Abstract:

Based on the sea surface wind data at 10 m during 1979 to 2018 from European Center for MediumRange Weather Forecasts (ECMWF), the Growing Hierarchical Self-Organizing Map (GHSOM) method were used to analyze the seasonal variation and interannual anomaly variation characteristics of near-surface wind field over the South China Sea (SCS). Four feature patterns are extracted in the first-layer GHSOM from original wind field data, which highly summarize the seasonal variation characteristics, and the second-layer results reveal the monthly variation characteristics. Four anomaly feature patterns also are extracted in the first-layer GHSOM network and they are anticyclonic wind anomaly, cyclonic wind anomaly, southwest wind anomaly and northeast wind anomaly patterns, respectively. Anticyclonic and cyclonic wind anomaly patterns are closely related to ENSO events with time lags by three months and five months comparing with Niño3.4 index. Anticyclonic and cyclonic wind anomalies also show asymmetry, that is, the amplitude of anticyclonic wind anomaly is obviously larger than that of cyclonic wind anomaly. The occurrence frequency of the northeast wind anomaly pattern is greater than that of the southwest wind anomaly pattern. The more SOM patterns in the second layer expose particulars of anomaly wind.

Key words: sea surface wind, Growing Hierarchical Self-Organizing Map, season variability, inter-annual anomaly variability, South China Sea