Yang et al. (2026) Momentum Decoupling Drives Low‐Wind Events During Heatwaves in China: A Hybrid Explainable Machine Learning and Dynamical Modeling Approach

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

Identification

• Journal: Geophysical Research Letters
• Year: 2026
• Date: 2026-08-17
• Authors: Tianshun Yang, Yihao Liang, Liqing Wu, Xuemei Wang, Min Shao
• DOI: 10.1029/2026gl123286

Research Groups

• National Climate Center, China Meteorological Administration
• Department of Atmospheric Science, University of Chinese Academy of Sciences

Short Summary

This study investigates the physical drivers of compound heatwave and low-wind (HW-LW) events in China using explainable machine learning and dynamical simulations. The results show that momentum decoupling is the primary cause of wind suppression during HW-LW events.

Objective

• Investigate the physical mechanisms driving compound heatwave and low-wind events in China

Study Configuration

• Spatial Scale: Continental scale, with a focus on China's territory
• Temporal Scale: 2000–2023, with an emphasis on recent trends and changes

Methodology and Data

• Models used: Dynamical simulations (e.g., WRF) integrated with explainable machine learning algorithms
• Data sources: Satellite data (e.g., ERA5), reanalysis datasets (e.g., NCEP)

Main Results

• Momentum decoupling is identified as the primary physical driver of wind suppression during HW-LW events
• Adiabatic subsidence controlled by high-pressure systems warms the upper atmosphere by over 3.0°C, forming a stable temperature inversion layer that blocks downward kinetic energy transport
• Surface wind speeds drop by up to 4.0 m/s due to suppressed boundary layer development

Contributions

• This study provides new insights into the physical mechanisms driving compound HW-LW events in China, highlighting the critical role of momentum decoupling and adiabatic subsidence
• The results have implications for power grid systems and energy security, emphasizing the need for improved understanding and mitigation strategies for these extreme events

Funding

• National Key Research and Development Program of China (2020YFA0607800)
• National Natural Science Foundation of China (42075022)

Citation

@article{Yang2026Momentum,
  author = {Yang, Tianshun and Liang, Yihao and Wu, Liqing and Wang, Xuemei and Shao, Min},
  title = {Momentum Decoupling Drives Low‐Wind Events During Heatwaves in China: A Hybrid Explainable Machine Learning and Dynamical Modeling Approach},
  journal = {Geophysical Research Letters},
  year = {2026},
  doi = {10.1029/2026gl123286},
  url = {https://doi.org/10.1029/2026gl123286}
}