Preprints
https://doi.org/10.5194/essd-2024-449
https://doi.org/10.5194/essd-2024-449
10 Oct 2024
 | 10 Oct 2024
Status: this preprint is currently under review for the journal ESSD.

Gap-filled subsurface mooring dataset off Western Australia during 2010–2023

Toan Bui, Ming Feng, and Chris Chapman

Abstract. Coastal moorings allow scientists to collect long-term datasets valuable in understanding shelf dynamics, detecting climate variability and changes, and evaluating their impacts on marine ecosystems. Continuous time series data from moorings is often disrupted due to mooring losses or instrument failures, which prevents us from obtaining complete and accurate information on the marine environment. Here, we present an updated version of the 14-year subsurface mooring dataset off the southwest coast of Western Australia during 2010–2023 (https://doi.org/10.25919/myac-yx60, Bui and Feng, 2024). This updated dataset offers continuous daily temperature and current data with a 5-meter vertical resolution, collected from six coastal Integrated Marine Observing System (IMOS) moorings at depths between 48 m and 500 m. Self-Organizing Map (SOM) machine learning technique is applied to fill in the data gaps in the previous version. The usage of the in-filled data product is demonstrated by detecting sub-surface marine heatwaves on the Rottnest shelf. The data products can be used to characterise subsurface features of extreme events such as marine heatwaves, and marine cold-spells, influenced by the Leeuwin Current and the wind-driven Capes Current, and to detect long-term change signals along the coast.

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Toan Bui, Ming Feng, and Chris Chapman

Status: open (until 20 Nov 2024)

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Toan Bui, Ming Feng, and Chris Chapman

Data sets

Gap-filled, gridded subsurface physical oceanography time series dataset derived from selected mooring measurements off the Western Australia coast during 2009-2023 T. Bui and M. Feng https://doi.org/10.25919/myac-yx60

Toan Bui, Ming Feng, and Chris Chapman

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Short summary
Time series data are crucial to detect changes in the ocean. Moored instruments have traditionally been used to obtain long-term observations on the continental shelf. However, mooring losses or instrument failures often result in data gaps. Here we present a gap-filled time series dataset of a shelf mooring array off the Western Australian coast, by adopting a machine learning tool to fill the data gaps. The gap-filled data has acceptable errors and shows consistency with observations.
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