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Geoeffectiveness of solar wind interplanetary magnetic structures
Journal of Atmospheric and Solar-Terrestrial Physics 73 (2011) 1380–1384
Contents lists available at ScienceDirect
Journal of Atmospheric and Solar-Terrestrial Physics journal homepage: www.elsevier.com/locate/jastp
Geoeffectiveness of solar wind interplanetary magnetic structures M.V. Alves n, E. Echer, W.D. Gonzalez ~ Jose´ dos Campos, SP, Brazil Instituto Nacional de Pesquisas Espaciais (INPE)–Av. Astronautas, 1758, 12201-970 Sao
a r t i c l e in f o
a b s t r a c t
Article history: Received 30 March 2010 Received in revised form 20 July 2010 Accepted 21 July 2010 Available online 29 July 2010
We address the geoeffectiveness of three interplanetary structures in the interplanetary space: magnetic clouds (MCs), interplanetary shocks (IPSs), and corotating interaction regions (CIRs). The geoeffectiveness is evaluated using the geomagnetic indices Kp, AE, and Dst. We find that MCs are more geoeffective than IPSs, or CIRs. The average values of magnetic indices are significantly enhanced during disturbed periods associated with MCs, IPSs and CIRs, compared to the whole interval. The highest effect is noted for MC disturbed periods. Results obtained for the three data sets are used to derive a theoretical (continuous) probability distribution function (PDF) by fitting the histograms representing the percentage of events against the intervals of magnetic index. PDFs allow estimation of the probability of a given level of geomagnetic activity to be reached after the detection, by in situ solar wind observations, of a given interplanetary structure approaching the Earth. & 2010 Elsevier Ltd. All rights reserved.
Keywords: Solar wind Geomagnetic storm Interplanetary magnetic structures Probability distribution functions
1. Introduction Solar wind–magnetosphere coupling has been widely studied in the last decades, with in situ data from several spacecraft observations and ground-based data from geomagnetic and upper atmosphere stations spread worldwide. Magnetic reconnection between the anti-parallel solar wind and diurnal magnetopause magnetic fields is the main cause for the energy transfer between the solar wind and the magnetosphere. Magnetic reconnection is favored when interplanetary magnetic field (IMF) presents an intense, long duration and southward oriented (in geocentric solar coordinate) magnetic field Z-component (Dungey, 1961; Akasofu, 1981; Gonzalez and Tsurutani, 1987; Gonzalez et al., 1994; Echer et al., 2005a). Origin of this intense and long duration southward (Bs) IMF is the interplanetary shock (IPS), the sector boundary crossings of the heliospheric current sheet (Burlaga, 1995; Gonzalez et al., 1999), and the solar wind (SW) interplanetary structures, such as stream interaction regions or corotating interaction regions (CIRs), magnetic clouds (MCs), and other interplanetary remnants of coronal mass ejections (ICMEs; Gonzalez et al., 1999). The geoeffectiveness and geomagnetic impacts of these SW interplanetary structures have been investigated by several authors. Gosling et al. (1991) analyzed 171 shocks and 191 ICMEs occurring between August 1978 and October 1982 using data from ISEE-3 and values of the Kp index. The authors concluded that 44% of ICMEs, 53% of shocks, and 85% of all events were associated with Kpmax Z5. Jurac et al. (2002) analyzed 107 shocks occurring between 1995 and 2000,
n
Corresponding author. Fax: + 55 12 3945 6810. E-mail addresses: [email protected], [email protected] (M.V. Alves).
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using WIND data. Considering all these events, they have obtained that 21% of the shocks were followed by geomagnetic storms with Dstr 100 nT. Among the perpendicular shocks, 40% were followed by Dstr 100 nT, while for non-perpendicular shocks they happen for just 10–15%. Wu and Lepping (2002) concentrated their attention to 34 MCs observed between 1995 and 1998, using WIND data. Their results show that 88% of MCs were followed by magnetic storms (Dstr 50 nT), associated with electric field Ey present in the sheath or MC itself. Zhang et al. (2004) used 104 MCs occurring between 1998 and 2002 to infer that 80% of MCs are followed by Dstr 30 nT, 56% by Dstr 50 nT, and 34% by Dstr 100 nT. Huttunen et al. (2005) used the data from 73 MCs occurring between 1997 and 2003 to infer that 71% of all MCs were followed by Dstr 50 nT. Among these, 22% of the events were associated with Ey in the sheath fields, while 49% were associated with MC electric fields. Echer and Gonzalez (2004) investigated the geoeffectiveness of several interplanetary structures occurring within 1973–2001, in terms of the Dst response. Alves et al. (2006) presented a complementary study considering only the passage by Earth of CIRs for the period of 1964–2003. Echer et al. (2006) analyzed the geoeffectiveness of MCs, IPSs, and CIRs, for the period 1964–2003. Their results show that, among the magnetic interplanetary structures, MCs are more geoeffective than shocks or CIRs. However, the percentage of CIRs that are followed by moderate or intense geomagnetic activity as measured by AE index is similar to the percentage of IPSs followed by moderate or intense geomagnetic activity as measured by AE index. A review of the geoeffectiveness of coronal mass ejections (CMEs) and flares was given by Yermolaev et al. (2005). They addressed a comparison of data analysis methods and
M.V. Alves et al. / Journal of Atmospheric and Solar-Terrestrial Physics 73 (2011) 1380–1384
quantitative estimation of the results given by different methods. According to them, the geoeffectiveness of CMEs, for example, can change from 35% up to 71%, depending on the statistics used. However, when the data set includes 38–132 CMEs, the interval is reduced to 35–50%, in good agreement with each other. Estimation of geoeffectiveness for solar flares is 30–40%, very close to the one for CMEs. More recently, Zhang et al. (2007) identified solar coronal mass ejection (CME) sources for 88 major geomagnetic storms (Dstr 100 nT) occurring between 1996 and 2005. Some of the events show complex solar wind flows and complex geomagnetic activity, which are, probably, the result of multiple halo-CMEs interacting in interplanetary space. Within these events, only one is caused by a corotating interaction region. They also found that the most geoeffective CMEs originate within a latitude strip of 301. Gopalswamy (2008) summarized the properties of CMEs and high-speed streams and