ISSN 00014338, Izvestiya, Atmospheric and Oceanic Physics, 2011, Vol. 47, No. 5, pp. 584–602. © Pleiades Publishing, Ltd., 2011. Original Russian Text © S.A. Sitnov, 2011, published in Izvestiya AN. Fizika Atmosfery i Okeana, 2011, Vol. 47, No. 5, pp. 634–652.
Spatial–Temporal Variability of the Aerosol Optical Thickness over the Central Part of European Russia from MODIS Data S. A. Sitnov Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Pyzhevskii per. 3, Moscow, 119017 Russia email:
[email protected] Received October 19, 2010; in final form, February 21, 2011
Abstract—Satellite observations of the aerosol optical thickness (AOT) at the wavelength 0.55 µm over the central part of European Russia (52°–59° N; 29°–45° E) in 2000–2009 obtained with the help of MODIS instruments (Terra and Aqua satellites) are analyzed. Descriptive statistics and frequency distributions of the AOT values observed over the central part of European Russia, Moscow, and the background region are pre sented. The AOT spatial distributions are described, and some their features are interpreted. The variations of the AOT on different time scales are analyzed. The relation that seasonal and weekly AOT variations have with the variability of aerological characteristics is investigated. The results of an analysis of satellite AOT observations are compared with the data of groundbased observations. Keywords: MODIS instrument, aerosol optical thickness, spatial distribution, temporal variability, seasonal cycle, weekly cycle. DOI: 10.1134/S0001433811050100
MODIS INSTRUMENTS AND THE ALGORITHM FOR THE RETRIEVAL OF AEROSOL OPTICAL THICKNESS
INTRODUCTION Atmospheric aerosol is an important factor con trolling the radiation balance of the Earth’s atmo sphere and the turnover of water in nature. Particles suspended in the air, reflecting and absorbing the solar radiation, additionally heat the air and cool the under lying surface. Such particles, acting as condensation nuclei, affect the processes of cloud formation, thereby changing the Earth’s albedo [1–6]. Satellite observations of aerosol characteristics are an integral part of the presentday system of global and regional monitoring of atmospheric pollutants [7, 8]. Analyzing the fields of the aerosol optical thickness (AOT) makes it possible to localize in space emission sources, elucidate their origin (natural or anthropo genic), and construct empirical–statistical models of aerosol pollution [9]. The goal of this work is to study the spatial–tempo ral AOT variability over the central part of the Euro pean Russia territory (ERT) on the basis of data of AOT observations with MODIS satellite instruments. The central ERT part is one of the most urbanized regions of the Russian Federation; it is where the Mos cow agglomeration is located (the largest one in Europe), and the problem of aerosol pollution in this region is very topical.
Moderate Resolution Imaging Spectroradiometer (MODIS) instruments were installed on the Terra and Aqua satellites launched on January 18, 1999, and May 4, 2002, respectively, into a polar solarsynchro nous orbits about 705 km high with an inclination of 98° and a period of 98 min. The MODIS is a spec trometer which forms an image on the basis of mea surements of reflected solar and produced by the Earth’s surface and atmosphere radiation in 36 spectral channels within a wavelength range of 0.45–14.36 µm [10]. The optical system of the instrument performs scanning across the satellite motion direction in the range of angles ±55° (corresponding to the 2330kmwide band on the surface), ensuring daily global coverage by observations of the region of extratropical latitudes. The algorithm for the AOT (τ) calculation over land, which takes into account the spectral dependence of the underlying surface reflectivity, is based on an anal ysis of the intensities of the reflected solar radiation at four wavelengths (0.47, 0.66, 2.13, and 1.24 µm) and the use of regional models of continental aerosol selected on the basis of analyzing the ratio of the inten sities at wavelengths of 0.47 and 0.66 at different scat tering angles. The AOT at the wavelength 0.55 µm (τ0.55) is determined through the interpolation of the retrieved values of τ0.47 and τ0.66 in accordance with the Angström law [11, 12].
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The calculated error of the AOT retrieval over land is ±0.05 ± 0.15 τ [11]. The results of a comparison of the MODIS data with the data of 29 European stations of the AERONET network [13] indicate that the MODIS instrument overestimates small AOT values and underestimates large AOT values (the regression AERONET line τMODIS on τ 0.55 has the form 0.55 MODIS AERONET + 0.04; R = 0.76). A compari τ 0.55 = 0.85τ 0.55 son of the longterm monthly mean values of τ0.55 cal culated from the MODIS and AERONET data over Europe showed that the difference between τMODIS and 0.55 AERONET depends on the season [14]. Positive differ τ 0.55 ences are observed from February through August and attain the maximum value 0.02 in April and June, whereas negative differences are observed from Sep tember to January and attain the value –0.03 in Octo ber and January. The negative differences between AERONET and τ 0.55 in September–November and in τ MODIS 0.55 January are statistically significant. DATA We used daily data of the third level (L3) of the fifth collection in the period 2000–2009. The L3 data are observations averaged inside the grid cells 1° × 1° (lat itude × longitude) [15]. At the latitude of Moscow, the cell 1° × 1° corresponds to the spatial dimensions 111 km × 63 km. These data are obtained from the Level 1 and Atmosphere Archive and Distribution System (LAADS Web), which can be found at http://ladsweb.nascom.nasa.gov. The time series of daily satellite observations of the cloud cover with the MODIS instruments in the period from February 24, 2000, through December 31, 2009, as well as nitrogen dioxide (NO2), sulfur dioxide (SO2), and formalde hyde (HCHO) with the Ozone Monitoring Instru ment/Aura (OMI) in the period from October 2, 2004, through December 31, 2009, were obtained from the Giovanni system of remote access (http://disc.sci. gsfc.nasa.gov/giovanni), which was developed and run by the NASA GES DISC [16]. The data of daytime (12:00 UTC) radiosonde launches at the Dolgoprud nyi (55.9° N, 37.5° E), Bologoe (57.9° N, 34.1° E), Smolensk (54.8° N, 32.1° E), and Ryazan (54.6° N, 39.7° E) stations taken from the archive of aerological observations at Wyoming State University (United States) (http://weather.uwyo.edu) and encompassing the period from January 1, 2000, through December 31, 2009, were also invoked for our analysis. In addition, we used daily data on the surface temperature and amount of precipitation obtained in the period from January 1, 2000, through December 31, 2008, at the Moscow meteorological station (50.8° N, 37.6° E) located in the territory of the All Russian Exhibition Center. These data are stored in the RIHMIWDC and can be obtained with the aid of the Aisori network IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
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technology of remote access (http://aisori.meteo.ru/ climat). RESULTS AND DISCUSSION Spatial Distribution of the AOT Figure 1a shows the spatial distribution of the AOT over Europe calculated from the monthly mean values of τ0.55 in the periods from April to September 2000– 2009 (anomalously high AOT values in September 2002 caused by forest and peat fires were excluded from consideration). The latitudeoriented region of increased AOT values, which extends from the British Isles to the Volga River and has the local AOT maxima over northern France (0.25), eastern Germany (0.23), and southern Poland (0.24), can be identified in this figure. Local AOT maxima are clearly recognizable over Kiev and cities of eastern Belarus (0.19), as well as over Moscow (0.19) and Kazan (0.16). Taking into consideration the direction of the zonal AOT gradient at midlatitudes over Europe and the prevalence of westerly winds, this region can be interpreted as a trace of the transboundary transport of pollutants from countries of Western, Central, and Eastern Europe to the ERT. At the same time, the local AOT maxima near Moscow and Kazan point to the presence of local aerosol sources associated with these agglomerations. The longterm AOT distribution over the ERT center in the periods from March to November 2000– 2009 is spatially heterogeneous (Fig. 1b). The region of increased τ0.55 values extends from the Russia– Belarus frontier in the southwest to Nizhni Novgorod in the northeast. The local AOT maximum (0.18), shifted to the southwest from the Moscow center, is observed in the center of the region. Moscow is located in the righthand upper quadrant of the grid cell 1° × 1° bounded by the coordinates 55°–56° N and 37°–38° E, and the southward shift of the maximum is an artifact caused by ascribing all AOT values in the cell to its center (55.5° N, 37.5° E). The eastward shift reflects the characteristic asymmetry of the spatial pollution of the Moscow agglomeration. It can also be seen from the figure that the plume of aerosol pollution from the Moscow megacity is oriented predominantly in the eastward direction. Over the ERT, increased τ0.55 values are also noted over cities of eastern Belarus (0.17) and Nizhni Novgorod (0.16), whereas the northwestern and southeastern parts of this region, in particular, Penza oblast, are cleanest (0.12). The AOT values over Penza oblast will be further considered as regional back ground values, because, unlike northwestern AOTs, they have a lower variability and a higher correlation with regional mean AOT values and more completely represent the annual cycle. Figure 1b indicates that the anthropogenic emission of pollutants associated with the vital activity of the Moscow megacity is one of the factors causing the aerosol pollution of the central Vol. 47
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SITNOV (a)
N 70
April–October 2000–2009
τ
65
0.5
Helsinki Oslo
60
Stockholm
0.4 St. Petersburg
55
Kazan
0.3 0.225
Mogilev
Berlin
Moscow
Warsaw
Brussels
0.2
Gomel’
0.18
Leipzig
50
Katowice
Lille
Kiev
0.1535
Milan
0.125
45
0.1 0.075
40
0.05 Teheran
35 –10
–5
0
5
10
15
20
25
30
35
40
45
Rybinsk waterstorage reservoir
58
0.1 4
Kostroma
Lake Ilmen
Bologoe
τ
Volga R. .
