Statistical characteristics of atmospheric gravity wave in the mesopause region observed with a sodium lidar at Beijing, China

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Statistical characteristics of atmospheric gravity wave in the mesopause region observed with a sodium lidar at Beijing, China Shaohua Gong, Guotao Yang, Jiyao Xu, Jihong Wang, Sai Guan, Wei Gong, Jun Fu

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Cite this article as: Shaohua Gong, Guotao Yang, Jiyao Xu, Jihong Wang, Sai Guan, Wei Gong, Jun Fu, Statistical characteristics of atmospheric gravity wave in the mesopause region observed with a sodium lidar at Beijing, China, Journal of Atmospheric and Solar-Terrestrial Physics, http://dx.doi.org/10.1016/j.jastp.2013.03.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Statistical characteristics of atmospheric gravity wave in the mesopause region observed with a sodium lidar at Beijing, China Shaohua Gong a,b , Guotao Yanga,*, Jiyao Xua, Jihong Wang a, Sai Guana, Wei Gongc, Jun Fub a

State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China b School of Physics and Electronics Engineering, Hainan Normal University, Haikou, 571158, China c State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430072, China Phone: +86-10-62586358 Fax: +86-10-62586358 E-mail: [email protected]

Abstract Nightly and seasonal variations of atmospheric gravity wave (AGW) activity in the sodium layer are investigated by analyzing 253 nights of sodium lidar observations. These observations were made from April 2010 to September 2011 at Yanqing, Beijing ( 40.3D N ,116.2D E ). Vertical wavelengths, periods and phase velocities of 162 quasi-monochromatic AGWs are extracted from the lidar data. An average parameter relationship Oz

0.212Tob0.544 is found. Nightly statistical results show that the activity of

AGW has maximum from 22--24 LST in night. Monthly statistical results reveal that AGW activity maximizes in summer and minimizes in winter. Seasonal variation characteristic of AGW activity is interpreted based on a qualitative analysis of probable main sources in Northern China. Influences of seasonally varying temperature and *

Corresponding author. Tel.: +86-10-62586358; fax: +86-10-62586358. E-mail address: [email protected]. (Guotao Yang)

background winds on AGW activity are discussed. Keywords: sodium lidar observation, atmospheric gravity waves, wave sources and propagations. 1. Introduction Atmospheric gravity waves (AGWs) are now believed to play a central role in establishing the large-scale circulation and structure of the middle and upper atmosphere. Our understanding of AGWs on many fronts has been advanced by numerous theoretical, numerical, and observational studies since the pioneering works of Hines (1960). Those fronts include AGW sources, parameters, propagation dynamics, variations with altitude, seasonal and geographic variability, and implications of various parameterizations for atmospheric circulation and structure (Nappo, 2002; Wright, 2011). Several important models and theories have been derived, such as Dewan and Good’s (1986) linear instability

theory,

Hines’ (1991)

Doppler-spreading

theory,

Dewan’s

(1994)

saturated-cascade theory, and Gardner’s (1994) diffusive filtering theory. These have provided some explanations to the vertical wave number spectrum, based on different physical mechanisms for dissipating wave energy (including shear and convective instabilities, cascade process, wave-induced Doppler effects and wave-induced diffusion). As an effective method for investigating AGW activities, sodium lidar observations can provide good understanding of AGW phenomena in the mesopause region, owing to their high temporal and spatial resolutions. The sodium layer is an excellent tracer of 2

