The climate emergency has arrived and is accelerating more rapidly than most scientists anticipated, and many of them are deeply concerned. The adverse effects of climate change are much more severe than expected, and now threaten both the biosphere and humanity. There is mounting evidence linking increases in extreme weather frequency and intensity to climate change. The year 2020, one of the hottest years on record, also saw extraordinary wildfire activity in the Western United States and Australia, a Siberian heat wave with record high temperatures exceeding 38 degrees C (100.4 degrees Fahrenheit) within the Arctic circle, a record low for October Arctic sea ice extent of 2.04 million square miles, an Atlantic hurricane season resulting in more than $46 billion in damage, and deadly floods and landslides in South Asia that displaced more than 12 million people.
Growing human and ecological costs due to increasing wildfire are an urgent concern in policy and management, particularly given projections of worsening fire conditions under climate change. Thus, understanding the relationship between climatic variation and fire activity is a critically important scientific question. Different factors limit fire behavior in different places and times, but most fire-climate analyses are conducted across broad spatial extents that mask geographical variation. This could result in overly broad or inappropriate management and policy decisions that neglect to account for regionally specific or other important factors driving fire activity. We developed statistical models relating seasonal temperature and precipitation variables to historical annual fire activity for 37 different regions across the continental United States and asked whether and how fire-climate relationships vary geographically, and why climate is more important in some regions than in others. Climatic variation played a significant role in explaining annual fire activity in some regions, but the relative importance of seasonal temperature or precipitation, in addition to the overall importance of climate, varied substantially depending on geographical context. Human presence was the primary reason that climate explained less fire activity in some regions than in others. That is, where human presence was more prominent, climate was less important. This means that humans may not only influence fire regimes but their presence can actually override, or swamp out, the effect of climate. Thus, geographical context as well as human influence should be considered alongside climate in national wildfire policy and management.
The low-elevation chaparral shrublands of southern California have long been occupied and modified by humans, but the magnitude and extent of human impact has dramatically increased since the early 1900s. As population growth started to boom in the 1940s, the primary form of habitat conversion transitioned from agriculture to urban and residential development. Now, urban growth is the primary contributor, directly and indirectly, to loss and fragmentation of chaparral landscapes. Different patterns and arrangements of housing development confer different ecological impacts. We found wide variation in the changing extent and pattern of development across the seven counties in the region. Substantial growth in lower-density exurban development has been associated with high frequency of human-caused ignitions as well as the expansion of highly flammable non-native annual grasses. Combined, increases in fire ignitions and the extent of grassland can lead to a positive feedback cycle in which grass promotes fire and shortens the fire-return interval, ultimately extirpating shrub species that are not adapted to short fire intervals. An overlay of a 1930s vegetation map with maps of contemporary vegetation showed a consistent trend of chaparral decline and conversion to sage scrub or grassland. In addition, those areas type-converted to grassland had the highest fire frequency over the latter part of the twentieth century. Thus, a continuing trend of population growth and urban expansion may continue to threaten the extent and intactness of remaining shrubland dominated landscapes. Interactions among housing development, fire ignitions, non-native grasses, roads, and vehicle emissions make fire prevention a complex endeavor. However, land use planning that targets the root cause of conversion, exurban sprawl, could address all of these threats simultaneously.
Understanding where and how fire patterns may change is critical for management and policy decision-making. To map future fire patterns, statistical correlative models are typically developed, which associate observed fire locations with recent climate maps, and are then applied to maps of future climate projections. A potential source of uncertainty is the common omission of static or dynamic vegetation as predictor variables. We therefore assessed the sensitivity of future fire projections to different combinations of vegetation maps used as explanatory variables in a statistically based fire modeling framework. We compared models without vegetation to models that incorporated static vegetation maps and that included output from a dynamic vegetation model that imposed three scenarios of fire and one scenario of land use change. We mapped projected future probability of all and large fires (> = 40 ha) under two climate scenarios in a heterogeneous study area spanning a large elevational gradient in the Sierra Nevada, California, USA. Results showed high model sensitivity to the treatment of vegetation as a predictor variable, particularly for models of large fire probability and for models accounting for wildfire effects on vegetation, which lowered future fire probability. Some scenarios resulted in opposite directional trends in the extent and probability of future fire, which could have serious implications for policy and management resource allocation. Model sensitivity resulted from high relative importance of vegetation variables in the baseline models and from large predicted changes in vegetation, particularly when simulating wildfire. Although statistical fire models often omit vegetation due to uncertainty, model sensitivity demonstrated here suggests a need to account for that uncertainty. Coupling statistical and processed based models may be a promising approach to reflect a more plausible range of scenarios.
