Diverging Ozone Trends Above Western North America: Boundary Layer Decreases Versus Free Tropospheric Increases
Bibliographic record
Abstract
Abstract This study has produced an improved percentile and seasonal (median) trend estimate of free tropospheric ozone above western North America (WNA), through a data fusion of ozonesonde, lidar, commercial aircraft, and field campaign measurements. Our method combines heterogeneous data sets according to the consensus data characteristics and inherent uncertainty in order to produce our best fused product. In response to different data collection environments (in situ or ground‐based), we investigate the ozone variability based on a wide range of percentiles, which is preferable for trend detection due to tropospheric ozone's high degree of heteroscedasticity (i.e., inconsistent trends and variability between different ozone percentiles). We then compare the ozone trends and variability above the California sub‐domain to the full WNA region for better understanding of the correlations between different regional scales. In California, the 1995–2021 percentile (from the 5th to 95th) and seasonal trends are clearly positive in terms of high signal‐to‐noise ratios. The magnitude of the trends is generally weaker over WNA compared to California, but reliable positive trends can still be found between the 10th and 70th percentiles, as well as winter and summer, whereas autumn shows a negative trend over the same period. In addition, dozens of rural surface sites across the region are selected to represent the boundary layer variability. In contrast to increasing free tropospheric ozone, we find overall strong negative surface trends since 1995, with the greatest divergence found in summer. Throughout the analysis implications of the COVID‐19 economic downturn on ozone variability are discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".