Indicators of Long-Term Changes in Middle Atmosphere Transports

Available online at www.sciencedirect.com

SCIENCE DIRECT.

doi: lO.l016/SO273-1177(03)00670-7

Pergamon

www.elsevier.com/locate/asr

INDICATIONS OF LONG-TERM CHANGES IN MIDDLE ATMOSPHERE TRANSPORTS

D. Offermannl’, M. Donnerl, P. Knieling l, K. Hamilton2, A. Menze13, B. Naujokat4 and P.Winkler5

1 Physics Department, Wuppertal University, &097 Wuppertal, Germany 2SOEST, University of Hawaii, Honolulu, HI 96822, USA 3Dept. of Ecology, TU Munich, 85354 R-&sing, Germany

41nstitute for Meteorology, Ree University of Berlin, 12165 Berlin, Germany 5Met . Observatoy, Deutscher Wetter&end, 82383 Hohenpeissenberg, Germany

ABSTRACT

Upper mesosphere temperatures are derived from Meinel OH* emissions. They show large variations of various kinds. Annual mean temperatures measured at Wuppertal (51°N, 7”E) since 1981 do not show a significant long-term trend: the value obtained is 0.02 K f 0.09 K per year. Monthly trends, on the contrary, are fairly substantial. To take a new and different approach to middle atmosphere long-term behavior, two new parameters are suggested for analysis: the short-period variance c2 of the temperature, and the Equivalent Summer Duration (ESD). 1.) The variance o2 itself shows long-term variations, which are correlated with temperature fluctuations at some lower altitude. This behavior is in line with the “downward control principle” and thus indicative of changes in atmospheric transports. 2.) The ESD parameter is defined in analogy to the duration of the vegetation period on the ground. A threshold temperature of 200 K at 87 km is chosen to define the beginning and end of summer. This yields an ESD on the order of 150 days. The ESD values at Wuppertal show a solar cycle effect which needs to be corrected for. Sensitivity of OH temperatures to solar activity at Wuppertal has been analyzed for this purpose. A sensitivity of 0.03 K f 0.016 K per Fl0.7~ flux unit was obtained. The corrected ESD values at the mesopause show a substantial increase of 18 days (11%) in 20 years (extrapolated). Respective vegetation period data have been published recently and show an increase of 20 days (13Y) o in 20 years (Zhou et al. 2001). They are in line with phenological data presented here. In the stratosphere summer length is estimated from the spring/autumn circulation reversals. Also here substantial changes are found, with a (negative) gradient of 19 days (13%) in 20 years. Considerable changes in middle atmosphere dynamics are thus indicated even though the statistics of some of the data are still limited. The mesopause ESD increases are compatible with a long-term change of the semi-annual oscillation (SAO). The stratospheric data suggest that the variations seen could be (in part) a climate fluctuation rather than a manmade trend, as data from two earlier decades show behavior that is opposite. 0 2003 COSPAR. Published by Elsevier Ltd. All rights reserved.

INTRODUCTION Middle atmosphere temperatures are mostly radiation controlled in the lower part of the altitude region,

and dynamically controlled in the upper part. Long-term satellite measurements in the mesosphere and stratosphere have been available since 1979 (e. g. Stratospheric Sounding Unit (SSU), Bailey et al., 1993). Mesopause temperatures have been monitored at several ground stations for a long time by measuring various bands of the Meinel OH* emissions (e. g. Bittner et al., 2000, 2002, Lysenko et al., 1999). At this altitude (87 km) temperature is found to result from a number of strong and competing influences of similar magnitudes: the most pronounced feature is a seasonal variation with a swing of up to 30 Kpp (peak to peak). Superimposed on it are various types of waves (tides, planetary waves, gravity waves, etc.). This is shown in Figure la for the year 1994. A three-component harmonic fit has been calculated for the data. The

Adv. Space Res. Vol. 32, No. 9, pp. 1675-1684,2003 Q 2003 COSPAR. Published by Elsevier Ltd. All rights reserved Printed in Great Britain 0273-1177/$30.00 + 0.00

1676 D. Offemmn et al.

residues between measured data and the fit are given in Figure lb. They are very large at times, reaching up to 15 Kpp.(Figure lc shows a schematics of the seasonal variation which is used below.) Further effects of similar magnitude are solar cycle influences (e. g. Semenov and Shefov, 1999; Gavrilyeva and Ammosov, 2002). Finally, long-period (subdecadal) variations of considerable magnitude and so-far unexplained origin are observed (for an overview see Bittner et al., 2000).

