Increasing ocean stratification over the past half-century


Seawater generally forms stratified layers with lighter waters near the surface and denser waters at greater depth. This stable configuration acts as a barrier to water mixing that impacts the efficiency of vertical exchanges of heat, carbon, oxygen and other constituents. Previous quantification of stratification change has been limited to simple differencing of surface and 200-m depth changes and has neglected the spatial complexity of ocean density change. Here, we quantify changes in ocean stratification down to depths of 2,000 m using the squared buoyancy frequency N2 and newly available ocean temperature/salinity observations. We find that stratification globally has increased by a substantial 5.3% [5.0%, 5.8%] in recent decades (1960–2018) (the confidence interval is 5–95%); a rate of 0.90% per decade. Most of the increase (~71%) occurred in the upper 200 m of the ocean and resulted largely (>90%) from temperature changes, although salinity changes play an important role locally.

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Fig. 1: Climatological mean and long-term trends for global mean ocean density and squared buoyancy frequency (N2) over 1960–2018.
Fig. 2: Trends of N2 over depth and the percent change.
Fig. 3: Time evolution of the 0–2,000 m ocean stratification changes.
Fig. 4: Spatial patterns of the 0–2,000 m stratification (N2) trends and the contributions from temperature and salinity.

Data availability

The data are available in the following links. IAP (; NCEI (; EN4 (; Ishii (; and ORAS4 ( For SST: ERSSTv.5 (; COBE2 (; and HadSST3 ( Also, data are available from the corresponding author on reasonable request. Raw figures and data are available from and Source data are provided with this paper.

Code availability

The source codes used to make the calculations and plots in this paper are available at and from the corresponding author on request.


  1. 1.

    Rhein, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 215–315 (IPCC, Cambridge Univ. Press, 2013).

  2. 2.

    de Lavergne, C., Palter, J. B., Galbraith, E. D., Bernardello, R. & Marinov, I. Cessation of deep convection in the open Southern Ocean under anthropogenic climate change. Nat. Clim. Change 4, 278–282 (2014).

    Article  Google Scholar 

  3. 3.

    Balaguru, K., Foltz, G. R., Leung, L. R. & Emanuel, K. A. Global warming-induced upper-ocean freshening and the intensification of super typhoons. Nat. Commun. 7, 13670 (2016).

    CAS  Article  Google Scholar 

  4. 4.

    DeVries, T., Holzer, M. & Primeau, F. Recent increase in oceanic carbon uptake driven by weaker upper-ocean overturning. Nature 542, 215–218 (2017).

    CAS  Article  Google Scholar 

  5. 5.

    Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, eaam7240 (2018).

    Article  CAS  Google Scholar 

  6. 6.

    Keeling, R. F., Körtzinger, A. & Gruber, N. Ocean deoxygenation in a warming world. Annu. Rev. Mar. Sci. 2, 199–229 (2010).

    Article  Google Scholar 

  7. 7.

    Fu, W., Randerson, J. T. & Moore, J. K. Climate change impacts on net primary production (NPP) and export production (EP) regulated by increasing stratification and phytoplankton community structure in the CMIP5 models. Biogeosciences 13, 5151–5170 (2016).

    Article  Google Scholar 

  8. 8.

    Durack, P. J. Ocean salinity and the global water cycle. Oceanography 28, 20–31 (2015).

    Article  Google Scholar 

  9. 9.

    Cheng, L., Abraham, J. P., Hausfather, Z. & Trenberth, K. E. How fast are the oceans warming? Science 363, 128–129 (2019).

    CAS  Article  Google Scholar 

  10. 10.

    Bindoff, N. et al. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) Ch. 5 (IPCC, 2019).

  11. 11.

    Yamaguchi, R. & Suga, T. Trend and variability in global upper-ocean stratification since the 1960s. J. Geophys. Res. Oceans 124, 8933–8948 (2019).

    Article  Google Scholar 

  12. 12.

    Abraham, J. P. et al. A review of global ocean temperature observations: implications for ocean heat content estimates and climate change. Rev. Geophys. 51, 450–483 (2013).

    Article  Google Scholar 

  13. 13.

    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  Article  Google Scholar 

  14. 14.

    Cheng, L. et al. Improved estimates of ocean heat content from 1960 to 2015. Sci. Adv. 3, e1601545 (2017).

    Article  Google Scholar 

  15. 15.

    Cheng, L. et al. Improved estimates of changes in upper ocean salinity and the hydrological cycle. J. Clim. (2020).

  16. 16.

    Rahmstorf, S. et al. Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation. Nat. Clim. Change 5, 475–480 (2015).

    Article  Google Scholar 

  17. 17.

