EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Machine learning for non-orographic gravity waves in a climate model

Steven Hardiman1, Adam Scaife1,2, Annelize van Niekerk1, Rachel Prudden1, Aled Owen1, Samantha Adams1, Tom Dunstan1, Nick Dunstone1, and Melissa Seabrook1
Steven Hardiman et al.
  • 1Met Office, Exeter, Devon, UK
  • 2Department of Mathematics, University of Exeter, Exeter, Devon, UK

There is growing use of machine learning algorithms to replicate sub-grid parametrisation schemes in global climate models.  Parametrisations rely on approximations, thus there is potential for machine learning to aid improvements.  In this study, a neural network is used to mimic the behaviour of the non-orographic gravity wave scheme used in the Met Office climate model, important for stratospheric climate and variability.  The neural network is found to require only two of the six inputs used by the parametrisation scheme, suggesting the potential for greater efficiency in this scheme.  Use of a one-dimensional mechanistic model is advocated, allowing neural network hyperparameters to be trained based on emergent features of the coupled system with minimal computational cost, and providing a test bed prior to coupling to a climate model.  A climate model simulation, using the neural network in place of the existing parametrisation scheme, is found to accurately generate a quasi-biennial oscillation of the tropical stratospheric winds, and correctly simulate the non-orographic gravity wave variability associated with the El Nino Southern Oscillation and stratospheric polar vortex variability.  These internal sources of variability are essential for providing seasonal forecast skill, and the gravity wave forcing associated with them is reproduced without explicit training for these patterns.

How to cite: Hardiman, S., Scaife, A., van Niekerk, A., Prudden, R., Owen, A., Adams, S., Dunstan, T., Dunstone, N., and Seabrook, M.: Machine learning for non-orographic gravity waves in a climate model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1244,, 2023.