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Trends in the thermosphere derived from global ionosonde observations
Pergamon www.elsevier.com/locate/asr
Adv. Space Res. Vol.28, No. 7, pp.997-1006,200l 0 2001 COSPAR. Published by Elsevier Science Ltd. All rights reserved
Printed in Great Britain 0273-l 177/01 $20.00 + 0.00 PII: SO273-1177(01)00462-8
TRENDS IN THE THERMOSPHERE DERIVED FROM GLOBAL IONOSONDE OBSERVATIONS J. Bremer Leibniz-Institutfiir Atmosphdrenphysik, Schloss-Str. 6, D-18225 Kiihlungsborn, Germany ABSTRACT
Long-term ionospheric data series have been used to derive trends in the E region (foE and h’E), in the Fl region (foFl), and in the F2 region (foF2, hmF2). To detect these small trends from ionospheric data the solar and geomagnetically induced variations have carefully to be eliminated. Different methodical approaches have been developed and are discussed in this paper. The trends in the E layer (decrease of h’E and increase of foE) and in the Fl layer (increase of foF1) are in general agreement with model predictions assuming an increasing atmospheric greenhouse effect. Especially in the E layer the experimental trends are, however, markedly stronger than the theoretical values. In the F2 layer the scatter of the derived experimental trends for the different stations is rather high and no significant mean global trends could be estimated. 0 2001 COSPAR.Published by Elsevier Science Ltd. All rights reserved. INTRODUCTION Whereas first ionosonde observations started in the UK during the thirties of the 20thcentury, since the fifties at many measuring stations of the world continuous ionospheric observations began. Therefore, a great amount of ionosonde data are available which can be used to investigate long-term trends in the thermosphere. Such trend investigations were initiated by the modelling results ofRoble and Dickinson (1989) and Rishbeth (1990). They predicted a lowering of peak heights of the F2 layer by 1.5-20km and of the E layer by about 2.5 km assuming a doubling of the atmospheric greenhouse gas C02. Modelling results with the TIGCM (thermosphere/ionosphere general circulation model of the NCAR, Boulder) by Rishbeth and Roble (1992) supported these results and predicted additionally a decrease of the peak electron density of the F2 layer (foF2 decrease of 0.2-0.5 MHz) and increases of the peak electron densities in the foF 1 and E layers (foF1 increase 0.3-0.5 MHz, foE increase 0.05-0.08 MHz). Experimental trends from ionosonde data have been derived for one or only few stations by Bremer (1992), Givishvili et al. (1995), Ulich and Turunen (1997a), Jarvis et al. (1998) and Sharma et al. (1999). Most of these investigations are concerned with trends in the F2 region, only some include also parameters of the E and/or F 1 layer (Givishvili et al. and Sharma et al.). During recent years the thermospheric trend analyses have been extended to investigations with a lot of different stations to get more representative results. Most of these papers deal with trends in the ionospheric F2 region (Ulich and Turunen (1997b): hmF2 for 68 stations, Upadhyay and Mahajan (1998): foF2 and hmF2 for 3 1 stations, Bencze et al. (1998): hmF2 for 10 stations, Danilov and Mikhailov (1999): foF2 for 22 stations, Marin et al. (2000): hmF2 for 27 stations). Bremer ( 1998) investigated trends of foE, h’E, foF 1, foF2, and hmF2 for 3 1 European stations. In the present paper these investigations of thermospheric trends are extended to all data which were available from stations all over the world. Most data were found at CD-ROMs of the National Geophysical DataCenter, Boulder, Colorado, USA and from the WDC-C at the Rutherford Appleton Laboratory, Chilton, Didcot, UK. DATA ANALYSIS
For the trend analysis monthly median values of different ionosonde standard parameters have been used. As an example in Fig. 1 median values of foF2 and hmF2 from observations at Juliusruh (54.6”N, 13.4“E) are shown for December at two different hours (0 and 12 LMT) together with the corresponding data of solar and geomagnetic activity (solar sunspot number R and geomagnetic activity index Ap) in the lower part of this figure. As to be seen from Fig. 1 the ionospheric parameters markedly depend on the solar (and geomagnetic) activity. Changes up to about 10 MHz in foF2 and more than 100 km in hmF2 may be caused by variations of the solar activity during a solar cycle. If long-term trends with clearly smaller amplitudes will be derived from such ionospheric data, the solar induced part has carefully to be eliminated. This can be done by simple regression analyses. 997
J. Bremer
998 16,..,,
.
.I..,
.,..
,.
.,
1975
,.
1980
.,
I...
1986
.I
1990
.
I...,
1995
year
Fig. 1. Long-term variation of foF2 and hmF2 for December at 0 and 12 LMT from observations at Juliusruh (54.6”N, 13.4OE) together with the corresponding variations of solar sunspot number R and geomagnetic Ap index.
Different regression models have been tested. Using experimental values X (X=foE, h’E, foF1, foF2, or hmF2) for each hour (O-23 UT) and each month the following regression equations have been calculated: Xth=A+B*R
(1)
Xth=A+B*R+CR2
(2)
Xth=A+B*R+C.Ap
(3)
In Eqs. (1) - (3) normally the solar sunspot number (R) is used to describe the solar activity, this index can however also be replaced by the solarlO. cm radio flux (F10.7) or the new EUV proxy E10.7 (Tobiska et al., 2000). For the geomagnetic activity the planetary index Ap is used in Eq. (3). With the results of the regression analyses according to Eqs. (l), (2) or (3) the deviations or relative deviations AX of the experimental hourly data Xexp from the corresponding theoretical values Xth are calculated: AX=X
exp - Xth
AX = (Xexp - Xth) / Xth
(4) (5)
Using these AX data linear trends are derived by simple regression calculations: AX = a + b * year
(6)
With Eq. (6) linear trend parameters b can be estimated for each hour and each month. To get more reliable results, in this paper the hourly and monthly AX values obtained after Eq. (4) or (5) have been averaged to get mean yearly AX values, and yearly trends for each station have been calculated by means of Eq. (6). The significance of the trend parameter b can easily be tested by Fisher’s F parameter: F = r2 * (N - 2) / (1 - r2)
(7)
In this formula r is the correlation coefficient between AX and year after Eq. (6) and N is the number of years with data in the trend analysis. The significance levels for the F parameter can be found in Taubenheim (1969).