gravity wave perturbations because steep sodium density gradients at the bottom and top of the layer can enhance these perturbations. Many studies of AGW intensities, spectra and wave field characteristics in the mesopause region have been made with sodium lidars in United States, South pole and Brazil [e.g., Senft and Gardner, 1991; Gardner and Voelz., 1987; Collins et al., 1994; Yang et al., 2006, 2008b]. However, since AGW characteristics generally vary with location, more detailed observational evidence is needed. Further, detailed understanding of generation mechanisms and resulting wave characteristics is still lacking for accurate description of AGW effects within global models. Since early 2010, 253 nightly observations of sodium lidar at Beijing have been done with support from the Chinese Meridian Project. After analysis of these observation data, we present the first lidar study of AGW activity in the mesopause region above Beijing. We address nightly, monthly and seasonal variabilities of this activity in this paper. Reasons behind the evolution of these characteristics are discussed, based on qualitative analyses of AGW sources and background atmospheric winds. 2. Instrumentation and Data Analysis Methodology Beijing lidar station locates at the suburb of Yanqing county ( 40.3D N ,116.2D E ), and its sodium lidar mainly consists of three parts, laser system, optical receiving system and signal processing system. The probing beam is generated by a Nd:YAG laser-pumped dye laser, and its wavelength is precisely controlled by employing a Na fluorescence cell to excite the resonance transition of Na atoms in sodium layer. The beam is set to 3

pointing at zenith, and its typical energy and divergence are about 30 mJ and 1 mrad . The repetition rate of Nd:YAG laser is 30 Hz . Backscattered fluorescence photons from sodium layer are collected with a receiving telescope (diameter is )950 mm ). The field-of-view of telescope is set to about 3 mrad . An optical filter with 1 nm bandwidth, a cooled PMT, a fast amplifier and a time-resolved photon counter are used for optical signal detection. Data is finally collected and saved in a computer. For an individual lidar profile, the photon counts are accumulated for every 5000 laser pulses in 3 min. The time resolution is 3 min , and the altitude resolution is 96 m (0.64 Ps photon counting gates). From April 2010 to September 2011, lidar observation has been constantly done in 253 suitable nights. A database of 2208 hours was obtained under the efforts of our group members. Monthly distributions of observing nights and hours are respectively plotted in Fig.1. The observing hours are significantly longer in winter than in summer because summer is the rainy season of Northern China. As a nightly measurement, more observations are during the middle of night and least during the hours just sunset and just before sunrise. All data are selected in this work except those data during the periods of sodium burst ( Nas ) in 46 nights. The sodium density profiles are spatially and temporally low-pass filtered with a cutoff of about 2 km and 15 min. The criterion described by Beatty et al. (1992) is applied, and only those wavelike perturbations that occur in several successive density profiles and exhibit coherent downward phase progression are considered to be quasi-monochromatic AGWs. (In general, the sodium density profile perturbed by an 4

AGW presents a monochromatic wavelike, and the ridge and trough are evenly distributed on the both sides of the each note.) The technique developed by Yang et al. (2008a) is employed to extract the parameter values of each AGW. Based on the fundamental theory developed by Gardner and Voelz (1985; 1987), when the steady state profile of sodium layer is horizontally homogeneneous, the layer density response to a monochromatic AGW can be written as ns ( r , t )

K K n0 ( z  JH ln[1  [ Ae Ez (J  1)] cos(Zt  k ˜ r )]) K K {1  [ A e Ez (J  1)] cos(Zt  k ˜ r )}

(1)

where ns (r , t ) is the density of sodium layer, and n0 ( z ) is the density of unperturbed K sodium layer; Ae Ez , Z , k , E are the wave amplitude, wave frequency,

wave

number vector and amplitude growth factor of AGW respectively; J is the ratio of

K specific heats (~1.4), H is the atmospheric scale height (~6 km), and r

xxˆ  zzˆ is

the position vector. When the wave amplitude is not large, the linear perturbation can only be taken into account, and formula (1) will be transformed into Eq. (2):

ns (r , t ) | n0 ( z )  [n0 ( z )  JH

dn0 ( z ) Ae Ez ]˜ cos(Zt  k ˜ r ) dz J 1

(2)

Because the sodium density perturbations caused by AGWs are directly related to the associated atmospheric density fluctuations ( rAtmosphere ), the relative sodium density fluctuations ( rSodium ) can be expressed in terms of the atmospheric density fluctuations ( rAtmosphere ) as following (Gardner and Voelz, 1985):

