A comparison of the MATCHES and NCEP1 databases for use in Australian east coast low studies

Gray, Jim; Jaffrés, Jasmine B.D.; Verdon‐Kidd, Danielle C.; Hewson, Michael; Clarke, John Mason; Pepler, Acacia; English, Nathan B.

doi:10.1016/j.wace.2021.100400

Cited by 1 publication

(8 citation statements)

References 41 publications

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“…The relatively short period covered within this study limits the detection of long‐term, decadal‐scale trends. A longer instrumental record of ECC tracks that mirrors the historical documentation (and includes recent events) would provide a more robust analysis of temporal trends and potentially more definite relationships with multi‐decadal climate drivers (Gray et al, 2021). An ECC study covering a longer historical period could be explored using the 20th‐century reanalysis ensemble (similar to Browning & Goodwin, 2016; Pepler et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Because we have restricted tracks based on their presence in the ECL box, this may have excluded relevant events (i.e. storms with land‐based impact) outside the box (Gray et al, 2021). Future ECC studies could be improved by defining a new area or filtering processes inclusive of Tasmania and ITL systems that form within the Gulf of Carpentaria — comprising land‐based areas of eastern Australia and not just the offshore region.…”

Section: Discussionmentioning

confidence: 99%

“…By combining polygons with a dispersion statistic (variance to mean ratio) to measure the statistical significance of clusters, potentially affected regions within the study area become apparent. A modification of this method has previously been used in Australian ECL studies, whereby one geographical polygon (herein referred to as the 'ECL box'; 25 to 40 S and eastern coastline to 160 E) has been used to pre-classify tracks as ECLs before analysis (Cavicchia et al, 2019;Dowdy, Mills, Timbal, & Wang, 2013;Gray et al, 2021;Pepler et al, 2016;Speer et al, 2009Speer et al, , 2021). An alternative approach that did not rely on a defined geographical polygon was adopted by Wolff et al (2016).…”

Section: Cyclone Clustering Methodsmentioning

confidence: 99%

“…To be included in the dataset, events had to be a closed low with a maximum Laplacian (MLAP) value of 0.25 or greater and have at least one data point within the ECL box (25°–40°S—and eastern coastline to 160°E; Speer et al, 2009; Browning & Goodwin, 2013; Dowdy, Mills, & Timbal, 2013; Pepler, Coutts‐Smith, & Timbal, 2014; Pepler et al, 2015; Pepler, 2020b). In addition, included tracks were required to have points with a Laplacian value of 0.25 or greater in the ECL box (Gray et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…For this study, the Niño 3.4 index (representing ENSO, 5 S-5 N, 170 -120 W; Risbey et al, 2009) (Kim et al, 2011;Mailier et al, 2006;Mumby et al, 2011;Nath et al, 2015;Pinto et al, 2013;Wolff et al, 2016). Therefore, the methods and ECC definition outlined in Gray et al (2021) were used to create this study's dataset. Following the same criteria, ECC events were defined using the Laplacian method and the NCEP1 dataset from Pepler (2020a;2020b).…”

Section: Climate Indicesmentioning

confidence: 99%

See 4 more Smart Citations

Characterizing Australia's east coast cyclones (1950–2019)

Gray

Verdon‐Kidd

Jaffrés

et al. 2023

Intl Journal of Climatology

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East coast cyclones (ECCs) provide an essential reprieve from dry periods across eastern Australia. They also deliver flood‐producing rains with significant economic, social and environmental impacts. Assessing and comparing the influence of different types of cyclones is hindered by an incomplete understanding of ECC typology, given their widely variable spatial and temporal characteristics. This study employs a track‐clustering method (probabilistic, curve‐aligned regression model) to identify key cyclonic pathways for ECCs from 1950 to 2019. Six spatially independent clusters were successfully distinguished and further sub‐classified (coastal, continental and tropical) based on their genesis location. The seasonality and long‐term variability, intensity (maximum Laplacian value ± 2 days) and event‐based rainfall were then evaluated for each cluster to quantify the impact of these lows on Australia. The highest quantity of land‐based rainfall per event is associated with the tropical cluster (Cluster 6), whereas widespread rainfall was also found to occur in the two continental clusters (clusters 4 and 5). Cyclone tracks orientated close to the coast (clusters 1, 2 and 3) were determined to be the least impactful in terms of rainfall and intensity, despite being the most common cyclone type. In terms of interannual variability, sea surface temperature anomalies suggest an increased cyclone frequency for clusters 1 (austral winter) and 4 (austral spring) during a central Pacific El Niño. Furthermore, cyclone incidence during IOD‐negative conditions was more pronounced in winter for clusters 1, 2, 3— and clusters 4 and 5 in spring. All cyclones also predominantly occurred in SAM‐positive conditions. However, winter ECCs for clusters 1 and 3 had a higher frequency in SAM‐negative. This new typology of ECCs via spatial clustering provides crucial insights into the systems that produce extreme rainfall across eastern Australia and should be used to inform future hazard management of cyclone events.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%