Yaroslavl’
Vo lga R
Tver’ 4 0.1
57
Ivanovo Nizhni Novgorod
Vladimir 0.14
56
55 Е
March–November 2000–2009
(b) N
50
0.16
Moscow
Vitebsk
16 0.
55 Smolensk 6 0.1
Kaluga
Ryazan
Tula
Orsha
0.14
54
4 0.1
Mogilev
Penza oblast
Orel
53 30
0.175 0.170 0.165 0.160 0.155 0.150 0.145 0.140 0.135 0.130 0.125 0.120 0.115
Bryansk
31
32
33
34
35
36
37
28
39
40
41
42
43
44 Е
Fig. 1. Spatial distribution of the AOT at the wavelength 0.55 (τ0.55) in 2000–2009 (a) in April–October over Europe and (b) in March–November over the ERT center. The data for crosshatched regions are absent. IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
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Table 1. Correlation coefficients of local longterm monthly mean AOT values over the central part of European Russia (52°– 59° N, 29°–45° E) in neighboring months of the year Calculated parameters
March– April
April– May
May– June
June– July
July– August
r 95% confidence interval N
0.33 0.15–0.50
0.42 0.25–0.56
0.45 0.29–0.59
0.61 0.45–0.70
0.62 0.49–0.72
0.65 0.53–0.75
109
112
112
112
112
112
ERT. The advection of pollutants from the eastern regions of Belarus is evidently one more factor. The annual AOT evolution over the central ERT is illustrated by Fig. 2, showing the spatial distributions of longterm monthly mean values of τ0.55 in the period from March to November. Significant spatial differences between the AOT values in early spring and late fall are explained by the fact that, due to the weak development of convection processes, the spatial structure of the AOT field in early spring and late fall predominantly reflects the aerosol pollution of the lower atmospheric layer, which is of a local character. The differences between the AOT values over Moscow and over the areas located to the west and east of the capital are minimal in April. The evolution of the spa tial AOT distribution from March to April, from April to May, and from October to November points to sub stantial changes in the AOT field caused by the inten sification of synopticscale dynamic processes in these periods. The spatial AOT distribution changes to a lesser extent from May to September. In these months, the region of increased AOT values oriented in the SW–NE direction is observed over the ERT, and the local AOT maximum is noted over Moscow. Table 1 presents the correlation coefficients for arrays of local monthly mean AOT values in adjacent months of the year, which gives us an idea of the coherence of AOT field variations from one month to another. These results indicate that the AOT field experiences the largest variations in October–November, whereas the spatial AOT distribution changes insignificantly in June–September. The longterm monthly mean AOT values averaged over the regional area are also pre sented in Fig. 2. The plot shows that the AOT increases from February to April (0.23), decreases from April to June (0.10), increases from June to July (0.17), weakly varies from July to August, and decreases from August to November (0.08). The similarity of annual AOT varia tions in different localities of the region indicates that these variations are mainly caused by natural factors. The seasonal AOT distribution, characterized by the spring and summer maxima and an appreciable AOT decrease in June, was noted previously many times [14, 17, 18]. The spring AOT maximum is asso ciated with the removal of snow cover and the intensi fication of the supply of soil particles into the atmo IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
August– September– October– September October November 0.38 0.13 0.21–0.53 –0.10–0.34 112
78
sphere caused by this process, whereas the summer maximum is associated with the prevalence of anticy clonic weather and southerly turbid air masses in the region [18]. The spring and fall maxima of the aerosol content were observed not only over the ERT, but also over the Siberia [19, 20]. In particular, large AOT val ues in April and their decrease from spring to summer were noted when the MODIS data over the Tomsk region were analyzed in [21]. Comparison of Data from Satellite and GroundBased Observations Figure 3 compares the results of AOT observations with the use of the MODIS instruments with results from groundbased AOT observations. The ground based AOT observations in the region are carried out at two stations of the AERONET network [22] located in the territory of the Meteorological Observatory of Moscow State University (MO MSU) and at the Zvenigorod Scientific Station (ZSS) of the Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences. The AOT is also calculated on the basis of observations of direct solar radiation fluxes at the sta tions belonging to the Russian actinometric network [18, 23]. Figure 3a shows the longterm monthly mean values of τ0.55 over Moscow calculated from satellite data (cell 55°–56° N, 37°–38° E) and the longterm annual cycle of τ0.55 from the MO MSU calculations based on the use of observations of the integral trans parency of the atmosphere in the period 1955–2004 [18]. It can be seen from this figure that, although the seasonal AOT variations obtained from groundbased and satellite data are similar, on the whole, the MODIS instrument underestimates the AOT, which is most noticeable in June (0.07) and November (0.05). The underestimation of satellite data is caused, in par ticular, by the absence of powerful volcanic eruptions in the period 2000–2009, as well as by distinctions in the AOT presentations by local groundbased observa tions and satellite data reduced to the grid 1° × 1° (the Moscow agglomeration occupies no more than 25% of the area of the cell 55°–56° N, 37°–38° E). Figure 3b shows the longterm monthly mean values of τ0.55 obtained from the data (L2) of the CIMEL photome ter over the ZSS in the period 2006–2009 and the longterm monthly mean values of τ0.55 obtained from Vol. 47
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SITNOV
Kaluga
Mogilev
Rybinsk water storage reservoir Lake Ilmen Bologoe
R.
Vitebsk 0.25 Smolensk Orsha
Nizhnii Novgorod
Vo lga
R.
Vladimir
0. 1 5 Vitebsk
5 0.1
Smolensk Kaluga
Orsha
Ryazan Tula
Mogilev Bryansk
Rybinsk water storage reservoir Bologoe Tver
Kostroma
Vo lga R.
Lake Ilmen
Penza oblast
Orel
Yaroslavl
Vladimir
Nizhnii Novgorod
Smolensk
0.09
Ryazan
Kaluga
Orsha
Tula Mogilev Bryansk
Penza oblast
Orel
0.08 0.07
Ryazan
Kaluga Tula
Mogilev Bryansk
Penza oblast
Orel
Rybinsk water storage reservoir Lake Ilmen Bologoe 0.1
Tver
September Kostroma
0.15
Volga R.
