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Plio-Pleistocene climate sensitivity evaluated using high-resolution CO2 records

Nature volume 518, pages 49–54 (2015)Cite this article

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An Addendum to this article was published on 19 August 2015

Abstract

Theory and climate modelling suggest that the sensitivity of Earth’s climate to changes in radiative forcing could depend on the background climate. However, palaeoclimate data have thus far been insufficient to provide a conclusive test of this prediction. Here we present atmospheric carbon dioxide (CO2) reconstructions based on multi-site boron-isotope records from the late Pliocene epoch (3.3 to 2.3 million years ago). We find that Earth’s climate sensitivity to CO2-based radiative forcing (Earth system sensitivity) was half as strong during the warm Pliocene as during the cold late Pleistocene epoch (0.8 to 0.01 million years ago). We attribute this difference to the radiative impacts of continental ice-volume changes (the ice–albedo feedback) during the late Pleistocene, because equilibrium climate sensitivity is identical for the two intervals when we account for such impacts using sea-level reconstructions. We conclude that, on a global scale, no unexpected climate feedbacks operated during the warm Pliocene, and that predictions of equilibrium climate sensitivity (excluding long-term ice-albedo feedbacks) for our Pliocene-like future (with CO2 levels up to maximum Pliocene levels of 450 parts per million) are well described by the currently accepted range of an increase of 1.5 K to 4.5 K per doubling of CO2.

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Figure 1: Records of late Pliocene/early Pleistocene .
Figure 2: Relationship between δ11B, climate forcing from CO2 and δ18O.
Figure 3: Pleistocene and late Pliocene time series.
Figure 4: Cross plots of forcing and temperature response.
Figure 5: Probability density functions of the slope from regressions of temperature against climate forcing.

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Acknowledgements

This study used samples provided by the International Ocean Discovery Program (IODP). We thank A. Milton at the University of Southampton for maintaining the mass spectrometers used in this study. S. Cherry and T. Garlichs are acknowledged for their help with sample preparation and we thank D. Liebrand for his assistance with time series analysis. This study was funded by NERC grants NE/H006273/1 to R.D.P., G.L.F., D.J.L. and D.N.S. (which supported M.A.M.-B. and M.P.S.B.) and NE/I006346/1 to P.F.S. and G.L.F. M.A.M.-B. was also supported by the European Community through a Marie Curie Fellowship and E.J.R. was supported by 2012 Australian Laureate Fellowship FL120100050. G.L.F. also wishes to acknowledge the support of Yale University (as Visiting Flint Lecturer).

Author information

Author notes

  1. M. A. Martínez-Botí and G. L. Foster: These authors contributed equally to this work.

Authors and Affiliations

  1. Ocean and Earth Science, University of Southampton, National Oceanography Centre Southampton, Southampton, SO14 3ZH, UK,

    M. A. Martínez-Botí, G. L. Foster, T. B. Chalk & E. J. Rohling

  2. Research School of Earth Sciences, The Australian National University, Canberra, Australian Capital Territory 2601, Australia,

    E. J. Rohling

  3. Centre for Earth, Planetary, Space and Astronomical Research, The Open University, Milton Keynes, MK7 6AA, UK,

    P. F. Sexton

  4. School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK,

    D. J. Lunt

  5. The Cabot Institute, University of Bristol, Bristol BS8 1UJ, UK,

    D. J. Lunt, R. D. Pancost, M. P. S. Badger & D. N. Schmidt

  6. Organic Geochemistry Unit, School of Chemistry, University of Bristol, Bristol BS8 1TS, UK,

    R. D. Pancost & M. P. S. Badger

  7. School of Earth Sciences, University of Bristol, Wills Memorial Building, Bristol, BS8 1RJ, UK,

    D. N. Schmidt

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  1. M. A. Martínez-Botí
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Contributions

M.A.M.-B. and T.B.C. collected the data and G.L.F. performed all relevant calculations. P.F.S. helped with sample preparation for δ11B analysis and refined the age models used for ODP Sites 999 and 662. G.L.F., M.A.M.-B. and T.B.C. constructed the first draft of the manuscript and all authors contributed specialist insights that helped refine the manuscript. G.L.F., R.D.P., D.J.L. and D.N.S. conceived the study.

