Abstract
The presence of large Northern Hemisphere ice sheets and reduced greenhouse gas concentrations during the Last Glacial Maximum fundamentally altered global ocean–atmosphere climate dynamics1. Model simulations and palaeoclimate records suggest that glacial boundary conditions affected the El Niño–Southern Oscillation2,3, a dominant source of short-term global climate variability. Yet little is known about changes in short-term climate variability at mid- to high latitudes. Here we use a high-resolution water isotope record from West Antarctica to demonstrate that interannual to decadal climate variability at high southern latitudes was almost twice as large at the Last Glacial Maximum as during the ensuing Holocene epoch (the past 11,700 years). Climate model simulations indicate that this increased variability reflects an increase in the teleconnection strength between the tropical Pacific and West Antarctica, owing to a shift in the mean location of tropical convection. This shift, in turn, can be attributed to the influence of topography and albedo of the North American ice sheets on atmospheric circulation. As the planet deglaciated, the largest and most abrupt decline in teleconnection strength occurred between approximately 16,000 years and 15,000 years ago, followed by a slower decline into the early Holocene.
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Acknowledgements
This work was supported by US National Science Foundation (NSF) grants 0537593, 0537661, 0537930, 0539232, 1043092, 1043167, 1043518 and 1142166. Field and logistical activities were managed by the WAIS Divide Science Coordination Office at the Desert Research Institute, USA, and the University of New Hampshire, USA (NSF grants 0230396, 0440817, 0944266 and 0944348). The NSF Division of Polar Programs funded the Ice Drilling Program Office (IDPO), the Ice Drilling Design and Operations (IDDO) group, the National Ice Core Laboratory (NICL), the Antarctic Support Contractor, and the 109th New York Air National Guard. W.H.G.R. was funded by a Leverhulme Trust Research Project Grant. All HadCM3 model simulations were carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol (http://www.bris.ac.uk/acrc/). We thank P. J. Valdes and J. S. Singarayer for providing their model simulations, as well as the groups that provided climate model data as part of the PMIP2/3.
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T.R.J., W.H.G.R. and E.J.S. designed the project and led the writing of the paper. T.R.J., J.W.C.W., E.J.S. and B.R.M. contributed water isotope measurements. W.H.G.R. conducted HadCM3 simulations and led model analysis. T.R.J., K.M.C., E.J.S. and J.W.C.W. developed the diffusion-correction calculations. B.R.M. contributed change point detection algorithms and power density ratio calculations. All authors discussed the results and contributed input to the manuscript.
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Extended data figures and tables
Extended Data Figure 1 Signal detection.
a, Relative amplitudes for 1-yr, 2-yr, 3-yr and 4-yr periods calculated for 500-yr spectral data windows, normalized to the value for the annual signal in the most recent data window. Both the climate and diffusion affects the amplitude of these high-frequency signals. However, it is mainly the effects of diffusion that cause the loss of the annual signal at >14 kyr ago. Similarly, the 2-yr period is lost between 17 kyr ago and 19 kyr ago, when the rate of diffusion for WDC was highest16. Periods >3 yr survive diffusion throughout the past 31 kyr. b, An example of a deconvolution calculation showing the observed δD water isotope record (that is, raw data; dotted red line), and the diffusion-corrected record (black line). Although this calculation was not used for the data presented in this paper, it serves as a visual aid in understanding how the diffusion-corrected water isotope record would look in the time domain. We performed all diffusion-correction calculations in the frequency domain to reduce uncertainty. c, The power density spectrum for the Southern Oscillation Index from 1951 to 2017 (black; 95% confidence intervals in grey), compared with a red noise null hypothesis (red) calculated from the average of 100 power spectrums of synthetic data that have the same autocorrelation and variance as the Southern Oscillation Index. The Southern Oscillation Index has power greater than the red noise across a broad spectral peak between 2 and 17 yr, which can be subdivided into a 2–7-yr high-frequency peak and an 8–17-yr peak. Owing to the limited temporal span of modern observations, multi-decadal spectral estimates of the Southern Oscillation Index cannot be adequately defined. d, Diffusion-corrected relative amplitudes using 500-yr windows of WDC water isotope data. e, The difference in age of consecutive 5-mm WDC water isotope samples (blue) and a 500-yr sliding average (red). f, The WDC accumulation rate63, inverted. The accumulation (in metres of ice equivalent per year), and by extension the difference in age of consecutive 5-mm WDC water isotope samples in e, undergoes large changes during the deglaciation at around 18.5 kyr ago, occurring 2.5 kyr before the change point in teleconnection strength at about 16 kyr ago.
Extended Data Figure 2 Change point detection.
a, WDC 4–15-yr variability for 500-yr data windows (dashed lines are 1σ uncertainties; see Methods). b, Regression test algorithm to determine the first significant change in the WDC 4–15-yr variability in a. The first coloured data point below the P-value significance threshold occurs 16.44 kyr ago. c, Example of the diffusion-correction calculation for a 100-yr data window centred on 15.54 kyr ago; raw data (black), diffusion-corrected data (blue), Gaussian fit (red) with dashed 1σ uncertainty bounds (see Methods). The same calculation is made for 500-yr data windows. d, The subset of diffusion-corrected 4–15-yr amplitudes (green) calculated at 100-yr resolution. The values are normalized to the amplitude value at 16.24 kyr ago, which represents the change point towards smaller amplitudes.
