We investigate the ionospheric response to solar Extreme
Ultraviolet (EUV) variations using different proxies, based on solar EUV
spectra observed from the Solar Extreme Ultraviolet Experiment (SEE) onboard
the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED)
satellite, the F10.7 index (solar irradiance at 10.7 cm), and the Bremen
composite Mg-II index during January 2003 to December 2016. The daily mean
solar proxies are compared with global mean Total Electron Content (GTEC)
values calculated from global IGS TEC maps. The preliminary analysis shows a
significant correlation between GTEC and both the integrated flux from SEE
and the Mg II index, while F10.7 correlates less strongly with GTEC. The
correlations of EUV proxies and GTEC at different time periods are presented.
An ionospheric delay in GTEC is observed at the 27 days solar rotation period
with the time scale of about ∼1–2 days. An experiment with the
physics based global 3-D Coupled Thermosphere/Ionosphere Plasmasphere
electrodynamics (CTIPe) numerical model was performed to reproduce the
ionospheric delay. Model simulations were performed for different values of
the F10.7 index while keeping all the other model inputs constant. Preliminary
results qualitatively reproduce the observed ∼1–2 days delay
in GTEC, which is might be due to vertical transport processes.
Introduction
The ionospheric E and F regions are important layers of the Earth's
atmosphere (above ∼60 km), which are created due to ionization
of various species like nitrogen, atomic oxygen, and molecular oxygen. The
ionosphere is built through absorbing solar extreme ultraviolet (EUV)
radiation and soft X-rays mainly at wavelengths below 105 nm. The EUV
radiation is not emitted at constant rates and varies at different
timescales, including short-term variability (minutes (flares), daily, 27 days Carrington rotation, and seasonal) and long term variability (11 years
solar cycle). The long term variability is expected to be greater than the
short term variability (Woods and Rottman, 2002). For instance, the He II
EUV emission line can change by a factor of 2 during the 11 years solar
cycle and ∼50 % during the 27 days rotation period, and the
variation in EUV can be 30 % at ∼100 nm and 100 % at
∼10 nm during the solar rotation period (e.g. Lean et al.,
2001, 2011). During solar flares, the X-rays and the solar EUV regions may
be enhanced by more than a factor of 50 and less than a factor of 2,
respectively (Woods and Eparvier, 2006). Short-term solar variability is
part of space weather, so that ionospheric parameters like the Total
Electron Content (TEC) and the ionospheric height (McNamara and Smith, 1982)
are influenced by space weather. TEC is the vertically integrated electron
density of the ionosphere which is usually given in TEC units (1 TECU = 1016electrons m-2). The ionospheric variability due to changes in
solar activity has been studied extensively by various researchers (e.g.,
Jakowski et al., 1991; Rishbeth, 1993; Su et al., 1999; Forbes et al., 2000;
Liu et al., 2006; Afraimovich et al., 2008; Lee et al., 2012; Jacobi et al.,
2016, and references therein). Such studies are of great importance for improving
our understanding of the solar influence on radio communication and
navigation systems like Global Navigation Satellite Systems (GNSS). Radio
waves are refracted by the ionosphere, which in turn is affected by the
solar activity.
Due to unavailability of direct EUV measurements before the space age, the
variation in TEC is frequently compared against solar proxies, with the most
common one being the F10.7 index, which is the irradiance at a wavelength of
10.7 cm, usually given in solar flux units (sfu, 10-22 W m-2 Hz-1) (Tapping, 1987; Rishbeth, 1993; Maruyama, 2010). Other indices
are the Bremen composite MG-II index (the core to wing ratio of the MG-II
line) (Maruyama, 2010), or EUV-TEC which have been introduced by Unglaub et
al. (2011) to name only a few. The long-term and short-term relation between
EUV and different solar proxies (F10.7 index, Mg-II index, Sunspot number)
has been reported in previous studies (Dudok de Wit et al., 2009; Chen et
al., 2012; Wintoft, 2011). In comparison to the short term variability, the
long-term variations of EUV radiation are better represented by the solar
proxies (Chen et al., 2012). All the proxies not always perfectly describe
the solar activity (Dudok de Wit et al., 2009), and their capability in
reproducing EUV depends on wavelength and time scale. Chen et
al. (2011) suggested that the F10.7 index is not able to produce the solar activity
level during the minima of solar cycle 23, and Chen et al. (2012) showed
that the MG-II index is a better representative of SOHO EUV in the
wavelength range 26–34 nm than the F10.7 index. On the other hand, good
correlation has been observed between ionospheric parameters and F10.7 index
during Autumn–Winter of the years 2003 to 2005 (Oinats et al., 2008).