Yaroslavl
0.14
Ivanovo Nizhnii Novgorod Vladimir
Moscow
0.13
Vitebsk
0.12 0.11
Smolensk Orsha
Kaluga
Ryazan
0.10
Tula
Mogilev
Penza oblast
Orel
Bryansk
Rybinsk water storage reservoir Lake Ilmen Bologoe Tver
Kostroma Yaroslavl
0.09
October
0.12 0.118 0.11 0.1 0.09 0.08 0.07 0.06
Volga R.
Ivanovo Moscow Vitebsk Smolensk Orsha
Vladimir
Kaluga
Nizhnii Novgorod
Ryazan Tula
Mogilev
Penza oblast
Orel Bryansk
Rybinsk water storage reservoir Lake Ilmen Bologoe Tver
Kostroma November Volga R. Yaroslavl
Moscow
0.15 0.13
Ivanovo Nizhnii Novgorod Vladimir
Vitebsk
0.11 0.09
0.15
Smolensk Kaluga
Orsha
0.07 0.05
Ryazan Tula
Mogilev Bryansk
τ 0.21 0.20 0.19 0.18 0.17 0.16 0.15 0.14 0.13 0.12
Penza oblast
Orel
0.03
0 .1 5
Lake Ilmen
Rybinsk water storage reservoir Bologoe Tver
R.
30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
Moscow
Kostroma July Volga R. Yaroslavl Ivanovo Nizhnii Vladimir Novgorod
Vitebsk
54
Smolensk Orsha
0.11 0.10
56 55
0.13
Vitebsk
0.12
Ivanovo 0.1 Vitebsk
0.21 0.20 0.19 0.18 0.17 0.16 0.15 0.14 0.13 0.12 0.11 0.10
June
0.1 Volga R.
Moscow
0.27 0.26 0.25 0.24 0.23 0.22 0.21 0.20 0.19 0.18 0.17
5 0.1
0.1
57
Vo lga
Ivanovo
53 58
May
Kostroma Volga R. Yaroslavl
Moscow
55 54
Penza oblast
Orel
Tver
57 56
Ryazan
Rybinsk water storage reservoir Bologoe
Lake Ilmen
53 58
Kaluga
Bryansk
55 54
Vladimir
5
Mogilev
57 56
0 .2
Nizhnii Novgorod
Tula
53 58
5
Ivanovo Moscow 0.25
56
54
April
Kostroma Volga R. Yaroslavl
0.2
0. 2
Tver
57
55
Penza oblast
Orel Bryansk
53 58
Ryazan Tula
Tver
aR .
Smolensk Orsha
Vo lg a
54
Rybinsk water August storage reservoir Kostroma 0.15 Volga R. R. a Yaroslavl g l o V Ivanovo Nizhnii Moscow Vladimir Novgorod
Bologoe
Vo lg
Vitebsk
55
Lake Ilmen
R.
56
30 31 32 33 34 35 36 37 3839 40 4142 43 44
Vo lga
57
τ 0.28 0.26 0.24 0.22 0.20 0.18 0.16 0.14 0.12 0.10
R.
Lake Ilmen
Rybinsk water March storage reservoir Kostroma Volga R. Bologoe R. ga Yaroslavl’ Tver’ Vol 0.2 Ivanovo Nizhnii Moscow Novgorod Vladimir 0.2
58
0 .2
N 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
Vo lg a
588
Smolensk Orsha
Kaluga
Ryazan
Tula Mogilev Bryansk
Orel
Penza oblast
53 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 E
0.21 0.20 0.19 0.18 0.17 0.16 0.15 0.14 0.13
τ 0.3
AOT 52°–59° N, 29°–45° E
0.2 σ
Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.
0.1
0
Fig. 2. Spatial distributions of the monthly mean AOT values in 2000–2009 over the ERT center. The data for crosshatched regions are absent. The histogram shows the longterm monthly mean AOT values averaged over the area of the ERT central part 52°–59° N, 29°–45° E). Vertical bars show the rms deviations of the mean values. IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
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τ 0.3
(а)
589
(b)
0.2
0.2
Δτ
2
0.05 2
0.1
0.1 1
3 0
0 1
2
4
3
5 6 7 8 9 10 11 12 Month of the year
τ 0.4
0
1
–0.05 1
2
3
4
5 6 7 8 9 10 11 12 Month of the year
(c)
(d)
Δτ 0.10
0.3 2
0.2
0.05
0.1
3 2 1
1
0 1 τ 0.4
1
2006
2007
1
2008
1
1
2009
2
0 3
4
5 6 7 8 9 10 11 12 Month of the year (e)
0.3 0.2 0.1
2 1
0 1 2000
1 2001 1 2002
1 2003 1 2004 1 2005 1 2006
1 2007
1 2008 1 2009 (f)
0.4 0.3 0.1 2 1
0.1 0 1
2000
1
2001
1
2002
1
2003
1
2004
1
2005
1
2006
1
2007
1
2008
1
2009
Fig. 3. Longterm monthly mean τ0.55 values over (a) Moscow: (1) from MODIS data in the period 2000–2009 and (2) from the data of MO MSU in the period 1955–2004; (b) over the ZSS IAP RAS; (1) from MODIS data in the period 2000–2009, (2) from data of the CIMEL photometer (AERONET) in the period 2006–2009, and (3) their difference (these data are adapted; the scale is on the right). (c) The monthly mean τ0.55 values over the ZSS: (1) from MODIS and (2) CIMEL data. (d) The difference between the longterm monthly mean AOT values: (1) over Moscow and the background region, (2) over Moscow and the ERT center, and (3) over Moscow and the ZSS from MODIS data. (e) The monthly mean τ0.55 values over the town of Nizhnedevitsk: (1) from MODIS data and (2) from data of the Nizhnedevitsk actinometric station. (f) The monthly mean τ0.55 values over the Kamennaya Steppe sanctuary: (1) from MODIS data and (2) from data of the Kamennaya Steppe actinometric station. Vertical bars show the rms deviations of the longterm monthly mean AOT values. IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
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Table 2. Longterm monthly mean values of τ0.55 in the period 2000–2009 calculated from the data of satellite and groundbased observations for different seasons of the year and their differences (standard deviations for the sample with the size N are given in pa rentheses) Nizhnedevitsk Period of year March–May June–August September–October March–October
Kamennaya Steppe
MODIS
Station
ΔMODIS – Station
MODIS
Station
ΔMODIS – Station
N
0.15 (0.08) 0.14 (0.06) 0.10 (0.06) 0.13 (0.07)
0.17 (0.05) 0.18 (0.05) 0.13 (0.04) 0.16 (0.05)
–0.02 –0.04 –0.03 –0.03
0.16 (0.09) 0.16 (0.07) 0.11 (0.09) 0.14 (0.08)
0.16 (0.07) 0.20 (0.09) 0.13 (0.06) 0.17 (0.08)
0.00 –0.04 –0.02 –0.03
26 30 20 76
the MODIS data in the vicinity of the ZSS (55°–56° N, 36°–37° E) in the period 2000–2009. It can be seen from this figure that, from August to November, the MODIS AOT values are smaller by 0.04–0.07 than the CIMEL AOT values; however, part of this difference is evidently caused by the absence of an adaptation of the data sets. For the adapted sets (if the MODIS and CIMEL data are simultaneously available), the mean AOT values are very close and are approximately 0.15 (Fig. 3c). In this case, the correlation coefficient between the monthly mean MODIS and CIMEL AOT values is 0.91 (0.76, 0.97) (here and below, their 95% confidence intervals are presented in parentheses near the correlation coefficients). Nevertheless, in the course of the year, substantial differences are observed between the satellite and groundbased AOT data: from +0.04 in July to –0.06 in November (Fig. 3b). Figures 3e and 3f show the monthly mean values of τ0.55 obtained from the data of the Nizhnedevitsk (51.6° N, 38.4° E) and Kamennaya Steppe (51.1° N, 40.7° E) actinometric stations in the period 2000– 2009 [24], as well as the monthly mean values of τ0.55 from the MODIS data over these stations. The results of a comparative analysis of the satellite and ground based data indicate that the satellite τ0.55 values are sys tematically smaller than the τ0.55 values determined from the data of actinometric stations (Table 2). In the period from March to October, this difference aver ages to 0.03. Figure 1b points to the noticeable influence that the Moscow agglomeration has on the AOT. For the period from March to November, the mean AOT dif ferences over Moscow and the ZSS, Moscow and the ERT center as a whole, and over Moscow and Penza oblast are about 0.03, 0.04, and 0.05, respectively (Fig. 3d), and these differences increase in the warm period of the year. In particular, in May, the difference between the AOTs over Moscow and the background region attains 0.11. The mean difference between the AOTs over Moscow and the background region (Δτ = 0.05) can serve as a rough estimate of the anthropogenic contribution to the aerosol pollution of Moscow (annual mean). It should be noted that, according to the data of simultaneous observations of the AOTs in Moscow and at the ZSS with the CIMEL photome
