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<front>
<journal-meta>
<journal-id journal-id-type="publisher">EGUsphere</journal-id>
<journal-title-group>
<journal-title>EGUsphere</journal-title>
<abbrev-journal-title abbrev-type="publisher">EGUsphere</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">EGUsphere</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub"></issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/egusphere-2025-5624</article-id>
<title-group>
<article-title>MErSiM v1.0: Resolving Biases in Global Silicate Weathering Model with A Data-Driven Surface Erosion Module</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Zhao</surname>
<given-names>Jiaxi</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Liu</surname>
<given-names>Yonggang</given-names>
<ext-link>https://orcid.org/0000-0001-8844-2185</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Hu</surname>
<given-names>Yongyun</given-names>
<ext-link>https://orcid.org/0000-0002-4003-4630</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of  Physics, Peking University, Beijing 100871, China</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Institute of Carbon Neutrality, Peking University, Beijing, China</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>Institute of Ocean Research, Peking University, Beijing, China</addr-line>
</aff>
<pub-date pub-type="epub">
<day>26</day>
<month>01</month>
<year>2026</year>
</pub-date>
<volume>2026</volume>
<fpage>1</fpage>
<lpage>44</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Jiaxi Zhao et al.</copyright-statement>
<copyright-year>2026</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://egusphere.copernicus.org/preprints/2026/egusphere-2025-5624/">This article is available from https://egusphere.copernicus.org/preprints/2026/egusphere-2025-5624/</self-uri>
<self-uri xlink:href="https://egusphere.copernicus.org/preprints/2026/egusphere-2025-5624/egusphere-2025-5624.pdf">The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2026/egusphere-2025-5624/egusphere-2025-5624.pdf</self-uri>
<abstract>
<p>The silicate weathering feedback is a key planetary thermostat regulating Earth&apos;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 &lt;sup&gt;10&lt;/sup&gt;Be-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 &amp;times; 10&lt;sup&gt;12&lt;/sup&gt; mol C yr&lt;sup&gt;-1&lt;/sup&gt;) 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&apos;s response to glacial-interglacial climate change is comparable to previous work, its feedback to intense warming (4&amp;times;CO&lt;sub&gt;2&lt;/sub&gt;) 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 &quot;sluggish&quot; 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.</p>
</abstract>
<counts><page-count count="44"/></counts>
<funding-group>
<award-group id="gs1">
<funding-source>National Natural Science Foundation of China</funding-source>
<award-id>42488201</award-id>
<award-id>42225606</award-id>
</award-group>
<award-group id="gs2">
<funding-source>National Key Research and Development Program of China Stem Cell and Translational Research</funding-source>
<award-id>2022YFF0800200</award-id>
</award-group>
</funding-group>
</article-meta>
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