Output of numerical weather prediction models is nowadays used to provide point-specific weather information (e.g., weather apps) - what is extremely challenging in mountainous terrain. Increasingly, it is also used as input for applied models for, e.g., hydrology or health-related forecasts, energy smart-net regulations and potential assessment, economic decision models or ecological budgeting. Similarly, climate services in relation to climate change call for our ability to correctly model scenarios for future climate states. Mountainous areas not only seem to exhibit a stronger climate sensitivity (e.g., stronger presently observed temperature increase over mountains than in the global average) and are thought to be particularly vulnerable, but also pose a particularly challenging task to the climate modelling community due to unresolved processes, terrain representation and scale interactions. Due to longer integration times, possible errors in surface-atmosphere exchange will likely have an even stronger impact for the assessment of input data for climate services modelling (the entire range of energy, agriculture, health, hydrology applications) than for Earth System services in the weather prediction time-range. Atmospheric composition, finally, is not only relevant with respect to climate forcing, but also – on shorter time scales – in view of air pollution. Mountainous terrain does not only trigger its characteristic pollution threats (such as smog episodes in a stably stratified valley), but also largely increases the complexity by introducing air chemistry as another process that needs to be taken into account. The interactions between chemical transformations and turbulent diffusion are complicated by additional length and timescales related to meso-scale processes in complex terrain.

This contribution summarizes the state of affairs for ‘TEAMx’, i.e. the most relevant scientific questions and corresponding TEAMx research goals and the ongoing and planned activities with respect to numerical modelling and the experimental phase.