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Amplified Arctic–boreal fire regimes from permafrost thaw feedbacks

Nature Geoscience volume 19, pages 279–290 (2026) Cite this article

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Abstract

Widespread permafrost thaw—indicated by the thickening of the soil active layer, which undergoes seasonal freeze–thaw cycles above the permafrost—has important implications for the thermal, hydrological and ecological processes of Arctic–boreal ecosystems. Yet, how thaw-driven biophysical and ecological feedbacks alter Arctic–boreal fire regimes remains poorly understood. Here we quantify these feedbacks by integrating long-term observations of soil active layer thickness (1997–2018) with a causal inference model and a space-for-time approach. We show that the thickening of the soil active layer can amplify summer fire regimes across the Arctic–boreal region, resulting in intensified fire weather conditions, increased burned area and higher fire emissions. The fire amplifications were driven by thaw-induced reductions in surface albedo and soil moisture that intensify summer warming and atmospheric aridity, further reinforced by enhanced vegetation growth and the desiccation of soil organic matter. These cascading feedbacks may create a positive fire–climate feedback loop, whereby more burning accelerates carbon release from vegetation and soil, further destabilizing northern ecosystems. Our findings highlight the potential escalating fire and climate risks initiated by permafrost thaw, emphasizing the urgent need for immediate climatic action to mitigate climate change and the compounding impacts of wildfires and permafrost degradation.

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Fig. 1: Climatic feedbacks from deepening ALT on amplified summer meteorological drought conditions across the northern permafrost zone (>45° N).
The alternative text for this image may have been generated using AI.
Fig. 2: Feedbacks from deepening ALT on vegetation growth and below-ground fuel dryness across the northern permafrost zone (>45° N).
The alternative text for this image may have been generated using AI.
Fig. 3: Changes in fire weather, burned area and fire emission induced by deepening ALT over 1997–2018.
The alternative text for this image may have been generated using AI.
Fig. 4: Conceptual framework of the permafrost–fire–climate feedback in northern high latitudes.
The alternative text for this image may have been generated using AI.

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Data availability

All data used in this study are freely and publicly accessible via the following links: CALM ALT field measurements, http://www.gwu.edu/~calm/; ESA permafrost ALT for the Northern Hemisphere, v3.0 and v 4.0, https://climate.esa.int/en/projects/permafrost/, GLASS FVC V40, https://github.com/IPCC-WG1/Atlas/tree/main; Circum-Arctic Map of Permafrost and Ground-Ice Conditions, https://nsidc.org/data/GGD318/versions/2; FT-ESDR, http://files.ntsg.umt.edu/data/FT_V5/, ERA5-Land, https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means; TerraClimate, https://www.climatologylab.org/terraclimate.html; ESA PFTs dataset v2.0.8, https://maps.elie.ucl.ac.be/CCI/viewer/download.php; GMTED2010, https://www.usgs.gov/coastal-changes-and-impacts/gmted2010 and GFED4.1s, https://www.globalfiredata.org/. A high-resolution reanalysis of global fire weather data that considers an overwintering procedure is available via Zenodo at https://doi.org/10.5281/zenodo.3626193 (ref. 55). GFED5 is available via Zenodo at https://doi.org/10.5281/zenodo.7668424 (ref. 61). The 500-m fuel consumption dataset is available via Zenodo at https://doi.org/10.5281/zenodo.7229674 (ref. 26). GIMMS LAI4g is available via Zenodo at https://doi.org/10.5281/zenodo.8281930 (ref. 63). Data for Figs. 1–3 and Extended Data Figs. 4–10 are available via Zenodo at https://doi.org/10.5281/zenodo.17567754 (ref. 81).

Code availability

The code for the analysis is available via Zenodo at https://doi.org/10.5281/zenodo.17567754 (ref. 81).

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Acknowledgements

This work was funded by the National Natural Science Foundation of China (grant no. 42125101) and the CAS Interdisciplinary Innovation Team (grant no. JCTD-2020-05). J.P. was funded by European Research Council Synergy grant no. ERC-SyG-2013-610028 IMBALANCE-P. J.P. was also financially supported by the Fundación Ramon Areces grant no. ELEMENTAL-CLIMATE, the Spanish Government grant no. PID2019-110521GB-I00 and the Catalan Government grant no. SGR 2017-1005. C.M.Z. was funded by the SNF Ambizione grant no. PZ00P3_193646. S.V. acknowledges funding from the European Research Council (ERC) through a Consolidator grant under the European Union’s Horizon 2020 research and innovation programme (grant no. 101000987). This work was also supported by the Science and Technology Programme of Guangdong (grant no. 2024B1212070012).

