Processes controlling the surface temperature signature of the Madden–Julian Oscillation in the thermocline ridge of the Indian Ocean

Abstract

During boreal winter, there is a prominent maximum of intraseasonal sea-surface temperature (SST) variability associated with the Madden-Julian Oscillation (MJO) along a Thermocline Ridge located in the southwestern Indian Ocean (5 o S−10°S, 60°E-90°E; TRIO region). There is an ongoing debate about the relative importance of air-sea heat fluxes and oceanic processes in driving this intraseasonal SST variability. Furthermore, various studies have suggested that interannual variability of the oceanic structure in the TRIO region could modulate the amplitude of the MJOdriven SST response. In this study, we use observations and ocean general circulation model (OGCM) experiments to quantify these two effects over the 1997−2006 period. Observational analysis indicates that Ekman pumping does not contribute significantly (on average) to intraseasonal SST variability. It is, however, difficult to quantify the relative contribution of net heat fluxes and entrainment to SST intraseasonal variability from observations alone. We therefore use a suite of OGCM experiments to isolate the impacts of each process. During 1997−2006, wind stress contributed only 20% of the intraseasonal SST variability (averaged over the TRIO region), while heat fluxes contributed to 70%, with forcing by shortwave radiation (75%) dominating the other flux components (25%). This estimate is consistent with an independent air-sea flux product, which indicates that shortwave radiation contributes 68% of intraseasonal heat flux variability. The time scale of the heat-flux perturbation, in addition to its amplitude, is also important in controlling the intraseasonal SST signature, with longer periods favouring a larger response. There are also strong year-to-year variations in the respective role of heat fluxes and wind stress. Intraseasonal-wind stress dominates the SST signature for one (in 2001) and contributes significantly to another (in 2000) of the 5 strong cooling events identified in both observations and the model (2 in 1999, 1 in 2000, 2001 and 2002). Interannual variations of the subsurface thermal structure associated with the Indian Ocean Dipole or El Niño/La Niña events modulate the MJOdriven SST signature only moderately (by up to 30%), mainly by changing the temperature of water entrained into the mixed layer. Our results therefore suggest that the primary factor that controls year-to-year changes in the amplitude of TRIO, intraseasonal SST anomalies is the amplitude and timescale of the intraseasonal heat-flux perturbations.

The 30–50-Day Intraseasonal Oscillation of SST and Precipitation in the South Tropical Indian Ocean

“…The oscillation generates in Region B, then propagates eastward, and strengthens in Region A. We investigate the variability of the heat fluxes, a major contributor of the intraseasonal SST variability [16,[42][43][44][45]. The intensity of 30-50-day net heat flux peaks during March and February in Regions A and B, respectively (Figure 8a), which is consistent with SST and precipitation (Figures 3b and 5b).…”

Processes of 30–90 days sea surface temperature variability in the northern Indian Ocean during boreal summer

“…The approach followed in this paper is very similar to the one followed in Jayakumar et al (2011) for the winter season. We summarize it below.…”

“…Second, quantify the respective contributions of atmospheric heat fluxes and oceanic processes in the intraseasonal (30-90 days) SST response over both the Arabain Sea and Bay of Bengal. In a companion paper, we used a combination of observations and OGCM experiments to understand processes of intraseasonal variability in the 5°S-10°S band of the Indian Ocean during boreal winter (Jayakumar et al 2011). In this paper, we will use a similar strategy to investigate these processes over the northern Indian Ocean during boreal summer.…”

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

“…[10] The model simulates Indian Ocean temperature, currents and salinity variability very well [Thompson et al, 2006[Thompson et al, , 2008[Thompson et al, , 2009Jayakumar et al, 2011;Parekh et al, 2011;Vialard et al, 2012]. [11] Having simulated the currents well at the RAMA mooring location, it is important to examine whether the model captures the horizontal structure of the jet as well.…”