Fire Weather Conditions Research Articles

Identifying Local-Scale Weather Forcing Conditions Favorable to Generating Iberia’s Largest Fires

The Mediterranean region is characterized by the frequent occurrence of summer wildfires, representing an environmental and socioeconomic burden. Some Mediterranean countries (or provinces) are particularly prone to large fires, namely Portugal, Galicia (Spain), Greece, and southern France. Additionally, the Mediterranean basin corresponds to a major hotspot of climate change, and anthropogenic warming is expected to increase the total burned area due to fires in Mediterranean Europe. Here, we propose to classify summer large fires for fifty-four provinces of the Iberian Peninsula according to their local-scale weather conditions and fire danger weather conditions. A composite analysis was used to investigate the impact of local and regional climate drivers at different timescales, and to identify distinct climatologies associated with the occurrence of large fires. Cluster analysis was also used to identify a limited set of fire weather types, each characterized by a combination of meteorological conditions. For each of the provinces, two significant fire weather types were identified—one dominated by high positive temperature anomalies and negative humidity anomalies, and the other by intense zonal wind anomalies with two distinct subtypes in the Iberian Peninsula., allowing for the identification of three distinct regions.

Attributing Increases in Fire Weather to Anthropogenic Climate Change Over France

Anthropogenic climate change is widely thought to have enhanced fire danger across parts of the world, including Mediterranean regions through increased evaporative demand and diminished precipitation during the fire season. Previous efforts have detected increases in fire danger across parts of southern Europe but a formal attribution of the role of anthropogenic climate forcing has not been undertaken. Here, we attempt to disentangle the confounding effects of anthropogenic climate change and natural variability on observed increases in fire danger in France over the past six decades, with a focus on the fire-prone Mediterranean region. Daily fire weather and fire-related drought indices were computed from a reanalyses dataset covering the 1958-2017 period. Anthropogenic signals in meteorological variables were isolated using 17 climate models and then removed from observations to form a set of counterfactual observations free of anthropogenic climate change. Our results show that anthropogenic climate change is responsible for nearly half of the long-term increases in fire weather and fire-related drought conditions across the Mediterranean region and have significantly elevated the likelihood of summers with extreme fire danger. Fire danger conditions such as those observed during the near-record breaking 2003 fire season have a 500 years) of occurrence in the absence of anthropogenic climate change, compared to a probability of ∼10% (return interval ∼10 years) under today’s climate accounting for anthropogenic climate change. Our approach provides modernized estimates of current fire danger levels and expected return levels of extreme fire seasons considering climate change, which may help inform fire management agencies and decision making.

The Proximal Drivers of Large Fires: A Pyrogeographic Study

Variations in global patterns of burning and fire regimes are relatively well measured, however the degree of influence of the complex suite of biophysical and human drivers of fire remains controversial and incompletely understood. Such an understanding is required in order to support current fire management and to predict the future trajectory of global fire patterns in response to changes in these determinants. In this study we explore and compare the effects of four fundamental controls on fire, namely the production of biomass, its drying, the influence of weather on the spread of fire and sources of ignition. Our study area is southern Australia, where fire is currently limited by either fuel production or fuel dryness. As in most fire-prone environments, the majority of annual burned area is due to a relatively small number of large fires. We train and test an Artificial Neural Network’s ability to predict spatial patterns in the probability of large fires (> 1,250 ha) in forests and grasslands as a function of proxies of the four major controls on fire activity. Fuel load is represented by predicted forested biomass and remotely sensed grass biomass, drying is represented by fraction of the time monthly potential evapotranspiration exceeds precipitation, weather is represented by the frequency of severe fire weather conditions and ignitions are represented by the average annual density of reported ignitions. The response of fire to these drivers is often non-linear. Our results suggest that fuel management will have limited capacity to alter future fire occurrence unless it yields landscape-scale changes in fuel amount, and that shifts between, rather than within, vegetation community types may be more important. We also find that increased frequency of severe fire weather could increase the likelihood of large fires in forests but decrease it in grasslands. These results have the potential to support long-term strategic planning and risk assessment by fire management agencies.

Fuel treatment effectiveness in the context of landform, vegetation, and large, wind-driven wildfires.

