Sun and Geosphere, 2016;

11(1): 37 - 47

ISSN 2367-8852

Long-term EEJ variations by using the improved EE-index
Akiko Fujimoto1, Teiji Uozumi1, Shuji Abe1, Hiroki Matsushita2, Shun Imajo2,
Jose K. Ishitsuka3, Akimasa Yoshikawa1
1

International Center for Space Weather Science and Education, Kyushu University,Japan
2
Department of Earth and Planetary Sciences, Kyushu University, Japan
3
Observatorio de Huancayo, Instituto Geofisico del Peru, Peru
E mail (fujimoto@icswse.kyushu-u.ac.jp).
Accepted 8 January, 2016

Abstract: In 2008, International Center for Space Weather Science and Education, Kyushu University (ICSWSE) proposed
the EE-index, which is an index to monitor the equatorial geomagnetic phenomena. EE-index has been improved with thedevelopment of the MAGnetic Data Acquisition System and the Circum-pan Pacific Magnetometer Network(MAGDAS/CPMN) and the enormous archive of MAGDAS/CPMN data over 10 years since the initial article. Using theimproved EE-index, we examined the solar cycle variation of equatorial electrojet (EEJ) by the time series analysis for
EUEL (one part of EE-index) at Ancon in Peru and the solar activity from September 18, 1998 to March 31, 2015. We foundthat the long-term variation of daily EEJ peak intensity has a trend similar to that of F10.7 (the solar activity). The power
spectrum of the daily EEJ peak has clearly two dominant peaks throughout the analysis interval: 14.5 days and 180 days
(semi-annual). The solar cycle variation of daily EEJ peak correlates well with that of F10.7 (the correlation coefficient0.99). We conclude that the daily EEJ peak intensity is roughly determined as the summation of the long-period trend of
the solar activity resulting from the solar cycle and day-to-day variations caused by various sources such as lunar tides,
geometric effects, magnetospheric phenomena and atmospheric phenomena. This work presents the primary evidence forsolar cycle variations of EEJ on the long-term study of the EE-index.
© 2016 BBSCS RN SWS. All rights reserved
Keywords: EE-index, equatorial electrojet, MAGDAS/CPMN, space weather

Introduction
In 2008, International Center for Space Weather
Science and Education, Kyushu University (ICSWSE)
proposed the monitoring index for equatorial electrojet(EEJ) by using MAGnetic Data Acquisition System andthe Circum-pan Pacific Magnetometer Network(MAGDAS/CPMN) (Yumoto and the CPMN group,
2001; Yumoto and the MAGDAS group, 2006, 2007)
data, which we called as EE-index (Uozumi et al., 2008).
EE-index has been developed with the object of
separating the magnetic disturbances in theequatorial region into the global and local magnetic
variations and also monitoring quantitatively various
electromagnetic phenomena in real time. The first
paper (Uozumi et al., 2008) of EE-index provided one
month MAGDAS/CPMN magnetic field data to explain
the algorithm of the index. EE-index consists of twoparts: EDst and EUEL index. Uozumi et al. (2008) definedthat EDst (the equatorial disturbance storm time) index
represents the global magnetic variation including
disturbances in the equatorial region caused bysudden storm commencement (SSC) and ring current
and a part of magnetospheric polar disturbances suchas substorms and DP2 effects. EUEL index is given by
the subtraction of EDst index from the relative Hcomponent (ERs, the definition is given in the Material
and methods). EUEL shows the total overhead currents
involved in EEJ/Sq at the equatorial region without the
global common magnetic effects changing momentby moment. The positive and negative of EUEL index
indicate the magnetic effects generated by eastward

and westward currents at the concerned station,
respectively.
The EEJ is known to flow eastward in a narrow
latitude band (~±3°) along the magnetic equator.
Therefore, its magnetic contribution is expected tooverlap the planetary Sq effects near the magnetic
equator. When a single latitude chain of stations is
considered, it appears that the EEJ effectsuperimposes that of the planetary Sq in a certain
latitude band across the magnetic equator(Onwumechili, 1967; Fambitakoye and Mayaud, 1976a,
1976b; Fambitakoye, Mayaud, and Richmond, 1976).
In the early study of EEJ, researchers have revealed
the morphology of EEJ during the quiet time in theterms of the variation of the “daily range” or “regulardaily variation”. These variables provide the total
overhead currents on the dip-equator and divide into
two components: EEJ and Sq currents. Chapman andRaja Rao (1965) presented the daily range as thedifference between the midday mean and the
midnight mean on a given day of the northwardmagnetic component. The regular daily variation isdefined as the deviation between a given instant andthe night level (zero level). Fambitakoye and Mayaud(1976a) determined the zero level by interpolating
linearly between two midnights neighboring the day
considered.
The manner of estimating the daily variation of EEJ
and Sq current magnetic effect is strongly supportedby the fact of the daytime ionospheric wind dynamo
theory. It is well known that the circulation of currents inSpecial Edition “2015 UN/Japan Workshop on Space Weather”

