Mountains are the natural water towers of the world through the capture and storage of atmospheric moisture. Beneficially, in many mountain systems this stored water (e.g., snowpack) is also provided at a time at which precipitation is scarce, yet water demand is high. Yet, the water resource boon that mountains provide can quickly become a bane under extreme conditions that accelerate runoff and lead to the endangerment of the communities that live beneath them. Albeit rare, these extreme rain-on-snow and rain-instead-of-snow events are important drivers of water resource management.

In this work, we utilize several observationally based products and regional climate models to explore mountain snowpack as both a boon and bane to water resource management in three distinct regions: the California Sierra Nevada, the Upper Colorado Rocky Mountains, and the Susquehanna River Basin in the Northeastern United States. These regions were selected in Project Hyperion as they provide a testbed to engage with water stakeholders on the management decisions unique to each region and a means to scientifically evaluate several seasonal snow climates that are shaped by distinct atmosphere-land interactions.

These observational products and modeling tools are evaluated using a multi-metric evaluation framework, the snow water equivalent (SWE) triangle, that isolates their agreements and disagreements in representing the seasonal cycle of snowpack. We then assess if these tools are fit-for-purpose in representing extreme events that lead to both rain-on-snow, and rain-instead-of-snow. We do this using a novel framework that directly connects storm-based event filtering with hydrologic based impact metrics.

Several climate models use the two stable water isotopologues (HDO and H₂¹⁸O) to simulate climate water cycles in global and regional scales. These isotoplogues are also used in paleoclimatic reconstructions based on ice core, speleotherm and other archives. Climatic modelling and paleoreconstructions are especially difficult in mountain regions, since meteorological parameters (including isotopologues of atmospheric precipitation) are known with insufficient spatial resolution for these regions, especially in North Asia. In this study we present the results of isotopic composition measurements for atmospheric precipitation and snow cover sampled in foothills of Altai during 2014-2018. The isotopic composition of precipitation varies widely, about 35‰ for δ¹⁸О, 210‰ for δD, and 50‰ for d-excess. The results of the isotopic analysis of the integral samples of snow cover are consistent with the average values of the isotopic composition of the precipitations that formed this cover. Thus, with the interpretation of the results, the data on the snow cover isotopic composition on Altai foothills can be used as an alternative data source instead isotopic composition of precipitations in cold season. Based on backward trajectories of air masses (model HYSPLIT, >3500 trajectories) and isotopic composition of precipitation, five regions have been identified as sources of the atmospheric moisture which precipitated in foothills Altai. It has been shown that Atlantic and Arctic Oceans are the dominant source (>50%) of precipitation in foothills Altai, but in the warm season Central Asian sources make a significant contribution (up to 20%). Comparison of the results of the isotope analysis of precipitation and the ECHAM5-wiso modelling data showed a good agreement. The found relations can be used as the reliable transfer functions for the climate modelling and paleoreconstructions in Altai Mountains.

Climate change and its socio-environmental impacts in the European Alps are investigated in the context of the TRAJECTORIES initiative (https://trajectories.univ-grenoble-alpes.fr/). A part of this research is based on climate simulations produced with the regional model MAR that has been applied with a resolution of 7 km over 1902-2010, using the ERA20C reanalysis as boundary condition. Model experiment has been used to detect precipitation changes over the last century. The model reproduces well the observed variability of precipitation, but probably overestimate its magnitude. However, the discrepancy found between different observational datasets limits the possibility to assess the model skill. Contrasted seasonal trends have been found over the 20^th century. An increase of winter precipitation is simulated over the North-western part of the Alps at altitude higher than 1500m. A decrease of summer precipitation is found everywhere but at altitude higher than 1500m. Precipitation changes have locally exceeded 50% over the last century. Seasonal maximum as well as variance of precipitation has increased from 20% to 40% mainly over the North-western part of the Alps and during the winter. The number of wet days (P>1mm) has decreased by 10% to 50% mainly in the South-eastern part of the Alps and during the summer. Because of large internal variability, precipitation changes are significant (pvalue<0.05) only when considering their evolution over long period, typically 60-100 years. Precipitation changes are accompanied by a general increase of temperature over the Alps during all the seasons except for winter at altitude higher than 1500m where the temperature signal is small and not significant in the simulation. A general increase of the wind velocity ranging between 5% and 25% over the last century is also found for all the seasons. Other research activities based on statistical adjustment, climate scenario and the links with socio-environmental studies will be described.

