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Antarctic offshore polynyas linked to Southern Hemisphere climate anomalies

Nature volume 570, pages 319–325 (2019)Cite this article

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Abstract

Offshore Antarctic polynyas—large openings in the winter sea ice cover—are thought to be maintained by a rapid ventilation of deep-ocean heat through convective mixing. These rare phenomena may alter abyssal properties and circulation, yet their formation mechanisms are not well understood. Here we demonstrate that concurrent upper-ocean preconditioning and meteorological perturbations are responsible for the appearance of polynyas in the Weddell Sea region of the Southern Ocean. Autonomous profiling float observations—collected in 2016 and 2017 during the largest polynyas to form near the Maud Rise seamount since 1976—reveal that the polynyas were initiated and modulated by the passage of severe storms, and that intense heat loss drove deep overturning within them. Wind-driven upwelling of record strength weakened haline stratification in the upper ocean, thus favouring destabilization in 2016 and 2017. We show that previous Weddell polynyas probably developed under similarly anomalous conditions, which are associated with a mode of Southern Hemisphere climate variability that is predicted to strengthen as a result of anthropogenic climate change.

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Fig. 1: Polynyas of 1974, 2016 and 2017 in relation to profiling float trajectories near Maud Rise.
Fig. 2: Storms, sea ice concentration and mixed-layer salinity at Maud Rise in 2016 and 2017.
Fig. 3: Local meteorology and heat loss during the 2016 polynya.
Fig. 4: Hydrographic observations from Maud Rise from 2011–2018.
Fig. 5: Relationships between past polynyas near Maud Rise and climate forcing from 1972–2018.

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Data availability

The data analysed in this article are all publicly available, with the exception of updates to the UW Calibrated O2 package, described below:

Ocean bathymetry data were obtained from the ETOPO1 1 Arc-Minute Global Relief Model114 at https://doi.org/10.7289/V5C8276M (accessed February 2017).

Sea ice concentration data were obtained for the period 1972–1977 from the NSIDC Nimbus-5 ESMR v1 product53 at https://doi.org/10.5067/W2PKTWMTY0TP (accessed February 2017); for the period 1978–2017 from the merged NASA Goddard v3 product54 at https://doi.org/10.7265/N59P2ZTG (accessed October 2018); for January 2018–February 2019 from the NOAA/NSIDC Near-Real-Time CDR v1 product56 at https://doi.org/10.7265/N5FF3QJ6 (accessed February 2019); and for the period 2002–2019 from the University of Bremen ASI AMSR-E and AMSR2 v5 products57,58 at https://seaice.uni-bremen.de/sea-ice-concentration/ (accessed February 2019).

Profiling float temperature and salinity measurements were obtained from the US-GODAE GDAC70 at http://www.usgodae.org/ftp/outgoing/argo (accessed October 2018). Dissolved oxygen measurements for floats 5904468 and 5904471 were obtained from the SOCCOM quality-controlled archive74 at https://doi.org/10.6075/J02J6968 (accessed January 2019) and for float 5903616 from the UW Calibrated O2 package, v1.175 at http://runt.ocean.washington.edu/o2 (accessed August 2016), with updated dissolved oxygen profiles provided by R. Drucker (personal communication, August 2017).

Shipboard and elephant seal temperature and salinity measurements were obtained from the World Ocean Database 2018 prerelease77 with August 2018 additions at http://www.nodc.noaa.gov/OC5/SELECT/dbsearch/dbsearch.html (accessed October 2018).

Gridded climatological ocean temperature fields were obtained from the 2018 WAGHC91 at http://icdc.cen.uni-hamburg.de/1/daten/ocean/waghc (accessed January 2018).

The monthly SAM index98 was obtained for the period 1972–2019 at http://legacy.bas.ac.uk/met/gjma/sam.html (accessed February 2019).

Monthly and daily ERA-I atmospheric reanalysis fields99 were obtained for the period 1979–2018 using the Python MARS API, described at https://confluence.ecmwf.int/display/WEBAPI/ (accessed February 2019).

