A novel spatiotemporal analysis of snow cover pattern over Finland based on ERA5-land reanalysis

As global temperatures rise due to climate change, snow-covered areas in high-latitude regions such as Finland exhibit increasing variability. Variations in Arctic snow cover can significantly impact the ecosystem, hydrological cycle, biodiversity, and many other physical processes. Consistent and detailed assessments of long-term changes in relevant snow cover pattern (SCP) features, including timing of snow accumulation and melt (phenology), duration of snow cover, and the number of snow-free days are crucial for understanding the regional dynamics of the water resources. This study aims to analyze the time series of SCP features in ERA5-Land reanalysis data for Finland. Prevalent SCP assessments exclude critical SCP features, such as the day of the year when maximum snow cover extent in each pixel start and end, which are essential for a thorough spatiotemporal analysis. This study addresses these gaps by analyzing four SCP features in each pixel: the snow onset date, first day of maximum snow cover extent, last day of maximum snow cover extent, and last day of snow cover. Based on ERA5-Land data from 2000 to 2020 and a novel method using convolution kernels and Hadamard-product-based weighting combined with K-means clustering, Finland was clustered into four distinct snow regions based on SCP features. In the largest cluster (114,738 km²) the duration of maximum snow cover extent (D_max) was 189 days of the total 220 days of snow cover duration (D_total). Conversely, the smallest cluster in southern and coastal areas covering 41,630 km², experienced D_max of 85 within 123 days of D_total. Mann-Kendall trend analysis revealed a significant extension of springtime snow cover in northern Finland, while southern and coastal areas experienced reduced winter snow-cover durations. Using K-nearest neighbours method and based on the mentioned four clusters, the 20 annual SCP features images of Finland were classified. The effect of air temperature and precipitation in the annual classification’s results and SCP variability in each region were also investigated. In this regard, we quantified the deviations of SCP form their cluster centroid during snow accumulation period, the period with maximum snow cover extent, and snowmelt period. The classification of annual SCP variations further demonstrated relationships between SCP dynamics and variations in air temperature and precipitation. SCP is particularly susceptible to near-zero air temperature fluctuations, whose effects can be further amplified by precipitation anomalies.