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Título:	Skilful forecasting of global fire activity using seasonal climate predictions
Autor/a:	Turco, Marco; Jerez, Sonia; Doblas-Reyes, Francisco J.; AghaKouchak, Amir; Llasat, Maria Carmen; Provenzale, Antonello
Otros autores:	Barcelona Supercomputing Center
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.
Abstract:	This work was partially funded by the EU H2020 Project 641762 “ECOPOTENTIAL: Improving Future Ecosystem Benefits through Earth Observations” and the SERVFORFIRE project of the ERA-NET for Climate Services, ERA4CS. M. Turco was supported by the Spanish Juan de la Cierva Programme (IJCI-2015-26953). F.J. Doblas- Reyes was supported by the H2020 IMPREX (GA 641811) and EUCP (GA 776613) projects. A.A. was partially supported by the National Oceanic and Atmospheric Administration (NOAA) award NA14OAR4310222, National Aeronautics and Space Administration (NASA) award NNX15AC27G, and National Science Foundation (NSF) INFEWS grant EAR 1639318. Special thanks to Esteve Canyameras and Xavier Castro for helpful discussions on the study.
Abstract:	Peer Reviewed
Materia(s):	-Àrees temàtiques de la UPC::Energies -Climate science -Seasonal prediction (Meteorology) -Season prediction -Skilful predictions of fire -Previsió del temps -Clima--Observacions
Derechos:	Attribution-NonCommercial-NoDerivs 4.0 Spain http://creativecommons.org/licenses/by-nc-nd/4.0/es/
Tipo de documento:	Artículo - Versión publicada Artículo
Editor:	Nature Publishing Group
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