Wildfires have reached an unprecedented scale in recent fire seasons of the Northern Hemisphere as demonstrated by the summers of 2021 and 2022. Severe fire seasons, characterized by heat, drought and windy conditions, might become even more frequent and will extend to more temperate regions in northern latitudes under global warming. Still, quantifying the effects of climate change on future fire danger is challenging because natural variability hides trends of increasing fire danger in climate model simulations in future potentially fire-prone areas. Single Model Initial-Condition Large Ensembles (SMILEs) help scientists to distinguish climate trends from natural variability. Here, we leverage the capabilities of SMILEs to assess future changes in fire weather conditions in a currently non-fire-prone area in Central Europe. The study area covers four heterogeneous landscapes, namely the Alps, the Alpine Foreland (southern parts), the lowlands of the Southern German Escarpment, and the eastern mountain ranges of the Bavarian Forest (northern parts). We use a SMILE of the Canadian regional climate model version 5 (CRCM5-LE) under the RCP 8.5 scenario from 1980 to 2099 to analyze trends in fire danger quantified by the globally applicable Canadian Fire Weather Index (FWI).

Our results show the strongest increases for the median (50th percentile) and extreme (90th percentile) FWI in the northern parts of the study area during the late summer months July, August and September. The southern, more alpine parts are affected less strongly and show high fire danger mostly in August by the end of the 21st century for the median FWI. Over the whole study area, we find that the extreme FWI in the present climate period will become much more frequent at the end of the century. In the South German Escarpment and Eastern Mountain Ranges, the climate change trend exceeds natural variability in the late 2040’s. Due to weaker variability, the time of emergence is reached in the Alps and Alpine Foreland in the early 2040's.

These results demonstrate that the CRCM5-LE is a suitable dataset to disentangle climate trends from natural variability in a multivariate fire danger metric. Our study emphasizes that regions with a low fire danger under current climate conditions will experience weather conditions facilitating the development of potentially uncontrollable wildfires in a warming climate.

How to cite: Miller, J., Böhnisch, A., Ludwig, R., and Brunner, M.: Impacts of climate variability and change on regional fire weather in heterogeneous landscapes of Central Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-271, https://doi.org/10.5194/egusphere-egu23-271, 2023.

Forest fires strongly depend on local weather conditions. Weather conditions conducive for occurrence and growth of fires is known as “fire weather”. This work investigates how climate change can affect the future fire weather in Indian forests using the Canadian Forest Fire Danger Rating System – Fire Weather Index (CFFDRS-FWI), a well-known fire danger assessment system. To drive this model, we used a high-resolution dynamically downscaled climate projection DSCESM for a baseline (2006-2015) and an end-century (2091-2100) period to compute the metric ‘Fire Weather Index (FWI)’. We divided the forest areas of the country into 5 zones based on climate and forest types viz., Himalayan (HIM), Northeast (NE), Central India (CEN), Deccan (DEC) and Western Ghats (WG) zones. Then, we developed thresholds for five fire weather danger classes using the baseline FWI in conjunction with observed fire count from MODIS active fire data. The baseline and future FWI, fire weather danger, Seasonal Severity Ratings (SSR) and characteristics of the fire weather season were compared to estimate the effect of climate change on forest fire danger.

Results show that there is considerable heterogeneity in the baseline as well as future fire weather danger across, and even within, the different forest zones of India. Climate change is likely to have a strong effect on fire weather. Days exceeding the Very High FWI threshold are likely to increase by about 30-40 days by the end-century despite a modest increase of about 5% in annual FWI values. SSR analysis suggests a maximum increase in fire disturbances during the pre-monsoon months of March-April-May.  About 55% of forest area over India will experience increased fire danger in this season. The least effect will be in the post monsoon season in September-October-November. The fire season is also expected to lengthen up to 59 days depending upon the forest type. The forests which are most likely to be affected by fire disturbances by end-century are moist deciduous and evergreen forests in the northern WG, mixed dry deciduous forests in central and southern CEN, Pine and Sal forests in the HIM and the scrub forests in the DEC zone. Climate change is unlikely to affect the fire weather danger in the NE.

