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Semi Empirical Model of Global Warming Including Cosmic Forces, Greenhouse Gases, and Volcanic Eruptions


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In this paper, the author describes a semi empirical climate model (SECM) including the major forces which have impacts on the global warming namely Greenhouse Gases (GHG), the Total Solar Irradiance (TSI), the Astronomical Harmonic Resonances (AHR), and the Volcanic Eruptions (VE). The effects of GHGs have been calculated based on the spectral analysis methods. The GHG effects cannot alone explain the temperature changes starting from the Little Ice Age (LIA). The known TSI variations have a major role in explaining the warming before 1880. There are two warming periods since 1930 and the cycling AHR effects can explain these periods of 60 year intervals. The warming mechanisms of TSI and AHR include the cloudiness changes and these quantitative effects are based on empirical temperature changes. The AHR effects depend on the TSI, because their impact mechanisms are proposed to happen through cloudiness changes and TSI amplification mechanism happen in the same way. Two major volcanic eruptions, which can be detected in the global temperature data, are included. The author has reconstructed the global temperature data from 1630 to 2015 utilizing the published temperature estimates for the period 1600 – 1880, and for the period 1880 – 2015 he has used the two measurement based data sets of the 1970s together with two present data sets. The SECM explains the temperature changes from 1630 to 2015 with the standard error of 0.09 ⁰C, and the coefficient of determination r2 being 0.90. The temperature increase according to SCEM from 1880 to 2015 is 0.76 ⁰C distributed between the Sun 0.35 ⁰C, the GHGs 0.28 ⁰C (CO2 0.22 ⁰C), and the AHR 0.13 ⁰C. The AHR effects can explain the temperature pause of the 2000s. The scenarios of four different TSI trends
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Physical Science International Journal
15(2): 1-14, 2017; Article no.PSIJ.34187
ISSN: 2348-0130
Semi Empirical Model of Global Warming Including
Cosmic Forces, Greenhouse Gases, and Volcanic
Antero Ollila
Department of Civil and Environmental Engineering (Emer.), School of Engineering, Aalto University,
Espoo, Finland.
Author’s contribution
The sole author designed, analyzed, interpreted and prepared the manuscript.
Article Information
DOI: 10.9734/PSIJ/2017/34187
Mohd Rafatullah, Division of Environmental Technology, School of Industrial Technology, Universiti Sains Malaysia,
Abbas Mohammed, Blekinge Institute of Technology, Sweden.
José Martínez Reyes, Trajectory of Engineering in Energy, University of the Ciénega of Michoacán State, México.
Francisco Bulnes, Department In Mathematics And Engineering, Technological Institute of High Studies of Chalco, Mexico.
Shu-Lung Kuo, Department of Environ, Engineering & Science, Open University of Kaohsiung, Taiwan.
A. Ayeshamariam, Department of Physics, Khadir Mohideen College, India.
Jorge F. Carrasco, University of Magallanes, Chile.
Complete Peer review History:
Received 17
May 2017
Accepted 28
June 2017
Published 3
July 2017
In this paper, the author describes a semi empirical climate model (SECM) including the major
forces which have impacts on the global warming namely Greenhouse Gases (GHG), the Total
Solar Irradiance (TSI), the Astronomical Harmonic Resonances (AHR), and the Volcanic Eruptions
(VE). The effects of GHGs have been calculated based on the spectral analysis methods. The
GHG effects cannot alone explain the temperature changes starting from the Little Ice Age (LIA).
The known TSI variations have a major role in explaining the warming before 1880. There are two
warming periods since 1930 and the cycling AHR effects can explain these periods of 60 year
intervals. The warming mechanisms of TSI and AHR include the cloudiness changes and these
quantitative effects are based on empirical temperature changes. The AHR effects depend on the
TSI, because their impact mechanisms are proposed to happen through cloudiness changes and
TSI amplification mechanism happen in the same way. Two major volcanic eruptions, which can be
detected in the global temperature data, are included. The author has reconstructed the global
temperature data from 1630 to 2015 utilizing the published temperature estimates for the period
Original Research Article
Ollila; PSIJ, 15(2): 1-14, 2017; Article no.PSIJ.34187
1600 – 1880, and for the period 1880 – 2015 he has used the two measurement based data sets of
the 1970s together with two present data sets. The SECM explains the temperature changes from
1630 to 2015 with the standard error of 0.09°C, and the coefficient of determination r
being 0.90.
The temperature increase according to SCEM from 1880 to 2015 is 0.76°C distributed between the
Sun 0.35°C, the GHGs 0.28°C (CO
0.22°C), and the AHR 0.13°C. The AHR effects can e xplain
the temperature pause of the 2000s. The scenarios of four different TSI trends from 2015 to 2100
show that the temperature decreases even if the TSI would remain at the present level.
