Coupling of Indo-Pacific climate variability over the last millennium (original) (raw)

Data availability

The coral δ18O data needed to reproduce the results are available at the World Data Service for Paleoclimatology at http://www.ncdc.noaa.gov/paleo/study/28451. Archived data includes coral δ18O and δ18O anomaly data, U-Th age data, reconstructed positive IOD event years, and the moving 30-year standard deviation of July–December IOD variability.

Change history

This Article was amended to correct a minor error in the Acknowledgements.

A Correction to this paper has been published: https://doi.org/10.1038/s41586-021-04318-0

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Acknowledgements

This research was supported by an Australian Research Council QEII Fellowship to N.J.A. (DP110101161; including C.-C.S., H.C. and R.L.E.) and the ARC Centre of Excellence for Climate Extremes (CLEX; CE170100023; to N.J.A., N.M.W., M.H.E. and C.C.U.). Further support to N.J.A. was provided by ARC Discovery Project DP140102059 and Future Fellowship FT160100029. B.E. was supported by an Australian Research Training Program scholarship and B.C.D. received scholarship support from the ARC Centre of Excellence for Climate System Science (CE110001028). M.H.E. is also supported by the Earth Science and Climate Change Hub of the Australian Government’s National Environmental Science Programme (NESP). C.C.U. acknowledges support by the US National Science Foundation (AGS-1602455). C.-C.S. thanks the Science Vanguard Research Program of the Ministry of Science and Technology (108-2119-M-002-012) and the Higher Education Sprout Project of the Ministry of Education, Taiwan, Republic of China (108L901001) for support. H.C. acknowledges support by the National Natural Science Foundation of China (NSFC 41888101). We gratefully acknowledge the Ministry of Research, Technology and Higher Education, and the Director of Intellectual Property Management as Secretary of the Coordinating Team for Foreign Research Permit (TKPIPA) for the research permit in Indonesia. Fieldwork was carried out in 2001 under research permit 2889/II/KS/2001, supported by W. Hantoro and the Indonesian Institute of Sciences. We thank W. Hantoro, B. Suwargadi, D. Prayudi, I. Suprianto, M. Gagan, K. Glenn, T. Watanabe, H. Scott-Gagan, and K. Sieh for assistance with fieldwork, J. Cowley, J. Cali, D. Becker, A. Kimbrough, S. Wong, B. Plunkett, S. Sosdian, H. Scott-Gagan and C.-H. Hsu for laboratory support, the NCAR CESM1 modelling group for making their last millennium ensemble simulations available, and Australia’s National Computational Infrastructure and CLEX Computational Modeling Systems team for data hosting and support. We acknowledge Python Software Foundation (Python version 3.7.2), MathWorks Inc. (MATLAB Release 2014a) and R. Pawlowicz (M_Map mapping package for MATLAB, version 1.4g) for software used in analysis and figure generation. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government. We thank J. Addison and R. Halley (US Geological Survey) for internal reviews of this manuscript before submission, and S. Eggins for leadership and guidance.

Author information

Author notes

  1. Jennifer B. Wurtzel
    Present address: New South Wales Department of Primary Industries, Orange, New South Wales, Australia

Authors and Affiliations

  1. Research School of Earth Sciences, The Australian National University, Canberra, Australian Capital Territory, Australia
    Nerilie J. Abram, Nicky M. Wright, Bethany Ellis, Bronwyn C. Dixon, Jennifer B. Wurtzel & David Heslop
  2. ARC Centre of Excellence for Climate Extremes, The Australian National University, Canberra, Australian Capital Territory, Australia
    Nerilie J. Abram & Nicky M. Wright
  3. ARC Centre of Excellence for Climate System Science, The Australian National University, Canberra, Australian Capital Territory, Australia
    Bethany Ellis & Bronwyn C. Dixon
  4. School of Geography, University of Melbourne, Melbourne, Victoria, Australia
    Bronwyn C. Dixon
  5. Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia
    Matthew H. England
  6. ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, New South Wales, Australia
    Matthew H. England & Caroline C. Ummenhofer
  7. Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, USA
    Caroline C. Ummenhofer
  8. Earthquake Science Center, United States Geological Survey, Menlo Park, CA, USA
    Belle Philibosian
  9. Research Centre of Geotechnology, Indonesian Institute of Sciences (LIPI), Bandung, Indonesia
    Sri Yudawati Cahyarini
  10. High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University, Taipei, Taiwan
    Tsai-Luen Yu & Chuan-Chou Shen
  11. Research Center for Future Earth, National Taiwan University, Taipei, Taiwan
    Tsai-Luen Yu & Chuan-Chou Shen
  12. Global Change Research Center, National Taiwan University, Taipei, Taiwan
    Chuan-Chou Shen
  13. Institute of Global Environmental Change, Xi’an Jiaotong University, Xi’an, China
    Hai Cheng
  14. Department of Geology and Geophysics, University of Minnesota, Minneapolis, MN, USA
    Hai Cheng & R. Lawrence Edwards

