Tuesday 23 August 2005

P1

PT0110
Evaluation of the geostrophic current derived from TRITON data
Ueki, Iwao¹, Ando, Kentaro¹, Kuroda, Yoshifumi¹
1 IORGC/JAMSTEC, Japan
Author email: uekii@jamstec.go.jp
The sea surface temperature (SST) variation in the warm pool region is an important element of climate variability. Monitoring of SST in that region is done mainly by mooring buoy and satellite observations in these days. In this study, using Triangle Trans-Ocean buoy Network (TRITON) data, we evaluate the geostrophic approximation in the Pacific warm pool. Using Tropical Atmosphere and Ocean (TAO) data and Temperature-Salinity (T-S) relationship derived from 11 Conductivity-temperature-depth (CTD) cast sections, Picaut et al. (JGR 1989) evaluated the geostrophic approximation at the equator. They showed the good agreement between geostrophic and observed current in the point of view of variation, with the large mean difference (70 cm/s for 4 month mooring). They concluded that the error of current estimation was mainly caused by variations of T-S relationship and spatial resolution of the moorings. TRITON buoy observe temperature and conductivity at originally every 10 minutes, therefore, can capture detailed T-S variation that enable to eliminate the estimation error. Results indicate that the good agreement between geostrophic and observed current is found not only variation but also mean current (Mean difference is 7 cm/s for 42 month mooring). The large error, however, has been occurred during wind disturbance was dominant. As pointed out by Picaut et al., neglected processes in the geostrophic approximation may cause this error. Volume transport above thermocline across 156E section from 8N to 5S was also calculated and it was indicated that the transport was mainly controlled by volume transport within the equatorial band. This is consistent with our understandings of task of equatorial Kelvin waves. In the next step, we will attempt to evaluate warm water volume transport derived from the geostrophic approximation and to describe the variation, for deeper understanding and improvement of the predictability of ENSO.

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