PhysAtm1709033VisheratinIKOR

ISSN 0001-4338, Izvestiya, Atmospheric and Oceanic Physics, 2017, Vol. 53, No. 9, pp. 894–903. © Pleiades Publishing, Ltd., 2017.
Original Russian Text © K.N. Visheratin, A.F. Nerushev, M.D. Orozaliev, Zheng Xiangdong, Sun Shumen, Liu Li, 2017, published in Issledovanie Zemli iz Kosmosa, 2017, No. 1,
pp. 59–68.
STYDYING SEAS AND OCEANS
FROM SPACE
Temporal Variability of Total Ozone in the Asian Region Inferred
from Ground-Based and Satellite Measurement Data
K. N. Visheratina, *, A. F. Nerusheva, M. D. Orozalievb, Zheng Xiangdongc, Sun Shumend, and Liu Lid
aResearch
and Production Association Typhoon, Obninsk, 249038 Russia
Kyrgyz State National University, Bishkek, 720033 Kyrgyz Republic
c
Chinese Academy of Meteorological Sciences, Beijing, China
d
Chengdu Institute of Information Technology, Chengdu, China
*e-mail: kvisher@rpatyphoon.ru
b
Received October 2, 2015
Abstract⎯This paper reports investigation data on the temporal variability of total ozone content (TOC) in
the Central Asian and Tibet Plateau mountain regions obtained by conventional methods, as well as by spectral, cross-wavelet, and composite analyses. The data of ground-based observation stations located at Huang
He, Kunming, and Lake Issyk-Kul, along with the satellite data obtained at SBUV/SBUV2 (SBUV merged
total and profile ozone data, Version 8.6) for 1980–2013 and OMI (Ozone Monitoring Instrument) and TOU
(Total Ozone Unit) for 2009–2013 have been used. The average relative deviation from the SBUV/SBUV2
data is less than 1% in Kunming and Issyk-Kul for the period of 1980–2013, while the Huang He Station is characterized by an excess of the satellite data over the ground-based information at an average deviation of 2%.
According to the Fourier analysis results, the distribution of amplitudes and the periods of TOC oscillations
within a range of over 14 months is similar for all series analyzed. Meanwhile, according to the cross-wavelet
and composite analyses results, the phase relationships between the series may considerably differ, especially
in the periods of 5–7 years. The phase of quasi-decennial oscillations in the Kunming Station is close to the
11-year oscillations of the solar cycle, while in the Huang He and Issyk-Kul stations the TOC variations go
ahead of the solar cycle.
Keywords: total ozone content, temporal variability, ground-based and satellite measurements, Asian Region,
spectral, cross-wavelet, and composite analyses
DOI: 10.1134/S000143381709033X
INTRODUCTION
The spatial distribution and temporal variability of
the total ozone content (TOC) and their causes have
been a relevant problem of atmospheric physics for
many years. It has been solved by different measurement methods with the help of ground-based and satellite equipment, as well as by statistical analysis and
mathematical modeling. With the development of
space observation techniques, measurements of atmospheric ozone characteristics using various satellite
instruments become more and more applicable (Timofeev, 2010).
The problem of the TOC variability at different
time scales has been studied in a large number of works
using both ground-based and satellite measurement
data (for example, (Labow et al., 2013; Chehade et al.,
2014; WMO, 2014)) and numerous references in
them). The TOC measurement data obtained with the
help of different ground-based and satellite systems
were compared and studied in detail in (Fioletov et al.,
2008; Labow et al., 2013). The recent investigation
(Izrael et al., 2014) has been focused on discrepancies
in the TOC measurement data obtained with the help
of the ground-based UV and visible range equipment
(Dobson, Brewer spectral devices and French SAOZ
spectrometer, as well as M-124 Russian filter instruments) and nadir scanning space-based systems. It has
made it possible to identify discrepancies in the TOC
linear trends, even when calculated using the measurement data of two ground-based instruments located at
one point.
