ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-12-3529-2020Year-round record of near-surface ozone and O3 enhancement events
(OEEs) at Dome A, East AntarcticaYear-round record of near-surface ozone and OEEs at Dome A, East AntarcticaDingMinghudingminghu@foxmail.comhttps://orcid.org/0000-0002-1142-6598TianBiaoAshleyMichael C. B.PuteroDavidehttps://orcid.org/0000-0002-9721-1036ZhuZhenxiWangLifanYangShihaiLiChuanjinXiaoCundeState Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, ChinaState Key Laboratory of Cryospheric Science, Northwest Institute of
Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000,
ChinaSchool of Physics, University of New South Wales, Sydney 2052,
AustraliaCNR–ISAC, National Research Council of Italy, Institute of
Atmospheric Sciences and Climate,corso Fiume 4, 10133, Turin, ItalyPurple Mountain Observatory, Chinese Academy of Sciences, Nanjing
210034, ChinaNanjing Institute of Astronomical Optics & Technology, Chinese
Academy of Sciences, Nanjing 210042, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology,
Beijing Normal University, Beijing 100875, China
Dome A, the summit of the East Antarctic Ice Sheet, is an area
challenging to access and is one of the harshest environments on Earth. Up
until recently, long-term automated observations from Dome A (DA) were only
possible with very low power instruments such as a basic meteorological
station. To evaluate the characteristics of near-surface O3, continuous
observations were carried out in 2016. Together with observations at the
Amundsen–Scott Station (South Pole – SP) and Zhongshan Station (ZS, on the
southeast coast of Prydz Bay), the seasonal and diurnal O3
variabilities were investigated. The results showed different patterns
between coastal and inland Antarctic areas that were characterized by high
concentrations in cold seasons and at night. The annual mean values at the
three stations (DA, SP and ZS) were 29.2±7.5, 29.9±5.0 and 24.1±5.8 ppb, respectively. We investigated the effect of
specific atmospheric processes on near-surface summer O3 variability,
when O3 enhancement events (OEEs) are systematically observed at DA
(average monthly frequency peaking at up to 64.5 % in December). As deduced
by a statistical selection methodology, these O3 enhancement events
(OEEs) are affected by significant interannual variability, both in their
average O3 values and in their frequency. To explain part of this
variability, we analyzed the OEEs as a function of specific atmospheric
processes: (i) the role of synoptic-scale air mass transport over the
Antarctic Plateau was explored using the Lagrangian back-trajectory analysis Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) method,
and (ii) the occurrence of “deep” stratospheric intrusion events was
investigated using the Lagrangian tool STEFLUX. The specific atmospheric
processes, including synoptic-scale air mass transport, were analyzed by the
HYSPLIT back-trajectory analysis and the potential source contribution
function (PSCF) model. Short-range transport accounted for the O3
enhancement events (OEEs) during summer at DA, rather than efficient local
production, which is consistent with previous studies of inland Antarctica.
Moreover, the identification of recent (i.e., 4 d old) stratospheric-intrusion events by STEFLUX suggested that deep events only had a minor
influence (up to 1.1 % of the period, in August) on deep events
during the variability in near-surface summer O3 at DA. The deep
events during the polar night were significantly higher than those during
the polar day. This work provides unique data on ozone variation at DA and
expands our knowledge of such events in Antarctica. Data are available at
10.5281/zenodo.3923517 (Ding and Tian, 2020).
Introduction
Ozone (O3) is a natural atmospheric component that is found in both the
stratosphere and the troposphere and plays a major role in the atmospheric
environment through radiative and chemical processes. O3 does not
have direct natural sources such as emission from the ground or vegetation but rather is produced in the atmosphere, and its concentration ranges from
a few parts per billion near the Earth's surface to approximately a few parts per million in the
stratosphere. Stratospheric O3, which is produced as a result of the
photolysis of molecular oxygen, forms a protective layer against the UV
radiation from the sun. By contrast, throughout the troposphere and at the
surface, O3 is considered a secondary short-lived air pollutant (Monks
et al., 2015), and O3 itself is a greenhouse gas, such that a
reduction in concentration has a direct influence on radiative forcing
(Mickley et al., 1999; IPCC, 2013; Stevenson et al., 1998).
O3 photochemical production in the troposphere occurs via hydroxyl
radical oxidation of carbon monoxide (CO), methane (CH4) and
non-methane hydrocarbons (generally referred to as NMHCs) in the presence of
nitrogen oxides (NOx) (Monks et al., 2015). As these precursors are
localized and their lifetimes are generally short, the distribution of
near-surface O3, which is produced from anthropogenic precursors, is
also localized and time-variant. In the presence of strong solar radiation
with λ<424 nm, volatile organic compounds (VOCs) and NOx
(NO+NO2), O3 is photochemically produced and can accumulate to reach
a hazardous level during favorable meteorological conditions (Davidson,
1993; Wakamatsu et al., 1996). In the case of NOx-rich air, NO2 is
produced and accumulates via the reaction between NO and HO2 or
RO2 (peroxy radicals), which is followed by the accumulation of
O3. However, in the case of NOx-poor air, these proxy radicals react
with O3 and lead to O3 loss (Lin et al., 1988). Experiments
conducted in Michigan (Honrath et al., 2000a) and Antarctica (Jones et al.,
2000) found that NOx can be produced in surface snow. This production
appears to be directly driven by incident radiation and photolysis of
nitrate deposited in the snow (Honrath et al., 2000a, b).
