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• W2802019503 abstract "Abstract Atmospheric ozone loss through dry deposition to vegetation is a critically important process for both air quality and ecosystem health. The majority of atmospheric chemistry models calculate dry deposition using a resistance‐in‐series parameterization by Wesely (1989), which is dependent on many environmental variables and lookup table values. The uncertainties contained within this parameterization have not been fully explored, ultimately challenging our ability to understand global scale biosphere‐atmosphere interactions. In this work, we evaluate the GEOS‐Chem model simulation of ozone dry deposition using a globally distributed suite of observations. We find that simulated daytime deposition velocities generally reproduce the magnitude of observations to within a factor of 1.4. When correctly accounting for differences in land class between the observations and model, these biases improve, most substantially over the grasses and shrubs land class. These biases do not impact the global ozone burden substantially; however, they do lead to local absolute changes of up to 4 ppbv and relative changes of 15% in summer surface concentrations. We use MERRA meteorology from 1979 to 2008 to assess that the interannual variability in simulated annual mean ozone dry deposition due to model input meteorology is small (generally less than 5% over vegetated surfaces). Sensitivity experiments indicate that the simulation is most sensitive to the stomatal and ground surface resistances, as well as leaf area index. To improve ozone dry deposition models, more measurements are necessary over rainforests and various crop types, alongside constraints on individual depositional pathways and other in‐canopy ozone loss processes." @default.
• W2802019503 created "2018-05-17" @default.
• W2802019503 creator A5012693145 @default.
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• W2802019503 date "2018-01-04" @default.
• W2802019503 modified "2023-10-14" @default.
• W2802019503 title "Investigating Dry Deposition of Ozone to Vegetation" @default.
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• W2802019503 doi "https://doi.org/10.1002/2017jd027278" @default.
• W2802019503 hasPublicationYear "2018" @default.
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