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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. This article is categorized under:Paleoclimates and Current Trends > Modern Climate ChangePaleoclimates and Current Trends > Detection and AttributionAssessing Impacts of Climate Change > Observed Impacts of Climate Change [ABSTRACT FROM AUTHOR]

Extratropical cyclones are responsible for the majority of total and extreme rainfall in many regions of the extratropics, including in southern Australia. Using an ensemble of projections from 12 regional climate models, we show that both the number of cyclone days and total cyclone-related rainfall are projected to decline across southern Australia during the twenty-first century. This is a robust signal in projections across models and explains more than 80% of projected declines in total cool-season rainfall in the region. Using cyclone-centered composites, we show that cyclone intensity based on wind speed is projected to decrease but cyclone maximum rainfall is projected to increase by close to 7% K−1 in the southeast. This results in a shift in the distribution of cyclone rainfall, with a decrease in moderate rainfall but little change or an increase in extreme rainfall. Significance Statement: Extratropical cyclones are very important for southern Australian rainfall, and they are expected to become less frequent in a warming climate. Our research shows this explains at least 80% of the projected decline in cool-season rainfall in southern Australia. However, the frequency of extratropical cyclones with very heavy rainfall may increase, particularly in Tasmania and the southeast coast, with increases in rainfall intensity in line with the Clausius–Clapeyron relationship. This contributes to increases in the frequency of very heavy rainfall in the future. [ABSTRACT FROM AUTHOR]

Reconstructed weather datasets, such as reanalyses based on model output with data assimilation, often show systematic biases in magnitude when compared with observations. Postprocessing approaches can help adjust the distribution so that the reconstructed data resemble the observed data as closely as possible. In this study, we have compared various statistical bias-correction approaches based on quantile–quantile matching to correct the data from the Twentieth Century Reanalysis, version 2c (20CRv2c), with observation-based data. Methods included in the comparison utilize a suite of different approaches: a linear model, a median-based approach, a nonparametric linear method, a spline-based method, and approaches that are based on the lognormal and Weibull distributions. These methods were applied to daily data in the Australian region for rainfall, maximum temperature, relative humidity, and wind speed. Note that these are the variables required to compute the forest fire danger index (FFDI), widely used in Australia to examine dangerous fire weather conditions. We have compared the relative errors and performances of each method across various locations in Australia and applied the approach with the lowest mean-absolute error across multiple variables to produce a reliable long-term bias-corrected FFDI dataset across Australia. The spline-based data correction was found to have some benefits relative to the other methods in better representing the mean FFDI values and the extremes from the observed records for many of the cases examined here. It is intended that this statistical bias-correction approach applied to long-term reanalysis data will help enable new insight on climatological variations in hazardous phenomena, including dangerous wildfires in Australia extending over the past century. [ABSTRACT FROM AUTHOR]

Cyclones can be identified from gridded pressure data at different levels of the troposphere, with vertical structure known to influence the temporal development and impacts of midlatitude cyclones. However, studies of midlatitude cyclones typically focus on cyclones identified at a single atmospheric level. This paper examines how the frequency of vertically organized or deep cyclones varies around the world, with a focus on southeastern Australia. About 50% of global cyclones identified from mean sea level pressure show a coherent vertical structure extending to at least 500 hPa, based on ERA-Interim reanalysis data, and shallow cyclones are most common in the global midlatitudes. Using a combination of reanalysis data and satellite-based rainfall and lightning, we show that in southeast Australia deep cyclones have higher intensities, longer durations, and more severe winds and rainfall than either shallow surface cyclones or upper-level cyclones with no surface low, motivating a three-dimensional approach for future cyclone analyses. [ABSTRACT FROM AUTHOR]

The subtropical part of the eastern Australian seaboard experiences intense cyclonic activity. The severe damage caused by the intense storms in the region, known as east coast lows (ECLs), has motivated a number of recent studies. Cyclones in this region appear to be driven by a combination of different (barotropic and baroclinic) formation mechanisms, consistent with the view emerging in the last decades that cyclones span a continuous spectrum of dynamical structures, with the barotropically driven tropical cyclone and the baroclinically driven extratropical cyclone being only the extremes of such a spectrum. In this work we revisit the climatology of cyclone occurrence in the subtropical east coast of Australia as seen in a global reanalysis, systematically applying classification criteria based on the cyclone vertical structure and thermal core. Moreover, we investigate the underlying processes driving the cyclone rapid intensification by means of an atmospheric limited-area energetics analysis. We show that ECLs have different spatial patterns according to the cyclone thermal structure, with the fraction of hybrid cyclones being larger toward the tropics and closer to the coast. Moreover, we find that explosively deepening cyclones in this region are driven by a different combination of processes with respect to the global case, with barotropic processes in the surrounding environment having a more dominant role in the energetics of cyclone rapid intensification. The findings of this work contribute to understanding the physical processes underlying the formation and intensification of Australian east coast lows and the associated coastal damage and risk. [ABSTRACT FROM AUTHOR]

Lightning is a natural hazard that can lead to the ignition of wildfires, disruption and damage to power and telecommunication infrastructures, human and livestock injuries and fatalities, and disruption to airport activities. This paper examines the ability of six statistical and machine-learning classification techniques to distinguish between nonlightning and lightning days at the coarse spatial and temporal scales of current general circulation models and reanalyses. The classification techniques considered were 1) a combination of principal component analysis and logistic regression, 2) classification and regression trees, 3) random forests, 4) linear discriminant analysis, 5) quadratic discriminant analysis, and 6) logistic regression. Lightning-flash counts at six locations across Australia for 2004-13 were used, together with atmospheric variables from the ERA-Interim dataset. Tenfold cross validation was used to evaluate classification performance. It was found that logistic regression was superior to the other classifiers considered and that its prediction skill is much better than using climatological values. The sets of atmospheric variables included in the final logistic-regression models were primarily composed of spatial mean measures of instability and lifting potential, along with atmospheric water content. The memberships of these sets varied among climatic zones. [ABSTRACT FROM AUTHOR]