Medium-frequency radar studies of gravity-wave seasonal variations over Hawaii (22 N, 160 W)

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D20, 4655, doi: /2002jd003131, 2003 Medium-frequency radar studies of gravity-wave seasonal variations over Hawaii (22 N, 160 W) Nikolai M. Gavrilov Atmospheric Physics Department, Saint-Petersburg State University, Petrodvorets, Saint Petersburg, Russia Dennis M. Riggin and David C. Fritts Colorado Research Associates Division, Northwest Research Associates, Boulder, Colorado, USA Received 2 November 2002; revised 9 July 2003; accepted 6 August 2003; published 31 October [1] Using simple numerical filters, estimates of wind variances with period bands of 0.1 to 1 hour and 1 to 5 hours have been derived from data taken with the medium-frequency radar on the island of Kauai, Hawaii (22 N, 160 W). The observations cover altitudes of 70 to 90 km and extend over the years The results show seasonal and interannual variations of the wind variances, which can be attributed to atmospheric gravity waves. The mean zonal wind has mainly an annual variation below an altitude of 83 km and a semiannual variation above. The gravity waves have maximum intensity at the solstices below 83 km, but at higher altitudes the times of the maxima shift to the equinoxes. Numerical simulations suggest this behavior results from the dependence of gravity-wave generation and propagation on the background wind and temperature. INDEX TERMS: 3384 Meteorology and Atmospheric Dynamics: Waves and tides; 3334 Meteorology and Atmospheric Dynamics: Middle atmosphere dynamics (0341, 0342); 3309 Meteorology and Atmospheric Dynamics: Climatology (1620); KEYWORDS: gravity waves, middle atmosphere, radar measurements, seasonal variations Citation: Gavrilov, N. M., D. M. Riggin, and D. C. Fritts, Medium-frequency radar studies of gravity-wave seasonal variations over Hawaii (22 N, 160 W), J. Geophys. Res., 108(D20), 4655, doi: /2002jd003131, Introduction [2] Internal gravity waves (IGWs) play an important role in the mesosphere and lower thermosphere (MLT). Whether propagating from below, or originating in situ, dissipating IGWs produce turbulence and substantial deposition of momentum and energy, and influence the general circulation, thermal structure, and composition of the MLT. IGWs have been studied extensively during the last decades (see reviews by Fritts [1984], Hirota [1997], McLandress [1998], Fritts and Alexander [2003]). Substantial information about IGWs in the MLT has been obtained using radar techniques [Vincent, 1984; Fritts and Vincent, 1987; Ebel et al., 1987; Manson and Meek, 1993]. Radar measurements provide continuous data, which can be used to infer seasonal and interannual variations of IGW intensity in the MLT [Tsuda et al., 1990; Nakamura et al., 1993a, 1993b, 1996a; Gavrilov et al., 1996, 2000]. [3] Since 1990, systematic measurements of wind velocity at altitudes of km have been made at Kauai, Hawaii (22 N, 160 W) using a medium-frequency (MF) radar. These data have been used for studies of tides and planetary waves [Fritts and Isler, 1994; Isler and Fritts, 1995; Nakamura et al., 1997], as well as for IGW studies Copyright 2003 by the American Geophysical Union /03/2002JD003131$09.00 [Taylor et al., 1995; Isler and Fritts, 1996; Isler et al., 1997]. [4] In this paper we address the seasonal variations of the short-period wind velocity variances attributed to atmospheric IGWs and their interannual changes in the middle atmosphere over Hawaii. Two simple numerical frequency filters are used to extract wind variances with timescales of hour and 1 5 hours from Hawaii MF radar data. The data analysis methods are described in section 2. Seasonal variations of the mean wind and wind variances composited by month over the years are presented in section 3, and interannual changes are also examined. A numerical model of the propagation of the IGW spectrum in the atmosphere with realistic profiles of wind, temperature, and dissipation is used to interpret the Hawaii MF radar observations in section 4. Some of the observed peculiarities of IGW seasonal variations over Hawaii are also discussed. 2. Data Analysis Methods [5] The Hawaii MF radar provides wind velocity estimates with a 2 min time resolution and 2 km height bins. The radar oversamples the returns in height so the actual height resolution is 4 km. At each height bin between 70 and 90 km we obtain hourly mean averages of the zonal and meridional wind velocity components and the corresponding hourly variances. These variances provide ACL 16-1

2 ACL 16-2 GAVRILOV ET AL.: GRAVITY-WAVE SEASONAL VARIATIONS OVER HAWAII waves. To increase statistical reliability, we use in (1) only hourly averages for which n i is greater than a threshold value N. As discussed later, N depends on the particular type of analysis. Figure 1. Theoretical power transmission functions of HF (dashed line) and LF (solid line) numerical frequency filters, which selects harmonics with periods and 1 5 hours, respectively. information about wind perturbations with timescales up to 1 hour, and will henceforth be designated as highfrequency (HF) variances. [6] To obtain information about longer-period wind perturbations we take the differences between consecutive hourly velocity values at given altitudes. Calculation of such differences is equivalent to a numerical filter passing harmonics with periods of 1 5 hours and