Trends in the Thermosphere
999
The quality of the experimental data which were normally taken from CD-ROMs have carefully to be checked as sometimes wrong data are included in the files or discontinuities occur in the data sets. If the quality of the data is not sufficient the data were rejected from the trend analyses. The mean durations of the ionosonde data series used in the analyses are about 30 years for foE, foF2, and hmF2 and about 24 years for foF 1 and h’E. METHODICAL
INVESTIGATIONS
As explained above the strong solar and geomagnetic influence upon the different ionosonde parameters have carefully to be removed as the trends in the ionospheric data are rather small. Therefore, a lot of investigations have been carried out to eliminate the solar activity caused variation. Bremer (1998) demonstrated that the derived trends are not markedly influenced by the choice of the regression models presented in Eqs. (1) - (3), especially the deviations using the models (2) or (3) are very small. Therefore, in agreement with the results of Bremer (1998) in this paper in general the model (3) is used. For the solar activity parameter used in Eqs. (1) - (3) different indices are available, the solar sunspot number R, the solar 10.7 cm radio flux F10.7 or a new index E10.7 (Tobiska et al., 2000) which describes the variability of the solar EUV spectrum. In Fig. 2 mean trends have been derived for three different ionospheric parameters (hmF2, h’F, h’E) measured at Juliusruh (54.6”N, 13.4’E) after elimination of the solar and geomagnetic influence according toEq. (3) with different solar activity indices. The full lines were obtained if the solar sunspot number R, the dotted lines if F10.7 values, and the dashed lines if El 0.7 data have been used. The individual yearly AX values (mainly for X= hmF2 , but also for foF2 and foF1 not shown here) differ from each other. The deviations from the mean trend curves are largest for the E10.7 index, especially to be seen for the year 1957, but also for the years 1967-1972. The derived mean trends, however, are not very different (also true for the foE, foF1 and foF2 data of Juliusruh not shown here). Therefore, the use of the solar activity index is not very critical and each of the three indices investigated can be used in trend analyses. As on the other side the significance level of the trends is not improved by use of the new index E10.7, in this paper mainly the solar sunspot number is used in agreement with earlier investigations (Bremer, 1998).
1960
1965
1970
1975
1980
1986
1990
1995
year Fig. 2. Yearly trends of different ionosonde parameters (hmF2, h’F and h’E) of the station Juliusruh after elimination of the solar and geomagnetic influences due to Eq. (3) using different solar activity indices (R: full lines with open squares, F10.7: dotted lines with full dots, E 10.7: dashed lines with crosses).
As mentioned above, trends can be derived from simple differences AX after Eq. (4) as used by Bremer (1992, 1998) or relative differences AX after Eq. (5) as introduced by Danilov and Mikhailov (1999). In Fig. 3 some examples are presented for trend analyses with different ionosonde data (foE, foF 1, foF2) of the station Juliusruh using simple differences after Eq. (4) (full lines) as well as relative differences afterEq. (5) (dashed lines). The differences between the derived trends are very small (also valid for liE and hmF2). Therefore, both methods can be used. In this paper AX values after Eq. (4) are analysed.
J. Bremer
1000
az!..,....,....,....,....,..~.,~...,....,...~” 1960
1966
1970
1975
1980
1985
19SfJ
1995
y-r
Fig. 3. Yearly trends of different ionosonde parameters (foF2, foF1 and foE) of the station Juliusruh after elimination of the solar and geomagnetic influences due to Eq. (3) and use of Eq. (4) (full curves belonging to the left ordinate) or Eq. (5) (dashed curves belonging to the ordinate on the right side).
Another problem known since many years is the socalled hysteresis effect in the ionospheric F2 region (Ostrow and PoKempner, 1952). After that foF2 varies differently with solar sunspot number during the rising and the falling parts of the solar cycles. In the upper part of Fig. 4 this behaviour can be seen for foF2 noon values of Juliusruh for the months January and July. The data for the rising parts of the solar cycles are represented by dots with full regression lines, the data of the falling parts by crosses with dashed regression lines. As can be seen from the upper part of Fig. 4 the differences between both regression lines may be different for different months. In the lower part of Fig. 4 the foF2 trends have been estimated for the three regression models after Eqs. (1) - (3). The results of the normal trend analysis (use of all data for a common analysis) are presented by the full lines with dots. The dashed lines with crosses have been derived taking into account the different regressions between foF2 and the solar activity R for the rising and falling parts of the solar cycles. The differences of the derived trends are very small. The same result was obtained for hmF2 values not shown here. Therefore, after our investigations the influence of the hysteresis effect in trend analyses is not very strong. One reason of this small influence may be that the hysteresis effect is different at different months. As here yearly AfoF2 or AhmF2 values have been analysed the hysteresis effect of different months may compensate each other. On the other side the influence of the hysteresis effect may additionally be reduced by the influence of the geomagnetic Ap index in Eq. (3) as found by Kane (1992).
0
50
103
150
ml
0
R
50
103
150
x-n
250
0.6 03 00
Fig. 4. Upper part: Variation of monthly foF2 values (Juliusruh, noon) in dependence on solar activity (R) for rising (dots, full lines) and falling parts of the solar cycles (crosses, dashed lines). Lower part: Yearly foF2 trends (Juliusruh) for different models after Eqs. (1) - (3) using all data together (dots, full lines) and with different regression curves for the rising and falling parts of the solar cycle (crosses, dashed lines).
In this paper the hmF2 values have been derived from the M(3000)F2 data by the very simple formula ofshimazaki (1955) which does not include the influence of underlying ionospheric layers hmF2 = 1490 / M(3000)F2 - 176
(8)
Trends in the Thermosphere
1001
After more detailed investigations of Ulich (2000) the estimation of hmF2 by this equation is only a very rough approach. This fact should be taken into consideration if the trends presented in this paper are compared with other hmF2 trend results. The advantage of the formula of Shimazaki is, however, that it only depends on the parameter M(3000)F2 and, therefore, the amount of available data increases significantly and many stations can be analysed for . , all 24 hours . EXPERIMENTAL TRENDS The results presented in the following have been derived on the basis of Eqs. (3), (4), and (6). Some examples of foE trends in dependence on latitude are shown in Fig. 5 using data of 12 stations in Western Europe. The results partly agree with trends published in Bremer (1998), only some stations have been updated by the observations until 1999. All trends shown here are positive. The significance levels of the individual regression lines have been estimated with the Fisher’s F parameter of Eq. (7). Trends with a significance level of more than 95 % are represented by dots and full lines, trends with significance levels smaller than 95 % by open squares with dashed lines. Trend analyses as shown in Fig. 5 have been carried out 0.2 using all available data series for the following five 0.0 ionosonde parameters foE, h’E, foF 1, foF2, and hmF2 from E stations all over the world. In Fig. 6 the results of the w 0.2 individual trend analyses are presented in dependence on $$ 0.0 geographic latitude (left part) and longitude (right part) of 0.2 : : : : : all stations analysed. In the upper part of Fig. 6 the trends LAMNCN for the critical frequencies foF2, foF 1, and foE are shown, cm- (48.TN) whereas in the lower part the trends of the height 0.2 : : : : : : : : : : parameters hmF2 and liE are presented. Significant trends 0.0: gKy L^: are marked by black dots, non significant trends by grey dots. The full lines represent the global mean values and 0.2 : : : : : : : : : : the dashed lines the zero level. The numbers given in the SW/A 0.0 (42. PI’J individual figures of the different parameters describe the 0.21: : : : : : : : : : 1 total numbers of the positive and negative trends. The corresponding numbers of significant trends are given in brackets. The following conclusions can be drawn from the * 0.2! : : : : : : : : : : 1 results presented in Fig. 6: - The trends derived from the observations of the individual stations differ markedly from each other. x1.2’..,. . . . . . . .,....,....,....,....,....,....,....,.. .-J Often stations not very far from each other have 50 55 60 65 70 75 80 85 90 95 clearly different trends. year - The trends do not depend neither on geographic latitude nor on longitude. There is also no dependence Fig. 5. Yearly foE trends for different stations in the on geomagnetic latitude (not shown here). western part of Europe after elimination of the solar - In the E region positive foE trends dominate. From 72 and geomagnetic influences due to Eq. (3) in trends 52 are positive and 20 negative, from 28 dependence on geographic latitude. Significant significant trends are 22 positive and only 6 negative. trends (95 %) are marked by full dots and full lines, The trends in h’E are predominantly negative with 19 non significant trends by open squares and dashed negative and 11 positive values, for the significant lines. trends the corresponding ratio is even more pronounced with 8 negative and only 3 positive trends. - The F 1 region is mainly characterized by positive foF 1 trends. From 5 1 trends are 40 positive and 11 negative, from 14 significant trends are 13 positive and only one negative. - In the F2 region the scatter of the individual trends is extremely strong. The foF2 trends are only slightly dominated by 58 negative against 48 positive values, whereas the difference between the significant trends is very small with 9 negative and 8 positive trends. The hmF2 trends are nearly symmetrically distributed around the zero level with 46 positive and 42 negative trends, from 35 significant trends are 18 positive and 17 negative.