ters, the AOT differences at the wavelength 0.50 µm between Moscow and the ZSS under the stable condi tions of October 2006 varied within the range 0.01– 0.06 [25]. Seasonal AOT Cycle: Relation to Local Meteorological Conditions Local meteorological conditions substantially affect the intensity of aerosolformation processes, as well as the advection, accumulation, and dispersion of pollutants. Figure 4 shows the seasonal cycles of aero logical characteristics over the ERT center calculated from the radiosonde data obtained at the Dolgoprud nyi, Smolensk, Bologoe, and Ryazan stations in the period 2000–2009. The comparison of Figs. 3a and 3c with Figs. 4a and 4b shows that the secondary AOT maximum coincides with the seasonal maxima of tem perature and humidity in the region. Evidently, high temperature and humidity are responsible for the sup ply of a large amount of vapors of aerosolforming compounds into the atmosphere and favor the intensi fication of photochemical processes, as well as the condensation and coagulation growth of particles [20]. It can be seen from the comparison of Figs. 1a, 1b, and 2 with Fig. 4 that, in March–April, southwest erly winds favor the advection of pollutants from east ern Belarus and Ukraine into the ERT, whereas in May–July, northwesterly winds ensure the advection of cleaner air masses into the region. The positive cor relation of the AOT with the meridional wind compo nent is observed from April to September in the height interval below 5 km: the AOT maxima are noted in the periods of southerly winds, whereas the July AOT maximum coincides with the short period, when the wind changes its direction toward the north (Fig. 4e). This feature is explained by the AOT increase from north to south over the ERT [23]. The AOT decrease from April to June falls on the period of the greatest static instability of the lower troposphere (Fig. 4d), which favors the outflow of pollutants from the surface layer upward and their subsequent dispersion by winds. It can be seen from Fig. 4c that the local maxi mum of the zonal wind component is noted in May– June against the background of the zonal wind velocity
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SPATIAL–TEMPORAL VARIABILITY OF THE AEROSOL OPTICAL THICKNESS (а)
4
10
5
6
7
8
9 10 11 12
9
9 8 7 6 5 4 3 2 1 0
9
7
u, m s–1 (c)
9
5 7
5
3
1
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0.25 0.5
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5 6
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9 10 11 12 γ, °C km–1 (d)
9 8 7 6 5 4 3 2 1 0
7
6 7
7.5
7 6
3 4
2
3
1
9 10 11 12
8
0.125
7
0
m, g kg–1 (b)
3
–1 0
9 8 7 6 5 4 3 2 1 0
4
t, °C
5
Z, km 9 8 7 6 –30 5 4 –20 3 2 –10 1 0 1 2 3
5 4 3
7
6
4
5
6
7
8
9 10 11 12
v, m s–1 (e) 1 0.5 –1
–1.5
–1 –0.5
9 8 7 6 5 4 3 2 1 0
–0.5
0.5
1
1
2
3
4
1
0.5
5 6 7 8 9 10 11 12 Month of the year
591
(f)
Ri
9 8 7 6 5 4 3 2 1 0
30
22 22
22 38 30
6
1
2
3
4
5
14
22
6 7 8 9 10 11 12 Month of the year
Fig. 4. Monthly mean values of (a) temperature, (b) watervapor mixing ratio, (c) zonal and (d) meridional wind components, (e) vertical temperature gradient, and (f) Richardson number calculated from data of the daytime (12:00 UTC) radiosonde launches at the Dolgoprudnyi, Smolensk, Bologoe, and Ryazan stations in 2000–2009. In Fig. 7e, northerly winds are marked by color and broken contours, the zero contour is omitted, and the density of gradient coloring increases with increasing parameter values.
increase with height. The AOT increase from June to August falls on the period of increased dynamic stabil ity of the free troposphere (Fig. 4f), which prevents the outflow of pollutants from the atmospheric boundary layer into the middle troposphere. A joint analysis of the seasonal cycles of the zonal and meridional wind components indicates that, in the atmospheric bound ary layer, the wind vector predominantly has the WSW direction, which is responsible for the characteristic orientation of the aerosol pollution plume (Fig. 1b). IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
The noticeable southward displacement of the “cap” of aerosol pollution over the Moscow region in June compared with August (Fig. 2) reflects distinctions in the directions of the meridional wind components in these months (Fig. 4e). Precipitation is one of the key factors controlling the content of pollutants in the atmospheric air. Figure 5a shows the longterm monthly mean values of the daily mean amount of precipitation (R) with an intensity of more than 0.1 mm/day and the number of rainy days Vol. 47
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R, mm/day 8 R ≥ 0.1
N, % 56
τ 0.30
52
0.25
48
0.20
44
0.15
40
0.10
36 9 10 11 12 22 (b)
0.05
20
0.25
18
0.20
16
0.15
14
0.10
12 9 10 11 12 32 (c)
0.05
(а)
6
N
4 R τ 2
1
0.52
2
3
4
5
6
7
8
0.1 ≤ R ≤ 1
0.48
0.44
0.40 1 4.4
2
3
4
5
6
7
8
1 ≤ R ≤ 10
4.2 28 4.0
0.30
0.30 0.25 0.20
24
3.8
0.15
3.6 20 3.4 3.2 1
2 3 24 R > 10 22
4
20
0.10
16 9 10 11 12 12 (d)
0.05
10
0.25
8
0.20
6
0.15
4
0.10
2 5 6 7 8 9 10 11 12 Month of the year
0.05
5
6
7
8
0.30
18 16 14 12
1
2
3
4
Fig. 5. Monthly mean values of the daily precipitation amount R (thick solid lines) and the number of rainy days N (dotted lines) in percent of the number of days in the cor responding calendar month for precipitation with the intensity (a) R ≥ 0.1 mm day–1; (b) 0.1 ≤ R < 1 mm day–1; (c) 1 ≤ R ≤ 10 mm day–1; and (d) R ≥ 10 mm day–1 calcu lated from data of the Moscow meteorological station (All Russian Exhibition Center) over the period 2000–2008. The monthly mean AOT values over Moscow (thin solid lines) are based on the MODIS data of 2000–2009.
