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Abstract. Climate change and increased fire are eroding theresilience of boreal forests. This is problematic because boreal vegetationand the cold soils underneath store approximately 30 % of all terrestrialcarbon. Society urgently needs projections of where, when, and why borealforests are likely to change. Permafrost (i.e., subsurface material thatremains frozen for at least 2 consecutive years) and the thicksoil-surface organic layers (SOLs) that insulate permafrost are importantcontrols of boreal forest dynamics and carbon cycling. However, both arerarely included in process-based vegetation models used to simulate futureecosystem trajectories. To address this challenge, we developed acomputationally efficient permafrost and SOL module named the Permafrost andOrganic LayEr module for Forest Models (POLE-FM) that operates at finespatial (1 ha) and temporal (daily) resolutions. The module mechanisticallysimulates daily changes in depth to permafrost, annual SOL accumulation, andtheir complex effects on boreal forest structure and functions. We coupledthe module to an established forest landscape model, iLand, and benchmarkedthe model in interior Alaska at spatial scales of stands (1 ha) tolandscapes (61 000 ha) and over temporal scales of days to centuries. Thecoupled model generated intra- and inter-annual patterns of snowaccumulation and active layer depth (portion of soil column that thawsthroughout the year) generally consistent with independent observations in17 instrumented forest stands. The model also represented the distributionof near-surface permafrost presence in a topographically complex landscape.We simulated 39.3 % of forested area in the landscape as underlain bypermafrost, compared to the estimated 33.4 % from the benchmarkingproduct. We further determined that the model could accurately simulate mossbiomass, SOL accumulation, fire activity, tree species composition, andstand structure at the landscape scale. Modular and flexible representationsof key biophysical processes that underpin 21st-century ecologicalchange are an essential next step in vegetation simulation to reduceuncertainty in future projections and to support innovative environmentaldecision-making. We show that coupling a new permafrost and SOL module to anexisting forest landscape model increases the model's utility for projectingforest futures at high latitudes. Process-based models that representrelevant dynamics will catalyze opportunities to address previouslyintractable questions about boreal forest resilience, biogeochemicalcycling, and feedbacks to regional and global climate.

 

Abstract. The annual area burned due to wildfires in the western United States (WUS) increased bymore than 300 % between 1984 and 2020. However, accounting for the nonlinear, spatially heterogeneous interactions between climate, vegetation, and human predictors driving the trends in fire frequency and sizes at different spatial scales remains a challenging problem for statistical fire models. Here we introduce a novel stochastic machine learning (SML) framework, SMLFire1.0, to model observed fire frequencies and sizes in 12 km × 12 km grid cells across the WUS. This framework is implemented using mixture density networks trained on a wide suite of input predictors. The modeled WUS fire frequency matches observations at both monthly (r=0.94) and annual (r=0.85) timescales, as do the monthly (r=0.90) and annual (r=0.88) area burned. Moreover, the modeled annual time series of both fire variables exhibit strong correlations (r≥0.6) with observations in 16 out of 18 ecoregions. Our ML model captures the interannual variability and the distinct multidecade increases in annual area burned for both forested and non-forested ecoregions. Evaluating predictor importance with Shapley additive explanations, we find that fire-month vapor pressure deficit (VPD) is the dominant driver of fire frequencies and sizes across the WUS, followed by 1000 h dead fuel moisture (FM1000), total monthly precipitation (Prec), mean daily maximum temperature (Tmax), and fraction of grassland cover in a grid cell. Our findings serve as a promising use case of ML techniques for wildfire prediction in particular and extreme event modeling more broadly. They also highlight the power of ML-driven parameterizations for potential implementation in fire modules of dynamic global vegetation models (DGVMs) and earth system models (ESMs). 

Inter-annual climate variability (hereafter climate variability) is increasing in many forested regions due to climate change. This variability could have larger near-term impacts on forests than decadal shifts in mean climate, but how forests will respond remains poorly resolved, particularly at broad scales. Individual trees, and even forest communities, often have traits and ecological strategies—the legacies of exposure to past variable conditions—that confer tolerance to subsequent climate variability. However, whether local legacies also shape global forest responses is unknown. Our objective was to assess how past and current climate variability influences global forest productivity. We hypothesized that forests exposed to large climate variability in the past would better tolerate current climate variability than forests for which past climate was relatively stable. We used historical (1950–1969) and contemporary (2000–2019) temperature, precipitation, and vapor pressure deficit (VPD) and the remotely sensed enhanced vegetation index (EVI) to quantify how historical and contemporary climate variability relate to patterns of contemporary forest productivity. Consistent with our hypothesis, forests exposed to large temperature variability in the past were more tolerant of contemporary temperature variability than forests where past temperatures were less variable. Forests were 19-fold times less sensitive to contemporary temperature variability where historical inter-annual temperature variability was 0.66 °C (two standard deviations) greater than the global average historical temperature variability. We also found that larger increases in temperature variability between the two study periods often eroded the tolerance conferred by the legacy effects of historical temperature variability. However, the hypothesis was not supported in the case of precipitation and VPD variability, potentially due to physiological tradeoffs inherent in how trees cope with dry conditions. We conclude that the sensitivity of forest productivity to imminent increases in temperature variability may be partially predictable based on the legacies of past conditions.