having a transmission maximum at a period of 2 hours. Henceforth, we will denote variances derived using this filter as lowfrequency (LF) variances. The calculated transmission functions of the HF and LF filters are shown in Figure 1. One can see that the HF and LF filters pass periods of hour and 1 5 hours, respectively. [7] After calculating hourly mean velocities and HF and LF filtered variances, we calculate monthly averages of the fields taking account the number of valid velocity measurements (n i ) during each hour, i.e., 3. Observational Results [9] The methods described in the previous section are now applied to the Hawaii MF radar data for the years The mean seasonal variations were obtained by averaging the mean wind and variance fields over months, and then making a composite average of the individual monthly estimates over all of the available years. The substantial amount of data allows us to restrict the average to hourly estimates derived for n i > 20 (67% of the maximum possible valid estimates per hour) Average Seasonal Variations [10] Figure 2 presents a composite average of the mean wind components observed by the Hawaii MF radar from 1990 to Below altitudes of 83 km the zonal wind in Figure 2 has mainly an annual variation with an eastward wind maximum in winter and a westward wind maximum in summer. Above 83 km the mean zonal wind has an increasingly semiannual variation with an additional maximum of eastward wind in summer. [11] Seasonal variations of the mean meridional wind in Figure 2 are more complicated, possibly a superposition of annual and semiannual harmonics with their phases variable in height. The appearance of the eastward winds above 83 km in Figure 2 might be connected with the mesopause semiannual oscillation (MSAO). Observations with other radars X ¼ X i X i n i = X i n i ; ð1þ where X i denotes the hourly mean wind velocities or HF and LF wind variances obtained during the particular month. The monthly estimates of mean winds and variances are restricted to km, the heights where we have highest confidence in the data. [8] The MF radar data have gaps due to instrumental failure and lack of sufficient receiver signal correlation. At lower altitudes the percentage of missing data can be substantial. This makes it difficult to obtain reliable hourly mean wind velocities and variances. We found that lower values of n i tend to cause an overestimate of hourly wind variances, especially when n i < 7. Therefore the absolute magnitude of the wind variances below 75 km should be treated with caution, and should not be directly compared with values at higher altitudes. However, these lower altitude variances are useful in a relative sense, for understanding the seasonal and interannual dependence of gravity Figure 2. Height-seasonal distributions of horizontal wind components over Hawaii in a composite average over years Contours are at 8 m s 1 intervals.

3 GAVRILOV ET AL.: GRAVITY-WAVE SEASONAL VARIATIONS OVER HAWAII ACL 16-3 Figure 3. Same as Figure 2, but for HF wind variances with timescales hour. Figure 4. Same as Figure 2, but for LF wind variances with timescales 1 5 hours. [Manson et al., 1991; Nakamura et al., 1996b] and satellites [Burrage et al., 1996; Garcia et al., 1997] as well as numerical MSAO simulations [Sassi and Garcia, 1997; Garcia and Sassi, 1999; Medvedev and Klaassen, 2001] show similar eastward winds in summer located above the westward jets at altitudes of km (depending on the latitude). Additional discussion of these variations is provided in section 5. [12] Figures 3 and 4 show height-seasonal variations of HF and LF wind variances. One can see clear seasonal changes of the variances with the main maximum in winter and a secondary maximum in summer below an altitude of 83 km (more clearly evident for the HF component in Figure 3). These winter and summer maxima of the wind variances are in agreement with previous measurements with the Japanese MU radar [e.g., Tsuda et al., 1990; Nakamura et al., 1993a, 1993b; Gavrilov et al., 2000]. Above 83 km, Figures 3 and 4 show an increasing tendency for spring and autumn maxima in IGW activity. The aforementioned peculiarities of the average seasonal behavior of the mean wind and perturbation variances are seen more clearly in Figures 5 7, representing the variations at different fixed altitudes. [13] Plots of the HF and LF variances of zonal wind in Figures 6 and 7 show changes in the character of seasonal variations of IGW intensity as a function of height. The equinox maxima in the meridional wind variance fields (Figures 6 and 7) are substantially smaller than those for zonal variance above 85 km. Figures 6 and 7 also show the spring maximum in the meridional wind variance to be stronger than the autumn maximum. [14] Similar transitions in the seasonal behavior of the wind variances from solstice maxima near the mesopause to equinox maxima at altitudes near and above 100 km have been observed at Collm observatory (Germany, 52 N, 15 E). The Collm instrument uses the low-frequency D1 radio wave reflection method for measuring the ionospheric drift velocities at altitudes km [see Gavrilov et al., Figure 5. (left) Mean zonal and (right) meridional winds averaged over at selected altitudes over Hawaii. Vertical bars show standard deviations of the values. An offset of 40 m s 1 (20 m s 1 ) is added to each consecutive curve for the zonal (meridional) component.

