Sudden Stratospheric Warmings in the Northern Hemisphere Observed With IASI
Bibliographic record
Abstract
Abstract Sudden Stratospheric Warming events (SSW) are extreme phenomena during which stratospheric temperature can increase by tens of degrees in a few days. They are due to the propagation and breaking of the planetary waves, leading to a perturbation of the polar vortex. SSWs also influence polar ozone concentrations and midlatitude weather. The Infrared Atmospheric Sounding Interferometers (IASI) monitor atmospheric composition and temperature globally since 2007, and they are ideal to observe the changes of temperature and ozone during SSWs. Since the launch of the first IASI, there have been several SSWs in the Northern Hemisphere, including eight major events that are investigated in this study. We find that during major SSWs, the temperature anomaly propagates from 10 hPa to the lower stratosphere and the maximum anomaly at 200 hPa is correlated to the maximum anomaly at 10 hPa. During these events, negative anomalies of temperature in Europe and Russia and positive anomalies in Canada and Greenland are often observed at 750 hPa. The cold air outbreaks that usually follow major SSWs are responsible for anomalies of −15 K. Finally, we look at the evolution of the total ozone column following major events. Major SSWs lead to higher springtime ozone concentrations and the ozone anomaly in March is correlated to the duration of the positive temperature anomaly at 10 hPa. These results show the potential of the IASI mission and its successors, IASI‐New Generation, for the study of SSWs and their effects on weather and atmospheric composition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".