Previous studies have shown that the near-surface O3 of Antarctica may
be influenced by a number of climate-related variables (Berman et al.,
1999), such as the variation in UV flux caused by the variation in O3
column concentration over Antarctica (Jones and Wolff, 2003; Frey et al.,
2015); the accumulation and transport of long-distance, high-concentration
air masses (e.g., Legrand et al., 2016); and the depth of continental mixing
layers. Many studies have observed summer episodes of “O3 enhancement
events” (OEEs) in the Antarctic interior (e.g., Crawford et al., 2001;
Legrand et al., 2009; Cristofanelli et al., 2018), and they have attributed this
phenomenon to the NOx emissions from the snowpack and subsequent photochemical
O3 production (for example, Jones et al., 2000). Moreover, this may
provide an input source for the entire Antarctic region (for example,
Legrand et al., 2016; Bauguitte et al., 2011). As the solar irradiance and
the nitrate aerosol concentration increase, the emission of NOx will
increase through the photodenitrification process of the summer snowpack
(e.g., NO3-+hv→NO2+O-; O-+H+→OH;
Honrath et al., 2000a; Warneck and Wurzinger, 1989). Helmig et al. (2008a, b)
provided further insight into the vigorous photochemistry and O3
production that result from the highly elevated levels of NOx in the
Antarctic surface layer. During stable atmospheric conditions, which are
typically observed during low-wind and fair-sky conditions, O3
accumulated in the surface layer can reach up to twice its background
concentration. Neff et al. (2008a) showed that shallow mixing layers
associated with light winds and strong surface stability can be among the
dominant factors leading to high NO levels. As shown by Cristofanelli et al. (2008) and Legrand et al. (2016), the photochemically produced O3 in
the planetary boundary layer (PBL) over the Antarctic Plateau can affect the O3 variability
thousands of kilometers away from the emission area, due to air mass transport.
The near-surface O3 concentrations at high-elevation sites can also
be increased by the downward transport of O3-rich air from the
stratosphere during deep convection and stratosphere-to-troposphere
transport (STT) events. Moreover, the stratospheric O3 in the polar
regions can be transferred to the troposphere not only during intrusion
events but also as a result of slow but prolonged subsidence (e.g., Gruzdev
et al., 1993; Roscoe, 2004; Greenslade et al., 2017). The earliest
studies, carried out with the aircraft flight NSFC-130 over the Ellsworth
Mountains of Antarctica in 1978, found that mountainous terrain may induce
atmospheric waves that propagate through the tropopause. The tropospheric
and stratospheric air may be mixed, leading to an increase in the
tropospheric O3 concentration (e.g., Robinson et al., 1983). Radio
soundings at the Resolute and Amundsen–Scott stations also showed the
existence of transport from the stratosphere to the troposphere, and the
flux could reach up to 5×1010molcm-2s-1 (e.g., Gruzdev
et al., 1993). Recently, Traversi et al. (2014, 2017) suggested that the
variability in air mass transport from the stratosphere to the Antarctic
Plateau could affect the nitrate content in the lower troposphere and the
snowpack.
Currently, the climatology of tropospheric O3 over Antarctica is
relatively understudied because observations of year-round near-surface
O3 have been tied to manned research stations. These stations are
generally located in coastal Antarctica, except for the South Pole (SP) and
Dome C continental stations on the East Antarctic Plateau. Thus, the only
information currently available for the vast region between the coast and
plateau are spot measurements of boundary layer O3 during summer from
scientific traverses (e.g., Frey et al., 2015) or airborne campaigns (e.g.,
Slusher et al., 2010). Moreover, the vertical profile of O3 in the
troposphere cannot be measured by satellites because the high density of
O3 in the stratosphere leads to the inaccurate estimation of
tropospheric O3 by limb-viewing sensors. Estimates of total O3 in the troposphere have been made by subtracting the stratospheric O3 column (determined by a limb-viewing sensor) from the total column of
O3 (measured by a nadir-viewing sensor) (Fishman et al., 1992). In
other words, tropospheric profiles cannot be obtained by satellites, and we
cannot examine the spatial distribution of near-surface O3 from
space. As a result of these limitations, a dearth of information exists
regarding the spatial gradient of near-surface O3 across Antarctica
and how it varies throughout the year.
To better understand the spatial variations and the source–sink mechanisms
of near-surface O3 in Antarctica, near-surface O3 concentrations were measured during 2016 at Dome A (DA) and Zhongshan
Station (ZS). Together with records from Amundsen–Scott Station (SP), we
analyzed specific processes that affect the intra-annual variability in
surface O3 over the East Antarctic Plateau; in particular, we
determined (i) the synoptic-scale air mass transport within the Antarctic
interior and (ii) the role of STT. This study broadens the
understanding of the spatial and temporal variations in the near-surface
O3 concentration and transport processes that impact tropospheric
O3 over high plateaus.
Site and method descriptionNear-surface ozone observations
There are several methods to measure the concentration of ozone, including
ultraviolet spectrophotometry, iodometry, sodium indigo disulfonate
spectrophotometry, gas chromatography, chemiluminescence, fluorescence
spectrophotometry and long-path differential optical absorption
spectrometry (e.g., Wang et al., 2017). Of them ultraviolet
spectrophotometry is the most popular for surface ozone monitoring and is
applied in many commercial instruments. The common ones, such as Thermo 49C
(Liu et al., 2006), API 400E (Sprovieri et al., 2003), ESA O342M (Lei and Min, 2014) and Ecotech 9810B (Moura et al., 2011), have been used in many
regions for their larger measuring range and high precision, but they are
expensive and need plenty of power supply and regular maintenance. Recently,
more and more studies have chosen portable ozone monitors (POMs), such as Model 205
Aeroqual Series 500 POM, due to their advantages of small volume, low price,
low energy consumption and good applicability for field observation (e.g.,
Johnson et al., 2014; Lin et al., 2015; Sagona et al., 2018). In Antarctica,
only a few stations have carried out continuous ozone monitoring, and all of
them were equipped with the common types, that is the Thermo and Ecotech types, as far as
we know.
