Evidence for El Niño–Southern Oscillation (ENSO) influence on Arctic CO interannual variability through biomass burning emissions
Bibliographic record
Abstract
A global chemical transport model is used in conjunction with measurements from surface stations to study the importance of biomass burning and meteorology in driving Arctic carbon monoxide (CO) interannual variability (IAV). Simulations with yearly varying fire emissions capture 66%–93% of CO IAV and a simulation with yearly varying meteorology but fixed fire emissions captures 0–25%, showing that biomass burning variability is the dominant driver of surface CO IAV. Observed CO anomalies are found to be significantly correlated with El Niño (0.58 < r < 0.64, 99% confidence level (CL)) and results indicate that this is due to ENSO's influence on fire emissions. Boreal Alaska, Canada and north‐east Siberia are found to contribute 59% to total Arctic fire CO and 67% to Arctic fire CO IAV. Analysis of meteorological fire drivers in these regions suggests that ENSO affects winter/spring precipitation, driving the Arctic/ENSO relationship.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".