THE EFFECTS OF CLIMATE VARIABILITY AND METEOROLOGICAL CONDITIONS ON THE ATMOSPHERIC NITROGEN CYCLE IN THE UNITED STATES

Abstract

Human activities have significantly increased reactive nitrogen (N) emissions and atmospheric deposition, causing a range of negative impacts on human and ecosystem health. The controlling factors for amount of N deposition and atmospheric transport are not well understood. This research aims to fill the gap by investigating the effects of inter-annual climate variability associated with the El Niño Southern Oscillation (ENSO) on the atmospheric N cycle in the U.S. Wavelet analysis on seasonal inorganic N wet deposition measured at the National Atmospheric Deposition Program and the NINO3.4 SST climate index indicated that up to 62% and 53% of the 2- to 6-year variations of precipitation and N wet deposition in the U.S., respectively, can be explained by ENSO activity. During El Niño winters, N wet deposition rates were above normal in the southern U.S., while La Niña events were associated with higher N wet deposition to the Cascades, the Rocky Mountains and the Great Lakes regions. Atmospheric N budgets for the western U.S. and its sub-regions were compiled using the Weather Research and Forecasting and the Community Multi-scale Air Quality (WRF-CMAQ) modeling system. The modeling study showed that total N deposition and net N transport over the western U.S. during the 1997/98 El Niño event vs. the 1998/99 La Niña event differed by 2-10% and 5-58%, respectively, with respect to the average of the two events. To support human and environmental impact studies, there is a need for improving model predictions at different time scales for time period ranging from episodic few days to multiple years. Spectral analysis revealed that the diurnal (11-36 hours) and baseline (> 21 days) components are the most important time scales present in observed ozone (O3), nitrogen dioxide (NO2), and fine particulate matters (PM2.5) concentrations in the western U.S. Together these components captured more than 75% of the total variances. The CMAQ model results analyzed for different temporal components indicated that improvement in the baseline component could improve overall model performance for episodic to multi-year studies.