1998
DOI: 10.1175/1520-0442(1998)011<2668:mlmoiv>2.0.co;2
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Mixed Layer Modeling of Intraseasonal Variability in the Tropical Western Pacific and Indian Oceans

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Cited by 171 publications
(147 citation statements)
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“…It is well known that the MJO has a large impact on the sea surface temperature (SST) in the Indian Ocean as increased zonal winds increase latent heat flux, induce vertical mixing, and increased cloud cover reduces solar insolation (e.g., Shinoda et al 1998;Shinoda 2005;Shinoda et al 2013a;Zhang 2005Zhang , 2013. In addition to mixed layer (ML) temperature variations from local heat fluxes, there is a dynamic wave response causing the horizontal or vertical advection of heat and freshwater.…”
Section: Introductionsupporting
confidence: 89%
“…It is well known that the MJO has a large impact on the sea surface temperature (SST) in the Indian Ocean as increased zonal winds increase latent heat flux, induce vertical mixing, and increased cloud cover reduces solar insolation (e.g., Shinoda et al 1998;Shinoda 2005;Shinoda et al 2013a;Zhang 2005Zhang , 2013. In addition to mixed layer (ML) temperature variations from local heat fluxes, there is a dynamic wave response causing the horizontal or vertical advection of heat and freshwater.…”
Section: Introductionsupporting
confidence: 89%
“…Significant SST variations in the tropical Indian and western Pacific warm pool regions are generally observed during the MJO events because of anomalous surface fluxes of heat and momentum, e.g., [31][32][33][34][35][36]. Figure 7 shows the SST at 80.5°E, equator from high resolution blended analysis and RAMA buoy data.…”
Section: Sea Surface Temperaturementioning
confidence: 97%
“…During the convectively active phase of the MJO, there are strong surface winds and no warm layer can form. As a result, diurnal warm layers tend to increase the amplitude of intraseasonal perturbations of the SST (e.g., Shinoda and Hendon 1998;Bernie et al 2005). These diurnal warm layers influence the atmospheric variability from diurnal to intraseasonal time scales and improve the MJO predictability in a GCM (Woolnough et al 2007).…”
Section: Figmentioning
confidence: 99%