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.
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