Extended Data Figure 3 ASL variability.
Panels a and b show the variance σ2 of the indices ZASL (black) and Zlocal (blue). a, The indices are computed from monthly mean data, then filtered with a 4–15-yr band pass filter. b, The indices are computed from annual mean output. We compute ZASL as the mean 500-hPa height in the region 55°–70° S and 195°–240° E. The blue lines show ZASL after linearly removing the SSTpac time series from the 500-hPa height field; this is the ASL variability unrelated to the tropical Pacific (Zlocal; see Methods). The markers are unfilled if the variance of ZASL at each time slice is 95% significantly different (F-test) to that of the pre-industrial period. c, Map of the change in the variance of the 500-hPa height field between 21 kyr ago and the pre-industrial period. d, The variability that is linearly related to ENSO, Zpac (this is the part removed from ZASL to yield Zlocal). e, The variability with the effect of ENSO linearly removed (see Methods; this the equivalent of Zlocal). Changes not attributable to ENSO occur to the north of the Amundsen Sea, while changes over the Amundsen Sea are related to ENSO. In c–e the contour intervals are 40 m2, with colours changing every 80 m2. Negative contours are dashed.
Extended Data Figure 4 Composites of the 500-hPa height field for ENSO events.
a–c, Composite for the ‘Pre-industrial’ (a), ‘Full 21ka’ (b) and the difference between the ‘Full 21ka’ and ‘Pre-industrial’ simulations (c). Contours are filled (not white) when statistical significance exceeds 95% using a Monte Carlo test. Negative contours are dashed. In a and b the contours are plotted every 5 m and colours saturate at ±25 m. The thin blue box shows where the ASL index is computed. In c contours are plotted every 2.5 m and colours saturate at ±12.5 m.
Extended Data Figure 5 Tropical Pacific-to-ASL teleconnection strength, computed using the ‘Full 28–0ka’ simulations.
a, Average 500-hPa geopotential height anomaly in the Amundsen Sea region, computed within the purple box shown in Extended Data Fig. 4. These values are derived from composites constructed using only the lower limits on the size of ENSO events (see main text and Methods). b, The t-score is indicated by the red line. Values outside the shaded red region are 95% statistically different from the pre-industrial value.
Extended Data Figure 6 Composites of the 500-hPa height field for ENSO events with and without upper bound.
a, The teleconnection without upper limit using the ‘Full 21ka’ simulation. b, The teleconnection with upper limit using the ‘Full 21ka’ simulation. c, The difference in teleconnection between the ‘Full 21ka’ simulation and the ‘Pre-industrial’ simulation without upper limit. d, The same difference as c with upper limit. In all panels, negative contours are dashed, and contours are filled (not white) when statistical significance exceeds 95%using a Monte Carlo test. In a and b, the contours are plotted every 5 m and colours saturate at ±25 m. In c and d, contours are plotted every 2.5 m and colours saturate at ±12.5 m.
Extended Data Figure 7 Composite maps of the 500-hPa height field for ENSO events in the PMIP2/3 models.
For each model in a–k, the top panel shows the ‘Pre-industrial’ composite and the bottom panel the difference between the ‘Full 21ka’ and ‘Pre-industrial’ simulations. In the bottom panel, contours are filled (non-white) when statistical significance exceeds 95% using a Monte Carlo test. In the top panels, contours are plotted at 5-m intervals, with colours saturating at 25 m. In the bottom panels, contours are plotted at 2.5-m intervals, with colours saturating at ±12.5 m. Negative contours are dashed. The reduced statistical significance in these panels compared to those shown in Extended Data Figs 4, 6, 8 and 9 is due to the shorter data series available in the PMIP2/3 archives.
Extended Data Figure 8 Sensitivity of composite maps to different sets of boundary conditions 21 kyr ago.
All plots show the difference between 21-kyr and 0-kyr composites. The composites are constructed using both upper and lower limits on the size of ENSO events. Contours are plotted every 2.5 m (negative contours are dashed) and colours saturate at ±12.5 m. Contours are filled (not white) when statistical significance exceeds 95% using a Monte Carlo test. a, ‘Full 21ka’ simulation. b, ‘21ka Orbit + GHG’ simulation. c, ‘21ka Ice Sheets’ simulation. d, ‘21ka Shelf Exp.’ simulation. e, ‘21ka LCIS only’ simulation. f, ‘21ka LCIS albedo’ simulation. Each of these simulations is fully defined in Supplementary Data.
Extended Data Figure 9 Annual mean anomalies of precipitation and sea surface temperature.
Maps of anomalies 21 kyr ago relative to the pre-industrial period. a, ‘Full 21ka’ simulation. b, ‘21ka Ice Sheets’ simulation. c, ‘21ka LCIS only’ simulation. d, ‘21ka Shelf Exp.’ simulation. Annual means are calculated from 100 years of output. Contour intervals for precipitation are 1 mm per day, and for sea surface temperature are 0.5 °C. Land areas are shown in grey. Note that the temperature colour scale in a ranges from −4 °C to 0 °C. This accounts for the mean greenhouse gas cooling that is seen in the ‘Full 21ka’ simulation.
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Jones, T., Roberts, W., Steig, E. et al. Southern Hemisphere climate variability forced by Northern Hemisphere ice-sheet topography. Nature 554, 351–355 (2018). https://doi.org/10.1038/nature24669
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DOI: https://doi.org/10.1038/nature24669
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