A large ensemble simulation by LPJmL-SPITFIRE to characterize extreme wildfire events in terms of fire danger, burned area and fire carbon emissions

Le résumé fourni par la source

This dataset supports the findings of the manuscript 'Ensemble-based global fire modeling as a tool to characterize extreme wildfire events' by Andreia F. S. Ribeiro, Maik Billing, Kirsten Thonicke, Werner von Bloh, Jakob Wessel, Sabine Undorf, Matthias Forkel, and Jakob Zscheischler. The dataset contains output from the process-based vegetation-fire model LPJmL-SPITFIRE (Oberhagemann et al., 2025) for three fire-related variables: fire danger index (mfiredi), burned area (mburnt_area), and fire carbon emissions (mfirec). The zip archives consist of 40 ensemble members by forcing LPJmL-SPITFIRE with the bias-adjusted and downscaled climate model ACCESS-ESM1-5 large ensemble. The standalone NetCDF files are obtained by forcing LPJmL-SPITFIRE with GSWP3-W5E5 reanalysis forcing. For more information, please read the README.txt and the associated article or contact the authors of the publication. References: Oberhagemann L, Billing M, von Bloh W, Drüke M, Forrest M, Bowring S, Hetzer J, Ribalaygua Batalla J, Thonicke K (2025). Sources of Uncertainty in the Global Fire Model SPITFIRE: Development of LPJmL-SPITFIRE1.9 and Directions for Future Improvements. GMD, https://doi.org/10.5194/gmd-18-2021-2025 By using this dataset, you agree to cite both the LPJmL-SPITFIRE model paper above and the companion manuscript (citation to be updated upon publication).

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.