Keywords: Climate change; climate model; cosmic forces; global warming; greenhouse gases.
The Intergovernmental Panel on Climate
Change (IPCC) has published five assessment
reports (AR) about the climate change.
According to IPCC the climate change is almost
totally due to the concentration increases of GH
gases (97.9%) since the industrialization 1750.
The Radiative Forcing (RF) value of the year
2011 corresponds the temperature increase of
1.17°C, which is 37.6% greater than the
observed temperature increase 0.85°C [1].
Because of the temperature pause since 2000,
the error of this model is now about 49%. This
great error of the IPCC’s model means that the
approach of IPCC can be questioned. One
obvious reason is that IPCC mission is limited to
assess only human-induced climate change. In
this paper, other climate changing forces
Greenhouse Gases (GHG), the Total Solar
Irradiance (TSI), the Astronomical Harmonic
Resonances (AHR), and the Volcanic Eruptions
(VE), are analyzed and their impacts on the
global temperature are quantified on the
theoretical and empirical ways. The objective of
this paper is to construct a global temperature
data set from 1610 to 2015 and to combine the
above listed climate change forces on the
theoretical and empirical grounds to explain the
temperature changes during this period.
Table 1. List of symbols, abbreviations, and acronyms
Anthropogenic global warming
Astronomic harmonic resonances
Assessment report of IPCC
Gravity center of the solar system
Cloud forcing
Climate sensitivity
Climate sensitivity parameter (=λ)
Equilibrium climate sensitivity
Galactic cosmic rays
GH gas
Goddard Institute for Space Studies
Temperature data set of Hadley Centre
The Intergovernmental Panel on Climate Change
International Satellite Cloud Climatology Project
Little ice age
Northern hemisphere
Radiative forcing change
Semi empirical climate model
Southern hemisphere
Transient climate sensitivity
Proxy temperature estimate
Measured temperature
T-est + T-rec
University of Alabama in Huntsville
Volcanic eruptions
Ollila; PSIJ, 15(2): 1-14, 2017; Article no.PSIJ.34187
Table 1 includes all the symbols, abbreviations,
acronyms, and definitions used repeatedly in this
2.1 Estimated Global Temperature from
1610 to 1890
There is no generally accepted temperature data
for the period from 1610 to 1890 because there
have been no global temperature recording
methods available. Therefore, all the global
temperatures for this period are estimated using
different proxy methods. The author has selected
three commonly used proxy data sets namely
Briffa et al. [2]
applying tree ring density data,
Moberg et al. [3]
applying tree ring data and lake
and ocean sediment data, and Ljungqvist [4]
applying nine different proxy methods. The data
sets are normalized to give zero Celsius degrees
for the period from 1877 to 1883, because the
year 1880 is generally used as a starting point for
the recorded temperatures. The three data sets
and the average data set is depicted in Fig. 1.
All three temperature graphs show the same kind
of overall trend from 1610 to 1890. The
temperature decrease caused by the Tambora
eruption in 1815 can clearly be seen but the
Krakatoa temperature decrease is significantly
smaller. It appears that the graph of Briffa et al.
[2] is the most sensitive for temperature changes.
It is natural that the graph of Ljungqvist [4] is the
most insensitive in regard to changes because
the averaging of the nine different proxies
smoothens out the changes. The arithmetical
average of these three different proxy
temperatures may be a good compromise
regarding sensitivity for temperature changes.
The three data sets applied cover the northern
hemisphere (NH) only. The NH and SH satellite
data sets of the University of Arizona in
Huntsville (UAH) [5] shows that the difference of
the average values from 1980 to 2015 is only
0.013°C. This small difference means that it is
justified to use NH temperatures as the global
temperature change as well.
2.2 Recorded Global Temperature from
1880 to 2015
[6] temperature data set starts from
1850 but the coverage of the data is not very
good. A special problem has been detected in
different data set versions of GISS [7]. The
versions of the year 2000 and 2016 of this data
and the satellite temperature data set of UAH are
depicted in Fig. 2.
Fig. 1. Estimated global temperature T-Est constructed as the average of three temperature
Ollila; PSIJ, 15(2): 1-14, 2017; Article no.PSIJ.34187
Fig. 2. The temperature data versions 2000 and 2016 of NASA/GISS [7] and UAH [5]
The temperature increase from 1880 to 2000 has
been 0.47°C according to GISS version 2000,
and the increase for the same period has been
1.09°C according to version 2016. As one can
see in Fig. 2, the version 2016 (GISS-16)
temperature in 1880 has been much lower but
the temperature in 2000 is higher than the one of
the version 2000. Also, the average temperature
of GISS-16 [7] during the period from 2000 to
2015 is 0.26°C higher than that of UAH
These new versions have been adjusted in the
name of data homogenization. Another
suspicious element in the GISS data sets is the
warming during 1930s. The extreme weather
events like heat waves and draughts in USA [8,9]
related to high temperatures show that in 1930s
these events have been stronger and more
frequent than during the 2000s. Therefore, the
author has been looking for older data before
1979, which was the starting year of UAH
temperature data set.