Authors

  1. Nerilie J. Abram
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  2. Nicky M. Wright
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  3. Bethany Ellis
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  4. Bronwyn C. Dixon
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  5. Jennifer B. Wurtzel
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  6. Matthew H. England
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  7. Caroline C. Ummenhofer
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  8. Belle Philibosian
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  9. Sri Yudawati Cahyarini
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  11. Chuan-Chou Shen
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Contributions

N.J.A. designed the study and led the analysis, interpretation and writing. B.E., B.C.D. and J.B.W. contributed to coral sample milling and geochemical analysis. N.J.A. and N.M.W. led the model analysis with assistance from M.H.E. and C.C.U. B.P. aided in chronology development for the fossil coral records, S.Y.C. provided coral palaeoclimate expertise, T.-L.K., C.-C.S., H.C. and R.L.E. carried out U–Th analyses, and D.H. helped with the statistical tests. All authors contributed to discussions during preparation of the manuscript.

Corresponding author

Correspondence toNerilie J. Abram.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature thanks Matthias Prange, Diane M. Thompson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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 Location map and coral δ18O–climate relationships.

a, Coral samples used in this study were collected from the southern Mentawai Islands, offshore of Sumatra in the eastern Indian Ocean. Study sites are Tinopo (coral TT01-A-1b), Saomang (corals SMG01-A-2, SMG01-A-4a, SMG01-A-5b and SMG01-A-10c), Pororogat (cores P01-C-2a and 2b), Silabu (coral NP01-A-3) and Siruamata (coral SI94-A-6). b, c, Relationships between modern coral δ18O (TT01-A-1b) and instrumental records51,52 of local SST (b) and precipitation (c) at 3° S, 100° E. Crosses show monthly average relationship and filled circles show relationship for July–December averages.

Extended Data Fig. 2 Observed and modelled IOD correlations.

a–d, Correlations of the DMI with SST (upper) and precipitation (lower) in instrumental data (a, c) and in the CESM1-LME simulations (b, d). Correlations are for July–December IOD season averages and cover 1982–2018 (a), 13 simulations of 850–2005 (15,028 years) (b), 1979–2018 (c) and 12 simulations of 850–2005 (13,872 years) (d). The study area (3° S, 100° E) is marked with yellow stars. e, f, Moving correlations of local SST (e) and precipitation (f) with the DMI in the CESM1-LME simulations for 30-year (light) and 100-year (dark) windows, demonstrating a significant (P < 0.01) and stable relationship through time of climate anomalies at our study site with the DMI.

Extended Data Fig. 3 Linearity of the response of rainfall in the southern Mentawai Islands to positive IOD events.

a–d, July–December averages of precipitation in the southern Mentawai Islands (3° S, 100° E) are plotted against the DMI (a, b) and local SST (c, d). Left-side panels (a, c) show relationship in instrumental data (1982–2018), and right-side panels (b, d) show relationship in full-forcing CESM1-LME simulations (12 atmospheric simulations each spanning 850–2005). e, f, Moving correlation (e) and moving slope (f) of the linear relationship of local SST with precipitation in the CESM1-LME simulations for 30-year (light) and 100-year (dark) windows. The instrumental and model data demonstrate the strong (P < 0.01) and stable linear response of rainfall at the study site to IOD variability, particularly during positive IOD events (positive DMI values and cool SST anomalies).

Extended Data Fig. 4 Coral data used in this study.

a, Monthly-resolution data (dark) and 7-year filter (light) of the nine coral δ18O records used in this study. b, Monthly δ18O anomalies after removal of 7-year filter. c, Mean annual δ18O cycle of the modern coral from our study area (black), along with 25–75% (dark shading) and 5–95% (light shading) distributions around the mean. Coloured curves give the mean annual δ18O cycle of each fossil coral record. Internal chronologies of the coral records were established by assigning the annual δ18O maxima to October, the coolest month on average in the southern Mentawai Islands (Methods). Details of the coral samples and δ18O records are provided in the Supplementary Information.

Extended Data Fig. 5 Composite time series for positive IOD events.

a, b, Composite records (bold curves) of coral δ18O anomalies for all positive IOD events (thin green curves based on 33 events) (a) and all extreme positive IOD events (thin blue curves based on 10 events) (b) in the last millennium coral reconstruction. Composites are aligned such that positive IOD events occur within July–December of year 0.