5

1 rSodium | 

JH dn0 n0 dz rAtmosphere |  J 1

1

JH dn0

K K n0 dz Ae Ez cos(Zt  k ˜ r ) J 1

One may calculate the sodium variation with 'ns

(3)

ns  n0 , and obtain the

atmospheric density fluctuations ( rAtmosphere ) using Eq.(3). Then, the linear wave perturbation of atmospheric density (

K K 'N | Ae Ez cos(Zt  k ˜ r ) ) can be calculated, and N

the value of vertical wavelength O z can be obtained by measuring the average distance between each pair of adjacent antinodes in wave perturbation. The vertical phase velocity

cz can be measured by calculating the downward moving velocity of wave peaks in density profile sequence. The observed period Tob can be calculated with c z

O z Tob .

The following is an example of this data analysis methodology. The density profiles of sodium layer presenting in Fig. 2 were obtained on Sept. 28, 2010. According to the criterion introduced above, it is easy to find from the density profile sequence that a quasi-monochromatic AGW is propagating in sodium layer. Its vertical phase velocity

cz

0.261 ms 1 can be obtained by measuring the moving downward speed of those

wave peaks. To extract the values of other wave parameters from this sodium density profile sequence, the density profiles observed by lidar are firstly averaged in time to form an average density profile in Fig. 3 (a) (the blue curve). Then a connection layer (the dot green curve) is constructed by connecting the midpoints between each ridge and trough of the average density profile. And an approximated background layer (the dash red curve) can be obtained after the connection layer is smoothed using a three-point moving 6

average. Since the density profile of unperturbed sodium layer generally distributes in Gaussian, n0 ( z )

c 2S V 0

exp[ ( z  z0 ) 2 2V 02 ] , the approximated background layer

can be improved by fitting it with two Gaussians in Fig. 3 (b) employing Levenberg-Marquard method.

And the improved background layer (the dash red curve)

is regarded as the reasonable background layer (steady sodium layer). For this Levenberg-Marquard fit, its correlation coefficient is R values of two Gaussians respectively are

0.99952 , and the parameter

c1 1.933 u 103 ,

V 1 2.95 km ; c2 1.903 u 103 , z0, 2 87.08 km , V 2

z0,1

92.95 km ,

3.05 km .

According to Eq. (3), the relative sodium density perturbation ( rSodium ) and the atmospheric density fluctuations ( rAtmosphere ) are calculated and plotted in Fig. 3 (c). The linear AGW perturbation of atmospheric density is calculated and plotted in Fig. 3 (d). After the wave perturbation curve is spatially high-pass filtered with a cutoff of 4.5 km , a vertical wavelength Oz

3.59 km is finally obtained by measuring the average distance

between each pair of adjacent antinodes in Fig. 3 (d). (The corresponding leakage in this high-pass filtering process is also plotted in Fig. 3 (d) with dash-dot line.) And the period is calculated to be Tob

3.82h with Tob

Oz cz .

Taking this approach, both vertical wavelengths and observed periods of quasi-monochromatic waves can be accurately obtained from those sequences of wave-like Na density profiles in different nights. The estimates of vertical wavelengths

7

are limited by the results of Levenberg-Marquard fitting, and the biases are generally less than 2%. Because the observed period is calculated with Tob

Oz cz , the uncertainty is

determined by the errors in vertical wavelength measurements and vertical phase velocity measurements. For the resultant errors in cz measurement are typically 5-8%, the errors in measurement of observed periods are generally less than 10%. In fact, because of the influence of mean wind field in the Na layer, the observed period should be the Doppler shifted wave period rather than the intrinsic period, and the former should be generally larger than the latter (Gardner and Voelz, 1987, Eqs. (20)). Moreover, for the case that the wind speed varies much at different sodium layer altitudes along the wave propagation direction, the coherent phase progression in density profiles will be distorted, and those waves will not be considered as monochromatic gravity waves in terms of the employed criterion. So our statistics are also biased towards waves that experienced a uniform Doppler shift or which propagated approximately normal to the mean flow.

