Evaluation of ozone trends in the mesosphere/lower thermosphere using a new merged dataset of ozone profiles
Bibliographic record
Abstract
Abstract. In recent years, the need for high-quality long-term mesospheric ozone records has become increasingly evident, as they are essential for understanding chemical, dynamical, and radiative processes in the middle and upper atmosphere and their coupling with the lower layers. Here, we present a new merged dataset of ozone profiles in the mesosphere and lower thermosphere (METEOR-O₃), created from several limb-viewing satellite instruments: HALOE, GOMOS, MIPAS, ACE-FTS, MLS, and SOFIE. The dataset covers the period from 1991 to 2023 and provides deseasonalized ozone anomalies in 10° latitude bins between 80° S and 80° N, from approximately 22 km to 100 km. The deseasonalized ozone anomalies are used for global and seasonal trend analysis. The results show positive upper stratospheric ozone trends in both hemispheres, with magnitudes of 1–2 % per decade between 35 and 45 km, indicating continued ozone recovery consistent with previous assessments. In contrast, mesospheric ozone (above ~60 km) exhibits negative trends of 1–3 % per decade, with the strongest decreases of about 8–10 % per decade near the mesopause. Seasonal analyses confirm positive trends in the upper stratosphere across all seasons and persistent negative trends in the upper mesosphere, strongest at high latitudes above 80 km. The METEOR-O₃ dataset provides the first global, long-term merged record suitable for detailed studies of mesospheric/lower thermospheric ozone variability and trend evaluation, providing valuable information for model validation and assessments of upper atmospheric changes.
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 | 0.002 |
| 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".