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Solar and geomagnetic activity effects on climate at regional and global scales: Case study—Romania
ARTICLE IN PRESS Journal of Atmospheric and Solar-Terrestrial Physics 71 (2009) 1727– 1735
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Solar and geomagnetic activity effects on climate at regional and global scales: Case study—Romania V. Dobrica a,, C. Demetrescu a, C. Boroneant b, G. Maris a a b
Institute of Geodynamics, 19-21 J. L. Calderon Street, Bucharest, Romania National Meteorological Administration, Bucharest, Romania
a r t i c l e in fo
abstract
Article history: Received 31 October 2007 Received in revised form 6 March 2008 Accepted 23 March 2008 Available online 20 April 2008
We analyze 100–150 years-long temperature and precipitation records from 14 meteorological stations in Romania, in connection with long-term trends in solar and geomagnetic activities. The comparison of solar (sunspot number) and geomagnetic (aa index) parameters with the mean air temperature over the Romanian territory, at interdecadal timescales, shows positive correlation coefficients, while the comparison with the mean precipitation shows negative correlation coefficients. The correlation of climatic parameters seems to be stronger for geomagnetic activity than for solar activity. The Romanian temperature series are examined in the context of other European stations and of averages on the European, northern hemisphere, and global scale, respectively. Long-term (interdecadal and centennial) trends and differences between local trends and average trends for larger areas are discussed. The study indicates that solar and geomagnetic activity effects are present on the 22-year Hale cycle timescale. The temperature variation on this timescale lags the solar/geomagnetic ones by 5–9 years. & 2008 Elsevier Ltd. All rights reserved.
Keywords: Solar–terrestrial relationship Solar and geomagnetic activity Surface air temperature and precipitation
1. Introduction A large body of evidence shows that the variable solar energy output is related to terrestrial climate variability (see, e.g., the volume edited by Pap and Fox (2004) and references therein). The solar activity can influence Earth’s climate in various ways and on different time scales, either directly, by long-term changes of radiative output of the Sun affecting the energy balance of the Earth’s surface, or/and indirectly, via the effects of the solar wind on the magnetosphere (resulting in the so-called geomagnetic activity) and further on the upper atmosphere, and via the modulation of the galactic cosmic ray flux by the combined effects of heliospheric and terrestrial magnetic fields. The solar influence on climate cannot be measured
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directly, but correlations between solar activity and climate parameters were found, such as the well-known correlation between the average temperature of the northern hemisphere and the length of the solar cycle (Friis-Christensen and Lassen, 1991). Labitzke and van Loon (1988) and Reid (1991) pointed out to a relationship between the 11-year solar cycle and stratospheric parameters (the geopotential heights and the temperature), on one hand, and sea-surface temperature anomalies, on the other hand. Perhaps the best evidence of the relationship between the solar activity and climate was provided by the coincidence of the Maunder Minimum in the solar activity (1645–1715) with a period of very low temperatures in Europe, known as the Little Ice Age. Lohmann et al. (2004) discussed the relationship between variations given by the well-known Schwabe, Hale and Gleissberg cycles in the solar irradiance and variations of the seasurface temperature and the sea-level pressure. The influence of solar activity on precipitation, which was less studied than the influence on temperature, was evidenced in the case of the Beijing area (Zhao et al.,
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2004), and of three locations in South Africa (King, 1975). Variations with periods of 11, 22, 33, and 72 years were found in the precipitation from the Beijing area. At the three South African locations the smoothed annual total rainfall varies with the double sunspot cycle. Geomagnetic activity as a forcing agent of the climatic variations was studied in several papers (Cliver et al., 1998; Bucha and Bucha Jr., 1998, 2002; El-Borie and Al-Thoyaib, 2006; Valev, 2006). The authors of these studies found statistically significant correlation coefficients at the 99% confidence level between geomagnetic activity and several climatic parameters, such as the sealevel atmospheric pressure and the surface air temperature. In areas such as the middle and southern Europe, the south-eastern part of North America and the western Atlantic, positive correlation coefficients were found, while in the northern Atlantic and Canada, negative ones characterize the correlation (Bucha and Bucha Jr., 1998, 2002). In addition, in terms of 11-year running averages, there is a similar variation of geomagnetic indices that characterize the solar quiet daily variation, driven by the ionospheric dynamo, which in turn is controlled by the UV solar radiation, the sunspot number, the solar irradiance, the geomagnetic activity and the global mean temperature (Le Moue¨l et al., 2005). The latter also pointed out to a divergence of the temperature trend versus the general geomagnetic trend after 1990, and considered it as an evidence for the effect of anthropogenic greenhouse gases. The sunspot numbers data series was reconstructed, at the multi-millennial scale, based on 10Be and 14C data from ice cores (Usoskin et al., 2003), and on the concentration of 14C in tree rings (Solanki et al., 2004). The reconstructed sunspot number showed relationships with the various reconstructions of the Earth’s surface temperature (Usoskin et al., 2005). The long-term relationship on the timescales of the Hale and Gleissberg cycles between the solar activity, as described by the sunspot number, the solar irradiance, as reconstructed by Lean et al. (1995); Lean (2000); Solanki et al. (2002); Krivova et al. (2003, 2007) and the geomagnetic activity, as described by several geomagnetic indices, has recently been discussed by Demetrescu and Dobrica (2008). Solar and 1 AU heliospheric variability, as well as the variability of the geomagnetic field with external sources were shown to have common properties on the aforementioned timescales. In the present paper, we go beyond correlating climate parameters with the solar and geomagnetic activities at the timescale of a solar 11-year cycle and attempt to quantitatively characterize longer-term solar/geomagnetic signatures or signals in temperature and precipitation. We are aware of the fact that the relationship between the solar variability and geomagnetic activity is far better understood than the possible causal links with climate of either solar or geomagnetic variabilities. Such links and the relative contribution of either solar or geomagnetic effects on climate, as well as the associated physical processes are still a matter of debate and of future studies. In view of possible mechanisms involving geomagnetic
phenomena as a nonlinear enhancer of the influence the solar activity has on climate (e.g. Bucha and Bucha Jr., 1998, 2002), we shall compare climatic variability with both solar and geomagnetic variabilities. In our analysis we do not attempt to decide on physical processes behind the correlations we find, but point to some of their characteristics at longer time scales than currently approached. Romania has a robust and reliable data set of long records of air temperature and precipitation (14 stations, 1850–2004), which could contribute, in the context of data on larger geographical scales, such as Europe, northern hemisphere (NH), and entire Earth, to elucidate some details of the long-term variations, otherwise averaged out in time series at larger geographical scales. Accordingly, the long-term variations of climatic parameters over Romania are discussed in Section 2 of the paper, and the solar/geomagnetic signatures in them in Section 3. The Section 4 is dedicated to long-term solar/ geomagnetic signatures in the Romanian, European, northern hemisphere, and world average temperatures. The conclusions are presented in Section 5.