Large wildfires (>50,000ha) are becoming increasingly common in semiarid landscapes of the western United States. Although fuel reduction treatments are used to mitigate potential wildfire effects, they can be overwhelmed in wind-driven wildfire events with extreme fire behavior. We evaluated drivers of fire severity and fuel treatment effectiveness in the 2014 Carlton Complex, a record-setting complex of wildfires in north-central Washington State. Across varied topography, vegetation, and distinct fire progressions, we used a combination of simultaneous autoregression (SAR) and random forest (RF) approaches to model drivers of fire severity and evaluated how fuel treatments mitigated fire severity. Predictor variables included fuel treatment type, time since treatment, topographic indices, vegetation and fuels, and weather summarized by progression interval. We found that the two spatial regression methods are generally complementary and are instructive as a combined approach for landscape analyses of fire severity. Simultaneous autoregression improves upon traditional linear models by incorporating information about neighboring pixel burn severity, which avoids type I errors in coefficient estimates and incorrect inferences. Random forest modeling provides a flexible modeling environment capable of capturing complex interactions and nonlinearities while still accounting for spatial autocorrelation through the use of spatially explicit predictor variables. All treatment areas burned with higher proportions of moderate and high-severity fire during early fire progressions, but thin and underburn, underburn only, and past wildfires were more effective than thin-only and thin and pile burn treatments. Treatment units had much greater percentages of unburned and low severity area in later progressions that burned under milder fire weather conditions, and differences between treatments were less pronounced. Our results provide evidence that strategic placement of fuels reduction treatments can effectively reduce localized fire spread and severity even under severe fire weather. During wind-driven fire spread progressions, fuel treatments that were located on leeward slopes tended to have lower fire severity than treatments located on windward slopes. As fire and fuels managers evaluate options for increasing landscape resilience to future climate change and wildfires, strategic placement of fuel treatments may be guided by retrospective studies of past large wildfire events.

Assessing Boreal Peat Fire Severity and Vulnerability of Peatlands to Early Season Wildland Fire

Globally peatlands store large amounts of carbon belowground with 80% distributed in boreal regions of the northern hemisphere. Climate warming and drying of the boreal region has been documented as affecting fire regimes, with increased fire frequency, severity and extent. While much research is dedicated to assessing changes in boreal uplands, few research efforts are focused on the vulnerability of boreal peatlands to wildfire. In this case study, an integration of field data collection, land cover mapping of peatland types and Landsat-based fire severity mapping was conducted for four spring wildfires where peatlands are abundant in northeastern Alberta Canada. The goal was to better understand if peatlands burn more or less preferentially than uplands in spring fires and how severely the organic soil layers (peat) of different peatland ecotypes burn. Spatial comparisons and statistical analysis showed that proportionally bogs are more likely to burn in spring Alberta wildfires than other ecosystem types, even upland conifer. When fire weather conditions for the duff layers are high, we found that fens become more vulnerable to burning. In addition, bogs experienced greater severity of burn to the peat layers than fens. Due to the small sample size of peat severity in uplands and limited geographic area of this case study, we were unable to assess if bogs are burning more severely than uplands. Further analysis and Landsat algorithm development for organic soil fire severity in peatlands and uplands is needed to more fully understand trends in belowground consumption for wildfires of all seasons and boreal ecotypes.

Seamless climate change projections and seasonal predictions for bushfires in Australia

Spatio-temporal variations in fire weather conditions are presented based on various data sets, with consistent approaches applied to help enable seamless services over different time scales. Recent research on this is shown here, covering climate change projections for future years throughout this century, predictions at multi-week to seasonal lead times and historical climate records based on observations. Climate projections are presented based on extreme metrics with results shown for individual seasons. A seasonal prediction system for fire weather conditions is demonstrated here as a new capability development for Australia. To produce a more seamless set of predictions, the data sets are calibrated based on quantile-quantile matching for consistency with observations-based data sets, including to help provide details around extreme values for the model predictions (demonstrating the quantile matching for extremes method). Factors influencing the predictability of conditions are discussed, including pre-existing fuel moisture, large-scale modes of variability, sudden stratospheric warmings and climate trends. The extreme 2019–2020 summer fire season is discussed, with examples provided on how this suite of calibrated fire weather data sets was used, including long-range predictions several months ahead provided to fire agencies. These fire weather data sets are now available in a consistent form covering historical records back to 1950, long-range predictions out to several months ahead and future climate change projections throughout this century. A seamless service across different time scales is intended to enhance long-range planning capabilities and climate adaptation efforts, leading to enhanced resilience and disaster risk reduction in relation to natural hazards.

