Global atmospheric impacts of aerosols emitted from the 2023 Canadian wildfires
Bibliographic record
Abstract
Wildfires are a central but relatively unexplored component of the Earth system. Severe wildfire events can lead to intense destruction of both nature and property, as was the case during the anomalously intense 2023 Canadian wildfire event. Last year, approximately 5% of the total forest area of Canada burned [1] [2], which is the highest wildfire damage Canada has ever sustained [1].Conditions pertaining to climate change and modifications in atmospheric conditions are considered to be responsible for this record series of wildfires [3]. Increasing mean temperatures and decreasing humidity in the region has exacerbated wildfire risk. Carbon emissions from the 2023 Canadian wildfires have been the highest on record [4], including large amounts of carbonaceous aerosol which can exert substantial atmospheric radiative forcing. Also, Canadian fire emissions contributed around 20% of global emissions from vegetation fires. Thus, beyond the well-known health risks of wildfire emission compounds, it is important to also study the consequences of these emissions on large-scale atmospheric composition and meteorological behavior.In this work, the global and regional atmospheric impact of the previously mentioned series of wildfires is investigated using the EC-Earth3 and UKESM1 earth system models. Simulated atmospheric conditions with and without the wildfire emissions, as provided by the Copernicus Atmosphere Monitoring Service (CAMS) Global Fire Assimilation System (GFAS), are compared through atmospheric modelling in the context of the Canadian 2023 fire season. The investigation reveals the connections between the emissions produced by this series of wildfires and atmospheric phenomena of importance, such as large-scale circulation, temperature patterns, and precipitation.[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] Barnes, Clair, et al. "Climate change more than doubled the likelihood of extreme fire weather conditions in eastern Canada." (2023).[4] “Copernicus: Emissions from Canadian wildfires the highest on record – smoke plume reaches Europe”. Atmosphere Monitoring Service, Copernicus. Retrieved January 4, 2024.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".