In recent years, direct solar EUV flux measurements are available from
various satellites such as the Solar EUV Experiment (SEE) onboard the
Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite
(Woods et al., 2000, 2005), and the Extreme Ultraviolet Variability
Experiment (EVE) onboard the Solar Dynamics Observatory (SDO) (Woods et al.,
2012; Pesnell et al., 2012). However, due to degradation of EUV measuring
instruments solar proxies may be more suitable (BenMoussa et al., 2013), or
repeated calibration is necessary. The availability of the direct EUV
measurements provide an opportunity for comparing EUV with different solar
proxies (e.g., Jacobi et al., 2016).
Various researchers had observed a delayed response of ∼1–2 days in
TEC or global mean TEC (GTEC) with respect to solar activity changes (e.g.
Jakowski et al., 1991; Oinats et al., 2008; Afraimovich et al., 2008; Min et
al., 2009; Lee et al., 2012; Jacobi et al., 2016). Hocke (2008) showed the
11 years, 1 year, and 27 days oscillations of GTEC and the Mg-II index with a
high correlation coefficient. Lee et al. (2012) studied the correlation and
time lag at the 27 days solar rotation period using GPS TEC and in situ
electron density measurements from the CHAMP and GRACE satellites. They found
a 1-day difference of the time delay in the northern and southern hemisphere.
Jakowski et al. (1991) used a 1-D numerical model to explain the delay of
∼1–2 days. The study concluded that the delay might be due to slow
diffusion of atomic oxygen at 180 km, which was produced due to dissociation
of molecular oxygen in the lower altitude.
In recent years numerical, empirical, and physics-based
thermosphere/ionosphere models have been developed to characterize
ionospheric dynamics. Among them are the Coupled Thermosphere Ionosphere
Plasmasphere Electrodynamics (CTIPe, Fuller-Rowell and Rees, 1983; Millward
et al., 2001; Codrescu et al., 2012), the International Reference Ionosphere
(IRI, Rawer et al., 1978; Bilitza et al., 2011) and
Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM,
Roble et al., 1988). These models play an important role in upper
atmospheric studies (e.g., Negrea et al., 2012; Fedrizzi et al., 2012). To
simulate solar variability, models are frequently driven by proxies like
F10.7 index or the Mg-II index. The F10.7 index is the most widely used
index in upper atmosphere research to represent the solar variability due to
the availability of continuous measurements since 1947 (Woods et al., 2005).
The solar EUV variability can be better represented by the improved F10.7
index using 81 days running mean (e.g., Viereck et al., 2001; Liu et al.,
2006). The CTIPe model uses a modified F10.7 index, which is the average of
the previous day value of the F10.7 index and the average of the previous 41
days (Codrescu et al., 2012). Fitzmaurice et al. (2017) used the CTIPe model
to understand the influence of solar activity on the ionosphere/thermosphere
during the geomagnetic storm. They reported that solar activity has the
greatest effect on model simulated TEC.
The main aim of the present study is to find out the correlation and time
delay between GTEC and solar proxies based on data from January 2003 to
December 2016. To derive the periodicities in GTEC and solar proxies, the
wavelet coherence and cross-wavelet method have been utilized. Preliminary
results of a CTIPe model experiment to estimate the delay at the solar
rotation time scale will also be presented.
Data and model descriptionData sources
In this work, we use daily global TEC maps from the International GNSS
Service (IGS, Hernandez-Pajares et al., 2009) provided by NASA's CDDIS
(Noll, 2010) data archive service (CDDIS, 2017). Gridded global TEC data is
available at a time resolution of 2 h and on a spatial grid of
2.5∘×5∘ in latitude-longitude. For the analysis of the
correlation between solar proxies and GTEC, we have selected three commonly
used solar proxies, namely daily values of the F10.7 index, the Bremen
composite Mg-II index, and the integrated EUV flux from the TIMED/SEE
satellite. The F10.7 index and TIMED/SEE measurements are taken from the
LISIRD (DeWolfe et al., 2010) database. The NASA TIMED satellite was
launched in 2001 and carried four instruments (GUVI, SABER, SEE and TIDI).