 

Escalating burned area in western US forests punctuated by the 2020 fire season has heightened the need to explore near-term macroscale forest-fire area trajectories. As fires remove fuels for subsequent fires, feedbacks may impose constraints on the otherwise climate-driven trend of increasing forest-fire area. Here, we test how fire-fuel feedbacks moderate near-term (2021–2050) climate-driven increases in forest-fire area across the western US. Assuming constant fuels, climate–fire models project a doubling of  forest-fire area compared to 1991–2020. Fire-fuel feedbacks only modestly attenuate the projected increase in forest-fire area. Even models with strong feedbacks project increasing interannual variability in forest-fire area and more than a two-fold increase in the likelihood of years exceeding the 2020 fire season. Fuel limitations from fire-fuel feedbacks are unlikely to strongly constrain the profound climate-driven broad-scale increases in forest-fire area by the mid-21st century, highlighting the need for proactive adaptation to increased western US forest-fire impacts.

 

Streamflow often increases after fire, but the persistence of this effect and its importance to present and future regional water resources are unclear. This paper addresses these knowledge gaps for the western United States (WUS), where annual forest fire area increased by more than 1,100% during 1984 to 2020. Among 72 forested basins across the WUS that burned between 1984 and 2019, the multibasin mean streamflow was significantly elevated by 0.19 SDs ( P < 0.01) for an average of 6 water years postfire, compared to the range of results expected from climate alone. Significance is assessed by comparing prefire and postfire streamflow responses to climate and also to streamflow among 107 control basins that experienced little to no wildfire during the study period. The streamflow response scales with fire extent: among the 29 basins where >20% of forest area burned in a year, streamflow over the first 6 water years postfire increased by a multibasin average of 0.38 SDs, or 30%. Postfire streamflow increases were significant in all four seasons. Historical fire–climate relationships combined with climate model projections suggest that 2021 to 2050 will see repeated years when climate is more fire-conducive than in 2020, the year currently holding the modern record for WUS forest area burned. These findings center on relatively small, minimally managed basins, but our results suggest that burned areas will grow enough over the next 3 decades to enhance streamflow at regional scales. Wildfire is an emerging driver of runoff change that will increasingly alter climate impacts on water supplies and runoff-related risks. 

Climate change and natural disturbances are catalysing forest transitions to different vegetation types, but whether these new communities are resilient alternate states that will persist for decades to centuries is not known. Here, we test how changing climate, disturbance and biotic interactions shape the long‐term fate of a deciduous broadleaf forest type that replaces black spruce after severe wildfires in interior Alaska, USA.

We simulated postfire deciduous forest that replaced black spruce after severe fires in 2004 for tens to hundreds of years under different climate scenarios (contemporary, mid 21st century, late 21st century), fire return intervals (11–250 years), distances to seed source (50–1,000 m) and browsing intensities (background, moderate, chronic). We identified combinations of conditions where deciduous forest remained the dominant vegetation type and combinations where it returned to black spruce forest, transitioned to mixed forest (where deciduous species and black spruce co‐dominate) or converted to nonforest.

Deciduous forest persisted in 86% of simulations and was most resilient if fire return intervals were short (≤50 years). When transitions to another vegetation type occurred, mixed forest was most common, particularly when fire return intervals were long (>50 years) and the nearest seed source was 500 m or farther. Moderate and chronic browsing also reduced deciduous sapling growth and survival, helping black spruce compete if fire return intervals were long and seed source was distant. Dry soils occasionally caused conversion to nonforest following short‐interval fire when simulations were forced with a late 21st‐century climate scenario that projects warming and increased vapor pressure deficit. Return to black spruce forest almost never occurred.

Synthesis. Conversion from black spruce to deciduous forest is already underway at regional scales in interior Alaska, and similar transitions have been widely observed throughout the North American boreal biome. We show that this boreal deciduous forest type is likely a resilient alternate state that will persist through the 21st century, which is important, because future vegetation outcomes will shape biophysical feedbacks to regional climate and influence subsequent disturbance regimes.

 

Changing climate and disturbance regimes are increasingly challenging the resilience of forest ecosystems around the globe. A powerful indicator for the loss of resilience is regeneration failure, that is, the inability of the prevailing tree species to regenerate after disturbance. Regeneration failure can result from the interplay among disturbance changes (e.g., larger and more frequent fires), altered climate conditions (e.g., increased drought), and functional traits (e.g., method of seed dispersal). This complexity makes projections of regeneration failure challenging. Here we applied a novel simulation approach assimilating data‐driven fire projections with vegetation responses from process modeling by means of deep neural networks. We (i) quantified the future probability of regeneration failure; (ii) identified spatial hotspots of regeneration failure; and (iii) assessed how current forest types differ in their ability to regenerate under future climate and fire. We focused on the Greater Yellowstone Ecosystem (2.9 × 10⁶ ha of forest) in the Rocky Mountains of the USA, which has experienced large wildfires in the past and is expected to undergo drastic changes in climate and fire in the future. We simulated four climate scenarios until 2100 at a fine spatial grain (100 m). Both wildfire activity and unstocked forest area increased substantially throughout the 21st century in all simulated scenarios. By 2100, between 28% and 59% of the forested area failed to regenerate, indicating considerable loss of resilience. Areas disproportionally at risk occurred where fires are not constrained by topography and in valleys aligned with predominant winds. High‐elevation forest types not adapted to fire (i.e.,Picea engelmannii–Abies lasiocarpaas well as non‐serotinousPinus contortavar.latifoliaforests) were especially vulnerable to regeneration failure. We conclude that changing climate and fire could exceed the resilience of forests in a substantial portion of Greater Yellowstone, with profound implications for carbon, biodiversity, and recreation.

 

Abstract Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways towards mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.