Spatial variability in Arctic-boreal pyroregions shaped by climate and human influence

Spatial variability in Arctic-boreal pyroregions shaped by climate and human influence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spatial variability in Arctic-boreal pyroregions shaped by climate and human influence Rebecca Scholten, Sander Veraverbeke, Yang Chen, James Randerson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3932189/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Sep, 2024 Read the published version in Nature Geoscience → Version 1 posted You are reading this latest preprint version Abstract Fire activity in Arctic and boreal regions is rapidly increasing with severe consequences for climate and human health. Long-term variations in fire frequency and intensity within regions characterize fire regimes. The spatial variability in Arctic-boreal fire regimes and their climatic and anthropogenic drivers, however, remain poorly understood. Here, we developed an object-based fire tracking system to map the sub-daily evolution of all circumpolar Arctic-boreal fires between 2012 and 2023 using 375m Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections. This dataset characterizes the ignition time, location, size, duration, spread, and intensity of individual fires. We used the resulting fire atlas to classify the Arctic-boreal biomes into seven distinct pyroregions with unique climatic and geographic environments. The pyroregions exhibited varying responses to environmental drivers, with boreal North America, eastern Siberia, and northern tundra regions showing the highest sensitivity to climate and lightning density. Anthropogenic factors also played an important role in influencing fire number and size, interacting with other factors. Understanding the spatial variability of fire regimes and its interconnected drivers in the Arctic-boreal domain is important for improving future predictions of fire activity and identifying areas at risk for extreme events. Earth and environmental sciences/Natural hazards Earth and environmental sciences/Biogeochemistry Earth and environmental sciences/Environmental social sciences/Climate-change impacts Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Main text Many Arctic and boreal regions have experienced unprecedented fire activity in the last decade. Major regional fire complexes occurred in eastern Siberia during 2019, 2020, and 2021, in Alaska in 2015 and 2022, and in Canada in 2014 and 2023. Most recently, in the summer of 2023, a significant expanse of Canada witnessed a record level of burning that likely reversed long-term trends in carbon storage within forests 1 . Extreme fire seasons in these regions were primarily caused by warm summer temperatures and high lightning activity 2 , 3 . Furthermore, land-atmosphere feedbacks due to changes in snowmelt timing, as well as shifts in the polar jet stream linked to a warming climate have been shown to influence these large fire events 4 , 5 . There is ample evidence that recent boreal fire extremes are driven by climate change 2 , 4 , 6 , 7 , however, little is known as to why different regions repeatedly experience unprecedented extremes, while fire activity in other boreal areas appears to remain relatively constant. Evidence for the drivers of spatial variability in fire activity has been compiled on a global scale, with major variations caused by differences in vegetation, climate, and human impact 8 – 11 . While these studies reveal significant variability in Arctic-boreal regions, the underlying causes have not yet been explored. Various regional studies have highlighted the importance of extreme climatic conditions conducive to fuel drying and fire spread for extreme fire seasons in boreal regions 2 , 7 , 12 . Furthermore, ignition limitations govern Arctic-boreal fire occurrences 10 , 13 . However, most studies of contemporary and future Arctic-boreal fire activity have focused on the fire-prone regions of western North America and eastern Siberia, and less is known about what drives fire activity in other Arctic-boreal regions, including those with a strong anthropogenic influence. To date, we miss a comprehensive understanding of the driving factors of spatial variability in Arctic-boreal fire activity. Understanding how climate, fuels and human activity spatially vary, interact and shape Arctic-boreal fire regimes is necessary to improve predictions of future Arctic-boreal fire activity. Pyroregions are regions characterized by a similar fire regime 14 , and are defined by the long-term variability in fire number, size, intensity, duration, burned area, and timing and length of the fire season 8 , 15 . Fire regime properties can substantially differ between ecoregions. It is thus crucial to assess the sensitivity of these different fire regime properties to climatic and anthropogenic drivers, as well as fuel amount and composition. Due to the absence of accurate and long-term records of individual fires in many areas of the Arctic-boreal region, studies evaluating Arctic-boreal fire activity often rely on satellite-derived products of active fires and burned area. While these raster products are useful for assessing regional burned area and fire occurrence and intensity, they do not offer insights about the ignition location and timing, sub-daily spread rate, and the temporal evolution of individual fires. While some near-global datasets are available that derived such object-based fire information from remotely sensed burned areas 16 – 19 , they do not provide full spatial coverage of Arctic tundra and boreal forest biomes and have often not been optimized for fire dynamics in these regions. The Visible Infrared Imaging Radiometer Suite (VIIRS) launched in 2012 provides global active fire data at a 375 m spatial resolution and with a sub-daily revisit time. This data enables detailed tracking of individual fires and offers new insights into fire behavior, as demonstrated for example by the Fire Events Database for California 20 . Here, we developed an Arctic-boreal fire atlas using a fire event tracking system based on VIIRS active fire locations from the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite, which records fire growth in half-daily intervals. This fire atlas contains information about every recorded fire detected by at least one VIIRS observation between 2012 and 2023 within the circumpolar Arctic and boreal biomes. We recorded the number of ignitions and their location and timing, 12-hour spread rates, and the final fire size and duration and fire intensity for each fire. We aggregated or averaged fire characteristics from the individual fires into grid cells of 100 by 100 km to assess spatial variability in fire dynamics across the Arctic-boreal domain. Since science quality archive data from VIIRS was only available between 2012 and 2021, we only used these years for this characterization of fire regimes. Based on the maps of fire regime properties, we identified seven distinct pyroregions using an unsupervised clustering algorithm. We further evaluated the influence of anthropogenic and climatic drivers, including the role of fuel availability and lightning, on these pyroregions. Patterns of fire activity in Arctic-boreal regions From 2012 through 2021, we recorded 26504 fires burning a total area of 112 Mha in Arctic-boreal forest and tundra regions (Fig. 1 A). Among these fires, we detected 11 that were larger than 5000 km 2 . The largest fire documented in our database occurred in eastern Siberia in 2021, encompassed 35 separate ignition locations, and burned an area of 15759 km 2 (Fig. 1 B). Burned area varied considerably from year to year, from 7 Mha in 2017 to 18 Mha in 2012 (Extended Data Fig. 1 ). Near real time VIIRS data revealed that 2023 set a new record burning 21 Mha by the end of October. Notably, years with large burned area were not always coinciding with years with many fires. While fire numbers showed a significantly decreasing decadal trend (-138 fires/year, p = 0.004), fire sizes have increased by 2.9 km 2 per year (p = 0.059) between 2012 and 2023. Areas with high fire frequency were concentrated in several hotspot regions in the continental interior of both continents, including in central, eastern, and southern Siberia and central Canada (Fig. 2 ). Low annual burned area occurred in western Eurasia and northeastern Canada and in northern tundra regions. Burned area was highest overall in regions where a high density of fire number co-occurred with large fire sizes (Fig. 2 B,C). Eurasia displayed distinct latitudinal gradients in fire density and the timing of fire starts (Fig. 2 D,F), with many early-season fires occurring in southern Siberia and the largest fires burning in northern forests. The regions with highest fire number density were identified around Lake Baikal in southern Siberia and in Yakutia, whereas the largest fires occurred in central and northeastern Siberia, and in the Northwest Territories and Quebec in Canada. There were substantial differences in fire intensities between continents (Fig. 2 E), with the highest fire radiative power recorded in Canada and lowest values in European Russia and western Siberia. Arctic-boreal pyroregions Using the Arctic-boreal fire atlas and a clustering approach, we identified seven Arctic-boreal pyroregions with unique combinations of fire density, fire size, fire duration, fire intensity, area burned, and fire start timing and duration (Table 1 , Extended Data Fig. 2 ). The pyroregions were mostly geographically contiguous (Fig. 3 ). We named the pyroregions based on fire frequency (rare/frequent), fire