Improving the understanding between climate variability and observed extremes of global NO<sub>2</sub> over the past 15 years
Bibliographic record
Abstract
Abstract This work addresses the relationship between major dynamical forcings and variability in NO 2 column measurements. The dominating impact in Northern Southeast Asia is due to El Niño-Southern Oscillation (ENSO); in Indonesia, Northern Australia and South America is due to Indian Ocean Dipole (IOD); and in Southern China Land and Sea, Populated Northern China, Siberia, Northern and Arctic Eurasia, Central and Southern Africa, and Western US and Canada is due to North Atlantic Oscillation (NAO). That NO 2 pollution in Indonesia is modulated by IOD contradicts previous work claiming that the emissions in Indonesia are a function of El Niño impacting upon Aerosol Optical Depth and Fire Radiative Power. Simultaneous impacts of concurrent and lagged forcings are derived using multi-linear regression, demonstrating ENSO impacts future NO 2 variability, enhancing NO 2 emissions 7–88 weeks in the future, while IOD and NAO in some cases increase the emissions from or the duration of the future burning season as measured by NO 2 . This impact will also extend to co-emitted aerosols and heat, which may impact the climate. In all cases, lagged forcings exhibit more impact than concurrent forcings, hinting at non-linearity coupling with soil moisture, water table, and other dynamical effects. The regression model formed demonstrates that dynamical forcings are responsible for over 45% of the NO 2 emissions variability in most non-urban areas and over 30% in urban China and sub-arctic regions. These results demonstrate the significance of climate forcing on short-lived air pollutants.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".