3. Results and Discussion 3.1. Observation results From April 2010 to September 2011, there were 162 quasi-monochromatic waves distinguished over the total observation period of 2208 hours (253 nights). A wave observed in early nighttime that fade away and reappear later, with slightly different wavelength or period, was regarded as the same wave. Specially, the waves appearing in 8

the process of Nas were not taken into account of wave event numbers. For the event of Nas , it can be frequently observed at our observing site. Nas event occurs in 78 nights in our 253 nights’ observation data and its occurrence frequency in different months is shown in Fig. 4 (a). The occurrence frequency is calculated as the total number of Nas events divided by the total observation hours in one month. In our statistics, Nas event is identified with the criteria: the maximum density of Nas peak must be twice of more than twice higher than that of the local background density, the width of Nas (FWHM) must be less than 3 km, and it can last for three or more successive lidar profiles (Clemesha, 1994). For example, in Fig.4(b), there is a strong Nas occurring near the altitude of 95 km during the local standard time (LST) period 22:50--00:30. Although the sodium density profiles present monochromatic wavelike structure below the altitude of 90 km and the wavelike perturbation exhibits coherent downward phase progression, this case can not be counted as a wave event because it is not certain that whether this wavelike perturbation was caused by a real gravity wave or by this strong Nas . Following the data analysis method of Section 2, parameter values of the AGWs were precisely extracted. There were N=162 quasi-monochromatic AGWs observed on different nights, and their vertical wavelengths and observed periods are respectively plotted versus month in Fig.5. Statistics show that their vertical wavelengths ( O z ) ranged from 2 km to 12.88 km , with average vertical wavelength 3.80 km . The corresponding observed periods ( Tob ) were from 1.03 h to 16.48 h , with annual mean 3.56 h . Histograms of these parameters are also plotted in Fig. 6. The number of AGWs 9

clearly decreases with increasing wavelength and observed period, and typical values of AGW parameters Tob

are

vertical

wavelength

1 ~ 4 hour , vertical phase velocity cz

Oz

2 ~ 4 km , observed period

0.2 ~ 0.5 ms 1 . Those observation results

reveal that the lidar-observed quasi-monochromatic AGWs in the mesopause region above Beijing are usually dominated by short-period waves and the waves with a longer period than 4 h are less common. Relationships between O z and Tob have been analyzed and plotted on a log-log scale in Fig. 7. It is evident that there is a strong systematic relationship between O z and Tob . After a power-law maximum likelihood (ML) algorithm is used to fit the data points of the figure in the form Oz

CTob , a relationship Oz p

0.212Tob0.544 is obtained for all

observations. The correlation coefficient from this fit is r 2

0.881 even though some

large Doppler errors in Tob are expected. This systematic relationship ( Oz v Tob

0.544

)

derived at Beijing agrees well with the prediction of Gardner’s diffusive filtering theory [Gardner, 1994], and it means only those waves which have the largest amplitudes and roughly fall along the diffusive damping limit ( Oz

(2SDT )1 / 2 ) are easy observed by

lidar in mesopause region. Relationships between O z and Tob in the summer (March through August) and winter (September through February) are also analyzed with the same method in Fig. 7. They are respectively Oz

0.287Tob0.489 in the summer and Oz

0.162Tob0.590 in the winter.

It is found that the value of slope p and coefficient C change considerably from summer to winter. That means, at Beijing, the seasonal variation of AGW activities is 10

very obvious. Nearly all previous lidar observations indicate a strong systematical relationship between O z and Tob , and the values of slope are similar to the result derived by us ( p | 1 2 ). At Illinois ( 40 D10' N ,88D10'W ), an observation Oz

0.40Tob0.55 is reported by

Gardner and Voelz (1987). Later, a similar relationship Oz

0.24Tob0.57 is presented by

Collins et al. (1996) in their study of AGW activity using Na Wind/Temperature lidar. At São José dos Campos ( 23D S ,46DW ), a low-latitude location, a relationship

Oz

0.42Tob0.43 is reported by Yang et al. (2008a).