In 1974, the Governing Board of the National
Research Council of USA established a
Committee for the Global Atmospheric Research
Program. This committee consisted of tens of the
front-line climate scientists in USA and their
major concern was to understand in which way
the changes in climate could affect human
activities and even life itself. A stimulus for this
special activity was not the increasing global
temperature but the rapid temperature decrease
since 1940. There was a common threat of a
new ice age. The committee published in behalf
of National Academy of Sciences the report [10]
by name “Understanding Climate Change – A
Program for Action” in 1975. The committee had
used the temperature data published by Budyko
[11], which shows the temperature peak of 1930s
and cooling to 1969. This digitized temperature
graph from 1880 to 1969 is depicted in Fig. 3.
The temperature peak of 30s is little bit lower in
the graph published by Hansen [12]. There is
another global data graph published by Angell
and Korshover [13] from 1957 to 1975 following
the trend of Byduko [11] but because it so short a
period, it has not been used. In constructing the
recorded global temperature data set T-rec, the
author has used the average of Budyko [11] and
Hansen [12] data from 1880 to 1969. The
temperature change from 1969 to 1979 is
covered by the GISS-16 data and thereafter by
UAH [5]. The UAH data has been normalized to
GISS-16 [7] by equaling the average values from
1979 to 1981. All these data set values are
depicted in Fig. 3.
The constructed data set T-rec is normalized by
averaging the decade 1880 to be same as that of
T-est. The constructed T-rec shows the peak
value of 1930s to be about 0.25°C lower than the
2000s. The same difference in the GISS-00
(version 2000) is about 0.3°C and in the GISS-16
version the difference is about 0.6°C. As
references, there are GISS-16 [7] and HadCrut4
[6] temperature graphs also depicted in Fig. 3.
Ollila; PSIJ, 15(2): 1-14, 2017; Article no.PSIJ.34187
Fig. 3. The different recorded temperature data set and the constructed data set T-rec
The difference between these data sets and T-
rec is even 0.35°C during the period from 1880
to 1950. In the late 1970s the differences
between different data sets are almost
neglectable, when the present warming was in
the early phase. A general conclusion is that the
history (1880-1960) of the global temperature of
the new versions of GISS is getting colder and
the newer temperatures of 2000s are getting
warmer. These changes, which happen always in
the newer versions of GISS, arouse doubts of
justification of these changes. Soon et al. [14]
have analyzed that the rural land-based
meteorological stations data results into a
temperature trend, which deviate from the official
temperature trends especially during 30s, it is
very close to T-rec calculated in this study. Their
conclusion is that the urban heat island
syndrome of meteorological stations has caused
a bias into the measurement data. Therefore, the
author considers that the T-rec constructed from
the older global temperature data sets is more
reliable than the GISS [5] and HadCRUT4 [7]
temperature data. The combination of the T-est
and T-rec is labelled as T-comp, which is valid
from 1630-2015.
3.1 Temperature Impacts of Greenhouse
Gases (GHG)
According to IPCC
the climate change is almost
totally due to the concentration increases of GH
gases since the industrialization 1750 and the
global warming can be calculated using Eq. (1)
dT = CSP * RF (1)
Where dT is the temperature change (K) since
1750, CSP (also marked by λ) is a climate
sensitivity parameter (K/Wm
) and RF is
radiative forcing (Wm
) caused by GH gases
and other drivers. The total RF in AR5 [15] was
2.34 Wm
in 2011 and the RF value of solar
irradiance was 0.05 Wm
, which means 2.1%
positive contribution. The CSP is nearly invariant
parameter according to IPCC [15] having a
typical value about 0.5 K/(Wm
The transient climate sensitivity (TRC) according
to Eq. (1) and the RF value of 3.7 Wm
for CO
is 1.85°C [16] and it is close to the average TRC
1.75°C (from 1.0°C to 2.5°C) reported in the AR5
[1]. The equilibrium climate sensitivity (ECS)
reported in AR5 [1] is in the range 1.5°C to
4.5°C, which means the average ECS to be
3.0°C. Several researchers have reported much
lower ECS values than 3.0°C (the best estimates
/ the minimum values): Aldrin [17] 2.0°C / 1.1°C;
Bengtson & Schwartz [18] 2.0 C / 1.15 C; Otto
et al. [19] 2.0°C / 1.2, and Lewis [20] 1.6°C /
1.2°C. In the above referred studies the RF [16]
value of 3.7 Wm
for CO
has been used. It
means that the CSP values of these studies are
essentially lower than 0.5 K/Wm
and it means
that there is no positive water feedback. Harde
[21] has used spectral analysis methods and the
Ollila; PSIJ, 15(2): 1-14, 2017; Article no.PSIJ.34187
two-layer climate model in calculating the ECS
values and his result is 0.6°C. Ollila [22] has also
reported the ECS value of 0.6°C by utilizing
spectral analysis and no water feedback in CSP
and in RF formula:
dT = 0.27 K/(Wm
) * 3.12 *ln (CO
/280) (2)
Where CO
is the actual CO
(ppm). The warming effect of CO
according to
Eq. (2) until to 2015 is 0.28°C. Ollila [23] has
shown that the total precipitable water (TPW)
changes are neglectable from 1979 to 2015
challenging the assumption of the constant
relative humidity assumption of IPCC. Ollila [24]
has combined the warming effects of CH
O into one linear equation based on the
spectral analysis calculations:
dT = -0.5558 + 0.0003176 * Year (3)
The temperature increase by CH
and N
O from
1750 to 2015 is 0.083°C according to Eq. (3).
