynamic Properties Intergovernmental Oceanographic Commission, Manuals and Guides No. 56, UNESCO (English) (IOC, SCOR and IAPSO, 2010).

  49. 49.

    Sen, P. K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).

    Google Scholar 

  50. 50.

    Kendall, A. M. G. A new measure of rank correlation. Biometrika 30, 81–93 (1938).

    Google Scholar 

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We acknowledge and thank the numerous principal investigators, researchers and technicians who have contributed to the BATS and Hydrostation S time-series projects since their inception. Additional thanks go to the officers and crew of the RV Weatherbird I, RV Weatherbird II and RV Atlantic Explorer. This work was funded by NSF grant OCE-1633215 and supported by the French National Program LEFE/INSU project SOMOVAR (North-Atlantic subtropical ocean: mechanisms of observed and projected low-frequency variability).

Author information




N.R.B. and R.J.J. are principal investigators for the BATS/Hydrostation S projects and coordinated the sampling and analysis of the data. S.W.S., N.R.B. and R.J.J. designed the study and performed the BATS/Hydrostation S analyses. S.W.S. and G.M. investigated the reanalysis datasets. S.W.S. wrote the manuscript with contributions from N.R.B., R.J.J. and G.M.

Corresponding author

Correspondence to Samuel W. Stevens.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Terrence Joyce, Olga Sato and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Time-series plots of STMW core properties.

Time-series of (a) <19C surface outcropping denoted by black vertical lines; (b) absolute salinity; (c) potential density anomaly; and (d) temperature gradient per 100 m from January 1988 to September 2019. Temperature gradient was calculated as the maximum temperature gradient, per metre, found in the identified STMW layer, multiplied by 100. Grey scatter points represent the full resolution time-series, solid black lines with white markers represent annual means, red shading represents the 95% confidence limits of the annual means, and red lines represent 2010–2018 trends (p-values < 0.05). Weak STMW pentad (2014–2018) is indicated by a cyan box. Annual means are expressed with 95% confidence limits calculated as the product of \({\sigma }_{\overline{x}}\) multiplied by the sample t-scores. Slopes are calculated using a Sen-Theil estimator \(\pm 2{\sigma }_{\overline{x}}\) of the slope. See methods section for further information on statistics.

Extended Data Fig. 2 Statistical comparison of STMW parameters for the two weakest STMW periods.

All date ranges are inclusive. The Argo and ORAS4 datasets are incomplete for certain periods. Ranges are expressed as the 95% confidence intervals of the mean properties within a given period, calculated as the product of σx and the sample t-scores. See methods section for further information on statistics.

Extended Data Fig. 3 Map of Argo float profiles.

Heat map denotes the amount of Argo profiles performed in 1 × 1grid cells within the formation zone. Scattered points denote the locations of individual profiles in within the formation zone.

Extended Data Fig. 4 Table detailing different STMW definitions.

Different STMW definitions and associated physical classifications employed in Extended Data Fig. 5.

Extended Data Fig. 5 Time-series plots of STMW properties based on different identification algorithms.

Data were averaged into annual means and the resulting signal was smoothed using a three-year moving mean. Red lines represent LS definition, blue lines represent MS definition, green lines represent DO definition, orange lines represent DI definition, olive lines represent BT definition, and brown lines represent BH definition. See Extended Data Fig. 4 for details of STMW definitions.

Extended Data Fig. 6 NAO-STMW correlation time-series.

Lag correlation plot for three-year centred moving average of the NAO DJFM index and the three primary measures of STMW strength. Negative lag occurs when NAO DJFM index leads STMW properties, positive lag occurs when NAO DJFM index lags STMW properties. Filled markers denote lags at which correlations are significant with p-values < 0.05.

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Stevens, S.W., Johnson, R.J., Maze, G. et al. A recent decline in North Atlantic subtropical mode water formation. Nat. Clim. Chang. 10, 335–341 (2020).

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