2. Long-term variations of climatic parameters over Romania The variations of annual mean of surface air temperature (SAT) and annual precipitation (P), during the period 1850–2004, at 14 meteorological stations over the Romanian territory (Fig. 1) are plotted in Fig. 2a and b, respectively. The displayed variations are anomalies relative to the means calculated over the time interval 1961–1990. In spite of the regional climatic differences on the Romanian territory, the variations are similar at all analyzed stations, thus justifying an additional analysis on the mean values over the Romanian territory. The main characteristics of the variations as revealed by the two plots are the coherence of the variation at all stations and the existence of interdecadal and longer variations. Spectral analysis applied to data using the multipletaper method (MTM) (http://www.atmos.ucla.edu/tcd/ ssa/; Ghil et al., 2002) reveals variations of short period (2–7 years, significance level 84–92% for temperature, 91–99% for precipitation), decadal variations with a period of 11 years (significance level 90% for temperature and 85% for precipitation) and variations with longer periods, 22 and/or 30 years and even longer (significance level 95% for temperature, 85–90% for precipitation). In Fig. 3a and b, the power spectra of the SAT and P in case of Sibiu meteorological station (the longest available time series) are shown. The annual means of surface air temperature, T, and annual precipitation, P, averaged over the 14 meteorological stations are shown in Fig. 4a and b. As it can be noticed, the temperature is generally increasing in the analyzed time interval (a linear trend of 0.68 1C/century) and the precipitation is decreasing (a linear trend of 51.47 mm/century). It should be pointed out that before 1900 such mean values for the territory is based on less and less number of stations (e.g. before 1880, on only 2 stations, Bucharest and Sibiu).
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Fig. 1. Location of the 14 meteorological stations (triangles) over the Romanian territory.
Temperature spectrum - Sibiu
2
3.5
79y
3 Power spectral density
SAT (°°C)
1 0 -1 -2 -3
34y
2.5 2
11y
3y
1 0.5 0
1850 1870 1890 1910 1930 1950 1970 1990 2010 Year
0
800 600
Precipitation spectrum - Sibiu 7y
19y Power spectral density
P (mm)
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Frequency (cycle/year) x 104
5
400 200 0 -200
4y
1.5
3y
4 31y
3
11y
2
1 -400 1850 1870 1890 1910 1930 1950 1970 1990 2010 Year Fig. 2. Surface air temperature (a) and precipitation (b) at the 14 stations relative to their means over the time interval 1961–1990.
0
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Frequency (cycle/year)
Fig. 3. Spectral analysis by multi-taper method (MTM) of the surface air temperature (a) and precipitation (b) at the Sibiu meteorological station.
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2
T (°°C)
1 0 -1 -2 1850 1870 1890 1910 1930 1950 1970 1990 2010 Year 600
P (mm)
400 200 0 -200 1850 1870 1890 1910 1930 1950 1970 1990 2010 Year Fig. 4. Average surface air temperature (a) and precipitation (b) over the 14 meteorological stations in Romania (black), interdecadal trend (red), centennial trend (blue and green, see text).
After filtering out short period variations, the series are smoothed using a running filter with a window of 11 years (the red line in Fig. 4), defining an interdecadal trend, T11. It is found that a 30-year variation appears in T11, with amplitudes of 0.3–0.4 1C, while in the interdecadal trend of mean precipitation, P11, a 22-year variation appears, with an amplitude of 35 mm. Further filtering with running windows of 22 and 30 years was successively applied on T11 and P11. The smoothed time series are also given in Fig. 4 (blue and, respectively, green lines) (T22, T30 and, respectively, P22 and P30). They define a centennial trend in data. The 22-, 30-year and longer (centennial scale) variations seen in the 11-year running averages of air temperature and precipitation might be related to solar and geomagnetic phenomena occurring at similar timescales (Hale and Gleissberg cycles of solar activity), as will be shown in Section 3. 3. Relationship between solar and geomagnetic activity and the climatic parameters for Romania As indicators of solar and geomagnetic activities, we used the annual means of the sunspot number, R (http:// www.ngdc.noaa.gov/stp/SOLAR/ftp.sunspotnumber.html), and, respectively, the annual means of the aa geomagnetic index (http://isgi.cetp.ipsl.fr/des_aa.htm). A correction of +3 nT was added to aa data prior to 1957, according to Svalgaard and Cliver (2007). We preferred the aa index to other geomagnetic activity indices, such as the Inter-Hour Variability (IHV, Svalgaard and Cliver, 2007) and the
Inter-Diurnal Variability (IDV, Svalgaard and Cliver, 2005), considering the length of their time series. The aa geomagnetic index (Mayaud, 1972, 1980) is compiled from the range of variations of the geomagnetic field over periods of 3 h (the K index) at two near-antipodal geomagnetic observatories in England and in Australia; by definition, the solar quiet time variation is removed from the data. While aa and IHV are responding to the product BV2, where B is the interplanetary magnetic field (IMF) strength and V is the solar wind velocity at 1 AU (Svalgaard and Cliver, 2007), the IDV index is linked only to the IMF strength (Svalgaard and Cliver, 2005), but all three show similar variations in terms of 11-year running averages and at Hale and Gleissberg time scales (Demetrescu and Dobrica, 2008). By applying the same multi-taper method of spectral analysis, which we applied to the climatic parameters, to the yearly means both of the sunspot number, R, and of the aa geomagnetic index, in the time interval 1868–2000, Prestes et al. (2006) showed that the significant period is the well-known period of about 11 years. A variation with a period of about 22 years (Hale cycle) in R and aa was discussed by several authors (e.g. Mursula et al., 2001; Russell and Mulligan, 1995; Cliver et al., 1996; Demetrescu and Dobrica, 2008 and references therein). Also, different spectral analysis techniques applied to the sunspot number time series show peaks at various frequencies in the Gleissberg cycle range (60–120 years). For instance, Rozelot (1994) reports peaks at 58.5 and 97.2 years, while Prestes et al. (2006) find a peak at 100.7 years and Rogers et al. (2006) indicate peaks at 87, 97.5, 102 years, depending on the method used. We investigated the correlation of the mean surface air temperature, T, and precipitation, P, over Romania and the solar, R, and geomagnetic (aa) parameters for the interdecadal, the 22- and the 30-year trends (Fig. 5a–c). In order to make the intercomparison, all parameters were referred to their means over the common time interval (1873–1995, 1883–1984 and, 1898–1970, respectively) and scaled with their standard deviations about the mean. In Fig. 5a, we compare the running averages with a window of 11 years. A reversed scale was used for P. As it can be seen, there is a positive correlation between surface air temperature and solar and geomagnetic activity, and a negative correlation in the case of precipitation. The correlation coefficients are given in Table 1. An improvement is found compared with the correlation in the case of raw (annual averages) data series (correlation coefficients: rT,R ¼ 0.25, rT,aa ¼ 0.31, rP,R ¼ 0.10, rP,aa ¼ 0.26). In terms of running averages with a window of 22 years, applied to time series in Fig. 5a, the investigated parameters are given in Fig. 5b, and the corresponding correlation coefficients in Table 2. The results of averaging the 22-year averaged series with a running window of 30 years, are presented in Fig. 5c and Table 3. The correlation coefficients listed in Tables 1–3 are statistically significant at 99% confidence level. It is found that the climatic signal in the surface air temperature is in phase with the geomagnetic and solar activity and out of phase in the case of precipitation on all investigated timescales and the correlation is improved
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3
Standard deviation
2 1
Table 2 The correlation coefficients, in case of 22-year running averages of the mean surface air temperature, T, and precipitation, P, over Romania with the solar, R, and geomagnetic, aa, indices
T -P R aa
0
R
aa
T P
0.69 0.72
0.70 0.75
Table 3 The correlation coefficients, in case of 30-year running averages of the mean surface air temperature, T, and precipitation, P, over Romania with the solar, R, and geomagnetic, aa, indices
-3 1850 1870 1890 1910 1930 1950 1970 1990 2010 Year 2 Standard deviation
Series
-1 -2
Series
R
aa
T P
0.86 0.88
0.89 0.91
1 0 -1 -2 -3 1850 1870 1890 1910 1930 1950 1970 1990 2010 Year 2
Standard deviation
1731
1 0 -1 -2
higher than the correlation between the climatic parameters and the solar activity. Such a behavior was also noticed by Bucha and Bucha Jr. (1998) for short-term, magnetic storm timescale, variations in solar and geomagnetic activities compared with climatic parameters. In conclusion, there is a significant correlation between, on one hand, the solar and geomagnetic activity (sunspot number, R, and the geomagnetic index, aa) and, on the other hand, the climatic parameters over Romania when they are referred to the long-term trends. However, differences in the general trends of temperature as compared with solar and geomagnetic trends can be noticed in the first half of the 20th century in all plots of Fig. 5. They can be understood in the context of European and global data.