described their geoeffectiveness, focusing on the intense storms of solar cycle 23. Since that was the first solar cycle during which solar, interplanetary, and geospace data were available, he was able to present several analyses, such as identifying the occurrence of magnetic storms driven structures (CMEs or CIR) along the solar cycle as well as their origin location in the Sun. One of his main results was the confinement of solar sources of major storms, independent of the driver, to low latitudes ( 7301) throughout the cycle. He also pointed out that only CMEs and coronal holes originating close to the solar disk center cause intense storm. He used the term geoeffectivess in the sense of ring current enhancement (Dst). The interplanetary origin of intense magnetic storms during solar cycle 23 was also studied by Zhang et al. (2007), Gonzalez et al. (2007), and Echer et al. (2008). All the mentioned works are part of a major effort to predict geomagnetic storms. To be able to do so, it is of prime importance to know their cause in the Sun and solar wind. In this work we assess the geoeffectiveness of the three main interplanetary structures found in interplanetary space, near the Earth: MCs, IPSs, and CIRs. The geoeffectiveness is evaluated with the geomagnetic indices Kp (ap), AE, and Dst peak values, within 2 days after the interplanetary structure has passed near-Earth orbit. We obtain a number of events following each interplanetary structure. Our results confirm that shocks driven by MCs are the most geoeffective structures, among the ones analyzed, in every type of magnetospheric activity (Echer et al., 2006). The results are then used to find fitted (continuous) probability distribution functions (PDFs). These PDFs are obtained after fitting the empirical distribution histograms by distribution functions, representing the percentage of events against intervals of magnetic index. The knowledge of probability distribution functions is important in schemes to forecast space weather, since it allows us to estimate the probability of a given level of geomagnetic activity reached following a certain interplanetary structure.
2. Methods The selection of events analyzed in this paper has been described elsewhere (Echer and Gonzalez, 2004; Alves et al., 2006, Echer et al., 2006). The selection of CIRs, structures formed when high speed solar wind streams overtake slow solar wind streams as they propagate outwards, was based on the basic physical features of the corotating high-speed streams with respect to interplanetary plasma and field parameters, as described by Alves et al. (2006). The events were checked to see if they were not interplanetary remnants of coronal mass ejection (ICME). CIR events associated with flares, ICMEs, magnetic clouds (MCs), and shocks were not included. Storms that were caused by the interaction of MCs or shocks with CIRs, i.e., the cases when
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Z-component of the magnetic field (Bz) causing the storm was not only the CIR field, but also an altered field due to the interaction with a different magnetic structure were also excluded. Magnetic clouds are a subset of interplanetary remnants of CMEs (ICMEs) and they are recognized in interplanetary data by their signatures: large-scale smooth field rotations; enhanced magnetic field magnitude; and decreased proton temperature/ proton beta (Echer et al., 2006). Since our main interest is to verify the geoeffectiveness of MCs, we have not considered the ones that present By rotation with Bz to north, which are, in general, not geoeffective. We do not make a difference if the geomagnetic storm is caused by the MC itself or by the sheath region. Selection of shocks followed the same procedure of Echer and Gonzalez (2004). Shocks are generated when the relative velocity between the interplanetary structure and the slow solar wind is higher than the magnetosonic velocity. The most usual type of shock, the fast forward, shows a simultaneous jump in plasma parameters (speed, density, and temperature) and in magnetic field strength. Usually, a very disturbed Bz component is observed after the shock (Gonzalez et al., 1999). Since interplanetary shock waves are observed in solar wind usually associated with the ICMEs, it is more likely to observe geomagnetic response associated to other fields, besides the fields associated with the shock itself. For MCs and CIRs there is not much problem of contamination by other structures. Here, for the complete data set of shocks, we have taken the maximum/minimum value of geomagnetic indices (peak value) within 2 days after the shock, independent of the shock being followed or not by other structure (MC or CIR). Among the 830 IPSs considered here, 115 were shocks driven by MC events. The events analyzed in this paper, 830 IPSs, 170 MCs and 727 CIRs, occurred between 1964 and 2003. The geoeffectiveness is evaluated with the geomagnetic indices Kp, AE and Dst peak values, within 2 days after the interplanetary structure was observed near 1 AU. We perform standard statistical analysis for 9 data sets (3 structures and 3 indices). We derive PDF for each data set from the analysis of empirical distribution parameters and by fitting the histograms (Spiegel, 1961). Tables 1–3 show the main statistical parameters related to the geomagnetic indices, Dst, AE and ap (Kp), respectively. The columns of the tables refer to minimum (min), maximum (max) and average Table 1 Statistical parameters for Dst for all periods (N in this case is the number of points used) and the disturbed period after shocks, MCs, and CIRs (1964–2003). Param.
Dst shocks
Dst MCs
Dst CIRs
Dst all
AV SD Min. Max. Median N
71.9 62.7 472 10 55 830
94.7 54.5 288 5 82 170
43.2 24.2 131 9 38 747
16.3 23.9 589 81 12 350,640
Table 2 Statistical parameters of AE for all periods (N in this case is the number of points used) and for the disturbed period after shocks, MCs, and CIRs (1964–2003). Param.
AE Shocks
AE MCs
AE CIRs
AE all
AV SD Min. Max. Median N
870 396 106 2780 840 830
1007 344 104 2155 1014 170
792 280 116 2300 786 747
218 211 5 3195 140 241,032
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values (AV), standard deviation (SD), median and the total number of events (N). Besides the results for the data sets of MCs, CIRs, and shocks, for comparison, we also list the statistical parameters considering the time series of the indices for the whole period (1964–2003); N in this case represents the number of points we use in the time series for the respective geomagnetic indices.
