State of Wildfires 2024-25: ConFLAME Future Projections

ConFLAME future projection and isimip attribution outputs are split by region, using the same regional naming convention as in previous years, with each directory name ending in -2425 to denote the 2024/25 report version: Amazon-2425 – Northeast Amazonia Pantanal-2425 – Pantanal–Chiquitano LA-2425 – Southern California Congo-2425 – Congo Basin Each region directory contains time series outputs from ConFLAME simulations driven by ISIMIP climate data, covering both historical and future projection experiments. The data structure is similar to the attribution outputs found at 10.5281/zenodo.15641876, but differs in experiment types and the inclusion of multiple climate models. Directory Structure <<region>>/time_series/<<experiment>>/... 1. Attribution-style experiments (ISIMIP3a; 2000–2019) For the following experiments: early_industrial – Simulations under pre-industrial climate counterfactual – Simulations with climate change signal removed factual – Simulations with observed historical climate The structure continues as: <<range>>/<<members or percentiles>>/<<metric>>/<<variable>>.csv Range: mean – Regional mean burned area pc-0.95 – Burned area for the grid cells with the highest 5% BA Members or Percentiles: members – Time series for all ensemble members from the posterior percentiles – Precomputed percentile ranges over all members Metric: absolute – Burned area in km² or equivalent climatology – Long-term monthly mean burned area anomaly – Deviation from climatology for that month ratio – Burned area divided by climatology 2. Future projections (ISIMIP3b; Historical: 2000–2014, SSPs: 2015–2100) For the following experiments: historical – Simulations with observed climate up to 2014 ssp126 – Low-emissions scenario ssp370 – Middle-of-the-road scenario ssp585 – High-emissions scenario The structure continues as: model/<<model>>/<<range>>/<<members or percentiles>>/<<metric>>/<<variable>>.csv Models: GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR MRI-ESM2-0 UKESM1-0-LL Important Notes This repo does not include the samples directory (full spatial posterior draws) because these can exceed 1 TB per region. You can easily regenerate samples by installing ConFLAME from:https://github.com/douglask3/Bayesian_fire_modelsand running the provided namelist for the desired experiment.