To develop effective long-term strategies, natural resource managers need to account for the projected effects of climate change as well as the uncertainty inherent in those projec- tions. Vegetation models are one important source of projected climate effects. We explore results and associated uncertainties from the MC2 Dynamic Global Vegetation Model for the Pacific Northwest west of the Cascade crest. We compare model results for vegetation cover and carbon dynamics over the period 1895–2100 assuming: 1) unlimited wildfire igni- tions versus stochastic ignitions, 2) no fire, and 3) a moderate CO2 fertilization effect versus no CO2 fertilization effect. Carbon stocks decline in all scenarios, except without fire and with a moderate CO2 fertilization effect. The greatest carbon stock loss, approximately 23% of historical levels, occurs with unlimited ignitions and no CO2 fertilization effect. With sto- chastic ignitions and a CO2 fertilization effect, carbon stocks are more stable than with unlimited ignitions. For all scenarios, the dominant vegetation type shifts from pure conifer to mixed forest, indicating that vegetation cover change is driven solely by climate and that significant mortality and vegetation shifts are likely through the 21st century regardless of fire regime changes.
Climate and land use patterns are expected to change dramatically in the coming century, raising concern about their effects on wildfire patterns and subsequent impacts to human communities. The relative influence of climate versus land use on fires and their impacts, however, remains unclear, particularly given the substantial geographical variability in fire-prone places like California. We developed a modeling framework to compare the importance of climatic and human variables for explaining fire patterns and structure loss for three diverse California landscapes, then projected future large fire and structure loss probability under two different climate (hot-dry or warm-wet) and two different land use (rural or urban residential growth) scenarios. The relative importance of climate and housing pattern varied across regions and according to fire size or whether the model was for large fires or structure loss. The differing strengths of these relationships, in addition to differences in the nature and magnitude of projected climate or land use change, dictated the extent to which large fires or structure loss were projected to change in the future. Despite this variability, housing and human infrastructure were consistently more responsible for explaining fire ignitions and structure loss probability, whereas climate, topography, and fuel variables were more important for explaining large fire patterns. For all study areas, most structure loss occurred in areas with low housing density (from 0.08 to 2.01 units/ha), and expansion of rural residential land use increased structure loss probability in the future. Regardless of future climate scenario, large fire probability was only projected to increase in the northern and interior parts of the state, whereas climate change had no projected impact on fire probability in southern California. Given the variation in fire-climate relationships and land use effects, policy and management decision-making should be customized for specific geographical regions.
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over two decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other 5 and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. Here we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models.
The relationship between annual variation in area burned and seasonal temperatures and precipitation was investigated for the major climate divisions in California. Historical analyses showed marked differences in fires on montane and foothill landscapes. Based on roughly a century of data, there are five important lessons on fire–climate relationships in California: (1) seasonal variations in temperature appear to have had minimal influence on area burned in the lower elevation, mostly non-forested, landscapes; (2) temperature has been a significant factor in controlling fire activity in higher elevation montane forests, but this varied greatly with season – winter and autumn temperatures showed no significant effect, whereas spring and summer temperatures were important determinants of area burned; (3) current season precipitation has been a strong controller of fire activity in forests, with drier years resulting in greater area burned on most United States Forest Service (USFS) lands in the state, but the effect of current-year precipitation was decidedly less on lower elevation California Department of Forestry and Fire Protection lands; (4) in largely grass-dominated foothills and valleys the magnitude of prior-year rainfall was positively tied to area burned in the following year, and we hypothesise that this is tied to greater fuel volume in the year following high rainfall. In the southern part of the state this effect has become stronger in recent decades and this likely is due to accelerated type conversion from shrubland to grassland in the latter part of the 20th century; (5) the strongest fire–climate models were on USFS lands in the Sierra Nevada Mountains, and these explained 42–52% of the variation in area burned; however, the models changed over time, with winter and spring precipitation being the primary drivers in the first half of the 20th century, but replaced by spring and summer temperatures after 1960.
Fire activity has increased in western US aridland ecosystems due to increased human-caused ignitions and the expansion of flammable exotic grasses. Because many desert plants are not adapted to fire, increased fire activity may have long-lasting ecological impacts on native vegetation and the wildlife that depend on it. Given the heterogeneity across aridland ecosystems, it is important to understand how trends and drivers of fire vary, so management can be customized accordingly. We examined historical trends and quantified the relative importance of and interactions among multiple drivers of fire patterns across five aridland ecoregions in southeastern California from 1970 to 2010. Fire frequency increased across all ecoregions for the first couple decades, and declined or plateaued since the 1990s; but area burned continued to increase in some regions. The relative importance of anthropogenic and biophysical drivers varied across ecoregions, with both direct and indirect influences on fire. Anthropogenic variables were equally important as biophysical variables, but some contributed indirectly, presumably via their influence on annual grass distribution and abundance. Grass burned disproportionately more than other cover types, suggesting that addressing exotics may be the key to fire management and conservation in much of the area
Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, either using well-founded empirical relationships or process-based models with good predictive skill. A large variety of models exist today and it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project – FireMIP, an international project to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we summarise the current state-of-the-art in fire regime modelling and model evaluation, and outline what lessons may be learned from FireMIP.