This study is one of the first attempts to quantify the effects of climate change on forest fire hazard in India. It has significant policy implications and can be valuable for fire management authorities for designing appropriate fire suppression and mitigating measures.

How to cite: Barik, A. and Baidya Roy, S.: Impact of climate change on the fire weather in Indian forests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10618, https://doi.org/10.5194/egusphere-egu23-10618, 2023.

This work investigates the physical interactions and feedback between wildfires and the atmosphere using the coupled atmosphere-fire spread modelling system, WRF-SFIRE. The Figueira da Foz forest fire, occurred in Portugal in October 2017, which ocurred in association with hurricane Ophelia, was simulated under two different scenarios of fuel moisture settings, one static and one dynamic. Results show an underestimation of burnt area in the dynamic case, while static fuel moisture has shown a very high agreement. Pyrocumulus formed during late afternoon with a very dry base and more humid top, creating conditions favourable for the occurrence of downbursts, with very high Convective Available Potential Energy values. Lifted Condensation Level increased above the fire front as moisture was transported upwards, increasing surface temperature. Official reports show an overestimation of fuel moisture near the surface, leading to high CAPE values, compared to near zero values reported by vertical soundings. Relative Humidity values were higher by 30% when compared to weather station observations, and temperatures approximately 4ºC lower. Further model testing is needed to provide more accurate surface temperature and moisture simulations, to allow a more accurate fire progression representation and energy exchange, and improve the modelling of potential convective events. 

How to cite: Vaz, R., Silva, R., Cardoso Pereira, S., Carvalho, A., Carvalho, D., and Rocha, A.: Modelling of a wildfire in Portugal using a fully coupled atmosphere-fire spread modelling system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1415, https://doi.org/10.5194/egusphere-egu23-1415, 2023.

Mega-fires are wildfires that burn an area greater than 10.000 hectares. Despite being a minority in relation to the total number of fires, they are the one with the greatest negative impact on society and the environment. Associated with this wildfire type, the phenomenon of pyro-convection has been reported in several cases. Strong pyro-convective activity can lead to the formation of clouds within the smoke plume, also known as pyro-cumulus (PyroCu) or pyro-cumulonimbus (PyroCb). In 2017, Portugal recorded 11 mega-fires, of which 8 occurred on the 15th October. Since the photographic evidence of the formation of a PyroCu cloud, the chosen case study was the Quiaios mega-fire. The study aims to simulate the impact of a fire in the atmosphere, as well as the large-scale meteorological conditions that were affecting Portugal during the mega-fires. For this purpose, two numerical simulations were performed using the MesoNH atmospheric model: a coupled simulation with the ForeFire fire propagation model, with 3 nested domains with resolution of 2000m, 400m and 80m (300 by 300 grid points), and a large-scale non-coupled simulation, with a 15km resolution (300 by 250 grid points) to study the large-scale conditions. The coupled simulation allowed identifying the formation of a PyroCu cloud composed by different species of hydrometeors, namely graupel and rain droplets. The pyro-cloud developed inside the plume due the vertical transport of water vapor to higher levels. In the context of large scale, the simulation well represented the evolution of hurricane Ophelia, showing the change in wind direction from Southeast to Southwest in Portuguese territory, which created a favourable condition to the intensification of the active fires and the development of PyroCb clouds during the late afternoon. This study was funded by national funds through FCT-Foundation for Science and Technology, I.P. under the PyroC.pt project (Ref. PCIF/MPG/0175/2019).

How to cite: Campos, C., Couto, F. T., Purificação, C., Filippi, J.-B., Baggio, R., and Salgado, R.: Modelling pyro-convective activity and the meteorological conditions leading to mega-fires, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8967, https://doi.org/10.5194/egusphere-egu23-8967, 2023.

