Articles | Volume 6, issue 8
07 Aug 2009
 | 07 Aug 2009

Intercomparison and assessment of turbulent and physiological exchange parameters of grassland

E. Nemitz, K. J. Hargreaves, A. Neftel, B. Loubet, P. Cellier, J. R. Dorsey, M. Flynn, A. Hensen, T. Weidinger, R. Meszaros, L. Horvath, U. Dämmgen, C. Frühauf, F. J. Löpmeier, M. W. Gallagher, and M. A. Sutton

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 SVAT-type 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 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.

Final-revised paper