in Moscow (N) in percent of the number of days in the corresponding calendar month for the periods from March to November 2000–2008. It can be seen that, in the period from June to November, the AOT posi tively correlates with the intensity of precipitation, whereas in the period from March to June, the R and AOT variations do not display any unambiguous cor respondence. At the same time, a noticeable correla tion is detected when the AOT variations and the number of rainy days are compared. The correlation coefficient of the monthly mean N and AOT values in the period from March to November is –0.67 (–0.92, –0.01), and it attains –0.95 (–0.99, –0.60) in March–August. For March to November, Figs. 5b–5d show the distributions of the longterm daily mean amounts of weak (0.1 ≤ R < 1), moderate (1 ≤ R ≤ 10), and strong (R > 10) precipitation, which form, respec tively, 34, 56, and 10% of its total amount, as well as the numbers of days with the precipitation of the cor responding gradations. It can be seen from these fig ures that, for all of the gradations, the local minima of the precipitation intensity coinciding with the local AOT minima are noted in June, and the maxima of precipitation coinciding with the local AOT maxima are noted in July. Nevertheless, no unambiguous inter relation is noted between monthtomonth changes in weak and strong precipitation and AOT variations. A closer interrelation is traceable between the AOT and moderate precipitation. It can be seen from Fig. 5c that the AOT positively correlates with the daily amount of moderate precipitation and negatively cor relates with the number of days when moderate pre cipitation was observed. In the period from March to November, the correlation coefficient between the longterm monthly mean values of the daily amount of moderate precipitation and the AOT is 0.62 (–0.07, 0.91), and the correlation coefficient between the frac tion of the number of days with moderate precipitation and the AOT in this period reaches –0.66 (–0.92, 0.13). Figure 6a shows the spatial distribution of the rms deviations of the monthly mean AOT anomalies (dif ferences between the current monthly mean AOT val ues and the corresponding longterm monthly mean AOT values). This distribution points to the relatively high AOT variability at the boundaries between Smo lensk and Tver and Kostroma and Ivanovo oblasts and the low AOT variability over Penza oblast. The vari ability of monthly mean AOT anomalies—to a greater extent than the variability of monthly mean AOT val ues—reflects local factors affecting the AOT evolution in the region. Figure 6b shows the spatial distribution of the correlation coefficients (r) between the AOT anomalies over Moscow and the AOT anomalies over other localities of the region, which clearly demon strates the influence that the megacity has on the region. This figure indicates that the relation of the AOT anomalies over Moscow and the AOT anomalies over the southwestern and northeastern parts of the
IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
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2011
SPATIAL–TEMPORAL VARIABILITY OF THE AEROSOL OPTICAL THICKNESS (а)
N Lake Ilmen
Kostroma Volga R. Yaroslavl
Bologoe Vo lga R.
57
rms
Rybinsk water storage reservoir
0.06
58
Tver
0.07
Ivanovo 0 .0 7
Vladimir
Nizhnii Novgorod
Moscow
56 0.07
Vitebsk
0.05
55 0.06
Smolensk
54
Kaluga
Orsha
593
Ryazan
Tula
Mogilev
Penza oblast
0.0 5
Orel Bryansk
τ 0.075 0.0725 0.07 0.0675
0.065 0.0625 0.06 0.0575 0.055 0.0525 0.05 0.0475 0.045 0.0425 0.04
53 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 E r (b) Rybinsk water storage reservoir Bologoe
Kostroma Volga R. Yaroslavl
R. lga o V
0 .6
Tver
57
0. 6
0. 6
Lake Ilmen
5 0.
0.5
58
Ivanovo
0.7
6 0.
Vladimir
Nizhnii Novgorod
Moscow
56 Vitebsk
55 Smolensk
Ryazan
Tula
0 .7
54
0.8
Kaluga
Orsha
0.7
Mogilev 0.5
0. 6
0.5
0.6
Orel
Penza oblast
1 0.95 0.90 0.85 0.80 0.75 0.70 0.65 0.60 0.55 0.50 0.45
Bryansk
53 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 Sr > 0.75 (c) 21
Rybinsk water storage reservoir Bologoe
Kostroma Volga R. Yaroslavl
S, %
Ivanovo
21
21
21
Tver
57
Vo lga
R.
Lake Ilmen
21
58
Moscow
Nizhnii Novgorod
21
56
Vladimir
Vitebsk 21
55 Smolensk
54
Kaluga
Orsha
Tula
Mogilev
Ryazan
Penza oblast
6
Bryansk
6
Orel
33 30 27 24 21 18 15 12 9 6
53 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 E Fig. 6. Spatial distributions of (a) rms deviations of local monthly mean AOT anomalies; (b) correlation coefficients between the AOT anomalies over Moscow (grid cell 55°–56° N, 37°–38° E) and the AOT anomalies over the remaining cells of the grid 1° × 1° of the ERT central part (contour r = 0.75 is colored in white); and (c) areas (in percent of the area of the ERT central part) encompassed by contours r = 0.75 in the calculations of the correlation coefficients between the AOT anomalies over each cell of the grid and the AOT anomalies over the remaining cells of the grid. IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
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% 10
Moscow ERT center Background region
8
6 4
2
0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 τ
Fig. 7. Frequency distributions of τ0.55 values over the ERT center, Moscow, and the background region (Penza oblast).
region is isotropic. The contour r = 0.75 outlines approximately 19% of the territory presented in Fig. 6b. In order to estimate the influence of other localities on the region, similar calculations were per formed for all cells 1° × 1° of the ERT central part. Figure 6c shows the spatial distribution of the areas (in percentage of the total area of the region) encom passed by the contours r = 0.75 in the calculations of the correlation coefficients of AOT anomalies with respect to each such cell. The results indicate that the eastern part of Vladimir oblast (and not the Moscow agglomeration) has the maximal influence on the AOT of the region (Fig. 6c). Figure 7 shows the frequency distributions of the daily τ0.55 values over the ERT center, Moscow, and the background region. These distributions are calculated for the range of τ0.55 variations from 0 to 1 at gradations of 0.02 (the number of AOT observations exceeding 1 will form, respectively, 0.4, 1, and 0.1% of the total number of observations in the specified regions). The descriptive statistics of AOT observations are pre sented in Table 3. It should be noted that the number of observations over the background region was larger than over Moscow. Taking into consideration the geo
graphic proximity of the regions and their identical geometric dimensions, this result seems unexpected. To some extent, it can be associated with the influence that aerosols of anthropogenic origin have on the cloudiness. An increase in the concentration of aero sol particles acting as condensation nuclei favors an increase in the amount of droplets in a cloud and, pro vided that the liquidwater content in a cloud remains the same, a decrease in droplet sizes, which increases the lifetime and albedo of a cloud [5]. These factors hamper the AOT retrieval and can cause a decrease in the number of observations over Moscow. The distri butions shown in Fig. 7 are far from normal ones and differ substantially from each other. The most proba ble AOT value over the ERT center (as a whole) and Moscow is 0.15, whereas, over the background region, for which the distribution curve is characterized by a considerable irregularity, the most probable AOT value is 0.07 (from the distribution smoothed by the moving average for three gradations, this value is 0.09). It is also seen from Fig. 7 that the distribution of the τ0.55 frequencies over Moscow is bimodal, i.e., the second ary maximum of the distribution is centered at the value τ0.55 = 0.25. The presence of two modes in the AOT distribution over Moscow (0.08–0.12 and 0.20– 0.24) was noted previously in the data of τ0.50 observa tions with a CIMEL photometer in September 2001– June 2002 [27]. Weekly Cycle of the AOT The content of aerosol particles in the air over urbanized regions is largely controlled by anthropo genic factors which reflect the weekly cycle of human related activities. As is noted above, the annual anthropogenic addition to the AOT over Moscow is, on average, about 0.05. This value can also serve as a reference point for an a priori upper estimate of the peaktopeak amplitude of a possible weekly AOT cycle over Moscow. It should be noted that the scatter of daily τ0.55 values is very large (the standard deviation of daily data is 0.22), and the weekly AOT variations are virtually indiscernible against the background of substantially larger AOT variations from one month to another (Fig. 3a) and from one day to another. At the same time, considerable τ0.55 variations are more likely caused by the supply of combustion products into the region, the inflow of turbid air masses, or the intense aerosol washout by precipitation than by anthropo
Table 3. Descriptive statistics of daily τ0.55 values over the central part of European Russia, Moscow and the background region in the period 2000–2009 Region Central part of the ERT Moscow Background region
Mode
Median
Asymmetry
Excess
Quantity
0.156 0.147 0.069
0.166 0.196 0.138
4.33 5.25 3.04
35.63 41.81 21.95
3963 1657 2153
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SPATIAL–TEMPORAL VARIABILITY OF THE AEROSOL OPTICAL THICKNESS
0.070
τ ≤ 0.1
(а)
18%
τ 0.185 0.180
0.05 ≤ τ ≤ 0.3 70%
(b)
τ
τ ≤ 0.8 99%
(c)
0.24
Moscow
τ 0.075
595
0.175 0.065 0.170 0.165
0.055
0.160 0.168
0.050 0.068
21% (d)
0.064
0.22 (e) 81%
0.20
0.164
99%
(f) ERT center
0.060
0.160 0.18
0.060 0.156 0.056 1 2 3 4 5 6 7 1 Day of the week
1 2 3 4 5 6 7 1 Day of the week
1 2 3 4 5 6 7 1 Day of the week
Fig. 8. Averagings of the AOT values for different days of the week satisfying the conditions: (a, d) τ0.55 ≤ 0.1; (b, e) 0.05 ≤ τ0.55 ≤ 0.3; and (c, f) τ0.55 ≤ 0.8 over Moscow (a–c) and over the ERT center (d–f). Numerals (%) indicate the fraction of observations for the given group from the total number of observations. Vertical bars show the rms deviations of the mean values. Dotted lines rep resent the smoothing by the weighted (1–2–1) moving average.