size (small/large) and intensity or fire start timing (cool/intense/early) Pyroregions with low fire number densities, namely rare-small-early (RSE), rare-small-cool (RSC) and rare-small-intense (RSI), also exhibited small fire sizes and were located predominantly in circumpolar northern tundra regions and northern Europe (Fig. 3 ). We identified three pyroregions characterized by frequent and large fires: frequent-large-cool (FLC) in western and central Siberia, frequent-large-early (FLE) in the eastern Siberian Republic of Sakha and western Alaska, and frequent-large-intense (FLI) in central and eastern Canada. FLI comprised the largest fires on average. Southern Siberia was notably distinct from other boreal pyroregions, displaying frequent, small, and early-season fires (frequent-small-early, FSE). The seven pyroregions exhibited significant differences in climate, human influence, and vegetation properties (Extended Data Figs. 3 – 5 ). Pyroregions dominated by small, early-season fires (frequent-small-early and rare-small-early) showed the largest anthropogenic impact, occurring in regions with low levels of wilderness area. In contrast, pyroregions dominated by large fires, such as frequent-large-early and frequent-large-intense, occurred in regions with higher percentages of wilderness area. Pyroregions with a strong anthropogenic influence exhibited either exceptionally high (e.g., in frequent-small-early) or low (e.g., in rare-small-early) ignition numbers. The highest vapor pressure deficit (VPD) levels were observed in the frequent-small-early and frequent-large-cool pyroregions located in the strongly continental climate zone of southern and central Siberia, followed by the frequent-large-intense pyroregion in central and eastern boreal North America. Pyroregions with higher VPD in Eurasia (FSE and FLC) were also associated with higher aboveground biomass and lightning densities (Table 1 , Extended Data Fig. 4 ). The frequent-large-early, rare-small-cool and rare-small-intense pyroregions had the lowest aboveground biomass levels, suggesting that fuel limitations may restrict the fire activity in these clusters. The pyroregions showed a clear division according to tree species, with deciduous needle-leaved larch species dominating in the frequent-small-early, frequent-large-cool and frequent-large-early pyroregions and evergreen needle-leaved conifers dominating in the frequent-large-intense pyroregion (Extended Data Fig. 4 ). Mixed forests with a large fraction of broadleaved trees prevailed in the rare-small-early and frequent-small-early pyroregions. Climatic and anthropogenic drivers of spatial variability in fire regimes Distinct spatial patterns of fire activity in Arctic-boreal regions may be caused by spatial variations in climate and fire weather, differences in fuel load and structure, and the influence of humans on ignition and fire suppression. Our domain-wide grid cell-based linear models, which used the multi-annual average of VPD in the month of maximum VPD, lightning density, aboveground biomass, wilderness fraction and cropland and pasture fraction as predictor variables, explained 30%, 29%, and 13% of the spatial variability in burned area, fire number and fire size, respectively (Extended Data Table 1 ). Models incorporating tree species yielded slightly superior results overall compared to models without them, particularly in predicting fire radiative power (R 2 = 0.30 vs R 2 = 0.14, Extended Data Table 1 ). This observation aligns with the earlier research of Rogers et al. 21 , who demonstrated that the presence of coniferous, fire-embracing tree species, prevalent in boreal North America, leads to higher fire intensity in contrast to the prevalence of fire-avoidant tree species dominating large areas of Eurasia. VPD is a strong driver of fire activity, since it regulates fuel moisture and thus governs ignition efficiency and fire spread 22 . The multi-annual average maximum VPD was the strongest predictor for spatial patterns of burned area (partial Spearman correlation ρ part = 0.50), fire number (ρ part = 0.45), fire size (ρ part = 0.39), and duration (ρ part = 0.35), but not fire intensity, which may be linked with species-specific fire traits (Fig. 4 A). Pyroregions with large fire sizes (FLC, FLE, and FLI) showed the highest sensitivity to VPD, whereas rare-small-early, rare-small-intense and frequent-small-intense were less sensitive to spatial variations in VPD. This suggests that anthropogenic activities or a larger fraction of broadleaf forest may attenuate the climate sensitivity of boreal fire regimes. Lightning and fire number were positively correlated in the frequent-large-early and rare-small-intense pyroregions (Fig. 4 B), with a smaller effect on burned area. Regions with higher lightning density displayed negative partial correlations between lightning and fire number. This counter-intuitive relationship may arise due to a dominance of anthropogenic ignitions (for example in frequent-small-early, southern Siberia) or strong fire suppression (for example in rare-small-cool, northern Europe 23 , 24 ). Importantly, while lightning occurrence may thus only limit fire number in a fourth of the Arctic-boreal domain, these include recent fire hotspots in eastern Siberia and western Alaska. Fuel moisture constraints may, however, be more important than strike density for initiating a fire start in large parts of boreal North America and central Siberia. The fraction of wilderness was the best single predictor of fire intensity (ρ part = 0.40), and also correlated positively with fire size and duration across the full domain (ρ part = 0.34 and ρ part = 0.25, Fig. 4 D). Land use and anthropogenic activities further significantly modulated the influence of climate and fuel availability on the spatial distribution of fire properties (Fig. 5 ). For example, in human-dominated regions with a low wilderness fraction, the sensitivity of fire size to VPD was lower than in more remote areas with a high wilderness fraction (Fig. 5 C). The weaker VPD response in areas with a stronger human footprint may be a result of fire suppression and increased landscape fragmentation 25 . Indeed, while VPD was a better spatial predictor for fire size overall, 86% of grid cells with an average fire size larger than 100 km 2 had a wilderness fraction greater than 50%. Burned area, fire number, and fire size also all had diverging responses to fuel density in low and high wilderness areas (Fig. 5 D-F), again highlighting the potential importance of fire suppression in human-dominated ecosystems. Positive relationships between fire regime properties and aboveground biomass prevailed in pyroregions with a higher wilderness fraction (frequent-large-cool, frequent-large-early, frequent-large-intense and rare-small-cool). In contrast, the anthropogenically dominated frequent-small-early pyroregion exhibited negative correlations between aboveground biomass and fire size and intensity (Fig. 4 C). Response of Arctic-boreal pyroregions to interannual climate variability The specific environmental conditions that shape fire regimes in different pyroregions may also strongly modulate the sensitivity of fire activity to interannual variation and long-term trends in climate. We therefore investigated the interannual correlation of fire regime properties with summer VPD within each pyroregion to assess which pyroregions were most sensitive to climatic variations. VPD showed highest interannual correlation with burned area, ignition and fire size in pyroregions with large fire sizes, particularly in Siberia (Extended Data Table 2). Fire activity in the rare-small-cool pyroregion located largely in Northern tundra regions was sensitive to VPD to a lesser degree. Fire activity, and in particular fire sizes in the rare-small-early, rare-small-intense and frequent-small early pyroregions were least sensitive to VPD. This indicates that interannual variations in weather drive ignitions and spread especially in remote pyroregions. Pyroregions which experienced fire extremes in recent years, such as Central and Eastern Siberia, and Central Canada (Extended Data Fig. 6) showed a strong climate sensitivity. Implications for future Arctic-boreal fire activity Arctic-boreal regions are warming nearly four times faster than the rest of the Earth 26 , and fire activity is projected to increase due to associated decreases in fuel moisture 27 and increases in lightning ignitions 13 , 28 . Intensifications of regional fire regimes have already been observed within the Arctic Circle 2 and in parts of Canada 7 , 29 and Alaska 30 . Here, we show that the sensitivity of fire activity to a warmer and drier climate varies substantially between pyroregions. In line with observed emerging trends, we found that fire activity is most sensitive to climate in boreal North America, eastern Siberia and northern tundra regions. Conversely, regions in southern Siberia and Europe may be more resilient to increases in heatwaves and droughts, since a larger human footprint in these areas resulted in more fragmented fuels. Projected increases in Arctic-boreal lightning activity with climate warming 13 are particularly important in driving future increases of fire activity in western Alaska and eastern Siberia, where fire number was sensitive to lightning strike density. Notably, these regions, sensitive to lightning, have already experienced recent fire extremes 2 – 4 , underscoring the potential impact of lightning-caused fire complexes on annual burned area. Furthermore, rising temperatures may enhance ignition efficiency in currently