Skilful forecasting of global fire activity using seasonal climate predictions

Marco Turco (), Sonia Jerez, Francisco J. Doblas-Reyes, Amir AghaKouchak, Maria Carmen Llasat and Antonello Provenzale
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Marco Turco: University of Barcelona
Sonia Jerez: Department of Physics, University of Murcia
Francisco J. Doblas-Reyes: Barcelona Supercomputing Center (BSC)
Amir AghaKouchak: University of California
Maria Carmen Llasat: University of Barcelona
Antonello Provenzale: National Research Council (CNR)

Nature Communications, 2018, vol. 9, issue 1, 1-9

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-05250-0

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