J. Bremer
0.00
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Fig. 6 Trends of different ionosonde parameters (foF2, foF1, foE, hmF2, h’E) derived at individual stations in dependence on latitude (left part) and longitude (right part). Significant trends (95 %) are presented by black, other trends by grey dots. The global mean values are shown by full, zero levels by dashed lines. The numbers in individual pictures are the total numbers of the positive and negative trends (in brackets the corresponding numbers of significant trends).
Trends in the Thermosphere
As the trends do not show a dependence neither on latitude nor on longitude, in Fig. 7 frequency distributions of the trends of all 5 parameters (foF2, foF1, foE, hmF2, h’E) are presented. Negative trends are marked by black bars, positive by hatched bars. The mean values derived from all trend data are marked by arrows at the top of each frequency distribution. In agreement with the results of Fig. 6 also in Fig. 7 it can be seen that the distributions in the F2 region are more or less symmetric around zero, whereas the distributions in the Fl and E regions are more asymmetric with mean values clearly apart from zero. In Table 1 the global mean trend values are compiled with the mean error calculated after Taubenheim (1969) by the following formula: Error(95 %) = t95 (N- 1) . SD / JN
(9)
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35 3l 25 xl 15
15
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a5
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Here t95 is the 95 % significance level of the Students test, N is the number of stations and SD the standard deviation derived from the individual trend values. In Table 1 the mean trend values for all five parameters investigated are compiled together with the error levels calculated afterEq. (9). As clearly to be seen, only the global trends of foE and foF 1 are significant different from zero with a reliability of more than 95 %. All other mean trends have markedly smaller significance levels.
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. .
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‘5 IO 5 0 0.00
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Fig. 7. Frequency distributions of trends derived from global ionosonde observations.
Table 1. Mean experimental trends and error limits (95%) derived from trend analyses of different ionosonde parameters at different stations.
F2 Region
Parameter
N
Mean Trend
Error (95 %)
foF2
106
-0.0014 MHz/year
f 0.0025 MHz/year
hmF2
88
0.0053 km/year
* 0.0770 km/year
Fl Region
foF 1
51
0.0028 MHz/year
+ 0.0011 MHz/year
E Region
foE
72
0.0016 MHz/year
+ 0.0008 MHz/year
h’E
30
-0.0370 km/year
f 0.0720 km/year
DISCUSSION One important question is: Can the derived trends be explained by the an increasing atmospheric greenhouse effect? Therefore, the derived mean ionospheric trends will be compared with model results ofRishbeth (1990) and Rishbeth and Roble (1992). The predicted changes of different thermospheric parameters due to doubling of the greenhouse gases (mainly C02) and the experimental mean trends from ionosonde observations are presented in Table 2. As the model calculations have been carried out for a doubling of the greenhouse gases it is necessary to look at the real changes of these gases during the last 40 years, corresponding to the main time interval ofionosonde observations investigated in this paper. After Houghton et al. (1996) and Brasseur and de Rudder (1987) an effective increase of the greenhouse gases of about 20 % can be assumed for the last 40 years. Taking into account this increase, the changes of the different experimental values for a doubling of the greenhouse gases can easily be extrapolated assuming a linear dependence between the content of the atmospheric greenhouse gases and the ionospheric effect, these estimated values are called CO2*2 (exp) in Table 2. A comparison of the experimental with the model trends in Table 2 demonstrates that in the E region the trends qualitatively agree, the amplitudes of the experimental trends are, however, stronger by a factor of 3 - 5 than the predicted values. The measured foE increase is also in qualitative agreement with negative trends of the ion ratio Ir\lO+/O2+] as derived by Danilov and Smimova (1997) from mass spectrometer observation at E region heights. This negative trend should increase the effective recombination coefficient as the dissociative recombination coefficient of NO+ is larger than that of 02+ ,and therefore a positive trend of the electron density can be expected. The negative trend of the ion ratio [NO+/o2+] could be caused by the negative NO trend as expected from model calculations for an increasing greenhouse effect (Roble and Dickinson, 1989; Beig and Mitra, 1997; Chakrabarty, 1997).
1004
J. Bremer
Table 2. Mean experimental trends of different ionospheric parameters and expected changes of these data assuming doubling of the atmospheric Greenhouse gases (CO2 * 2). The model data (mod) are from Rishbeth (1990) and Rishbeth and Roble ( 1992).
As to be seen from the mean results compiled in Table 2 in the F 1 region the agreement between experimental and theoretical values is relatively good, the mean experimental trend in foF1 is only slightly stronger than the value predicted by model calculations. From satellite observations with a perigee altitude of about 350 kmKeating et al. (2000) found clear indications of a decrease of the atmospheric density caused by a cooling of the upper atmosphere due to an increasing atmospheric greenhouse effect. From the investigated trends of ionosonde parameters of the F2 layer, however, such a clear confirmation of the greenhouse effect could not be found. Whereas the mean foF2 trend agrees surprisingly well with the model prediction, the mean experimental hmF2 trend has even a different sign compared with the theoretically expected trend. As to be seen from the data of Table 1 the global mean values of the trends in the F2 region are accompanied by large mean errors due to the very strong scatter of the individual trend values. Therefore, the estimated global mean trends of foF2 and of hmF2 are not representative for the F2 region. Here the variability is too strong to get significant mean trends. The reason of this variability is not quite clear. As to be seen in Fig. 6 there is no clear dependence of the individual trends on latitude and longitude. Nevertheless there are some indications of regional differences of the individual trends as discussed by Bremer (1998) which possibly could be dynamically induced. But there are additional investigations necessary in the future to get a better understanding of the trends in the F2 region. One reason for the strong variability of individual trends can be technical changes during the long-term ionosonde observations or changes in the evaluation procedures. In Fig. 8 some examples are presented with possible steps in some h’E data series. In Bremer (1998) other examules can be found for discontinuities in hmF2 trends. If such technically caused changes are not detected the derived trends could be incorrect. It should be one of the tasks for the future to test the quality of the data of the individual stations more accurate to remove data series of stations with low quality observations. CONCLUSIONS From world wide long-term ionosonde observations trends have been derived for different characteristic ionospheric parameters in the E, Fl, and F2 regions. The main conclusions can be summarized as follows: Long-term trends are markedly smaller than the solar and geomagnetically induced variations of the investigated ionospheric parameters. Therefore, the solar induced part has carefully to be eliminated in the trend analysis procedure. Trends in the E region (lowering of h’E and increase of foE) are in qualitative agreement with model predictions assuming an increasing atmospheric greenhouse effect. The experimental trends are however 3 - 5 times stronger than the model result. The mean trend in the F 1 region (increase of foF 1) is in good agreement with model calculations for an increasing greenhouse effect. The scatter of the individual F2 region trend data (foF2 and hmF2) is very high, and the estimated mean global trends are relatively small and not significant different
tLancap (12.0%)
L
J
:
60
66
70
75 year
80
85
90
Fig. 8. Examples of Ah’E trends with sudden discontinuities which could be caused by technical changes.