Regional seasonality of fire size and fire weather conditions across Australia's northern savanna

Australia’s northern savannas have among the highest fire frequencies in the world. The climate is monsoonal, with a long, dry season of up to 9 months, during which most fires occur. The Australian Government’s Emissions Reduction Fund allows land managers to generate carbon credits by abating the direct emissions of CO2 equivalent gases via prescribed burning that shifts the fire regime from predominantly large, high-intensity late dry season fires to a more benign, early dry season fire regime. However, the Australian savannas are vast and there is significant variation in weather conditions and seasonality, which is likely to result in spatial and temporal variations in the commencement and length of late dry season conditions. Here, we assess the temporal and spatial consistency of the commencement of late dry season conditions, defined as those months that maximise fire size and where the most extreme fire weather conditions exist. The results demonstrate that significant yearly, seasonal and spatial variations in fire size and fire weather conditions exist, both within and between bioregions. The effective start of late dry season conditions, as defined by those months that maximise fire size and where the most extreme fire weather variables exist, is variable across the savannas.

Climate Change Increases the Potential for Extreme Wildfires

AbstractPyrocumulonimbus (pyroCb) wildfires cause devastation in many regions globally. Given that fire‐atmosphere coupling is associated with pyroCbs, future changes in coincident high index values of atmospheric instability and dryness (C‐Haines) and near‐surface fire weather are assessed for southeastern Australia using a regional climate projection ensemble. We show that observed pyroCb events occur predominantly on forested, rugged landscapes during extreme C‐Haines conditions, but over a wide range of surface fire weather conditions. Statistically significant increases in the number of days where both C‐Haines and near‐surface fire weather values are conducive to pyroCb development are projected across southeastern Australia, predominantly for November (spring), and less strongly for December (summer) in 2060‐2079 versus 1990‐2009, with future C‐Haines increases linked to increased 850‐hPa dewpoint depression. The increased future occurrence of conditions conducive to pyroCb development and their extension into spring have implications for mitigating these dangerous wildfires and urbanizing fire‐prone landscapes.

Future changes in extreme weather and pyroconvection risk factors for Australian wildfires

Extreme wildfires have recently caused disastrous impacts in Australia and other regions of the world, including events with strong convective processes in their plumes (i.e., strong pyroconvection). Dangerous wildfire events such as these could potentially be influenced by anthropogenic climate change, however, there are large knowledge gaps on how these events might change in the future. The McArthur Forest Fire Danger Index (FFDI) is used to represent near-surface weather conditions and the Continuous Haines index (CH) is used here to represent lower to mid-tropospheric vertical atmospheric stability and humidity measures relevant to dangerous wildfires and pyroconvective processes. Projected changes in extreme measures of CH and FFDI are examined using a multi-method approach, including an ensemble of global climate models together with two ensembles of regional climate models. The projections show a clear trend towards more dangerous near-surface fire weather conditions for Australia based on the FFDI, as well as increased pyroconvection risk factors for some regions of southern Australia based on the CH. These results have implications for fields such as disaster risk reduction, climate adaptation, ecology, policy and planning, noting that improved knowledge on how climate change can influence extreme wildfires can help reduce future impacts of these events.

Simulating the effects of weather and climate on large wildfires in France

Abstract. Large wildfires across parts of France can cause devastating damage which puts lives, infrastructure, and the natural ecosystem at risk. In the climate change context, it is essential to better understand how these large wildfires relate to weather and climate and how they might change in a warmer world. Such projections rely on the development of a robust modeling framework linking large wildfires to present-day atmospheric variability. Drawing from a MODIS product and a gridded meteorological dataset, we derived a suite of biophysical and fire danger indices and developed generalized linear models simulating the probability of large wildfires (>100 ha) at 8 km spatial and daily temporal resolutions across the entire country over the last two decades. The models were able to reproduce large-wildfire activity across a range of spatial and temporal scales. Different sensitivities to weather and climate were detected across different environmental regions. Long-term drought was found to be a significant predictor of large wildfires in flammability-limited systems such as the Alpine and southwestern regions. In the Mediterranean, large wildfires were found to be associated with both short-term fire weather conditions and longer-term soil moisture deficits, collectively facilitating the occurrence of large wildfires. Simulated probabilities on days with large wildfires were on average 2–3 times higher than normal with respect to the mean seasonal cycle, highlighting the key role of atmospheric variability in wildfire spread. The model has wide applications, including improving our understanding of the drivers of large wildfires over the historical period and providing a basis on which to estimate future changes to large wildfires from climate scenarios.

Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia

Considerable investments are made in managing fire risk to human assets, including a growing use of fire behaviour simulation tools to allocate expenditure. Understanding fire risk requires estimation of the likelihood of ignition, spread of the fire and impact on assets. The ability to estimate and predict risk requires both the development of ignition likelihood models and the evaluation of these models in novel environments. We developed models for natural and anthropogenic ignitions in the south-eastern Australian state of Victoria incorporating variables relating to fire weather, terrain and the built environment. Fire weather conditions had a consistently positive effect on the likelihood of ignition, although they contributed much more to lightning (57%) and power transmission (55%) ignitions than the 7 other modelled causes (8–32%). The built environment played an important role in driving anthropogenic ignitions. Housing density was the most important variable in most models and proximity to roads had a consistently positive effect. In contrast, the best model for lightning ignitions included a positive relationship with primary productivity, as represented by annual rainfall. These patterns are broadly consistent with previous ignition modelling studies. The models developed for Victoria were tested in the neighbouring fire prone states of South Australia and Tasmania. The anthropogenic ignition model performed well in South Australia (AUC = 0.969) and Tasmania (AUC = 0.848), whereas the natural ignition model only performed well in South Australia (AUC = 0.972; Tasmania AUC = 0.612). Model performance may have been impaired by much lower lightning ignition rates in South Australia and Tasmania than in Victoria. This study shows that the spatial likelihood of ignition can be reliably predicted based on readily available meteorological and biophysical data. Furthermore, the strong performance of anthropogenic and natural ignition models in novel environments suggests there are some universal drivers of ignition likelihood across south-eastern Australia.

Global Emergence of Anthropogenic Climate Change in Fire Weather Indices

AbstractChanges in global fire activity are influenced by a multitude of factors including land‐cover change, policies, and climatic conditions. This study uses 17 climate models to evaluate when changes in fire weather, as realized through the Fire Weather Index, emerge from the expected range of internal variability due to anthropogenic climate change using the time of emergence framework. Anthropogenic increases in extreme Fire Weather Index days emerge for 22% of burnable land area globally by 2019, including much of the Mediterranean and the Amazon. By the midtwenty‐first century, emergence among the different Fire Weather Index metrics occurs for 33–62% of burnable lands. Emergence of heightened fire weather becomes more widespread as a function of global temperature change. At 2 °C above preindustrial levels, the area of emergence is half that for 3 °C. These results highlight increases in fire weather conditions with human‐caused climate change and incentivize local adaptation efforts to limit detrimental fire impacts.

Forest fuel bed ignitability under marginal fire weather conditions in Eucalyptus forests

Fires burning under marginal fire-weather conditions tend to be patchy in terms of their spatial coverage. This patchiness is partially driven by variability in the ignitability of the fuel bed. An understanding of fuel-bed ignitability through space and time would help fire managers to more effectively carry out prescribed burns to achieve desired levels of burn coverage in Eucalyptus forests. We sought to identify the key fuel-bed attributes influencing ignitability under marginal weather conditions. We recorded ignition successes and failures at 45 points within 5 operational prescribed burns and used the data to build logistic regression models to predict the probability of ignition as a function of fuel-bed attributes. Models were ranked using an information theoretic approach. The four highest ranked models explained 48–54% of the variance in ignitability. Surface fine-fuel moisture content (FFMC) and overall fuel hazard (i.e. fuel arrangement) were the strongest predictors of ignitability, occurring in all four highest ranking models. Both surface FFMC and overall fuel hazard were negatively related to ignition likelihood, contradicting a commonly assumed positive relationship between fuel hazard and flammability. Our field method to measure ignition success could be applied across more prescribed burns to develop operationally useful models of ignitability.

