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Solar Activity, Lightning and Climate
Devendraa Siingh • R. P. Singh • Ashok K. Singh • M. N. Kulkarni •
A. S. Gautam • Abhay K. Singh
Received: 10 April 2010 / Accepted: 9 May 2011 / Published online: 21 May 2011� Springer Science+Business Media B.V. 2011
Abstract The physics of solar forcing of the climate and long term climate change is
summarized, and the role of energetic charged particles (including cosmic rays) on cloud
formation and their effect on climate is examined. It is considered that the cosmic ray-
cloud cover hypothesis is not supported by presently available data and further investi-
gations (during Forbush decreases and at other times) should be analyzed to further
examine the hypothesis. Another player in climate is lightning through the production of
NOx; this greenhouse gas, water vapour in the troposphere (and stratosphere) and carbon
dioxide influence the global temperature through different processes. The enhancement of
aerosol concentrations and their distribution in the troposphere also affect the climate and
may result in enhanced lightning activity. Finally, the roles of atmospheric conductivity on
the electrical activity of thunderstorms and lightning discharges in relation to climate are
discussed.
Keywords Solar irradiance � Galactic cosmic rays � Thunderstorm/Lightning � Upper
tropospheric water vapour � Atmospheric chemistry � Aerosols � Climate
1 Introduction
The terrestrial climate is driven by solar energy incident on the Earth’s atmo-
sphere, * 1.365 kWm-2 at the top of the atmosphere. On average, about 50% of the
energy from the Sun is absorbed by the surface and about 30% is reflected back into the
space. The reflected energy consists of *20% reflected by clouds, 6% backscattered by air
D. Siingh (&) � M. N. Kulkarni � A. S. GautamIndian Institute of Tropical Meteorology, Pune 411 008, Indiae-mail: [email protected]; [email protected]
R. P. Singh � A. K. SinghDepartment of Physics, Banaras Hindu University, Varanasi 211 005, India
A. K. SinghPhysics Department, Lucknow University, Lucknow 226 007, India
123
Surv Geophys (2011) 32:659–703DOI 10.1007/s10712-011-9127-1
and *4% reflected by the surface (Hanslmeier 2007). About 16% is absorbed by the
atmosphere (mostly by aerosol and dust particles in the troposphere, and by ozone). About
79% of the received energy is returned to space in the form of thermal radiation in the long
wave infrared part of the spectrum. Some 21% is transmitted from the Earth’s surface to
the atmosphere by conduction (termed the sensible heat flux), and by latent heat flux
(through the evaporation of H2O leading to the surface cooling). The variation in the
climate may be attributed, at least in part, to the variation of solar energy incident on the
Earth’s atmosphere.
The variation of solar energy incident on the Earth’s atmosphere depends on solar
activity, which varies on time scales from hours to billions of years, and depends on various
processes operating inside/on the surface of the Sun. It is also affected by the rotation of the
Sun, the precession of the Earth and the obliquity of the Earth’s rotational axis and changes
due to the eccentricity of Earth’s orbit (Berger et al. 2003). The Sun’s magnetic field varies
in a complex manner, including the well known sunspot cycle of *11 years periodicity and
longer period modulations. The total solar irradiance (the total electromagnetic power per
unit area of cross section at the top of the Earth’s atmosphere) varies not only through the
solar cycle (Frohlich 2006) but also in a much more complex manner.
The radiation incident on the terrestrial atmosphere is affected by the different atmo-
spheric constituents. For example, additional ozone in the stratosphere reduces the
downward short wavelength (ultraviolet) fluxes and increases the upward long wavelength
fluxes. The systematic change in the global average temperature termed global warming is
mainly attributed to changes in the concentrations of atmospheric constituents which
absorb infrared radiation, the so called greenhouse gases. These produce a radiative forcing
of *3 Wm-2 (IPCC 2007). Scafetta and West (2006, 2008) suggest that solar variability
accounts for *69% of global warming in the twentieth century, and 25–35% warming
since 1980. This is about a factor of two larger warming than in prior studies (Camp and
Tung 2007). Benestad and Schmidt (2009) discussed the weaknesses of the method
adopted by Scafetta and West (2006, 2008) and showed that the most likely contribution
from solar forcing to global warming is 7 ± 1% for the twentieth century and with a
negligible contribution to warming since 1980. The long term drift in the radiative forcing
is of great importance in climate change; it is *0.017 Wm-2 per decade (Frohlich 2006,
Gray et al. 2010). The current rate of increase in trace greenhouse gas radiative forcing is
*0.30 Wm-2 per decade (Hofmann et al. 2008).
The global climate change in terms of increasing concentrations of greenhouse gases in
the atmosphere and variations of solar radiation has been summarized in the report of
Intergovernmental Panel on Climate Change (2007). The report has also stressed that some
weather conditions including flash floods are often related to intense thunderstorms and
lightning activity. The strong dependence of the lightning strike rate on cloud top prop-
erties (Williams 1985), the correlation of lightning flash rate with the surface temperature
(Williams 1992), the association of intense rainfall with the lightning flash rate (Price and
Federmesser 2006), the effect of doubling the amount of CO2 in the atmosphere on changes
in cloud height (Price and Rind 1994), the production of NOx by lightning and thereby
modification of the amount of ozone in the atmosphere (Schumann and Huntrieser 2007),
etc., are some of the factors which stress the present need to understand the relation
between solar activity, lightning and climate.
The major driver of climate is the Sun, which is also at the origin of thunderstorm
formation leading to rainfall and the production of lightning. The convective processes
caused by differential heating at the Earth’s surface lead to both rainfall and lightning. The
deeper and stronger updrafts in convection may produce lightning (Baker et al. 1999;
660 Surv Geophys (2011) 32:659–703
123
Boccippio 2001), whereas modest lifting results in rainfall (Williams and Stanfill 2002;
Williams 2005). Chronis et al. (2004) discussed the possibility of using lighting as a proxy
of intense convective rainfall in remote regions. This was supported by the analysis of
satellite data showing a positive connection between Mediterranean lightning activity and
convective rainfall and also between the El Nino cycle and thunderstorms (Price and
Federmesser 2006). The intense thunderstorms in eastern Africa influence the stability of
the African Easterly waves, which ultimately may result in Atlantic hurricanes about a
week later (Price et al. 2007).
In this paper we briefly discuss direct and indirect forcing of solar irradiance on climate,
the impact of energetic charged particles (in the solar wind, solar energetic proton events
associated with solar flares, and cosmic rays) on climate through cloud formation and
consequent atmospheric cooling or warming. Also, the global observation of lightning, the
association of lightning with the amount of upper tropospheric water vapour (UTWV) and
atmospheric aerosols, and the impact of lightning on atmospheric constituents and their
relation with climate are discussed. Modifications of the electrical conductivity of the
atmosphere affect the vertical electric current between the surface and the ionosphere
which can affect cloud formation and result ultimately in changes to the climate.
2 Long Term Climate Variations, Solar Irradiance and Cosmic Rays
One main agent responsible for climate variation is the variation in solar irradiance and/or
variation of the solar spectrum (Haigh et al. 2010). Other factors responsible for variations
in the Earth’s climate are variations in the concentrations of atmospheric constituents
(partly controlled by the Sun), galactic cosmic rays (GCRs), processes taking place at the
surface of the Earth, and man-made changes in the troposphere (King 1975). The contri-
bution of each process to climate is qualitatively discussed but a quantitative estimate for
each separate process is not available (Gray et al. 2005).
There are three probable mechanisms which link solar variability with variations of
climate (Carslaw et al. 2002): (a) changes in solar irradiance leading to changes in heat
input to the lower atmosphere, (b) changes in solar ultraviolet radiation coupled to changes
in the ozone concentration affecting the heat budget of the stratosphere, and (c) solar and
galactic cosmic rays (GCR) affecting cloud formation. These effects are modulated by long
term solar activity, by changes of the galactic and solar cosmic rays and by changes of the
Earth’s magnetic field.
Figure 1 shows a schematic diagram of the direct and indirect effects at different
altitudes of solar radiation changes (either total solar irradiance (TSI) or ultraviolet (UV))
on climate with respect to Smax (solar maxima), GCR and corpuscular radiation (Kodera
and Kuroda 2002). The TSI affects the surface temperature, whereas UV affects the
stratospheric temperature through the modification of the ozone concentration there.
Upward propagating planetary waves from the troposphere to the stratosphere are sensitive
to the background winds present. Positive feedback (rF [ 0) arises through the wind
anomaly which moves poleward and downward with time and grows significantly in
amplitude (Kodera and Kuroda 2002). The wave forcing (rF [ 0) weakens the strength of
large scale equatorial upwelling (w* in Fig. 1). In solar maxima (Smax) years the polar
lower stratosphere is colder because of a less disturbed polar vortex. In Smin (solar minima)
years the effect would be reversed (Kodera and Kuroda 2002). In the return upwelling arm
(equatorial region) weak circulation during Smax years will result in less adiabatic cooling,
resulting in a warmer equatorial stratosphere. In this mechanism a small temperature
Surv Geophys (2011) 32:659–703 661
123
(1–2 K) anomaly at the equatorial stratopause is transferred to the lower polar stratosphere
in the amplified form. This feedback mechanism also modulates the transport of ozone
(Hood 2004; Gray et al. 2009). The current I flowing in the global electric circuit (GEC)
generated in thunderclouds is also shown in Fig. 1.
