Periodic Reporting for period 1 - CliRSnow (Statistically combine climate models with remote sensing to providehigh-resolution snow projections for the near and distant future.)
Berichtszeitraum: 2018-10-01 bis 2020-09-30
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.
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.
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.