3. Results Fig. 1 shows the histogram distribution of the geomagnetic indices Dst, upper panels; AE, central panels, and; ap, bottom panels; for CIRs, left column; IPSs, central column, and; MCs, right column. Histogram distribution of Dst for MCs has been presented by Echer et al. (2005b), for the period 1966–2001 (149 MCs), and for CIRs by Alves et al. (2006). The dotted lines superposed to the histograms (percentage of raw counts per bin) in Fig. 1 are the fitted distribution functions we have obtained. It can be seen that Table 3 Statistical parameters for ap (Kp) for all period (N in this case is the number of points used) and for the disturbed period after shocks, MCs and CIRs (1964–2003). Param.
ap Shocks
ap MCs
ap CIRs
ap all
AV SD Min. Max. Median N
72 67 3 400 48 830
92 63 7 300 80 170
48 29 9 236 39 747
14 19.6 0 400 7 116,880
only AE distributions can be recognized as nearly normally distributed, while ap and Dst show a representative number of events on extreme limits (tail) of their distribution; for ap, an exponential behavior is observed. The normality W-test of Shapiro–Wilk (Royston, 1982) shows that Dst and ap are not normally distributed at the 0.05 significance level, while AE is normally distributed for the MC and CIR data sets. AE is not normally distributed at 0.05 significance level for IPSs. However, when removing the extreme values, the distribution becomes Gaussian at the 0.05 W-test. The parameter A3 measures the asymmetry, pointing out how different from a normal distribution, for extremes, a distribution is (A3¼0 for a normal distribution). We obtain negative values for A3 using Dst data set ( 2.0 for shocks, 1.1 for MCs, and–0.9 for CIRs), meaning an asymmetry on the left (on the right if we take–Dst). For ap, the A3 values are high and positive (1.9 for shocks, 0.9 for MCs, and 1.5 for CIRs), indicating an asymmetry to the right. For AE, the values of A3 are lower than for the other indices, suggesting a lower degree of asymmetry (Spiegel, 1961). Based on these results, we decided to fit Gaussian functions to AE frequency distributions. For Dst, the kurtosis test showed that the distribution function could be described by a right-skewed Gaussian function (using ORIGIN software). Finally, for ap distributions, we judged both from the statistical parameters and from a visual inspection that an exponential fit would be more acceptable. Fig. 2 gives the derived distribution functions, which we called theoretical distribution functions, whereas the histograms in Fig. 1 can be seen as empirical distribution functions. From the theoretical distributions, we can estimate the probability of each geomagnetic activity level be reached after an interplanetary structure is detected.
Fig. 1. Histograms and line fitting of the histograms for geomagnetic indices Dst, upper panels; AE, central panels; and ap, bottom panels; for CIRs, left column; shocks, central column, and; MCs, right column. The geomagnetic indices Kp (ap), AE, and Dst are peak values within 2 days after the interplanetary structure has passed nearEarth orbit.
M.V. Alves et al. / Journal of Atmospheric and Solar-Terrestrial Physics 73 (2011) 1380–1384
Fitted distribution function, P, for Dst, upper panel of Fig. 1, is given by the right-skewed Gaussian function, P ¼ y0 þ A expðexpðzÞz þ 1Þ, where z ¼(x xc)/w, and x is the value of the geomagnetic index Dst. For AE, central panel of Fig. 1, A xxc 2 P ¼ y0 þ exp 2 , 1=2 w wðp=2Þ where x is the value of AE. For ap P ¼ A1 ðexpðx=t1 Þ þ y0 , where x is the value of ap. The values of the adjusting parameters are given in Table 4. To evaluate the goodness of fit of our statistical model we obtain the value of R2. In regression, the R2 coefficient of determination is a statistical measure of how well the regression line approximates the real data points. An R2 of 1.0 indicates that the regression line perfectly fits the data. Table 4, last line, gives the values of R2 for the
Fig. 2. Fitted distribution functions for Dst (upper panel), AE (central panel), and Kp (ap) (bottom panel) for the three interplanetary structures used in this work.
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fitting curves presented in this paper. They indicate that the best fittings are obtained for AE (R2 40.89); for Dst, the chosen function is also a good fitting (R2 40.8), and is able to include the extreme values of Dsto 100 nT; for ap, fittings are not so reliable, but they provide an easy way to handle the function in order to estimate the probability of occurrence of a certain event as measured by ap. It can be seen that the peaks of the distributions for Dst and AE are at higher index values for MCs than for shocks and CIRs. Shocks and CIRs have peaks close to each other, with slightly higher values for shocks. Most CIRs concentrate around lower index values. For ap indices, the exponential distribution shows that we have higher probabilities for CIRs and shocks with low value of ap index. MC distribution shows higher probability of occurrence than CIRs or shocks for high ap index values.
