You are here:
Evaluation and improvement of the Surface Tiled Aerosol and Gaseous Exchange (STAGE) resistance model with long term ozone fluxes at multiple sites
Citation:
Bash, J., C. Hogrefe, B. Cheng, K. Alapaty, J. Walker, D. Schwede, AND O. Clifton. Evaluation and improvement of the Surface Tiled Aerosol and Gaseous Exchange (STAGE) resistance model with long term ozone fluxes at multiple sites. AMS 2022, Virtual, TX, January 23 - 27, 2022.
Impact/Purpose:
The STAGE box model was applied to meteorological, site, and flux data at seven long term O3 micrometeorological flux datasets. These sites represent evergreen needleleaf, deciduous broadleaf, grass land and shrub biomes/plant functional types. Both estimated fluxes and deposition velocities were evaluated. A month of data, representing approximately 1.5% to 29.5% of the observed data, was randomly sampled from each of the data sets for the aggregate evaluation to prevent the overrepresentation of sites with longer data records. In aggregate, the STAGE model overestimated fluxes by approximately 13% and deposition velocities by 7%. The modeled deposition velocities and fluxes were underestimated in the winter and nighttime conditions and overestimated in the afternoon and summer at most sites. Modeled biases at individual sites ranged from 42% to -10% and 34% to -18% for fluxes and deposition velocities respectively. To constrain modeled processes both Ohm’s law and the first Kirchhoff current law was applied to the resistance model to infer fluxes at stomatal, cuticular and soil surfaces. These inferred sinks indicate that the STAGE model was underestimating the flux to snow and cuticular surfaces and overestimating fluxes to soil, wet cuticular, and stomatal surfaces. To extrapolate these findings to other biomes/plant functional types, model processes were correlated with observed plant traits from the global plant trait (TRY) database. A significant relationship, p<0.001, was found between the inferred cuticular resistance and leaf mass per area indicating that deposition to cuticular surfaces is higher for plant functional types with thicker waxy epicuticular surfaces. Updates to these resistances improved the model ability to capture diel and seasonal variability seen in the observations and, when implemented, in a regional scale model.
Description:
The STAGE box model was applied to meteorological, site, and flux data at seven long term O3 micrometeorological flux datasets. These sites represent evergreen needleleaf, deciduous broadleaf, grass land and shrub biomes/plant functional types. Both estimated fluxes and deposition velocities were evaluated. A month of data, representing approximately 1.5% to 29.5% of the observed data, was randomly sampled from each of the data sets for the aggregate evaluation to prevent the overrepresentation of sites with longer data records. In aggregate, the STAGE model overestimated fluxes by approximately 13% and deposition velocities by 7%. The modeled deposition velocities and fluxes were underestimated in the winter and nighttime conditions and overestimated in the afternoon and summer at most sites. Modeled biases at individual sites ranged from 42% to -10% and 34% to -18% for fluxes and deposition velocities respectively. To constrain modeled processes both Ohm’s law and the first Kirchhoff current law was applied to the resistance model to infer fluxes at stomatal, cuticular and soil surfaces. These inferred sinks indicate that the STAGE model was underestimating the flux to snow and cuticular surfaces and overestimating fluxes to soil, wet cuticular, and stomatal surfaces. To extrapolate these findings to other biomes/plant functional types, model processes were correlated with observed plant traits from the global plant trait (TRY) database. A significant relationship, p<0.001, was found between the inferred cuticular resistance and leaf mass per area indicating that deposition to cuticular surfaces is higher for plant functional types with thicker waxy epicuticular surfaces. Updates to these resistances improved the model ability to capture diel and seasonal variability seen in the observations and, when implemented, in a regional scale model.
