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Recent recovery of Antarctic Bottom Water formation in the Ross Sea driven by climate anomalies

Abstract

Antarctic Bottom Water (AABW) supplies the lower limb of the global overturning circulation, ventilates the abyssal ocean and sequesters heat and carbon on multidecadal to millennial timescales. AABW originates on the Antarctic continental shelf, where strong winter cooling and brine released during sea ice formation produce Dense Shelf Water, which sinks to the deep ocean. The salinity, density and volume of AABW have decreased over the last 50 years, with the most marked changes observed in the Ross Sea. These changes have been attributed to increased melting of the Antarctic Ice Sheet. Here we use in situ observations to document a recovery in the salinity, density and thickness (that is, depth range) of AABW formed in the Ross Sea, with properties in 2018–2019 similar to those observed in the 1990s. The recovery was caused by increased sea ice formation on the continental shelf. Increased sea ice formation was triggered by anomalous wind forcing associated with the unusual combination of positive Southern Annular Mode and extreme El Niño conditions between 2015 and 2018. Our study highlights the sensitivity of AABW formation to remote forcing and shows that climate anomalies can drive episodic increases in local sea ice formation that counter the tendency for increased ice-sheet melt to reduce AABW formation.

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Fig. 1: Recovery of AABW formed in the Ross Sea.
Fig. 2: Link between DSW salinity, sea ice production and import.
Fig. 3: Sea ice changes in the Ross Sea.
Fig. 4: Interannual variability of winds and sea ice in the western Amundsen Sea.
Fig. 5: Anomalous climate forcing between 2015 and 2018.
Fig. 6: Schematic illustrating the physical mechanisms driving enhanced AABW formation in the Ross Sea.

Data availability

Oceanographic data were collected and made publicly available by the International Global Ship-based Hydrographic Investigations Program (http://www.go-ship.org/), by the National Oceanic and Atmospheric Administration (NOAA) (https://www.nodc.noaa.gov), by the International Argo Program and the national programmes that contribute to it (http://www.argodatamgt.org; http://www.argo.ucsd.edu; http://argo.jcommops.org), by the Italian National Antarctic Research Programme (http://morsea.uniparthenope.it) and during Nathaniel B. Palmer expeditions in 2013 and 2018 as part of TRACERS (https://doi.org/10.1594/IEDA/320068) and CICLOPS (https://www.bco-dmo.org/dataset/783911). Atmospheric data are provided by the ECMWF ERA5 reanalysis (https://go.nature.com/2J6PvuY). The National Snow and Ice Data Center provides the satellite-derived data used in this study: sea ice concentration and brightness temperature (https://nsidc.org/data/G02202/versions/3; https://nsidc.org/data/AU_SI12/versions/1; https://nsidc.org/data/smmr_ssmi) and sea ice motion (https://nsidc.org/data/NSIDC-0116/versions/4).

Climate indices are provided by the British Antarctic Survey (SAM: https://legacy.bas.ac.uk/met/gjma/sam.html) and by the National Centre for Atmospheric Research (SOI: https://go.nature.com/2Tz3TOJ).

Code availability

MATLAB scripts used for the analyses described in this study can be obtained from the corresponding author on reasonable request.

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Acknowledgements

This research was supported by the Centre for Southern Hemisphere Oceans Research (CSHOR), a partnership between the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the Qingdao National Laboratory for Marine Science, as well as the Antarctic Climate and Ecosystems Cooperative Research Centre, the Australian Antarctic Program Partnership and the Earth Systems and Climate Change Hub of the Australian Government’s National Environmental Science Program. A.S., P.R.H. and A.C.N.G. acknowledge funding from NERC (grant number NE/SO11994/1). F.A.H. was supported by SNSF grant numbers P2EZP2_175162 and P400P2_186681 and NSF grant number PLR-1425989.

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Contributions

A.S. and S.R.R. conceived the study. A.S., A.F. and S.R.R. analysed the oceanographic data. S.R.R., P.C., P.F., G.B. and A.M.M. collected part of the oceanographic data used in this study. A.S., P.R.H., T.T. and N.K. analysed the sea ice and atmospheric data. F.A.H. and A.C.N.G. provided essential insight in the interpretation of the observations. A.S. wrote the manuscript with input from all the coauthors.

Corresponding author

Correspondence to Alessandro Silvano.

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Extended data

Extended Data Fig. 1 Easterly winds at the shelf break of the western Ross Sea.

Following Gordon et al.19, winds are obtained by rotating the coordinate system clockwise by 40° to follow shelf break isobaths and are spatially averaged in the box 70S°-75°S, 175°E-175°W. Monthly data are in black while data temporally averaged between February and April are in red. The ‘Feb-Apr winds’ are shown because the strongest wind-driven export of DSW from the western Ross Sea continental shelf occurs in these months19.

