4D-Var Inversion of European NH3 Emissions Using CrIS NH3 Measurements and GEOS-Chem Adjoint With Bi-Directional and Uni-Directional Flux Schemes.

We conduct the first 4D-Var inversion of NH3 accounting for NH3 bi-directional flux, using CrIS satellite NH3 observations over Europe in 2016. We find posterior NH3 emissions peak more in springtime than prior emissions at continental to national scales, and annually they are generally smaller than the prior emissions over central Europe, but larger over most of the rest of Europe. Annual posterior anthropogenic NH3 emissions for 25 European Union members (EU25) are 25% higher than the prior emissions and very close (<2% difference) to other inventories. Our posterior annual anthropogenic emissions for EU25, the UK, the Netherlands, and Switzerland are generally 10%-20% smaller than when treating NH3 fluxes as uni-directional emissions, while the monthly regional difference can be up to 34% (Switzerland in July). Compared to monthly mean in-situ observations, our posterior NH3 emissions from both schemes generally improve the magnitude and seasonality of simulated surface NH3 and bulk NH x wet deposition throughout most of Europe, whereas evaluation against hourly measurements at a background site shows the bi-directional scheme better captures observed diurnal variability of surface NH3. This contrast highlights the need for accurately simulating diurnal variability of NH3 in assimilation of sun-synchronous observations and also the potential value of future geostationary satellite observations. Overall, our top-down ammonia emissions can help to examine the effectiveness of air pollution control policies to facilitate future air pollution management, as well as helping us understand the uncertainty in top-down NH3 emissions estimates associated with treatment of NH3 surface exchange.

Towards a coupled paradigm of NH3 -CO2 biosphere-atmosphere exchange modelling.

Stomatal conductance, one of the major plant physiological controls within NH3 biosphere-atmosphere exchange models, is commonly estimated from semi-empirical multiplicative schemes or simple light- and temperature-response functions. However, due to their inherent parameterization on meteorological proxy variables, instead of a direct measure of stomatal opening, they are unfit for the use in climate change scenarios and of limited value for interpreting field-scale measurements. Alternatives based on H2 O flux measurements suffer from uncertainties in the partitioning of evapotranspiration at humid sites, as well as a potential decoupling of transpiration from stomatal opening in the presence of hygroscopic particles on leaf surfaces. We argue that these problems may be avoided by directly deriving stomatal conductance from CO2 fluxes instead. We reanalysed a data set of NH3 flux measurements based on CO2 -derived stomatal conductance, confirming the hypothesis that the increasing relevance of stomatal exchange with the onset of vegetation activity caused a rapid decrease of observed NH3 deposition velocities. Finally, we argue that developing more mechanistic representations of NH3 biosphere-atmosphere exchange can be of great benefit in many applications. These range from model-based flux partitioning, over deposition monitoring using low-cost samplers and inferential modelling, to a direct response of NH3 exchange to climate change.

The hidden cost of using low-resolution concentration data in the estimation of NH3 dry deposition fluxes.

Long-term monitoring stations for atmospheric pollutants are often equipped with low-resolution concentration samplers. In this study, we analyse the errors associated with using monthly average ammonia concentrations as input variables for bidirectional biosphere-atmosphere exchange models, which are commonly used to estimate dry deposition fluxes. Previous studies often failed to account for a potential correlation between ammonia exchange velocities and ambient concentrations. We formally derive the exact magnitude of these errors from statistical considerations and propose a correction scheme based on parallel measurements using high-frequency analysers. In case studies using both modelled and measured ammonia concentrations and micrometeorological drivers from sites with varying pollution levels, we were able to substantially reduce bias in the predicted ammonia fluxes. Neglecting to account for these errors can, in some cases, lead to significantly biased deposition estimates compared to using high-frequency instrumentation or corrected averaging strategies. Our study presents a first step towards a unified correction scheme for data from nation-wide air pollutant monitoring networks to be used in chemical transport and air quality models.

Land Use Specific Ammonia Deposition Velocities: a Review of Recent Studies (2004-2013).

Land use specific deposition velocities of atmospheric trace gases and aerosols-particularly of reactive nitrogen compounds-are a fundamental input variable for a variety of deposition models. Although the concept is known to have shortcomings-especially with regard to bi-directional exchange-the often limited availability of concentration data and meteorological input variables make it a valuable simplification for regional modeling of deposition fluxes. In order to meet the demand for an up-to-date overview of recent publications on measurements and modeling studies, we compiled a database of ammonia (NH3) deposition velocities published from 2004 to 2013. Observations from a total of 42 individual studies were averaged using an objective weighing scheme and classified into seven land use categories. Weighted average and median deposition velocities are 2.2 and 2.1 cm s-1 for coniferous forests, 1.5 and 1.2 cm s-1 for mixed forests, 1.1 and 0.9 cm s-1 for deciduous forests, 0.9 and 0.7 cm s-1 for semi-natural sites, 0.7 and 0.8 cm s-1 for urban sites, 0.7 and 0.6 cm s-1 for water surfaces, and 1.0 and 0.4 cm s-1 for agricultural sites, respectively. Thus, values presented in this compilation were considerably lower than those found in former studies (e.g., VDI 2006). Reasons for the mismatch were likely due to different land use classification, different averaging methods, choices of measurement locations, and improvements in measurement and in modeling techniques. Both data and code used for processing are made available as supplementary material to this article.