Comment on egusphere-2024-3873

Abstract

Abstract. Tropospheric ozone depletion events (ODEs) occurring in the Arctic spring are a unique photochemical phenomenon in which the boundary layer ozone drops rapidly to near-zero levels. However, the criterion for identifying ODEs remains inconsistent among different studies, which may influence conclusions regarding the characteristics of ODEs. To address this issue, in this study, we applied various criteria used in previous studies to identify springtime ODEs at Utqiagvik, Arctic (the BRW station), based on observational data spanning 23 years (2000–2022), and investigated the influences of implementing different criteria. We compared three types of criteria: traditional methods (fixed thresholds), variability-based methods (considering mean and standard deviation), and machine learning methods (Isolation Forest), and found that criteria using fixed thresholds (e.g., 10 ppbv) and relative thresholds based on monthly average ozone levels are more suitable for capturing ODEs at BRW compared to other criteria. Results applying these appropriate criteria all reveal a significant decline in ODE occurrence frequency over the investigated 23 years, particularly in April. However, implementing relative thresholds or more stringent thresholds instead of the 10 ppbv threshold would display a more significant decline in the number of ODE hours across these 23 years. Further investigation of meteorological conditions indicates that ODEs at BRW are more prevalent under northerly and northeasterly winds with moderate wind speeds, at lower temperatures, and higher pressures, while severe ODEs are more associated with lower wind speeds and temperatures below 256 K. This research highlights the importance of selecting appropriate criteria to accurately identify ODEs.

Authors 6

1. Xiaochun Zhu

   China Meteorological Administration · Nanjing University of Information Science and Technology

   Affiliation as printed

   China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, China

2. Le Cao corresponding

   China Meteorological Administration · Nanjing University of Information Science and Technology

   Affiliation as printed

   China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, China

3. Xin Yang

   British Antarctic Survey · Natural Environment Research Council

   Affiliation as printed

   British Antarctic Survey, Natural Environment Research Council, Cambridge, UK

4. Simeng Li Aachen Institute of Environmental Sciences (CML)

   Leiden University

   Affiliation as printed

   Institute of Environmental Sciences, Universiteit Leiden, Leiden, 2333 CA, the Netherlands

5. Jiandong Wang

   China Meteorological Administration · Nanjing University of Information Science and Technology

   Affiliation as printed

   China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, China

6. Tianliang Zhao

   China Meteorological Administration · Nanjing University of Information Science and Technology

   Affiliation as printed

   China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing, 210044, China