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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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Autores principales: Turco, Marco, Jerez, Sonia, Doblas-Reyes, Francisco J., AghaKouchak, Amir, Llasat, Maria Carmen, Provenzale, Antonello
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6045620/
https://www.ncbi.nlm.nih.gov/pubmed/30006529
http://dx.doi.org/10.1038/s41467-018-05250-0
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author Turco, Marco
Jerez, Sonia
Doblas-Reyes, Francisco J.
AghaKouchak, Amir
Llasat, Maria Carmen
Provenzale, Antonello
author_facet Turco, Marco
Jerez, Sonia
Doblas-Reyes, Francisco J.
AghaKouchak, Amir
Llasat, Maria Carmen
Provenzale, Antonello
author_sort Turco, Marco
collection PubMed
description 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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spelling pubmed-60456202018-07-16 Skilful forecasting of global fire activity using seasonal climate predictions Turco, Marco Jerez, Sonia Doblas-Reyes, Francisco J. AghaKouchak, Amir Llasat, Maria Carmen Provenzale, Antonello Nat Commun Article 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. Nature Publishing Group UK 2018-07-13 /pmc/articles/PMC6045620/ /pubmed/30006529 http://dx.doi.org/10.1038/s41467-018-05250-0 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Turco, Marco
Jerez, Sonia
Doblas-Reyes, Francisco J.
AghaKouchak, Amir
Llasat, Maria Carmen
Provenzale, Antonello
Skilful forecasting of global fire activity using seasonal climate predictions
title Skilful forecasting of global fire activity using seasonal climate predictions
title_full Skilful forecasting of global fire activity using seasonal climate predictions
title_fullStr Skilful forecasting of global fire activity using seasonal climate predictions
title_full_unstemmed Skilful forecasting of global fire activity using seasonal climate predictions
title_short Skilful forecasting of global fire activity using seasonal climate predictions
title_sort skilful forecasting of global fire activity using seasonal climate predictions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6045620/
https://www.ncbi.nlm.nih.gov/pubmed/30006529
http://dx.doi.org/10.1038/s41467-018-05250-0
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