Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Matters Arising
  • Published:

Limitations of ice cores in reconstructing temperature seasonality

Nature volume 637, pages E1–E6 (2025)Cite this article

Subjects

Matters Arising to this article was published on 01 January 2025

arising from: T. R. Jones et al. Nature https://doi.org/10.1038/s41586-022-05411-8 (2023).

Resolving seasonal variations in long proxy records is an outstanding challenge in paleoclimate research. Jones et al.1 (referred to as J23) report on the continuous reconstruction of seasonal temperatures over the last 11,000 years from the West Antarctic Ice Sheet Divide ice-core (WDC) record. Here, we show that, in this record, most of the seasonal temperature signal is lost during the recording process, and correcting for this loss would lead to unreasonable reconstructed changes in Holocene temperature seasonality. We propose that, contrary to J23’s interpretation of Holocene seasonal temperature evolution, the observed changes in the ice-core signal’s seasonality may be influenced by time-varying depositional conditions and the uncertainty of the diffusion correction.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Comparison of ice-core derived and reanalysis-based surface temperature shows that most of the seasonal cycle is lost in the recording process.
Fig. 2: Implications of the signal loss for the reconstructed amplitude of Holocene seasonality changes.
Fig. 3: Comparison of different published diffusion-length estimates for δD for WDC.

Data availability

All datasets used in this study are in public repositories as cited or from the Source Figure Data of J23. The diffusion-length estimates from ref. 13 were digitized from the figure data.

Code availability

The code and data to reproduce the results are available via Zenodo at https://doi.org/10.5281/zenodo.1328524719 (ref. 19).

References

  1. Jones, T. R. et al. Seasonal temperatures in West Antarctica during the Holocene. Nature 613, 292–297 (2023).

    Article  ADS  PubMed  PubMed Central  MATH  CAS  Google Scholar 

  2. Steig, E. J., Grootes, P. M. & Stuiver, M. Seasonal precipitation timing and ice core records. Science 266, 1885–1886 (1994).

    Article  ADS  PubMed  CAS  Google Scholar 

  3. Laepple, T. et al. On the similarity and apparent cycles of isotopic variations in East Antarctic snow pits. Cryosphere 12, 169–187 (2018).

    Article  ADS  Google Scholar 

  4. Fisher, D. A., Reeh, N. & Clausen, H. B. Stratigraphic noise in the time series derived from ice cores. Ann. Glaciol. 7, 76–83 (1985).

    Article  ADS  MATH  Google Scholar 

  5. Johnsen, S. J. et al. in Physics of Ice Core Records, Vol. 159 (ed. Hondoh, T.) 121–140 (Hokkaido Univ. Press, 2000).

  6. Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).

    Article  ADS  MATH  Google Scholar 

  7. White, J. et al. Stable isotopes of ice in the transition and glacial sections of the WAIS Divide deep ice core. US Antarctic Program (USAP) Data Center https://doi.org/10.15784/601274 (2019).

  8. Jones, T. R. Seasonal temperatures in West Antarctica during the Holocene. US Antarctic Program (USAP) Data Center https://doi.org/10.15784/601603 (2022).

  9. Barnes, P. R. F., Wolff, E. W. & Mulvaney, R. A 44 kyr paleoroughness record of the Antarctic surface. J. Geophys. Res. 111, D03102 (2006).

  10. Casado, M., Münch, T. & Laepple, T. Climatic information archived in ice cores: impact of intermittency and diffusion on the recorded isotopic signal in Antarctica. Clim. Past 16, 1581–1598 (2020).

    Article  Google Scholar 

  11. Hirsch, N. et al. Stratigraphic noise and its potential drivers across the plateau of Dronning Maud Land, East Antarctica. Cryosphere 17, 4207–4221 (2023).

    Article  ADS  MATH  Google Scholar 

  12. Münch, T. & Laepple, T. What climate signal is contained in decadal- to centennial-scale isotope variations from Antarctic ice cores? Clim. Past 14, 2053–2070 (2018).

