气候变化领域集成服务门户(CCPortal): Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales

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DOI	10.1029/2019MS001955
	Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales
	Chen Y.; Randerson J.T.; Coffield S.R.; Foufoula-Georgiou E.; Smyth P.; Graff C.A.; Morton D.C.; Andela N.; van der Werf G.R.; Giglio L.; Ott L.E.
发表日期	2020
ISSN	19422466
卷号	12 期号:9
英文摘要	Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire-prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region-specific seasonality, long-term trends, recent fire observations, and climate drivers representing both large-scale climate variability and local fire weather. We cross-validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near-real-time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system. ©2020. The Authors.
英文关键词	autoregression; El Niño–Southern Oscillation (ENSO); fire forecasting; ocean climate indices; statistical model; vapor pressure deficit
语种	英语
scopus关键词	Atmospheric composition; Forecasting; Large scale systems; Smoke; Autoregressive integrated moving average models; Climate variability; Ecosystem functions; Effective management; Exogenous variables; Forecasting system; Optimized parameter; Time series modeling; Fires; carbon cycle; climate change; emission; seasonality; weather forecasting
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156644
作者单位	Department of Earth System Science, University of California, Irvine, CA, United States; Department of Civil and Environmental Engineering, University of California, Irvine, CA, United States; Department of Computer Science, University of California, Irvine, CA, United States; Department of Statistics, University of California, Irvine, United States; Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United States; Faculty of Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Department of Geographical Sciences, University of Maryland, College Park, MD, United States; Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, United States
推荐引用方式 GB/T 7714	Chen Y.,Randerson J.T.,Coffield S.R.,et al. Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales[J],2020,12(9).
APA	Chen Y..,Randerson J.T..,Coffield S.R..,Foufoula-Georgiou E..,Smyth P..,...&Ott L.E..(2020).Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales.Journal of Advances in Modeling Earth Systems,12(9).
MLA	Chen Y.,et al."Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales".Journal of Advances in Modeling Earth Systems 12.9(2020).