large scale oscillations of temperature during the DYANA campaign

large scale oscillations of temperature during the DYANA campaign

Jourrmlof Amo.~pher~c4 weeks) and the shortest periods (around 5 days) were consistently observed in the whole altitude regime. and were, therefore, f...
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Long period/large scale oscillations of temperature during the DYANA campaign M. BITTNER,* D. OFFERMANN,* I. V. BUGAEVA,~ G. A. KOKIN,~ J. P. KOSHELKOV,? A. KRIvoLumw,t D. A. TARASENKO,? M. GIL-OJEDA ,$ A. HAUCHECORNE,#F.-J. L~~BKEN,!I B. A. DE LA MORENA,~] A. MOURIER,** H. N.&K.&iW,‘ft K. I. OYAMA,$$ F. J. SCHMIDLIN,$# I. SOULE,~~~~L. THOMAS~~ and T. TSUDA*** * Physics Department, University of Wuppertal, 5600 Wuppertal I, Germany ; t Central Aerological Observatory, Dolgoprudny, Moscow Region, Russia; $ Grupos Scientificos, INTA Torrejon de Ardoz. Spain ; 5Service d’Aeronomie du CNRS, 91370 Verrieres-le-Buisson, France ; 11 Physikalisches Institut. University of Bonn, NuBallee 12,530O Bonn, Germany ; 7 Estacion des sondeos de El Arenosillo, Mazagon, Huelva, Spain; ** Centre d’Essais des Landes, 401 I5 Biscarosse AIR, France; tt The National Institute for Environmental Studies, lbaraki 305, Japan ; $$ISAS, 3-l- I Yoshinodai, Sagamihara. Kanagawa. Japan ; $$NASA Goddard Space Flight Center, Wallops Island, U.S.A. ; 11 alAETE Canadian Forces Base, Cold Lake, Alberta TOA 2 MO, Canada; q]T Department of Physics, University College of Wales. Aberystwyth SY23 3BZ, U.K. : *** Kyoto University, Radio Atmospheric Science Center, Uji. Kyoto 61 I, Japan (Rrceit)ed infinalfbrm

4 Januar~~ 1994;

accrptrtl

I4 January 1994)

Abstract-During the DYANA campaign (January-March 1990) vertical temperature profiles were measured in the middle atmosphere above 11rocket and four lidar stations in the northern hemisphere. Strong temperature variations were observed, especially at the medium to high latitude stations. Time series analysis was applied, and most oscillations were found to be quasi-periodic only, and restricted to certain altitude levels. Solely the longest periods (>4 weeks) and the shortest periods (around 5 days) were consistently observed in the whole altitude regime. and were, therefore, further analysed. These temperature variations were found to be compatible with the assumption that the Quasi-Stationary Planetary Wave No. I (QSW I) of the CIRA 1990 Model Atmosphere was modulated with the respective periods. Especially close similarity of the vertical phase structures was obtained. The amplitudes measured were, however, much larger than those of the model, and hence some amendment to the model may be appropriate. The importance of QSW 1 modulation appears to be considerable, as more than 50% of the temperature variance could be ascribed to it. The DYANA results were checked by an analysis of respective data from two other campaigns (Winter Anomaly campaign, 1976, and MAP/WINE campaign, 1984). Essentially the same results were obtained. Considering the strength of QSW I control, the midwinter middle atmosphere shows itself to be quite strongly and persistently structured in both the vertical and horizontal directions. This applies to all parts of the stratosphere and mesosphere

1. INTRODUCTION It is well-known that an important part of the dynamics of the winter middle atmosphere is characterized by wavelike structures of planetary scale. These disturbances can be aptly described in terms of the socalled external Rossby waves or waves of the second class. Stationary waves have been analysed, for instance by SCHOEBERLand GELLER (1976, 1977) or BARNETT and LABITZKE (I 990). Detailed reviews on travelling waves are given, for instance by MADDEN (1979) and SALBY (1984). In recent years extensive investigations into such wave structures, especially for the travelling components in the stratosphere have been performed by means of satellite borne instruments, and these investigations are in general in agree-

ment with the theoretical results (e.g. HIROTA and HIROOKA, 1984). Recent rocket measurements appear to indicate, however, that the vertical structure of large scale waves may at least occasionally differ considerably from Lamb wave modes. Using a harmonic analysis based on the least-squares technique, OFFERMANNet nl. (1987) found peculiar structures in the vertical distribution of amplitudes and phases, indicating the presence of so-called ‘quiet atmospheric layers’ with variations that were minimum at the layer altitude and were anticorrelated below and above these layers. These structures were found to occur in long period oscillations larger than about 30 days as well as in oscillations with shorter periods of a few days. It is the purpose of this work to investigate such

1675

1676

M. BITTNERef al

structures on a broader data base with high vertical resolution. This will be achieved mainly within the frame of the international DYANA campaign (Dynamics Adapted Network for the Atmosphere). A campaign overview and details of the scientific objectives are given by OFFERMANN(1994). A variety of temperature measurements taken during the DYANA campaign at various places in the northern hemisphere have been investigated with respect to long period variations. In order to analyse the data the following techniques were used : a harmonic analysis based on the least-squares method, a discrete Fourier Transform, the maximum entropy method, and a cross-correlation analysis. The latter three methods were used especially to carry out the analysis for oscillations with periods of less than 30 days. They were used in combination to verify the results of the different techniques and to identify possible artefacts.

2. MEASUREMENTS Figure 1 shows the geographical distribution of the observational sites that were used for this analysis. The type of measurement technique, number of measurements taken, and time period covered at each station are given in Table 1. Vertical temperature profiles with high resolution (< 1 km) obtained by a considerable number of meteorological rockets were analysed. Most of the rockets were Datasondes, falling spheres and M-IOOB systems. Payload descriptions are given for instance by SCHMIDLINet al. (1991) and in OFFERMANN and BITTNER (1989). In order to extend the altitude regime covered down to the ground level the rocket soundings were supported by radiosonde releases. To reduce tidal effects, only launches performed at nearly the same local time at each station were taken into account. A complete list of flights is given by OFFERMANN (1991). Details are given in OFFERMANNand BITTNER (1990). About 100 LIDAR observations from four stations were also included in the analysis. Furthermore, nightly mean temperatures derived from near infrared emissions of excited hydroxyl molecules (OH,,.,,) near the mesopause, and daily mean ground pressure data obtained at Andoya (60 N, 16-E) were analysed. For the study of the long term variations (r 3 30 days), rocket and LIDAR data from the MAP/WINE campaign (February 1984) (VON ZAHN, 1987) and the Winter Anomaly campaign (January/February 1976) (OFFERMANN, 1979) were also used. Recently a data evaluation problem was detected for failing spheres in general (F. J. SCHMIDLIN, pers. commun.). This is serious above 80 km under certain

circumstances. Such altitudes are not considered in the present analysis. Minor effects (<2 K) could be present between about 70-80 km, which would, however, not influence the results of this paper.

