Solar activity impact on
the Earth’s upper
atmosphere
review paper SG1.1.4
presented by Ivan Kutiev
5-9 November 2012
9ESSW, Brussels
1
Authors
Kutiev*, Ivan(1), Ioanna Tsagouri(2), Loredana Perrone(2) ,Dora
Pancheva(1), Plamen Mukhtarov(1), Andrei Mikhailov(4), Jan Lastovicka(5),
Norbert Jakowski(6), Dalia Buresova(5), Estefania Blanch(7), Borislav
Andonov(1), , David Altadill(7), Sergio Magdaleno(9), Mario Parisi(8) and J.
Miquel Torta(7)
1 National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences,
2 Institute for Space Applications and Remote Sensing, National Observatory of Athens, Greece
3 Istituto Nazionale di Geofisica e Vulcanologia , Italy
4 Institute of Terrestrial Magnetism, Ionosphere, and Radio Propagation, Russian Academy of
Sciences.
5 Institute of Atmospheric Physics ASCR, Czech Republic,
6 Institute of Communications and Navigation, German Aerospace Center,
7 Ebro Observatory, University Ramon Llull – CSIC, Spain
8 Dipartimento di Fisica, Università degli Studi di Roma, Italy
9 Atmospheric Sounding Station “El Arenosillo”, INTA, Huelva, Spain
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Content
2. Medium- and long term ionospheric response to the changes in
solar and geomagnetic activity
2.1 Response of low-latitude ionosphere to 27-day variations of solar and geomagnetic
activity
2.2 Thermosphere- ionosphere coupling in response to recurrent geomagnetic activity
2.3 Long-term trends in the upper atmosphere and ionosphere and space
weather/climate
2.4 Latitude dependent response of TEC to solar EUV changes
2.5. Ionospheric behaviour during prolonged minimum of the 23/24 solar cycle
3. Storm-time ionospheric response to the solar and geomagnetic
forcing
3.1 Ionospheric response to geomagnetic activity during prolonged solar minimum
3.2 Polar cap absorption event of May 2005 in Antarctica
3.3 E-layer dominated ionosphere
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Content – cont.
4. Modeling and forecasting techniques
4.1 Empirical model of the TEC response to the geomagnetic activity over the
North American region
4.2 Advances in the development of ionospheric forecasting models
4.3 Real time forecasting tool for hmf2 at midlatitudes combining quiet and
disturbance hmf2 models
4.4 DIAS effective sunspot number: an indicator of the ionospheric activity
level over Europe
4.5 Retrieval of thermospheric parameters from routine ionospheric
observations
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Selected topics
Medium-term ionospheric response to solar activity:
27-days oscillations of TEC
Amplitude of 27-days periodicity
2000 2001 2002 2003 2004 2005 2006 2007 2008
0.60
80
rTEC at 27 deg
rTEC at 38 deg
F107
oscillation in both rTEC27 (red
line) and rTEC38 (green line)
2000-2008. The magnitude of
the 27-day oscillation in F10.7 is
40
0.40
20
rTEC
for the whole period of analysis
60
F10.7
The magnitude of 27-days
0
0.20
shown with the scale on the right
(Kutiev et al. 2012).
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0.00
years 2000-2008
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Selected topics
Medium-term ionospheric response to solar activity:
27-days oscillations of TEC
Top: rTEC variations between days 180 and 270, in 2004, superimposed by a periodic
variation (solid black curve), with period close to 27 days. Vertical arrows mark the start of
some of these deviations, which coincide with the storms onsets (Kutiev et al. 2012).
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Selected topics
Medium-term ionospheric response to solar activity:
9-days planetary waves in foF2 and hmF2
9-d (s=0) PW Response in foF2
Modip Latitude (degree)
70
50
0.29
70
0.24
50
30
0.19
10
6.3
5.3
30
4.3
10
3.3
0.14
-10
-10
-30
0.09
-50
0.04
-70
0
90
180
270
360
450
100
30
50
10
0
-10
-30
-50
-70
0
90
180
270
360
450
540
1.3
-50
0.3
0
150
50
2.3
-30
-70
540
Phase Diff. 9-d (s=0) (foF2-PI)
70
Modip Latitude (degree)
9-d (s=0) PW Response in hmF2
90
180
270
360
450
540
Phase Diff. 9-d (s=0) (hmF2-PI)
70
150
50
100
30
50
10
0
-50
-10
-100
-30
-150
-50
-50
-100
-150
-70
0
90
Day Number (start 1 October 2007)
180
270
360
450
540
Day Number (start 1 October 2007)
(upper row of plots) Extracted from the COSMIC data 9-day zonally symmetric (s=0) waves
seen in the ionospheric parameters: foF2 (left plot) and hmF2 (right plot); (bottom row of
plots) Phase difference between the ionospheric parameters foF2 and the PI (left plot) and
between hmF2 and PI (right plot); the thick white line shows the zero phase difference.
