Mountains create their own weather through topographic modification of the prevailing synoptic meteorology. As a result, wildfires in mountainous terrain may exhibit erratic and extreme behaviour as ridges and valleys modify the prevailing winds. Here we present a case study analysis of a wildfire that occurred in mountainous terrain in subtropical eastern Australia. The wildfire was observed to transition between a wind-driven and plume-driven wildfire on at least three occasions with a periodicity of around 60 minutes. The Weather Research and Forecasting (WRF) model was used to investigate the surface windfield and vertical thermodynamic properties of the atmosphere. Results from the WRF simulations aligned with observational data indicating that topographic lifting caused by only moderate changes in terrain may have contributed to the coupling of the wildfire plume to an elevated layer of humidity leading to rapid pyrocumulus (pyroCu) development. Strong horizontal wind shear caused the pyroCu to detach from the wildfire on at least three occasions with a subsequent return to wind-driven wildfire behaviour. Our results highlight the importance of understanding the influence of what may be perceived as only subtle to moderate changes in terrain on local meteorological conditions and wildfire behaviour.

How to cite: McGowan, H., Guyot, A., Sturman, A., Seifried, V., and Dale, T.: Wind to plume driven wildfire cycles caused by topographically forced wildfire – atmosphere coupling, Southeast Queensland, Australia., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-583, https://doi.org/10.5194/egusphere-egu26-583, 2026.

Anomalies in sea surface temperature of the tropical Pacific Ocean, are associated with changes in precipitation and humidity patterns in the Amazon region. Temperature increases (El Niño) causes abnormal dry seasons, making the forest more susceptible to wildfires. The decrease (La Niña) in ocean temperatures implies abnormal increases in rainfall, especially in the northern region of Amazon. Between 2019 and 2024, the municipality of Santarém (Pará, Brazil), eastern Amazon, was affected by an increase in forest fires, leading the local government to declare an environmental emergency state in 2024. The objective of this study was to characterize burned areas and changes in land cover in Santarém during the recent ENSO period (2019 to 2024). Annual data on Land Use and Land Cover (LULC) from MapBiomas (Collection 10), and monthly data of burned area from MapBiomas Fogo (Collection 4) were used. National Oceanic and Atmospheric Administration's (NOAAs) Weather Prediction Center data was used to define El Niño (EN), La Niña (LN) and “no anomalies” months (Regular - Rg). LULC was analyzed in small properties (SMp > 300 hectares (ha)), medium-sized (MS, 300 to 1125 ha), large properties (LP >1125 ha), and smallholdings (SMs < 300 ha), as well as indigenous lands (ILs), quilombola areas (Qa), and conservation units (Cs). These data were extracted from SICAR (National Rural Environmental Registry System). For EN periods, 19 months between 2019 and 2024 were analyzed, with 62,962.56 ha of burned areas. For LN (28 months), 15,799.59 ha were burned. During Rg (25 months), 60,109.20 ha of burned area were detected. During EN and Rg periods, Forest Formation (FF) was the most affected coverage, with 36,016.92 ha (EN, 57%) and 39,183.03 (Rg, 65%). For LN, the highest burned coverage was Pasture (Pt), with 8,537.94 ha burned. Mostly small properties (SMp) were affected, with 1,249.29 ha (EN) and 1,124.82 ha (Rg) of FF scorched (1.87%). Pt areas were also affected in SMp (2.07%), accounting for 4.84% of the total. For the protected areas, Cs had 5.63% of the total burned area, with 3,913.47 ha (EN), 2,574.09 ha (Rg) and 1,330.56 ha (LN), mostly in FF. ILs had 1.04% of the total, mostly during EN, with 1,287.27 ha. Other classes (MS, LP, SMs and Qa) accounted for only 1.06% of the burned area. Areas without SICAR classification had the largest burned area (87.43% of the total). These patterns raise concern, given that burning persists in forest areas that remain unprotected and unmonitored. The occurrence of climatic phenomena that induce drier vegetation and less precipitation in the Amazon enable increases in  burned area associated with anthropic activities. Implementing fire-prevention measures in vulnerable areas is crucial, and it is equally important to account for these climatic periods. Investment in public policies for environmental education and fire mitigation are essential for transforming these scenarios, in order to mitigate the effects of climate change.

How to cite: Freitas, S., Dutra, D., Haddad, I., Mataveli, G., Escada, M. I., and Aragão, L.: Fires during recent El Niño and La Niña periods in Eastern Amazon, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1269, https://doi.org/10.5194/egusphere-egu26-1269, 2026.