However, it should be noticed that

there are still slight variations in observed p and C values at different locations. For example, for a given wave with a typical period of 3 h, its wavelength will be observed to be about 4.6 km at Illinois (about 6.9 km for Gardner and Voelz’s observations), about 3.9 km at São José dos Campos, and about 3.6 km at our observation site. This suggests that AGW activity has its own regional characteristics at different locations. In addition, the associated rms errors in coefficient C and slope p of this ML fit have been calculated and listed in Table 1. The errors are dominated by the scatter in data due to Doppler effects. (Note that the correlation coefficient r 2 is calculated from the raw data and does not depend on the curve fitting algorithm.) Table 1. rms errors in the measured power-law relationships between Oz and Tob . Coefficient and Slope Estimation errors

Total observation C 0.212 p 0.544 2.3%

1.2%

C

Summer 0.287 p 0.489 2.8%

1.5%

C

Winter 0.162 p 0.590 2.0%

1.0%

11

3.2. Analysis of main sources To our knowledge, AGWs can be produced by mesoscale meteorological and irregular motions, and the main contributing factors may be from hydrodynamic sources of momentum, mass, and heat. Generally, at a specific location, convection and topography are the most obvious sources, although other sources may be significant at certain sites or be associated with specific large-scale dynamics (Fritts and Alexander, 2003). The intensities of waves generated below 20 km altitude are usually much stronger than those generated above this altitude, because of the growth of AGW amplitudes associated with the decease in atmospheric density (Gavrilov and Fukao, 1999). Hence, for the typical AGWs observed with sodium lidar, the main contribution may come from topography and convection in troposphere and stratosphere below 20 km. Following the method of Senft and Gardner (1991), the distance between the observation site and tropospheric source can be estimated by multiplying Tob TB by the height of the sodium layer. Where TB is Brunt-Vaisala period and it is approximately 5 min near the mesopause. The annual mean of Tob is 3.56 hours (Fig. 5). That suggests that tropospheric sources are about 3750 km away from Beijing, on average. Since those observed periods are typically 1 ~ 4 h (in Fig. 6(b)), it can be estimated that AGW sources are principally 1050 ~ 4300 km away from the observation site. At these distances, orographic forcing and convective heating over Qinghai-Tibet Plateau may be 12

the most probable sources of the typical AGWs observed at Beijing. This plateau is the highest in the world, and is situated in the southwestern Chinese mainland. It constitutes about one quarter of the area of China, and numerous AGWs may be generated above it [Alexander et al., 2008]. Basing on the observations of HF Doppler arrays, Wan et al. [1998] and Xu et al. [2008] report that the topography and convection above this plateau are the primary sources of medium-scale AGWs above central China. A similar conclusion is also made by Ding et al. [2011] when they investigated the traveling ionospheric disturbances over China with GPS network. Therefore, it may be reasonable to regard the topography and convection above this plateau as the primary sources of AGWs observed by our lidar. For the 162 AGWs observed at Beijing, the wave event rate (WER) in different period intervals has been calculated in different seasons and it is plotted in Fig.8.

The

WER is calculated as the total number of wave events divided by the total observation hours over one season. The figure shows that the WER of AGWs with Tob between 1 h and 4 h is greater than those with other periods. This means that the AGW activities are dominated by components of these typical waves ( Tob  (1,4) hours in Fig. 6(b)). By contrasting the WER of waves across different seasons, one can find that AGW activity has an apparent minimum in winter and these typical AGWs are more active in spring and summer. This seasonal variation is similar to that of tropospheric temperature, and it suggests that seasonal variation of AGW activity is mainly related to the seasonal temperature variation in the troposphere. As is known, convection is a dominant gravity 13

wave source in the troposphere, and the frequency and intensity of AGW source are related to temperature (Beres et al., 2005). When temperature is increases, convection that involves thermal forcing associated with latent heat release becomes stronger and more frequent, thereby generating more AGWs during its interaction with overlying stable layers (McLandress et al., 2000). Based on the above qualitative analyses, it is reasonable to think that AGW activities observed in the mesopause region above Beijing are mainly related to the topography and convection over Qinghai-Tibet Plateau. To those typical AGWs ( Tob  (1,4) hours in Fig. 6(b)), they could be mainly generated by the convections, and their activity may be dominantly influenced by the seasonal variations of convections. Here, we need address that the mentioned above is a qualitative analysis on the AGW sources and the quantitative studies are beyond the scope of this paper.

