"Despite large progress in modelling of atmospheric processes and computing capabilities and concentrated efforts to increase complexity of the atmospheric models, the assessment of accuracy of natural atmospheric climate variability, its predictability and interaction with anthropogenic influences is far from well understood. This project aims to advance scientific understanding of dynamical properties of the atmosphere and climate systems over many spatial and temporal scales.It is proposed to study atmospheric balance and predictability in terms of the energy percentage which is associated with various types of motions, balanced or Rossby-type of motions and unbalanced or inertio-gravity motions. This representation of the atmosphere is called the normal-mode function representation and it is a heart of methodology proposed in this project.The projects is built on theoretical foundation set in 1970s at the National Center for Atmospheric Research in USA and with the support of original developers it will apply normal-mode function representation tool to issues for which it could not have been reliably applied earlier. The project relies on accomplishments of the proposal’s PI in weather and data assimilation modeling which this project will extend to new research areas.The project will quantify balance in analysis datasets and ensemble forecasting systems and use the results as a starting point for climate model assessment for their ability to represent the present climate and possible changes of balance in model simulations of future climate scenarios. Results will allow dynamical classification of climate models based on their balance properties. Predictability issues will be studied by comparing temporal variability of balance in the forecasts in terms of various spatial scales. An important project outcome will be a free-access, user-friendly tool for carrying out a physically-based analysis of weather and climate model outputs."