Nature Communications (Jul 2020)

Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions

  • Janni Yuval,
  • Paul A. O’Gorman

DOI
https://doi.org/10.1038/s41467-020-17142-3
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 10

Abstract

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Machine learning has been used to represent small-scale processes, such as clouds, in atmospheric models but this can lead to instability in simulations of climate. Here, the authors demonstrate a use of machine learning in an atmospheric model that leads to stable simulations of climate at a range of grid spacings.