Influence of the Madden–Julian Oscillation (MJO) on tropical cyclones affecting Tonga in the Southwest Pacific
- Tu’uholoaki, Moleni, Espejo, Antonio, Sharma, Krishneel, Singh, Awnesh, Wandres, Moritz, Damlamian, Herve, Chand, Savin
- Authors: Tu’uholoaki, Moleni , Espejo, Antonio , Sharma, Krishneel , Singh, Awnesh , Wandres, Moritz , Damlamian, Herve , Chand, Savin
- Date: 2023
- Type: Text , Journal article
- Relation: Atmosphere Vol. 14, no. 7 (2023), p.
- Full Text:
- Reviewed:
- Description: 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. © 2023 by the authors.
- Authors: Tu’uholoaki, Moleni , Espejo, Antonio , Sharma, Krishneel , Singh, Awnesh , Wandres, Moritz , Damlamian, Herve , Chand, Savin
- Date: 2023
- Type: Text , Journal article
- Relation: Atmosphere Vol. 14, no. 7 (2023), p.
- Full Text:
- Reviewed:
- Description: 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. © 2023 by the authors.
Subseasonal prediction framework for tropical cyclone activity in the Solomon Islands region
- Haruhiru, Alick, Chand, Savin, Sultanova, Nargiz, Ramsay, Hamish, Sharma, Krishneel, Tahani, Lloyd
- Authors: Haruhiru, Alick , Chand, Savin , Sultanova, Nargiz , Ramsay, Hamish , Sharma, Krishneel , Tahani, Lloyd
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 43, no. 12 (2023), p. 5763-5777
- Full Text:
- Reviewed:
- Description: 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. © 2023 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.
- Authors: Haruhiru, Alick , Chand, Savin , Sultanova, Nargiz , Ramsay, Hamish , Sharma, Krishneel , Tahani, Lloyd
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 43, no. 12 (2023), p. 5763-5777
- Full Text:
- Reviewed:
- Description: 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. © 2023 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.
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