Factors that affect the timing of the dispatch of initial attack resources to forest fires in northeastern Ontario, Canada

The success of forest fire initial attack systems is believed to be affected by many factors including the initial attack response time. Despite the fact that fire managers typically strive to dispatch initial attack resources to most fires soon after they are reported in order to minimise their response time, they may not always be able to do so as the timing of the initial attack dispatch can be influenced by many factors. We examine the effects of the following factors on the initial attack dispatch process: the daily fire load (the number of fires reported each day), the time of day the fire was reported, fire weather conditions, fire cause and the month of the fire season, on the probability that initial attack resources are dispatched on the day that a fire is reported. Logistic regression methods are used to analyse a dataset composed of 4532 forest fires that were reported in our study area in a portion of northeastern region of Ontario, Canada, during 1963–2012 fire seasons. Our results indicate that the time of day a fire is reported, the total number of fires reported on that day and the Initial Spread Index are key factors that influence the timing of the initial attack response in our study area.

The protective action decision model: when householders choose their protective response to wildfire

Confronted with an imminent bushfire threat, Australian householders can respond by evacuating, waiting to see what the bushfire does before deciding, or remaining to defend property from the fire. This decision-making is central to householders’ survival because late evacuation has been associated with fatalities. North Americans who seek alternatives to mandatory evacuation from wildfires are confronted with similar choices.Lindell and Perry’s Protective Action Decision Model (PADM) has been extensively used to analyse the behaviour and decision-making of people subject to threat from a wide range of hazards and has acquired the status of a theory of hazard decision-making. It has not been widely used for the study of hazards in Australia. This paper addresses the question of how the PADM can be applied to the analysis of behaviour in Australian bushfires and North American wildfire, and whether the model requires adjusting for effective use in a context where householders choose their protective response rather than having decisions made for them.The paper addresses Australian bushfire policy, which emphasises the dangers of staying to defend during extreme fire weather conditions and promotes leaving early as the safer option but allows householders to decide whether to ‘stay or go’ during a bushfire. This Australian policy approach impacts key elements of the PADM, most importantly how long-run hazard adjustments, preparatory, mitigating and equipping actions, effect the perception of evacuating or remaining. The paper concludes that the PADM should be modified to reflect the complexity of choice confronting Australian bushfire prone householders and North Americans considering alternatives to mandatory evacuation.

Suppression resource decisions are the dominant influence on containment of Australian forest and grass fires

Fire agencies aim to contain wildfires before they impact on life, property and infrastructure and to reduce the risk of damage to the environment. Despite the large cost of suppression, there are few data on the success of suppression efforts under varying weather, fuel and resource scenarios. We examined over 2200 forest and 4600 grass fires in New South Wales, Australia to determine the dominant influences on the containment of wildfires. A random forest modelling approach was used to analyse the effect of a range of human and environmental factors. The number of suppression resources per area of fire were the dominant influence on the containment of both forest and grass fires. As fire weather conditions worsened the probability of containment decreased across all fires and as fuel loads and slope increased the probability of containment decreased for forest fires. Environmental controls limit the effectiveness of wildfire management. However, results suggest investment in suppression resources and strategic fuel management will increase the probability of containment.

An assessment of climate, weather, and fuel factors influencing a large, destructive wildfire in the Knysna region, South Africa

BackgroundIn June 2017, wildfires burned 15 000 ha around the town of Knysna in the Western Cape, destroying > 800 buildings, > 5000 ha of forest plantations, and claiming the lives of seven people. We examined the factors that contributed to making this one of the worst fires on record in the region.ResultsOne third of the area that burned was in natural vegetation (mainly fynbos shrublands), and more than half was in plantations of invasive alien (non-native) pine trees, or in natural vegetation invaded by alien trees. We used satellite imagery to assess burn severity in different land cover types by comparing pre- and post-fire images to estimate biomass consumed. We used daily weather data from two weather stations to calculate fire danger and drought indices over 70 years, and compared the fire weather conditions during the 2017 Knysna fires to the long-term weather record. The amount of biomass consumed was significantly higher in plantations of invasive alien trees, and in fynbos invaded by alien trees, than in uninvaded fynbos, providing support for the contention that invasion by alien trees increases the impact and difficulty of control of wildfires. Fire danger indices on the days of the fires were in the top 0.1 to 0.2% of days in the historic record, indicating that fire weather conditions were extreme but not unprecedented. The fires were preceded by a prolonged drought, and 18-month running means for two drought indices were the highest on record.ConclusionThe severity of the fires was exacerbated by very high fire danger conditions, preceded by an unprecedented drought, and further worsened by the conversion of natural fynbos vegetation to plantations, and invasion of vegetation by alien trees. Historical fire suppression also resulted in fuel buildups, further aggravating the problem of fire control, while residential development within and adjacent to fire-prone areas increased the risks faced by residents. Our results support calls to control invasive alien plants, reduce commercial planting of invasive alien trees, strictly regulate development in areas of high fire risk, and maintain awareness of the need for fire-wise practices.