Trends in the Thermosphere
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1005
from zero. Therefore, a conclusive comparison of these mean values with model results of an atmospheric greenhouse effect is impossible. From the trend results presented here it cannot be decided if there is a remarkable global trend in the F2 layer caused by an increasing greenhouse effect. The origin of the observed strong differences between the trends derived for the individual stations is not quite clear. Mainly in the F2 region dynamical effects may play an important role which could cause regionally different trends. But also inhomogeneities in the data series due to technical changes of the equipment or changes of the evaluation algorithms have to be taken into consideration.
ACKNOWLEDGEMENTS The author thanks R. Conkright of the NGDC, Boulder, Colorado, USA and M. Dick from RAL, Chilton, Didcot, UK for getting a large amount of ionosonde data on CD-ROMs and K. Tobiska from FDCXPL, Pasadena, USA for new data of the solar EUV proxy El 0.7. REFERENCES Beig, G., and A. P. Mitra, Atmospheric and ionospheric response to trace gas perturbations through the ice age to the next century in the middle atmosphere, J. Atmos. Solar-Terr. Phys., 59, 1245- 1259, 1997. Bencze, P., G. Sole, L. F. Alberta, and A. Poor, Long-term changes of hmF2: possible latitudinal and regional variations, Proc. of the 2nd COST 251 Workshop, 30-3 1 March 1998, Side, Turkey, RAL UK, 107-l 13, 1998. Brasseur, G., and A. de Rudder, The potential impact on atmospheric ozone and temperature of increasing trace gas concentrations, J. Geophys. Res., 92, 10903-10920, 1987. Bremer, J., Ionospheric trends in mid-latitudes as a possible indicator of the atmospheric greenhouse effect, J. Atmos. Terr. Phys., 54, 1505-I 5 11, 1992. Bremer, J., Trends in the ionospheric E and F regions over Europe, Ann. Geophysicae, 16,986-996, 1998. Chakrabarty, D. K., Mesopause scenario on doubling of C02, Adv. Space Res., 20, (11)2 117-2 125, 1997. Danilov, A., D., and A. V. Mikhailov, Spatial and seasonal variations of foF2 long-term trends&m. Geophysicae, 19, 1239-1243, 1999. Danilov, A. D., and N. V. Smirnova, Long-term trends in the ion composition in the E region (in Russian),Geomagn. Aeron., 37, (4), 35-40, 1997. Givishvili, G. V., L. N. Leshchenko, 0. P. Shmeleva, and T. G. Ivanidze, Climatic trends of the mid-latitude upper atmosphere and ionosphere, 1 Atmos. Terr. Phys., 57,87 l-874, 1995. Houghton; J. T., L. G. Meira Filho, B. A. Callander, N. Harris, A. Kattenberg, and K. Maskell, Climate Change 1995, Contribution of WGI to the Second Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, 1996. Jarvis, M. J.,-B. Jenkins, and G. A. Rodgers, Southern hemisphere observations of a long-term decrease in F region altitude and thermospheric wind providing possible evidence for global thermospheric cooling, J0Geophys. Res., 103,20775-20787, 1998. Kane, R. P.; Solar cycle variation of foF2, J. Atmos. Terr. Phys., 54, 1201-1205, 1992. Keating, G. M., R. H. Tolson, and M. S. Bradford, Evidence of long term global decline in the Earthh thermcspheric densities apparently related to anthropogenic effects, Geophys. Res. Lett., 27, 1523-1526,200O. Marin, D., A. V. Mikhailov, B. A. de la Morens, and M. Herraiz, Long-term hmF2 trends in the Eurasian longitudinal sector on the ground-based ionosonde observations, Physics and Chemistry of the Earth (submitted), 2000. Ostrow, S. M., and M. PoKempner, The differences in the relationship between ionospheric critical frequencies and sunspot number for different sunspot cycles, J. Geophys. Res., 57,473-480, 1992. Rishbeth, H., A greenhouse effect in the ionosphere ?, Planet. Space Sci., 38, 945-948, 1990. Rishbeth, H., and R. G. Roble, Cooling of the upper atmosphere by enhanced greenhouse gases - Modelling of thermospheric and ionospheric effects, Planet. Space Sci., 40, 10 1 l- 1026, 1992. Roble, R. G., and R. E. Dickinson, How will changes in carbon dioxide and methane modify the mean structure of the mesophere and thermosphere?, Geophys. Res. Lett., 16, 144 l- 1444, 1989. Sharma, S., H. Chandra, and G. D. Vyas, Long term trends over Ahmedabad, Geophys. Res. Lett., 26,433-436, 1999. Shimazaki, T., World wide daily variations in the height of the maximum electron density in theionospheric F2 layer, J. Radio Res. Labs., Japan, 2, 85-97, 1955. Taubenheim,J., Statistische Auswertung geophysikalischer und meteorologischer Daten, Akad. Verlagsgesellschaft Geest und Portig K.-G., Leipzig, 1969. Tobiska, W. K., D. Bouver, and C. McGuigan, Validation of the solar EUV proxy E10.7,J. Geophys. Res. (submitted), 2000. Ulich, T., Solar variability and long-term trends in the ionosphere, Sodankyll Geophysical Observatory Publications, No. 87, University of Oulu, Finland, 2000. Ulich,and E. Turunen, Evidence for long-term cooling of the upper atmosphere, Geophys. Res. Lett., 24, 11031106, 1997a
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Ulich, T., and E. Turunen, Long-term behaviour of ionospheric F2 layer peak height on a global scale, Paper presented at Session 2.18 of the 8th Scientific Assembly of IAGA, Uppsala, 1997b. Upadhyay, H. O., and K. K. Mahajan, Atmospheric greenhouse effect and ionospheric trends, Geophys. Res. Lett., 25, 3375-3378, 1998.
