Abstract
Numerical weather and climate prediction systems necessitate accurate land surface–atmosphere fluxes, whose determination typically replies on a suite of parameterization schemes. The authors present a field investigation over tall grass in a Beijing suburb, where the aerodynamic roughness length (z0) and zero-plane displacement height (d) are found to be 0.02 and 0.44 m, respectively (the value of d is close to two-thirds the average grass height during this field experiment). Both z0 and d values are then used as input parameters of an analytic model of flux footprint; the footprint model reveals that eddy-covariance flux measurements are mainly representative of the tall grass surface concerned herein, potential influences from anthropogenic sources in this suburban area notwithstanding. Based on the “fair weather” data (with an energy balance ratio of 0.89), the authors evaluate four parameterizations of turbulent surface fluxes, namely, a total of three traditional “iterative” schemes and one “noniterative” scheme developed recently to reduce computational time. Their performances are intercompared in terms of the estimations of the sensible heat flux and two turbulence components (the friction velocity and temperature scale). In weakly stable to unstable conditions, two schemes are recommended here for their good performance overall; the first scheme stems jointly from the work of Högström and Beljaars and Holtslag, and the second stems from that of Li et al.. For this tall grass surface, the choice of z0/z0h = 100 (z0h is the thermal roughness length) is more appropriate than another choice of 10, because the former produces comparatively accurate sensible heat flux estimations.