Amundsen–Scott Station (South Pole, SP), Kunlun Station (Dome A,
DA) and Zhongshan Station (ZS) locations in Antarctica.
The Kunlun Station (80∘25′02′′ S, 77∘06′59′′ E; altitude
4087 m) is located in the DA area, on the summit of the East Antarctic Ice
Sheet (Fig. 1). The only continuous power supply is the PLATO-A
observatory, which can also provide internet access via the Iridium
satellite network (a detailed introduction to the PLATO observatory can be found
in Lawrence et al., 2009). Due to the limitation of energy consumption and
conditions encountered during transportation from the coast to the dome, larger
monitors such as Thermo 49i cannot be used. Thus, on 1 January 2016, we
deployed a Model 205 Dual Beam Ozone Monitor (205 2B) during the 33rd
Chinese National Antarctic Research Expedition. The instrument has been
certified by the Environmental Protection Agency (EPA) and makes use of two detection
cells to improve its precision, baseline stability and response time. In
the dual-beam instrument, UV light intensity measurements I0
(O3-scrubbed air) and I (unscrubbed air) are performed simultaneously
(Wang et al., 2017). It is the fastest UV-based O3 monitor available to date, with a small size, light weight and low power requirements
(Table S1 in the Supplement). A quick response is particularly
desirable for unattended stations and aircraft and balloon measurements. In
Dome A, we use a Teflon pipeline to connect the free air at ∼4 m
above the surface with the instrument. At the inlet of the pipeline, a Thermo
47 mm filter was used to block snow particles. During the observation, the
instrument was set at the sampling frequency of once an hour, and the data
were transmitted to the observatory computer through RS232 and sent to
Beijing by satellite.
The Zhongshan Station (69∘22′12′′ S, 76∘21′49′′ E;
altitude 18.5 m) is located at the edge of the East Antarctic Ice Sheet
(Fig. 1). The atmospheric chemistry observatory was constructed at the
Swan Ridge, northwest of the Nella Fjord, where we installed a UV-absorption near-surface O3 analyzer (EC9810A) for long-term near-surface
O3 monitoring in January 2008. The air inlet was 4 m above the surface
and connected to the analyzer through the Teflon pipe. The observational
frequency was 3 min, and the data were transferred in real time to Beijing.
Furthermore, to prevent data losses, a CR1000 data logger was used to record
the data output in real time. Every 3 months, the O3 analyzer was
calibrated using the EC9811 O3 calibrator, and five standard
concentrations of O3 gas were generated for each calibration. The
calibration concentration and measured concentration underwent correlation
analysis, and seasonal calibration results were generated every 3 months. In 2016, five calibrations were made, and the appropriate correlation
coefficients (r) were all greater than 0.9995.
The Amundsen–Scott Station (89∘59′51.19′′ S, 139∘16′22.41′′ E; altitude 2835 m) is located at SP and operated by the
United States. In 2016, a Thermo 49C ozone monitor was used and 5 min
and 1 h data were uploaded to GAW (Global Atmosphere Watch) every month.
The record used in this paper was downloaded from the Earth System Research
Laboratory Global Monitoring Division under the National Oceanic and Atmospheric Administration (NOAA; https://www.esrl.noaa.gov/gmd/dv/data, last access: 17 December 2020).
The hourly data of these stations collated here are available at 10.5281/zenodo.3923517 (Ding and Tian, 2020).
Calibration process and results
Generally, the zero point, span point and operation parameters of the
O3 monitor should be checked before each operation. The zero point
should be checked regularly during continuous observation. While such
regular calibration was done at Global Atmosphere Watch (GAW) and
Zhongshan Station, it was not possible at DA due to the lack of logistic
support and the extreme environment. To minimize the error and evaluate the
accuracy of the experiment, a UV-absorption O3 calibrator Thermo 49i-PS
was used to examine the Model 205. The calibration procedure follows China's
environmental protection standard “Ambient air–Determination of ozone–Ultraviolet photometric method” (HJ 590-2010)
(http://www.mee.gov.cn/gkml/sthjbgw/sthjbgg/201808/t20180815_451411.htm, last access: 16 December 2020) which is more strict than that of the US EPA (USEPA, 2008):
the slope of the calibration curve ranges between 0.95 and 1.05, and the intercept
ranges between -5 and 5 ppb. Instruments used in the calibration process include
a DOA-P512-BN air compressor (USA), in addition to the Thermo 49i-PS O3
calibrator and the Model 205 O3 monitor. Before each test, the O3
calibrator and the O3 monitor were turned on and preheated for 12 h, and the measuring range was set to 400 ppb. We first generated a zero
concentration using the Thermo 49i-PS, and, once the analyzer response had
stabilized on zero reading, we adjusted the Model 205's internal zero
setting to matches the zero air source. Then, O3 airflow at the 400 ppb
level was generated and injected into the analyzer, and a correction factor
was calculated based on the observed value, which was then loaded into the
Model 205 configuration.
