• Introduction
• Measurements and Analysis Technique
• Analysis and Climatology
• Summary and Conclusions
• Acknowledgments
• References

FIGURES

• Figures 1a, 1b
• Figure 2
• Figure 3
• Figure 4
• Figure 5

A climatology of stratospheric gravity wave activity is presented for the ten year period from 1990 to 1999 . The climatology is based on high vertical resolution radiosonde data of routine soundings from Munich (48° N, 12° E) and Stuttgart (49° N, 9° E), i.e. stations located close to the northern baseline of the Alps. Part of this study contributes to the cooperative 'SPARC Gravity Wave Initiative' coordinated by R.A. Vincent of the University of Adelaide, Australia.

The data are analyzed in two different stratospheric altitude ranges: a fixed one is applied for the contribution to the global climatology. A new adaptive analysis range is defined. This range takes daily variations of the tropopause height into account. Results obtained with this adaptive altitude range are contrasted with those from the fixed one. The seasonal variation of stratospheric energy density is more pronounced when analyzing with the adaptive altitude range. The use of the fixed altitude range leads to an overestimation of stratospheric energy density in summer.

In order to classify the different excitation mechanisms of gravity waves, the dependence of stratospheric wave energy density on tropospheric wind conditions is investigated. Maximum stratospheric wave energy densities are found to be connected to large tropospheric wind shears. For the investigated region the jet stream excitation seems to be the most important mechanism for the generation of stratospheric gravity waves.

Introduction

Internal gravity waves are the cause for most of the spatial and temporal fluctuations in stratospheric temperature and wind. For example, the adiabatic cooling near the crest of hydrostatic mountain waves favors the nucleation of supercooled droplets and thus the formation of polar stratospheric clouds (e.g. Wirth et al., 1999). Upward propagating gravity waves transport momentum to the middle atmosphere and are now well appreciated to play a dominant role in driving the circulation of the atmosphere (e.g. Alexander and Rosenlof., 1996). Since the present GCMs and climate models are not able to resolve the whole gravity wave spectrum, a better characterization of stratospheric gravity wave activity in terms of wave energies, periods, phase speeds, propagation directions, and spectral parameters is needed. This can be achieved by means of observational, theoretical and modeling studies.

The present work constitutes the South-German contribution to the global gravity wave climatology within SPARC, see Vincent, this issue. This climatology is based on high vertical resolution radiosonde data of about 16 groups in 13 countries, and follows the previous work of Allen and Vincent, 1995 and Vincent et al., 1997. A significant improvement for our region is reached by analyzing the data with the new adaptive altitude range. Our results help to better understand the different sources of gravity wave activity in the lower stratosphere (Nastrom and Fritts, 1992 and Fritts and Nastrom, 1992).

Measurements and Analysis Technique

For our analysis 10 years data of twice daily radiosonde launches (00 UT, 12 UT) from Munich-Oberschleißheim (MO) and Stuttgart (STU) are used. Altogether these are 14608 profiles of high vertical resolution radiosonde data. Since there are few gaps in the data (due to uncomplete or no measurements), not all of the profiles are suitable for the analysis. However, about 95% of the profiles are analyzed. The stored data of each sounding include profiles of temperature, horizontal wind speed and direction, relative humidity, and ascent rate of the balloon. In this analysis only the profiles of temperature and horizontal wind components are used. The accuracy of the temperature measurements is about 0.1 K. For the wind speed measurements it ranges from 1 to 2 ms ^-1 , depending on the horizontal distance to the station, since it is measured by radar tracking which leads to more inaccurate measurements for larger distances. The vertical resolution is 50  m for the temperature measurements and about 150 m for the wind speed measurements. Väisälä, RS 80 radiosondes are used and reach maximum ceilings of about 30 to 35 km in our region.

As an example, selected vertical profiles of temperature and horizontal wind speed for MO, January 1995 are shown in Figure 1. For clearness the curves are shifted from each other by 10 K and 10 ms ^-1 , respectively. An unperturbed background profile (T₀ , u₀, v₀) is defined by fitting a quadratic polynomial to the measured profile in the respective altitude range (blue dashed curves). The local deviations (T', u', v' ) from this background state are considered as due to gravity waves. For this approach see e.g. Allen and Vincent, 1995; Whiteway, 1999. For our analysis two altitude ranges are applied: a fixed one as for the global climatology within SPARC (FAR = z_T - z_B , where z_B= Z_TP+ 2 km, z_T = z_B+ 7 km and Z_TP = 11 km is the climatological zonally averaged tropopause height (Hoinka, 1998) for the investigated geographical region). The new adaptive altitude range is defined by AAR = z_T - z_B , where z_B(t)=z_TP(t) + 4 km, z_T (t) = z_B(t) + 11 km and z_TP(t) is the time dependent tropopause height. The altitude ranges FAR and AAR are indicated in the figure by bright and dark shadings, respectively.

The lower edge of the AAR shows very clearly the strong variability of the tropopause height in just one month. The thermal definition of the tropopause (WMO, 1957) is used to obtain its height. Within our 10 year analysis we find tropopause heights ranging from 6 to 15 km, and a mean value of about 11 km which agrees very well with the zonally averaged value of Hoinka, 1998. Thus, if one uses the FAR, there are some profiles with the tropopause lying within the analyzed range. An inversion at the tropopause (which is typical for northern midlatitudes) would then be misinterpreted as part of a wave fluctuation. Eventually, this overestimates the contribution to the calculated energy density for the respective profile. In order to avoid misinterpretation due to tropopause effects we defined the above AAR, which represents a constant range in altitude referring to the temporal varying tropopause. To ensure that there will be really no effects from the tropopause, this altitude range starts 4  km above z_TP(t).