3. DATAANALYSIS 3.1. Models used Our aim is to gain some insight into the spectral structure of the process that is lying behind the discrete time series measured during the campaign. To do so we attempted to describe the spectrum in terms of a statistical model. Three different mathematical models have been found to be useful in spectral analysis (e.g. ROBINSONand DURRANI, 1986) : autoregressive (AR-) models, moving average (MA-) models, and the mixed autoregressive-moving average (ARMA-) models. Each type of model gives rise to a spectrum which has a different characteristic shape. For example, the spectrum of a typical autoregressive model will exhibit poles, while the spectrum of a typical moving average model will have nulls. This means that the use of different models will most likely yield different frequencies, amplitudes, and phases (ROBINSON and TREITEL, 1980). Since it is a priori not clear which spectrum reflects best the probabilistic structure of the underlying process (GUTOWSKI rt ul., 1978) we decided to use more than one model for estimating the power spectrum. This way we obtained an idea of the consistent dominant spectral elements. The ARMA-model contains both AR as well as MA components, and evidently is the most general of the three representations considered here. Nevertheless, we have restricted ourselves to the AR- and the MAmodel in this work. This is because up to now there seems to be no criterion (comparable with the FPEcriterion for the AR-model, see below) which might determine the correct order of the ARMA-model (e.g. BUTTKUS, 1991). A classical and widely used method of power spectrum estimation for a discrete time series I!. k = I ,2. ., N is the discrete Fourier Transform as a realization of the MA-model. It is well-known that discrete and relatively short time series (typically 1640 points in our case) result in leakage and aliasing effects. Furthermore, measurements are always noisy and will, therefore, produce a pronounced noise in the power spectrum resulting in a wildly oscillating curve. A detailed description of such effects is given for instance in SCHLITTCENand STREITBERG(1987). The conventional technique to obtain a consistent estimate of the power spectrum is to convolve the power spectrum with a spectral window function. The main prob-

Temperature

Fig. 1. Distribution

of rocket and lidar stations

lem with this is that the resulting densities at the single frequencies are no longer statistically independent. An alternative is to use the unsmoothed power spectrum, provided the statistical behaviour of the spectral estimator used is known. SCARCLE (1982) developed a modified discrete Fourier Transform for unevenly spaced data based on the work of LOMB (1973, hereafter referred to as L&S. He showed that this technique is equivalent to a pure harmonic least-squares analysis. We believe that the AR-model is best handled with the maximum entropy method, hereafter referred to as MEM. MEM attempts to fit, in a least squares sense, an AR-model to an input time series (e.g. ULRYCH and BISHOP, 1975). This method exhibits an excellent frequency resolution and has no problems

oscillations

during

DYANA

(0

= rocket station

; * = lidar)

with spectral side bands. It is best qualified to handle short time series using the Burg-algorithm. However, there is an inherent difficulty in the determination of the correct order (filter length) N of the polynomial in the model to be fitted to the input time series. Indeed, there exists no general algorithm for finding the best order, though a number of criteria and recipes have been given. Using too many degrees of freedom means to overfit the data may result in a splitting of spectral features and can produce additional structures. Otherwise, an oversimple a priori hypothesis leads to a poor approximation and yields a coarsely smoothed power spectrum. Therefore, one needs to find a compromise between a simple a priori hypothesis and a good fit. Quite a nmmber of methods has been proposed. A detailed discussion of some com-

167X

M. BITTNER of ul. Table

1, Measurement

stations,

techniques,

and time periods

covered

Station

Instrument

Cold Lake 54 N 110 W

Datsonde Radiosonde

16 January-16 I6 January-16

Ships (Ushakov/Krenkel) 53 N 35 W

M-1OOB Radiosonde

5 January-9 5 January-9

El Arenosillo 37N6W

Datasonde Radiosonde

IX January-15 I5 January-15

March March

1990 1990

I7 19

Aberystwyth 52N3W

Lidar

12 January-13

March

1990

x

CEL 44Nl

Falling Sphere Radiosonde Lidar

I5 January-15 I5 January-15 12 January-20

March March March

1990 1990 1990

9 IO

30

OHP 44N6E

Lidar

IO January

19 March

1990

31

Essen 50 N 7’E

Radlosonde

I5 January-

10 March

1990

55

Bodo 67 N I4 E

Radiosonde

I1 January-15

March

1990

IX

Andoya 69 N I6 E

Datasonde Falling Sphere

1 I January-14 12 January-1

March

1990

20 17

ESRANGE 68 N2l E

Radiosonde

I I January-15

March

1990

?I

Volgogrdd 48 N 44 E

M-IOOB Radiosonde

3 January-14 March 3 January- 21 March

1990 1990

I9 21

Helss Island Xl N58 E

M-IOOB Radiosonde

IO January -14 March 10 January-14 March

1990 1990

IX I9

Balkhash 47 N 74 E

M-IOOB Radiosonde

2 January-20 2 January-~20

1990 1990

II II

Sain Shand 48 N 107 E

M-l00B Radiosonde

Uchinoura 31 N 131 E

Datsonde Falling Sphere MT-135-51 Radiosonde

I7 January-5 February 1990 17 February-26 February 1990 2 I February 1990 17 January 26 February 1990

4 3 I Ii

Shigaraki 34 N 136 E

Radiosonde

26 January

21

Tsukuba 36 N 140 E

Lidar

Ryori 39 N I41 E

MT-135

W

Period

27 December 27 December

P

FPE(N)

=

M+(N+ 1) M-(NS-l)O’

where 0 is the residual squared error for an Nth length filter and M is the length of the time series. FPE should be minimal at the smallest possible filter length N.

March March

1990 1990

I5 I7

1990 1990

1X 1x

1 March 1990

March March

1989-2X February 1989 28 February

27 February

of measurements

1990 1990

1990

IO IO

IO January~-14

March

1990

9

IO January-14

March

1990

9

The number of measurements used in this work at each station nights available. The order of stations is from west to east.

mon criteria is given by LANDERS and LACCISS (1977). To determine the filter length, the final prediction error (FPE) suggested by AKAIKE (1969, 1974) was used here :

March March

Number

is also given. At lidar stations

this means the number

of

Considering the above uncertainties we also used a heuristic criterion to determine the order of the model to cross-check the order resulting from the FPEcriterion. GUTOWSKI ef (11.(I 978) suggested using the partial correlation coefficient (PCC)h, as an indicator for the order, using the fact that the Burg algorithm minimizes the average of the sum of both the mean square prediction and the mean square hindsight error (for details see ROBINSONand TREITEL, 1980). As soon as the correct order is reached all information has

Temperature been removed from the time series and both the prediction and retrospection error series will be left uncorrelated. As a rule of thumb the order N of relative short time series (M < 100) can be taken as

A4

N 2 -3

(for example OLBERG and RAKOCKI, 1984). It turned out that in practice the difference in the order suggested by the PCC- and the FPE-criterion within this work is typically about one to two. The MEM-algorithms currently available require evenly spaced data points. that is data gaps are not tolerated. Their application is, therefore, restricted to densely populated time series, but even our best measured series exhibit data gaps of I-2. possibly 3 days. Gaps in our data were interpolated using MEM in its capacity as a linear prediction filter. Thus gaps are filled by applying the prediction filter on the equidistant subset of data prior to the gaps. The prediction error was typically 8-l 8%. Problems associated with the stability of the filter (ROBINSONand TREITEL, 1980) were not serious. 3.2. Compur-ken

of&fkent

unalysis

techniques

To study the characteristics of the various techniques mentioned above, they were applied to a typical temperature time series without data gaps. Figure 2 shows a series of nightly mean temperatures as they have been derived from the near infra-red emissions of excited hydroxyl molecules using a ground based grating spectrometer (SCHEER rt al., 1994). The techniques were first applied to the complete time series. Data gaps were then successively created to study the stability of the various techniques. The resulting power spectra are plotted in Fig. 3. Figure 3(a) shows MEM results in the upper panel. They exhibit the characteristic smooth power spectrum with high frequency resolution and strong peaks with steep gradients. An algorithm given by PRESS et al. (1990) was used. The horizontal dashed line represents the 90% significance level (OLBERG and RAKO~KI, 1984). Obviously, there are two poles with periods of 10.5 days and 4.4 days, A peak near the 2 day period is omitted because it is too near the the Nyquist frequency. The second spectrum was calculated using a computer program by SCIIEER based on the L&S algorithm. The algorithm represents a single pass in the iterative method used in SCHEER et al. (1994). It reveals the typical structure for MA-models. It is not smooth but shows a strong variability, especially at low periods. The frequency resolution is good. Dominant periods are 1 I .2 and 4.6 days. The next curve shows the power

