Large-scale impacts of the 2023 Canadian wildfires on the Northern Hemisphere atmosphere
Bibliographic record
Abstract
The study of wildfires is crucial to understanding the Earth system, as severe wildfire events can lead to intense degradation of nature and property. The record-breaking 2023 Canadian wildfire event best represents this, with approximately 5% of the total forest area of Canada burned [1] [2], resulting in biomass burning (BB) emissions quantitatively comparable to the annual fossil fuel emissions of large nations [3], and with the highest Canadian carbon emissions on record [4]. Increased mean temperatures along with decreased humidity in the region due to climate change are considered responsible for this record series of wildfires [5], as increasing mean temperatures along with decreasing humidity in the region led to increased fire risk.Large amounts of carbonaceous aerosols can exert substantial atmospheric radiative forcing, thus it is important to study the consequences of these emissions on large-scale atmospheric composition and meteorological behavior. In this work, global and local atmospheric impacts of this historic wildfire event are analyzed using the EC-Earth3 earth system model [6] in its standard AerChem configuration. BB emissions from the Copernicus Atmosphere Monitoring Service (CAMS) Global Fire Assimilation System (GFAS) were used as input in the model to produce two 10-member ensembles simulations, with and without the 2023 Canadian wildfire emissions. The results are analyzed, and the differences in various modelled atmospheric quantities between the two ensembles are spatially cross-correlated to determine connections between atmospheric anomalies and wildfire intrusions.Modelled monthly changes in radiative effects, cloud cover, large-scale circulation, and temperature patterns throughout the North Hemisphere and Canada are found as a result of the 2023 BB emissions, and the mechanisms via which these can be caused are discussed and explained. These changes include the long-range transport of the BB pollutants in the troposphere and the stratosphere with marked impacts on cloud cover and on temperatures at low and high altitudes, differential cooling over the Canadian region due to a dual influence of direct and indirect effects of AOD increases, and even large-scale circulation anomalies which led to cooling as far as in Eastern Siberia. We find that the modelled temperature anomalies between the two ensembles caused by the wildfire-generated aerosols can be as intense as -5.44 °C locally, while the modelled average hemispheric temperature anomaly is equal to -0.91 °C.[1] "Fire Statistics". Canadian Interagency Forest Fire Centre. Retrieved January 4, 2024.[2] “The State of Canada’s Forests: Annual Report”. 2022. Canadian Minister of Natural Resources.[3] Byrne, Brendan, et al. "Carbon emissions from the 2023 Canadian wildfires" Nature. 2024 835-839.[4] “Copernicus: Emissions from Canadian wildfires the highest on record – smoke plume reaches Europe”. Atmosphere Monitoring Service, Copernicus. Retrieved January 4, 2024.[5] Barnes, Clair, et al. "Climate change more than doubled the likelihood of extreme fire weather conditions in eastern Canada" 2023.[6] Döscher, Ralf, et al. "The EC-earth3 Earth system model for the climate model intercomparison project 6." Geoscientific Model Development Discussions. 2021 1-90.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".