    Gleckler, P. J., Durack, P. J., Stouffer, R. J., Johnson, G. C. & Forest, C. E. Industrial-era global ocean heat uptake doubles in recent decades. Nat. Clim. Change 6, 394–398 (2016).

    Article  Google Scholar 

  18. 18.

    Durack, P. J. & Wijffels, S. E. Fifty-year trends in global ocean salinities and their relationship to broad-scale warming. J. Clim. 23, 4342–4362 (2010).

    Article  Google Scholar 

  19. 19.

    Tokarska, K. B., Hegerl, G. C., Schurer, A. P., Ribes, A. & Fasullo, J. T. Quantifying human contributions to past and future ocean warming and thermosteric sea level rise. Environ. Res. Lett. 14, 074020 (2019).

    CAS  Article  Google Scholar 

  20. 20.

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

    Article  Google Scholar 

  21. 21.

    Levitus, S. et al. World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010. Geophys. Res. Lett. (2012).

  22. 22.

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

    Article  Google Scholar 

  23. 23.

    Ishii, M. & Kimoto, M. Reevaluation of historical ocean heat content variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr. 65, 287–299 (2009).

    Article  Google Scholar 

  24. 24.

    Durack, P. J., Gleckler, P. J., Landerer, F. W. & Taylor, K. E. Quantifying underestimates of long-term upper-ocean warming. Nat. Clim. Change 4, 999–1005 (2014).

    Article  Google Scholar 

  25. 25.

    Trenberth, K. E. The definition of El Niño. Bull. Am. Meteorol. Soc. 78, 2771–2778 (1997).

    Article  Google Scholar 

  26. 26.

    Zheng, F., Zhang, R. & Zhu, J. Effects of interannual salinity variability on the barrier layer in the western-central equatorial Pacific: a diagnostic analysis from Argo. Adv. Atmos. Sci. 31, 532–542 (2014).

    Article  Google Scholar 

  27. 27.

    Qu, T., Song, Y. T. & Maes, C. Sea surface salinity and barrier layer variability in the equatorial Pacific as seen from Aquarius and Argo. J. Geophys. Res. Oceans 119, 15–29 (2014).

    Article  Google Scholar 

  28. 28.

    AchutaRao, K. M. et al. Simulated and observed variability in ocean temperature and heat content. Proc. Natl Acad. Sci. USA 104, 10768–10773 (2007).

    CAS  Article  Google Scholar 

  29. 29.

    Chen, X. & Tung, K. K. Global surface warming enhanced by weak Atlantic overturning circulation. Nature 559, 387–391 (2018).

    CAS  Article  Google Scholar 

  30. 30.

    Santer, B. D. et al. Causes of differences in model and satellite tropospheric warming rates. Nat. Geosci. 10, 478–485 (2017).

    CAS  Article  Google Scholar 

  31. 31.

    Li, X., Xie, S. P., Gille, S. T. & Yoo, C. Atlantic-induced pan-tropical climate change over the past three decades. Nat. Clim. Change 6, 275–279 (2016).

    Article  Google Scholar 

  32. 32.

    Meehl, G. A., Hu, A., Santer, B. D. & Xie, S. P. Contribution of the Interdecadal Pacific Oscillation to twentieth-century global surface temperature trends. Nat. Clim. Change 6, 1005–1008 (2016).

    Article  Google Scholar 

  33. 33.

    Trenberth, K. E. Has there been a hiatus? Science 349, 691–692 (2015).

    CAS  Article  Google Scholar 

  34. 34.

    Kosaka, Y. & Xie, S. P. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501, 403–407 (2013).

    CAS  Article  Google Scholar 

  35. 35.

    Tokinaga, H. et al. Regional patterns of tropical Indo-Pacific climate change: evidence of the Walker Circulation weakening. J. Clim. 25, 1689–1710 (2011).

    Article  Google Scholar 

  36. 36.

    Shi, J. R., Xie, S. P. & Talley, L. D. Evolving relative importance of the Southern Ocean and North Atlantic in anthropogenic ocean heat uptake. J. Clim. 31, 7459–7479 (2018).

    Article  Google Scholar 

  37. 37.

    Du, Y. et al. Decadal trends of the upper ocean salinity in the tropical Indo-Pacific since mid-1990s. Sci. Rep. 5, 16050 (2015).

    CAS  Article  Google Scholar 

  38. 38.

    Haine, T. W. N. et al. Arctic freshwater export: status, mechanisms, and prospects. Glob. Planet. Change 125, 13–35 (2015).

    Article  Google Scholar 

  39. 39.

    Carmack, E. C. et al. Freshwater and its role in the arctic marine system: sources, disposition, storage, export, and physical and biogeochemical consequences in the Arctic and global oceans. J. Geophys. Res. Biogeosci. 121, 675–717 (2016).

