Drought is known as a “creeping disaster” because drought impacts are usually noticed months or years after a drought begins. In the Pacific Island Countries and Territories (PICTs), there is almost no ability to tell when a drought will begin or end, especially for droughts other than meteorological droughts. Monitoring, forecasting and managing drought in the PICTs is complex due to the variety of different ways droughts occur, and the diverse direct and indirect causes and consequences of drought, across the PICT region. For example, the impacts of drought across the PICTs vary significantly depending on (i) the type of drought (e.g. meteorological drought or agricultural drought); (ii) the location (e.g. high islands versus atolls); (iii) socioeconomic conditions in the location affected by drought; and (iv) cultural attitudes towards the causes of drought (e.g. a punishment from God versus a natural process that is potentially predictable and something that can be managed). This paper summarises what is known and unknown about drought impacts in the PICTs and provides recommendations to guide future research and investment towards minimising the negative impacts of droughts when they inevitably occur in the PICTs.

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 1969During the period /1970During the period -2018During the period /2019 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.

Southwest Pacific nations are among some of the worst impacted and most vulnerable globally in terms of tropical cyclone (TC)-induced flooding and accompanying risks. This study objectively quantifies the fractional contribution of TCs to extreme rainfall (hereafter, TC contributions) in the context of climate variability and change. We show that TC contributions to extreme rainfall are substantially enhanced during active phases of the Madden–Julian Oscillation and by El Niño conditions (particularly over the eastern southwest Pacific region); this enhancement is primarily attributed to increased TC activity during these event periods. There are also indications of increasing intensities of TC-induced extreme rainfall events over the past few decades. A key part of this work involves development of sophisticated Bayesian regression models for individual island nations in order to better understand the synergistic relationships between TC-induced extreme rainfall and combinations of various climatic drivers that modulate the relationship. Such models are found to be very useful for not only assessing probabilities of TC- and non-TC induced extreme rainfall events but also evaluating probabilities of extreme rainfall for cases with different underlying climatic conditions. For example, TC-induced extreme rainfall probability over Samoa can vary from ~ 95 to ~ 75% during a La Niña period, if it coincides with an active or inactive phase of the MJO, and can be reduced to ~ 30% during a combination of El Niño period and inactive phase of the MJO. Several other such cases have been assessed for different island nations, providing information that have potentially important implications for planning and preparing for TC risks in vulnerable Pacific Island nations.

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.

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.