Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales.

ABSTRACT: 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 ac

SUBMITTER: Chen Y 

PROVIDER: S-EPMC7540459 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature