This work identifies and documents a suite of large-scale drivers of rainfall variability in the Australian region. The key driver in terms of broad influence and impact on rainfall is the El Niño–Southern Oscillation (ENSO). ENSO is related to rainfall over much of the continent at different times, particularly in the north and east, with the regions of influence shifting with the seasons. The Indian Ocean dipole (IOD) is particularly important in the June–October period, which spans much of the wet season in the southwest and southeast where IOD has an influence. ENSO interacts with the IOD in this period such that their separate regions of influence cover the entire continent. Atmospheric blocking also becomes most important during this period and has an influence on rainfall across the southern half of the continent. The Madden–Julian oscillation can influence rainfall in different parts of the continent in different seasons, but its impact is strongest on the monsoonal rains in the north. The influence of the southern annular mode is mostly confined to the southwest and southeast of the continent. The patterns of rainfall relationship to each of the drivers exhibit substantial decadal variability, though the characteristic regions described above do not change markedly. The relationships between large-scale drivers and rainfall are robust to the selection of typical indices used to represent the drivers. In most regions the individual drivers account for less than 20% of monthly rainfall variability, though the drivers relate to a predictable component of this variability. The amount of rainfall variance explained by individual drivers is highest in eastern Australia and in spring, where it approaches 50% in association with ENSO and blocking.

Climate and weather variables such as rainfall, temperature, and pressure are indicators for hazards such as tropical cyclones, floods, and fires. The impact of these events can be due to a single variable being in an extreme state, but more often it is the result of a combination of variables not all of which are necessarily extreme. Here, the combination of variables or events that lead to an extreme impact is referred to as a compound event. Any given compound event will depend upon the nature and number of physical variables, the range of spatial and temporal scales, the strength of dependence between processes, and the perspective of the stakeholder who defines the impact. Modeling compound events is a large, complex, and interdisciplinary undertaking. To facilitate this task we propose the use of influence diagrams for defining, mapping, analyzing, modeling, and communicating the risk of the compound event. Ultimately, a greater appreciation of compound events will lead to further insight and a changed perspective on how impact risks are associated with climate‐related hazards. WIREs Clim Change 2014, 5:113–128. doi: 10.1002/wcc.252 This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models Assessing Impacts of Climate Change > Representing Uncertainty

Since 1995, a large region of Australia has been gripped by the most severe drought in living memory, the so‐called “Big Dry”. The ramifications for affected regions are dire, with acute water shortages for rural and metropolitan areas, record agricultural losses, the drying‐out of two of Australia's major river systems and far‐reaching ecosystem damage. Yet the drought's origins have remained elusive. For Southeast Australia, we show here that the “Big Dry” and other iconic 20th Century droughts, including the Federation Drought (1895–1902) and World War II drought (1937–1945), are driven by Indian Ocean variability, not Pacific Ocean conditions as traditionally assumed. Specifically, a conspicuous absence of Indian Ocean temperature conditions conducive to enhanced tropical moisture transport has deprived southeastern Australia of its normal rainfall quota. In the case of the “Big Dry”, its unprecedented intensity is also related to recent higher temperatures.

The analysis of climatological data often involves statistical significance testing at many locations. While the field significance approach determines if a field as a whole is significant, a multiple testing procedure determines which particular tests are significant. Many such procedures are available, most of which control, for every test, the probability of detecting significance that does not really exist. The aim of this paper is to introduce the novel “false discovery rate” approach, which controls the false rejections in a more meaningful way. Specifically, it controls a priori the expected proportion of falsely rejected tests out of all rejected tests; additionally, the test results are more easily interpretable. The paper also investigates the best way to apply a false discovery rate (FDR) approach to spatially correlated data, which are common in climatology. The most straightforward method for controlling the FDR makes an assumption of independence between tests, while other FDR-controlling methods make less stringent assumptions. In a simulation study involving data with correlation structure similar to that of a real climatological dataset, the simple FDR method does control the proportion of falsely rejected hypotheses despite the violation of assumptions, while a more complicated method involves more computation with little gain in detecting alternative hypotheses. A very general method that makes no assumptions controls the proportion of falsely rejected hypotheses but at the cost of detecting few alternative hypotheses. Despite its unrealistic assumption, based on the simulation results, the authors suggest the use of the straightforward FDR-controlling method and provide a simple modification that increases the power to detect alternative hypotheses.