Climate Influences Wildfire Activity Through Opportunity: An Event-Scale Perspective

Introduction Extreme wildfires are increasingly prevalent worldwide, driving significant forest area loss and severe environmental and socioeconomic impacts (Cunningham et al. 2024). The Mediterranean, in particular, is projected to face heightened fire risks due to climate change-induced drier conditions and lower fuel moisture (de Rivera et al. 2020). However, the drivers of extreme wildfires remain poorly understood. Current fire models, typically calibrated on global fire datasets, are primarily designed to estimate annual total burned areas and struggle to capture the unique behaviours of extreme wildfires (Forrest et al. 2024). Furthermore, correlation-based approaches, which dominate current modelling efforts, may fail to identify the underlying causal drivers of these events and are poorly suited for extrapolation to changing conditions. Causal discovery methods, which aim to identify cause-and-effect relationships from observational data, offer a promising pathway to uncover the mechanisms driving extreme wildfires. While increasingly applied in environmental sciences, their use in wildfire prediction remains limited (de Rivera et al. 2020, Zhang et al. 2024, Zhao et al. 2024).This study will use causal discovery to identify key drivers of extreme wildfire in the Mediterranean, and further integrate the causal graphs into a stand-alone model of wildfire spread. This approach aims to move beyond correlation-based models, improve our understanding of extreme wildfire behaviour and inform more robust mitigation strategies. Study Area and Data We will use the Mesogeos dataset (Kondylatos et al. 2023), designed for wildfire modelling in the Mediterranean region. Spanning 17 years (2006–2022) at a 1 km² spatial and daily temporal resolution, it includes meteorological variables (e.g., temperature, wind speed), vegetation indices (e.g., NDVI, LAI), and human activity indicators (e.g., population density, road proximity). Wildfire data include MODIS fire ignitions and burned areas from EFFIS. Methods Extreme Wildfire Definition and Sampling In this study, we define extreme wildfires as those that are exceptionally large in size. To identify these events, we will first extract the final burned areas associated with each fire ignition recorded in the Mesogeos dataset. Since the classification of large fires is inherently subjective and varies by region, we will adopt a data-driven approach based on an absolute quantitative threshold. Specifically, we will define extreme wildfires as those exceeding the 99th percentile of fire sizes, though this threshold may be adjusted to align with extreme fire events documented in national fire reports. While this method provides a straightforward and reproducible way to define extreme events, we acknowledge its limitations. Future work will refine this approach by incorporating region-specific thresholds and additional contextual factors to improve geographic relevance. Phase I: Causal Discovery Using local variables from Mesogeos, averaged over final burned areas and lagged to time t, we will estimate causal graphs for extreme events via Python’s Tigramite library with the PCMCI method (Runge et al. 2019). PCMCI detects time-lagged causal associations in large nonlinear datasets through iterative conditional independence testing. To ensure robustness, we will assess graph stability across hyperparameters and selected drivers, and validate graphs through expert knowledge. Phase II: Causal Fire Spread Model We will develop a fire spread model incorporating causal mechanisms from Phase I. This model will integrate spatiotemporal fire dynamics, causal dependencies constraining fire spread, and dynamic weather and fuel inputs. By explicitly modeling causal interactions, it aims to improve early warning systems and risk assessments under future climate scenarios. The causal model’s performance will be benchmarked against statistical models to evaluate its predictive accuracy and robustness. Expected Results We expect that the data-driven approach proposed in this study will enhance the predictability of extreme wildfires by reducing confounding effects and capturing key drivers of extreme fire events. Compared to purely statistical approaches, incorporating causal structures should lead to more reliable predictions, particularly in out-of-sample applications or under changing environmental conditions. Furthermore, the causal fire spread model will provide insights into how climate, vegetation, and anthropogenic factors interact to drive fire spread, supporting fire prevention and mitigation strategies.

