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Sensitivity of Holocene East Antarctic productivity to subdecadal variability set by sea ice

Nature Geoscience volume 14, pages 762–768 (2021)Cite this article

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Abstract

Antarctic sea-ice extent, primary productivity and ocean circulation represent interconnected systems that form important components of the global carbon cycle. Subdecadal to centennial-scale variability can influence the characteristics and interactions of these systems, but observational records are too short to evaluate the impacts of this variability over longer timescales. Here, we use a 170-m-long sediment core collected from Integrated Ocean Drilling Program Site U1357B, offshore Adélie Land, East Antarctica to disentangle the impacts of sea ice and subdecadal climate variability on phytoplankton bloom frequency over the last ~11,400 years. We apply X-ray computed tomography, Ice Proxy for the Southern Ocean with 25 carbon atoms, diatom, physical property and geochemical analyses to the core, which contains an annually resolved, continuously laminated archive of phytoplankton bloom events. Bloom events occurred annually to biennially through most of the Holocene, but became less frequent (~2–7 years) at ~4.5 ka when coastal sea ice intensified. We propose that coastal sea-ice intensification subdued annual sea-ice break-out, causing an increased sensitivity of sea-ice dynamics to subdecadal climate modes, leading to a subdecadal frequency of bloom events. Our data suggest that projected loss of coastal sea ice will impact the influence of subdecadal variability on Antarctic margin primary productivity, altering food webs and carbon-cycling processes at seasonal timescales.

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Fig. 1: Area of study and example bloom event after sea-ice break-out.
Fig. 2: Simplified sediment deposition model for U1357B.
Fig. 3: Holocene proxy records in Adélie Land.
Fig. 4: EHA of greyscale data and XRF Si/Ti productivity proxies.

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Data availability

The raw greyscale data, light laminae depths, light laminae sand percent, XRF silicon, XRF titanium and HBI diene data for IODP Site U1357B are available at https://doi.org/10.1594/PANGAEA.933380.

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Acknowledgements

This research used samples and data provided by IODP expedition 318, sponsored by the US National Science Foundation (NSF) and participating countries under the management of the Consortium for Ocean Leadership, including the Australian and New Zealand International Ocean Discovery Program Consortium. Funding was provided by Royal Society Te Apārangi Marsden Fund (18-VUW-089 to R.M.M. and 15-VUW-131 to N.A.N.B.) and the New Zealand Ministry of Business, Innovation and Employment through the Antarctic Science Platform (ANTA1801). Funding was also provided by the New Zealand Ministry of Business, Innovation and Employment Strategic Science Investment Fund (SSIF) through GNS Science (grant 540GCT32). We acknowledge funding from the Dumont d’Urville NZ-France Science and Technology Programme, MARICE project (Marine and Ice core reconstruction of East Antarctic sea ice variability over the past 2,000 years) (project nos. 45455NF and 19-VUW-047-DDU Catalyst Fund, RSNZ). J.E. and X.C. acknowledge funding by the ERC StG ICEPROXY (203441), the ANR CLIMICE and the FP7 Past4Future (243908) projects. F.J.J.-E. was funded by project 201830I092 (Spanish Research Council). C.E. and F.J.J.-E acknowledge funding by the Spanish Ministry of Science and Innovation (grant CTM2017-89711-C2-1-P), co-funded by the European Union through FEDER funds. C.R.R. was funded by a University of Otago research grant and a L’Oréal-UNESCO For Women in Science Australia and New Zealand Fellowship. The Natural Environment Research Council funded K.E.A. (CENTA PhD; NE/L002493/1) and J.B. (standard grant Ne/I00646X/1). Y.Y. was funded by the Japan Society for Promotion of Science (JSPS) grant no. JP20H00193. S.F.P. was supported by National Science Foundation grant OPP-0732796. R.B.D. was supported by National Science Foundation grants PLR-1644118 and OCE-1129101. The authors acknowledge the Norwegian Polar Institute’s Quantarctica package, and the use of imagery from the NASA Worldview application (https://worldview.earthdata.nasa.gov/), part of the NASA Earth Observing System Data and Information System.

Author information

Authors and Affiliations

  1. GNS Science, Lower Hutt, New Zealand

    Katelyn M. Johnson & Nancy A. N. Bertler

  2. Antarctic Research Centre, Victoria University of Wellington, Wellington, New Zealand

    Katelyn M. Johnson, Robert M. McKay, Anya Albot, Nancy A. N. Bertler & Huw J. Horgan

