Tropical cyclone climatology, variability, and trends in the Tonga region, Southwest Pacific

Tu’uholoaki, Moleni; Singh, Awnesh; Espejo, Antonio Guerrero; Chand, Savin S.; Damlamian, Hervé

doi:10.1016/j.wace.2022.100483

Cited by 4 publications

(20 citation statements)

References 69 publications

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“…Climatologically, Nuku'alofa is expected to experience ~2.6 TCs a year. The majority of TCs entering the Tonga region are from the northwest, reaching maximum intensity as they track closer to Nuku'alofa [55]. Theoretically, this northwest approach of TCs directs the body of water at the left-forward quadrant of TCs, where winds are strongest and consequently storm surges are maximum, towards Nuku'alofa.…”

Section: Study Sitementioning

confidence: 97%

“…In the present study, we investigate the storm surge and waves generated by TC Harold by using a range of numerical models. Unlike the case studies of Hoeke et al [36], Merrifield et al [52], Wandres et al [53], and Hoeke et al [54] in the Pacific, which focused on events triggered by distant-source weather systems (i.e., mid-latitude low-pressure systems and distant TCs), TC Harold made a direct hit ("direct hit", defined when the eye is within one degree of the land; see [55]) on Nuku'alofa, the capital of Tonga and triggered an extreme water level that caused coastal inundation and damage in the coastal areas of Tongatapu, the main island of Tonga. In particular, we investigate the performance of the coupled ADCIRC + SWAN model and the uncoupled ADCIRC model in the hindcast of the storm surges and total water levels, as well as the relative contributions of the tide, wave setup, wind surge, and atmospheric pressure to the total water levels associated with the severe TC Harold.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Mechanisms Responsible for Extreme Coastal Water Levels and Flooding during Severe Tropical Cyclone Harold in Tonga, Southwest Pacific

Tu’uholoaki

Espejo

Wandres

et al. 2023

JMSE

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The South Pacific region is characterised by steep shelves and fringing coral reef islands. The lack of wide continental shelves that can dissipate waves makes Pacific Island countries vulnerable to large waves that can enhance extreme total water levels triggered by tropical cyclones (TCs). In this study, hindcasts of the waves and storm surge induced by severe TC Harold in 2020 on Tongatapu, Tonga’s capital island, were examined using the state-of-the-art hydrodynamic and wave models ADCIRC and SWAN. The contributions of winds, atmospheric pressure, waves, and wave-radiation-stress-induced setup to extreme total water levels were analysed by running the models separately and two-way coupled. The atmospheric pressure deficit contributed uniformly to the total water levels (~25%), while the wind surge was prominent over the shallow shelf (more than 75%). Wave setup became significant at locations with narrow fringing reefs on the western side (more than 75%). Tides were dominant on the leeward coasts of the island (50–75%). Storm surge obtained from the coupled run without tide was comparable with the observation. The wave contribution to extreme total water levels and inundation was analysed using XBEACH in non-hydrostatic mode. The model (XBEACH) was able to reproduce coastal inundation when compared to the observed satellite imagery after the event on a particular coastal segment severely impacted by coastal flooding induced by TC Harold. The coupled ADCIRC+SWAN underestimated total water levels nearshore on the reef flat and consequently inundation extent as infragravity waves and swash motion are not resolved by these models. The suite of models (ADCIRC+SWAN+XBEACH) used in this study can be used to support the Tonga Meteorological Service Tropical Cyclone Early Warning System.

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Section: Study Sitementioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Quantifying Mechanisms Responsible for Extreme Coastal Water Levels and Flooding during Severe Tropical Cyclone Harold in Tonga, Southwest Pacific

Tu’uholoaki

Espejo

Wandres

et al. 2023

JMSE

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“…Note that Niño3.4 is preferred because it is slightly more skilful than alternative predictors such as Niño3, Niño4, Southern Oscillation Index (SOI) and the ENSO longitude index (for predictor evaluations, see, e.g., Haruhiru et al, 2023, unpublished). The ENSO–TC connection is well‐reported in the SI region (Haruhiru et al, 2022; Maru et al, 2018), the wider SWP (e.g., Diamond et al, 2013; Dowdy et al, 2012; Ramsay et al, 2012), the Australian region (e.g., McDonnell & Holbrook, 2004; Nicholls, 1985; Ramsay et al, 2008), and the Fiji, Samoa and Tonga region (Chand & Walsh, 2011b; Tu'uholoaki et al, 2022). Synergistic relationship between the MJO and ENSO can also modulate TC activity in the South Pacific (e.g., Chand & Walsh, 2010; Hall et al, 2001).…”

Section: Potential Climate Predictorsmentioning

confidence: 99%

Subseasonal prediction framework for tropical cyclone activity in the Solomon Islands region

