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Importance of wind and meltwater for observed chemical and physical changes in the Southern Ocean

Nature Geoscience volume 13, pages 35–42 (2020)Cite this article

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Abstract

The Southern Ocean south of 30° S represents only one-third of the total ocean area, yet absorbs half of the total ocean anthropogenic carbon and over two-thirds of ocean anthropogenic heat. In the past, the Southern Ocean has also been one of the most sparsely measured regions of the global ocean. Here we use pre-2005 ocean shipboard measurements alongside novel observations from autonomous floats with biogeochemical sensors to calculate changes in Southern Ocean temperature, salinity, pH and concentrations of nitrate, dissolved inorganic carbon and oxygen over two decades. We find local warming of over 3 °C, salinification of over 0.2 psu near the Antarctic coast, and isopycnals are found to deepen between 65° and 40° S. We find deoxygenation along the Antarctic coast, but reduced deoxygenation and nitrate concentrations where isopycnals deepen farther north. The forced response of the Earth system model ESM2M does not reproduce the observed patterns. Accounting for meltwater and poleward-intensifying winds in ESM2M improves reproduction of the observed large-scale changes, demonstrating the importance of recent changes in wind and meltwater. Future Southern Ocean biogeochemical changes are likely to be influenced by the relative strength of meltwater input and poleward-intensifying winds. The combined effect could lead to increased Southern Ocean deoxygenation and nutrient accumulation, starving the global ocean of nutrients sooner than otherwise expected.

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Fig. 1: Observed physical changes.
Fig. 2: Observed biogeochemical changes.
Fig. 3: SS decadal changes.
Fig. 4: WMS decadal changes.
Fig. 5: Ocean ventilation changes.
Fig. 6: Future impact of wind and meltwater.

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Data availability

The SOCCOM data are available as a 12 March 2019 quality-controlled snapshot at https://doi.org/10.6075/J01G0JKT. The results from the standard, wind, meltwater and wind–meltwater RCP 8.5 simulations are freely available from the corresponding author. Shipboard data are available at https://odv.awi.de/data/ocean/glodap-v2-bottle-data/ and the GLODAP synthesis product is available at https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2/. Argo data are available at http://www.Argodatamgt.org/.

Code availability

GFDL ESM2M model code is publicly available at https://github.com/mom-ocean.

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Acknowledgements

This work was sponsored by the NSF’s SOCCOM Project under NSF Award No. PLR-1425989, with additional support from the NOAA and NASA. N.L.W. was supported by NOAA PMEL through the NRC post-doctoral programme and this is PMEL contribution no. 4834. Data were collected and made freely available by the SOCCOM Project funded by the National Science Foundation, Division of Polar Programs (NSF Award No. PLR -1425989), supplemented by NASA, and by the International Argo Program and the NOAA programmes that contribute to it (http://www.Argo.ucsd.edu, http://Argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System. We thank the SOCCOM, Argo and GLODAP teams for making the data available online.

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Authors and Affiliations

  1. Department of Geosciences, University of Arizona, Tucson, AR, USA

    Ben Bronselaer & Joellen L. Russell

  2. Geophysical Fluid Dynamics Laboratory (NOAA), Princeton University Forrestal Campus, Princeton, NJ, USA

    Ben Bronselaer, Michael Winton & John P. Dunne

  3. Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA

    Ben Bronselaer, Robert M. Key & Jorge L. Sarmiento

  4. College of Marine Science, University of South Florida, Saint Petersburg, FL, USA

    Nancy L. Williams

  5. Pacific Marine Environmental Laboratory (NOAA), Seattle, WA, USA

    Richard A. Feely

  6. Monterey Bay Aquarium Research Institute, Moss Landing, CA, USA

    Kenneth S. Johnson

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Contributions

B.B. performed the simulations with help from J.L.R. and M.W. B.B., J.L.R. and M.W. analysed the data with help from N.L.W., R.M.K., R.A.F., K.S.J., J.L.S. (for observations) and J.P.D. (for simulations). All authors wrote the manuscript.

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Correspondence to Ben Bronselaer.

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Extended data

Extended Data Fig. 1 Argo and SOCCOM anomalies.

onal mean differences in a, temperature, b, salinity, and c, density between 2014–2019 combined Argo and SOCCOM data, and pre-2005 shipboard data. The comparison is only shown where there is overlap between both datasets on an 8x3◦ horizontal grid. Solid and dashed lines show zonal mean shipboard and Argo/SOCCOM isopycnal surfaces respectively. Grey shows areas with insufficient data. Panel d shows the number of grid boxes per 3 degrees of latitude covered by the SOCCOM data only and by the Argo+SOCCOM data in the shipboard comparison. The stippling indicates where the differences are not significant at the 90% level. Panels e and f show the upper 1000 m difference between Argo+SOCCOM and pre-2005 shipboard data for temperature and salinity respectively. The stippling indicates where the differences are not significant at the 90% level.

Extended Data Fig. 2 Spatial coverage of data.

upper 1000 m difference between SOCCOM and pre-2005 shipboard data for a, temperature, b, salinity, c, oxygen, d, dissolved inorganic carbon (DIC), e, nitrate and f, pH. All data was regridded onto a 8° × 3° horizontal grid prior to calculating differences. The stippling indicates where the differences are not significant at the 90% level.