4. Summary and conclusions In this work we address the geoeffectiveness of the three main interplanetary structures found in interplanetary space, near the Earth: MCs, IPSs, and CIRs. The geoeffectiveness is evaluated with the geomagnetic indices Kp (ap), AE, and Dst peak values, within 2 days after the interplanetary structure has passed near-Earth orbit, and the number of events following each interplanetary structure was obtained. A statistical analysis of these three data sets shows that the transient interplanetary structures, MCs or IPSs, are more geoeffective than CIRs. MCs are the most geoeffective structures as measured by the three geomagnetic indices used here. The geoeffectiveness of the structures analyzed here as measured by the AE index is very similar, observed by the fitting parameters in Table 4. The ooccurrence of combined structures (IP+MC) seems to be more geoeffective for every class of geomagnetic activity than the occurrence of isolated interplanetary structures (Echer et al., 2005b). Since usually there is a region between the MC and the driven shock, the sheath, further studies should address the geoeffectivity of this region (Badruddin and Singh, 2009; Gopalswamy, 2009). From Tables 1–3 it is also possible to notice how the presence of magnetic structures changes the values of average values for the magnetic index. For example, for Dst the average values for IPSs, MCs, and CIRs are 4.4, 5.8, and 2.6 larger than the average of all the periods, respectively. For AE index, the ratio values are 4.0 for IPSs, 4.6 for MCs, and 3.6 for CIRs. For ap index, the values are 5.1 for IPSs, 6.6 for MCs, and 3.4 for CIRs. It is important to mention that the extreme value for Dst, 589 nT, is related to an unknown structure. This value is associated with a huge magnetic storm occurred in 1989 with no interplanetary data available for analysis. We also obtain, by fitting the histograms, a probability distribution function for the three data sets. Dst and ap are not normally distributed, while AE is normally distributed for MC and CIR data sets. AE is not normally distributed for IPSs; however, when removing the extreme values, the distribution function becomes a Gaussian. Based on these results, we decided to fit by
Table 4 Values of parameters found on the fitted distribution functions: for Dst, P¼ y0 + A exp( exp( z) z + 1), where z ¼(x xc)/w, and x is the value of Dst; for AE, P ¼y0 +(A/(w(p/ 2)1/2))exp( 2(x xc/w))2), where x is the value of AE; and for ap, P ¼A1 exp( exp( x/t1) + y0), where x is the value of ap. Dst
y0 xc w A R2
AE
ap
MCs
IPs
CIRs
MCs
IPs
CIRs
0.06 64.45 38.21 9.30 0.82
0.22 37.6 29.80 10.82 0.97
0.045 31.25 18.34 19.56 0.99
0.15 992.5 567.6 4563.0 0.9
0.03 830.9 822.1 5202.8 0.89
0.01 772.7 547.7 4989.6 0.94
y0 A1 t1 –
MCs
IPs
CIRs
4.85 12.25 355.55 – 0.43
0.28 12.72 89.11 – 0.69
1.19 15.38 97.52 – 0.54
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M.V. Alves et al. / Journal of Atmospheric and Solar-Terrestrial Physics 73 (2011) 1380–1384
Gaussian functions the AE histograms. For Dst, the kurtosis test showed us that the distribution function could be described by a right-skewed Gaussian function. Finally, for ap distributions, we judged both from the statistical parameters and from a visual inspection that an exponential fit would be more acceptable. The probability distributions obtained in this work can be used for space weather forecast given by a probability of an observed interplanetary solar wind magnetic structure be followed by every type of magnetic activity. This type of statistical result could also be used in models that try to simulate long periods of space weather activity, i.e., the space climate around the geospace.
Acknowledgments We thank the ACE SWEPAM and MAG teams for making their data publicly available, and the NSSDC at Goddard Space Flight Center for providing the OMNIweb database. Thanks to National Geophysical Data Center and to the World Data Center-Geomagnetism for the Kp, Dst, and AE geomagnetic indices. E.E. thanks both FAPESP (2007/52533-1) and CNPq/PQ (300211/2008-2) for research support. M.V.A thanks CNPq (304865/2007-9) for research support. W.D.G thanks FAPESP (2008/06650-9) for financial support. References Akasofu, S.I., 1981. Energy coupling between the solar wind and the magnetosphere. Space Sci. Rev. 28, 121–190. Alves, M.V., Echer, E., Gonzalez, W.D., 2006. Geoeffectiveness of corotating interaction regions as measured by Dst index. J. Geophys. Res. 111, A07S05. doi:10.1029/2005JA011379. Badruddin, Singh, Y.P., 2009. Geoeffectiveness of magnetic cloud, shock/sheath, interaction region, high-speed stream and their combined occurrence. Planet. Space Sci. 57, 318–331. Burlaga, L.F., 1995. Interplanetary Magnetohydrodynamics, Oxford Un. Dungey, J.W., 1961. Interplanetary magnetic field and the auroral zones. Phys. Rev. Lett. 6, 47–48. Echer, E., Gonzalez, W.D., 2004. Geoeffectiveness of interplanetary shocks, magnetic clouds, sector boundary crossings and their combined occurrence. Geophys. Res. Lett. 31, L09909. doi:10.1029/2003Gl019199. Echer, E., Gonzalez, W.D., Guarnieri, F.L., Dal Lago, A., Vieira, L.E.A., 2005a. Introduction to space weather. Adv. Space Res. 35, 855–865.
