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Interdecadal change of interannual variability and predictability of two types of ENSO

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Abstract

A significant interdecadal climate shift of interannual variability and predictability of two types of the El Niño-Southern Oscillation (ENSO), namely the canonical or eastern Pacific (EP)-type and Modoki or central Pacific (CP) type, are investigated. Using the retrospective forecasts of six-state-of-the-art coupled models and their multi-model ensemble (MME) for December–January–February during the period of 1972–2005 along with corresponding observed and reanalyzed data, we examine the climate regime shift that occurred in the winter of 1988/1989 and how the shift affected interannual variability and predictability of two types of ENSO for the two periods of 1972–1988 (hereafter PRE) and 1989–2005 (hereafter POST). The result first shows substantial interdecadal changes of observed sea surface temperature (SST) in mean state and variability over the western and central Pacific attributable to the significant warming trend in the POST period. In the POST period, the SST variability increased (decreased) significantly over the western (eastern) Pacific. The MME realistically reproduces the observed interdecadal changes with 1- and 4-month forecast lead time. It is found that the CP-type ENSO was more prominent and predictable during the POST than the PRE period while there was no apparent difference in the variability and predictability of the EP-type ENSO between two periods. Note that the second empirical orthogonal function mode of the Pacific SST during the POST period represents the CP-type ENSO but that during the PRE period captures the ENSO transition phase. The MME better predicts the former than the latter. We also investigate distinctive regional impacts associated with the two types of ENSO during the two periods.

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Notes

  1. The Niño3 index is an area-average of the sea surface temperature in the region of 150°W–90°W and 5°S–5°N.

  2. The El Niño Modoki index or EMI is defined as [SSTA]C–0.5[SSTA]E–0.5[SSTA]W, where the square bracket with a subscript represents the area-mean SSTA, averaged over one of the three regions specified as the central (C:165°E–140°W, 10°S–10°N), eastern (E: 110°W–70°W, 15°S–5°N), and western (W: 125°E–145°E, 10°S–20°N).

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Acknowledgments

We appreciate the anonymous three reviewers for their helpful comments and suggestions. This research was carried out in APEC Climate Center which is fully funded by Korea Meteorological Administration (KMA), Republic of Korea. J.-B. Ahn was supported by Rural Development Administration Cooperative Research Program for Agriculture Science and Technology Development under Grant Project No. PJ009953, Republic of Korea. A. Alessandri was partially supported by the European Commission’s Seventh Framework Research Programme projects CLIMITS under the grant agreement 303208 and SPECS under the grant agreement 308378. This work was also supported by IPRC, which is supported in part by JAMSTEC, NOAA, and NASA. This is the SOEST publication number 9070 and IPRC publication number 1042.

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Authors and Affiliations

  1. APEC Climate Center (APCC), Pusan, Republic of Korea

    Hye-In Jeong

  2. Pusan National University, Pusan, Republic of Korea

    Hye-In Jeong & Joong-Bae Ahn

  3. Institute of Environmental Studies, Pusan National University, Pusan, 609-735, Republic of Korea

    June-Yi Lee

  4. Agenzia Nazionale per le nuove Tecnologie, l’energia e lo sviluppo economico sostenibile, Rome, Italy

    Andrea Alessandri

  5. University of Hawaii/International Pacific Research Center, Honolulu, HI, USA

    Andrea Alessandri

  6. Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, VIC, Australia

    Harry H. Hendon

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  1. Hye-In Jeong
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Correspondence to June-Yi Lee.

Appendix

Appendix

The DJF differences between observed and predicted mean SST are shown in Fig. 12. Most of individual models and MME prediction have a cold bias over the equatorial eastern Pacific. On the other hands, the coupled models and their MME well reproduce the spatial distribution of observed mean SST showing higher skill of pattern correlations.

Fig. 12
figure 12

DJF difference between observed mean SST and one-month lead MME prediction (a) and individual models (b–g) for 1972–2005. Pattern correlation coefficients between the observed and model predicted mean SST are also given at the top of each figures

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Jeong, HI., Ahn, JB., Lee, JY. et al. Interdecadal change of interannual variability and predictability of two types of ENSO. Clim Dyn 44, 1073–1091 (2015). https://doi.org/10.1007/s00382-014-2127-3

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