moisture-limited pyroregions, heightening their susceptibility to concurrent increases in lightning 28 . In parallel with the Arctic-boreal regions transitioning into a warmer climate with an increasing likelihood of compounding extremes 31 , abrupt biome shifts have been observed 32 , 33 and projected 34 , 35 , with major implications for fire regimes. Some biome shifts may exert a positive feedback on fire activity, such as forest transitions from more open and older stands to denser and younger in Siberian larch forests 35 , 36 , shrub expansion in tundra areas 37 , 38 or tree line shifts 39 , 40 . Negative feedbacks can emerge through forest transitioning from flammable conifers to less flammable deciduous forests 41 – 44 , regeneration failures 45 – 47 , or decadal self-limitation of fire occurrence and spread 48 . While fuels were generally less influential than climate in shaping spatial patterns of fire activity, we found that fuel load was as an important driver of fire intensity and burned area in more remote pyroregions. Fuel type also governed fire activity, with pyroregions with higher fractions of deciduous broadleaf forests displaying lower fire activity. Furthermore, while the effects of climate warming and fires on aboveground biomass have been extensively studied, the sensitivity of belowground peat carbon pools to these changes remains poorly understood 49 , 50 . Arctic-boreal regions contain extensive peatlands, for example in the western Siberian lowlands and the Hudson plains. These peatlands currently experience relatively limited fire activity. Thus, boreal peatlands may be relatively resistant to fires under current climate and permafrost conditions due to hydrologic self-regulation 50 – 52 . However, data on the presence and hydraulic state of peatlands are scarce in many Arctic-boreal regions, hindering predictions about the sensitivity of peatland burning to climate warming and associated permafrost degradation and ecosystem shifts 49 . Continued expansion of agriculture, logging and resource extraction, and wildland-urban interfaces 53 , 54 increases human vulnerability to fire, but may also strongly influence fire regimes 55 . Anthropogenic activities influence fire activity directly, through intentional or unintentional ignition and fire suppression, and indirectly through fuel management, logging and fragmentation 56 , 57 . Human activities also increase peatland vulnerability to fires through land use changes that enhance drainage and degrade peatlands 50 , 51 . While many anthropogenic activities currently suppress fire activity, the combination of a warming climate and long-term fire prevention practices in many populated boreal regions may increase the risk of escaped fires in vulnerable areas 57 . Ongoing efforts to better represent fire-suppressing and fire-inducing effects of anthropogenic presence in fire models are therefore critical to improve the representation of boreal fires 58 , 59 . Understanding the interplay between fuels, climate and ignition sources and their varying importance in different pyroregions is vital for improving future predictions of changing Arctic-boreal fire regimes. Our analysis uniquely identifies these interconnected drivers of spatial variability in fire regimes, and shows that some Arctic-boreal pyroregions, in particular those that experienced recent fire extremes, exhibit a strong climate sensitivity, while fire activity in pyroregions of southern Siberia and northern Europe is strongly controlled by fuel availability and fragmentation as a result of anthropogenic activity. Table 1 Average and standard deviation of fire and environmental characteristics of Arctic-boreal pyroregions. Fire regime properties are based on data between 2012 and 2021. Vapor pressure deficit and lightning strike density are multi-year (2012–2021) averages over the boreal fire season (March – October). Burned area Total burned area Fire density Fire size Fire duration Fire radiative power Fire start Vapor pressure deficit Lightning strike density Above-ground biomass Wilderness fraction (% yr − 1 ) (Mha yr − 1 ) (10 − 5 km − 2 yr − 1 ) (km 2 ) (days) (Wm − 2 ) (Julian day) (kPa) (10 − 5 km − 2 d − 1 ) (Mg/ha) (%) Rare Small Early 0.01 ± 0.01 0.01 ± 0.01 0.17 ± 0.21 3.1 ± 1.9 4.8 ± 3.0 39.8 ± 15.6 174 ± 29 0.49 ± 0.19 5.41 ± 6.01 45.4 ± 41.3 28.8 ± 39.5 Rare Small Cool 0.11 ± 0.27 0.41 ± 0.25 0.47 ± 0.47 16.5 ± 17.4 6.1 ± 2.9 68.4 ± 23.0 192 ± 10 0.48 ± 0.17 1.55 ± 2.07 21.7 ± 23.4 64.5 ± 36.9 Frequent Small Early 0.91 ± 0.72 1.13 ± 0.89 6.85 ± 4.47 23.1 ± 17.6 8.5 ± 1.8 67.0 ± 12.1 148 ± 17 0.74 ± 0.16 7.49 ± 5.01 64.7 ± 27.7 20.3 ± 30.0 Frequent Large Cool 1.23 ± 1.01 4.50 ± 3.10 3.25 ± 1.61 45.8 ± 30.0 13.1 ± 3.4 69.1 ± 13.8 194 ± 16 0.74 ± 0.16 3.49 ± 3.05 54.3 ± 25.9 57.9 ± 33.8 Frequent Large Early 0.52 ± 0.42 1.36 ± 1.16 1.59 ± 1.29 49.3 ± 31.6 8.2 ± 1.8 76.4 ± 12.4 178 ± 6 0.51 ± 0.14 0.86 ± 1.18 20.0 ± 80.0 80.1 ± 18.2 Frequent Large Intense 0.51 ± 0.50 3.42 ± 3.15 1.02 ± 0.72 63.0 ± 36.7 10.1 ± 3.6 122.0 ± 19.1 188 ± 7 0.58 ± 0.17 2.50 ± 3.46 34.9 ± 24.2 86.2 ± 20.4 Rare Small Intense 0.11 ± 0.27 0.21 ± 0.31 0.35 ± 0.48 30.9 ± 38.0 7.9 ± 5.8 113.8 ± 44.4 196 ± 23 0.48 ± 0.20 2.23 ± 3.82 32.6 ± 35.2 64.9 ± 35.3 Declarations Acknowledgements Funding: This work was funded by the Dutch Research Council (NWO) through Vidi grant 016.Vidi.189.070 (Fires Pushing Trees North) awarded to S.V. S.V. acknowledges the European Research Council through a Consolidator grant under the European Union’s Horizon 2020 and innovation program (grant agreement No. 101000987). JTR received funding support from NASA’s Modeling, Analysis, and Prediction (80NSSC21K1362), Arctic and Boreal Vulnerability Experiment (80NSSC23K0140), and Earth Information System research programs and the US. Dept of Energy RUBISCO Science Focus Area. Author contributions: All authors designed the research. Y.C. and R.C.S. contributed to the fire tracking code. R.C.S. performed the analysis, with input from the other authors, and drafted the paper. All authors participated in manuscript editing. Code availability The fire tracking code used for the Arctic-boreal fire atlas is freely accessible via https://zenodo.org/doi/10.5281/zenodo.10611948. Data availability All data used for this research are freely available.Arctic-boreal fire atlas data from 2012 to 2023 can be accessed via Pangaea (submission in process). VIIRS active fire locations can be downloaded from the University of Maryland (https://modis-fire.umd.edu) and NASA’s Fire Information for Resource Management System (https://firms.modaps.eosdis.nasa.gov/). ERA5 reanalysis data can be retrieved from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu). The GlobBiomass data can be found via Pangaea (https://doi.pangaea.de/10.1594/PANGAEA.894711). Lightning density form the World Wide Lightning Location Network can be found at Zenodo (https://zenodo.org/records/6007052). Human Footprint Maps can be downloaded from UNEP-GRID-Geneva (https://datacore-gn.unepgrid.ch/geonetwork/srv/api/records/a967c8b4-3169-4848-a624-f14946b53a24). References Byrne, B. et al. Unprecedented Canadian forest fire carbon emissions during 2023. 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Climatic warming strengthens a positive feedback between alpine shrubs and fire. Glob. Chang. Biol. 23 , 3249–3258 (2017). Mekonnen, Z. A. et al. Arctic tundra shrubification: a review of mechanisms and impacts on ecosystem carbon balance. Environ. Res. Lett. 16 , (2021). Esper, J. & Schweingruber, F. H. Large-scale treeline changes recorded in Siberia. Geophys. Res. Lett. 31 , (2004). Dial, R. J., Maher, C. T., Hewitt, R. E. & Sullivan, P. F. Sufficient conditions for rapid range expansion of a boreal conifer. Nature 608 , 546–551 (2022). Mekonnen, Z. A., Riley, W. J., Randerson, J. T., Grant, R. F. & Rogers, B. M. Expansion of high-latitude deciduous forests driven by interactions between climate warming and fire. Nat. Plants 5 , (2019). Wang, J. A. et al. Extensive land cover change across Arctic-Boreal Northwestern North America from disturbance and climate forcing. Glob. Chang. Biol. 00 , 1–16 (2019). Johnstone, J. F. et al. Factors shaping alternate successional trajectories in burned black spruce forests of Alaska. Ecosphere 11 , (2020). Kim, J. E., Wang, J. A., Li, Y., Czimczik, C. I. & Randerson, J. T. Wildfire-induced increases in photosynthesis in boreal forest ecosystems of North America. Glob. Chang. Biol. 30 , 1–23 (2024). Baltzer, J. L. et al. Increasing fire and the decline of fire adapted black spruce in the boreal forest. Proc. Natl. Acad. Sci. 118 , e2024872118 (2021). Burrell, A. L. et al. Climate change, fire return intervals and the growing risk of permanent forest loss in boreal Eurasia. Sci. Total Environ. 831 , 154885 (2022). Barrett, K. et al. Postfire recruitment failure in Scots pine forests of southern Siberia. Remote Sens. Environ. 237 , 111539 (2020). Buma, B., Hayes, K., Weiss, S. & Lucash, M. Short-interval fires increasing in the Alaskan boreal forest as fire self-regulation decays across forest types. Sci. Rep. 12 , 1–10 (2022). Loisel, J. et al. Expert assessment of future vulnerability of the global peatland carbon sink. Nat. Clim. Chang. 11 , 70–77 (2021). Turetsky, M. R. et al. Global vulnerability of peatlands to fire and carbon loss. Nat. Geosci. 8 , 11–14 (2015). Wilkinson, S. L. et al. Wildfire and degradation accelerate northern peatland carbon release. Nat. Clim. Chang. 13 , (2023). Turetsky, M. R. et al. Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands. Nat. Geosci. 