Echer, E., Alves, M.V., Gonzalez, W.D., 2005b. A statistical study of magnetic cloud parameters and geoeffectiveness. J. Atmos. Sol.–Terr. Phys. 67, 839–852. doi:10.1016/j.jastp.2005.02.010. Echer, E., Gonzalez, W.D., Alves, M.V., 2006. On the geomagnetic effects of solar wind interplanetary magnetic structures. Space Weather 4, S06001. doi:10.1029/2005SW000200. Echer, E., Gonzalez, W.D., Tsurutani, B.T., 2008. Interplanetary conditions leading to superintense geomagnetic storms (Dstr 250 nT) during solar cycle 23. Geophys. Res. Lett. 35, L06S03. doi:10.1029/2007GL031755. Gonzalez, W.D., Tsurutani, B.T., 1987. Criteria of interplanetary parameters causing intense magnetic storms (Dst o 100 nT). Planet. Space Sci. 35, 1101–1109. Gonzalez, W.D., Joselyn, J.A., Kamide, Y., Kroehl, H.W., Rostoker, G., Tsurutani, B.T., Vasyliunas, V.M., 1994. What is a geomagnetic storm? J. Geophys. Res. 99, 5771–5792. Gonzalez, W.D., Tsurutani, B.T., Clua de Gonzalez, A.L., 1999. Interplanetary origin of geomagnetic storms. Space Sci. Rev. 88, 529–562. doi:10.1023/ A:1005160129098. Gonzalez, W.D., Echer, E., Clua-Gonzalez, A.L., Tsurutani, B.T., 2007. Interplanetary origin of intense geomagnetic storms (Dsto 100 nT) during solar cycle 23. Geophys. Res. Lett. 34 (L06101), 1–4. doi:10.1029/2006GL028879. Gosling, J.T., McComas, D.J., Phillips, J.L., Bame, S.J., 1991. Geomagnetic activity associated with earth passage of interplanetary shock disturbances and coronal mass ejections. J. Geophys. Res. 96, 7831–7839. Gopalswamy, N., 2008. Solar connections of geoeffective magnetic structures. J. Atmos. Sol. Terr. Phys). doi:10.1016/j.jastp.2008.06.010. Gopalswamy, N., 2009. The CME link to geomagnetic storms. In: Proceedings of the International Astronomical Union, vol. 5, Cambridge University Press, pp. 326–335, doi:10.1017/S1743921309992870. Huttunen, K.E.J., Schwenn, R., Bothmer, V., Koskinen, H.E.J., 2005. Properties and geoeffectiveness of magnetic clouds in the rising, maximum and early declining phases of solar cycle. Ann. Geophys. 23, 625–641. Jurac, S., Kasper, J.C., Richardson, J.D., Lazarus, A.J., 2002. Geomagnetic disturbances and their relationship to interplanetary shock parameters. Geophys. Res. Lett. 29, 1463. doi:10.1029/2001GL014034. Royston, J.P., 1982. Algorithm AS 181: the W test for normality. Appl. Stat. 31, 176–180. Spiegel, M.R., 1961. Statistics Theory and Problems of Statistics. McGraw-Hill, NY. Wu, C.-C., Lepping, R.P., 2002. Effects of magnetic clouds on the occurrence of geomagnetic storms: the first 4 years of wind. J. Geophys. Res. 107, 1314. doi:10.1029/2001JA000161. Yermolaev, Y.I., Yermolaev, M.Y., Zastenker, G.N., Zelenyi, L.M., Petrukovich, A.A., Sauvaud, J.-A., 2005. Statistical studies of geomagnetic storm dependencies on solar and interplanetary events: a review. Planet. Space Sci. 53, 189–196. Zhang, JCh, Liemohn, V., Kozyra, J.U., Lynch, B.J., Zurbuchen, T.H., 2004. A statistical study f the geoeffectiveness of magnetic clouds during high solar activity years. J. Geophys. Res. 109, A09101. doi:10.1029/2004JA010410. Zhang, J., et al., 2007. Solar and interplanetary sources of major geomagnetic storms (Dst r 100 nT) during 1996–2005. J. Geophys. Res. 112, A10102. doi:10.1029/2007JA012321.
Contents lists available at ScienceDirect
Journal of Atmospheric and Solar-Terrestrial Physics journal homepage: www.elsevier.com/locate/jastp
Geoeffectiveness of solar wind interplanetary magnetic structures M.V. Alves n, E. Echer, W.D. Gonzalez ~ Jose´ dos Campos, SP, Brazil Instituto Nacional de Pesquisas Espaciais (INPE)–Av. Astronautas, 1758, 12201-970 Sao
a r t i c l e in f o
a b s t r a c t
Article history: Received 30 March 2010 Received in revised form 20 July 2010 Accepted 21 July 2010 Available online 29 July 2010
We address the geoeffectiveness of three interplanetary structures in the interplanetary space: magnetic clouds (MCs), interplanetary shocks (IPSs), and corotating interaction regions (CIRs). The geoeffectiveness is evaluated using the geomagnetic indices Kp, AE, and Dst. We find that MCs are more geoeffective than IPSs, or CIRs. The average values of magnetic indices are significantly enhanced during disturbed periods associated with MCs, IPSs and CIRs, compared to the whole interval. The highest effect is noted for MC disturbed periods. Results obtained for the three data sets are used to derive a theoretical (continuous) probability distribution function (PDF) by fitting the histograms representing the percentage of events against the intervals of magnetic index. PDFs allow estimation of the probability of a given level of geomagnetic activity to be reached after the detection, by in situ solar wind observations, of a given interplanetary structure approaching the Earth. & 2010 Elsevier Ltd. All rights reserved.
Keywords: Solar wind Geomagnetic storm Interplanetary magnetic structures Probability distribution functions
1. Introduction Solar wind–magnetosphere coupling has been widely studied in the last decades, with in situ data from several spacecraft observations and ground-based data from geomagnetic and upper atmosphere stations spread worldwide. Magnetic reconnection between the anti-parallel solar wind and diurnal magnetopause magnetic fields is the main cause for the energy transfer between the solar wind and the magnetosphere. Magnetic reconnection is favored when interplanetary magnetic field (IMF) presents an intense, long duration and southward oriented (in geocentric solar coordinate) magnetic field Z-component (Dungey, 1961; Akasofu, 1981; Gonzalez and Tsurutani, 1987; Gonzalez et al., 1994; Echer et al., 2005a). Origin of this intense and long duration southward (Bs) IMF is the interplanetary shock (IPS), the sector boundary crossings of the heliospheric current sheet (Burlaga, 1995; Gonzalez et al., 1999), and the solar wind (SW) interplanetary structures, such as stream interaction regions or corotating interaction regions (CIRs), magnetic clouds (MCs), and other interplanetary remnants of coronal mass ejections (ICMEs; Gonzalez et al., 1999). The geoeffectiveness and geomagnetic impacts of these SW interplanetary structures have been investigated by several authors. Gosling et al. (1991) analyzed 171 shocks and 191 ICMEs occurring between August 1978 and October 1982 using data from ISEE-3 and values of the Kp index. The authors concluded that 44% of ICMEs, 53% of shocks, and 85% of all events were associated with Kpmax Z5. Jurac et al. (2002) analyzed 107 shocks occurring between 1995 and 2000,
n
Corresponding author. Fax: + 55 12 3945 6810. E-mail addresses: [email protected], [email protected] (M.V. Alves).
