Downscaling fire weather extremes from historical and projected climate models
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Downscaling fire weather extremes from historical and projected climate models Piyush Jain1,2
· Mari R. Tye3 · Debasish Paimazumder4 · Mike Flannigan2
Received: 7 February 2020 / Accepted: 8 September 2020 / © Crown 2020
Abstract An important aspect of predicting future wildland fire risk is estimating fire weather— weather conducive to the ignition and propagation of fire—under realistic climate change scenarios. Because the majority of area burned occurs on a few days of extreme fire weather, this task should be able to resolve fire weather extremes. In this paper, we present a statistical downscaling procedure based on distribution based scaling (DBS) to bias correct the Fire Weather Index (FWI), part of the Canadian Forest Fire Danger Rating System, as calculated from modeled climate data. Our study area is western Canada (British Columbia and Alberta) and we consider both an historical control period (1990–2000) and three future time periods (2020–2030, 2050–2060, and 2080–2090). The historical data used to calibrate the DBS procedure comprises weather station data and weather from the North American Regional Reanalysis (NARR), whereas the future climate projections are the output of three regional climate models, corresponding to different model parameterizations and downscaled from the NCAR Community Earth System Model under the RCP 8.5 scenario. By fitting a truncated Weibull distribution to observed and modeled FWI values, our method is able to reproduce historical extremes in fire weather indices as determined by the distribution of annual potential spread days, which are defined as days with FWI values greater than 19. Moreover, by calibrating the DBS procedure with gridded reanalysis data as well as station observations, we are able to project future spread day distributions over the entire study area. The results of this study show the DBS procedure leads to a greater number of projected annual spread days at most locations compared with estimates using the uncorrected model output, and that all three RCM models show positive increases in potential annual spread days for the 2050–2060 and 2080–2090 time periods. Keywords Wildfires-Fire · Weather · Index-Spread days-Statistical downscaling-Bias correction- Climate change Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10584-020-02865-5) contains supplementary material, which is available to authorized users. Piyush Jain
[email protected]
Extended author information available on the last page of the article.
Climatic Change
1 Introduction There is a pressing need to understand how future wildland fire regimes will be shaped by changes in the climate, fire ignitions, land use, and vegetation and other factors such as socio-economic conditions. The scientific consensus is that future climate regimes will be associated with an increase in both the frequency and intensity of extreme weather events (Field et al. 2012). Understanding how changes in extreme weather will manifest at the regional or loca
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