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Adv. Space Res. Vol.28, No. 7, pp.997-1006,200l 0 2001 COSPAR. Published by Elsevier Science Ltd. All rights reserved
Printed in Great Britain 0273-l 177/01 $20.00 + 0.00 PII: SO273-1177(01)00462-8
TRENDS IN THE THERMOSPHERE DERIVED FROM GLOBAL IONOSONDE OBSERVATIONS J. Bremer Leibniz-Institutfiir Atmosphdrenphysik, Schloss-Str. 6, D-18225 Kiihlungsborn, Germany ABSTRACT
Long-term ionospheric data series have been used to derive trends in the E region (foE and h’E), in the Fl region (foFl), and in the F2 region (foF2, hmF2). To detect these small trends from ionospheric data the solar and geomagnetically induced variations have carefully to be eliminated. Different methodical approaches have been developed and are discussed in this paper. The trends in the E layer (decrease of h’E and increase of foE) and in the Fl layer (increase of foF1) are in general agreement with model predictions assuming an increasing atmospheric greenhouse effect. Especially in the E layer the experimental trends are, however, markedly stronger than the theoretical values. In the F2 layer the scatter of the derived experimental trends for the different stations is rather high and no significant mean global trends could be estimated. 0 2001 COSPAR.Published by Elsevier Science Ltd. All rights reserved. INTRODUCTION Whereas first ionosonde observations started in the UK during the thirties of the 20thcentury, since the fifties at many measuring stations of the world continuous ionospheric observations began. Therefore, a great amount of ionosonde data are available which can be used to investigate long-term trends in the thermosphere. Such trend investigations were initiated by the modelling results ofRoble and Dickinson (1989) and Rishbeth (1990). They predicted a lowering of peak heights of the F2 layer by 1.5-20km and of the E layer by about 2.5 km assuming a doubling of the atmospheric greenhouse gas C02. Modelling results with the TIGCM (thermosphere/ionosphere general circulation model of the NCAR, Boulder) by Rishbeth and Roble (1992) supported these results and predicted additionally a decrease of the peak electron density of the F2 layer (foF2 decrease of 0.2-0.5 MHz) and increases of the peak electron densities in the foF 1 and E layers (foF1 increase 0.3-0.5 MHz, foE increase 0.05-0.08 MHz). Experimental trends from ionosonde data have been derived for one or only few stations by Bremer (1992), Givishvili et al. (1995), Ulich and Turunen (1997a), Jarvis et al. (1998) and Sharma et al. (1999). Most of these investigations are concerned with trends in the F2 region, only some include also parameters of the E and/or F 1 layer (Givishvili et al. and Sharma et al.). During recent years the thermospheric trend analyses have been extended to investigations with a lot of different stations to get more representative results. Most of these papers deal with trends in the ionospheric F2 region (Ulich and Turunen (1997b): hmF2 for 68 stations, Upadhyay and Mahajan (1998): foF2 and hmF2 for 3 1 stations, Bencze et al. (1998): hmF2 for 10 stations, Danilov and Mikhailov (1999): foF2 for 22 stations, Marin et al. (2000): hmF2 for 27 stations). Bremer ( 1998) investigated trends of foE, h’E, foF 1, foF2, and hmF2 for 3 1 European stations. In the present paper these investigations of thermospheric trends are extended to all data which were available from stations all over the world. Most data were found at CD-ROMs of the National Geophysical DataCenter, Boulder, Colorado, USA and from the WDC-C at the Rutherford Appleton Laboratory, Chilton, Didcot, UK. DATA ANALYSIS
For the trend analysis monthly median values of different ionosonde standard parameters have been used. As an example in Fig. 1 median values of foF2 and hmF2 from observations at Juliusruh (54.6”N, 13.4“E) are shown for December at two different hours (0 and 12 LMT) together with the corresponding data of solar and geomagnetic activity (solar sunspot number R and geomagnetic activity index Ap) in the lower part of this figure. As to be seen from Fig. 1 the ionospheric parameters markedly depend on the solar (and geomagnetic) activity. Changes up to about 10 MHz in foF2 and more than 100 km in hmF2 may be caused by variations of the solar activity during a solar cycle. If long-term trends with clearly smaller amplitudes will be derived from such ionospheric data, the solar induced part has carefully to be eliminated. This can be done by simple regression analyses. 997
J. Bremer
998 16,..,,
.
.I..,
.,..
,.
.,
1975
,.
1980
.,
I...
1986
.I
1990
.
I...,
1995
year
Fig. 1. Long-term variation of foF2 and hmF2 for December at 0 and 12 LMT from observations at Juliusruh (54.6”N, 13.4OE) together with the corresponding variations of solar sunspot number R and geomagnetic Ap index.
Different regression models have been tested. Using experimental values X (X=foE, h’E, foF1, foF2, or hmF2) for each hour (O-23 UT) and each month the following regression equations have been calculated: Xth=A+B*R
(1)
Xth=A+B*R+CR2
(2)
Xth=A+B*R+C.Ap
(3)
In Eqs. (1) - (3) normally the solar sunspot number (R) is used to describe the solar activity, this index can however also be replaced by the solarlO. cm radio flux (F10.7) or the new EUV proxy E10.7 (Tobiska et al., 2000). For the geomagnetic activity the planetary index Ap is used in Eq. (3). With the results of the regression analyses according to Eqs. (l), (2) or (3) the deviations or relative deviations AX of the experimental hourly data Xexp from the corresponding theoretical values Xth are calculated: AX=X
exp - Xth
AX = (Xexp - Xth) / Xth
(4) (5)
Using these AX data linear trends are derived by simple regression calculations: AX = a + b * year
(6)
With Eq. (6) linear trend parameters b can be estimated for each hour and each month. To get more reliable results, in this paper the hourly and monthly AX values obtained after Eq. (4) or (5) have been averaged to get mean yearly AX values, and yearly trends for each station have been calculated by means of Eq. (6). The significance of the trend parameter b can easily be tested by Fisher’s F parameter: F = r2 * (N - 2) / (1 - r2)
(7)
In this formula r is the correlation coefficient between AX and year after Eq. (6) and N is the number of years with data in the trend analysis. The significance levels for the F parameter can be found in Taubenheim (1969).
Trends in the Thermosphere
999
The quality of the experimental data which were normally taken from CD-ROMs have carefully to be checked as sometimes wrong data are included in the files or discontinuities occur in the data sets. If the quality of the data is not sufficient the data were rejected from the trend analyses. The mean durations of the ionosonde data series used in the analyses are about 30 years for foE, foF2, and hmF2 and about 24 years for foF 1 and h’E. METHODICAL
INVESTIGATIONS
As explained above the strong solar and geomagnetic influence upon the different ionosonde parameters have carefully to be removed as the trends in the ionospheric data are rather small. Therefore, a lot of investigations have been carried out to eliminate the solar activity caused variation. Bremer (1998) demonstrated that the derived trends are not markedly influenced by the choice of the regression models presented in Eqs. (1) - (3), especially the deviations using the models (2) or (3) are very small. Therefore, in agreement with the results of Bremer (1998) in this paper in general the model (3) is used. For the solar activity parameter used in Eqs. (1) - (3) different indices are available, the solar sunspot number R, the solar 10.7 cm radio flux F10.7 or a new index E10.7 (Tobiska et al., 2000) which describes the variability of the solar EUV spectrum. In Fig. 2 mean trends have been derived for three different ionospheric parameters (hmF2, h’F, h’E) measured at Juliusruh (54.6”N, 13.4’E) after elimination of the solar and geomagnetic influence according toEq. (3) with different solar activity indices. The full lines were obtained if the solar sunspot number R, the dotted lines if F10.7 values, and the dashed lines if El 0.7 data have been used. The individual yearly AX values (mainly for X= hmF2 , but also for foF2 and foF1 not shown here) differ from each other. The deviations from the mean trend curves are largest for the E10.7 index, especially to be seen for the year 1957, but also for the years 1967-1972. The derived mean trends, however, are not very different (also true for the foE, foF1 and foF2 data of Juliusruh not shown here). Therefore, the use of the solar activity index is not very critical and each of the three indices investigated can be used in trend analyses. As on the other side the significance level of the trends is not improved by use of the new index E10.7, in this paper mainly the solar sunspot number is used in agreement with earlier investigations (Bremer, 1998).
