Characteristics of the Northern Australian Rainy Season

Smith, Ian; Wilson, Louise; Suppiah, Ramasamy

doi:10.1175/2008jcli2109.1

Cited by 50 publications

(70 citation statements)

References 27 publications

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“…The accumulated annual rainfall is calculated from July of the previous year to June of the next year. The result shown here is consistent with the rainy window adopted by Smith et al (2008), from 1 September to 30 April. This simple estimate is satisfactory for our purposes, because we are not interested in the actual dates of the monsoon, but rather in the difference between the start and end of the rainy season during the composited Modoki years compared to the climatology.…”

Section: The Australian Monsoon Response To Modoki Eventssupporting

confidence: 91%

“…2. Smith et al (2008) also showed a shorter rainy season for northern Australia with traditional El Niñ o events.…”

Section: The Australian Monsoon Response To Modoki Eventsmentioning

confidence: 90%

“…To estimate the large-scale onset and retreat dates of the rainy season, we have adopted a similar definition to that used by Nicholls (1984) and Smith et al (2008). The onset of a rainy season is defined as the day when the accumulated rainfall reaches 15% of the annual total, and the end date when 85% of total rainfall is accumulated.…”

Section: The Australian Monsoon Response To Modoki Eventsmentioning

confidence: 99%

“…Averaged over the Australian region to the north of 208S the delay is about 7.4 days, while the retreat is approximately 6.5 days, that is, about one week. If the rainy window is taken from September to April, as in Smith et al (2008), the onset and retreat are both offset by just over 1 week. Although a difference of 1 week in the advance and retreat of the monsoon does not appear too large, it is sufficient to produce significant change to the normal monthly rainfall totals.…”

Section: The Australian Monsoon Response To Modoki Eventsmentioning

confidence: 99%

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Australian Monsoon Variability Driven by a Gill–Matsuno-Type Response to Central West Pacific Warming

et al. 2010

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The objective of this study is to investigate the mechanisms that cause the anomalous intensification of tropical Australian rainfall at the height of the monsoon during El Niñ o Modoki events. In such events, northwestern Australia tends to be wetter in January and February when the SST warming is displaced to the central west Pacific, the opposite response to that associated with a traditional El Niñ o. In addition, during the bounding months, that is, December and March, there is below-average rainfall induced by an anomalous Walker circulation. This behavior tends to narrow and intensify the annual rainfall cycle over northwestern Australia relative to the climatology, causing a delayed monsoonal onset and an earlier retreat over the region. Observational datasets and numerical experiments with a general circulation model are used to examine the atmospheric response to the central west Pacific SST warming. It is shown here that the increase of precipitation, particularly in February, is phased locked to the seasonal cycle when the intertropical convergence zone is displaced southward and the South Pacific convergence zone is strengthened. An interaction between the interannual SST variability associated with El Niñ o Modoki events and the evolution of the seasonal cycle intensifies deep convection in the central west Pacific, driving a Gill-Matsuno-type response to the diabatic heating. The westward-propagating disturbance associated with the Gill-Matsuno mechanism generates an anomalous cyclonic circulation over northwestern Australia, leading to convergence of moisture and increased precipitation. The Gill-Matsuno-type response overwhelms the subsidence of the anomalous Walker circulation associated with Modoki events over Australia during the peak of the monsoon.

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Section: The Australian Monsoon Response To Modoki Eventssupporting

confidence: 91%

“…2. Smith et al (2008) also showed a shorter rainy season for northern Australia with traditional El Niñ o events.…”

Section: The Australian Monsoon Response To Modoki Eventsmentioning

confidence: 90%

Section: The Australian Monsoon Response To Modoki Eventsmentioning

confidence: 99%

Section: The Australian Monsoon Response To Modoki Eventsmentioning

confidence: 99%

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Australian Monsoon Variability Driven by a Gill–Matsuno-Type Response to Central West Pacific Warming

et al. 2010

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“…The MEP and ST(DEP) represented the climatological mean of EP and standard deviation of DEP for each calendar day, respectively. The base period for calculating MEP and ST(DEP) needs to be at least 30 years ) so we used 1971-2000, which is a common period used for analysis of Australian climate (Smith et al, 2008). During the computation process, if DEP continued to be negative for more than 2 days, the duration of summation D was increased by the number of days for which the DEP was negative which had no upper limit.…”

Section: Calculation Of the Effective Drought Indexmentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

270

102

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The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed datadriven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

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Observed and projected intra‐seasonal variability of Australian monsoon rainfall

Moise

Smith

Brown

et al. 2019

Intl Journal of Climatology

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Indices derived from daily rainfall time series are used to measure “burst” features of the northern Australia monsoon, corresponding to one or more days of heavy rainfall. These indices include number of burst days, numbers and durations of burst events, and average intensity. The results using observational data show how these features can vary from one year to the next, and how they can vary from the station scale (Darwin) to the regional scale (northern Australia). The results from Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model simulations under both historical and future greenhouse gas conditions have also been analysed and indicate how well models can capture these features and how they might change by the end of the 21st century under a high emissions scenario. While most models provide a reasonable simulation of present‐day burst features, there is little consensus for a significant change to seasonal rainfall totals when looking at the full CMIP5 ensemble. A subset of models with detectable skills with respect to the Madden‐Julian Oscillation shows evidence for an increase in the seasonal total rainfall amount and most other monsoon metrics, except a slight decrease in the number of burst events. This is consistent with a basic thermodynamic response to warming and consistent with findings elsewhere. However, the Australian monsoon is strongly influenced by the large‐scale circulation and there remains some doubt about whether we can confidently diagnose all the changes to monsoon bursts that could occur given the limited ability of many of the current generation of models to simulate tropical cyclones, the Madden‐Julian Oscillation and other relevant features.

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Characteristics of the Northern Australian Rainy Season

Cited by 50 publications

References 27 publications

Australian Monsoon Variability Driven by a Gill–Matsuno-Type Response to Central West Pacific Warming

Australian Monsoon Variability Driven by a Gill–Matsuno-Type Response to Central West Pacific Warming

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Observed and projected intra‐seasonal variability of Australian monsoon rainfall

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