The recent development of high-resolution climate models offers a promising approach in improving the simulation of precipitation, clouds, and temperature. However, higher grid spacing is also a promising feature to improve the simulation of snow cover as it allows to represent topography in more detail and to resolve convection explicitly. In this study, we analyze the snow cover in a set of decade-long high-resolution climate simulation with a horizontal grid spacing of 2.2 km over the greater Alpine region. We compare it against observations and lower resolution models (12 and 50 km), which use a parameterization of convection. The simulations are integrated using the COSMO (Consortium for Small-Scale Modeling) model.

The validation of snow water equivalents (SWE) in the simulation of present-day climate driven by ERA-Interim, against an observational dataset, reveals that the high-resolution simulation clearly outperforms simulations with coarse grid spacings. These simulations underestimate the cumulative amount of SWE over the whole annual cycle by 33% (12 km simulation) and 56% (50 km simulation) while the high-resolution simulation shows a difference smaller than 1%.

Comparison of scenario simulations driven by GCM MPI-ESM-LR (2081-2090 RCP8.5 vs 1991-2000) reveals a strong decrease of SWE over the Alps consistent with previous studies. Previous studies had found that the relative decrease becomes gradually smaller with elevation, but this finding was limited to low and intermediate altitudes (as a 12 km simulation is only capable resolving the topography up to ~2500 m asl). In the current study, we find that the height gradient reverses sign and relative reductions in snow cover increase above 3000 m asl. This more pronounced decline emphasizes the value of the higher grid spacing. Overall, we show that high-resolution climate models offer a promising approach in improving the simulation of snow cover in Alpine terrain.

Snow is an important component of many mountainous regions as it influences the magnitude and timing of river runoff, causes natural hazards (avalanches) and is a crucial economical factor for areas that depend on winter tourism. In climate models, snow cover plays an important role through its control of near-surface temperature and its impact on the atmosphere through the snow-albedo feedback. Its representation in kilometre-scale climate simulations is, via an enhanced representation of hypsometry and precipitation patterns, considerably improved compared to coarser-scale simulations. Nonetheless, biases in snow cover duration (SCD) remain, as indicated by an evaluation of a COSMO simulation (~2 km) with satellite-derived snow-cover products for the European Alps. These biases reveal a clear dependence on slope aspect, where SCD is overestimated for south-facing grid cells and underestimated for north-facing cells. SCD is, among other factors, controlled by incoming short- and longwave radiation, which contribute to the surface energy balance. Thus, a likely cause for these biases is the unaccounted influence of topography on incoming surface radiation. This influence is neglected in most state-of-the-art regional climate models (RCMs). For simulations with intermediate grid spacing (> 10 km), the neglect is justified by the inability of the model to resolve typical length scales of valleys and mountain ridges. In high-resolution (~2 km) simulations, however, such topographic features are at least partially resolved.

To address the biases in SCD, COSMO is run with a scheme that corrects incoming surface long- and shortwave radiation. Radiative fluxes are thereby modified according to local slope angle/aspect, shadowing due to surrounding terrain and the local sky view factor. Results indicate that biases in SCD can be considerably reduced with this scheme. It is expected that the improvements will be even more pronounced in RCM simulations with higher horizontal resolutions.

The representation of the mountain cryosphere in climate models is critical owing to the scale mismatch between climate model grid resolutions, typically ranging between 100 and 10 km, and the scales at which snow-related processes occur, which is finer than 1 km.

