Preprints
https://doi.org/10.5194/gmd-2024-31
https://doi.org/10.5194/gmd-2024-31
Submitted as: model description paper
 | 
06 Mar 2024
Submitted as: model description paper |  | 06 Mar 2024
Status: a revised version of this preprint is currently under review for the journal GMD.

The Global Forest Fire Emissions Prediction System version 1.0

Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul Makar, and Dan Thompson

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in real time for global air-quality forecasting. The model uses a bottom-up approach, based on remotely-sensed hotspot locations and global databases linking burned area per hotspot to ecosystem-type classification at a 1-km resolution. Unlike other global forest fire emissions models, GFFEPS provides dynamic estimates of fuel consumption and fire behaviour based on the Canadian Forest Fire Danger Rating System. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3-hour time steps (in contrast to non-dynamic models that use fixed consumption rates and require collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in near-real-time forecasting applications as well as historical simulations for which data are available. A study was conducted running GFFEPS through a six-year period (2015–2020). Regional annual total smoke emissions, burned area and total fuel consumption per unit area as predicted by GFFEPS were generated to assess model performance over multiple years and regions. The model distinguished grass-dominated regions from forested, while also showed high variability in regions affected by El Niño and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including GFAS, GFED4.1s and FINN1.5/2.5. GFFEPS estimated values lower than GFAS/GFED (80 %/74 %), and estimated values similar to FINN1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. An effort is underway to validate the model, with further developments and improvements expected.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul Makar, and Dan Thompson

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2024-31', Anonymous Referee #1, 14 Apr 2024
    • AC2: 'Reply on RC1', Kerry Anderson, 29 May 2024
    • AC3: 'Reply on RC1', Kerry Anderson, 29 May 2024
  • RC2: 'Comment on gmd-2024-31', Anonymous Referee #2, 25 Apr 2024
    • AC1: 'Reply on RC2', Kerry Anderson, 29 May 2024
    • AC4: 'Reply on RC2', Kerry Anderson, 29 May 2024
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul Makar, and Dan Thompson
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul Makar, and Dan Thompson

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Short summary
The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.