Future changes in extreme weather and pyroconvection risk factors for Australian wildfires.

Dowdy, A., H. Ye, A. Pepler, M. Thatcher, S. Osbrough, J. Evans, G. Di Virgilio, and N. McCarthy
Scientific Reports, 9(1), 10073, doi: 10.1038/s41598-019-46362-x, 2019.


Extreme wildfires have recently caused disastrous impacts in Australia and other regions of the world, including events with strong convective processes in their plumes (i.e., strong pyroconvection). Dangerous wildfire events such as these could potentially be influenced by anthropogenic climate change, however, there are large knowledge gaps on how these events might change in the future. The McArthur Forest Fire Danger Index (FFDI) is used to represent near-surface weather conditions and the Continuous Haines index (CH) is used here to represent lower to mid-tropospheric vertical atmospheric stability and humidity measures relevant to dangerous wildfires and pyroconvective processes. Projected changes in extreme measures of CH and FFDI are examined using a multi-method approach, including an ensemble of global climate models together with two ensembles of regional climate models. The projections show a clear trend towards more dangerous near-surface fire weather conditions for Australia based on the FFDI, as well as increased pyroconvection risk factors for some regions of southern Australia based on the CH. These results have implications for fields such as disaster risk reduction, climate adaptation, ecology, policy and planning, noting that improved knowledge on how climate change can influence extreme wildfires can help reduce future impacts of these events.

Key Figure

Figure 6. Spatial changes in near-surface fire weather condition for three different modelling methods. Changes are shown for the number of days per year that the FFDI exceeds a threshold value, based on changes from the period 1990–2009 to the period 2060–2079. Results are presented for the number of days per year that FFDI is above 25 for different data sets: (a) GCMs; (b) CCAM; and (c) WRF. Results are also presented for the number of days per year that FFDI is above its historical period 95th percentile for different data sets: (d) GCMs; (e) CCAM; and (f) WRF. Coloured regions represent locations where at least two thirds of the ensemble members for each modelling method agree of the sign of the change.

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