| 03 Nov 2026
Terrestrial Ecosystem Model in R (TEMIR) version 2.0: developing a crop model to simulate the response of maize and soybean to elevated CO2 and O3 concentrations
Abstract. Surface ozone (O3) pollution threatens global crop production, and future projections indicate severe risks to regional food security under worsening air pollution. Concurrently, rising atmospheric carbon dioxide (CO2) 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 CO2 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 CO2 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 66–75 %, 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.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Geoscientific Model Development.
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