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Antarctic icebergs reorganize ocean circulation during Pleistocene glacials

Nature volume 589, pages 236–241 (2021)Cite this article

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Abstract

The dominant feature of large-scale mass transfer in the modern ocean is the Atlantic meridional overturning circulation (AMOC). The geometry and vigour of this circulation influences global climate on various timescales. Palaeoceanographic evidence suggests that during glacial periods of the past 1.5 million years the AMOC had markedly different features from today1; in the Atlantic basin, deep waters of Southern Ocean origin increased in volume while above them the core of the North Atlantic Deep Water (NADW) shoaled2. An absence of evidence on the origin of this phenomenon means that the sequence of events leading to global glacial conditions remains unclear. Here we present multi-proxy evidence showing that northward shifts in Antarctic iceberg melt in the Indian–Atlantic Southern Ocean (0–50° E) systematically preceded deep-water mass reorganizations by one to two thousand years during Pleistocene-era glaciations. With the aid of iceberg-trajectory model experiments, we demonstrate that such a shift in iceberg trajectories during glacial periods can result in a considerable redistribution of freshwater in the Southern Ocean. We suggest that this, in concert with increased sea-ice cover, enabled positive buoyancy anomalies to ‘escape’ into the upper limb of the AMOC, providing a teleconnection between surface Southern Ocean conditions and the formation of NADW. The magnitude and pacing of this mechanism evolved substantially across the mid-Pleistocene transition, and the coeval increase in magnitude of the ‘southern escape’ and deep circulation perturbations implicate this mechanism as a key feedback in the transition to the ‘100-kyr world’, in which glacial–interglacial cycles occur at roughly 100,000-year periods.

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Fig. 1: Modelled iceberg trajectories and meridionally averaged SST and meltwater distribution for the pre-industrial and LGM Atlantic Southern Ocean.
Fig. 2: Palaeoceanographic proxy records from the mid-to-late Pleistocene APcomp.
Fig. 3: IRDMAR versus δ13Cbenthic lead–lag relationship.
Fig. 4: The evolution of the APcomp IRDMAR record in the time and frequency domain with respect to orbital forcing, global climate and inter-hemispheric phasing.

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Data availability

All newly presented data are available at https://doi.org/10.1594/PANGAEA.921315.

Code availability

Code for all data analysis presented is available at https://github.com/AidanStarr/Starr_et_al_2020 and code for the Pyberg model is available at https://github.com/trackow/pyberg.

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Acknowledgements

This research used samples and/or data provided by the International Ocean Discovery Program (IODP). Funding for this research was provided by The Natural Environmental Research Council GW4+ Doctoral Training Partnership (A.S.) and NERC grant NE/P000037/1 (I.R.H.). A.S. acknowledges further funding through the Antarctic Science International Bursary. X.Z. acknowledges funding from Lanzhou University (number 225000-830006) and National Key R&D programme of China (number 2018YFA0606403). F.J.J.-E. acknowledges funding through Spanish Ministry of Science and Innovation (grant CTM2017-89711-C2-1-P), co-funded by the European Union through FEDER funds. G.K. acknowledges funding by the German Helmholtz national REKLIM initiative and the BMBF project PalMod. L. Owen, S. Slater, A. Nedebragt and D. Muir are thanked for laboratory assistance.

Author information

Authors and Affiliations

  1. School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK

    Aidan Starr, Ian R. Hall, Stephen Barker, H. J. L. van der Lubbe & H. J. L. van der Lubbe

  2. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany

    Thomas Rackow, Gregor Knorr, Xun Gong & Jens Gruetzner

  3. Center for Pan Third Pole Environment (Pan-TPE), Key Laboratory of Western China’s Environmental Systems, (Ministry of Education), College of Earth and Environmental Science, Lanzhou University, Lanzhou, China

    Xu Zhang

  4. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences (CAS), Beijing, China