The TOC variations over vast areas are of special
interest, in particular, major mountain systems of the
Central Asia Region (the Himalayas, Tan-Shan,
Pamir–Alai, and Tibet). The mountain systems have
a considerable effect on characteristics of the jet
streams, one of the most important factors controlling the TOC spacetime variability. The influence
of the major mountain systems extends to an altitude
that is 3–4 times higher than that of the mountains.
Local orographically caused jet streams to appear over
the Central Asian mountain systems, whose orientation generally coincides with the western wind trend.
894
TEMPORAL VARIABILITY OF TOTAL OZONE IN THE ASIAN REGION INFERRED
895
208
347
209
Fig. 1. Location of ground-based observation stations: (208) Huang He, (209) Kunming, and (347) Issyk-Kul.
Their occurrence is caused by drastic differences in
characteristics of the air masses over the plains and
mountains, especially at the boundary between them.
Mountain systems and the Tibet Plateau, warming up
in summer and cooling down in winter, affect the local
circulation features (Atmosphere…, 1991).
Ground-based TOC monitoring stations are few in
this area. Long-term continuous TOC measurements
have been carried out since 1979 at the Issyk-Kul Station in the northwestern part of the studied region and
at the Huang He Station near Beijing and, since 1980,
at the Kunming Station in the southeast of China.
Additional observations were initiated at four stations
since 1990, including the Lhasa Station in the Tibet
Plateau. The observations at these stations are distinguished by a relatively drastic variability in TOC with a
high TOC content at the stations located in the northern part of the studied region and a relatively low TOC
content in the south and over the Tibet Plateau (Zhou
et al., 1995; Visheratin et al., 2006; Bian et al., 2011).
The lower TOC areas of the Tibet Plateau, especially
in summer, are explained largely by effect of the convective flows (Zhou et al., 1995; Bian et al., 2011).
Another possible reason for the TOC latitudinal variability, in our opinion, is the abovementioned peculiarity of the atmospheric circulation, including the
presence of high-altitude jet streams.
IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
This investigation objective is to study the TOC temporal variability in the Asian Region by comparing the
longest term measurement data obtained at the groundbased TOC monitoring stations and the satellite measurement data obtained with the SBUV/SBUV2, OMI,
and TOU instruments. The conventional methods of
statistical and spectral analyses are supplemented by
composite and cross-wavelet analyses.
CHARACTERISTICS OF THE USED DATA
The ground-based and satellite measurement data
and the corresponding equipment used in the analysis
are described below in brief.
We have used the average monthly TOC values for
1980–2013 obtained at three observation stations
included in the global ozonometric network (WOUDC,
2013). Huang He (No. 208), Kunming (No. 209), and
Issyk-Kul (No. 347) stations are located at a distance of
3000 km from each other and cover a major part of the
Central Asian and Tibet Plateau mountain systems
(Fig. 1).
The Huang He Station (No. 208) is located near
Beijing (39.9° N, 117.0° E, 50 m above sea level); the
Kunming Station (No. 209) is located in the southeast
of China (25.0° N, 102.7° E., 1920 m above sea level),
in the subtropical highlands. Measurements at the
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Table 1. Parameters of formula (1) according to groundbased and satellite measurement data on TOC for the period
of 1980–2013
Station
G208
S208
G209
S209
G347
S347
А12
37.6
39.7
19.8
17.8
31.7
33.4
Р12
А6
Р6
S0
–0.04
0.12
–1.58
–1.40
–0.01
–0.08
0.6
0.7
3.8
4.8
7.2
6.5
2.65
1.45
–2.27
–2.37
–0.32
–0.42
338.9
335.3
263.4
266.4
334.4
325.9
B*120
–0.2
–2.2
1.2
0.4
–7.9
–3.1
Huang He and Kunming stations are conducted using
Dobson No. 075 and Dobson No. 003 devices, respectively. Both instruments are periodically calibrated
with a regional standard, Dobson No. 116, Japanese
Meteorological Agency (JMA). The Issyk-Kul Station
(No. 347) is located on the northern shore of the highaltitude Lake Issyk-Kul (Kyrgyz Republic) (42.6° N,
77.0° E., 1650 m above sea level). The TOC measurements are carried out with the help of a spectrophotometric device (SPSD) using a multiwave method
(Semenov et al., 1983). The random error of a single
TOC measurement is 0.6%. The verification measurements have been carried out with Dobson No. 108
(Voeikov Main Geophysical Observatory) and Brewer
No. 44 (Research and Production Association
Typhoon) spectrophotometers in order to determine
the systematic error of TOC measured with the SPSD.