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Author notes

  1. These authors contributed equally: Jialing Li, Gengke Lai.

Authors and Affiliations

  1. The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Jialing Li & Chaoyang Wu

  2. International Institute for Earth System Sciences, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing University, Nanjing, China

    Jialing Li & Gengke Lai

  3. Department of Earth and Environmental Sciences, Vanderbilt University, Nashville, TN, USA

    Lin Meng

  4. Department of Environmental Systems Science, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland

    Constantin M. Zohner

  5. CSIC, Global Ecology Unit, CREAF-CSIC-UAB, Barcelona, Spain

    Josep Peñuelas

  6. CREAF, Barcelona, Spain

    Josep Peñuelas

  7. Department of Earth Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

    Sander Veraverbeke

  8. Geography Research Unit, University of Oulu, Oulu, Finland

    Jan Hjort

  9. Laboratoire des Sciences du Climat et de lˊEnvironnement, CEA-CNRS-UVSQ, Université Paris Saclay, Gif-sur-Yvette, France

    Philippe Ciais

  10. Department of Earth System Science, University of California, Irvine, Irvine, CA, USA

    Yang Chen

  11. National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Xin Li

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Contributions

C.W. designed the research. J.L., G.L. and C.W. wrote the first draft of the article. J.L. and G.L. analysed the data and performed the research. J.L. conducted the visualization. All authors assessed the research analyses and contributed to the writing of the article.

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Correspondence to Chaoyang Wu.

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Nature Geoscience thanks Sumanta Chatterjee and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editor: Xujia Jiang and Carolina Ortiz Guerrero, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Schematic of causal discovery using CCM model, illustrated with the example “ALT causes VPD”.

a, Year-to-year variations of ALT and VPD serve as inputs to the CCM model. b, The CCM model detects causality based on non-linear state space reconstruction. It first constructs shadow manifolds for ALT (MALT,t) and VPD (MVPD,t) in a three-dimensional state space using the original (ALT(t); VPD(t)) and two time-lagged series (ALT(t-τ), ALT(t-2τ); VPD(t-τ), VPD(t-2τ)). Predict skill (ρ) is then used to assess whether MVPD,t can reliably predict the state of ALT. c, If ρ converges as the increase of time series length (L), it suggests that ALT causally influences VPD. This convergence is assessed by calculating Spearman’s rank correlation between ρ and L, identifying causality only when the correlation is significantly positive (Methods). Maps created in ArcGIS (v.10.8) using a base map derived from the IPCC-WG1 Atlas GitHub repository80.

Extended Data Fig. 2 Schematic of the space-for-time approach to derive the sensitivities of various climatic factors (for example, VPD) to the deepening ALT.

This method assumes that the adjacent pixels share the same background climate with the central pixel within the spatial moving window (set as 9×9 pixels in our study). It can therefore detect the one-way signal representing the climatic feedback from the deepening ALT. a-b, Adjacent pixels were selected based on criteria that can exclude the confounding effects of vegetation dynamics (LAI and FVC), land-cover changes (main land-cover type, LC; fraction of main land-cover type, LC_fraction), topography (DEM), and permafrost type. c, Theil-Sen slope estimator was applied to estimate sensitivity by pooling all selected adjacent pixels and the central pixel itself together. This method was also used to estimate the sensitivities of vegetation growth (FVC and LAI) and below-ground fuel dryness (FFMC, DMC, and DC) to ALT. The constraints of vegetation dynamics on the selection of adjacent pixels were not considered when calculating the sensitivity of FVC and LAI to ALT. The shaded area in c indicates the 95% confidence interval of fitting line. Maps created in ArcGIS (v.10.8) using a base map derived from the IPCC-WG1 Atlas GitHub repository80.

Extended Data Fig. 3 A summary flowchart of the methodologies used in this study.

a, Permafrost thaw amplifies Arctic–boreal fire regimes through three potential feedback effects: intensifying meteorological drought, enhancing vegetation growth, and exacerbated below-ground fuel dryness. To investigate these mechanisms, we employed CCM model to qualitatively assess causal relationships (b) and a space-for-time approach to quantificationally estimate the sensitivities of these factors to permafrost thaw (c). Subsequently, to provide a preliminary estimation of changes in fire regimes induced by permafrost thaw, we applied a XGBoost-based model to predict changes in fire weather conditions (d), a non-linear model to estimate changes in burned area (e), and a bottom-up model to calculate associated changes in fire emissions (f). Uncertainty propagation was incorporated throughout the estimation of changes in fire regimes using Monte Carlo simulations.

Extended Data Fig. 4 Causal discovery by CCM model for the feedback effects of the deepening ALT on meteorological variable (VPD; a), vegetation growth (FVC and LAI; b and c), and below-ground fuel dryness (FFMC, DMC, and DC; d-f) based on ALT ground measurements from the CALM network.