1679

oscillations

spectrum due to an harmonic analysis (denoted ‘HA’). It exhibits qualitatively the same structure with almost the same dominant periods as L&S. This is not surprising since the definitions of the power spectra of both techniques are equivalent (SCARGLE, 1982). The dashed-dotted line represents the 90% significance level. A power spectrum based on an FFT is given at the bottom. It uses an algorithm given in BLOOMFIELD (1976) and also shows dominant features around 11 and 4.5 days. It exhibits, however, a poor frequency resolution. Figure 3(b))(d) shows results ofthese techniques for the data set with increasing gaps. FFT was omitted in Fig. 3(b)-(d) because it cannot handle data gaps. It is interesting to see that the results remain comparable until case 2d where the number of data points has been reduced to about 60%. The power spectra of the harmonic analysis and the L&S-technique look quite similar. We carried out a detailed comparison between the two in order to determine which to use. The method used in SCHEERet (11.(1994) applies an approximate deconvolution to the spectra by successively subtracting the highest peak, then computing a new spectrum, etc. This procedure is equivalent to the harmonic analysis if one fits a sinusoid to the data, computes the residuals. fits a second sine function, etc. This so-called ‘one step mode’ yields exactly the same results as L&S does. This method was further developed by KUECHLER (1989) who showed that the variance of the time series residues can be much reduced if one always varies simultaneously all parameters of the actual and former sinusoids. This procedure we call the ‘all step mode’. It means. for instance. that when fitting the second sine function, the period, amplitude, and phase of the first sine function are free parameters again and thus iterated (OFFERMANNet cd., 1987). A detailed comparison of the ‘one step mode’ and the ‘all step mode‘ showed that the ‘all step mode’ allows one to tind a much better parameter vector in the least-squares scheme. Details are given by BITTNER (1992). Of course. it is always necessary to investigate the statistical consistency of the resulting fit. This was done by performing a chi-square test (BITTNER, 1992). As a result of the comparison, we chose to use the harmonic analysis (all step mode) in this work. 3.3. Sn?oothin,q of~ertical

temperature

profiles

It is well-known that outliers in a time series can easily turn a least-squares fit into nonsense because their probability of occurrence in the assumed normal distribution is so small that the maximum likelihood estimator is able to distort the whole curve to try to

M. BITTNERP~U~.

1680

200 240

200 240

IS

20 Time January

30

25 [day

of the

35

40

year1 February

Fig. 2. Time series of nightly mean temperatures derived from NIR OH*-measurements above February 1990). Data gaps are introduced that increase from (b) to (d). (17 January-8

Andoya

Temperature

--

oscillations

HEM (PEF=Ett

lo3

- LLS .-. Hh .*-FFT

- - MEM lPEF=Il -

119

--. HA

102

I 10-l

5

1

d IO-*

2

Period

2

60

10

Cdl

Perlo#

__HEH - US .__HA

Period

Cd3

10

60

fd3

[PEF=81

(PEF=Bi

Perlod td3

Fig. 3. Spectral analysis of the time series shown in Fig. 2(a)-(d) using different spectral estimators. The curves are dispiaced vertically by two decades relative to each other. Horizontal dashed lines represent the 90% significance level for MEM and HA, respectively.

1682

M. BITTNERetal.

bring them, mistakenly, into line. The effect of time series outliers was discussed for instance by MARTIN (1983) and DE BAKKER (I 988). Gravity waves are wellknown to be responsible for producing considerable deviations from the mean background temperature profiles at least occasionally (FRITTS, 1984 ; HAUC‘HECORNEet cd., 1991). We, therefore, treated these strong small scale deviations as outliers in the time series since we wanted to look for long period oscillations. In order to reduce this error source a low-pass filter based on a cubic spline function with uniformly spaced breakpoints was designed. This was to filter out the gravity waves and similar variations from the vertical temperature profiles, without distorting real structures such as the stratopause. The viability of this smoothing procedure was checked by applying a Monte Carlo simulation. Starting from a realistic smooth temperature profile (“true profile”) 100 random synthetic gravity wave structures within a realistic wavelength and amplitude spectrum were superimposed on the ‘true’ profile and smoothed afterwards by the technique described. This procedure allowed us to calculate a distribution function of the deviations from the ‘true’ values and hence to determine the 95% confidence interval of the smoothed profile. It was found to be & 1.5 K. Details are given by BITTNER(~~~~).

4.RESULTS 4.1,Temporul rrolution qf’temperatures

aboce Andoya

The main features of stratospheric-mesospheric dynamics during winter 1989-90 are described by OFFERMANN (1994), NAUJOKAT et al. (1990). and BUGAEVAet al. (1994). Strong variations were seen in the temporal evolution of the temperature field at various stations. As a typical example the situation at Andoya will be discussed below. Figure 4 shows smoothed temperature profiles as they were obtained at this station by meteorological rockets during the campaign period. The profiles in the lower atmosphere are from radiosonde soundings above Bodo which is about 300 km south of Andoya. The picture clearly shows that the two halves of the observation period (1 I January-1 5 March) behave quite differently. There is a sharp transition between the two parts of the campaign around 10 February. The first part shows a pronounced stratopause with temperatures around 280 K and very low temperatures in the lowest stratosphere. These structures are Battened considerably in the second part: the stratopause has cooled down, while the lower stratosphere has warmed up. This behaviour was studied in more

60

180 200 220 240 260 280 300 Temperature

CKI

Fig. 4. Smoothed vertical temperature profiles obtained by meteorological rockets above Andoya and by radiosondes above Bodo (- 300 km south of Andoya). detail with regard to the temporal evolution of temperatures at various altitude levels. Figure 5 shows temperature time series at 5 km altitude steps. The symbols represent temperature residuals with respect to the mean temperature for each altitude level. The data clearly show a difference in behaviour of the time series in the first part of the observation period until about 10 February, and in the part afterwards. The first part is characterized by strong temperature oscillations especially around the stratopause, while the second part is much more quiet. Note that the transition between the two parts is marked by a sharp peak in temperatures. This feature was identified by NAUJOKAT et ul. (1990) as being a pronounced minor stratospheric warming. In addition to the short term fluctuations. the temperature time series of Fig. 5 shows long term changes. This is seen more clearly from Fig. 6, where harmonic functions were fitted to the data points. This picture also indicates vertical phase shifts in these long term variations. Similar behaviour was found at all measurement stations at high and middle latitudes during the campaign. 4.2. Long and .shorter period oscillations Oscillations with periods smaller than about 30 days will be referred to as short period oscillations here. In

Temperature

20

1

10

1683

Mar

Feb

Jan

IO

oscillations

20

1

10

all

80

75

75

70

70

65

65

60

60

55

55

50

50

45

45

40

40

35

35

30

30

25

20

15

10

5

0

11.01.90 (21:21 UT)

Time

Fig. 5. Temporal development of smoothed residues at 5 km altitude steps, with respect

Chows3

temperatures shown in Fig. 4. The symbols to the mean temperatures (horizontal dashed altitude level.

represent the lines) at each

M. BITTNER ei ai

I684

10

f,

Mar

Feb

Jan

20

1

10

I

I

I

20

1

10

t-1

,

60

60

75

65

60

60

I IOK *

/

l!!,llll,,,,,JII,,,,,,/,

0 / 11.01.90

500

1OOD

,,,;,l,,,

0 ISa0

Time thoursl

(21:21 UT) Fig.6. Long period

oscillation (T = 69.4 d) in the tem~rat~ire~ above Andoya analysis. Time period covered is 1 I January-14 March

as modelled 1990.

by an harmonic

Temperature

20 0

2

4

6

Amplitude

8

10

12

14

oscillations

-3

-2

-1 Phase

CKI

0

1

2

3

Cradl

Fig. 7. Vertical distribution of fit amplitudes (a) and phases (b) for a long period oscillation analysis of Andoya temperatures (11 January-8 February 1990). Bars indicate the 1~ confidence interval. The fit accounts for 42.4% of the data variance.

