Significant Contribution of Severe Ozone Loss to the Siberian‐Arctic Surface Warming in Spring 2020
Bibliographic record
Abstract
Abstract Severe ozone loss and significant surface warming anomalies in the Siberian Arctic were observed in spring 2020. Here, we show that the anomalous surface warming was likely related to the ozone loss. The dramatic Arctic ozone loss in March was shifted to Siberia in April and May, which largely cools the lower stratosphere and leads to an increase of high clouds by modifying the static stability in the upper troposphere. This further results in an increase of longwave radiation at surface which likely contributes to surface warming. Multiple linear regression demonstrates that ozone loss contributes most of the surface warming in April, while the Arctic Oscillation and ice‐albedo feedback play a minor role. In May, both ozone loss and ice‐albedo feedback contribute to the surface warming. These results support that surface warming in the Siberian Arctic could occur in April and May when severe ozone loss occurs in March.
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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 | 0.000 |
| 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) | 0.000 | 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".