The calibration record of the ozone monitor.
DateSpan pointThermo 49i-PSModel 205(ppb)(ppb)(ppb)5 Oct 20150-0.790.262019.9920.733534.9935.355050.0250.736564.9665.718079.9980.4810099.99100.43120119.96120.316 May 20170-0.710.512020.0021.683534.9536.955050.0152.176564.9867.378079.9982.88100100.00103.00120119.92124.10
After the calibration of the internal zero and span settings, a second stage of
calibration was performed involving multi-point verification to check the
response and stability of the analyzer. On 5 October 2015 (before the
instrument was shipped) and 6 May 2017 (the day that the instrument was
transported back from Antarctica), a zero and seven upscale points (0, 20, 35,
50, 65, 80, 100, 120 ppb), encompassing the full scale of the observation
range (Table 1), were generated by the Thermo 49i-PS to test the Model 205
analyzer. Each point was observed for 15 min, during the last 10 min of
which readings of the calibrator and analyzer were taken every minute. Based
on this experiment, the slope and intercept of the calibration curve were
calculated by least squares. The results are shown in Table 2; it can be
concluded that the slopes of the linear correction curve were 0.99936 and
1.02520, and the intercepts were 0.53861 and 0.85220l, which fulfilled the
requirements of HJ 590-2010 and USEPA.
Stability test of the ozone monitor.
TimeSlopeStandardInterceptStandarduncertaintyuncertainty5 Oct 20150.999360.001950.538610.136726 May 20171.025200.002640.852200.18491Average1.012280.002300.695410.16082Standard error0.018270.000490.221740.03408
Another challenge when monitoring the atmosphere is the stability of the
analyzer, which includes the analyzer's response time. Similarly to the
regular calibration, its calibration could not be performed during the observation
period, but it was reassuring that the Model 205 was still in good condition
when we did the multi-point verification in May 2017, as shown in Table 2.
The slope and intercept of the two calibration curves changed little, and the
standard uncertainties were small. To further test the stability, data
consistency was also examined, and the mean absolute deviation between two
adjacent values was only 0.09 ppb. The largest difference was 0.61 ppb,
indicating that the analyzer was stable and reliable.
Before analysis, a variance test was used to remove abnormal data based on
the Laida criterion method, which assumes that the records obeyed a normal
distribution. The formula is xi-x>3σ,
where xi is the measured value, x is the time series mean
and σ is the standard deviation. After processing,
99.3 %, 99.6 % and 89.3 % of the hourly mean data were retained from
the Amundsen–Scott Station, Zhongshan Station and Kunlun Station,
respectively.
Air mass back-trajectory calculations
Gridded meteorological data for backward trajectories in the Hybrid
Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were obtained
from the Global Data Assimilation System (GDAS1) operated by NOAA with 1∘×1∘ horizontal resolution and 23 vertical levels, from
1000 to 20 hPa (http://www.arl.noaa.gov/gdas1.php, last access: 17 December 2020).
The HYSPLIT backward-trajectory air mass model was previously applied to
atmospheric research in Antarctica (Legrand et al., 2009; Hara et al.,
2011). We used the HYSPLIT model in this paper to analyze the impact of
varying air mass sources and the intrusion of stratospheric O3.
Backward trajectories and clusters were calculated using the NOAA HYSPLIT model (Draxler and
Rolph, 2003; http://ready.arl.noaa.gov/HYSPLIT.php, last access: 17 December 2020), which is a free
software plug-in for MeteoInfo (Wang, 2014; http://meteothink.org/, last access: 17 December 2020). The
backward trajectories' starting height was set at 20 m above the surface; the total run time was 120 h for each backward trajectory, and each run
was performed in time intervals of 6 h (00:00, 06:00, 12:00, 18:00 UTC).
The integral error part of the trajectory calculation error can be estimated
by simulating the backward trajectory at the end of the forward trajectory
and comparing the differences in the tracks. The starting point of the
backward integration is set as (80.42∘ S, 77.12∘ E; 20 m a.g.l.); the backward integration is 120 h. Then the point reached
at this time is taken as the starting point, and a forward simulation is
made for 120 h. In this simulation experiment, the contribution of
integration error to trajectory calculation error is very small within the
first 72 h. With the extension of integration time, the integration
error slightly increases.
The air mass trajectories were assigned to distinct clusters according to
their moving speed and direction using a k-means clustering algorithm (Wong,
1979). As this study focused on the transport pathway of O3,
the clustering result with the smallest number was selected as done by Wang
(2014). It was found that three clusters perform best to represent the
meteorological characteristics of the transport pathways at DA. This number
was then selected as the expected number of air mass trajectory clusters. A
more detailed clustering procedure using the k-means algorithm can be found
in Wang (2014).
Potential source contribution function
The observation of a secondary maximum of O3 in November–December at
the inland Antarctic sites was first reported for SP by Crawford et al. (2001) and was attributed to photochemical production induced by high NOx
levels in the atmospheric surface layer, which were generated by the
photodenitrification of the Antarctic snowpack (same as Davis et al.,
2001). At Dome C (DC), a secondary maximum in November–December 2007 was also
reported by Legrand et al. (2009), proving that photochemical production of
O3 in the summer takes place over a large part of the Antarctic
Plateau. A further study by Legrand et al. (2016) found that the highest
near-surface O3 summer values were observed within air masses that
spent extensive time over the highest part of the Antarctic Plateau before
arriving at DC. To investigate the possible influence of synoptic-scale air
mass circulation on the occurrence of OEEs at DA, 5 d HYSPLIT
back-trajectories were analyzed (Fig. 9). We used the potential source
contribution function (PSCF; see, e.g., Hopke et al., 1995; Brattich et al.,
2017) to calculate the conditional probabilities and identify the
geographical regions related to the occurrence of no-O3 enhancement events (NOEEs) and OEEs at DA
(Fig. 7).