The incidence and severity of forest fires are linked to the interaction between climate, fuel and topography. Increased warming and drying in the future is expected to have a significant impact on the risk of forest fire occurrence. An increase in fire risk is linked to the synchronous relationship between climate and fuel moisture conditions. A warmer, drier climate will lead to drier forest fuels that will in turn increase the chance of successful fire ignition and propagation. This interaction will increase the severity of fire weather, which, in turn, will increase the risk of extreme fire behaviour. A warmer climate will also extend fire season length, which will increase the likelihood of fires occurring over a greater proportion of the year. In this study of the North Okanagan area of British Columbia, Canada, the impacts of climate change of fire potential were evaluated using the Canadian Forest Fire Danger Rating System and multiple climate scenario analysis. Utilizing this approach, a 30% increase in fire season length was modelled to occur by 2070. In addition, statistically significant increases in fire severity and fire behaviour were also modelled. Fire weather severity was predicted to increase by 95% during the summer months by 2070 while fire behaviour was predicted to shift from surface fire‐intermittent crown fire regimes to a predominantly intermittent‐full crown fire regime by 2070 onwards. An increase in fire season length, fire weather severity and fire behaviour will increase the costs of fire suppression and the risk of property and resource loss while limiting human‐use within vulnerable forest landscapes. An increase in fire weather severity and fire behaviour over a greater proportion of the season will increase the risks faced by ecosystems and biodiversity to climatic change and increase the costs and difficulty of achieving sustainable forest management.

This chapter presents passion as one of the key elements leading to extreme behavior and outcomes. It first defines extremism as an infrequent phenomena entailed by an underlying motivation of high intensity or magnitude. The second section introduces the concept of passion and the predominant theory on passion, the Dualistic Model of Passion. According to this model, passion is a strong inclination for an important, meaningful, and self-defining activity in which one invests time and energy. It posits that there are two types of passion, harmonious and obsessive, that, given their specific characteristics, respectively lead to moderate and extreme consequences. The next two subsections uncover the role of passion in extreme interpersonal (e.g., violent activism, romantic and sexual behaviors, aggression, hatred, and dehumanization) and intrapersonal (e.g., addiction, burnout, and health problems) behaviors and outcomes. Overall, this chapter reveals that obsessive passion leads to a number of extreme interpersonal and intrapersonal consequences, whereas harmonious passion is related to less extreme outcomes such as mainstream and peaceful behaviors, positive relationship quality, and healthy behaviors. Finally, the last section offers some conclusions on passion and extremism.

Trace elements and stable isotopes are commonly used in chronologically formed biominerals as proxies of temperature and/or salinity in estuarine and marine environments. To accurately use the chemistry of biominerals as salinity proxies, understanding the consistency of dissolved element-salinity relationships across spatiotemporal scales is essential. We examined relationships between dissolved Ba:Ca, Sr:Ca, Mg:Ca, Mn:Ca, δ18O, and salinity on regional, local, and seasonal scales in the lower portions of subtropical estuaries (salinities 15–42 ppt) of Texas, including locations where seasonal alternations between negative and positive estuaries can occur. Across all spatiotemporal scales, Ba:Ca displayed a negative linear relationship within the sampled salinity range and was elevated at sites furthest from the ocean and lowest at locations closest to the Gulf of Mexico. This pattern remained consistent as the neutral estuary switched from negative to positive after a rain event. On regional scales, δ18O displayed a positive linear relationship with salinity and was strongly related to evaporation rates. On local and seasonal scales, evaporation-enriched δ18O at upper enclosed estuarine sites and this pattern was consistent over time including periods of reverse estuary conditions. Dissolved Sr:Ca and Mg:Ca varied linearly with salinity on regional scales but displayed minimal variation across temporal scales within an estuary. High variability in dissolved Mn:Ca-salinity relations was found at all spatiotemporal scales, with localized episodic peaks of Mn:Ca possibly due to sediment disturbance. Although dissolved Ba:Ca and δ18O were not predictably related to salinity on local scales, consistent up-estuary enrichment and lower-estuary depletion make these two constituents reliable proxies for animal movements across the ocean–estuary gradient as recorded in chronologically formed biominerals.

