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Improved Global Understanding of Ozone Damage to Crops and Trees
  • +3
  • Jyoti Singh,
  • Danica Lombardozzi,
  • Ella Walmsley,
  • Lili Xia,
  • Manuel T. Lerdau,
  • Alan Robock
Jyoti Singh
Rutgers University

Corresponding Author:[email protected]

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Danica Lombardozzi
National Center for Atmospheric Research
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Ella Walmsley
Rutgers University
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Lili Xia
Rutgers University
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Manuel T. Lerdau
University of Virginia
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Alan Robock
Rutgers University
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Abstract

Tropospheric O3 damage to plants significantly affects global vegetation productivity, yet accurately predicting this damage remains challenging. This study develops a parameterization to globally predict ozone damage by integrating a combination of factors: cumulative uptake of ozone (CUO), O3 concentration, stomatal conductance (Gs), and total exposure time. We compiled experimental data from over 200 peer-reviewed publications spanning 50 years, focusing on the responses of various crops and trees to chronic ozone exposure. Our analysis reveals that while CUO alone has a weak relationship with changes in photosynthesis and stomatal conductance under O3 stress, combining CUO with O3 concentration, Gs, and total exposure time significantly strengthens the predictive power. This combined approach was validated across diverse categories from experimental data, including plant types, tree age, exposure systems, types of control air, rooting environments, and ozone concentration bins. We found photosynthesis exhibited a weaker response relationship than stomatal conductance, indicative of underlying responses to O3 stress that could not be captured by the variables and methods used in this study. Our results underscore the complexity of predicting O3 damage and highlight the importance of synthesizing multiple predictors. Future research should incorporate other environmental stressors, e.g., heat, drought, and elevated CO2 levels, to enhance the accuracy of O3 damage models. This study provides a significant advancement in incorporating O3 damage parameterization for global crop and land surface modeling.
28 Aug 2024Submitted to ESS Open Archive
29 Aug 2024Published in ESS Open Archive