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Principal component analysis (PCA) used by meteorologists and oceanographers is a powerful tool for the analysis of the spatial and temporal variability of physical fields .This study is aimed at applying “ quasi-local PCA for singular factor ” to make the cumulative percentage for the first principal component as great as possible , so that the many-dimension problem can be reduced to a single-dimension one , and at combining PCA with stepwise regression analysis to parameterize the relationship between El Nino events and the anomalies in hydrographic factors along 137°E in summer.The results show that the hydrography on 30-50 m levels at 7-9° N along 137° E in summer is very closely correlated with El Nino events because of the thermocline movement caused by enhanced upwelling in this area during El Nino years .
Principal component analysis (PCA) used by meteorologists and oceanographers is a for tool for the analysis of the spatial and temporal variability of physical fields. This study is aimed at applying “quasi-local PCA for singular factor ” to make the cumulative percentage for the first principal component as great as possible, so that the many-dimension problem can be reduced to a single-dimension one, and at combining PCA with stepwise regression analysis to parameterize the relationship between El Nino events and anomalies in hydrographic factors along 137 ° E in summer.The results show that the hydrography on 30-50 m levels at 7-9 ° N along 137 ° E in summer is very closely correlated with El Nino events because of the thermocline movement caused by enhanced upwelling in this area during El Nino years.