QBO‐dependent relation between electron precipitation and wintertime surface temperature
Bibliographic record
Abstract
Recent research has shown that energetic particle precipitation into the upper atmosphere can change ion and neutral chemistry, e.g., by enhancing NOxconcentration in the mesosphere, which, in turn, can affect stratospheric ozone balance under appropriate conditions. It has been suggested that this may affect the surface temperatures at high latitudes by modulating tropospheric circulation. Motivated by such results, we compare here the wintertime energetic electron precipitation (EEP) with North Atlantic Oscillation (NAO) and surface air temperature (SAT) in the Northern Hemisphere. We use the recently recalibrated energetic electron data from the Medium Energy Proton and Electron Detector instrument of the National Oceanic and Atmospheric Administration (NOAA)/Polar Orbiting Environment Satellites in two energy ranges (30–100 keV and 100–300 keV), the NAO index from NOAA, and the NASA Goddard Institute for Space Studies surface temperature analysis for years 1980–2010. We find a statistically significant correlation between EEP and the NAO index and also between EEP and SAT in certain geographic regions. The strongest negative correlation is found in Northeast Canada/Greenland, while the strongest positive correlation is found in North Siberia/Barents Sea, in agreement with similar studies using global geomagnetic activity as a proxy for particle precipitation. We find higher correlation when the two winters (1984/1985 and 2003/2004) of unprecedentedly strong sudden stratospheric warmings are excluded. We also find that the different phases of quasi‐biennial oscillation (QBO; observed at 30 hPa) lead to dramatically different correlation patterns, with easterly QBO producing considerably stronger and spatially wider correlation and larger temperature response than westerly QBO.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".