Trends and patterns in annually burned forest areas and fire weather across the European boreal zone in the 20th and early 21st centuries
Bibliographic record
Abstract
Fire remains one of the main natural disturbance factors in the European boreal zone and understanding climatic forcing on fire activity is important for projecting effects of climate change on ecosystem services in this region. We analyzed records of annually burned areas in 16 administrative regions of the European boreal zone (countries or administrative units within countries) and fire weather variability to test for their spatio-temporal patterns over the 1901-2017 period. Over the 1992-2017 period, the region exhibited large variability in forest fire activity with the fire cycles varying from ~1600 (St. Petersburg region) to ~37000 years (Finland). The clustering of administrative units in respect to their burned area, suggested the presence of sub-regions with synchronous annual variability in burned areas. Large fire years (LFYs) in each of the clusters were associated with the development of the high pressure cell over or in immediate proximity of the regions in question in July, indicating climatic forcing of LFYs. Contingency analysis indicated that there was no long-term trend in the synchrony of LFYs observed simultaneously in several administrative units. We documented a trend towards higher values of Monthly Drought Code (MDC) for the months of April and May in the western (April) and northern (April and May) sections. The significant positive correlation between biome-wide fire activity index and June SNAO (Summer North Atlantic Oscillation) (r = 0.53) pointed to the importance of large-scale atmospheric circulation, in particular the summer European blocking pattern, in controlling forest fires across EBZ. The forest fire activity of the European boreal zone remains strongly connected to the annual climate variability. Higher frequency of strongly positive SNAO states in the future will likely synchronize years with a large area burned across the European boreal zone.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".