Can atmospheric chemistry deposition schemes reliably simulate stomatal ozone flux across global land covers and climates?
Bibliographic record
Abstract
Abstract. Over the past few decades, ozone risk assessments for vegetation have been developed based on stomatal O3 flux since this metric is more biologically meaningful than the traditional concentration-based approaches. However, uncertainty remains in the ability to simulate stomatal O3 fluxes accurately. Here, we investigate stomatal O3 fluxes simulated by six common air pollution deposition models across various land cover types worldwide. The Tropospheric Ozone Assessment Report (TOAR) database, a large collection of measurements worldwide, provides hourly O3 concentration and meteorological data which are used to drive the models at 9 sites. The models estimated summertime O3 deposition velocities of between 0.5–0.8 cm s-1, mostly in agreement with the literature. Simulations of canopy conductance (Gst) showed differences between models that varied by land cover type with correlation coefficients of 0.75, 0.80 and 0.85 for forests, crops and grasslands. The model differences were determined by especially soil moisture and VPD depending upon the model constructs. Finally, the range of PODy simulations at each site across models was most in agreement for crops (3 to 11 mmol O3 m-2) < forests (10 to 23 mmol O3 m-2) < grasslands (24 to 26 mmol O3 m-2). Nevertheless, ensemble model median response estimates gave results consistent with the literature in terms of those sites where O3 damage is most likely to occur. Overall, this study is an important first step in developing and evaluating tools for broad-scale assessment of O3 impact on vegetation within the framework of TOAR phase II.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".