We provide an overview of the state-of-the-art global and regional climate model simulations from CMIP5 and EURO-CORDEX experiments by comparing the model representations of the snow water equivalent climatology over the Greater Alpine Region (4–19°E, 43–49°N) in the last decades (1980-2005). We explore the impact of the horizontal resolution on snow water equivalent simulations exploiting an additional set of five simulations performed with the Earth-System Model EC-Earth run at increasing spatial resolutions, from ~125 to 16 km. We assess the differences in the drivers of snow processes (air temperature, total precipitation, snowfall) and in the snow water equivalent outputs. Results show that, compared to lower resolution runs, in the finest-resolution runs colder temperatures and a slightly higher amount of snow precipitation lead to a significantly thicker snow depth. The spatial resolution is found to play a crucial role on snowpack simulations, and high-resolution modelling is required to reliably reproduce snowpack dynamics.

A possible strategy to achieve high-resolution snowpack simulations useful for climatic studies can be to employ climate model meteorological outputs as forcings for snowpack models run in off-line mode at the desired spatial resolution but over a limited domain. An analysis of the sensitivity of selected snowpack models to the type and accuracy of the meteorological forcing will be presented.

The enhancement of warming rates with elevation, or Elevation-Dependent Warming (EDW), is one of the regional manifestations of global warming. Sentinels of climate and environmental changes, mountain regions have overall experienced more rapid and intense warming rates compared to the globally-averaged temperature increase in the recent decades, leading to serious impacts both on high-altitude mountain ecosystems and downstream.

In this work we analyse an ensemble of simulations from one state-of-the-art Global Climate Model (EC-Earth) run at five different spatial resolutions, from ∼125 to ∼16 km, with the aim of investigating the impact of the model resolution on the representation and drivers of EDW in three mountain regions of the Northern Hemisphere mid-latitudes - the Colorado Rocky Mountains, the Greater Alpine Region and the Tibetan Plateau–Himalayas.

Our results show that the changes in albedo and in downward longwave radiation are the more frequent EDW drivers in all regions and seasons. In the Tibetan Plateau-Himalayas and in the Greater Alpine Region, an additional driver is the change in specific humidity. We also find that, while generally the model ability in simulating the existence of EDW in the different regions does not depend on the model spatial resolution, specific EDW characteristics such as its intensity and the relative role of different driving mechanisms may be different in simulations performed at different spatial resolutions.

Overall, our results indicate that the model resolution only plays a crucial role in small areas such as the Alps, where a too coarse resolution would lead to an underrepresentation of the highest altitudes. In fact, elevational dependence of warming, as well as of other mechanisms or variables, could not be easily identified if the range of altitudes is too limited.
 

The coarse grid spacing of global circulation models necessitates the application of climate downscaling to investigate the local impact of a changing global climate. Difficulties arise for data sparse regions in complex topography which are computationally demanding for dynamic downscaling and often not suitable for statistical downscaling due to the lack of high quality observational data. The Intermediate Complexity Atmospheric Research Model (ICAR) is a physics-based model that can be applied without relying on measurements for training and is computationally more efficient than dynamic downscaling models. This study presents the first in-depth evaluation of multi-year precipitation time series generated with ICAR on a 4 x 4 km² grid for the South Island of New Zealand for the eleven-year period from 2007 to 2017. It focuses on complex topography and evaluates ICAR at eleven of which are situated in the Southern Alps. ICAR is diagnosed with standard skill scores and the effect of model top elevation, topography, season, atmospheric background state and synoptic weather patterns on these scores are investigated. The results show a strong dependence of ICAR skill on the choice of the model top elevation. Furthermore, ICAR is found to provide added value over its ERA-Interim reanalysis forcing data set, improving mean squared errors (MSE) up to 53%. It performs similarly during all seasons, while flow of higher linearity and atmospheric stability were found to increase skill scores. ICAR scores are highest during weather patterns associated with flow perpendicular to the Southern Alps and lowest for flow parallel to the alpine range. While measured precipitation is underestimated by ICAR, these results may be improved upon by further observational tuning or bias correction techniques. Based on these findings ICAR shows the potential to generate downscaled fields for long term impact studies in data sparse regions with complex topography.