Solar irradiance measurements from the TIMED/SEE instrument are available
since 22 January 2002 (Woods et al., 2005). The SEE instrument is designed
to measure the soft X-rays and EUV radiation from 0.1 to 194 nm with the
resolution and accuracy of 0.1 nm and ∼10–20 %,
respectively. SEE includes two instruments, the EUV grating spectrograph and
the XUV photometer system (Woods et al., 2000). We have used the daily
integrated value of solar irradiance from 5.5 to 105.5 nm wavelength.
CTIPe model description
Temporal variations of normalized datasets of GTEC (blue),
SEE-EUV flux (black), F10.7 index (red), and Mg-II index (magenta) during year
2003 to 2016. The curves are vertically offset each by 2.
The CTIPe model is a global, 3-D, time-dependent, physics-based numerical
model. It consists of four components, namely (a) a neutral thermosphere
model (Fuller-Rowell and Rees, 1980), (b) a mid- and high-latitude
ionosphere convection model (Quegan et al., 1982), (c) a plasmasphere and
low latitude ionosphere model (Millward et al., 1996), and (d) an
electrodynamics model (Richmond et al., 1992), which run simultaneously and
are fully coupled. The thermosphere model is solving the equation of
momentum, continuity, and energy to calculate global temperature, density,
wind components, and atmospheric neutral composition. The parameters
calculated from the thermosphere code are used to calculate production,
loss, and transport of plasma. The transport terms consider ExB drift and
interactions of ionised and neutral particles under the influence of the
magnetospheric electric field (Codrescu et al., 2012). In the high latitude
model, the atomic ions of O+ and H+ are calculated by solving the
momentum, energy, and continuity equations, and the model includes vertical
diffusion, horizontal transport, ion-ion, and ion neutral processes in the
height range of 100 to 10 000 km. The contribution from N2+, O2+,
NO+ and N+ are additionally added below 400 km. The mid and low
latitude ionosphere model is also calculating H+, O+ ions, and
electrons as does the high latitude model. The numerical solution of the
composition equation with the energy and momentum equations describe the
transport, turbulence, and diffusion of atomic oxygen, molecular oxygen and
nitrogen (Fuller-Rowell and Rees, 1983). The latitude/longitude resolution
is 2∘/18∘. In the vertical direction, the atmosphere
is divided into 15 levels in logarithmic pressure starting from a lower
boundary at 1 Pa to ∼500 km altitude at an interval of one
scale height. The corresponding geometric heights are variable depending on
temperature and therefore on the solar and magnetic activity. External
inputs are required to drive the model like solar UV and EUV, Weimer
electric field, TIROS/NOAA auroral precipitation, and tidal forcing. The
F10.7 index is used in an artificial manner as input solar proxy to
calculate ionization, heating, and oxygen dissociation processes in the
ionosphere. For the simulation, the Hinteregger et al. (1981) reference
solar spectrum driven by variations of input F10.7 is used in the model.
More description of CTIPe is available in Codrescu et al. (2008, 2012).
Results and discussionCorrelation between TEC and solar EUV proxies
To study the long-term variations in GTEC and EUV proxies, datasets from
2003 to 2016 have been used. Figure 1 shows the normalized time series of
GTEC, SEE EUV flux, the F10.7 index, and the MG-II index. All data has been
normalized by subtracting the mean and dividing by the respective standard
deviation. The data represent the decreasing and increasing parts of solar
cycle 23 and 24, respectively. As the solar radiation plays a major role in
the electron production, the correlation of GTEC with solar EUV or EUV
proxies must be significant and is also correlated at the 27 days solar
rotation period. Figure 2 shows the cross correlation between GTEC and (a)
TIMED/SEE integrated EUV flux (left panel), and (b) F10.7 index (right
panel) from 1 January 2003 to 31 December 2016. Since we do not consider the
seasonal cycle here, a low-pass filter with a cut off period of three
months was applied to the data before.