As in Yin et al. (2017), the potential source contribution function (PSCF)
assumes that back trajectories arriving at times of high concentrations
likely point to significant pollution directions (Ashbaugh et al., 1985).
This function was often applied to locate air masses associated with high
levels of near-surface O3 at different sites (Kaiser et al., 2007;
Dimitriou and Kassomenos, 2015). In this study, the PSCF was calculated
using HYSPLIT trajectories. The top of the model was set to 10 000 m a.s.l.
The PSCF values for the grid cells in the study domain were calculated by
counting the trajectory segment endpoints that terminated within each cell
(Ashbaugh et al., 1985). If the total number of endpoints that fall in a
cell is nij and there are mij points for which the measured
O3 parameter exceeds a criterion value selected for this parameter,
then the conditional probability, the PSCF, can be determined as
PSCFij=mijnij.
The concentrations of a given analyte greater than the criterion level are
related to the passage of air parcels through the ijth cell during transport
to the receptor site. That is, cells with high PSCF values are associated
with the arrival of air parcels at the receptor site, which has near-surface
O3 concentrations that are higher than the criterion value. These cells
are indicative of areas with “high potential” contributions of the
constituent. Identical PSCFij values can be obtained from cells with
very different counts of back-trajectory points (e.g., grid cell A with
mij=5000 and nij=10000 and
grid cell B with mij=5 and nij=10). In this extreme situation, grid cell A has 1000 times more air parcels
passing through it than grid cell B. Because the particle count in grid cell B is sparse, the PSCF values in this cell are highly uncertain. To explain the uncertainty due to the low values of nij,
the PSCF values were scaled by a weighting function Wij (Polissar et
al., 1999). The weighting function reduced the PSCF values when the total
number of endpoints in a cell was less than approximately 3 times the
average number of endpoints per cell. In this case, Wij was set as follows:
2Wij(NOEE)=1.00nij>12Nave0.7012Nave>nij>3Nave0.423Nave>nij>1.5Nave0.05Nave>nij,3Wij(OEE)=1.00nij>8Nave0.708Nave>nij>2Nave0.422Nave>nij>1Nave0.05Nave>nij,
where Nave represents the mean nij of all grid cells. The weighted PSCF
values were obtained by multiplying the original PSCF values by the
weighting factor.
Near-surface O3 variabilityMean concentration
At the DA, SP and ZS sites, the annual mean concentrations of near-surface
O3 were 29.2±7.5 ppb, 29.9±5.0 ppb and 24.1±5.8 ppb, respectively; the maximum annual mean concentration reached 42.5, 46.4 and 32.8 ppb, respectively; and the minimum annual mean
concentrations were 14.0, 10.9 and 9.9 ppb, respectively. The inland
stations are characterized by higher annual mean concentrations than the
coastal station.
Time series of near-surface O3 at SP, DA and ZS during
2016. Yellow (gray) shading identifies the polar day (night).
There were also obvious differences between the polar day and the polar night at all
stations. In Fig. 2, we define the polar-day and polar-night windows by the day
of year margins and have used different color shading to identify the
polar day and polar night. The average concentrations of near-surface
O3 during the polar night at the DA, SP and ZS sites were 34.1±4.3, 31.5±3.9 and 28.7±1.3 ppb, respectively, and
much lower concentrations appeared during the non-polar night, with
corresponding values of 26.1±7.0, 28.1±5.8 and 23.1±5.9 ppb, respectively. Interestingly, SP had the highest
near-surface O3 concentration during the non-polar night, whereas at DA
the highest concentration occurred during the polar night, and the largest
variation also occurred at this site.
Seasonal variation
In this part, we define October–March as the warm season and April–September as the cold
season, which is similar to the definition of polar day and night.
Monthly average and statistical parameters of near-surface O3
at SP, DA and ZS during 2016.
In agreement with previous studies (Oltmans and Komhyr, 1976; Gruzdev et al.,
1993; Ghude et al., 2005), the concentrations of near-surface O3 at
the three stations were high and less variable during the cold season and
low and more variable during the warm season (Fig. 3). In Antarctica, the
emissions of O3 precursors are generally less than those at middle and
low latitudes, whereas ultraviolet radiation is relatively strong; thus,
when solar radiation occurs, the depletion effect is much greater than the
effects from photochemical reactions during the warm season (Schnell et al.,
1991). As explained by previous studies, during the polar night, due to the
lack of light, the photochemical reactions stopped. Moreover, due to the lack
of loss effect, the O3 concentration gradually increased and the
fluctuations became smaller. During the polar night, the monthly variation
in surface O3 at ZS was lower than that at DA but higher than
that at SP. However, due to strong UV radiation in the low-latitude
areas and the presence of bromine-controlled O3 depletion events in
coastal areas, ZS shows large seasonal variations during the non-polar
night (Wang et al., 2011; Prados-Roman et al., 2017). However, at SP, the largest standard deviation was observed in December, similarly
to the characteristics at Dome C station (DC) from November to December
(Legrand et al., 2009; Cristofanelli et al., 2018). Figures 2 and 3 indicate
that the near-surface O3 showed obviously larger variations at DA
than at SP during the polar night, since, due to the different geographical
location, the meteorological conditions of DA and SP are different. The
abnormal fluctuation in O3 concentration over DA during the polar
night may be related to its special geographical environment.