2.1 Solar Irradiance and Climate (Direct Mechanism)
The variation in solar irradiance may result from variations in (a) nuclear conditions in the
Sun’s core, (b) the solar magnetic field generated below the convective zone, and
(c) orbital conditions, such as solar rotation, changes in the Earth’s orbit or inclination of
the Sun’s rotational axis with respect to the ecliptic plane. These processes are expected to
produce very long term changes, up to several million years (Shaviv 2003). The evolution
of solar magnetic field, the structure of the solar wind and coronal mass ejections (CMEs)
(Wagner 1984; Gopalswamy 2004; Singh et al. 2010) involve time scales ranging from
hours to years (Williams et al. 2001; Kudela et al. 2002; Labitzke and Matthes 2003;
Muscheler et al. 2003; Delmonte et al. 2005; Christiansen et al. 2007). Details of these
variations are given in Table 1, which summarizes the time scale, origin of the variations
Fig. 1 The direct and indirect effects of total solar irradiance changes (TSI) and UV radiation changes withrespect to solar maximum conditions, as well as corpuscular radiation effects (energetic charged particlesand GCR), on climate are schematically shown. T is the atmospheric temperature; SST is the sea surfacetemperature. The thin blue arrows represent the global electric circuit (GEC). Here w* denotes theweakened equatorial upwelling at solar maximum associated with reduced planetary wave forcing. W* isvertical velocity, U is wind speed and F is forcing at the top of the atmosphere. The black arrow representstransport associated with planetary waves, AO is the Arctic Oscillation, NAO is the North AtlanticOscillation, ENSO is the El Nino Southern Oscillation, QDO is the Quasi-Decadal Oscillation, and QBO isthe Quasi-Biennial Oscillation. The two dashed purple arrows denote the coupling between the stratosphereand the troposphere and the coupling between the ocean and the atmosphere (after Kodera and Kuroda,2002; Gray et al. 2010)
662 Surv Geophys (2011) 32:659–703
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Table 1 Processes responsible for solar variability, their origin, time scales and the climate parametersaffected (modified from Christiansen et al. 2007)
Time scale Solar origin Climate effect Reference
*109 years Solar evolution andgalactic arm traverses(encounter of intensecosmic ray bursts)
Reduced solar irradianceand enhanced low cloudcover cause glaciations;colder climate, reducedprecipitation, morevigorous large scaleatmospheric circulation
Shaviv (2003),Christiansen et al. (2007)
*105 years TSI variations caused byorbital changes due toeccentricity of Earth’sorbit
Change in seasonalduration and intensity ofTSI. Effects are oppositein each hemisphere, andlargest at low latitudes
Berger et al. (1984), Petitet al. (1999), Imbrie et al.(1992)
*4.1 9 104 years TSI variation due toobliquity of the Earth’srotational axis
Equal effects of TSI in bothhemispheres, strongerpole wards. Moreradiation at summer poleat large obliquity
Berger et al. (1984), Petitet al. (1999), Imbrie et al.(1992)
*2.3 9 104 years TSI variation due toprecession of Earth’srotational axis
Different seasonal cyclestructures, produceing acomplex effect onclimate
Berger et al. (1984), Petitet al. (1999), Imbrie et al.(1992)
*2.3 9 103 years(Hallstatt cycle)
TSI variation due tomodulation of the solardynamo
Lower temperatures,reduced precipitationand changed large scaleatmospheric circulation
Denton and Karlen (1973),Damon and Jirikowic(1992)
*205 years (deVries cycle)
TSI variation due tomodulation of the solardynamo
Solar forcing amplifiedthrough ocean–atmosphere interactions,changing sea surfacetemperatures and sea-iceextent
Muscheler et al. (2003),Delmonte et al. (2005)
88 years(Gleissbergcycle)
TSI variation due tomodulation of the solardynamo
Changes of globaltemperature (both airand ocean temperatures)and of atmosphericcirculation. Climaticfluctuations induced atthe turning points of thiscycle
Hoyt and Schatten (1997),Fairbridge (1967)
22 years (Halecycle)
TSI variation due tooscillation of the solardynamo
Changes of temperatureand precipitation.Droughts in US followthis pattern
Mitchell et al. (1979)
11 years (Schwabecycle)
TSI variation due tooscillation of the solardynamo. About 0.1%variation of TSI betweensolar maximum andminimum
As a result of changes ofozone concentration,geopotential height andtemperatures in thestratosphere vary,modifying meriodionalcirculation, low cloudcoverage and Arcticoscillations
Labitzke and Matthes(2003), Shindell et al.(1999), Svensmark(1998), Reid (1991),Friis-Christensen andLassen (1991)
Surv Geophys (2011) 32:659–703 663
123
and related climate parameters (Christiansen et al. 2007). The time scales involved due to
variations in the precession of the Earth’s rotation axis and obliquity and eccentricity of the
Earth’s orbit are 23,000, 41,000 and 100,000 years, respectively (Petit et al. 1999).
The most well known 11 year sunspot cycle, the Schwabe cycle (1844), the 22 year
Hale cycle and their modulation, with a period of about 88 years, known as the Gleissberg
cycle, are evident in sunspot data (Frick et al. 1997; Ogurtsov et al. 2002), solar cosmic ray
activity (McCracken et al. 2001) and solar energetic particle (SEP) events (Reames 2004).
During the Maunder Minimum (1645–1715), sunspots were almost absent (Fig. 2a). High
sunspot numbers coincide with warm weather and low sunspot numbers with cool weather
(Hoyt and Schatten 1997).
Figure 2b shows the variation of reconstructed total solar irradiance from 1610–2003
based on sunspot data (Lean et al. 2002) and the solar cycle amplitude for secular varia-
tions (Solanki and Krivova 2003). Lean et al. (2002) have used a 2.7 Wm-2 increase in
total solar irradiance since the Maunder Minimum, whereas Solanki and Krivova (2003)
used a 2.15 Wm-2 increase. This long term positive drift in TSI between the Maunder
Minimum and the present day is somewhat uncertain. Foukal et al. (2004) suggested that
there may not be any change, whereas Krivova et al. (2007) considered a smaller positive
drift than previously estimated (Lean et al. 2002). Krivova et al. (2007) found a value of
1.3 Wm-2 with an uncertainty range of 0.9–1.5 Wm-2. The reconstructed total solar
irradiance has been compared with independent records of solar activity obtained from
cosmogenic isotopes in tree-rings and ice-cores (Beer et al. 1994; Lean et al. 1995). The
correlation between solar irradiance and northern hemisphere surface temperature is
*0.86 in the pre-industrial period (1610–1800 years) (Lean et al. 1995).
The global surface temperature is usually used as the key parameter for demonstrating
the average state of the Earth’s climate. Therefore, the average global sea surface tem-
perature (SST) anomaly and running mean of the annual sunspot number for the period
1860–1985 are shown in Fig. 3a (Reid 1991, 2000; Christiansen et al. 2007); they show
similar variations. This finding has been corroborated by White et al. (1997) using two
independent SST data sets, i.e. surface marine weather observations (1900–1991) and upper
ocean bathythermograph temperature profiles (1955–1994). They reported the maximum
global average temperature change of 0.08 ± 0.02 K on the 11 year period scale and
0.14 ± 0.02 K on the 22 year cycle. The highest correlation with ocean temperatures lags
solar activity by 1–2 years, a time scale expected for the upper layers of the ocean (\100 m)
to reach radiative equilibrium following a perturbation in the total solar irradiance.
Figure 3b shows the variation of solar cycle length from less than 9 years to more than
12 years and the northern hemisphere land temperature anomaly from 1860 to 1990 (Friis-
Christensen and Lassen 1991; Christiansen et al. 2007); a shorter solar cycle, which could
indicate higher activity, corresponds to warmer conditions. This assumption is not based on
Table 1 continued
Timescale
Solar origin Climate effect Reference
1.3 years Solar dynamo bifurcation(observed periodicity insolar wind velocity)
May change atmospheric circulation andgeomagnetic activity
Paularena et al.(1995), Kudelaet al. (2002)
27 days Solar rotation results in 27 dayvariability of solar UVradiation
Photochemistry, composition (ozone),temperature and dynamics ofstratosphere are modified
Williams et al.(2001)
664 Surv Geophys (2011) 32:659–703
123
physical arguments and doubts have been expressed on its ability to represent solar irra-
diance (Solanki et al. 2000). The northern hemisphere surface temperature has been
considered because most of the southern hemisphere is covered with sea whose temper-
ature lags the air temperature over the ocean. Comparing Fig. 3a, b, lower temperatures
during the 1970 s occur when the Sun is less active than usual. There is breakdown in such
a correlation after 1985 which is attributed to an increasingly dominant effect of the
greenhouse gases responsible for global warming (Thejll and Lassen 2000). The surface
temperature (both sea and land) includes contributions from many factors besides green-
house gases, such as ENSO, volcanic eruptions, aerosol changes, and other anthropogenic
contributions.
Lean and Rind (2008) performed a multivariate analysis using the best available esti-
mates of natural and anthropogenic impacts on surface temperature, regionally as well as
globally, and showed that natural influences produce *0.2 K warming during major
ENSO events, *0.3 K cooling following larger volcanic eruptions and 0.1 K warming
1600 1700 1800 1900 2000 2100
0
25
50
75
100
125
150
175
200
Sun
spot
Num
ber
Year
(a)
(b)
Fig. 2 a Smoothed yearly variation of sunspot number for the period 1610–2003. The 11 year sunspotcycle modulated with a 88 year Gleissberg cycle is clearly seen with the distinct Maunder Minimum(1645–1715) when there were no visible sunspots (reproduced after Christiansen et al. 2007). b Thevariation of reconstructed total solar irradiance [black (Lean, 2000) and red (Gray et al. 2005)] for the period1610–2003 (after Gray et al. 2005)
Surv Geophys (2011) 32:659–703 665
123
near the maximum of the recent solar cycle. The temperature changes due to a volcanic
eruption last about 1–2 years (Trenberth et al. 2007). They showed that solar forcing
contributed negligible long-term warming in the past 25 years and 10% of the warming in
the past 100 years, not 69% as claimed by Scafetta and West (2008). Volcanoes inject a
large quantity of dust and gases such as SO2 into the atmosphere which lead to the
formation of aerosols. Enhanced aerosol amounts reduce the solar energy input to the
atmosphere resulting in cooling, over a period of about 2 years following a major eruption
(Hansen et al. 1992).
Using ocean heat content anomaly data for the period 1993–2008, Lyman et al. (2010)
estimated a global warming trend of 0.63 ± 0.28 W/m2. Positive radiative imbalance was
also reported by Trenberth and Fasullo (2010). Knox and Dauglass (2010) claimed that the
result of Lyman et al. (2010) is not representative of the recent (2003–2008) warming/
Fig. 3 a 11 year running mean of annual sunspot numbers (upper panel thin curve) from 1870 to 1985, andthe mean global sea surface temperature anomaly (lower panel thin curve) for the period 1860–1980. Thebold curve represents a 7th degree polynomial least squares fit to the data. The units for the lower curve are0.01 K departures from the 1951–1980 average (after Christiansen et al. 2007). b Solar cycle length (SCL, inyears, right hand scale, dotted curve) versus the northern hemisphere temperature anomaly over land(T, degrees K, solid curve) for the period 1860–2000 (after Christiansen et al. 2007)
666 Surv Geophys (2011) 32:659–703
123
cooling rate because of a flattening of ocean heat content anomaly data that occurred
around 2001–2002; using data only for the period 2003–2008, they showed a cooling, not
warming. Recently, Easterling and Wehner (2009) suggested that naturally induced surface
temperature changes can dominate current anthropogenic warming of *0.2�C per decade.
Lockwood and Frohlich (2007) demonstrated that the observed rapid rise in global mean
temperature after 1985 cannot be ascribed to solar variability, no matter how much the
solar variation is amplified. Lockwood et al. (2010a, b) presented evidence for a long term
drift in the solar UV irradiance, which produces intense effects on both stratospheric and
tropospheric winds and temperature. As a result a top-down climate effect may arise; this
may be out of phase with the bottom-up solar forcing (Lockwood et al. 2010a). In the top-
down model, climate–chemistry models in the stratosphere become very important and
could change the spatial response patterns. Thus top-down climate effects become very
important for making regional/seasonal climate predictions.
Solar irradiance affects the stratospheric temperature and zonal winds which also
exhibit 11 year solar cycle variations (Crooks and Gray 2005; Shibata and Deushi 2008;
Frame and Gray 2010). The response is a maximum in the tropical upper stratosphere
where the percentage change in ozone also has its maximum response to solar cycle
variations (Soukharev and Hood 2006). Gray et al. (2009) reported that half of the
temperature response is due to the solar irradiance change and half due to the additional
ozone feedback mechanism. A second region of statistically significant temperature
response is the tropical and subtropical lower stratosphere, indicative of changes in net
equatorial upwelling rates (Shibata and Kodera 2005; Gray et al. 2009). The polar winter
vortex in the Smax years is less disturbed and the polar lower stratosphere is colder than
average because of the weaker adiabatic heating in the descending arm of the Brewer-
Dobson (B-D) circulation. As a result, the return upwelling arm of the B-D circulation at
the equator is weakened, leading to less adiabatic cooling and a warmer equatorial lower
stratosphere.
Zonal winds in the upper stratosphere and lower mesosphere also show a positive
response with 11 year solar cycle changes, especially in the winter hemisphere (Crooks
and Gray 2005; Frame and Gray 2010). The zonal wind anomaly propagates downward
with time during winter seasons (Kodera and Kuroda 2002) and wave-mean-flow inter-
actions are involved in producing the response (Kodera et al. 2003). The easterly and
westerly zonal winds in the equatorial lower stratosphere exhibit the quasi-biennial
oscillation (QBO), showing the presence of solar activity effects in the stratosphere
(Labitzke 1987; Labitzke and van Loon 1988; Baldwin et al. 2001; Gray et al. 2010). It is
essential to improve our understanding of the vertical structure of the stratospheric tem-
perature response to solar cycle variations at tropical latitudes. Data of spectrally-resolved
irradiances from SORCE satellite/SIM (spectral irradiance monitor) measurements for
longer duration could be used to understand complex zonal wind and stratospheric tem-
perature anomaly. Recently Haigh et al. (2010) showed that the observed spectral changes
by the SIM instrument lead to a significant decline from 2004 to 2007 in stratospheric
ozone below an altitude of 45 km and an increase above this altitude. They have also
shown that solar radiation forcing of surface climate is out of phase with solar activity, a
finding contrary to the current expectations (Lean and Rind 2009; Cahalan et al. 2010).