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Akiko Fujimoto, et.al. Long-term EEJ variations by using the improved EE-index

the lower ionosphere, denoted as the daytime
ionospheric wind dynamo, causes EEJ and Sq currents.
Since the source of the ionospheric dynamo is the solar
daily radiations, the magnetic effect of this dynamo is
generally negligible during the nighttime. Additionally,
the magnetic effects attributed the magnetosphericcurrent systems (e.g., the Chapman-Ferraro current on
the magnetopause and the ring-current, which are
estimated through the Dst index) exist during thedaytime as well as the nighttime. Thus, during the quiet
time it is acceptable to definition of the daily range or
the regular daily variation. However, they are notsuitable for the geomagnetic disturbed time such as
during magnetic storms because the nightside
magnetic variability is not stable or constant.
The further understanding of EEJ mechanisms needs
to separate the daily range/regular daily variation into
the EEJ and Sq current magnetic effects. To isolate the
EEJ effect from the planetary Sq, different approaches
are used, depending on the workers and on available
datasets. Some will use pairs of stations located at thesame longitude (Rastogi, Chandra, and Yumoto, 2013).
One station of the pair is chosen at the magnetic
equator, and the other must be chosen enough far
away from the EEJ influence (hereafter called pairstations method). The weakness of this approach relieson the fact that the planetary Sq also varies asfunction of latitudes across the magnetic equator.
Hamid et al. (2014) analyzed EUEL index (which is one
part of EE-index) to examine the relationship between
EEJ and Sq, with the CM4 global current model(Sabaka, Olsen, and Purucker, 2004) to minimize thelatitudinal uncertainty of EEJ/Sq. More accurate
approach is the use of a latitude chain, enoughextended in latitude to include both the latitude

profiles of the Sq and the EEJ effects (Rigoti et al., 1999).
In that case, the Sq is represented by the backgroundsignal, which can be fitted and removed.
Based on the several manners described above,
EEJ has been studied by many researchers in order toexplain the mechanisms of EEJ. Some features of EEJ
variations have been revealed since its discovery at
Huancayo in Peru (Forbes, (1981) for a review). The
following characteristics are provided by the groundbased magnetometer data: diurnal, semi-diurnal andsemi-annual variability (Chapman and Raja Rao, 1965;
Rastogi, Alex, and Patil, 1994), day-to-day variations(Fambitakoye and Mayaud, 1976b; Kane and Trivedi,
1980; Doumouya et al., 1998), counter equatorial
electrojet (CEJ), the dependence of solar radio flux
(F10.7) (Rastogi and Iyer, 1976; Rastogi, Alex, and Patil,
1994), latitudinal structure (Rigoti et al., 1999), local
time and longitudinal dependence of EEJ (Doumouya
et al., 2003).
These evidences have been provided by the
analysis of the magnetic quiet EEJ. Since there is no
significant magnetic variation at the midnight during
the magnetic quiet time, the past researchers haveused the constant nighttime level to determine themagnetic variations affected by the EEJ/Sq currents(Chapman and Raja Rao, 1965). Many studies of quiettime EEJ variations have been reported over the past
decades, whereas the disturbance-time EEJ variability
has never enough been studied. The traditional
determination manner of the daily magnetic variations
is useless for the magnetic disturbance time, forexample, magnetic storms. It is because we cannot
properly consider the magnetic field variations
affected by the ring current during the magnetic
storms.

Figure 1: A map of MAGDAS stations used in this paper. The stations marked with the black filled circle except for CXI (yellow filled
circle) are used in the EE-index procedure. CXI is only used for validating whether low-latitude stations can be used as the proxy ofmagnetic field intensity at the dip-equator stations along the same longitude. The detailed descriptions are given in the section ofMaterials and methods. CXI belongs to the Ocean Hemisphere network Project (OHP), University of Tokyo.

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Sun and Geosphere, 2016;