order to model the long term change, a harmonic analysis (with one period only) was applied to the temperature time series at each altitude level for all stations available. The vertical resolution is 1 km. While amplitudes and phases were allowed to vary with height, it was required that the period should be the same throughout all altitudes at a given station. Here we restrict our discussion to the situation at Andoya as a typical example. Because the minor warming was very strong at Andoya, the long term changes at Andoya have been determined for the first part (1 I January-8 February 1990) and the second part (14 February-14 March 1990) of the campaign separately. An optimum harmonic fit was obtained using a sinusoidal oscillation with nearly 36 and 31 day periods, respectively. In both cases the fit accounts for about 43% of the total variance. It should be stressed that the values of the periods found must be used with caution because the length of the time series given influences these periods. This is already seen when comparing these periods with the one given in Fig. 6. The vertical structure of the oscillation determined for the first part of the campaign is very pronounced and is given in Fig. 7. Figure 7(a) shows the amplitude distribution with height. The horizontal bars represent the one sigma

confidence region of the chi-square test. The overall structure is characterized by pronounced minima at altitudes around 29, 47 and 63 km, with high values below and above these levels. The corresponding phase profile is shown in Fig. 7(b). It is interesting to note that there appear to be considerable phase changes at those altitudes where the amplitude minima occur in Fig. 7(a). Thus we again find, as in earlier analyses, that during the time period observed, layers existed in the atmosphere above Andoya which show only little variation in temperature (‘quiet layers’). In between these there are regions with strong variations. The phase structures of Fig. 7 show that the variations above and below the quiet layers are approximately anticorrelated. To check whether this result (that the quiet layers) is just a special feature of the measurement site chosen, we analysed all other stations that were operated during DYANA (the whole campaign period). We also analysed the data of earlier campaigns such as MAP/WINE (February 1984) and the Winter Anomaly campaign (January/February 1976). We found similar behaviour in all cases

(BITTNER, 1992). As was already discussed, considerable changes in the dynamical behaviour of middle atmosphere temperatures occurred in early February. As a conse-

16X6

M. BITTNERet nl

quence, the time series recorded within the first and second parts of the campaign time period showed clearly different statistics and spectral properties. This means that stationarity will be a problem if one tries to analyse the complete time series as a whole in a spectral analysis. Furthermore, calculation of skewness for some chosen time series showed that there are. at least occasionally, deviations from Gaussianity. Therefore, to guarantee at least some quasistationarity and linearity we decided to divide the data sets into two parts corresponding with the prewarming and postwarming time period. On the other hand such a separation turned out to be problematic for some stations because of insufficient data density. As an example, the results obtained at Andoya will be discussed here. There was a large number of rocket flights performed at Andoya. They yielded 37 temperature altitude profiles at our standard time during the two months of the campaign (18 in the first part, 19 in the second part). The first part covers the time period until 8 February. A harmonic analysis was applied to the data. Vertical resolution was 1 km. The altitude regime extended from 30 to 69 km. As the harmonic analysis now took into account several oscillations, we had to restrict the altitude regime because of lack of data at the highest and lowest levels. The resulting power spectrum for the first part clearly showed four strong spectral components with periods of about 35.5, 1 I. 1, 4.3 and 4.9 days. A fit taking into account the ‘trend period’ of 35.5 days and the next two dominant oscillations with 11.1 days and 4.3 days is presented in Fig. 8. Because of the limited number of data points at each altitude level, it appears to be unwarranted to fit more than three independent oscillations. This is because the number of free fit parameters will then get close to the number of data points for one altitude level. To test the goodness of the fit, a chi-square test was performed. The result is also given in Fig. 8 and is seen to depend on altitude. While the fit is well confirmed above about 56 below 40 km. the deviations of the model from the data obviously cannot be explained by random fluctuations in the intermediate region. The model should, therefore, be rejected at these altitudes. However, the shortness of the time series should be kept in mind, which means that only a few ‘outhers’ can affect this test quite seriously. To demonstrate this, the star-marked data in the altitude region from 54 to 60 km were omitted, as they show a large deviation from the model. The resulting chi-square test then changes considerably, as is indicated by the broken line in Fig. 8. Therefore, the data in the intermediate altitude region need to be investigated in more detail. To do so, MEM

was applied to the time series at each altitude level. Data gaps were interpolated by the technique described above. MEM was restricted to the altitude regime from 36 to 64 km, because at higher and lower levels there were data gaps larger than 3 days. The result is given in Fig. 9. The I 1.5day oscillation found by the harmonic analysis turns out to be restricted to the altitude regime above about 55 km and seems to recur at altitudes below about 40 km. The relatively large frequency region of high significance indicates that this oscillation may be quasiperiodic (GRASSBERGER,1991). A period ofabout 4.5 days was the only one found to be persistent throughout the whole altitude region, with a small gap at about 47 km, and a tendency to somewhat larger periods near 5 days above about 60 km. This tendency was obviously responsible for the occurrence of the peak at 4.9 days in the power spectrum derived by the harmonic analysis. The fact that the 11 day oscillation is absent in the altitude region between about 40 and 55 km may, therefore, explain the poor quality of the harmonic fit in this altitude region. As a consequence, it is necessary to omit this 11 day oscillation from a harmonic analysis of the whole altitude region. Doing this, an optimum harmonic fit was obtained using a sinusoid with 4.3 days period. (The long oscillation of nearly 36 days shows itself to be unaffected.) The vertical structure of this oscillation is presented in Fig. 10. The amplitude is characterized by two strong maxima at about 38 and 56 km altitude. and by pronounced minima at 68. 47 and 30 km. It is interesting to note that the gap in the MEM analysis (Fig. 9) occurs around 47 km, i.e. where the signal of the oscillation is weak. The phase structure of the oscillation shows a considerable phase change (by about 180 ) at the same height at 47 km, whereas above and below this altitude phase changes are rather small. This means that there is a ‘quiet layer’ at 47 km in this short period structure, resulting in an anticorrelation of temperatures below and above this level. Two further ‘quiet layers’ appear to be indicated at 68 km and below 30 km. Another data set with a sampling rate comparable with that of Andoya was obtained in southern France. Lidar and metrocket measurements were performed at CEL (Centre d’Essais de Landes) and OHP (Observatoire Haute Provence). The two stations are located at the same latitude (44-N) and are about 550 km apart. This is a short distance compared with the wavelength of planetary waves. It was. therefore, assumed that large scale dynamics were the same at the two sites, and the temperatures obtained at OHP and at CEL were treated as a single set of data. Following the same procedure as for the Andoya data. a

1687

Temperature oscillations

l2 Z!,,,.

62

8

6 46 42

I

2 25.5K

I2 11.01.90

6

10 Time

14

18

22

26

0

30

50 Prob

Cdaysl

100 [%I

(21:21 UT)

Fig. 8. Comparison of measured (diamonds) and modelled temperatures above Andoya during the first halfof the DYANA campaign (11 January-8 February 1990) at 4 km altitude steps. Superposed oscillations are of 35.5, 4.3 and 11 .l day periods. Flags at diamonds point towards the fit curves they belong to. The right hand part of the figure shows the results of a chi-square test.

harmonic analysis was applied to the data of the first part of the campaign in the altitude region from 30 to 80 km. Again MEM was performed for each altitude level and showed that there is a pronounced variability of the longer period components (up to 18 days) with height. An oscillation around 3-5 days is the only one to persist in the whole altitude region. An harmonic analysis using only this oscillation and the long period oscillation discussed above yields a vertical structure very similar to that for Andoya (Fig. I I). Minima in amplitude associated with clear phase jumps, that is, ‘quiet layers’ are found at about 51 and 68 km, respectively. These altitudes are similar to those obtained at Andoya. Two further ‘quiet layers’ appear to be indicated below 30 and above 78 km. Division of the middle atmospheric data sets into

first and second halves of the campaign was not possible for any of the other stations because of sparse data coverage. Harmonic analyses were, therefore, performed using the whole data sets. In consequence the resulting dominant periods must be interpreted with caution. They can be regarded as rough indications of oscillations only. Vertical structures of these oscillations are, therefore, inconclusive, except for the longest periods. The results are summarized in Table 2. 5. DISCUSSION

5. I. Long term clzungrs Seasonal temperature

temperature variations will affect the to some extent over the period of the

M. BITTNER ef u/

1688

70

, , . Maximum (

-

,

,

,

,

,

,

,

,

,

(

,

,

Spectral Density

Spectral Range of

Period

95% Sigmficance

Cdaysl

Fig. 9. Significant (>95% significance) spectral components for the same temperatures as in Fig. 8, but analysed by

means of the maximum entropy method. Data gaps were interpolated (see text).

