Large-scale impacts of the 2023 Canadian wildfires on the Northern Hemisphere atmosphere
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».