4 , 27–31 (2011). Robinne, F. N., Parisien, M. A. & Flannigan, M. D. Anthropogenic influence on wildfire activity in Alberta, Canada. Int. J. Wildl. Fire 25 , 1131–1143 (2016). Bartsch, A. et al. Expanding infrastructure and growing anthropogenic impacts along Arctic coasts. Environ. Res. Lett. 16 , (2021). Schug, F. et al. The global wildland – urban interface. (2023) doi:10.1038/s41586-023-06320-0. Kukavskaya, E. A. et al. Influence of logging on the effects of wildfire in Siberia. Environ. Res. Lett. 8 , 2002–2011 (2013). Calef, M. P., Varvak, A., McGuire, A. D., Chapin, F. S. & Reinhold, K. B. Recent changes in annual area burned in interior Alaska: The impact of fire management. Earth Interact. 19 , 1–17 (2015). Hantson, S. et al. The status and challenge of global fire modelling. Biogeosciences 13 , 3359–3375 (2016). Lasslop, G. et al. Global ecosystems and fire: Multi‐model assessment of fire‐induced tree‐cover and carbon storage reduction. Glob. Chang. Biol. 26 , 5027–5041 (2020). Sexton, J. O. et al. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. Int. J. Digit. Earth 6 , 427–448 (2013). Additional Declarations There is NO Competing Interest. Supplementary Files RCSpyrogeoedfiguresfinal.docx Extended Data Figures and Tables RCSpyrogeomethodscombined.docx Methods and supplementary figures Cite Share Download PDF Status: Published Journal Publication published 02 Sep, 2024 Read the published version in Nature Geoscience → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3932189","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":275153681,"identity":"96728c75-346d-4666-8fb5-ef48abea4b8d","order_by":0,"name":"Rebecca Scholten","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYFACHiTGAyDFD8QHGBiYidSSkMDAINlAshaDA2Aebi3m7b0HH/yosWOQbz978EHiD5s84xvJDw8wVFgnNuDQInPmXLJhz7FkBoMzeckGCQlpxWY30oAWnUnHqUVCIsdMmrGBmcFAgsdMIiHhcOK2GzkMBxjbDuPWIv/G/DdjQz2D/Aywlv+Jm2eAtPzDowVoODNjw2EGhhtgLQcSN0iAtDTg0cKTlyzZc+w4j8GZHGODhLTkxBlnnhkcSDiWboxTC/vZgx9+1FTLybefMXzwwcYusb89+fGHDzXWsri0wAAPKjeBgPJRMApGwSgYBfgBAGSBWIaK2xBFAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0144-0572","institution":"University of California, Irvine","correspondingAuthor":true,"prefix":"","firstName":"Rebecca","middleName":"","lastName":"Scholten","suffix":""},{"id":275153682,"identity":"1afd76e4-d7d9-4097-b014-b15037dbd3d3","order_by":1,"name":"Sander Veraverbeke","email":"","orcid":"https://orcid.org/0000-0003-1362-5125","institution":"Vrije Universiteit Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Sander","middleName":"","lastName":"Veraverbeke","suffix":""},{"id":275153683,"identity":"0c3f6f79-88d8-4023-86c1-d2e795b8d96a","order_by":2,"name":"Yang Chen","email":"","orcid":"https://orcid.org/0000-0002-0993-7081","institution":"University of California, Irvine","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Chen","suffix":""},{"id":275153684,"identity":"c1607779-e070-43f9-8ac0-8b3fe1984f46","order_by":3,"name":"James Randerson","email":"","orcid":"https://orcid.org/0000-0001-6559-7387","institution":"University of California, Irvine","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Randerson","suffix":""}],"badges":[],"createdAt":"2024-02-05 23:51:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3932189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3932189/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41561-024-01505-2","type":"published","date":"2024-09-02T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51777759,"identity":"670ca15b-f49a-49c6-b8f5-ce3105dc7ca8","added_by":"auto","created_at":"2024-02-28 21:06:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":452818,"visible":true,"origin":"","legend":"\u003cp\u003eArctic-boreal fire atlas. \u003cstrong\u003eA\u003c/strong\u003e, Map of all fires in the Arctic-boreal biomes between 2012 and 2021. Fire perimeters are labelled by their year of burning. Arctic and boreal biome boundaries are shaded in dark and light gray. \u003cstrong\u003eB\u003c/strong\u003e, The spread of the largest fire in the database, which was recorded in eastern Siberia during 2021. Yellow stars represent fire ignition points. Colors of the half-daily perimeters represent the day of burning, from dark to light. The black bounding box in B represents the location of the fire in A. Background image: 2015 Global Forest Cover Change Tree Cover 30m\u003csup\u003e60\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3932189/v1/0e6f2004235e6bd26cfabb46.jpg"},{"id":51777760,"identity":"d53e37ca-872e-4370-9875-4d33215ca342","added_by":"auto","created_at":"2024-02-28 21:06:42","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":693272,"visible":true,"origin":"","legend":"\u003cp\u003eCircumpolar maps of fire characteristics derived from the Arctic-boreal fire atlas (2012-2021). \u003cstrong\u003eA\u003c/strong\u003e, Annual percentage burned area. \u003cstrong\u003eB\u003c/strong\u003e, Fire number density. \u003cstrong\u003eC\u003c/strong\u003e, Average fire size, \u003cstrong\u003eD\u003c/strong\u003e, Average fire duration. \u003cstrong\u003eE\u003c/strong\u003e, Average 95\u003csup\u003eth\u003c/sup\u003e percentile of FRP per fire as detected by VIIRS. \u003cstrong\u003eF\u003c/strong\u003e, Average start month of fires (April – October).\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3932189/v1/9683be5593653f8138e276fb.jpeg"},{"id":51777761,"identity":"85ad48f9-17ee-4ea7-aa17-3a9506201998","added_by":"auto","created_at":"2024-02-28 21:06:42","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":185222,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical distribution of Arctic-boreal pyroregions. The cluster uncertainty is expressed through the transparency of each grid cell. RSE: rare-small-early, RSC: rare-small-cool, FSE: frequent-small-early, FLC: frequent-large-cool, FLE: frequent-large-early, FLI: frequent-large-intense, RSI: rare-small-intense. See Table 1 for the fire and environmental characteristics of the pyroregions.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3932189/v1/5fa505a2007126c5961d2dc2.jpeg"},{"id":51777766,"identity":"13a8bb39-5e4d-4bc6-b21b-7abcfd014911","added_by":"auto","created_at":"2024-02-28 21:06:43","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":254487,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial relationships between environmental variables and fire regime properties in the seven Arctic-boreal pyroregions. Relationships are expressed as partial Spearman’s correlations. Environmental variables include vapor pressure deficit (\u003cstrong\u003eA\u003c/strong\u003e), lightning density (\u003cstrong\u003eB\u003c/strong\u003e), aboveground biomass (\u003cstrong\u003eC\u003c/strong\u003e) and wilderness fraction (\u003cstrong\u003eD\u003c/strong\u003e). Colors indicate the strength and direction of correlation. Values are given for all partial correlations with p \u0026lt; 0.05. RSE: rare-small-early, RSC: rare-small-cool, FSE: frequent-small-early, FLC: frequent-large-cool, FLE: frequent-large-early, FLI: frequent-large-intense, RSI: rare-small-intense. See Fig. 3 for the geographical extent of the pyroregions.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3932189/v1/3cdeee37147c2ec2aa639661.jpeg"},{"id":51778019,"identity":"48adb316-6d7f-4007-8988-80209ea05f72","added_by":"auto","created_at":"2024-02-28 21:14:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":275459,"visible":true,"origin":"","legend":"\u003cp\u003eThe dependence of fire regime properties (burned area, ignition density, and fire size) on climate (vapor pressure deficit) and fuel (aboveground biomass) are modulated by the fraction of wilderness. Scatter plots for vapor pressure deficit (\u003cstrong\u003eA-C\u003c/strong\u003e) and aboveground biomass (\u003cstrong\u003eD-F\u003c/strong\u003e) and burned area (\u003cstrong\u003eA\u003c/strong\u003e, \u003cstrong\u003eD\u003c/strong\u003e), ignition density (\u003cstrong\u003eB\u003c/strong\u003e, \u003cstrong\u003eE\u003c/strong\u003e) and fire size (\u003cstrong\u003eC\u003c/strong\u003e, \u003cstrong\u003eF\u003c/strong\u003e). All y-axes are log-scaled. Lines and shaded areas refer to linear regressions and 95\u003csup\u003e% \u003c/sup\u003econfidence interval based on groups of 0-20% wild (pink) and 80-100% wild (green). Relationships of burned area and fire size with vapor pressure deficit are weaker in the presence of humans. Relationships between fire regime properties and aboveground biomass reverse in the presence of humans.\u003c/p\u003e","description":"","filename":"image5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3932189/v1/ee28f53272d9229a47e5c184.jpg"},{"id":63864509,"identity":"bdc85c9a-a147-4c49-baf6-9204870f13ab","added_by":"auto","created_at":"2024-09-03 07:06:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2466844,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3932189/v1/c8f920cb-e1c7-40c1-bb65-e96d887851a4.pdf"},{"id":51777762,"identity":"65b7bfbd-6a56-4071-8426-3dba0f883a36","added_by":"auto","created_at":"2024-02-28 21:06:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2358097,"visible":true,"origin":"","legend":"\u003cp\u003eExtended Data Figures and Tables\u003c/p\u003e","description":"","filename":"RCSpyrogeoedfiguresfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-3932189/v1/070c7bd4eeee8c4a022ee132.docx"},{"id":51777765,"identity":"b049b7a3-43eb-4153-bb80-3acd4c6a60f1","added_by":"auto","created_at":"2024-02-28 21:06:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":601218,"visible":true,"origin":"","legend":"\u003cp\u003eMethods and supplementary figures\u003c/p\u003e","description":"","filename":"RCSpyrogeomethodscombined.docx","url":"https://assets-eu.researchsquare.com/files/rs-3932189/v1/e5361aacca81c2571809807c.