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.
Tropical cyclone activity in the Solomon Islands region : climatology, variability, and trends
- Haruhiru, Alick, Chand, Savin, Turville, Christopher, Ramsay, Hamish
- Authors: Haruhiru, Alick , Chand, Savin , Turville, Christopher , Ramsay, Hamish
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 43, no. 1 (2023), p. 593-614
- Full Text:
- Reviewed:
- Description: This study examines the climatology, variability, and trends of tropical cyclones (TCs) affecting the Solomon Islands (SI) territory, in the wider southwest Pacific (SWP), using the South Pacific Enhanced Archive for Tropical Cyclones (SPEArTC) database. During the period 1969/1970–2018/2019, 168 TCs were recorded in the SI territory. A cluster analysis is used to objectively partition these tracks into three clusters of similar TC trajectories to obtain better insights into the effects of natural climate variability, particularly due to the El Niño–Southern Oscillation (ENSO) phenomenon, which otherwise is not very apparent for TCs when considered collectively in the SI region. We find that TCs in clusters 1 and 3 show enhanced activity during El Niño phase, whereas TCs in cluster 2 are enhanced during La Niña and neutral phases. In addition to being modulated by ENSO, TCs in clusters 2 and 3 show statistically significant modulation at an intraseasonal timescale due to the Madden–Julian Oscillation (MJO) phenomenon. There are also some indications through sophisticated Bayesian modelling that TCs in clusters 2 and 3 are slightly influenced by the Interdecadal Pacific Oscillation (IPO). These results can have substantial implications for cluster-specific development of TC prediction schemes for the SI region. © 2022 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.
- Authors: Haruhiru, Alick , Chand, Savin , Turville, Christopher , Ramsay, Hamish
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 43, no. 1 (2023), p. 593-614
- Full Text:
- Reviewed:
- Description: This study examines the climatology, variability, and trends of tropical cyclones (TCs) affecting the Solomon Islands (SI) territory, in the wider southwest Pacific (SWP), using the South Pacific Enhanced Archive for Tropical Cyclones (SPEArTC) database. During the period 1969/1970–2018/2019, 168 TCs were recorded in the SI territory. A cluster analysis is used to objectively partition these tracks into three clusters of similar TC trajectories to obtain better insights into the effects of natural climate variability, particularly due to the El Niño–Southern Oscillation (ENSO) phenomenon, which otherwise is not very apparent for TCs when considered collectively in the SI region. We find that TCs in clusters 1 and 3 show enhanced activity during El Niño phase, whereas TCs in cluster 2 are enhanced during La Niña and neutral phases. In addition to being modulated by ENSO, TCs in clusters 2 and 3 show statistically significant modulation at an intraseasonal timescale due to the Madden–Julian Oscillation (MJO) phenomenon. There are also some indications through sophisticated Bayesian modelling that TCs in clusters 2 and 3 are slightly influenced by the Interdecadal Pacific Oscillation (IPO). These results can have substantial implications for cluster-specific development of TC prediction schemes for the SI region. © 2022 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.
Tropical cyclone prediction for the Solomon Islands region
- Authors: Haruhiru, Alick
- Date: 2023
- Type: Text , Thesis , PhD
- Full Text:
- Description: Tropical cyclones (TCs) are among the costliest natural disasters impacting the Solomon Islands in the southwest Pacific due to its high vulnerability and low adaptive capacity to the hazard. Strong winds coupled with heavy rainfall often have devastating consequences on life and property. Occurrence of TCs in the Solomon Islands region – defined here as 5°–15°S and 155°–170°E – have large year-to-year variability over the period 1970-2019, ranging from TC numbers as low as zero to up to eight in some years. Geographically, the region spans the spatial phase change of the major climatic driver in the South Pacific, the El Niño Southern Oscillation (ENSO), and so the year-to-year variability of TCs here do not have any defined pattern. This creates a ‘predictability barrier’ for seasonal (and even sub-seasonal) prediction of TCs in the region. To circumvent the issue of TC predictability in the Solomon Islands region, I first objectively defined the total observed TCs into three specific clusters. Cluster-specific TCs showed improved patterns of variability with respect to natural modes of climate variability such as ENSO, the Madden Julian Oscillations (MJO) and Interdecadal Pacific Oscillations (IPO). I then developed sophisticated statistical prediction models for TCs in each cluster at seasonal and sub-seasonal timescales using ENSO, the MJO and IPO as main predictors. Overall, the results showed enhanced predictability skills of TCs up to several months in advance compared with methods that are currently being used by the Solomon Islands Meteorological Service. It is anticipated that improved seasonal and sub-seasonal predictions of TCs at various timescales can help disaster management agencies in the Solomon Islands with appropriate plannings and decision-making to lessen risks associated with TC events.
