Linear Model Predictions of Time Averages

Abstract

Abstract Linear prediction models applicable to a basic nonlinear two-level quasi-geostrophic model and extended range forecasting are described. One prediction model is linearized around the nonlinear model baroclinic climatological state and solved via an expansion in normal modes. The skill of these predictions are superior to persistence forecasts of daily events for at least 20 days and time averages for at least 90 days. As might be expected, initial states that project strongly onto the linear baroclinic model slow modes provide skillful forecasts at long forecast lags (seasons), which thus provides a prediction of the quality of the prediction and a possible explanation as to why persistence and forecast skill have been found to be correlated at long lags. An equivalent method for partitioning extended range forecast quality is provided via an EOF expansion. Initial states strongly projecting onto the first and dominant EOF mode are predicted best by the linear baroclinic model. This dominant EOF ...