Independent calibration issues of the SIM instrument remain, however.
The solar irradiance is linked with the magnetic variability of the Sun; however, the
physical process of linkage is not clearly understood, although magnetic observations have
been used to model the observed solar irradiance variability. The surface temperature,
stratospheric temperature and zonal wind show 11 year solar cycle variations.
Surv Geophys (2011) 32:659–703 667
123
2.2 Solar UV Changes and Ozone (Indirect Mechanism)
The total solar irradiance discussed in the previous section could explain the trend of the
changes in climate, but its magnitude and other effects were both underestimated (Stott
et al. 2003). Computations also showed an underestimate of the near surface temperature
(North and Wu 2001) and tropospheric temperature (Hill et al. 2001) by a factor of 2–3.
The deficiency requires us to look into other mechanisms, which may be termed indirect
mechanisms of solar irradiance, and also the direct influence of solar wind, energetic
charged particles and cosmic rays.
Ultraviolet (UV, wavelengths\300 nm) radiation reinforces the solar irradiance effect
via variations in ozone concentrations and a corresponding response in stratospheric
temperature and dynamics (Balachandran and Rind 1995; Shindell et al. 1999; Haigh 2003;
Matthes et al. 2004). Model computations show significantly less latitudinal structure and
an underestimation of the ozone concentration in the upper stratosphere, whereas in the
lower stratosphere computations match the observed changes (Shindell et al. 1999; Gray
et al. 2005). The computation of annual mean temperature changes for solar minimum and
maximum conditions with relevant total solar irradiance input did not compare well with
observations, specially the secondary temperature maximum near the equator in the lower
stratosphere and the temperature signal at high latitudes (Austin et al. 2003; Langematz
et al. 2005; Haigh et al. 2005).
Although UV radiation is a small portion of the total solar irradiance, it has a relatively
large 11 years SC (solar cycle) variation. About 100% variation is present at wavelengths
near 100 nm, 6% near 200 nm (where O2 dissociation and O3 production occurs) and up to
4% variation in the region 240–320 nm (where absorption of stratospheric ozone is
dominant) (Gray et al. 2010). Harder et al. (2009), based on measurements of solar spectral
irradiance (SSI) by the SORCE/SIM instrument, suggest that variations in UV may be
much larger (by a factor of 4–6) than previously assumed. Such a large variation may result
in a very different response in both stratospheric ozone and temperature (Roy and Haigh
2010). Enhanced UV radiation produces additional heating in the stratosphere, leading to
increased positive feedback discussed earlier. Even the additional production of ozone
reduces the downward shortwave fluxes and increases the long wave fluxes. Thus the
feedback mechanism is reinforced. The feedback mechanism driving global circulation
ultimately results in changes in lower atmospheric dynamics (Rind et al. 2002; Gleisner
and Thejll 2003), which may feedback on the chemical composition of the atmosphere;
because the reaction rates of the chemical species are temperature sensitive. Thus, a small
change of the solar UV radiation in the stratosphere could produce significant dynamical
change in the lower atmosphere and affect climate. However, the forcing due to feedback is
not properly understood and quantified; further data analysis and modeling are needed.
Smith et al. (2007) forecast a rapid warming after 2008. Keenlyside et al. (2008),
accounting for meridional circulation, forecast the opposite, ‘‘that global surface temper-
ature may not increase over the next decade, as natural climate variations in the North
Atlantic and tropical Pacific temporarily offset the projected anthropogenic warming’’.
Lean and Rind (2009) forecast that ‘‘from 2009 to 2014 projected rises in anthropogenic
influences and solar irradiance will increase the global surface temperature by
0.15 ± 0.03�C, at a rate 50% greater than predicted by IPCC’’ (2007). Lean and Rind
(2009) suggested that, as a result of declining solar activity in the subsequent 5 years, the
average temperature in 2019 could be only 0.03 ± 0.01�C warmer than in 2014. Benestad
and Schmidt (2009) predicted a likely contribution of solar forcing to global warming to be
7 ± 1% for the twentieth century, with negligible contribution to warming since 1980.
668 Surv Geophys (2011) 32:659–703
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The climate forecast is based on the current trend of variations in climate variables and
the dubious assumption that the response will continue to be linear over the next few
decades. Even processes which were unimportant in the recent past may become important.
For example, Arctic sea ice changes can lead to a rapid amplification of the high latitude
response and become important. An enhanced average surface temperature can cause ice
melting of the Himalayan region and produce nonlinear changes in local and global climate
response. Abrupt industrialization and sudden commencement of war will also cause an
anthropogenic effect larger than predicted by a linear response. An enhancement in pol-
lution will reinforce changes in climate variables which may not be linear (Gray et al.
2010). These factors make climate modeling and forecasting a very difficult task.
2.3 High Energy Charged Particles (Solar Wind, Magnetospheric Energetic
Charged Particles)
Plasma and energetic charged particles emitted from the Sun in the form of the solar wind,
solar energetic particles (SEPs), solar flares, and galactic cosmic rays (GCRs) could also
play a role in the Earth’s weather/climate. The solar wind coupled with the magnetosphere
drives ionospheric currents at high latitudes (Rycroft et al. 2000; Singh et al. 2004, Siingh
et al. 2005, 2007, 2008; Tinsley 2008) producing modifications to the global electric circuit
(GEC), which may couple to the neutral atmosphere. North Atlantic Oscillations (NAO),
possibly a manifestation of solar wind electric field effects on atmospheric pressure over
the North Atlantic, cause significant climate variations over a wide range of scales in the
Atlantic sector (Marshall et al. 2001). It is claimed that the effect of solar wind induced
geomagnetic activity on the stratosphere is comparable to that of UV flux variations
between solar minimum and maximum (Arnold and Robinson 2001; Bucha and Bucha
1998). The coupling solar wind-magnetosphere-ionosphere-atmosphere could be through
charged particles precipitating from the magnetosphere or through magnetospheric current
systems closing in the ionosphere and thereby depositing Joule heating energy to the
thermosphere, the uppermost layer of the atmosphere. These particles could ionize the
atmosphere, changing the number density of ions and changing the vertical current flowing
in the GEC. The effect could fluctuate by several orders of magnitude between quiet times
and magnetic storm/substorm conditions. Christiansen et al. (2007) have shown that the
energies involved are extremely small compared to the total solar irradiance or flux of UV
radiation; thus, some amplifying mechanism (currently unknown) is required for the
magnetosphere-ionosphere system to have a significant impact on the lower atmosphere,
the troposphere, and hence on climate.
The energetic charged particles both of solar origin and precipitated from the magne-
tosphere during wave-particle interactions create ionization directly or via bremsstrahlung
radiation in the middle atmosphere. They can cause an effect on the GEC (Farrell and
Desch 2002), and alter atmospheric chemistry by affecting the concentration of NOx, O3
and sulphate aerosols (Jackman et al. 1995, 2006). Thus the Earth’s radiation budget could
also be affected. The charged particles could also affect the nucleation of water droplets
and the formation of clouds (Tinsley 2000). Energetic solar proton events could produce
small holes in Arctic ozone layer (Shumilov et al. 1995; Gray et al. 2009), could trigger the
formation of cirrus clouds (Gray et al. 2005), which decrease the flux of solar radiation to
the Earth’s surface and hence affect the evolution of weather systems (Pudovkin and
Babushkina 1992).
Energetic electrons of magnetospheric origin produce NOx in the mesosphere and
thermosphere, which could be transported by polar downwelling into the winter polar
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stratosphere where it can affect ozone abundances (Randall et al. 2005, 2006). Seppala
et al. (2004, 2007a) studied the relative contributions of ionization due to solar proton
events, energetic electron precipitation and low energy (1–10 keV) electron precipitation
on NOx production and its subsequent downward transport to the upper stratosphere. They
showed that NOx generated from the large solar proton storm (the 2003 Halloween event)
was transported into the upper stratosphere in agreement with model simulations, whereas
aurorally generated NOx descended later in the winter. Seppala et al. (2007b) showed that
night time NO2 enhancement during polar winters in the Arctic and Antarctic was well
correlated with high energy charged particle precipitation. They further showed that the
NO2 column density in both hemispheres was linearly correlated with geomagnetic activity
(Ap and Dst). Rodger et al. (2007) showed that lightning generated whistler-induced
electron precipitation was a significant source of energy input into some part of the lower
ionosphere but did not play a significant role in the neutral chemistry of the mesosphere.
Such effects are limited to the polar vortex zone. Outside this zone, the effect is insig-
nificant except during very large events (Thomas et al. 2007; Ganguly 2010). Some studies
(Callis et al. 2000, 2001; Langematz et al. 2005) reported that energetic particle induced
NOx effects on ozone at low latitudes may be comparable to the effects of solar UV
radiation, but this was not supported by the recent data analysis of Marsh et al. (2007).
Hence, based on present evidence/knowledge, it may be argued that the effect of energetic
particle produced NOx has little effect on the climate. Rigorous data analysis and simu-
lations are required to either accept or reject the above idea.
2.4 Cosmic Rays and Clouds
Cosmic rays also affect climate through the cloud cover/cloud formation, rather than direct
energy input into the atmosphere which is *10-5 times smaller than the solar input
(Frohlich and Lean 1998, Siingh 2008; Siingh and Singh 2010; Singh et al. 2010, 2011).
Ney (1959) originated the idea that cosmic ray changes could directly influence the
weather. Dickinson (1975) considered that the modulation of the galactic cosmic ray
(GCR) flux by solar activity might affect cloudiness and hence the climate. He proposed
that GCR-induced ions could trigger the formation of sulphate aerosols (and cloud con-
densation nuclei, CCN) and hence could influence cloudiness. Tinsley (2000) proposed
another route through the global electric circuit (GEC). GCR-induced ions in the atmo-
sphere modify the vertical current flowing through the atmosphere. The vertical current
passing through clouds causes charging of local droplets and aerosols at the lower and the
upper boundaries of the clouds (Tinsley 2000), as recently observed at a lower cloud
boundary by Nicoll and Harrison (2010).
Charging can modify the microphysics of clouds and hence the GEC could provide a
possible link between solar variability and clouds. The vertical current Jz of the GEC is
known to flow through regions of water droplets and clouds. Bennett and Harrison (2009)
argued that the vertical conductivity gradient could be greater than 10 fSm-2 at the cloud
boundary. During cloudy (overcast) conditions the vertical current density was found to be
inversely proportional to the cloud optical thickness (Nicoll and Harrison 2010). Thick
cloud reduced Jz by 12% compared to thin cloud. Recently Harrison and Usoskin (2010)
showed that the lower troposphere vertical current density and potential gradient were
significantly increased at CR maximum (solar minimum), with a proportional change
greater than that of the CR change.
The net effect on climate depends on the altitude of the GCR-created clouds. If the
cloud is enhanced at lower altitudes then the dominant effect would be increased reflection/
670 Surv Geophys (2011) 32:659–703
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scattering of incoming short wave solar radiation due to their high albedo, leading to a
cooling effect. However, in the case of enhancement of cloud amounts at higher altitudes,
that would cause trapping of outgoing long wavelength radiation, resulting in a warming of
the atmosphere.