    Article  MATH  Google Scholar 

  13. Kahle, E. C., Holme, C., Jones, T. R., Gkinis, V. & Steig, E. J. A generalized approach to estimating diffusion length of stable water isotopes from ice-core data. J. Geophys. Res. Earth Surf. 123, 2377–2391 (2018).

    Article  ADS  CAS  Google Scholar 

  14. Fudge, T. J. et al. Variable relationship between accumulation and temperature in West Antarctica for the past 31,000 years. Geophys. Res. Lett. 43, 3795–3803 (2016).

    Article  ADS  MATH  Google Scholar 

  15. WAIS Divide Project Members. Onset of deglacial warming in West Antarctica driven by local orbital forcing. Nature 500, 440–444 (2013).

    Article  ADS  Google Scholar 

  16. Jones, T. R. et al. Southern Hemisphere climate variability forced by Northern Hemisphere ice-sheet topography. Nature 554, 351–355 (2018).

    Article  ADS  PubMed  MATH  CAS  Google Scholar 

  17. Jones, T. R. et al. Water isotope diffusion in the WAIS Divide ice core during the Holocene and last glacial. J. Geophys. Res. Earth Surf. 122, 290–309 (2017).

    Article  ADS  MATH  CAS  Google Scholar 

  18. Sigl, M. et al. WAIS Divide Deep ice core 0-68 ka WD2014 chronology [dataset]. PANGAEA https://doi.org/10.1594/PANGAEA.902577 (2019).

  19. Münch, T. & Laepple, T. Code for Nature Matters Arising publication by Laepple et al. (v.1.0.0). Zenodo https://doi.org/10.5281/zenodo.13285247 (2024).

Download references

Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme Starting Grant SPACE (grant agreement number 716092). T.M. was supported by the Informationsinfrastrukturen Grant of the Helmholtz Association as part of the DataHub Earth and Environment and F.S. was supported by the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 955750.

Author information

Authors and Affiliations

  1. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany

    T. Laepple, T. Münch, N. Hirsch, F. Shaw & M. Hörhold

  2. University of Bremen, MARUM – Center for Marine Environmental Sciences and Faculty of Geosciences, Bremen, Germany

    T. Laepple

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

    M. Hörhold

Authors
  1. T. Laepple
    View author publications

    Search author on:PubMed Google Scholar

  2. T. Münch
    View author publications

    Search author on:PubMed Google Scholar

  3. N. Hirsch
    View author publications

    Search author on:PubMed Google Scholar

  4. F. Shaw
    View author publications

    Search author on:PubMed Google Scholar

  5. M. Hörhold
    View author publications

    Search author on:PubMed Google Scholar

Contributions

All authors collaborated on the design of the study, the interpretation of the results and the writing of the manuscript. T.L. led the study and performed the numerical analyses.

Corresponding author

Correspondence to T. Laepple.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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 Loss of seasonal cycle amplitude shown in the spectral domain.

Power spectral density of reanalysis daily surface air temperature at the coring site6, the WDC δD data covering the same time-period7 and as an example for the deeper ice record, the diffusion corrected and raw J23 WDC δD data of a time-window of the same length 5ka BP (as in J23, Fig. 1a). Shading indicates 90% confidence intervals. The ratio of the variance in the frequency band around the annual cycle (1/1.2-1.2) yr−1 (vertical dashed lines) is 0.222 (1979-2005) and 0.212 (5ka BP diffusion corrected) showing that only about 20% of the amplitude is preserved. The results are not sensitive to the choice of the time-window.

Extended Data Fig. 2 J23 seasonal amplitudes in δD8 (black) and WDC accumulation rate7 (blue), show a similar temporal evolution over the Holocene.

Shown are 1000 year running mean values as used in J23.

Extended Data Fig. 3

Comparison of the diffusion length used in J23 (black) and the diffusion length derived from the ratio of the corrected and uncorrected J23 annual variance (red) calculated on 140 yr windows every 70 years. Gray shading is ±1sd range of the estimation uncertainties as provided by J23.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Laepple, T., Münch, T., Hirsch, N. et al. Limitations of ice cores in reconstructing temperature seasonality. Nature 637, E1–E6 (2025). https://doi.org/10.1038/s41586-024-08181-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41586-024-08181-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing