Coherent Structures Modulate Atmospheric Surface Layer Flux-Gradient Relationships.

Area of Science:

• Atmospheric science
• Fluid dynamics
• Turbulence research

Background:

• Monin-Obukhov similarity theory (MOST) is a cornerstone for modeling atmospheric surface layer processes.
• MOST relates turbulent fluxes to mean vertical gradients but neglects large coherent flow structures.
• These structures significantly impact turbulent fluctuations and transport, leading to deviations from MOST predictions.

Purpose of the Study:

• To introduce a new dimensionless parameter that quantifies the influence of large coherent structures on flux-gradient relationships.
• To demonstrate that this parameter can explain observed deviations from MOST.
• To enhance the accuracy of atmospheric transport models.

Main Methods:

• Incorporating large-scale streamwise velocity, filtered using a low-pass kernel, into dimensional analysis.
• Developing a new dimensionless parameter, α(x,t), representing the effect of coherent structures.
• Validating the parameter using atmospheric observations and large-eddy simulations.

Main Results:

• The new parameter α(x,t) effectively captures the modulating influence of large coherent structures.
• Deviations from MOST predictions are demonstrably linked to the magnitude of α(x,t).
• Coherent structures were shown to cause alternating loading and unloading of the mean velocity gradient.

Conclusions:

• The proposed parameter α(x,t) offers a significant advancement over traditional MOST.
• Accounting for large-scale structures improves the physical basis of flux-gradient relationships.
• This work provides a pathway for more accurate predictions in weather, climate, and hydrological models.