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Abrupt changes in the global carbon cycle during the last glacial period


During the last glacial period, atmospheric carbon dioxide (CO2) closely followed Antarctic temperature on millennial timescales. This strong correlation between Antarctic climate and atmospheric CO2 has led to suggestions that reorganizations of Southern Ocean circulation and/or biogeochemistry were the dominant cause of these variations. However, recent work also revealed centennial-scale changes in CO2 that appear unrelated to Antarctic climate and may represent additional modes of carbon cycle variability. Here we present a high-resolution CO2 record from the last glacial period from an ice core drilled in West Antarctica. This reconstruction precisely defines the timing of millennial and centennial CO2 variations with respect to Antarctic temperature and abrupt changes in Northern Hemisphere climate during Heinrich stadials and Dansgaard–Oeschger events. On the millennial scale, CO2 tracks Antarctic climate variability, but peak CO2 levels lag peak Antarctic temperature by more than 500 years. Centennial-scale CO2 increases of up to 10 ppm occurred within some Heinrich stadials, and increases of ~5 ppm occurred at the abrupt warming of most Dansgaard–Oeschger events. Regression analysis suggests that the CO2 variations can be explained by a combination of one mechanism operating on the timescale of Antarctic climate variability and a second responding on the timescale of Dansgaard–Oeschger events. Consistent with our statistical analysis, carbon cycle box-model simulations illustrate a plausible scenario where Southern Hemisphere processes contribute the majority of the CO2 variability during the last glacial period, but Northern Hemisphere processes are the crucial drivers of centennial-scale variability.

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Fig. 1: Ice-core records of polar climate, greenhouse gas variability and lead–lag correlations.
Fig. 2: Box-model experiments illustrating a plausible scenario for last glacial period CO2 variability.
Fig. 3: Centennial-scale greenhouse gas variability within Heinrich stadials.

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Data Availability

The WAIS Divide CO2 data are publicly available at the NOAA NCEI Database ( and US Antarctic Program Data Center (

Code Availability

Model results are summarized in the supplemental material, and the entire suite of Monte Carlo simulations and box-model code is available from T.K.B. upon request.


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This work was funded by NSF grant 0944764 to E.J.B. and Royal Society University Research Fellowship (grant URF\R1\180366) to T.K.B. We thank M. Kalk and T. Alig for assistance in measuring the CO2 data. We appreciate the support of the WAIS Divide Science Coordination Office at the Desert Research Institute (DRI) of Reno, Nevada, and the University of New Hampshire for the collection and distribution of the WAIS Divide ice core and related tasks (NSF grants 0230396, 0440817, 0944348 and 0944266). Additional support for this research came from the NSF Office of Polar Programs through their support of the Ice Drilling Program Office and the Ice Drilling Design and Operations group; the US National Ice Core Laboratory, for curation of the core; and the 109th New York Air National Guard, for airlift to Antarctica.

Author information

Authors and Affiliations



T.K.B. and S.A.M. contributed equally. E.J.B. and S.A.M. designed the study. S.A.M. measured the CO2 data and performed the preliminary analysis. T.K.B. performed time series and modelling analyses and led the writing of the paper with input from S.A.M. and E.J.B.

Corresponding authors

Correspondence to Thomas K. Bauska or Shaun A. Marcott.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Geoscience thanks Julia Gottschalk and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: James Super.

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

Extended data

Extended Data Fig. 1 A comparison of the WD to other ice core records of atmospheric CO2.

A comparison of ice core CO2 records including WD (this study), Byrd19, Taylor Glacier23,38, Talos Dome20, EDC (mechanical crushing58 and sublimation data59), Siple Dome24,26 and EDML20. All the ice core records have been smoothed with splines to preserve the millennial-scale variability and then subtracted from the WD record to show any offsets. Note that millennial-scale differences in the CO2 offset are susceptible to small errors in the various ice core chronologies (AICC2012) relative to the WD2014. Sub-panels show the raw data for each individual core record.

Extended Data Fig. 2 Mean differences in ice core CO2 relative to the WD Record.

*Note only plateaus in CO2 used for comparison **Original Taylor Dome12 timescale is too uncertain to quantify millennial-scale offsets. See Neff, 2014 for details on site data61.

