Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections
The ensemble spread of climate models is often interpreted as the uncertainty of the projection, but this is not always justified. Applying learning algorithms to an ensemble of climate predictions allows for a significant uncertainty reduction of projected global mean surface temperatures compared...
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| Main Authors: | , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2020
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| Subjects: | |
| Online Access: | https://doaj.org/article/091cdd00c72a4b78b45a73bb4d21935a |
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| Summary: | The ensemble spread of climate models is often interpreted as the uncertainty of the projection, but this is not always justified. Applying learning algorithms to an ensemble of climate predictions allows for a significant uncertainty reduction of projected global mean surface temperatures compared to the ensemble spread. |
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