Periodic Reporting for period 1 - CliRSnow (Statistically combine climate models with remote sensing to providehigh-resolution snow projections for the near and distant future.)
Période du rapport: 2018-10-01 au 2020-09-30
Seasonal snow cover plays a key role in mountain socio-ecosystems but it is threatened by climate change. Snow accumulates in winter and subsequently melts in spring and summer, thus releasing significant water amounts downstream. The water availability from snow impacts hydro-power generation and agriculture far beyond the mountain ranges, while the snow cover directly affects vegetation phenology and species distribution. Moreover, snow plays an important role in defining mountain people’s identities and is a major influence on mountain economy via winter tourism.
With increasing temperatures and potentially changing precipitation patterns, the timing and abundance of snow will change. In order to determine the impacts of changing snow patterns, climate information is crucial, which can identify the impact of different emission scenarios as well as temporal and spatial patterns of change. The main challenge in determining future snow cover lies in the interaction between the large-scale weather forcing and small-scale topographical control on snow.
The overall objective of CliRSnow is to produce climatological information on snow cover in the European Alps for the past and the future. This is achieved by combining in-situ data, regional climate models and remote sensing to provide high-resolution projections of snow cover fraction for different scenarios of greenhouse gas concentrations, and putting these in context to past changes.
With increasing temperatures and potentially changing precipitation patterns, the timing and abundance of snow will change. In order to determine the impacts of changing snow patterns, climate information is crucial, which can identify the impact of different emission scenarios as well as temporal and spatial patterns of change. The main challenge in determining future snow cover lies in the interaction between the large-scale weather forcing and small-scale topographical control on snow.
The overall objective of CliRSnow is to produce climatological information on snow cover in the European Alps for the past and the future. This is achieved by combining in-situ data, regional climate models and remote sensing to provide high-resolution projections of snow cover fraction for different scenarios of greenhouse gas concentrations, and putting these in context to past changes.
Within CliRSnow, the first Alpine-wide collection of in-situ snow depth data has been created, which served as foundation for an important publication on Alpine snow climatology and past trends. It showed that 84% of variability in daily snow depth is explained by five spatial gradients, which reflect the topography of the Alps and the related temperature and precipitation patterns. In the period 1971-2019, mean snow depth decreased on average 8.4% per decade, with stronger and more significant trends at the seasons and elevations where the transition between snow and snow-free occurs. In addition, the open sharing of the data has led to it being re-used in a variety of scientific disciplines.
Twenty years of high-resolution remote sensing data on snow cover fraction and duration have been processed using temporal and spatial filters to remove clouds, and thus allowing a climatological assessment on the current snow cover duration patterns in the Alps. This confirmed the previous findings from in-situ snow depth data regarding patterns of snow cover in the Alps with a complete spatial coverage.
The evaluation of snow cover fraction and snow depth from regional climate models using in-situ and remote sensing data showed an overall good agreement between models and observations and good capacity of the regional climate model to reproduce snow, if systematic biases were accounted for. These systematic biases were, in order of importance, topography mismatches, temperature biases, and precipitation biases.
Finally, statistical bias-correction and downscaling routines were employed to projections of snow cover fraction from regional climate models using remote sensing data. These reduced ensemble uncertainties in the future projections, and were able to increase the resolution of the snow cover duration projections. The results indicate that over the whole Greater Alpine Region the snow cover area is almost halved for the 2071-2100 period for a high emission scenario (ie., 4-5°C global warming), while under a low emission scenario (1.5-2°C global warming) reductions amount to only one sixth of the 2000-2020 snow cover area.
The results of CliRSnow on past and future snow cover in the European Alps have been disseminated two-fold. First, via four peer-reviewed articles in international journals as well as three data sets and programming code in open public and institutional repositories, thus allowing re-use and further research. Second, in aggregated and summarized form to the general public and stakeholders, which includes, in addition to hard numbers, also the wider societal, ecological, and economical implication of snow cover and its changes. This includes, among others, an online dossier on snow, dashboards with interactive visualizations on the project homepage, and an e-learning course.
Twenty years of high-resolution remote sensing data on snow cover fraction and duration have been processed using temporal and spatial filters to remove clouds, and thus allowing a climatological assessment on the current snow cover duration patterns in the Alps. This confirmed the previous findings from in-situ snow depth data regarding patterns of snow cover in the Alps with a complete spatial coverage.
The evaluation of snow cover fraction and snow depth from regional climate models using in-situ and remote sensing data showed an overall good agreement between models and observations and good capacity of the regional climate model to reproduce snow, if systematic biases were accounted for. These systematic biases were, in order of importance, topography mismatches, temperature biases, and precipitation biases.
Finally, statistical bias-correction and downscaling routines were employed to projections of snow cover fraction from regional climate models using remote sensing data. These reduced ensemble uncertainties in the future projections, and were able to increase the resolution of the snow cover duration projections. The results indicate that over the whole Greater Alpine Region the snow cover area is almost halved for the 2071-2100 period for a high emission scenario (ie., 4-5°C global warming), while under a low emission scenario (1.5-2°C global warming) reductions amount to only one sixth of the 2000-2020 snow cover area.
The results of CliRSnow on past and future snow cover in the European Alps have been disseminated two-fold. First, via four peer-reviewed articles in international journals as well as three data sets and programming code in open public and institutional repositories, thus allowing re-use and further research. Second, in aggregated and summarized form to the general public and stakeholders, which includes, in addition to hard numbers, also the wider societal, ecological, and economical implication of snow cover and its changes. This includes, among others, an online dossier on snow, dashboards with interactive visualizations on the project homepage, and an e-learning course.
With the first Alpine-wide study on past snow depth, CliRSnow pushed the state-of-the-art in Alpine snow climatology by showing how snow cover patterns match the already known climatic patterns of temperature and precipitation in the Alps, and that these influence both the daily variability as well as long-term trends of snow depth. The methodological framework on bias-correction and downscaling that has been developed, and, for the first time, successfully applied within the project, serves as proof-of-concept and opened up the path to statistical corrections of snow cover information from regional climate models using remote sensing data.
The information on future snow cover is expected to help stakeholders and policy makers in judging the impacts of greenhouse gas emission scenarios, and thus in better framing the impact and necessity of climate action. Finally, outreach and communication activities in online and print media raised public awareness on the importance of snow cover as water storage in mountains as well as its wider impacts locally and downstream.
The information on future snow cover is expected to help stakeholders and policy makers in judging the impacts of greenhouse gas emission scenarios, and thus in better framing the impact and necessity of climate action. Finally, outreach and communication activities in online and print media raised public awareness on the importance of snow cover as water storage in mountains as well as its wider impacts locally and downstream.