Abstract
Satellite-derived, long-term snow cover extent (SCE) documents broad-scale climatic variability and change of the cryosphere. SCE is a highly dynamic Essential Climate Variables (ECVs; e.g. GCOS-200, 2016) for the cryosphere used to assess its current state, long-term changes, and the rate of these change. Thus, SCE time series fill essential knowledge gaps on past snow occurrences and support our understanding of spatiotemporal variance in snow cover. This is of special interest in mountainous alpine areas, such as the European Alps, which are hotspots of biodiversity and particularly vulnerable to climate change. However, a weak correlation was found in summer of greening predominated in warming areas and snow cover recression, though significant (Rumpf et al., 2022).
This contribution presents a spatiotemporal analysis for the European Alps based on a novel, public accessible Level-2 and -3 fractional snow cover (FSC) data set (1981-2021) from the Advanced Very High Resolution Sensor (AVHRR). The FSC product is canopy-corrected, gap-filled for Level-3, and includes an uncertainty estimation as well as user-oriented quality flags, while the spatial resolution is defined by AVHRR’s effective footprint size of 1.1 x 1.1 km at nadir. Data source is the extended European daily AVHRR LAC L1C data set (version v04; processed in 2022) dating from 1981 until today, hosted by the University of Bern, Switzerland. To avoid misclassification and better discriminate between clouds and snow, the newest PPS probabilistic cloud mask (PPS v2021.1 by NWSAF/SMHI, Sweden) has been applied following a sensitivity analysis. Canopy-corrected FSC has been estimated using the adapted SCAMOD method (Metsämäki et al., 2015, Weber et al., 2021).
Based on this novel data set, we analyze spatiotemporal variability in seasonal snow cover related to elevation-dependent warming and in combination with climate data provide insights into the controls of this variability. Our study is focused on the European Alpine mountain region, which is particularly vulnerable and sensitive to climate change. In addition to a decrease in snow cover duration (SCD) and spatial extent (Hüsler et al., 2014), it has been observed that temperature increases particularly rapid at the current boundary of the cryosphere (e.g., snowline region) due to the snow-albedo effect (Pepin et al., 2022 and therewithin). To assess these changes and temporal trends using the novel data set, we derived seasonal snow cover metrics for specific Alpine catchments. Seasonal and annual SCA anomaly as well as mean SCD were calculated for different elevation bands of the Alps and subregions of Switzerland over the entire time range to analyze long-term changes. The future analysis of respective climate data (i.e., temperature and precipitation) allows to determine controls of long-term variability in these highly sensitive environments.