On the dynamics of rock glaciers

<p>Beyond global mean temperatures, anthropogenic land cover change (LCC) can have significant impacts at regional and seasonal scales but also on extreme weather events to which human, natural and economical systems are highly vulnerable. However, the effects of LCC on extreme events remain either largely unexplored at global and regional scale and/or without consensus. Here, using several Earth System Models under two different LCC scenarios (the RCP8.5 and RCP2.6 Representative Concentration Pathways) and analyzing 20 extreme weather indices, we find future LCC substantially modulates projected weather extremes particularly at regional level.</p><p>On average by the end of the 21<sup>st</sup> century, under RCP8.5 and RCP2.6 scenarios, future LCC robustly lessens global projections of high rainfall extremes. Accounting for LCC diminishes regional projections of heavy precipitation days or consecutive wet days by more than 50% in southern Africa or northeastern Brazil but intensifies projected dry days in eastern Africa by 30%. LCC do not substantially affect projections of global and regional temperature extremes projections (<5%), but it can impact global rainfall extremes 2.5 times more than global mean rainfall projections.</p><p>Under RCP2.6 scenario, global LCC impacts are similar but of lesser magnitude while at regional scale in Amazon or Asia, LCC enhances drought projections. We investigate the underlying biophysical drivers behind those projected changes.</p><p>We stress here that multi-coupled modelling frameworks incorporating all aspects of land use-land cover change and more model-data benchmarking are needed for reliable projections of extreme events.</p><div> <div> </div> </div>

Recent destructive fire seasons around the world indicate the emergence of novel fire regimes, characterized by high-intensity burning and extreme fire behavior. While the contribution of individual factors can be debated, the scientific literature concludes that fire weather is one prominent driver of fire activity. Moreover, there is growing evidence that climate change is escalating the frequency, severity and extend of wildfires around the world. Simply put, wildfires are changing because we change the conditions in which they occur. Although the importance of weather to wildfire activity has been documented since the 1930s, there is still a lot of research effort in advancing our knowledge on the drivers and the processes that lead to the development of extreme fire weather and behavior. Here we investigate the synoptic and mesoscale fire weather dynamics associated with 9 extreme wildfires in Greece during the period from 2009—2022. We select wildfires that took place within this period to exploit the increased availability of surface weather data from the automatic weather stations network of the National Observatory of Athens as ground-truth for evaluating the numerical simulations and studying surface fire weather. The selection of the examined wildfires is based on the extremeness of satellite-derived daily growth rates of burnt area as well as the environmental and socio-economic impacts. To assess the fire weather dynamics associated with each event we conduct numerical simulations with the Weather Research and Forecasting (WRF) model initialized with ERA5 reanalysis data from the European Centre for Medium-range Weather Forecasts (ECMWF). Our synoptic and mesoscale analysis of the WRF simulations illustrates the dominant atmospheric processes that drive fire weather conditions, such as the horizontal and vertical transport of dry, warm and high-momentum air. Furthermore, our analysis demonstrates a distinct separation in atmospheric dynamics between the wind-driven and plume-dominated (i.e., wildfires that are accompanied by pyroconvection) wildfires. Finally, our work highlights the added value of high-resolution simulations to better simulate fire weather conditions in areas with complex topography such as Greece, and we discuss potential implications for fire weather forecasting.Acknowledgments This work has been supported financially by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the "2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers" (Project Number: 00559, Project Acronym: FLAME).