As mentioned in the introduction section, mountainous topography and mountain
waves may disturb advection transport in the stratosphere and lead to
downward transportation to the troposphere (Robinson et al., 1983). DA is on
the summit of the East Antarctic Ice Sheet, and the tropospheric depth is
only ∼4.6 km (Liang et al., 2015), which favors exchange
between the stratosphere and troposphere. However, the topography in this
area is very flat and creates a disadvantage for mountain waves. Does
O3 transport occur? We will analyze and discuss this question in
Sect. 4.
Mean diurnal variations in near-surface O3 concentrations at SP (a), DA (b) and ZS (c) during 2016.
Standard deviations of mean diurnal variation in near-surface
O3 concentrations at SP, DA and ZS during 2016.
Diurnal variation
To characterize the typical monthly O3 diurnal variations at the three
stations, we analyzed the mean diurnal variations in O3 at the three
stations (Fig. 4) and the standard deviation of the mean diurnal
variations (Fig. 5). At the DA site, the mean diurnal concentrations for
each month were relatively steady, with the standard deviation of the mean
diurnal concentration for each month being lower than 0.4 ppb. At SP,
the mean diurnal concentrations were less variable as well. Except for
December, the standard deviation of the mean diurnal concentration was lower
than 0.3 ppb. At ZS, except for October, the standard deviation of the mean
diurnal concentration was greater than that at the other two stations. In
particular, the standard deviation of the mean diurnal concentration of ZS
in September, November and December exceeded 0.5 ppb. Obviously, the average
daily concentration fluctuation in Zhongshan Station was obviously different
with the two inland stations, which can be attributed to their background
climates. In spring, ozone depletion events (ODEs) occur frequently at Zhongshan Station. And this
phenomenon is always accompanied by abrupt weather transition from continental
dominant to oceanic dominant; in other words, the BrO brought by northerly
wind from sea ice areas could lead to serious ozone depletion (Wang et
al., 2011; Ye et al., 2017). In contrast, at inland stations like DA and SP,
there were rarely ODEs.
On the whole, the mean diurnal variations in different time periods were not
obvious, and the mean diurnal concentrations of the three stations
fluctuated within a range of less than 1 ppb. The magnitude of the diurnal
variation was low, which is similar to the variations in other Antarctic
stations, Neumayer and Marambio for instance (Nadzir et al., 2018).
Ozone under OEEs at the Kunlun StationIdentification of OEEs
Our method to select the days characterized by OEEs is based on the
procedure used in Cristofanelli et al. (2018). First, a sinusoidal fit is
used to calculate the O3 annual cycle not affected by the OEEs; then a
probability density function (PDF) of the deviations from the sinusoidal fit
is calculated, with the application of a Gaussian fit to the obtained PDF.
As reported in Giostra et al. (2011), the deviations from the Gaussian
distribution (calculated by using the Origin® 9 statistical tool)
can be used to identify observations affected by non-background variability.
We computed the further Gaussian fitting of PDF points beyond 1σ
(standard deviation) of the Gaussian PDF and determined the non-background
O3 daily values that may be affected by “anomalous” O3 enhancement. The intersection of the two fitting curves is taken as our
screening threshold (3.4 ppb at SP, 3.4 ppb at DA and 2.5 ppb at ZS).
Figure 6a, b and c show OEE days and NOEE days at these three stations,
while Fig. 6d, e and f report the distribution frequency of OEE days.
(a–c) The OEEs and (d–f) averaged distribution of
OEE occurrence among the different months of 2016 at the three stations.
Monthly frequency=number of OEE days for each monthnumber of days in the
month; annual frequency=number of OEE days for
each monthtotal number of OEE days.
In total, 42 d at DA were found to be affected by anomalous OEEs:
14.3 % in January, 2.4 % in May, 14.3 % in June, 4.8 % in July,
11.9 % in August, 4.8 % in November and 47.6 % in December (Fig. 6e,
blue bars). This result clearly indicates that half of the anomalous days
occurred in December, followed by January and June. At SP, 36 d with OEEs
were found in 2016: 44.4 % in January, 30.6 % in November and 25 % in
December (Fig. 6d, gray bars). Apparently, OEEs occur only in summertime
at this measurement site. ZS was characterized by more days with OEEs: 53 d in April (34.0 %), followed by September (18.9 %), January
(13.2 %), October (11.3 %), November (11.3 %), December (5.7 %)
March (3.8 %) and May (1.9 %) (Fig. 6f, yellow bars).
From the results above, it can be seen that SP was characterized by
concentrated OEE occurrences, and ZS had the most scattered OEEs pattern. In
addition, all OEEs at SP and ZS occurred during the Antarctic warm season,
and no OEEs were present during the polar night, similarly to the pattern
observed at DC (Cristofanelli et al., 2018). In contrast, the OEEs also
occurred during the polar night at DA, and the number of OEE occurrence days
accounted for up to 33 % of the total number of events throughout the
year. This is the main reason for the large variations in daily average
concentration during the polar night at DA.