-3 -4
4. Romanian data in the context of European and global data
-5 1850 1870 1890 1910 1930 1950 1970 1990 2010 Year Fig. 5. The mean over Romania of surface air temperature, T (red), and precipitation, P (black), compared with solar, R (blue), and geomagnetic, aa (green), activity in terms of 11- (a), 22- (b) and 30-year (c) running averages. Note the reversed scale for P. Curves are reduced to their means over the common time interval (1873–1995, a; 1883–1984, b; 1898–1970, c) and scaled with their standard deviations about the mean as a unit.
Table 1 The correlation coefficients, in case of 11-year running averages of the mean surface air temperature, T, and precipitation, P, over Romania with the solar, R, and geomagnetic, aa, indices Series
R
aa
T P
0.54 0.59
0.57 0.62
when the length of the running window is increased. On the long-term scale, the relationship between the climatic parameters and the geomagnetic activity seems to be
Instrumental information from other meteorological stations in Europe dates back to the end of the 17th century (London, Central England—1659). Reconstructions on the northern hemisphere and global scales (e.g. Moberg et al., 2005; Jones et al., 2006), go further back (1500). It appears interesting that the Romanian data follow the long-term trends observed at such scales, as it is well known that remarkable regional differences are found in climate trends (Mann et al., 2003). Our analysis will refer to SAT data. In Fig. 6, we superimposed 11-year running means of several temperature time series from individual European stations, sampling the latitudinal band 45–55 N, namely: Central England (Manley, 1974; Parker et al., 1992), Hohenpeissenberg (Germany), Vienna (Austria), Prague (Czech Republic), (Jan Safanda, personal communication, 2005) and Bucharest (Romania). We included several published averages for certain areas, namely Europe (Mitchell and Jones, 2005, based on instrumental data for 1900–2004 and reconstructions by Luterbacher et al. (2004) and Xoplaki et al. (2005) for 1500–1900), northern
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Central England Hohenpeissenberg Vienna Prague Bucharest Europe Northern Hemisphere Globe
1.5
1
SAT (°C)
0.5
0
-0.5
-1
-1.5 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000 Year Fig. 6. Anomaly of the surface air temperature, relative to the means over the time interval 1961–1990, in terms of 11-year running averages, for individual stations and for Europe, northern hemisphere and entire Earth (see text for data sources).
4
Romania Europe Northern Hemisphere Globe R aa
3
Standard deviation
2 1 0 -1
peak, while the temperature at individual stations and the European average are depressed. A comparison in terms of 11-year running averages (Fig. 7) of a local temperature record (the average for the Romanian territory, T), of continental, of northern hemisphere and of global temperature averages with the solar and geomagnetic activities reveals: (1) the well-known correlation between solar and geomagnetic activities and the northern hemisphere mean temperature (FriisChristensen and Lassen, 1991; Le Moue¨l et al., 2005), also closely followed by the mean temperature at global scale; (2) the marked discrepancy in trend after 1980–1990, envisaging the possible emergence of the effect of anthropogenic greenhouse gases (Le Moue¨l et al., 2005); (3) the faster increase of temperature in comparison to solar and geomagnetic activities before 1940–1950; and (4) the differences in case of local (Romanian average, Europe average) temperature variations as compared with averages at larger geographical scale (NH, Globe). Smoothing the interdecadal variations by 22-year running averages (Fig. 8) better illustrates these conclusions. We note here that the discrepancies mentioned above under (3), were also discussed by Friis-Christensen and Lassen (1991) and by Le Moue¨l et al. (2005). The former found out that using another solar parameter instead of sunspot numbers, namely the length of sunspot cycle, removes the apparent lag of the solar activity curve relative to the surface temperature. The latter explain what they call a slight lag of geomagnetic and solar parameters with respect to temperature, by a difference in temperature data quality before and after 1960. We find (see below) that the faster increase of temperature in comparison with solar/geomagnetic activities before 1940–1950 might be explained by a 40–50-year variation that characterize the long trend temperature variation. Examining Fig. 8 also reveals that the 30-year variation, present in Romanian data, is also visible, with smaller amplitudes, in case of Europe average time
-2 4
Romania Europe Northern Hemisphere Globe R aa
-3 1900
1950
2000
Year Fig. 7. Comparison in terms of 11-year running averages, between the mean value of the surface air temperature in Romania, Europe, northern hemisphere, Globe and solar, R, and geomagnetic, aa index, activity. Curves are reduced to the their means over the common time interval (1873–1995) and scaled with their standard deviations about the mean as a unit.
2 Standard deviation
1850
0
-2 hemisphere (Moberg et al., 2005 for 1500–1979, based on tree-ring and lake/ocean sediments data; Jones et al., 2006 for 1980–2005, instrumental data) and entire Earth (Jones et al., 2006 for 1856–2005, instrumental data). At a first glance, the similarity of the variations at individual stations and notable amplitude differences with respect to mean values for the entire Europe or for the northern hemisphere can be noticed. We also note that between 1930 and 1940 the NH average shows a
-4 1850
1900
1950
2000
Year Fig. 8. Data of Fig. 7 smoothed with a 22-year running averages filter. Curves are reduced to the their means over the common time interval (1883–1984) and scaled with their standard deviations about the mean as a unit.