3.2 Temperature Impacts of Total Solar
Irradiance (TSI) Changes
The second element in the SECM is TSI (Total
Solar Irradiance) changes caused by activity
variations of the Sun. The TSI changes have
been estimated by applying different proxy
methods. Lean [25] has used sunspot darkening
and facular brightening data. Hoyt and Schatten
[26] have used three different indices namely
sunspot structure, solar cycle, and equatorial
solar rotation rate data. Bard [27]
has used
C and
Be production rates in
evaluating the solar magnetic variability. These
TSI trends based on these three methods are
depicted in Fig. 4. There are similarities and
differences between these three trends.
The author has selected the data set of Lean
which is available to 2000 and combined
this data to the data of PMOD data set [28] from
2000 onward. According to this data, TSI has
increased 2.75 Wm
since the 1650’s as
depicted in Fig. 4. The direct warming impact can
be calculated by Eq. (4) derived from the Earth’s
energy balance
T = (TSI*(1-α)/4s))
where α is the Earth’s total albedo, and s is
Stefan-Bolzmann constant. The dependency of
the Earth’s albedo on the cloudiness can be
calculated based on the three pairs of cloudiness
and albedo values [29]. Cloudiness-% values are
0%, 66%, and 100%. The cloudiness-% of 66 is
the average all-sky value of the present climate.
The corresponding albedo values can be
calculated according to the albedo specification
by dividing the total reflected shortwave radiation
flux by the total solar radiation flux (324 Wm
53/342, 104.2/342, and 120/342). These three
pairs of data are fitted to the second order
α = 0.15497 + 0.0028623 * cloudiness-% -
0.000009 * (cloudiness-%)
Fig. 4. TSI from 1610 to 2014 Lean [25], PMOD [28], Bard [27] and Hoyt and Schotten [26] and
the global temperature T-Comp
Ollila; PSIJ, 15(2): 1-14, 2017; Article no.PSIJ.34187
McIntyre & McKitrick [30], Alley [31], Ljungvist
[4], and Esper et al.
[32] have come into
conclusion that there have been at least two
warm periods about 1000 and 2000 years ago.
The direct irradiance changes have not been big
enough to explain these changes, because the
direct temperature impact by TSI change from
1650 to 2015 is 0.12°C. In the pioneer research
Svensmark [33] has introduced evidence about
the phenomena in which solar cycle variations
modulate galactic cosmic ray (GCR) fluxes in the
earth’s atmosphere, which phenomenon could
cause clouds to form. They argued that cosmic
ray particles collide with particles in atmosphere,
inducing electrical charges on them and
nucleating clouds. Svensmark et al. [34] have
found further evidences about this mechanism by
studying the coronal mass ejections from the
Sun. They found that low clouds contain less
liquid water following cosmic ray decreases
caused by the Sun. This mechanism amplifies
the impacts of the original changes in the Sun’s
activity on the Earth’s climate but the researchers
have not been able to calculate the quantitative
effects of TSI changes.
The author has calculated the empirical warming
effects of TSI changes on the three periods:
1665 1703, 1844 1873, and 1987-1991, see
Table 2. The periods are selected so that the
positive and negative temperature effects of
Astronomic Harmonic Resonances (AHR) during
these periods compensate each other, see
section 4. The first period acts as a reference
period, when the warming impacts of the Sun are
zero. The observed temperature changes caused
by the TSI changes during the two other
periods are calculated by subtracting the
dT caused by the GH gases from the observed
dT-comp. The cloudiness-% of the selected
periods are calculated applying Eq. (4) and Eq.
