Warm Arctic-Cold Eurasia pattern helps predict spring wildfire burned area in West Siberia

Zhicong Yin, Yijia Zhang, Shengping He and Huijun Wang ()
Additional contact information
Zhicong Yin: Nanjing University of Information Science & Technology
Yijia Zhang: Nanjing University of Information Science & Technology
Shengping He: University of Bergen and Bjerknes Centre for Climate Research
Huijun Wang: Nanjing University of Information Science & Technology

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Extreme wildfires have devastating impacts on multiple fronts, and associated carbon greatly heats the earth’s climate. Whether and how to predict wildfires becomes a critical question. In this study, we find that the preceding-winter “warm Arctic-cold Eurasia” (WACE) pattern significantly enlarges the spring burned area in West Siberia. The winter WACE and accompanying snow reduction result in dryness and vegetation exposure in West Siberia in spring, increasing fire risks. A multiple linear regression model is constructed that successfully predicts the spring burned area in West Siberia one season in advance (R-squared coefficient=0.64). The same predictors also well predict the corresponding fire carbon emissions. Independent predictions for spring burned area in 2019 and 2020 are very close to observations, with a mean absolute percentage error of only 3.0%. The findings of this study provide a possibility for guarding humans against extreme wildfires and predicting sharp rises in carbon emissions.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-53470-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53470-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-53470-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().