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Predictability of the Indian Ocean Dipole in the coupled models

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Abstract

In this study, the Indian Ocean Dipole (IOD) predictability, measured by the Indian Dipole Mode Index (DMI), is comprehensively examined at the seasonal time scale, including its actual prediction skill and potential predictability, using the ENSEMBLES multiple model ensembles and the recently developed information-based theoretical framework of predictability. It was found that all model predictions have useful skill, which is normally defined by the anomaly correlation coefficient larger than 0.5, only at around 2–3 month leads. This is mainly because there are more false alarms in predictions as leading time increases. The DMI predictability has significant seasonal variation, and the predictions whose target seasons are boreal summer (JJA) and autumn (SON) are more reliable than that for other seasons. All of models fail to predict the IOD onset before May and suffer from the winter (DJF) predictability barrier. The potential predictability study indicates that, with the model development and initialization improvement, the prediction of IOD onset is likely to be improved but the winter barrier cannot be overcome. The IOD predictability also has decadal variation, with a high skill during the 1960s and the early 1990s, and a low skill during the early 1970s and early 1980s, which is very consistent with the potential predictability. The main factors controlling the IOD predictability, including its seasonal and decadal variations, are also analyzed in this study.

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Acknowledgments

This work was supported by the National Science Foundation of China (41276029, 41530961, 91528304, 41321004), National Programme on Global Change and Air-Sea Interaction (GASI-IPOVAI-06), the National Basic Research Program (2013CB430302), the NSERC (Natural Sciences and Engineering Research Council of Canada) Discovery Grant, the project of State Key Laboratory of Satellite Ocean Environment Dynamics (SOEDZZ1404), the Zhejiang Provincial Natural Science Foundation of China (LQ15D060004), and project of the Second Institute of Oceanography (QNYC201501).

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Authors and Affiliations

  1. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Hangzhou, 310012, China

    Huafeng Liu, Youmin Tang, Dake Chen & Tao Lian

  2. Environmental Science and Engineering, University of Northern British Columbia, Prince George, BC, V2N4Z9, Canada

    Youmin Tang

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  1. Huafeng Liu
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Correspondence to Youmin Tang.

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Liu, H., Tang, Y., Chen, D. et al. Predictability of the Indian Ocean Dipole in the coupled models. Clim Dyn 48, 2005–2024 (2017). https://doi.org/10.1007/s00382-016-3187-3

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