Impact of 2050 climate change on North American wildfire: consequences for ozone air quality
Bibliographic record
Abstract
Abstract. We estimate future area burned in the Alaskan and Canadian forest by the mid-century (2046–2065) based on the simulated meteorology from 13 climate models under the A1B scenario. We develop ecoregion-dependent regressions using observed relationships between annual total area burned and a suite of meteorological variables and fire weather indices, and apply these regressions to the simulated meteorology. We find that for Alaska and western Canada, almost all models predict significant (p < 0.05) increases in area burned at the mid-century, with median values ranging from 150 to 390 %, depending on the ecoregion. Such changes are attributed to the higher surface air temperatures and 500 hPa geopotential heights relative to present day, which together lead to favorable conditions for wildfire spread. Elsewhere the model predictions are not as robust. For the central and southern Canadian ecoregions, the models predict increases in area burned of 45–90 %. Except for the Taiga Plain, where area burned decreases by 50 %, no robust trends are found in northern Canada, due to the competing effects of hotter weather and wetter conditions there. Using the GEOS-Chem chemical transport model, we find that changes in wildfire emissions alone increase mean summertime surface ozone levels by 5 ppbv for Alaska, 3 ppbv for Canada, and 1 ppbv for the western US by the mid-century. In the northwestern US states, local wildfire emissions at the mid-century enhance surface ozone by an average of 1 ppbv, while transport of boreal fire pollution further degrades ozone air quality by an additional 0.5 ppbv. The projected changes in wildfire activity increase daily summertime surface ozone above the 95th percentile by 1 ppbv in the northwestern US, 5 ppbv in the high latitudes of Canada, and 15 ppbv in Alaska, suggesting a greater frequency of pollution episodes in the future atmosphere.
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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".