Model Error and Predictability over Different Timescales in the Lorenz96 systems

Prediction problems have been described by Lorenz as falling into two categories. Problems which depend on the initial condition, such as short to medium range weather forecasting, are described as ‘predictions of the first kind’, while problems that depend on boundary rather than initial con-ditions, such as in many cases the longer term climatology, are referred to as predictions of the second kind. Both kinds of prediction will be af-fected by error in the model equations used to approximate the true sys-tem. In this paper, we examine predictability over different time scales for the medium-dimensional Lorenz ’96 systems. Models are constructed for the purposes of optimising both short-range prediction and climatological behavior, and studied over a range of forcings for which they show peri-odic, quasi-periodic, or chaotic behavior. It is shown that, for the models discussed here, there is a link between short and long-range predictability, which holds independently of the effects of chaos. The role of stochas-tic terms is considered, and the possible implications for atmospheric or oceanographic modelling are discussed. 2