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variation and seems to be absent at northern hemisphere and globe scales. A comparative study of a number of local temperature time series, which is beyond the scope of this paper, might give some indications on the nature of this variation. We only mention here that a 30-year variation was noticed by Mursula et al. (2001) in case of solar activity, using the group sunspot number series. Subtracting the time series presented in Fig. 8 from those in Fig. 7 gives the so-called 22-year variation, related to the Hale cycle in the solar activity. Demetrescu and Dobrica (2008) presented a comparison of geomagnetic activity, described by the aa, IHV and IDV indices, with the sunspot number, as a proxy for the solar activity, and with the reconstructed total solar irradiance (Lean et al., 1995; Lean, 2000; Krivova et al., 2007), and showed that the interdecadal variability is explained by the superposition of the 22-year signature of the Hale cycle on a variation at the Gleissberg cycle timescales. In Fig. 9, we superimpose the 22-year variation of temperature at individual stations and the same signal in the European average. In order to make the time series comparable, curves are reduced to their means over the common time interval (1883–1983) and scaled with their standard deviations about the mean as a unit. Again, the similar pattern of variation, but with different amplitudes, can be noticed. Discrepancies evident before 1890 could be a result of less accuracy in data. The corresponding NH curve was not plotted, due to the mentioned dissimilarity in the time interval 1930–1940 of the NH average as compared with the European data, which would strongly show up in this kind of plot.
4
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A cross correlation analysis on temperature and solar/ geomagnetic 22-year variations shows that the average temperature at northern hemisphere and global scales lag behind the solar and geomagnetic activities by about 5 years (correlation coefficients Globe: rT,R ¼ 0.34; NH: rT,R ¼ 0.46) and, respectively, 6 years (Globe: rT,aa ¼ 0.49; NH: rT,aa ¼ 0.59). All these correlation coefficients are statistically significant at the 99% level. The local temperatures (individual stations and European average) show a larger lag, of 7–9 years. To illustrate this kind of correlation, we superimposed on the 22-year temperature variations (Fig. 9) the corresponding one in the geomagnetic aa index (red curve), shifted forward in time by 8 years. Further smoothing the time series of Fig. 8 with a longer window running averages filter, at the timescale of the Gleissberg cycle (60–120 years, Demetrescu and Dobrica (2008)), to obtain a possible Gleissberg cycle signature in the temperature time series, is not relevant because of the relative short time interval covered by data series. However, the Central England time series, which goes back to 1659 AD, seems to indicate the presence of a 40–50-year variation (not shown), superimposed on a longer-term trend. With present data sets we cannot decide whether this variation is a harmonic of the Gleissberg variation or is simply a result of the internal variability of the climate system. However, this variation explains the faster increase of temperatures in comparison with solar/geomagnetic parameters, observed before 1940–1950 (Fig. 8). We did not consider for this treatment longer temperature time series, based on proxy data (e.g. Moberg et al., 2005), due to discrepancies with measured data in the common time interval (Fig. 6), on one hand, and to the well-known incapacity of proxies to reveal very low-frequency climate fluctuations (e.g. Esper et al., 2004), on the other.
3 5. Conclusions
Standard deviation
2 1 0 -1 -2 -3 -4 1850
1900
1950
2000
Year Fig. 9. The 22-year variation of temperature at individual stations (Central England, Hohenpeissenberg, Vienna, Prague, Bucharest) (full line) and at European scale (dashed line), as compared with the 22-year variation in the geomagnetic activity (red line). The aa curve is shifted forward in time by 8 years. Curves are reduced to the their means over the common time interval (1883–1983) and scaled with their standard deviations about the mean as a unit.
Fourteen long time series (1850–2004) of air temperature and precipitation in Romania, several temperature time series from other European stations in the latitude interval 45–55 N (Central England, Hohenpeissenberg– Germany, Vienna–Austria, Prague–Czech Republic), and average temperature reconstructions for Europe, northern hemisphere and entire Earth were analyzed in terms of correlation with the solar and geomagnetic activity on the Hale and Gleissberg timescales, as represented by the sunspot number and, respectively, by the geomagnetic aa index time series. Having in view the similarity of variations observed for all Romanian stations, in the case of both the air temperature and precipitation, the analysis was carried out on averages for the Romanian territory. A positive correlation of the mean air temperature over Romania and a negative one of the mean precipitation over Romania were found with the solar/geomagnetic activity, in terms of 11-, 22- and 30-year successive lowpass running averages filters applied to annual values after filtering out shorter, 2–7 years, variations seen in data. The correlation of all such parameters is largely
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controlled by the long-term trends, and the correlation coefficients increase with the amplitude of the window of the running average applied to the data series. On the long-term scale, the correlation between the climatic parameters and the geomagnetic activity seems to be more effective compared with the correlation between climatic parameters and solar activity. Romanian temperature data were examined in the context of longer time series from the other European stations and averages on continental, northern hemisphere, and global scales. The variability in terms of the 11-year running averages appears quite similar at all stations. The time series of average values on continental, northern hemisphere and global scales is characterized by smaller amplitudes than the time series from individual stations. A 22-year, Hale cycle related, variation in data, superimposed on a longer-term variation, is responsible for the 22-year-scale fluctuations seen in the 11-year filtered time series. A cross-correlation analysis showed that the 22-year variation in temperature lags behind the corresponding solar/geomagnetic variations by 5–9 years depending on the spatial scale at which the analyzed temperature time series are representative. An analysis of variations on centennial (Gleissberg) timescales is not significant in view of the insufficient length of observed time series. However, the longest one, the Central England time series, seems to indicate the presence of a 40–50-year variation superimposed on a longer-term trend, variation that could explain features like the faster increase of temperature, in comparison to solar/geomagnetic activities, before 1940–1950. The similarity of the variation pattern, as well as amplitude differences between local records and averages on large geographical scale can be understood in terms of large-scale atmospheric circulation patterns, influenced by solar/geomagnetic forcing on the analyzed timescales, but with local intensity differences. By no means, however, the present analysis can decide on, or discriminate between, various forcings at work, including the anthropogenic one. The relative contribution of either solar or geomagnetic effects on climate and/or the physical processes actually responsible for the observed correlations are a topic for future studies.
Acknowledgements The study has been supported by the Institute of Geodynamics (Project 2/2005–2006) and the Ministry of Education and Research (Project MENER 405/2004, PN-II projects No. 81-021/2007 and 151/2007). We thank Prof. G.P. Gregori and an anonymous reviewer for suggestions that helped improving the manuscript. References Bucha, V., Bucha Jr., V., 1998. Geomagnetic forcing of changes in climate and in the atmospheric circulation. Journal of Atmospheric and Solar-Terrestrial Physics 60, 145–169. Bucha, V., Bucha Jr., V., 2002. Geomagnetic forcing and climatic variations in Europe, North America and in the Pacific Ocean. Quaternary International 91, 5–15.