Svensmark and Friis-Christensen (1997) analysed International Satellite Cloud Clima-
tology Project (ISCCP) data and neutron monitor cosmic ray data. They reported a cor-
relation between cosmic ray variations and low altitude cloud cover variations. Sun and
Bradley (2002) reanalyzed the same data sets, included additional satellite-based and
ground-based data; they showed that no meaningful relationship between cloud cover and
GCR could be found. They extended the analysis for cloud cover for the period 1987–1993
and separated the low, middle and high altitude cloud level analyses for the data collected
over the North Atlantic (0�–60�N). They reported correlation coefficients between GCR
and cloud cover to be 0.19, 0.07 and 0.00, respectively. Laut (2003) re-analysed the graphs
of Svensmark and Friis-Christensen (1997) and Svensmark (1998) and showed that the
apparent strong correlation displayed on these graphs might be due to incorrect handling of
the data.Svensmark (2007) compared data from the International Satellite Cloud Climatology
Project and the Huancayo cosmic-ray station and showed that cloud cover and cosmic rays
show almost no correlation at high ([6.5 km) and middle (6.5–3.2 km) altitude, but a good
match at low altitudes (\3.2 km). Figure 4 shows the variation of low altitude cloud
amount and change in cosmic rays for the period 1982–2005 (Svensmark 2007). He
considered that plenty of cosmic rays are always present at high altitudes in the atmosphere
but they and the ions that they produce are in short supply at low altitudes; thus, increases/
decreases in the cosmic ray flux due to changes in scattering by fluctuations of the
interplanetary magnetic field may have more consequences at lower altitudes than at high
altitudes. Svensmark et al. (2007) claimed that electrons released in the atmosphere by
cosmic rays act as good catalysts and significantly accelerate the formation of stable ultra-
small clusters of water molecules and sulphuric acid, the building blocks for the cloud
condensation nuclei. However, this mechanism does not have quantitative support (Sorokin
and Arnold 2009).
Low level clouds cover more than a quarter of the Earth and exert a strong cooling
effect at the Earth’s surface. Figure 4 shows that a change of *2% in low altitude clouds
during the 23rd solar cycle could result in a *1.2 W m-2 change in the solar energy heat
input at the Earth’s surface. Using this argument, one may try to explain some part of
global warming in the twentieth century as a reduction in cosmic ray fluxes which may
have caused a decrease in low cloud coverage. This argument is also favoured if one looks
into the past history of terrestrial climate. Figure 5 shows changes in the flux of galactic
cosmic rays from 1700 to 2005 (Svensmark 2007). Lower altitude cloud amounts (from
Fig. 4) normalized to observational cosmic ray data from Climax for the period 1953 to
2005 are also shown in the figure. The high cosmic ray activity during the Maunder
Minimum (1645–1715), when the area of sunspots was extremely small and the solar
magnetic field was exceptionally weak, coincided with the coldest phase of climate known
as the Little Ice Age (Eddy 1976; Kirkby 2007; Siingh 2008; Siingh and Singh 2010; Singh
et al. 2011). The Dalton minimum of the early nineteenth century, another cold phase, also
coincides with the enhanced cosmic ray counts. Svensmark (2007) claimed that Fig. 4
reveals the overall trend in the past, between cold 1700 and warm 2000 years, of high
resolution climate-related switch between high and low cosmic ray counts. Even the
reconstruction of the sea surface temperature anomaly and relative cosmic ray flux during
the past 600 million years show four switches from warm ‘hot house’ to cold ‘ice house’
Surv Geophys (2011) 32:659–703 671
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during the Phanerozoic periods. This was explained in terms of four encounters of the solar
system with spiral arms of the Milky Way galaxy which might have resulted in locally
enhanced cosmic ray fluxes (Shaviv and Veizer 2003; Svensmark 2007).
Kernthaler et al. (1999) studied the possible relation between GCRs and different types
of clouds; they could not find any clear relationship between individual cloud types and
GCR. Erlykin et al. (2009a) also searched for cosmic ray effects on major low level cloud
components (stratiform and cumuliform) considering different sensitivities to the cloud
condensation nuclei. Cumuliform clouds have much higher upthrust velocities and hence
are expected to be less affected by the cosmic ray induced cloud modification. However,
even this approach of dividing low level clouds into stratiform and cumuliform could not
produce any support for the GCR and low cloud hypothesis. The evidence of long term
correlation between low cloud cover (LCC) and the absence of short term correlation
indicates that there is no direct causal connection between GCR and LCC. Erlykin et al.
(2009b) proposed that, under certain circumstances, GCR-produced showers could leave a
signature in cloud ‘‘near vertical or cigar shaped clouds’’. However, such structures have
not yet been observed. Sloan and Wolfendale (2008) analysed the ISCCP infrared data for
cloud cover and GCR data during the period of solar cycle 22 and estimated that less than
23% of the 11 year cycle change in the globally averaged cloud cover is due to the change
in the rate of ionisation from the solar modulation of cosmic rays.
The hypothesis of cloud cover and GCRs based on the variations in the concentration
and efficiency of CCN through ionization during a Forbush decrease (Fd) was also studied
(Palle Bago and Butler 2001; Harrison and Stephenson 2006; Todd and Kniveton 2001,
2004; Kniveton 2004; Kristjansson et al. 2008; Harrison and Ambaum 2010). Palle Bago
and Butler (2001) analyzed ISCCP cloud data and Irish radiation data combined but could
not find any correlation between cloud cover and Fd. Harrison and Stephenson (2006) used
radiation measurements to infer cloud cover and reported a positive correlation between
cloud cover and Fd for UK sites. Significant correlations between the Fd and high clouds at
high altitudes from ISCCP data were reported in several studies (Todd and Kniveton 2001,
2004; Kniveton 2004). However, significant inhomogeneities in the ISCCP data sets have
been pinpointed (Evan et al. 2007).
Kristjansson et al. (2008) considered the high spatial and spectral resolution of MODIS
(Moderate-resolution Imaging Spectro-radiometer) data to study cloud microphysical
Fig. 4 Variation of low cloud cover and cosmic ray counts (monthly average counts) (red line) recorded atHuancayo for the period 1985–2005 (after Svensmark 2007)
672 Surv Geophys (2011) 32:659–703
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parameters such as droplet size, water content and optical depth, in addition to cloud cover.
They analyzed data corresponding to 22 Fd events but failed to find statistically significant
correlations between GCRs and any of these four cloud parameters. They considered
separately six intense Fd events but even in these cases a consistent GCR signal in cloud
parameters could not be deciphered. Kristjansson et al. (2008) commented that ‘‘whether
such a signal exists at all can not be ruled out on the basis of the present study, due to the
small number of cases and because the strongest Fd events indicate slightly higher cor-
relations than the average events’’.
Calogovic et al. (2010) analysed cloud data during six Fd events in detail but could not
find any response of global/local cloud cover to Fd at any altitude and latitude. Svensmark
et al. (2009) analysed data for low clouds during 26 Fd events and claimed significant
reductions in cloud water content (SSM/I data), cloud cover (MODIS, ISCCP data) and
aerosol concentrations (AERONET). Laken et al. (2009) re-analysed the liquid water cloud
fraction (LCF) data obtained by MODIS and found the LCF variations to be unrelated to Fd
events. Thus, the relationship between GCRs and clouds could not be established defini-
tively for these specific periods of satellite-retrieved cloud data. However, this does not
rule out the significance of GCRs in cloud formation processes.
Cosmic rays produce ionization in the atmosphere, which maximizes at around
10–20 km altitude (Neher 1971). Hence any likely effect should be in the high altitude
cloud rather than in low level cloud as claimed (Svensmark 1998, 2007). Erlykin et al.
(2009c) tried to estimate the effect of extra-ionization produced in the atmosphere on
clouds by ionizing agents—cosmic rays, radon and nuclear fallout—but, in a broad search,
could not find evidence for a role of ions in enhancing the probability of cloud droplet
formation, at least in the lower atmosphere (below 3.2 km altitude).
Simulation results of Yu and Turco (2001) showed that charged clusters have a larger
probability of resisting evaporation than uncharged ones, indicating a beneficial influence
on particle formation by GCRs. Kazil et al. (2006) and Yu et al. (2008) presented
Fig. 5 Variation of changes in galactic cosmic ray fluxes derived from 10Be data (light blue), open solarcoronal flux (blue) and low altitude cloud amount (orange) from Fig. 4 scaled and normalized to theobservational cosmic ray data from Climax (red) for the period 1953–2005; note that both scales areinverted (after Svensmark 2007)
Surv Geophys (2011) 32:659–703 673
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simulation results under various atmospheric conditions and found support for the role of
electrical charge for aerosol nucleation. However, no physical process is known through
which the nucleated aerosols could grow to large enough sizes to be of climate relevance.
The hypothesis of GCR-induced ionisation and formation of CCN has been tested in a
climate model that calculates aerosol microphysics in response to GCR (Pierce and Adams
2009). They found that GCR-induced changes in CCN are two orders of magnitude too
small to account for observed changes in cloud properties. Carslaw (2009) has discussed
the negative result of Pierce and Adams (2009) and argued that CCN may be more
sensitive and model dependent and there could be different models/different conditions not
explored by them, where GCR-induced CCN may account for observed changes in cloud
properties. He has also suggested that ‘‘ion-aerosol near-cloud’’ mechanism (Carslaw et al.
2002) is more probable and attention should be shifted to this mechanism.
The observed long term correlation between GCRs and LCC could be explained con-
sidering the influence of solar activity (Erlykin et al. 2009d). The enhanced solar irradiance
causes a rise of the mean surface temperature (Christiansen et al. 2007) and results in
enhanced vertical convection. From below 3 km warm air rises to greater heights and
causes the LCC to decrease and the medium cloud cover (MCC) to increase. During high
solar activity the strength and turbulence level of the interplanetary magnetic field becomes
high, leading to an enhanced shielding of GCRs. Thus, the flux of GCRs reaching the
Earth’s surface decreases. In this way GCRs and LCC both decrease during high solar
activity. Based on this argument no causal connection between GCRs and LCC could be
established (Erlykin et al. 2009d).
The enhanced low altitude cloud coverage and consequent cooling is not valid over the
Antarctic ice sheets, which are dazzling white snow having a higher albedo than the cloud
tops. Snow surfaces act as a better scatterer than the cloud surface so that cloud cover
causes warming rather than cooling. Satellite and meteorological measurements confirm
this (Svensmark 2007).
The cloud cover, cosmic ray and climate hypothesis does not find support from data
analysis and interpretations. Kirkby (2007) reviewed the relation between solar/cosmic
rays and climate variability. He had discussed diverse reconstructions of past climate
change (last millennium, last 10 ky, last 3 My, last 550 My) revealing clear association
with cosmic ray variations recorded in cosmogenic isotope archives, providing evidence
for solar/cosmic ray forcing of climate variability. Physical mechanisms relating cosmic
ray and clouds were also discussed and the need for a thorough experimental study of the
fundamental microphysical interactions between cosmic rays and clouds was emphasised.
Finally he has discussed the upcoming experimental facility known as CLOUD (Cosmics
Leaving OUtdoor Droplets) at CERN (Switzerland). Some of the problems proposed to be
studied are ion-induced nucleation, growth of condensation nuclei into cloud condensation
nuclei, activation of cloud condensations nuclei into cloud droplets, ice particle formation,
collision efficiency of aerosols and droplets and freezing mechanism of polar stratospheric
clouds. Recently Duplissy et al. (2010) based on CLOUD experiments reported a few
events to be related to ion-induced nucleation or ion-ion recombination to form stable
neutral clusters. This seems to be inconclusive evidence on the role of small ions in cloud
formations in ambient atmospheric conditions. In the complex physical process induced by
cosmic ray produced ionisation, such as (a) the production of cloud condensation nuclei
acting as seeds for the cloud droplet formation and (b) modification produced in the GEC
which influences the cloud charging process, there are many unanswered questions (Gray
et al. 2005; Siingh and Singh 2010; Singh et al. 2011). Precise measurements of electrical
parameters from the top to the bottom of clouds, together with measurements of size of
674 Surv Geophys (2011) 32:659–703
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aerosol particles and droplet parameters and precipitation current could provide some
answers to the understanding of involved complex mechanism in cloud formation.