Corresponding author

Correspondence to G. L. Foster.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Maps of modern mean annual and SST labelled with site locations.

a, Map of sites used for reconstructions with the mean annual modern from the reconstruction of ref. 21. b, Map of the sites (and labelled with their depths) used to generate the SST stack with mean annual modern SST from the World Ocean Atlas 2013 (ref. 89). mbsl, metres below sea level, where DSDP is the Deep Sea Drilling Project. Figures constructed and data visualized in Ocean Data View90.

Extended Data Figure 2 Comparisons of boron-isotope-based estimates with other methodologies and archives.

a, Estimates of from published δ11B records compared to ice-core CO2 (red line; refs 27, 28, 29). The dotted line is for = 278 μatm. In a the data of ref. 20 (blue circles) have been recalculated in the same manner as described here for the Pliocene, including using the G. ruber δ11B–pH calibration of ref. 18. The error band encompasses 68% (dark blue) and 95% (light blue) of 10,000 Monte Carlo simulations of (see main text). Also shown are the G. sacculifer-based δ11B– record of ref. 30 (green circles). In this case error bars (±25 µatm) are as determined in that study. Despite similar analytical uncertainty, the smaller error bars for the ref. 30 data result from these authors not propagating the δ11B–pH calibration uncertainty and considering a smaller range in temperature, salinity and alkalinity uncertainty than in this study (±0.76 °C, ±1 psu, ±27 µmol kg−1 versus ±3 °C, ±3 practical salinity units (psu), ±175 µmol kg−1 with a flat probability in this study). b, δ11B-based record generated here (blue closed circles and 95% and 68% uncertainty bands) with from the δ13C of alkenones from published studies. See Fig. 1 legend for details. c, δ11B-based record generated here (blue closed circles and 95% and 68% uncertainty bands) with from previous δ11B-based studies and from plant stomata. See Fig. 1 legend for details. d–f, Comparison of cross plots of CO2 forcing and ΔMAT for our high-resolution δ11B–CO2 record (d), published alkenone CO2 data (e) and published low-resolution δ11B–CO2 data (f). In each panel the slopes of regression lines fitted through the data are labelled (±1 standard error, se). In d ice-core CO2 data are shown as red open circles and Pliocene δ11B–CO2 as open blue circles. In e and f, ice-core CO2 data are shown in grey for clarity. In e, alkenone CO2 data are from the following sources: ODP 1208 (orange16), ODP 806 (purple16); ODP 925 (brown49); ODP 999 (green circles25; green squares26). In c δ11B–CO2 data are from ODP 999 (blue25 and red23).

Extended Data Figure 3 Probability density functions for equivalently aged samples from ODP Site 662 and ODP Site 999.

Each panel, labelled with age (in units of kyr ago), shows the probability density function for a given estimate of from ODP Site 662 (red) and ODP Site 999 (blue). In most instances equal age samples are compared, but in some cases either where variability is high and/or equivalent age samples are absent, we show neighbouring samples from ODP Site 999 (for example, bottom left and right). This comparison indicates that although the mean of ODP 662 tends to be higher than ODP 999, there is always a high degree of overlap between the estimates from the two sites.