- Description: Doctor of Philosophy
- Authors: Haruhiru, Alick
- Date: 2023
- Type: Text , Thesis , PhD
- Full Text:
- Description: Tropical cyclones (TCs) are among the costliest natural disasters impacting the Solomon Islands in the southwest Pacific due to its high vulnerability and low adaptive capacity to the hazard. Strong winds coupled with heavy rainfall often have devastating consequences on life and property. Occurrence of TCs in the Solomon Islands region – defined here as 5°–15°S and 155°–170°E – have large year-to-year variability over the period 1970-2019, ranging from TC numbers as low as zero to up to eight in some years. Geographically, the region spans the spatial phase change of the major climatic driver in the South Pacific, the El Niño Southern Oscillation (ENSO), and so the year-to-year variability of TCs here do not have any defined pattern. This creates a ‘predictability barrier’ for seasonal (and even sub-seasonal) prediction of TCs in the region. To circumvent the issue of TC predictability in the Solomon Islands region, I first objectively defined the total observed TCs into three specific clusters. Cluster-specific TCs showed improved patterns of variability with respect to natural modes of climate variability such as ENSO, the Madden Julian Oscillations (MJO) and Interdecadal Pacific Oscillations (IPO). I then developed sophisticated statistical prediction models for TCs in each cluster at seasonal and sub-seasonal timescales using ENSO, the MJO and IPO as main predictors. Overall, the results showed enhanced predictability skills of TCs up to several months in advance compared with methods that are currently being used by the Solomon Islands Meteorological Service. It is anticipated that improved seasonal and sub-seasonal predictions of TCs at various timescales can help disaster management agencies in the Solomon Islands with appropriate plannings and decision-making to lessen risks associated with TC events.
- Description: Doctor of Philosophy
Severe tropical cyclones over southwest Pacific Islands : economic impacts and implications for disaster risk management
- Deo, Anil, Chand, Savin, McIntosh, R. Duncan, Prakash, Bipen, Holbrook, Neil, Magee, Andrew, Haruhiru, Alick, Malsale, Philip
- Authors: Deo, Anil , Chand, Savin , McIntosh, R. Duncan , Prakash, Bipen , Holbrook, Neil , Magee, Andrew , Haruhiru, Alick , Malsale, Philip
- Date: 2022
- Type: Text , Journal article
- Relation: Climatic Change Vol. 172, no. 3-4 (2022), p.
- Full Text:
- Reviewed:
- Description: Tropical cyclones (TCs) are amongst the costliest natural hazards for southwest Pacific (SWP) Island nations. Extreme winds coupled with heavy rainfall and related coastal hazards, such as large waves and high seas, can have devastating consequences for life and property. Effects of anthropogenic climate change are likely to make TCs even more destructive in the SWP (as that observed particularly over Fiji) and elsewhere around the globe, yet TCs may occur less often. However, the underpinning science of quantifying future TC projections amid multiple uncertainties can be complex. The challenge for scientists is how to turn such technical knowledge framed around uncertainties into tangible products to inform decision-making in the disaster risk management (DRM) and disaster risk reduction (DRR) sector. Drawing on experiences from past TC events as analogies to what may happen in a warming climate can be useful. The role of science-based climate services tailored to the needs of the DRM and DRR sector is critical in this context. In the first part of this paper, we examine cases of historically severe TCs in the SWP and quantify their socio-economic impacts. The second part of this paper discusses a decision-support framework developed in collaboration with a number of agencies in the SWP, featuring science-based climate services that inform different stages of planning in national-level risk management strategies. © 2022, The Author(s).
- Authors: Deo, Anil , Chand, Savin , McIntosh, R. Duncan , Prakash, Bipen , Holbrook, Neil , Magee, Andrew , Haruhiru, Alick , Malsale, Philip
- Date: 2022
- Type: Text , Journal article
- Relation: Climatic Change Vol. 172, no. 3-4 (2022), p.
- Full Text:
- Reviewed:
- Description: Tropical cyclones (TCs) are amongst the costliest natural hazards for southwest Pacific (SWP) Island nations. Extreme winds coupled with heavy rainfall and related coastal hazards, such as large waves and high seas, can have devastating consequences for life and property. Effects of anthropogenic climate change are likely to make TCs even more destructive in the SWP (as that observed particularly over Fiji) and elsewhere around the globe, yet TCs may occur less often. However, the underpinning science of quantifying future TC projections amid multiple uncertainties can be complex. The challenge for scientists is how to turn such technical knowledge framed around uncertainties into tangible products to inform decision-making in the disaster risk management (DRM) and disaster risk reduction (DRR) sector. Drawing on experiences from past TC events as analogies to what may happen in a warming climate can be useful. The role of science-based climate services tailored to the needs of the DRM and DRR sector is critical in this context. In the first part of this paper, we examine cases of historically severe TCs in the SWP and quantify their socio-economic impacts. The second part of this paper discusses a decision-support framework developed in collaboration with a number of agencies in the SWP, featuring science-based climate services that inform different stages of planning in national-level risk management strategies. © 2022, The Author(s).
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