ArticleScience advances2024
Australia's Tinderbox Drought: An extreme natural event likely worsened by human-caused climate change.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Fire-Moisture Interactions Shape Recovery Trajectories Across Temperate Vegetation Communities.Global change biology · 2026Article
- Compounding Disasters and Australian Youth Mental Health: A Time of Significant (di)stress.Early intervention in psychiatry · 2026Article
- The Response and Recovery of Carbon and Water Fluxes in Australian Ecosystems Exposed to Severe Drought.Global change biology · 2025Article
- ERA5-Drought: Global drought indices based on ECMWF reanalysis.Scientific data · 2025Article
- A review of the relationship between mental health, wellbeing and drought resilience in rural, regional and remote communities.Frontiers in psychiatry · 2025Review
- Let's celebrate Earth Day as the Age of Open Science.Science advances · 2024Article
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Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We examine the characteristics and causes of southeast Australia's Tinderbox Drought (2017 to 2019) that preceded the Black Summer fire disaster. The Tinderbox Drought was characterized by cool season rainfall deficits of around -50% in three consecutive years, which was exceptionally unlikely in the context of natural variability alone. The precipitation deficits were initiated and sustained by an anomalous atmospheric circulation that diverted oceanic moisture away from the region, despite traditional indicators of drought risk in southeast Australia generally being in neutral states. Moisture deficits were intensified by unusually high temperatures, high vapor pressure deficits, and sustained reductions in terrestrial water availability. Anthropogenic forcing intensified the rainfall deficits of the Tinderbox Drought by around 18% with an interquartile range of 34.9 to -13.3% highlighting the considerable uncertainty in attributing droughts of this kind to human activity. Skillful predictability of this drought was possible by incorporating multiple remote and local predictors through machine learning, providing prospects for improving forecasting of droughts.
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