Associations between Australian climate drivers and extreme weekly fire danger

Abstract

Aims We investigate the associations between major Australian climate drivers and extreme weekly fire danger throughout the year. Methods We use a composite-based approach, relating the probability of top-decile observed potential fire intensity to the positive and negative modes of the El Niño Southern Oscillation, Indian Ocean Dipole, Madden–Julian Oscillation, Southern Annular Mode, split-flow blocking and Subtropical Ridge Tasman Highs, both concurrently and at a variety of lag times. Key results The chance of extreme fire danger increases over broad regions of the continent in response to El Niño and positive Indian Ocean Dipole events, the negative mode of the Southern Annular Mode, split-flow Blocking Index and Subtropical Ridge Tasman High, and Madden–Julian Oscillation phases 5, 6, 2 and 8 in Austral summer, autumn, winter and spring respectively. These relationships exist not only concurrently, but also when a climate event occurs up to 6 months ahead of the season of interest. Conclusions These findings highlight the importance of considering the influence of diverse climate drivers, at a range of temporal lag periods, in understanding and predicting extreme fire danger. Implications The results of this study may aid in the development of effective fire management strategies and decision-making processes to mitigate the impacts of fire events in Australia.

A Statistical Forecasting Model for Extremes of the Fire Behaviour Index in Australia

“…These reconstructions further demonstrate the potential for such linear techniques to be used as predictive tools in their own right in fire risk forecasting, or for the statistical relationships to be used to improve dynamical forecasting systems [38]. There are limits to the extent to which climatic processes can be described or explained in terms of linear relationships, as it is acknowledged [39][40][41] that many nonlinearities and asymmetries [19,20,42] exist in climate dynamics. Rainfall, in particular, is vulnerable in this respect, as large rainfall events often occur on small spatial and temporal scales (e.g., convective cell thunderstorms, cut-off lows) driven more by sub-synoptic processes.…”

“…These, in turn, are influenced by large-scale climate drivers such as the El Niño Southern Oscillation, Indian Ocean Dipole, and Southern Annular Mode, among others [15]. These drivers have been shown to impact the extremes of temperature [16][17][18], fire weather, and fire risk [19,20]. As many of these drivers can be forecast in advance [21], knowledge of their states could be utilised in forecasting severe fire danger.…”

“…Non-linear and particularly machine-learning approaches can hide or obscure the nature of the relationships used to make predictions, thereby making a forecast that is not only unable to be checked for logic, but which also cannot be learnt from. Considering these respective merits and drawbacks, as well as the strong ability of simple techniques to demonstrate relationships between fire danger and climate processes [19,20], which are well replicated by more advanced dynamical systems [19,43], we use climate driver indices to develop a multiple logistic regression model for forecasting cases of extreme fire danger based on climate driver activity. The application of this model is demonstrated through three case studies covering recent impactful fire events in Australia-the Canberra bushfires of 2003, the Black Saturday bushfires of 2009, and the Pinery fire of 2015.…”

Climate Driver Influences on Prediction of the Australian Fire Behaviour Index

“…The choice of climate drivers (Table 1) is based upon the results of previous analyses showing their significant influences on Australian climate (e.g., [23][24][25][26][27]) and fire danger [16,28]. These are the El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Southern Annular Mode (SAM), Madden-Julian Oscillation (MJO), split-flow blocking and persistent modes of high pressure in the region of the Tasman Sea (STRH).…”

“…These are the El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Southern Annular Mode (SAM), Madden-Julian Oscillation (MJO), split-flow blocking and persistent modes of high pressure in the region of the Tasman Sea (STRH). Following the methods used by Marshall et al [16] and Taylor et al [28], we use meteorological data from NCEP/NCAR Reanalysis 1 [29] to compute climate driver indices and define the high and low polarities when the driver is >1 SD above or below its 2003-2017 mean. For the observed and ACCESS-S2 simulated FBI, we stratify the dates in the datasets according to when the climate driver under investigation is in the phase of interest.…”