Future Fire Weather Projections CanLEAD-FWI Version 1.0

Canadian wildfire activity is changing in part because human-caused climate change is causing more hot and dry weather conditions conducive to wildfires, also referred to as “fire weather” (Kirchmeier-Young et al., 2019). Fire weather is described by a system called the Canadian Forest Fire Weather Index (FWI) System, developed by Natural Resources Canada (Natural Resources Canada, n.d.). The FWI System is widely used for wildfire planning in Canada and comprises codes and indices that reflect the potential impact of weather on different aspects of wildfire. Each of these provides different measures of hot, dry and/or windy conditions and their potential impact on wildfire. 


The first three FWI System components are called the fuel moisture codes because they describe the dryness of wildfire fuel at different layers of the forest floor based on weather conditions. These codes are the Fine Fuel Moisture Code (FFMC), the Duff Moisture Code (DMC), and the Drought Code (DC). The last three FWI System components describe how wildfires might behave if started. These components are the Buildup Index (BUI), the Initial Spread Index (ISI), and the Fire Weather Index (FWI). Each of the FWI System codes and indices becomes larger as the weather becomes drier, hotter, and/or windier – as fire weather gets worse. Due to differences in forest type (the wildfire fuel), FWI System values are interpreted differently depending on the region. For example, each Province and Territory uses the FWI System in slightly different ways to understand fire danger (e.g., different thresholds for “extreme” fire danger). Knowledge of regional characteristics is key for using FWI System values in local planning. 


This dataset consists of a single model large ensemble and a single scenario of statistically downscaled climate model projections for all six FWI System components and the fire season length (FFMC, DMC, DC, BUI, ISI, FWI, and fire_season, respectively), on a 0.5° latitude/longitude grid for Canada. Fire season length is determined using daily maximum temperature (tmax) following the methods of Wotton and Flannigan (1993). Spring start-up (start of calculations, and active fire season) occurs on the fourth day following three consecutive days of tmax >12 °C. Autumn shut-down (end of active fire season and beginning of overwintering) occurs on the fourth day after three consecutive days of tmax <5 °C. All of the FWI System components are available to download as an ensemble of daily values from 1950 to 2100 for one emission scenario called Representative Concentration Pathway 8.5 (RCP8.5, van Vuuren et al., 2011). The fire season length is provided as an annual count of days in the fire season. 


This dataset is based on the input data from CanLEAD-CanRCM4-EWEMBI (CanLEADv1; Cannon et al., 2021), a 50-member single model initial-condition large ensemble. Driven by historical (1950–2005) and upper-bound Representative Concentration Pathway 8.5 (RCP8.5, 2006–2100) scenario forcing (van Vuuren et al., 2011), each CanESM2-LE member was dynamically downscaled to 0.44° (∼50 km) resolution using the Canadian Regional Climate Model Version 4 (CanRCM4; Scinocca et al., 2016). Next, CanRCM4-simulated near-surface minimum and maximum daily temperatures, as well as daily average precipitation rate, relative humidity, and wind speed, were adjusted using multivariate bias correction (Cannon, 2018) to the observationally constrained EWEMBI dataset (Frieler et al., 2017, Lange, 2019) after interpolation to a 0.5° latitude/longitude grid (NAM-44i). The bias-corrected values listed here were used to compute the daily FWI System components and the annual fire season length. 


In addition, for the grid boxes where weather station data exists, station-based projections were created through another bias adjustment applied to station-based observations of FWI System values using Quantile Delta Mapping (QDM, Cannon et al., 2015). QDM preserves relative changes in model projections in all quantiles after bias correction. The bias adjustment at station locations was applied to FWI System components rather than to the weather-related variables from which they are computed. 


This data provides future projections of fire weather, helping users respond to shifting conditions and meet the growing demand for projected climate impact data, which supports informed, long-term decision-making on wildfire management. 


References: 


Cannon, A. J., Alford, H., Shrestha, R. R., Kirchmeier‐Young, M. C., & Najafi, M. R. (2021). Canadian Large Ensembles Adjusted Dataset version 1 (CanLEADv1): Multivariate bias‐corrected climate model outputs for terrestrial modelling and attribution studies in North America. Geoscience Data Journal, 9(2), 288–303. https://doi.org/10.1002/gdj3.142 


Cannon, A. J. (2018). Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dynamics, 50(1–2), 31–49. https://doi.org/10.1007/s00382-017-3580-6 


Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015). Bias Correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? Journal of Climate, 28(17), 6938–6959. https://doi.org/10.1175/jcli-d-14-00754.1 


Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski, L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K., Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R., Stevanovic, M., . . . Yamagata, Y. (2017). Assessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geoscientific Model Development, 10(12), 4321–4345. https://doi.org/10.5194/gmd-10-4321-2017 


Kirchmeier‐Young, M. C., Gillett, N. P., Zwiers, F. W., Cannon, A. J., & Anslow, F. S. (2019). Attribution of the influence of Human‐Induced climate change on an extreme fire season. Earth S Future, 7(1), 2–10. https://doi.org/10.1029/2018ef001050


Lange, S. (2019). EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI). (n.d.). https://dataservices.gfz-potsdam.de/pik/showshort.php?id=escidoc:3928916 


Natural Resources Canada (NRCan). [no date]. Background Information: Canadian Forest Fire Weather Index (FWI) System. Accessed on: 2023-04-27. Available at: https://cwfis.cfs.nrcan.gc.ca/background/summary/fwi 


Scinocca, J. F., Kharin, V. V., Jiao, Y., Qian, M. W., Lazare, M., Solheim, L., Flato, G. M., Biner, S., Desgagne, M., & Dugas, B. (2015). Coordinated global and regional climate modeling*. Journal of Climate, 29(1), 17–35. https://doi.org/10.1175/jcli-d-15-0161.1 


Van Vliet, L., Fyke, J., Nakoneczny, S., Murdock, T. Q., & Jafarpur, P. (2024). Developing user-informed fire weather projections for Canada. Climate Services, 35, 100505. https://doi.org/10.1016/j.cliser.2024.100505


Van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., & Rose, S. K. (2011). The representative concentration pathways: an overview. Climatic Change, 109(1–2), 5–31. https://doi.org/10.1007/s10584-011-0148-z 


Wotton, B. M., & Flannigan, M. D. (1993). Length of the fire season in a changing climate. The Forestry Chronicle, 69(2), 187–192. https://doi.org/10.5558/tfc69187-2

Organization: Environment and Climate Change Canada | Environnement et Changement climatique Canada