Links between tropical Pacific seasonal, interannual and orbital variability during the Holocene

Journal name:
Nature Geoscience
Volume:
9,
Pages:
168–173
Year published:
DOI:
doi:10.1038/ngeo2608
Received
Accepted
Published online

Abstract

The El Niño/Southern Oscillation (ENSO) is the leading mode of interannual climate variability. However, it is unclear how ENSO has responded to external forcing, particularly orbitally induced changes in the amplitude of the seasonal cycle during the Holocene. Here we present a reconstruction of seasonal and interannual surface conditions in the tropical Pacific Ocean from a network of high-resolution coral and mollusc records that span discrete intervals of the Holocene. We identify several intervals of reduced variance in the 2 to 7yr ENSO band that are not in phase with orbital changes in equatorial insolation, with a notable 64% reduction between 5,000 and 3,000 years ago. We compare the reconstructed ENSO variance and seasonal cycle with that simulated by nine climate models that include orbital forcing, and find that the models do not capture the timing or amplitude of ENSO variability, nor the mid-Holocene increase in seasonality seen in the observations; moreover, a simulated inverse relationship between the amplitude of the seasonal cycle and ENSO-related variance in sea surface temperatures is not found in our reconstructions. We conclude that the tropical Pacific climate is highly variable and subject to millennial scale quiescent periods. These periods harbour no simple link to orbital forcing, and are not adequately simulated by the current generation of models.

At a glance

Figures

  1. Location and ENSO sensitivity of proxy archives.
    Figure 1: Location and ENSO sensitivity of proxy archives.

    Circles denote corals; stars denote molluscs. Contours denote biocarbonate δ18O composites (‰ per °C of NINO3.4 SST) derived from the model of ref. 39 driven by NCEP OI analysis v2 SST and SODA 2.2.4 SSS over 1981–2010 boreal winters (Supplementary Figs 8 and 9). δ18O values were regressed onto NINO3.4 SST to highlight relationships to ENSO. The three equatorial study regions (west, centre and east) are delineated by rectangles. Note that refs 11,12,15,16,18,19,20,21 all use data from Kiritimati (1° 53′ N,157° 24′ W).

  2. Distribution of seasonal and interannual variability in models and observations.
    Figure 2: Distribution of seasonal and interannual variability in models and observations.

    Top: changes in ENSO variance and AC amplitude over the Holocene. Left column: changes in ENSO-band (2–7yr) variance between fossil and modern samples in the west (top), centre (middle) and east (bottom). Horizontals bars mark the period covered by each data set; except for molluscs from the Peruvian coast, these are narrower than the symbol width. Ellipses represent uncertainties about these ratios in both dimensions: the width represents a 95% CI for the central date of each sample, based on reported analytical uncertainty on radiometric ages; the vertical component is a 95% CI for the variance ratio, obtained through a block-bootstrap procedure (Methods). Unity (no change) is marked by a dashed grey line. Similar statistics derived from PMIP3 models on 50-yr windows are depicted on side panels for piControl and midHolocene experiments. Solid lines are kernel density estimates of those distributions (Methods), whereas dashed lines indicate their median. Right column: idem for AC amplitude.

  3. Link between interannual and seasonal variability in models and observations.
    Figure 3: Link between interannual and seasonal variability in models and observations.

    Link between ENSO variance and the seasonal cycle in proxy observations (top) and PMIP3 models (bottom). The observed values are for all seasonally resolved records from the Pacific during the whole of the past 10kyr. The simulated values are based on 50-year segments from the midHolocene and piControl simulations. On top, symbology as in Fig. 1. On the bottom, triangles denote the median of piControl simulations, squares the median of midHolocene simulations. Data from the EP, CP and WP were pooled together, scaled by their interquartile range so their uncertainties on both axes are commensurate. An orthogonal regression (total least squares, TLS) fit is presented for both data sets, together with approximate 95% CIs (dashed lines) and probability density (grey contours) obtained through bootstrap resampling (Methods).

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Author information

Affiliations

  1. Department of Earth Sciences, University of Southern California, Los Angeles, California 90089, USA

    • J. Emile-Geay &
    • Y. Zhou
  2. School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA

    • K. M. Cobb
  3. Institut des Sciences de lEvolution, Université de Montpellier, CNRS, IRD, EPHE, Montpellier 34095, France

    • M. Carré
  4. IPSL/LSCE, unité mixte CEA-CNRS-UVSQ, Gif sur Yvette 91191, France

    • P. Braconnot
  5. Sorbonne Universités, UPMC Univ. Paris 6, LOCEAN/IPSL, UMR 7159, CNRS-IRD-MNHN, 75005 Paris, France

    • J. Leloup
  6. Centre for Past Climate Change and School of Archaeology, Geography and Environmental Sciences (SAGES), University of Reading, Whiteknights, Reading RG6 6AB, UK

    • S. P. Harrison
  7. Université Bordeaux, UMR CNRS 5805 EPOC, Allee Geoffroy St Hilaire, 33615 Pessac cedex, France

    • T. Corrège
  8. School of Earth and Environmental Sciences, University of Wollongong, Wollongong, New South Wales 2522, Australia

    • H. V. McGregor
  9. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Laver Building, North Park Road, Exeter EX4 4QE, UK

    • M. Collins
  10. University of Edinburgh, School of GeoSciences, James Hutton Road, Edinburgh EH9 3FE, UK

    • R. Driscoll &
    • A. Tudhope
  11. Paleoclimats Paleoenvironnements Bioindicateurs, Université de Nantes, LPGNantes, 2 rue de la Houssinière, Nantes 44300, France

    • M. Elliot
  12. Institut für Geowissenschaften, Universität Kiel, D-24118 Kiel, Germany

    • B. Schneider

Contributions

J.E.G. designed the study, performed the analysis, led the writing, and prepared the manuscript. P.B. coordinated the synthesis. M.Carré, K.M.C., T.C., M.E. and R.D. contributed data and/or analysis. P.B. and J.L. analysed simulations and contributed to writing. Y.Z. processed PMIP3 output and generated some of the supplementary figures. A.T., B.S. and M.Collins provided input in the analysis and interpretation. J.E.G., K.M.C., M.Carré, S.P.H., H.V.M., T.C., P.B. and A.T. wrote the paper. All authors reviewed the manuscript.

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