Abstract. Current deposition schemes used in atmospheric chemical transport models do not generally account for bi-directional exchange of ammonia (NH₃). Bi-directional exchange schemes, which have so far been applied at the plot scale, can be included in transport models, but need to be parameterised with appropriate values of the ground layer compensation point (χ_g), stomatal compensation point (χ_s) and cuticular resistance (R_w). We review existing measurements of χ_g, χ_s as well as R_w and compile a comprehensive dataset from which we then propose generalised parameterisations. χ_s is related to Γ_s, the non-dimensional ratio of [NH₄⁺]_apo and [H⁺]_apo in the apoplast, through the temperature dependence of the combined Henry and dissociation equilibrium. The meta-analysis suggests that the nitrogen (N) input is the main driver of the apoplastic and bulk leaf concentrations of ammonium (NH_{4 apo}, NH_{4 bulk}). For managed ecosystems, the main source of N is fertilisation which is reflected in a peak value of χ_s a few days following application, but also alters seasonal values of NH_{4 apo} and NH_{4 bulk}. We propose a parameterisation for χ_s which includes peak values as a function of amount and type of fertiliser application which gradually decreases to a background value. The background χ_s is based on total N input to the ecosystem as a yearly fertiliser application and N deposition (N_dep). For non-managed ecosystems, χ_s is parameterised based solely on the link with N_dep.

For R_w we propose a general parameterisation as a function of atmospheric relative humidity (RH), incorporating a minimum value (R_w(min)), which depends on the ratio of atmospheric acid concentrations (SO₂, HNO₃ and HCl) to NH₃ concentrations. The parameterisations are based mainly on datasets from temperate locations in northern Europe making them most suitable for up-scaling in these regions (EMEP model for example). In principle, the parameterisations should be applicable to other climates, though there is a need for more underpinning data, with the uncertainties being especially large for tropical and subtropical conditions.