Long-term trends of surface ozone and its influencing factors at the Mt Waliguan GAW station, China – Part 2: The roles of anthropogenic emissions and climate variability

Abstract

Abstract. Inter-annual variability and long-term trends in tropospheric ozone are both environmental and climate concerns. Ozone measured at Mt Waliguan Observatory (WLG, 3816 m a.s.l.) on the Tibetan Plateau over the period of 1994-2013 has increased significantly by 0.2-0.3 ppbv yr −1 during spring and autumn but shows a much smaller trend in winter and no significant trend in summer. Here we explore the factors driving the observed ozone changes at WLG using backward trajectory analysis, chemistry-climate model hindcast simulations (GFDL AM3), a trajectory-mapped ozonesonde data set, and several climate indices. A stratospheric ozone tracer implemented in GFDL AM3 indicates that stratosphere-to-troposphere transport (STT) can explain ∼ 60 % of the simulated springtime ozone increase at WLG, consistent with an increase in the NW air-mass frequency inferred from the trajectory analysis. Enhanced STT associated with the strengthening of the mid-latitude jet stream contributes to the observed high ozone anomalies at WLG during the springs of 1999 and 2012. During autumn, observations at WLG are more heavily influenced by polluted air masses originating from South East Asia than in the other seasons. Rising Asian anthropogenic emissions of ozone precursors are the key driver of increasing autumnal ozone observed at WLG, as supported by the GFDL AM3 model with time-varying emissions, which captures the observed ozone increase (0.26 ± 0.11 ppbv yr −1 ). AM3 simulates a greater ozone increase of 0.38 ± 0.11 ppbv yr −1 at WLG in autumn under conditions with strong transport from South East Asia and shows no significant ozone trend in autumn when anthropogenic emissions are held constant in time. During summer, WLG is mostly influenced by easterly air masses, but these trajectories do not extend to the polluted regions of eastern China and have decreased significantly over the last 2 decades, which likely explains why summertime ozone measured at WLG shows no significant trend despite ozone increases in eastern China. Analysis of the Trajectory-mapped Ozonesonde data set for the Stratosphere and Troposphere (TOST) and trajectory residence time reveals increases in direct ozone transport from the eastern sector during autumn, which adds to the autumnal ozone increase. We further examine the links of ozone variability at WLG to the quasibiennial oscillation (QBO), the East Asian summer monsoon (EASM), and the sunspot cycle. Our results suggest that the 2-3-, 3-7-, and 11-year periodicities are linked to the QBO, EASM index, and sunspot cycle, respectively. A multivariate regression analysis is performed to quantify the relative Published by Copernicus Publications on behalf of the European Geosciences Union. contributions of various factors to surface ozone concentrations at WLG. Through an observational and modelling analysis, this study demonstrates the complex relationships between surface ozone at remote locations and its dynamical and chemical influencing factors.

Long-term changes of regional ozone in China: implications for human health and ecosystem impacts

“…GCH is a polluted rural site located in Hebei Province and about 110 km southwest of Beijing. More details about the sites can be found in publications (Lin et al, 2008(Lin et al, , 2009a(Lin et al, , 2009b(Lin et al, , 2010(Lin et al, , 2011Ma et al, 2014;Xu et al, 1998Xu et al, , 2008Xu et al, , 2016Xu et al, , 2018Jin et al, 2016). All GAW sites are far away from direct anthropogenic impacts.…”

“…Both WLG and XGLL are high mountain sites that are less impacted by anthropogenic emissions (see Table S1). Transport of air masses from the free troposphere and even the lower stratosphere makes significant contributions to surface ozone at both sites (Ding et al, 2006;Ma et al, 2014;Xu et al, 2016Xu et al, , 2018. At WLG, the mountain-valley breeze plays an important role in the diurnal-seasonal cycles of ozone, with daytime and nighttime ozone being mainly influenced by boundary layer air and free-tropospheric air, respectively (Xu et al, 2016).…”

“…This result differs from those of Cooper et al (2012), who focused on somewhat different period . Changing emissions of ozone precursors are thought to be responsible for the differences in ozone trends (Zhang et al, 2016) though climate variability also has important impact on the variation of surface ozone (Barnes et al, 2016;Okamoto et al, 2018;Xu et al, 2018;Sun et al, 2019). In addition, variations of aerosol level may exert influences on surface ozone by changing photolysis rate and heterogeneous reactions (Qu et al, 2018).…”

Exploring 2016–2017 surface ozone pollution over China: source contributions and meteorological influences

“…These ozone precursors are emitted intensively in China from anthropogenic sources, including emissions from industry, plant powers, residential areas, and transportation (Li et al, 2017a;Zheng et al, 2018;Wang et al, 2019). Significant ozone production driven by intense anthropogenic emissions has been observed and modeled in many urban regions in China (Wang et al, 2006;Ran et al, 2009;Xue et al, 2014;Li et al, 2017b;Tan et al, 2018a). While anthropogenic contribution can be reduced by emission control measures, background ozone, defined as ozone concentration in the absence of domestic anthropogenic sources, presents a base below which ozone air quality cannot be improved by domestic emission controls.…”

Year-round record of near-surface ozone and O<sub>3</sub> enhancement events (OEEs) at Dome A, East Antarctica

“…Several methods can be applied to study STT events. One method is the chemistry-climate hindcast model GFDL-AM3, which Lin et al (2017) used to evaluate the increasing anthropogenic emissions in Asia, and Xu et al (2018) used to examine the impact of direct tropospheric ozone transport at the 330 Waliguan Station. Stratosphere-to-Troposphere Exchange Flux (STEFLUX) is a novel tool to quickly obtain reliable identification of SI events occurring at a specific location and during a specified time window, and the tool was recently developed by Putero et al (2016).…”