Dr Richard Brinkman1, Dr Claire Spillman2, Dr Roger Beeden3, Dr Jessica Benthuysen1, Mr Craig Steinberg1, Dr Neal Cantin1, Dr Grant Smith2, Dr William Skirving4
1Australian Institute Of Marine Science, , , 2Bureau of Meteorology, , , 3Great Barrier Reef Marine Park Authority, , , 4NOAA, ,
Mass coral bleaching occurred on the Great Barrier Reef (GBR) in 2016 and 2017 as part of a
continuous global bleaching event that started in late 2014 and impacted coral reefs worldwide. Mass bleaching occurs during extended periods of elevated ocean temperature. It has the potential to result in significant and widespread loss of coral, and compromise the ecological, cultural, social and economic benefits and services provided by healthy coral reefs. The length and severity of the 2016-2017 event resulted in significant coral mortality over a large proportion of the GBR.
Monitoring of sea surface temperature (SST) via remote sensing has historically provided a synoptic view of anomalous ocean heating and underpinned operational products for monitoring and predicting bleaching. Advances in the resolution and accuracy of dynamical seasonal forecasts from coupled oceanographic and atmospheric models, increased in situ observations and the application of highly resolved regional & local ocean models are improving our ability to forecast anomalous environmental conditions and move beyond surface-focused products to integrate vertically resolved subsurface observations and model outputs.
Reef managers have a range of management responses that can be implemented at different space and time scales to help manage local impacts and improve resilience to global pressures. The selection and application of tactical and strategic response actions requires access to validated long-term, seasonal and short-term forecasts of environmental condition. This presentation will outline the current state and future direction of operational products to predict coral bleaching, and support management of the GBR and reefs worldwide.
Biography:
Bio to come