Over mountainous terrain, the atmospheric structure becomes much more complex than homogeneous terrain in terms of the Atmospheric Boundary Layer (ABL). In the context of interaction between fire and atmosphere, abrupt changes in the ABL wind often lead to erratic and turbulent flow in the fire environment and expose firefighters to dangerous conditions. The study aims to characterize the ABL conditions associated with the largest forest fire that occurred in Portugal in 2019. The fire event took place in Vila de Rei county, which is surrounded by hills and valleys with large differences in altitudes. In order to study the regional scale, a numerical simulation was performed using the Meso-NH atmospheric model, configured with 500 × 500 grid points at 2500 m horizontal resolution, between 19 July at 0000 UTC and 25 July 2019 at 0000 UTC. The simulation covered the Iberian Peninsula and corresponds to the period when the fire burned more than 9,000 hectares in Vila de Rei. Such a simulation helped to characterize the lower troposphere, which contributed to the evolution of the ABL height over the days. Results indicate that the simulated ABL evolution is characterized by the presence of a coastal low-level jet with a maximum wind speed of 10 meters per second at ~ 600 meters’ altitude (1800 UTC of 20 July).  ABL height calculated from Richardson number method depicted a growing in the morning that reached a peak height by mid-afternoon. The ABL height ranged from 500 to 900 m throughout the afternoon and evening during the entire study period. Besides the identification of the fire weather conditions, this study also highlights the factors that contributed to the lower values of the ABL height in the wildfire event. This study was funded by national funds through FCT-Foundation for Science and Technology, I.P. under the PyroC.pt project (Ref. PCIF/MPG/0175/2019).

How to cite: Purificação, C., Campos, C., Henkes, A., and Couto, F. T.: Modelling the atmospheric factors determining the evolution of the boundary layer during a wildfire event, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14223, https://doi.org/10.5194/egusphere-egu23-14223, 2023.

We will present approaches to modeling wildfire dynamics using the IIASA’s wildFire cLimate impacts and Adaptation Model (FLAM). FLAM operates with a daily time step and uses mechanistic algorithms to parametrize the impacts of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. Validation on historical data and future projections under climate change scenarios will be discussed at various scales and resolutions.  We will present results for the following case-studies: (i) projections of global burned areas driven by climate change scenarios until 2100; (ii) modeling burned areas and adaptation options in Europe; (iii) modeling burned areas and their feedback to land-use change in Indonesia with a particular emphasis on extreme fires due the impacts of El Niño southern oscillation using historical data and the delta approach for future scenarios; (iv) regional variability and driving forces behind forest fires in Sweden. Our results support international analyses that, irrespective of changes in management, it is evident that climate change is very likely to increase the frequency and impact of wildland fires in the coming decades.

How to cite: Krasovskiy, A., Corning, S., Boere, E., Khabarov, N., Cimdins, R., and Kraxner, F.: Modeling wildfire dynamics and future projections under climate change scenarios: the FLAM approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16190, https://doi.org/10.5194/egusphere-egu23-16190, 2023.

Wildfire events are driven by complex interactions of the climate and anthropogenic interventions. Predictions of future wildfire events, their extremity, and their impact on the environment and economy must account for the interactions between these drivers. Economic policy and land use decisions influence the susceptibility of an area to climate extremes, the probability of burning, and future decision making. To better understand how climate-driven drought events and adaptation efforts affect burned area, agricultural production losses, and land use decisions, we developed a storyline approach centered on Indonesia’s 2015 fire events, which saw significant production losses of palm oil – a product imported by the EU chiefly as a biofuel – surpassing 7%. We explored analogous events under three warming conditions and two palm oil sector adaptation scenarios using two storylines: ensemble mean climate and high aridity conditions. We employed a model chain consisting of IIASA's wildfire climate impacts and adaptation model (FLAM) and the partial equilibrium global biosphere management model (GLOBIOM) to predict burned area and assess resultant production losses in the oil palm sector in Indonesia. To quantify the changes in burned area, we applied a delta approach based on the different degrees of global warming that can be expected. To define fire-induced oil palm losses and associated economic impacts, we combined the burned areas from FLAM with land-use change and productivity estimates from GLOBIOM. We found that the total burned area and production loss increased across the projections and climate warming by up to 25%, with only minor differences between storylines. By varying characteristics of regional climate change features, we found that these results are spatially explicit and robust across projections. Our results highlight the importance of including future warming and drought conditions in predicting oil palm losses and land use decision making. They leave room to explore how climatic and economic impacts could be mitigated through economic and land use management policies affecting Indonesia and the EU.