(5). The cloudiness-% of 1987-1991 is
practically same as the one from the ISCCP data
set [35].
Table 2. TSI, albedo, cloudiness, and
temperature changes during three periods
Period TSI,
°C Albedo Cloud.-
The relationship between the temperature
change and the cloudiness-% change can be
fitted by the 2. order polynomial, which is slightly
dT = -457777.75 + 671.93304 * TSI
0,2465316 * TSI
where dT (°C) is the temperature change by the
TSI. During the analyzed period from 1630-2015
the corresponding albedo and temperature
changes are calculated by Eq. (4) and Eq. (5).
The temperature change of 0.50°C caused by
TSI change of 2.67 Wm
can be divided
between the direct impact of TSI change 0.1C
and the cloudiness-% decrease of 2.67%
causing the temperature increase of 0.38°C.
Cloud forcing according to Eq. (4) and Eq. (5) is
1.7°C/cloudiness-% and this relationship is
included in Eq. (6). In this analysis, the
cloudiness-% decrease from 68.5 to 66 explains
the amplification of TSI increase. Because we do
not have real cloudiness measurements before
1980, we do not know exactly what have been
the real cloudiness variations before that year.
Kauppinen et al. [36] and Ollila [29]
reported that the cloudiness forcing is -0.1
°C/cloudiness-% using two different approaches.
According to this cloud forcing, the cloudiness-%
change needed to explain the temperature
change would be from 69.45 to 66.0. Anyway,
the empirical result is that the relatively small
cloudiness changes can explain, why the
temperature effect of the TSI changes are
amplified by a factor = 0.5°C / 0.12°C = 4.2.
3.3 Temperature Impacts of Astronomical
Harmonic Resonances (AHR)
The third element of the SECM is a phenomenon
called Astronomical Harmonic Resonances
(AHR). This approach has proposed Scafetta
[37]. He found that large climate oscillations with
peak-to-trough amplitude of about 0.1°C and
0.25°C, and periods of about 20 and 60 years,
respectively, are synchronized to the orbital
periods of Jupiter (29.4 years) and Saturn
(11.87) years.
Ermakov et al. [38] have proposed that the
influence mechanism of the AHR happens
through the variations of space dust entering the
Earth’s atmosphere. The estimates of daily dust
amount vary from 400 to 10000 tons. The optical
measurement of the Infrared Astronomical
Satellite (IRAS) revealed in 1983 that the Earth is
embedded in a circumsolar toroid ring of dust
Ollila; PSIJ, 15(2): 1-14, 2017; Article no.PSIJ.34187
[39], Fig. 5. This dust ring co-rotates around the
Sun with Earth and it locates from 0.8 AU to 1.3
AU from the Sun [40].
In the wake of the Earth is the permanent trail of
dust particles having about 10% greater density
than the background zodiacal cloud. The darker
spots in Fig. 5 represent higher concentrations of
dust. Gold [39] has pointed out that the small
particles in the Solar System spiral toward the
Sun but they may become trapped in resonances
with the planets. This should result the
circumsolar dust cloud, which is not uniform.
Dermott et al. [41]
have shown by numerical
simulations that the trailing density of the cloud is
higher than the leading density and this is
confirmed by the IRAS quantitative
measurements. Simulations show that the
dust particles are trapped in a 5:6 resonance
with the Earth with the results that their paths
are not symmetric about the Sun-Earth
line. According to Dermott et al. [41]
this asymmetric nature of the heliocentric dust
cloud leads to greater dust amount
encountering the Earth during September-
October when the Earth is closest to the trailing
Variations in dust amounts happen during a
longer time scale depending on the periodicities
of the planets, which can move the dust cloud
position in the Earth’s orbit. Scafetta [37] has
proposed that the climate can also be directly
influenced by the magnetic field oscillations
caused by the perturbations of the planets. AHR
resonance, collective synchronization and
feedback mechanisms could amplify the eects
of a weak external periodic forcing. In the same
way that galactic cosmic rays (GCR) cause
ionization in the atmosphere, dust particles can
do the same phenomenon. In this respect, the
cosmic ray model and the cosmic dust model
have a common meeting point but the original
reasons are different: The Sun activity changes
and planetary periodical motions as illustrated in
Fig. 6.