Climate change has intensified the frequency and severity of forest fires, driving a global increase in fire-induced forest losses. Although extreme forest fires have been extensively studied at regional scales, the global characteristics of extreme fire-induced forest loss (EFFL) remain poorly understood. Here, we used high-resolution remote-sensing data to assess the spatiotemporal dynamics, ecological impacts, and climatic drivers of EFFL events worldwide. Our results show that from 2001 to 2023, EFFLs were widespread across global forest ecosystems, with boreal forests having the highest frequency and extent, contributing to 57.53% and 58.37% of the global EFFLs, respectively. During this period, the frequency of EFFLs increased 2.02-fold. Moreover, 8.79% of endangered forest vertebrates (662 species) experienced EFFLs in >10% of their geographic range, and 1.76 × 109 tons of aboveground biomass (AGB) was affected. High-risk regions, including the Amazon rainforest, Southeast Asia, and southeastern Australia, face severe threats to species conservation and carbon stocks. The occurrence of EFFLs was consistently associated with dry and hot anomalies. Our findings underscore the urgent need for enhanced forest monitoring and conservation, and provide a scientific basis for developing targeted fire prevention and management strategies.

Holocene wildfire on the Qinghai-Tibetan Plateau–witness of abrupt millennial timescale climate events

Levoglucosan has been extensively used as a biomarker for tracing vegetation fire emissions in atmospheric aerosols, ice cores, and lake sediments. Precipitation can scavenge levoglucosan from the atmosphere to the Earth’s surface. However, almost no previous research has investigated the variability of levoglucosan in precipitation. This research reports levoglucosan records in precipitation samples collected from March 2018 to September 2019 in Lhasa, on the southern Tibetan Plateau. Although the event-based levoglucosan variations seem random, the variability on monthly or seasonal time scales is highly correlated to vegetation fire changes along the Himalayas and surrounding regions. In addition, extreme wildfires in southern Central Asia also affect levoglucosan records in precipitation at Lhasa, especially during the summer. Our results indicate that local vegetation fires are not the major sources of levoglucosan in precipitation in Lhasa. In addition, the annual levoglucosan flux at Lhasa is much higher than that in ice cores on the Tibetan Plateau. This study provides important insights into levoglucosan records in precipitation over the Tibetan Plateau, highlighting how levoglucosan variations could reflect fire changes from a single event to seasonal or annual time scales.

Exceptionally large areas burned in 2014 in central Northwest Territories (Canada), leading members of the Tłı̨chǫ First Nation to characterize this year as ‘extreme’. Top-down climatic and bottom-up environmental drivers of fire behavior and areas burned in the boreal forest are relatively well understood, but not the drivers of extreme wildfire years (EWY). We investigated the temporal and spatial distributions of fire regime components (fire occurrence, size, cause, fire season length) on the Tłı̨chǫ territory from 1965 to 2019. We used BioSIM and data from weather stations to interpolate mean weather conditions, fuel moisture content and fire-weather indices for each fire season, and we described the environmental characteristics of burned areas. We identified and characterized EWY, i.e., years exceeding the 80th percentile of annual area burned for the study period. Temperature and fuel moisture were the main drivers of areas burned. Nine EWY occurred from 1965 to 2019, including 2014. Compared to non-EWY, EWY had significantly higher mean temperature (>14.7°C) and exceeded threshold values of Drought Code (>514), Initial Spread Index (>7), and Fire Weather Index (>19). Our results will help limit the effects of EWY on human safety, health and Indigenous livelihoods and lifestyles.

Observed and predicted hydrological changes in C‐rich boreal ecosystems have the ability to change the transport trajectory of biogeochemical constituents from the land to ecologically, economically, and culturally important coastal systems. Yet, most of our current understanding of biogeochemical fluxes and cycling across salinity gradients stem from observations of large and urbanized riverine systems, which overlooks the numerically abundant smaller systems. In this study, we conducted a baseline assessment of the biogeochemical constituents across salinity gradients among two adjacent small systems in the boreal zone. Dissolved iron (DFe) and its ratio with dissolved organic carbon (DFe : DOC) were the most sensitive indicators for small catchment heterogeneity. These parameters were the best indicators of change among coastal systems across regional and seasonal scales. Our results also confirm consistencies in common optical measures (SUVA254 and S[275–295/350–400]) and DOC to nitrogen ratios that may adequately provide representation of biogeochemical composition on a regional scale. Simultaneous variation in biogeochemical parameters across particulate and dissolved pools during the summer‐to‐fall transition period indicate this as an important timeframe for targeted investigation of the linkages between biogeochemical parameters and coastal ecosystem functioning. By providing some key spatial and temporal constraints on biogeochemical fluxes among boreal river‐estuaries, our findings indicate that DFe and DFe : DOC ratios should be used to design research aimed at capturing regional and coastal ecosystem scale biogeochemical fluxes to inform Earth System Models.

