Large-scale impacts of the 2023 Canadian wildfires on the Northern Hemisphere atmosphere
Bibliographic record
Abstract
Abstract Climate change projections are expected to increase wildfire activity in many world regions in the coming decades because of rising mean temperatures and hydrological changes. This increase is especially pronounced in the high latitudes, and the large-scale weather impact of the resulting increased emissions has not been comprehensively explored so far. In this study, we investigate the impacts of the extreme Canadian wildfires of 2023 with a state-of-the-art Earth system model, comparing modelled atmospheric conditions with and without wildfire emissions. We find significant increases in aerosol abundances throughout much of the Northern Hemisphere (NH), with wildfire aerosols leading to a hemispheric surface air temperature decrease. This modelled cooling takes place because of direct aerosol-radiation interactions, amplified by cloud cover and circulation modifications. Influences of aerosols on meteorology are pronounced even in parts of the NH where smoke transport is minimal. Our results highlight the importance of modelling wildfire-generated aerosols, as extreme fires can lead to local and remote long-lasting hemispheric temperature anomalies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".