Climate Dynamics

, Volume 43, Issue 7–8, pp 1973–1991 | Cite as

Projection of wildfire activity in southern California in the mid-twenty-first century

Article

Abstract

We estimate area burned in southern California at mid-century (2046–2065) for the Intergovernmental Panel on Climate Change A1B scenario. We develop both regressions and a parameterization to predict area burned in three ecoregions, and apply present-day (1981–2000) and future meteorology from the suite of general circulation models to these fire prediction tools. The regressions account for the impacts of both current and antecedent meteorological factors on wildfire activity and explain 40–46 % of the variance in area burned during 1980–2009. The parameterization yields area burned as a function of temperature, precipitation, and relative humidity, and includes the impact of Santa Ana wind and other geographical factors on wildfires. It explains 38 % of the variance in area burned over southern California as a whole, and 64 % of the variance in southwestern California. The parameterization also captures the seasonality of wildfires in three ecoregions of southern California. Using the regressions, we find that area burned likely doubles in Southwestern California by midcentury, and increases by 35 % in the Sierra Nevada and 10 % in central western California. The parameterization suggests a likely increase of 40 % in area burned in southwestern California and 50 % in the Sierra Nevada by midcentury. It also predicts a longer fire season in southwestern California due to warmer and drier conditions on Santa Ana days in November. Our method provides robust estimates of area burned at midcentury, a key metric which can be used to calculate the fire-related effects on air quality, human health, and the associated costs.

Keywords

Wildfire Ensemble projection Southern California Santa Ana wind 

Notes

Acknowledgments

We would like to thank Zhiming Kuang and Brian F. Farrell for useful advice in diagnosing Santa Anas in GCMs. We are grateful for the helpful discussion with Dr. Yufang Jin at University of California, Irvine. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM), for making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. This work was funded by STAR Research Assistance agreement R834282 awarded by the U.S. Environmental Protection Agency (EPA). Although the research described in this article has been funded wholly or in part by the EPA, it has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. Research reported in this publication was supported in part by the NASA Air Quality Applied Science Team and the National Institutes of Health (NIH) under Award Numbers 1R21ES021427 and 5R21ES020194. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Supplementary material

382_2013_2022_MOESM1_ESM.doc (156 kb)
Supplementary material 1 (DOC 155 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xu Yue
    • 1
    • 2
  • Loretta J. Mickley
    • 1
  • Jennifer A. Logan
    • 1
  1. 1.School of Engineering and Applied SciencesHarvard UniversityCambridgeUSA
  2. 2.School of Forestry and Environmental StudiesYale UniversityNew HavenUSA

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