    CAS  Article  Google Scholar 

  40. 40.

    Swart, N. C., Gille, S. T., Fyfe, J. C. & Gillett, N. P. Recent Southern Ocean warming and freshening driven by greenhouse gas emissions and ozone depletion. Nat. Geosci. 11, 836–841 (2018).

    CAS  Article  Google Scholar 

  41. 41.

    Purkey, S. G. & Johnson, G. C. Antarctic bottom water warming and freshening: contributions to sea level rise, ocean freshwater budgets, and global heat gain. J. Clim. 26, 6105–6122 (2013).

    Article  Google Scholar 

  42. 42.

    Haumann, F. A., Gruber, N., Münnich, M., Frenger, I. & Kern, S. Sea-ice transport driving Southern Ocean salinity and its recent trends. Nature 537, 89–92 (2016).

    CAS  Article  Google Scholar 

  43. 43.

    Boyce, D. G., Lewis, M. R. & Worm, B. Global phytoplankton decline over the past century. Nature 466, 591–596 (2010).

    CAS  Article  Google Scholar 

  44. 44.

    Schmidtko, S., Stramma, L. & Visbeck, M. Decline in global oceanic oxygen content during the past five decades. Nature 542, 335–339 (2017).

    CAS  Article  Google Scholar 

  45. 45.

    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. Oceans 117, C04031 (2012).

    Article  Google Scholar 

  46. 46.

    Li, S. et al. The Pacific Decadal Oscillation less predictable under greenhouse warming. Nat. Clim. Change 10, 30–34 (2019).

    Article  Google Scholar 

  47. 47.

    Kuhlbrodt, T. & Gregory, J. M. Ocean heat uptake and its consequences for the magnitude of sea level rise and climate change. Geophys. Res. Lett. (2012).

  48. 48.

    Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds. Stocker, T. F. et al.) 1029–1136 (IPCC, Cambridge Univ. Press, 2013).

  49. 49.

    Ishii, M. et al. Accuracy of global upper ocean heat content estimation expected from present observational data sets. SOLA 13, 163–167 (2017).

    Article  Google Scholar 

  50. 50.

    IOC, SCOR & IAPSO The International Thermodynamic Equation of Seawater—2010: Calculation and Use of Thermodynamic Properties (UNESCO, 2010).

  51. 51.

    Huang, B. et al. Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J. Clim. 30, 8179–8205 (2017).

    Article  Google Scholar 

  52. 52.

    Hirahara, S., Ishii, M. & Fukuda, Y. Centennial-scale sea surface temperature analysis and its uncertainty. J. Clim. 27, 57–75 (2014).

    Article  Google Scholar 

  53. 53.

    Kennedy, J. J., Rayner, N. A., Smith, R. O., Parker, D. E. & Saunby, M. Reassessing biases and other uncertainties in sea surface temperature observations measured in situ since 1850: 1. Measurement and sampling uncertainties. J. Geophys. Res. Atmos. (2011).

  54. 54.

    Kennedy, J. J., Rayner, N. A., Smith, R. O., Parker, D. E. & Saunby, M. Reassessing biases and other uncertainties in sea surface temperature observations measured in situ since 1850: 2. Biases and homogenization. J. Geophys. Res. Atmos. (2011).

  55. 55.

    Reiniger, R. F. & Ross, C. K. A method of interpolation with application to oceanographic data. Deep Sea Res. 15, 185–193 (1968).

    Google Scholar 

  56. 56.

    Cheng, L. & Zhu, J. Benefits of CMIP5 multimodel ensemble in reconstructing historical ocean subsurface temperature variations. J. Clim. 29, 5393–5416 (2016).

    Article  Google Scholar 

  57. 57.

    Cheng, L. & Zhu, J. Uncertainties of the ocean heat content estimation induced by insufficient vertical resolution of historical ocean subsurface observations. J. Atmos. Ocean. Technol. 31, 1383–1396 (2014).

    Article  Google Scholar 

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This study is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDB42040402), National Key R&D Program of China (grant no. 2017YFA0603202) and Key Deployment Project of Centre for Ocean Mega-Research of Science, CAS (grant no. COMS2019Q01). The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Author information




L.C. initiated and conceptualized this study and performed the subsample test. G.L. performed all the stratification analyses. All authors contributed to the figure generation, interpretation of results and the preparation of the manuscript.

Corresponding authors

Correspondence to Lijing Cheng or Jiang Zhu.

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Extended data

Extended Data Fig. 1 Climatological means and long-term linear trends of ocean temperature and salinity.

a, Climatological mean and b, linear trend of zonal mean potential temperature. c, Climatological mean and d, linear trend of zonal mean absolute salinity. The stippled areas in b and d denote the signals significant at 90% confidence level.

