Seasonality and predictability of the Indian Ocean dipole mode: ENSO forcing and internal variability


Title: Seasonality and predictability of the Indian Ocean dipole mode: ENSO forcing and internal variability

Journal: Journal of Climate, 28: 8021-8036

Authors: YANG Y., S.-P. Xie*, L. -X. Wu, Y. Kosaka, N.-C. Lau, and G.A. Vecchi

Abstract:This study evaluates the relative contributions to the Indian Ocean dipole (IOD) mode of interannualvariability from the El Niño–Southern Oscillation (ENSO) forcing and ocean–atmosphere feedbacks internalto the Indian Ocean. The ENSO forcing and internal variability is extracted by conducting a 10-membercoupled simulation for 1950–2012 where sea surface temperature (SST) is restored to the observed anomaliesover the tropical Pacific but interactive with the atmosphere over the rest of the World Ocean. In theseexperiments, the ensemble mean is due to ENSO forcing and the intermember difference arises from internalvariability of the climate system independent of ENSO. These elements contribute one-third and two-thirdsof the total IOD variance, respectively. Both types of IOD variability develop into an east–west dipole patternbecause of Bjerknes feedback and peak in September–November. The ENSO forced and internal IOD modesdiffer in several important ways. The forced IOD mode develops in August with a broad meridional patternand eventually evolves into the Indian Ocean basin mode, while the internal IOD mode grows earlier in June,is more confined to the equator, and decays rapidly after October. The internal IOD mode is more skewedthan the ENSO forced response. The destructive interference of ENSO forcing and internal variability canexplain early terminating IOD events, referred to as IOD-like perturbations that fail to grow during borealsummer. The results have implications for predictability. Internal variability, as represented by preseason seasurface height anomalies off Sumatra, contributes to predictability considerably. Including this indicator ofinternal variability, together with ENSO, improves the predictability of IOD.