Fig. 5. A schematic picture of the circumsolar dust cloud reproduced by the author according
to the presentation of the numerical simulations [40]
Fig. 6. The influence mechanisms of TSI changes and Astronomic Harmonic Resonances
Ollila; PSIJ, 15(2): 1-14, 2017; Article no.PSIJ.34187
Ollila [24] has analyzed that using the graphical
data of Ermakov et al. [38]
and the GH gas
warming effects, the correlation between the
combined model (AHR, TSI and GH gases) and
the real temperature data is very good with the
coefficient of correlation r
being 0.957 from 1880
to 2015. The calculated correlation in this case is
not based on the quantified warming effects of
AHR and TSI. The major objective of this paper
is to assess the quantified effects AHR and TSI
changes on the global temperature starting from
the LIA. The periodicities caused by Jupiter and
Saturn can be found in the calculated speed
variations of the Sun around the Solar System
Barycenter (SSB). The author has used the
Horizon’s application of NASA [42] in depicting
the graphs in Fig. 7.
In Fig. 7 is also depicted the variations of the
maximum speed values (blue line) of the 20
years’ cycles. This graphical line is 11 years
running average. It should be noticed that the
speed variations are not fully symmetric around
the average speed and thus the temperature
effects are also asymmetric. The 60-year’s cycle
can be easily detected. These temperature
effects of the AHR changes are based on the
speed changes of the Sun. The magnitude of the
AHR effect is calculated on the empirical basis.
The change from 1941 peak temperature
+0.185°C to the minimum temperature -0.15°C in
1962 is used to estimate the AHR impact:
dT = -6.43125 + 418.75 * SS (7)
Where dT (°C) is the temperature change and
SS is the Sun speed (kms
Because the TSI variations and the AHR
variations finally happen through the cloudiness
changes, these effects cannot be summarized
directly. The average cloudiness-% according to
ISCCP [35] is about 66% and the average cloud
layer is from 1.6 km to 4.0 km [43]. When the low
activity of the Sun has increased cloudiness to its
maximum value, the cloudiness growth by
nucleation process increase cannot increase the
cloudiness anymore. It means that 1) the
humidity in the atmosphere is not adequate to
increase the cloudiness area over the drier areas
of the globe even though the nucleation process
has increased or 2) the AHR actually changes
the thickness and the mass of the existing clouds
but these changes do not change the area of the
clouds. When the Sun’s activity is in maximum,
the cloudiness changes by AHR can have a full
effect, because in these conditions the nucleation
process controls the amount of cloudiness. In
calculating this relationship, the author has used
a factor, which has a sinusoidal dependency on
the TSI value: TSI of 1363.43 Wm
during the
LIA gives factor value zero and the TSI value of
1366.2 Wm
during the present maximum gives
the value = 1. The sinusoidal dependency
smoothens the changes close to the maximum
and the minimum TSI fluxes.
3.4 Temperature Impacts of Major
Volcanic Eruptions (VE)
The strong volcanic eruptions, which have the
Volcanic Explosivity Index (VEI) 5 or 6, have
capacity to create eruption columns reaching the
stratosphere [44]. The best documented eruption
of this kind was the Mt. Pinatubo eruption in
1991. The aerosol cloud covered the latitudes
from 60S to 60N after three months and in six
months the cloud was uniform over the
hemispheres [45]. These kinds of eruptions
typically reduce the global temperature by 0.5°C
from 2 to 5 years. During the period from 1600 to
2015 there has been four volcanic eruptions with
VEI index 5-7 namely Tambora 1815, Krakatoa
1883, Novarupta 1912, and Pinatubo 1991. In
the global temperature record constructed in this
research, the eruptions of Tambora and Krakatoa
can be identified but the Novarupta ja Pinatubo
effects disappear in the 11 years running mean
presentation. The temperature effects of both
eruptions have been estimated in the same way.
The temperature decrease starting from the
eruption year and the consecutive years have
been -0.5°C, -0.35°C, -0.1°C, and -0.05°C.
3.5 The Summary of the SECM
Temperature Effects
The estimated and observed temperature T-
comp and the temperature by the SECM are
depicted in Fig. 8. All temperatures are smoothed
by 11 years running average. The average
values of the SECM and T-comp are normalized
to be the same for the period from 1630 to 2015.
This figure shows that the global temperature
does not follow the monotonically increasing
temperature effect of GH gases. The major driver
of the climate change is the Sun. The AHR
explain the strong temperature peaks of 30’s and
the now in 2000’s. Without the AHR effects the
total explanation power of SECM would be much
weaker since 1900. Because the temperature
effects depend on the Sun activity, the
magnitude of AHR effects disappears totally in
1600s. The coefficient of correlation r
= 0.90 for
the period from 1630 to 2015 and the standard
error of estimate is 0.09°C.