4 ACL 16-4 GAVRILOV ET AL.: GRAVITY-WAVE SEASONAL VARIATIONS OVER HAWAII Figure 6. Same as Figure 5, but for HF wind variance with timescales hour. An offset of 60 m 2 s 2 is added to each consecutive curve. 2001]. The Collm measurements also showed a difference between the seasonal variations of zonal and meridional wind variances with smaller autumn maxima for the meridional component. Comparing the Hawaii and Collm results, the transition in seasonal behavior of the mean winds and velocity variances appears to be a function of latitude occurring at lower altitudes (83 km) over Hawaii, than at Collm (100 km) Seasonal Variations in Different Years [15] To study possible changes in seasonal behavior over the decade, we analyze the seasonal changes of the mean wind and velocity variances averaged for consecutive 4-year intervals of Hawaii MF radar measurements; years , and Seasonal variations of the mean wind at different altitudes are shown in Figure 8. They are similar in all years at corresponding heights. At 88 and 92 km altitudes one can see smaller eastward and larger westward flows during [16] Figures 9 and 10 represent seasonal changes of the wind velocity variances. At corresponding altitudes the seasonal variations of the wind variances have the same character over the decade, but there are some differences in their magnitudes. At 72 km altitude the HF wind variances in Figure 9 have maximum values during , smaller interannual changes at altitudes of km, and minimum values during at km. The LF wind variances in Figure 10 show small interannual changes at altitudes of km and larger magnitudes during at altitudes of km, which is opposite to that of the HF component in Figure 9. Maxima in both the HF and LF variances in Figures 9 and 10 tend to form around the equinox maxima at altitudes above 83 km, as was previously shown in Figures 6 and 7. [17] Figure 11 displays height variations of the mean winds averaged for different months over 1-month time Figure 7. Same as Figure 5, but for LF wind variance with timescales 1 5 hours. An offset of 60 m 2 s 2 is added to each consecutive curve. intervals. Plots of the mean zonal wind show that the transition to semiannual wind variations above 83 km in Figures 1 and 4 corresponds to the heights over which the mean flow reverses from westward to eastward during summer. During winter the zonal wind is eastward at all Figure 8. (left) Mean zonal and (right) meridional winds averaged over years (thick solid lines), (thin solid lines) and (dashed lines) at selected altitudes over Hawaii. Vertical bars show the standard deviations of the values. An offset of 50 m s 1 is added to each consecutive curve.

5 GAVRILOV ET AL.: GRAVITY-WAVE SEASONAL VARIATIONS OVER HAWAII ACL 16-5 Figure 9. Same as Figure 8, but for HF wind variance with timescales hour. An offset of 100 m 2 s 2 is added to each consecutive curve. Figure 10. Same as Figure 8, but for LF wind variance with timescales 1 5 hours. Each curve is offset by 100 m 2 s 2. altitudes in Figure 11. A reversal may occur above 90 km, but it is above the heights that we analyze in this study. The meridional mean wind in Figure 11 has a more complicated structure with reversals of direction between 70 and 90 km in all seasons. The altitude structures of the zonal and meridional winds in Figure 11 are similar in different years. [18] Figures 12 and 13 show height profiles of HF and LF wind variances for different months averaged over specified time intervals. The variances generally increase with altitude above 76 km. Figures 12 and 13 also show an increase in variances below 75 km, especially for the shorter-period HF component. This increase may be partly an artifact of the greater proportion of missing data at lower altitudes (see section 2). In summer, Figures 12 and 13 show a tendency for localized maxima of wind variances to form at altitudes between 80 and 85 km, which is most evident in the HF component during years These maxima may be connected with the diminution of the HF variances at high altitudes during these years (see below). [19] Inspection of the height profiles for different years in Figure 12 reveals that the magnitudes of the HF wind variances are largest during below an altitude of 75 km. Above 85 km the HF magnitudes are smallest during Magnitudes of the LF variances in Figure 13 are largest during the years above 85 km. Below an altitude of 75 km, LF variance magnitudes are larger during , but the increase is smaller than that for HF variances seen in Figure 12. Therefore interannual variations of gravity-wave variances may depend on altitude. 4. Numerical Modeling [20] To estimate the qualitative contributions of different factors to the seasonal variations of IGW intensity, we performed numerical modeling of the average character- Figure 11. Vertical profiles of the (top) mean zonal and (bottom) meridional winds averaged over years (thick solid lines), (thin solid lines) and (dashed lines) at selected altitudes over Hawaii. The horizontal bars show the standard deviations of the values. Consecutive heights are offset by 80 m s 1. Vertical dashed lines show zero values for each respective month.

