Unprecedented Spring 2020 Ozone Depletion in the Context of 20 Years of Measurements at Eureka, Canada
Bibliographic record
Abstract
Abstract In the winter and spring of 2019/2020, the unusually cold, strong, and stable polar vortex created favorable conditions for ozone depletion in the Arctic. Chemical ozone loss started earlier than in any previous year in the satellite era and continued until late March, resulting in the unprecedented reduction of the ozone column. The vortex was located above the Polar Environment Atmospheric Research Laboratory in Eureka, Canada (80°N, 86°W) from late February to the end of April, presenting an excellent opportunity to examine ozone loss from a single ground station. Measurements from a suite of instruments show that total column ozone was at an all‐time low in the 20‐year data set, 22–102 DU below previous records set in 2011. Ozone minima (<200 DU), enhanced OClO and BrO slant columns, and unusually low‐HCl, ClONO 2 , and HNO 3 columns were observed in March. Polar stratospheric clouds were present as late as 20 March, and ozonesondes show unprecedented depletion in the March and April profiles (to <0.2 ppmv). While both chemical and dynamical factors lead to reduced ozone when the vortex is cold, the contribution of chemical depletion (based on the variable correlation of ozone and temperature) was exceptional in spring 2020 when compared to typical Arctic winters. Mean chemical ozone loss over Eureka was estimated to be 111–126 DU (27%–31%) using April measurements and passive ozone from the SLIMCAT chemical transport model. While absolute ozone loss was generally smaller in 2020 than in 2011, percentage ozone loss was greater in 2020.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".