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Climate warming increases extreme daily wildfire growth risk in California

Nature volume 621, pages 760–766 (2023)Cite this article

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Abstract

California has experienced enhanced extreme wildfire behaviour in recent years1,2,3, leading to substantial loss of life and property4,5. Some portion of the change in wildfire behaviour is attributable to anthropogenic climate warming, but formally quantifying this contribution is difficult because of numerous confounding factors6,7 and because wildfires are below the grid scale of global climate models. Here we use machine learning to quantify empirical relationships between temperature (as well as the influence of temperature on aridity) and the risk of extreme daily wildfire growth (>10,000 acres) in California and find that the influence of temperature on the risk is primarily mediated through its influence on fuel moisture. We use the uncovered relationships to estimate the changes in extreme daily wildfire growth risk under anthropogenic warming by subjecting historical fires from 2003 to 2020 to differing background climatological temperatures and aridity conditions. We find that the influence of anthropogenic warming on the risk of extreme daily wildfire growth varies appreciably on a fire-by-fire and day-by-day basis, depending on whether or not climate warming pushes conditions over certain thresholds of aridity, such as 1.5 kPa of vapour-pressure deficit and 10% dead fuel moisture. So far, anthropogenic warming has enhanced the aggregate expected frequency of extreme daily wildfire growth by 25% (5–95 range of 14–36%), on average, relative to preindustrial conditions. But for some fires, there was approximately no change, and for other fires, the enhancement has been as much as 461%. When historical fires are subjected to a range of projected end-of-century conditions, the aggregate expected frequency of extreme daily wildfire growth events increases by 59% (5–95 range of 47–71%) under a low SSP1–2.6 emissions scenario compared with an increase of 172% (5–95 range of 156–188%) under a very high SSP5–8.5 emissions scenario, relative to preindustrial conditions.

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Fig. 1: Illustration of the method.
Fig. 2: Influence of anthropogenic warming on the risk of extreme wildfire behaviour historically and in the future.
Fig. 3: Physical conditions and mechanisms most responsible for shifts in the risk of extreme wildfire growth.
Fig. 4: Influence of anthropogenic warming on the risk of extreme daily wildfire growth over the lifetimes of the Carr Fire and the North Complex Fire.

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Data availability

The Weather Research and Forecasting model used for the predictor data (Supplementary Information 3) is an open source and can be downloaded from GitHub (https://github.com/wrf-model/WRF/releases). MODIS fire products that were used for the predictand data (Supplementary Information 2) can be downloaded from FIRMS (https://firms.modaps.eosdis.nasa.gov/active_fire/) and the CMIP6 climate model data (Supplementary Information 4) can be downloaded from IPCC WGI Interactive Atlas (https://interactive-atlas.ipcc.ch/regional-information). Source data are provided with this paper.

Code availability

The code for this study is archived at GitHub (https://github.com/ptbrown31/Climate-Driven-Risk-of-Extreme-Wildfire-in-California).

References

  1. Coop, J. D. et al. Extreme fire spread events and area burned under recent and future climate in the western USA. Glob. Ecol. Biogeogr. 31, 1949–1959 (2022).

    Article  Google Scholar 

  2. Juang, C. S. et al. Rapid growth of large forest fires drives the exponential response of annual forest-fire area to aridity in the western United States. Geophys. Res. Lett. 49, e2021GL097131 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. Keeley, J. E. & Syphard, A. D. Large California wildfires: 2020 fires in historical context. Fire Ecol. 17, 22 (2021).

    Article  Google Scholar 

  4. Wang, D. et al. Economic footprint of California wildfires in 2018. Nat. Sustain. 4, 252–260 (2021).

    Article  Google Scholar 

  5. Bowman, D. M. J. S. et al. Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol. 1, 0058 (2017).

    Article  Google Scholar 

  6. Starrs, C. F., Butsic, V., Stephens, C. & Stewart, W. The impact of land ownership, firefighting, and reserve status on fire probability in California. Environ. Res. Lett. 13, 034025 (2018).

    Article  ADS  Google Scholar 

  7. Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).

    Article  ADS  Google Scholar 

  8. Seneviratne, S. I. et al. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.), pp. 1513–1766 (Cambridge Univ. Press, 2021).

  9. Mandel, J. et al. Recent advances and applications of WRF–SFIRE. Nat. Hazards Earth Syst. Sci. 14, 2829–2845 (2014).

    Article  ADS  Google Scholar 

  10. Rabin, S. S. et al. The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions. Geosci. Model Dev. 10, 1175–1197 (2017).

