Extension of Large Fire Emissions From Summer to Autumn and Its Drivers in the Western US

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 degrees 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. Plain Language Summary Global warming has been raising temperature and drying out the western US. The increasingly warmer climate influences the seasonal water cycle over the western US and changes wildfire activity and its seasonality. Explainable artificial intelligence (XAI) is a set of useful tools for interpreting the predictions made by the machine learning (ML) models. Leveraging the power of XAI and a statistical clustering method, we built a ML model to predict the fire emissions over the western US and grouped the grids with large fire emissions by which predictors have larger contributions to the large fire emissions. We identified four groups of large fire emissions controlled by abundant fuel and extreme, moderate, and weak drying conditions, respectively. The drying conditions are contributed by multiple factors, including drought, local dryness, and fire-favorable large-scale meteorological patterns (high temperature, high pressure, and low relative humidity). Additionally, the large fire emission peak of the first three groups extends from summer to autumn. The increased fire emissions in autumn are caused by warmer temperature, decreased fuel moisture, along with concurrent fire-favorable large-scale meteorology and drought. These findings underscore the importance of drying in increasing the autumn risk of large fire emissions across the western US.

Automated summary

The burned areas in the western US have increased ten-fold since the 1980s, driven by factors such as rising temperatures, changing precipitation patterns, and extended drought. Using machine learning and explainable artificial intelligence, a study examined the predictors contributing to large fire emissions and identified fuel load and dryness levels as major drivers. Furthermore, it found that the risk of large fire emissions in autumn has increased due to decreased fuel moisture and concurrent fire-favorable meteorological patterns.