11(1): 37 - 47

We also believe the importance of the study on
geomagnetic disturbances as well as on quiet timemagnetic variations. For example, the equatorial
plasma bubble in the night side is involved withmagnetic storms. In order to understand themechanisms of plasma bubble, we need quantitatively
to analysis the equatorial magnetic variation during
the magnetic disturbance time as well as the quiet
time. Today human activity and society extend to thespace. The plasma bubble is well known to cause thecommunication failure between satellites and groundstations. The monitoring of space weather environmentis needed to allow us the safety life involving the space
weather. ICSWSE provides the real time EE-index on our
web site, in terms of the monitoring of space weather
environment. The consecutive monitoring of equatorial
magnetic variations requires an indicator unaffectedby the magnetospheric environment.
In order to achieve the objective of space weather
monitoring, the possible solution is the development of
the daily range method since the appropriate station
pair may not always exist for applying to two stations
pair method. We use the nighttime magnetic variations
as the dynamic reference base level by using themultiple equatorial magnetometer observations
spread through the longitude direction. The
advantage of this new method is that it is possible tomonitor the equatorial magnetic field variations
changing from moment to moment. Additionally we
can evaluate the equatorial magnetic variation withthe same ruler regardless of magnetic environment(quiet/disturbance) of the magnetosphere.
Many past studies (utilizing the daily range method
and the pair stations method) discussed themechanism or morphology of the quiet time EEJ, due
to the aforementioned reasons. Moreover thesuperposed EEJ variations in past papers showed therelationship between the EEJ intensity and UT/LT(Universal Time/Local Time). However few reports havebeen issued in terms of the time series analysis for the
long-term comparison between the EEJ variations andsolar
activity/magnetospheric/atmosphericphenomena because the successive time informationis removed from the data. In contrast, our new index
enables us to study the time series analysis for revealing
the relation between EEJ and 11-year solar
cycle/climate changes through several decades withhigh time resolution. EE-index has been improved interms of using long-term MAGDAS/CPMN archives andmultiple equatorial magnetometer data (Figure 1)
since this index was produced in 2008. In the present
paper, we describe the improvement of EE-index andintroduce the application example as the long-term
EEJ variations compared with the solar activity.

equator. EUEL shows the localized variations in themagnetic field at each individual station. The processof obtaining the primary EE-index (EDst and EUEL) issummarized as follows: (a) use the dip-equatorial H
component in the time series of magnetometer data(the data sampling time is one minutes, theMAGDAS/CPMN magnetometer stations are locatedat within ± 3 degrees in latitude), (b) calculate therelative magnetic field (ERS(m), S and m indicate
indicate the station and time, respectively): obtainedby subtraction of the median value throughout thewhole data used in the procedure from the original H
component data for each individual stations, (c)determine EDst using the averaged night timemagnetic field ERs(m)|LT=18-06, all selected stations arelocated between LT = 18 and 06:

Materials and methods
EE-index is divided into the global and local
components named as EDst and EUEL, respectively.
EEindex = EDst + EUEL
(1)
EDst represents the simultaneous magnetic field
variations appearing throughout the entire magnetic

ISSN 2367-8852(2)
Where N(m)|LT=18-06 indicates the number of stations
located in the nighttime sector (LT = 18-06).
Considering that the nightside ionospheric conductivity
is small compared to dayside conductivity, thelocalized ionospheric current is limited in the nighttime
sector. Consequently, the observed magnetic field at
the nightside is mostly the variations resulted in the
global phenomena such as the equatorial ring current.
(d) Finally get EUELS(m) by subtracting 6-h runningaverage of EDst(m) (labeled EDst6h(m)) from ERs(m)for each individual stations. The positive and negativeof EUELS(m) represent the magnetic variations
produced by eastward currents and westward currents,
respectively. We expressly define the positive of
EUELS(m) as EUS(m) and negative of EUELS(m) as
ELS(m).
As described in Uozumi et al. (2008), thefundamental algorithm of EE-index is an improved
method of the traditional manner as referred to
Chapman and Raja Rao (1965). That is the daily EEJ
intensity is provided by the derivation of the midnightmagnetic field value from the time series of magnetic
field value. In the traditional method, the midnightmagnetic field value is adopted to the reference baselevel with the hypothesis that is the midnight value isstable for an entire single day. Rastogi and Iyer (1976)
showed that around midnight the magnetic fieldremained constant during a low sunspot year andchangeable during a high sunspot year. This suggeststhat the fixed reference value is an unsuitableparameter for determining the total intensity of EEJ
variations. In our method, however, the reference levelvaries with time. In order to obtain the reference levelvalue, the multiple dip-equatorial stations, which are
spread worldwide, are used in our procedure: 4
stations in Uozumi et al. (2008).
Uozumi et al. (2008) left the lower accuracy in thedetermination of the reference level due to few
stations used in the procedure. Thus we tried to
improve matters in terms of using more stations than
Uozumi et al. (2008) for the calculation of EDst. The
number of MAGDAS/CPMN network stations has
increased since the first EE-index paper was publishedSpecial Edition “2015 UN/Japan Workshop on Space Weather”

