Blanco et al. (2025) Comparison of Spatial Patterns of Aridity in Argentina Based on Different Climatic Indices and Datasets

⚠️ 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: 2025-12-10
• Authors: Pedro S. Blanco, Moira E. Doyle
• DOI: 10.1002/joc.70224

Research Groups

Not specified in the provided abstract.

Short Summary

This study evaluates the spatial patterns of aridity in Argentina from 1961 to 2020 by comparing various climatic indices and gridded datasets. It finds that aridity indices based on mean temperature or potential evapotranspiration (especially Thornthwaite) are more consistent, with CRU and WORLDCLIM performing better than ERA5, which shows significant errors in certain conditions.

Objective

• To analyze the spatial patterns of aridity in Argentina during 1961–2020 by comparing different climatic indices and datasets, and to assess how these choices affect the representation of aridity patterns and their changes.

Study Configuration

• Spatial Scale: Argentina
• Temporal Scale: 1961–2020, analyzed at annual and seasonal scales.

Methodology and Data

• Models used:
  • Potential Evapotranspiration (PET) estimated using Thornthwaite, Holdridge, and Hargreaves–Samani methods.
  • Various aridity indices (selected for frequent use and complementary perspectives).
• Data sources:
  • Monthly precipitation and temperature data from observations.
  • Gridded datasets: CRU (Climatic Research Unit), WORLDCLIM, and ERA5 (ECMWF Reanalysis v5).
  • Performance of gridded data evaluated using statistical comparison metrics against observations.

Main Results

• Aridity indices based on mean temperature or potential evapotranspiration (PET), particularly those using the Thornthwaite method, are more consistent in representing climate patterns in Argentina compared to indices based on temperature ranges.
• CRU and WORLDCLIM datasets exhibited similar performance, characterized by relatively low biases, high spatial coherence with observations, and good representation of statistical distributions.
• ERA5 showed significant errors in representing climate categories and estimating changes in aridity, especially during summer and in areas with complex topography.

Contributions

• Provides a comparative assessment of different aridity indices and gridded datasets (CRU, WORLDCLIM, ERA5) for characterizing aridity patterns in Argentina.
• Highlights the critical importance of carefully selecting both the aridity index and the dataset, demonstrating how these choices significantly impact the representation of aridity patterns and their changes.
• Offers crucial insights for climate monitoring and decision-making in regions vulnerable to desertification.

Funding

Not specified in the provided abstract.

Citation

@article{Blanco2025Comparison,
  author = {Blanco, Pedro S. and Doyle, Moira E.},
  title = {Comparison of Spatial Patterns of Aridity in Argentina Based on Different Climatic Indices and Datasets},
  journal = {International Journal of Climatology},
  year = {2025},
  doi = {10.1002/joc.70224},
  url = {https://doi.org/10.1002/joc.70224}
}