Articles | Volume 19, issue 14
https://doi.org/10.5194/gmd-19-6857-2026
https://doi.org/10.5194/gmd-19-6857-2026
Model description paper
 | 
28 Jul 2026
Model description paper |  | 28 Jul 2026

MErSiM v1.0: resolving biases in global silicate weathering model with a data-driven surface erosion module

Jiaxi Zhao, Yonggang Liu, and Yongyun Hu

Data sets

MErSiM v1.0: Machine learning derived Erosion and Silicate weathering Model; code and data of Zhao et al. (2025) GMD Jiaxi Zhao et al. https://doi.org/10.5281/zenodo.18015309

Model code and software

MErSiM v1.0: Machine learning derived Erosion and Silicate weathering Model; code and data of Zhao et al. (2025) GMD Jiaxi Zhao et al. https://doi.org/10.5281/zenodo.18015309

Download
Short summary
By using artificial intelligence and geological measurements, we built a machine learning model that accurately shows how landscapes erode. With this module included we developed a new silicate weatherig model, named MErSiM v1.0, which corrected a major overestimation of weathering flux in models simulating Earth’s long-term carbon cycle. This revealed that Earth's natural ability to remove atmospheric carbon dioxide is profoundly weaker under intense warming than previously understood.
Share