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Akiko Fujimoto, et.al. Long-term EEJ variations by using the improved EE-index

in 2008. Now there are 12 dip-equator stations coveringwhole longitudinal sectors (within±3 degrees inlatitude). The past EE-index in Uozumi et al. (2008) used
at most two-nightside stations. Consequently EDst
remains of the disturbed magnetic field variations suchas the substorm positive bay observed during thesubstorm. The reference base level is calculated byusing the dip-equatorial data obtained from at most
10-nightside stations and at least 2-nightside stations.
The EDst in the renewed EE-index monitors the global
magnetic disturbed variations with higher accuracy
than Uozumi et al. (2008).
The most critical problem for operating the EE-index
is that there are few dip-equatorial MAGDAS/CPMN
stations in the Pacific Ocean as shown in Figure 1. We
assumed that the magnetic field at a low-latitude
station estimates the equatorial magnetic field along
the same longitudinal line during the nighttime sector.
It is because the nightside ionospheric conductivity isalmost equivalent between the dip-equator and thelow latitude, whereas there is significant difference
between two latitudinal regions for the daysideconductivity. In order to validate this assumption, we
examined the relationship of the magnetic field
intensity between CXI (CXI data is provided from the
Ocean Hemisphere network Project (OHP), University ofTokyo) as the dip-equatorial station and EWA as the
low-latitude station. The 1-min. H component inmagnetic field data from 1999 January 1 to 2000
December 31 was analyzed. We calculated theequatorial estimated H component value (Hequator)
from H component observed at EWA station (HEWA) byusing the correcting function for the latitudinal effect,
Hequator=HEWA / cos(ΦEWA)(3)
Where ΦEWA is the geomagnetic latitude (gmlat)
value of EWA station. We found the good correlation
between the observed magnetic field (HCXI) at CXI
and the Hequator value estimated from HEWA with thecorrelation coefficient 0.83 (not shown). The function of
the linear regression analysis between HCXI
andHequator is

HCXI = 1.03 Χ Hequator – 7.05

(4)

The slope value of the function suggests that thenighttime magnetic field is controlled by the current
system such as the ring current and themagnetospheric tail current which widely influence
along the latitude, without the variations affected by
the ionospheric current. Figure 2 shows the magnetic
field data at CXI and EWA with SYM-H (provided byWorld Data Center (WDC) for Geomagnetism Kyoto
University). The SYM-H is used to show the global
magnetic disturbance such as the magnetic storm inthis paper. As shown in Figure 2, there are similarvariabilities between CXI and EWA during the nighttime.
We conclude that the equatorial estimated H
component from the low-latitude H component is
acceptable to the magnetic field intensity at theequator.
As described above, the magnetic field dataobtained from low-latitude stations can be used as the40

proxy of the intensity at the dip-equatorial stations
along the same longitude. Now it must be noted that
this estimation is applied to the low latitude stations,whereas the magnetic field of dip-equatorial and offdip stations are used without the latitudinal correction.
Thus, the former is the magnetic field variations at
gmlat = 0 and the latter is at gmlat ≠ 0. This processconsequently results in two different quantities. In order
to avoid this matter, we assume that the equatorial
estimated magnetic field intensity can be calculated
from data recorded at any station in less than low
latitude (<± 25 degrees). That is, the equation (3) would
be applied to the magnetic field data at not only low
latitude stations (located within |10-25| degrees inlatitude) but also off–dip stations (within |3-10|degrees
in latitude) and dip-equatorial stations (< |3| degrees
in latitude). Hence,
Hdip=HS / cos(Φs)(5)
Where S indicates any station located within ±25
degrees in latitude, Hdip means the H component
value at gmlat = 0. The division value on the right side
(1/cos(ΦS)) is less than 1.1 when the latitude value isless than 25. This means that the value estimated by
the equation (5) has errors with at most 10% for the
lower latitude. The errors decrease with decreasing
latitudinal value.
The equation (5) applies to the calculation of EDst
index. Dst index is determined by the same manner interms of the latitudinal correction of H-component
magnetic field. This latitudinal correction applies to thestandardization of the low-latitudinal magnetic fielddata into the dip magnetic field value. The correction
is appropriate since the magnetic effect during
magnetic storms is assumed to be affected by the ring
current existing far from the surface of the earth. EDst
index indicates the magnetic effect of themagnetospheric currents including the ring current. We
believe that the latitudinal correction is useful toestimate the magnetospheric currents.
In the past, the EE-index assumed that themagnetic field value at the inside the narrow channel(<±3 degrees in latitude) of the EEJ band as themagnetic field intensity at gmlat = 0. Using theimproved assumption explained above, we obtain theestimated magnetic field intensity on the dip equatorfrom any magnetometer station not exactly locatedon dip-equator (that is, all of MAGDAS/CPMN stations).
This means that EDst is calculated by using 29
MAGDAS/CPMN stations located from dip-equator to
low-latitude: 12 dip-equatorial (the narrow channel), 7
off-dip and 10 low-latitude stations (Table 1, except for
CXI). The latest EE-index procedure is as follows:
Step1. Use the magnetometer data obtained from the
MAGDAS/CPMN stations located within ±25
degrees in latitude, with one minute resolution
Step2. Calculate the relative magnetic field for eachindividual station: ERS(m)Step3. Estimate the dip-equatorial magnetic field value(gmlat = 0) by applying the equation (5) for
each individual stations during the nighttime
sector: ERdip(m)|LT=18-06 (this is the estimatedSpecial Edition “2015 UN/Japan Workshop on Space Weather”

