State of Wildfires 2024–2025
Notice bibliographique
Résumé
Abstract. Climate change is increasing the frequency and intensity of extreme wildfires globally, yet our understanding of these high-impact events remains uneven and shaped by media attention and regional research biases. The State of Wildfires project systematically tracks global and regional fire activity of each annual fire season, analyses the causes of prominent extreme wildfire events, and projects the likelihood of similar events occurring in future climate scenarios. This, its second annual report, covers the March 2024 to February 2025 fire season. During the 2024–2025 fire season, fire-related carbon (C) emissions totalled 2.2 Pg C, 9 % above average and the sixth highest on record since 2003, despite below-average global burned area (BA). Extreme fire seasons in South America's rainforests, dry forests, and wetlands and in Canada's boreal forests pushed up the global C emissions total. Fire C emissions were over 4 times above average in Bolivia, 3 times above average in Canada, and ∼ 50 % above average in Brazil and Venezuela. Wildfires in 2024–2025 caused 100 fatalities in Nepal, 34 in South Africa, and 31 in Los Angeles, with additional fatalities reported in Canada, Côte d'Ivoire, Portugal, and Türkiye. The Eaton and Palisades fires in Southern California caused 150 000 evacuations and USD 140 billion in damages. Communities in Brazil, Bolivia, Southern California, and northern India were exposed to fine particulate matter at concentrations 13–60 times WHO's daily air quality standards. We evaluated the causes and predictability of four extreme wildfire episodes from the 2024–2025 fire season, including in Northeast Amazonia (January–March 2024), the Pantanal–Chiquitano border regions of Brazil and Bolivia (August–September 2024), Southern California (January 2025), and the Congo Basin (July–August 2024). Anomalous weather created conditions for these regional extremes, while fuel availability and human ignitions shaped spatial patterns and temporal fire dynamics. In the three tropical regions, prolonged drought was the dominant fire enabler, whereas in California, extreme heat, wind, and antecedent fuel build-up were compounding enablers. Our attribution analyses show that climate change made extreme fire weather in Northeast Amazonia 30–70 times more likely, increasing BA roughly 4-fold compared to a scenario without climate change. In the Pantanal–Chiquitano, fire weather was 4–5 times more likely, with 35-fold increases in BA. Meanwhile, our analyses suggest that BA was 25 times higher in Southern California due to climate change. The Congo Basin's fire weather was 3–8 times more likely with climate change, with a 2.7-fold increase in BA. Socioeconomic changes since the pre-industrial period, including land-use change, also likely increased BA in Northeast Amazonia. Our models project that events on the scale of 2024–2025 will become up to 57 %, 34 %, and 50 % more frequent than in the modern era in Northeast Amazonia, the Pantanal–Chiquitano, and the Congo Basin, respectively, under a medium–high scenario (SSP370) by 2100. Climate action can limit the added risk, with frequency increases held to below 15 % in all three regions under a strong mitigation scenario (SSP126). In Southern California, the future trajectory of extreme fire likelihood remains highly uncertain due to poorly constrained climate–vegetation–fire interactions influencing fuel moisture, though our models suggest that risk may decline in future. This annual report from the State of Wildfires project integrates and advances cutting-edge fire observations and modelling with regional expertise to track changing global wildfire hazard, guiding policy and practice towards improved preparedness, mitigation, adaptation, and societal benefit. Thirteen new datasets and model codebases presented in this work are available from the State of Wildfires Project's Zenodo community, including updated annual statistics on wildfire extent (Jones et al., 2025; https://doi.org/10.5281/zenodo.15525674), outputs from modelling of fire causality using PoF model (Di Giuseppe, 2025; https://doi.org/10.24433/CO.8570224.v1) and codebase for the extreme event attribution/projections model, ConFLAME (Barbosa et al., 2025a, https://doi.org/10.5281/zenodo.16790787).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 ».