Extended Data Figure 4 Probability density functions of and benthic δ18O and time series analysis.

a, Probability density functions of the residuals of δ11B– about the long-term trend for the late Pliocene (this study; blue line), the mid-Pleistocene30 (green line) and late Pleistocene19,20 (red line). Dashed vertical lines show the upper and lower limit (labelled) encompassing 90% of the data. The residual of the ice-core CO2 record27,28,29 about the long-term mean for 0–0.8 Myr ago plus a random noise equivalent to ±35 μatm (the typical δ11B–CO2 uncertainty) is shown as a black dashed probability density function. b, Probability density functions of the residual of LR04 benthic δ18O from the long-term trend for the late Pleistocene (red), mid-Pleistocene (green) and late Pliocene (blue). Dashed vertical lines show the upper and lower limit (labelled) encompassing 90% of the data. In contrast to , δ18O clearly exhibits an increase in variability over the last 3.3 Myr. c, d, Evolutive power spectral analyses of Pliocene (c) and resampled δ18O (ref. 22) (d). The evolutive power spectra was computed using the fast Fourier transform of overlapping segments with a 300-kyr moving window. Before spectral analysis, all series were notch-filtered to remove the long-term trend (bandwidth = 0.005), and interpolated to 12-kyr intervals (the real resolution of our record is ∼13.5 kyr).

Extended Data Figure 5 Summary of sea-level records used to calculate ΔFLI.

In a and b the red curve is from ref. 13 (R14) based on the planktic δ18O from the Mediterranean Sea and the methods developed for the Red Sea by ref. 93. We have removed those intervals identified as possible sapropel (organic-matter-rich sediments) events and linearly interpolated across gaps in the original record. The black curve is the sea-level record from an inversion of the benthic oxygen isotope record of ref. 76 (tuned to LR04 here) using an ice sheet model35 (VDW11). The blue curve in a is based on the planktic/bulk δ18O from the Red Sea44 for the interval 0–520 kyr and the paired Mg/Ca and benthic δ18O from the deep South Pacific for the interval 520–800 kyr (ref. 45) (R09+E12). The green curve in b is based on a scaling of the LR04 δ18O stack to indicators of sea level from sequence stratigraphy (ref. 46 recalculated by ref. 12). In each the uncertainty in the reconstruction at 95% confidence is shown by an appropriately coloured error band. Marine isotope stages mentioned in text are labelled. RSL, relative sea-level change (in metres), relative to the modern value.

Extended Data Figure 6 Stacked sea surface temperature record.

a, b, Number of records that contribute to the SST stack through time. c, d, Uncertainty in the SST stack due to analytical uncertainty (at 95% confidence; red band) and showing the influence of jacknifing (that is, removing one record at a time; grey lines show maximum and minimum). Note that the jacknifing illustrates that no single record has an undue influence on the SST stack.

Extended Data Figure 7 Comparison of global SST from the HadSST3 data set with SST HadISST1 from ODP sites.

a, Historic global mean annual sea surface temperature anomaly from the HadSST3 data set86,87 (red circles) and mean SST at locations above the ODP sites that make up the SST stack from HadISST1 (blue; local SST). Thick red and blue lines are non-parametric smoothers through both data sets. b, Cross plot of global mean annual SST and local SST. The regression line determined using linear regression has a slope of ∼1 and intercept of close to 0, so local SST captures the global trend well. The shaded blue band in b represents the 95% confidence interval of the regression line.

Extended Data Figure 8 The influence of TA and δ11Bsw on determinations of Sp using linear regression.

a, b, Artificial δ11B record (where δ11B foram is the boron isotopic composition of an artificial foraminifera; a) and temperature record (b). c, d, Cross plot and regressions of δ11B–ΔFCO2 and global temperature for TA dramatically varying in the range 2,000–2,600 µmol kg−1 (TA; c) and δ11Bsw from 38.8‰ to 40.4‰ (d). The slopes of the regressions, which are very similar regardless of parameter choice, are colour-coded and listed in the bottom right-hand corner of c and d. e, f, Probability density function of slope for regressions of Pliocene-aged ΔMAT against ΔFCO2 (e) and ΔFCO2,LI (f), where TA is decreasing by 200 µmol kg−1 (dashed) and increasing by 200 µmol kg−1 (dotted). Note that despite large variations in TA the slope of the regressions do not change greatly.

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Martínez-Botí, M., Foster, G., Chalk, T. et al. Plio-Pleistocene climate sensitivity evaluated using high-resolution CO2 records. Nature 518, 49–54 (2015). https://doi.org/10.1038/nature14145

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