Tropical Indian Ocean variability revealed by self-organizing maps

The tropical Indian Ocean climate variability is investigated using an artificial neural network analysis called self-organizing map (SOM) for both observational data and coupled model outputs. The SOM successfully captures the dipole sea surface temperature anomaly (SSTA) pattern associated with the Indian Ocean Dipole (IOD) and basin-wide warming/cooling associated with ENSO. The dipole SSTA pattern appears only in boreal summer and fall, whereas the basin-wide warming/cooling appears mostly in boreal winter and spring owing to the phase-locking nature of these phenomena. Their occurrence also undergoes significant decadal variation. Composite diagrams constructed for nodes in the SOM array based on the simulated SSTA reveal interesting features. For the nodes with the basin-wide warming, a strong positive SSTA in the eastern equatorial Pacific, a negative Southern Oscillation, and a negative precipitation anomaly in East Africa are found. The nodes with the positive IOD are associated with a weak positive SSTA in the central equatorial Pacific or positive SSTA in the eastern equatorial Pacific, a positive (negative) sea level pressure anomaly in the eastern (western) tropical Indian Ocean, and a positive precipitation anomaly over East Africa. The warming in the central equatorial Pacific appears to correspond to El Niño Modoki discussed recently. These results suggest usefulness of SOM in studying large-scale ocean–atmosphere coupled phenomena.

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