The systematic deviations of the TOC measurement
results obtained with the SPSD and Dobson No. 108
and Brewer No. 44 spectrophotometers do not exceed
2% (Visheratin et al., 2006).
The satellite measurement data obtained over the
points with coordinates of the ground-based ozonometric stations were compiled on the basis of several
databases. Until recently, the TOC spacetime variations were commonly analyzed using the satellite measurement data obtained with TOMS and OMI supplemented with the SBUV/SBUV2–TOMS-SBUV
merged total ozone data (for example, (Fioletov et al.,
2008; Visheratin and Kuznetsov, 2012; Chehade et al.,
2013). However, the latest version of the merged database was released in 2011 (Vers. 8) and has not been
updated since that time. Therefore, we have used the
data recommended in (Labow et al., 2013; McPeters
et al., 2013) that are based on the SBUV/SBUV2 measurements (SBUV merged total and profile ozone
data, Vers. 8.6). It should be noted that one of reasons
for the recommendations by (Labow et al., 2013;
McPeters et al., 2013) is a systematic offset between
the EP-TOMS data and currently functioning OMI,
which has not been explained yet. Therefore, a comparison with the data of the Chinese TOU (Total Ozon
Unit) device installed on the FengYun-3/A satellite
(Wang et al., 2010, 2012; Bai et al., 2013) is of interest.
Our investigation has been carried out using the
TOC values over the points with geographic coordinates of the ground-based stations (overpass data)
provided in the SBUV merged total and profile ozone
data, Vers. 8.6 (hereinafter, V86) (ftp://toms.gsfc.
nasa./pub/sbuv/MERGED), for the period of 1980–
2013; OMI data (http://avdc.gsfc.nasa.gov) for 2009–
2013; and TOU data for 2009–2013 (http://satellite.
cma.gov.cn).
Both ground-based and satellite data have minor
gaps in measurements. Therefore, such omissions
were filled in advance. For this purpose, a linear trend
has been subtracted from the series, and the annual
and semiannual harmonics parameters have been
determined by Fourier analysis. An amplitude and a
phase of the analyzed series are shown in Table 1.
The variation parameters given in Table 1 are significant within 1 sigma. The S0 and B trends (U/decade)
have been calculated by the average annual data and are
not significant for all series. The general function
approximating the annual and semiannual components
of the TOC variations has the following form:
Y = S(0) + B 120N + A12 sin(P12 + 2π N 12)
+ A6 sin(P6 + 2π N 6),
(1)
where А12 and А6 are amplitudes (U), Р12 and Р6 are
phases of annual and semiannual harmonics (radians), S0 (U) and B (U/decade) are trend parameters,
and N is the serial number of the month beginning in
January 1980.
The TOC values calculated by (1) have been added
instead of the omissions in the original series. Additional calculations with the inclusion of the harmonics
with a period of 3 and 4 months resulted in a slight difference. In Table 1 and hereinafter in the paper, for the
sake of brevity, the ground-based station data are indicated by station numbers with a letter G (ground)
added, while the satellite data are indicated with station numbers with a letter S (satellite). Upon a relatively good agreement between the ground-based and
satellite parameters, the phases of half-year harmonics
are greatly different for the Huang He Station (208),
and all stations are characterized by differences in the
trend value.
ANALYSIS OF THE RESULTS
One of the conventional methods for comparing
the ground-based and satellite data (Fioletov et al.,
2008, Bai et al., 2013) is calculating the relative deviation (Δ):
Δ = (G(i) – S(i))/G(i).
(2)
The relative deviation values are shown in Fig. 2.
Station 208 is characterized by some excess of the
ground measurement data over the satellite data at an
average Δ of about 2% throughout the studied period.
