Impacts of Spring East Pacific SST and Arctic Sea Ice on Interannual Variability of Summer Burned Area in boreal North America
Bibliographic record
Abstract
Wildfires pose an increasing threat to boreal forest and tundra ecosystems in boreal North America (Alaska and northwestern Canada), as their frequencies rise under global warming. These fires exhibit strong interannual variability that is influenced by regional atmospheric circulation. However, potential impacts of remote boundary forcings on regional fires and the underlying mechanisms remain unclear. This study provides a comprehensive analysis on the impacts of spring sea surface temperature (SST) and sea ice on interannual variability of burned area in this region during fire season (summer) from 1997 to 2020 using GFED5 burned area, SST and sea ice concentration data from the Met Office Hadley Centre, and ERA5 reanalysis data. Results show that in spring a warmer SST in the East Pacific and reduction of sea ice in the northern Chukchi Sea lead independently to an increase in burned area in boreal North America. The correlation coefficients between the SST and sea ice factors with the burned area in boreal North America are 0.43 and –0.44 respectively. The SST-fire relationships can be explained as follows: A warm SST anomaly in the East Pacific triggers a northeastward-propagated Rossby wave, inducing a high-pressure anomaly over boreal North America in spring. Consequently, this circulation anomaly causes a higher surface temperature and thus vegetation growth or drying. As temperatures rise and lightning activity intensifies in summer, burned area increases. On the other hand, the process of sea ice affecting burned area is different. A reduction in sea ice coverage in the northern Chukchi Sea leads to a decrease in surface albedo, resulting in an increase in heat flux. The heat release persists from spring to summer and causes a high-pressure circulation anomaly in boreal North America in summer, which suppresses regional water vapor convergence and precipitation, reducing soil moisture and surface air humidity and increasing vapor pressure deficit (VPD) thereby promoting fuel flammability.
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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 | 0.000 |
| 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 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".