Insights into the Role of Meteorology on Improving Model-measurement Agreement of Oxidants in Forested Environments
Bibliographic record
Abstract
Within forest canopies, biogenic emissions and anthropogenic pollutants interact through complex chemicalreactions, impacting atmospheric composition, climate, and ecosystem processes. The need to understand the biosphere-atmosphere exchange of heat, momentum, and chemical species has led to a significant body of work on the transport and fate of molecules within and above plant canopies. The impact of these canopies on the fast oxidation chemistry responsible for regulating the lifetime of greenhouse gases is still insufficiently understood. This is largely owing to the complexity of the chemical reactions involved and the lack of fully explicit physical descriptions of in-canopy turbulence and deposition. This thesis investigates the role that improved meteorological representation can play in reducing model error for simulated chemistry and improving our understanding of oxidation chemistry in forested environments. I examine long-term air quality monitoring data to show evidence that ozone mixing ratios in much of the United States are impacted by the presence of vegetation through the ability of plants to remove ozone from the atmosphere, and that this dry deposition sink is regulated by vapor pressure deficit. Examining the role of forest canopies in modulating chemistry further, I used the FORest Canopy Atmosphere Transfer (FORCAsT) model to simulate dynamics and reactions in a forest at the University of Michigan Biological Station (UMBS). I found that updating modeled meteorology by assimilating observations improves simulations of primary species, but key discrepancies in oxidation products exist, suggesting possible changes to branching ratios in the chemical mechanism may be needed. I use the micrometeorology measurements I made at the UMBS in conjunction with high resolution volatile organic compound (VOC) data to determine the impact of turbulence on VOCs, providing novel data for future model validation and parameterization. Further, I simulate the impact of turbulence on chemical reactions by incorporating the turbulence-induced covariance between chemical reactants in a box model and show that turbulent fluctuations strongly impact reactions involving short-lived radicals, leading to significant concentration changes, at canopy height. This work contributes to our understanding of the impact that meteorology plays on oxidation chemistry in and above forests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".