Do climate teleconnections modulate wildfire-prone conditions over the Iberian Peninsula?
Bibliographic record
Abstract
Abstract Climate teleconnections (CT) synchronize and influence weather features such as temperature, precipitation and, subsequently, drought and fuel moisture in many regions across the globe. CTs thus may be related to cycles in wildfire activity, and thereby help fire managers to anticipate fire-prone weather conditions as well as envisaging their future evolution. A wide number of CTs modulate weather in the Iberian Peninsula (IP), exerting different levels of influence at different spatial and seasonal scales on a wide range of weather factors. In this work, we investigated the link between the most relevant CT patterns in the IP and fire activity and danger, exploring different spatial and temporal scales of aggregation. We analyzed a period of 36 years (1980–2015) using historical records of fire events (>100 ha burned) and the Canadian Fire Weather Index (FWI). Cross-correlation analysis was performed on monthly time series of CTs and fire data. Results pointed towards the North Atlantic Oscillation (in the western half of the IP) and Mediterranean Oscillation Index (along the Mediterranean coast) as the key CTs boosting burned area (BA) and fire weather danger in the IP. Both CTs relate to the relative position of the Azorean anticlone, fostering hazardous fire weather conditions during their positive phases, i.e. low rainfall and warm temperature leading to low fuel moisture content. The Scandinavian pattern index also played an important role in the western half of the Peninsula, linked to a decrease in rainfall during its negative phases. Nonetheless, the association between the CTs and BA (up to 0.5 Pearson’s R p < 0.05) was weaker than the observed between CTs and FWI (up to 0.75 Pearson’s R p < 0.05).
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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; both teacher heads agree on what is shown here.
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".