Abstract
The Indian Ocean Dipole (IOD) affects climate and rainfall across the world, and most severely in nations surrounding the Indian Ocean1,2,3,4. The frequency and intensity of positive IOD events increased during the twentieth century5 and may continue to intensify in a warming world6. However, confidence in predictions of future IOD change is limited by known biases in IOD models7 and the lack of information on natural IOD variability before anthropogenic climate change. Here we use precisely dated and highly resolved coral records from the eastern equatorial Indian Ocean, where the signature of IOD variability is strong and unambiguous, to produce a semi-continuous reconstruction of IOD variability that covers five centuries of the last millennium. Our reconstruction demonstrates that extreme positive IOD events were rare before 1960. However, the most extreme event on record (1997) is not unprecedented, because at least one event that was approximately 27 to 42 per cent larger occurred naturally during the seventeenth century. We further show that a persistent, tight coupling existed between the variability of the IOD and the El Niño/Southern Oscillation during the last millennium. Indo-Pacific coupling was characterized by weak interannual variability before approximately 1590, which probably altered teleconnection patterns, and by anomalously strong variability during the seventeenth century, which was associated with societal upheaval in tropical Asia. A tendency towards clustering of positive IOD events is evident in our reconstruction, which—together with the identification of extreme IOD variability and persistent tropical Indo-Pacific climate coupling—may have implications for improving seasonal and decadal predictions and managing the climate risks of future IOD variability.
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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
11 March 2020
This Article was amended to correct a minor error in the Acknowledgements.
12 January 2022
A Correction to this paper has been published: https://doi.org/10.1038/s41586-021-04318-0
References
Saji, N. H., Goswami, B. N., Vinayachandran, P. N. & Yamagata, T. A dipole mode in the tropical Indian Ocean. Nature 401, 360–363 (1999).
Webster, P. J., Moore, A. M., Loschnigg, J. P. & Leben, R. R. Coupled ocean–atmosphere dynamics in the Indian Ocean during 1997–98. Nature 401, 356–360 (1999).
Cai, W. et al. Projected response of the Indian Ocean Dipole to greenhouse warming. Nat. Geosci. 6, 999–1007 (2013).
Han, W. et al. Indian Ocean decadal variability: a review. Bull. Am. Meteorol. Soc. 95, 1679–1703 (2014).
Abram, N. J., Gagan, M. K., Cole, J. E., Hantoro, W. S. & Mudelsee, M. Recent intensification of tropical climate variability in the Indian Ocean. Nat. Geosci. 1, 849–853 (2008).
Cai, W. et al. Increased frequency of extreme Indian Ocean Dipole events due to greenhouse warming. Nature 510, 254–258 (2014).
Weller, E. & Cai, W. Realism of the Indian Ocean Dipole in CMIP5 models: the implications for climate projections. J. Clim. 26, 6649–6659 (2013).
Cai, W. et al. Pantropical climate interactions. Science 363, eaav4236 (2019).
Ummenhofer, C. C. et al. What causes southeast Australia’s worst droughts? Geophys. Res. Lett. 36, L04706 (2009).
Cai, W., Cowan, T. & Raupach, M. Positive Indian Ocean Dipole events precondition southeast Australia bushfires. Geophys. Res. Lett. 36, (2009).
Abram, N. J., Gagan, M. K., McCulloch, M. T., Chappell, J. & Hantoro, W. S. Coral reef death during the 1997 Indian Ocean dipole linked to Indonesian wildfires. Science 301, 952–955 (2003).
Hashizume, M., Chaves, L. F. & Minakawa, N. Indian Ocean Dipole drives malaria resurgence in East African highlands. Sci. Rep. 2, 269 (2012).
Cai, W., Sullivan, A. & Cowan, T. Climate change contributes to more frequent consecutive positive Indian Ocean Dipole events. Geophys. Res. Lett. 36, L23704 (2009).
Cai, W. et al. Stabilised frequency of extreme positive Indian Ocean Dipole under 1.5 °C warming. Nat. Commun. 9, 1419 (2018).
