Towards a Unified Data-Driven Boundary Layer Momentum Flux Parameterization for Ocean and Atmosphere

TL;DR

This paper introduces a unified, data-driven neural network-based parameterization for turbulent momentum fluxes in oceanic and atmospheric boundary layers, improving climate model accuracy across regimes.

Contribution

A novel neural network approach trained on LES data to predict boundary layer momentum fluxes, unifying oceanic and atmospheric turbulence modeling.

Findings

Outperforms traditional schemes in boundary layer wind profile prediction.

Maintains robustness with up to 30% flux bias.

Generalizes well to unseen LES cases.

Abstract

Boundary layer turbulence, particularly the vertical fluxes of momentum, shapes the evolution of winds and currents and plays a critical role in weather, climate, and biogeochemical processes. In this work, a unified, data-driven parameterization of turbulent momentum fluxes is introduced for both the oceanic and atmospheric convective boundary layers. An artificial neural network (ANN) is trained offline on coarse-grained large-eddy simulation (LES) data representing a wide range of turbulent regimes in both fluids. By normalizing momentum flux profiles with their surface values, we exploit a self-similar structure across regimes and fluids, enabling joint training. The ANN learns to predict vertical profiles of subgrid momentum fluxes from mean wind or current profiles, capturing key physical features such as upgradient fluxes that are inaccessible to traditional first-order closure schemes. When implemented online in the Single Column Atmospheric Model (SCAM), the ANN parameterization consistently outperforms the SCAM baseline parameterization in replicating the evolution of the boundary layer wind profiles from the LES, especially under convective conditions, with errors reduced by a factor of 2-3 across regimes. ANN performance remains robust even when the surface momentum flux is biased up to 30\%, and generalization is confirmed by testing on LES cases excluded from the training dataset. This work demonstrates the potential of machine learning to create unified and physically consistent parameterizations across boundary layer systems in climate models.