Variability of southwest Pacific tropical cyclone track geometry over the last 70 years

Abstract

Variability in tropical cyclone (TC) track morphology, as it evolves post genesis, presents continued challenges in accurately forecasting TC movement. Therefore, an improved understanding of TC track climatology is essential, given that TCs are one of the most critical natural hazards in the southwest Pacific (SWP) region. We examine the historical variability of TC tracks within the SWP over the last 70 years (1948-2017) using 6-hourly track data obtained from the South Pacific Enhanced Archive of Tropical Cyclones (SPEArTC) database. A probabilistic clustering technique is applied to separate TC tracks into distinct groups in order to assess the primary cyclone trajectories for the region and its relationship with the El Niño-Southern Oscillation (ENSO). TC tracks are also classified into four sinuosity categories: straight, recurving, sinuous and highly sinuous; and their spatial and temporal characteristics subsequently analysed. The results of the cluster analysis identified five optimal groups of TC tracks, four of which exhibited southeast propagation, except for the southwest moving tracks in Cluster 5. Temporally, significant trends were observed over the last seven decades, with Clusters 1, 3 and 4 becoming less frequent with a substantial increase in the occurrence of Cluster 2 tracks (representing TCs east of dateline), a geometry favoured by El Niño conditions. Further, the sinuosity analysis revealed continued dominance of straight TCs within the eastern SWP with a tendency of encountering TCs of other morphology types. Conversely, the western SWP region is typically exposed to highly sinuous tracks. We also observed a significant decrease (increase) in TCs with straight and quasi-straight (highly sinuous) tracks, particularly during the last decade. These findings suggest that combined cluster analysis and TC track sinuosity analysis is an important tool in generalising the TC track regimes, refining predicted trajectories and understanding impacts on SWP island nations.

Location, Area, Curvature and Temporal Trends of Coral Sea Tropical Cyclone Tracks, Over 50 Years

“…the increasing number of tracks with a curving or sinuous property moving east to west and the corresponding decrease in straight or quasi-straight tracks (as shown by percentage in table 3), is consistent with findings by Terry & Gienko (2011) on SWP TC sinuosity. Curving or sinuous tracks have more complex trajectoriesthey can recurve, trace back on themselves or loop (Sharma et al 2021).…”

“…The sinuosity categories for the entire southwest Pacific (SWP) that were developed by Sharma et al (2021) are used here to describe the degree of track curvature.…”

“…Also, many recent studies using TC track data have been conducted in the Northern Hemisphere (e.g., Bell et al 2020;Bhaskar Rao, Srinivas, & Satyanarayana 2019;Ge & Colle 2019;Kelly et al 2018;Liu, Pan, Huang, Zheng, & He 2019;Zhang, Zhang, Shi, & Yao 2020). However, while historical records of TC tracks as recorded by geo-spatial location has the potential to yield new insight into TC behaviour, the body of research into TC track spatial geometry and curvature and the evolution of these characteristics post genesis is far less prevalent (Sharma, Magee, & Verdon-Kidd 2021).…”

“…These characteristics can be used to index and group cyclones in order to assess and understand the movements of cyclones within a region and to predict future changes to TC behaviour at a sub-regional level, based on defined statistical groupings of chosen track parameters. The studies of this type that have been done to date can be categorised into a few types: A) Fitting polynomial regression curves to the TC tracks and then grouping by similarity of the polynomial regressions (e.g., Ramsay, Camargo, & Kim 2012;Sharma et al 2021); B) K-means clustering of a selected n-dimensional vector compiled from chosen TC characteristics (e.g., Yan, Xu, Ma, & Ma 2018); C) K-means clustering of TC mass moments (e.g., Nakamura, Lall, Kushnir, & Camargo 2009); D) Grouping using the Fuzzy C-means algorithm (e.g., Liu et al 2019).…”

Detection and verification of tropical cyclones and depressions over the South Pacific Ocean basin using ERA‐5 reanalysis dataset

“…2 in Ramsay et al ., 2012, or fig. 3 in Sharma et al ., 2021). To be consistent with the availability of ERA‐5 reanalysis (as discussed in the next section), only data for the period 1979/80 to 2018/19 have been used in the analysis.…”