Is Portugal Starting to Burn All Year Long? The Transboundary Fire in January 2022

Couto, Flavio T.; Santos, Filippe L.M.; Campos, Cátia; Andrade, Nuno; Purificação, Carolina; Salgado, Rui

doi:10.3390/atmos13101677

Cited by 17 publications

(5 citation statements)

References 44 publications

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“…However, pyroCb cloud formation is not the only factor producing sudden changes in the evolution of the fire front. For instance, as shown in several studies [10][11][12][13], complex terrain also affects fire propagation. Therefore, forecasting the behavior of the fire front under the extreme conditions induced by the fire-atmosphere interaction is one of the most challenging issues that firefighters have to face.…”

Section: Introductionmentioning

confidence: 94%

Triggering Pyro-Convection in a High-Resolution Coupled Fire–Atmosphere Simulation

Couto,

Filippi,

Baggio

et al. 2024

Fire

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This study aimed to assess fire–atmosphere interactions using the fully coupled Meso-NH–ForeFire system. We focused on the Pedrógão Grande wildfire (28,914 ha), which occurred in June 2017 and was one of the deadliest and most damaging fires in Portugal’s history. Two simulations (control and fully coupled fire–atmosphere) were performed for three two-way nested domains configured with horizontal resolutions of 2 km, 0.4 km, and 0.08 km, respectively, in the atmospheric model Meso-NH. Fire propagation was modeled within the innermost domain with ForeFire, which solves the fire front with a 20 m resolution, producing the heat and vapor fluxes which are then injected into the atmospheric model. A simplified homogeneous fuel distribution was used in this case study. The fully coupled experiment helped us to characterize the smoke plume structure and identify two different regimes: (1) a wind-driven regime, with the smoke plume transported horizontally southward and in the lower troposphere, and (2) a plume-dominated regime, in which the simulated smoke plume extended vertically up to upper levels, favoring the formation of a pyro-cloud. The simulations were compared, and the results suggest that the change in the fire regime was caused by an outflow that affected the main fire front. Furthermore, the fully coupled simulation allowed us to explore the change in meteorology caused by an extreme fire, namely through the development of a pyro-cloud that also induced outflows that reached the surface. We show that the Meso-NH–ForeFire system may strongly contribute to an improved understanding of extreme wildfires events and associated weather phenomena.

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Section: Introductionmentioning

confidence: 94%

Triggering Pyro-Convection in a High-Resolution Coupled Fire–Atmosphere Simulation

Couto,

Filippi,

Baggio

et al. 2024

Fire

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“…Southern European countries such as Portugal, Spain, Greece, and Italy, characterized by having a Mediterranean climate, are among the ones most affected by wildfires in summer, and several studies suggest that climate change may alter wildfire activity behavior in this region (e.g., [1,2]). For instance, January 2022 was the winter month with the highest number of fire outbreaks in Portugal, also indicating possible changes in the fire activity regime [3,4]. Portugal has a long history of forest fires, with the most critical years in terms of total burned area being 2003, 2005, and 2017.…”

Section: Introductionmentioning

confidence: 99%

ECMWF Lightning Forecast in Mainland Portugal during Four Fire Seasons

Campos,

Couto,

Santos

et al. 2024

Atmosphere

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The study evaluated the ECMWF model ability in forecasting lightning in Portugal during four fire seasons (019–2022). The evaluation was made based on lightning data from the national lightning detector network, which was aggregated into resolutions of 0.5° and 1° over 3 h periods and analyzed from statistical indices using two contingency tables. The results showed that the model overestimates the lightning occurrence, with a BIAS greater than 1, with a success rate of 57.7% (49%) for a horizontal resolution of 1° (0.5°). The objective analysis was complemented by the spatial lightning distribution analysis, which indicated a time lag between the two data, i.e., the model started predicting lightning before its occurrence and finished the prediction earlier. Furthermore, such analysis revealed the lightning distribution being consistent with some weather patterns. The findings of this study provide insights into the applicability of the ECMWF lightning forecast data in the context of forecasting natural forest fires in Portugal.

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“…Climate has a strong influence on global wildfire activity, with the frequency and intensity of wildfires increasing in many regions due to climate change (Couto et al., 2022 ; Jolly et al., 2015 ; Moritz et al., 2014 ). Wildfires occur at the intersection of dry weather, available biomass fuel and ignition sources (Moritz et al., 2005 ).…”

Section: Introductionmentioning

confidence: 99%

Fire‐Pollutant‐Atmosphere Components and Its Impact on Mortality in Portugal During Wildfire Seasons

de Souza Fernandes Duarte,

Salgueiro,

Costa

et al. 2023

GeoHealth

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This study analyzed fire‐pollutant‐meteorological variables and their impact on cardio‐respiratory mortality in Portugal during wildfire season. Data of burned area, particulate matter with a diameter of 10 or 2.5 μm (μm) or less (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind speed, aerosol optical depth and mortality rates of Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease, and Asthma (ASMA), were used. Only the months of 2011–2020 wildfire season (June–July–August–September‐October) with a burned area greater than 1,000 ha were considered. Principal component analysis was used on fire‐pollutant‐meteorological variables to create two indices called Pollutant‐Burning Interaction (PBI) and Atmospheric‐Pollutant Interaction (API). PBI was strongly correlated with the air pollutants and burned area while API was strongly correlated with temperature and relative humidity, and O3. Cluster analysis applied to PBI‐API divided the data into two Clusters. Cluster 1 included colder and wetter months and higher NO2 concentration. Cluster 2 included warmer and dried months, and higher PM10, PM2.5, CO, and O3 concentrations. The clusters were subjected to Principal Component Linear Regression to better understand the relationship between mortality and PBI‐API indices. Cluster 1 showed statistically significant (p‐value < 0.05) correlation (r) between RSDxPBI (rRSD = 0.58) and PNEUxPBI (rPNEU = 0.67). Cluster 2 showed statistically significant correlations between RSDxPBI (rRSD = 0.48), PNEUxPBI (rPNEU = 0.47), COPDxPBI (rCOPD = 0.45), CSDxAPI (rCSD = 0.70), RSDxAPI (rCSD = 0.71), PNEUxAPI (rPNEU = 0.49), and COPDxAPI (rPNEU = 0.62). Cluster 2 analysis indicates that the warmest, driest, and most polluted months of the wildfire season were associated with cardio‐respiratory mortality.

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Is Portugal Starting to Burn All Year Long? The Transboundary Fire in January 2022

Cited by 17 publications

References 44 publications

Triggering Pyro-Convection in a High-Resolution Coupled Fire–Atmosphere Simulation

Triggering Pyro-Convection in a High-Resolution Coupled Fire–Atmosphere Simulation

ECMWF Lightning Forecast in Mainland Portugal during Four Fire Seasons

Fire‐Pollutant‐Atmosphere Components and Its Impact on Mortality in Portugal During Wildfire Seasons

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