Haruhiru

Chand

Sultanova

et al. 2023

Intl Journal of Climatology

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Recently, we developed seasonal prediction schemes with improved skill to predict tropical cyclone (TC) activity up to 3 months in advance for the Solomon Islands (SI) region (5°–15°S, 155°–170°E) using sophisticated Bayesian regression techniques. However, TC prediction at subseasonal timescale (i.e., 1–4 weeks in advance) is not being researched for that region despite growing demands from decision makers at sectoral level. In this paper, we first assess the feasibility of developing subseasonal prediction frameworks for the SI region using a pool of predictors that are known to affect TC activity in the region. We then evaluate multiple predictor combinations to develop the most appropriate models using a statistical approach to forecast weekly TC activity up to 4 weeks in advance. Predictors used include indices of various natural climate variability modes, namely the Madden–Julian Oscillation (MJO), the El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and the Interdecadal Pacific Oscillation (IPO). These modes often have robust physical and statistical relationships with TC occurrences in the SI region and the broader southwest Pacific territory as shown by preceding studies. Additionally, we incorporate TC seasonality as a potential predictor given the persistence of TCs occurring more in certain months than others. Note that a model with seasonality predictor alone (hereafter called the “climatology” model) forms a baseline for comparisons. The hindcast verifications of the forecasts using leave‐one‐out cross‐validation procedure over the study period 1975–2019 indicate considerable improvements in prediction skill of our logistic regression models over climatology, even up to 4 weeks in advance. This study sets the foundation for introducing subseasonal prediction services, which is a national priority for improved decision making in sectors like agriculture and food security, water, health and disaster risk mitigation in the Solomon Islands.

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“…Tonga is ranked third in the world in terms of vulnerability to disasters [4], with expected damage valued at US $15.8 million per year (see [5]). Climatologically, Tonga is affected by 2.6 TCs per year (see [6]) and lies in the latitudinal band where TCs normally reach their maximum intensity (see [7]) and extreme TC-induced wave height (see [8]). The number of intense TCs affecting Tonga has also been found to increase, although not statistically significantly (see [6]).…”

Section: Introductionmentioning

confidence: 99%

“…Climatologically, Tonga is affected by 2.6 TCs per year (see [6]) and lies in the latitudinal band where TCs normally reach their maximum intensity (see [7]) and extreme TC-induced wave height (see [8]). The number of intense TCs affecting Tonga has also been found to increase, although not statistically significantly (see [6]). Notable TCs that affected Tonga in the past were TC Isaac in 1982, TC Hina in 1997, TC Waka in 2001, TC Heta in 2004, TC Ian in 2014, TC Gita in 2018, and TC Harold in 2020.…”

Section: Introductionmentioning

confidence: 99%

Influence of the Madden–Julian Oscillation (MJO) on Tropical Cyclones Affecting Tonga in the Southwest Pacific

et al. 2023

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The modulating influence of the Madden–Julian oscillation (MJO) on tropical cyclones (TCs) has been examined globally, regionally, and subregionally, but its impact on the island scale remains unclear. This study investigates how TC activity affecting the Tonga region is being modulated by the MJO, using the Southwest Pacific Enhanced Archive of Tropical Cyclones (SPEArTC) and the MJO index. In particular, this study investigates how the MJO modulates the frequency and intensity of TCs affecting the Tonga region relative to the entire study period (1970–2019; hereafter referred to as all years), as well as to different phases of the El Niño southern oscillation (ENSO) phenomenon. Results suggest that the MJO strongly modulates TC activity affecting the Tonga region. The frequency and intensity of TCs is enhanced during the active phases (phases six to eight) in all years, including El Niño and ENSO-neutral years. The MJO also strongly influences the climatological pattern of genesis of TCs affecting the Tonga region, where more (fewer) cyclones form in the active (inactive) phases of the MJO and more genesis points are clustered (scattered) near (away from) the Tonga region. There were three regression curves that best described the movement of TCs in the region matching the dominant steering mechanisms in the Southwest Pacific region. The findings of this study can provide climatological information for the Tonga Meteorological Service (TMS) and disaster managers to better understand the TC risk associated with the impact of the MJO on TCs affecting the Tonga region and support its TC early warning system.

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Tropical cyclone climatology, variability, and trends in the Tonga region, Southwest Pacific

Cited by 4 publications

References 69 publications

Quantifying Mechanisms Responsible for Extreme Coastal Water Levels and Flooding during Severe Tropical Cyclone Harold in Tonga, Southwest Pacific

Quantifying Mechanisms Responsible for Extreme Coastal Water Levels and Flooding during Severe Tropical Cyclone Harold in Tonga, Southwest Pacific

Subseasonal prediction framework for tropical cyclone activity in the Solomon Islands region

Influence of the Madden–Julian Oscillation (MJO) on Tropical Cyclones Affecting Tonga in the Southwest Pacific

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