1364-6826/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.jastp.2010.07.024
using WIND data. Considering all these events, they have obtained that 21% of the shocks were followed by geomagnetic storms with Dstr 100 nT. Among the perpendicular shocks, 40% were followed by Dstr 100 nT, while for non-perpendicular shocks they happen for just 10–15%. Wu and Lepping (2002) concentrated their attention to 34 MCs observed between 1995 and 1998, using WIND data. Their results show that 88% of MCs were followed by magnetic storms (Dstr 50 nT), associated with electric field Ey present in the sheath or MC itself. Zhang et al. (2004) used 104 MCs occurring between 1998 and 2002 to infer that 80% of MCs are followed by Dstr 30 nT, 56% by Dstr 50 nT, and 34% by Dstr 100 nT. Huttunen et al. (2005) used the data from 73 MCs occurring between 1997 and 2003 to infer that 71% of all MCs were followed by Dstr 50 nT. Among these, 22% of the events were associated with Ey in the sheath fields, while 49% were associated with MC electric fields. Echer and Gonzalez (2004) investigated the geoeffectiveness of several interplanetary structures occurring within 1973–2001, in terms of the Dst response. Alves et al. (2006) presented a complementary study considering only the passage by Earth of CIRs for the period of 1964–2003. Echer et al. (2006) analyzed the geoeffectiveness of MCs, IPSs, and CIRs, for the period 1964–2003. Their results show that, among the magnetic interplanetary structures, MCs are more geoeffective than shocks or CIRs. However, the percentage of CIRs that are followed by moderate or intense geomagnetic activity as measured by AE index is similar to the percentage of IPSs followed by moderate or intense geomagnetic activity as measured by AE index. A review of the geoeffectiveness of coronal mass ejections (CMEs) and flares was given by Yermolaev et al. (2005). They addressed a comparison of data analysis methods and
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quantitative estimation of the results given by different methods. According to them, the geoeffectiveness of CMEs, for example, can change from 35% up to 71%, depending on the statistics used. However, when the data set includes 38–132 CMEs, the interval is reduced to 35–50%, in good agreement with each other. Estimation of geoeffectiveness for solar flares is 30–40%, very close to the one for CMEs. More recently, Zhang et al. (2007) identified solar coronal mass ejection (CME) sources for 88 major geomagnetic storms (Dstr 100 nT) occurring between 1996 and 2005. Some of the events show complex solar wind flows and complex geomagnetic activity, which are, probably, the result of multiple halo-CMEs interacting in interplanetary space. Within these events, only one is caused by a corotating interaction region. They also found that the most geoeffective CMEs originate within a latitude strip of 301. Gopalswamy (2008) summarized the properties of CMEs and high-speed streams and described their geoeffectiveness, focusing on the intense storms of solar cycle 23. Since that was the first solar cycle during which solar, interplanetary, and geospace data were available, he was able to present several analyses, such as identifying the occurrence of magnetic storms driven structures (CMEs or CIR) along the solar cycle as well as their origin location in the Sun. One of his main results was the confinement of solar sources of major storms, independent of the driver, to low latitudes ( 7301) throughout the cycle. He also pointed out that only CMEs and coronal holes originating close to the solar disk center cause intense storm. He used the term geoeffectivess in the sense of ring current enhancement (Dst). The interplanetary origin of intense magnetic storms during solar cycle 23 was also studied by Zhang et al. (2007), Gonzalez et al. (2007), and Echer et al. (2008). All the mentioned works are part of a major effort to predict geomagnetic storms. To be able to do so, it is of prime importance to know their cause in the Sun and solar wind. In this work we assess the geoeffectiveness of the three main interplanetary structures found in interplanetary space, near the Earth: MCs, IPSs, and CIRs. The geoeffectiveness is evaluated with the geomagnetic indices Kp (ap), AE, and Dst peak values, within 2 days after the interplanetary structure has passed near-Earth orbit. We obtain a number of events following each interplanetary structure. Our results confirm that shocks driven by MCs are the most geoeffective structures, among the ones analyzed, in every type of magnetospheric activity (Echer et al., 2006). The results are then used to find fitted (continuous) probability distribution functions (PDFs). These PDFs are obtained after fitting the empirical distribution histograms by distribution functions, representing the percentage of events against intervals of magnetic index. The knowledge of probability distribution functions is important in schemes to forecast space weather, since it allows us to estimate the probability of a given level of geomagnetic activity reached following a certain interplanetary structure.
2. Methods The selection of events analyzed in this paper has been described elsewhere (Echer and Gonzalez, 2004; Alves et al., 2006, Echer et al., 2006). The selection of CIRs, structures formed when high speed solar wind streams overtake slow solar wind streams as they propagate outwards, was based on the basic physical features of the corotating high-speed streams with respect to interplanetary plasma and field parameters, as described by Alves et al. (2006). The events were checked to see if they were not interplanetary remnants of coronal mass ejection (ICME). CIR events associated with flares, ICMEs, magnetic clouds (MCs), and shocks were not included. Storms that were caused by the interaction of MCs or shocks with CIRs, i.e., the cases when
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Z-component of the magnetic field (Bz) causing the storm was not only the CIR field, but also an altered field due to the interaction with a different magnetic structure were also excluded. Magnetic clouds are a subset of interplanetary remnants of CMEs (ICMEs) and they are recognized in interplanetary data by their signatures: large-scale smooth field rotations; enhanced magnetic field magnitude; and decreased proton temperature/ proton beta (Echer et al., 2006). Since our main interest is to verify the geoeffectiveness of MCs, we have not considered the ones that present By rotation with Bz to north, which are, in general, not geoeffective. We do not make a difference if the geomagnetic storm is caused by the MC itself or by the sheath region. Selection of shocks followed the same procedure of Echer and Gonzalez (2004). Shocks are generated when the relative velocity between the interplanetary structure and the slow solar wind is higher than the magnetosonic velocity. The most usual type of shock, the fast forward, shows a simultaneous jump in plasma parameters (speed, density, and temperature) and in magnetic field strength. Usually, a very disturbed Bz component is observed after the shock (Gonzalez et al., 1999). Since interplanetary shock waves are observed in solar wind usually associated with the ICMEs, it is more likely to observe geomagnetic response associated to other fields, besides the fields associated with the shock itself. For MCs and CIRs there is not much problem of contamination by other structures. Here, for the complete data set of shocks, we have taken the maximum/minimum value of geomagnetic indices (peak value) within 2 days after the shock, independent of the shock being followed or not by other structure (MC or CIR). Among the 830 IPSs considered here, 115 were shocks driven by MC events. The events analyzed in this paper, 830 IPSs, 170 MCs and 727 CIRs, occurred between 1964 and 2003. The geoeffectiveness is evaluated with the geomagnetic indices Kp, AE and Dst peak values, within 2 days after the interplanetary structure was observed near 1 AU. We perform standard statistical analysis for 9 data sets (3 structures and 3 indices). We derive PDF for each data set from the analysis of empirical distribution parameters and by fitting the histograms (Spiegel, 1961). Tables 1–3 show the main statistical parameters related to the geomagnetic indices, Dst, AE and ap (Kp), respectively. The columns of the tables refer to minimum (min), maximum (max) and average Table 1 Statistical parameters for Dst for all periods (N in this case is the number of points used) and the disturbed period after shocks, MCs, and CIRs (1964–2003). Param.
