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Skilful forecasting of global fire activity using seasonal climate predictions.


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.

SUBMITTER: Turco M 

PROVIDER: S-EPMC6045620 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Skilful forecasting of global fire activity using seasonal climate predictions.

Turco Marco M   Jerez Sonia S   Doblas-Reyes Francisco J FJ   AghaKouchak Amir A   Llasat Maria Carmen MC   Provenzale Antonello A  

Nature communications 20180713 1


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-  ...[more]

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