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Spatial variability in Arctic-boreal pyroregions shaped by climate and human influence","fulltext":[{"header":"Main text","content":"\u003cp\u003eMany Arctic and boreal regions have experienced unprecedented fire activity in the last decade. Major regional fire complexes occurred in eastern Siberia during 2019, 2020, and 2021, in Alaska in 2015 and 2022, and in Canada in 2014 and 2023. Most recently, in the summer of 2023, a significant expanse of Canada witnessed a record level of burning that likely reversed long-term trends in carbon storage within forests\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Extreme fire seasons in these regions were primarily caused by warm summer temperatures and high lightning activity\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Furthermore, land-atmosphere feedbacks due to changes in snowmelt timing, as well as shifts in the polar jet stream linked to a warming climate have been shown to influence these large fire events\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. There is ample evidence that recent boreal fire extremes are driven by climate change\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, however, little is known as to why different regions repeatedly experience unprecedented extremes, while fire activity in other boreal areas appears to remain relatively constant.\u003c/p\u003e\n\u003cp\u003eEvidence for the drivers of spatial variability in fire activity has been compiled on a global scale, with major variations caused by differences in vegetation, climate, and human impact\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. While these studies reveal significant variability in Arctic-boreal regions, the underlying causes have not yet been explored. Various regional studies have highlighted the importance of extreme climatic conditions conducive to fuel drying and fire spread for extreme fire seasons in boreal regions\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Furthermore, ignition limitations govern Arctic-boreal fire occurrences\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, most studies of contemporary and future Arctic-boreal fire activity have focused on the fire-prone regions of western North America and eastern Siberia, and less is known about what drives fire activity in other Arctic-boreal regions, including those with a strong anthropogenic influence. To date, we miss a comprehensive understanding of the driving factors of spatial variability in Arctic-boreal fire activity. Understanding how climate, fuels and human activity spatially vary, interact and shape Arctic-boreal fire regimes is necessary to improve predictions of future Arctic-boreal fire activity.\u003c/p\u003e\n\u003cp\u003ePyroregions are regions characterized by a similar fire regime\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and are defined by the long-term variability in fire number, size, intensity, duration, burned area, and timing and length of the fire season\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Fire regime properties can substantially differ between ecoregions. It is thus crucial to assess the sensitivity of these different fire regime properties to climatic and anthropogenic drivers, as well as fuel amount and composition. Due to the absence of accurate and long-term records of individual fires in many areas of the Arctic-boreal region, studies evaluating Arctic-boreal fire activity often rely on satellite-derived products of active fires and burned area. While these raster products are useful for assessing regional burned area and fire occurrence and intensity, they do not offer insights about the ignition location and timing, sub-daily spread rate, and the temporal evolution of individual fires. While some near-global datasets are available that derived such object-based fire information from remotely sensed burned areas\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, they do not provide full spatial coverage of Arctic tundra and boreal forest biomes and have often not been optimized for fire dynamics in these regions. The Visible Infrared Imaging Radiometer Suite (VIIRS) launched in 2012 provides global active fire data at a 375 m spatial resolution and with a sub-daily revisit time. This data enables detailed tracking of individual fires and offers new insights into fire behavior, as demonstrated for example by the Fire Events Database for California\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHere, we developed an Arctic-boreal fire atlas using a fire event tracking system based on VIIRS active fire locations from the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite, which records fire growth in half-daily intervals. This fire atlas contains information about every recorded fire detected by at least one VIIRS observation between 2012 and 2023 within the circumpolar Arctic and boreal biomes. We recorded the number of ignitions and their location and timing, 12-hour spread rates, and the final fire size and duration and fire intensity for each fire. We aggregated or averaged fire characteristics from the individual fires into grid cells of 100 by 100 km to assess spatial variability in fire dynamics across the Arctic-boreal domain. Since science quality archive data from VIIRS was only available between 2012 and 2021, we only used these years for this characterization of fire regimes. Based on the maps of fire regime properties, we identified seven distinct pyroregions using an unsupervised clustering algorithm. We further evaluated the influence of anthropogenic and climatic drivers, including the role of fuel availability and lightning, on these pyroregions.\u003c/p\u003e\n\u003cp\u003ePatterns of fire activity in Arctic-boreal regions\u003c/p\u003e\n\u003cp\u003eFrom 2012 through 2021, we recorded 26504 fires burning a total area of 112 Mha in Arctic-boreal forest and tundra regions (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Among these fires, we detected 11 that were larger than 5000 km\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The largest fire documented in our database occurred in eastern Siberia in 2021, encompassed 35 separate ignition locations, and burned an area of 15759 km\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). Burned area varied considerably from year to year, from 7 Mha in 2017 to 18 Mha in 2012 (Extended Data Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Near real time VIIRS data revealed that 2023 set a new record burning 21 Mha by the end of October. Notably, years with large burned area were not always coinciding with years with many fires. While fire numbers showed a significantly decreasing decadal trend (-138 fires/year, p\u0026thinsp;=\u0026thinsp;0.004), fire sizes have increased by 2.9 km\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e per year (p\u0026thinsp;=\u0026thinsp;0.059) between 2012 and 2023.\u003c/p\u003e\n\u003cp\u003eAreas with high fire frequency were concentrated in several hotspot regions in the continental interior of both continents, including in central, eastern, and southern Siberia and central Canada (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Low annual burned area occurred in western Eurasia and northeastern Canada and in northern tundra regions. Burned area was highest overall in regions where a high density of fire number co-occurred with large fire sizes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB,C). Eurasia displayed distinct latitudinal gradients in fire density and the timing of fire starts (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD,F), with many early-season fires occurring in southern Siberia and the largest fires burning in northern forests. The regions with highest fire number density were identified around Lake Baikal in southern Siberia and in Yakutia, whereas the largest fires occurred in central and northeastern Siberia, and in the Northwest Territories and Quebec in Canada. There were substantial differences in fire intensities between continents (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE), with the highest fire radiative power recorded in Canada and lowest values in European Russia and western Siberia.\u003c/p\u003e\n\u003cp\u003eArctic-boreal pyroregions\u003c/p\u003e\n\u003cp\u003eUsing the Arctic-boreal fire atlas and a clustering approach, we identified seven Arctic-boreal pyroregions with unique combinations of fire density, fire size, fire duration, fire intensity, area burned, and fire start timing and duration (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Extended Data Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The pyroregions were mostly geographically contiguous (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). We named the pyroregions based on fire frequency (rare/frequent), fire size (small/large) and intensity or fire start timing (cool/intense/early) Pyroregions with low fire number densities, namely rare-small-early (RSE), rare-small-cool (RSC) and rare-small-intense (RSI), also exhibited small fire sizes and were located predominantly in circumpolar northern tundra regions and northern Europe (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). We identified three pyroregions characterized by frequent and large fires: frequent-large-cool (FLC) in western and central Siberia, frequent-large-early (FLE) in the eastern Siberian Republic of Sakha and western Alaska, and frequent-large-intense (FLI) in central and eastern Canada. FLI comprised the largest fires on average. Southern Siberia was notably distinct from other boreal pyroregions, displaying frequent, small, and early-season fires (frequent-small-early, FSE).