Preprints
https://doi.org/10.5194/egusphere-2025-5624
https://doi.org/10.5194/egusphere-2025-5624
26 Jan 2026
 | 26 Jan 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

Abstract. The silicate weathering feedback is a key planetary thermostat regulating Earth's long-term climate, yet process-based models of this mechanism suffer from biases. The widely-used weathering model, when driven by stream power erosion laws, systematically overestimates weathering fluxes in the tropics and predicts a global total flux nearly double the observation-based estimates. This study demonstrates that this discrepancy partially originates from a poorly constrained erosion submodule. To resolve this, we developed a new global erosion model using a Random Forest algorithm trained on ~4,000 10Be-derived, basin-averaged erosion rates. Our data-driven model explains 90 % of the variance in the observational erosion data, far exceeding the performance of the traditional Stream Power Incision Model (SPIM) and other existing approaches. By integrating this newly developed erosion module into a commonly used framework, we created a revised silicate weathering model, named MErSiM v1.0 (Machine-learning derived Erosion and Silicate-weathering Model). This new model successfully eliminates the systematic tropical overestimation, and its predicted global total flux (~3.1 × 1012 mol C yr-1) is now in better agreement with observations. More fundamentally, MErSiM resolves a critical trade-off in the original framework, now able to simultaneously match both the global total flux and the watershed-scale spatial pattern of weathering. Sensitivity experiments reveal that while MErSiM's response to glacial-interglacial climate change is comparable to previous work, its feedback to intense warming (4×CO2) is profoundly attenuated (a 42 % increase vs. 149 % in the original model). This dampened sensitivity stems from a structural shift to a more supply-limited weathering regime, a finding supported by a newly calibrated set of "sluggish" chemical kinetic parameters. This work delivers a comprehensively evaluated and observationally constrained model, which suggests that the silicate weathering feedback may be a weaker climate stabilizer under extreme greenhouse conditions than previously thought.

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Jiaxi Zhao, Yonggang Liu, and Yongyun Hu

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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

Jiaxi Zhao, Yonggang Liu, and Yongyun Hu
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Latest update: 26 Jan 2026
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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.
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