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Abrupt ice-age shifts in southern westerly winds and Antarctic climate forced from the north

Nature volume 563, pages 681–685 (2018)Cite this article

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Abstract

The mid-latitude westerly winds of the Southern Hemisphere play a central role in the global climate system via Southern Ocean upwelling1, carbon exchange with the deep ocean2, Agulhas leakage (transport of Indian Ocean waters into the Atlantic)3 and possibly Antarctic ice-sheet stability4. Meridional shifts of the Southern Hemisphere westerly winds have been hypothesized to occur5,6 in parallel with the well-documented shifts of the intertropical convergence zone7 in response to Dansgaard–Oeschger (DO) events— abrupt North Atlantic climate change events of the last ice age. Shifting moisture pathways to West Antarctica8 are consistent with this view but may represent a Pacific teleconnection pattern forced from the tropics9. The full response of the Southern Hemisphere atmospheric circulation to the DO cycle and its impact on Antarctic temperature remain unclear10. Here we use five ice cores synchronized via volcanic markers to show that the Antarctic temperature response to the DO cycle can be understood as the superposition of two modes: a spatially homogeneous oceanic ‘bipolar seesaw’ mode that lags behind Northern Hemisphere climate by about 200 years, and a spatially heterogeneous atmospheric mode that is synchronous with abrupt events in the Northern Hemisphere. Temperature anomalies of the atmospheric mode are similar to those associated with present-day Southern Annular Mode variability, rather than the Pacific–South American pattern. Moreover, deuterium-excess records suggest a zonally coherent migration of the Southern Hemisphere westerly winds over all ocean basins in phase with Northern Hemisphere climate. Our work provides a simple conceptual framework for understanding circum-Antarctic temperature variations forced by abrupt Northern Hemisphere climate change. We provide observational evidence of abrupt shifts in the Southern Hemisphere westerly winds, which have previously documented1,2,3 ramifications for global ocean circulation and atmospheric carbon dioxide. These coupled changes highlight the necessity of a global, rather than a purely North Atlantic, perspective on the DO cycle.

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Fig. 1: Records of abrupt glacial climate variability.
Fig. 2: Antarctic climate response to DO warming.
Fig. 3: Deuterium excess and the Southern Hemisphere westerlies.
Fig. 4: Attribution of the atmospheric mode of Antarctic temperature variability.

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Data availability

Source Data (WDC sulfur data, volcanic tie points and water isotope data on synchronized chronologies) and derived products (stacks, PCA results, etc.) are available in the online version of the paper and in the NOAA palaeoclimate data archive.

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Acknowledgements

This work is funded by the US National Science Foundation (NSF), through grants ANT-1643394 (to C.B. and J.J.W.), ANT-1643355 (to T.J.F. and E.J.S) and AGS-1502990 (to F.H.); the Swiss National Science Foundation through grant 200021_143436 (to H.S.); the CNRS/INSU/LEFE projects IceChrono and CO2Role (to F.P.); JSPS KAKENHI grants 15H01731 (to K.G.-A., H.M. and M.H.), 15KK0027 (to K.K.) and 26241011 (to K.K., S.F. and H.M.); MEXT KAKENHI grant 17H06320 (to K.K., H.M. and R.U.); the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement 610055 (to J.B.P.); and the NOAA Climate and Global Change Postdoctoral Fellowship programme, administered by the University Corporation for Atmospheric Research (F.H.). We acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the NSF. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy under contract number DE-AC05-00OR22725. This is TALDICE publication number 52.

Reviewer information

Nature thanks N. Abram, S. Davies and T. Stocker for their contribution to the peer review of this work.