Sun and Geosphere, 2016;

11(1): 37 - 47

ISSN 2367-8852

value on the dip equator from any station
located at LT = 18-06)
Step4. Determine EDst(m) by averaging the dipequatorial
estimatedmagnetic
field
ERdip(m)|LT=18-06 during the night time
Step5. Convert EDst(m) into EDstS(m)for eachindividual station by correcting the latitudinal
effectStep6. Get EUELS(m) by subtracting EDst S,6h(m) from
ERS(m) for each individual station
Here we provide the comparison of the results
calculated from three method (the daily range, EEindex: EDst and EUEL indices and the pair stations
method), in order to suggest that EE-index is the useful
tool on the monitoring of equatorial magnetic

variations. Figure 3 and 4 show each behavior of all
methods for the quiet and disturbance time magnetic
variations, respectively. Note that we use the mannerof Chapman and Raja Rao (1965) as thedetermination of the daily range. The primaryobjective of providing these figures is to show thedifference between the daily range method and EEindex approach, caused by the different manner ofreference level: the stable or drastic night magnetic
field references. The presented magnetic field data
are obtained from two stations of MAGDAS network.
One is DAV (-1.02 gmlat) as the dip station, the other is
MND (-6.91gmlat) as the off-dip station. The period

Table 1: Geographical and geomagnetic coordinates of the stations. The latest EE-index uses the stations except for CXI. The
used stations will be changed in the future due to operating MAGDAS/CPMN observations.
stationgeographic
latitude(°)

geomagnetic

Name

code

Addis Abeba

longitude(°)

latitude(°)

longitude(°)

AAB

9.04

38.77

0.18

110.47

Abuja

ABU

8.99

7.39

-1.53

79.4

Amami Oshima

AMA

28.17

129.3321.11

200.88Ancon

ANC-11.77282.850.77

354.33

Bac Lieu

177.96BCL

9.3

105.71-0.66

Bengkulu

BKL

-3.8

102.31-15.13

173.6

Cagayan De Oro

CDO

8.4

124.63-1.1

196.66

Cebu

CEB

10.36

123.91

2.53

195.06

Davao

DAV

7

125.4

-1.02

196.54

DarwinDAW-12.41130.92-21.91202.81

Eusebio

EUS

-3.88

321.57

-3.64

34.21

Ewa Beach

202

21.67

269.52

EWA

21.3

Gunung Sitoli

GSI

1.29

97.61

-7.53

169.49Hualien

HLN

23.9

121.5516.86

193.05Ica

ICA-14.09284.26-1.56

356.16

Ilorin

ILR

8.5

4.68

-1.82

76.8

Khartoum

KRT

15.3

32.32

5.69

103.8

Lagos

LAG

6.4

3.27

-3.04

75.33

Legazpi

LGZ

13.1

123.74

3.54

195.56

Langkawi

LKW

6.3

99.78

-2.32

171.29

Liwa

LWA

-5

104.06-16.19175.33

ManadoMND

1.44

124.84-6.91

196.06

Muntinlupa

MUT

14.37

121.02

6.79

192.25

Nairobi

NAB

-1.1

36.48

-10.65108.18Pare Pare

PRP

-3.6

119.4

-12.38190.75Sicincin

SCN

-0.5

100.3

-12.11

171.66

Tuguegarao

TGG

17.66

121.7610.26

193.05

Trivandrum

TIR

8.48

76.95

-0.37

149.11

Yap

YAP

9.5

138.081.49

209.06

Christmas Island

CXI

1.91

202.512.64

273.89Special Edition “2015 UN/Japan Workshop on Space Weather”

41Akiko Fujimoto, et.al. Long-term EEJ variations by using the improved EE-index

demonstrated in Figure 2 and 3 is 12th June 2010 and
15th February 2012 in local time (125 degrees east
longitude), respectively. On 12th June 2010, themagnetosphere is quiet: Kp index=0 and International
Q-Days (Q1: the most quiet day in the month). On 15th
February 2012, the magnetosphere spent disturbancetime: Kp~3-4, International D-Days (D1: the most
disturbance day in the month). Kp, Q-days and D-days
are obtained from GFZ German Research Centre forGeosciences.
For the magnetic quiet time shown in Figure 3(b),there is no remarkable difference between the daily
range method (grey solid line) and EUEL (red dashedline) of EE-index during daytime. The result isreasonable because the reference zero level of EEindex (EDst value) is almost stable during the daytime
in the present event and there is little difference
between two midnights neighboring the day
considered for the daily range method. On the other
hand, the slight difference exists during the nighttime.
EUEL value of EE-index during the nighttime(local time = 21-24) shows zero since EDst (green solidline) estimates successfully the magnetic effect of themagnetospheric currents. In both methods, the CEJ isclearly found around the evening.

