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.
Subscribe to Journal
Get full journal access for 1 year
only $17.42 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The data are available in the following links. IAP (http://188.8.131.52/cheng/); NCEI (https://www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT/); EN4 (https://www.metoffice.gov.uk/hadobs/en4/download-en4-2-1.html); Ishii (https://climate.mri-jma.go.jp/pub/ocean/ts/); and ORAS4 (http://apdrc.soest.hawaii.edu/datadoc/ecmwf_oras4.php). For SST: ERSSTv.5 (https://www1.ncdc.noaa.gov/pub/data/cmb/ersst/v5/netcdf/); COBE2 (https://psl.noaa.gov/data/gridded/data.cobe2.html); and HadSST3 (https://www.metoffice.gov.uk/hadobs/hadsst3/data/download.html). Also, data are available from the corresponding author on reasonable request. Raw figures and data are available from http://184.108.40.206/cheng/ and https://doi.org/10.6084/m9.figshare.12771116. Source data are provided with this paper.
The source codes used to make the calculations and plots in this paper are available at http://220.127.116.11/cheng/ and from the corresponding author on request.
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).
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).
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).
DeVries, T., Holzer, M. & Primeau, F. Recent increase in oceanic carbon uptake driven by weaker upper-ocean overturning. Nature 542, 215–218 (2017).
Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, eaam7240 (2018).
Keeling, R. F., Körtzinger, A. & Gruber, N. Ocean deoxygenation in a warming world. Annu. Rev. Mar. Sci. 2, 199–229 (2010).
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).
Durack, P. J. Ocean salinity and the global water cycle. Oceanography 28, 20–31 (2015).
Cheng, L., Abraham, J. P., Hausfather, Z. & Trenberth, K. E. How fast are the oceans warming? Science 363, 128–129 (2019).
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).
Yamaguchi, R. & Suga, T. Trend and variability in global upper-ocean stratification since the 1960s. J. Geophys. Res. Oceans 124, 8933–8948 (2019).
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).
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).
Cheng, L. et al. Improved estimates of ocean heat content from 1960 to 2015. Sci. Adv. 3, e1601545 (2017).
Cheng, L. et al. Improved estimates of changes in upper ocean salinity and the hydrological cycle. J. Clim. https://doi.org/10.1175/JCLI-D-20-0366.1 (2020).
Rahmstorf, S. et al. Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation. Nat. Clim. Change 5, 475–480 (2015).
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).
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).
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).
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).
Levitus, S. et al. World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010. Geophys. Res. Lett. https://doi.org/10.1029/2012GL051106 (2012).
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).
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).
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).
Trenberth, K. E. The definition of El Niño. Bull. Am. Meteorol. Soc. 78, 2771–2778 (1997).
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).
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).
AchutaRao, K. M. et al. Simulated and observed variability in ocean temperature and heat content. Proc. Natl Acad. Sci. USA 104, 10768–10773 (2007).
Chen, X. & Tung, K. K. Global surface warming enhanced by weak Atlantic overturning circulation. Nature 559, 387–391 (2018).
Santer, B. D. et al. Causes of differences in model and satellite tropospheric warming rates. Nat. Geosci. 10, 478–485 (2017).
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).
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).
Trenberth, K. E. Has there been a hiatus? Science 349, 691–692 (2015).
Kosaka, Y. & Xie, S. P. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501, 403–407 (2013).
Tokinaga, H. et al. Regional patterns of tropical Indo-Pacific climate change: evidence of the Walker Circulation weakening. J. Clim. 25, 1689–1710 (2011).
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).
Du, Y. et al. Decadal trends of the upper ocean salinity in the tropical Indo-Pacific since mid-1990s. Sci. Rep. 5, 16050 (2015).
Haine, T. W. N. et al. Arctic freshwater export: status, mechanisms, and prospects. Glob. Planet. Change 125, 13–35 (2015).
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).
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).
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).
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).
Boyce, D. G., Lewis, M. R. & Worm, B. Global phytoplankton decline over the past century. Nature 466, 591–596 (2010).
Schmidtko, S., Stramma, L. & Visbeck, M. Decline in global oceanic oxygen content during the past five decades. Nature 542, 335–339 (2017).
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).
Li, S. et al. The Pacific Decadal Oscillation less predictable under greenhouse warming. Nat. Clim. Change 10, 30–34 (2019).
Kuhlbrodt, T. & Gregory, J. M. Ocean heat uptake and its consequences for the magnitude of sea level rise and climate change. Geophys. Res. Lett. https://doi.org/10.1029/2012GL052952 (2012).
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).
Ishii, M. et al. Accuracy of global upper ocean heat content estimation expected from present observational data sets. SOLA 13, 163–167 (2017).
IOC, SCOR & IAPSO The International Thermodynamic Equation of Seawater—2010: Calculation and Use of Thermodynamic Properties (UNESCO, 2010).
Huang, B. et al. Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J. Clim. 30, 8179–8205 (2017).
Hirahara, S., Ishii, M. & Fukuda, Y. Centennial-scale sea surface temperature analysis and its uncertainty. J. Clim. 27, 57–75 (2014).
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. https://doi.org/10.1029/2010JD015218 (2011).
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. https://doi.org/10.1029/2010JD015220 (2011).
Reiniger, R. F. & Ross, C. K. A method of interpolation with application to oceanographic data. Deep Sea Res. 15, 185–193 (1968).
Cheng, L. & Zhu, J. Benefits of CMIP5 multimodel ensemble in reconstructing historical ocean subsurface temperature variations. J. Clim. 29, 5393–5416 (2016).
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).
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.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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 a–d 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.
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).
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) (https://psl.noaa.gov/data/correlation/nina34.data).
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.
About this article
Cite this article
Li, G., Cheng, L., Zhu, J. et al. Increasing ocean stratification over the past half-century. Nat. Clim. Chang. (2020). https://doi.org/10.1038/s41558-020-00918-2