Figure 2 shows a strong correlation between normalized GTEC and integrated EUV
flux (black) with a maximum correlation coefficient of 0.90 and shows a
weaker correlation with the F10.7 index (red) with a maximum correlation
coefficient of 0.84. Also, we have analyzed the correlation between GTEC and
Mg-II index which shows a good correlation with a correlation coefficient of
0.89 (figure not shown). Jacobi et al. (2016) analyzed GTEC and SDO/EVE
integrated EUV flux data from 2011 to 2014 and they also found a good
correlation of about 0.89. Unglaub et al. (2011, 2012) have shown that the
GTEC is more strongly correlated with the EUV-TEC proxy than with the F10.7
index. Figure 2 shows a delay of ∼1–2 days in GTEC with
respect to both SEE flux and F10.7 index, which confirms earlier analyses
e.g. by Jacobi et al. (2016).
Cross-correlation of GTEC with SEE-EUV flux (black) and
F10.7 index (red). Positive values denote GTEC lagging SEE-EUV or F10.7,
respectively.
Wavelet analysis
(a, b): cross-wavelet transform of the GTEC with (a)
SEE-EUV flux and (b) F10.7 index. (c, d): wavelet coherence of the GTEC
with (c) SEE-EUV flux and (d) F10.7 index. The cone of influence is shown by
a black line. Significant values are surrounded by a black line. The arrows
show the phase relationship: in-phase pointing right, anti-phase pointing
left, while downward direction means that GTEC is leading.
(a) input F10.7 index values for CTIPe model simulation,
(b) simulated zonal mean TEC, (c) normalized data of F10.7 index and
modelled GTEC, and (d) cross-correlation between F10.7 index and modelled
GTEC.
In order to investigate the oscillations in the time series of GTEC and all
EUV proxies in more detail, the continuous wavelet transform (CWT) method
has been applied. The cross wavelet transform is constructed using 2 CWTs,
which shows common high energies of the two time series and relative phase
(Grinsted et al., 2004). We have used a Morlet mother wavelet. Furthermore,
the wavelet coherence method is used to calculate significant coherence
using Monte Carlo methods (Grinsted et al., 2004). Wavelet coherence can be
calculated using 2 CWTs which shows the local correlation between the time
series. All data has been normalized by subtracting the mean and dividing by
the respective standard deviation.
The cross wavelet spectra between GTEC and both SEE–EUV flux and F10.7
index are shown in Fig. 3a and b, respectively.
GTEC shows common high power with SEE-EUV flux and F10.7 at scales of 16–32 days during 2003 to 2005 and during 2009 to 2016. During those times when
the coherence is significant, GTEC is in phase with SEE-EUV and F10.7. Much
less power at the 27 days periodicity is observed from 2007 to 2009, which
is the extended part of solar cycle 23.
The magnitude squared coherence of GTEC with SEE-EUV flux and the F10.7
index is shown in Fig. 3c and d, respectively. The coherence spectrum
shows the time and period range where the two time series co-vary. As shown
in both figures, a high correlation is observed at the 27 days periodicity.
The magnitude squared coherence between GTEC and SEE flux is very high at 27 days periodicity, while GTEC and F10.7 behave less coherent. In comparison
to the cross wavelet in Fig. 3a, b, wavelet coherence shows larger
significant regions in Fig. 3c, d.
Variation in TEC using varying F10.7 values in CTIPe model
(a, b, c): Normalized modelled GAOID and input F10.7 data
at three different pressure levels. (d, e, f): Corresponding
cross-correlations between F10.7 and modelled GAOID.
The CTIPe model has been used to simulate the ionospheric variability and to
estimate the ionospheric delay due to solar variability. The model was run
for 15 March 2013 conditions (Kp index = 3) and simulates TEC by varying
the F10.7 index values in an artificial manner as input, keeping all the
other input parameters constant. The input lower boundary in the CTIPe model
is specified by the output of the Whole Atmospheric Model (WAM) (Akmaev,
2011). For the experiment, the model was first run for 30 days with constant
input to reach a diurnally reproducible global temperature pattern, and then
F10.7 was modified. Figure 4a shows the chosen F10.7 index values as input
for the model, which vary from 80 to 120 sfu during one complete solar
rotation period.