3 Lightning Discharges and Lightning Activity
3.1 Thunderstorms and GCR Induced Discharges
The occurrence of lightning discharges is a clear manifestation of processes that generate,
separate and lastly neutralize electrical charges in nature. The generation of charge during
thunderstorm development can be grouped under inductive and non-inductive charging
mechanisms. The inductive process relies on the pre-existing electric field to induce
polarisation charges in suspended particles in the atmosphere so as to enhance the electric
field (Yair 2008; Saunders 1993, 2008; Siingh et al. 2008). Such polarisation in the
presence of fair weather electric field will cause an excess positive charge to accumulate in
the lower part of the particle, while negative charge will be located preferably in the upper
part. The non-inductive process is independent of electric field and charge transfer takes
place during particle collisions, drop break–up, ion charging and convection process
(Saunders 2008). During graupel and ice particle collisions, the smaller ice crystals are
charged positively and carried to the upper region whereas large graupel particles charge
negatively and descend relative to the smaller particles due to gravity (Yair 2008; Siingh
et al. 2008). Charging takes place due to temperature differences of the interacting particles
(Saunders 1993). Drop break-up charging occurs when a bubble containing positively
charged liquid inside collides with stronger particles/bubbles. Bursting of such bubbles
releases positively charged jet droplets which are carried into clouds by the local air
current (Blanchard 1963). In the ion charging process, ions produced by energetic particles
are selectively captured and transported to the cloud site (Saunders 2008) by natural
convection. Charge separation is a very sensitive function of temperature, and usually
occurs between the levels where the temperature lies between 0�C and -40�C (Saunders
2008).
Experimental evidence as well as Monte-Carlo calculations suggest that the electric
field strength in the clouds is much lower than the threshold electric field required to
initiate electrical breakdown (Marshall et al. 2005; Dwyer et al. 2006; Stolzenburg et al.
2007). This inhibits the application of the breakdown discharge process to explain the
numerous lightning discharges observed during the active period of a thunderstorm.
Gurevich and Zybin (2001, 2005) proposed the runaway breakdown mechanism operating
at a lower excitation threshold to explain lightning discharges. In the runaway mechanism,
high energy particles (GCRs) in the presence of an electric field generate secondary
electrons of various energies due to the ionization of neutral molecules. The secondary
electrons having sufficiently high energy are accelerated by the electric fields to become
‘‘runaway’’ electrons. This process is repeated many times and, as a result, an exponential
growing avalanche of runaway electrons develops. Simultaneously an immensely large
number of slow electrons are also generated which leads to electric breakdown. In the air,
the conventional breakdown electric field at atmospheric pressure is *23 kVcm-1 and the
runaway breakdown electric field is *2.16 kVcm-1 (Gurevich et al. 2009a). Accurate
measurements of electric fields in thunderstorm show that the field strength never reaches
the value required for conventional discharges (Marshall et al. 2005; Stolzenburg et al.
2007). Thus, runaway breakdown is much more probable than the conventional breakdown
in creating lightning discharges in a thunderstorm. GCRs with energies *1014–1016 eV
Surv Geophys (2011) 32:659–703 675
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could initiate extensive air showers (EASs) and produce energetic secondary electrons and
initiate lightning discharges (Gurevich et al. 2009a, b; Chubenko et al. 2009; Chilingarian
et al. 2009). However, it is not yet possible to identify the percentage of lightning dis-
charges triggered by GCRs. This is a preliminary result and much more work is to be done
before this route of GCR effect on climate can be considered to be effective. It will also
need to be quantified.
A discharge in the presence of a large number of energetic electrons should be
accompanied by a strong pulse of gamma rays. In a number of experiments, a burst of
gamma radiation during thunderstorms has been observed (Eack et al. 1996; Chubenko
et al. 2003; Dwyer et al. 2005; Howard et al. 2008). Gurevich et al. (2009b) reported 4
events of runaway breakdown while an EAS passes through a thunderstorm. Pulses of
gamma radiation in coincidence with EAS were recorded. Exactly at the EAS trigger
moment a strong pulse of radio emission (0.1–30 MHz) frequency band is also measured.
One event was recorded during the initial phase of a thunderstorm and three during the
main phase.
Chubenko et al. (2009) measured the energy spectrum of gamma radiation during the
stepped leader of lightning and the total energy is estimated as *10-3 to 10-2 J. The
measurements were carried out at the Tien-Shan mountain cosmic ray observatory situated
at the height of 3.34 km above sea level, which is an average height of thunderclouds at
Tien-Shan. Thunderstorms are abundant above the observatory during May–September.
The spectrum of gamma-ray emissions observed in the balloon experiment (Eack et al.
1996) and at Tien Shan is in good agreement with the runaway breakdown theory. Pow-
erful emissions of terrestrial gamma ray flashes (TGFs) observed on the CGRO and
RHESSI satellites (Smith et al. 2005) are correlated with high-altitude lightning (Dwyer
and Smith 2005); therefore, they may have resulted from the runaway electron breakdown
effect.
3.2 Lightning Activity and Solar Activity
The dependence of climate on various factors associated with lightning has enhanced
interest in the study of lightning. NASA satellites using sensors such as OTD (Optical
Transient Detector) and LIS (Lightning Imaging Sensor) are continuously supplying
important information on lightning’s spatial and temporal distribution around the planet
(Christian et al. 2003). Analyses of these data yield a global flash rate 44 ± 5 flashes per
second (Christian et al. 2003; Ushino 2003). Higher flash rates have also been reported
(Kotaki and Katoh 1983; Mackerras et al. 1998). The lightning activity is mainly con-
centrated over the tropics; *78% of all lightning flashes occur between ±30� latitude.
There are far more over the land than over the ocean; the average land/ocean occurrence
ratio being about 10:1 (Christian et al. 2003).
Figure 6 shows the variation of sunspot number and lightning flash rates during the
period 1988–2010 (sunspot cycle 22, 23). Lightning flash rates are for the geographical
regions covering the USA, Brazil and the Indian Peninsular region. In the Indian Peninsular
region lightning flash rates and sunspot numbers show an opposite behaviour, whereas
observations at Sao Paulo (Brazil) do not follow that trend—they show maxima when the
sunspot number shows maxima (Pinto and Pinto 2008). There is a secondary maximum
when the sunspot number shows a decreasing trend. Lightning frequency over the USA
shows a minimum when the sunspot number is a maximum and then it shows an almost
constant behaviour (www.slac.stanford.edu/cgi-wrap/getdoc/slac-wp-02-ch11g-kirkby.pdf).
Thus, a complex relation between sunspot number and lightning flash rate is observed.
676 Surv Geophys (2011) 32:659–703
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Based on these limited data it is very difficult to draw any conclusion about the relation
between sunspot number and lightning activity.
Fritz (1878) made the first attempt to correlate thunderstorm frequencies with the rel-
ative sunspot numbers, analysing data collected at different stations in Europe and North
America between 1755 and 1875 and showed a positive correlation at some stations and at
others a negative one. Later on Brooks (1934) using worldwide data reported a low
correlation at mid-latitude and enhanced correlation towards the pole and the equator. He
also found the interesting result that the correlation coefficient changed significantly over
relatively short distances. Aniol (1952) based on data from South Germany between 1851
and 1950 reported an insignificant correlation coefficient (-0.02) for the whole period,
-0.55 for the years 1889–1913 and ?0.74 for the years 1923–1944.
Stringfellow (1974) studied the annual variation of 5 year running means of lightning
incidence in Britain and sunspot number for the years 1930–1973 and reported an
important result relating to an underlying cyclic variation with a period of about 11 year
and in phase with the solar cycle. The correlation coefficient was found to be *0.8.
Schlegel and Fullekrug (1999) showed that Schumann resonance (SRs) of the Earth-
ionosphere cavity and constituting the longest wavelength ELF waves are definitely
affected by solar activity such as solar electron events and solar proton events. On the other
hand, globally correlated amplitude variations of SRs were found to associate with the
solar rotation period (Fullekrug and Fraser-Smith 1996) and that was attributed to geo-
magnetic activity.
Schlegel et al. (2001) studied correlation between lightning frequency and solar activity
parameters (sunspot number, Ap and F10.7 indices, cosmic ray flux) using data from the
German lightning detection system between the years 1992 and 2000. They obtained a
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
10
20
30
40
50
60
70
80
90
100
110
120
130
140
0
25
50
75
100
125
150
175
200
225
250
(b) USA
(c) Indian Peninsular Region (Pereira et al., 2010)
(d) Sao Paulo (Brazil), (Pinto and Pinto, 2008)
(a) Sun Spot Number
Years
Lightning Flashes (X 103), Peninsular India Lightning Frequency (x 106/years), USA Lightning Flashes (X 103), Brazil
Mon
thly
Ave
rage
Sun
Spo
t Num
ber
Fig. 6 a Yearly variation of sunspot number; b yearly variation of lightning flash counts for Sao Paulo(Brazil) along with a linear fit and three point adjacent averaging smoothing (reproduced from Pinto andPinto 2008); c yearly variation of lightning flash counts for USA along with a linear fit and three pointadjacent averaging smoothing (www.slac.stanford.edu/cgi-wrap/getdoc/slac-wp-02-ch11g-kirkby.pdf);d yearly variation of lightning flash counts for the Indian Peninsular Region (India) along with a linear fitand three point adjacent averaging smoothing (from Pereira Felix et al. 2010)
Surv Geophys (2011) 32:659–703 677
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significant correlation of lightning frequency with Ap (geomagnetic index) and sunspot
number and a significant anti-correlation with CR flux. The study relating lightning
amplitude variation with solar/geomagnetic parameters did not yield significant results,
maybe because the amplitude is most probably controlled by meteorological factors and
the electrification process in thunderstorms. During the period of low CR fluxes the
atmosphere is less conducting and therefore a higher electric field can build up causing
more lightning. Even a reduced CR flux (enhanced solar activity) may affect the global
distribution of weather conditions (such as modifications of the vorticity area index, global
circulation pattern) causing thunderstorms.
Figure 7 shows the global distribution of lightning averaged over 9 years of observa-
tions of the NASA OTD (April 1995–March 2000) and LIS (January 1998–December
2003) satellite data. The annual global lightning activity peaks in the summer hemisphere
in agreement with the seasonal migration of the ITCZ (Inter Tropical Conversion Zone)
and the atmospheric circulation patterns (Price 2006). During spring and fall, the distri-
bution of lightning is fairly symmetric about the equator.
The diurnal variation (Fig. 8) shows that thunderstorms are generally active in the
afternoon local time sector as a result of solar forcing, which maximises between 1600 and
1700 h. However, over the oceans, the thunderstorms are equally distributed during the
daytime, since the ocean temperatures are mostly constant throughout the daytime whereas
the land surface temperature shows a strong diurnal variation. Figure 9 shows the annual
variation of total flash rates decomposed into land and ocean (Fig. 9a), northern and
southern hemisphere (Fig. 9b) and tropics and subtropics (Fig. 9c) (Christian et al. 2003).
