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Abstract. Snowpack models simulate the evolution of the snow stratigraphy based on meteorological inputs and have the potential to support avalanche risk management operations with complementary information relevant for their avalanche hazard assessment, especially in data-sparse regions or at times of unfavorable weather and hazard conditions. However, the adoption of snowpack models in operational avalanche forecasting has been limited, predominantly due to missing data processing algorithms and uncertainty around model validity. Thus, to enhance the usefulness of snowpack models for the avalanche industry, numerical methods are required that evaluate and summarize snowpack model output in accessible and relevant ways. We present algorithms that compare and assess generic snowpack data from both human observations and models, which consist of multidimensional sequences describing the snow characteristics of grain type, hardness, and age. Our approach exploits Dynamic Time Warping, a well-established method in the data sciences, to match layers between snow profiles and thereby align them. The similarity of the aligned profiles is then evaluated by our independent similarity measure based on characteristics relevant for avalanche hazard assessment. Since our methods provide the necessary quantitative link to data clustering and aggregating methods, we demonstrate how snowpack model output can be grouped and summarized according to similar hazard conditions. By emulating aspects of the human avalanche hazard assessment process, our methods aim to promote the operational application of snowpack models so that avalanche forecasters can begin to build an understanding of how to interpret and trust operational snowpack simulations.

ABSTRACT. A third-order linear glacier length model is used to analyze if the retreat of Hintereisferner in the Austrian Alps over the past 160 years is exceptional, or lies within the range of the natural variability inherent to a stationary climate. A detailed uncertainty analysis takes into account glacier geometry, model parameters and initial conditions. A Monte Carlo ensemble strengthens the result that the observed retreat cannot be explained by natural variability and therefore affirms regional climate change. Finally the observed temperature trend at Hintereisferner lies outside the range of natural variability from an ensemble of climate models, but is consistent with the modeled range of responses to anthropogenic forcing.

Abstract. Snowpack models can provide detailed insight about the evolution of the snow stratigraphy in a way that is not possible with direct observations. However, the lack of suitable data aggregation methods currently prevents the effective use of the available information, which is commonly reduced to bulk properties and summary statistics of the entire snow column or individual grid cells. This is only of limited value for operational avalanche forecasting and has substantially hampered the application of spatially distributed simulations, as well as the development of comprehensive ensemble systems. To address this challenge, we present an averaging algorithm for snow profiles that effectively synthesizes large numbers of snow profiles into a meaningful overall perspective of the existing conditions. Notably, the algorithm enables compiling of informative summary statistics and distributions of snowpack layers, which creates new opportunities for presenting and analyzing distributed and ensemble snowpack simulations.

Abstract. Avalanche warning services increasingly employ large-scale snow stratigraphy simulations to improve their insight into the current state of the snowpack. These simulations contain information about thin, persistent critical avalanche layers that are buried within the snowpack and are fundamental drivers of avalanche hazard. However, the data volume, data complexity, and unknown validity have so far limited the value of the simulations for operational decisions. We attribute this at least partially to a lack of research that validates the simulations for their capability to represent the existence and instability of known critical layers at the regional scale. To address this knowledge gap, we present methods that enable meaningful comparisons between regional assessments of avalanche forecasters and snowpack simulations that are distributed across entire forecast regions. We applied these methods to operational data sets of ten winter seasons and three public forecast regions in western Canada and thereby quantified the performance of the Canadian weather and snowpack model chain to represent persistent critical avalanche layers. We found that the overall probability of detecting a known critical layer in the simulations can be as high as 75 % when accepting a low probability of 40 % that any simulated layer is actually of operational concern in reality. Furthermore, we explored patterns that characterize which layers were represented well and which were not. Faceted layers, for example, were captured well but also caused most false alarms, whereas surface hoar layers tended to be less prevalent but in return were mostly of operational concern when modeled. Overall, our results suggest that the simulations provide a valuable starting point for targeted field observations as well as a rich complementary information source that can help alert forecasters about the existence of specific critical layers or provide an independent perspective on their instability. However, we do not believe that the existing model chain is sufficiently reliable to generate assessments purely based on simulations. We conclude by presenting our vision of a real-time operational validation suite that can help forecasters develop a better understanding of the simulations' strengths and weaknesses by continuously comparing assessments and simulations in a user-friendly manner.