Simulating lightning NO production in CMAQv5.2: performance evaluations.

Area of Science:

• Atmospheric Chemistry
• Air Quality Modeling
• Environmental Science

Background:

• Lightning is a significant source of nitric oxide (NO) in the atmosphere.
• Accurate representation of lightning NO (LNO) in air quality models is crucial for predicting ground-level and aloft ozone (O3) and nitrogen deposition.
• Existing models have uncertainties in simulating LNO impacts on atmospheric chemistry.

Purpose of the Study:

• To assess the impact of different lightning nitric oxide (LNO) production schemes within the Community Multiscale Air Quality (CMAQ) model.
• To evaluate the model's predictions of nitrogen oxides (NOx) and ozone (O3) against observational data.
• To determine the influence of LNO on ground-level air quality, aloft atmospheric chemistry, and nitrate wet deposition.

Main Methods:

• Utilized hourly O3 and NOx data from the U.S. EPA Air Quality System (AQS) for ground-level evaluation.
• Employed ozonesonde and P-3B aircraft measurements from the DISCOVER-AQ campaign for vertical profile analysis.
• Assessed nitrate wet deposition using data from the National Atmospheric Deposition Program's National Trends Network (NADP NTN).

Main Results:

• LNO impacts on surface O3 vary regionally; it can reduce underestimation in some areas and increase overestimation in others.
• LNO significantly improves modeled vertical O3 distributions, reducing underestimation aloft and overestimation near the surface.
• Inclusion of LNO reduces biases and errors in nitrate wet deposition predictions across most of the domain.

Conclusions:

• The hNLDN scheme, using hourly lightning data, demonstrated the best performance across all evaluation metrics (ground-level, vertical, and wet deposition).
• The mNLDN scheme also showed improved performance, while the pNLDN scheme offered better nitrate wet deposition estimates than the base model when lightning data was unavailable.
• Accurate LNO parameterization is essential for improving air quality model predictions of ozone and nitrogen deposition.