Subseasonal to Seasonal Effects of Climate Drivers on Australian Fire Danger

Abstract

This dissertation contains research that examines the influence of large-scale climate drivers on Australian metrics of fire danger, contributing to a more nuanced understanding of fire risk management and potentially aiding in preparation and mitigation of fire-related threats. Australian ecosystems, societies and values are increasingly threatened by changes to established fire regimes and dynamics, as well as the introduction of the Australian Fire Danger Rating System, which supersedes existing rating systems and incorporates new understandings associated with fire behaviour.

By posing a series of targeted questions I have aimed to expand our understanding of large-scale climate patterns on fire risk factors. The questions are: 1. How do the major drivers of Australian climate (El Nino Southern Oscillation, Southern Annular Mode, Indian Ocean Dipole, Madden-Julian Oscillation, Split-flow Blocking and Subtropical Ridge Tasman Highs) influence fire danger in terms of the Australian Fire Danger Rating System metrics of Fireline Intensity and Fire Behaviour Index (FBI) across different Australian regions in all seasons? 2. How accurately does the Australian Bureau of Meteorology's official ACCESS-S2 model capture the observed relationships between climate driver phases and FBI? 3. Does the historical forecast accuracy of the FBI vary significantly during different climate driver phases? 4. Considering the relationships between climate drivers and the FBI, what specific approaches can be implemented to enhance operational decision-making during and preceding fire seasons?

Composite analyses and logistic regression were applied to examine how climate drivers influence fire danger metrics across Australia. The composite analysis reveals the year-round impact of these drivers on fire risk, generally aligning with their known characteristics and areas of influence. The Bureau of Meteorology's ACCESS-S2 model is generally effective in replicating observed fire danger patterns, but accuracy can vary regionally and seasonally. The research utilises this analysis to develop a complementary statistical model for extreme fire danger episodes. While this alternative model exhibits a tendency to overpredict fire risk, integrating forecasts from both systems could lead to a more accurate and risk-informed approach to fire season preparation and management. Additionally, the ACCESS-S2 model's strength in subseasonal to seasonal climate forecasting holds promise for extending fire danger prediction lead times.

The research highlights that all major climate drivers significantly impact fire potential through their effects on weather and fuel conditions. This knowledge is critical for predicting fire season severity and developing risk mitigation strategies.

This research provides a critical link between climate drivers and fire danger, informing fire prediction and mitigation strategies. In developing a novel statistical model for extreme fire danger episodes and exploring the potential of combining it with existing models, this thesis contributes to improved forecasting. These findings highlight the relationships between climate and fire danger, while also emphasising the importance of clear communication about forecast uncertainties and stakeholder expertise in interpreting model limitations. Show more