I,, I / I I ,, I I,, ,, I,, I,, I / / I, 1,) I 0 30 60 90 120150190210240270300330360

DOY

Fig. 1. a) Seasonal variation of OH* temperatures (87 km,Wuppertal, 51”N/7”E, 1994). b) Residues from a harmonic fit. c) Schematics of seasonal variation. ESD is defined by a 200 K threshold (see text).

MESOPAUSE TEMPERATURE TRENDS Temperatures at the mesopause have been much studied in recent years, especially for long-term trends

of the mean temperatures. One motivation was to find an indicator for (anthropogenic) climate changes in the troposphere near the ground. Quite different results have been observed at different stations and for different time periods: about the largest trend values (-0.7K/year) have been published by Golitsyn et al. (1996) and Lysenko et al. (1999) for Moscow (56”N/37”E) since 1957, whereas Bittner et al. (2002) have found about zero trend at Wuppertal (51”N/7”E) since 1980. The Wuppertal annual trend data are given in Figure 2. Annual mean temperature deviations from the reference year 1994 are shown (see Bittner et al., 2002). The fit line shows a O.O4K/year trend, which is statistically not significant. The same data have also been analyzed on a monthly basis, i. e. a trend has been calculated for all mean January values available, and similarly for the other months. The results are shown in Figure 3. Relatively large trend values are obtained in certain months (O.l5K/year). If the mean of all monthly values is taken, however, an almost vanishing value is obtained again (O.OaK/year), which is much smaller than its standard deviation (O.OSK/year). There appears to be a systematic oscillation in the monthly values, as is indicated by a two-component harmonic fit in Figure 3 (solid curve). The reason for this is discussed below. All trend values mentioned here are relatively small if compared to the other temperature variations mentioned above. The largest trend values are on the order of 1% of the annual swing, and a few percent of the other

Long-Term Changes in Middle Atmosphere Dynamics 1611

8

8

ii -4

-8

81 82 83 84 85 88 87 88 89 90 91 92 93 94 95 98 97 98 99 00 01

Time (year)

Fig. 2. Annual mean OH temperatures at Wuppertal. Data are relative to year 1994.

, I / I

1 1 1 I I

-0.2 I I I I I I I I I I I I 0 30 60 90 120 150 180 210 240 270 300 330 360

DOY Fig. 3. Monthly trends of OH temperatures at Wuppertal during the course of the year. Mean trend is 0.02 f 0.09 K/a (dashed line). Data are relative to year 1994.

disturbances. To determine trend values on this order requires all superimposed influences such as seasonal, wave, and other variations to be measured at high accuracy. That is a difficult task. It might therefore be worth looking for atmospheric parameters other than the annual mean temperature To to describe the long-term behavior of the mesopause. They should - if varied - leave 2’0 unchanged.

There are two other parameters that appear suitable to characterize the gross behavior of the seasonal temperature variations: 1) the swing S of the annual variations, 2) the angle (Y or the width of the temperature summer valley in Figure lc. The latter is called “Equivalent Summer Duration” (ESD) in the following for reasons discussed below. It is possible to find different combinations of these two parameters that have the same mean temperature To.

1678 D. Offermann et al.

Table 1. Correlation of wave activity with temperature. Temperature variance u2 is taken to represent wave activity at a given altitude level. It correlates with the temperature at the next lower level. Sensitivity b is : b = AT/Ac2.

50°N/5”E 70”N/15’E 87 km (OHI

1

(la; I E) (Ia; 1 y b = 0.21 K/K2 b = 0.32 K/K2

1 hPa (48 km) (SW

(Ia; I iz (la; 1 Ki) b = 0.17 K/K2 b = 0.22 K/K2

10 hPa (31 km) (SW

SWING ANALYSIS The temperature swing consists of roughly two parts: long-period, i. e. seasonal variations with annual,

