Atmospheric rivers as mediators between climate teleconnections and burned area variability in North America
Bibliographic record
Abstract
This study identifies atmospheric rivers (ARs) as key mediators linking large-scale climate teleconnections, the El Niño-Southern Oscillation (ENSO), Pacific-North American pattern (PNA), and Arctic Oscillation (AO), to variations in vegetation activity (NDVI) and burned area (BA) across North America. The results highlight the central role of ARs in shaping regional fire regimes and improving prospects for seasonal fire prediction. Distinct spatial and lag-dependent responses emerge: ENSO-driven precipitation promotes vegetation greening in northwestern Canada at longer lags, whereas browning dominates Alaska and northeastern Canada. The PNA exerts a dominant influence, suppressing NDVI across the eastern United States and central Canada at longer lags, while promoting greening in Alaska at shorter ones. AO effects often counter those of ENSO, driving vegetation drying in the southern United States and southwestern Canada at short lags, and in central Canada and Alaska at longer timescales. ARs exert a strong control over burned area, particularly across northern Canada and Alaska. When AR variability is incorporated, much of the fire enhancement previously attributed to teleconnection phases is reversed, indicating that AR-teleconnection interactions play a pivotal role in modulating the timing and magnitude of vegetation and fire responses across North America. The interactions between atmospheric rivers and large-scale climate teleconnections modulate the timing and magnitude of vegetation and fire responses across North America, according to remote sensing, teleconnection index, and reanalysis data analyses.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".