The nonlinear principal component analysis (NLPCA), via a neural network approach, was applied to thermocline anomalies in the tropical Pacific. While the tropical sea surface temperature (SST) anomalies had been nonlinearly mapped by the NLPCA mode 1 onto an open curve in the data space, the thermocline anomalies were mapped to a closed curve, suggesting that ENSO is a cyclic phenomenon. The NLPCA mode 1 of the thermocline anomalies reveals the nonlinear evolution of the ENSO cycle with much asymmetry for the different phases: The weak heat accumulation in the whole equatorial Pacific is followed by the strong El Niño, and the subsequent strong drain of equatorial heat content toward the off-equatorial region precedes a weak La Niña. This asymmetric ENSO evolution implies that the nonlinear instability enhances the growth of El Niño, but dwarfs the growth of La Niña. The nonlinear ENSO cycle was found to have changed since the late 1970s. For the pre-1980s the ENSO cycle associated with the thermocline is less asymmetrical than that during the post-1980s, indicating that the nonlinearity of the ENSO cycle has become stronger since the late 1970s.
All Science Journal Classification (ASJC) codes
- Atmospheric Science