6 ACL 16-6 GAVRILOV ET AL.: GRAVITY-WAVE SEASONAL VARIATIONS OVER HAWAII generation may be provided by Lighthill-type nonlinear interactions of mesoscale meteorological motions [see Lighthill, 1952, 1978; Stein, 1967], which account, in part, for the mesoscale structure of the atmosphere. Both convective and hydrodynamic IGW sources are assumed to be randomly distributed within the atmosphere. Gavrilov and Fukao [1999] supposed that each elementary wave source generates its own IGW with random observable frequency (s), horizontal phase speed (c = s/k), and azimuth of propagation (j). [24] The wave harmonics from different sources produce a statistical ensemble of IGWs. Equation (2) may be solved for a selection of IGW harmonics with an arbitrary set of s i, c j, and j k values. Then, assuming a probability distribution function for the s values, the average variances associated with the ensemble of IGW harmonics generated by random sources can be obtained. [25] The strength of the wave sources (s) in (2) can depend on s, c, and j. While the atmospheric IGW spectrum is almost certainly not separable [Gardner, 1996], it is often not a bad approximation [Fritts and VanZandt, 1987], and in this simple model we suppose that sðs; c; j; v 0 ; N Þ ¼ Sv ð 0 ; NÞF s ðsþf c ðþ c ðjþ; ð3þ Figure 12. Same as Figure 11, but for HF wind variance with timescales hour. The curves are offset by 100 m 2 s 2. where N is the Brunt-Väisälä frequency and v 0 is the mean wind speed. The functions F s (s) and F c (c) are used according to Gavrilov and Fukao [1999]. These functions are assumed to decrease at large s and small and large c, as it might be expected for turbulent flows [Monin and istics of a spectrum of propagating IGW harmonics in the atmosphere Numerical Model and Background Atmosphere [21] The numerical model used in our study is described in detail by Gavrilov and Fukao [1999]. The model assumes that the atmospheric wave fields can be decomposed into a spectrum of sinusoidal harmonics. The model calculates vertical distributions of parameters of a set of IGW harmonics representing a wave spectrum. [22] In a stationary and horizontally homogeneous background atmosphere, the balance of the wave action is valid for each wave harmonic [see Gavrilov and Fukao, ¼ r 0 w sv N d 2 V 2 ; F az ¼ r 0 m V 2 2 ; ð2þ where F az is the vertical flux of the wave action, w and m are the intrinsic frequency and vertical wave number, V is the amplitude of IGW horizontal velocity, r 0 is the background density, and N d is the rate of IGW dissipation. Contributions to dissipation in the N d factor include turbulent and molecular viscosity and heat conduction, radiative heat exchange, and ion drag [see Gavrilov, 1990]. [23] The parameter s in (2) describes the strength of nonlinear wave sources of mass, momentum and heat in the atmosphere [see Gavrilov and Fukao, 1999]. Substantial contribution to these sources at the frequencies of IGWs at low latitudes may be produced by random convective motions [Alexander and Holton, 1997]. Additional IGW Figure 13. Same as Figure 11, but for LF wind variances with timescales 1 5 hours. The curves are offset by 100 m 2 s 2.