\u003c/p\u003e\n\u003cp\u003eThe seven pyroregions exhibited significant differences in climate, human influence, and vegetation properties (Extended Data Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Pyroregions dominated by small, early-season fires (frequent-small-early and rare-small-early) showed the largest anthropogenic impact, occurring in regions with low levels of wilderness area. In contrast, pyroregions dominated by large fires, such as frequent-large-early and frequent-large-intense, occurred in regions with higher percentages of wilderness area. Pyroregions with a strong anthropogenic influence exhibited either exceptionally high (e.g., in frequent-small-early) or low (e.g., in rare-small-early) ignition numbers. The highest vapor pressure deficit (VPD) levels were observed in the frequent-small-early and frequent-large-cool pyroregions located in the strongly continental climate zone of southern and central Siberia, followed by the frequent-large-intense pyroregion in central and eastern boreal North America. Pyroregions with higher VPD in Eurasia (FSE and FLC) were also associated with higher aboveground biomass and lightning densities (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Extended Data Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The frequent-large-early, rare-small-cool and rare-small-intense pyroregions had the lowest aboveground biomass levels, suggesting that fuel limitations may restrict the fire activity in these clusters. The pyroregions showed a clear division according to tree species, with deciduous needle-leaved larch species dominating in the frequent-small-early, frequent-large-cool and frequent-large-early pyroregions and evergreen needle-leaved conifers dominating in the frequent-large-intense pyroregion (Extended Data Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Mixed forests with a large fraction of broadleaved trees prevailed in the rare-small-early and frequent-small-early pyroregions.\u003c/p\u003e\n\u003cp\u003eClimatic and anthropogenic drivers of spatial variability in fire regimes\u003c/p\u003e\n\u003cp\u003eDistinct spatial patterns of fire activity in Arctic-boreal regions may be caused by spatial variations in climate and fire weather, differences in fuel load and structure, and the influence of humans on ignition and fire suppression. Our domain-wide grid cell-based linear models, which used the multi-annual average of VPD in the month of maximum VPD, lightning density, aboveground biomass, wilderness fraction and cropland and pasture fraction as predictor variables, explained 30%, 29%, and 13% of the spatial variability in burned area, fire number and fire size, respectively (Extended Data Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Models incorporating tree species yielded slightly superior results overall compared to models without them, particularly in predicting fire radiative power (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.30 vs R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.14, Extended Data Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). This observation aligns with the earlier research of Rogers et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, who demonstrated that the presence of coniferous, fire-embracing tree species, prevalent in boreal North America, leads to higher fire intensity in contrast to the prevalence of fire-avoidant tree species dominating large areas of Eurasia.\u003c/p\u003e\n\u003cp\u003eVPD is a strong driver of fire activity, since it regulates fuel moisture and thus governs ignition efficiency and fire spread\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The multi-annual average maximum VPD was the strongest predictor for spatial patterns of burned area (partial Spearman correlation \u0026rho;\u003csub\u003epart\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.50), fire number (\u0026rho;\u003csub\u003epart\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.45), fire size (\u0026rho;\u003csub\u003epart\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.39), and duration (\u0026rho;\u003csub\u003epart\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.35), but not fire intensity, which may be linked with species-specific fire traits (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Pyroregions with large fire sizes (FLC, FLE, and FLI) showed the highest sensitivity to VPD, whereas rare-small-early, rare-small-intense and frequent-small-intense were less sensitive to spatial variations in VPD. This suggests that anthropogenic activities or a larger fraction of broadleaf forest may attenuate the climate sensitivity of boreal fire regimes.\u003c/p\u003e\n\u003cp\u003eLightning and fire number were positively correlated in the frequent-large-early and rare-small-intense pyroregions (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB), with a smaller effect on burned area. Regions with higher lightning density displayed negative partial correlations between lightning and fire number. This counter-intuitive relationship may arise due to a dominance of anthropogenic ignitions (for example in frequent-small-early, southern Siberia) or strong fire suppression (for example in rare-small-cool, northern Europe\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e). Importantly, while lightning occurrence may thus only limit fire number in a fourth of the Arctic-boreal domain, these include recent fire hotspots in eastern Siberia and western Alaska. Fuel moisture constraints may, however, be more important than strike density for initiating a fire start in large parts of boreal North America and central Siberia.\u003c/p\u003e\n\u003cp\u003eThe fraction of wilderness was the best single predictor of fire intensity (\u0026rho;\u003csub\u003epart\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.40), and also correlated positively with fire size and duration across the full domain (\u0026rho;\u003csub\u003epart\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.34 and \u0026rho;\u003csub\u003epart\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.25, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). Land use and anthropogenic activities further significantly modulated the influence of climate and fuel availability on the spatial distribution of fire properties (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). For example, in human-dominated regions with a low wilderness fraction, the sensitivity of fire size to VPD was lower than in more remote areas with a high wilderness fraction (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). The weaker VPD response in areas with a stronger human footprint may be a result of fire suppression and increased landscape fragmentation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Indeed, while VPD was a better spatial predictor for fire size overall, 86% of grid cells with an average fire size larger than 100 km\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e had a wilderness fraction greater than 50%.\u003c/p\u003e\n\u003cp\u003eBurned area, fire number, and fire size also all had diverging responses to fuel density in low and high wilderness areas (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD-F), again highlighting the potential importance of fire suppression in human-dominated ecosystems. Positive relationships between fire regime properties and aboveground biomass prevailed in pyroregions with a higher wilderness fraction (frequent-large-cool, frequent-large-early, frequent-large-intense and rare-small-cool). In contrast, the anthropogenically dominated frequent-small-early pyroregion exhibited negative correlations between aboveground biomass and fire size and intensity (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eResponse of Arctic-boreal pyroregions to interannual climate variability\u003c/p\u003e\n\u003cp\u003eThe specific environmental conditions that shape fire regimes in different pyroregions may also strongly modulate the sensitivity of fire activity to interannual variation and long-term trends in climate. We therefore investigated the interannual correlation of fire regime properties with summer VPD within each pyroregion to assess which pyroregions were most sensitive to climatic variations. VPD showed highest interannual correlation with burned area, ignition and fire size in pyroregions with large fire sizes, particularly in Siberia (Extended Data Table\u0026nbsp;2). Fire activity in the rare-small-cool pyroregion located largely in Northern tundra regions was sensitive to VPD to a lesser degree. Fire activity, and in particular fire sizes in the rare-small-early, rare-small-intense and frequent-small early pyroregions were least sensitive to VPD. This indicates that interannual variations in weather drive ignitions and spread especially in remote pyroregions. Pyroregions which experienced fire extremes in recent years, such as Central and Eastern Siberia, and Central Canada (Extended Data Fig.\u0026nbsp;6) showed a strong climate sensitivity.