Stevenson, S., Fox-Kemper, B., Jochum, M., Rajagopalan, B. & Yeager, S. G. ENSO model validation using wavelet probability analysis. J. Clim. 23, 5540–5547 (2010).
Wittenberg, A. T. Are historical records sufficient to constrain ENSO simulations? Geophys. Res. Lett. 36, L12702 (2009).
Meyers, G., McIntosh, P., Pigot, L. & Pook, M. The years of El Niño, La Niña, and interactions with the tropical Indian Ocean. J. Clim. 20, 2872–2880 (2007).
Abram, N. J. et al. Optimized coral reconstructions of the Indian Ocean Dipole: an assessment of location and length considerations. Paleoceanography 30, 1391–1405 (2015).
Thompson, D. M., Ault, T. R., Evans, M. N., Cole, J. E. & Emile-Geay, J. Comparison of observed and simulated tropical climate trends using a forward model of coral δ18O. Geophys. Res. Lett. 38, L14706 (2011); correction 38, L19705 (2011).
Konecky, B. L., Noone, D. C. & Cobb, K. M. The influence of competing hydroclimate processes on stable isotope ratios in tropical rainfall. Geophys. Res. Lett. 46, 1622–1633 (2019).
Cobb, K. M., Charles, C. D., Cheng, H. & Edwards, R. L. El Nino/Southern Oscillation and tropical Pacific climate during the last millennium. Nature 424, 271–276 (2003).
Abram, N. J. et al. Early onset of industrial-era warming across the oceans and continents. Nature 536, 411–418 (2016).
Maher, N., McGregor, S., England, M. H. & Gupta, A. S. Effects of volcanism on tropical variability. Geophys. Res. Lett. 42, 6024–6033 (2015).
Sigl, M. et al. Timing and climate forcing of volcanic eruptions for the past 2,500 years. Nature 523, 543–549 (2015).
Gergis, J. L. & Fowler, A. M. A history of ENSO events since A.D. 1525: implications for future climate change. Clim. Change 92, 343–387 (2009).
Grove, R. & Adamson, G. in El Niño in World History (eds Grove, R. & Adamson, G.) 49–79 (Palgrave Macmillan, 2018).
Boomgaard, P. in Asian Population History (eds Liu, T. et al.) 451 (Oxford Univ. Press, 2001).
Cobb, K. M. et al. Highly variable El Nino/Southern Oscillation throughout the Holocene. Science 339, 67–70 (2013).
Grothe, P. R. et al. Enhanced El Niño-Southern Oscillation variability in recent decades. Geophys. Res. Lett. 46, https://doi.org/10.1029/2019GL083906 (2019).
Stuecker, M. F. et al. Revisiting ENSO/Indian Ocean Dipole phase relationships. Geophys. Res. Lett. 44, 2481–2492 (2017).
Rustic, G. T., Koutavas, A., Marchitto, T. M. & Linsley, B. K. Dynamical excitation of the tropical Pacific Ocean and ENSO variability by Little Ice Age cooling. Science 350, 1537–1541 (2015).
Dätwyler, C., Abram, N. J., Grosjean, M., Wahl, E. R. & Neukom, R. El Niño–Southern Oscillation variability, teleconnection changes and responses to large volcanic eruptions since AD 1000. Int. J. Clim. 39, 2711–2724 (2019).
Freund, M. B. et al. Higher frequency of Central Pacific El Nino events in recent decades relative to past centuries. Nat. Geosci. 12, 450–455 (2019).
Tierney, J. E., Smerdon, J. E., Anchukaitis, K. J. & Seager, R. Multidecadal variability in East African hydroclimate controlled by the Indian Ocean. Nature 493, 389–392 (2013).
Goodkin, N. F. et al. East Asian monsoon variability since the sixteenth century. Geophys. Res. Lett. 46, 4790–4798 (2019).
Denniston, R. F. et al. Expansion and contraction of the Indo-Pacific tropical rain belt over the last three millennia. Sci. Rep. 6, 34485 (2016).