In recent years, California has experienced increasingly severe wildfire events, leading to substantial socio-economic losses and ecological degradation. Against this backdrop, this study aims to identify key indicators influencing forest fire occurrence, assess forest fire vulnerability, and examine vegetation cover changes in Butte County, California. First, we analyze and visualize 14 wildfire-relevant environmental and anthropogenic factors, capturing climatic, topographic, and land-use characteristics of the study area. To address multicollinearity among variables, the Variance Inflation Factor (VIF) is employed, resulting in the selection of 11 non-collinear indicators. Based on these selected variables, a Boosted Regression Tree (BRT) model is applied to evaluate spatial patterns of wildfire vulnerability in Butte County. Finally, we employ Vegetation Fractional Cover (VFC) to quantify post-fire vegetation cover changes, enabling an assessment of wildfire impacts on vegetation dynamics. The results indicate that rainfall, land use, and topographic conditions exert significant influences on wildfire vulnerability in Butte County. Moreover, VFC analysis reveals a notable decline in vegetation cover surrounding fire locations between July 2024 and September 2024, highlighting the short-term ecological impacts of recent wildfire events.

How to cite: Tong, C.: Wildfire Vulnerability Modeling and Vegetation Cover Change in Butte: An Analysis Based on Boosted Regression Tree, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4145, https://doi.org/10.5194/egusphere-egu26-4145, 2026.

Extreme wildfires are one of the most destructive natural phenomena in our world. Modeling of these fires is challenging due to large uncertainties in model parameters as well as initial and boundary conditions. High-resolution observations of fire behavior can be used to reduce modeling uncertainties. However, adequate observational systems and methods are scarce.

We will present airborne based mid-wave infrared (MWIR) and long-wave infrared (LWIR) imagery with high spatial and temporal resolution of extreme fires that occurred in California around September 2022. This data was captured using the SJSU Wildfire Imaging Suite during the California Fire Dynamics Experiment (CalFiDE) campaign together with meteorological data both from ground-based and airborne instruments.

We will show a comparison of our airborne observations with the infrared spectral imagery captured by the spaceborne MODIS and VIIRS instruments during the campaign. The cross-platform comparison will address the spatial extent of the fire as well as the differences in the registered radiance values. 

This rich dataset, including more than 400 overpasses over multiple days and multiple extreme fires, provides a unique detailed view of the active wildfire behavior during these fire events. 

Acknowledgements: This work was supported by the U.S. National Science Foundation under award number 2053619, the U.S. National Oceanic and Atmospheric Administration during the CalFiDE campaign, and the EU COST Action NERO (CA22164).

How to cite: Stockmans, T., Klofas, A., Clements, C. B., Giesige, C. C., Goldbeck-Dimon, E., Manoj Santhi, S., Olivera Prieto, P., and Valero, M. M.: Airborne infrared observations of extreme wildfires in California 2022, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6694, https://doi.org/10.5194/egusphere-egu26-6694, 2026.

Extreme wildfires present an increasing environmental and socio-economic concern across Europe. However, the absence of comprehensive observational datasets of fire behaviour restricts the capacity to analyse, model, and manage extreme wildfire events effectively. This work aims to develop a unified and standardized dataset of extreme wildfire events in Europe based on reliable official sources, thus contributing to a consistent reference for research and management applications.

Fire event information was gathered from European fire management agencies. The data has been filtered and organized to capture the most relevant variables for extreme fire behaviour analysis, such as the temporal evolution of the burned area and the fire perimeter. Data processing and validation was performed in QGIS and outputs were exported in GeoJSON format to ensure easy integration in any geographic information system. The produced dataset allows the temporal and spatial reconstruction of fire progression. The overarching goal is to develop a comprehensive dataset of extreme wildfire events across Europe to support research and modelling efforts through shared, high-quality and standardized data resources.

Acknowledgements: This research initiative is based upon work from COST Action NERO, CA22164, supported by COST (European Cooperation in Science and Technology).

How to cite: Olivera Prieto, P., Fernández Anez, N., Berčák, R., Stockmans, T., Manoj Santhi, S., Giannaros, T. M., and Valero, M. M.: Building a Unified Framework for Extreme Wildfire Data in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6760, https://doi.org/10.5194/egusphere-egu26-6760, 2026.