campaign, and could modify or even be the source of the long period oscillations described above. This possibility was investigated by BITTNER (1992) and was found not to be the case, primarily because the vertical amplitude and phase structures were dissimilar. The question to be resolved is, therefore, how to understand the nature of the oscillations of long and also of short periods that were found in the data especially at Andoya and in France. This would allow one to explain a large part of the strong temperature variability that was observed during the campaign and which is for instance shown in Fig. 4. As an example, 57% of the variance during the first part of the campaign (11 January-8 February 1990) is covered by the 35 day and 4.3 day oscillations at Andoya (see Table 2). In France about 50% of the variance is due to the respective periods. Understanding these oscillations means being able to explain the peculiar structures of their vertical amplitude and phase distributions (Figs 7, 10 and 11). A first hint concerning the long term variations is given in Fig. 12. Here the mean of the measured temperature profiles (Andoya, first part of the campaign) is compared with the CIRA 1990 zonal mean tem-

peratures and the zonal mean plus the wave No. I model that is also contained in the CIRA 1990 tables, The CIRA wave model gives amplitudes and phases for the quasi-stationary planetary waves (QSW) Nos I and 2 for each month and in 10 latitude steps. Wave No. 2 will not be considered here as it appears to be of minor importance (see the CIRA tables : NAUJ~KAT rt al., 1990 ; BUCAEVAet (II., 1994). The peculiar finding in Fig. 12 is that the CIRA zonal mean curve intersects with the wave model and with the DYANA measurements at about the same altitude. A similar result was obtained at most of the other stations that took temperature data at medium to high latitudes, (At low latitudes the wave amplitudes are small, and an accurate determination of the intersection altitude is difficult.) This suggests that the quasi-stationary wave No. I (QSW 1) was active during the campaign. Figure 12 also indicates that the amplitude of QSW 1 may have been much stronger than predicted by the CI RA tables. The vertical amplitude and phase structures of QSW 1 are shown in Fig. 13. as given by the CIRA 1990 tables for the latitude of Andoya (interpolated). From Fig. 13 a schematic visualization of a quasistationary planetary wave is derived in Fig. 14. The vertical line in Fig. 14 represents the location of a given rocket launch place (or lidar station) and the vertical trajectory where the temperature data are measured. It is obvious from the picture that at a fixed rocket station one will measure identical altitude profiles during all of the campaign, as long as the planetary wave is stationary in space and time (and no other disturbances are present). In other words : as long as the wave is stable, neither its amplitude nor its phase structure can be determined from measurements at one station (as the zonal mean is not really known). The structures seen in Fig. 7 cannot be explained. This situation changes as soon as the planetary wave starts to change. Two types of variations can occur : 1. The amplitude of the wave changes (see vertical arrow in Fig. 14). This occurs when the wave is excited at the beginning of its activity period, and when it decays at the end of that period. However, there may also be changes or modulations of the excitation strength within this period which can produce many variations of the wave amplitude. This is demonstrated by Fig. 15 which shows the amplitudes of planetary wave No. 1 at 30 and 10 hPa during the DYANA campaign (Berlin analysis). Long term as well as short term variations are seen. The changes in wave amplitude are not observed at all locations. Figure 14 shows that there would be

Temperature oscillations

I689

90 ““t

a0

80

70

20 -

20”“““““““’ -3 -2

1234567 Amplitude CKI

-1

0

1

2

3

Phase Cradl

11 January-8 by an harmonic analysis. The fit accounts for 14.8% of the data variance. The bars represent the IG confidence interval.

Fig. 10. Vertical structure of the 4.3 day oscillation above Andoya for the time period February

1990 as modelled

no variations seen at the intersection points of the temperature curves with their respective zonal means (dashed lines). These points lie for instance on the dash-dotted line given in Fig. 14, which shows the phase tilt of the wave. Repeated vertical temperature soundings above the ground station along the vertical line in Fig. 14 would, therefore, show zero temperature variations at level 4 (as long as the wave is stationary in space). At the levels above and below, temperature variations would be observed, and they would be anticorrelated, as is seen in Fig. 14. The measurement station of Fig. 14, would, therefore, observe a ‘quiet layer’ (QL) at altitude level 4. (We shall call it a ‘type 1 QL’ in what follows.) This could be an explanation for (part of) the structures seen in Fig. I. A measurement station more to the east would see its type 1 QL at a lower altitude, a station more to the west at higher altitude according to the dash-dotted line in Fig. 14. If several stations are available at various longitudes (as during DYANA), they should see an altitude distribution of their QLs that follows the phase tilt of the quasi-stationary planetary wave. If a sufficient number of stations is available it.

therefore, appears possible to check on the vertical phase structures of QSW 1 shown in Fig. 13. The amplitudes of QSW 1, on the other hand, cannot be determined. Only lower limits can be derived. This can be demonstrated by means of the data shown in Fig. 15. If the variations seen in that picture are approximated by one harmonic function in time (as was done above), the amplitude of this fit function will be equal to or smaller than one-half of the maximum amplitudes shown in Fig. 15. This is because the fit algorithm does not know the zonal mean and determines its own zero line which is the mean of the measured variations, that is the mean of the amplitude values in Fig. 15. In our analysis only part of the time interval of Fig. I5 was used for the harmonic fit. As a consequence our fit amplitudes are about one-half of the range of real amplitudes present during the time span analysed. Our fit amplitudes (shown in Fig. 7) are, therefore, definitely smaller than 50% of the maximum amplitudes present during the time covered by the fit. 2. The second type of variation results from wave migrations. If the wave is not stationary in space, but quasi-stationary only, that is if it moves to and

1690

M. BITTNER et al.

80

20 1234567 Amplitude CKI

-3

-2

-1

0

1

2

3

Phase Cradl

Fig. 11. Vertical structure of the 4.9 day oscillation above southern France for the time period 10 January 8 February 1990 as modelled by an harmonic analysis. The fit accounts for about 14.6% of the data variance. The bars represent the la confidence level.

in the longitudinal direction, the temperature measurements at a given place and altitude would see time variations too (see the horizontal arrow in Fig. 14). Again, however, there are certain altitude levels above a given station where these temperature variations are zero or relatively small. These are the altitudes where a wave maximum or minimum is met (e.g. level 9 in Fig. 14). Again there are temperature variations observed at the levels above and below, and they are anticorrelated. Hence another type of Quiet Layer is observed, which is called ‘type 2 QL’ in what follows. It can be used to explain the structures of Fig. 7 too. Again the altitude distribution of type 2 QLs follows the phase curve of the quasi-stationary planetary wave. Concerning the variations in the type 2 QLs, their amplitudes could be as large as the real amplitudes of the planetary wave if the longitudinal migration is as large as one wavelength (360”). If the migration is between 180” and 360”, the full amplitude can be seen as a time variation only if the rocket place is in a favourable position with respect to the wave (near a crossing of the amplitude curve with the zonal mean curve in Fig. 14). If the wave movement is less than fro

180’ in longitude, only a fraction of the wave amplitude will show up in the temporal fit curve. In Fig. 16 the migration of planetary wave No. 1 at 30 hPa is given for the time interval of DYANA (Berlin analysis). It shows a substantial eastward shift during the first part of the campaign, when the wave amplitudes were large. The shift is 50” longitude at 70’N, that is at Andoya. This shift is, however, not large enough to allow for a full mapping of the real amplitude by the measured temperature time variations at that station. The time variation amplitude represents less than one-half of the real amplitude even at the most favourable locations. During the DYANA campaign, amplitude variations and position shifts of the QSW 1 occurred simultaneously (Figs 15 and 16). One can derive from Fig. 14, however, that the combined action of these two variations for our observed time interval cannot produce a fit amplitude that is larger than 50% of the real QSW 1 amplitude (at Andoya). Assuming that the longitudinal shift was about the same at all altitudes, the real wave amplitudes must have been at least twice as large as those shown in Fig. 7.

