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Deep learning to represent subgrid processes in climate models
The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because o...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
National Academy of Sciences
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6166853/ https://www.ncbi.nlm.nih.gov/pubmed/30190437 http://dx.doi.org/10.1073/pnas.1810286115 |