Analysis of variability of tropical Pacific sea surface temperatures

Neural network forecasts of the tropical Pacific sea surface temperatures

A nonlinear forecast system for the sea surface temperature (SST) anomalies over the whole tropical Pacific has been developed using a multi-layer perceptron neural network approach, where sea level pressure and SST anomalies were used as predictors to predict the five leading SST principal components at lead times from 3 to 15 months. Relative to the linear regression (LR) models, the nonlinea...

Tropical Pacific Sea-Surface Temperatures and Preceding Sea Level Pressure Anomalies in the Subtropical North Pacific

Simulated Sea Surface Salinity variability in the tropical Pacific

[1] Sea Surface Salinity (SSS) variability from a hindcast run of an oceanic general circulation model (OGCM) forced by daily NCEP-NCAR reanalysis from 1990 to 2001 is analyzed. The purpose is to test the capability of the model in terms of the salinity simulation and provide insights for the future SSS observation from space. With daily forcing, the model can reproduce SSS change of the tropic...

Pacific Sea Surface Temperatures in the Twentieth Century: An Evolution-Centric Analysis of Variability and Trend

A consistent analysis of natural variability and secular trend in Pacific SSTs in the twentieth century is presented. By focusing on spatial and temporal recurrence, but without imposition of periodicity constraints, this single analysis discriminates between biennial, ENSO, and decadal variabilities, leading to refined evolutionary descriptions, and between these natural variability modes and ...

Application of Entropy Ensemble Filter in Neural Network Forecasts of Tropical Pacific Sea Surface Temperatures

Recently, the Entropy Ensemble Filter (EEF) method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging) method. This method uses the most informative training data sets in the model ensemble rather than all ensemble members created by the conventional bagging. In this study, we evaluate, for the first time, the application of the EEF method in Neural Network (N...