Human-environmental drivers and impacts of the globally extreme 2017 Chilean fires.

In January 2017, hundreds of fires in Mediterranean Chile burnt more than 5000km2, an area nearly 14 times the 40-year mean. We contextualize these fires in terms of estimates of global fire intensity using MODIS satellite record, and provide an overview of the climatic factors and recent changes in land use that led to the active fire season and estimate the impact of fire emissions to human health. The primary fire activity in late January coincided with extreme fire weather conditions including all-time (1979-2017) daily records for the Fire Weather Index (FWI) and maximum temperature, producing some of the most energetically intense fire events on Earth in the last 15-years. Fire activity was further enabled by a warm moist growing season in 2016 that interrupted an intense drought that started in 2010. The land cover in this region had been extensively modified, with less than 20% of the original native vegetation remaining, and extensive plantations of highly flammable exotic Pinus and Eucalyptus species established since the 1970s. These plantations were disproportionally burnt (44% of the burned area) in 2017, and associated with the highest fire severities, as part of an increasing trend of fire extent in plantations over the past three decades. Smoke from the fires exposed over 9.5 million people to increased concentrations of particulate air pollution, causing an estimated 76 premature deaths and 209 additional admissions to hospital for respiratory and cardiovascular conditions. This study highlights that Mediterranean biogeographic regions with expansive Pinus and Eucalyptus plantations and associated rural depopulation are vulnerable to intense wildfires with wide ranging social, economic, and environmental impacts, which are likely to become more frequent due to longer and more extreme wildfire seasons.

Exploratory analysis of lightning-ignited wildfires in the Warren Region, Western Australia

An exploratory analysis of lightning-ignited wildfire data for the Warren Region of Western Australia was carried out for the period from April 1976 to December 2016. Temporal patterns in the series were examined in terms of characterizing the seasonal cycle, and detecting long-term trends and changes in seasonality over time. A generalized additive modelling approach was used to ensure that temporal features were determined by the data rather than a priori assumed mathematical forms (e.g. linear or low-order polynomial functions). The spatial organization of the data was evaluated using concepts from the theory of stochastic point processes. Results indicate a strong seasonality in the monthly lightning ignition series, the presence of a long-term trend and an interaction between trend and seasonality. There is also strong evidence of spatial variation in the number of ignitions per unit area in terms of location and distance from nearest ignition. Within the Warren Region, observation platforms for fire detection and reporting protocols have remained stable over the period of record, and changes in land use are unlikely to have altered the pattern of lightning ignition. Thus, the above results might reflect an interplay between: landscape attributes (e.g. vegetation classes, elevation, slope, aspect); changes in rainfall and fuel moisture; changes in fuel management practices; and, perhaps, an increase in the frequency of dry thunderstorms and fire weather conditions.

The 2017 North Bay and Southern California Fires: A Case Study

Two extreme wind-driven wildfire events impacted California in late 2017, leading to 46 fatalities and thousands of structures lost. This study characterizes the meteorological and climatological factors that drove and enabled these wildfire events and quantifies their rarity over the observational record. Both events featured key fire-weather metrics that were unprecedented in the observational record that followed a sequence of climatic conditions that enhanced fine fuel abundance and fuel availability. The North Bay fires of October 2017 occurred coincident with strong downslope winds, with a majority of burned area occurring within the first 12 hours of ignition. By contrast, the southern California fires of December 2017 occurred during the longest Santa Ana wind event on record, resulting in the largest wildfire in California’s modern history. Both fire events occurred following an exceptionally wet winter that was preceded by a severe four-year drought. Fuels were further preconditioned by the warmest summer and autumn on record in northern and southern California, respectively. Finally, delayed onset of autumn precipitation allowed for critically low dead fuel moistures leading up to the wind events. Fire weather conditions were well forecast several days prior to the fire. However, the rarity of fire-weather conditions that occurred near populated regions, along with other societal factors such as limited evacuation protocols and limited wildfire preparedness in communities outside of the traditional wildland urban interface were key contributors to the widespread wildfire impacts.