Temperature Table 2. Dominant Station

oscillation

periods

obtained

and Metrocket

64.0 d 4.2 d 15.3 d

I .03 d 0.15 d 0.43 d

58.7% 13.0% 10.1%

Radiosd.

and Metrocket

64.1 d 17.5 d 6.3 d

I .65 d 0.45 d 0.18 d

48.5% I5.2% 13.1 %

Metrocket

39.7 d 4.4 d

0.58 d 0.23 d

32.7% 8.9”/0

1990

Lidar

1990

Lidar

36.5 d 4.2 d 27.6 d 16.6 d r._ 57d

0.58 0.13 0.60 0.32 0.15

d d d d d

64.4% I Y.4 “/a 32.7% 21.0% 13.7%

1990

Ltdar

March

1990

Lidar

31.9 6.5 19.1 18.0 10.2 4.2

0.53 0.26 0.30 0.43 0.23 0.14

d d d d d d

38 3% 16:3% I 8 .3 % 26. I % 74 8% ;4:7%

February

1990

Lidar and Metrocket

El Arenosillo 37 ‘N 06’ W

15January-l

5 March

CEL 44’N OI’W

12 January-7

February

10 January-S

14 February-16

10 January-8

9 February-16

15January-8 13 February-l

Bodo 67.N 14’E

1I January-8 I5 February-l

Andoya 69 N 16,E

ESRANGE 68’N 21 ME

March

March

March

February

1990

1990

1990

March

1990

February

1990

Radiosd.

6 March

1990

Radiosd.

February

1990

Radiosd.

1990

Radiosd.

5 March

I1 January-S

February

1990

Metrocket

17 January-8

February

1990

OH*

17 January-8

February

1990

14 February-l

4 March

1990

14 February-l

6 March

I 990

OH*

March

1990

p,

14 February--l6

I I January-8 15 February-15

stations

Radiosd.

5 January-9

Essen 50’N 07’ E

time series and other data at various Period

Ships 53”N 35’,W

17 February-16

1691

Data

16 January-16

Southern France (CEL:OHP combined)

from temperature

Time interval

Cold Lake 54:N 110 W

OHP 44 ‘N 06’ E

oscillations

February

March

Metrocket

1990

Radiosd.

1990

Radiosd.

d d d d d d

la-Error

Variance

34.4 d

0.43 d

X.0?/”

IX.2 d 4.9 d

0.32 d 0.25 d

I6 1‘/” 14.6%

28.3 d 15.4d 8.3 d

0.41 d 0.27 d 0.23 d

21.2”/” 20.8% 12.1%

24.7 7.6 3.9 19.7 3.4 10.5

d d d d d d

0.53 0.23 0.08 0.50 0.06 0.21

d d d d d d

35.0% 19.1% 10.2”/0 42.8% 13 .8 ‘%, 12.7%

30.4 4.9 31.x 19.6

d d d d

0.58 0.13 0.54 0.25

d d d d

5 I 1% 41.9% 66.0% 23. I %

35.5 11.1 4.3 10.7 4.6 16.7 7.4 4.0 31.2 17.6 5.3 30.2 16.7 3.6 8.3 5.3 3.3

d d d d d d d d d d d d d d d d d

0.53 d 0.36 d 0.12 d 0.25 d O.lOd 0.37 d 0.23 d 0.12d 0.48 d 0.33 d 0.12 d 0.32 d 0.04 d 0.15 d 0.21 d 0.15 d 0.05 d

42.4% 17.0% 14.8% 5.5.9% 16.3?” 40.2% 18.3”/,, 15.6% 44. 1u/u 7 5 Y“/” :0:6X 13 --. 7”$ , 20.9% 15 .9 %I 32.5% 25.2”/0 10.3%

4.1 7.3 14.6 4.0 16.7

d d d d d

0.12 0.23 0.38 0.08 0.25

32.2% 30.9% 19.5% 60.9% 28.O”Xl

d d d d d

M.

1692

BITTNER et ~1.

Table 2. (continued) Time interval

station Volgograd 48 ‘N 44’ E

Heiss Island 81 N 58’E

Data

Period

2c?-Error

Variance

50.6 5.6 15.3 69.0 19.8 14.6

I .23 0.23 0.35 1.68 0.63 0.32

d d d d d d

38.0% 20.1% 13.4% 32.3% 21.3% 10.3%

10 January-14

March

1990

Metrocket

d d d d d d

3 JanuaryyZl

March

1990

Radiosd.

10 January-14

March

1990

Metrocket

74.2 d 8.9 d 14.5 d

2.20 d 0.25 d 0.43 d

36.5% 22.6% 15.3%

Uchinoura 31’N 131’E

17 January-26

February

1990

Radiosd.

20.1 d 4.4 d

0.61 d 0.14 d

41.7% 26.8%

Shigaraki 34 N 136 E

26 January-27

February

1990

Radiosd.

7.5 d 11.5 d 5.72 d

0.23 d 0.28 d 0.15 d

59.2% 16.6% 10.6%

7.0 d 31.5 d

0.25 d 0.63 d

55.5% 16.2%

Tsukuba 36 N 140 E

24 January-13

March

1990

Lidar

Also given are the time intervals covered, the measurement techniques used. and 20-errors of the oscillation periods. The amount of variance of the time series that can be explained by the respective oscillations is shown, too. P,, is the daily mean ground pressure, and OH* stands for nightly mean temperatures derived from hydroxyl IR-emissions at about 86 km. The order ofstations is from west to east. _

110 I’,

, , / , / , , , , , , , , , , , , , , , , , , , , , , , , / ‘_I

- -

100

Median (Metrocket)

-

Median (Radiosondel

90 I_ .-. Zoral Mean -

-~ CIRA

wave

ICIRA 19901 mdel

80 70 7 = 0 ; 1 Q

60 5o 40 30 20

0

11 ’ ’ ’ ’ 1’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ ’ Y-t?’ ’ ’ ’ ’ ’ ’ ’ ’ 1’ 11 180 200 220 240 260 280 300 Temperature

CKI

Fig. 12. Mean temperatures measured above Andoya during the first part of DYANA (11 January-8 February 1990). Bars denote maximum data scatter. CIRA 1990 zonal mean temperatures and CIRA Planetary Wave No. 1 model values are shown for comparison.

It must be stressed that the factor by which the real wave amplitudes are larger than those shown in Fig. 7 is different at all altitudes. The altitude dependence of this factor is not known. The only information available is that it is larger than two. Nevertheless an important conclusion can be drawn from the comparison of Figs 7 and 13 : the amplitude maximum at about 40 km is S-9 K in the CIRA tables. The DYANA measurements show, however, that it was larger than I3 K x 2, i.e. 326 K during January/ February 1990. The CIRA amplitude at about 55 km is 8 K, whereas the DYANA data show >9 K x 2. i.e. 3 18K. Similar results are obtained at almost all of the DYANA stations (from respective analyses discussed below). It thus appears that the CIRA amplitudes of QSW I at medium and high latitudes are too low by a substantial factor. This could be a consequence of the fact that the CIRA climatology used data from three to four winters, which tends to decrease the amplitude because of the pronounced year to year variability of the quasistationary planetary waves (for example vAK LooN et al., 1973). It could further be due to the low vertical resolution of the satellite measurements used for the CIRA climatology (BARNETT, 1980). It is interesting to note in this context that the analysis of an earlier rocket campaign (MAP/WINE) yielded results very similar to those obtained during DYANA (SPANG et cd., 1994). Simultaneous amplitude variations and position shifts generate quiet layers of either type discussed

Temperature

oscillations

1693 80

60

60

30

20

10

0

5

10

Amplitude

15

20

Il,I,

I

-3

III

-2

-1

0

Phase

CKI

I

1

2

,

3

Cradl

Fig. 13. Vertical structure of the quasi-stationary planetary wave No 1 after CIRA 1990 for Andoya amphtudes, (b) phases (zero radian means wave maximum IS at 90 eastern longitude)

(a)

above. These would superimpose, and could possibly interfere with each other. Selection of an analysis interval could thus influence the type of QL found, if there are periods of predominant amplitude variations, and others with stronger position shifts. As an example, the analysis of the whole campaign period (instead of the first part only) at Andoya smears out the middle QL in Fig. 7, and shifts the upper and

-

10 hPa

.-.

30 hPa

I

I

West

East

S-

Longitude I

Fig. 14. Schematic representation of a quasi-stationary planetary wave in temperature. Dashed horizontal lines denote aonal mean temperatures. Vertical line indicates temperature measurement by rockets or lidar above a fixed ground station.