It should be noted that this station was distinguished
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TEMPORAL VARIABILITY OF TOTAL OZONE IN THE ASIAN REGION INFERRED
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(a)
10
5
0
–5
–10
1980
1985
1990
1995 2000
(b)
2005
2010
1985
1990
1995 2000
(c)
2005
2010
1985
1990
1995 2000
Year
2005
2010
10
5
Δ, %
by the systematic data offset in comparison with the
SCIAMACHY and OMI data in (Zheng and Wei,
2010). As for stations Nos. 209 and 347, the average Δ
value is less than 1% throughout the studied period.
Meanwhile, we should note the underestimated results
of station 347 in the period of 2000–2011. The reasons
for such a TOC trend at station 347 require additional
studies. The linear deviation trend between the
ground-based and satellite measurement data is the
greatest for station 208, yet is negligible like for the
other two stations. In general, the ground-based and
satellite measurement data are in good agreement at all
three stations.
As follows from an analysis of the TOMS and
OMI data (Labow et al., 2013), there is a systematic
discrepancy (the OMI values are lower than those of
TOMS) caused the recommendations to use the
SBUV/SBUV2 data.
Figure 3 shows the comparison results obtained for
the ground-based data and V86, OMI and TOU for
2009–2013. In the case of station 208, the V86, OMI,
and TOU data are in good agreement with each other,
while the average satellite values throughout the studied
period are 2.7, 2.5, and 1.6%, respectively, lower than
the ground-based data. As for station 209, the systematic underestimation of the satellite data is observed
only for the OMI device, reaching 2%. The V86 and
TOU data are consistent with each other and with the
data of station 209 within 1%. As for station 347, the
period until 2011 can be pointed out, when the satellite
data were on average above the ground-based measurement values. Since 2011, the satellite data were in good
agreement with each other and exceeded station 347
measurement values by 2%, on average throughout the
studied period. We should note one more common
trend well-defined for stations Nos. 208 and 209: the
greatest differences from the ground-based measurement data (5–7%) were noted in the summer period.
In general, the SBUV/SBUV2, OMI, and TOU
data are in good agreement with each other over stations Nos. 347 and 208 located in the northern part of
the considered region. Above station 209 located in
the southeast in a mountain area, the OMI results are
understated by 2–3%, on average.
Let us consider the comparison of the groundbased and satellite measurement data more thoroughly
by spectral, cross-wavelet, and composite analysis.
The spectral analysis has been carried out using the
modified Lomb–Scargle (LS) Fourier transform (Scargle, 1982), having a number of advantages in comparison with other spectral analysis methods. The linear
trend has been preliminary removed from the data. The
value of the spectral harmonics (peaks) in the spectra
has been estimated by the method of (Scargle, 1982;
Baluev, 2008). Due to the fact that the annual harmonic
amplitudes are 6–10 times greater than amplitudes of
the longer period oscillations, the time series have been
preliminary “whitened,” that is to say, the oscillations
897
0
–5
–10
1980
10
5
0
–5
–10
1980
Fig. 2. Relative deviation Δ (%) between the ground-based
and satellite (V86) measurement data on TOC for the period
of 1980–2013: (a) Huang He Station (208), (b) Kunming
Station (209), and (c) Issyk-Kul Station (347).
with periods from 3 to 13 months have been removed
(annual oscillation and its harmonics). The algorithms
for calculations by various spectral methods are
described in detail in (Visheratin and Karmanov, 2008).
The results of the spectral analysis are shown in
Fig. 4 separately for the ground-based and satellite
data. For demonstrative purposes, the spectra in the
region of 14–30 and 30–150 months are shown separately. Although the annual harmonic amplitude is
almost twice as low at station 209 than that at stations
208 and 347 located in the northern part of the studied
region (Table 1), the variation amplitudes with a
period of more than 14 months are similar to those
obtained at stations 208 and 347. A spectral structure
of the satellite and ground-based data is almost similar
for both the main (significant at the level of 0.05)
oscillations and a number of other oscillations. The
distribution of amplitudes and oscillation periods in
the region of more than 14 months is very similar for
all analyzed series. Table 2 shows, as an example, the
minimum and maximum periods of main oscillations
over three considered stations and average amplitudes
of these oscillations.
