The contributions of long-lived nitrous oxide (N2O) to global climate and environment have received increasing attention. Especially, atmospheric nitrogen (N) deposition has substantially increased in recent decades due to the extensive use of fossil fuels in industry, which strongly stimulates the N2O emissions of terrestrial ecosystem. Several models have been developed to simulate the impacts of environmental factors on N2O emission from soil, but there are still large differences in the simulations of N2O emission and their responses to atmospheric deposition over global or regional scales. Using observations from N addition experiments in a subtropical forest, this study compared five widely-used N2O modules or algorithms (i.e. the N2O modules of DayCENT, PnET-NDNDC and DyN, and the algorithm of NOE and NGAS) to investigate their performances for reproducing N2O emission, and especially the impacts of two forms of N additions (i.e. NH4+-N and NO3--N, respectively) of two levels (low and high) on N2O emission. In general, the five modules reproduced the seasonal variations of N2O emission. Under the high levels of N addition compared to low ones for both NH4+-N and NO3--N, however, not all modules can reproduce larger N2O emission. Relatively larger N2O emissions in measurements due to NH4+N compared to NO3--N additions were not indicated neither in all the modules. Moreover, there were substantial differences in simulating the ratios of N2O emission from nitrification and denitrification processes due to disagreements in the structure of these modules or algorithms. The comparison highlights the need to improve the representation of N2O production and diffusion processes. At the same time, it also highlights the application of WFPS in the model methodology as a key scheme that mediates the two microbial processes, i.e. nitrification and denitrification, could probably improve the performances of N2O models in future research.

[1] New aerosol modules of global ( circulation and chemical transport) models are evaluated. These new modules distinguish among at least five aerosol components: sulfate, organic carbon, black carbon, sea salt, and dust. Monthly and regionally averaged predictions for aerosol mass and aerosol optical depth are compared. Differences among models are significant for all aerosol types. The largest differences were found near expected source regions of biomass burning ( carbon) and dust. Assumptions for the permitted water uptake also contribute to optical depth differences ( of sulfate, organic carbon, and sea salt) at higher latitudes. The decline of mass or optical depth away from recognized sources reveals strong differences in aerosol transport or removal among models. These differences are also a function of altitude, as transport biases of dust do not always extend to other aerosol types. Ratios of optical depth and mass demonstrate large differences in the mass extinction efficiency, even for hydrophobic aerosol. This suggests that efforts of good mass simulations could be wasted or that conversions are misused to cover for poor mass simulations. In an attempt to provide an absolute measure for model skill, simulated total optical depths ( when adding contributions from all five aerosol types) are compared to measurements from ground and space. Comparisons to the Aerosol Robotic Network (AERONET) suggest a source strength underestimate in many models, most frequently for ( subtropical) tropical biomass or dust. Comparisons to the combined best of Moderate-Resolution Imaging Spectroradiometer ( MODIS) and Total Ozone Mapping Spectrometer ( TOMS) indicate that away from sources, model simulations are usually smaller. Particularly large are discrepancies over tropical oceans and oceans of the Southern Hemisphere, raising issues on the treatment of sea salt in models. Totals for mass or optical depth in many models are defined by the absence or dominance of only one aerosol component. With appropriate corrections to that component ( e. g., to removal, to source strength, or to seasonality) a much better model performance can be expected. Still, many important modeling issues remain inconclusive as the combined result of poor coordination ( different emissions and meteorology), insufficient model output ( vertical distributions, water uptake by aerosol type), and unresolved measurement issues ( retrieval assumptions and temporal or spatial sampling biases).