How to cite: Corning, S., Boere, E., Krasovskiy, A., Lessa-Derci-Augustynczik, A., and Kraxner, F.: Flammable Futures – A storyline of climatic and land-use change impacts on wildfire extremes in Indonesia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10736, https://doi.org/10.5194/egusphere-egu23-10736, 2023.

Climate change has increased the vulnerability of boreal forests and grasslands to wildfires. An increase in high-latitude wildfires has resulted in the deterioration of air quality, particularly in Western Siberia, where PM_2.5 levels have increased at >1 µg m^-3 year^-1 during 1998−2020. Arctic wildfire carbon emissions have doubled during the last 20 years, and in Siberia they have shifted northwards. Using fire air pollutant emissions data from the Quick Fire Emissions Dataset (QFED), we create two emissions scenarios for Arctic Council member nations, with and without wildfires. PM_2.5 is simulated for the two scenarios using the Community Earth System Model, which we evaluate using in-situ measurement data, finding a large negative bias in wildfire plumes. To correct this underestimation we use a high resolution PM_2.5 reanalysis dataset, improving agreement with observations. Bias-corrected scenarios are used to estimate air quality degradation and resulting health impacts due to Arctic Council nation wildfires across the Northern Hemisphere high- and mid-latitudes for the period 2001-2020. We use the Global Exposure Mortality Model to estimate the health impacts of chronic exposure to Arctic wildfire-attributed PM_2.5­. We find that health impacts are highly variable, with 25,000−55,000 premature deaths yearly, with most of the health burden falling on nations outside the Arctic Council, particularly China, due to transboundary transport of Siberian wildfire PM_2.5. Health impacts have decreased during our study period partly due to the northwards shift in wildfires.

How to cite: Silver, B., Arnold, S., Emmons, L., Reddington, C., and Conibear, L.: Health impacts of wildfire smoke in the Arctic, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9394, https://doi.org/10.5194/egusphere-egu23-9394, 2023.

Wildfires are a major source of atmospheric aerosols and can have significant impacts on air quality and radiative forcing. In our work, we have utilized the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to study the impact of wildfires on aerosol pollution and meteorological feedbacks, focusing on the geographical area of Greece as a test case. We focus on the summer season of 2021, during which intense wildfire activity occurred in the country. We have performed sensitivity experiments with and without emissions from fires as a way to quantify the impact of such emissions on atmospheric pollutants, AOD, radiative forcing and weather variables (temperature, humidity, winds). Our results show that wildfires can have a significant impact on AOD, with the magnitude of the effect depending on the size and intensity of the wildfire and the meteorological conditions. Our recent work has shown that fire prone areas determined using the FWI index seem to link closely with the areas in Greece with the highest burnt area. Since the emitted aerosols are an important parameter, we test how well the emissions correlate to the FWI. Overall, our work demonstrates the potential role of wildfires in the evolution of short-term weather conditions via fire pollution radiative forcing, and provides new insights into the mechanisms leading to such effects.

How to cite: Rovithakis, A. and Voulgarakis, A.: Studying air pollution and weather feedbacks from wildfires over Greece using WRF-Chem, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11003, https://doi.org/10.5194/egusphere-egu23-11003, 2023.

The identification of areas susceptible to fire is critical for planners, managers, and decision makers in developing effective mitigation strategies. At Climate X we are producing risk estimates to help businesses and communities mitigate and adapt for climate change related losses. Climate X provides risk scores and expected financial losses from a range of physical hazards.  The risks posed by wildfire are increasing in many regions and especially within the wildland–urban interface. We developed a machine learning model to predict changes in fire risk at 90m that can be applied globally. The approach combines meteorological drivers of fire weather utilising CORDEX regional climate models with local fire susceptibility modelling trained from Earth observation records of fire occurrence.  By 2100, we find an average 7% increase in fire risk across the US and western Europe under the rcp8.5 scenario. Our results demonstrate how the combined application of machine learning, climate and Earth observation data can provide time sensitive assessments of fire risk at global scale.

How to cite: Brennan, J., Burke, C., Reveley, G., Woodhouse, S., Mitchell, H., Leach, N., and Ramsamy, L.: Towards global prediction of fire risk in a changing climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8082, https://doi.org/10.5194/egusphere-egu23-8082, 2023.