Links Between Carbon Monoxide and Climate Indices for the Southern Hemisphere and Tropical Fire Regions
Bibliographic record
Abstract
Abstract In the Southern Hemisphere and tropics, the main contribution to carbon monoxide (CO) variability is from fire emissions, which are connected to climate through the availability, type, and dryness of fuel. Here we assess the data‐driven relationships between CO and climate, aiming to predict atmospheric loading during fire seasons. Observations of total column CO from the Measurements Of Pollution In The Troposphere satellite instrument are used to build a record of monthly anomalies between 2001 and 2016, focusing on seven biomass burning regions of the Southern Hemisphere and tropics. With the exception of 2015, the range of absolute variability in CO is similar between regions. We model CO anomalies in each of the regions using climate indices for the climate modes: El Niño–Southern Oscillation, Indian Ocean Dipole, Tropical South Atlantic, and Antarctic Oscillation. Stepwise forward and backward variable selection is used to choose from statistical regression models that use combinations of climate indices, at lag times between 1 and 8 months relative to CO anomalies. The Bayesian information criterion selects models with the best predictive power. We find that all climate mode indices are required to model CO in each region, generally explaining over 50% of the variability and over 70% for tropical regions. First‐order interaction terms of the climate modes are necessary, producing greatly improved explanation of CO variability over single terms. Predictive capability is assessed for the Maritime Southeast Asia and the predicted peak CO anomaly in 2015 is within 20% of the measurements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".