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Contents lists available at ScienceDirect
Journal of Atmospheric and Solar-Terrestrial Physics journal homepage: www.elsevier.com/locate/jastp
Solar and geomagnetic activity effects on climate at regional and global scales: Case study—Romania V. Dobrica a,, C. Demetrescu a, C. Boroneant b, G. Maris a a b
Institute of Geodynamics, 19-21 J. L. Calderon Street, Bucharest, Romania National Meteorological Administration, Bucharest, Romania
a r t i c l e in fo
abstract
Article history: Received 31 October 2007 Received in revised form 6 March 2008 Accepted 23 March 2008 Available online 20 April 2008
We analyze 100–150 years-long temperature and precipitation records from 14 meteorological stations in Romania, in connection with long-term trends in solar and geomagnetic activities. The comparison of solar (sunspot number) and geomagnetic (aa index) parameters with the mean air temperature over the Romanian territory, at interdecadal timescales, shows positive correlation coefficients, while the comparison with the mean precipitation shows negative correlation coefficients. The correlation of climatic parameters seems to be stronger for geomagnetic activity than for solar activity. The Romanian temperature series are examined in the context of other European stations and of averages on the European, northern hemisphere, and global scale, respectively. Long-term (interdecadal and centennial) trends and differences between local trends and average trends for larger areas are discussed. The study indicates that solar and geomagnetic activity effects are present on the 22-year Hale cycle timescale. The temperature variation on this timescale lags the solar/geomagnetic ones by 5–9 years. & 2008 Elsevier Ltd. All rights reserved.
Keywords: Solar–terrestrial relationship Solar and geomagnetic activity Surface air temperature and precipitation
1. Introduction A large body of evidence shows that the variable solar energy output is related to terrestrial climate variability (see, e.g., the volume edited by Pap and Fox (2004) and references therein). The solar activity can influence Earth’s climate in various ways and on different time scales, either directly, by long-term changes of radiative output of the Sun affecting the energy balance of the Earth’s surface, or/and indirectly, via the effects of the solar wind on the magnetosphere (resulting in the so-called geomagnetic activity) and further on the upper atmosphere, and via the modulation of the galactic cosmic ray flux by the combined effects of heliospheric and terrestrial magnetic fields. The solar influence on climate cannot be measured
Corresponding author. Tel.: +40 21 3172126; fax: +40 21 3172120.
E-mail addresses: [email protected], [email protected] (V. Dobrica). 1364-6826/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jastp.2008.03.022
directly, but correlations between solar activity and climate parameters were found, such as the well-known correlation between the average temperature of the northern hemisphere and the length of the solar cycle (Friis-Christensen and Lassen, 1991). Labitzke and van Loon (1988) and Reid (1991) pointed out to a relationship between the 11-year solar cycle and stratospheric parameters (the geopotential heights and the temperature), on one hand, and sea-surface temperature anomalies, on the other hand. Perhaps the best evidence of the relationship between the solar activity and climate was provided by the coincidence of the Maunder Minimum in the solar activity (1645–1715) with a period of very low temperatures in Europe, known as the Little Ice Age. Lohmann et al. (2004) discussed the relationship between variations given by the well-known Schwabe, Hale and Gleissberg cycles in the solar irradiance and variations of the seasurface temperature and the sea-level pressure. The influence of solar activity on precipitation, which was less studied than the influence on temperature, was evidenced in the case of the Beijing area (Zhao et al.,
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2004), and of three locations in South Africa (King, 1975). Variations with periods of 11, 22, 33, and 72 years were found in the precipitation from the Beijing area. At the three South African locations the smoothed annual total rainfall varies with the double sunspot cycle. Geomagnetic activity as a forcing agent of the climatic variations was studied in several papers (Cliver et al., 1998; Bucha and Bucha Jr., 1998, 2002; El-Borie and Al-Thoyaib, 2006; Valev, 2006). The authors of these studies found statistically significant correlation coefficients at the 99% confidence level between geomagnetic activity and several climatic parameters, such as the sealevel atmospheric pressure and the surface air temperature. In areas such as the middle and southern Europe, the south-eastern part of North America and the western Atlantic, positive correlation coefficients were found, while in the northern Atlantic and Canada, negative ones characterize the correlation (Bucha and Bucha Jr., 1998, 2002). In addition, in terms of 11-year running averages, there is a similar variation of geomagnetic indices that characterize the solar quiet daily variation, driven by the ionospheric dynamo, which in turn is controlled by the UV solar radiation, the sunspot number, the solar irradiance, the geomagnetic activity and the global mean temperature (Le Moue¨l et al., 2005). The latter also pointed out to a divergence of the temperature trend versus the general geomagnetic trend after 1990, and considered it as an evidence for the effect of anthropogenic greenhouse gases. The sunspot numbers data series was reconstructed, at the multi-millennial scale, based on 10Be and 14C data from ice cores (Usoskin et al., 2003), and on the concentration of 14C in tree rings (Solanki et al., 2004). The reconstructed sunspot number showed relationships with the various reconstructions of the Earth’s surface temperature (Usoskin et al., 2005). The long-term relationship on the timescales of the Hale and Gleissberg cycles between the solar activity, as described by the sunspot number, the solar irradiance, as reconstructed by Lean et al. (1995); Lean (2000); Solanki et al. (2002); Krivova et al. (2003, 2007) and the geomagnetic activity, as described by several geomagnetic indices, has recently been discussed by Demetrescu and Dobrica (2008). Solar and 1 AU heliospheric variability, as well as the variability of the geomagnetic field with external sources were shown to have common properties on the aforementioned timescales. In the present paper, we go beyond correlating climate parameters with the solar and geomagnetic activities at the timescale of a solar 11-year cycle and attempt to quantitatively characterize longer-term solar/geomagnetic signatures or signals in temperature and precipitation. We are aware of the fact that the relationship between the solar variability and geomagnetic activity is far better understood than the possible causal links with climate of either solar or geomagnetic variabilities. Such links and the relative contribution of either solar or geomagnetic effects on climate, as well as the associated physical processes are still a matter of debate and of future studies. In view of possible mechanisms involving geomagnetic
phenomena as a nonlinear enhancer of the influence the solar activity has on climate (e.g. Bucha and Bucha Jr., 1998, 2002), we shall compare climatic variability with both solar and geomagnetic variabilities. In our analysis we do not attempt to decide on physical processes behind the correlations we find, but point to some of their characteristics at longer time scales than currently approached. Romania has a robust and reliable data set of long records of air temperature and precipitation (14 stations, 1850–2004), which could contribute, in the context of data on larger geographical scales, such as Europe, northern hemisphere (NH), and entire Earth, to elucidate some details of the long-term variations, otherwise averaged out in time series at larger geographical scales. Accordingly, the long-term variations of climatic parameters over Romania are discussed in Section 2 of the paper, and the solar/geomagnetic signatures in them in Section 3. The Section 4 is dedicated to long-term solar/ geomagnetic signatures in the Romanian, European, northern hemisphere, and world average temperatures. The conclusions are presented in Section 5.
