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Global burned area increasingly explained by climate change

Nature Climate Change volume 14, pages 1186–1192 (2024)Cite this article

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Abstract

Fire behaviour is changing in many regions worldwide. However, nonlinear interactions between fire weather, fuel, land use, management and ignitions have impeded formal attribution of global burned area changes. Here, we demonstrate that climate change increasingly explains regional burned area patterns, using an ensemble of global fire models. The simulations show that climate change increased global burned area by 15.8% (95% confidence interval (CI) [13.1–18.7]) for 2003–2019 and increased the probability of experiencing months with above-average global burned area by 22% (95% CI [18–26]). In contrast, other human forcings contributed to lowering burned area by 19.1% (95% CI [21.9–15.8]) over the same period. Moreover, the contribution of climate change to burned area increased by 0.22% (95% CI [0.22–0.24]) per year globally, with the largest increase in central Australia. Our results highlight the importance of immediate, drastic and sustained GHG emission reductions along with landscape and fire management strategies to stabilize fire impacts on lives, livelihoods and ecosystems.

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Fig. 1: Contribution to burned area from climate change, DHFs and all combined.
Fig. 2: Dominant driver of global burned area change varies across the distribution.
Fig. 3: Climate change-induced increase in burned area (overburning) on the rise.

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Data availability

Model and model input data are available from the ISIMIP data repository (https://doi.org/10.48364/ISIMIP.446106)38. The observational satellite burned area products FireCCI5.1 (https://doi.org/10.5285/58f00d8814064b79a0c49662ad3af537) and GFED5 (https://doi.org/10.5281/zenodo.7668424) are freely available.

Code availability

Scripts for the preprocessing and analysis are available via GitHub at https://github.com/SeppeLampe/Global-Burned-Area-Increasingly-Explained-By-Climate-Change.

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Acknowledgements

C.B. was funded by the Met Office Climate Science for Service Partnership (CSSP) Brazil project which is supported by the Department for Science, Innovation & Technology (DSIT). C.B. and N.C. were supported by the Met Office Hadley Centre Climate Programme funded by DSIT. S.L. was supported by a PhD Fundamental Research Grant by Fonds Wetenschappelijk Onderzoek—Vlaanderen (11M7723N). Part of the resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government. D.I.K. was supported by the Natural Environment Research Council as part of the LTSM2 TerraFIRMA project. H.H. is supported by US Department of Energy (DOE), Office of Science (Lab Directed Res & Dev (LDRD) 29IN290162:80941). The Pacific Northwest National Laboratory (PNNL) is operated for DOE by the Battelle Memorial Institute under contract DE-AC05-76RLO1830. M.F. used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID 1202. F.L. is supported by the National Key R&D Program of China (2022YFE0106500). W.L. is supported by the National Key R&D Program of China (grant no. 2019YFA0606604). J.C. is supported by the National Key R&D Program of China (2022YFF0801904). W.T. and M.M. acknowledge funding from the EU Horizon 2020 research and innovation programme (SPARCCLE). L.N. is supported by the Strategic Research Area ‘Modelling the regional and global Earth system’, MERGE, funded by the Swedish government and the simulations were enabled by resources provided by LUNARC, The Centre for Scientific and Technical Computing at Lund University. M.M. received funding from the German Federal Ministry of Education and Research (BMBF) under the research projects QUIDIC (01LP1907A) and is based on work from COST Action CA19139 PROCLIAS (process-based models for climate impact attribution across sectors), supported by COST (European Cooperation in Science and Technology). S.H. acknowledges support from the Max Planck Tandem group programme and from Universidad del Rosario within the programme of Fondos de arranque. A.I. was supported by the JSPS KAKENHI grant no. JP21H05318. We also thank the ISIMIP core team for making these simulations possible.

