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Turco M, Jerez S, Doblas-Reyes FJ, et al. Skilful forecasting of global fire activity using seasonal climate predictions. Nat Commun. 2018;9(1):2718doi: 10.1038/s41467-018-05250-0.
Turco, M., Jerez, S., Doblas-Reyes, F. J., AghaKouchak, A., Llasat, M. C., & Provenzale, A. (2018). Skilful forecasting of global fire activity using seasonal climate predictions. Nature communications, 9(1), 2718. https://doi.org/10.1038/s41467-018-05250-0
Turco, Marco, et al. "Skilful forecasting of global fire activity using seasonal climate predictions." Nature communications vol. 9,1 (2018): 2718. doi: https://doi.org/10.1038/s41467-018-05250-0
Turco M, Jerez S, Doblas-Reyes FJ, AghaKouchak A, Llasat MC, Provenzale A. Skilful forecasting of global fire activity using seasonal climate predictions. Nat Commun. 2018 Jul 13;9(1):2718. doi: 10.1038/s41467-018-05250-0. PMID: 30006529; PMCID: PMC6045620.
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Nat Commun. 2018 Jul 13;9(1):2718. doi: 10.1038/s41467-018-05250-0.

Skilful forecasting of global fire activity using seasonal climate predictions.

Nature communications

Marco Turco, Sonia Jerez, Francisco J Doblas-Reyes, Amir AghaKouchak, Maria Carmen Llasat, Antonello Provenzale

Affiliations

  1. Department of Applied Physics, University of Barcelona, 08028, Barcelona, Spain. [email protected].
  2. Regional Atmospheric Modeling Group, Department of Physics, University of Murcia, 30100, Murcia, Spain.
  3. Barcelona Supercomputing Center (BSC), Carrer de Jordi Girona 29-31, 08034, Barcelona, Spain.
  4. ICREA, Pg. Luís Companys 23, 08010, Barcelona, Spain.
  5. Department of Civil and Environmental Engineering, Center for Hydrometeorology and Remote Sensing, University of California, Irvine, CA, 92697, USA.
  6. Department of Applied Physics, University of Barcelona, 08028, Barcelona, Spain.
  7. Institute of Geosciences and Earth Resources (IGG), National Research Council (CNR), 56124, Pisa, Italy.

PMID: 30006529 PMCID: PMC6045620 DOI: 10.1038/s41467-018-05250-0

Abstract

Societal exposure to large fires has been increasing in recent years. Estimating the expected fire activity a few months in advance would allow reducing environmental and socio-economic impacts through short-term adaptation and response to climate variability and change. However, seasonal prediction of climate-driven fires is still in its infancy. Here, we discuss a strategy for seasonally forecasting burned area anomalies linking seasonal climate predictions with parsimonious empirical climate-fire models using the standardized precipitation index as the climate predictor for burned area. Assuming near-perfect climate predictions, we obtained skilful predictions of fire activity over a substantial portion of the global burnable area (~60%). Using currently available operational seasonal climate predictions, the skill of fire seasonal forecasts remains high and significant in a large fraction of the burnable area (~40%). These findings reveal an untapped and useful burned area predictive ability using seasonal climate forecasts, which can play a crucial role in fire management strategies and minimise the impact of adverse climate conditions.

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