Neural‐Network Parameterization of Subgrid Momentum Transport in the Atmosphere

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.

Regional climate change: consensus, discrepancies, and ways forward

“…The current generation of coupled models also suffer from significant structural uncertainty related to the empirical parameterizations of subgrid scale physics. Machine learning methods have emerged as a promising new tool for parameterization development (e.g., Rasp et al, 2018;Wang P. et al, 2022;Yuval and O'Gorman, 2023;Beucler et al, 2024), for correcting (Bretherton et al, 2022) and understanding (Silva et al, 2022;Wang S. S. C. et al, 2022) model biases, and for downscaling (e.g., Leinonen et al, 2021;Miralles et al, 2022;Sekiyama et al, 2023). Furthermore, causal discovery and interpretable and explainable Artificial Intelligence (XAI) are also potentially useful for identifying drivers of the discrepancies and understanding where and why models are deviating from observations (e.g., Gregory et al, 2023).…”

Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer Using Neural Networks

“…Studies show that neural networks can be used in idealized model configurations, and recently, the use of machine learning has emerged in realistic GCMs. Artificial neural networks (ANNs) have been shown to improve sub-grid momentum transport in atmospheric models (Yuval & O'Gorman, 2023), predict precipitation and fluxes , while in ocean models they have been used to improve the parameterization of free convection (Ramadhan et al, 2023). Liang et al (2022) applied deep neural networks to predict temperature and salinity evolution in the OSBL at a weather station (Station Papa).…”

Climate-invariant machine learning

“…We use the term ML here to broadly describe algorithms that learn a task from data without being explicitly programmed for that task. Applications of ML in atmospheric science include the emulation of radiative transfer algorithms [e.g., (6)(7)(8)(9)], momentum fluxes [e.g., (10)(11)(12)(13)] and microphysical schemes [e.g., (14)(15)(16)], the bias correction of climate predictions [e.g., (17,18)], the detection and classification of clouds and storms [e.g., (19)(20)(21)(22)], and the development of subgrid-scale "closures" (i.e., representation based on coarse-scale processes only) from high-resolution simulation data [e.g., (23)(24)(25)], which is the main application discussed here.…”