Ollila; PSIJ, 15(2): 1-14, 2017; Article no.PSIJ.34187
Fig. 7. The speed variations of the sun around the solar system Barycenter
Fig. 8. The estimated and observed temperature T-comp and the temperature by SECM. All
temperatures are smoothed by 11 years running mean
The average contributions of the different climate
forcing elements during the centuries and in year
2015 have been summarized in Table 3.
The Sun’s contribution is the greatest but the
warming effect of GHGs is steadily increasing
having the impact of 37.3% in 2015. The average
contribution of AHRs is zero in the long run but
during the shorter periods they may be positive
or negative.
3.6 The Future Temperature Scenarios by
the SECM
The possible scenarios depending on the future
changes in the Sun’s activity can be easily
Ollila; PSIJ, 15(2): 1-14, 2017; Article no.PSIJ.34187
calculated using the SECM. The author has
selected four different scenarios with different
decreasing TSI trends in 35 years: Scenario 1,
TSI decrease –3 Wm
; scenario 2, TSI decrease
–2 Wm
; scenario 3, TSI decrease –1 Wm
scenario 4, TSI decrease 0 Wm
. After the
decrease phase, the TSI flux stays at the same
level to 2100. These scenarios are depicted in
Fig. 9.
The behavior of the Sun has been difficult to
predict for researchers. The two dynamos model
of the Sun developed by Shepherd et al. [46]
explains very well the Sun’s activity during the
last three solar cycles. This model predicts that
the Sun’s activity approaches the conditions,
where the Sun spots disappear almost totally
during the next two solar cycles like during the
Maunder minimum. The AHR effect explains,
why the present temperature pause has
continued so long, because the positive peak
duration is exceptionally long, Fig. 7. Because
the AHR effect also turn to a decreasing phase
after 2020, the temperature would start to
gradually decrease regardless of the Sun’s
activity change trend. In Fig. 9 the temperatures
according to the IPCC model are depicted for the
years 2005, 2011 and 2016. The error in
comparison to the observed temperature is very
clear and if the temperature does not increase in
the coming years, the error is becoming
The constructed average global temperature T-
comp is a combination of the average of the
three historical proxy data sets from 1610 to
1890 and the combination of observed
temperature data sets from 1889 to 2015. The
correctness and accuracy is difficult to estimate,
because the measurement based data sets
deviate from each other up to 0.3°C in yearly
values. The three selected temperature proxy
graphs show the same kind of trends before
1890 explaining for example the temperature
decrease by the Tambora eruption in 1815. The
temperature measurements starting from 1880
show almost as great differences as the proxy
temperature graphs. The author has used the
average of two different temperature
measurement data sets published in 1975 [10]
and the other in 1981 [12] for the period from
1889 to 1979. Because these data sets were
before the warming period since 1975,
there has been no pressure to show any extra
warming trend as it may now be a case. The
author has used the UAH data set from 1980
onward. There was practically no difference
between the temperature trends of UAH and
GISS from 1979 to 2005 published before 2008
but thereafter the difference has increased to
0.26°C arousing doubts about the accuracy of
latest version of GISS-16.
Many research studies show that Pacific Decal
Oscillation (PDO) phenomenon causes climate
variations in the Pacific Basin and in the North
America. The ENSO (El Nino-Southern
Oscillation) causes also very clear climate
impacts. The Atlantic Multi-decadal Oscillation
(AMO) correlates with the sea surface
temperature of the North Atlantic Ocean. By
analyzing the long-term PDO index and the AMO
index, it can be found that they follow quite well
the general temperature trend of the Earth. For
example, the high temperature periods of 1930’s
and 2000’s happen at the same time as the
maximum values of PDO and AMO index. The
author’s conclusion is that the oscillation
phenomena like PDO and AMO are not the real
root causes of the long-term climate change but
they have the common origin.
The warming impact of GH gases has increased
from 0% in 1750 to 37% in 2015. The Astronomic
Harmonic Resonances (AHR) can explain the
temperature peaks of the 1930’s and the present
warming period since 2000. The change in Sun
activity explains the low temperatures during the
LIA. Therefore, these climate forces should be
included into the overall climate model.