Figure 2: The magnetic field (H component) at CXI and EWA with SYM-H. EWA data are corrected for the latitudinal effect. The left
panel shows the time series data and the right panel illustrates the magnetic field variations in the polar coordinate viewed from
north during April 14 – 18, 2000.

Figure 3: Schematic illustration of the daily range method, EE-index and two pair stations method during the geomagnetic quiet time on
12th June 2010 in the local time (125 degrees east longitude), from June 11 (1540 UT) to 12 (1539 UT) 2010. DAV is a dip-equatorstation and MND is an off-dip station. (a) raw magnetic field data at DAV and MND, (b) the daily range of DAV (grey solid line) andMND (grey dashed line), EUEL variation of DAV (red dashed line) and EDst variation (green solid line), (c) two pair stations method,using the pair of DAV and MND for the daily range (grey solid line) and EUEL (red dashed line).

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Sun and Geosphere, 2016;

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ISSN 2367-8852

Figure 4: Schematic illustration of the daily range method, EE-index and two pair stations method during the geomagnetic disturbancetime as the same manner of Figure 2, on 15th February 2012 in the local time (125 degrees east longitude), from February 14 (1540
UT) to 15 (1539 UT), 2012.

In contrast, for the magnetic disturbance time
(Figure 4 (b)), there is noticeable difference between
the daily range method and EUEL of EE-index. EUEL
removes successfully the magnetic effect ofmagnetospheric current, as EDst, from the original
observational magnetic field data. Therefore, thenighttime EUEL variation roughly exists around zero. Two
midnights level neighboring the day considered arewidely different, since the magnetic variation of thedaily range remains the magnetic effect of themagnetospheric currents. The results suggest that EEindex is the useful indicator during the magnetic
disturbance time.
Fortunately, the pair of stations is available forisolating EEJ effect from the Sq variation. The behaviorof two pair method is also presented for the exampleof the isolation of EEJ variation from Sq variation in
Figure 3 (c) and 4 (c). The grey line is calculated byusing DAV and MND of the daily range. The reddashed line is obtained by subtracting EUELMND from
EUELDAV. There is no outstanding difference between
two methods. Note that the appropriate station pairmay not always exist for applying to two stations pairmethod.
Using the improved EE-index, the time series analysis(the line chart and the spectral analysis) are executedfor EUEL at ANC and the solar activity from September
18, 1998 to March 31, 2015 in this paper, in order toreveal influences of the solar activity on the EEJ
intensity throughout one solar cycle. The solar radio
emission at 10.7 cm (F10.7) is used in our analysis as theindicator of solar activity. F10.7 data are obtained fromtheGSFC/SPDFOMNIWeb
interfaceat
http://omniweb.gsfc.nasa.gov (King and Papitashvili,2005). We used Dst value (provided by WDC of Kyoto
University) as EDst from 1998 to 2004, because there
are few stations for determining EDst during these

periods. As evaluated by Uozumi et al. (2008), thissubstitution is reasonable for the long-term analysis. In
this paper, the term “daily EEJ peak” is used to refer tothe maximum of the dayside EUEL intensity. Note that
we analyze not EEJ variations but the total equatorial
magnetic field on the magnetic equator. The EEJ
intensity is typically maximum around 1100 LT during
the low sunspot year and around 1200 LT during thehigh sunspot year (Rastogi and Iyer, 1976). Hence thedaily EEJ peak is selected between 1000 and 1400 LT
for the individual day. The parameters used in the
spectral analysis for daily EEJ peak (1-day samplingrate) are as follows: 2-year FFT window with thehumming window and sliding 30 days along the timeseries.

Results
Figure 5 (a) and (b) show the hourly averaged EUEL
intensity in color coding for UT hour (vertical axis) andday (horizontal axis, tick labels are given by year) andF10.7 for day (horizontal axis, the same manner of EUEL
tick labels). Comparing Figure 5 (a) and 5 (b), it isobvious that the long-term trend of EUEL intensity
corresponds to F10.7. The variability in F10.7 shows two
peaks of the solar activity during the analyzed interval.
One is around 2002 in solar cycle 23 (1996 - 2008), theother is around 2014 in solar cycle 24 (2009 - ). The
peak of F10.7 in solar cycle 23 is larger than the peak insolar cycle 24. The similar peaks and the long-term
trend are found in the EUEL intensity around the local
noon (1500 -1800 UT), which are denoted with warm
color (Figure 5 (a)).
In order to see the relationship between F10.7 andthe intensity of the daily EEJ peak during local noon,
we demonstrated the time series plot (line chart) of
both F10.7 and the daily EEJ peak in Figure 5 (c). The
temporal day-to-day F10.7 variations are different fromSpecial Edition “2015 UN/Japan Workshop on Space Weather”

