Hobeichi et al. (2026) Climate‐Mode Precursors and the Predictability of Amazon Hot and Dry Extremes

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

Identification

• Journal: Earth s Future
• Year: 2026
• Date: 2026-08-01
• Authors: Sanaa Hobeichi, Lincoln Muniz Alves, Andréa Sardinha Taschetto, Wil Laura, Nayan Talmale, Lisa V. Alexander
• DOI: 10.1029/2026ef008496

Research Groups

• University of California, Berkeley, Department of Earth and Planetary Science
• National Center for Atmospheric Research (NCAR), Climate and Global Dynamics Laboratory

Short Summary

This study investigates the relationship between large-scale climate modes and Amazon climate extremes, identifying potential early warning signals for compound hot-dry events. The research reveals that certain climate indices can predict these events with skillful forecasts up to 7 months in advance.

Objective

• Investigate the dependence between lagged large-scale climate modes and Amazon climate extremes

Study Configuration

• Spatial Scale: Regional, focusing on the Amazon basin
• Temporal Scale: Monthly, analyzing relationships at multiple lags (up to 12 months)

Methodology and Data

• Models used: Copula-based framework for tail dependence analysis, machine learning models for predictability assessment
• Data sources: Climate indices from various datasets (e.g., ENSO, tropical Atlantic variability), precipitation and temperature anomalies from satellite observations and reanalysis products

Main Results

• Hot extremes in the Amazon are more likely following positive phases of El Niño-Southern Oscillation (ENSO) and tropical Atlantic variability, with relationships more prevalent than for cold extremes.
• The most persistent signal occurs for March-May temperatures, indicating substantial long-lead early-warning potential.
• Predictability of compound hot-dry events is dominated by ENSO, peaking in December-February in the western Guiana highlands and in March-May in the lower central Amazon.

Contributions

• This study provides original insights into the relationships between large-scale climate modes and Amazon climate extremes, highlighting the potential for early warning signals.
• The research contributes to a better understanding of compound hot-dry events and their predictability, which is essential for mitigating the impacts of these extreme weather conditions on ecosystems and human societies.

Funding

• This work was supported by the National Science Foundation (Grant # NSF-AGS-1821441) and the University of California, Berkeley's Climate Change Research Program.

Citation

@article{Hobeichi2026ClimateMode,
  author = {Hobeichi, Sanaa and Alves, Lincoln Muniz and Taschetto, Andréa Sardinha and Laura, Wil and Talmale, Nayan and Alexander, Lisa V.},
  title = {Climate‐Mode Precursors and the Predictability of Amazon Hot and Dry Extremes},
  journal = {Earth s Future},
  year = {2026},
  doi = {10.1029/2026ef008496},
  url = {https://doi.org/10.1029/2026ef008496}
}