• Introduction
• Quasibiennial oscillations in sunspot numbers and stratospheric winds
• QBO and surface air temperature response to solar activity
• Instability of the relations
• Quasibiennial oscillations in North-South solar asymmetry
• Conclusions
• References

FIGURES

• Figure 1
• Figure 2
• Figure 3
• Figures 4a, 4b
• Figures 5a, 5b
• Figures 6a, 6b
• Figure 7
• Figure 8
• Figure 9
• Figure 10

One of the main problems in solar-climatic influences is the instability of the relations found. Different authors have reported both positive and negative correlations between solar activity and surface air temperatures. We have performed a detailed study making use of global, hemispheric and zonal temperature estimations, as well as of data from individual meteorological stations with long measurement records, to show that the sign of the correlation changes regularly in consecutive centennial solar cycles and seems determined by the North-South asymmetry of solar activity: the correlation is positive when the Northern solar hemisphere is the more active one, and negative when more active is the Southern solar hemisphere. On the other hand, the sign of the correlation between solar activity and different climatic elements reveals a similar dependence on the phase of the quasibiennial oscillation of stratospheric winds (QBO). QBO signals have been identified in a number of geophysical parameters, e.g. sea level pressure, ozone distribution, Earth’s rotation, and its existence has been shown in solar activity parameters as sunspot numbers, solar radio flux at 10.7 cm, green coronal activity, solar neutrino flux, etc. In the present paper we show that QBO exists in solar North-South asymmetry as well, and discuss the relation between the quasibiennial oscillation in solar asymmetry and in stratospheric zonal winds, and its possible implications on climate.

Introduction

Since the discovery of the solar activity periodicity, attempts have been made to find a connection between the solar cycle and the terrestrial climate [1]. However, the results are ambiguous. For different periods and locations, both positive and negative correlations have been found between solar activity parameters and meteorological elements like surface air temperature, sea level pressure, precipitation, etc. [2] Labitzke and van Loon [3] have demonstrated that the picture significantly improves if the data is stratified according to the phase of the quasibiennial oscillation of stratospheric winds (QBO). This fact has no explanation. Moreover, the mechanism of the quasibiennial oscillation itself is not clear, though QBO signals have been identified in a number of geophysical parameters. It was only natural to seek for a connection with solar activity, and similar periodicities were found in solar parameters like sunspot numbers [4], solar radio flux [5], solar neutrino [6], green coronal acticity [7], etc. This fact could be interpreted as an indication that the quasibiennial oscillations in stratospheric winds are triggered by the quasibiennial oscillations in solar activity.

Quasibiennial oscillations in sunspot numbers and stratospheric winds

Different authors have found “similar” but not exactly equal periodicities in both solar and geophysical parameters [4-10]. This differences may be due to the fact that time series with different discretizartion and lengths have been used, or that the processes are independent. To compare correctly the periodicities in solar activity and 30 hPa QBO [11], in the present study the time series have been reduced to equal lengths - 552 monthly values, from January 1953 to December 1998, and the same filtering procedure has been applied. The quasibiennial oscillation in solar activity is very weak compared to the much more powerful 11-year, 5-7 year and other cycles, and an appropriate filtering procedure must be applied in order to identify it. To this end, we have designed a digital bandpass filter with finite-duration response using Fourier approximation of the desired transfer function and a Kaiser window [12]. As the period studied is relatively short, some compromise had to be found between the quality of the filter and the inevitable reduction of the time series. The input parameters we finally chose were low cut frequency f_L = 0.02 month^-1, high cut frequency f_H = 0.05 month^-1, ripple d = 0.05 and transition width D f = 0.02 month^-1. Thus we obtained a 63-point filter, and the time series length decreased at each side by 32 points. The quality of the filter was tested on modeled time series with predefined periodicities of 21.3 and 26.5 months with equal amplitudes, 66 months with a two times higher amplitude, and 132 months with a five times higher amplitude. The Fourier analysis of the filtered time series yields peaks at 21.3 and 27.2 months and a much lower broad peak at 70.0 months - Fig.1.

In Fig.2 the raw QBO data are compared to the filtered values. The amplitudes of the filtered values are slightly shifted with respect to the raw ones, however no periodicities are lost and none are added.

The Fourier analysis of the QBO data gives a single peak at 28.44 months, and in sunspot numbers periodicities at 28.44 months, 23.27 months and 39.40 months are found - Fig.3. If these different peaks do not correspond to independent oscillations, and taking into account the discrete character of the oscillation, their existence indicates that the oscillation has a variable duration [13]. Besides, it has been shown that the period of QBO is not constant either. Therefore, a moving normal window with a width of 128 months (corresponding approximately to one 11-year solar cycle) has been applied with a step of 64 months, looking for periods between 20 and 50 months. In Table 1 the periods of the peaks are presented together with the periods corresponding to 0.7 of the amplitude of the respective peak (in brackets).

Table 1

In some time intervals (April 1966-November 1976, April 1982 - November 1992) the peaks coincide, in others (December 1960 - July 1971, December 1976 - July 1987, August 1987 –May 1996) they partly overlap, and in still others (August 1955 - March 1966, August 1971 - March 1982) stratospheric winds exhibits clear quasibiennial oscillation, while no such periodicity is obvious in sunspot numbers. Therefore, it does not seem probable that the sunspot activity quasibiennial oscillations are an indicator for QBO in stratospheric winds.