Yan, H. et al. Dynamics of the intertropical convergence zone over the western Pacific during the Little Ice Age. Nat. Geosci. 8, 315 (2015).
Yancheva, G. et al. Influence of the intertropical convergence zone on the East Asian monsoon. Nature 445, 74–77 (2007).
Ma, T. et al. East Asian winter monsoon impacts the ENSO-related teleconnections and North American seasonal air temperature prediction. Sci. Rep. 8, 6547 (2018).
Zhang, P. et al. A test of climate, sun, and culture relationships from an 1810-year Chinese cave record. Science 322, 940–942 (2008).
Otto-Bliesner, B. L. et al. Climate variability and change since 850 CE: an ensemble approach with the Community Earth System Model. Bull. Am. Meteorol. Soc. 97, 735–754 (2016).
Thirumalai, K., DiNezio, P. N., Tierney, J. E., Puy, M. & Mohtadi, M. An El Niño mode in the glacial Indian Ocean? Paleoceanogr. Paleoclimatol. 34, 1316–1327 (2019).
Holland, P. R., Bracegirdle, T. J., Dutrieux, P., Jenkins, A. & Steig, E. J. West Antarctic ice loss influenced by internal climate variability and anthropogenic forcing. Nat. Geosci. 12, 718–724 (2019).
Purich, A. & England, M. H. Tropical teleconnections to Antarctic sea ice during austral spring 2016 in coupled pacemaker experiments. Geophys. Res. Lett. 46, 6848–6858 (2019).
Henley, B. J. et al. A tripole index for the Interdecadal Pacific Oscillation. Clim. Dyn. 45, 3077 (2015).
Ummenhofer, C. C., Biastoch, A. & Böning, C. W. Multidecadal Indian Ocean variability linked to the Pacific and implications for preconditioning Indian Ocean Dipole events. J. Clim. 30, 1739–1751 (2017).
Zhang, L., Du, Y. & Cai, W. Low-frequency variability and the unusual Indian Ocean Dipole events in 2015 and 2016. Geophys. Res. Lett. 45, 1040–1048 (2018).
Abram, N. J. et al. Seasonal characteristics of the Indian Ocean Dipole during the Holocene epoch. Nature 445, 299–302 (2007).
Guemas, V. et al. The Indian Ocean: the region of highest skill worldwide in decadal climate prediction. J. Clim. 26, 726–739 (2013).
PAGES 2K Consortium. Global multi-decadal variability in global temperature reconstructions and simulations over the Common Era. Nat. Geosci. 12, 643–649 (2019).
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C. & Wang, W. An improved in situ and satellite SST analysis for climate. J. Clim. 15, 1609–1625 (2002).
Adler, R. et al. The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere 9, 138 (2018).
Sieh, K. et al. Earthquake supercycles inferred from sea-level changes recorded in the corals of west Sumatra. Science 322, 1674–1678 (2008).
Natawidjaja, D. H. et al. Source parameters of the great Sumatran megathrust earthquakes of 1797 and 1833 inferred from coral microatolls. J. Geophys. Res. Solid Earth 111, B06403 (2006).
Zachariasen, J., Sieh, K., Taylor, F. W., Edwards, R. L. & Hantoro, W. S. Submergence and uplift associated with the giant 1833 Sumatran subduction earthquake: evidence from coral microatolls. J. Geophys. Res. Solid Earth 104, 895–919 (1999).
McGregor, H. V. & Abram, N. J. Images of diagenetic textures in Porites corals from Papua New Guinea and Indonesia. Geochem. Geophys. Geosyst. 9, Q10013 (2008).
DeLong, K. L., Quinn, T. M., Taylor, F. W., Shen, C.-C. & Lin, K. Improving coral-base paleoclimate reconstructions by replicating 350 years of coral Sr/Ca variations. Palaeogeogr. Palaeoclimatol. Palaeoecol. 373, 6–24 (2013).
Shen, C.-C. et al. High-precision and high-resolution carbonate 230Th dating by MC-ICP-MS with SEM protocols. Geochim. Cosmochim. Acta 99, 71–86 (2012).