1960
1965
1970
1975
1980
1986
1990
1995
year Fig. 2. Yearly trends of different ionosonde parameters (hmF2, h’F and h’E) of the station Juliusruh after elimination of the solar and geomagnetic influences due to Eq. (3) using different solar activity indices (R: full lines with open squares, F10.7: dotted lines with full dots, E 10.7: dashed lines with crosses).
As mentioned above, trends can be derived from simple differences AX after Eq. (4) as used by Bremer (1992, 1998) or relative differences AX after Eq. (5) as introduced by Danilov and Mikhailov (1999). In Fig. 3 some examples are presented for trend analyses with different ionosonde data (foE, foF 1, foF2) of the station Juliusruh using simple differences after Eq. (4) (full lines) as well as relative differences afterEq. (5) (dashed lines). The differences between the derived trends are very small (also valid for liE and hmF2). Therefore, both methods can be used. In this paper AX values after Eq. (4) are analysed.
J. Bremer
1000
az!..,....,....,....,....,..~.,~...,....,...~” 1960
1966
1970
1975
1980
1985
19SfJ
1995
y-r
Fig. 3. Yearly trends of different ionosonde parameters (foF2, foF1 and foE) of the station Juliusruh after elimination of the solar and geomagnetic influences due to Eq. (3) and use of Eq. (4) (full curves belonging to the left ordinate) or Eq. (5) (dashed curves belonging to the ordinate on the right side).
Another problem known since many years is the socalled hysteresis effect in the ionospheric F2 region (Ostrow and PoKempner, 1952). After that foF2 varies differently with solar sunspot number during the rising and the falling parts of the solar cycles. In the upper part of Fig. 4 this behaviour can be seen for foF2 noon values of Juliusruh for the months January and July. The data for the rising parts of the solar cycles are represented by dots with full regression lines, the data of the falling parts by crosses with dashed regression lines. As can be seen from the upper part of Fig. 4 the differences between both regression lines may be different for different months. In the lower part of Fig. 4 the foF2 trends have been estimated for the three regression models after Eqs. (1) - (3). The results of the normal trend analysis (use of all data for a common analysis) are presented by the full lines with dots. The dashed lines with crosses have been derived taking into account the different regressions between foF2 and the solar activity R for the rising and falling parts of the solar cycles. The differences of the derived trends are very small. The same result was obtained for hmF2 values not shown here. Therefore, after our investigations the influence of the hysteresis effect in trend analyses is not very strong. One reason of this small influence may be that the hysteresis effect is different at different months. As here yearly AfoF2 or AhmF2 values have been analysed the hysteresis effect of different months may compensate each other. On the other side the influence of the hysteresis effect may additionally be reduced by the influence of the geomagnetic Ap index in Eq. (3) as found by Kane (1992).
0
50
103
150
ml
0
R
50
103
150
x-n
250
0.6 03 00
Fig. 4. Upper part: Variation of monthly foF2 values (Juliusruh, noon) in dependence on solar activity (R) for rising (dots, full lines) and falling parts of the solar cycles (crosses, dashed lines). Lower part: Yearly foF2 trends (Juliusruh) for different models after Eqs. (1) - (3) using all data together (dots, full lines) and with different regression curves for the rising and falling parts of the solar cycle (crosses, dashed lines).
In this paper the hmF2 values have been derived from the M(3000)F2 data by the very simple formula ofshimazaki (1955) which does not include the influence of underlying ionospheric layers hmF2 = 1490 / M(3000)F2 - 176
(8)
Trends in the Thermosphere
1001
After more detailed investigations of Ulich (2000) the estimation of hmF2 by this equation is only a very rough approach. This fact should be taken into consideration if the trends presented in this paper are compared with other hmF2 trend results. The advantage of the formula of Shimazaki is, however, that it only depends on the parameter M(3000)F2 and, therefore, the amount of available data increases significantly and many stations can be analysed for . , all 24 hours . EXPERIMENTAL TRENDS The results presented in the following have been derived on the basis of Eqs. (3), (4), and (6). Some examples of foE trends in dependence on latitude are shown in Fig. 5 using data of 12 stations in Western Europe. The results partly agree with trends published in Bremer (1998), only some stations have been updated by the observations until 1999. All trends shown here are positive. The significance levels of the individual regression lines have been estimated with the Fisher’s F parameter of Eq. (7). Trends with a significance level of more than 95 % are represented by dots and full lines, trends with significance levels smaller than 95 % by open squares with dashed lines. Trend analyses as shown in Fig. 5 have been carried out 0.2 using all available data series for the following five 0.0 ionosonde parameters foE, h’E, foF 1, foF2, and hmF2 from E stations all over the world. In Fig. 6 the results of the w 0.2 individual trend analyses are presented in dependence on $$ 0.0 geographic latitude (left part) and longitude (right part) of 0.2 : : : : : all stations analysed. In the upper part of Fig. 6 the trends LAMNCN for the critical frequencies foF2, foF 1, and foE are shown, cm- (48.TN) whereas in the lower part the trends of the height 0.2 : : : : : : : : : : parameters hmF2 and liE are presented. Significant trends 0.0: gKy L^: are marked by black dots, non significant trends by grey dots. The full lines represent the global mean values and 0.2 : : : : : : : : : : the dashed lines the zero level. The numbers given in the SW/A 0.0 (42. PI’J individual figures of the different parameters describe the 0.21: : : : : : : : : : 1 total numbers of the positive and negative trends. The corresponding numbers of significant trends are given in brackets. The following conclusions can be drawn from the * 0.2! : : : : : : : : : : 1 results presented in Fig. 6: - The trends derived from the observations of the individual stations differ markedly from each other. x1.2’..,. . . . . . . .,....,....,....,....,....,....,....,.. .-J Often stations not very far from each other have 50 55 60 65 70 75 80 85 90 95 clearly different trends. year - The trends do not depend neither on geographic latitude nor on longitude. There is also no dependence Fig. 5. Yearly foE trends for different stations in the on geomagnetic latitude (not shown here). western part of Europe after elimination of the solar - In the E region positive foE trends dominate. From 72 and geomagnetic influences due to Eq. (3) in trends 52 are positive and 20 negative, from 28 dependence on geographic latitude. Significant significant trends are 22 positive and only 6 negative. trends (95 %) are marked by full dots and full lines, The trends in h’E are predominantly negative with 19 non significant trends by open squares and dashed negative and 11 positive values, for the significant lines. trends the corresponding ratio is even more pronounced with 8 negative and only 3 positive trends. - The F 1 region is mainly characterized by positive foF 1 trends. From 5 1 trends are 40 positive and 11 negative, from 14 significant trends are 13 positive and only one negative. - In the F2 region the scatter of the individual trends is extremely strong. The foF2 trends are only slightly dominated by 58 negative against 48 positive values, whereas the difference between the significant trends is very small with 9 negative and 8 positive trends. The hmF2 trends are nearly symmetrically distributed around the zero level with 46 positive and 42 negative trends, from 35 significant trends are 18 positive and 17 negative.
