Climate drivers of fire#

Overview#

This diagnostic includes:

burnt fraction
fire weather control
fuel load/continuity control

The diagnostic relies on the processing of fire climate drivers through the ConFire model (dedicated branch from the GitHub repository available at douglask3/Bayesian_fire_models) and is based on Jones et al. (2024). The diagnostic computes the burnt fraction for each grid cell based on a number of drivers. Additionally, the respective controls due to fire weather and fuel load/continuity are computed. The stochastic control corresponds to the unmodelled processed influencing to fire occurrence. The ESMValTool diagnostic includes only the relevant part of the evaluation code (see douglask3/Bayesian_fire_models for now) as the model run is done offline beforehand. The corresponding result files are made available through a Zenodo archive which can be retrieved inside the diagnostic. The archive is published under the DOI 10.5281/zenodo.14917244, but a specific version of the files can be specified using the corresponding DOI, e.g. the default link to https://zenodo.org/records/14917245. The other option is to provide a local directory which contains the necessary model files.

The ConFire model relies on a variety of observational datasets (see references):

Global Fire Emissions Database version 5
MODIS MOD44B
ESA CCI Biomass
ISIMIP3a GSWP3-W5E5 dataset

Note: If custom ConFire run files are to be used for the diagnostic, they need to be present inside the auxiliary directory defined in the user configuration file config_user.yml.

Available recipes and diagnostics#

Recipes are stored in esmvaltool/recipes/:

recipe_ref_fire.yml

Diagnostics are stored in esmvaltool/diag_scripts/fire/:

fire_diagnostic.py: main diagnostic script calling a util function from diagnostic_run_ConFire.py.
diagnostic_run_ConFire.py: script containing utils functions to run the ConFire model.

User settings in recipe#

Script fire_diagnostic.py

Required settings for script
- var_order: list of climate drivers in the order corresponding to the one specified in the corresponding file from the confire_param directory.
Optional settings for script
- confire_param: path to the directory containing the required files to run the ConFire model or Zenodo URL to retrieve files from a Zenodo archive. If custom files are used, the corresponding directory needs to be present inside the auxiliary data directory defined inside the user configuration. This defaults to the original Zenodo archive otherwise.
- remove_vpd_files: Removing or not the computed vapor pressure deficit files. It will only apply if the vapor pressure deficit is part of var_order. This defaults to False.
- remove_confire_files: Removing or not the files produced during the ConFire model evaluation. This defaults to False.
Required settings for variables

Optional settings for variables

Required settings for preprocessor

Optional settings for preprocessor
Script diagnostic_run_ConFire.py

Required settings for script
- confire_param: path to the directory containing the required files to run the ConFire model (or downloaded files from a Zenodo archive).
- files_input: list containing tuples of variable name and file path for each climate driver present in var_order.
Optional settings for script
- model_name: string containing the input data model for legending the plots.
- timerange: string containing the time range of the input data for legending the plots.
- project: string or list containing the input data project(s).
- experiment: string or list containing the input data experiment(s).
Required settings for variables

Optional settings for variables

Required settings for preprocessor

Optional settings for preprocessor

Variables#

pr (atmos, monthly mean, longitude latitude time)
tasmax (atmos, monthly mean, longitude latitude time)
treeFrac (land, monthly mean, longitude latitude time)
vegFrac (land, monthly mean, longitude latitude time)
cveg (land, monthly mean, longitude latitude time)
tas (atmos, monthly mean, longitude latitude time): used to compute vpd.
hurs (atmos, monthly mean, longitude latitude time): used to compue vpd.

References#

Jones, M. W., Kelley, D. I., Burton, C. A., Di Giuseppe, F., Barbosa, M. L. F., Brambleby, E., Hartley, A. J., Lombardi, A., Mataveli, G., McNorton, J. R., Spuler, F. R., Wessel, J. B., Abatzoglou, J. T., Anderson, L. O., Andela, N., Archibald, S., Armenteras, D., Burke, E., Carmenta, R., Chuvieco, E., Clarke, H., Doerr, S. H., Fernandes, P. M., Giglio, L., Hamilton, D. S., Hantson, S., Harris, S., Jain, P., Kolden, C. A., Kurvits, T., Lampe, S., Meier, S., New, S., Parrington, M., Perron, M. M. G., Qu, Y., Ribeiro, N. S., Saharjo, B. H., San-Miguel-Ayanz, J., Shuman, J. K., Tanpipat, V., van der Werf, G. R., Veraverbeke, S., and Xanthopoulos, G.: State of Wildfires 2023–2024, Earth Syst. Sci. Data, 16, 3601–3685, https://doi.org/10.5194/essd-16-3601-2024, 2024.
Yang Chen, Joanne Hall, Dave van Wees, Niels Andela, Stijn Hantson, Louis Giglio, Guido R. van der Werf, Douglas C. Morton, & James T. Randerson. (2023). Global Fire Emissions Database (GFED5) Burned Area (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7668424.
DiMiceli, C., Sohlberg, R., Townshend, J. (2022). MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. Accessed 2025-04-01 from https://doi.org/10.5067/MODIS/MOD44B.061.
Santoro, M.; Cartus, O. (2024): ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021, v5.01. NERC EDS Centre for Environmental Data Analysis, 22 August 2024. https://dx.doi.org/10.5285/bf535053562141c6bb7ad831f5998d77.
Stefan Lange, Matthias Mengel, Simon Treu, Matthias Büchner (2022): ISIMIP3a atmospheric climate input data (v1.0). ISIMIP Repository. https://doi.org/10.48364/ISIMIP.982724.