Abstract: An important step in understanding the climate system is simulating and studying the past climate variability, using oceanic models, atmospheric models, or both. Toward this goal, long-term wind stress data, as the forcing ...

Abstract: n this study, the predictability of the Pacific?North American (PNA) pattern is evaluated on time scales from days to months using state-of-the-art dynamical multiple-model ensembles including the Canadian Historical ...

Abstract: In this study, a breeding analysis was conducted for a hybrid coupled El Niño?Southern Oscillation (ENSO) model that assimilated a historic dataset of sea surface temperature (SST) for the 120 yr between 1881 and 2000. ...

Abstract: Performance of an advanced, derivativeless, sigma-point Kalman filter (SPKF) data assimilation scheme in a strongly nonlinear dynamical model is investigated. The SPKF data assimilation scheme is compared against standard ...

Abstract: The advent of the feed-forward neural network (N) model opens the possibility of hybrid neural?dynamical models via variational data assimilation. Such a hybrid model may be used in situations where some variables, difficult ...

Abstract: In this study, ensemble predictions of the El Niño?Southern Oscillation (ENSO) were conducted for the period 1981?98 using two hybrid coupled models. Several recently proposed information-based measures of predictability, ...