  3. Andaluz Institute of Earth Sciences, CSIC-University of Granada, Granada, Spain

    Johan Etourneau, Francisco J. Jiménez-Espejo & Carlota Escutia

  4. EPHE, PSL University, Paris, France

    Johan Etourneau

  5. UMR 5805 EPOC CNRS, University of Bordeaux, Bordeaux, France

    Johan Etourneau & Xavier Crosta

  6. Biogeochemistry Center, JAMSTEC, Yokosuka, Japan

    Francisco J. Jiménez-Espejo

  7. Department of Geology, University of Otago, Dunedin, New Zealand

    Christina R. Riesselman

  8. Department of Marine Science, University of Otago, Dunedin, New Zealand

    Christina R. Riesselman

  9. School of Geography, Earth, and Environmental Sciences, University of Birmingham, Birmingham, UK

    James Bendle & Kate E. Ashley

  10. Institute for Space–Earth Environmental Research (ISEE), Nagoya University, Nagoya, Japan

    Masako Yamane

  11. Analytical Center for Environmental Study, Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Japan

    Yusuke Yokoyama

  12. School of Earth and Environmental Sciences, City University of New York - Queens College, New York, NY, USA

    Stephen F. Pekar

  13. School of Earth, Energy, and Environmental Sciences, Stanford University, Stanford, CA, USA

    Robert B. Dunbar

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Contributions

K.M.J., R.M.M. and N.A.N.B designed the study and wrote the paper with input from all authors. K.M.J., R.M.M. and H.J.H. analysed the X-ray CT data. R.M.M. and A.A. conducted the grain size analyses. J.E. produced the HBI data. F.J.J.-E. produced the XRF geochemical data. C.R.R. conducted the opal (%BSi) measurements. M.Y. and Y.Y. analysed and provided the compound-specific 14C ages. R.B.D. and C.E. were lead proponents of the ancillary IODP expedition 318 proposal to core IODP Site 1357. All authors contributed to the interpretations of data and finalization of the manuscript.

Corresponding author

Correspondence to Katelyn M. Johnson.

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The authors declare no competing interests.

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Peer review information Nature Geoscience thanks Kaarina Weckström and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor(s): 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 Corrected and calibrated radiocarbon age models for U1357B and MD03-2601.

Age Model for U1357B (blue symbols show calibrated and corrected ages, and blue line of calculated Bayesian age-depth curve, using BACON R package; dotted lines are 2 sigma uncertainty) and MD03-2601 (red symbols and lines as for U1357B) show that sediment advection is a regional signal as sedimentation rates covary. Compound specific ages from U1357A are shown in green with 2 sigma uncertainties. U1357B is a longer core with 87 14C dates, leading to a much higher resolution age model. U1357B also includes the last 1,000 years, which is lacking in MD03-2601.

Extended Data Fig. 2 Grain size analysis of light laminae.

Light laminae in three intervals (Interval 1: ~4.5 to present; Interval 2: ~8-4.5 ka; Interval 3: ~11.4-8 ka;) have a dominant silt-fine sand mode and are less than 125 µm. Sand percent provides a measure of this coarse silt to fine sand mode and is used as a proxy to capture the upper values of relative current strength.

Extended Data Fig. 3 Correlations of Adélie Drift Proxies.

(a,b) Overlay plot and correlation between laminae counts (frequency of biological blooms) and biogenic mass accumulation rates (advection of biological material by wind driven current strength). (c,d) Overlay plot and correlation between sand percent (grain size proxy for wind driven current strength) and biogenic mass accumulation rates (advection of biological material by wind driven current strength). (d,e) Overlay plot and correlation between sand percent (grain size proxy for wind driven current strength) and laminae counts (frequency of biological blooms).

Extended Data Fig. 4 Image Analysis example.

From left to right: Line-scan core photo, CT image, raw greyscale curve with light laminae picks in orange, and XRF titanium data. The CT image visually enhances the laminations, and provides a sub-mm resolved greyscale curve that better captures the rapid shifts in sedimentation. Frequencies from the manually picked laminae (Fig. 3d) match those extracted from the greyscale curve (Fig. 4).

Extended Data Fig. 5 Greyscale data in relation sedimentological changes at the site.

Greyscale data compared to various sedimentological proxies of the core. (a) GRA Bulk Density (b) Natural Gamma Radiation (NGR) (c) XRF peak area of silicon (d) XRF peak area of titanium (e) XRF peak area of silicon/titanium ratio used to indicate long-term changes in biological productivity (f) Greyscale curve from the CT images. XRF, GRA Bulk Density, and Greyscale interpolated to 5 cm. Black curves indicate a robust LOESS smoothing using 2% of the data points for all data sets. GRA Bulk Density and NGR data from ref. 17.

Extended Data Fig. 6 Holocene Mean Changepoints.

Calculation of shifts in the mean of various Holocene records using statistical changepoint analysis. This method identifies the point at which the mean of a timeseries changes most significantly by finding the point at which the total residual error from the mean of each section is minimized (ref. 80,81). Shifts in Adélie Land proxies (a-d) occur between ~4.3 and ~4.7 ka.

Extended Data Fig. 7 IBRD in U1357B.

Section 19H5 (169.98-170.67 m) from U1357B is the only portion of core with substantial IBRD. The top of this section is dated to ~11.365ka.

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Supplementary Figs. 1 and 2, and Table 1.

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Johnson, K.M., McKay, R.M., Etourneau, J. et al. Sensitivity of Holocene East Antarctic productivity to subdecadal variability set by sea ice. Nat. Geosci. 14, 762–768 (2021). https://doi.org/10.1038/s41561-021-00816-y

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