Dst shocks
Dst MCs
Dst CIRs
Dst all
AV SD Min. Max. Median N
71.9 62.7 472 10 55 830
94.7 54.5 288 5 82 170
43.2 24.2 131 9 38 747
16.3 23.9 589 81 12 350,640
Table 2 Statistical parameters of AE for all periods (N in this case is the number of points used) and for the disturbed period after shocks, MCs, and CIRs (1964–2003). Param.
AE Shocks
AE MCs
AE CIRs
AE all
AV SD Min. Max. Median N
870 396 106 2780 840 830
1007 344 104 2155 1014 170
792 280 116 2300 786 747
218 211 5 3195 140 241,032
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values (AV), standard deviation (SD), median and the total number of events (N). Besides the results for the data sets of MCs, CIRs, and shocks, for comparison, we also list the statistical parameters considering the time series of the indices for the whole period (1964–2003); N in this case represents the number of points we use in the time series for the respective geomagnetic indices.
3. Results Fig. 1 shows the histogram distribution of the geomagnetic indices Dst, upper panels; AE, central panels, and; ap, bottom panels; for CIRs, left column; IPSs, central column, and; MCs, right column. Histogram distribution of Dst for MCs has been presented by Echer et al. (2005b), for the period 1966–2001 (149 MCs), and for CIRs by Alves et al. (2006). The dotted lines superposed to the histograms (percentage of raw counts per bin) in Fig. 1 are the fitted distribution functions we have obtained. It can be seen that Table 3 Statistical parameters for ap (Kp) for all period (N in this case is the number of points used) and for the disturbed period after shocks, MCs and CIRs (1964–2003). Param.
ap Shocks
ap MCs
ap CIRs
ap all
AV SD Min. Max. Median N
72 67 3 400 48 830
92 63 7 300 80 170
48 29 9 236 39 747
14 19.6 0 400 7 116,880
only AE distributions can be recognized as nearly normally distributed, while ap and Dst show a representative number of events on extreme limits (tail) of their distribution; for ap, an exponential behavior is observed. The normality W-test of Shapiro–Wilk (Royston, 1982) shows that Dst and ap are not normally distributed at the 0.05 significance level, while AE is normally distributed for the MC and CIR data sets. AE is not normally distributed at 0.05 significance level for IPSs. However, when removing the extreme values, the distribution becomes Gaussian at the 0.05 W-test. The parameter A3 measures the asymmetry, pointing out how different from a normal distribution, for extremes, a distribution is (A3¼0 for a normal distribution). We obtain negative values for A3 using Dst data set ( 2.0 for shocks, 1.1 for MCs, and–0.9 for CIRs), meaning an asymmetry on the left (on the right if we take–Dst). For ap, the A3 values are high and positive (1.9 for shocks, 0.9 for MCs, and 1.5 for CIRs), indicating an asymmetry to the right. For AE, the values of A3 are lower than for the other indices, suggesting a lower degree of asymmetry (Spiegel, 1961). Based on these results, we decided to fit Gaussian functions to AE frequency distributions. For Dst, the kurtosis test showed that the distribution function could be described by a right-skewed Gaussian function (using ORIGIN software). Finally, for ap distributions, we judged both from the statistical parameters and from a visual inspection that an exponential fit would be more acceptable. Fig. 2 gives the derived distribution functions, which we called theoretical distribution functions, whereas the histograms in Fig. 1 can be seen as empirical distribution functions. From the theoretical distributions, we can estimate the probability of each geomagnetic activity level be reached after an interplanetary structure is detected.
Fig. 1. Histograms and line fitting of the histograms for geomagnetic indices Dst, upper panels; AE, central panels; and ap, bottom panels; for CIRs, left column; shocks, central column, and; MCs, right column. The geomagnetic indices Kp (ap), AE, and Dst are peak values within 2 days after the interplanetary structure has passed nearEarth orbit.
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Fitted distribution function, P, for Dst, upper panel of Fig. 1, is given by the right-skewed Gaussian function, P ¼ y0 þ A expðexpðzÞz þ 1Þ, where z ¼(x xc)/w, and x is the value of the geomagnetic index Dst. For AE, central panel of Fig. 1, A xxc 2 P ¼ y0 þ exp 2 , 1=2 w wðp=2Þ where x is the value of AE. For ap P ¼ A1 ðexpðx=t1 Þ þ y0 , where x is the value of ap. The values of the adjusting parameters are given in Table 4. To evaluate the goodness of fit of our statistical model we obtain the value of R2. In regression, the R2 coefficient of determination is a statistical measure of how well the regression line approximates the real data points. An R2 of 1.0 indicates that the regression line perfectly fits the data. Table 4, last line, gives the values of R2 for the
Fig. 2. Fitted distribution functions for Dst (upper panel), AE (central panel), and Kp (ap) (bottom panel) for the three interplanetary structures used in this work.