Queen Maud Land pressure records (see Methods section ‘Meteorological station records’) were obtained from the READER archive110 at http://legacy.bas.ac.uk/met/READER (accessed February 2019) and the NOAA-NCEI ISD111 at http://www.ncdc.noaa.gov/isd (accessed February 2019).

Code availability

Analytical scripts used to generate the figures in this paper are available at https://github.com/ethan-campbell.

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Acknowledgements

We thank A. Wong, R. Drucker, J. Plant, T. Maurer and K. Johnson for assistance with float data calibration, and all others involved in float and sensor design, construction, calibration and deployment for their contributions. Data were collected and made freely available by the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project, which is funded by the US National Science Foundation, Division of Polar Programs (NSF PLR-1425989), supplemented by NASA, and by the International Argo Program and the NOAA programmes that contribute to it. The Argo Program is part of the Global Ocean Observing System. E.C.C. acknowledges funding from the University of Washington (UW) Program on Climate Change, ARCS Foundation, and US Department of Defense through the National Defense Science & Engineering Graduate (NDSEG) Fellowship Program. E.A.W., S.C.R., M.R.M. and L.D.T. acknowledge funding from NSF PLR-1425989, and S.C.R. from NOAA grant NA15OAR4320063. G.W.K.M. acknowledges the support of the Canada Fulbright Foundation, UW Jackson School of International Studies, and the Natural Sciences and Engineering Research Council of Canada. C.E.B. was supported by the Scripps Undergraduate Research Fellowship (SURF) programme.

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Authors and Affiliations

  1. School of Oceanography, University of Washington, Seattle, WA, USA

    Ethan C. Campbell, Earle A. Wilson & Stephen C. Riser

  2. Department of Physics, University of Toronto, Toronto, Ontario, Canada

    G. W. Kent Moore

  3. Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario, Canada

    G. W. Kent Moore

  4. School of the Earth, Ocean and Environment, University of South Carolina, Columbia, SC, USA

    Casey E. Brayton

  5. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Matthew R. Mazloff & Lynne D. Talley

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  1. Ethan C. Campbell
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  2. Earle A. Wilson
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Contributions

E.C.C., E.A.W. and S.C.R. conceived the study and E.C.C. wrote the initial manuscript. E.C.C. and E.A.W. analysed the hydrographic data and together with G.W.K.M. analysed the sea ice and reanalysis data. S.C.R. led the float design and construction and together with L.D.T. coordinated SOCCOM float deployments. All authors interpreted results and provided input to the final manuscript.

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Correspondence to Ethan C. Campbell.

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Extended data figures and tables

Extended Data Fig. 1 Locations of observations used to construct hydrographic climatologies for the Maud Rise and eastern Weddell regions.

Observations from 1970 to 2001 are shown together (top left); observations from 2002 to 2018 are represented by one panel per year. Included are float profiles from the Argo GDAC (filled circles) as well as shipboard (open squares) and instrumented seal (open triangles) casts from the World Ocean Database (see Methods section ‘Hydrographic data’). Colours indicate seasons. Bathymetric contours (intervals of 750 m) highlight Maud Rise and the Antarctic continental shelf. Concentric circles represent radii of 250 km and 500 km from Maud Rise, encompassing the Maud Rise and eastern Weddell regions, respectively (see Methods section ‘Regions’).

Extended Data Fig. 2 Sea ice concentration during the 2016 polynya.

Daily SIC from AMSR2-ASI around Maud Rise from 24 July to 17 August 2016, encompassing the main polynya event, followed by selected SIC fields from AMSR2-ASI during the late-winter 2016 polynya south of Maud Rise (bottom row; note different map area). Estimated locations of SOCCOM profiling floats 5904471 and 5904468 (see Methods section ‘Hydrographic data’) are marked in blue; a circle marker and profile number indicate that a hydrographic profile was obtained on that date. Bathymetry shallower than 3,500 m is contoured at intervals of 500 m to highlight Maud Rise (centre of July–August images) as well as Astrid Ridge (bottom right of October–November images), an extension of the Antarctic continental shelf.