semi-annual, and ter-annual periods on the one hand (solid fit line in Figure la). The other part is made up of short-period oscillations (shorter than a few weeks) superimposed as shown in Figure la and lb. The swing of the seasonal variations is found to be strongly correlated with solar activity (as measured by the Fi0.7~ flux). This will not be pursued here. The short period “noise” (Figure lb) has been analyzed to some extent by Bittner et al. (2000,2002), to determine the contribution of various waves. Moreover, a correlation is obtained between the “noise intensity” (i. e. the temperature variance o2 from Figure lb and similar data from the years 1980-1998) at the OH layer altitude and the temperature at some lower level (1hPa). If this variance is assumed to be a proxy for the action of breaking waves on the atmospheric circulation, this correlation may be interpreted as a consequence of the Downward Control Principle ( Haynes et al., 1991; Bittner et al., 2002). Similar correlations are also obtained in the stratosphere. These and some more results are summarized in Table 1. Three altitude levels at two different latitudes in the European longitude sector have been analyzed. (The Wuppertal OH data are given at the coordinates 50”N/5”E.) Positive correlations are obtained in all four cases. (The correlation significance is around 95%. The lags given are such that the higher altitude g2 disturbance leads the lower altitude temperature variation.) The sensitivities b of the lower altitude temperature to the disturbance intensity u2 at the higher altitude are also given in Table 1. They can be compared to a study of the sensitivity of mean circulation in a global circulation model (SKYHI, Hamilton et al., 1995, 1999). Respective responses in the SKYHI model are in qualitative agreement in three (of the four) cases shown (Bittner et al., 2002).

EQUIVALENT SUMMER DURATION (ESD) It is easily seen from Figure lc that the ESD parameter can in principle change without a variation of the

annual mean temperature To. As this mean temperature does not show long-term changes at Wuppertal (Figure 2) it would be interesting to look for any such variations in ESD. There is another motivation for an ESD analysis: It has been well known for quite some time that the vegetation period (in the northern hemisphere) has changed: it starts earlier in spring by up to two weeks, and lasts longer in autumn by up to one week. Exact figures depend on geographical location and parameter studied. Zhou et al. (2001) have analyzed the normalized difference vegetation index (NDVI) and found a total increase of the growing season duration of about 18 days for midlatitude (40-70”N) Eurasia in 18 years since 1982. At a total season length of roughly 150 days presently this is an ESD increase of 0.67% per year or 13% in 20 years


(extrapolated). As we want to check whether there is a relation between climate effects on the ground and the mesopause temperatures, we have chosen the ZOOK temperature level to define beginning and end of “mesopause summer” (summer temperatures being lower than winter temperatures at these altitudes). A summer length somewhat longer than 150 days is obtained in this way, which is taken to be equivalent to the growing season duration. It is therefore called the “Equivalent Summer Duration” (ESD) in the following.

E ml.:..:. .:_ .:_ _I_ 1. :. .: .l..fK. .,_ 3. I.: .I .:. ~1. :~ .:. .:E. 1.: .: -1 .:_ _I_ .:. _z_ .:. .:.!.I

0 I50 100 150 200 250 300 350

DOY

Fig. 4. Seasonal temperature variations composed of annual and semi-annual components. Red curve shows an increase of summer length of about three weeks (see text).

Figure 4 shows how a change in ESD can come about. The picture shows two modeled seasonal variations of temperature, each consisting of an annual and a semi-annual component (for simplicity). The mean annual temperature 2’0 of the two curves is the same. Annual and semi-annual amplitudes and phases have been analyzed for the Wuppertal OH temperatures since 1987. They show small to moderate trends (Bittner et al., 2002). Maximum possible changes from 1987 until 2001 (including the error bars) were used as differences between the blue and red curves in Figure 4. The difference between the two curves is therefore an exaggeration of the real atmospheric behavior. This is to make it more visible. Most of it is due to changes of the semi-annual component of the red curve: its amplitude was decreased from 7 K to 4 K, and its phase was increased from DOY 80 to DOY 101. The resulting increase in ESD is about three weeks.

If the blue curve is subtracted from the red curve in Figure 4 the differences show a systematic seasonal variation which resembles the fit curve and data shown in Figure 3. This will be analysed in detail in the future.

A proper ESD analysis requires accurate determination of the components forming the seasonal temperature variation. This is possible only if the data density and homogeneity is high. Density and homogeneity are demonstrated in Figure 1 which is typical of the Wuppertal measurements. At Wuppertal there are about 19 daily values available per month, with a 25% variation about this mean during the course of the year.

