The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record for climate applications
Bibliographic record
Abstract
In this study, we present the satellite-based GOME-type Total Ozone Essential Climate Variable (GTO-ECV) Climate Data Record (CDR), which has been developed in the framework of national and the European Space Agency’s Climate Change Initiative (ESA-CCI+) ozone projects. GTO-ECV covers the past 26 years (1995-2021), and it is regularly extended as part of the European Union Copernicus Climate Change Service (EU-C3S2) ozone project. The GTO-ECV CDR combines space-based observations from a series of seven nadir-viewing low earth orbit sensors of the GOME-type (Global Ozone Monitoring Experiment). The latest additions were measurements from GOME-2/MetOp-C and from the TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P. All instruments measure the upwelling solar radiation reflected or scattered in the Earth's atmosphere and from its surface in the ultraviolet, visible, and near-infrared spectral range. Total ozone columns are retrieved using the GOME Direct Fitting approach (GODFIT version 4) and the inter-sensor consistency is excellent. We combine the individual data sets into one homogenized record that provides monthly means with global coverage and a spatial resolution of 1°x1°. The data record can be used for various climate applications regarding the long-term evolution of the atmospheric ozone layer including inter-annual variability and decadal trends on global and regional scales, or the evaluation of Chemistry-Climate Model simulations. Of particular interest is the search for signs of ozone recovery. Thanks to the Montreal Protocol the stratospheric concentrations of ozone depleting substances have been declining since the late 1990s and a slow healing of the ozone layer is expected. In this study, we report on the spatial and seasonal distribution of ozone trends and on the possible impact of climate change.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.012 | 0.009 |
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".