Lightning activity over the oceans is about 5 flashes per second at all times. However, the
distribution over the land surface peaks in the months of July and August, varying between
31 and 49 flashes per second; the maximum flash rate for the northern hemisphere is
significantly larger than for the southern hemisphere. Sato et al. (2008) showed that
thunderstorms/lightning are mainly continental phenomena and occur more often in the
Fig. 7 Global distribution of lightning (annual flash rate) from 9 years of observations of the NASA OTD(1995–2000) and LIS (1998–2003) instruments (http://sprg.ssl.berkeley.edu/atmos/tgf/UCB-TGF.pdf)
678 Surv Geophys (2011) 32:659–703
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northern hemisphere summer than in the southern hemisphere summer. Figure 9c shows
lightning activity for the globe subdivided into zones of 5�S–5� N latitude, 10�S–10�N
latitude, 20�S–20�N latitude and 30�S–30�N latitude to separate the tropical and sub-
tropical contributions.
Satori et al. (2009) analysed Schumann resonances recorded at Nagycenk (Hungary)
and satellite data from the OTD and the LIS. They showed that more lightning was
observed in the tropical and extra tropical land regions during the warm El Nino periods,
especially in South East Asia, along with minor contribution from oceanic regions, a result
consistent with that reported in Fig. 9c. In the cold La Nina phase, global lightning is
slightly suppressed.
LIS/OTD observations do not provide information about the intensity of lightning fla-
shes (Christian et al. 2003) which is available from other observations (Baker et al. 1995,
1999; Schumann and Huntrieser 2007). Very high frequency (VHF) electromagnetic
radiation observed at ground stations has been used to collect lightning statistics which are
independent of day/night and ocean/land differences (Bondiou-Clergerie et al. 2004; Boeck
et al. 2004; Noble et al. 2004).
Recently Kumar and Kamra (2010) analyzed TRMM (Tropical Rain Measuring
Mission) satellite data over three islands (Carnicobar, Little Andaman and North
Andaman) situated in the south of the Bay of Bengal, in order to resolve the question of size
of an island which is able to exhibit a land–ocean contrast. They have shown that the flash
density and flash rate increase with the increasing area of island. Their results favour the
traditional thermal hypothesis but could not distinguish between the thermal and aerosol
hypothesis (Williams et al. 2004) for the enhancement of lightning activity over islands.
3.3 Lightning Activity, Rainfall and Surface Temperature
Lightning discharges are the result of electrical activity in thunderstorms. The electrical
activity and rainfall are associated with the microphysics and dynamics of deep convective
clouds (Williams et al. 1989), a result supported by a positive correlation between rainfall
and lightning activity in individual thunderstorms (Petersen and Rutledge 1998; Price and
Federmesser 2006; Gungle and Krider 2006). A quite different scenario emerges when data
are analysed on a regional basis over long time scales. The analysis of the long term data
from the TRMM satellite provide three thunderstorm ‘‘chimneys’’ which, ranked from the
most active to the least active, are 1- Africa, 2- South America and 3- South East Asia. The
ranking based on rainfall is 1- South East Asia, 2- South America, and 3- Africa (Christian
et al. 2003; Price 2000, 2009). Thus, the long term data display the opposite relationship
between regional/global lightning activity and rainfall. This paradox is also supported by
the presence of lightning maxima over Argentina, North America, South Africa and the
Himalayas, which are not regions with the highest rainfall (Price 2009).
Williams and Satori (2004) studied the differences between tropical Africa and
tropical South America; they showed that the main difference between lightning activity
in Africa and South America is due to Africa being hotter and drier than South America.
A drier surface may permit the development of a deeper reservoir of unstable air during
the day and can lead to higher cloud base heights and a suppression of warmer rain
coalescence between the 0�C isotherms (Williams et al. 2004, 2005). Carey and Buffalo
(2007) demonstrated that thunderstorms with predominantly positive lightning were
more probable in a drier climate. The above paradoxical behaviour may also be due to
differences in geography, meteorology or other local factors in these regions (Price
2009).
Surv Geophys (2011) 32:659–703 679
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In order to understand the relation between lightning activity and rainfall, model sim-
ulation studies have also been carried out. These show that lightning activity will increase
in a warmer climate (Price and Rind 1994; Grenfell et al. 2003; Shindell et al. 2006), a
result consistent with the suggestion based on the study of seasonal variations in lightning
activity on the Tibetan Plateau (Toumi and Qie 2004). Model studies also show that, for
every 1�K of global warming, a *10% increase in lightning activity may arise (Shindell
et al. 2006; Futyan and Del Genio 2007). Nath et al. (2009) analyzed satellite data for a
5 year period (1995–1999) over land and ocean regions of India and claimed that a cooling
of 1�C in the surface temperature results in the reduction of ~3.5 thunderstorms per month
per station.
This result is in accordance with the suggestion of Kandalgaonkar et al. (2005) that
every rise of 1�C in surface air temperature may correspond to a 20–40% enhancement of
the lightning flash density. Del Genio et al. (2007) showed that in a warmer environment
the frequency of thunderstorms decreases but the updrafts in the developed thunderstorms
are strengthened by *1 m/s due to a rise in the height of the freezing level; as a result the
developed thunderstorm will be much more intense and hence result in a higher lightning
flash rate. This is consistent with the observations of Williams et al. (2005), yet it con-
tradicts the claims of Williams et al. (2000) that changes of global lightning activity are
mainly caused by changes in the number of thunderstorms. Price (2009) reported that
‘‘drier climate conditions result in fewer thunderstorms and less rainfall; the thunderstorms
that do occur are more explosive, resulting in more lightning activity’’.
The role of surface temperature and humidity on the lightning activity has been
investigated and discussed briefly. Different regions on the surface of the Earth differ in
elevation and topography, atmospheric circulation patterns, proximity to the ocean,
vegetation, industrialization and pollution, all of which may influence the lightning
activity and hence climate. The effects of all these parameters on lightning activity
need to be studied and modelling studies including these factors should be carried
out.
Fig. 8 Diurnal variation (local time) of the global lightning over the land and oceans (http://sprg.ssl.berkeley.edu/atmos/tgf/UCB-TGF.pdf)
680 Surv Geophys (2011) 32:659–703
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Fig. 9 The annual cycle of global flash rate calculated with a 55 day moving average and decomposed into(a) land and ocean contributions, (b) northern and southern hemisphere contributions, and (c) tropical andsubtropical contributions (after Christian et al. 2003)
Surv Geophys (2011) 32:659–703 681
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4 Lightning, Aerosols and Climate
Studies conducted in different geographic locations on the globe show the urban area effect
on local weather (Orville et al. 2001; Steiger et al. 2002; Soriano and Pablo 2002). Almost all
of them indicate the possible role of air pollution. Westcott (1995) for the first time showed
the urban effect on enhancing cloud-to-ground (CG) lightning activity. Later on many
studies showed that the percentage of positive CG flashes decreases and the total number of
flashes (flash density) increases as an effect of urban area/pollution (Steiger et al. 2002;
Soriano and Pablo 2002; Naccarato et al. 2003; Koren et al. 2005; Stallins and Rose 2008;
Kar et al. 2009). However, no evidence of an increase in peak current of either positive or
negative CG flashes has been reported. These results are explained considering that urban air
pollution contains increased particulate matter/aerosols, which act as cloud condensation
nuclei (CCN). Urban pollution also affects the charge separation mechanism (Jayaratne et al.
1983) in thunderstorms and may enhance negative CG lightning activity (Steiger et al. 2002;
Kar et al. 2009). High concentrations of pollutants in supercooled cloud droplets lead to
negative charging of graupel at higher cloud temperatures, which could extend towards the
cloud base, covering the positive charge centre region (Pruppacher and Klett 1997). This
newly created stretched region of negative charge makes a tripolar charge distribution in the
thunderstorm (MacGorman and Rust 1998). As a result more negative CG flashes are pro-
duced. Enhanced negative CG flashes decrease the relative frequency of positive CG flashes.
Williams et al. (1999) proposed that under continental and polluted boundary layer
conditions, the available liquid water in the storm updraft is shared by an innumerable
number of small droplets, thereby suppressing the mean droplet size and thwarting the
coalescence process. The more available cloud water reaches the mixed phase region to
participate in excess cloud buoyancy, in precipitation formation, and in electric charge
separation and increased lightning activity (Orville et al. 2001; Williams et al. 2002).
Rosenfeld and Lensky (1998) and Kar et al. (2009) showed a deep mixed-phase zone and
ice forming at higher levels for clouds over polluted regions compared to less polluted rural
clouds.
Satellite measurements have shown a dramatic increase of the aerosol concentration over
many countries in Asia due to urbanization/industrialization (Lelieveld et al. 2001; Massie
et al. 2004). This might enhance deep convection and mixed phase processes leading to
elevated electrification and enhanced lightning activity (Zhang et al. 2004). Koren et al.
(2005) analyzed aerosol optical depth and cloud-top pressure data from satellites, studied
the correlation between the aerosol concentration and the structural properties of clouds, and
suggested cloud invigoration by aerosols/pollutants. Recently Altaratz et al. (2010) ana-
lyzed lightning data from WWLLN (World Wide Lightning Location Network) measure-
ments and aerosol and cloud data from Aqua-MODIS. They showed the intricate and
complex relations between the aerosols (biomass burning smoke particles) and lightning
activity in the Amazon basin. They have shown that low aerosol loading increases the CCN
concentration and produces invigoration of the clouds whereas higher values of aerosol
loading inhibit deep convection clouds and diminish their electrical activity.
Aerosols can also affect cloud properties by altering the microphysical processes of
clouds absorbing and scattering solar radiation by modifying the droplet size distributions.
This affects condensation and evaporation rates, latent heat release and collision coales-
cence efficiency (Rosenfeld et al. 2008). In the case of the Amazon basin, heavy smoke
from fires reduced the cloud droplet size (Altaratz et al. 2010) which resulted in a delay of
precipitation onset causing stronger updraft, large hail and intense convection (Andreae
et al. 2004). Cloud simulations have also shown that an enhancement in aerosols/pollutants
682 Surv Geophys (2011) 32:659–703
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in moist unstable atmosphere can induce clouds to develop stronger updrafts and down-
drafts, grow taller and trigger secondary storm development (Khain et al. 2005; van den
Heever et al. 2006). The absorption and scattering of solar radiation by aerosols result in
the cooling of the surface below the aerosol layer; this stabilizes the lower atmosphere. The
surface fluxes are suppressed, inhibiting deep convective cloud formation (Koren et al.
2004, 2008; Davidi et al. 2009).
The microphysical processes linking aerosols with cloud processes occur in two ways,
namely: (a) by the formation of cloud condensation nuclei (CCN) (Yu and Turco 2001) and
(b) by the modification of the population of freezing of super-cooled water drops (Tinsley
et al. 2001; Tripathi and Harrison 2002). CCN formation can take place via ion induced
nucleation. The observed major ions in the troposphere and stratosphere are complex cluster
ions, containing H2SO4, H2O, HNO3, (CH3)2CO and CH3CN molecules (Viggiano and
Arnold 1995). Out of these gases H2SO4 is a powerful nucleating agent. In the atmosphere,
the OH reaction with SO2 leads to the formation of H2SO4, which rapidly condenses on pre-
existing aerosols. Measurements of SO2 concentrations reveal a high degree of spatial
variability (Speidel et al. 2007). This implies highly variable concentrations of H2SO4 and
hence highly variable nucleation rates and particle growth rates. Thus, the formation and
growth of CCN depend on the spatial and temporal variations of SO2 concentrations in the
upper troposphere. Therefore, precise information on SO2 concentrations in the upper
troposphere and their spatial and temporal variability are needed for use in climate models.
The transport of H2SO4 aerosols to the lower troposphere as well as into the stratosphere
requires careful attention; such clouds markedly affect the Earth’s albedo.
