Improving Model Representation of Reduced Nitrogen in the Greater Yellowstone Area

Abstract:

Human activity, including fossil fuel combustion and agriculture has greatly increased the amount of reactive nitrogen (RN) in the atmosphere and its subsequent deposition to land. Increases in deposition of RN compounds can adversely affect sensitive ecosystems and is a growing problem in many natural areas. The National Park Service in conjunction with Colorado State University researchers and assistance from the Forest Service conducted the Grand Teton Reactive Nitrogen Deposition Study (GrandTReNDS) involving spatially and temporally detailed measurements of RN during spring/summer 2011. In this work it was found that during summer months at the high elevation site Grand Targhee, 62% of the nitrogen deposition was due to reduced nitrogen, about equally split between dry and wet deposition, oxidized nitrogen accounted for 27% of the total, and the remaining was wet deposited organic nitrogen. An important next step to GrandTReNDS is the use of chemical transport models (CTMs) to estimate source contributions to RN in the park. Given the large contribution of reduced nitrogen species to total nitrogen deposition in the park, understanding and properly characterizing ammonia in CTMs is critical to estimating the total nitrogen deposition. A model performance evaluation of the CAMx uni-directional model and CMAQ bi-direction and uni-directional 2011 model simulations versus GrandTReNDS and other datasets was conducted. Preliminary results suggest that, in some areas, model performance of ambient ammonia concentration is more sensitive to the spatial resolution of the model and the accuracy of the spatial representation of emissions than to the incorporation of bi-directional flux. Additional model sensitivity runs, including sensitivity to resolution (with and without bi-directional flux capabilities), changes to model estimated ammonia dry deposition velocities, and improved representation of the spatial distribution of ammonia emissions, are used to identify the best set of options for GrandTReNDS modeling, and to provide a measure of uncertainties. This will help atmospheric scientists identify deficiencies in the models and inform future model development.