Figure 4 shows two stable oscillations with periods
of about 21 and 28 months. Oscillations with a period
of about 28 months (quasi-biennial oscillations,
QBO) are commonly related to variations in the equaVol. 53
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(a)
15
V86
10
OMI
TOU
5
0
–5
–10
2009
2010
2011
2012
2013
2014
2012
2013
2014
2012
2013
2014
(b)
15
10
Δ, %
5
0
–5
–10
2009
2010
2011
(c)
15
10
5
0
–5
–10
2009
2010
2011
Year
Fig. 3. Relative deviation Δ(%) between the ground-based and satellite (V86, OMI, TOU) measurement data on TOC for the
period of 2009–2013, stations (a) Huang He, (b) Kunming 4, and (c) Issyk-Kul.
torial stratospheric wind (Gray and Pyle, 1989). The
range which the TOC quasi-biennial oscillations can
be attributed to is limited to the periods from 24–25 to
35–36 months, because only these oscillations are
characterized by the dramatic maxima exactly above
the equator and the symmetrical minima near (10–
12)°S and (10–12)°N. The equatorial region is characterized by the most intensive oscillations with periods
of 28–29 and 32–33 months, while the maxima of
extratropical quasi-biennial oscillations are observed
in both hemispheres near 40–50° (Visheratin and
Kuznetsov, 2012).
It was assumed that the beats between the basic frequencies could lead to oscillations at the combination
frequencies (Bohme, 1965). It was proposed to modulate TOC QBO by circulation processes with a period
that is equal to the annual harmonic period (Tung and
Yang, 1994). The multiplicative modulation model
proposed in this paper can be presented as follows:
⎛ 2πt ⎞
⎡
⎛ 2π t ⎞⎤
⎟
⎢ A + B sin ⎜ T ⎟⎥ sin ⎜T
⎣
⎝ 12 ⎠⎦
⎝ QBO ⎠
⎛
⎞
⎛
⎞
⎛
⎞
= A sin ⎜ 2πt ⎟ + B sin ⎜ 2π t ⎟ + B cos ⎜ 2πt ⎟ ,
⎝ T− ⎠ 2
⎝ T+ ⎠
⎝TQBO ⎠ 2
(3)
Table 2. Basic periods (Tmin, Tmax, months) and average amplitudes (A, DU) of oscillations over stations 208, 209, and 347
based on satellite data
Tmin
Tmax
A
120.0
125.0
2.9
89.0
90.0
2.9
58.0
59.0
2.3
45.0
49.0
2.4
34.7
36.3
2.4
31.0
31.7
1.7
27.2
28.1
3.5
22.8
23.2
3.1
20.9
21.3
3.4
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18.3
18.6
1.9
15.0
15.4
2.4
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TEMPORAL VARIABILITY OF TOTAL OZONE IN THE ASIAN REGION INFERRED
(a)
4
4
2
2
0
0
(b)
4
Spectral amplitude, DU
899
4
2
2
0
4
0
4
(c)
2
2
0
4
0
4
(d)
2
2
0
6
0
6
(e)
4
4
2
2
0
6
0
6
(f)
4
4
2
2
0
0
15
20
25
30
50
100
150
Period, month
200
Fig. 4. TOC spectral amplitudes inferred from the ground-based measurement data: (a) G208, (c) G209, and (e) G347, and satellite data (b) S208, (d) S209, and (f) S347. Horizontal straight lines correspond to a confidence probability of 95%.
where А and В are constant and variable parts of the
annual oscillation Т12, TQBO is a period of QBO, and Т_
and Т+ are oscillation periods corresponding to the difference and the total of original frequencies Т12 and
TQBO. For the data from Table 2: Т_ = 1/(1/12 – 1/28) =
21 months and Т+ = 1/(1/12 + 1/28) = 8.4 months.