    Xu Zhang

  5. Department of Earth and Environmental Sciences, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA

    Sidney R. Hemming & Sophia K. V. Hines

  6. Faculty of Science, Vrije University, Amsterdam, The Netherlands

    H. J. L. van der Lubbe & H. J. L. van der Lubbe

  7. Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN, USA

    Melissa A. Berke & Alejandra Cartagena-Sierra

  8. Department of Geography, University of Sheffield, Sheffield, UK

    Grant R. Bigg

  9. Instituto Andaluz de Ciencias de la Tierra (CSIC-UGR), Armilla, Spain

    Francisco J. Jiménez-Espejo & Francisco J. Jimenez Espejo

  10. Research Institute for Marine Resources Utilization (Biogeochemistry Program), JAMSTEC, Yokosuka, Japan

    Francisco J. Jiménez-Espejo, Francisco J. Jimenez Espejo & Masako Yamane

  11. Hubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan, China

    Xun Gong

  12. National Centre for Polar and Ocean Research, Ministry of Earth Sciences, Goa, India

    Nambiyathodi Lathika

  13. International Ocean Discovery Program, Texas A&M University, College Station, TX, USA

    Leah J. LeVay

  14. Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA

    Rebecca S. Robinson

  15. Department of Earth Sciences, Utrecht University, Utrecht, The Netherlands

    Martin Ziegler

  16. Earth and Environmental Sciences, University of Technology Queensland, Brisbane, Queensland, Australia

    Luna Brentegani

  17. EPOC, UMR CNRS 5805, University of Bordeaux, Pessac, France

    Thibaut Caley & Julien G. Crespin

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

    Christopher D. Charles & Richard D. Norris

  19. Department of Geology, Northern Illinois University, Sycamore, IL, USA

    Jason J. Coenen

  20. Natural Sciences Department, School of Earth and Environmental Sciences, Hostos Community College (C.U.N.Y.), Bronx, NY, USA

    Allison M. Franzese

  21. Second Institute of Oceanography (SOA), Key Laboratory of Submarine Science, Hangzhou, People’s Republic of China

    Xibin Han

  22. Geologisches Institut, Universität Köln, Cologne, Germany

    Janna Just

  23. Institute of Earth Sciences, University of Heidelberg, Heidelberg, Germany

    Andreas Koutsodendris

  24. Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwashi, Japan

    Kaoru Kubota

  25. Institute for Geosciences, Universidade Federal Fluminense (UFF), Rio de Janeiro, Brazil

    Thiago Pereira dos Santos

  26. Chemistry Department, University of Otago, Dunedin, New Zealand

    John M. Rolison

  27. Bjerknes Centre for Climate Research, Bergen, Norway

    Margit H. Simon

  28. Department of Geosciences, University of Bremen, Bremen, Germany

    Deborah Tangunan

  29. Laboratory of Plateau Lake Ecology and Global Change, Yunnan Normal University, Kunming, People’s Republic of China

    Hucai Zhang

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  3. Stephen Barker
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  5. Xu Zhang
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Consortia

Expedition 361 Science Party

  • Ian R. Hall
  • , Sidney R. Hemming
  • , Leah J. LeVay
  • , Stephen Barker
  • , Melissa A. Berke
  • , Luna Brentegani
  • , Thibaut Caley
  • , Alejandra Cartagena-Sierra
  • , Christopher D. Charles
  • , Jason J. Coenen
  • , Julien G. Crespin
  • , Allison M. Franzese
  • , Jens Gruetzner
  • , Xibin Han
  • , Sophia K. V. Hines
  • , Francisco J. Jimenez Espejo
  • , Janna Just
  • , Andreas Koutsodendris
  • , Kaoru Kubota
  • , Nambiyathodi Lathika
  • , Richard D. Norris
  • , Thiago Pereira dos Santos
  • , Rebecca S. Robinson
  • , John M. Rolison
  • , Margit H. Simon
  • , Deborah Tangunan
  • , H. J. L. van der Lubbe
  • , Masako Yamane
  •  & Hucai Zhang

Contributions

IODP Expedition 361 was led by I.R.H. and S.R.H. All Expedition 361 Science Party members contributed to sample collection. I.R.H. formulated the research. A.S. performed laboratory analysis. A.S. performed data analysis with input from S.B., I.R.H., G.K., and H.J.L.v.d.L. T.R. performed Pyberg experiments and X.Z. performed COSMOS experiments. A.S. wrote the manuscript with input from I.R.H., S.B., G.K., T.R. and X.Z. All authors contributed to the interpretation of results and commented on the final manuscript.

Corresponding authors

Correspondence to Aidan Starr or Ian R. Hall.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Helen Bostock, Till Wagner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Schematic representation of the ‘southern escape’ mechanism described in this study.

a, An idealized representation of the Southern Ocean during interglacial conditions. The front-facing panel gives meridional-averaged overturning circulation93, with circulation divided into an upper cell and lower cell. Arrow width represents the relative strength of each circulation cell (wider being stronger). The top panel represents the ocean surface, with icebergs calving from the Antarctic Ice Sheet (AIS) and following a northward then eastward trajectory. The blue shading represents iceberg meltwater being entrained into the lower cell as icebergs melt south of the main Antarctic Circumpolar Current (ACC) belt. Orange wavy arrows represent brine rejection from sea-ice formation. The peach-coloured band represents the subtropical frontal zone (STFZ) delineating the subtropical regime to the north and the (sub)Antarctic regime to the south. b, As in a, for conditions during glacial inception; that is, the transition from interglacial to glacial conditions. The major change from a is the northward displacement of iceberg trajectories, resulting in much of the meltwater spreading northwards and being entrained in the upper cell, instead of the lower cell. Second, colder surface conditions facilitate extended sea-ice cover, and subsequent increase in brine rejection. The lower cell experiences less-positive buoyancy forcing from the combination of less iceberg meltwater and more brine rejection, and thus becomes stronger. The southern escape of meltwater into the upper cell has not yet occurred in high enough amounts to perturb the upper cell and hence the geometries of the overturning cells are unchanged from a. c, As in a and b, for full glacial conditions (occurring after b.). Here the southern escape of meltwater has successfully perturbed the upper cell, which is now weaker and has contracted upwards. The now-dominant lower cell has thus increased in volume.