SOLAR CYCLE EFFECTS As concerns the solar cycle effects already mentioned, Figure 2 indicates their influence on 2’0: two of the

largest temperature excursions (enhancements) occur around the maxima of solar cycles No. 22 and 23 (see years 1990/91 and 2000). The OH temperatures at Wuppertal have therefore been correlated with solar activity (as represented by the Fi0.7~ radio flux in units 1O-22 W/m2Hz). This was done on a monthly basis in the same way as the trend analysis in Figure 3. The results are shown in Figure 5, which gives the monthly sensitivity of temperature to solar radio flux. The values obtained vary considerably during


-0.02

1 2 3 4 5 6 7 a 9 10 11 12 Month

Fig. 5. Monthly OH temperatures analyzed for solar activity influences. Sensitivities are the slopes of the regression curves correlating monthly mean temperature-s with annual mean solar fluxes. Solar activity is given in F10.7~ flux units. Mean sensitivity is 0.03 f 0.016 K/flux unit (dashed line).

the course of the year. The mean annual sensitivity is relatively small: 0.03K / flux unit. This means a temperature swing of 4.5 K from solar maximum to solar minimum in a cycle of 150 flux units (70 to 220 FU).

ESD TRENDS In principle the ESD is influenced by geomagnetic activity such that it decreases if the activity increases.

This easily follows from Figure lc assuming that the temperature profile is raised by the activity and that the temperature threshold defining the ESD remains fixed. In detail things are more complicated because the geomagnetic influence on the annual profile possibly is not uniform during the year (Figure 5). In a first approximation we therefore have correlated the ESD values with the solar Fr0.7~ fluxes. A linear fit function was calculated, which was subsequently used to correct the measured ESD values for solar cycle effects. The result is shown in Figure 6a. ESD values obtained are around 150 days or somewhat larger. The data show a relatively large scatter. Nevertheless a trend of the ESD values can be seen, indeed, with an increase in summer length of about 15 days (10%) in 20 years (extrapolated).

The procedure described is rather coarse. Therefore a more refined analysis was performed in addition. For this purpose every daily data point measured at Wuppertal was corrected. for geomagnetic influences by means of the data shown in Figure 5. The monthly results shown in that Figure have been interpolated in time to the DOY value of the data point in question, and used for its correction. Subsequently the seasonal variations of all years were matched by three-component harmonic fits as usual. From these curves new ESD values were obtained with the summer/winter threshold of 200K as before. The results are shown in Figure 6b. The picture shows beginning and end of summer and the changes of these dates since 1987. Two opposite trends are obtained that imply a trend in ESD, too. This resulting change in ESD is 12 days in 13 years or 18.5 days (117) o in 20 years (extrapolated). It is thus similar to the values obtained from the approximate analysis in Figure 6a. (The ESD values are around 160 days. They are somewhat higher than in Figure 6a, which is due to the geomagnetic correction applied).

There are more measured data at Wuppertal than shown in Figure 6. The data before 1987 are, however, not homogeneous enough to allow for three component harmonic fits, and have therefore been omitted from the present analysis. As concerns the reason of the ESD increase Figure 4 gives a hint: it suggests a long- term variation of the semi-annual oscillation (SAO) as a candidate, with the greatest influence stemming from its phase shift. A detailed analysis of this is beyond the scope of the present paper and will be given in the future.


; 8 160 ‘S L

s 155 8 E 150 I

.z s

145

w 140

265 7 120

0 260 c3 115

E 255 110

s 250 ‘s 105

P 245 ‘E 100

240

235 87 88 00 SO 91 92 93 94 95 96 97 90 99 00 01

Year

Fig. 6. a) Equivalent summer duration (ESD) measured at Wuppertal (87 km) since 1988. The fit line shows an increase of 15 days in 20 years (extrapolated). An approximate correction for the F 10. 7 m flux has been applied (see text). b) Beginning and end of summer. An improved F1o.7~ correction has been applied. The resulting ESD increase is 18.5 days in 20 years (extrapolated, see text).

PHENOLOGICAL DATA There is a wealth of phenological data indicating changes in the vegetation period or “summer length”.

Studies in Europe and North America have revealed that spring phases, such as leaf unfolding or flowering of plants, have occurred earlier progressively since the 1960s. Changes in autumn events are less pronounced and more variegated. In total, the length of growing season has increased in some areas by up to 3.6 days per decade over the past 50 years (Walther et al., 2002; Menzel and Estrella, 2001). This extension of the growing season is also mirrored by results derived from the northern hemisphere NDVI satellite data (e.g. Zhou et al., 2001).

