A possible link between wildfire aerosol and North American Monsoon precipitation in Arizona–New Mexico
Bibliographic record
Abstract
ABSTRACT Previous research highlights the dominant role of Pacific sea surface temperatures (SSTs) and their associated large‐scale teleconnections in modulating the North American monsoon (NAM). At the regional scale, feedbacks associated with land‐surface boundary conditions have been shown to provide ‘memory’ in the system. Here, a previously unexplored second‐order linkage between aerosol generated by late‐spring wildfires and subsequent summer precipitation delivered by the NAM in the Arizona‐New Mexico (AZNM) region is proposed. Correlations between June/July organic carbon and elemental carbon (OC/EC) in Inter‐agency Monitoring of Protected Visual Environments (IMPROVE) aerosol data (Gila Wilderness and Bandelier) and NAM precipitation in southern AZNM over the period 1994–2012 are shown to be negative (r = –0.4), suggesting that active antecedent wildfire seasons tend to be immediately followed by a weak late summer monsoon. This result is consistent with a previous study linking precipitation and area burned in wildfires in AZNM. A survey of extant literature suggests a sound basis for potential mechanisms related to convective processes and cloud microphysics, and furthermore suggests that this forcing could be of similar magnitude to well‐documented land‐based second‐order NAM forcings (antecedent snowpack and soil moisture, vegetation, and mineral dust). Based on these results, we believe that the role of aerosols in modulating summer precipitation deserves further investigation both observationally and in modeling studies. If indeed wildfire smoke does contribute to the modulation of NAM intensity, by virtue of its close temporal association with NAM season, it may well represent a factor that could contribute effectively to improved seasonal prediction of summer precipitation in the NAM region.
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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 | 0.001 | 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) | 0.001 | 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".