Discovery Early Career Researcher Award - Grant ID: DE210100749

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DE210100749

Funding Status
Active

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Funded Activity Summary

Machine learning of subgrid ocean physics for global ocean models. Climate projections require simulations with ocean-climate models for hundreds of years. Computational resources limit the resolution of our models for such long runs, meaning that some key physical processes remain unresolved and must be parameterised. This project uses machine learning to find new parameterisations for unresolved ocean processes. These new parameterisations will be implemented into computationally cheaper coarse-resolution ocean models, thereby enhancing these models' representation of the ocean circulation. This project expects to reveal the dynamics of unresolved processes, to improve the accuracy of climate projections and to provide a proof-of-concept for how machine learning can be used in ocean and climate science.

Funded Activity Details

Start Date: 28-05-2021

End Date: 27-05-2024

Funding Scheme: Discovery Early Career Researcher Award

Funding Amount: $434,030.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Physical Oceanography | Geophysical Fluid Dynamics | Knowledge Representation and Machine Learning | Oceanography

ANZSRC Socio-Economic Objective (SEO)

Climate Variability (excl. Social Impacts) | Climate Change Models |

Other Keywords

Climate Change Models | Climate Variability (excl. Social Impacts) | Geophysical Fluid Dynamics | Knowledge Representation and Machine Learning | Oceanography | Physical Oceanography

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