Examining the role of biophysical feedback on simulated temperature extremes during the Tinderbox Drought and Black Summer bushfires in Southeast Australia

The Tinderbox Drought (2017–2019) was one of the most severe droughts recorded in Australia. The extreme summer air temperatures (>40°C) combined with drought, contributed to the unprecedented Black Summer bushfires in 2019–20 over southeast Australia. Whilst the temperature extremes were largely driven by synoptic processes, it is important to understand to what extent interactions between land and atmosphere played a role. In this study, we use the WRF-LIS-CABLE land-atmosphere coupled model to examine the impacts of changes in leaf area index (LAI) and albedo by contrasting simulations with climatological and time-varying LAI and albedo. We analyse the impact of these biophysical feedbacks on temperature extremes and fire risk during the Tinderbox Drought and the Black Summer bushfires. Remote-sensing data showed a decrease in LAI (0.1–4.0 m2 m–2) over the three years of the drought along the southeast coast of Australia relative to the long-term climatology, while albedo increased inland (0.02–0.14). These changes in LAI and albedo were accompanied by an overall decrease in daily maximum temperature (Tmax) in the vast majority of interior regions (by ∼0.5°C) and, in the 2019–20 summer, by a clear increase in Tmax in the coastal regions of up to ∼1°C. Increased albedo explained most of the decreases in Tmax inland, whereas increases in Tmax along the coasts were mostly associated with LAI declines. The magnitude of the impact of biophysical changes on temperature demonstrates the potential impact that would be missed in simulations that assumed fixed vegetation properties. Finally, we only found a small impact from LAI and albedo changes on the fire risk (as measured by the fuel moisture index) preceding the Black Summer bushfires, suggesting these biophysical feedbacks did not significantly modulate fire risk. Our results have implications for coupled simulations relying on climatological LAI and albedo, including operation weather and seasonal climate predictions.