3.3. Nightly and seasonal variation characteristics To investigate the nightly variation of AGW activity, histograms of nightly WER are plotted versus local standard time (LST) in Fig.9, and an average wave event rate (WER) of 0.222 hr 1 is found. The WER for one local time interval is computed by using the total number of wave events observed during this time interval to divide with the times of observation experiment we carried out in this time interval. (The number of experiments carried out in each LST interval is correspondingly given in Fig.9.) In Fig.9, the WER roughly increases from 17--24 LST and decreases from 24--07 LST, having maximum 14

from 22--24 LST. It means, in the mesopause region over Beijing, AGW is most active near midnight and passive in dusk and dawn. In contrast with lidar observation results reported elsewhere, it is found that the nightly variation of AGW activity commonly varies with observation sites. Gardner and Voelz (1987) indicated that the period of greatest AGW activity at Illinois was from 2100--2200 LST, and the corresponding WER was more than twice that of any other hour. Yang et al. (2008b) stated that the nightly distribution of the quasi-monochromatic WER was irregular at Campos ( 23D S ,46D W ). Both these observations of nighttime AGW activity are different from ours. The discrepancy may be ascribed to different properties of AGW sources in the troposphere, or a lower stratosphere at the observation sites. However, Gardner and Voelz (1987) asserted that it is difficult to postulate that a source mechanism in the lower atmosphere is important to nightly distributions of WER, because AGW propagation to the sodium layer requires more than 20 hours. Perhaps it is more reasonable that the nightly variation is mainly determined by the influence of the background environment. It is know that during their upward propagation, AGWs need suffer considerable absorption, reflection, dissipation and filtration from the background atmosphere; and only a few AGWs can survive in the mesopause region. Hence, those “blocking effects” from background environment may be crucial to AGW propagation, and they may represent important influences on our observation of nightly variation of AGW activity near the mesopause. To study seasonal variation of AGW activity, the WER in each month has been 15

calculated and plotted with curve (a) in Fig.10. Here, the monthly WER is calculated as the total number of wave events divided by the total observation hours in one month. It is shown that the monthly WER for total AGWs maximizes in July and minimizes in December. This means that the strongest and weakest AGW activity occur around summer and winter solstices, respectively. Since the quantity of observing nights and hours varies between months in Fig. 1, some deviation of the WER values may be introduced in the statistic results. Therefore, to obtain a more reliable seasonal WER distribution, a least squares fit is applied to the data to generate curve (c), which is plotted as a red solid line in Fig. 10. It is seen that the WER gradually increases from winter to summer and decreases from summer to winter. Summer values are typically 2 to 3 times larger than winter values. Those illustrate that AGW activity at the observing site is most active in summer and passive in winter. During the winter to summer phase, AGW activity becomes stronger month by month, with just the opposite trend during summer to winter. However, at other observation sites, different seasonal variations of AGW activity in the mesosphere have been reported (Senft and Gardner, 1991; Yang et al., 2008a; Reisin and Scheer, 2004; Wilson et al., 1991; Gavrilov et al., 2003), and different formation mechanisms for those seasonal variations were proposed (Hirota et al., 1997; Gavrilov et al., 1999). But, most believed that AGW activities have their own regional characteristics at different locations and the seasonal variation of AGW activity is principally ascribed to the seasonal change of temperature and background winds in troposphere and 16

