Abstract

Since its inception in the 1940s, Monin-Obukhov similarity theory (MOST), which relates turbulent fluxes to mean vertical gradients in the lower atmosphere, has become ubiquitous for predicting surface fluxes of quantities transported by the flow in numerical weather, climate, and hydrological forecasting models. Despite its widespread use, MOST does not account for the effects of large coherent structures in the flow, which modulate the amplitude of turbulent fluctuations, and are responsible for a large fraction of the total transport. Herein, we demonstrate that the incorporation of the large-scale streamwise velocity u_{l}(x,t)=G_{δ}⋆u(x,t), where G_{δ} is a low-pass filtering kernel, into dimensional analysis leads to an additional dimensionless parameter α(x,t), which captures the modulating influence of these structures on flux-gradient relationships. Atmospheric observations and large-eddy simulations are used to demonstrate that observed deviations from MOST can indeed be explained by this new parameter; coherent structures induce an alternating loading and unloading of the mean velocity gradient near the surface.

Highlights

  • Since its inception in the 1940s, Monin-Obukhov similarity theory (MOST), which relates turbulent fluxes to mean vertical gradients in the lower atmosphere, has become ubiquitous for predicting surface fluxes of quantities transported by the flow in numerical weather, climate, and hydrological forecasting models

  • MOST does not account for the effects of large coherent structures in the flow, which modulate the amplitude of turbulent fluctuations, and are responsible for a large fraction of the total transport

  • We demonstrate that the incorporation of the large-scale streamwise velocity ulðx; tÞ 1⁄4 Gδ⋆uðx; tÞ, where Gδ is a low-pass filtering kernel, into dimensional analysis leads to an additional dimensionless parameter αðx; tÞ, which captures the modulating influence of these structures on flux-gradient relationships

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Summary

Introduction

Since its inception in the 1940s, Monin-Obukhov similarity theory (MOST), which relates turbulent fluxes to mean vertical gradients in the lower atmosphere, has become ubiquitous for predicting surface fluxes of quantities transported by the flow in numerical weather, climate, and hydrological forecasting models. We demonstrate that the incorporation of the large-scale streamwise velocity ulðx; tÞ 1⁄4 Gδ⋆uðx; tÞ, where Gδ is a low-pass filtering kernel, into dimensional analysis leads to an additional dimensionless parameter αðx; tÞ, which captures the modulating influence of these structures on flux-gradient relationships.

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