On the dynamics of ozone depletion events at Villum Research Station in the High Arctic
Ozone depletion events (ODEs) occur every spring in the Arctic and have implications for the atmospheric oxidizing capacity, radiative balance, and mercury oxidation. Here we comprehensively analyze ozone, ODEs, and their connection to meteorological and air mass history variables through statistica...
| Main Authors: | , , , |
|---|---|
| Format: | Text |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://doi.org/10.5194/egusphere-2024-1676 https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1676/ |
| Summary: | Ozone depletion events (ODEs) occur every spring in the Arctic and have implications for the atmospheric oxidizing capacity, radiative balance, and mercury oxidation. Here we comprehensively analyze ozone, ODEs, and their connection to meteorological and air mass history variables through statistical analyses, back-trajectories, and machine learning (ML) from observations at Villum Research Station, Station Nord, Greenland. We show that the ODE frequency and duration peak in May followed by April and March, which is likely related to air masses spending more time over sea ice and increases in radiation from March to May. Back-trajectories indicate that, as spring progresses, ODE air masses spend more time within the mixed layer and the geographic origins move closer to Villum. ODE frequency and duration are increasing during May (low confidence) and April (high confidence), respectively. Our analysis revealed that ODEs are favorable under sunny, calm conditions with air masses arriving from northerly wind directions with sea ice contact. The ML model was able to reproduce the ODE occurrence and illuminated that radiation, time over sea ice, and temperature were the most important variables for modeling ODEs during March, April, and May, respectively. Several variables displayed threshold ranges for contributing to the positive prediction of ODEs vs Non-ODEs, notably temperature, radiation, wind direction, time spent over sea ice, and snow. Our ML methodology provides a framework for investigating and comparing the environmental drivers of ODEs between different Arctic sites and can be applied to other atmospheric phenomena (e.g., atmospheric mercury depletion events). |
|---|