Simulating lightning NO production in CMAQv5.2: evolution of scientific updates
Kang, D., K. Pickering, D. Allen, K. Foley, Cheung Wong, R. Mathur, AND S. Roselle. Simulating lightning NO production in CMAQv5.2: evolution of scientific updates. Geoscientific Model Development . Copernicus Publications, Katlenburg-Lindau, Germany, 12(7):3071–3083, (2019). https://doi.org/10.5194/gmd-12-3071-2019
Lightning is one of the major natural sources to produce nitrogen oxides (NOX) which contribute to the tropospheric ozone (O3) formation and other air quality related processes. It is important to incorporate the most up-to-date science and observational data to quantify lightning NOx emissions in time and space in air quality models.
This work describes the lightning nitric oxide (LNO) production schemes in the Community Multiscale Air Quality (CMAQ) model. We first document the existing LNO production scheme and vertical distribution algorithm. We then describe updates that were made to the scheme originally based on monthly National Lightning Detection Network (mNLDN) observations. The updated scheme uses hourly NLDN (hNLDN) observations. These NLDN-based schemes are good for retrospective model applications when historical lightning data are available. For applications when observed data are not available (i.e., air quality forecasts and climate studies that assume similar climate conditions), we have developed a scheme that is based on linear and log-linear parameters derived from regression of multiyear historical NLDN (pNLDN) observations and meteorological model simulations. Preliminary assessment for total column LNO production reveals that the mNLDN scheme overestimates LNO by over 40 % during summer months compared with the updated hNLDN scheme that reflects the observed lightning activity more faithfully in time and space. The pNLDN performance varies with year, but it generally produced LNO columns that are comparable to hNLDN and mNLDN, and in most cases it outperformed mNLDN. Thus, when no observed lightning data are available, pNLDN can provide reasonable estimates of LNO emissions over time and space for this important natural NO source that influences air quality regulations.