Can southern Australian rainfall decline be explained? A review of possible drivers

Abstract

Southern Australia's rainfall is highly variable and influenced by factors across scales from synoptic weather to large-scale circulation and remote climate modes of variability. Anthropogenic climate change and natural variability modulate these factors and their interactions. However, studies often focus on changes in selected parts of the climate system with less emphasis on the system as a whole. As such, it is difficult to gain a complete understanding of how southern Australia's rainfall responds to broad-scale changes in the climate system. We step through the existing literature on long-term changes in synoptic-to-large-scale atmospheric circulation and drivers of climate variability to form a more complete story of rainfall changes across southern Australia. This process reveals that the most robust change is the observed winter decline in rainfall as it is consistent with several changing climatic factors: decreasing rainfall from weather systems, strengthening subtropical ridge, poleward shifts in the Hadley Cell and the Southern Annular Mode, and increasing frequency of positive Indian Ocean Dipole events. In other seasons, particularly summer, changes in atmospheric circulation and drivers may not agree with observed rainfall changes, highlighting gaps in our knowledge of atmospheric dynamics and climate change processes. Future work should focus on research across temporal-and spatial-scales, better understanding of jet interactions, the influence of stratospheric processes on the troposphere, and instances of contrasting trends in drivers and southern Australian rainfall changes.

Examining past and projecting future: an 800-year streamflow reconstruction of the Australian Murray river

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Australia’s Tinderbox Drought: An extreme natural event likely worsened by human-caused climate change

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Discerning the influence of climate variability modes, regional weather features and time series persistence on streamflow using Bayesian networks and multiple linear regression

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