Observational ozone data over the global oceans and polar regions: The TOAR-II Oceans data set version 2024
Bibliographic record
Abstract
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 data sets and provides insights into key photochemical and depositional loss processes that control the concentrations, spatio-temporal variability of ozone, and the physico-chemical 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 data sets. Here, we present the first comprehensive collection of ozone data over the oceans and the polar regions. The overall data set consists of 77 ship cruises/buoy-based observations and 48 aircraft-based campaigns. The data set, 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 spatio-temporal distribution and long-term trends over the defined 11 ocean/polar regions. The data sets from ships, buoys, and aircrafts are complemented with an ozonesonde data set from 29 launch sites or field campaigns, and by 21 non-polar and 17 polar ground-based stations data sets. The data were filtered by using backward trajectories calculated with the HYSPLIT model from the individual observation points to extract essentially oceanic observations, defined as air masses that have travelled over oceans for 72 hours or more, which were further tested with the coincident Radon observations. The oceanic and polar data thus selected showed typically flat diurnal patterns at high latitudes and daytime decreases (11–16 %) at low latitudes, indicating the adequacy of the data collection and processing procedures, as well as the potential for further studies of processes with statistical robustness and coverage. The ship/buoy- and aircraft-based data sets presented here will supplement the land-based ones in the TOAR-II database to provide a fully global assessment of tropospheric ozone.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".