本征非平稳性特征及其对CO2通量涡旋协方差测量的影响

IF 1.7 4区地球科学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Nonlinear Processes in Geophysics Pub Date : 2022-03-24 DOI:10.5194/npg-29-123-2022

Lei Liu, Yu Shi, F. Hu

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引用次数: 2

摘要

摘要在涡流协方差法中，平稳性是一个关键的假设，而涡流协方差法被广泛用于计算湍流通量。已经提出了许多方法来诊断归因于外部非湍流流动的非平稳性。本文主要研究湍流随机性引起的固有非平稳性。采用趋势波动分析方法对北京市中心城区CO2湍流通量进行了IN量化。结果表明，IN在CO2湍流通量中是常见的，是一种与惯性亚范围湍流有关的小尺度现象。CO2湍流通量的小尺度IN可以用Ornstein-Uhlenbeck (OU)过程作为一级近似来模拟。仿真结果表明，为避免IN的影响，通量平均时间应大于27 s。此外，不考虑IN的非平稳性诊断方法可能会对某些参数进行错误诊断。

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Characteristics of intrinsic non-stationarity and its effect on eddy-covariance measurements of CO₂ fluxes

Abstract. Stationarity is a critical assumption in the eddy-covariance method that is widely used to calculate turbulent fluxes. Many methods have been proposed to diagnose non-stationarity attributed to external non-turbulent flows. In this paper, we focus on intrinsic non-stationarity (IN) attributed to turbulence randomness. The detrended fluctuation analysis is used to quantify IN of CO2 turbulent fluxes in the downtown of Beijing. Results show that the IN is common in CO2 turbulent fluxes and is a small-scale phenomenon related to the inertial sub-range turbulence. The small-scale IN of CO2 turbulent fluxes can be simulated by the Ornstein–Uhlenbeck (OU) process as a first approximation. Based on the simulation results, we find that the flux-averaging time should be greater than 27 s to avoid the effects of IN. Besides, the non-stationarity diagnosis methods that do not take into account IN would possibly make a wrong diagnosis with some parameters.

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来源期刊

Nonlinear Processes in Geophysics 地学-地球化学与地球物理

CiteScore

4.00

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： Nonlinear Processes in Geophysics (NPG) is an international, inter-/trans-disciplinary, non-profit journal devoted to breaking the deadlocks often faced by standard approaches in Earth and space sciences. It therefore solicits disruptive and innovative concepts and methodologies, as well as original applications of these to address the ubiquitous complexity in geoscience systems, and in interacting social and biological systems. Such systems are nonlinear, with responses strongly non-proportional to perturbations, and show an associated extreme variability across scales.