The balances of mixing ratios and segregation intensity: a case study from the field (ECHO 2003)
Abstract. An inhomogeneous mixing of reactants causes a reduction of their chemical removal compared to the homogeneously mixed case in turbulent atmospheric flows. This can be described by the intensity of segregation IS being the covariance of the mixing ratios of two species divided by the product of their means. Both terms appear in the balance equation of the mixing ratio and are discussed for the reaction between isoprene and OH for data of the field study ECHO 2003 above a deciduous forest. For most of these data, IS is negatively correlated with the fraction of mean OH mixing ratio reacting with isoprene. IS is also negatively correlated with the isoprene standard deviation. Both findings agree with model results discussed by Patton et al. (2001) and others. The correlation coefficient between OH and isoprene and, therefore, IS increases with increasing mean reaction rate. In addition, the balance equation of the covariance between isoprene and OH is applied as the theoretical framework for the analysis of the same field data. The storage term is small, and, therefore, a diagnostic equation for this covariance can be derived. The chemical reaction term Rij is dominated by the variance of isoprene times the quotient of mixing ratios of OH and isoprene. Based on these findings a new diagnostic equation for IS is formulated. Comparing different terms of this equation, IS and Rij show a relation also to the normalised isoprene standard deviation. It is shown that not only chemistry but also turbulent and convective mixing and advection – considered in a residual term – influence IS. Despite this finding, a detection of the influence of coherent eddy transport above the forest according to Katul et al. (1997) on IS fails, but a relation to the turbulent and advective transport of isoprene variance is determined. The largest values of IS are found for most unstable conditions with increasing buoyant production, confirming qualitatively model predictions by Ouwersloot et al. (2011).