Extended Data Fig. 2 APcomp age model and composite record construction.

a, The appending of U1475 to the bottom of MD02-2588 using δ18Obenthic with the new APcomp composite depth scale on the right-hand axis. b, Age-depth tie points (circles) with the median and 95% confidence bounds for the deterministic age model. Depth is given on the APcomp scale. c, The δ18Obenthic from this study (purple) and the tuning target, the global δ18Obenthic stack53 (light grey), and the implied linear sedimentation rates (black dashed line) and calculated MAR (green solid line) from the final age model in the lower subplot.

Extended Data Fig. 3 SEM imaging and mineralogy of IRD grains from the APcomp.

a, An SEM image of a quartz grain from site U1475 with an enlarged view of surface microtextures in the inset panel. b–d, SEM images of IRD grains with examples of relative peak intensities for elements derived from EDS point analyses. The images show spectra typical of quartz (b), of garnet (almandine member; c) and of K-feldspar (orthoclase; d). Scale bars are given in white on all SEM images.

Extended Data Fig. 4 Comparison of δ13Cbenthic and water mass ‘end-members’.

a, Authigenic εNd (143Nd/144Nd) isotope record from the deep equatorial Atlantic (ODP site 154-929 from ref. 20; top, green), Smoothed δ13Cbenthic stacks for northern-sourced water (NSW), Pacific deep water (PDW) and southern-sourced water (SSW) end-members (see Methods for stack construction and constituent core sites) and the APcomp δ13Cbenthic record. Selected MISs are shown as grey vertical shading. The bottom time series in a shows %NSW at the APcomp calculated using a binary mixing model with NSW and a PDW (grey) or SSW (purple) end-members. b, Scatter plots of APcomp δ13Cbenthic versus %NSW calculated with an SSW (left) and PDW (middle) end-member, and versus the εNd isotope record of ref. 20 (right). Pearson’s correlation coefficient, r2, is given. c, Pre-industrial δ13C of dissolved organic carbon (from ref. 94) along a north–south (blue) and then east–west (red) transect, with the APcomp position shown as a white circle.

Extended Data Fig. 5 Time-series analysis algorithm tests.

a, b, Testing the peak-lag algorithm to detect relationships in a series of surrogate time series with known lags applied. The ‘actual lag’ axes represent the known lag time imposed between pairs of the surrogate series and the ‘calculated lag’ axes show the lag estimated by the algorithm. A perfect performance would manifest as a 1:1 straight line through the scatter points. In b, the violin plots show the mean, median and kernel probability-density estimates of calculated lags from 104 iterations of the test. c, Results of the ‘glacial accumulation’ algorithm with each shaded curve representing IRDMAR (green) and δ18Obenthic (purple) integrated and normalized to 100% within each glacial cycle. Above, the δ18Obenthic record is shown (purple solid line) with green triangles denoting the peak interglacials identified and the dashed black line showing the δ18Obenthic threshold above which the transitions from glacial to interglacial conditions are defined. d, Gaussian kernel-based cross-correlation (gXCF)86 function for APcomp IRDMAR versus δ13Obenthic and δ13Cbenthic. The horizontal lines show the 95% Monte Carlo confidence levels for significant cross-correlation.

Extended Data Fig. 6 COSMOS model experiment results.

a–c, Annual mean sea surface height (SSH; shaded) in experiments for pre-industrial (a) LGM (b) and 27 ka (c). Solid (dashed) contours represent 90% (15%) sea-ice concentration. d–f, As in a–c, for climatological annual mean sea surface temperature (SST). g, Anomaly of mean annual SSH between 27 ka and LGM experiments. Solid (dashed) contours give 90% (15%) sea-ice concentration (black and white lines represent LGM and 27 ka, respectively). h, As in g, for SST.

Extended Data Fig. 7 Pyberg model experiment results.

a, c, e, Spatial distribution of meltwater input estimated for 1° × 1° cells by Pyberg when forced by COSMOS outputs for pre-industrial (PI; a), LGM (c), and LGM 27 ka (LGM27ka; e) conditions. In purple are observed modern iceberg trajectories from the BYU Giant Iceberg database95 (QSCAT); the modelled pre-industrial trajectories appear substantially longer than the observed, probably because icebergs are tracked in Pyberg even after they become too small to be identified and hence tracked by modern observational techniques. b, d, f, Zonally averaged meltwater estimates for pre-industrial (b), LGM (d) and LGM27ka (f) experiments. The average is taken for each latitude between 0 and 50° E; in other words, this shows the latitudinal distribution of meltwater across the Indian–Atlantic Ocean Gateway.

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Starr, A., Hall, I.R., Barker, S. et al. Antarctic icebergs reorganize ocean circulation during Pleistocene glacials. Nature 589, 236–241 (2021). https://doi.org/10.1038/s41586-020-03094-7

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