stratosphere below 20 km (Gavrilov and Jacobi, 2004; Gavrilov et al., 2003). When the temperature is high, the intensity and frequency of AGW sources will be enhanced for the stronger and more common tropospheric convections, and more AGWs will be generated. During the obliquely upward propagation process, AGWs need suffer severe “blocking effects” (such as absorption, dissipation and filtering) from the background winds (Gavrilov et al., 1999; Medeiros et al., 2003). For AGWs propagating at the same direction as the background wind, they are easy absorbed into the mean flow and hard to survive in the mesopause region. Contrarily, for AGWs propagating opposite to the mean wind, they are easy to pass through and survive in the mesopause region for the weak “blocking effects” from background winds. When convection over Qinghai-Tibet Plateau is regarded as the dominant source of lidar-observed AGWs, the observation results in Fig. 10 can be interpreted, based on the analysis of influences from seasonally varying temperature and background winds. To the monthly varied background winds in tropospheric and lower-stratospheric segments over Beijing, they have been statistically studied by Zhang and Yi (2007, Fig. 3) used the data from Radiosonde observation. And here, for comparison, the altitude-season distribution of mean horizontal wind above Northern China is calculated with the mean horizontal wind model HWM93 (http:// modelweb.gsfc.nasa.gov/) and plotted in Fig. 11. (Since the mean zonal wind is much stronger than the mean meridional wind, only the seasonal variation of the former is usually taken into account in analysis.) Both of the analysis results show that the seasonal distribution of mean zonal wind 17

exhibits an apparent semiannual symmetry, westward wind in spring and summer, and eastward wind in autumn and winter. Minima of the mean zonal wind below 20 km altitude occur in July, August and September (Zhang and Yi (2007), Fig. 4). The average temperature in troposphere and lower-stratosphere over China maximizes in summer. More AGWs are generated by strong and frequent convection above Qinghai-Tibet Plateau.

Also in summer, the mean zonal wind above Northern

China is westward (Fig. 11). AGWs traveling northeastward from the plateau to Beijing suffer fewer blocking effects during their upward propagation, because the propagation direction in the horizontal opposes the background wind. Consequently, more waves are observed in the mesopause region by lidar in summer, and the WER also has a summer maximum (Fig. 10). In China, atmosphere temperature near the ground is a minimum in winter. Fewer AGWs are generated above Qinghai-Tibet Plateau because this colder temperature makes convection relatively weak and less common. Moreover, the mean zonal wind is eastward in winter (Fig. 11). Because of blocking effects from the background wind, AGWs traveling northeastward from the plateau to Beijing are severely dissipated and absorbed during their obliquely upward propagations. Further, most are completely blocked by the mean flow, and disappear in the stratosphere and mesosphere. Therefore, fewer waves survive in the mesopause region to be detected by lidar, and the WER has a winter minimum (Fig. 10). Using the same rationale, the seasonal variation of AGW activity in Fig. 10 can be 18

explained. The increase of tropospheric temperature between winter and summer generates stronger and more frequent convection. However, zonal winds slowly change from eastward to westward (Fig. 11). Thus, the blocking effects of background winds to AGWs propagating northeastward from the plateau to Beijing weaken. As a consequence, more AGWs are observed with time in the mesopause region. In contrast, from summer to winter, the source intensity and frequency decreases because of decreasing tropospheric temperature. Also, zonal winds slowly change from westward to eastward in mesosphere. As a result, the blocking effects of the mean flow to the northeastward propagating AGWs gradually strengthen. To attain more insights on the seasonal dependence of AGW activity, we have calculated the WER for long-period AGWs (periods are longer than 7 hours in Fig. 5) in different months. These are plotted by curve (b) in Fig. 10. We find that the WER distributes semiannually and the long-period AGWs seldom occur from July through September. Comparing curve (b) with the seasonal distribution of horizontal zonal winds below 20 km in Fig.11, it is interesting that both have the same semiannual variation tendency. Also, both have their minima in July, August and September. This temporal correspondence indicates that the activity of the long-period AGWs correlates with zonal winds below 20 km . And it suggests that the long period AGWs are possibly generated by the topography of the Qinghai-Tibet Plateau when the seasonal monsoon winds flow over it. During July through September, fewer topographic AGWs are generated since the horizontal zonal wind is minimized in the troposphere. Hence, there is almost no 19