Author information

Author notes

  1. Michael Sigl

    Present address: Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

Authors and Affiliations

  1. College of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA

    Christo Buizert, Justin J. Wettstein & Feng He

  2. Laboratory of Environmental Chemistry, Paul Scherrer Institute, Villigen, Switzerland

    Michael Sigl

  3. Department of Chemistry ‘Ugo Schiff’, University of Florence, Florence, Italy

    Mirko Severi

  4. Department of Earth and Space Science, University of Washington, Seattle, WA, USA

    Bradley R. Markle, T. J. Fudge & Eric J. Steig

  5. Geophysical Institute and Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway

    Justin J. Wettstein & Harald Sodemann

  6. Desert Research Institute, Nevada System of Higher Education, Reno, NV, USA

    Joseph R. McConnell

  7. Centre for Ice and Climate, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

    Joel B. Pedro

  8. Antarctic Climate & Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, Tasmania, Australia

    Joel B. Pedro

  9. National Institute for Polar Research, Tachikawa, Tokyo, Japan

    Kumiko Goto-Azuma, Kenji Kawamura, Shuji Fujita, Hideaki Motoyama & Motohiro Hirabayashi

  10. Department of Chemistry, Biology and Marine Science, University of the Ryukyus, Okinawa, Japan

    Ryu Uemura

  11. Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, Venice, Italy

    Barbara Stenni

  12. Université Grenoble Alpes, CNRS, IRD, IGE, Grenoble, France

    Frédéric Parrenin

  13. Center for Climatic Research, Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, WI, USA

    Feng He

Authors
  1. Christo Buizert
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  2. Michael Sigl
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Contributions

Data analysis by C.B., M. Severi, M. Sigl and J.J.W.; manuscript preparation by C.B.; volcanic ice-core synchronization by M. Sigl, M. Severi, C.B., J.R.M., F.P., S.F. and T.J.F.; GCM simulations and interpretation by F.H., J.B.P. and J.J.W.; moisture tagging/tracing experiments by B.R.M. and H.S.; ice-core water isotope analysis by K.G.-A., K.K., H.M., M.H., R.U., B.S. and E.J.S.; all authors discussed the results and contributed towards improving the final manuscript.

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Correspondence to Christo Buizert.

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Extended data figures and tables

Extended Data Fig. 1 Volcanic synchronization of Antarctic ice cores.

a, Age offset between the WD201437,38 (WDC) and AICC201236 (TAL, EDML, EDC) age scales, with each dot representing a volcanic tie point. Yellow and blue triangles denote the timing of TAL–EDC and EMDL–EDC volcanic ties32,36, respectively. b, Overview of synchronizations between the ice cores used in this study. Grey arrows indicate previously published synchronizations and coloured arrows denote synchronizations performed here. Synchronizations within Antarctica are based purely on volcanic links; synchronization between WDC and NGRIP (Greenland) are based on atmospheric CH4 (green arrow).

Extended Data Fig. 2 Age uncertainty due to volcanic synchronization.

a, Interpolation uncertainty (1σ) for four different values of L (the spacing between two adjacent volcanic tie points), based on the layer-counted WD2014 age scale38. b, Interpolation uncertainty in synchronizing the four ice cores to WD2014 chronology. Grey vertical lines give the timing of DO events.

Extended Data Fig. 3 Site-specific stacks of δ18O and dln.

a, Stack of NGRIP δ18O (teal; left axis) and WDC CH4 (green; right axis) during DO warming. b, As in a, but for DO cooling. c, Stack of Antarctic δ18O at the indicated locations (see key) during DO warming. d, As in c, but for DO cooling. e, Stack of Antarctic dln at the indicated locations during DO warming. f, As in e, but for DO cooling. Isotope ratios are on the VSMOW scale.

Extended Data Fig. 4 Proportionality of the atmospheric response.

a–f, Comparison of stacks of just the major DO events (those following Heinrich events, namely, DO events 0, 1, 4, 8, 12 and 14; a, c, e) and just the minor DO events (the remainder; b, d, f). a, b, Stacks of NGRIP δ18O (left axis) and CH4 (right axis). c, d, Stacks of Antarctic δ18O at the indicated locations. e, f, As in c and d, but with the Antarctic mean subtracted. g, Proportionality of the atmospheric response for individual events (numbered). The NGRIP event size is found via regression of individual NGRIP events to the multi-event NGRIP δ18O stack normalized to unit variance (Fig. 2a). The Antarctic atmospheric response is found via multiple linear regression of single-site individual events to the atmospheric and oceanic modes (Fig. 2e). Shown are the average (dots) and standard deviation (error bars) of the response at EDC, DF and EDML (the cores with the strongest atmospheric response); the EDML response is multiplied by −1 because it has the opposite sign of the response at DF and EDC. Red and blue dots denote the major and minor DO events, respectively. Isotope ratios are on the VSMOW scale.

