From Australian Oceans Data Network

Oceanographic Drivers of Bleaching in the GBR: from observations to predictions (NESP TWQ 4.2, AIMS)

Created 12/03/2025

Updated 12/03/2025

This project seeks to understand how local, regional and global oceanographic and meteorological processes influence the severity and spatial variability of thermally driven coral bleaching for the Great Barrier Reef (GBR) and Torres Strait. All available relevant environmental observations of the recent bleaching events will be gathered to be more easily discoverable to researchers and managers via a gateway/summary webpage. The data will be used to assess how well the eReefs models are modelling the recent warming during bleaching events and then analyse the reasons behind the variations of bleaching response. 3D versions of remotely sensed bleaching products by NOAA and BoM will be produced and a seasonal prediction capability for marine heatwaves will be developed. The research is structured in three main components: 1 – Summary of oceanographic conditions during the 2015-17 bleaching years 2 – Hydrodynamics of bleaching and improved predictions 3 – Improved seasonal predictions of marine heatwaves in the GBR This project is funded by the National Environment Science Program's Tropical Water Quality Hub

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Field Value
Title Oceanographic Drivers of Bleaching in the GBR: from observations to predictions (NESP TWQ 4.2, AIMS)
Language eng
Licence notspecified
Landing Page https://devweb.dga.links.com.au/data/dataset/5b3dda68-ba64-4105-976e-a7c3de3d166e
Contact Point
CSIRO Oceans & Atmosphere
reception@aims.gov.au
Reference Period 11/11/2019
Data Portal data.gov.au

Data Source

This dataset was originally found on data.gov.au "Oceanographic Drivers of Bleaching in the GBR: from observations to predictions (NESP TWQ 4.2, AIMS)". Please visit the source to access the original metadata of the dataset:
https://devweb.dga.links.com.au/data/dataset/oceanographic-drivers-of-bleaching-in-the-gbr-from-observations-to-predictions-nesp-twq-4-2-aim

No duplicate datasets found.