Embedding machine-learnt sub-grid variability improves climate model precipitation patterns
Abstract Parameterisation schemes within General Circulation Models are required to capture cloud processes and precipitation formation but exhibit long-standing known biases. Here, we develop a hybrid approach that tackles these biases by embedding a Multi-Output Gaussian Process trained to predict...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
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| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-024-01885-8 |
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