Interrelationship between Indian Ocean Dipole (IOD) and Australian Tropical Cyclones

Saha, Kamal Kumar; Wasimi, Saleh A.

doi:10.7763/ijesd.2013.v4.431

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…The TCs occurring in the northern IO are found to be in good agreement with the PDI, especially during the premonsoon season (Sebastian and Behera 2015). It is also observed that the IO SST strongly controls the TCs occurring in the Pacific region (Zhan et al 2014), the Australian basin (Saha and Wasimi 2013), the South China Sea (Wang et al 2013), and Korean waters (Choi et al 2015). SST not only aids in TC genesis but can intensify a TC and change its path (Michaels et al 2006;Yu and McPhaden 2011).…”

Section: Introduction a Sst And Associated Weather Phenomenamentioning

confidence: 52%

Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

Patil

Deo

2018

Journal of Atmospheric and Oceanic Technology

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The prediction of sea surface temperature (SST) on the basis of artificial neural networks (ANNs) can be viewed as complementary to numerical SST predictions, and it has fairly sustained in the recent past. However, one of its limitations is that such ANNs are site specific and do not provide simultaneous spatial information similar to the numerical schemes. In this work we have addressed this issue by presenting basin-scale SST predictions based on the operation of a very large number of individual ANNs simultaneously. The study area belongs to the basin of the tropical Indian Ocean (TIO) having coordinates of 30°N–30°S, 30°–120°E. The network training and testing are done on the basis of HadISST data of the past 140 yr. Monthly SST anomalies are predicted at 3813 nodes in the basin and over nine time steps into the future with more than 20 million ANN models. The network testing indicated that the prediction skill of ANNs is attractive up to certain lead times depending on the subbasin. The ANN models performed well over both the western Indian Ocean (WIO) and eastern Indian Ocean (EIO) regions up to 5 and 4 months lead time, respectively, as judged by the error statistics of the correlation coefficient and the normalized root-mean-square error. The prediction skill of the ANN models for the TIO region is found to be better than the physics-based coupled atmosphere–ocean models. It is also observed that the ANNs are capable of providing an advanced warning of the Indian Ocean dipole as well as abnormal basin warming.

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Section: Introduction a Sst And Associated Weather Phenomenamentioning

confidence: 52%

Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

Patil

Deo

2018

Journal of Atmospheric and Oceanic Technology

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“…This article uncovers a new relationship between Indian Ocean SST variability and SWP TC genesis. While previous research had noted a significant relationship between Australian region TC genesis and the IOD (Liu and Chan, ; Saha and Wasimi, ; Wijnands et al ., ; Ramsay et al ., ), this has not been explored for the wider SWP region. Of the four Indian Ocean indices investigated (DMI, IOD E, IOD W and II), those that represent individual poles of the IOD (or regional SST variability) were shown to have the most substantial and statistically significant relationship with SWP TC genesis.…”

Section: Discussionmentioning

confidence: 99%

“…They found that the co‐occurrence of La Niña and IOD negative events produced higher TC counts in the Australian region, compared to La Niña events alone. Furthermore, a study by Saha and Wasimi () found statistically significant correlations between the western (eastern) poles of the IOD and TC counts in northeast Australia (137.5°–160°E) at r = −0.45 ( r = −0.58). Wijnands et al .…”

Section: Introductionmentioning

confidence: 99%

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On the relationship between Indian Ocean sea surface temperature variability and tropical cyclogenesis in the southwest Pacific

Magee

Verdon‐Kidd

2018

Intl Journal of Climatology

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In the southwest Pacific (SWP) tropical cyclones (TCs) account for 76% of the regions natural disasters and have substantial economic, physical and environmental impacts on people and places. Therefore, information is needed to better understand when and where TCs are likely to occur, as this can aid in preparedness and planning. While there is a well‐established relationship between Pacific Ocean sea surface temperature (SST) variability and tropical cyclogenesis (TC genesis) in the SWP, it does not fully explain the historical spatial and temporal variability observed. Therefore, this study aims to look beyond the Pacific and establish a new relationship between Indian Ocean SST variability and SWP TC genesis. This is achieved by statistically relating indices of Indian Ocean SST variability to SWP TC genesis positions. The physical mechanisms driving these observed relationships are then established by studying changes in the environmental conditions conducive to TC genesis. This analysis shows that Indian Ocean SST variability significantly modulates the clustering of SWP TC genesis, where warmer (cooler) SSTs in the eastern and western regions of the Indian Ocean result in a statistically significant north/east (south/west) migration of TC genesis by up to 950 km. Importantly, this relationship is shown to be consistent when the El Niño/Southern Oscillation (ENSO, the dominant Pacific mode) is in an inactive phase (ENSO neutral). Favourable TC genesis parameters including warm SSTs, increased relative humidity, anomalously negative 700 hPa vorticity, anomalously negative and low absolute 200–850 hPa vertical wind shear account for the observed shift in clustering. Furthermore, we show that the combined effect of ENSO/Indian Ocean SST variability results in varying risk profiles for island nations of the region, with the two climate modes either enhancing or suppressing individual impacts. Significantly, the findings from this study provide an opportunity for meteorological agencies to improve seasonal SWP TC outlooks.

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“…Ramsay et al (2017) found that including Indian Ocean SST variability alongside predictors of ENSO can assist in mitigating any loss in the predictive skill of ENSO-driven TC forecasts post-1998. Other studies have also investigated the role of Indian Ocean SST variability on TCs in the AR (Liu and Chan 2012;Saha and Wasimi 2013) and areas of the overlapping southwest Pacific (SWP) basin (135°E-120°W; Magee and Verdon-Kidd, 2018). For the AR and subregions, Wijnands et al (2015) found that the Dipole Mode Index (DMI) was the most frequently used index used in support vector regression models, when considered amongst a host of other indices, including eight ENSO indices.…”

Section: Introductionmentioning

confidence: 99%

Using Indicators of ENSO, IOD, and SAM to Improve Lead Time and Accuracy of Tropical Cyclone Outlooks for Australia

Magee

Kiem

2020

Journal of Applied Meteorology and Climatology

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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°S-40°S, 90°E-160°E) and its four sub-regions. We test ten 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 pre-season TC count outlooks generated between April-October for the AR TC season (November-April) and in-season TC count outlooks generated between November-January for the remaining AR TC season are tested. Results demonstrate skilful 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= 0.59-0.78 and covariates explaining between 35-60% of the variance in TC counts. The dependence of models on indices representing Indian Ocean sea surface temperature (SST) highlights the importance of the Indian Ocean for TC occurrence in this region. Importantly, generating rolling monthly pre-season 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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Interrelationship between Indian Ocean Dipole (IOD) and Australian Tropical Cyclones

Abstract: Abstract-Indian

Cited by 7 publications

References 8 publications

Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

On the relationship between Indian Ocean sea surface temperature variability and tropical cyclogenesis in the southwest Pacific

Using Indicators of ENSO, IOD, and SAM to Improve Lead Time and Accuracy of Tropical Cyclone Outlooks for Australia

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