Observed links between heatwaves and wildfires across Northern high latitudes
Bibliographic record
Abstract
Abstract Data on Arctic and Sub-Arctic summer heat events are limited due to the sparse network of surface observation stations. Here, we analyze large heat events within 60°–80°N using land surface temperature (LST) observations from the moderate resolution imaging spectroradiometer (MODIS) sensor aboard the Terra satellite. Our heatwave (HW) detection method uses exceedances of the climatological 90th percentile of LST across summer months, and a spatio-temporal density-based clustering algorithm to distinguish space-time coherent events across Northern Hemispheric high latitudes for the summers of 2000–2022. We find a close link between HW duration, spatial extent and amplitude across the study region (correlations ranging from 0.63 to 0.73). MODIS-derived burned area data show that wildfire seasons are significantly correlated to summer HW activity, particularly in Siberia (r = 0.87 at p < 0.05) and Alaska and NW Canada (r = 0.45 at p < 0.05), and are also spatially co-located. MODIS active fire data also show substantial increase during larger heat events. For the strongest HWs in Siberia, the peak in daily fire count (from the MODIS active fire archive) lags behind peak HW activity. We conclude that there is a close link between intense fire summers and extensive HWs over boreal and shrub tundra regions (Interior Alaska, the Canadian Prairies and Eastern Siberia).
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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 | 0.001 | 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".