Shen et al. (2026) Divergent Forest Cover Changes Dominate Inter‐Model Spread in Near‐Surface Wind Speed Trends Over China

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

Identification

• Journal: Geophysical Research Letters
• Year: 2026
• Date: 2026-08-22
• Authors: Cheng Shen, Zhi‐Da Sun, Xu Yang, Hui‐Shuang Yuan, Youli Chang
• DOI: 10.1029/2026gl124057

Research Groups

Not specified in the provided text.

Short Summary

This study identifies forest cover changes as the primary driver of uncertainty in global climate model simulations of near-surface wind speed (NSWS) over China.

Objective

• To identify the primary drivers of the wide inter-model spread in near-surface wind speed (NSWS) simulations over China.

Study Configuration

• Spatial Scale: China (specifically concentrated in North and Northeast China).
• Temporal Scale: Not specified (Climate projection period).

Methodology and Data

• Models used: Global climate models (GCMs).
• Data sources: Model simulations of NSWS and forest cover changes.

Main Results

• Forest cover changes account for approximately 74% of the inter-model spread in NSWS projections.
• The inter-model spread is most pronounced in North and Northeast China.
• Models simulating extensive forest loss exhibit weak or negligible NSWS trends, as deforestation smooths the land surface and counteracts background surface stilling.
• Models simulating limited forest loss or forest gain show pronounced declines in NSWS.

Contributions

• Quantifies the impact of land-cover change (specifically forests) on the uncertainty of wind speed projections, highlighting that constraining forest cover data is critical for improving the reliability of future wind energy assessments.

Funding

Not specified in the provided text.

Citation

@article{Shen2026Divergent,
  author = {Shen, Cheng and Sun, Zhi‐Da and Yang, Xu and Yuan, Hui‐Shuang and Chang, Youli},
  title = {Divergent Forest Cover Changes Dominate Inter‐Model Spread in Near‐Surface Wind Speed Trends Over China},
  journal = {Geophysical Research Letters},
  year = {2026},
  doi = {10.1029/2026gl124057},
  url = {https://doi.org/10.1029/2026gl124057}
}