Bao et al. (2025) How Cloud Feedbacks Modulate the Tibetan Plateau Thermal Forcing: A Lead–Lag Perspective

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

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-12-29
• Authors: Fangling Bao, Husi Letu, Ri Xu
• DOI: 10.3390/rs18010122

Research Groups

Not available from the provided text.

Short Summary

This study investigates the interaction between the Tibetan Plateau's thermal forcing and cloud feedbacks by applying an improved cloud-classification algorithm to CERES and ERA5 data. It reveals a vertical redistribution of clouds and complex lead-lag relationships among cloud cover, snowfall, radiation, and heat fluxes, highlighting the critical role of cloud-radiation-snowfall interactions.

Objective

• To systematically analyze the trends and lead–lag relationships among cloud vertical structure, surface radiation, cloud radiative forcing (CRF), heat fluxes, snowfall, and the Tibetan Plateau Monsoon Index (TPMI) to understand the interaction of the Tibetan Plateau's thermal forcing with cloud feedbacks.

Study Configuration

• Spatial Scale: Tibetan Plateau
• Temporal Scale: 2001–2023 (for data generation); analysis of trends and lead-lag relationships (monthly to multi-month scales).

Methodology and Data

• Models used: An improved cloud-classification algorithm (integrating cloud microphysical properties to enhance low-cloud detection).
• Data sources: CERES data (2001–2023), ERA5 reanalysis data.

Main Results

• A vertical cloud redistribution is observed over the Tibetan Plateau, characterized by a decrease in high cloud cover (HCC) and an increase in low cloud cover (LCC).
• HCC is strongly synchronized with snowfall and significantly influences surface radiation.
• Net cloud radiative forcing (CRF) and sensible heat flux exhibit delayed responses, peaking approximately one month after HCC leads.
• Composite analysis of winter low-HCC events shows that reduced HCC suppresses snowfall, weakens net CRF, and reduces sensible heat flux after approximately one to two months.
• The Tibetan Plateau Monsoon Index (TPMI) demonstrates a significant response around month zero to low-HCC events.
• These findings underscore the crucial role of cloud–radiation–snowfall interactions in modulating the thermal forcing of the Tibetan Plateau.

Contributions

• Development and application of an improved cloud-classification algorithm, enhancing low-cloud detection over the Tibetan Plateau.
• Generation of a long-term (2001–2023) cloud-type dataset for the Tibetan Plateau.
• First systematic analysis of trends and lead-lag relationships among cloud vertical structure, surface radiation, cloud radiative forcing, heat fluxes, snowfall, and the Tibetan Plateau Monsoon Index.
• Elucidation of the complex interplay between cloud, radiation, and snowfall as a key modulator of Tibetan Plateau thermal forcing.

Funding

Not available from the provided text.

Citation

@article{Bao2025How,
  author = {Bao, Fangling and Letu, Husi and Xu, Ri},
  title = {How Cloud Feedbacks Modulate the Tibetan Plateau Thermal Forcing: A Lead–Lag Perspective},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs18010122},
  url = {https://doi.org/10.3390/rs18010122}
}