Cluster-based characterization of multi-dimensional tropospheric ozone variability in coastal regions: an analysis of lidar measurements and model results
<p>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 mesoscale and have proven to be arduous to simulate using chemical transport...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-12-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/22/15313/2022/acp-22-15313-2022.pdf |
| Summary: | <p>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 mesoscale and have proven to be arduous to simulate using chemical
transport models (CTMs). We present a clustering analysis of multi-dimensional measurements from ozone lidar in
conjunction with both an offline GEOS-Chem chemical-transport model (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 five ozone profile
curtain clusters. The established five 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. An overall evaluation of the models reveals good agreement (<span class="inline-formula"><i>R</i>≈0.70</span>) in the low-level altitude range (0 to 2000 <span class="inline-formula">m</span>), with a
low and unsystematic bias for GEOS-Chem and a high systemic positive bias for GEOS-CF. The mid-level (2000–4000 <span class="inline-formula">m</span>) performances show a high
systematic negative bias for GEOS-Chem and an overall low unsystematic bias for GEOS-CF and a generally weak agreement to the lidar observations
(<span class="inline-formula"><i>R</i>=0.12</span> and 0.22, respectively). Evaluating cluster-by-cluster model performance reveals additional model insight that is overlooked in the
overall model performance. Utilizing the full vertical and diurnal ozone distribution information specific to lidar measurements, this work provides
new insights on model proficiency in complex coastal regions.</p> |
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| ISSN: | 1680-7316 1680-7324 |