4.4 Article

Influence of Canopy Seasonal Changes on Turbulence Parameterization within the Roughness Sublayer over an Orchard Canopy

Journal

JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
Volume 55, Issue 6, Pages 1391-1407

Publisher

AMER METEOROLOGICAL SOC
DOI: 10.1175/JAMC-D-15-0205.1

Keywords

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Funding

  1. Center for Multiscale Modeling of Atmospheric Processes (CMMAP) at Colorado State University (NSF) [ATM-0425247, G-3045-9]

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In this observational study, the role of tree phenology on the atmospheric turbulence parameterization over 10-m-tall and relatively sparse deciduous vegetation is quantified. Observations from the Canopy Horizontal Array Turbulence Study (CHATS) field experiment are analyzed to establish the dependence of the turbulent exchange of momentum, heat, and moisture, as well as kinetic energy on canopy phenological evolution through widely used parameterization models based on 1) dimensionless gradients or 2) turbulent kinetic energy (TKE) in the roughness sublayer. Observed vertical turbulent fluxes and gradients of mean wind, temperature, and humidity, as well as velocity variances, are used in combination with empirical dimensionless functions to calculate the turbulent exchange coefficient. The analysis shows that changes in canopy phenology influence the turbulent exchange of all quantities analyzed in this study. The turbulent exchange coefficients of those quantities are twice as large near the canopy top for a leafless canopy than for a full-leaf canopy under unstable and near-neutral conditions. This turbulent exchange coefficient difference is related to the differing penetration depths of the turbulent eddies organized at the canopy top, which increase for a canopy without leaves. The TKE and dissipation analysis under near-neutral atmospheric conditions additionally shows that TKE exchange increases for a leafless canopy because of reduced TKE dissipation efficiency relative to that when the canopy is in full-leaf stage. The study closes with discussion surrounding the implications of these findings for parameterizations used in large-scale models.

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