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Andrew D. Magee and Anthony S. Kiem

Abstract

Catastrophic impacts associated with tropical cyclone (TC) activity mean that the accurate and timely provision of TC outlooks are important to people, places, and numerous sectors in Australia and beyond. In this study, we apply a Poisson regression statistical framework to predict TC counts in the Australian region (AR; 5°–40°S, 90°–160°E) and its four subregions. We test 10 unique covariate models, each using different representations of the influence of El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and southern annular mode (SAM) and use an automated covariate selection algorithm to select the optimum combination of predictors. The performance of preseason TC count outlooks generated between April and October for the AR TC season (November–April) and in-season TC count outlooks generated between November and January for the remaining AR TC season are tested. Results demonstrate that skillful TC count outlooks can be generated in April (i.e., 7 months prior to the start of the AR TC season), with Pearson correlation coefficient values between r = 0.59 and 0.78 and covariates explaining between 35% and 60% of the variance in TC counts. The dependence of models on indices representing Indian Ocean sea surface temperature highlights the importance of the Indian Ocean for TC occurrence in this region. Importantly, generating rolling monthly preseason and in-season outlooks for the AR TC season enables the continuous refinement of expected TC counts in a given season.

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Danielle C. Verdon-Kidd and Anthony S. Kiem

Abstract

Water management in Australia has traditionally been carried out on the assumption that the historical record of rainfall, evaporation, streamflow, and recharge is representative of current and future climatic conditions. However, in many circumstances, this does not adequately address the potential risks to supply security for towns, industry, irrigators, and the environment. This is because the Australian climate varies markedly due to natural cycles that operate over periods of several years to several decades. There is also serious concern about how anthropogenic climate change may exacerbate drought risk in the future. In this paper, the frequency and severity of droughts are analyzed during a range of “climate states” (e.g., different phases of the Pacific, Indian, and/or Southern Oceans) to demonstrate that drought risk varies markedly over interannual through to multidecadal time scales. Importantly, by accounting for climate variability and change on multitemporal scales (e.g., interdecadal, multidecadal, and the palaeo scale), it is demonstrated that the risk of failure of current drought management practices may be better assessed and more robust climate adaptation responses developed.

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Thomas A. McMahon, Anthony S. Kiem, Murray C. Peel, Phillip W. Jordan, and Geoffrey G. S. Pegram

Abstract

This paper introduces a new approach to stochastically generating rainfall sequences that can take into account natural climate phenomena, such as the El Niño–Southern Oscillation and the interdecadal Pacific oscillation. The approach is also amenable to modeling projected affects of anthropogenic climate change. The method uses a relatively new technique, empirical mode decomposition (EMD), to decompose a historical rainfall series into several independent time series that have different average periods and amplitudes. These time series are then recombined to form an intradecadal time series and an interdecadal time series. After separate stochastic generation of these two series, because they are independent, they can be recombined by summation to form a replicate equivalent to the historical data. The approach was applied to generate 6-monthly rainfall totals for six rainfall stations located near Canberra, Australia. The cross correlations were preserved by carrying out the stochastic analysis using the Matalas multisite model. The results were compared with those obtained using a traditional autoregressive lag-one [AR(1)], and it was found that the new EMD stochastic model performed satisfactorily. The new approach is able to realistically reproduce multiyear–multidecadal dry and wet epochs that are characteristic of Australia’s climate and are not satisfactorily modeled using traditional stochastic rainfall generation methods. The method has two advantages over the traditional AR(1) approach, namely, that it can simulate nonstationarity characteristics in the historical time series, and it is easy to alter the decomposed time series components to examine the impact of anthropogenic climate change.

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Michelle Ho, Danielle C. Verdon-Kidd, Anthony S. Kiem, and Russell N. Drysdale

Abstract

Recent advances in the collection and analysis of paleoclimate data have provided significant insights into preinstrumental environmental events and processes, enabling a greater understanding of long-term environmental change and associated hydroclimatic risks. Unfortunately, it is often the case that there is a dearth of readily available paleoclimate data from regions where such insights and long-term data are most needed. The Murray–Darling basin (MDB), known as Australia’s “food bowl,” is an example of such a region where currently there are very limited in situ paleoclimate data available. While previous studies have utilized paleoclimate proxy records of large-scale climate mechanisms to infer preinstrumental MDB hydroclimatic variability, there is a lack of studies that utilize Australian terrestrial proxy records to garner similar information. Given the immediate need for improved understanding of MDB hydroclimatic variability, this paper identifies key locations in Australia where existing and as yet unrealized paleoclimate records will be most useful in reconstructing such information. To identify these key locations, rainfall relationships between MDB and non-MDB locations were explored through correlations and principal component analysis. An objective analysis using optimal interpolation was then used to pinpoint the most strategic locations to further develop proxy records and gain insights into the benefits of obtaining this additional information. The findings reveal that there is potential for the future assembly of high-resolution paleoclimate records in Australia capable of informing MDB rainfall variability, in particular southeast Australia and central-northern Australia. This study highlights the need for further investment in the development of these potential proxy sources to subsequently enable improved assessments of long-term hydroclimatic risks.

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Danielle G. Udy, Tessa R. Vance, Anthony S. Kiem, Neil J. Holbrook, and Mark A. J. Curran

Abstract

Weather systems in the southern Indian Ocean (SIO) drive synoptic-scale precipitation variability in East Antarctica and southern Australia. Improved understanding of these dynamical linkages is beneficial to diagnose long-term climate changes from climate proxy records as well as informing regional weather and climate forecasts. Self-organizing maps (SOMs) are used to group daily 500-hPa geopotential height (z500; ERA-Interim) anomalies into nine regional synoptic types based on their dominant patterns over the SIO (30°–75°S, 40°–180°E) from January 1979 to October 2018. The pattern anomalies represented include four meridional, three mixed meridional–zonal, one zonal, and one transitional node. The frequency of the meridional nodes shows limited association with the phase of the southern annular mode (SAM), especially during September–November. The zonal and mixed patterns were nevertheless strongly and significantly correlated with SAM, although the regional synoptic representation of SAM+ conditions was not zonally symmetric and was represented by three separate nodes. We recommend consideration of how different synoptic conditions vary the atmospheric representation of SAM+ in any given season in the SIO. These different types of SAM+ mean a hemispheric index fails to capture the regional variability in surface weather conditions that is primarily driven by the synoptic variability rather than the absolute polarity of the SAM.

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