Extended Data Fig. 6 Extreme positive IOD events in the last millennium.

a, b, Detailed comparison of coral δ18O anomalies during the 1997 (a) and 1675 (b) extreme positive IOD events, showing monthly average anomalies (thick dark blue lines), the non-interpolated δ18O anomaly data (thin light blue lines) and the raw δ18O anomaly measurements demonstrating replicate analyses across the extreme positive IOD events (grey crosses). c, Details of the extreme positive IOD event years identified in the last millennium IOD reconstruction (Fig. 2), giving the peak monthly mean δ18O anomaly and the July–December mean δ18O anomaly for each event, and the magnitude of these isotopic anomalies relative to the 1997 extreme positive IOD event. We note that coral δ18O data for the 1877 event is based on a previously published northern Mentawai Islands sample48, and its magnitude is assessed relative to 1997 coral δ18O data from the northern Mentawai Islands. See Methods for details on chronological constraints and uncertainties on absolute fossil coral ages.

Extended Data Fig. 7 Mid-millennium shift in IOD and ENSO variability.

a, b, Distributions of coral δ18O before 1590 and from 1590 onwards demonstrate changes in IOD (a) and ENSO (b) variability during the mid-millennium. Identification of a mid-millennium shift in IOD–ENSO variability to around 1590 (Fig. 3) is based on the time when the 30-year IOD variability in the sixteenth-century coral sequence transitioned from negative to positive amplitude anomalies (relative to 1961–1990). Distributions shown here are based on July–December average data for the IOD reconstruction, and July–June average data for the ENSO reconstruction, with δ18O normalized relative to the 1961–1990 interval. For the IOD reconstruction the distributions are derived from 196 years of coral data before 1590, and 286 years of coral data from 1590 onwards. For the ENSO reconstruction the distributions are derived from 364 years of coral data before 1590, and from 216 years of data after 1590. Statistical testing (Kolmogorov–Smirnov test) indicates that the pre-1590 distributions are significantly narrower (reduced range of variability) than distributions of data from 1590 onwards for both the IOD and ENSO reconstructions (P = 0.0004 for IOD changes, and P = 0.04 for ENSO changes). Moving application of this distribution testing further confirms that minimum P values are achieved if the mid-millennium shift is placed during the 1590s; specifically 1591 (1598) based on the Kolmogorov–Smirnov (Wilcoxon rank-sum) method.

Extended Data Fig. 8 Recurrence times between positive IOD events.

a, b, Distributions for the recurrence times between positive IOD events in the coral reconstruction (blue) (a) and the CESM1-LME simulations (black) (b). Vertical black line denotes the mean interval between positive IOD events across the full timeseries. c, The cumulative probability of subsequent positive IOD events, based on years since the previous event, was assessed across the last millennium IOD reconstruction using a Kaplan–Meier estimate (blue curve) with 95% confidence bounds (shading).

Extended Data Fig. 9 Coupling of IOD–ENSO variability in last millennium simulations.

Composite maps as in Fig. 4c–j, but based on 10-year standard deviation of IOD variability. a–d, Composite maps of standard deviation of July–December SST anomalies (a), and July–December averages of anomalies of SST (b), depth of the 20 °C isotherm (D20) (c) and mean sea level pressure (MSLP; shading) and surface wind stress (arrows) (d), calculated across all 10-year intervals where July–December IOD variability was below the 10th percentile in the CESM1-LME full forcing ensemble. e–h, as in a–d, but composited across all 10-year intervals where IOD variability was above the 90th percentile. Data are shown only for grid cells where distributions between intervals of low and high IOD variability are significantly (P < 0.05) different based on a Kolmogorov–Smirnov test. Composite maps show the same spatial pattern of climate anomalies as in Fig. 4, but with greater amplitude owing to the shorter compositing window.

Extended Data Fig. 10 Decadal modulation of positive IOD event frequency in last millennium simulations.

a, b, Scatter plots of the modelled DMI (a) against the Tripole Index (TPI) of the Interdecadal Pacific Oscillation, and for 10-year positive IOD event numbers against 10-year averages of the TPI (b). Correlation statistics in a and b are given for aggregated data from all 13 full forcing simulations, and values in square brackets give the range of values calculated across individual simulations. c–j, Composite climate anomalies during decades of rare and frequent positive IOD events. c–f, Composite maps of standard deviation σ of annual SST anomalies (c), and annual averages of anomalies of SST(d), depth of the 20 °C isotherm (D20) (e), and mean sea level pressure (shading) and surface wind stress (arrows) (f), calculated across all 10-year intervals containing 1 or fewer positive IOD events. g–j, As in c–f, but composited across all 10-year intervals containing 4 or more positive IOD events. Data are shown in c–j only for grid cells where distributions between intervals of rare and frequent IOD events are significantly (P < 0.05) different based on a Kolmogorov–Smirnov test.

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Abram, N.J., Wright, N.M., Ellis, B. et al. Coupling of Indo-Pacific climate variability over the last millennium.Nature 579, 385–392 (2020). https://doi.org/10.1038/s41586-020-2084-4

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