Extended Data Fig. 5 Reduction of the number of events in the δ18O stacks.

a, PC1, when stacking n = 2 and n = 8 randomly selected events. The thick line and shaded area represent the mean and ±1σ, respectively, obtained from 50,000 runs. The vertical yellow bands denote the 200-yr period after the abrupt DO event at t = 0. b, As in a, but for PC2. c, Fraction of signal variance explained by PC1 and PC2, when stacking 2 or 8 randomly selected events. Colour coding as in a and b.

Extended Data Fig. 6 Robustness of the atmospherically forced warming pattern.

a, PCA as a function of window length, with the fraction of variance explained by PC1 and PC2. b, Comparison of PC1 at a 400-yr window length to PC2 at a 2,000-yr window length, showing the crossover of the atmospheric response (that is, from PC1 to PC2) as a function of window length. c, Lag time of the Antarctic PC1 and PC2 response as a function of window length, assessed using the BREAKFIT55 and RAMPFIT56 routines (see Methods for the selection of routine). d, EOF1 at a 400-yr window length, expressed in units of δ18O (‰). e, EOF2 at a 2,000-yr window length, expressed in units of δ18O (‰). f, Slope of linear fit to δ18O stacks in the interval t = 0 to t = +200 yr, shown as the change (in ‰) during these 200 years. Isotope ratios are on the VSMOW scale.

Extended Data Fig. 7 Antarctic climate response to DO cooling.

a, Stack of NGRIP δ18O. b, Stack of Antarctic δ18O at indicated locations. c, As in b, but with the Antarctic mean subtracted. d, First two principal components of the Antarctic δ18O stacks, with the fraction of variance explained (offset for clarity). The lines show the BREAKFIT (PC1) and RAMPFIT (PC2) fits. e, f, Empirical orthogonal functions EOF1 and EOF2 associated with PC1 and PC2 in d, scaled to show the magnitude in units of δ18O (‰). Isotope ratios are on the VSMOW scale.

Extended Data Fig. 8 Moisture sources of Antarctic ice cores and the SAM.

a, Mass-weighted probability distribution functions of Antarctic moisture sources for the five ice cores of interest (5 × 10−5 deg−2 contour lines; the area-integrated probability distribution function equals 1). Distributions are calculated from reanalysis data30,67 using a Lagrangian source diagnostic21,66 (Methods). Parallels are plotted in 15° increments of latitude and meridians in 45° increments of longitude. b, SST (black)69 and relative humidity (RH; grey)70 as a function of latitude. The coloured curves give the latitudinal source distribution during a negative SAM phase (solid curves; SAM index <0) and a positive SAM phase (dashed curves; SAM index >0). The solid and open dots show the first moment of the source distribution during negative and positive SAM phases, respectively. We note that during a positive SAM phase, moisture sources for all core locations are located closer to the Antarctic continent. Source distribution data were obtained using water tagging experiments8 in the CAM (Methods).

Extended Data Fig. 9 SAM-like variability in zonal near-surface winds.

a, ERA-Interim reanalysis (1979–2016 annual means) zonal wind speed at a height of 10 m, regressed onto the SAM index (here the first principal component of sea-level pressure variability south of 20° S), expressed in metres per second per standard deviation in the index. b, As in a, but for internal variability in the CCSM3 TraCE model simulation25,62 during glacial climate before freshwater forcing of Heinrich stadial 1 (19.5 kyr bp to 19.01 kyr bp, decadal means). c, As in a, but for the response to North Atlantic freshwater forcing in the CCSM3 TraCE model (19.1 kyr bp to 18.9 kyr bp, decadal means, with the freshwater forcing applied at 19 kyr bp).

Extended Data Table 1 Change-point analysis of Antarctic response
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Buizert, C., Sigl, M., Severi, M. et al. Abrupt ice-age shifts in southern westerly winds and Antarctic climate forced from the north. Nature 563, 681–685 (2018). https://doi.org/10.1038/s41586-018-0727-5

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