Extreme wildfires have become more frequent in many regions worldwide in recent years. Compared with moderate events, the most intense wildfires, especially those that generate pyrocumulonimbus (pyroCb) clouds, exert disproportionately large impacts on the Earth system and cause substantial socioeconomic losses. High‑fidelity modeling is a critical tool for studying wildfire behavior, identifying key drivers, and quantifying their impacts. Here, we improve the pyroCb representation in the global Energy Exascale Earth System Model (E3SM) by leveraging its kilometer-scale regionally refined model (RRM) capability, integrating satellite-based high-resolution (hourly, 500 m) fire emissions, and incorporating fire-related parameterizations. Compared with conventional global simulations at coarse resolution (approximately 100 km), the kilometer-scale grid spacing over the fire source region substantially improves the simulation by explicitly resolving more fire-related dynamic and thermodynamic processes. In the meanwhile, the RRM configuration enables seamless smoke transport and interactions between the fine and coarse meshes and allows efficient simulation of downstream fire aerosol spatiotemporal distributions in regions where high resolution is less critical. The simulations capture essential pyroCb features, e.g., cloud height, spatiotemporal evolution, and convective intensity, as observed by satellite and ground measurements for different cases occurred in California. Sensitivity experiments suggest that pyroCb formation in our simulations is not controlled by a single dominant factor, but instead emerges from the coupled interactions of multiple fire-atmosphere processes. Furthermore, we use the global RRM to investigate the mechanisms of stratospheric aerosol injection and examine implications for seasonal and longer predictability. Because these simulations include interactive chemistry and aerosol schemes, we also evaluate the impacts of wildfires on surface air quality.

How to cite: Tang, Q., Ke, Z., Zhang, J., Chen, Y., Ding, X., Randerson, J., Zhang, Y., and Chen, G.: Extreme wildfire simulations using kilometer-scale regionally refined E3SM, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8792, https://doi.org/10.5194/egusphere-egu26-8792, 2026.

Large wildfires are a major source of atmopsheric aerosol. The lifetime of the smoke aerosol in the atmosphere, and thus their impact on the climate, is strongly controlled by the altitude in which the smoke is injected into the atmopshere. While most fires release their smoke in the lower troposphere, so called pyrocumulunimbus (PyroCb) events have the potential to transport smoke aerosol upwards deep into the troposphere and even into the lower stratosphere, extending the lifetime of the smoke by several orders of magnitude. These PyroCbs are thunderstorms that are triggered by extreme heat release and occasionally form above particularly intense wildfires.

For example, during the extreme PyroCb event now often referred to as the “Australian New Year’s Eve Event” of 2019/2020, deep pyro-convective plumes generated by record-breaking wildfires injected vast quantities of smoke into the tropopause region that are comparable to those of a major volcanic eruption. It is therefore crucial to understand which fires produce deep PyroCbs and why. In this study, we investigate the critical heat emission threshold at which shallow wildfire smoke plumes transition into pyroCbs that penetrate deep into the tropopause region. We further examine the sensitivity of the pyroCbs to further changes in the total amount of heat released by the fire and analyze how changes in the sensible heat emissions and water vapor release impact plume dynamics. 

To do that, using case studies of extreme fires such as the Australian New Years Eve PyroCb event, we perform semi-idealized simulations with a regional high-resolution atmospheric model. Based on the so simulated plumes, we uncover a pronounced bimodal behavior of the fire-induced convection with an abrupt onset of pyroCb formation when the sensible heat flux emissions by the fire exceeds 50kW m^-2. We show, that whenever cloud formation is present within the plume, the plume top height is mainly controlled by the sum of the sensible and latent heat flux by the fire, while the ratio between the two plays a subordinate role. Increasing either heat flux will simultanously raise  both the plume water content and temperature anomaly within the cloud.  These results show the importance of accurate estimates of heat and moisture released by fires for predicting pyroCb development. Encouragingly, these results suggest that a reliable estimate of the total heat flux might be sufficient to characterize the behavior of pyroCbs, reducing the need for detailed partitioning of sensible and latent heat.

How to cite: Müller, J., Senf, F., and Tegen, I.: How Sensible Heat Release and Water Vapor Emissions from Fires Impact the Characteristics of Pyro-convective Plumes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10023, https://doi.org/10.5194/egusphere-egu26-10023, 2026.