2. Long-term variations of climatic parameters over Romania The variations of annual mean of surface air temperature (SAT) and annual precipitation (P), during the period 1850–2004, at 14 meteorological stations over the Romanian territory (Fig. 1) are plotted in Fig. 2a and b, respectively. The displayed variations are anomalies relative to the means calculated over the time interval 1961–1990. In spite of the regional climatic differences on the Romanian territory, the variations are similar at all analyzed stations, thus justifying an additional analysis on the mean values over the Romanian territory. The main characteristics of the variations as revealed by the two plots are the coherence of the variation at all stations and the existence of interdecadal and longer variations. Spectral analysis applied to data using the multipletaper method (MTM) (http://www.atmos.ucla.edu/tcd/ ssa/; Ghil et al., 2002) reveals variations of short period (2–7 years, significance level 84–92% for temperature, 91–99% for precipitation), decadal variations with a period of 11 years (significance level 90% for temperature and 85% for precipitation) and variations with longer periods, 22 and/or 30 years and even longer (significance level 95% for temperature, 85–90% for precipitation). In Fig. 3a and b, the power spectra of the SAT and P in case of Sibiu meteorological station (the longest available time series) are shown. The annual means of surface air temperature, T, and annual precipitation, P, averaged over the 14 meteorological stations are shown in Fig. 4a and b. As it can be noticed, the temperature is generally increasing in the analyzed time interval (a linear trend of 0.68 1C/century) and the precipitation is decreasing (a linear trend of 51.47 mm/century). It should be pointed out that before 1900 such mean values for the territory is based on less and less number of stations (e.g. before 1880, on only 2 stations, Bucharest and Sibiu).
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Fig. 1. Location of the 14 meteorological stations (triangles) over the Romanian territory.
Temperature spectrum - Sibiu
2
3.5
79y
3 Power spectral density
SAT (°°C)
1 0 -1 -2 -3
34y
2.5 2
11y
3y
1 0.5 0
1850 1870 1890 1910 1930 1950 1970 1990 2010 Year
0
800 600
Precipitation spectrum - Sibiu 7y
19y Power spectral density
P (mm)
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Frequency (cycle/year) x 104
5
400 200 0 -200
4y
1.5
3y
4 31y
3
11y
2
1 -400 1850 1870 1890 1910 1930 1950 1970 1990 2010 Year Fig. 2. Surface air temperature (a) and precipitation (b) at the 14 stations relative to their means over the time interval 1961–1990.
0
0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Frequency (cycle/year)
Fig. 3. Spectral analysis by multi-taper method (MTM) of the surface air temperature (a) and precipitation (b) at the Sibiu meteorological station.
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2
T (°°C)
1 0 -1 -2 1850 1870 1890 1910 1930 1950 1970 1990 2010 Year 600
P (mm)
400 200 0 -200 1850 1870 1890 1910 1930 1950 1970 1990 2010 Year Fig. 4. Average surface air temperature (a) and precipitation (b) over the 14 meteorological stations in Romania (black), interdecadal trend (red), centennial trend (blue and green, see text).
After filtering out short period variations, the series are smoothed using a running filter with a window of 11 years (the red line in Fig. 4), defining an interdecadal trend, T11. It is found that a 30-year variation appears in T11, with amplitudes of 0.3–0.4 1C, while in the interdecadal trend of mean precipitation, P11, a 22-year variation appears, with an amplitude of 35 mm. Further filtering with running windows of 22 and 30 years was successively applied on T11 and P11. The smoothed time series are also given in Fig. 4 (blue and, respectively, green lines) (T22, T30 and, respectively, P22 and P30). They define a centennial trend in data. The 22-, 30-year and longer (centennial scale) variations seen in the 11-year running averages of air temperature and precipitation might be related to solar and geomagnetic phenomena occurring at similar timescales (Hale and Gleissberg cycles of solar activity), as will be shown in Section 3. 3. Relationship between solar and geomagnetic activity and the climatic parameters for Romania As indicators of solar and geomagnetic activities, we used the annual means of the sunspot number, R (http:// www.ngdc.noaa.gov/stp/SOLAR/ftp.sunspotnumber.html), and, respectively, the annual means of the aa geomagnetic index (http://isgi.cetp.ipsl.fr/des_aa.htm). A correction of +3 nT was added to aa data prior to 1957, according to Svalgaard and Cliver (2007). We preferred the aa index to other geomagnetic activity indices, such as the Inter-Hour Variability (IHV, Svalgaard and Cliver, 2007) and the
Inter-Diurnal Variability (IDV, Svalgaard and Cliver, 2005), considering the length of their time series. The aa geomagnetic index (Mayaud, 1972, 1980) is compiled from the range of variations of the geomagnetic field over periods of 3 h (the K index) at two near-antipodal geomagnetic observatories in England and in Australia; by definition, the solar quiet time variation is removed from the data. While aa and IHV are responding to the product BV2, where B is the interplanetary magnetic field (IMF) strength and V is the solar wind velocity at 1 AU (Svalgaard and Cliver, 2007), the IDV index is linked only to the IMF strength (Svalgaard and Cliver, 2005), but all three show similar variations in terms of 11-year running averages and at Hale and Gleissberg time scales (Demetrescu and Dobrica, 2008). By applying the same multi-taper method of spectral analysis, which we applied to the climatic parameters, to the yearly means both of the sunspot number, R, and of the aa geomagnetic index, in the time interval 1868–2000, Prestes et al. (2006) showed that the significant period is the well-known period of about 11 years. A variation with a period of about 22 years (Hale cycle) in R and aa was discussed by several authors (e.g. Mursula et al., 2001; Russell and Mulligan, 1995; Cliver et al., 1996; Demetrescu and Dobrica, 2008 and references therein). Also, different spectral analysis techniques applied to the sunspot number time series show peaks at various frequencies in the Gleissberg cycle range (60–120 years). For instance, Rozelot (1994) reports peaks at 58.5 and 97.2 years, while Prestes et al. (2006) find a peak at 100.7 years and Rogers et al. (2006) indicate peaks at 87, 97.5, 102 years, depending on the method used. We investigated the correlation of the mean surface air temperature, T, and precipitation, P, over Romania and the solar, R, and geomagnetic (aa) parameters for the interdecadal, the 22- and the 30-year trends (Fig. 5a–c). In order to make the intercomparison, all parameters were referred to their means over the common time interval (1873–1995, 1883–1984 and, 1898–1970, respectively) and scaled with their standard deviations about the mean. In Fig. 5a, we compare the running averages with a window of 11 years. A reversed scale was used for P. As it can be seen, there is a positive correlation between surface air temperature and solar and geomagnetic activity, and a negative correlation in the case of precipitation. The correlation coefficients are given in Table 1. An improvement is found compared with the correlation in the case of raw (annual averages) data series (correlation coefficients: rT,R ¼ 0.25, rT,aa ¼ 0.31, rP,R ¼ 0.10, rP,aa ¼ 0.26). In terms of running averages with a window of 22 years, applied to time series in Fig. 5a, the investigated parameters are given in Fig. 5b, and the corresponding correlation coefficients in Table 2. The results of averaging the 22-year averaged series with a running window of 30 years, are presented in Fig. 5c and Table 3. The correlation coefficients listed in Tables 1–3 are statistically significant at 99% confidence level. It is found that the climatic signal in the surface air temperature is in phase with the geomagnetic and solar activity and out of phase in the case of precipitation on all investigated timescales and the correlation is improved
