Neural‐Network Parameterization of Subgrid Momentum Transport in the Atmosphere
Name
J Adv Model Earth Syst - 2023 - Yuval - Neural‐Network Parameterization of Subgrid Momentum Transport in the Atmosphere.pdf
Description
Published version
Size
1.62 MB
Format
Adobe PDF
Checksum (MD5)
d983b777f6a0bd9fe1b01fdfd7d1be40
Author(s)
Yuval, Janni
O’Gorman, Paul A
Date Issued
April 6, 2023
Journal
Journal of Advances in Modeling Earth Systems
Publisher
American Geophysical Union
Citation
Yuval, J., & O’Gorman, P. A. (2023). Neural-network parameterization of subgrid momentum transport in the atmosphere. Journal of Advances in Modeling Earth Systems, 15, e2023MS003606.
Version
Final published version
Abstract
Attempts to use machine learning to develop atmospheric parameterizations have mainly focused on subgrid effects on temperature and moisture, but subgrid momentum transport is also important in simulations of the atmospheric circulation. Here, we use neural networks to develop a subgrid momentum transport parameterization that learns from coarse‐grained output of a high‐resolution atmospheric simulation in an idealized aquaplanet domain. We show that substantial subgrid momentum transport occurs due to convection. The neural‐network parameterization has skill in predicting momentum fluxes associated with convection, although its skill for subgrid momentum fluxes is lower compared to subgrid energy and moisture fluxes. The parameterization conserves momentum, and when implemented in the same atmospheric model at coarse resolution it leads to stable simulations and tends to reduce wind biases, although it over‐corrects for one configuration tested. Overall, our results show that it is challenging to predict subgrid momentum fluxes and that machine‐learning momentum parameterization gives promising results.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
10.1029/2023ms003606
