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Based on the simple ocean data assimilation(SODA) reanalysis dataset from the University of Maryland and the method of Empirical Orthogonal Functions(EOF),the characteristics of interannual and interdecadal variabilities of the equatorial Pacific subsurface oceanic temperature anomaly(SOTA) are captured.The first and second modes of the equatorial Pacific SOTA in the interannual and interdecadal variations are found respectively and the effect of the second mode on the ENSO cycle is discussed.Results show that the first mode of SOTA’s interannual and interdecadal variabilities exhibit a dipole pattern,indicating that the warm and cold temperature anomalies appear simultaneously in the equatorial subsurface Pacific.The second mode shows coherent large-scale temperature anomalies in the equatorial subsurface Pacific,which is a dominant mode in the evolution of ENSO cycle.The temporal series of the second mode has a significant lead correlation with the Ni?o-3.4 index,which can make a precursory prediction signal for ENSO.The function of this prediction factor in SOTA is verified by composite and case analyses.
Based on the simple ocean data assimilation (SODA) reanalysis dataset from the University of Maryland and the method of empirical Orthogonal Functions (EOF), the characteristics of interannual and interdecadal variabilities of the equatorial Pacific subsurface oceanic temperature anomaly (SOTA) are captured. first and second modes of the equatorial Pacific SOTA in the interannual and interdecadal variations are found respectively and the effect of the second mode on the ENSO cycle is discussed. Results show that the first mode of SOTA’s interannual and interdecadal variabilities exhibit a dipole pattern, indicating that the warm and cold temperature anomalies appear simultaneously in the equatorial subsurface Pacific the second mode shows coherent large-scale temperature anomalies in the equatorial subsurface Pacific, which is a dominant mode in the evolution of ENSO cycle.The temporal series of the second mode has a significant lead correlation with the Ni? o-3.4 index, which can make a pre cursory prediction signal for ENSO. The function of this prediction factor in SOTA is verified by composite and case analyzes.