43Akiko Fujimoto, et.al. Long-term EEJ variations by using the improved EE-index

the daily EEJ peak. The fluctuation in the long-term of
F10.7 has a tendency to increase with increase of thevalue of F10.7, whereas the daily EEJ peak consists of
the long-term trend and the irregular fluctuation.
The results of the spectral analysis are shown in
Figure 6. The power spectrum of the daily EEJ peak has
clearly two dominant peaks throughout the analysis
interval in the left panel: 14.5 days and 180 days (semiannual), which correspond to lunar tides (Gasperini
and Forbes, 2014) and the Russell-McPherron Effect(Russell and McPherron, 1973), respectively. In contrast,
F10.7 power spectrum shows no continuous peaks
through solar cycles (the right panel in Figure 6).
The daily EEJ peak power spectrum is stronger
around the solar maximum years than the solar
minimum years. In other words, there is a lack ofspectrum power between 2007 and 2009 except for
the frequency of the lunar tides and semi-annual
variations. A similar tendency appears in F10.7 power
spectrum for all of frequencies.
We extended the analysis for the solar cycle
dependence in both F10.7 and the daily EEJ peak.
Figure 7 (a) shows the 540 days running averaged daily
EEJ peak and F10.7, in order to remove lower periodvariations (for example day-to-day, solar rotation,
annual and semi-annual variations) from the daily EEJ
peak and F10.7. The grey line in Figure 7 (a) shows theintensity 1.4 times the smoothed daily EEJ peak values
shifted downward by 80. The solar cycle variations ofamplified daily EEJ peak (the grey line) have a goodcorrelation with that of F10.7, with the correlation
coefficient 0.99.
The subtraction of long-period trends from the daily
EEJ peak and F10.7 variations is shown in the bottom
panel of Figure 7 (b). The differences of F10.7
significantly depend on the solar cycle: F10.7
differential values increase during the solar maximum
years and decrease during the lower solar activity. The
daily EEJ peak differential variations (the black line)
show the dominant semi-annual variations (the yellow
line results from calculating the 81 days runningaverage in order to removing the lower periodvariations than semi-annual period). There are solar
cycle modulations of semi-annual daily EEJ peakvariations. The semi-annual variability slightly depends
on the solar activity.

Discussion
The results of our analysis are as follows:
The long-term variation of daily EEJ peak intensity
has a trend similar to that of F10.7 (the solar
activity).
2. The dominant spectrum powers of daily EEJ peakoccur at 14.5 days and 180 days throughout twosolar cycles. In contrast, F10.7 has no dominant
spectrum peaks throughout the analyzed interval.
3. The solar cycle variation of daily EEJ peakcorrelates well with that of F10.7 (the correlation
coefficient 0.99).
1.

Figure 5 : The long-term variations of EUEL/daily EEJ peak and F10.7. (a) the hourly averaged EUEL intensity in color coding for UT hour(vertical axis) and year (horizontal axis), (b) F10.7 for year (horizontal axis) and (c) the time series plot (line chart) of both daily
F10.7 and the daily EEJ peak. In (a), the lack of data is denoted with white color. 1 sfu = 10 -22Wm-2Hz-1.

44

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Sun and Geosphere, 2016;

11(1): 37 - 47

ISSN 2367-8852

Figure 6: A dynamic spectrum from 1999 to 2014. The left panel shows the power spectrum of daily EEJ peak. The right panel is F10.7.
The horizontal dash lines denote the 14.5 days and 180 days.

Figure 7: Long-period variations of daily EEJ peak and F10.7. (a) is the 540-days running averaged values and (b) shows the differencesof them from the daily value. In both panels, the red and black line indicates daily F10.7 and the daily EEJ peak, respectively. In
the panel (a), the grey line represents the daily EEJ peak intensity 1.4 times the smoothed daily value shifted downward by 80. In
the panel (b), the yellow line indicates the smoothed daily EEJ peak value by calculating the 81 days running average.
1 sfu = 10-22Wm-2Hz-1.

