Hybrid coupled modeling of the tropical Pacific using neural networks

To investigate the potential for improving hybrid coupled models (HCM) of the tropical Pacific by the use of neural network (NN) methods for nonlinear regression, NN was introduced for the nonlinear parametrization of the subsurface temperature in the Lamont ocean model, and for the nonlinear estimation of the wind stress anomalies (WSA) from the sea surface temperature anomalies (SSTA). For comparison, corresponding linear regression (LR) models were also built. By combining the NN or the LR version of the ocean model and the atmospheric model, four HCMs resulted. For the coupled model Niño3 SSTA spectrum, using NN in the ocean model produced a much broader spectrum than using LR, which gave basically a single narrow spectral peak. Using NN in the atmospheric model in addition to the ocean model further broadened the SSTA spectrum, yielding a spectrum with two main peaks as observed. Principal component analysis (PCA) and nonlinear PCA (NLPCA) were used to analyze the SSTA and WSA. By comparing the NLPCA mode 1 and the PCA mode 1, we found that all the coupled models (including the original Lamont coupled model) were too linear compared to the observations. However, using NN in the ocean model and in the atmospheric model was able to alleviate the weak nonlinearity in the coupled models.