Cheng, H. et al. Improvements in 230Th dating, 230Th and 234U half-life values, and U–Th isotopic measurements by multi-collector inductively coupled plasma mass spectrometry. Earth Planet. Sci. Lett. 371–372, 82–91 (2013).
Shen, C.-C. et al. Variation of initial 230Th/232Th and limits of high precision U–Th dating of shallow-water corals. Geochim. Cosmochim. Acta 72, 4201–4223 (2008).
Philibosian, B. et al. Earthquake supercycles on the Mentawai segment of the Sunda megathrust in the seventeenth century and earlier. J. Geophys. Res. Solid Earth 122, 642–676 (2017).
Gagan, M. K. et al. Coral 13C/12C records of vertical seafloor displacement during megathrust earthquakes west of Sumatra. Earth Planet. Sci. Lett. 432, 461–471 (2015).
Gao, C., Robock, A. & Ammann, C. Volcanic forcing of climate over the past 1500 years: an improved ice core-based index for climate models. J. Geophys. Res. 113, D23111 (2008).
Smith, T. M., Reynolds, R. W., Peterson, T. C. & Lawrimore, J. Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880-2006). J. Clim. 21, 2283–2296 (2008).
Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108, 4407 (2003).
Chan, D., Kent, E. C., Berry, D. I. & Huybers, P. Correcting datasets leads to more homogeneous early-twentieth-century sea surface warming. Nature 571, 393–397 (2019).
Philibosian, B. et al. Rupture and variable coupling behavior of the Mentawai segment of the Sunda megathrust during the supercycle culmination of 1797 to 1833. J. Geophys. Res. Solid Earth 119, 7258–7287 (2014).
McGregor, H. V., Fischer, M. J., Gagan, M. K., Fink, D. & Woodroffe, C. D. Environmental control of the oxygen isotope composition of Porites coral microatolls. Geochim. Cosmochim. Acta 75, 3930–3944 (2011).
Grove, R. & Adamson, G. in El Niño in World History (eds Richard Grove & George Adamson) 81–92 (Palgrave Macmillan, 2018).
Natawidjaja, D. H. et al. Paleogeodetic records of seismic and aseismic subduction from central Sumatran microatolls, Indonesia. J. Geophys. Res. Solid Earth 109, B04306 (2004).
Plummer, C. T. et al. An independently dated 2000-yr volcanic record from Law Dome, East Antarctica, including a new perspective on the dating of the 1450s CE eruption of Kuwae, Vanuatu. Clim. Past 8, 1929–1940 (2012).
Esper, J., Büntgen, U., Hartl-Meier, C., Oppenheimer, C. & Schneider, L. Northern Hemisphere temperature anomalies during the 1450s period of ambiguous volcanic forcing. Bull. Volcanol. 79, 41 (2017).
Guillet, S. et al. Climate response to the Samalas volcanic eruption in 1257 revealed by proxy records. Nat. Geosci. 10, 123–128 (2017).
Lavigne, F. et al. Source of the great A.D. 1257 mystery eruption unveiled, Samalas volcano, Rinjani Volcanic Complex, Indonesia. Proc. Natl Acad. Sci. USA 110, 16742–16747 (2013).
Philibosian, B. et al. An ancient shallow slip event on the Mentawai segment of the Sunda megathrust, Sumatra. J. Geophys. Res. Solid Earth 117, (2012).
Lim, E.-P. & Hendon, H. H. Causes and predictability of the negative Indian Ocean Dipole and its impact on La Niña during 2016. Sci. Rep. 7, 12619 (2017).
Ren, H.-L. & Jin, F.-F. Niño indices for two types of ENSO. Geophys. Res. Lett. 38, L04704 (2011).
Conroy, J. L. et al. Spatiotemporal variability in the δ18O-salinity relationship of seawater across the tropical Pacific Ocean. Paleoceanography 32, 484–497 (2017).
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.
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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.
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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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DOI: https://doi.org/10.1038/s41586-020-2084-4
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