QBO and surface air temperature response to solar activity

Labitzke and van Loon [3] demonstrated strong negative correlations between solar activity and surface air temperature measured in individual meteorological stations in the Northern hemisphere for the west phase of the QBO. Fig.4 and 5 present the mean hemispheric temperatures in the Northern and Southern hemispheres [14] for local winter (January and July, respectively) for west and east phases of QBO.

No good correlations can be seen like the ones presented in [3]. The explanation could be that the response is not the same in the whole hemisphere, so ne

For the west phase, statistically significant correlations (p<0.05) were found only in the low latitude zone (± 23.6^o), and only in two periods - from May to July and from October to December - Fig.6a. The correlation is positive and in Spring-Summer it persists for all three months even if only in May QBO is in the west phase.

For the east QBO phase, statistically significant correlations between surface air temperature and solar activity are observed only at high latitudes (>64.2^o) from December to April: negative in December, January and April (fig.6b) and positive in February.

Instability of the relations

For individual meteorological stations, as seen from [3], much better correlations can be found in the west QBO phase between surface air temperatures and sunspot numbers than for zonal and moreover hemispheric averages. However, in some but not all of them, a change of the sign of the correlation is observed around 1970 (Fig.7).

Changes in the sign of the correlation between solar activity and meteorological elements have been reported by a number of authors (see for example [2] and the references therein) based on nonstratified data. This fact is one of the main arguments against the solar activity influence upon climate. We have made a compilation of the available published results reporting positive or negative correlations between solar activity and surface air temperature in different locations (Fig.8).

It can be seen that the cases of positive and negative correlations are fairly well grouped, with the sign of the correlation depending on the period studied and not on the location, and changing in consecutive secular cycles. This observation is confirmed from reconstructions of global, hemispheric and zonal temperatures (Fig.9): in the end of the 18^th century high temperatures are observed in solar minimum and low ones - at high solar activity, and about 1920-30 the situation changes to the opposite.

As the global reconstructions are relatively short, to study earlier epochs data from individual meteorological stations with long measurement records were used from the Global Historical Climatology Network Temperature Database (GHCN) of NCDC [16]. The time series for each station was divided into subseries yielding the best correlation with solar activity. In Fig.10 the relative number of the stations with statistically significant positive and negative correlation (along the positive and negative parts of the Y-axis, respectively) is shown together with the secular (or “Gleissberg”) solar activity cycle.

Fig.10 confirms that in the 18^th century, in the vast majority of the available stations the surface air temperature was positively correlated with solar activity in the 11-year solar cycle, this correlation changed to negative in the 19^th century and to positive again in the 20^th century. As pointed out in [2], for the interpretation of the changes of correlation, other solar parameters should be considered apart from the sunspot numbers. As such parameter changing in consecutive secular solar cycles has been identified the North-South solar activity asymmetry [17] defined as A=(S_N-S_S)/(S_N+S_S) where S_N and S_S stand for the given solar activity parameter (sunspot number, sunspot area, number of major flares, etc.) in the Northern and Southern hemisphere, respectively. Negative A seen in the 19^th century means more active Southern solar hemisphere and is associated with negative correlation between solar activity and surface air temperature in the 11-year cycle, and positive A in the 20^th century - more active Northern hemisphere and positive correlation. As seen in Fig.10, the change in the sign of the correlation does not occur simultaneously all over the globe. About 1970 the asymmetry becomes negative and part of the stations begin showing negative correlations. This is the period when a change in the sign of the correlation is observed also in part of the stations in data stratified according to the phase of the stratospheric winds QBO. It could therefore be supposed that the factor determining the way in which solar activity affects climate is the North-South solar activity asymmetry while the phase of the QBO facilitates or hinders such an influence.

Quasibiennial oscillations in North-South solar asymmetry

If the oscillations of the sunspot numbers themselves are not an indicator for the stratospheric quasibiennial oscillations, maybe such an indicator are the oscillations of sunspot asymmetry. To check this, a spectral analysis of asymmetry was made using a time series with the same length and covering the same interval, the same filtering procedure and moving normal window. The results are presented in Table 2.

Table 2

In the whole interval studied (January 1953 - December 1998), the prevailing periodicity in solar asymmetry is 25.6 months, and the peaks in stratospheric winds QBO lie within 0.7 of the amplitude of the asymmetry QBO peaks. So it seems more probable that if the oscillations in stratospheric winds are in some way or another related to the Sun, the parameter responsible for such an influence is the solar asymmetry rather than the level of the solar activity itself.

Conclusions

1. The quasibiennial oscillations in both sunspot numbers and stratospheric winds have variable durations which in different periods may or may not coincide. Therefore, it is not probable that the solar quasibiennial oscillations are an indicator for the quasibiennial oscillations in stratospheric winds. A more probable indicator are the quasibiennial oscillations in North-South solar activity asymmetry.

2. When the data is stratified according to the phase of the stratospheric winds QBO, strong correlations can be found between solar activity and surface air temperature for individual locations. No such correlations are found for hemispheric averages. For zonal averages, in the west QBO phase the temperature at low latitudes is in phase with solar activity from May to July and from October to December. On the contrary, in the east QBO phase the effect can be seen at high latitudes from December to April, the correlation being negative and less stable.

3. The sign of the correlation between solar activity and surface air temperature changes in consecutive secular solar cycles and seems ruled by the North-South solar asymmetry. The phase of the stratospheric winds QBO does not determine the sign of the correlation, but rather enhances or suppresses this influence.

References

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