Revisiting the evidence of increasing springtime ozone mixing ratios in the free troposphere over western North America
Bibliographic record
Abstract
We present a 20 year time series of in situ free tropospheric ozone observations above western North America during springtime and interpret results using hindcast simulations (1980–2014) conducted with the Geophysical Fluid Dynamics Laboratory global chemistry-climate model (GFDL AM3). Revisiting the analysis of Cooper et al. (2010), we show that sampling biases can substantially influence calculated trends. AM3 cosampled in space and time with observations reproduces the observed ozone trend (0.65 ± 0.32 ppbv yr−1) over 1995–2008 (in simulations either with or without time-varying emissions), whereas AM3 “true median” with continuous temporal and spatial sampling indicates an insignificant trend (0.25 ± 0.32 ppbv yr−1). Extending this analysis to 1995–2014, we find a weaker ozone trend of 0.31 ± 0.21 ppbv yr−1 from observations and 0.36 ± 0.18 ppbv yr−1 from AM3 “true median.” Rising Asian emissions and global methane contribute to this increase. While interannual variability complicates the attribution of ozone trends, multidecadal hindcasts can aid in the estimation of robust confidence limits for trends based on sparse observational records.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".