\u003c/p\u003e\n\u003cp\u003eImplications for future Arctic-boreal fire activity\u003c/p\u003e\n\u003cp\u003eArctic-boreal regions are warming nearly four times faster than the rest of the Earth\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and fire activity is projected to increase due to associated decreases in fuel moisture\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and increases in lightning ignitions\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Intensifications of regional fire regimes have already been observed within the Arctic Circle\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and in parts of Canada\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and Alaska\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Here, we show that the sensitivity of fire activity to a warmer and drier climate varies substantially between pyroregions. In line with observed emerging trends, we found that fire activity is most sensitive to climate in boreal North America, eastern Siberia and northern tundra regions. Conversely, regions in southern Siberia and Europe may be more resilient to increases in heatwaves and droughts, since a larger human footprint in these areas resulted in more fragmented fuels.\u003c/p\u003e\n\u003cp\u003eProjected increases in Arctic-boreal lightning activity with climate warming\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e are particularly important in driving future increases of fire activity in western Alaska and eastern Siberia, where fire number was sensitive to lightning strike density. Notably, these regions, sensitive to lightning, have already experienced recent fire extremes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, underscoring the potential impact of lightning-caused fire complexes on annual burned area. Furthermore, rising temperatures may enhance ignition efficiency in currently moisture-limited pyroregions, heightening their susceptibility to concurrent increases in lightning\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn parallel with the Arctic-boreal regions transitioning into a warmer climate with an increasing likelihood of compounding extremes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, abrupt biome shifts have been observed\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and projected\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, with major implications for fire regimes. Some biome shifts may exert a positive feedback on fire activity, such as forest transitions from more open and older stands to denser and younger in Siberian larch forests\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, shrub expansion in tundra areas\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e or tree line shifts\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Negative feedbacks can emerge through forest transitioning from flammable conifers to less flammable deciduous forests\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, regeneration failures\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, or decadal self-limitation of fire occurrence and spread\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. While fuels were generally less influential than climate in shaping spatial patterns of fire activity, we found that fuel load was as an important driver of fire intensity and burned area in more remote pyroregions. Fuel type also governed fire activity, with pyroregions with higher fractions of deciduous broadleaf forests displaying lower fire activity. Furthermore, while the effects of climate warming and fires on aboveground biomass have been extensively studied, the sensitivity of belowground peat carbon pools to these changes remains poorly understood\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Arctic-boreal regions contain extensive peatlands, for example in the western Siberian lowlands and the Hudson plains. These peatlands currently experience relatively limited fire activity. Thus, boreal peatlands may be relatively resistant to fires under current climate and permafrost conditions due to hydrologic self-regulation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. However, data on the presence and hydraulic state of peatlands are scarce in many Arctic-boreal regions, hindering predictions about the sensitivity of peatland burning to climate warming and associated permafrost degradation and ecosystem shifts\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eContinued expansion of agriculture, logging and resource extraction, and wildland-urban interfaces\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e increases human vulnerability to fire, but may also strongly influence fire regimes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Anthropogenic activities influence fire activity directly, through intentional or unintentional ignition and fire suppression, and indirectly through fuel management, logging and fragmentation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Human activities also increase peatland vulnerability to fires through land use changes that enhance drainage and degrade peatlands\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. While many anthropogenic activities currently suppress fire activity, the combination of a warming climate and long-term fire prevention practices in many populated boreal regions may increase the risk of escaped fires in vulnerable areas\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Ongoing efforts to better represent fire-suppressing and fire-inducing effects of anthropogenic presence in fire models are therefore critical to improve the representation of boreal fires\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Understanding the interplay between fuels, climate and ignition sources and their varying importance in different pyroregions is vital for improving future predictions of changing Arctic-boreal fire regimes. Our analysis uniquely identifies these interconnected drivers of spatial variability in fire regimes, and shows that some Arctic-boreal pyroregions, in particular those that experienced recent fire extremes, exhibit a strong climate sensitivity, while fire activity in pyroregions of southern Siberia and northern Europe is strongly controlled by fuel availability and fragmentation as a result of anthropogenic activity.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAverage and standard deviation of fire and environmental characteristics of Arctic-boreal pyroregions. Fire regime properties are based on data between 2012 and 2021. Vapor pressure deficit and lightning strike density are multi-year (2012\u0026ndash;2021) averages over the boreal fire season (March \u0026ndash; October).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBurned area\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal burned area\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFire density\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFire size\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFire duration\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFire radiative power\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFire start\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVapor pressure deficit\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLightning strike density\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAbove-ground biomass\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWilderness fraction\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(% yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Mha yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e km\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(km\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(days)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Wm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Julian day)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(kPa)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e km\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Mg/ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRare Small Early\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.17 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.1 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.8 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.8 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e15.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e174 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.49 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.41 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e6.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.4 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e41.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.8 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e39.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRare Small Cool\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.11 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.41 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.47 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.5 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e17.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.1 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e2.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.4 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e23.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e192 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.48 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.55 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e2.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.7 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e23.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.5 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e36.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFrequent Small Early\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.13 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.85 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e4.