    Article  ADS  CAS  Google Scholar 

  11. Goss, M. et al. Climate change is increasing the likelihood of extreme autumn wildfire conditions across California. Environ. Res. Lett. 15, 094016 (2020).

    Article  ADS  Google Scholar 

  12. Williams, A. P. et al. Observed impacts of Anthropogenic climate change on wildfire in California. Earths Future 7, 892–910 (2019).

    Article  ADS  Google Scholar 

  13. Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Seager, R. et al. Climatology, variability, and trends in the U.S. vapor pressure deficit, an important fire-related meteorological quantity. J. Appl. Meteorol. Climatol. 54, 1121–1141 (2015).

    Article  ADS  Google Scholar 

  15. Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  16. van Oldenborgh, G. J. et al. Attribution of the Australian bushfire risk to anthropogenic climate change. Nat. Hazards Earth Syst. Sci. 21, 941–960 (2021).

    Article  ADS  Google Scholar 

  17. Abatzoglou, J. T., Williams, A. P. & Barbero, R. Global emergence of Anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).

    Article  ADS  Google Scholar 

  18. Jain, P., Castellanos-Acuna, D., Coogan, S. C. P., Abatzoglou, J. T. & Flannigan, M. D. Observed increases in extreme fire weather driven by atmospheric humidity and temperature. Nat. Clim. Change 12, 63–70 (2022).

    Article  ADS  Google Scholar 

  19. Wang, S. S.-C., Qian, Y., Leung, L. R. & Zhang, Y. Identifying key drivers of wildfires in the contiguous US using machine learning and game theory interpretation. Earths Future 9, e2020EF001910 (2021).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  20. Huang, Y., Jin, Y., Schwartz, M. W. & Thorne, J. H. Intensified burn severity in California’s northern coastal mountains by drier climatic condition. Environ. Res. Lett. 15, 104033 (2020).

    Article  ADS  Google Scholar 

  21. Elia, M. et al. Estimating the probability of wildfire occurrence in Mediterranean landscapes using Artificial Neural Networks. Environ. Impact Assess. Rev. 85, 106474 (2020).

    Article  Google Scholar 

  22. Satir, O., Berberoglu, S. & Donmez, C. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomat. Nat. Hazards Risk 7, 1645–1658 (2016).

    Article  Google Scholar 

  23. Werth, P. A. et. al. Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers, and Meteorologists. General Technical Report PNW-GTR-891 (U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2016).

  24. Schär, C., Frei, C., Lüthi, D. & Davies, H. C. Surrogate climate-change scenarios for regional climate models. Geophys. Res. Lett. 23, 669–672 (1996).

    Article  ADS  Google Scholar 

  25. Patricola, C. M. & Wehner, M. F. Anthropogenic influences on major tropical cyclone events. Nature 563, 339–346 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  26. Otto, F. E. L. Attribution of weather and climate events. Annu. Rev. Environ. Resour. 42, 627–646 (2017).

    Article  Google Scholar 

  27. French, N. H. F., Kasischke, E. S. & Williams, D. G. Variability in the emission of carbon-based trace gases from wildfire in the Alaskan boreal forest. J. Geophys. Res. 108, FFR 7-1–FFR 7-11 (2003).

    Google Scholar 

  28. Flannigan, M. D., Logan, K. A., Amiro, B. D., Skinner, W. R. & Stocks, B. J. Future area burned in Canada. Clim. Change 72, 1–16 (2005).

    Article  ADS  CAS  Google Scholar 

  29. Conard, S. G. et al. Determining effects of area burned and fire severity on carbon cycling and emissions in Siberia. Clim. Change 55, 197–211 (2002).

    Article  CAS  Google Scholar 

  30. Potter, B. E. & McEvoy, D. Weather factors associated with extremely large fires and fire growth days. Earth Interact. 25, 160–176 (2021).

    Article  ADS  Google Scholar 

  31. Gutierrez, A. A. et al. Wildfire response to changing daily temperature extremes in California’s Sierra Nevada. Sci. Adv. 7, eabe6417 (2021).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  32. Philip, S. et al. A protocol for probabilistic extreme event attribution analyses.Adv. Stat. Climatol. Meteorol. Oceanogr. 6, 177–203 (2020).

    Article  ADS  Google Scholar 

  33. Bindoff, N. L. et al. in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (ed. Stocker, T. F.), pp. 857–952 (Cambridge Univ. Press, 2013).

  34. Duane, A., Castellnou, M. & Brotons, L. Towards a comprehensive look at global drivers of novel extreme wildfire events. Clim. Change 165, 43 (2021).

    Article  ADS  Google Scholar 

  35. Woollings, T. et al. Blocking and its response to climate change. Current Clim. Change Rep. 4, 287–300 (2018).

    Article  Google Scholar 

  36. Swain, D. L. A shorter, sharper rainy season amplifies California wildfire risk. Geophys. Res. Lett. 48, e2021GL092843 (2021).

    Article  ADS  Google Scholar 

  37. Zhuang, Y., Fu, R., Santer, B. D., Dickinson, R. E. & Hall, A. Quantifying contributions of natural variability and anthropogenic forcings on increased fire weather risk over the western United States. Proc. Natl Acad. Sci. 118, e2111875118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Stephens, S. L. et al. Drought, tree mortality, and wildfire in forests adapted to frequent fire. BioScience 68, 77–88 (2018).

    Article  Google Scholar 

  39. Westerling, A. L. Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150178 (2016).

    Article  Google Scholar 

  40. Ma, W. et al. Assessing climate change impacts on live fuel moisture and wildfire risk using a hydrodynamic vegetation model. Biogeosciences 18, 4005–4020 (2021).

    Article  ADS  MathSciNet  CAS  Google Scholar 

Download references

Acknowledgements

San José State’s Wildfire Interdisciplinary Research Center (WIRC) is supported by the National Science Foundation (NSF)’s Industry–University Cooperative Research Center (IUCRC) Program (award number 2113931). We thank the Pacific Gas and Electric Meteorology Operations and Fire Science team for useful discussion throughout the project. We acknowledge the teams at DTN (https://www.dtn.com/), Atmospheric Data Solutions (http://www.atmosphericdatasolutions.com/) and Sonoma Technology (http://www.sonomatech.com/) for data collection and preprocessing. We also acknowledge M. Voss for technical support and A. J. Eiserloh, R. Bagley and P. Pall for valuable discussions. This project was partially funded by a contract (C6909) between the San José State University Research Foundation and Pacific Gas and Electric titled ‘Understanding Extreme Fire Weather Conditions Using a 30-Year High-Resolution WRF Model Dataset’. A.M. was supported by the Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy as part of the Regional and Global Model Analysis program area within the Earth and Environmental Systems Modeling Program under contract number DE-AC02-05CH11231.

Author information

Authors and Affiliations

  1. Climate and Energy Team, The Breakthrough Institute, Berkeley, CA, USA

    Patrick T. Brown

  2. Wildfire Interdisciplinary Research Center (WIRC), San José State University, San Jose, CA, USA

    Patrick T. Brown, Holt Hanley, Adam K. Kochanski & Craig B. Clements

  3. Energy Policy and Climate Program, Johns Hopkins University, Baltimore, MD, USA

    Patrick T. Brown

  4. Department of Meteorology and Climate Science, San José State University, San Jose, CA, USA

    Holt Hanley, Adam K. Kochanski & Craig B. Clements

  5. KSBW News, Salinas, CA, USA

    Holt Hanley

  6. Climate and Ecosystems Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Ankur Mahesh

  7. Earth and Planetary Science, University of California, Berkeley, Berkeley, CA, USA

    Ankur Mahesh

  8. Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA

    Colorado Reed

  9. Pacific Gas and Electric Company, Oakland, CA, USA

    Scott J. Strenfel

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

    Steven J. Davis

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Contributions

S.J.S. introduced P.T.B. and H.H. to the broad conceptual framework used in this study (using machine learning models to predict the risk of extreme wildfire growth), and P.T.B. and H.H. worked together to refine the conceptual framework for climate-change-related applications. P.T.B. conceived of the specific methodology of the study, performed all the analyses and wrote an initial draft of the paper. A.M. and C.R. contributed to the technical and philosophical aspects of the machine learning experiment design and validation. S.J.D. created a schematic for Fig. 1 and helped refine communication (including figure design) throughout the study. A.K.K. and C.B.C. provided domain expertise in wildfire science, advising on which predictor variables to use. P.T.B., H.H., A.M., C.R., S.J.S., S.J.D., AKK. and C.B.C. all contributed to the interpretation of results and refinement of the manuscript.

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Correspondence to Patrick T. Brown.

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Supplementary information

Supplementary Information

The Supplementary Information file provides further methodological details and sensitivity tests of the results of the study. It is divided into subsections, which include 27 figures and 4 tables.

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Brown, P.T., Hanley, H., Mahesh, A. et al. Climate warming increases extreme daily wildfire growth risk in California. Nature 621, 760–766 (2023). https://doi.org/10.1038/s41586-023-06444-3

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