Predictability of a Stochastically Forced Hybrid Coupled Model of El Niño

Publisher Website
Google Scholar
Abstract
The El Niño–Southern Oscillation (ENSO) phenomenon is modeled as a stochastically driven dynamical system. This was accomplished by adding to a Hybrid Coupled Model (HCM) of the tropical Pacific ocean–atmosphere system a stochastic wind stress anomaly field that was derived from observations. The model exhibits irregular interannual fluctuations, whose space–time characteristics resemble those of the observed interannual climate variability in this region. To investigate the predictability of the model, the authors performed ensemble integrations with different realizations of the stochastic wind stress forcing. The ensembles were initialized at various phases of the model’s ENSO cycle simulated in a 120-yr integration with a particular noise realization. The numerical experiments indicate that the ENSO predictability is severely limited by the stochastic wind stress forcing. Linear stochastic processes were fitted to the restart ensembles in a reduced state space. A predictability measure based ... Abstract The El Niño–Southern Oscillation (ENSO) phenomenon is modeled as a stochastically driven dynamical system. This was accomplished by adding to a Hybrid Coupled Model (HCM) of the tropical Pacific ocean–atmosphere system a stochastic wind stress anomaly field that was derived from observations. The model exhibits irregular interannual fluctuations, whose space–time characteristics resemble those of the observed interannual climate variability in this region. To investigate the predictability of the model, the authors performed ensemble integrations with different realizations of the stochastic wind stress forcing. The ensembles were initialized at various phases of the model’s ENSO cycle simulated in a 120-yr integration with a particular noise realization. The numerical experiments indicate that the ENSO predictability is severely limited by the stochastic wind stress forcing. Linear stochastic processes were fitted to the restart ensembles in a reduced state space. A predictability measure based ...

This publication has 0 references indexed in Scilit: