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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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).
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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.
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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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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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DOI: https://doi.org/10.1038/s41586-023-06444-3
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