Skilful forecasting of global fire activity using seasonal climate predictions

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...

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Detalles Bibliográficos
Autores: Turco, Marco, Jerez, Sonia, Doblas-Reyes, Francisco|||0000-0002-6622-4280, AghaKouchak, Amir, Llasat, Maria Carmen, Provenzale, Antonello
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/119384
Acceso en línea:https://hdl.handle.net/2117/119384
https://dx.doi.org/10.1038/s41467-018-05250-0
Access Level:acceso abierto
Palabra clave:Climate science
Seasonal prediction (Meteorology)
Season prediction
Skilful predictions of fire
Previsió del temps
Clima--Observacions
Àrees temàtiques de la UPC::Energies
Descripción
Sumario: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.