Bongio et al. (2025) A Statistically Based Method to Estimate Long‐Term Daily Air Temperature at High Elevations

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: International Journal of Climatology
• Year: 2025
• Date: 2025-12-03
• Authors: Marco Bongio, Giovanni Baccolo, Riccardo Scotti, Carlo De Michele
• DOI: 10.1002/joc.70208

Research Groups

Swiss meteorological research institutions (inferred from the study location and nature)

Short Summary

This study develops a statistical methodology to reconstruct daily air temperature time series at the Jungfraujoch (3571 m a.s.l.) in Switzerland from 1900, using observations from lower-altitude stations. The reconstructed series provides a robust, computationally efficient benchmark for evaluating temperature anomalies and studying elevation-dependent warming, achieving performance comparable to existing high-resolution datasets with fewer data requirements.

Objective

• To reconstruct daily air temperature time series at the Jungfraujoch (3571 m a.s.l.), the highest permanently manned meteorological station in Switzerland, for the period dating back to 1900, using observations from 30 lower-altitude stations (485–2691 m a.s.l.).

Study Configuration

• Spatial Scale: Jungfraujoch (3571 m a.s.l.) and 30 lower-altitude stations (485–2691 m a.s.l.) across Switzerland.
• Temporal Scale: Daily air temperature time series from 1900 to 2023 (reconstructed for 1900-1933, observed for 1933-2023).

Methodology and Data

• Models used: Statistical methodology, ensemble simulation.
• Data sources: Observations from 30 lower-altitude meteorological stations (485–2691 m a.s.l.), observations from Jungfraujoch (1933–2023), and two high-resolution gridded datasets (HISTALP and Imfeld et al. 2023) for validation.

Main Results

• The selection of stations with temporally consistent long-term observations is critical for reconstruction accuracy.
• Model performance, efficiency, and errors are primarily influenced by altitude.
• The Kling–Gupta Efficiency (KGE) is an appropriate metric for defining the ensemble simulation, considering correlation and bias on the mean and standard deviation values.
• The ensemble simulation enhances the temporal consistency of the estimated time series and mediates latitudinal and longitudinal gradients.
• The developed methodology achieves comparable performance to existing datasets despite low-data requirements, with greater computational efficiency.
• The estimated time series can serve as a benchmark for evaluating time series anomalies and for a deeper analysis of the elevation-dependent warming issue.

Contributions

• Development of a robust and computationally efficient statistical methodology for reconstructing long-term daily air temperature time series at high-altitude stations with limited historical data.
• Creation of a new, validated long-term (1900-2023) daily air temperature dataset for the Jungfraujoch, providing a valuable benchmark for climate studies.
• Demonstration of a method that achieves high performance comparable to existing high-resolution gridded datasets while requiring fewer input data.

Funding

Not explicitly mentioned in the abstract.

Citation

@article{Bongio2025Statistically,
  author = {Bongio, Marco and Baccolo, Giovanni and Scotti, Riccardo and Michele, Carlo De},
  title = {A Statistically Based Method to Estimate Long‐Term Daily Air Temperature at High Elevations},
  journal = {International Journal of Climatology},
  year = {2025},
  doi = {10.1002/joc.70208},
  url = {https://doi.org/10.1002/joc.70208}
}