genic factors changing with weekly periodicity. Figure 8 shows the AOT values over Moscow (Figs. 8a–8c) and over the ERT center (Figs. 8d–8f) averaged for indi vidual days of the week which are calculated for three groups of observations. The observations satisfying the condition τ0.55 ≤ 0.1 (conditionally background pollu tion), which amount to 18% of all AOT observations over Moscow and 21% of regional observations, were included into the first group. The data satisfying the condition 0.05 ≤ τ0.55 ≤ 0.3 (moderate pollution), which amount, respectively, to 70 and 81% of observa tions, were included in the second group. The third group included all observations except for those yield ing extremely high AOT values. The observations of this group satisfy the condition τ0.55 ≤ 0.8 and amount to 99% of all observations (in both of the regions). It can be seen from Fig. 8 that the mean AOT values for different days of the week for all groups of observations exhibit intraweek changes; however, there are substan tial distinctions between local and regional signals, as well as between signals from different groups. Specific features of individual signals are clearly traceable on the signals smoothed by the moving average with the weights 1–2–1. Observations of the first group are characterized by reduced AOT values on weekends and increased AOT values on working days. However, such a form of the signal over Moscow is explained solely by a very low Saturday value of the AOT (Figs. 8a, 8d). Data of the second group are character ized by reduced AOT values from Tuesday to Thursday and by increased AOT values from Saturday to Mon IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
day; in this case the amplitudes of weekly signals (local and regional) are close to each other (Figs. 8b, 8e). In observations of the third group, the local and regional signals are substantially different (Figs. 8c, 8f). In observations of this group over Moscow, the weekly signal manifests itself most noticeably: the peakto peak amplitude of the smoothed signal with a mini mum on Tuesday and a maximum on Sunday reaches 0.02. This value is comparable with the annual mean estimate of the anthropogenic effect. The regional sig nal is substantially weaker, and its minimum is shifted to Tuesday. Over the background region, weekly AOT signals are not diagnosed, which testifies to the anthropogenic origin of weekly AOT variations. Dis tinctions between the weekly AOT signals can be explained by distinctions in the microstructures and the chemical compositions of aerosols, as well as by different climatic conditions and meteorological regimes over the urban agglomeration, the background region, and the region as a whole. In order to analyze the statistical significance (α) and the confidence probability (1 – α) of the differ ences of the mean AOT values over individual days of the week, we used the statistic
Z =
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X d1 − X d 2 2 ⎞ + N d1sd 2 ⎛ 1 + 1 N d1 + N d 2 − 2 ⎜⎝ N d1 N d 2 ⎟⎠
2 N d1sd 2
2011
,
(1)
596
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under the assumption that Z follows Student’s t distri bution. In (1), X d1 and X d 2 are the mean values; s d21 and sd22 are the rms deviations; and N d1 and N d 2 are the numbers of AOT observations on the days of the week d1 and d2, respectively. The calculations show that, at weak pollution, the Saturday AOT value over Moscow with the confidence probability 95% and higher is smaller than the AOT value on any other day of the week (Fig. 8a) and the Saturday and Sunday values of τ0.55 over the ERT center are smaller than their Tues day and Thursday values (Fig. 8d). If the pollutions of the region are moderate, the Tuesday value of τ0.55 is smaller than its Sunday value, and their difference (τ0.55) is also statistically significant (Fig. 8e). In these conditions over Moscow, the confidence probability τ0.55 for Monday and Wednesday is 93% and for Satur day and Wednesday it is 86% (Fig. 8b). For the third group of data, the confidence probability Δτ0.55 on Sunday and Friday over Moscow reaches 95%, and on Sunday and Tuesday over the ERT center it is 85% (Fig. 8f). It would be of interest to elucidate the signif icance of the AOT differences averaged over several days of the weak and, in particular, over weekend and working days. The calculations show that, for the third group of data over Moscow, the confidence probability of the differences between the mean Δτ0.55 values from Wednesday to Friday and from Saturday to Monday exceeds 95% (Z = 2.62), whereas for the second group of data, the confidence probability of the differences between the mean Δτ0.55 values from Tuesday to Thursday and from Sunday to Monday exceeds 95%. However, these calculations disregard the autocorrela tion of these observations. The autocorrelation functions (ACFs) for the series of daily AOT observations are presented in Fig. 9. Over Moscow, the ACFs calculated from data of two instru ments (MODIS/Terra and MODIS/Aqua) are very similar and display a periodicity close to the weekly one (Fig. 9a). The AOT variations over the ERT center are pronounced more weakly (Fig. 9c), whereas the 17day periodicity can be traced in the AOT variations over the background region (Fig. 9e). These facts also testify in favor of the anthropogenic origin of the weekly AOT signal over Moscow. Figures 9b–9f show ACFs smoothed by the moving average over 15 days for the intervals of lags 7–353 days. It can be seen from these figures that the ACT based on the MODIS/Terra data are similar over all regions with an approximately 4month periodicity. This result reflects the presence of the spring and summer local AOT maxima (sepa rated by a period of 3–4 months (Fig. 3a)) and a sim ilarity of the annual AOT evolution over the ERT cen ter (as a whole) and its individual regions. In order to more exactly determine the time scale of the shortperiod variability of the AOT ACF over Moscow, the parts of the ACF in the range of lags from 3 to 103 days were filtered: for suppressing noise, the
ACFs were smoothed by the weighted (1–2–1) mov ing average, and the quadratic cycles were extracted from the smoothed ACFs. The resulting residual series were subjected to spectral analysis. Figure 10 shows the power spectral densities of the residual time series calculated with the use of the Welch method with a Hanning window 85 days wide. It is seen from this fig ure that the main power of the signals is concentrated in the range of periods of 7.5–8.5 days. The spectral peaks of the MODIS/Aqua and MODIS/Terra signals correspond to the period of 7.6 and 7.8 days, respec tively. Therefore, the ACF periodicity noted above and, consequently, the periodicity of AOT variations determined from the data of both instruments is very close to the weekly periodicity but not exactly equal to it; this periodicity should be more correctly called the quasiweekly periodicity. The results of a spectral anal ysis also indicate that variations with a period of 11.5 days are present in the observational data of both instruments. The ACF calculations also make it possible to take into account the autocorrelation of AOT observations in calculating the statistical significance of the differ ence between the mean values of two groups of obser vations. The autocorrelation of data can be taken into account by substituting Z in formula (1) for Z ' = Z t 0 T0, where t0 is the initial sampling interval (1 day) and T0 is the effective sampling interval. The doubled least ACF lag, at which the autocorrelation coefficient becomes smaller than 1/е, was assumed to be T0. The values of T0 calculated in this manner from the AOT data of the instrument MODIS/Aqua (Terra) over Moscow and over the ERT center are 3.7 (3.3) days and 5.0 (7.1) days, respectively. The calculations show that the use of even the smallest T0 value (3.3 days) radically reduces the confidence probability of all of the differences noted above, making them sta tistically insignificant. In particular, with allowance for autocorrelation, the confidence probability of the difference between the mean AOT values over Mos cow from Wednesday to Friday and from Saturday to Monday (Fig. 8c) is 85% (Z' = 1.44). The described procedure of estimating the significance of differences may be too rigorous; however, the question about the statistical reliability of the weekly AOT signals detected over Moscow seems to still be open. Sevenday aerosol variations were noted previously both in optical and microphysical parameters of sur face aerosol [28–33] and in the aerosol optical thick ness [26, 34–36] in regions characterized both by high [30] and low [32] levels of pollutions. Among others, 11day variations close to those detected in this work were also found in work [28]. The weekly cycles of the daily mean surface concentrations of aerosol particles smaller than 10 µm in size (PM10) and submicron aerosol were diagnosed, respectively, in Moscow and at the ZSS [37]. The manifestation of the weekly AOT periodicity over Moscow was noted in [38]. The
IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
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SPATIAL–TEMPORAL VARIABILITY OF THE AEROSOL OPTICAL THICKNESS r 0.8
r 0.4
(a)
0.6
597
(b)
0.2
0.4 0 0.2 –0.2
0 –0.2 0
7
14
21
28
35
42
49
56
63
–0.4 70
0.8
0 0.4
90
(c)
180
270
360
270
360
270
360
(d)
0.6
0.2
0.4 0 0.2 –0.2
0 –0.2 0
7
14
21
28
35
42
49
56
63
–0.4 70
0.8
0
90
180
0.4 (e)
(f)
0.6
0.2
0.4 0 0.2 –0.2
0 –0.2
0
7
14
21
28 35 Lag, days
42
49
56
63
70
–0.4 0
90
180 Lag, days
Fig. 9. Autocorrelation functions for the series of daily AOT observations over (a, b) Moscow; (c, d) ERT center; and (e, f) back ground region. (b, d, f) ACFs smoothed by the moving average over 15 days. Solid (dotted) lines indicate data of the MODIS Terra (Aqua) instrument.
weekly cyclicity of aerosol in the atmospheric surface layer always manifests itself as the weekend effect: the concentration of aerosol particles decreases on Satur day and Sunday (when it attains the minimum) [30, 31, 33, 37]. The weekly cyclicity of the AOT is more complex and displays regional distinctions. According to the data of the AERONET network, the AOT values (at 0.44 µm) on working days over US stations are, on average, larger by 3.8% than the AOT values on week ends [35]. In the west and center of Europe, reduced IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
AOT values are observed on Sunday and Monday and increased AOT values are seen from Wednesday to Fri day. However, in the east and south of Europe, it is the other way around. In particular, over Moscow (from AERONET data), the mean AOT value from Wednes day to Friday is smaller by 15% than the mean AOT value from Saturday to Monday [35]. This result agrees well with the results of this work. Distinctions between the effects based on the MODIS and AERONET data can be explained by a distinction in the wavelengths of Vol. 47
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Power spectral density
Period, days 40 20 13.3 10.0 8.0 6.7 5.7 5.0 4.4 4.0 0.20 0.16 0.12 0.08 0.04 0
0.05
0.1 0.15 0.2 Frequency, day–1
0.25
Fig. 10. Power spectral density of the ACF signals after fil tering. Solid (dotted) lines indicate data of the MODIS Terra (Aqua) instrument.
observations, in the height sensitivity of groundbased and satellite methods of the AOT retrieval, and in the larger spatial averaging of satellite data. The decrease in the surface aerosol concentration over the Moscow agglomeration on weekends is evi dently associated with a decrease in the intensity of anthropogenic emissions on these days. Therefore, the antiphase character of the weekly AOT cycle (Fig. 8c) and the weekly cycle of surface РМ10 concentrations obtained in [37] seems rather unexpected. At the same time, it is obvious that the direct comparison of AOT data and surface aerosol concentrations is not quite correct. Unlike local data, satellite AOT observations depend on the total aerosol content in the atmospheric column, its vertical distribution, and the optical prop erties of aerosol, which, in turn, are controlled by the chemical composition and sizes of aerosol particles. A joint analysis of the variabilities of admixtures and meteorological parameters can, to some extent, eluci date the situation. Figure 11 shows the atmospheric admixtures and meteorological parameters of the Moscow region, whose variabilities display the weekly cyclicity (CH2O data for the background region are an exception). The temperature, humidity, pressure, and Brunt–Väisälä frequency profiles are calculated on the basis of observations at the Dolgoprudnyi aerolog ical station, and the profiles of the zonal and meridi onal wind components are calculated on the basis of observations at the Dolgoprudnyi, Smolensk, Bolo goe, and Ryazan stations. Calculations of the profiles are based on the data of daytime (12:00 UTC) launches in the warm periods (April–September) of 2000–2009. It is seen from this figure that increased contents of gases–precursors of aerosol (NO2 and SO2) are characteristic of working days, while the weekly maxima of precursor gases fall on Sunday (Figs. 11c, 11e). The weekly variability of NO2 is explained by the fact that the largest part of the nitro
gen dioxide content in the troposphere over the Mos cow megacity is of anthropogenic origin. The weekly AOT variations (Fig. 11a) are in antiphase with the corresponding NO2 variations (Fig. 11c), and the cor relation coefficient between weekly variations in the AOT and NO2 is –0.78 (–0.96. –0.17). It should be noted that differences between the mean values of the AOT and SO2 on working days and weekends also have opposite signs (compare Fig. 11a and Fig. 11e). At the same time, AOT variations are in phase with pressure (Fig. 11f) and temperature (Fig. 11b) variations and, as a consequence, with variations in the tropopause height, as well as in the humidity in the height range from the Earth’s surface to 7 km (Fig. 11d). Compar ing Fig. 11a and Fig. 11b, we see that the watervapor content increase in the lower and middle troposphere coincides with the AOT increase, whereas its decrease coincides with the AOT decrease. The positive corre lation of the AOT and air humidity may be indicative that the weekly AOT cycle is caused by the processes of condensation growth of aerosol particles. These pro cesses lead to variations in the size and shape of aerosol particles, which, in turn, changes the complex refrac tive index of the dispersed substance and, as a conse quence, the optical characteristics of aerosol [40]. Humidity variations also affect the photochemical processes of particle formation, growth, and coagula tion [4]. At the same time, the weekly cycle of the watervapor content can cause the corresponding vari ations of hydroxyl in the troposphere [39], which forms as a result of the reaction H2O + O(1D) → OH + OH, and whose interaction with nitrogen dioxide, which leads to the formation of nitric acid in accordance with the reaction NO2 + OH + M → HNO3 + M can, in principle, explain the autocorrelation of the AOT and NO2 weekly cycles. The negative correlation between the cloudcover fraction and the surface temperature, which is evi dently caused by the influence of cloudiness on the intensity of solar radiation coming to the surface (compare Figs. 11i and 11k), is a very remarkable fea ture of the interrelation between weekly cycles of atmospheric parameters over the Moscow region. A similar relation between the weekly cycles of the cloudiness and surface temperature was detected in [40], where the observational data of 12 meteorologi cal stations of Germany in the period 1991–2005 were analyzed. The manifestation of the weekly cycle of the formaldehyde content in the atmospheric column over the background region with increased values of HCHO on Friday and Saturday and its reduced values on Monday and Tuesday was somewhat unexpected. The averaging over days of the week of the zonal (Fig. 11h) and meridional (Fig. 11j) wind components
