HighResMountains

Abstrakt: Mountains are among the ecosystems most sensitive to climate change and are experiencing more rapid changes in temperature than environments at lower elevations (Pepin et al., 2015). They play a major role in shaping the weather and climate through their impact on atmospheric flow, hydrology, and precipitation processes, and are often associated with extreme events like heavy precipitation and downslope windstorms. Despite its importance, the understanding of mountain weather and climate and how it will change with further warming of the atmosphere is still very limited. The lack of understanding is largely associated with the lack of reliable data over such a complex mountainous terrain. For example, observational data sets suffer from sparse and inhomogeneous station networks and are often of short temporal coverage, especially at subdaily time scales, which are needed to study local scale extreme events characteristic for mountainous regions like the Alps. On the other side, current conventional and probably the most used climate models and simulations are not able to properly represent the complex mountainous orography and processes related to them due to the coarse resolution (12-50 kilometres in regional and >50 kilometres in global climate models). Thus, in parallel to dynamical downscaling, different statistical downscaling and bias adjustment methods have been developed. These methods have been essential in producing the information on a local scale in the recent ÖKS15 Climate Scenarios for Austria (Chimani et al., 2016). These (nominal) 1 km scenarios have been produced by bias-adjusting 12 km EURO-CORDEX simulations, driven by CMIP5 (Coupled Model Intercomparison Project - 5) global climate models. Yet recent research (e.g., Maraun et al., 2017; Maraun & Widmann, 2018; Maraun et al., 2019) and the recent 6th assessment report of the IPCC (Doblas-Reyes et al., 2021) have highlighted the severe limitations of, in particular bias-adjustment methods when used to downscale to fine grids. Especially in mountain regions and in the presence of regional feedbacks, statistical approaches may have shortcomings at reliably simulating local climate change (Wilby et al., 2004; Maraun & Widmann, 2018).

With the advancements in computer power and continuous development of high performance systems, climate simulations with a kilometre-scale grid spacing are becoming feasible that are in principle able to represent local-scale topography, processes and feedbacks. Even though, they are still limited to shorter time periods (10 year-long simulations) and cover only selected areas, they offer very promising results for the simulations of precipitation, especially heavy precipitation on subdaily time scales (e.g., Ban et al., 2014, Prein et al., 2015). Furthermore, the first multi-model ensemble at the km-scale resolution emerged last year covering the greater Alpine region and decade-long periods (Ban et al., 2021, Pichelli et al., 2021) as a result of a large international effort invested by many research groups around Europe and as a part of the CORDEX (Coordinated Regional Climate Downscaling Experiment) Flagship Pilot Study (CORDEX-FPS) on Convective Phenomena over Europe and the Mediterranean (Coppola et al. 2019). It is a very unique dataset, with a great potential and only superficially explored, offering
a great opportunity to study the climate of the complex Alpine region.
With the availability of the new CMIP6 simulations, which are soon to be downscaled by the EURO-CORDEX initiative, new Austrian climate change scenarios are in preparation within the community-initiative ÖKS NextGen led by ZAMG. But especially to serve local user needs in the mountain regions of the Alps, the CORDEX-FPS ensemble may provide a breakthrough compared to standard EURO-CORDEX simulations. Given the bias adjustment limitations discussed today, this argument holds in particular also for the current bias-adjusted ÖKS15 simulations. Thus, an assessment and intercomparison of the local information for the Alps across the available datasets is of utmost importance.
HighResMountains is bringing together all these new emerging and high-resolution climate data with the overarching goal of understanding extreme events over the complex Alpine region and their changes with further warming of the atmosphere.
More specifically, the HighResMountains will address the following objectives:
O1. Engage with the regional climate consultants from ZAMG to find out what are the characteristics of the extreme  events that matter the most for the impact community in the Alpine region.
O2. Assess how these characteristics of extreme events, and local processes related to them, are represented in different recent and state-of-the-art climate data sets (observations and simulations, including ÖKS15).
O3. Develop a strategy on how different emerging data sets can be combined to provide credible information about local processes and extreme events.
O4. Understand how these extreme events and their associated processes will change with further warming of the atmosphere.
O5. Disseminate and communicate the resulting information to stakeholders and the wider public.
The ultimate goals of HighResMountains are to provide the next generation of ÖKS with i) a deeper understanding of extreme events and local processes related to them, and their changes, ii) information on the credibility of different datasets, and iii) a framework to combine these different data sets to distil clear and reliable information.