The maps display Spearman’s rank correlation coefficients between predictive skill (ρ) and the length of the time series (L) estimated by the CCM model. The base map shows different permafrost types (continuous, discontinuous, isolated, and sporadic). P and N indicate the percentage of positive and negative correlations between ρ and L, respectively. Maps created in ArcGIS (v.10.8) using a base map derived from the Circum-Arctic Map of Permafrost and Ground-Ice Conditions (https://nsidc.org/data/GGD318/versions/2), the IPCC-WG1 Atlas GitHub repository80 and our results81.

Extended Data Fig. 5 Comparisons across different permafrost types (continuous, discontinuous, isolated, and sporadic).

Bars indicate the regional means of the long-term ALT trend (ALT_trend) and the sensitivities of various variables (VPD, Albedo, T, RH, FVC, FFMC, DMC, and DC) to ALT.

Extended Data Fig. 6 Model accuracy of the XGBoost-based FWI models for the training and testing data sets across different GFED regions.

Each subplot shows the scatter of predicted versus actual FWI interannual variation (that is, the difference between two consecutive years), with R2 and RMSE indicating model performance. N indicates the sample sizes for the training and testing data sets. BONA, Boreal North America; TENA, Temperate North America; EURO, Europe; BOAS, Boreal Asia; CEAS, Central Asia. Since only a small portion of TENA lies above 45°N (Fig. S6), we combined BONA and TENA for training the XGBoost model.

Extended Data Fig. 7 Changes of active layer thickness (ΔALT) over 1997-2018 and their induced changes in summer air temperature (ΔT), relative humidity (ΔRH), vapor pressure deficit (ΔVPD), and fire weather index (ΔFWI) across the norther permafrost regions.

Bars indicate regional averages. Bars and error bars for ΔFWI are presented as mean values ±1 standard deviation of regional average derived from 10000 bootstrapped XGBoost model runs.

Extended Data Fig. 8 Modeling the relationships between changes in summer total burned area (ΔBA) and changes in summer fire weather variables using linear and cubic functions.

Fire weather variables include FWI and its annual 95th percentile (FWI95), as well as ISI and its annual 95th percentile (ISI95). a-b, Schematic of liner (a) and cubic fittings (b). R2 and mean absolute error (MAE) were used to evaluate model performance. Δ indicates the year-to-year variation (that is, the difference between two consecutive years). The green circles include long time series data within a 3×3 spatial moving window. The yellow circles represent the mean ΔBA values under 20 bins of ΔFWI. The blue line indicates the fitted linear or cubic regression. c-d, Spatial patterns of model accuracy for the linear (c) and cubic models (d), with labels indicating regional average R2 and MAE values. The cubic function was selected to model the relationship between ΔBA and ΔFWI, as it yielded the highest R2 (0.53; red label). Maps created with ArcGIS (v.10.8) using basemap data from the IPCC-WG1 Atlas GitHub repository80.

Extended Data Fig. 9 Changes in CO2 (ΔCO2) and CH4 (ΔCH4) emissions from fire induced by the deepening active layer thickness (ΔALT) during 1997-2018.

ΔCO2 and ΔCH4 induced by ΔALT based on the GFED4.1 s (a-b) and the 500-m fuel consumption (c-d) datasets. Emissions of CO2 and CH4 were estimated as the mean values from 10000 iterations of the Monte Carlo simulations, which consider the propagation of uncertainties in the emission estimates. Maps created with ArcGIS (v.10.8) using basemap data from the IPCC-WG1 Atlas GitHub repository80.

Extended Data Fig. 10 A synthesis of the cascade linking deepening ALT, summer fire-prone meteorological conditions, above-ground vegetation growth, and below-ground fuel dryness, as analyzed using a structural equation model (SEM).

Three pathways were designed to describe the feedback chains triggered by the deepening ALT and their interactions. Red arrows indicate positive path coefficients or increased effects, while green arrows indicate negative path coefficients or decreased effects. The SEM was conducted at the grid-cell level. Maps between connected variables show the spatial pattern of path coefficients across the Arctic-boreal region. FVC, fractional vegetation coverage; SM, surface soil moisture content; H: flux of surface sensible heat; T, air temperature; RH, relative humidity; VPD, vapor pressure deficit; CWD, climatic water deficit; FFMC, fine fuel moisture code; DMC, duff moisture code; DC, drought code. Maps created with ArcGIS (v.10.8) using basemap data from the IPCC-WG1 Atlas GitHub repository80.

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Li, J., Lai, G., Meng, L. et al. Amplified Arctic–boreal fire regimes from permafrost thaw feedbacks. Nat. Geosci. 19, 279–290 (2026). https://doi.org/10.1038/s41561-025-01894-y

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