Extended Data Fig. 3 Estimated anthropogenic and dynamical DIC changes.

Panels a, c and d show the anthropogenic component of DIC changes, for the observations, standard ESM2M simulation and wind-meltwater simulation, respectively. The anthropogenic DIC change is calculated by subtracting 117/16 × dNO from the total change in DIC, where dNO is the change in Nitrate and 117/16 is the Redfield Ratio between carbon and nitrogen. Panels b, e and f show the dynamically-induced DIC changes, for the observations, standard ESM2M simulation and wind- meltwater simulation, respectively. The dynamically induced changes are calculated as 117/16 × dNO. We choose to use nitrate for this calculation since it less influenced by temperature than oxygen, and the simulated nitrate changes are inconsistent with the changes in surface biological productivity, meaning they are dominated by dynamics. A simple 2-degree smoothing has been applied to facilitate interpretation of large-scale patterns.

Extended Data Fig. 4 Meltwater-only scenario simulated decadal changes.

5-member ensemble mean of the zonal mean differences in a, temperature, b, salinity, c, oxygen, d, dissolved inorganic carbon (DIC), e, nitrate and f, pH between years 2014-2019 of the meltwater-only RCP8.5 ESM2M simulation and years 1985–2005 of the standard simulation. Solid and dashed lines show zonal mean 1985–2005 and 2014–2019 isopycnal surfaces respectively. The stippling indicates where the differences are not significant at the 90% level (see Methods). Here, the model is not sub-sampled to show the whole Southern Ocean response to the added meltwater.

Extended Data Fig. 5 5×WM scenario simulated decadal changes.

5-member ensemble mean of the zonal mean differences in a, temperature, b, salinity, c, oxygen, d, dissolved inorganic carbon (DIC), e, nitrate and f, pH between years 2014-2019 of the 0.2×WMRCP8.5 ESM2M simulation and years 1985–2005 of the standard simulation, sub-sampled on the same grid as the observations. Solid and dashed lines show zonal mean 1985–2005 and 2014–2019 isopycnal surfaces respectively. The grey error bars indicate the range of uncertainty in isopycnal depths, as diagnosed from 75 20-years periods of a 1500-year pre-industrial ESM2M control simulation. The stippling indicates where the differences are significant at the 90% level. R-values in the bottom right corners of the panels give the correlation coefficient with the observed zonal mean changes in Figs. 1 and 2.

Extended Data Fig. 6 Observed oxygen changes.

zonal mean difference in oxygen due to a, changes in oxygen saturation (temperature-induced solubility change) and b, changes in ventilation and surface biological activity. The change due to ventilation and surface biology is calculated as the difference between the observed change in oxygen and the change in oxygen saturation.

Extended Data Fig. 7 Vertical profile of anomalies.

Change in a, salinity, b, temperature, c, oxygen, d, dissolved inorganic carbon (DIC), e, nitrate and f, pH between the periods 2014-2019 and 1985- 2005 averaged over the area South of 60◦S. The double black line shows the observed anomaly, the thin black line shows the 5-ensemble-member mean standard ESM2M simulation, the dotted blue line shows the ESM2M with only meltwater added, the dashed blue line shows the ESM2M with only winds increased and the solid blue line shows the melt+wind simulation. The orange lines shows the same as blue lines, but with the milder perturbation scenarios (0.2x wind and melt). The black shaded region shows the 90% uncertainty range due to natural variability from the ESM2M 5-member ensemble. The same shading applies to all profiles shown, but has been omitted for clarity of presentation.

Extended Data Fig. 8 Wind-only scenario simulated decadal changes.

5-member ensemble mean of the zonal mean differences in a, temperature, b, salinity, c, oxygen, d, dissolved inorganic carbon (DIC), e, nitrate and f, pH between years 2014-2019 of the wind-only RCP8.5 ESM2M simulation and years 1985–2005 of the standard simulation. Solid and dashed lines show zonal mean 1985- 2005 and 2014–2019 isopycnal surfaces respectively. The stippling indicates where the differences are not significant at the 90% level (see Methods). Here, the model is not sub-sampled to show the whole Southern Ocean response to the wind perturbation.

Extended Data Fig. 9 Seasonal bias correction.

Difference between the December-May and annual means for a, temperature, b, salinity, c, oxygen, d, DIC, e, nitrate, and f, pH in the SOCCOM dataset. The colored depth-latitude panels show the zonal mean difference, and the depth profile to the right of each colored panel show the corresponding zonal and meridional mean.

Extended Data Fig. 10 Table ED10.

Spatial correlations of the ESM2M standard historical-RCP8.5 (Fig. 3), WM (Fig. 5), and 5×WM (supplementary Fig. ED5) ensemble scenario anomalies with the observed anomalies, as well as the 90% uncertainty in the mean based on 5 ensemble members. The bold text indicates the largest correlation for each field.

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Bronselaer, B., Russell, J.L., Winton, M. et al. Importance of wind and meltwater for observed chemical and physical changes in the Southern Ocean. Nat. Geosci. 13, 35–42 (2020). https://doi.org/10.1038/s41561-019-0502-8

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