Climatic variability regulates the occurrence and extent of large fires in the subalpine forests of the Canadian Rockies
Bibliographic record
Abstract
Drought occurrence and wildfire activity in subalpine forests in Kootenay National Park (KNP) of the Canadian Rockies are studied by focusing on the interannual and multi-decadal variations in climate patterns of the Pacific Ocean. The question addressed is whether broad-scale climate patterns can regulate both large fire occurrence and fire-free periods causing fuel buildups. This study compared years of large wildfire outbreaks for the subalpine forests of KNP during the last three centuries using indices of climate patterns of the Pacific Decadal Oscillation (PDO) and El Niño-Southern Oscillation (ENSO). PDO and ENSO were correlated with drought and fire occurrence in KNP. A positive PDO–positive ENSO combination created extreme drought conducive to crown fires, indicating that the occurrence of high-severity wildfires in these generally moist/cool forests is strongly determined by climatic anomalies. Large fire activity is chiefly modulated by PDO compared to ENSO, because a negative PDO phase greatly decreased fire activity in the mid-twentieth century. Although this fire-free period is seemingly matched with a fire-suppression period, it may be attributable to a negative PDO, which increased precipitation in the region. This mid-century fire gap contributed to the accumulation of old forests serving as loaded fuels within the landscape and ultimately led to occurrences of crown fires as the PDO shifted to the positive phase. Thus, in addition to the fundamental importance of interannual variations in the Pacific that initiates a current-year severe drought, multi-decadal scale climate variability also influences the extent and severity of subsequent fires by modulating pre-fire landscape conditions. For fire management in subalpine areas of the Rockies, although drought occurrence is a primary concern and there are still uncertainties in the detailed changes in fire risk through the successional process, it is worth paying attention to fuel-loads of older forests in the landscape, which may lead to extensive large fires. Because the climatic teleconnection pattern is one of the main drivers of crown fire occurrence in the region in terms of creating current summer drought, and also for constructing a landscape structure prone to larger fires, more of a focus on short to long-term variations in the climate for wildfire management is needed in high-elevation forested landscapes.
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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 | 0.000 |
| 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 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".