Extended Data Fig. 2 Temperature anomaly time series and linear trends at surface and 200 m from 1960 to 2018 based on multiple datasets.

a, Time series and b, linear trends of sea surface temperature change. c, Time series and d, linear trends of 200 m temperature change. All time series are relative to a 1981–2010 baseline. Error bar in b and d denotes the 90% confidence interval of linear trend.

Extended Data Fig. 3 Spatial patterns of linear trend in the annual SST for different datasets from the 1971 to 2010.

a is the observational mean based on three independent SST products, including ERSST, COBE2, and HadSST3, b is IAP, c is EN4, and d is NCEI data. The stippled areas in ad denote the signals significant at 90% confidence level.

Extended Data Fig. 4 Annual mean vertical resolution at depths for all in situ temperature and salinity observations within 0–2000 m from 1960 to 2018.

Annual mean vertical resolution at depths for all in situ temperature a and salinity b observations within 0–2000 m from 1960 to 2018.

Extended Data Fig. 5 A schematic of the “vertical subsample test”.

This test is used to quantify the impacts of vertical resolution in ocean profile observation and vertical interpolation methods on the gridded product (IAP gap-filling method). Temperature and salinity data are processed with the same method.

Extended Data Fig. 6 Trends and per cent changes in global mean N2 vertical sampling errors from 1960 to 2018 for three interpolation methods.

a, Linear trends of N2 bias at each depth from surface to 2000m with an interval of 20 m at upper 500 m (100 m below 500 m) (same as Fig. 2a); b is same as a but for percentages of long-term changes relative to the 1981–2010 average of global mean N2. Three interpolation methods are included: Reiniger-Ross, Spline, and Linear interpolation. The dotted lines denote the observed N2 estimates. The shadings are 90% confidence intervals from 5000 realizations.

Extended Data Fig. 7 Global 0–2000 m mean N2 vertical sampling errors (VSE) associated with different vertical interpolation methods.

a for Reiniger-Ross (RR) method, b for spline interpolation and c for linear interpolation. d, Relative error in N2 changes with three different interpolation methods based on 5000 realizations. The VSE for different T/S high-resolution climatology fields subsampled by historical observation locations are shown as dots, with the solid line and the error bar for the median and 90% confidence interval (CI), respectively. The sticks in (d) denote the [5%, 95%] CI of the linear trends based on all realizations using Monte Carlo approach. The fitted Gaussian distribution is included for comparison in (d).

Extended Data Fig. 8 Annual mean 0–2000 N2 anomaly compared with the Niño 3.4 index.

a for the Global (Glb) and Pacific (Pac) Ocean, b for Atlantic (Atl), Indian (Ind) and Southern (So) oceans. For N2 time series, a high-pass filter with cut-off frequency of 1/102 (period of 8.5 years) is applied. To gain better illustration of interannual variability, all the time series are smoothed by a 13-month running smoother17 weighted by (1, 6, 19, 42, 71, 96, 106, 96, 71, 42, 19, 6, 1)/576. The correlation between Niño 3.4 index (shading) and N2 time series (solid lines) are provided at zero lag and * sign means it is statistically significant at the 90% confidence level. Niño3.4 index is obtained from the National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA/CPC) (

Extended Data Fig. 9 Results of decomposing analysis of 0–2000 m mean Ν2 change.

a, Spatial pattern of residual term (Res). b, Basin-mean linear trends of the observed change and its contributors (caused by the temperature, salinity and Res changes). The Res is calculated by the difference between the observed linear trends in N2 (see in Fig. 4a) and the sum of temperature and salinity contributions (see Fig. 4b, c). The dot and the error bar in panel (b) denote the median and 90% confidence interval, respectively.

Extended Data Fig. 10 Ensemble members of 0–2000 m mean Ν2 time series and frequency distribution of their trends.

a, 5000 realizations of global mean N2 time series and its ensemble median. b, Distribution of the estimated per cent changes of N2 from these 5000 realizations, with their ensemble median and 90% confidence interval (CI) shown in dashed red line and pink shading, respectively. The per cent changes are relative to 1981–2010 climatological N2. The fitted Gaussian distribution is included for comparison. The blue bar indicates the estimate (median and 90% CI) when only VES are taken into account in creating 5000 realizations; the green bar indicates the results when only horizonal and instrumental errors are taken into account.

Supplementary information

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Supplementary discussion, Figs. 1–3 and Tables 1–4.

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Statistical source data.

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Li, G., Cheng, L., Zhu, J. et al. Increasing ocean stratification over the past half-century. Nat. Clim. Chang. (2020).

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