Extended Data Fig. 3 Lead-lag correlation analysis of ice core data with percentile ranges.

Median and confidence intervals reported in years.

Extended Data Fig. 4 Alternative lead-lag analysis using one-box models with variable e-folding timescales.

The results of correlation of the various ice core proxy time-series against atmospheric CO2 after being passed through a series of one-box models. The x-axis shows the variable e-folding timescales of each run. Shading shows the uncertainty introduced from Monte Carlo error estimates in the delta-age history (90% CI) as in Fig. 1b (main text). The maximum reached in each curve indicates the apparent lead-lag with the horizontal bars indicate the range in maximum R2 values from the delta-age histories.

Extended Data Fig. 5 T-test and correlation analysis of CO2 response at onset of DO interstadials.

Individual interstadials are indicated with black marker and are labeled such that interstadials after an H-event are in blue and all others are labeled in grey. Grey shading shows the 95% confidence interval for the fits of the linear regressions.

Extended Data Fig. 6 Objective construction of the model ensemble solution.

Left panel. The forcings that enter the model with variable scaling (AABW formation scaled to WD-δ18O Antarctic sea-ice extent scaled to the log of WD Na, Subantarctic PO4 scaled to WD-Ca, and NADW formation scaled to Bermuda Rise Pa/Th, not shown Antarctic PO4 scaled to a combination of WD-Ca and sea ice extent). The maximum possible forcing is shown with the dotted red line along with the associated model response of atmospheric CO2 (black line). The red shading shows the range of forcings (mean and 1-sigma) that produce model predictions consistent with the ice core data (that is the ‘combined scenario’). The middle panel shows the same 1-sigma range for temperature forcings in the ‘combined scenario’. The right panel shows various model predictions including those that are used to select the ‘combined scenario’ constraint: atmospheric CO2, atmospheric δ13C-CO2 (including spline-smoothed data from refs. 44,59,60) and mean ocean temperature (MOT). All model results are slightly offset to match interglacial values in the data. Blue bars show the temporal range used in for each data constraint.

Extended Data Fig. 7 Maximum Proxy-to-Forcing Scaling Parameters and Maximum Predicted Changes in CO2.

*SST forcing is applied in the surface boxes but for convenience we report the predicted changes in mean ocean temperature (MOT).

Extended Data Fig. 8 Maximum effect of the major forcings in the full model ensemble.

The upper limit of forcings that enter the model prior to RMSE fitting are plotted on the left axes with the exception of temperature which is shown as the predicted change in MOT for the mean changes in temperature. The colours of the axes correspond to the colour of the trace (AABW = blue; Southern Ocean phosphate = orange; NADW = purple; temperature = grey). The corresponding predicted changes in CO2 to are plotted against the shown on the right axes in black. The figure is divided between the last glacial period on the left and the deglaciation on the right with larger ranges for all vertical axes during the deglaciation.

Extended Data Fig. 9 Lead-lag correlation analysis of model forcing and predicted CO2.

Lead-lag correlations for all model factorial experiments in which CO2 is driven soley by one forcing and lead-lag analysis of the combined scenario with forcings inputs from WD δ18O and NGRIP δ18O combined scenario.

Extended Data Fig. 10 Lead-lag analysis of model forcing and predictions with comparison to observations.

Left panel. Lead-lag correlation of factorial model experiments between proxy forcing and simulated CO2. Factorial experiments include: AABW formation (dark blue); ‘Iron Fertilization’/nutrient utilization (yellow); Antarctic sea ice extent (light blue); NADW formation (red); North Atlantic temperature (purple). Right panel. Lead-lag correlation of the WD-δ18O (dash dark blue) and NGRIP- δ18O (dashed purple) that enter the model as forcings against simulated CO2 in the ‘combined scenario’. This mimics the lead-lag correlation performed on the real ice core data and presented in the main body of the text between WD-CO2 and WD- δ18O (shaded blue envelope) and WD-δ18O and NGRIP-δ18O (shaded purple envelope).

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Supplementary Discussion and references.

Supplementary Data

Summary of box-model results.

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Bauska, T.K., Marcott, S.A. & Brook, E.J. Abrupt changes in the global carbon cycle during the last glacial period. Nat. Geosci. 14, 91–96 (2021).

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