Hamid et al. (2013) showed that the EEJ intensity
represented by the difference of EUEL between dipequator and off-dip stations had a similar long-period
trend of the solar F10.7 flux through one year during
2011. The long-term trend variations of EEJ, which issimilar to the solar cycle variation of F10.7, are foundby using the successive time series data in the present
paper. In contrast, the day-to-day variation of daily EEJ
peak correlates poorly with daily F10.7. These results
suggest that the long-period solar activity mainly
controls the trend of long-term variation in the intensityof daily EEJ peak. It is well known that the solar activity
has an 11-year cycle and the effect of the solar cycle
appears into the quantities representing the solar
activity such as the solar radiation (Hathaway, 2010, fora review).
The 14.5-day and semi-annual variations has strong
power spectrum throughout solar cycles, whereas

these F10.7 variations has no signal in Figure (6). Thissuggests that two variations of EEJ relate poorly to thesolar radiation and other sources control these EEJ
variations. The predominant sources are the lunar tides(Gasperini and Forbes, 2014) and the RussellMcPherron Effect (Russell and McPherron, 1973) for
14.5-day and semi-annual variations, respectively.
Lunar tide variations contribute to the ionosphericelectric field changes.
The day-to-day EEJ variations seem to be controlled
by other parameters related to the atmosphere rather
than F10.7 (solar radiation). Yamazaki et al. (2014)
found that the day-to-day EEJ variations during
magnetic quiet periods are mainly controlled by the
response of the zonal polarization electric field tovariable zonal winds. Recently many possible
explanations are proposed for the long-period trend
and the day-to-day variations of EEJ in terms ofSpecial Edition “2015 UN/Japan Workshop on Space Weather”

45

Akiko Fujimoto, et.al. Long-term EEJ variations by using the improved EE-index

atmospheric dynamics. The EEJ current is described by
the Cowling conductivity (Hirono, 1950a, 1950b) andthe eastward current along the dip-equator caused by
the tidal winds. The tidal winds and temperature
variations are well known to be attributed with thesolar activity (Forbes, 1978). Additionally thethermospheric density and ionospheric electron
density decrease with decreasing the solar EUV
irradiance (Liu et al., 2006; Solomon et al., 2010).
Because both decreasing density leads to lower
Cowling conductivity, the EEJ intensity decreases. We
need to further analyze the contribution of theatmospheric dynamics to EEJ in future work.
The daily EEJ peak often has a large amplitude (>400 nT) as shown Figure 5 (c). We found that theseamplified EEJ peaks correspond to magnetic storms.
Because the main topic of our analysis is the longperiod EEJ variations, we does not give detailed
discussions for such transient disturbances .There areuncertainties of the EE-index during the main phase ofmagnetic storms. The EE-index will be needed to
improve this matter.
We conclude that the daily EEJ peak intensity isroughly determined as the summation of the longperiod trend of the solar activity resulting from the solar
cycle and day-to-day variations caused by various
sources such as lunar tides, geometric effects,
magnetosphericphenomenaandatmosphericphenomena. Many past studies demonstrate thesimilarity between EEJ and F10.7, although that areprovided from the monthly averaged values
calculated by using solar quiet days (Rastogi and Iyer,
1976; Rastogi, Alex, and Patil, 1994). The EE-index allows
us to study the EEJ variations in terms of the time series
analysis with high time resolution, regardless of thegeomagnetic
environment(magnetic
quiet/disturbance). This work presents the primaryevidence for solar cycle variations of EEJ by using thelong-term study of the EE-index. The real-time EE-index
is published on the web of ICSWSE for the purpose ofmonitoring the equatorial magnetic variation involving
EEJ.

Acknowledgments
We wish to acknowledge that this paper was
originally presented at "2015 UN/Japan Workshop on
Space Weather". This event was supported in part by
JSPS Core-to-Core Program (B. Asia-Africa Science
Platforms), Formation of Preliminary Center for
Capacity Building for Space Weather Research. We
thank World Data Center (WDC) for Geomagnetism
Kyoto University for providing geomagnetic indices Dst.
We thank Goddard Space Flight Center/Space Physics
Data
Facility(GSFC/SPDF)OMNIWebathttp://omniweb.gsfc.nasa.gov for providing F10.7 solar
radiation data. Special thanks are due to the Ocean
Hemisphere network Project (OHP), University of Tokyo
for providing with the CXI magnetometer data. The PI
of MAGDAS/CPMN project, A. Yoshikawa, ICSWSE,
Kyushu Univ. very much appreciates 32 organizations
and Co-investigators around the world for their46

ceaseless cooperation and contribution to the
MAGDAS/CPMN project. The MAGDAS observation isfinancially supported by Japan Society for the
Promotion of Science (JSPS) as Grant-in-Aid forOverseas Scientific Survey (15253005, 18253005). The
MAGDAS/CPMN database is also supported by JSPS
for Publication of Scientific Research Results (128068,
138059, 148071, 158068, 168066, 188068, 198055,
208043), and National Institute of Information andCommunications Technology (NiCT) as funded
research. This work was supported by JSPS KAKENHI
Grant Number 15H05815.

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