Sensitivity of Ozone Dry Deposition to Ecosystem‐Atmosphere Interactions: A Critical Appraisal of Observations and Simulations
Bibliographic record
Abstract
Abstract The response of ozone (O 3 ) dry deposition to ecosystem‐atmosphere interactions is poorly understood but is central to determining the potential for extreme pollution events under current and future climate conditions. Using observations and an interactive dry deposition scheme within two dynamic vegetation land models (Geophysical Fluid Dynamics Laboratory LM3.0/LM4.0) driven by observation‐based meteorological forcings over 1948–2014, we investigate the factors controlling seasonal and interannual variability (IAV) in O 3 deposition velocities ( V d,O3 ). Stomatal activity in this scheme is determined mechanistically, depending on phenology, soil moisture, vapor pressure deficit, and CO 2 concentration. Soil moisture plays a key role in modulating the observed and simulated V d,O3 seasonal changes over evergreen forests in Mediterranean Europe, South Asia, and the Amazon. Analysis of multiyear observations at forest sites in Europe and North America reveals drought stress to reduce V d,O3 by ~50%. Both LM3.0 and LM4.0 capture the observed V d,O3 decreases due to drought; however, IAV is weaker by a factor of 2 in LM3.0 coupled to atmospheric models, particularly in regions with large precipitation biases. IAV in summertime V d,O3 to forests, driven primarily by the stomatal pathway, is largest (15–35%) in semiarid regions of western Europe, eastern North America, and northeastern China. Monthly mean V d,O3 for the highest year is 2 to 4 times that of the lowest, with significant implications for surface O 3 variability and extreme events. Using V d,O3 from LM4.0 in an atmospheric chemistry model improves the simulation of surface O 3 abundance and spatial variability (reduces mean biases by ~10 ppb) relative to the widely used Wesely scheme.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".