The main focus of this presentation is on the spatiotemporal dynamics of errors in global model-based forecasts of the atmospheric state. First, a review of the latest important results from the literature is provided. Then, the results of a new investigation, which is based on The THORPEX Interactive Grand Global Ensemble (TIGGE) data set are presented. This data set provides a unique opportunity to study the dynamics of model errors, because it includes data produced by the top global forecast systems of the world. These systems use a variety of techniques to represent the effects of the initial condition and model errors on the forecast errors. The point that exponential growth of the forecast errors at the baroclinically unstable scales play a central role in the error dynamics is reiterated.