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.
Description
Skilful forecasting of global fire activity using seasonal climate predictions | Nature Communications
%0 Journal Article
%1 turco2018skilful
%A Turco, Marco
%A Jerez, Sonia
%A Doblas-Reyes, Francisco J.
%A AghaKouchak, Amir
%A Llasat, Maria Carmen
%A Provenzale, Antonello
%D 2018
%J Nature Communications
%K MySeasonalCarbonWork fire seasonal
%N 1
%P 2718
%R 10.1038/s41467-018-05250-0
%T Skilful forecasting of global fire activity using seasonal climate predictions
%U https://doi.org/10.1038/s41467-018-05250-0
%V 9
%X 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.
@article{turco2018skilful,
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.},
added-at = {2018-07-13T15:34:37.000+0200},
author = {Turco, Marco and Jerez, Sonia and Doblas-Reyes, Francisco J. and AghaKouchak, Amir and Llasat, Maria Carmen and Provenzale, Antonello},
biburl = {https://www.bibsonomy.org/bibtex/2dca032b3fbe909adcb1cdea133d50642/pbett},
description = {Skilful forecasting of global fire activity using seasonal climate predictions | Nature Communications},
doi = {10.1038/s41467-018-05250-0},
interhash = {6669a0c266ba8782e17955ef6623962f},
intrahash = {dca032b3fbe909adcb1cdea133d50642},
issn = {20411723},
journal = {Nature Communications},
keywords = {MySeasonalCarbonWork fire seasonal},
number = 1,
pages = 2718,
refid = {Turco2018},
timestamp = {2019-08-20T17:13:26.000+0200},
title = {Skilful forecasting of global fire activity using seasonal climate predictions},
url = {https://doi.org/10.1038/s41467-018-05250-0},
volume = 9,
year = 2018
}