Analogously, the oscillation modulation with a period
of about 35 months leads to the oscillations with periods of 8.9 and 18.2 months. Oscillations with similar
periods are observed in the considered spectra, but
amplitudes of these oscillations are much lower than
the B/2 value. The additive modulation model can be
constructed in an analogous way (Kiefer, 2015). In this
case, the combination frequencies originating from
summing up of oscillations have doubled period values
relative to (3). The disadvantage of these interpretations consists of the absence or inadequate validity of
the physical mechanisms providing conditions for the
occurrence of a particular combination frequency.
IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
In the TOC spectrum there are also a number of
oscillations in the range of 4–5 and 8–13 years, which
are commonly related in the literature to the El Nino
phenomenon and the 11-year solar activity (SA) cycle.
The wavelet and composite analyses are used for a
more thorough analysis of these oscillations.
The cross-wavelet transformation for the joint
analysis of two time series was proposed in (Torrence
and Compo, 1998; Grinsted et al., 2004). This method
makes it possible to determine the correlation degree
and phase relations between two series in the time–
frequency space, as well as to estimate the correlation
ratios by the Monte Carlo method, taking into
account the red-noise processes (series autocorrelation). Figure 5a shows the cross-wavelet analysis data
on the TOC series inferred from the ground-based and
satellite data for station 208. Just as in the spectral
analysis, the linear and seasonal trends have been preliminary excluded from the time series.
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VISHERATIN et al.
(a)
0
–5
5
Period, month
16
32
64
TOC, DU
128
1980
4
2
0
–2
–4
1980
1985
1990
1995
2000
2005
2010
(b)
2
1
1985
1990
1995
2000
2005
3
2010
Year
Fig. 5. (a) Cross-correlation wavelet analysis of the total ozone series according to the ground-based and satellite data for Huang
He Station 208. The correlation degree (color scale) is given in r.u. A thick black line draws out the areas with a confidence interval
of more than 95%. The arrows show the relationship between the time-series phases: to the right, in phase; to the left, in antiphase; downwards, G208 variations are ahead of S208 by 90°; upwards, they are behind by 90°. (b) Composite series according
to the ground-based (1) and satellite (2) data in the range of 8–13 years and solar-activity variations (3).
As follows from Fig. 5a, the TOC interannual oscillations for a significant part of the periods and temporal
oscillations occur in the same phase. Meanwhile, in the
last decade, the maximum ozone oscillations from
ground-based measurements have been ahead of the
maximum satellite oscillations for periods of more than
90 months, and the oscillations have been close to the
antiphase for the periods of more than 140 months. The
peaks with periods of about 90 and 120 months correspond to these TOC long-term variations in the spectra
shown in Fig. 4. Figures 6a and 7a show the cross-wave
transformation results for stations 209 and 347.
The phase relationships between the satellite and
ground-based data in the long-period oscillation region
are in good agreement at these stations. Meanwhile,
some time ranges are marked by differences between the
satellite and ground-based data in the quasi-quinquennial oscillation region (4–6 years). Variations in ozone
and other atmospheric parameters with such periods
are commonly related to large-scale processes in the
Pacific Ocean such as the El Nino and La Nina phenomena and atmosphere center transformations
(Nerushev, 2003). Phase differences are the most
noticeable for station 209 located in the south of the
studied region. At this station, in the period from 1987
to 2005, the TOC variations in the region of 4–6 years
inferred from the satellite and ground-based data were
close to the antiphase variations.
The composite method has been used to estimate
the phase relationships in the field of quasi-decennial
oscillations (QDO); this method makes it possible to
filter out the oscillations in the selected spectrum segments (Visheratin, 2012). The composite temporal
series were formed as follows: the first stage involved a
calculation of the direct Fourier transformation (FT)
factors; the second stage involved the reverse FT to
form the temporal series (Figs. 5b–7b) containing the
total of harmonics in the range from 8 to 13 years. The
phase relationships between the ground-based and
satellite data for the station 208, obtained by the composite method, are in relatively good agreement with
the cross-wavelet analysis data (Fig. 5a) and confirm
discrepancies in the TOC QDO phases with the help
of the independent method. The reasons for such discrepancies are not clear.
