Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western US
Abstract Burned areas in the western US have increased ten‐fold since 1980s, which are attributable to multiple factors, including increasing heat, changing precipitation patterns, and extended drought. To better understand how these factors contribute to large fire emissions (gridded monthly fire e...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-07-01
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| Series: | Earth's Future |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2022EF003086 |
| _version_ | 1797771009949958144 |
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| author | S. S.‐C. Wang L. R. Leung Y. Qian |
| author_facet | S. S.‐C. Wang L. R. Leung Y. Qian |
| author_sort | S. S.‐C. Wang |
| collection | DOAJ |
| description | Abstract Burned areas in the western US have increased ten‐fold since 1980s, which are attributable to multiple factors, including increasing heat, changing precipitation patterns, and extended drought. To better understand how these factors contribute to large fire emissions (gridded monthly fire emissions >95th percentile of all the fire emissions in the western US; 0.009 Gg/month), we build a machine learning model to predict fire emissions (PM2.5) over the western US at 0.25° resolution, interpreted using explainable artificial intelligence (XAI). From the predictor contributions derived from XAI, we conduct k‐means clustering analysis to identify four clusters of predictor variables representing different drivers of large fire emissions. The four clusters feature the contributions of fuel load (Cluster 1) and different levels of dryness (Cluster 2–4), controlled by fuel moisture, drought condition, and fire‐favorable large‐scale meteorological patterns featuring high temperature, high pressure, and low relative humidity. In the past two decades, large fire emissions peak in summer. However, large fire emissions increased significantly in September and October in 2010–2020 relative to 2000–2009, extending the peak large fire emissions from summer to autumn. The larger enhancements of large fire emissions during autumn compared to summer are contributed by decreased fuel moisture, along with more frequent concurrent fire‐favorable large‐scale meteorological patterns and drought. These results highlight fuel drying as a common driver supported by multiple drivers, such as warmer temperature and more frequent synoptic patterns favorable for fires, in increasing the autumn risk of large fire emissions across the western US. |
| first_indexed | 2024-03-12T21:30:20Z |
| format | Article |
| id | doaj.art-336ca60f1ec846bd90b50fb56b28ef71 |
| institution | Directory Open Access Journal |
| issn | 2328-4277 |
| language | English |
| last_indexed | 2024-03-12T21:30:20Z |
| publishDate | 2023-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Earth's Future |
| spelling | doaj.art-336ca60f1ec846bd90b50fb56b28ef712023-07-27T19:18:31ZengWileyEarth's Future2328-42772023-07-01117n/an/a10.1029/2022EF003086Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western USS. S.‐C. Wang0L. R. Leung1Y. Qian2Atmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USAAtmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USAAtmospheric Sciences and Global Change Division Pacific Northwest National Laboratory Richland WA USAAbstract Burned areas in the western US have increased ten‐fold since 1980s, which are attributable to multiple factors, including increasing heat, changing precipitation patterns, and extended drought. To better understand how these factors contribute to large fire emissions (gridded monthly fire emissions >95th percentile of all the fire emissions in the western US; 0.009 Gg/month), we build a machine learning model to predict fire emissions (PM2.5) over the western US at 0.25° resolution, interpreted using explainable artificial intelligence (XAI). From the predictor contributions derived from XAI, we conduct k‐means clustering analysis to identify four clusters of predictor variables representing different drivers of large fire emissions. The four clusters feature the contributions of fuel load (Cluster 1) and different levels of dryness (Cluster 2–4), controlled by fuel moisture, drought condition, and fire‐favorable large‐scale meteorological patterns featuring high temperature, high pressure, and low relative humidity. In the past two decades, large fire emissions peak in summer. However, large fire emissions increased significantly in September and October in 2010–2020 relative to 2000–2009, extending the peak large fire emissions from summer to autumn. The larger enhancements of large fire emissions during autumn compared to summer are contributed by decreased fuel moisture, along with more frequent concurrent fire‐favorable large‐scale meteorological patterns and drought. These results highlight fuel drying as a common driver supported by multiple drivers, such as warmer temperature and more frequent synoptic patterns favorable for fires, in increasing the autumn risk of large fire emissions across the western US.https://doi.org/10.1029/2022EF003086wildfiresmachine learning |
| spellingShingle | S. S.‐C. Wang L. R. Leung Y. Qian Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western US Earth's Future wildfires machine learning |
| title | Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western US |
| title_full | Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western US |
| title_fullStr | Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western US |
| title_full_unstemmed | Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western US |
| title_short | Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western US |
| title_sort | extension of large fire emissions from summer to autumn and its drivers in the western us |
| topic | wildfires machine learning |
| url | https://doi.org/10.1029/2022EF003086 |
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