Table 3. The summary of warming effects during the centuries, %
Century Sun GHGs AHR Volcanoes
1700-1800 99.5 4.6 -4.0 0.0
1800-1900 70.6 21.5 17.4 -9.4
1900-2000 72.5 30.4 -2.9 0.0
2015 46.2 37.3 16.6 0.0
Ollila; PSIJ, 15(2): 1-14, 2017; Article no.PSIJ.34187
Fig. 9. Four scenarios from 2015 to 2100 using four different TSI change trends
The semi empirical climate model SECM has the
coefficient of correlation r
= 0.90 and the
standard error is 0.09°C. The SECM follows the
ups and downs of the T-comp very well. The TSI
variation is the major driving force of the
temperature increase having the contribution of
71-73% during 19
and 20
centuries. Lean et
al. [47] have carried out the correlation analysis
between the NH surface temperature and the
reconstructed solar irradiation and they found
that a solar induced warming was 0.51°C from
the LIA in the 1990’s and the correlation was
0.86. This result is in line with the results of this
study but the overall accuracy of SECM in this
study is better, because of GHG and AHR effects
The Anthropogenic Global Warming (AGW)
theory cannot explain any periods with
decreasing temperatures. It is also obvious that
the climate model of IPCC [1], which is based on
the sums of the radiative forcings (RF), gives
about 50% too high of a value in 2015. In this
study, the author has used the formula of Ollila
[22] in calculating the warming impact of CO
This formula does not assume the constant
relative humidity but the constant absolute
humidity both in the radiative forcing and in the
climate sensitivity parameter calculations.
The four scenarios calculated to 2100 show that
the temperature would start to decrease after
2020 even though the TSI level would stay at the
present level.
Author has declared that no competing interests
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... Looking at the historical versions of GISS data sets, this is not probable. The author's conclusion is that the best estimate for the global temperature data set is the combination of NAS and Hansen data from 1880 to 1969, the Paris climate agreement period from 1969 to 1979 covered by the GISS-2017 data set and thereafter by the UAH data set (Ollila, 2017b). The IPCC model calculated temperature for 2016 is 1.27°C. ...
... These same forces can explain the present temperature pause. The obvious culprit is the sun, which has shown a rather strong decrease in its activity (Ollila, 2017b). If the present temperature pause continues, there is no need to rush into a strong reduction of GH gas emissions. ...
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The mechanism and even the existence of the Atlantic Multidecadal Oscillation (AMO) have remained under debate among climate researchers, and the same applies to general temperature oscillations of 60‐90‐year period. The objective of this study is to show that these temperature oscillations are real and not artifacts and that these oscillations have different external cosmic origins. The authors has studied how well the variations of astronomical harmonic resonances (AHR) could explain the 60‐year temperature variations, which are based on instrumental records and on the tree‐ring data of the supra‐long Scots pine tree‐ring record for northern Finnish Lapland (subsequently called the Finnish timberline pine chronology (FTPC)), stretching to the year 5634 BC. Powerful volcanic eruptions have significant temperature‐decreasing impacts, and they are the major disturbances to eliminate in analysis. The similarities of the temperatures of tree‐ring trend and AHR trend are easy to observe even by the naked eye. The statistical analysis shows that these two signals are statistically related. The analyses also show that the well‐known Gleissberg cycle of 80‐90 years is the dominating cycle caused by the Suns’ activity changes but the observed 60‐year cycle can be connected to the AHR cyclicity.
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The author has reanalysed the warming effects of greenhouse (GH) gases utilising the latest HITRAN 2012 database and improved water continuum calculations in the spectral analysis tool. The contributions of GH gases in the GH effect in the all-sky conditions are found to be: H2O 81%, CO2 13%, O3 4%, CH4 & N2O 1%, and clouds 1%. Because the total absorption is already 93% from the maximum in the altitude of 1.6 km, which is the average global cloud base, the GH gas impacts are almost the same in the clear and all-sky conditions. The impacts of clouds are based on the normal cloudiness changes between the clear and cloudy skies. The positive impact of clouds is analysed and it is based on the warming impact of clouds during the night-time. The warming impact of CO2 is very nonlinear and it means that in the present climate the strength of H2O is 11.8 times stronger than CO2, when in the total GH effect this relationship is 6.2:1. The atmospheric Total Precipitable Water (TPW) changes during ENSO events are the essential parts of the ENSO process and they are not actually separate feedback processes. The TPW changes during the ENSO events almost double the original ENSO effects. On the other hand, during Mt. Pinatubo eruption and during the three latest solar cycles, the long-term water feedback effect cannot be found despite of rapid warming from 1980 to 2000. This empirical result confirms that the assumption of no water feedback in calculating the climate sensitivity of 0.6°C is justified. Because there is no long-term positive feedback, it explains why the IPCC model calculated temperature 1.2°C in 2015 is 44 % greater than the average 0.85ºC of the pause period since 2000.
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It follows from the analysis of observation data that the secular variation of the mean temperature of the Earth can be explained by the variation of short-wave radiation, arriving at the surface of the Earth. In connection with this, the influence of long-term changes of radiation, caused by variations of atmospheric transparency on the thermal regime is being studied. Taking into account the influence of changes of planetary albedo of the Earth under the development of glaciations on the thermal regime, it is found that comparatively small variations of atmospheric transparency could be sufficient for the development of quaternary glaciations. DOI: 10.1111/j.2153-3490.1969.tb00466.x