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.1 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e17.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.5 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.0 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e12.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.49 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e5.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.7 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e27.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.3 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e30.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFrequent Large Cool\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.23 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.50 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e3.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.25 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e1.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.8 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e30.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.1 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e3.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.1 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e13.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e194 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.49 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e3.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.3 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e25.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.9 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e33.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFrequent Large Early\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.52 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.36 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e1.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e1.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49.3 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e31.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.2 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76.4 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e12.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.51 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e1.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.0 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e80.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80.1 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e18.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFrequent Large Intense\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.51 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.42 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e3.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.0 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e36.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.1 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e3.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122.0 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e19.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e188 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.58 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.50 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e3.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.9 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e24.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86.2 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e20.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRare Small Intense\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.11 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.21 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.9 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e38.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.9 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e5.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113.8 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e44.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e196 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.48 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.23 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e3.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.6 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e35.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.9 \u0026plusmn;\u003c/p\u003e\n\u003cp\u003e35.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was funded by the Dutch Research Council (NWO) through Vidi grant 016.Vidi.189.070 (Fires Pushing Trees North) awarded to S.V. S.V. acknowledges the European Research Council through a Consolidator grant under the European Union\u0026rsquo;s Horizon 2020 and innovation program (grant agreement No. 101000987). JTR received funding support from NASA\u0026rsquo;s Modeling, Analysis, and Prediction (80NSSC21K1362), Arctic and Boreal Vulnerability Experiment (80NSSC23K0140), and Earth Information System research programs and the US. Dept of Energy RUBISCO Science Focus Area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e All authors designed the research. Y.C. and R.C.S. contributed to the fire tracking code. R.C.S. performed the analysis, with input from the other authors, and drafted the paper. All authors participated in manuscript editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u0026nbsp;\u003c/strong\u003eThe fire tracking code used for the Arctic-boreal fire atlas is freely accessible via https://zenodo.org/doi/10.5281/zenodo.10611948.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eAll data used for this research are freely available.Arctic-boreal fire atlas data from 2012 to 2023 can be accessed via Pangaea (submission in process). VIIRS active fire locations can be downloaded from the University of Maryland (https://modis-fire.umd.edu) and NASA\u0026rsquo;s Fire Information for Resource Management System (https://firms.modaps.eosdis.nasa.gov/). ERA5 reanalysis data can be retrieved from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu). The GlobBiomass data can be found via Pangaea (https://doi.pangaea.de/10.1594/PANGAEA.894711). Lightning density form the World Wide Lightning Location Network can be found at Zenodo (https://zenodo.org/records/6007052). 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Earth\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 427\u0026ndash;448 (2013).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3932189/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3932189/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFire activity in Arctic and boreal regions is rapidly increasing with severe consequences for climate and human health. Long-term variations in fire frequency and intensity within regions characterize fire regimes. The spatial variability in Arctic-boreal fire regimes and their climatic and anthropogenic drivers, however, remain poorly understood. Here, we developed an object-based fire tracking system to map the sub-daily evolution of all circumpolar Arctic-boreal fires between 2012 and 2023 using 375m Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections. This dataset characterizes the ignition time, location, size, duration, spread, and intensity of individual fires. We used the resulting fire atlas to classify the Arctic-boreal biomes into seven distinct pyroregions with unique climatic and geographic environments. The pyroregions exhibited varying responses to environmental drivers, with boreal North America, eastern Siberia, and northern tundra regions showing the highest sensitivity to climate and lightning density. Anthropogenic factors also played an important role in influencing fire number and size, interacting with other factors. Understanding the spatial variability of fire regimes and its interconnected drivers in the Arctic-boreal domain is important for improving future predictions of fire activity and identifying areas at risk for extreme events.\u003c/p\u003e","manuscriptTitle":"Spatial variability in Arctic-boreal pyroregions shaped by climate and human influence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 21:06:37","doi":"10.21203/rs.3.rs-3932189/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-geoscience","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ngeo","sideBox":"Learn more about [Nature Geoscience](http://www.nature.com/ngeo/)","snPcode":"","submissionUrl":"","title":"Nature Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9c30a211-020b-4885-9163-10cc21a5a868","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28998891,"name":"Earth and environmental sciences/Natural hazards"},{"id":28998892,"name":"Earth and environmental sciences/Biogeochemistry"},{"id":28998893,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change impacts"}],"tags":[],"updatedAt":"2024-09-03T07:06:03+00:00","versionOfRecord":{"articleIdentity":"rs-3932189","link":"https://doi.org/10.1038/s41561-024-01505-2","journal":{"identity":"nature-geoscience","isVorOnly":false,"title":"Nature Geoscience"},"publishedOn":"2024-09-02 04:00:00","publishedOnDateReadable":"September 2nd, 2024"},"versionCreatedAt":"2024-02-28 21:06:37","video":"","vorDoi":"10.1038/s41561-024-01505-2","vorDoiUrl":"https://doi.org/10.1038/s41561-024-01505-2","workflowStages":[]},"version":"v1","identity":"rs-3932189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3932189","identity":"rs-3932189","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}