Yang et al. (2026) Optimization of snow cover fraction parameterization in the Community Land Model: implementation and preliminary validation over the Tibetan Plateau

Identification

• Journal: Geoscientific model development
• Year: 2026
• Date: 2026-07-01
• Authors: Kai Yang, Chenghai Wang, Yang Cui, Lingyun Ai, Feimin Zhang, Pinghan Zhaoye
• DOI: 10.5194/gmd-19-5743-2026

Research Groups

• Institute of Mountain Hazards and Environment, Chinese Academy of Sciences
• Key Laboratory of Climate Resource Development and Disaster Prevention of Gansu Province
• Research and Development Center of Earth System Model, College of Atmospheric Sciences, Lanzhou University
• Ningxia Meteorological Bureau
• Changzhou Environmental Monitoring Center of Jiangsu Province

Short Summary

This study optimizes the snow cover fraction parameterization in the Community Land Model (CLM5) by accounting for topographic relief and non-growing-season low vegetation. The optimized scheme reduces winter snow cover biases over the Tibetan Plateau by 63% and improves surface albedo simulations.

Objective

• To optimize the snow cover fraction parameterization in CLM5 to reduce biases in simulating winter snow cover over the Tibetan Plateau.
• To investigate the effects of topographic relief and non-growing-season low vegetation on snow cover processes.

Study Configuration

• Spatial Scale: Regional scale, focusing on the Tibetan Plateau.
• Temporal Scale: Daily timescale for snowfall and snowmelt events.

Methodology and Data

• Models used: Community Land Model version 5 (CLM5).
• Data sources:
  • China meteorological forcing dataset v1.6 (CMFD 1.6) for meteorological forcing.
  • MODIS snow area ratio dataset (2000–2015) for validation of snow cover simulations.
  • Snow depth downscaling product (2000–2021) for analysis of snowmelt processes.

Main Results

• The optimized SCF parameterization scheme reduces winter snow cover biases over the Tibetan Plateau by 63%.
• The revised factor F is used to modify the N melt parameterization, which improves snowmelt simulations.
• The effects of topographic relief and non-growing-season low vegetation on snow cover processes are explicitly incorporated into the SCF parameterization.

Contributions

• This study provides a comprehensive optimization of the SCF parameterization in CLM5, addressing biases in simulating winter snow cover over the Tibetan Plateau.
• The optimized scheme improves surface albedo simulations and reduces cold surface temperature biases.

Funding

• This research was supported by the National Natural Science Foundation of China (Grant No. 42175044).
• The authors acknowledge funding from the Key Laboratory of Climate Resource Development and Disaster Prevention of Gansu Province.

Citation

@article{Yang2026Optimization,
  author = {Yang, Kai and Wang, Chenghai and Cui, Yang and Ai, Lingyun and Zhang, Feimin and Zhaoye, Pinghan},
  title = {Optimization of snow cover fraction parameterization in the Community Land Model: implementation and preliminary validation over the Tibetan Plateau},
  journal = {Geoscientific model development},
  year = {2026},
  doi = {10.5194/gmd-19-5743-2026},
  url = {https://doi.org/10.5194/gmd-19-5743-2026}
}