Figure 4b shows the zonal mean TEC simulated by the CTIPe model. The
global TEC distribution qualitatively reproduces real ionospheric
conditions, e.g. enhanced electron density near the equator due to the
fountain effect (Appleton, 1946; Hanson and Moffett, 1966; Sterling et al.,
1969). TEC varies according to the F10.7 index, but with a delay which can
be seen by comparing the TEC maximum with the one of F10.7 in Fig. 4a.
Figure 4c shows global mean values for the F10.7 index and the CTIPe TEC,
both normalized by subtracting the mean and dividing by the respective
standard deviation. A delay of about 1–2 days is observed. Figure 4d
shows the cross-correlation and thus the delay between the input F10.7 index
and TEC simulated by the CTIPe model. The delay introduced here may be due
to vertical transport processes or slow diffusion of atomic oxygen, which
has been suggested by Jakowski et al. (1991) as a possible process for the
ionospheric delay. In order to understand the possible delay mechanism in
the GTEC, the normalized modelled global mean atomic oxygen ion density
(GAOID) is shown in Fig. 5 (upper row) for different altitudes. The
corresponding cross correlations between the F10.7 index and GAOID are shown
in the lower panel. It is interesting to note that at pressure 1.9×10-2 Pascal in Fig. 5d there is only a small delay in GAOID with
respect to F10.7, but Fig. 5b, e and c, f show a larger delay of
∼1 day at greater altitudes. This preliminary analysis
indicates that vertical transport processes might play a role in the delay.
Summary and Conclusions
To contribute to the understanding of the long-term ionospheric behaviour
with respect to solar EUV variations we have analyzed data from 1 January
2003 to 31 December 2016. In this study, the strong correlation between GTEC
and solar proxies has been observed at the 27 days solar rotation period.
There is a particularly strong correlation between GTEC and integrated
SEE-EUV flux and the Mg II index, while F10.7 correlates less strongly with
TEC. We have also observed an ionospheric delay at the 27 days solar
rotation period with the time scale of 1–2 days between GTEC and all the
solar proxies considered, thereby confirming earlier results in the
literature.
To gain more insight into the possible reasons for the delay, we have run
the CTIPe model for 27 days and varied the input F10.7 index artificially
while keeping all the other conditions constant. Preliminary results show
that the model qualitatively reproduces the observed ionospheric delay of
∼1–2 days in GTEC with respect to the F10.7 index. An attempt
has been made to understand the delay process using GAOID simulated by the
CTIPe. The cross correlation analysis between the GAOID and the F10.7
indicates small delay at the lower pressure level and longer delay in higher
pressure levels, which suggests that transport processes might play a role
in the delay.
To conclude, in this first approach we have found that the CTIPe model is
able to reproduce the observed ionospheric delay. The results, however, are
only preliminary. In further studies with more realistic EUV changes, we
will also analyse photodissociation and ionization processes of atomic
oxygen, molecular oxygen, and molecular nitrogen in more detail to check the
validity of the results by Jakowski et al. (1991). Furthermore, we will
investigate the delay in the different ionospheric parameters on different
timescales by varying various model components (dissociation, ionization)
thereby investigating the physical processes responsible for the delay.
Data availability
IGS TEC data has been provided via NASA through
ftp://cddis.gsfc.nasa.gov/gnss/products/ionex/ (CDDIS, 2017). Daily F10.7 index and
TIMED/SEE version 3A spectra have been provided by LASP at
http://lasp.colorado.edu/lisird/noaa_radio_flux and
http://lasp.colorado.edu/lisird/data/timed_see_ssi_l3a (LASP, 2017), respectively. Mg-II
index has been provided by IUP at
http://www.iup.uni-bremen.de/UVSAT/Datasets/mgii (IUP, 2017).
Author contributions
CJ, RV, JB, ES, and MC designed the study. RV performed the CTIPe
model run with help from MC and CJ. RV analysed the data. CJ together
with RV drafted the first version of the text. All authors discussed
the results and provided critical feedback and contributed to the
final version of the manuscript.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “Kleinheubacher
Berichte 2017”. It is a result of the Kleinheubacher Tagung 2017,
Miltenberg, Germany, 25–27 September 2017
Acknowledgements
The study has been supported by Deutsche Forschungsgemeinschaft (DFG) through
grants no. BE 5789/2-1 and JA 836/33-1.
Edited by: Ralph Latteck Reviewed by: Matthias Förster and
one anonymous referee
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