7 GAVRILOV ET AL.: GRAVITY-WAVE SEASONAL VARIATIONS OVER HAWAII ACL 16-7 Yaglom, 1971]. The parameter (j) relates to the azimuth, j 0, of the mean wind as follows: h i 1=2= ðjþ ¼ ðb 1 þ cosðj j 0 ÞÞ 2 þðb 2 sinðj j 0 ÞÞ 2 ð 1 þ B1 Þ; ð4þ where B 1 and B 2 are constants. The average variance of horizontal velocity produced by this ensemble may be calculated as described by Gavrilov and Fukao [1999]. The model has been validated previously by a comparison of the calculated average wave characteristics with the results of radar observations of the IGW climatology in the middle and upper atmosphere. The numerical model reproduces different types of seasonal variations of IGW intensity [see Gavrilov and Fukao, 1999, 2001], which have the winter maximum and summer minimum in the upper troposphere and stratosphere and the solstice maxima and equinox minima in the mesosphere [see Murayama et al., 1994]. Gavrilov and Jacobi [2003] applied the numerical model for the interpretation of seasonal variations of the ionospheric drift velocity variances observed at altitudes of km with the D1 reflection method at Collm, Germany. The model reproduces a transition from the solstice maxima of the velocity variances observed near 85 km to the equinox maxima at altitudes near and above 100 km at Collm. [26] The main disadvantage of our numerical model is its dependence on altitude only. This means that the background fields and statistical characteristics of wave sources are assumed to be horizontally homogeneous. This assumption may not be valid for low-frequency IGWs, the energy of which propagates at extremely low elevation angles (a w/n), reaching high altitudes at distances of thousands of kilometers from their tropospheric sources. IGWs with periods up to hours and substantial horizontal phase speeds propagate energy from near the surface to an altitude of 100 km within a horizontal distance of km. At such horizontal scales, climatological characteristics of the background wind, temperature and wave sources may be relatively uniform in many cases, and the numerical model described above may be used valid. [27] An important random IGW sources at low latitudes might be convective atmospheric motions [Alexander and Holton, 1997]. At present, there is no adequate parameterizations of convectively generated IGWs. We might expect that the intensity of convective wave sources could depend on the mean Brunt-Väisälä frequency, N, which estimates a stability of the mean temperature profile influencing the conditions of convection development in the atmosphere. [28] Additional IGW generation may be produced by mesoscale meteorological and irregular motions, which produce mesoscale turbulence in the atmosphere. The main contribution to these IGW nonlinear hydrodynamic sources comes from the nonlinear advective accelerations involved in the hydrodynamic momentum equation [see Drobyazko and Krasilnikov, 1985]. Observations of the advective accelerations in the troposphere and stratosphere with the Japanese MU radar [see Gavrilov and Fukao, 2001] show their strong dependence on the mean wind velocity, v 0. Also, we may expect a dependence of s on N, which may influence the inferred intensity of turbulent and convective Figure 14. Height-seasonal distributions of the (top) background zonal and (middle) meridional winds. Also shown is the strength of (bottom) hydrodynamic wave sources over Hawaii, which were used for the numerical simulation of the IGW spectrum propagation. motions in the atmosphere. Gavrilov and Fukao [1999] expressed S(v 0, N ) in (3) in the form of Sv ð 0 ; NÞ ¼ S 0 v n 0 =N q ; ð5þ where S 0, n and q are constants. Equations (2) (5) are solved here for a set of IGW harmonics representing a statistical ensemble of waves propagating from random IGW sources. The background wind components for altitudes km are presented in Figure 14. For altitudes km and km the background temperature and winds were taken from the MSISE-90 and HWM-93 models [Hedin, 1991; Hedin et al., 1996] for different months of the year for the location of the Hawaii radar. At altitudes 0 30 km we used monthly mean temperatures and winds taken from the NCEP/NCAR Reanalysis database and averaged over the years At altitudes of km the mean winds were obtained from the Hawaii MF radar. [29] Equation (2) contains the rate of IGW dissipation (N d ), due to turbulent and molecular viscosity and heat conduction, ion drag, and radiative heat exchange. These characteristics, as well as coefficients of turbulent viscosity and heat conduction and dissipation of IGW harmonics at