J. Bremer
0.00
-0.02 -0.04
Q.04
0.015
0.015
F z g
0.010
0.010
0.006
0.005
0.000
0.m
-0.cn5
Q.005 0.015
0.015 52m
tbE
Iif $ z 2 ti
0.010
1.0 0.5
0.0
3
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$
-1.0
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0.0
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-02
a.4
Al.4 -90
40
-3
0
30
latitu&["]
60
900
90
180
270
360
longitu&["1
Fig. 6 Trends of different ionosonde parameters (foF2, foF1, foE, hmF2, h’E) derived at individual stations in dependence on latitude (left part) and longitude (right part). Significant trends (95 %) are presented by black, other trends by grey dots. The global mean values are shown by full, zero levels by dashed lines. The numbers in individual pictures are the total numbers of the positive and negative trends (in brackets the corresponding numbers of significant trends).
Trends in the Thermosphere
As the trends do not show a dependence neither on latitude nor on longitude, in Fig. 7 frequency distributions of the trends of all 5 parameters (foF2, foF1, foE, hmF2, h’E) are presented. Negative trends are marked by black bars, positive by hatched bars. The mean values derived from all trend data are marked by arrows at the top of each frequency distribution. In agreement with the results of Fig. 6 also in Fig. 7 it can be seen that the distributions in the F2 region are more or less symmetric around zero, whereas the distributions in the Fl and E regions are more asymmetric with mean values clearly apart from zero. In Table 1 the global mean trend values are compiled with the mean error calculated after Taubenheim (1969) by the following formula: Error(95 %) = t95 (N- 1) . SD / JN
(9)
1003
35 3l 25 xl 15
15
IO
IO
5
5 0
0
.g
I
-0.05
-1.0 -0.5 0.0
a5
1.0
0.5
1.0
10
‘s5
!
0 am I
Here t95 is the 95 % significance level of the Students test, N is the number of stations and SD the standard deviation derived from the individual trend values. In Table 1 the mean trend values for all five parameters investigated are compiled together with the error levels calculated afterEq. (9). As clearly to be seen, only the global trends of foE and foF 1 are significant different from zero with a reliability of more than 95 %. All other mean trends have markedly smaller significance levels.
0.05
0.00
OS0
,
.
.
I
.I.
0.01 m
. .
I.,
‘5 IO 5 0 0.00
0.01
WlWrl
am
a E
-1.0 -a5
0.0
td[knfyearl
Fig. 7. Frequency distributions of trends derived from global ionosonde observations.
Table 1. Mean experimental trends and error limits (95%) derived from trend analyses of different ionosonde parameters at different stations.
F2 Region
Parameter
N
Mean Trend
Error (95 %)
foF2
106
-0.0014 MHz/year
f 0.0025 MHz/year
hmF2
88
0.0053 km/year
* 0.0770 km/year
Fl Region
foF 1
51
0.0028 MHz/year
+ 0.0011 MHz/year
E Region
foE
72
0.0016 MHz/year
+ 0.0008 MHz/year
h’E
30
-0.0370 km/year
f 0.0720 km/year
DISCUSSION One important question is: Can the derived trends be explained by the an increasing atmospheric greenhouse effect? Therefore, the derived mean ionospheric trends will be compared with model results ofRishbeth (1990) and Rishbeth and Roble (1992). The predicted changes of different thermospheric parameters due to doubling of the greenhouse gases (mainly C02) and the experimental mean trends from ionosonde observations are presented in Table 2. As the model calculations have been carried out for a doubling of the greenhouse gases it is necessary to look at the real changes of these gases during the last 40 years, corresponding to the main time interval ofionosonde observations investigated in this paper. After Houghton et al. (1996) and Brasseur and de Rudder (1987) an effective increase of the greenhouse gases of about 20 % can be assumed for the last 40 years. Taking into account this increase, the changes of the different experimental values for a doubling of the greenhouse gases can easily be extrapolated assuming a linear dependence between the content of the atmospheric greenhouse gases and the ionospheric effect, these estimated values are called CO2*2 (exp) in Table 2. A comparison of the experimental with the model trends in Table 2 demonstrates that in the E region the trends qualitatively agree, the amplitudes of the experimental trends are, however, stronger by a factor of 3 - 5 than the predicted values. The measured foE increase is also in qualitative agreement with negative trends of the ion ratio Ir\lO+/O2+] as derived by Danilov and Smimova (1997) from mass spectrometer observation at E region heights. This negative trend should increase the effective recombination coefficient as the dissociative recombination coefficient of NO+ is larger than that of 02+ ,and therefore a positive trend of the electron density can be expected. The negative trend of the ion ratio [NO+/o2+] could be caused by the negative NO trend as expected from model calculations for an increasing greenhouse effect (Roble and Dickinson, 1989; Beig and Mitra, 1997; Chakrabarty, 1997).
1004
J. Bremer
Table 2. Mean experimental trends of different ionospheric parameters and expected changes of these data assuming doubling of the atmospheric Greenhouse gases (CO2 * 2). The model data (mod) are from Rishbeth (1990) and Rishbeth and Roble ( 1992).