Analysis and Climatology

The interannual variability of the stratospheric total energy density is determined on the basis of the 10 year data set for MO and STU. Figure 2 shows this quantity and the ratio of kinetic to potential energy density for MO and both altitude ranges (AAR = 11 km and FAR = 7 km). The results for STU exhibit a qualitatively similar behaviour, but with smaller energy densities mainly in winter, and are not shown. The potenial and kinetic energy densities are given by E_pot = 1/2(g/N)²(T'/T₀)² and E_kin = 1/2(u'² +v'²), respectively, where we neglected the vertical part of the kinetic energy density.

There is a strong half-yearly trend in stratospheric energy density which can be seen in Figure 2, i.e. high stratospheric energy densities in winter and lower ones in summer. Mean values for MO are 3.8  J/kg for JJA (summer), 9.2  J/kg for DJF (winter), and 6.3  J/kg for all data. For STU the mean values are 3.4  J/kg for JJA, 7.4  J/kg for DJF, and 5.4  J/kg for all data. The ratio of kinetic to potential energy density for MO (STU) is almost constant about 1.8 (1.7) during the entire year. The stratospheric energy densities derived from the FAR analysis show qualitatively the same seasonal behaviour, but they are smaller in winter and higher in summer than those derived from the AAR analysis. Power spectral analysis, which is a technique often used in the literature (e.g. VanZandt, 1982; Allen and Vincent, 1995), shows that the energy density increases with increasing vertical wavelength, Figure 3. Since larger altitude ranges can resolve longer wavelengths, the energy density should be larger for AAR =11  km than for FAR =7  km. In winter this is the case. In summer the opposite behaviour cannot be understood with the above explanation. However, this is an artifact and comes most likely from the above mentioned tropopause effect (for the FAR analysis) that occurs more often in summer, since the tropopause in summer is higher on average (Hoinka, 1998).

We now focus on the dependence of stratospheric energy density on tropospheric wind conditions, Figure 4, in order to identify the most important wave excitation mechanism north of the Alps. The tropospheric quantities are obtained between the altitudes z₀+ 1 km and  z_TP - 2 km, where z₀ is the altitude above sea level of the individual station. In a first step we determine the energy weighted angular distribution of mean tropospheric wind direction (30° bins, insets in Figure 4). This gives the energetic significance of different tropospheric wind directions. In order to clarify the difference in stratospheric energy density for MO and STU, the energy densities are normalized by the sum of MO and STU. Obviously, the tropospheric wind directions in the westerly sector show high positive correlation with the stratospheric energy density. The energy weighted mean tropospheric wind directions derived from this are 287° for MO and 282° for STU. These are compatible with the wind directions from which in our region most of the active weather systems as fronts come from.

In a second step we investigate whether critical level filtering inhibits the vertical propagation of the gravity waves and thus reduces the stratospheric wave energy density. To see that, we consider the energy weighted distribution of the tropospheric directional shear, Figure 4. Again, the energy densities are normalized by the sum of MO and STU. This distribution indicates that about 80 % of the normalized energy densities are correlated with directional shears between 0° and 90° . Thus high stratospheric energy density is associated with small tropospheric directional shear in agreement with the results in Whiteway, 1999.

Finally, in a third step, we consider mean relative statistical correlations between stratospheric energy density and magnitudes derived from the tropospheric wind speed, Figure 5. They show a strong contrast between summer (correlations between -0.1 and 0.1 ) and winter (correlations between 0.2 and 0.6 ). For the winter we find positive correlation of stratospheric energy density with the surface wind speed, larger correlation with the tropospheric wind shear, and even larger correlation with both, that means the product of surface wind speed and tropospheric wind shear. Thus, since surface wind speed and tropospheric wind shear are measures for the strength of the jetstream, there is a high positive corralation in our region between jetstream strength and observed stratospheric energy density. We remark that there is a significant difference between MO and STU only for January and February referring to the correlation between stratospheric energy density and tropospheric wind shear.

Summary and Conclusions

This study constitutes the South-German contribution to the global gravity wave climatology within SPARC (see Vincent, this issue). The results derived with the newly developed adaptive analysis height range were critically compared with those of the fixed altitude range used for the global climatology. Both methods give high stratospheric energy densities occuring mostly in winter, associated with large tropospheric wind shear, small tropospheric directional shear and large surface wind speed. Lower stratospheric energies are found in summer. For the investigated region the use of the AAR analysis shows a stronger half-yearly trend in stratospheric energy density than the FAR analysis, and makes thus seasons better comparable. It avoids the overestimation of stratospheric energy density of the FAR analysis in summer. Furthermore, higher energy densities are found for MO compared to STU. This could be due to the fact that Munich lies closer to the Alps than Stuttgart and some of the MO-profiles are thereby certainly influenced by the effect of these mountains. The dependence of stratospheric energy density on tropospheric wind conditions support the hypothesis that the main source of stratospheric gravity wave activity in the investigated region is the jet stream excitation. Up to now the relative contribution of convective or orographic excitation is not clear for the investigated region. Since the Alps are very complex, 90° propagating mountain waves are possibly a part of the sources for the observed stratospheric energy densities.

For further investigations idealized studies and realistic model studies are needed. Together with this observational study they should lead to a reliable identification of the different gravity wave generation mechanisms.

Acknowledgments

We thank the DWD Oberschleißheim for the free availability of the radiosonde data. We furthermore thank Jana Freund and Winfried Beer for their help in solving the difficulties with different data formats. We are particularly grateful to Ulrich Hamann for his help in analyzing the huge amount of data.

References

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