Extended Data Fig. 3 Evolution of sea ice concentration, air temperature and upper ocean properties at Maud Rise in 2016 and 2017.

Marked at the top are intense winter storm events near Maud Rise, as in Fig. 2 (also see Extended Data Fig. 4 and Methods section ‘Storm identification’). a, Average daily SIC within the Maud Rise region (63°–67° S, 0°–10° E) from NSIDC Merged (solid black line) and AMSR2-ASI (dashed black line) in 2016 and 2017, as in Fig. 2a. SIC climatology from NSIDC Merged (1978–2019) is shown as median (grey line) and 25–75% interquartile range (IQR; grey shading). Note the stretched y axis. Polynya extent is quantified (blue lines) during the 2016 and 2017 events (vertical blue shading). b, Six-hourly 2-m air temperature around Maud Rise (within 63°–67° S, 0°–10° E) from ERA-I reanalysis (black line). Climatology for 1979–2018 is shown as mean (grey line) and IQR (grey shading). c, Composite of average MLD in 2016 and 2017 measured by floats 5903616, 5904468 and 5904471 (black line; see Methods sections ‘Derived oceanographic quantities’ and ‘Composites of float time series’). MLD climatology for the Maud Rise region (R < 250 km from 65° S, 3° E) is shown as median (grey line) and IQR (grey shading); climatology for the eastern Weddell region away from Maud Rise (250 < R < 500 km) is presented for comparison (light brown dashed and dotted lines for median and IQR, respectively; see Methods section ‘Hydrographic climatologies’). d, Composite of average mixed-layer potential temperature (MLT) and MLT climatology presented as in c. e, Composite of the lowest observed upper-250-m freshwater anomaly (or ‘salt deficit’; see Methods sections ‘Derived oceanographic quantities’ and ‘Composites of float time series’) and freshwater anomaly climatology for the Maud Rise region presented as in c. Note the reversed y axis. Key changes quantified, from left to right, are 2016 freeze, climatological melt, 2016 melt, anomaly from climatology in January 2017, 2017 freeze, and change between 2017 freeze and 2017 polynya appearance.

Extended Data Fig. 4 Correspondence of sea ice loss episodes and major storms near Maud Rise.

a, b, Time series are shown for 2016 (a) and 2017 (b). Average daily SIC within the Maud Rise region (63°–67° S, 0°–10° E) from NSIDC Merged (solid black line) and AMSR2-ASI (dashed black line) is presented at the top for each year, as in Fig. 2a. SIC climatology from NSIDC Merged (1978–2019) is shown as median (grey line) and 25–75% interquartile range (grey shading). Note the stretched y axis. Daily changes in SIC are presented in the centre for each year, with negative changes in NSIDC Merged highlighted (dark yellow shading); both NSIDC Merged and AMSR2-ASI time series are smoothed using a 3-day right-edge running-mean filter. At the bottom for each year are the minimum daily sea-level pressure and maximum daily 10-m wind speed near Maud Rise (within 63–67° S, 0–10° E) from ERA-I reanalysis. Mean climatological values of these minimum/maximum metrics are shown (grey lines) to highlight the lack of a pronounced seasonal cycle. The most intense winter polar lows, as shown in Figs. 2, 3, Extended Data Fig. 3, are identified here using pressure and wind speed thresholds (dark yellow shading from dashed lines), and aggregated ‘storm days’ are numbered at the top and marked with vertical yellow bars (see Methods section ‘Storm identification’).

Extended Data Fig. 5 Winds and wind-induced sea ice divergence during the 2016 polynya.

50% SIC contours (black) from AMSR2-ASI show the daily polynya evolution from 22 July to 17 August 2016. Bathymetry shallower than 3,500 m is contoured at intervals of 500 m (light grey) to highlight Maud Rise (centre). Daily mean 10-m wind vectors from ERA-I reanalysis, subsampled as every fifth u-wind and every second v-wind vector, are plotted with a 20 m s−1 key as reference. Estimated daily mean wind-induced sea ice divergence (see Methods section ‘Atmospheric reanalysis’) is shaded such that red represents divergence and blue represents convergence.