Here we show as an example the dates of onset of leaf unfolding of Fagus sylvatica (beech) at the Me- teorological Observatory of Hohenpeissenberg (48”N, ll”E, Figure 7a). As the picture shows, this date has advanced in time by 11.6 days during the last two decades. Another example is that of Figure 7b which displays the mean annual deviations of leaf unfolding, leaf coloring, and the length of the growing season in Germany from their respective 1971-1990 means. The curves show the average response of four deciduous tree species (Quercus robur/oak, Fagus sylvatica/beech, Betula pendula/birch, Aesculus hip- pocastanum/chestnut), based on data from almost 1600 stations of the German Weather Service network (details in Menzel, 2002). Both curves of leaf unfolding are very similar (green fit lines in Figure 7a and 7b, respectively). They resemble the “beginning of summer” development in Figure 6b, though they are somewhat steeper. The data in Figure 7b for the duration of the growing season (10 day = 6% increase in 20 years) roughly agree with the ESD development contained in Figure 6b.

Even though the increase in summer duration appears to be similar at the mesopause and on the ground the two phenomena cannot be directly related. This is because the two “summer seasons” are phase shifted with respect to each other: the center of the summer season at 87 km occurs close to solstice, whereas the middle of the vegetation period is several weeks later (e. g. Zhou et al., 2001).

1682 D.OffemannetaZ.

- Leaf unfolding

10

5

0

-5

i 950 1985 i 990 1995 2ooo Year

Fig. 7. Phenological changes a) Leaf unfolding of Fagus sylvatica (beech) at the Meteorological Observatory Hohenpeissenberg b) Mean annual deviations of leaf unfolding, leaf coloring, and the length of the growing season for four tree species in Germany from their respective 1971 - 1990 means, 5 year running means, (see t-&t).

180

150

140

120

110

loo- -100

QO- , I I I, I I ,, I, >, I I -90

lQ65 70 75 80 85 90 95 2000

Year

Fig. 8. Stratospheric summer length at 30 hPa. Summer duration is based on reversal of meridional temperature gradients (circles, above) and on zonal wind reversal (diamonds, below). For details see text.

CIRCULATION TURN-AROUND IN THE STRATOSPHERE If there is a relationship - even an indirect one - between the increases in vegetation period and the

summer length near the mesopause, something similar should likely occur at intermediate altitudes, i. e. in


the stratosphere. To employ a different method for defining the length of summer in the stratosphere we have used the circulation reversal in spring and autumn. Times of spring / autumn turn-around at 30 hPa have been derived from the reversal of the mean temperature difference between 80”N and 50”N (positive or negative; Berlin analysis data). The results are shown in Figure 8 (circles). The time differences between autumn and spring turn-around are shown. There are strong variations in these data from year to year. Nevertheless a trend can be clearly seen in the last two decades. It shows a decrease in summer length, however, and thus is opposite to the mesosphere. This can be understood if the change is due to a phase shift of the SAO opposite to that one mentioned above. This will be further studied in the future. Summer length can also be defined as the difference in time of the actual wind reversal in the stratosphere. Such data are also shown in Figure 8 (diamonds), based on the times when the mean zonal wind component at 60” N reverses sign. These data also indicate a trend since 1980, though much smaller.

55 g go-

3 z

c” 80-

‘E c

‘6 70- m”

1955 60 65 70 75 80 85 90 95 2000

Year

Fig. 9. Day of spring circulation turn-around 1958 - 2000. Early data (diamonds) are from Entzian and Lauter (1982) later data (circles) are from the Berlin analysis. Data have been smoothed by a 3 year running mean (see text).

The trends are shown in Figure 8 by straight fit lines. These lines are not unambiguous, however. They would change considerably if one were to increase the fit interval for instance by five years (1975 - 2000). The reason is that the trend appears to change sign somewhere around year 1980, as is indicated by the dashed fit lines that represent the fit interval 1965-1980. This episode will be further discussed below. To obtain an estimate of a trend in circulation reversal in the following we use the mean of the two gradients (1980 - 2000) as a first approximation. This mean trend is almost 19 days (13%) in the 20 year period and therefore similar to the OH temperature results at Wuppertal. The turn-around points are about symmetric (& 5 days) with respect to the summer solstice. (Note that this stratospheric analysis is hemispheric, while the Wuppertal data are local!)

To study the question of episodic trend changes we show the times of spring turn-around since 1965 in Figure 9. Data are from reversal of meridional temperature gradients. They are taken as the “beginning of summer”. The shift of spring reversal time makes a far greater contribution to the trend in summer length in Figure 8 than the autumn data (not shown here). The data shown in Figure 9 are thus taken to be representative of the trend in summer length. A three-year running mean is shown. Going backward from the present time the data show a decline until 1975-1980, as indicated in Figure 8. Going back further the data trend, however, changes sign, and data increase to fairly high values in the years before 1970. Similar data from even earlier years have been published by Entzian and Lauter (1982, their Table 1). They are included in Figure 9 for comparison (diamonds). The two data sets agree reasonably well at times when


they overlap. At the earliest interval, 1958 - 1970, the combined data sets show high values for quite some time. They are as high as the highest at the end of the series shown in Figure 9 (year 2000). In conclusion one has to be cautious when interpreting summer duration data. It may well be that they show an episodic climate fluctuation and not a manmade climate change, or a mixture of both. Ongoing observations will reveal whether future data continue to increase and even go beyond the present level (and that of the sixties) or whether there will be a “saturation,, again.

