Observational ozone datasets over the global oceans and polar regions (version 2024)
Bibliographic record
Abstract
Studying tropospheric ozone over the remote areas of the planet, such as the open oceans and the polar regions, is crucial to understand the role of ozone as a global climate forcer and regulator of atmospheric oxidative capacity. A focus on the pristine oceanic and polar regions complements the available land-based datasets and provides insights into key photochemical and depositional loss processes that control the concentrations and spatiotemporal variability in ozone as well as the physicochemical mechanisms driving these patterns. However, an assessment of the role of ozone over the oceanic and polar regions has been hampered by a lack of comprehensive observational datasets. Here, we present the first comprehensive collection of ozone data over the oceans and the polar regions. The overall dataset consists of 77 ship cruises/buoy-based observations and 48 aircraft-based campaigns. The dataset, consisting of more than 630 000 independent ozone measurement data points covering the period from 1977 to 2022 and an altitude range from the surface to 5000 m (with a focus on the lowest 2000 m), allows systematic analyses of the spatiotemporal distribution and long-term trends over the 11 defined ocean/polar regions. The datasets from ships, buoys, and aircraft are complemented by ozonesonde data from 29 launch sites or field campaigns and by 21 non-polar and 17 polar ground-based station datasets. The datasets contain information on how long the observed air masses were isolated from land, as estimated by backward trajectories from the individual observation points. To extract observations representative of oceanic conditions, we recommend using a subset of the data with an isolation time of 72 h or longer, from the analysis with coincident radon observations. These filtered oceanic and polar data showed typically flat diurnal cycles at high latitudes, whereas daytime decreases in ozone (11 %–16 %) were observed at lower latitudes. The ship/buoy- and aircraft-based datasets presented here will supplement the land-based ones in the TOAR-II (Tropospheric Ozone Assessment Report Phase II) database to provide a fully global assessment of tropospheric ozone. The described dataset is available at https://doi.org/10.17596/0004044 (Kanaya et al., 2025).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.004 | 0.003 |
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".