Climatic Change

, Volume 139, Issue 3–4, pp 591–605 | Cite as

An investigation of future fuel load and fire weather in Australia

  • Hamish Clarke
  • Andrew J. Pitman
  • Jatin Kala
  • Claire Carouge
  • Vanessa Haverd
  • Jason P. Evans


We present an assessment of the impact of future climate change on two key drivers of fire risk in Australia, fire weather and fuel load. Fire weather conditions are represented by the McArthur Forest Fire Danger Index (FFDI), calculated from a 12-member regional climate model ensemble. Fuel load is predicted from net primary production, simulated using a land surface model forced by the same regional climate model ensemble. Mean annual fine litter is projected to increase across all ensemble members, by 1.2 to 1.7 t ha−1 in temperate areas, 0.3 to 0.5 t ha−1 in grassland areas and 0.7 to 1.1 t ha−1 in subtropical areas. Ensemble changes in annual cumulative FFDI vary widely, from 57 to 550 in temperate areas, −186 to 1372 in grassland areas and −231 to 907 in subtropical areas. These results suggest that uncertainty in FFDI projections will be underestimated if only a single driving model is used. The largest increases in fuel load and fire weather are projected to occur in spring. Deriving fuel load from a land surface model may be possible in other regions, when this information is not directly available from climate model outputs.


Climate change Wildland fire Bushfire CABLE WRF 



This study was supported by the ARC Centre of Excellence for Climate System Science (CE110001028) and by the NCI National Facility at the Australian National University, Australia. Regional climate data have been provided by the NARCLiM project funded by NSW Government Office of Environment and Heritage, University of New South Wales Climate Change Research Centre, ACT Government Environment and Sustainable Development Directorate and other project partners. Jason Evans was funded by the ARC Future Fellowship FT110100576.

Supplementary material

10584_2016_1808_Fig7_ESM.gif (235 kb)

Change from 1990 to 2009 to 2060–2079 for the GCMs considered, numbered by independence rank (from Evans et al. 2014). Models selected are MIROC3.2-medres (1), ECHAM5 (5), CCCM3.1 (9) and CSIRO-Mk3.0 (12). (GIF 234 kb)

10584_2016_1808_MOESM1_ESM.eps (412 kb)
High resolution image (EPS 411 kb)
10584_2016_1808_Fig8_ESM.gif (145 kb)

Scatterplots of BIOS2 mean annual NPP and mean annual fine litter from the same year (left) and the next year (right), in each climate zone. (GIF 144 kb)

10584_2016_1808_MOESM2_ESM.eps (303 kb)
High resolution image (EPS 302 kb)
10584_2016_1808_MOESM3_ESM.docx (16 kb)
ESM 3 (DOCX 15 kb)
10584_2016_1808_Fig9_ESM.gif (402 kb)

Change in mean annual fine litter from each ensemble member (GIF 402 kb)

10584_2016_1808_MOESM4_ESM.eps (459 kb)
High resolution image (EPS 458 kb)
10584_2016_1808_Fig11_ESM.gif (165 kb)

Change in mean annual cumulative FFDI from each ensemble member (GIF 165 kb)

10584_2016_1808_MOESM5_ESM.eps (311 kb)
High resolution image (EPS 311 kb)
10584_2016_1808_Fig10_ESM.gif (370 kb)

Present and future mean monthly fine litter (a-c) and FFDI (d-f) in temperate, grassland and subtropical climate zones. Unbroken line shows multimodel mean, dotted lines show ensemble minimum and maximum values. (GIF 369 kb)

10584_2016_1808_MOESM6_ESM.eps (413 kb)
High resolution image (EPS 413 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.ARC Centre of Excellence for Climate System Science and Climate Change Research CentreUniversity of New South WalesSydneyAustralia
  2. 2.Climate and Atmospheric Science Branch, NSW Office of Environment and HeritageParramattaAustralia
  3. 3.Centre for Environmental Risk Management of BushfiresUniversity WollongongWollongongAustralia
  4. 4.CSIRO Marine and Atmospheric ResearchCanberraAustralia

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