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Enhancing decadal snowfall forecasts in the Mediterranean mountains through informed atmospheric variability and climate data

  • Nazzareno Diodato
  • , Sara Rubinetti
  • , Gianni Bellocchi

Research output: Contribution to journalArticlepeer-review

Abstract

Forecasting decadal-scale snowfall, crucial for global water management, is challenging due to the complex interplay of environmental factors. This study projects the number of snowfall days (NSD) up to 2060, using the extensive time-series data from the Montevergine Observatory, southern Italy (1884–2022). We pioneered an innovative statistical model, accounting for antecedent time lags, and exogenous support from the Teleconnection–Climate Pattern Index, incorporating large-scale (Arctic oscillation) and smaller-scale (temperature) forcings. Our projections reveal the influence of decadal and multidecadal oscillations throughout the forecast period and suggest an increase in NSD after 2030, notably shifting in the 2040s to 2050s, averaging from about 20 to 30 snowfall days annually. The frequency of snowfall deficit years (reaching −1 standard deviation) remains, however, high in the first part of the forecast. Despite the limitation of a single-site study, this trend is consistent with projections from various regional circulation models for increased extreme snowloads in Italy.

Original languageEnglish
Pages (from-to)294-310
Number of pages17
JournalHydrological Sciences Journal
Volume70
Issue number2
DOIs
Publication statusPublished - 2025
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • Arctic oscillation
  • autoregressive model
  • decadal predictions
  • snowfall

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