• Energy Research

Existing descriptions of bi-directional ammonia (NH 3 ) land–atmosphere exchange incorporate temperature and moisture controls, and are beginning to be used in regional chemical transport models. However, such models have typically applied simpler emission factors to upscale the main NH 3 emission terms. While this approach has successfully simulated the main spatial patterns on local to global scales, it fails to address the environment- and climate-dependence of emissions. To handle these issues, we outline the basis for a new modelling paradigm where both NH 3 emissions and deposition are calculated online according to diurnal, seasonal and spatial differences in meteorology. We show how measurements reveal a strong, but complex pattern of climatic dependence, which is increasingly being characterized using ground-based NH 3 monitoring and satellite observations, while advances in process-based modelling are illustrated for agricultural and natural sources, including a global application for seabird colonies. A future architecture for NH 3 emission–deposition modelling is proposed that integrates the spatio-temporal interactions, and provides the necessary foundation to assess the consequences of climate change. Based on available measurements, a first empirical estimate suggests that 5°C warming would increase emissions by 42 per cent (28–67%). Together with increased anthropogenic activity, global NH 3 emissions may increase from 65 (45–85) Tg N in 2008 to reach 132 (89–179) Tg by 2100.

Abstract Seabird colonies alter the biogeochemistry of nearby ecosystems, while the associated emissions of ammonia (NH3) may cause acidification and eutrophication of finely balanced biomes. To examine the possible effects of future climate change on the magnitude and distribution of seabird NH3 emissions globally, a global seabird database was used as input to the GUANO model, a dynamic mass-flow process-based model that simulates NH3 losses from seabird colonies at an hourly resolution in relation to environmental conditions. Ammonia emissions calculated by the GUANO model were in close agreement with measured NH3 emissions across a wide range of climates. For the year 2010, the total global seabird NH3 emission is estimated at 82 [37–127] Gg year−1. This is less than previously estimated using a simple temperature-dependent empirical model, mainly due to inclusion of nitrogen wash-off from colonies during precipitation events in the GUANO model. High precipitation, especially between 40° and 60° S, results in total emissions for the penguin species that are 82% smaller than previously estimated, while for species found in dry tropical areas, emissions are 83–133% larger. Application of temperature anomalies for several IPCC scenarios for 2099 in the GUANO model indicated a predicted net increase in global seabird NH3 emissions of 27% (B1 scenario) and 39% (A2 scenario), compared with the 2010 estimates. At individual colonies, the net change was the result of influences of temperature, precipitation and relative humidity change, with smaller effects of wind-speed changes. The largest increases in NH3 emissions (mean: 60% [486 to −50] increase; A2 scenario for 2099 compared with 2010) were found for colonies 40°S to 65°N, and may lead to increased plant growth and decreased biodiversity by eliminating nitrogen sensitive plant species. Only 7% of the seabird colonies assessed globally (mainly limited to the sub-polar Southern Ocean) were estimated to experience a reduction in NH3 emission (average: −18% [-50 to 0] reduction between 2010 and 2099, A2 scenario), where an increase in precipitation was found to more than offset the effect of rising temperatures.

The aim of this study is to illustrate the importance of farm scale heterogeneity on nitrogen (N) losses in agricultural landscapes. Results are exemplified with a chain of N models calculating farm-N balances and distributing the N-surplus to N-losses (volatilisation, denitrification, leaching) and soil-N accumulation/release in a Danish landscape. Possible non-linearities in upscaling are assessed by comparing average model results based on (i) individual farm level calculations and (ii) averaged inputs at landscape level. Effects of the non-linearities that appear when scaling up from farm to landscape are demonstrated. Especially in relation to ammonia losses the non-linearity between livestock density and N-loss is significant (p > 0.999), with around 20-30% difference compared to a scaling procedure not taking this non-linearity into account. A significant effect of farm type on soil N accumulation (p > 0.95) was also identified and needs to be included when modelling landscape level N-fluxes and greenhouse gas emissions.