8 ACL 16-8 GAVRILOV ET AL.: GRAVITY-WAVE SEASONAL VARIATIONS OVER HAWAII critical and reflection levels, are calculated here as described by Gavrilov and Fukao [1999]). Influence of the critical layers leads in the model to stronger dissipation of IGW harmonics propagating in the direction of the mean wind and to the predominance of waves propagating in opposite direction [see Gavrilov, 1997]. Such IGW filtering provides in the model the wave accelerations of the mean flow directed mainly opposite to the direction of the stratomesospheric winds in the upper middle atmosphere [Gavrilov, 1997], which is consistent with recent views [Fritts and Alexander, 2003] Model Results [30] The numerical model involving equations (2) (5) was run for the background atmosphere representing different months of the year at the location of the Hawaii MF radar (see Figure 14). The numerical methods are further described by Gavrilov [1990]. The vertical step of integration was 250 m. The equations were solved for a set of IGW harmonics, where the dimensions denote the numbers of wave frequencies, horizontal phase speeds and azimuths, respectively. The IGW parameters cover the frequency ranges of s rad s 1, horizontal phase speeds of c m s 1, and azimuths of j deg. The first two grids were logarithmically spaced (proportional to ln s, and ln c) within the specified intervals. The values of the constants determining the spectral distributions of the wave sources in the model were the same as were used by Gavrilov and Fukao [1999] and Gavrilov et al. [2001]. In (4) we take B 1 = 0.3 and B 2 = 0.1. Parameters of the model were chosen to provide IGW power spectral slopes of s 5/3 and m 3 at large s and m corresponding to some observations and theoretical studies [VanZandt, 1982]. The parameters were chosen to give the best fit of the calculated results to observations (see below). Gavrilov and Fukao [1999] showed that this model can reproduce realistic seasonal variations of IGW intensity in the troposphere and mesosphere with n = 2 in (5). A strong dependence of the intensity of wave sources on the mean wind in the atmosphere is confirmed by recent MU radar measurements of nonlinear advective accelerations. Therefore in (5) we use the values n =2,S 0 = m 1 s 2 and q =2. [31] The calculated seasonal variations of standard deviations of wind speed produced by the model are presented in Figure 15. One can see that the amplitudes of the zonal IGW component have a primary maximum in the beginning of summer at 70 km altitude, and have two secondary maxima near the equinoxes at higher altitudes. This tendency is similar to that shown in Figures 6 and 7 regarding the transition of the IGW intensity from a solstice maximum at 70 km to an equinox maximum at 90 km. Consistent with Figures 6 and 7, the autumn maximum is smaller in Figure 15 for the meridional component than for the zonal component. [32] Seasonal variations in the model results are caused by changes in the background atmospheric fields and changes in the strength of the IGW hydrodynamic sourcesv (s), which according to (5) depend on the mean wind. The height-seasonal distribution of the strength of wave sources over Hawaii is shown in the lower panel of Figure 14. The tropospheric sources are weak compared to those in the stratomesospheric jets. This is different from the Figure 15. Calculated seasonal variations of (left) zonal and (right) meridional wind standard deviations produced by the ensemble of IGW harmonics with periods hours at different altitudes. approximate equality of tropospheric and stratomesospheric wave sources shown by Gavrilov and Fukao [1999] for the MU radar measurements in Shigaraki. This is a result of much stronger tropospheric jets over Japan (up to 90 m s 1 in January) than that over Hawaii (25 m s 1 ). [33] The relative contributions of wave sources at different altitudes were studied by calculating separately the IGW amplitudes produced by the wave sources located below and above 20 km altitude as shown in Figure 16. At altitudes of km the main contribution to the numerical results is made by wave sources located below 20 km. Therefore one might expect that the seasonal variations of IGW activity measured with the Hawaii MF radar would reflect the seasonal variations of lower atmosphere wave sources and the background wind and temperature profiles influencing IGW propagation. However, more detailed studies of IGW sources and middle atmosphere dynamics are required to understand the relative contributions of these different factors. [34] Results shown in Figures 15 and 16 are calculated for the parameters of numerical model specified above. We made also model runs with the model parameters varying in wide ranges of the wave source strengthens S 0 =(1 10) 10 6 m 1 s 2 in (5) and of different wave sources anisotropy B 1 =0 0.3 in (4). In all model runs we obtained seasonal

9 GAVRILOV ET AL.: GRAVITY-WAVE SEASONAL VARIATIONS OVER HAWAII ACL 16-9 different altitudes calculated using variable parameters of the numerical model are similar to Figures 15 and 16. Figure 16. Same as Figure 15, but for IGW spectra produced by wave sources located in the atmosphere below 20 km (solid lines) and above 20 km (dashed lines). variations of IGW variances very similar to Figures 15 and 16. Changes of the wave sources strength S 0 lead to respective changes in IGW variances in the upper atmosphere, which are substantially smaller than that of S 0.Thisisbecausethe substantial portion of IGW harmonics in the model reach saturation and their amplitudes do not practically depend on the wave sources strength above the saturation level [see Gavrilov, 1997]. Similar behaviour is characteristic for observed IGW spectra in the atmosphere [Fritts, 1984; Fritts and Alexander, 2003]. [35] Figures 15 and 16 are calculated for B 1 = 0.3 in (4). Other model runs show that the value of B 1 = 0 lead to an increase of calculated winter IGW variances of about times. This may