Author information

Author notes

  1. These authors contributed equally: Chantelle Burton, Seppe Lampe.

Authors and Affiliations

  1. Met Office Hadley Centre, Exeter, UK

    Chantelle Burton, Nikos Christidis & Eleanor Burke

  2. Vrije Universiteit Brussel, Department of Water and Climate, Brussels, Belgium

    Seppe Lampe & Wim Thiery

  3. UK Centre for Ecology and Hydrology, Wallingford, UK

    Douglas I. Kelley

  4. Faculty of Natural Sciences, Universidad del Rosario, Bogotá, Colombia

    Stijn Hantson

  5. Institute for Atmospheric and Climate Science, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland

    Lukas Gudmundsson

  6. Senckenberg Biodiversity and Climate Research Centre, Frankfurt, Germany

    Matthew Forrest & Gitta Lasslop

  7. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Jinfeng Chang

  8. Pacific Northwest National Laboratory, Richland, WA, USA

    Huilin Huang

  9. National Institute for Environmental Studies (NIES), Tsukuba, Japan

    Akihiko Ito

  10. The University of Tokyo, Tokyo, Japan

    Akihiko Ito

  11. Dalhousie University, Department of Earth and Environmental Sciences, Halifax, Nova Scotia, Canada

    Sian Kou-Giesbrecht

  12. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

    Wei Li

  13. Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

    Lars Nieradzik

  14. International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Fang Li

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

    Yang Chen & James Randerson

  16. Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany

    Christopher P. O. Reyer & Matthias Mengel

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  1. Chantelle Burton
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Contributions

C.B. and S.L. contributed equally to the analysis and writing. C.B., S.L., D.I.K., W.T., S.H., N.C., L.G. and M.F. designed the analysis. E.B., J.C., M.F., H.H., A.I., S.K.-G. and W.L. ran the model simulations and contributed data. Y.C. and J.R. contributed observational data. C.B., S.H., F.L., M.F., G.L. and C.P.O.R. co-ordinated the fire sector and simulations. All authors contributed to the final manuscript.

Corresponding author

Correspondence to Seppe Lampe.

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Extended data

Extended Data Fig. 1 Model validation for 4 selected regions.

Distribution of models and observations for probability distribution (top row), quantile-quantile plots (middle row) and power spectra (bottom row). All show relative anomaly compared to observations. In the QQ plots, models are plotted in colours against GFED5 (dotted lines) and Fire CCI5.1 (solid lines).

Extended Data Fig. 2 Description of the general workflow.

This Figure shows how the different climate forcing data (top row) leads to differences in modelled burned areas in the fire models (second row). These models are then weighted based on their ability to correctly model (in the factual simulations) the satellite-based burned area observations. Lastly, these weights are applied to construct a factual and counterfactual ensemble, which are then compared. Differences between these two ensembles are the result of climate change forcing.

Extended Data Fig. 3 Description of the weighting.

First, the burned area (BA) observations and simulations are transformed to relative anomalies. Then, we calculate the climatological RMSE and total NME of between the observational RA (monthly) and the factual simulated RA (monthly). From the RMSE, we generate random noise and add that to the simulated values. We repeat this process 1000 times, the bottom right plot is a visualization of the aggregation of these 1000 series (using yearly data instead of monthly for simplification), showing the median value for each model for each timestep along with the P2.5-P97.5. We then combine these 1000 series with the NME and the kneedle algorithm; to find the optimal σD and the according weights. This results in 1000 sets of weights (box shows the inter-quantile range (IQR) centred around the median, while the whiskers extend from the box by 1.5 times the IQR and the dots represent outliers), which are used in our analysis.

Extended Data Table 1 Fire model overview
Full size table
Extended Data Table 2 Global benchmarking for the ranked and annual NME scores used in the model weighing
Full size table

Supplementary information

Supplementary Information

Supplementary Text 1–5, Figs. 1–9 and Tables 1–6.

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Burton, C., Lampe, S., Kelley, D.I. et al. Global burned area increasingly explained by climate change. Nat. Clim. Chang. 14, 1186–1192 (2024). https://doi.org/10.1038/s41558-024-02140-w

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