|Title||Seasonality and predictability of the Indian Ocean Dipole Mode: ENSO forcing and internal variability|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Yang Y., Xie SP, Wu L.X, Kosaka Y, Lau NC, Vecchi GA|
|Journal||Journal of Climate|
|Type of Article||Article|
|Keywords||atmosphere; Atmosphere-ocean interaction; climate models; coupled gcm; el-nino; enso; equatorial pacific; Indian Ocean; interannual; interannual variability; mode; part i; sea-surface temperature; tropical pacific; variability; zonal|
This study evaluates the relative contributions to the Indian Ocean dipole (IOD) mode of interannual variability from the El Nino-Southern Oscillation (ENSO) forcing and ocean-atmosphere feedbacks internal to the Indian Ocean. The ENSO forcing and internal variability is extracted by conducting a 10-member coupled simulation for 1950-2012 where sea surface temperature (SST) is restored to the observed anomalies over the tropical Pacific but interactive with the atmosphere over the rest of the World Ocean. In these experiments, the ensemble mean is due to ENSO forcing and the intermember difference arises from internal variability of the climate system independent of ENSO. These elements contribute one-third and two-thirds of the total IOD variance, respectively. Both types of IOD variability develop into an east-west dipole pattern because of Bjerknes feedback and peak in September-November. The ENSO forced and internal IOD modes differ in several important ways. The forced IOD mode develops in August with a broad meridional pattern and 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 skewed than the ENSO forced response. The destructive interference of ENSO forcing and internal variability can explain early terminating IOD events, referred to as IOD-like perturbations that fail to grow during boreal summer. The results have implications for predictability. Internal variability, as represented by preseason sea surface height anomalies off Sumatra, contributes to predictability considerably. Including this indicator of internal variability, together with ENSO, improves the predictability of IOD.