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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>
<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-2026-5958</article-id>
<title-group>
<article-title>Terrestrial Ecosystem Model in R (TEMIR) version 2.0: developing a crop model to simulate the response of maize and soybean to elevated CO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; concentrations</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Pang</surname>
<given-names>Jacky Y. S.</given-names>
<ext-link>https://orcid.org/0000-0001-9472-8841</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Singh</surname>
<given-names>Aditya Abha</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Agrawal</surname>
<given-names>Shashi Bhushan</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Chintala</surname>
<given-names>Syam</given-names>
<ext-link>https://orcid.org/0000-0002-4535-2718</ext-link>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Feng</surname>
<given-names>Zhaozhong</given-names>
</name>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Tai</surname>
<given-names>Amos P. K.</given-names>
<ext-link>https://orcid.org/0000-0001-5189-6263</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="aff7">
<sup>7</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Department of Earth and Environmental Sciences, The Chinese University of Hong Kong, Hong Kong</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>Department of Botany, University of Lucknow, Lucknow, India</addr-line>
</aff>
<aff id="aff4">
<label>4</label>
<addr-line>Laboratory of Air Pollution and Global Climate Change, Department of Botany, Institute of Science, Banaras Hindu  University, Varanasi 221005, India</addr-line>
</aff>
<aff id="aff5">
<label>5</label>
<addr-line>Department of Civil Engineering, Indian Institute of Technology Hyderabad, Telangana, India</addr-line>
</aff>
<aff id="aff6">
<label>6</label>
<addr-line>School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, 210044,  China</addr-line>
</aff>
<aff id="aff7">
<label>7</label>
<addr-line>State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong</addr-line>
</aff>
<pub-date pub-type="epub">
<day>03</day>
<month>11</month>
<year>2026</year>
</pub-date>
<volume>2026</volume>
<fpage>1</fpage>
<lpage>40</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Jacky Y. S. Pang 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-2026-5958/">This article is available from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-5958/</self-uri>
<self-uri xlink:href="https://egusphere.copernicus.org/preprints/2026/egusphere-2026-5958/egusphere-2026-5958.pdf">The full text article is available as a PDF file from https://egusphere.copernicus.org/preprints/2026/egusphere-2026-5958/egusphere-2026-5958.pdf</self-uri>
<abstract>
<p>Surface ozone (O&lt;sub&gt;3&lt;/sub&gt;) pollution threatens global crop production, and future projections indicate severe risks to regional food security under worsening air pollution. Concurrently, rising atmospheric carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) can partially offset ozone damage by stimulating carbon assimilation and reducing stomatal ozone uptake. Here, a process-based crop module is integrated into the offline Terrestrial Ecosystem Model in R (TEMIR), enabling simulations of crop growth and yield under varying CO&lt;sub&gt;2&lt;/sub&gt; and ozone concentrations at site to global scales. We describe the module structure, evaluate performance against benchmark flux-tower observations for maize and soybean, and calibrate ozone damage parameters using gas-exchange data from field elevated CO&lt;sub&gt;2&lt;/sub&gt; or ozone fumigation experiments. For soybean, with ozone damage parameters calibrated using SoyFACE data, TEMIR reproduces ozone-induced relative yield loss with a mean error of 5 percentage points, substantially improving upon uncalibrated simulations (30 percentage points error), with photosynthetic declines closely mirroring yield reductions. In contrast for maize, simulations with parameters calibrated to leaf-level gas exchange overestimate relative biomass loss by 30 percentage points at 100 ppb ozone. Reconciling simulated biomass with observations requires reducing photosynthetic ozone sensitivity by 66&amp;ndash;75 %, demonstrating that maize biomass and yield are buffered against leaf-level photosynthetic impairment. These divergent responses across maize and soybean indicate that modelers must select ozone-sensitivity parameters aligned with specific research objectives (e.g., yield estimation versus carbon- and water-flux assessments). Ultimately, the TEMIR crop module enables comprehensive assessments of agricultural impacts across future climate and pollution scenarios, particularly in pollution hotspots such as the US, China, and India. By bridging physiological mechanisms and agronomic productivity, this framework provides science-based evidence on the agricultural co-benefits of air quality management, advancing climate-resilient food security and Sustainable Development Goals.</p>
</abstract>
<counts><page-count count="40"/></counts>
<funding-group>
<award-group id="gs1">
<funding-source>Research Grants Council, University Grants Committee</funding-source>
<award-id>C2048-24GF</award-id>
</award-group>
</funding-group>
</article-meta>
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