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

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Title: Enhancing decadal snowfall forecasts in the Mediterranean mountains through informed atmospheric variability and climate data.
Authors: Diodato, Nazzareno1 (AUTHOR), Rubinetti, Sara2 (AUTHOR), Bellocchi, Gianni1,3 (AUTHOR) gianni.bellocchi@inrae.fr
Source: Hydrological Sciences Journal/Journal des Sciences Hydrologiques. Feb2025, Vol. 70 Issue 2, p294-310. 17p.
Subjects: Circulation models, Statistical models, Autoregressive models, Water management, Standard deviations
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. [ABSTRACT FROM AUTHOR]
Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Enhancing decadal snowfall forecasts in the Mediterranean mountains through informed atmospheric variability and climate data.
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  Data: <searchLink fieldCode="AR" term="%22Diodato%2C+Nazzareno%22">Diodato, Nazzareno</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rubinetti%2C+Sara%22">Rubinetti, Sara</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bellocchi%2C+Gianni%22">Bellocchi, Gianni</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> gianni.bellocchi@inrae.fr</i>
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  Data: <searchLink fieldCode="JN" term="%22Hydrological+Sciences+Journal%2FJournal+des+Sciences+Hydrologiques%22">Hydrological Sciences Journal/Journal des Sciences Hydrologiques</searchLink>. Feb2025, Vol. 70 Issue 2, p294-310. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Circulation+models%22">Circulation models</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Autoregressive+models%22">Autoregressive models</searchLink><br /><searchLink fieldCode="DE" term="%22Water+management%22">Water management</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/02626667.2024.2427356
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 294
    Subjects:
      – SubjectFull: Circulation models
        Type: general
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Autoregressive models
        Type: general
      – SubjectFull: Water management
        Type: general
      – SubjectFull: Standard deviations
        Type: general
    Titles:
      – TitleFull: Enhancing decadal snowfall forecasts in the Mediterranean mountains through informed atmospheric variability and climate data.
        Type: main
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            NameFull: Diodato, Nazzareno
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            NameFull: Rubinetti, Sara
      – PersonEntity:
          Name:
            NameFull: Bellocchi, Gianni
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          Dates:
            – D: 01
              M: 02
              Text: Feb2025
              Type: published
              Y: 2025
          Identifiers:
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              Value: 70
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              Value: 2
          Titles:
            – TitleFull: Hydrological Sciences Journal/Journal des Sciences Hydrologiques
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