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Nat. Hazards Earth Syst. Sci., 15, 2037-2057, 2015
https://doi.org/10.5194/nhess-15-2037-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
11 Sep 2015
Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden
W. Yang, M. Gardelin, J. Olsson, and T. Bosshard Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
Abstract. As the risk of a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically downscaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.

Citation: Yang, W., Gardelin, M., Olsson, J., and Bosshard, T.: Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden, Nat. Hazards Earth Syst. Sci., 15, 2037-2057, https://doi.org/10.5194/nhess-15-2037-2015, 2015.
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Short summary
A distribution-based scaling approach was developed and proven useful as a post-process to correct systematic biases in climate modelling outputs (i.e. precipitation, temperature, relative humidity and wind speed) to facilitate the utilisation of climate projections in forest fire risk studies. The result showed reduction of bias in forcing data and an improved description of fire-risk-related indices. Concerning the future climate, southern Sweden is likely to become a more fire-prone region.
A distribution-based scaling approach was developed and proven useful as a post-process to...
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