Previous studies (e.g., Legrand et al., 2016; Cristofanelli et al., 2018)
carried out in DC showed that the O3 variability at DC could be
associated with processes occurring at long temporal scales. In addition,
the accumulation of photochemically produced O3 during transport of air
masses was the main reason for OEEs, whereas the stratospheric intrusion
events had only a minor influence on OEEs (up to 3 %). This finding cannot
explain the temporal occurrence pattern of OEEs at DA. To determine the
unknown cause, we investigated the synoptic-scale air mass transport and the
STT occurrence at the measurement site.
Likely source areas of surface O3 at Kunlun Station during
the NOEE (a) and OEE (b) identified using the PSCF (potential source
contribution function).
Backward HYSPLIT trajectories for each measurement day (gray lines
in panel a) and mean back trajectory for three HYSPLIT clusters (colored
lines in panel a; 3-D view shown in panel b) arriving at Kunlun Station
during NOEEs. Panel (c) shows the range of surface ozone concentrations
measured at DA by cluster. The error bar is the standard deviation of the same
cluster. Panels (d)–(f) are the same as panels (a)–(c) but for OEEs.
Role of synoptic-scale air mass transport
During NOEEs, the air masses arriving at DA mainly come from the west and
east of DA, and the 3-D clusters show that the air masses traveled over the
Antarctic Plateau before reaching DA (Fig. 8b). The difference in the
number of the three cluster trajectories is small, and the difference in the
corresponding cluster average concentrations is not large. Using the PSCF
results, we have identified air masses associated with higher surface ozone
at DA during NOEEs (Fig. 8a). The Antarctic Plateau to the east and west
of DA had high PSCF weight values (Fig. 7), which shows that, during
NOEEs, the potential source area of surface O3 for DA is mainly in the
inland plateaus in the east and west, and the area of high-PSCF-weight-value distribution in the east is larger than in other directions.
Monthly frequency distribution of clustering trajectories (Line 1,
2, 3) during NOEEs and OEEs.
Compared with NOEEs, the clustering results of trajectories during OEEs have
different characteristics. In OEEs, the air masses that arrived at DA were
prevalent from the north and from the west, and the 3-D clusters indicated
that 73 % of the air mass trajectories came from the area north of DA
(red line in Fig. 8e). The average concentrations of the three clusters
differ greatly (Fig. 8f), but they are all higher than those obtained for
NOEEs. It should be noted that 68 % of Line-2 cluster (green line in
Fig. 8d) occurred during the polar night (Fig. 9) and had a high average
O3 concentration (reached 36.3 ppb). This shows that the OEEs of the
polar night are more affected by the high-value O3 air masses over the
plateau west of DA than those during the polar day. Using the PSCF results,
during OEEs, we did not find a large area of high WPSCF values; the high
WPSCF value only appeared in the east and the north of DA over a limited
area. However, independently of the polar day or of the polar night, the
Line-1 cluster trajectory accounted for more than 60 % during OEEs. In
addition, the short distance of the Line-1 cluster trajectory indicates that the
air mass transport speed is slow, which is conducive to the accumulation of
O3 along the way. It can be seen from Fig. 8e that the characteristic
values of backward-trajectory clustering during OEEs are mostly lower than
200 m a.g.l. (supporting the role of snow as the source of near-surface
O3). As Fiebig et al. (2014) have proposed, the increase in O3
values in the near surface of central Antarctica may also be related to the
transport of free tropospheric air and aged pollution plumes from low
latitudes. In addition, Fig. 10 shows that the average O3 growth rate
reached 0.29 ppbh-1 during OEEs in the polar night, while the average O3
growth rate was -0.06ppbh-1 during NOEEs in the polar night (Fig. 10). The
statistical scatter distribution showed that 97 % of OEEs occurred when
the wind speed was lower than 4 ms-1. The overall average wind speed during
OEEs is also significantly lower than that of NOEEs. As Helmig et al. (2008a) have proposed, during stable atmospheric conditions (which typically
existed during low-wind and fair-sky conditions) ozone accumulates in the
surface layer, and its concentration increases rapidly.
Wind speed and ΔO3 statistical distribution around
OEEs (red dots) and NOEEs (black dots) at DA in the polar night. Here, ΔO3 represents the growth rate of near-surface O3 concentration,
calculated by the following equation:
ΔO3=the O3 concentration at Tn-the O3 concentration at Tn-1time
difference between Tn and Tn-1.
This finding confirms that the OEEs of DA are mainly caused by the
accumulation of high concentrations of air masses transported
nearby, and the synoptic-scale transport can favor the photochemical
production and the accumulation of O3 accumulation by air masses
traveling over the plateau near the north of DA before their arrival.
Role of STT eventsIdentification of deep STT events
Several methods can be applied to study stratosphere-to-troposphere
transport (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 Waliguan Station.
Stratosphere-to-Troposphere Exchange Flux (STEFLUX; Putero et al., 2016) is
a novel tool to quickly obtain reliable identification of STT events
occurring at a specific location and during a specified time window. STEFLUX
relies on a compiled stratosphere-to-troposphere exchange climatology,
making use of the ERA-Interim reanalysis dataset from the ECMWF and a
refined version of a well-established Lagrangian methodology. STEFLUX is
able to detect stratospheric intrusion events on a regional scale, and it
has the advantage of retaining additional information concerning the pathway
of stratosphere-affected air masses, such as the location of tropopause
crossing and other meteorological parameters along the trajectories.
We applied STEFLUX to assess the possible contribution of STT to
near-surface O3 variability in the DA region (i.e., STEFLUX “target
box”; for further details on the methodology see Putero et al., 2016), and
to identify the measurement periods possibly affected by “deep” STT
events (i.e., stratospheric air masses transferred down to the lower
troposphere). For this work, we set the top lid of the box at 500 hPa and
the following geographical boundaries: 79–82∘ S, 76–79∘ E. A deep STT event at Kunlun Station was determined if at
least one stratospheric trajectory crossed the 3-D target box.
