Forecasting Fire Season Severity in South America Using Sea Surface Temperature Anomalies
Open accessAbstract
Sea surface temperature anomalies can predict annual fire season severity in South America up to 3 to 5 months in advance.
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A study in Science found that sea surface temperatures thousands of miles away had a 0.9 correlation with forest fires in certain regions of the Amazon.
“The study found that the sea surface temperatures thousands of miles away had a 0.9 correlation with forest fires in certain regions of the Amazon.”
From A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) · cites this paper as [17]
The duration of the fire record only spanned 10 years, and these correlations were between time series of length 10.
“However, the duration of the fire record only spanned 10 years, and these correlations were between time series of length 10.”
From A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) · cites this paper as [17]
The link between sea surface temperatures and Amazon fires was not solely based on the 0.9 correlation between a length-10 time series.
“What makes it different from the Google Flu Trends study is that the link between sea surface temperatures and fires was not solely based on the 0.9 correlation between a length-10 time series.”
From A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) · cites this paper as [17]
The authors of the Science study explained a physically plausible mechanism by which sea surface temperatures would impact dryness and, as a result, forest fires several months later.
“Instead, the authors were able to explain a physically plausible mechanism that would lead sea surface temperatures to impact dryness, and as a result, forest fires several months later.”
From A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) · cites this paper as [17]
The Science study linked changes in sea surface temperatures to forest fires in the Amazon.
“As an illustrative example, take the Science study that linked changes in sea surface temperatures to forest fires in the Amazon.17”
From A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science (2014) · cites this paper as [17]
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References 33 · Cited by 231
- Authors
- Chen, Y., Randerson, J. T., Morton, D. C., DeFries, R. S., Collatz, G. J., Kasibhatla, P. S., Giglio, L., Jin, Y., & Marlier, M. E.
- Affiliations
- University of California, Irvine (US)
- Goddard Space Flight Center (US)
- Columbia University (US)
- Duke University (US)
- University of Maryland, College Park (US)
- Department of Earth System Science, University of California, Irvine, CA 92697, USA.
- Corresponding author
- Chen, Yang
- ORCID
- Venue
- Science
- Published
- 2011-11-11
- Volume
- 334
- Issue
- 6057
- Pages
- 787-791
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2066801828
- Cited by
- 231
- References
- 33
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 10 · 2023: 16 · 2022: 17 · 2021: 23 · 2020: 16 · 2019: 15 · 2018: 17 · 2017: 22 · 2016: 15 · 2015: 15 · 2014: 16 · 2013: 17 · 2012: 10
- FWCI
- 11.76
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Licence
- cc-by
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Fire effects on ecosystems
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
- Keywords
- sea surface temperature anomalies, Oceanic Niño Index, Atlantic Multidecadal Oscillation, Amazon fires, fire prediction, early warning systems, CO2 emissions, land use change, interannual climate variability
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