Impact of Indian Ocean Dipole and El Niño/Southern Oscillation wind‐forcing on the Wyrtki jets

Abstract

[1] Interannual variability of the Wyrtki jets is studied in the context of Indian Ocean Dipole (IOD) and El Niño and Southern Oscillation (ENSO) wind-forcing using a three dimensional numerical ocean model and observations. The boreal fall (October-November) Wyrtki jet is more significantly affected than the boreal spring (April-May) Wyrtki jet since both the IOD and ENSO tend to peak toward the end of the calendar year. Various statistical methods are used in an attempt to separate the impacts of the IOD and ENSO on these jets, with emphasis on the fall jet. The first two modes of an Empirical Orthogonal Function (EOF) decomposition account for about 90% and 85% of variability in zonal currents and wind stress respectively along the equator in the Indian Ocean, but EOF analysis does not cleanly separate out IOD and ENSO forcing and response. Partial correlation analysis reveals that IOD wind-forcing and zonal equatorial current response are stronger on average than for ENSO and extend further west across the basin. Composite analysis of IOD only, ENSO only, and combined IOD and ENSO years provides a complementary definition of the relative contributions of these two phenomena on Wyrtki jet variability and in general is consistent with the results of the partial correlation analysis.

Volume transports of the Wyrtki jets and their relationship to the Indian Ocean Dipole

“…Also, Gnanaseelan et al . [] showed from an ocean general circulation model simulation that while ENSO forcing affects Wyrtki jet variations, it is generally of secondary importance relative to forcing associated with the IOD. Thus, in this section we will interpret interannual variations in the transports of the Wyrtki jets primarily in terms of IOD influences.…”

“…Both the spring and the fall Wyrtki jets exhibit year‐to‐year variability (Figure a). However, the IOD is phase locked to the seasonal cycle with largest amplitudes in September–November (SON) (Figures b and c), so the fall jet is more strongly influenced by the IOD [ Gnanaseelan et al ., ]. Thus, the correlation between DMI, zonal wind stress and zonal transport is significantly higher during the latter half of the year (Figure b).…”

The interannual sea level variability in the Indian Ocean as simulated by an Ocean General Circulation Model

“…() and Gnanaseelan et al . (). The El Niño and IOD years defined by ORAS4 and CTL are identified by analysing Niño 3.4 index and Dipole mode index (DMI) and are found to be consistent with the year selection of above studies.…”

Temperature inversions and their influence on the mixed layer heat budget during the winters of 2006–2007 and 2007–2008 in the Bay of Bengal

“…The seasonal average (October to March) of wind stress anomalies, sea surface height anomaly (SSHA) and depth of 23°C isotherm (D23; proxy for thermocline) Figure shows the tendency for easterly wind anomalies in the EIO, negative SSHA and shallow thermocline in the EIO and in the BoB (particularly in the southern part of the BoB) during W67. Opposite tendencies occurred during W78, consistent with expectations based on ENSO and IOD variability during these two periods [ Saji et al ., ; Rao et al ., ; Vinayachandran et al ., ; Cai et al ., ; Xie et al ., ; Girishkumar et al ., ; Gnanaseelan et al ., ; Aparna et al ., ]. In this study, our focus will be on understanding year‐to‐year differences in temperature inversions at the 8°N, 90°E buoy location within this large scale context and how those inversions influence the mixed layer heat budget in W67 and W78.…”