How much global burned area can be forecast on seasonal time scales using sea surface temperatures

Bibliographic Data

ID15544908
AuthorsYang Chen (0000-0001-5943-3247, University of California, Irvine, corresponding author), Douglas C Morton (0000-0003-2226-1124, Goddard Space Flight Center), Niels Andela (0000-0002-8241-6143, Goddard Space Flight Center), Louis Giglio (0000-0001-6312-7955, University of Maryland, College Park), James T Randerson (0000-0001-6559-7387, University of California, Irvine)
Year2016
Volume11
Issue4
Pages045001-045001
Publication date2016-03-23
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/11/4/045001
OpenAlexW2321676588
LanguageEN
Citations received8
References cited58

Large-scale sea surface temperature (SST) patterns influence the interannual variability of burned area in many regions by means of climate controls on fuel continuity, amount, and moisture content. Some of the variability in burned area is predictable on seasonal timescales because fuel characteristics respond to the cumulative effects of climate prior to the onset of the fire season. Here we systematically evaluated the degree to which annual burned area from the Global Fire Emissions Database version 4 with small fires (GFED4s) can be predicted using SSTs from 14 different ocean regions. We found that about 48% of global burned area can be forecast with a correlation coefficient that is significant at a p 〈 0.01 level using a single ocean climate index (OCI) 3 or more months prior to the month of peak burning. Continental regions where burned area had a higher degree of predictability included equatorial Asia, where 92% of the burned area exceeded the correlation threshold, and Central America, where 86% of the burned area exceeded this threshold. Pacific Ocean indices describing the El Niño-Southern Oscillation were more important than indices from other ocean basins, accounting for about 1/3 of the total predictable global burned area. A model that combined two indices from different oceans considerably improved model performance, suggesting that fires in many regions respond to forcing from more than one ocean basin. Using OCI - burned area relationships and a clustering algorithm, we identified 12 hotspot regions in which fires had a consistent response to SST patterns. Annual burned area in these regions can be predicted with moderate confidence levels, suggesting operational forecasts may be possible with the aim of improving ecosystem management

Climatology · Forcing (mathematics · Predictability · Sea surface temperature · Atmospheric and Environmental Gas Dynamics · Atmospheric chemistry and aerosols · Environmental Science · Fire effects on ecosystems · Geology

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