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Standard deviation
2 1
Table 2 The correlation coefficients, in case of 22-year running averages of the mean surface air temperature, T, and precipitation, P, over Romania with the solar, R, and geomagnetic, aa, indices
T -P R aa
0
R
aa
T P
0.69 0.72
0.70 0.75
Table 3 The correlation coefficients, in case of 30-year running averages of the mean surface air temperature, T, and precipitation, P, over Romania with the solar, R, and geomagnetic, aa, indices
-3 1850 1870 1890 1910 1930 1950 1970 1990 2010 Year 2 Standard deviation
Series
-1 -2
Series
R
aa
T P
0.86 0.88
0.89 0.91
1 0 -1 -2 -3 1850 1870 1890 1910 1930 1950 1970 1990 2010 Year 2
Standard deviation
1731
1 0 -1 -2
higher than the correlation between the climatic parameters and the solar activity. Such a behavior was also noticed by Bucha and Bucha Jr. (1998) for short-term, magnetic storm timescale, variations in solar and geomagnetic activities compared with climatic parameters. In conclusion, there is a significant correlation between, on one hand, the solar and geomagnetic activity (sunspot number, R, and the geomagnetic index, aa) and, on the other hand, the climatic parameters over Romania when they are referred to the long-term trends. However, differences in the general trends of temperature as compared with solar and geomagnetic trends can be noticed in the first half of the 20th century in all plots of Fig. 5. They can be understood in the context of European and global data.
-3 -4
4. Romanian data in the context of European and global data
-5 1850 1870 1890 1910 1930 1950 1970 1990 2010 Year Fig. 5. The mean over Romania of surface air temperature, T (red), and precipitation, P (black), compared with solar, R (blue), and geomagnetic, aa (green), activity in terms of 11- (a), 22- (b) and 30-year (c) running averages. Note the reversed scale for P. Curves are reduced to their means over the common time interval (1873–1995, a; 1883–1984, b; 1898–1970, c) and scaled with their standard deviations about the mean as a unit.
Table 1 The correlation coefficients, in case of 11-year running averages of the mean surface air temperature, T, and precipitation, P, over Romania with the solar, R, and geomagnetic, aa, indices Series
R
aa
T P
0.54 0.59
0.57 0.62
when the length of the running window is increased. On the long-term scale, the relationship between the climatic parameters and the geomagnetic activity seems to be
Instrumental information from other meteorological stations in Europe dates back to the end of the 17th century (London, Central England—1659). Reconstructions on the northern hemisphere and global scales (e.g. Moberg et al., 2005; Jones et al., 2006), go further back (1500). It appears interesting that the Romanian data follow the long-term trends observed at such scales, as it is well known that remarkable regional differences are found in climate trends (Mann et al., 2003). Our analysis will refer to SAT data. In Fig. 6, we superimposed 11-year running means of several temperature time series from individual European stations, sampling the latitudinal band 45–55 N, namely: Central England (Manley, 1974; Parker et al., 1992), Hohenpeissenberg (Germany), Vienna (Austria), Prague (Czech Republic), (Jan Safanda, personal communication, 2005) and Bucharest (Romania). We included several published averages for certain areas, namely Europe (Mitchell and Jones, 2005, based on instrumental data for 1900–2004 and reconstructions by Luterbacher et al. (2004) and Xoplaki et al. (2005) for 1500–1900), northern
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Central England Hohenpeissenberg Vienna Prague Bucharest Europe Northern Hemisphere Globe
1.5
1
SAT (°C)
0.5
0
-0.5
-1
-1.5 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000 Year Fig. 6. Anomaly of the surface air temperature, relative to the means over the time interval 1961–1990, in terms of 11-year running averages, for individual stations and for Europe, northern hemisphere and entire Earth (see text for data sources).
4
Romania Europe Northern Hemisphere Globe R aa
3
Standard deviation
2 1 0 -1
peak, while the temperature at individual stations and the European average are depressed. A comparison in terms of 11-year running averages (Fig. 7) of a local temperature record (the average for the Romanian territory, T), of continental, of northern hemisphere and of global temperature averages with the solar and geomagnetic activities reveals: (1) the well-known correlation between solar and geomagnetic activities and the northern hemisphere mean temperature (FriisChristensen and Lassen, 1991; Le Moue¨l et al., 2005), also closely followed by the mean temperature at global scale; (2) the marked discrepancy in trend after 1980–1990, envisaging the possible emergence of the effect of anthropogenic greenhouse gases (Le Moue¨l et al., 2005); (3) the faster increase of temperature in comparison to solar and geomagnetic activities before 1940–1950; and (4) the differences in case of local (Romanian average, Europe average) temperature variations as compared with averages at larger geographical scale (NH, Globe). Smoothing the interdecadal variations by 22-year running averages (Fig. 8) better illustrates these conclusions. We note here that the discrepancies mentioned above under (3), were also discussed by Friis-Christensen and Lassen (1991) and by Le Moue¨l et al. (2005). The former found out that using another solar parameter instead of sunspot numbers, namely the length of sunspot cycle, removes the apparent lag of the solar activity curve relative to the surface temperature. The latter explain what they call a slight lag of geomagnetic and solar parameters with respect to temperature, by a difference in temperature data quality before and after 1960. We find (see below) that the faster increase of temperature in comparison with solar/geomagnetic activities before 1940–1950 might be explained by a 40–50-year variation that characterize the long trend temperature variation. Examining Fig. 8 also reveals that the 30-year variation, present in Romanian data, is also visible, with smaller amplitudes, in case of Europe average time
-2 4
Romania Europe Northern Hemisphere Globe R aa
-3 1900
1950
2000
Year Fig. 7. Comparison in terms of 11-year running averages, between the mean value of the surface air temperature in Romania, Europe, northern hemisphere, Globe and solar, R, and geomagnetic, aa index, activity. Curves are reduced to the their means over the common time interval (1873–1995) and scaled with their standard deviations about the mean as a unit.