ACKNOWLEDGEMENT The SSU data were supplied by the UK Meteorological Office under Contract ref. 097.

REFERENCES Bailey, M. J., A. O’Neill, and V. D. Pope, Stratospheric analyses produced by the United Kingdom Meteo-

rological Office; J. Appl. Meteos:, 32, 1472 - 1482, 1993. Bittner, M., D. Offermann, and H.-H. Graef, Mesopause temperature variability above a mid-latitude station

in Europe; J. Geophys.Res. 105, 2045 - 2058, 2000. Bittner, M., D. Offermann, H.-H. Graef, M. Dormer, and K. Hamilton, An 18-year time series of OH

temperatures and middle atmosphere decadal variations; J. Atmos. sol. Terr. Phys., 64, 1147 - 1166, 2002.

Entzian, G. and E. A. Lauter, Variations in the Springtime Reversal of the Startospheric Circulation; 2. Meteor., 32, 209 - 215, 1982.

Gavrilyeva, G. A. and P. P. Ammosov, Near-mesopause temperatures registered over Yakutia; J. Atmos. Sol. Terr. Phys., 64, 985 - 990, 2002.

Golitsyn, G. S., A. I. Semenov, N. N. Shefov, L. M. Fishkova, E. V. Lysenko, and S. P. Perov, Long-term temperature trends in the middle and upper atmosphere; Gwphys. Res. Let., 23, 1741 - 1744, 1996.

Hamilton, K., R. J. Wilson, J. D. Mahlman and L. Umschied, Climatology of the SKYHI troposphere- stratosphere-mesosphere general circulation model; J. Atmos. Sci., 52, 5-43, 1995.

Hamilton, K., R. J. Wilson und R. S. Hemler, Middle Atmosphere simulated with high vertical and horizontal resolution versions of a GCM: Improvements in the cold pole bias and generation of a QBO-like oscillation in the tropics; J. Atmos. Sco’., 56, 3829-3846, 1999.

Haynes, P. M., C. J. Marks, M. E. McIntyre, T. G. Shepherd and K. P. Shine, On the “downward control” of extratropical diabatic circulations by eddy-induced mean zonal forces; J. Atmos. Sci., 48, 651-678, 1991.

Lysenko, E.V., S.P. Perov, A.I. Semenov, N.N. Shefov, V.A. Sukhodoev, G.V. Givishvili, and L.N. Leshchenko, Long-term trends of the yearly mean temperature at heights from 25 to 110 km; Izvestiya, Atmos. Ocean. Phys., 35) 393 - 400) 1999.

Menzel, A. and N. Estrella, Plant phenological changes, In: Walther, G.-R., Burga, C.A. and Edwards, P.J., (Eds.) “Fingerprints” of &mate change - Adapted behatiour and shifting species ranges, Kluwer Academic/Plenum Publishers, New York and London, 123 - 137, 2001.

Menzel, A., Phenological anomalies in Germany and their relation to air temperature and NAO., Climatic Change, accepted, 2002.

Semenov, A. I. and N. N. Shefov, Empirical model of hydroxyl emission variations; ht. J. Geomagn. Aeron., 1) 229-242, 1999.

Walther, G.-R., E. Post, P. Convey, A. Menzel, C. Parmesan, T.J.C. Beebee, J.-M. Fromentin, 0. Hoegh- Guldberg and F. Bairlein, Ecological responses to recent climate change, Nature, 416, 389 - 395, 2002.

Zhou, L., C. J. Tucker, R. K. Kaufmann, D. Slayback, N. V. Shabanov, and R. B. Myneni, Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999; J. Gwphys. Res., 106, 20,069 - 20,083, 2001.

E-mail address of D. Offermann [email protected] Manuscript received 18 November 2002; revised 10 April 2003, accept& 10 April 2003.

Indicators of Long-Term Changes in Middle Atmosphere Transports

Documents

Transcript of Indicators of Long-Term Changes in Middle Atmosphere Transports