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Selected topics
Long-term trends in upper ionosphere
The role of space weather/climate in longterm changes and trends in the upper
atmosphere-ionosphere was more important
in the past, when it controlled the trends in
ionospheric parameters, than it is at present,
when the dominant controlling parameter
seems to be increasing concentration of CO2.
The figure: Model simulation of trends in foF2 and hmF2 at noon, longitude 0 o, as a
difference between the basic state and the state with doubled CO2 concentration. Dashed
curve – hmF2 for basic state; solid curve – hmF2 for doubled CO2. After Qian et al. (2008).
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Selected topics
E-layer dominated ionosphere (ELDI)
Left panel: Typical electron density profile derived from GPS RO measurements onboard
Formosat3/COSMIC, CHAMP and GRACE satellites. Right panel: Ellipse fit to the distribution
of ELDI profiles, which show enhanced E-layer ionization (red dots). The yellow stars mark the
focal points of the ellipse; the black star marks the center point of the circle fit which coincides
with the position of the geomagnetic pole. (Mayer & Jakowski, 2009).
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Selected topics
Ionospheric response to Kp changes is
composed by two phases, positive and
negative, with different duration and different
time delay.
Lat [deg]
Empirical ionosphere modeling:
TEC/Kp dependence with time delays
Lat [deg]
rVTECt    fTs KpTs t   fTl KpTl t  f LT 
The figure: Two-dimensional (latitude-lag
time) cross-correlation functions calculated
months of the year; the zero line is shown by
dashed white line (Andonov et al. 2012).
Lat [deg]
between the rVTEC and Kp-index for 6
60
55
50
45
40
35
30
25
20
15
10
-24 -16 -8
60
55
50
45
40
35
30
25
20
15
10
-24 -16 -8
60
55
50
45
40
35
30
25
20
15
10
-24 -16 -8
January
0
0
8 16 24 32 40 48 56 64 72
May
8 16 24 32 40 48 56 64 72
September
0
8 16 24 32 40 48 56 64 72
Time lag [hours]
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60
55
50
45
40
35
30
25
20
15
10
-24 -16 -8
60
55
50
45
40
35
30
25
20
15
10
-24 -16 -8
60
55
50
45
40
35
30
25
20
15
10
-24 -16 -8
March
0.36
0.28
0.2
0.12
0.04
-0.04
-0.12
-0.2
-0.28
0
8 16 24 32 40 48 56 64 72
July
0.36
0.28
0.2
0.12
0.04
-0.04
-0.12
-0.2
-0.28
0
8 16 24 32 40 48 56 64 72
November
0.36
0.28
0.2
0.12
0.04
-0.04
-0.12
-0.2
-0.28
0
8 16 24 32 40 48 56 64 72
Time lag [hours]
10
Selected topics
Empirical ionosphere modeling: foF2/SW parameters (SWIF)
SWIF combines the autoregression forecasting
algorithm, called Time Series AutoRegressive –
TSAR (Koutroumbas et al. 2008), with the
empirical Storm Time Ionospheric Model – STIM
(Tsagouri & Belehaki 2006, 2008) which
formulates the ionospheric storm time response
with solar wind parameters (magnitude of the IMF,
its rate of change and Bz). STIM provides a
correction factor to the quiet model TZAR). STIM
estimates the time delay in the ionospheric storm
onset and estimates the ionospheric storm time
response by taking into account the latitude and the
LT of the observation point at the storm onset.
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Selected topics
Empirical ionosphere modeling: hmF2 triggered by SW changes
Global quiet-time behavior of hmF2, has been
modeled by the Spherical Harmonic Analysis
(SHA) technique (Altadill et al. 2009). Blanch
(2009) has developed a model describing the storm
time deviation of the peak height ΔhmF2, which
uses a bell-shaped functions whose coefficients
depend on IMF configuration, local time and
season. Both models have been combined for
modeling hmF2 under both quiet and disturbed
conditions
hmF 2(t )  A1·e(t B1 )
2
/ C12
 A2 ·e(t B2 )
2
/ C22
The figure: Red points represent experimental
values and the red line shows the model prediction.
The black points represent the average data and the
black line corresponds the quiet time hmF2
prediction of SHA model. Vertical grey dashed line
indicates the triggering time.
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Selected topics
Retrieval of thermospheric parameters from ionospheric data
The figure: Reduced to the Millstone Hill
location and 400 km height CHAMP
neutral gas density observations along
with 10% error bars, MSISE-00 and
JB2006 model predictions and the model
extracted neutral gas density at 400 km
height (stars) using ISR Ne(h) profiles for
October 2002 (solar maximum).
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 x 10-15 g cm-3
A new method has been developed
(Mikhailov et al. 2012) to retrieve neutral
temperature Tn and composition [O],
[N2], [O2] from electron density profiles
in the daytime mid-latitude F2-region
under both quiet and disturbed conditions.
CHAMP data
MSISE-00
ISR Ne(h)
JB2006
11
10
9
8
7
6
3
5
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9 11 13 15 17 19 21 23 25 27 29 31
October, 2002
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Thank you
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