Contribution des échauffements stratosphériques à la variabilité et à l'évolution à long terme de la moyenne atmosphère : observations et modélisations numériques
Bibliographic record
Abstract
The influence of stratospheric conditions on the climate has recently become widely accepted. Coupling in the stratosphere-troposphere system causes large amplitude dynamical activity occurring in the middle atmosphere to have significant consequences on the troposphere's equilibrium and flow. Sudden stratospheric warmings are the clearest and strongest manifestation of such activity. This study focuses on the impact of these events on the variability of the middle atmosphere and on estimations of temperature trends. Another objective of our work is to acquire a better understanding of the evolution of a stratospheric warming, from the state of the atmosphere that initiates such an event to its consequences on both the middle atmosphere and the troposphere from a few days to a couple of months after it. First, we describe a novel methodology to perform a statistical analysis of long series and we apply it on a lidar measurement from the Haute-Provence Observatory. The large dynamical activity and the so-called background component can be distinguished, which allows to explain the differences observed between summer and winter. Second, the methodology is validated and its results are extended to a global scope thanks to a dataset from the Canadian Middle Atmosphere Model. Spatial differences are also explained by variations in the dynamical activity. Third, sensitivity tests are performed using RACCORD model to investigate what state of the atmosphere leads to a major stratospheric warming. Meteorological nudging is essential to produce a major warming, especially for split-type events. Last, our results are applied and compared to a case-study of winter 2012-2013.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".