I

,

I

I

,

I

I

1

10

20

I

IO

20

1

10

20

Jan

Feb

Mar

Fig. 15. Planetary Wave No. 1 amplitudes for 60 N at 10 hPa and 30 hPa during the DYANA campaign 1990.

M. BZTTXER ZI 01.

I694

- -

70°N

1

10

20

1

Jan Fig. 16. Migration of Planetary

10

20

10

20 Mar

Feb

Wave No. 1 phases (~xirn~) during level at 40 ‘N and 70 N.

lower QL to somewhat higher altitudes. We present here the data of the first part of the campaign (Fig. 7) because the amplitude of wave No. 1 was much smaller at Andoya during the second part (Fig. 15). The same applies to the Cold Lake data. The conclusions drawn below. however, are not affected by this data selection. Even if there are such interferences of QLs, their vertical phase structures should be unchanged, that is the altitude distribution of the resulting QLs-if measured by several stations along a latitude circleshould follow the QSW 1 phase curve (see Fig. 13). Long period harmonic analyses (with one oscillation period only) were, therefore, also attempted for all other stations participating in the DYANA campaign. As the number of soundings was mostly limited, the whole campaign period was used for these. The results consistently showed maxima and quiet layers in the amplitude and phase curves. The structures were. however, not always as pronounced as in Andoya. Especially at low latitudes, the amplitudes were sometimes small and the gradients weak. The results are plotted in Fig. 17. Stations were grouped according to their geographical latitude. The majority of DYANA stations were situated near to 50. northern latitude. Figure 17(a), therefore, shows the results of the stations between 44” and 54‘N. The altitudes of the quiet layers derived are plotted vs. the geographical longitudes of the stations. (A Cold Lake Station data point at 41 km was shifted by 180 .) Bars given with the measured points show the estimated uncertainty of the altitude of the respective QL. CIRA

1

DYANA.

Data are for the 30 hPa

1990 phases of QSW 1 are shown for comparison : the shaded areas give the range of longitudes where type I QLs occur in the CIRA wave model during the months of January, February, and March at 50 N. Four groups of measured QLs can be formed in Fig. 17(a), as indicated by the thin curves connecting the measured points. The upper three curves have gradients very similar to the CIRA phases. It thus appears that in the middle atmosphere our measured structures were controlled by the QSW 1. The allocation of the measured QLs to the two types discussed, or to mixtures of them, requires a more detailed analysis of the very many temperature profiles. This is beyond the scope of the present paper. Even though our phase determinations are in agreement with CIRA, it must be remembered that our amplitudes are not. The amplitudes during DYANA were much larger at many stations than those given in the CIRA wave model. This is especially true for the high altitudes. We thus have a strong indication that QSW I was very active even at the highest levels (70 km). At the lowest altitudes we find further quiet layers at all latitudes. They are shown as the lowest curve in Fig. 17(a). On the mean they occur at 9 km. They are obtained from the analysis of radiosonde data and are, therefore, statistically well founded. These QLs are not contained in the CIRA tables. Layers of low wind and density variations around this altitude have. however. been discussed by meteorologists (FAUST, 1968). Two earlier campaigns were performed in Europe

Temperature oscillations in the January/February sector of the year. Data obtained during the Winter Anomaly campaign 1976 in Spain (OITFERMANN,1979) were analysed in the same manner as the DYANA data. The same was done for the MAP/WINE campaign 1984 (VON ZAHN, 1987) with data measured at Andoya (Norway) and Observatoire Haute Provence (France). In this case data were used from that part of the campaign when the planetary wave No. 1 was strong (February 1984). Each data set yields quiet layers very similar to the (21)

90

80

70

3

16’)s

DYANA results, and the respective altitude levels are included in Fig. 17(a). They are near to the DYANA levels. This shows that the dynamical situation of the middle atmosphere observed during DYANA was not a peculiar one, but appears to have been typical of midwinter conditions with high wave No. 1 excitation. At high latitudes there were only two measurement stations available (Andoya, 69 N and Heiss Island, 8 1 N). Therefore, coherent structures as shown in Fig.

g 0

CIRA’SO Cold Lake

W Ships X _ +

Aberystwyth Volgograd

7 y: Balchasch - 0 A _ 0 q

(b)

Sayan

Shand

Southern

France

OHP-MAP/wine Essen

M. BITTNER et al.

I696 90

(c)

,,,,,,,‘,,,“,,,“,,,,‘,,,‘,,,‘,‘,,,,”,,,’,,’,”,,,,‘,‘,” G

80 -

-_ Z-

A --

60 7 y

CIRA’SO

A

Southern

0

OHP-MAP/wine

France

n

El Arenoslllo

0

El Arenosillo

+

Ryorl

0

Tsukuba

-

WAC

50

Longitude

Fig. 17. Altitude levels of ‘quiet layers’ vs. longitude. Points in brackets are uncertain. Shaded areas give the locations where the CIRA Quasi-Stationary Planetary Wave No. I has zero temperature amplitude (see text). (a) DYANA campaign for stations between 44 N and 54 N. CIRA phases are for 50’N (January March). Two points from MAP/WINE are also shown ; (b) DYANA and MAP/WINE campaigns for Andoya (69 N) and Heiss Island (81’N). CIRA phases are for 70 N (left side) and for 80 N (right side) ; (c) DYANA. MAP/WINE campaign and Winter Anomaly campaign (WAC) for 36.N to 44‘N. CIRA phases are for 40-N.

17a cannot be derived here. Nevertheless the data obtained fit the general picture developed above. This is seen from Fig. 17(b), which shows the QLs obtained at the two northerly stations. Three QLs are found again in the middle atmosphere above each station as in Fig. 17(a). They have about the same vertical distances as in Fig. 17(a). Data from the MAP/WINE campaign are also included and are near to the DYANA data. Four measurement stations were available between 36. and 44 N (if southern France is included again). Respective results and CIRA phases for 40’N are shown in Fig. 17(c). A Tsukuba station/l40 E data point at 59 km altitude has been shifted by 360’ in longitude to make the phase structure clearer. Data structure is similar to Fig. 17(a), that is the vertical distances between measured (groups of) QLs are about the same. Two data points from the MAP,/ WINE campaign and one point from the Winter Anomaly campaign are also included. They are in good agreement with DYANA data. CIRA phase structure is much more variable at this latitude, especially between 54 and 60 km. Also it is again difficult to see the coherent structure in the measured points. This is because the measurement stations cluster at two

longitudes. Nevertheless it appears that our data points follow the phase tilt of the CIRA QSW 1 at this latitude too. We have data from one more station not included in Fig. 17 (Uchinoura, 31 N, 131 E). Two QLs were found, and are in or at the edge of the CIRA phase area for 30 ‘N. In summary, we find a close similarity between the phase results at medium latitudes from campaigns of three different winters, and the CIRA model phases which are based on satellite data from three or four different winters. Hence, we tentatively attribute our results to the action of the QSW 1. The results at higher and lower latitudes appear to support-or at least do not contradict-this picture In this context it may be interesting to consider two geophysical parameters that are presently discussed in the context of Sun-atmosphere relationships : the sun spot activity and the phase of the Quasi-Biennial Oscillation QBO (LABITZKE and VAN LOON, 1989). These parameters were quite different during the three campaigns : the Winter Anomaly campaign was in the very minimum of the solar cycle with QBO phase West ; the DYANA campaign. on the contrary, was performed during solar maximum with QBO phase East; during the MAP/WINE campaign, solar

1697

Temperature oscillations t’

” r t - L” ’ s s ” ’ 1 1 ’ - ” s ”

n mz ”

LIDAR

l.half

Metrocket

1. half

1.half T,OH*,tPa,.half Radiosonde

16

LIDAR

2.half

Rocket

2.half

Radiosonde

2. half

TlOH+)&Po

2.half

Rocket total time period Radmsonde LIDAR

total time permd

total time perwd

6

t,,,,1,,,,,,,,,,,,,,,,,,,,,,,,,. 30°N

40°N

50°N

Geographical

600N

70°N

6O0N

Latitude

Fig. 18.Dominant oscillation periods obtained at various stations during the DYANA campaign.