2 Standard deviation
1850
0
-2 hemisphere (Moberg et al., 2005 for 1500–1979, based on tree-ring and lake/ocean sediments data; Jones et al., 2006 for 1980–2005, instrumental data) and entire Earth (Jones et al., 2006 for 1856–2005, instrumental data). At a first glance, the similarity of the variations at individual stations and notable amplitude differences with respect to mean values for the entire Europe or for the northern hemisphere can be noticed. We also note that between 1930 and 1940 the NH average shows a
-4 1850
1900
1950
2000
Year Fig. 8. Data of Fig. 7 smoothed with a 22-year running averages filter. Curves are reduced to the their means over the common time interval (1883–1984) and scaled with their standard deviations about the mean as a unit.
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variation and seems to be absent at northern hemisphere and globe scales. A comparative study of a number of local temperature time series, which is beyond the scope of this paper, might give some indications on the nature of this variation. We only mention here that a 30-year variation was noticed by Mursula et al. (2001) in case of solar activity, using the group sunspot number series. Subtracting the time series presented in Fig. 8 from those in Fig. 7 gives the so-called 22-year variation, related to the Hale cycle in the solar activity. Demetrescu and Dobrica (2008) presented a comparison of geomagnetic activity, described by the aa, IHV and IDV indices, with the sunspot number, as a proxy for the solar activity, and with the reconstructed total solar irradiance (Lean et al., 1995; Lean, 2000; Krivova et al., 2007), and showed that the interdecadal variability is explained by the superposition of the 22-year signature of the Hale cycle on a variation at the Gleissberg cycle timescales. In Fig. 9, we superimpose the 22-year variation of temperature at individual stations and the same signal in the European average. In order to make the time series comparable, curves are reduced to their means over the common time interval (1883–1983) and scaled with their standard deviations about the mean as a unit. Again, the similar pattern of variation, but with different amplitudes, can be noticed. Discrepancies evident before 1890 could be a result of less accuracy in data. The corresponding NH curve was not plotted, due to the mentioned dissimilarity in the time interval 1930–1940 of the NH average as compared with the European data, which would strongly show up in this kind of plot.
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A cross correlation analysis on temperature and solar/ geomagnetic 22-year variations shows that the average temperature at northern hemisphere and global scales lag behind the solar and geomagnetic activities by about 5 years (correlation coefficients Globe: rT,R ¼ 0.34; NH: rT,R ¼ 0.46) and, respectively, 6 years (Globe: rT,aa ¼ 0.49; NH: rT,aa ¼ 0.59). All these correlation coefficients are statistically significant at the 99% level. The local temperatures (individual stations and European average) show a larger lag, of 7–9 years. To illustrate this kind of correlation, we superimposed on the 22-year temperature variations (Fig. 9) the corresponding one in the geomagnetic aa index (red curve), shifted forward in time by 8 years. Further smoothing the time series of Fig. 8 with a longer window running averages filter, at the timescale of the Gleissberg cycle (60–120 years, Demetrescu and Dobrica (2008)), to obtain a possible Gleissberg cycle signature in the temperature time series, is not relevant because of the relative short time interval covered by data series. However, the Central England time series, which goes back to 1659 AD, seems to indicate the presence of a 40–50-year variation (not shown), superimposed on a longer-term trend. With present data sets we cannot decide whether this variation is a harmonic of the Gleissberg variation or is simply a result of the internal variability of the climate system. However, this variation explains the faster increase of temperatures in comparison with solar/geomagnetic parameters, observed before 1940–1950 (Fig. 8). We did not consider for this treatment longer temperature time series, based on proxy data (e.g. Moberg et al., 2005), due to discrepancies with measured data in the common time interval (Fig. 6), on one hand, and to the well-known incapacity of proxies to reveal very low-frequency climate fluctuations (e.g. Esper et al., 2004), on the other.
3 5. Conclusions
Standard deviation
2 1 0 -1 -2 -3 -4 1850
1900
1950
2000
Year Fig. 9. The 22-year variation of temperature at individual stations (Central England, Hohenpeissenberg, Vienna, Prague, Bucharest) (full line) and at European scale (dashed line), as compared with the 22-year variation in the geomagnetic activity (red line). The aa curve is shifted forward in time by 8 years. Curves are reduced to the their means over the common time interval (1883–1983) and scaled with their standard deviations about the mean as a unit.
Fourteen long time series (1850–2004) of air temperature and precipitation in Romania, several temperature time series from other European stations in the latitude interval 45–55 N (Central England, Hohenpeissenberg– Germany, Vienna–Austria, Prague–Czech Republic), and average temperature reconstructions for Europe, northern hemisphere and entire Earth were analyzed in terms of correlation with the solar and geomagnetic activity on the Hale and Gleissberg timescales, as represented by the sunspot number and, respectively, by the geomagnetic aa index time series. Having in view the similarity of variations observed for all Romanian stations, in the case of both the air temperature and precipitation, the analysis was carried out on averages for the Romanian territory. A positive correlation of the mean air temperature over Romania and a negative one of the mean precipitation over Romania were found with the solar/geomagnetic activity, in terms of 11-, 22- and 30-year successive lowpass running averages filters applied to annual values after filtering out shorter, 2–7 years, variations seen in data. The correlation of all such parameters is largely
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controlled by the long-term trends, and the correlation coefficients increase with the amplitude of the window of the running average applied to the data series. On the long-term scale, the correlation between the climatic parameters and the geomagnetic activity seems to be more effective compared with the correlation between climatic parameters and solar activity. Romanian temperature data were examined in the context of longer time series from the other European stations and averages on continental, northern hemisphere, and global scales. The variability in terms of the 11-year running averages appears quite similar at all stations. The time series of average values on continental, northern hemisphere and global scales is characterized by smaller amplitudes than the time series from individual stations. A 22-year, Hale cycle related, variation in data, superimposed on a longer-term variation, is responsible for the 22-year-scale fluctuations seen in the 11-year filtered time series. A cross-correlation analysis showed that the 22-year variation in temperature lags behind the corresponding solar/geomagnetic variations by 5–9 years depending on the spatial scale at which the analyzed temperature time series are representative. An analysis of variations on centennial (Gleissberg) timescales is not significant in view of the insufficient length of observed time series. However, the longest one, the Central England time series, seems to indicate the presence of a 40–50-year variation superimposed on a longer-term trend, variation that could explain features like the faster increase of temperature, in comparison to solar/geomagnetic activities, before 1940–1950. The similarity of the variation pattern, as well as amplitude differences between local records and averages on large geographical scale can be understood in terms of large-scale atmospheric circulation patterns, influenced by solar/geomagnetic forcing on the analyzed timescales, but with local intensity differences. By no means, however, the present analysis can decide on, or discriminate between, various forcings at work, including the anthropogenic one. The relative contribution of either solar or geomagnetic effects on climate and/or the physical processes actually responsible for the observed correlations are a topic for future studies.
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