In the second case, the electric field directly affects aerosol and cloud hydrometeor
processes on the edges of cloud boundary by modifying charge-mediated processes. Space
charges are created at cloud boundaries (Nicoll and Harrison 2010), and the region may
become turbulent. Increased ice nucleation and increased scavenging rates of aerosols may
take place. Measurements of the interactions between ions, aerosols and cloud particles are
required; these are difficult. Hence an alternate approach is to make such studies using
simulations and in laboratory experiments.
These discussions indicate that the spatial and temporal variations of the aerosol con-
tent, the availability of moisture in the troposphere and boundary layer instabilities are the
geographical variable phenomena that constrain thunderstorm formation and lightning
production (Stallins and Rose 2008). Even the concentration of aerosols induces cloud
formation in a very complex manner. Large aerosol concentrations may suppress cloud
formation. To investigate this hypothesis additional data from different geographical
locations over the short and long terms and simulation studies, including the contributions
of aerosol concentrations and their size distributions, are required.
5 Lightning, Upper Tropospheric Water Vapour and Climate
Atmospheric water vapour plays a key role in the atmosphere; it is an important trace gas
controlling weather and climate. It is the primary source of the hydroxyl radical in the
atmosphere (Levy 1971), and it influences the heterogeneous chemical reactions that
destroy stratospheric ozone (Rodriguez et al. 1988; Hofmann and Oltmans 1992). Thus an
increased water amount in the upper troposphere/stratosphere leads to a greater ozone loss
and decreased warming of the atmosphere. Climate models predict a 10% increase in water
vapour in the upper troposphere for every 1�K increase of temperature in that layer.
Some models predicted a 20% increase for every 1�K increase in surface temperature
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(Rind 1998). This sensitivity is greater than that predicted by the Clausius-Clapeyron
equation (*6% per 1�K change at 300�K) (Price 2000). This suggests that upper tropspheric
water vapour (UTWV) is greatly influenced by transport from the lower atmosphere.
The transport of water vapour during convection depends on temperature. An increase
in surface temperature enhances the intensity of convection which transports larger
amounts of water into upper level clouds and also increases the cloud height (Price 2000;
Price and Asfur 2006). Intense convection modifies the electrification processes in the
clouds and enhances the lightning frequency, which is correlated with the peak velocity of
updrafts (Baker et al. 1999). After the development stage, a thunderstorm decays which
results in the evaporation/sublimation of clouds and consequently a large amount of water
vapour is deposited in the upper troposphere. This process is shown schematically in
Fig. 10 (Price and Asfur 2006). They showed that about 6 h is required for the develop-
ment of a thunderstorm and the initiation of lightning discharges. A time lag of 8 h
between upper tropospheric cloud amount and upper tropospheric humidity has been
reported (Udelhofen and Hartmann 1995). However, Price (2000) reported a time lag of
*24 h between the lightning activity and upper tropospheric water vapour. Further, there
is a critical temperature (*295�K) above which the surface temperature has to be con-
sidered. Williams and Renno (1993) noted that at many tropical stations lightning activity
was not observed when the surface wet-bulb temperature was below 296 K. They reported
that an increase/decrease of surface temperature by 1 K could cause the lightning intensity
to increase/decrease by 25%. A similar sensitivity based on other studies was reported by
Williams (2005).
The correlations between lightning activity and climate variables (UTWV, humidity,
precipitation) are studied on diurnal temporal scales. It is not clear whether the same
Fig. 10 Schematic diagram showing the stages of development over a 30 h period of tropicalthunderstorms (Price and Asfur, 2006). a The morning temperature—density anomaly at the surface resultsin large-scale updrafts X. b In the afternoon the temperature rises, deep convection develops, continues intothe night hours and is accompanied by rainfall and lightning activity. The updraft also transports largeamounts of water into the upper troposphere. c During the decay of the storm, the rainout process and theentrainment with the dry environment lead to the evaporation-sublimation of the cloud. This deposits largeamounts of water vapour in the upper troposphere as shown in (d) (after Price and Asfur 2006)
684 Surv Geophys (2011) 32:659–703
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relation would hold for longer time scales such a monthly, seasonal, yearly and even longer
time scales. More studies covering different regions and data for longer time durations are
required to validate the dependence of lightning on UTWV and other variables. It is
believed that on long time scales enhanced tropical convection stabilizes the atmosphere
due to the vertical transport of water vapour to the upper troposphere where the maximum
warming is predicted to occur (Hansen et al. 1984). This is not in line with the proposed
mechanism of Price and Asfur (2006). Therefore detailed studies of lightning activity and
climate associated micro- phenomena are needed either to prove or disprove the hypothesis
of Price and Asfur (2006).
6 Lightning, Atmospheric Chemistry and Climate
The influence of lightning on climate occurs via the production of nitrogen oxides (NOx,
i.e. NO (nitric oxide) and NO2) followed by the production of ozone, one of the efficient
greenhouse gases. Since the initial proposal by von Liebig in 1827, lightning discharges
have been considered to be the largest natural sources of NOx production in the atmo-
sphere (Galloway et al. 2004; Schumann and Huntrieser 2007). The other sources are
microbial actions in the soil (Yienger and Levy 1995; Granier et al. 2004), oxidation of
atmospheric NH3 and the burning of biomass and fossil fuels (Olivier et al. 2005).
The global contribution to NOx from fossil fuel burning is *28–32 Tga-1, from bio-
mass burning *4–24 Tga-1, soil *4–16 Tga-1 (Lee et al. 1997), nitrogen oxide (N2O)
degradation in the stratosphere *0.1–1.0 Tga-1 (Martin et al. 2006), and aircraft exhausts
*0.7–1 Tga-1 (Eyers et al. 2005). Fossil fuel burning also includes NOx emission from
ships (*3–6 Tga-1) (Olivier et al. 2005). Lightning produced NOx amounts to *5 Tga-1
(Christian et al. 2003). The latitudinal distribution of NOx shows that the major contri-
bution comes from the northern hemisphere, in line with lightning activity. The share of
lightning contribution *10% on the global basis, but it rises to *23% in the 35�S–35�N
region (Bond et al. 2002; Neubert et al. 2008). The contribution from lightning was
computed using lightning data for the period 1998–2000 years and assuming production of
6.7 9 1026 and 6.7 9 1025 NO molecules for each cloud-to-ground and intra-cloud flash,
respectively (Bond et al. 2002; Neubert et al. 2008). The field experiment TROCCINOX
shows greatly enhanced concentrations in thunderstorms anvils and enhanced ozone
concentrations downwind (Huntrieser et al. 2007).
The global estimation of NOx production by lightning and thunderstorms is based on
only a few individual measurements; it remains highly uncertain because of the different
types of discharge and differing characteristics such as energy, length, peak current, tor-
tuosity, altitude, number of return strokes, etc. In addition to tropospheric discharges, the
upward discharges between cloud-tops and the ionosphere also contribute to the production
of NOx in the atmosphere (Enell et al. 2008; Sentman et al. 2008; Gordillio-Vazquez 2008,
Neubert et al. 2008; Siingh et al. 2008; Fadnavis et al. 2009). The NOx production per
upward discharge could be taken as *1023–1025 molecules (Neubert et al. 2008: Sentman
et al. 2008) and a total production *1031 molecules per year (based on three events per
minute), which is of the same order as the minimum production of NOx, N2O, N2O5 and
HNO3 by solar proton events during a quiet year (Singh et al. 2005). During an intense
thunderstorm, there could be a significant impact on the local production and budget of
NOx. Based on the measurements by the GOMOS instrument onboard the ENVISAT
satellite, Rodger et al. (2008) showed enhancements of NOx on a local basis but that was
not found on the larger regional scale.
Surv Geophys (2011) 32:659–703 685
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Nitrogen oxides critically affect the abundance of ozone (O3) and the hydroxyl radical
(OH) in the troposphere (Rohrer and Berresheim 2006). Figure 11 shows the dependence
of OH concentration and O3 production rate on the NOx mixing ratio (Ehhalt and Rohrer
1994; Schumann and Huntrieser 2007). Under clean air condition OH is mainly produced
by O3 photolysis and reactions of the resultant atomic oxygen with water vapour. Under
more polluted conditions, OH is also formed by the photolysis of NO2 during the oxidation
of carbon monoxide (CO), methane (CH4) and non-methane hydrocarbons (Jaegle et al.
2001; Olson et al. 2006).
Tropospheric ozone has an impact on the atmospheric radiative forcing (Hansen et al.
2005). Toumi et al. (1996) showed that a 100% increase in lightning activity enhances the
global mean radiative forcing via tropospheric O3 by *0.3 Wm-2. Hopkins (2003)
computed the global average total radiative forcing due to O3 formed by lightning induced
NOx (*6.5 Tga-1) to be *0.1 Wm-2. Model studies show an increase in lightning
activity and lightning induced NOx due to global warming (Price and Rind 1994; Unger
et al. 2006; Schumann and Huntrieser 2007). However, Stevenson et al. (2005) and
Sanderson et al. (2006) did not find any change in global lightning emissions over the
projected period 1990–2030, but found significant changes in their regional distribution.
Doherty et al. (2006) showed a correlation between the El Nino phenomena and ozone. In
fact both lightning occurrence and NOx increase during El Nino periods, explaining the
inter-annual ozone variability of *3% in the tropical upper atmosphere (Grewe 2007).
The reaction of NO2 with OH during daytime forms HNO3, and during the nighttime
NO2 reacts with O3 to form NO3 and oxidation of NO2 by NO3 forms N2O5 (van Noije
et al. 2006). The oxidation products leave the atmosphere in the form of ‘‘acid rain’’
(Logan 1983). Brasseur et al. (2006) have shown that an increased tropospheric water
vapour concentration may dampen O3 increases from increased lightning induced NOx.
Nitrogen oxides act as a feedback agent between lightning and climate, for which the
precise knowledge of the lightning-induced NOx production, distribution, and its role in the
production/destruction of ozone is important. In addition, a detailed understanding of the
oxidizing capacity of the atmosphere and the lifetime of the trace gases destroyed by
reactions with OH is essential in quantifying the feedback processes between climate and
Fig. 11 Variation of the OH concentration and the net production rate of O3 with the mixing ratio of NOx
shown. A steady state box model for the diurnal average in June at 10 km altitude and 45� latitude has beenused. The other parameters used are O3 mixing ratio: 100 nmol-1; H2O: 47 lmol mol-1 CO:120 nmol mol-1; CH4: 1,660 nmol mol-1 (Enhalt and Rohrer 1994; Schumann and Huntrieser 2007)
686 Surv Geophys (2011) 32:659–703
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lightning. There is large error in estimating the annual production of NOx by lightning
(Neubert et al. 2008; Schumann and Huntrieser 2007). The uncertainty range is much more
than the total production of NOx; hence future work should aim to reduce the uncertainty
range from the present value of ±3 Tga-1 (Schumann and Huntrieser 2007).
7 Atmospheric Conductivity, Lightning and Climate
The electrical conductivity of the atmosphere depends upon the distribution of both
positive and negative ions in the atmosphere. The principal source of ionization in the
lower and middle atmosphere is galactic cosmic rays; near the ground additional ionization
is produced due to the release of radioactive gases from the soil. Above 60 km altitude
solar far ultraviolet radiation becomes an important source. The solar cycle variation in
GCR flux could produce a 5% variation in the ionization of the troposphere and strato-
sphere at low latitudes and about 10–20% at high latitudes (Tinsley 2008). However,
during a magnetic storm, ionization due to energetic electron precipitation from the
magnetosphere, auroral electrons and auroral X-ray bremsstrahlung radiation, along with
that produced by proton bombardment (during solar proton events, SPEs) becomes sig-
nificant, especially in the high-latitude polar atmosphere (Kokorowski et al. 2006).