Extended Data Fig. 2 Salinification of dense waters on the eastern Ross Sea continental shelf.

a, Time series of absolute salinity (g kg-1) collected by a profiling float (WMO: 5904150) between late 2013 and early 2017 (map in c). b, same as a for conservative temperature (°C). d, e same a, b for another profiling float (WMO: 5904152) between late 2013 and early 2018. f, Time series of salinity averaged in the bottom 200 m of the water column for water cooler than -1.85 °C for float 5904150 (red) and 5904152 (black).

Extended Data Fig. 3 Link between DSW salinity and sea ice production.

a, Yearly sea ice formation (km 3) over the entire Ross Sea continental shelf (solid black), Ross Ice Shelf (red) and Terra Nova Bay (blue) polynyas. Polynyas are defined as areas where yearly sea ice formation is larger than 5 m (see Fig. 3). Solid green is DSW absolute salinity (g kg-1) in Terra Nova Bay17. b, Same as a for anomalies. c, Correlation between DSW salinity and yearly sea ice formation (colors the same as a, dashed black line represents the 99% confidence interval). Sea ice production leads DSW salinity.

Extended Data Fig. 4 Sea ice motion and fluxes.

Winter climatology of a, winds (m s-1) from ERA5, b, sea ice motion (m s-1) from Pathfinder and c, sea ice motion from the Kimura dataset. d, Winter sea ice area inflow (105 km2) into the southern Ross Sea through the meridional transect shown in red in a. Estimates are obtained using the wind-approximation for sea ice motion and CDR for sea ice concentration (green), Pathfinder for sea ice motion and CDR for sea ice concentration (blue), and the Kimura dataset for sea ice motion and AMSR-E/AMSR2 for sea ice concentration (red). The three estimates agree reasonably well with each other. e, Winter sea ice area flux through a transect that encloses the Ross Sea continental shelf (green line in a). The dashed (dotted) lines indicate sea ice flowing into (out of) the Ross Sea continental shelf. The net (solid) is a proxy for how much sea ice forms on the continental shelf. Note how the wind approximation fails in representing the export, but it well captures the inflow.

Extended Data Fig. 5 Link between sea ice formation and import.

a, In blue is the yearly (November to October) anomaly of sea ice area (105 km2) imported from the Amundsen Sea into the Ross Sea between 1992 and 2018, calculated across the gate shown in red in Extended Data Fig. 4a. Note that the y-axis is reversed. Overlaid in black is the yearly (March to October) anomaly of sea ice production (km3) over the Ross Sea continental shelf (as shown in Extended Data Fig. 3b). b, Summer (November to February) anomaly of imported sea ice area and early winter (March to May) sea ice production anomaly. c, Winter (March to October) anomaly of imported sea ice area and sea ice production anomaly in late winter (June to October). Correlations between time series shown in each panel are in red.

Extended Data Fig. 6 Ross Sea continental shelf spatial averages.

a, Southerly winds (m s-1). Monthly data are in black, while values temporally averaged over summer and winter months are in red and blue, respectively. b, Surface air temperature (°C). Black and red lines as in a. c, Sea ice area (105 km2) lost on the Ross Sea continental shelf due to sea ice melting (see sea ice concentration budget in the Methods). The values are obtained from the time integration of the residual term between November and February, further integrated over the continental shelf. Only negative values (which in summer is mostly due to melting) are included in the calculation.

Extended Data Fig. 7 Winter climate indices.

Anomalies of SAM (black) and SOI (red) during winter.

Extended Data Fig. 8 Wind multiple regression analysis.

As Fig. 5 but including the multiple regression of SOI and SAM onto winds, instead of showing geostrophic winds derived by the mean sea level pressure.

Extended Data Fig. 9 Sea ice concentration budget.

a (b) Winter climatology of the divergence (residual) term of the sea ice concentration budget (see Methods). Values indicate sea ice concentration change over the winter season associated with divergence/residual term. Positive values indicate a source of sea ice (that is more divergence is negative, while more sea ice formation is positive). c (d) Anomalies of the divergence (residual) term temporally averaged between 2015 and 2018. Climatologies are calculated using all available years (that is 2003-2010 and 2013-2018). Here anomalies are the deviations from the climatologies. Overlaid in a (c) are vectors representing the winter climatology (2015-2018 anomaly) of sea ice drift (‘Kimura’23).

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Silvano, A., Foppert, A., Rintoul, S.R. et al. Recent recovery of Antarctic Bottom Water formation in the Ross Sea driven by climate anomalies. Nat. Geosci. 13, 780–786 (2020). https://doi.org/10.1038/s41561-020-00655-3

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