Extreme fire weather conditions are becoming increasingly unprecedented worldwide, yet the full range of potentially high-impact extreme wildfires remains difficult to assess. Here we generate a large ensemble of wildfire simulations by forcing the process-based model LPJmL-SPITFIRE with a 40-member bias-adjusted and statistically downscaled climate model (ACCESS-ESM1-5). This enables robust sampling of extreme wildfire events and allows comparison against single realizations (using forcing from climate reanalysis GSWP3-W5E and from an individual climate model ensemble member, r1i1p1f1). We show that wildfire ensemble maxima typically exceed single realizations maxima, suggesting that using a single climate forcing misses a substantial portion of the plausible extreme wildfire events due to internal climate variability. Extreme fire impacts (carbon emissions and burned area) respond more strongly to internal climate variability than fire weather conditions, suggesting a strong vegetation-fire feedback sensitivity to the climate forcing. Additionally, the large ensemble simulations capture climate driver-fire relationships not captured by single realizations, where maximum impacts occur without maximum fire danger, and vice-versa, highlighting the critical role of other factors beyond weather conditions that contribute to whether fires become extreme. These findings demonstrate that modelling a large range of possible wildfire events using the full distribution of climate realizations can help identify the mechanisms leading to the most extreme events.

How to cite: Ribeiro, A., Thonicke, K., Billing, M., von Bloh, W., Wessel, J., Undorf, S., Forkel, M., and Zscheischler, J.: Sampling extreme wildfire events from LPJmL-SPITFIRE large ensemble simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10291, https://doi.org/10.5194/egusphere-egu26-10291, 2026.

Wildfires have become much more frequent in recent decades and are posing an increasing threat to human health and the surrounding environment. Beside generating aerosol particles predominantly in the accumulation mode, wildfires also emit giant aerosol particles (> 20 µm) and pyrometeors (> 2 mm), whose occurrence and transport in the atmosphere are not yet fully understood. This knowledge gap must be addressed in order to improve our understanding of the possible effects of these particles on the climate and to advance the early detection and tracking of wildfires using weather radar.

The NOAA/NASA joint aircraft field campaign FIREX-AQ of 2019 conducted systematic measurements of trace gases and aerosol particles in wildfire smoke plumes. During the project, we observed smoke particles up to a nominal diameter of 6.2 mm with state-of-the-art open-path instruments (Cloud, Aerosol, and Precipitation Spectrometer and Precipitation Imaging Probe; both manufactured by Droplet Measurement Technologies, Longmont, CO, USA) aboard the NASA DC-8 research aircraft at various distances from the wildfire. In total, 194 smoke plume encounters (“transects”) were investigated from nine different wildfires with some measured on multiple days. In this study we discuss the shape, occurrence, and transport of particles larger than 0.1 mm emitted by western US wildfires in the near- to mid-field from the source.

Giant aerosol particles and pyrometeors were found in the vast majority of the transects examined, with younger smoke containing more of the very massive particles than 4-hour old smoke. In only 4% of cases where the smoke age was less than 2 hours particles larger than 0.1 mm were absent. The largest particles, measuring up to over 4 mm, were observed during transects in which the Modified Combustion Efficiency (MCE) indicates flaming combustion conditions. All observed particles larger than 0.1 mm were analyzed based on their shape. The results show that the larger the particles are, the more elongated their shape is with median aspect ratios (ratio of major to minor axis length) of 5.2 for particles larger than 2.6 mm. Furthermore, a case study was considered in which we attempt to reconstruct the observed settling of pyrometeors with a size of about 3.5 mm with theoretical calculations.

How to cite: Schöberl, M., Tatsii, D., Dollner, M., Gattringer, A., Kupc, A., Schwarz, J. P., Holmes, C. D., Halliday, H. S., Hair, J. W., Fenn, M. A., Bui, P. T., Stohl, A., and Weinzierl, B.: Giant aerosol particles and pyrometeors emitted by western US wildfires: shape, occurrence, and transport, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21215, https://doi.org/10.5194/egusphere-egu26-21215, 2026.

Wildfires are becoming more frequent and severe in many fire-prone regions, with disproportionate impacts on carbon emissions, ecosystems, and society. However, existing fire and Earth system models still struggle to represent the highly localized and stochastic nature of extreme fire ignitions and to quantify their short-term impacts at fine spatial scales.

In this work, we develop a data-driven framework for next day active fire forecasting at sub-kilometre resolution by combining reanalysis meteorology with satellite fire observations. Our approach builds on recent advances in spatio-temporal deep learning from the AI community, in particular Earth system transformers and denoising diffusion probabilistic models. We use multi-year ERA5 meteorological fields together with static variables (topography, land cover, fuel proxies) as training data, and VIIRS active fire detections and pixel-level brightness/fire radiative power as targets.