be explained by IGW filtering by the mean wind. An increase in B 1 parameter in (4) leads to a relative increase in the wave sources strength in the direction of the mean wind at the altitude of IGW generation. In the troposphere the mean zonal wind is directed mainly to the east in all seasons. Therefore an increase in B 1 is mainly equivalent to larger relative strengths of sources of eastward propagating IGW in the model. These IGWs are subject of larger filtering out by eastward background winds in winter stratomesosphere due to stronger dissipation and critical levels [see Gavrilov, 1997]. This leads to smaller the average IGW variances for B 1 = 0.3 in Figures 15 and 16 in winter compared to that for B 1 = 0. However, all other main qualitative features of seasonal IGW variations at 5. Discussion [36] The seasonal dependence of the mean wind and IGW variances observed over Hawaii can be compared with measurements at other locations. Figures 2 and 5 show the transition from an annual to semiannual dependence of the mean zonal wind at an altitude of 83 km over Hawaii. There is a reversal of the zonal mean flow direction from westward to eastward during the summer at lower altitudes (see Figures 2 and 5), as observed by other radars, but the altitude of the reversal appears to depend on latitude. Measurements with the Japanese MU radar at Shigaraki (35 N, 136 E) show a summer reversal of the mean zonal wind at 90 km altitude [Nakamura et al., 1996b]. The ionospheric drift D1 method at Collm (52 N, 15 E) [Jacobi et al., 1997] reveals a similar wind reversal height. An analysis of the winds from a network of middle and high altitude radars [Manson et al., 1991] yielded altitudes of the mean wind reversal near and above 90 km with some indications that the wind reversal height may increase with latitude. This is confirmed by the analysis of data from Christmas Island MF radar (2 N, 130 W) and from the High Resolution Doppler Imager (HRDI) on the Upper Atmosphere Research Satellite (UARS), showing that the regions of eastward MSAO winds may go down to altitudes of km (sometimes as low as km) near the equator [see Burrage et al., 1996; Garcia et al., 1997]. [37] The wind reversal heights observed at Hawaii (see Figures 2 and 5) are intermediate between those at higher and lower altitudes mentioned above. This is consistent with a tendency for the wind reverse height to decrease at lower latitudes as noted by Manson et al. [1991] and Burrage et al. [1996]. The analysis of HRDI data for altitudes near 80 km [Burrage et al., 1996; Garcia et al., 1997] revealed a dependence of the MSAO eastward and westward wind phases on latitude. For latitude 22 these data predict westward winds in March May and July August with eastward winds during the other months. These terms correspond to the times of westward and eastward winds shown for Hawaii radar data in Figures 2 and 5 at altitude 84 km. [38] The analyses of HRDI data by Burrage et al. [1996] and Garcia et al. [1997] and of semiempirical HWM-93 wind model [Hedin et al., 1996] show that the regions of stratomesospheric eastward velocities existing in the winter hemisphere may penetrate into the lower thermosphere of the summer hemisphere. The height of this penetration is lowest near equator and increases at higher latitudes. This may explain the latitudinal dependence in the height of the wind reversal observed by radars. Numerical models also show this dependence and reveal the significant role of Kelvin and gravity waves in MSAO formation [see Sassi and Garcia, 1997; Garcia and Sassi, 1999; Medvedev and Klaassen, 2001]. [39] The climatological behavior in height and time of the IGW intensity maxima as a function of latitude near the mesopause has been less studied. Gavrilov et al. [2001] have analyzed variances of ionospheric drift velocity at altitudes km measured with the low-frequency D1

10 ACL GAVRILOV ET AL.: GRAVITY-WAVE SEASONAL VARIATIONS OVER HAWAII radio wave reflection method at Collm observatory. At 85 km the variance was found to maximize at solstice, but at higher altitudes there was a transition in the time of maximum intensity to an equinox maxima at heights of 100 km and above. A similar altitude transition in seasonal behavior of gravity-wave intensities is observed with the Hawaii MF radar (see Figures 6 and 7), but the transition occurs here at lower altitudes. However, at each latitude the change in the seasonal variation of gravity-wave variances occurs at the heights where zonal mean winds have a transition to semiannual variations. [40] The results of numerical modeling of seasonal variations of wind variances produced by a spectrum of IGW harmonics (performed for Collm observatory by Gavrilov et al. [2001] and herein for Hawaii) reveal that the main reasons for the observed height changes in IGW seasonal variations could be the seasonal changes in the background wind and temperature in the atmosphere. These background fields may change the conditions of IGW propagation and filtering, and may also produce seasonal variations in the strength of IGW nonlinear hydrodynamic generation. These hydrodynamic IGW sources are distributed throughout the atmosphere and tend to maximize in the regions of tropospheric and stratomesospheric jet streams. [41] The