Annual variation in deep STT events at Kunlun Station and the
annual variation in it that occurred at the same time as OEEs over the period
2016, obtained by STEFLUX.
Role of STT events at DA
The possible occurrence of stratospheric intrusion events and their role in
affecting the variability in near-surface O3 and tropospheric
air chemistry in Antarctica has been investigated in several studies
(Murayama et al., 1992; Roscoe, 2004; Stohl and Sodemann, 2010; Mihalikova
and Kirkwood, 2013; Traversi et al., 2014, 2017;
Cristofanelli et al., 2018). To provide a systematic assessment of the
possible influence of deep STT events on the near-surface O3
variability at Kunlun Station, we used the STEFLUX tool (see Sect. 4.3.1).
Figure 11 shows the distribution of the occurrence of deep STT events
over DA during the year. Although it is difficult to see a clear seasonal
cycle due to the low frequency of deep STT events, our results are in
agreement with previous studies, indicating STT influence of up to 2 % on
a monthly basis (Stohl and Sodemann, 2010; Cristofanelli et al., 2018).
According to our STEFLUX outputs, the highest frequency of deep STT
events was observed in May and August (1.1 %). The frequency of occurrence
of deep STT events identified by STEFLUX at Kunlun Station is about 1 order of magnitude lower than the occurrence of OEEs. Thus, a direct link of
STT with OEE interannual variability is unlikely, as also reported for the DC
station (Cristofanelli et al., 2018). Nevertheless, STT events can be a
source of nitrates for the Antarctic atmosphere through different processes,
thus indirectly affecting near-surface O3 concentrations and favoring
the presence of OEEs (Traversi et al., 2014, 2017).
Data availability
All data presented in this paper are available at 10.5281/zenodo.3923517 (Ding and Tian, 2020). The dataset
covers the hourly average concentrations of near-surface ozone at three
stations (i.e., SP, ZS, DA).
Summary
Based on the in situ monitoring data during 2016 at DA, the variation,
formation and decay mechanisms of near-surface O3 were studied and
compared with those at SP and ZS. The annual mean concentrations of
near-surface O3 at the DA, SP and ZS sites were 29.2±7.5,
29.9±5.0 and 24.1±5.8 ppb, respectively. The
near-surface O3 concentrations were clearly higher in the winter polar
night, with small fluctuations, than in the other seasons, which is
different from the patterns observed at low latitudes. The O3 over inland
areas was also higher than over the coast.
The diurnal variations showed nonsignificant regular patterns, and the range
of the average diurnal concentration fluctuation was less than 1 ppb at all
three stations. These findings suggest that the synoptic transport somehow
controls the overall O3 variability, as has been shown at SP and DC (Neff et al., 2008b; Cristofanelli et al., 2018).
At Kunlun Station, it is unlikely that there is a direct relationship
between STT and OEEs. The frequency of deep STT events identified by STEFLUX
is about an order of magnitude lower than OEEs and reaches its highest
frequency (1.1 %) in May and August. As deduced by the STEFLUX
application, deep STT events play a marginal role in steering the
occurrence of OEEs at DA via “direct” transport of O3 from the
stratosphere or the free troposphere to the surface. As explained in
Cristofanelli et al. (2018), this can be related to an underestimation of “young” (i.e., <4 d old) STT events by STEFLUX or to an insufficient spatial and vertical resolution from ERA-Interim to fully
resolve the complex STT in the Antarctic atmosphere (Mihalikova
and Kirkwood, 2013). Despite this, STT can still represent a source of
nitrates for the Antarctic snowpack, thus possibly affecting summer
photochemical O3 production. Therefore, it is important to carry out
further studies to better assess these processes.
The characteristics and mechanisms of near-surface O3 revealed in this
paper have important implications for better understanding the formation and
decay processes of near-surface O3 in Antarctica, especially over the
plateau areas. Nevertheless, the lack of observations restricted our ability
to amass more information. Long-term sustained observations at Dome A, Dome C, Dome F, SP, Vostok and other locations would greatly help in the
future.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-12-3529-2020-supplement.
Author contributions
MD and BT designed the experiments and wrote the manuscript;
MD carried out the experiments; BT analyzed the experimental
results. MD, BT and DP revised the manuscript;
DP run the STEFLUX tool. MCBA, ZZ, LW,
SY, JT, CL and CX discussed the results.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work is financially supported by the National Natural Science
Foundation of China (41771064), the Strategic Priority Research Program of
Chinese Academy of Sciences (XDA20100300) and the Basic Fund of the Chinese
Academy of Meteorological Sciences (2018Z001 and 2019Y010). The observations
were carried out by during the Chinese National Antarctic Research
Expedition at Zhongshan Station and Kunlun Station. We are also
grateful to NOAA for providing the HYSPLIT model and GFS meteorological
files. Yaqiang Wang is the developer of MeteoInfo and provided generous help
for the paper. PLATO-A was supported by the Australian Antarctic Division
and with NCRIS funding through Astronomy Australia Limited.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 41771064), the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA20100300) and the Basic Fund
of the Chinese Academy of Meteorological Sciences (grant nos. 2018Z001 and 2019Y010).
Review statement
This paper was edited by Alexander Kokhanovsky and reviewed by two anonymous referees.
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