Intercomparison and assessment of turbulent and physiological exchange parameters of grassland

Abstract

Abstract. Commonly, the micrometeorological parameters that underline the calculations of surface atmosphere exchange fluxes (e.g. friction velocity and sensible heat flux) and parameters used to model exchange fluxes with SVATtype parameterisations (e.g. latent heat flux and canopy temperature) are measured with a single set of instrumentation and are analysed with a single methodology. This paper evaluates uncertainties in these measurements with a single instrument, by comparing the independent results from nine different institutes during the international GRAMINAE integrated field experiment over agricultural grassland near Braunschweig, Lower Saxony, Germany. The paper discusses uncertainties in measuring friction velocity, sensible and latent heat fluxes, canopy temperature and investigates the energy balance closure at this site. Although individual 15-min flux calculations show a large variability between the instruments, when averaged over the campaign, fluxes agree within 2% for momentum and 11% for sensible heat. However, the spread in estimates of latent heat flux (λE) is Correspondence to: E. Nemitz (en@ceh.ac.uk) larger, with standard deviations of averages of 18%. The dataset averaged over the different instruments fails to close the energy budget by 20%, significantly larger than the uncertainties in the individual flux corrections. However, if the largest individual turbulent flux estimates are considered, energy closure can be achieved, indicating that the closure gap is within the spread of the measurements. The uncertainty in λE feeds results in an uncertainty in the bulk stomatal resistance, which further adds to the uncertainties in the estimation of the canopy temperature that controls the exchange. The paper demonstrated how a consensus dataset was derived, which is used by the individual investigators to calculate fluxes and drive their models.

Nitrous oxide emissions from managed grassland: a comparison of eddy covariance and static chamber measurements

“…By contrast, long-term averages of duplicated measurements are very close because the statistical variability averages out (e.g. Dämmgen et al, 2005;Nemitz et al, 2009).…”

Ammonia fluxes in relation to cutting and fertilization of an intensively managed grassland derived from an inter-comparison of gradient measurements

“…u * and all other meteorological parameters were measured by several institutes in the experiment. These estimates have been compared and evaluated by Nemitz et al (2009b), who provided a consensus dataset for use in the flux calculations here. The basic calculations of NH 3 fluxes derived here assume stationarity and homogeneity of the atmospheric conditions, such that the vertical flux results entirely from turbulent transfer in the vertical and that errors related to local advection (horizontal heterogeneity), storage (temporal instationarity) and chemical production or consumption in the surface layer are ignored.…”

“…These micrometeorological criteria were: u (1 m) <0.8 m s −1 , |L|<5 m and cumulative normalised footprint function (CNF) <67%. The cumulative normalised footprint function was calculated using the Kormann and Meixner (2001) formulation as described in Nemitz et al (2009b).…”

Advection of NH&lt;sub&gt;3&lt;/sub&gt; over a pasture field and its effect on gradient flux measurements

“…Using the measured fluxes, T a , u and RH were estimated at a reference height of 1 m above d. In the modelling approach, u * and L were filtered to consider only data where: u * >0.2 m s −1 and |L|>5 m. The roughness height (z 0 ) and the displacement height (d) were both estimated using a wind speed profile and retrieving d by minimising the linear regression between u(z) and ln(z−d). The roughness height z 0 was estimated using measured u * and wind speed profile, knowing d (Nemitz et al, 2009b).…”

“…No NH 3 surface exchange was considered in the fields located between the farm and the Field I, unless otherwise stated. As FIDES-2D applies to homogeneous fields, a single z 0 and a single d was considered for the whole distance downwind from the source, which were either taken from the micrometeorological dataset (Nemitz et al, 2009b) when modelling advection errors from the field, or fixed to z 0 =0.1 m and d=0.5 m when modelling advections errors from the farm, since taller canopies and heterogeneities were present between the farm and the field.…”