Fire danger
The research for this study used the daily climate output directly from an ensemble of versions of the HadCM3 climate model 2.5° × 3.75° latitude-longitude resolution [Gordon et al., 2000; Collins et al., 2001] driven by a scenario of CO2 concentrations arising from the high A1FI emissions scenario [IPCC, 2000] (this scenario neglects feedbacks between the climate and the carbon cycle [Cox et al., 2000] and does not include dynamic vegetation [Betts et al., 2004]).
Fig 1. Difference between areas of high FFDI in 2080s and the 1961-90 baseline, i.e. the new areas moving into the high FFDI or above by the 2080s.
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The ensemble contained 17 model runs, and of these 13 showed a global average temperature rise of 4 °C or higher by the 2080s.
The combined output data from this subset of models was used to calculate the McArthur Forest Fire Danger Index Mark 5 (FFDI) (A. G. McArthur, Grassland fire danger meter Mk I, published as slide rule, 1973). It is a weather-based fire index derived empirically in southeast Australia, but subsequently used elsewhere [Golding and Betts, 2008]. The FFDI indicates the probability of a fire starting, rate of spread, intensity, and difficulty of suppression.
Fire index
An index of 1 means that a fire will not burn, or burn so slowly that control presents little difficulty. An index of 100 means that fires will burn so fast and hot that control is virtually impossible. The index is divided into five fire danger ratings (low: 0-5, moderate: 5-12, high: 12-24, very high: 24-50, and extreme: 50-100) but for the purposes of this poster, the 'high' fire danger category was selected as a threshold.
Fig 2. All areas of high FFDI in 2080s. Orange shading indicates where the FFDI was high in the 1961-90 baseline. Red shading indicates the regions that have crossed the high fire danger threshold by the 2080s.
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As a purely meteorological measure, it does not take account of the availability of fuel. However, areas highlighted in the poster as crossing the high fire danger threshold are currently vegetated.
The results are shown in Figures 1 and 2.
References
Methodology applied in this study: Golding, N., and R. Betts (2008), Fire risk in Amazonia due to climate change in the HadCM3 climate model: Potential interactions with deforestation, Global Biogeochem. Cycles, 22, GB4007, doi:10.1029/2007GB003166
Betts R.A., Cox P.M., Collins M., Harris P.P., Huntingford C. and Jones C.D., 2004, The role of ecosystem-atmosphere interactions in simulated Amazonian precipitation decrease and forest dieback under global climate warming, Theoretical and Applied Climatology, 78(1-3), 157-175.
Collins M., Tett S.F.B. and Cooper C., 2001, The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments, Climate Dynamics, 17(1), 61-81.
Cox P.M., Betts R.A., Jones C.D., Spall S.A. and Totterdell I.J., 2000, Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 408, 184-187, doi:10.1038/35041539.
Golding N. and Betts R., 2008, Fire risk in Amazonia due to climate change in the HadCM3 climate model: Potential interactions with deforestation, Global Biogeochemical Cycles, 22, GB4007, doi:10.1029/2007GB003166.
Gordon C., Cooper C., Senior C.A., Banks H., Gregory J.M., Johns T.C., Mitchell J.F.B. and Wood R.A., 2000, The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Climate Dynamics, 16(2-3), 147-168.
IPCC, 2000, Land Use, Land-use Change, and Forestry. A Special Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, 377 pp.
