Simulating Lightning NO X Production in CMAQv5.2 Using mNLDN, hNLDN, and pNLDN Schemes: Performance Evaluation

Abstract. This study assesses the impact of the lightning NO X (LNO X ) production schemes in the CMAQ model (Kang et al., 2019) on ground-level air quality as well as aloft atmospheric chemistry through detailed evaluation of model predictions of nitrogen oxides (NO x ) and ozone (O 3 ) with corresponding observations for the U.S. For ground-level evaluations, hourly O 3 and NO x from the US EPA's AQS monitoring network are used to assess the impact of different LNOx schemes on model prediction of these species in time and space. Vertical evaluations are performed using ozonesonde and P-3B aircraft measurements during the DISCOVER-AQ campaign conducted in the Baltimore/Washington region during July 2011. The impact on wet deposition of nitrate is assessed using measurements from the National Atmospheric Deposition Program's National Trends Network (NADP/NTN). Compared with the base model (without LNO x ), the impact of LNO x on surface O 3 varies from region to region depending on the base model conditions. Overall statistics suggest that for regions where surface O 3 mixing ratios are already overestimated, the incorporation of additional NO x from lightning generally increased model overestimation of mean daily maximum 8-hr (DM8HR) O 3 by 1–2 ppb. In regions where surface O 3 is underestimated by the base model, LNO x can significantly reduce the underestimation and bring model predictions close to observations. Analysis of vertical profiles reveals that LNO x can significantly improve the vertical structure of modeled O 3 distributions by reducing underestimation aloft, and to a lesser degree decreasing overestimation near the surface. Since the base model underestimates the wet deposition of nitrate in most regions across the modeling domain except the Pacific Coast, the inclusion of LNO x leads to reduction in biases and errors and an increase in correlation coefficients at almost all the NADP/NTN sites. Among the three LNO x schemes described in Kang et al. (2019), the hNLDN scheme, which is implemented using hourly observed lightning flash data from National Lightning Detection Network (NLDN), performs best for the ground-level, vertical profiles, and wet deposition comparisons except that for the accumulated wet deposition of nitrate, the mNLDN scheme (the monthly NLDN-based scheme) performed slightly better. However, when observed lightning flash data are not available, the linear regression-based parameterization scheme, pNLDN, provides an improved estimate for LNO x compared to the base simulation that does not include LNO x .