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New approach for optimal perturbation method in ensemble climate prediction with empirical singular vector

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Abstract

In this study, a new method is developed to generate optimal perturbations in ensemble climate prediction. In this method, the optimal perturbation in initial conditions is the 1st leading singular vector, calculated from an empirical linear operator based on a historical model integration. To verify this concept, this method is applied to a hybrid coupled model. It is demonstrated that the 1st leading singular vector from the empirical linear operator, to a large extent, represents the fast-growing mode in the nonlinear integration. Therefore, the forecast skill with the optimal perturbations is improved over most lead times and regions. In particular, the improvement of the forecast skill is significant where the signal-to-noise ratio is small, indicating that the optimal perturbation method is effective when the initial uncertainty is large. Therefore, the new optimal perturbation method has the potential to improve current seasonal prediction with state-of-the-art coupled GCMs.

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Notes

  1. The STN ration can be expressed by correlation coefficient (r) as follows: \( \text{STN} = r^{2} /(1 - r^{2} ). \)

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Acknowledgments

This work was mostly done when J.-S. Kug visited CCSR during the summer in 2007. J.-S. Kug is partly supported by KORDI(PE98425, PP00720, PM55290). F.-F. Jin was partly supported by NSF grants ATM-0652145 and ATM-0650552 and NOAA grants GC01-229. M. Kimoto was supported by Innovative Program of Climate Change Projection for the 21st Century of the Ministry of Education, Culture, Sports, Science and Technology of Japan.

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Authors and Affiliations

  1. Korea Ocean Research and Development Institute, Ansan, Korea

    Jong-Seong Kug

  2. School of Earth and Environment Sciences, Seoul National University, Seoul, Korea

    Yoo-Geun Ham & In-Sik Kang

  3. Center for Climate System Research, University of Tokyo, Tokyo, Japan

    Masahide Kimoto

  4. Department of Meteorology, SOEST, University of Hawaii, Hawaii, USA

    Fei-Fei Jin

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  1. Jong-Seong Kug
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  2. Yoo-Geun Ham
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  3. Masahide Kimoto
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  4. Fei-Fei Jin
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  5. In-Sik Kang
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Correspondence to Jong-Seong Kug.

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Kug, JS., Ham, YG., Kimoto, M. et al. New approach for optimal perturbation method in ensemble climate prediction with empirical singular vector. Clim Dyn 35, 331–340 (2010). https://doi.org/10.1007/s00382-009-0664-y

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