numerical modeling in section 4 shows that the contribution of IGW sources located in the atmosphere above 20 km altitude to the IGW wind variance is relatively small at altitudes below 90 km, but may become comparable with the contribution of the lower altitude wave sources at altitudes near and above 100 km. Therefore stratomesospheric wave sources may substantially alter the character of IGW seasonal variations observed over Collm observatory [see Gavrilov et al., 2001], but their impact on the IGW variances observed with Hawaii MF radar at altitudes km is less evident (see Figure 16). [42] Careful analysis of the seasonal changes in tropospheric winds and IGW source strengths used in our calculations show the existence of maxima in tropospheric dynamic activity during spring and late summer. These variations in the tropospheric IGW sources may produce corresponding maxima in the IGW wind variance near the mesopause, which may increase and be shifted toward equinoxes due to substantial height changes of the zonal wind above altitudes 80 km over Hawaii (see Figures 15 and 16). A possible explanation for semiannual variations in IGW activity in the tropical tropostratosphere was shown by Ray et al. [1998], who analyzed a gravity-wave climatology in the tropics from the United Kingdom Meteorological Office (UKMO) global assimilated model meteorological data. Figure 10 of Ray et al. [1998] shows that at a pressure level of 2.1 mb and latitude of 22 the gravity wave zonal forcing maximizes in March April and August October, corresponding to the times of gravitywave variance maxima in our Figures 6 and 7 at altitudes above 84 km. Therefore IGWs may propagate to the MLT region from tropostratospheric sources. At low altitudes their amplitudes are small, but they grow exponentially with height. Above altitudes 85 km their amplitudes may become larger than background atmospheric variations and the waves become noticeable in the Hawaii radar data as semiannual variations. Ray et al. [1998] suggested that IGWs may play an important role in the semiannual oscillation in the tropical stratosphere. Propagating upward and interacting with the mean wind, IGWs may help to produce the MSAO in the tropical and subtropical middle atmosphere. This may explain why the semiannual variations of the zonal wind and wind variances are observed at the same heights in Hawaii radar data. [43] An interesting feature observed with Hawaii MF radar is the opposite interannual change in the HF and LF wind variances (period bands hour and 1 5 hours, respectively) at altitudes km. Figures 12 and 13 show that the LF variance increased during years , while the HF variance decreased during the same interval. This could be caused by differences in the strengths and spectral compositions of the wave sources and the influence of the longer-period LF component on the propagation and dissipation of the higher-frequency HF band of the IGW spectrum. Fritts and VanZandt [1987] showed that largerscale IGWs may induce wind and temperature fields, which can modify and modulate propagation conditions for smaller-scale IGWs. Also, when the amplitudes of less stable IGWs increase, they may break and produce intensive turbulence, suppressing other IGWs. Owing to this increased dissipation, the amplitudes of IGWs in other spectral bands may decrease during intervals of increased amplitudes of unstable waves. This coupling between IGWs of different scales due to Doppler-shifting and turbulent dissipation may explain the different interannual variations of longer- and shorter-period IGWs seen in Figures 12 and Conclusion [44] In this paper simple numerical filters were used to extract wind velocity variances with periods hour (HF band) and 1 5 hours (LF band). The mean zonal wind has mainly an annual variation below 83 km and a semiannual variation above. The wind variances associated with IGWs show a solsticial maximum below 83 km (larger in winter). Above 83 km the seasonal dependence undergoes a transition to a maximum amplitudes at the equinoxes. [45] A numerical model of the propagation of the IGW spectrum through the atmosphere is used to explain qualitatively the seasonal variations of wind variances at different altitudes. The numerical simulation shows that the peculiarities of the seasonal variations of IGW intensity could be explained by the seasonal variations in the strength of the lower atmosphere wave sources and the effect of the changing background wind and temperature profiles on IGW propagation in the MLT. Because of the very simple parameterization of gravity wave sources, our numerical simulation can give only a qualitative description. Further improvement of parameterizations of orographic, convective and hydrodynamic IGW sources is required to fully understand the influences. [46] Acknowledgments. This study was supported by the American Civil Research and Development Foundation (grant RG1-2074), the Russian Basic Research Foundation, the National Science Foundation (grants ATM and ATM ) and NASA contract NAG References Alexander, M. J., and J. R. Holton, A model study of zonal forcing in the equatorial stratosphere by convectively induced gravity waves, J. Atmos. Sci., 54, , 1997.

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