@Article{Tang_AdvClimChangRes_20211001,
 author		= {Rongyun Tang and Jiafu Mao and Mingzhou Jin and Anping Chen and Yan Yu and Xiaoying Shi and Yulong Zhang and Forrest M. Hoffman and Min Xu and Yaoping Wang},
 title		= {Interannual Variability and Climatic Sensitivity of Global Wildfire Activity},
 journal	= AdvClimChangRes,
 volume		= 12,
 number		= 5,
 pages		= {686--695},
 doi		= {10.1016/j.accre.2021.07.001},
 day		= 1,
 month		= oct,
 year		= 2021,
 abstract	= {Understanding historical wildfire variations and their environmental driving mechanisms is key to predicting and mitigating wildfires. However, current knowledge of climatic responses and regional contributions to the interannual variability (IAV) of global burned area remains limited. Using recent satellite-derived wildfire products and simulations from version v1.0 of the land component of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM land model [ELM] v1) driven by three different climate forcings, we investigated the burned area IAV and its climatic sensitivity globally and across nine biomes from 1997 to 2018. We found that 1) the ELM simulations generally agreed with the satellite observations in terms of the burned area IAV magnitudes, regional contributions, and covariations with climate factors, confirming the robustness of the ELM to the usage of different climate forcing sources; 2) tropical savannas, tropical forests, and semi-arid grasslands near deserts were primary contributors to the global burned area IAV, collectively accounting for 71.7\%--99.7\% of the global wildfire IAV estimated by both the satellite observations and ELM simulations; 3) precipitation was a major fire suppressing factor and dominated the global and regional burned area IAVs, and temperature and shortwave solar radiation were mostly positively related with burned area IAVs; and 4) noticeable local discrepancies between the ELM and remote-sensing results occurred in semi-arid grasslands, croplands, boreal forests, and wetlands, likely caused by uncertainties in the current ELM fire scheme and the imperfectly derived satellite observations. Our findings revealed the spatiotemporal diversity of wildfire variations, regional contributions and climatic responses, and provided new metrics for wildfire modeling, facilitating the wildfire prediction and management.}
}