The conductivity of the upper stratosphere and mesosphere occasionally increases when
there is an unusually large flux of energetic solar protons (*100 MeV) within the polar
cap (Von Biel 1992), or at mid latitudes (Stephenson and Scourfield 1991) during a
relativistic electron precipitation (REP) events. Even lightning-generated whistler mode
waves cause the precipitation of energetic electrons which may enhance the electrical
conductivity locally (Singh et al. 2004; Siingh et al. 2005, 2007, 2008; Rycroft et al. 2007;
Inan et al. 1988, 2007). Upward lightning discharges (sprites, elves, blue jets, blue starters
and gigantic jets) from the top of a thundercloud to the lower ionosphere cause local
electrical breakdown of the atmosphere and produce a transient and localized enhancement
in conductivity in addition to that produced by precipitated energetic electrons (Inan et al.
1988, 1996, 2007; Singh et al. 2005; Fadnavis et al. 2009). The conductivity inside a
thundercloud and in the fair-weather region depends on geomagnetic latitude, height and
aerosol pollution, varying between *10-14 Sm-1 (at the surface of the Earth) and
*10-7 Sm-1 in the ionosphere (at *80 km height) (Rycroft et al. 2008). The enhance-
ment in conductivity may lower the ionosphere above a thundercloud sufficiently to trigger
an upward lightning discharge between the cloud and the ionosphere (Armstrong 1987).
Variations in the vertical conductivity of the troposphere and stratosphere cause vari-
ations in the fair weather vertical current Jz. Apart from the variations caused by external
sources, the vertical current is also affected by day-to-day variations in the ionospheric
potential due to changes in the thunderstorm sources, the highly electrified deep convective
clouds mainly over the humid low latitude land areas of Africa, the Americas and northern
Australia/Indonesia (Tinsley et al. 2007). Regional and global changes in the conductivity
of the atmosphere due to changes in natural and anthropogenic aerosol loading also cause
variations in the ionospheric potential and hence in the vertical current. The ionospheric
potential is positively correlated with both global temperature and global lightning activity
(Markson and Price 1999). Reeve and Toumi (1999) considered that a warmer atmosphere
leads to a more deep convection resulting in higher ionospheric potentials.
The current Jz causes space charges to be formed at the top and bottom of clouds. These
may affect their microscopic/macroscopic processes and droplet formation related to
clouds in the fair weather region, and may produce an integrated effect which could result
Surv Geophys (2011) 32:659–703 687
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into short term/long term variations in weather/climate (Tinsley 2000; Williams 2005;
Price and Asfur 2006; Siingh et al. 2007, 2008). The space charge created attaches to
aerosol particles, cloud condensation nuclei (CCN) and ice forming nuclei (Tinsley et al.
2001). An increase in Jz may cause the enhancement of electro-scavenging and ice for-
mation, depending on the droplet size distribution (Tinsley 2000), or modify droplet
evaporation (Harrison and Ambaum 2008). This enhanced electroscavenging of larger
CCN and aerosol particles may protect the smaller CCN and aerosol particles from
scavenging (Tinsley 2004) and may result in a narrowing of the droplet size distribution.
As a consequence of this, there may be both reduced precipitation and enhanced cloud life.
These two processes simultaneously compete and the net result depends upon local tem-
perature, the aerosol environment, the GCR flux and cloud dynamics.
Measurements show the presence of droplet of radii *6 to 8 lm and positive charges of
80–90 electronic charges (e) in downdrafts at cloud tops and droplets of negative charges
(50–90 e) in updrafts near the cloud base (Beard et al. 2004). The magnitude and sign of
the charges near the cloud boundaries are consistent with the calculations of droplet
charging caused by the flow of Jz through clouds (Zhou and Tinsley 2007; Tinsley et al.
2007). The charging time constants range from minutes to hours, which are comparable
with the characteristic time scales of convection and turbulence in the troposphere (Tinsley
2008). This shows the need to develop a time dependent cloud and aerosol charging model,
including turbulence and convection processes.
Figure 12 shows variation of surface air temperature and observed thunder days in
Fairbanks, Alaska (65�N) for the period 1950–2005 (Williams 2009); a linear fit to the data is
shown. The variation has been considered for summer months (June, July, August and
September). The data show an increase of about 2�C over 68 years (Williams 2009) which is
correlated with the increase in the number of thunderstorm days. The enhancement in
thunderstorm activity as the surface temperature increases is in accordance with the
hypothesis that surface warming enhances the convection process and thunderstorm activity.
The response of atmospheric conductivity/vertical current and lightning to climate and
vice versa are known based on the analysis of short periods of data and different regions on
the Earth’s surface. Because both climate and lightning on a local/regional basis are also
influenced by a number of factors such as topography, ocean currents, atmospheric cir-
culation, vegetations, precipitation pattern, etc.; isolated regional and short term studies do
not reflect the global and long term behaviour. Therefore any modelling work on the basis
of short period input data would represent its applicability to that region only. For global
modelling, one requires input data over larger areas of the globe and the trend should be
valid for the long term.
Atmospheric temperature changes show correlations with thunderstorm activity and also
with the measured or inferred changes in the Earth-ionosphere vertical current density Jz.
These responses are consistent in onset time, duration and sign of the response. The pro-
duction of electric space charges at the conductivity gradient at the edges of clouds by the
flow of Jz through them (Nicoll and Harrison 2010) needs to be related to the electrical
scavenging and other cloud processes. However, their quantitative overall effect on cloud
formation, and the cloud cover at different latitudes and altitudes, remains to be worked out.
8 Summary
In this paper, we have briefly summarized our present understanding of the terrestrial
climate as affected by solar forcing, galactic cosmic rays (GCRs), lightning discharges and
688 Surv Geophys (2011) 32:659–703
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associated processes. The importance of solar irradiance on climate has been reviewed; it is
shown that the ‘‘little ice age’’ and other climate variations could be explained based on
observations/modelled variations in the intensity of galactic cosmic rays (Sect. 2.4).
Cosmic rays may influence the complex electrical processes involved in cloud develop-
ment. The tops of clouds scatter the Sun’s radiation back into space, producing a cooling
effect, and high level clouds reflect back long wavelength thermal radiation emitted by the
Earth, leading to a warming effect (Sect. 2.4).
Solar irradiation varies according to solar magnetic activity; however, the physical
processes linking magnetic activity to solar irradiance is not worked out. Solar irradiance
measurements, the trend of the variations and anthropogenic effects have been used to
forecast global warming over the next decades. Non-linear processes, which are hard to
model, are involved. The complexity of the physical processes involved, deficiencies in
long term data sets and further refinements in data analysis techniques require further
research to improve our understanding of climate forcing by the Sun and other processes.
Enhanced solar activity results in a decreased cosmic ray intensity reaching the Earth’s
surface due to enhanced GCR scattering by irregularities of the interplanetary magnetic
field. The correlation between GCR and cloud cover was initially proposed to be the result
of cosmic ray induced cloud formation. But reanalyses of the same satellite cloud data and
grouping them under different types of cloud did not support the cosmic ray induced cloud
cover hypothesis (Sect. 2.4). Even the detailed analysis using different cloud parameters
during Forbush decreases (sudden reduced cosmic ray intensity events) did not support the
cosmic ray cloud cover hypothesis. However, some weak signatures of GCR in cloud cover
during a rather small number of relatively intense Forbush decreases events are observed
1950 1960 1970 1980 1990 2000 2010
0
5
10
15
20
Num
ber
of T
hund
er D
ays
Years
Linear Fit
Summer Thunder Days,Fairbanks, AK
52
54
56
58
60
62
Tem
pera
ture
(0 F)
Linear fit
Summer Average Temperature,Fairbanks, AK 1950-2005
Fig. 12 Variation of a summer time surface air temperature (upper panel) and the number of thunder days(June, July, August, September) (lower panel) for Fairbanks, Alaska (after Williams 2009)
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(Sect. 2.4), or in analysis of non-satellite cloud data specific to a single location. There is
some experimental evidence that the initiation of lighting discharges may be caused by
high energy cosmic ray particles (Sect. 3.1). Enhanced solar activity causes enhanced
vertical convection (due to the increased Earth surface temperature) leading to increased
medium and high altitude cloud cover whereas low altitude cloud cover is decreased
(Sect. 2.4).
Recent observations show that, due to an increase in the Earth’s surface temperature,
both thunderstorm occurrence and lightning flash rates are enhanced, while model studies
show that the thunderstorm rate will decrease but the storms will be vigorous and may
produce higher flash rates (Sect. 3.2). Such model studies have not yet included the role of
aerosols which play significant role in cloud formation processes. Even the small, 1�C
change in surface temperature associated with global warming is expected to change the
aerosol distribution in the troposphere and the vegetation of the land surface, affecting the
distribution of humid air and the rainfall (Sect. 3.3). These parameters will affect thun-
derstorm formation and dynamics; their role should be included in any realistic model
study. Further, our present knowledge of lightning-climate relationships is based on very
limited data in space and time. Hence much more data collection and analysis, along with
theoretical studies, are required to understand future trends of lightning activity and their
relation to climate.
The enhanced surface temperature leads to enhanced convection and enhanced upward
transport of water vapour, creating a drier land surface (Sect. 5). Observations show that
intense rainfall is associated with less lightning and vice versa. These observations are
explained by assuming that drier climates have fewer but more intense thunderstorms, as
confirmed by model studies. Increased convective activity moistens the upper troposphere
by transporting additional water vapour upwards (Price and Asfur 2006). This enhanced
‘‘moisturising’’ due to UTWV may drive the maximum warming in climate models in a
double-CO2 world (Price 2009). The enhancement in surface temperature will also affect
the distribution of aerosols in the troposphere resulting in enhanced lightning activity
(Sect. 4). Thus, efforts should be made to separate the thermodynamic and aerosol effects
quantitatively in future modelling work.
Lightning discharges are a major source of NOx production in the atmosphere, which
affects the stratospheric ozone population, a major controller of solar ultraviolet radiation
reaching the Earth’s surface. The latitudinal and longitudinal distribution of lightning cause
similar variations in the production of atmospheric NOx. The spatial and temporal varia-
tions of aerosol content, UTWV, pollution, NOx, local surface temperature and boundary
layer instabilities are geographically varying phenomena that constrain thunderstorm
formation and the production of lightning. Hence, extensive study is required taking data
from different regions in order to arrive at more precise conclusions (Sects. 5, 6).
Meteorological effects, such as cloud cover changes, temperature changes in the tro-
posphere, and changes of UTWV in both disturbed and fair weather parts of the Earth
affect the electrical activity of thunderstorms/lightning discharges and the vertical con-
duction current density, Jz. The responses in the vertical current with changes in solar
activity and internal forcing are consistent in onset time, duration and sign of the current
(Sect. 7). The microphysical phenomena involved in the formation of cloud condensation
nuclei, the ion-assisted formation of ultrafine aerosols, electro-scavenging of ice-forming
nuclei, ice nucleation capability of charged aerosols, and the effects of cloud droplet charge
on precipitation efficiency are not yet quantitatively tested. More work in this area is
required.
690 Surv Geophys (2011) 32:659–703
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Acknowledgments This work was inspired by discussions which DS had with Prof. B.N. Goswami andsuggestions given by him during his previous work on the role of cosmic rays in the Earth’s atmosphere.RPS acknowledges the facilities provided by the Head, Department of Physics. BHU, Varanasi. The authorsthank the four anonymous reviewers for their critical comments which helped to improve this paper. Theyalso express their gratitude to Prof. M. J. Rycroft for his valuable suggestions.This work was supportedunder the collaboration programme of IITM, Pune and BHU, Varanasi, and also partially supported underCAWSES programme (DS). The authors thank Mr Kirankumar Johare for help with correcting the figures.
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