Preprints
https://doi.org/10.5194/egusphere-2024-1715
https://doi.org/10.5194/egusphere-2024-1715
18 Jun 2024
 | 18 Jun 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Constraining Light Dependency in Modeled Emissions Through Comparison to Observed BVOC Concentrations in a Southeastern US Forest

Namrata Shanmukh Panji, Deborah F. McGlynn, Laura E. R. Barry, Todd M. Scanlon, Manuel T. Lerdau, Sally E. Pusede, and Gabriel Isaacman-VanWertz

Abstract. Climate change will bring about changes in meteorological and ecological factors that are currently used in global-scale models to calculate biogenic emissions. By comparing long-term datasets of biogenic compounds to modeled emissions, this work seeks to improve understanding of these models and their driving factors. We compare speciated BVOC measurements at the Virginia Forest Research Laboratory located in Fluvanna County, VA, USA for the 2020 year with emissions estimated by MEGANv3.2. The emissions were subjected to oxidation in a 0-D box-model (F0AM v4.3) to generate timeseries of modeled concentrations. We find that default light-dependent fractions (LDFs) in the emissions model do not accurately represent observed temporal variability of regional observations. Some monoterpenes with a default light dependence are better represented using light-independent emissions throughout the year (LDFα-pinene=0, as opposed to 0.6), while others are best represented using a seasonally or temporally dependent light dependence. For example, limonene has the highest correlation between modeled and measured concentrations using LDF=0 for January through April and roughly 0.74–0.97 in the summer months, in contrast to the default value of 0.4. The monoterpenes β-thujene, sabinene, and γ-terpinene similarly have an LDF that varies throughout the year, with light-dependent behavior in summer, while camphene and α-fenchene follow light-independent behavior throughout the year. Simulations of most compounds are consistently underpredicted in the winter months compared to observed concentrations. In contrast, day-to-day variability in the concentrations during summer months are relatively well captured using the coupled emissions-chemistry model constrained by regional concentrations of NOx and O3.

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Namrata Shanmukh Panji, Deborah F. McGlynn, Laura E. R. Barry, Todd M. Scanlon, Manuel T. Lerdau, Sally E. Pusede, and Gabriel Isaacman-VanWertz

Status: open (until 21 Aug 2024)

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  • RC1: 'Comment on egusphere-2024-1715', Anonymous Referee #1, 10 Jul 2024 reply
  • RC2: 'Comment on egusphere-2024-1715', Anonymous Referee #2, 24 Jul 2024 reply
Namrata Shanmukh Panji, Deborah F. McGlynn, Laura E. R. Barry, Todd M. Scanlon, Manuel T. Lerdau, Sally E. Pusede, and Gabriel Isaacman-VanWertz
Namrata Shanmukh Panji, Deborah F. McGlynn, Laura E. R. Barry, Todd M. Scanlon, Manuel T. Lerdau, Sally E. Pusede, and Gabriel Isaacman-VanWertz

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Short summary
Climate change will bring about changes in parameters that are currently used in global-scale models to calculate biogenic emissions. This study seeks to understand the factors driving these models by comparing long-term datasets of biogenic compounds to modeled emissions. We note that the light-dependent fractions currently used in models do not accurately represent regional observations. We provide evidence for the time-dependent variation of this parameter for future modifications to models.