Atmospheric and Surface Climate Associated With 1986–2013 Wildfires in North America
Bibliographic record
Abstract
Abstract We analyze climate simulations conducted with the RegCM3 regional climate model on 50‐ and 15‐km model grids to diagnose the dependence of wildfire incidence and area burned variations on monthly climate long‐term means and anomalies over North America for the period 1986–2013. We created a new wildfire database by merging the Fire Program Analysis Fire‐Occurrence Database, the National Interagency Fire Center Fire History Data, and the Canadian National Fire Database. The database includes 2,083,865 daily fire starts that burned a total of 1.25 × 10 8 ha in North America. We derive long‐term climatologies, standardized gamma indices, and composite climate anomalies of atmospheric circulation (500‐hPa height and wind) and various surface fields (e.g., solar radiation, soil moisture, vapor pressure deficit, and latent and sensible heat fluxes) to illustrate the climatology of burned area. The immediate and lagged monthly atmospheric circulation and surface climate anomalies differentiate high‐ and low‐fire years and the role of El Niño–Southern Oscillation in wildfire occurrence. Our approach demonstrates the association of the seasonal cycles of wildfire and climate and the strong role of climatic variability in modulating the seasonal cycle as a control of wildfire on monthly time scales.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".