Abstract

Data from a subsurface mooring deployed in the western South China Sea shows clear intra-seasonal oscillations (ISO) at the period of 40~70 days. Analysis of remotelysensed sea surface height (SSH) anomalies in the same area indicates that these ISO signals propagate both eastward and westward. Time-longitude diagrams of ISO signals in SSH anomalies and wind-stress curl indicate that the eastward propagating SSH anomalies is forced by wind-stress curl. This is also confirmed by lag correlation between SSH anomalies and the wind-stress-curl index (wind stress curl averaged over 109.5ºE -115ºE and 12ºN -13.5ºN). Lag correlation of SSH anomaly suggests that the westward propagating signals are free Rossby waves.

Highlights

  • Intra-seasonal variability is the most dominant mode in the tropical atmosphere [Madden and Julian, 1972], with period of 30~90 days

  • EOF analysis of sea surface height (SSH) anomalies in the study area indicates that the five leading modes can explain

  • The eastward intra-seasonal signals are forced by intra-seasonal variations of wind-stress curl, while the westward intra-seasonal signals represent planetary waves

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Summary

Introduction

Intra-seasonal variability is the most dominant mode in the tropical atmosphere [Madden and Julian, 1972], with period of 30~90 days. In some regional circulation studies based on TOPEX/Poseidon (T/P) altimetry data, mesoscale variations in SSH anomalies were assumed to be a response to wind forcing [Liu et al, 2001]. Isoguchi and Kawamura [2006] showed that the summer upwelling/blooms off the South Vietnam coast and their offshore spreading occurs in sync with the intra-seasonal cycle of SST. Their results indicate that SSH anomalies can be interpreted in terms of the forced Rossby waves with an annual period. Their study revealed that the wind-stress curl dominates the occurrence and propagation of the forced Rossby waves. We analyze wind and SSH anomalies over the intra-seasonal time scale.

Data and method
Identifying intra-seasonal oscillation
Case study
Hypothesis
Conclusions

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