long-period AGW observed in the mesopause region. The WER maxima in June and October are hard to explain. In addition, in the above explanation, we think those AGWs observed at Beijing are from Qinghai-Tibet Plateau, and they travel northeast to Beijing. However, for the horizontal propagation directions of AGWs, it can not be measured with a single lidar. At Xinglong, Beijing ( 40.2D N ,117.4D E ), OH airglow imager observations show that the distribution of AGW propagation in horizontal direction is anisotropic and almost all waves propagate northeastward in summer at an altitude of ~87 km (Li et al., 2011). This result matches our assumption that most AGWs observed in the mesopause region in summer originate from the Qinghai-Tibetan Plateau. For the horizontal propagation directions of AGWs in winter, however, OH airglow imager observations are different. Therefore, to precisely understand the climatological properties of AGW activity in China, more cooperated measurement methods are desired.

4. Conclusions We obtained a lidar observation database of 2208 hours from 253 nights, during the period April 2010 to September 2011. The regional characteristics of AGWs activity in the mesopause region above Beijing ( 40.3D N ,116.2D E ) were statistically analyzed. The average WER of AGWs is 0.222 hr 1 , and the relationship between vertical wavelengths and periods is Oz

0.212Tob0.544 . AGW is most active near midnight and passive in dusk

and dawn, and seasonal variations of AGW activity present a summer-maximum and 20

winter-minimum

characteristic.

When

convection

and

topography

above

the

Qinghai-Tibet Plateau are regarded as the probable main sources of those lidar-observed AGWs, the seasonal variation characteristic are qualitatively interpreted. It is mainly related to the seasonally varied temperature and background winds in troposphere and lower-stratosphere. This work can assist understanding of the climatology of AGWs in mainland China, and benefit accurate parameterization of AGW effects within global models.

Acknowledgements S. Gong is financially supported by China Postdoctoral Science Foundation under the Grant No. 20110490609 and the Natural Science Foundation of China under the Grant No. 41264006. And this work is also partially supported by the Natural Science Foundation of China under the Grant No. 40905012 and No. 41174129 (Yang). We acknowledge the use of data from the Chinese Meridian Project.

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Figure caption list Fig.1.

Histograms of observing nights (blue column) and observing hours (green column) in different months.

Fig. 2.

Density profile sequence of sodium layer observed on Sept. 28, 2010.

Fig. 3.

Processes of extracting parameter values of AGW. (a) to obtain an approximated background layer, (b) to get the reasonable background layer with Levenberg-Marquard fit, (c) to get the sodium density fluctuation and atmosphere density fluctuation, and (d) measurement of the 24

wavelength. Fig. 4. (a) Monthly distribution of occurrence frequency of sodium burst event observed at Beijing; (b) An example for the case that wavelike structure appeared in sodium burst event. Fig. 5.

Nightly distributions of vertical wavelength and corresponding observed period.

Fig. 6.

Histograms of AGW parameter value. (a) vertical wavelength

O z , (b) observed period Tob ,

and (c) vertical phase velocity cz . Fig. 7.

Vertical wavelength O z versus its corresponding observed period Tob . Red solid line is the ML fit to data in form of O z

p CTob , where

C is the constant coefficient and p is the

slope of logarithmic plot. Fig. 8.

Seasonal variations of wave event rate with different observed periods.

Fig. 9. Local standard time dependence of wave event rate and the number statistic of experiments carried out in different time intervals. Fig. 10. Monthly distribution of wave event rate. Curve (a) is for the total AGWs, curve (b) is for the waves of which Tob t 7 hour , and curve (c) is the least square fit to data in curve (a). Fig. 11.

The altitude-season distribution of horizontal mean wind above Northern China.

25

Highlights: 1. Nightly and seasonal variations of AGW activities are investigated. 2. AGW activity has maximum in summer and minimum in winter. 3. Formation mechanism of regional characteristic is qualitatively discussed.

26

Figure_1

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Figure_3(a)

Figure_3(b)

original Gaussian 1 Gaussian 2 fitted

Density Profile

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