Abstract
Abstract. In a recent paper (Koga et al., 2007) it was shown that the intermittent nature of solar wind turbulence can be characterized by kurtosis and phase coherence index. In this paper, we apply these two nonlinear time series techniques to characterize the intermittent nature of atmospheric turbulence above and within the Amazon forest canopy using the day-time data of temperature and vertical wind velocity measured by a micrometeorological tower at two different heights. By applying kurtosis and phase coherence index to quantify the degree of phase coherence, we identify an enhanced scalar-velocity similarity for in-canopy turbulence compared to the above-canopy turbulence, during the interval of data analysis.
Highlights
Amazon rain forest plays a key role in the regional and global climate dynamics
This study indicates that for day-time conditions when there is an efficient turbulent mixing in the upper canopy and profile gradients are small, the radon-222 source/sink distributions show a high sensitivity to small measurement errors and the CO2 and H2O fluxes show a reasonable agreement with the eddy covariance measurements made above the forest canopy, which is not the case for night-time conditions when the CO2 profile gradients in the upper canopy are large due to reduced turbulent mixing
Our results prove that the atmospheric intermittent turbulence, above and within the Amazon forest canopy, is generated by the phase coherence due to nonlinear wave-wave interactions
Summary
Amazon rain forest plays a key role in the regional and global climate dynamics. One important problem for understanding the vegetation-atmosphere interactions in Amazonia is the turbulent exchange of scalar and momentum in the atmospheric boundary layer - above and within the forest canopy. An analysis of fine-scale canopy turbulence in an Amazon forest based on the generalized Tsallis’ thermostatistics theory was performed by Bolzan et al (2002) This investigation shows that the entropy parameter q from Tsallis’ non-extensive statistics, that controls the shape of the probability density function (PDF) of velocity. Using time series of turbulent velocity and scalar concentration collected in the atmosphere above a temperate pine forest, Katul et al (2006) demonstrated that Tsallis’ non-extensive thermostatistics provides a unifying framework to study two inter-connected problems: dissimilarity between scalars and velocity statistics within the inertial subrange and contamination of internal intermittency by external factors. The observational data of atmospheric turbulence is an admixture of deterministic signal and stochastic noise In such case, a demonstration of finite phase coherence is required to ascertain the nonlinear origin of non-Gaussian fluctuations.
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