Climate-assisted data-driven decadal snowfall predictions in the Swiss foothills

Nazzareno Diodato, Fredrik Charpentier Ljungqvist and Gianni Bellocchi

PLOS Climate, 2025, vol. 4, issue 7, 1-21

Abstract: Decadal-scale climate predictability is crucial for societal planning, not least in regions sensitive to winter extremes. This study predicts the number of heavy snowfall days in the Swiss Pre-Alpine Region (SPAR) through 2060, using a time-varying autoregressive model based on data from 1884 to 2023. The model integrates a pattern index that combines both large-scale (Arctic Oscillation and Dipole Model Index) and regional-scale (spring–winter temperature differential) climate forcings, capturing lagged seasonal effects. The results indicate a slight upward trend – about one to two additional heavy snowfall days by the 2050s – though not statistically significant, and consistent with regionally downscaled climate models. After 2045, variability is expected to rise, periods of snowfall deficits affecting year groups and a cluster of exceedance years emerging towards the end of the projection period. While extreme snowfall events are projected to become less frequent in the Northern Hemisphere, their intensity is unlikely to diminish. These findings enhance understanding of snowfall dynamics in the SPAR and contribute to broader insights into cryospheric changes. Data-driven models such as this one are valuable tools to contextualise historical hydroclimatic drivers and assessing inter-annual and inter-decadal variability in regional climate projections and their societal implications.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pclm00:0000592

DOI: 10.1371/journal.pclm.0000592

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