It is interesting to compare the TOC quasi-decennial oscillations with the 11-year solar activity (SA)
cycle. The phase relationships between TOC QDO
and 11-year SA cycle were previously discussed by the
example of the longest ozone series (Arosa Station,
Switzerland) and the TOC average zoned fields in
(Visheratin, 2012; Visheratin, 2015). According to
these works, the phase of TOC QDO maxima coincides with that of SA maxima in the tropic regions and
is ahead of the SA maxima in the middle latitudes of
the Northern Hemisphere. As follows from Fig. 5b, the
ozone oscillations were higher than SA variations in the
23rd solar-activity cycle, according to station 208 data;
in the current 24th cycle, the oscillations were close to
the antiphase. At station 347, the ozone variations are
also higher than the SA variations on average, which is
seen better when comparing the variation minima. At
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TEMPORAL VARIABILITY OF TOTAL OZONE IN THE ASIAN REGION INFERRED
–5
–4
–3
–2
(a)
0
–1
1
2
3
4
5
Period, month
16
32
64
128
1980
1985
1990
1995
5
TOC, DU
2000
2005
2010
(b)
2
1
3
0
–5
1980
1985
1990
1995
2000
2005
2010
Year
Fig. 6. Same as in Fig. 5 for Kunming Station 209.
–5
–4
–3
–2
–1
(a)
0
1
2
3
4
Period, month
16
32
64
128
TOC, DU
1980
5
1985
1990
1995
(b)
2000
2005
2
1
2010
3
0
–5
1980
1985
1990
1995
2000
2005
Year
Fig. 7. Same as in Fig. 5 for Issyk-Kul Station 347.
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station 209 located southwards, the TOC quasidecennial phase is similar to the SA 11-year phase.
CONCLUSIONS
We have analyzed the satellite and ground-based
measurement data on the total ozone content in the
studied region covering the Central Asian and Tibetan
Plateau mountains by both conventional methods and
spectral, cross-wavelet, composite analyses. For the
period of 1980–2013, a relative average deviation from
the SBUV/SBUV2 is less than 1% at the Kunming
(209) and Issyk-Kul (347) stations, while the Huang
He Station (208) is characterized by an excess of satellite measurement data over the ground-based data at
an average deviation of 2%. As for the SBUV/SBUV2,
OMI, and TOU satellite data for a shorter period,
from 2009 to 2013, in general, the measurement results
are consistent within 3%. The TOU data are in agreement with SBUV/SBUV2 within 1%.
A comparison of the satellite and ground-based
data by spectral, cross-wavelet, and composite analysis has made it possible to evaluate systematic offsets
between the analyzed data at a time–frequency plane
and, in addition, estimate the phase relationships
between the analyzed series. It stands to mention the
similarity of the interannual variation spectra at the
stations located at considerable distances from each
other and separated by large mountains. The characteristic regional jet streams can be one of possible factors of oscillation synchronization and spacetime variability of TOC (Atmosphere…, 1991). Meanwhile,
according to the cross-wavelet and composite analyses
results, with a slight relative deviation of the satellite
and ground-based data and the spectra similarity, the
phase relationships between the studied series can be
subject to considerable variations. For example, the
oscillation phases in the region of 5–7 years in 1990–
2000 are almost opposite at station 209. The phase
relationships are different between the 11-year solar
activity cycle and ozone variations inferred from the
satellite and ground-based data obtained at the stations located in the north and the south. The oscillation phase at the Kunming Station is similar to that of
the 11-year solar cycle, while TOC variations are
higher than the solar cycle variations at the Huang He
and Issyk-Kul stations.
ACKNOWLEDGMENTS
We thank NASA, NOAA, WOUDC, and SIDC
scientists for giving us access to their databases. This
work was carried out under cooperation between the
Federal Service for Hydrometeorology and Environmental Monitoring of Russia and Chinese Meteorology Department (project no. 1.3) with partial support
from the Russian Foundation for Basic Research
(projects nos. 14-05-00127, 14-05-90104).
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Translated by E. Maslennikova
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