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fitting curves presented in this paper. They indicate that the best fittings are obtained for AE (R2 40.89); for Dst, the chosen function is also a good fitting (R2 40.8), and is able to include the extreme values of Dsto 100 nT; for ap, fittings are not so reliable, but they provide an easy way to handle the function in order to estimate the probability of occurrence of a certain event as measured by ap. It can be seen that the peaks of the distributions for Dst and AE are at higher index values for MCs than for shocks and CIRs. Shocks and CIRs have peaks close to each other, with slightly higher values for shocks. Most CIRs concentrate around lower index values. For ap indices, the exponential distribution shows that we have higher probabilities for CIRs and shocks with low value of ap index. MC distribution shows higher probability of occurrence than CIRs or shocks for high ap index values.
4. Summary and conclusions In this work we address the geoeffectiveness of the three main interplanetary structures found in interplanetary space, near the Earth: MCs, IPSs, and CIRs. The geoeffectiveness is evaluated with the geomagnetic indices Kp (ap), AE, and Dst peak values, within 2 days after the interplanetary structure has passed near-Earth orbit, and the number of events following each interplanetary structure was obtained. A statistical analysis of these three data sets shows that the transient interplanetary structures, MCs or IPSs, are more geoeffective than CIRs. MCs are the most geoeffective structures as measured by the three geomagnetic indices used here. The geoeffectiveness of the structures analyzed here as measured by the AE index is very similar, observed by the fitting parameters in Table 4. The ooccurrence of combined structures (IP+MC) seems to be more geoeffective for every class of geomagnetic activity than the occurrence of isolated interplanetary structures (Echer et al., 2005b). Since usually there is a region between the MC and the driven shock, the sheath, further studies should address the geoeffectivity of this region (Badruddin and Singh, 2009; Gopalswamy, 2009). From Tables 1–3 it is also possible to notice how the presence of magnetic structures changes the values of average values for the magnetic index. For example, for Dst the average values for IPSs, MCs, and CIRs are 4.4, 5.8, and 2.6 larger than the average of all the periods, respectively. For AE index, the ratio values are 4.0 for IPSs, 4.6 for MCs, and 3.6 for CIRs. For ap index, the values are 5.1 for IPSs, 6.6 for MCs, and 3.4 for CIRs. It is important to mention that the extreme value for Dst, 589 nT, is related to an unknown structure. This value is associated with a huge magnetic storm occurred in 1989 with no interplanetary data available for analysis. We also obtain, by fitting the histograms, a probability distribution function for the three data sets. Dst and ap are not normally distributed, while AE is normally distributed for MC and CIR data sets. AE is not normally distributed for IPSs; however, when removing the extreme values, the distribution function becomes a Gaussian. Based on these results, we decided to fit by
Table 4 Values of parameters found on the fitted distribution functions: for Dst, P¼ y0 + A exp( exp( z) z + 1), where z ¼(x xc)/w, and x is the value of Dst; for AE, P ¼y0 +(A/(w(p/ 2)1/2))exp( 2(x xc/w))2), where x is the value of AE; and for ap, P ¼A1 exp( exp( x/t1) + y0), where x is the value of ap. Dst
y0 xc w A R2
AE
ap
MCs
IPs
CIRs
MCs
IPs
CIRs
0.06 64.45 38.21 9.30 0.82
0.22 37.6 29.80 10.82 0.97
0.045 31.25 18.34 19.56 0.99
0.15 992.5 567.6 4563.0 0.9
0.03 830.9 822.1 5202.8 0.89
0.01 772.7 547.7 4989.6 0.94
y0 A1 t1 –
MCs
IPs
CIRs
4.85 12.25 355.55 – 0.43
0.28 12.72 89.11 – 0.69
1.19 15.38 97.52 – 0.54
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Gaussian functions the AE histograms. For Dst, the kurtosis test showed us that the distribution function could be described by a right-skewed Gaussian function. Finally, for ap distributions, we judged both from the statistical parameters and from a visual inspection that an exponential fit would be more acceptable. The probability distributions obtained in this work can be used for space weather forecast given by a probability of an observed interplanetary solar wind magnetic structure be followed by every type of magnetic activity. This type of statistical result could also be used in models that try to simulate long periods of space weather activity, i.e., the space climate around the geospace.
Acknowledgments We thank the ACE SWEPAM and MAG teams for making their data publicly available, and the NSSDC at Goddard Space Flight Center for providing the OMNIweb database. Thanks to National Geophysical Data Center and to the World Data Center-Geomagnetism for the Kp, Dst, and AE geomagnetic indices. E.E. thanks both FAPESP (2007/52533-1) and CNPq/PQ (300211/2008-2) for research support. M.V.A thanks CNPq (304865/2007-9) for research support. W.D.G thanks FAPESP (2008/06650-9) for financial support. References Akasofu, S.I., 1981. Energy coupling between the solar wind and the magnetosphere. Space Sci. Rev. 28, 121–190. Alves, M.V., Echer, E., Gonzalez, W.D., 2006. Geoeffectiveness of corotating interaction regions as measured by Dst index. J. Geophys. Res. 111, A07S05. doi:10.1029/2005JA011379. Badruddin, Singh, Y.P., 2009. Geoeffectiveness of magnetic cloud, shock/sheath, interaction region, high-speed stream and their combined occurrence. Planet. Space Sci. 57, 318–331. Burlaga, L.F., 1995. Interplanetary Magnetohydrodynamics, Oxford Un. Dungey, J.W., 1961. Interplanetary magnetic field and the auroral zones. Phys. Rev. Lett. 6, 47–48. Echer, E., Gonzalez, W.D., 2004. Geoeffectiveness of interplanetary shocks, magnetic clouds, sector boundary crossings and their combined occurrence. Geophys. Res. Lett. 31, L09909. doi:10.1029/2003Gl019199. Echer, E., Gonzalez, W.D., Guarnieri, F.L., Dal Lago, A., Vieira, L.E.A., 2005a. Introduction to space weather. Adv. Space Res. 35, 855–865.
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