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Climate variability in the southern Indian Ocean as revealed by self-organizing maps

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Abstract

Using a non-linear statistical analysis called “self-organizing maps”, the interannual sea surface temperature (SST) variations in the southern Indian Ocean are investigated. The SST anomalies during austral summer from 1951 to 2006 are classified into nine types with differences in the position of positive and negative SST anomaly poles. To investigate the evolution of these SST anomaly poles, heat budget analysis of mixed-layer using outputs from an ocean general circulation model is conducted. The warming of the mixed-layer by the climatological shortwave radiation is enhanced (suppressed) as a result of negative (positive) mixed-layer thickness anomaly over the positive (negative) SST anomaly pole. This contribution from shortwave radiation is most dominant in the growth of SST anomalies. In contrast to the results reported so far, the contribution from latent heat flux anomaly is not so important. The discrepancy in the analysis is explained by the modulation in the contribution from the climatological heat flux by the interannual mixed-layer depth anomaly that was neglected in the past studies.

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Acknowledgments

The authors thank Associate Prof. Yukio Masumoto, Prof. Ichiro Yasuda, Associate Prof. Hisashi Nakamura, and Dr. Takeshi Doi for their helpful comments. They also thank two anonymous reviewers for their helpful comments. The SOM_PAK software was provided by the Neural Network Research Centre at the Helsinki University of Technology and is available at http://www.cis.hut.fi/research/som_pak. The OGCM was run on HITACHI SR11000/J1 of Information Technology Center, the University of Tokyo under the cooperative research with Center for Climate System Research, the University of Tokyo. The present research is supported by the Sasakawa Scientific Research Grant from The Japan Science Society, Japan Science and Technology Agency/Japan International Cooperation Agency through Science and Technology Research Partnership for Sustainable Development, and Japan Society for Promotion of Science through Grant-in-Aid for Scientific Research (B) 20340125 for the senior author.

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Authors and Affiliations

  1. Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, Japan

    Yushi Morioka, Tomoki Tozuka & Toshio Yamagata

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  1. Yushi Morioka
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  2. Tomoki Tozuka
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  3. Toshio Yamagata
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Correspondence to Toshio Yamagata.

Appendix

Appendix

We have calculated the averaged root mean square errors up to 25 types to check whether adoption of 3 × 3 types is a reasonable choice for pattern classification. Figure 13 shows minimum and maximum values of the root mean square errors after trying different sets of initial values for learning rate and neighborhood radius, and number of iteration. As seen in Fig. 13, the root mean square errors tend to decrease with increasing the number of types. Since the number of events classified into each type decreases, we need to introduce a trade-off here. The decreasing rate becomes small at around 9 types. However, the number of events within each type becomes smaller as the number of types increases. When we separate into 16 types, we do not have enough years to construct composite diagrams. Thus, we have decided to adopt 3 × 3 types as an optimum choice to classify 55 events.

Fig. 13
figure 13

Root mean square errors (RMS, in °C) for number of types of SOM. The square arrays from 1 × 1 through 5 × 5 are selected. The error bar shows a deviation from the mean RMS after trying different sets of initial values for learning rate and neighborhood radius, and number of iteration

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Morioka, Y., Tozuka, T. & Yamagata, T. Climate variability in the southern Indian Ocean as revealed by self-organizing maps. Clim Dyn 35, 1059–1072 (2010). https://doi.org/10.1007/s00382-010-0843-x

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