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NO2 (1015), molecule cm–2
Aerosol optical thickness
(а) 0.24 0.23 0.22 0.21 Mon Tues Wed Thu Fri Sat Sun Mon
8
(c)
7 6 5 Mon Tues Wed Thu Fri Sat Sun Mon
SO2
(e)
Cloudiness, % of cover
HCHO (1015), molecule cm–2
0.01DU Mon Tues Wed Thu Fri Sat Sun Mon
9.5
(g)
9.0 8.5 8.0 7.5 7.0 Mon Tues Wed Thu Fri Sat Sun Mon
(i)
56 54 52 50 48
Surface temperature, °С
Mon Tues Wed Thu Fri Sat Sun Mon
(k)
14.8 14.6
12 UTC t,°C 0.4 0.3 0.2 0.1 0 –0.1 –0.2 –0.3 –0.4
Height, km 9 8 7 6 5 4 3 2 1 0
Mon Tues Wed Thu Fri Sat Sun Mon
9 8 7 6 5 4 3 2 1
0 Mon Tues Wed Thu Fri Sat Sun Mon
10.9 10.8 10.7 10.6 (d)
0.1 0.06 0.02 –0.02 –0.06 –0.10 –0.14
p, hPa
9 8 7 6 5 4 3 2 1 0
(f)
0.7 0.5 0.3 0.1 –0.1 –0.3 –0.5
Mon Tues Wed Thu Fri Sat Sun Mon
9 8 7 6 5 4 3 2 1 0
u, m s–1 0.7 0.5 0.3 0.1 –0.1 –0.3 –0.5 –0.7 –0.9
Mon Tues Wed Thu Fri Sat Sun Mon 9 ν, m s–1
8 7 6 5 4 3 2 1 0
(h)
(j)
0.7 0.5 0.3 0.1 –0.1 –0.3 –0.5 –0.7 –0.9
Mon Tues Wed Thu Fri Sat Sun Mon
3
N, s–1
(l)
0.00015 5E–005 0 –5E–005 –0.00015 –0.00025
14.4 1
14.0 0 Mon Tues Wed Thu Fri Sat Sun Mon
(b) htrop
m, g kg–1
2
14.2
599
Mon Tues Wed Thu Fri Sat Sun Mon
Day of the week
Day of the week
Fig. 11. Values of (a) AOT, (c) NO2, (e) SO2, (i) cloudcover fraction, (k) surface temperature, (g) CH2O, (b) temperature profile, (d) watervapor mixing ratio, (f) pressure, (h) zonal wind, (j) meridional wind, and (l) Brunt–Väisälä frequency averaged for different days of the week. Local longterm weekly mean values are eliminated from the profiles. Positive (negative) deviations are shown by solid (dotted) isolines; zero isolines are omitted. The thick curve in Fig. 11b shows weekly variations in the tropopause height. IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS
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points to the presence of a clearly pronounced weekly cycle of the wind over the ERT center. Therefore, the weekly cycle of HCHO over the background region can be unrelated to local sources of pollutions but caused by the advection of this admixture from some other region. Figure 11m indicates that height varia tions in the statistical stability of the atmospheric boundary layer in the first half of the week are of a complex character. Nevertheless, it follows from a comparison between Fig. 11l and Fig. 11a that, on the whole, the decrease in the boundarylayer stability on working days favors the dispersion of pollutants and is accompanied by an AOT decrease, whereas the increase in the boundarylayer stability on weekends favors the accumulation of pollutants and is accompa nied by an AOT increase. CONCLUSIONS An analysis of AOT (τ0.55) observations with the MODIS instruments installed on the Terra and Aqua platforms in 2000–2009 made it possible to confirm the specific features of the evolution of AOT over the ERT center, which were previously known from the data of groundbased observations, and to reveal a number of previously undocumented regularities of the temporal AOT variability. The longterm spatial AOT distribution over the ERT center displays the local maximum (0.18) over Moscow, which is caused by the anthropogenic impact of the urban agglomeration. From March to Novem ber the main difference between the τ0.55 values over Moscow and the ERT center is 0.04, whereas in May that over Moscow and the background region reaches 0.11. The longterm spatial AOT distribution points to the advection of pollutants from countries of Central and Eastern Europe into the ERT. The seasonal AOT cycle over the ERT center forms mainly under the influence of natural factors. The annual AOT evolution is closely related to the local meteorological regime. In the period of snow thawing and the corresponding increase in the supply of soil particles into the atmosphere, southwesterly winds also favor the supply to the ERT of pollutants from eastern Belarus and Ukraine, whereas, in June, north erly winds favor the supply into the central part of the ERT of cleaner air from high latitudes. In April–June, a sharp increase in the number of days with moderate precipitation also favors a decrease in AOT. A comparison of the MODIS and AERONET AOT data over the ZSS indicates that the longterm mean AOT data calculated from satellite and ground based observations coincide and the MODIS and AERONET AOT variations from one month to another are correlated (the correlation coefficient between the monthly mean τ0.55 values is 0.91 (0.76, 0.97)). At the same time, this comparison revealed substantial seasonal distinctions between the satellite
and groundbased AOT data, which vary from +0.04 in July to –0.06 in November. An analysis of the monthly mean AOT anomalies over the ERT center points to the AOT over Moscow having a closer relation to the AOT over the southwest ern and northeastern parts of the region. The eastern part of Vladimir oblast (and not the Moscow agglom eration) has the maximal influence on the region as a whole. The most probable value of τ0.55 over the ERT cen ter and the Moscow agglomeration is 0.15. The distri bution of the frequency of τ0.55 repetition over Moscow is bimodal: the secondary maximum of the distribu tion is centered at the value 0.25. Over the background region, the most probable value of τ0.55 is 0.09. An analysis of the weekly AOT variations shows that the intraweek AOT distinctions manifest them selves more strongly over the Moscow agglomeration and more weakly over the ERT center as a whole, and they do not manifest themselves over the background region. The weekly AOT cycle over Moscow is charac terized by reduced τ0.55 values from Wednesday to Fri day and by increased τ0.55 values from Saturday to Monday. The peaktopeak of weekly AOT variations is about 10% of the mean value. A spectral analysis of τ0.55 variations shows that the period of weekly AOT variations is not equal to seven days exactly and lies between seven and eight days. The results of a spectral analysis also indicate that variations with the period 11.5 days are present in time series of the AOT. A study of the interrelation between weekly varia tions in the AOT and weekly variations in the contents of precursor gases and meteorological parameters reveals the positive correlation of the AOT with humidity, temperature, and pressure, which suggests the key role of condensation mechanisms, which con trol the number concentration and the size of aerosol particles in the formation of the weekly AOT cycle over Moscow. ACKNOWLEDGMENTS I am grateful to M.A. Sviridenkov for the possibility of using the data from AOT observations at the Zveni gorod station of the AERONET network, as well as to E.D. Makhotkina and I.N. Plakhina for the AOT data of the RSBD Aktinometriya. This work was supported by the Russian Foundation for Basic Research, project no. 110500704. REFERENCES 1. K. Ya. Kondrat’ev and M. A. Prokof’ev, “Atmospheric Aerosol and its Impact on Climate,” Izvestiya AN SSSR. Fiz. Atmos. Okeana 20 (11), 1055–1063 (1984). 2. V. E. Zuev, B. D. Belan, and G. O. Zadde, Optical Weather (Nauka, Novosibirsk, 1990) [in Russian].
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