Extended Data Fig. 6 Full set of profiling float hydrographic observations from Maud Rise from 2011–2018.

a–e, Complete depth sections of potential temperature (a), salinity (b), dissolved oxygen (c), buoyancy frequency squared (N2) (d) and convection resistance (see Methods section ‘Derived oceanographic quantities’) (e) from profiling floats 5903616 (left), 5904468 (centre) and 5904471 (right), as shown in Fig. 4. Individual profiles are marked at the top (black ticks). Mixed-layer depth is indicated in white. Vertical lines in each panel mark the start and end dates of the 2016 and 2017 polynyas. Along-trajectory SIC, primarily from AMSR2-ASI, is shaded at the top in black (see Methods section ‘Sea ice concentration data’).

Extended Data Fig. 7 Heat loss during the 2016 polynya estimated from hydrographic observations.

Heat flux estimates (n = 31; diamonds at the bottom correspond to histogram above) computed using potential temperature profiles from floats 5904468 and 5904471 following the opening of the 2016 polynya (see Methods section ‘Polynya heat flux estimates’). The dashed red line marks the average open-water ocean-atmosphere turbulent heat flux within the 2016 opening, estimated using a bulk flux algorithm as 252 W m−2 (see Methods section ‘Atmospheric reanalysis’).

Extended Data Fig. 8 Sea ice concentration during the 2017 polynya.

SIC from AMSR2-ASI around Maud Rise is shown every other day from 30 August to 2 December 2017. Estimated locations of SOCCOM profiling floats 5904471 and 5904468 are marked in blue; locations of profiling floats following an ice-free profile with a known position fix are marked in pink (see Methods section ‘Hydrographic data’). A circle marker and profile number indicate that a hydrographic profile was obtained on that date. Bathymetry shallower than 3,500 m is contoured at intervals of 500 m to highlight Maud Rise (centre) as well as Astrid Ridge (bottom right), an extension of the Antarctic continental shelf.

Extended Data Fig. 9 Additional relationships between past polynyas near Maud Rise, climate forcing, and sub-pycnocline temperatures.

a, Annual maximum polynya extent (bars) and number of polynya days (red diamonds; see Methods section ‘Polynya identification’), as in Fig. 5a. Maximum polynya extent is calculated for three SIC thresholds representing increasingly strict polynya definitions: 60%, 50% and 40%. Polynya days are quantified using the 60% threshold. Stars indicate years with incomplete or absent SIC records. Years with polynya activity at the 50% threshold are shaded vertically in red in b, c, and likewise in d, e, except vertical shading delimits the actual major polynya events. b, Mean sea-level pressure records from Queen Maud Land meteorological stations, 1972–2018 (see legend and Methods section ‘Meteorological station records’). c, Eastern Weddell average precipitation from ERA-I reanalysis between 1979 and 2018, shaded below its mean value to indicate years with polynya-favourable conditions (that is, lower atmosphere–ocean freshwater flux), consistent with Fig. 5. Time series in b and c are filtered using a two-year centred running mean to highlight longer-term fluctuations. d, Biannually binned eastern Weddell region (within 500 km of Maud Rise) shipboard, float and instrumented seal temperature observations at 258 m from 2002 to 2017, expressed as anomalies from WAGHC gridded hydrographic climatology (see Methods section ‘Sub-pycnocline temperature records’). Error bars denote median and 25–75% IQR. Violin plots summarize the data distribution for n > 10, and individual anomalies are shown for 5 ≤ n ≤ 10. Periods with n < 5 are not plotted. e, As in d, but showing average temperature anomalies from WAGHC climatology between 250–1,000 m (see Methods section ‘Sub-pycnocline temperature records’). See Extended Data Table 1 for trends and significance for c–e.

Extended Data Table 1 Correlations and trends for climate indices and sub-pycnocline temperature records
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Campbell, E.C., Wilson, E.A., Moore, G.W.K. et al. Antarctic offshore polynyas linked to Southern Hemisphere climate anomalies. Nature 570, 319–325 (2019). https://doi.org/10.1038/s41586-019-1294-0

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