Comment on acp-2022-133

Abstract

Coastal regions are susceptible to multiple complex dynamic and chemical mechanisms and emission sources that lead to frequently observed large tropospheric ozone variations. These large ozone variations occur on a meso-scale which have proven to be arduous to simulate using chemical transport models (CTMs). We present a clustering analysis of multi-dimensional measurements from ozone Light Detection And Ranging (LiDAR) in conjunction with both an offline GEOS-Chem CTM simulation and the online GEOS-Chem simulation GEOS-CF, to investigate the vertical and temporal variability of coastal ozone during three recent air quality campaigns: 2017 Ozone Water-Land Environmental Transition Study (OWLETS) 1, 2018 OWLETS 2, and 2018 Long Island Sound Tropospheric Ozone Study (LISTOS). We developed and tested a clustering method that resulted in 5 vertical ozone profile curtain clusters. The established 5 clusters all varied significantly in ozone magnitude vertically and temporally which allowed us to characterize the coastal ozone behavior. The lidar clusters provided a simplified way to evaluate the two CTMs for their performance of diverse coastal ozone cases. The two models have fair-to-good relationships with the lidar observations (R = 0.66 to 0.69) in the low-level altitude range (0 to 2000 m), with unsystematic bias for GEOS-Chem and systemically high bias for GEOS-CF. In the mid-level altitude range (2000 to 4000 m), both models have difficulty simulating the vertical extent and variability of ozone concentrations in all 5 clusters, with a weak relationship with the lidar observations (R = 0.12 and 0.22, respectively). GEOS-Chem revealed a systematic high negative bias and GEOS-CF an overall low unsystematic bias range. Using ozone vertical distribution from lidar measurements, this work provides new insights on model’s proficiency in complex coastal regions.