Identification of Non-Turbulent Motions for Enhanced Estimation of Land–Atmosphere Transport Through the Anisotropy of Turbulence
2025
Zihan Liu | Hongsheng Zhang | Xuhui Cai | Yu Song
Quantifying land&ndash:atmosphere transport remains crucial for advancing climate prediction and weather forecasting efforts. To improve turbulent flux estimation, the anisotropy of turbulence is taken into consideration. The parameters xB and yB, which quantify anisotropy degrees across motion scales, form trajectories in the barycentric map. Using the Hilbert&ndash:Huang transform, the scale-dependent properties of anisotropy in observational data from multiple sites are investigated. Analysis reveals consistent patterns in the average yB&minus:xB trajectories across stratification conditions: as scale increases, xB increases from 0.4 to 0.9, while yB initially climbs from 0.5 to 0.7 before declining to 0. Meanwhile, individual case trajectories sometimes deviate from this pattern, indicating contamination by non-turbulent motions that typically cause turbulent flux overestimation. Crucially, identifying the scale at which deviations occur allows effective separation of atmospheric turbulence from non-turbulent motions, which enables the reconstruction of turbulence data. Results demonstrate that corrected fluxes reduce overestimation inherent in traditional eddy covariance systems by approximately 30%, with enhancements for CO2 and air pollutants reaching 45&ndash:83%. Furthermore, the correlation between anisotropy and stratification suggests potential for refining similarity theories into a broader scope, such as carbon cycle assessment and pollution control. Therefore, anisotropy shows promise in quantifying the land&ndash:atmosphere transport.
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