Anthropogenic land cover change (LCC) can have significant impacts at regional and seasonal scales but also for extreme weather events to which socio-economical systems are vulnerable. However, the effects of LCC on extreme events remain either largely unexplored and/or without consensus following modelling over the historical period (often based on a single model), regional or idealized studies. Here, using simulations performed with five earth system models under common future global LCC scenarios (the RCP8.5 and RCP2.6 Representative Concentration Pathways) and analyzing 20 extreme weather indices, we find future LCC substantially modulates projected weather extremes. On average by the end of the 21st century, under RCP8.5, future LCC robustly lessens global projections of high rainfall extremes by 22% for heavy precipitation days (>10 mm) and by 16% for total precipitation amount of wet days (PRCPTOT). Accounting for LCC diminishes their regional projections by >50% (70%) in southern Africa (northeastern Brazil) but intensifies projected dry days in eastern Africa by 29%. LCC does not substantially affect projections of global and regional temperature extremes (<5%), but it can impact global rainfall extremes 2.5 times more than global mean rainfall projections. Under an RCP2.6 scenario, global LCC impacts are similar but of lesser magnitude, while at regional scale in Amazon or Asia, LCC enhances drought projections. We stress here that multi-coupled modelling frameworks incorporating all aspects of land use are needed for reliable projections of extreme events.

Increases in extreme precipitation greater than in the mean under increased greenhouse gases have been reported in many climate models both on global and regional scales. It has been proposed in a previous study that whereas global-mean precipitation change is primarily constrained by the global energy budget, the heaviest events can be expected when effectively all the moisture in a volume of air is precipitated out, suggesting the intensity of these events increases with availability of moisture, and significantly faster than the global mean. Thus under conditions of constant relative humidity one might expect the Clausius–Clapeyron relation to give a constraint on changes in the uppermost quantiles of precipitation distributions. This study examines if the phenomenon manifests on regional and seasonal scales also. Zonal analysis of daily precipitation in the HadCM3 model under a transient CO2 forcing scenario shows increased extreme precipitation in the tropics accompanied by increased drying at lower percentiles. At mid- to high-latitudes there is increased precipitation over all percentiles. The greatest agreement with Clausius–Clapeyron predicted change occurs at mid-latitudes. This pattern is consistent with other climate model projections, and suggests that regions in which the nature of the ambient flows change little give the greatest agreement with Clausius–Clapeyron prediction. This is borne out by repeating the analyses at gridbox level and over season. Furthermore, it is found that Clausius–Clapeyron predicted change in extreme precipitation is a better predictor than directly using the change in mean precipitation, particularly between 60°N and 60°S. This could explain why extreme precipitation changes may be more detectable then mean changes.

Abstract. During the last 20 years extreme wildfires have challenged firefighting capabilities. Often, the prediction of the extreme behaviour is essential for the safety of citizens and firefighters. Currently, there are several fire danger indices routinely used by firefighting services, but they are not suited to forecast extreme-wildfire behaviour at the global scale. This article proposes a new fire danger index, the extreme-fire behaviour index (EFBI), based on the analysis of the vertical profiles of the atmosphere above wildfires as an addition to the use of traditional fire danger indices. The EFBI evaluates the ease of interaction between wildfires and the atmosphere that could lead to deep moist convection and erratic and extreme wildfires. Results of this research through the analysis of some of the critical fires in the last years show that the EFBI can potentially be used to provide valuable information to identify convection-driven fires and to enhance fire danger rating schemes worldwide.