Abstract. The mountain cryosphere of mainland Europe is recognized to have important impacts on a range of environmental processes. In this paper, we provide an overview on the current knowledge on snow, glacier, and permafrost processes, as well as their past, current, and future evolution. We additionally provide an assessment of current cryosphere research in Europe and point to the different domains requiring further research. Emphasis is given to our understanding of climate–cryosphere interactions, cryosphere controls on physical and biological mountain systems, and related impacts. By the end of the century, Europe's mountain cryosphere will have changed to an extent that will impact the landscape, the hydrological regimes, the water resources, and the infrastructure. The impacts will not remain confined to the mountain area but also affect the downstream lowlands, entailing a wide range of socioeconomical consequences. European mountains will have a completely different visual appearance, in which low- and mid-range-altitude glaciers will have disappeared and even large valley glaciers will have experienced significant retreat and mass loss. Due to increased air temperatures and related shifts from solid to liquid precipitation, seasonal snow lines will be found at much higher altitudes, and the snow season will be much shorter than today. These changes in snow and ice melt will cause a shift in the timing of discharge maxima, as well as a transition of runoff regimes from glacial to nival and from nival to pluvial. This will entail significant impacts on the seasonality of high-altitude water availability, with consequences for water storage and management in reservoirs for drinking water, irrigation, and hydropower production. Whereas an upward shift of the tree line and expansion of vegetation can be expected into current periglacial areas, the disappearance of permafrost at lower altitudes and its warming at higher elevations will likely result in mass movements and process chains beyond historical experience. Future cryospheric research has the responsibility not only to foster awareness of these expected changes and to develop targeted strategies to precisely quantify their magnitude and rate of occurrence but also to help in the development of approaches to adapt to these changes and to mitigate their consequences. Major joint efforts are required in the domain of cryospheric monitoring, which will require coordination in terms of data availability and quality. In particular, we recognize the quantification of high-altitude precipitation as a key source of uncertainty in projections of future changes. Improvements in numerical modeling and a better understanding of process chains affecting high-altitude mass movements are the two further fields that – in our view – future cryospheric research should focus on.

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Soils are the product of a complex suite of chemical, biological, and physical processes. In spite of the importance of soils for society and for sustaining life on earth, our knowledge of soil formation rates and of the influence of biological activity on mineral weathering and geochemical cycles is still limited. In this paper we provide a description of the Damma Glacier Critical Zone Observatory and present a first synthesis of our multidisciplinary studies of the 150-yr soil chronosequence. The aim of our research was to improve our understanding of ecosystem development on a barren substrate and the early evolution of soils and to evaluate the influence of biological activity on weathering rates. Soil pH, cation exchange capacity, biomass, bacterial and fungal populations, and soil organic matter show clear gradients related to soil age, in spite of the extreme heterogeneity of the ecosystem. The bulk mineralogy and inorganic geochemistry of the soils, in contrast, are independent of soil age and only in older soils (>100 yr) is incipient weathering observed, mainly as a decreasing content in albite and biotite by coincidental formation of secondary chlorites in the clay fraction. Further, we document the rapid evolution of microbial and plant munities along the chronosequence.

In alpine and high-latitude regions, water resource decision making often requires large-scale estimates of snow amounts and melt rates. Such estimates are available through distributed snow models which in some situations can be improved by assimilation of remote sensing observations. However, in regions with frequent cloud cover, complex topography, or large snow amounts satellite observations may feature information of limited quality. In this study, we examine whether assimilation of snow water equivalent (SWE) data from ground observations can improve model simulations in a region largely lacking reliable remote sensing observations. We combine the model output with the point data using three-dimensional sequential data assimilation methods, the ensemble Kalman filter, and statistical interpolation. The filter performance was assessed by comparing the simulation results against observed SWE and snow-covered fraction. We find that a method which assimilates fluxes (snowfall and melt rates computed from SWE) showed higher model performance than a control simulation not utilizing the filter algorithms. However, an alternative approach for updating the model results using the SWE data directly did not show a significantly higher performance than the control simulation. The results show that three-dimensional data assimilation methods can be useful for transferring information from point snow observations to the distributed snow model.

Much effort has been invested in developing snow models over several decades, resulting in a wide variety of empirical and physically based snow models. For the most part, these models are built on similar principles. The greatest differences are found in how each model parameterizes individual processes (e.g., surface albedo and snow compaction). Parameterization choices naturally span a wide range of complexities. In this study, we evaluate the performance of different snow model parameterizations for hydrological applications using an existing multimodel energy-balance framework and data from two wellinstrumented alpine sites with seasonal snow cover. We also include two temperature-index snow models and an intensive, physically based multilayer snow model in our analyses. Our results show that snow mass observations provide useful information for evaluating the ability of a model to predict snowpack runoff, whereas snow depth data alone are not. For snow mass and runoff, the energy-balance models appear transferable between our two study sites, a behavior which is not observed for snow surface temperature predictions due to site-specificity of turbulent heat transfer formulations. Errors in the input and validation data, rather than model formulation, seem to be the greatest factor affecting model performance. The three model types provide similar ability to reproduce daily observed snowpack runoff when appropriate model structures are chosen. Model complexity was not a determinant for predicting daily snowpack mass and runoff reliably. Our study shows the usefulness of the multimodel framework for identifying appropriate models under given constraints such as data availability, properties of interest and computational cost.