Non‐turbulent motion identified from properties of transport and its influence on the calculation of turbulent flux

Author:

Liu Zihan1ORCID,Zhang Hongsheng1ORCID,Wei Wei2,Cai Xuhui3,Song Yu3,Zhang Xiaoye2

Affiliation:

1. Laboratory for Climate and Ocean‐Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics Peking University Beijing China

2. State Key Laboratory of Severe Weather and Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences China Meteorological Administration (CMA) Beijing China

3. State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Science and Engineering Peking University Beijing China

Abstract

AbstractTurbulent fluxes play a critical role in atmospheric science and are usually calculated from the eddy covariance system. However, the presence of motions of larger scale and the bias from observational instruments often affects the procurement of turbulent fluxes. Based on the Hilbert–Huang transform, the properties of transport can be defined and used to distinguish non‐turbulent motions from the observational data, and a new way of decomposing the variables is thereby put forward to reconstruct the turbulent flux series. To quantify the influence of non‐turbulent motions on the calculation of turbulent flux, the non‐turbulent motions extracted from the five‐level turbulence data of the Tianjin 255‐m meteorological tower from July 1 to August 31, 2017 are examined. The results reveal that the presence of non‐turbulent motion can lead to a universal overestimation of turbulent flux, and the degree of overestimation increases with the complexity and the intensity of non‐turbulent motion. According to the consequences above, an empirical relationship, together with the corresponding coefficients, is given to provide a guidance on the correction of turbulent fluxes in practical use, such as the simulation of atmospheric turbulence and the parameterization in meteorological and climate models.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Wiley

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