Weather prediction has improved tremendously over the last decades. Ultimately, however, there are limits in predictability due to the multi-scale, non-linear nature of atmospheric dynamics.
To further improve our mechanistic understanding of atmospheric dynamics and to further improve numerical weather forecasting it is increasingly important to better understand the physical and dynamical processes connecting atmospheric motions across temporal and spatial scales. This includes, for example, the identification of the limits of predictability of different weather systems. For longer time scales, identifying and understanding sources of predictability and variability is of crucial importance. This session will therefore discuss the current understanding of physical and dynamical processes determining predictability and variability on different scales.

Contributions are invited that attempt to improve our understanding of atmospheric dynamics or that link process-based, dynamical understanding and predictability aspects. Atmospheric phenomena on all spatial and temporal scales are of interest. Particularly welcome are contributions that focus on high-impact weather, the sub-seasonal to seasonal (S2S) timescale, and related extremes. This may include, but is not limited to, the influence of remote factors (e.g., the stratosphere, the Artic, or the tropics) on the midlatitudes, predictability in the tropics and polar regions, stationary and recurrent systems (e.g. associated with heat waves, cyclone clustering, heavy precipitation), or processes driving seasonal or interannual variability.