As to be seen from the mean results compiled in Table 2 in the F 1 region the agreement between experimental and theoretical values is relatively good, the mean experimental trend in foF1 is only slightly stronger than the value predicted by model calculations. From satellite observations with a perigee altitude of about 350 kmKeating et al. (2000) found clear indications of a decrease of the atmospheric density caused by a cooling of the upper atmosphere due to an increasing atmospheric greenhouse effect. From the investigated trends of ionosonde parameters of the F2 layer, however, such a clear confirmation of the greenhouse effect could not be found. Whereas the mean foF2 trend agrees surprisingly well with the model prediction, the mean experimental hmF2 trend has even a different sign compared with the theoretically expected trend. As to be seen from the data of Table 1 the global mean values of the trends in the F2 region are accompanied by large mean errors due to the very strong scatter of the individual trend values. Therefore, the estimated global mean trends of foF2 and of hmF2 are not representative for the F2 region. Here the variability is too strong to get significant mean trends. The reason of this variability is not quite clear. As to be seen in Fig. 6 there is no clear dependence of the individual trends on latitude and longitude. Nevertheless there are some indications of regional differences of the individual trends as discussed by Bremer (1998) which possibly could be dynamically induced. But there are additional investigations necessary in the future to get a better understanding of the trends in the F2 region. One reason for the strong variability of individual trends can be technical changes during the long-term ionosonde observations or changes in the evaluation procedures. In Fig. 8 some examples are presented with possible steps in some h’E data series. In Bremer (1998) other examules can be found for discontinuities in hmF2 trends. If such technically caused changes are not detected the derived trends could be incorrect. It should be one of the tasks for the future to test the quality of the data of the individual stations more accurate to remove data series of stations with low quality observations. CONCLUSIONS From world wide long-term ionosonde observations trends have been derived for different characteristic ionospheric parameters in the E, Fl, and F2 regions. The main conclusions can be summarized as follows: Long-term trends are markedly smaller than the solar and geomagnetically induced variations of the investigated ionospheric parameters. Therefore, the solar induced part has carefully to be eliminated in the trend analysis procedure. Trends in the E region (lowering of h’E and increase of foE) are in qualitative agreement with model predictions assuming an increasing atmospheric greenhouse effect. The experimental trends are however 3 - 5 times stronger than the model result. The mean trend in the F 1 region (increase of foF 1) is in good agreement with model calculations for an increasing greenhouse effect. The scatter of the individual F2 region trend data (foF2 and hmF2) is very high, and the estimated mean global trends are relatively small and not significant different
tLancap (12.0%)
L
J
:
60
66
70
75 year
80
85
90
Fig. 8. Examples of Ah’E trends with sudden discontinuities which could be caused by technical changes.
Trends in the Thermosphere
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1005
from zero. Therefore, a conclusive comparison of these mean values with model results of an atmospheric greenhouse effect is impossible. From the trend results presented here it cannot be decided if there is a remarkable global trend in the F2 layer caused by an increasing greenhouse effect. The origin of the observed strong differences between the trends derived for the individual stations is not quite clear. Mainly in the F2 region dynamical effects may play an important role which could cause regionally different trends. But also inhomogeneities in the data series due to technical changes of the equipment or changes of the evaluation algorithms have to be taken into consideration.
ACKNOWLEDGEMENTS The author thanks R. Conkright of the NGDC, Boulder, Colorado, USA and M. Dick from RAL, Chilton, Didcot, UK for getting a large amount of ionosonde data on CD-ROMs and K. Tobiska from FDCXPL, Pasadena, USA for new data of the solar EUV proxy El 0.7. REFERENCES Beig, G., and A. P. Mitra, Atmospheric and ionospheric response to trace gas perturbations through the ice age to the next century in the middle atmosphere, J. Atmos. Solar-Terr. Phys., 59, 1245- 1259, 1997. Bencze, P., G. Sole, L. F. Alberta, and A. Poor, Long-term changes of hmF2: possible latitudinal and regional variations, Proc. of the 2nd COST 251 Workshop, 30-3 1 March 1998, Side, Turkey, RAL UK, 107-l 13, 1998. Brasseur, G., and A. de Rudder, The potential impact on atmospheric ozone and temperature of increasing trace gas concentrations, J. Geophys. Res., 92, 10903-10920, 1987. Bremer, J., Ionospheric trends in mid-latitudes as a possible indicator of the atmospheric greenhouse effect, J. Atmos. Terr. Phys., 54, 1505-I 5 11, 1992. Bremer, J., Trends in the ionospheric E and F regions over Europe, Ann. Geophysicae, 16,986-996, 1998. Chakrabarty, D. K., Mesopause scenario on doubling of C02, Adv. Space Res., 20, (11)2 117-2 125, 1997. Danilov, A., D., and A. V. Mikhailov, Spatial and seasonal variations of foF2 long-term trends&m. Geophysicae, 19, 1239-1243, 1999. Danilov, A. D., and N. V. Smirnova, Long-term trends in the ion composition in the E region (in Russian),Geomagn. Aeron., 37, (4), 35-40, 1997. Givishvili, G. V., L. N. Leshchenko, 0. P. Shmeleva, and T. G. Ivanidze, Climatic trends of the mid-latitude upper atmosphere and ionosphere, 1 Atmos. Terr. Phys., 57,87 l-874, 1995. Houghton; J. T., L. G. Meira Filho, B. A. Callander, N. Harris, A. Kattenberg, and K. Maskell, Climate Change 1995, Contribution of WGI to the Second Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, 1996. Jarvis, M. J.,-B. Jenkins, and G. A. Rodgers, Southern hemisphere observations of a long-term decrease in F region altitude and thermospheric wind providing possible evidence for global thermospheric cooling, J0Geophys. Res., 103,20775-20787, 1998. Kane, R. P.; Solar cycle variation of foF2, J. Atmos. Terr. Phys., 54, 1201-1205, 1992. Keating, G. M., R. H. Tolson, and M. S. Bradford, Evidence of long term global decline in the Earthh thermcspheric densities apparently related to anthropogenic effects, Geophys. Res. Lett., 27, 1523-1526,200O. Marin, D., A. V. Mikhailov, B. A. de la Morens, and M. Herraiz, Long-term hmF2 trends in the Eurasian longitudinal sector on the ground-based ionosonde observations, Physics and Chemistry of the Earth (submitted), 2000. Ostrow, S. M., and M. PoKempner, The differences in the relationship between ionospheric critical frequencies and sunspot number for different sunspot cycles, J. Geophys. Res., 57,473-480, 1992. Rishbeth, H., A greenhouse effect in the ionosphere ?, Planet. Space Sci., 38, 945-948, 1990. Rishbeth, H., and R. G. Roble, Cooling of the upper atmosphere by enhanced greenhouse gases - Modelling of thermospheric and ionospheric effects, Planet. Space Sci., 40, 10 1 l- 1026, 1992. Roble, R. G., and R. E. Dickinson, How will changes in carbon dioxide and methane modify the mean structure of the mesophere and thermosphere?, Geophys. Res. Lett., 16, 144 l- 1444, 1989. Sharma, S., H. Chandra, and G. D. Vyas, Long term trends over Ahmedabad, Geophys. Res. Lett., 26,433-436, 1999. Shimazaki, T., World wide daily variations in the height of the maximum electron density in theionospheric F2 layer, J. Radio Res. Labs., Japan, 2, 85-97, 1955. Taubenheim,J., Statistische Auswertung geophysikalischer und meteorologischer Daten, Akad. Verlagsgesellschaft Geest und Portig K.-G., Leipzig, 1969. Tobiska, W. K., D. Bouver, and C. McGuigan, Validation of the solar EUV proxy E10.7,J. Geophys. Res. (submitted), 2000. Ulich, T., Solar variability and long-term trends in the ionosphere, Sodankyll Geophysical Observatory Publications, No. 87, University of Oulu, Finland, 2000. Ulich,and E. Turunen, Evidence for long-term cooling of the upper atmosphere, Geophys. Res. Lett., 24, 11031106, 1997a
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Ulich, T., and E. Turunen, Long-term behaviour of ionospheric F2 layer peak height on a global scale, Paper presented at Session 2.18 of the 8th Scientific Assembly of IAGA, Uppsala, 1997b. Upadhyay, H. O., and K. K. Mahajan, Atmospheric greenhouse effect and ionospheric trends, Geophys. Res. Lett., 25, 3375-3378, 1998.
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