The model consists of a deterministic spatio-temporal backbone that encodes the joint evolution of weather and surface conditions, coupled to a diffusion-based probabilistic head that predicts the distribution of future ignition locations and associated fire intensity. This design allows us to explicitly represent uncertainty in rare, extreme events while retaining high spatial resolution. We evaluate the system on multiple fire-prone regions and held-out seasons containing documented extreme fire episodes. Preliminary results show improved skill in localizing ignitions and capturing extreme-tail intensity compared to baseline statistical and convolutional models, particularly in top-k precision metrics relevant for operational targeting.

We plan to couple the predicted intensity fields with standard emission factors to estimate event-scale CO₂ emissions and explore the relative importance of meteorological and surface drivers using feature attribution techniques using causality discovery methods. Our findings illustrate the potential of modern probabilistic deep learning to bridge between high-resolution fire observations and Earth system applications, and to support the assessment and management of future extreme fires.

How to cite: Bai, Y., Athanasiou, G., Antonopoulos, D., Papoutsis, I., and Carvalhais, N.: Probabilistic forecasting of wildfire ignitions and intensity at sub-kilometre scale using diffusion models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18849, https://doi.org/10.5194/egusphere-egu26-18849, 2026.

Extreme wildfires and megafires in Mediterranean Europe generate disproportionate ecological, social, and economic impacts, yet the processes that govern transitions from large fires to the most extreme events remain insufficiently constrained. In particular, it is unclear whether the emergence of megafires is primarily controlled by short-term atmospheric fire-weather anomalies, antecedent drought-driven fuel preconditioning, or their compound interaction. Clarifying these mechanisms is critical for improving impact-oriented wildfire risk assessment and early warning.

Here, we analyse 11,403 summer wildfires (≥30 ha) that occurred across Mediterranean Europe between 2008 and 2022, including 44 megafires (≥10,000 ha). Fires are classified into four size categories (30–100 ha, 100–1,000 ha, 1,000–10,000 ha, and ≥10,000 ha) to explicitly examine transitions across wildfire size classes. Official fire perimeters from EFFIS are combined with the MESOGEOS environmental–fire datacube (daily, 1 km), integrating meteorological variables, drought indicators, and land-surface conditions.

Fast-reacting atmospheric drivers (air and land-surface temperature, relative humidity, precipitation, and wind speed) are characterized over a ±1-day window around the reported ignition date and aggregated as a 3-day mean to account for start-date uncertainty. Slow-reacting environmental controls are represented using multi-month antecedent drought indicators, including the Standardized Precipitation–Evapotranspiration Index (SPEI), capturing longer-term fuel moisture and stress conditions.

Across increasing fire-size classes, we observe a systematic intensification of hot, dry, and windy conditions near ignition, alongside progressively drier antecedent conditions. Drought indicators show marked stepwise deterioration from medium to very large fires, supporting a strong role of fuel preconditioning driven by prolonged moisture deficits. However, the transition from very large fires to megafires is distinguished less by further increases in drought severity and more by exceptional short-term fire-weather anomalies, particularly strong winds and anomalously high night-time land-surface temperatures.

Using Random Forest classification models with permutation-based feature importance and repeated cross-validation to address class imbalance, we identify a compact and interpretable set of predictors that consistently discriminate transitions toward extreme fire sizes. Night-time land-surface temperature and wind speed emerge as dominant drivers of megafire occurrence, while multi-month drought indicators play a secondary role at the uppermost tail. Complementary logistic regression analyses confirm coherent directions of effect and demonstrate meaningful predictive skill for rare extreme events.

Overall, our results support a compound but non-uniform mechanism: antecedent drought and fuel stress set the stage for very large fires, whereas megafires arise when this preconditioning coincides with extreme short-term fire-weather conditions, particularly persistent nocturnal heat and strong winds. These findings provide actionable insights for extreme-event-focused wildfire early warning and highlight the need to jointly address fuel management and short-term atmospheric extremes under a warming Mediterranean climate.

References:

Balch et al. (2022), Nature.
Fernandes et al. (2016), Journal of Geophysical Research: Biogeosciences.
Ghasemiazma et al.(2026), NPJ Natural Hazard (under revision).
Linley et al. (2022), Global Ecology and Biogeography.
Luo et al. (2024), Nature.
Ruffault et al. (2020), Scientific Reports.
Turco et al. (2017), Scientific Reports.

How to cite: Ghasemiazma, F., Tonini, M., Fiorucci, P., and Turco, M.: Why some wildfires become megafires: compound short-term fire weather and antecedent drought controls in Mediterranean Europe., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12505, https://doi.org/10.5194/egusphere-egu26-12505, 2026.