activity was medium, and QBO phase was east. No obvious influence of these parameters on our results has been found. CHANIN and KECKHUT (1991) correlated middle atmosphere temperatures measured by lidar in France with the solar 10.7 cm flux. Positive and negative correlation coefficients were obtained, depending on altitude layer, and were attributed to dynamical processes. Zero correlation was obtained at altitudes that are very near to those of the QLs measured during DYANA in France (Fig. 17(a)). This is a consistent result for quiet layers. 5.2. Shovtrr period oscilhtions Only for Andoya and southern France were the sampling rates of the data sets sufficiently high to analyse the shorter period oscillations for the prewarming and the postwarming period of the campaign separately. As an outstanding result a nearly monochromatic oscillation of about 5 days (4.2-6.5 days) was found to be present at both geographical sites. Detailed information about the periods and the 20errors is given in Table 2. This 5 day oscillation turned out to be present in both parts and to be persistent throughout the whole altitude region considered. This is in contrast to all longer periods analysed except the very longest ones. Oscillations within the 5 day period range were also found at nearly all other stations (Table 2, Fig. 18). Although the analysis for the data sets at all stations except Andoya and France had to use the whole campaign period, and is, therefore, questionable, this 5 day oscillation may be regarded as being truly present.

This is because of its persistence at Andoya and in southern France. Similarities to the well-known prominent westward migrating planetary wave mode (m = 1 ; n = 2) are obvious. This wave mode was identified and described by many authors (RODGERS, 1976 ; MADDEN, 1979 ; SALBY, 1984). Its period was observed to range between 4.5 and 6.2 days, and the amplitude exhibited variability on a time scale of the order of a month. It is relatively insensitive to background winds. In order to determine whether our oscillation can be interpreted as such a planetary wave mode, one has to perform a spatial analysis in addition to the temporal analyses presented here. This was attenuated, but did not yield conclusive results on the data base given (for details see BITTNER, 1992). There is another difficulty in understanding our 5 day oscillation : the vertical structures found (Figs 10 and 11) are quite different from what is reported in the literature cited above for the 5 day planetary wave. Instead of being fairly smooth they rather show quiet layers and intermediate maxima very similar to the slow variations discussed above (Fig. 7). These structures of the 5 day oscillations are at least as pronounced as those of the slow variations. It is furthermore surprising that the maxima and the quiet layers of either type of variation occur at about the same altitudes (except for the highest altitudes, compare Fig. 7 to Figs 10 and 11). It is, therefore, suggested that the 5 day oscillation should not be interpreted as a planetary wave of this period, but instead as a modulation of the QSW 1 as this offers a simple explanation: if the strength of the QSW I is

169X

M. BITTNERet al

modulated with a short period by some means (possibly by a 5 day planetary wave), then according to Fig. 14 temperature oscillations in the upper atmosphere must result that show maxima and quiet layers at the same altitudes as the slow variations discussed above. An amplitude modulation (of a few degrees) that fits this picture is seen indeed in the low altitude data (30 hPa) shown in Fig. 15 (between Y January and 11 February 1990). Oscillations of this type have been discussed in terms of vaccillations (for instance LF.OVYPt al., 1985). The modulation hypothesis implies that long and short period modulations superimpose if they are present simultaneously. They can, however, be easily separated if the larger oscillation is analysed first and taken out of the data. This was done in the present analysis. Besides the 5 day oscillation, harmonic components with periods ranging between about 11 and 21 days were observed at various stations (Table 2. Fig. 18). A detailed analysis of these oscillations was possible only at Andoya and in southern France because only at these stations was the number of soundings sufficiently large. They were found to be quasi-periodic and limited to certain altitudes. Especially at Andoya an oscillation with a period of about 11 days was clearly present during the prewarming part of the time series (Fig. 9). An oscillation with a period of nearly I 1 days was also found by SCHEERet al. (1994) using OH*-temperatures (-86 km) and was interpreted as a westward migrating planetary wave No. I. This period was absent in the postwarming part, Instead, a harmonic component with about 17 days was then observed (Table 2). In consequence, studies that analyse the campaign time period as a whole must be regarded with great caution. The latitudinal distribution of dominant oscillation periods as derived from various data sets (Table 2) is shown in Fig. 18. All in all, the picture is quite confusing. A conclusive pattern can hardly be detected. There is some indication of a clustering of the data points around the 5 days and 18 days period, and very vaguely around 11 days. It was said above that periods obtained from an analysis of the whole campaign time need not be present in the whole altitude regime analysed nor in the whole observation period.

6. CONCLUSIONS During the DYANA campaign, variations were observed in the atmosphere at a variety of rocket They were thoroughly analysed

strong temperature lower and middle and lidar stations. by several spectral

techniques. A number of oscillation periods in the range 480 days was determined (Table 2). Most of these appeared to have been quasi-periodic. They were limited to certain altitudes, and were, therefore, not further studied. Only the longest and the shortest periods were consistently found in the whole altitude regime. These two oscillations were quite dominant and made up for more than 50% of the temperature variance at medium to high latitudes. The vertical structures of amplitudes and phases of the long and short period oscillations were analysed in detail. They showed similarities with those of the Quasi-stationary Planetary Wave No. 1 (QSW 1) as given in the CIRA 1990 tables. It was, therefore. assumed for the analysis that a strong QSW 1 was excited during a large part of the DYANA campaign, the amplitude of which showed slow as well as fast modulations. Such modulations have peculiar consequences : surfaces of minimum temperature variance should result (so-called ‘quiet layers’) that are defined by the nodes or the extrema of the QSW 1. These surfaces should be tilted as the QSW 1 vertical phase curves are tilted. Determination of altitude levels with minimum temperature variation by rocket or lidar measurements at various longitudes should, therefore. yield data suitable to compare with the phase curves of the CIRA 1990 QSW 1 model. ‘Quiet layers’ were indeed found in the DYANA data at all measured stations. Their altitude structure was very similar to those of the QSW 1 phase, at least at medium latitudes. The DYANA measurements are therefore compatible with the assumption that a strong QSW I was excited, and that it controlled most of the temperature variance during the campaign. In between the quiet layers there is large temperature variation. Contrary to the apparent phase agreement of CIRA model and DYANA measurements, there are considerable differences in the amplitudes of temperature variations in between the quiet layers. The DYANA maximum amplitudes are larger than those of the CIRA QSW 1 model by a substantial factor, and hence some amendment of the CIRA model may be required. A detailed evaluation of this factor will be given in a separate paper. As strong coupling throughout the whole middle atmosphere was observed, it appears possible to model the high altitude temperature variations on the basis of low altitude radiosonde measurements (SPANG et cd.. 1994). It should be noted that the strong wave activity extended well into the upper mesosphere. It was checked whether the atmospheric conditions during DYANA were peculiar, and hence the above results not representative. For this purpose temperature data measured in January-February during

Temperature oscillations the Winter Anomaly campaign (1976) and the MAP/ WINE campaign (1984) were analysed in the same manner as the DYANA data. Very similar results were obtained. This yields support to the picture developed here. It thus sphere

appears

must

be

that

structured

medium

areas

high

with

quasi-stationary.

the

visualized

midwinter

in which

and and

low

middle

atmo-

as a three-dimensionally there

variability.

reappear

each year. One could, therefore,

are

intermediate

These

areas

at

are

the same locations expect to see features

I699

similar to those of temperature in other dynamical parameters such as wind or turbulence, and even in minor constituent distributions in the middle atmosphere. Ackno~~ledgements-We thank K. Labitzke and her collaborators at the Free University of Berlin for providing the planetary wave data in the lower stratosphere. The DYANA campaign was funded by the Bundesministerium fur Forschung und Technologie BMFT (Bonn) through Deutsche Agentur fur Raumfahrt-Angelegenheiten DARA (Bonn). The authors are also grateful to their respective institutions for support. We thank the anonymous referees for their

comments.

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KUE~HI.I:R R.

1989

NAUJOKAT B., LABITZKE K., LENSCHOW R., PETZ~LDT K. and WOHLFAHRT R.-C. OFFERMANN D. and BITTNER M.

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