A Machine Learning Based Snow Cover Parameterization for Common Land Model (CoLM)

Accurate representation of snow cover fraction (SCF) is vital for terrestrial simulation, as it significantly affects surface albedo and land surface radiation. In land models, SCF is parameterized using snow water equivalent and snow depth. This study introduces a novel machine learning-based parameterization, which incorporates the light-GBM regression algorithm and additional input features: surface air temperature, humidity, leaf area index, and the standard deviation of topography. The regression model is trained with input features from the Common Land Model (CoLM) simulations and the labels from the Moderate Resolution Imaging Spectroradiometer (MODIS) observations on a daily scale. Offline verification indicates significant improvements for the new scheme over multiple traditional parameterizations.

Moreover, this machine learning-based parameterization has been online coupled with the CoLM using the Message Passing Interface (MPI). In online simulations, it substantially outperforms the widely used Niu and Yang (2007) scheme, improving the root mean square errors and temporal correlations of SCF on 80% of global grids. Additionally, associated land surface temperature and hydrological processes also benefit from the enhanced estimation of SCF. The new solution also shows good portability as it also demonstrates similar enhancements when it is directly used in a global 1° simulation, even though it was trained at a 0.1° resolution.