ArticlePDF Available

The potency of carbon dioxide (CO2) as a greenhouse gas


Abstract and Figures

According to this study the commonly applied radiative forcing (RF) value of 3.7 Wm-2 for CO2 concentration of 560 ppm includes water feedback. The same value without water feedback is 2.16 Wm-2 which is 41.6 % smaller. Spectral analyses show that the contribution of CO2 in the greenhouse (GH) phenomenon is about 11 % and water’s strength in the present climate in comparison to CO2 is 15.2. The author has analyzed the value of the climate sensitivity (CS) and the climate sensitivity parameter () using three different calculation bases. These methods include energy balance calculations, infrared radiation absorption in the atmosphere, and the changes in outgoing longwave radiation at the top of the atmosphere. According to the analyzed results, the equilibrium CS (ECS) is at maximum 0.6 °C and the best estimate of  is 0.268 K/(Wm-2 ) without any feedback mechanisms. The latest warming scenarios of Intergovernmental Panel on Climate Change (IPCC) for different CO2 concentrations until the year 2100 include the same feedbacks as the 2011 warming i.e. only water feedback. The ECS value of 3.0 °C would mean that other feedback mechanisms should be stronger than water feedback. So far there is no evidence about these mechanisms, even though 40 % of the change from 280 ppm to 560 ppm has already happened. The relative humidity trends since 1948 show descending development which gives no basis for using positive water feedback in any warming calculations. Cloudiness changes could explain the recent stagnation in global warming.
Content may be subject to copyright. Development in Earth Science Volume 2, 2014
The Potency of Carbon Dioxide (CO2) as a
Greenhouse Gas
Antero Ollila
Department of Civil and Environmental Engineering (Emer.), School of Engineering, Aalto University, Espoo,
Finland, Otakaari 1, Box 11000, 00076 AALTO, Finland
According to this study the commonly applied radiative
forcing (RF) value of 3.7 Wm-2 for CO2 concentration of 560
ppm includes water feedback. The same value without water
feedback is 2.16 Wm-2 which is 41.6 % smaller. Spectral
analyses show that the contribution of CO2 in the
greenhouse (GH) phenomenon is about 11 % and water’s
strength in the present climate in comparison to CO2 is 15.2.
The author has analyzed the value of the climate sensitivity
(CS) and the climate sensitivity parameter () using three
different calculation bases. These methods include energy
balance calculations, infrared radiation absorption in the
atmosphere, and the changes in outgoing longwave
radiation at the top of the atmosphere. According to the
analyzed results, the equilibrium CS (ECS) is at maximum
0.6 °C and the best estimate of is 0.268 K/(Wm-2) without
any feedback mechanisms. The latest warming scenarios of
Intergovernmental Panel on Climate Change (IPCC) for
different CO2 concentrations until the year 2100 include the
same feedbacks as the 2011 warming i.e. only water
feedback. The ECS value of 3.0 °C would mean that other
feedback mechanisms should be stronger than water
feedback. So far there is no evidence about these
mechanisms, even though 40 % of the change from 280 ppm
to 560 ppm has already happened. The relative humidity
trends since 1948 show descending development which
gives no basis for using positive water feedback in any
warming calculations. Cloudiness changes could explain the
recent stagnation in global warming.
Strength of CO2; Climate Change; Global Warming; Climate
Sensitivity; Climate Sensitivity Parameter
The CS is calculated based on the doubling of the CO2
concentration from the pre-industrial concentration
from 280 ppm to 560 ppm. Before calculating these
future effects, climate science should find a good
understanding of the effect of CO2 on the present
greenhouse (GH) phenomenon and on the warming
starting 1750. It should be alarming that there is not
even a broad consensus on these figures. The values of
the CO2 effect on GH phenomenon vary in scientific
articles from 9% (Miskolczi and Mlynczak, 2004) to
about 33% (Pierrehumbert, 2011).
The primary effect of increased CO2 concentration
occurs in the lower part of the atmosphere, where GH
gases have absorbed 95% of the infrared radiation (IR)
emitted by the earth's surface up to 2 km high
(Ohmura, 1997; Ollila, 2012a). The secondary effect is
that the outgoing longwave radiation (OLR) at the top
of the atmosphere (TOA) is reduced, and IPCC names
this OLR change (IPCC, 2007a) the radiative forcing
(RF). Because the Earth must reach the radiative
energy balance, the third effect is the increase in the
surface temperature until the OLR is the same as the
incoming shortwave (SW) radiation. The changes are
so small that they can be analyzed only by
computational methods.
The global mean surface temperature change can be
calculated multiplying the RF change at TOA by
climate sensitivity parameter () according to IPCC
(2007a). The value and accuracy of is critical in
calculating the global warming value.
The objectives of this paper are to show that the
potency of CO2 as a GH gas is much lower than used
by IPCC, that the CS is much lower than used by IPCC
and finally that the global warming value is much
lower than calculated by IPCC. The calculation
methods are the same as used by IPCC, but the basic
differences are the water content of the atmosphere
and finally the relative humidity (RH) trends of the
The Strengths of Greenhouse Gases
IPCC (2013) claims that “The contribution of water
vapor to the natural greenhouse effect relative to that
of carbon dioxide (CO2) depends on the accounting
method, but can be considered to be approximately
Development in Earth Science Volume 2, 2014
two to three times greater.” There are no references to
any scientific papers supporting this claim. IPCC has
referred in its 2007 report (IPCC, 2007a) to the article
of Kiehl & Trenberth (1997). The author (Ollila, 2013a)
has shown that using the same US Standard
Atmosphere 76 with 12% less water he can get the
same results: H2O 60% and CO2 26% (60/26 = 2.3). This
atmosphere contains only 50 % of the real average
global atmosphere (AGA), see Table 1. The author has
concluded that the number “three times greater” could
refer to the article of Pierrehumbert (2011), which says
that CO2 absorption is not close to saturation and its
contribution in the tropical climate is about 33%.
Pierrehumbert shows no detailed calculations only
the claim above.
alt, km
Precipitated water in cm’s (prcm)
I have calculated the AGA profiles (Ollila, 2012a) by
combining the values of three climate zones published
by Ellingson et al. (1991) These profiles are available
also in the Spectral Calculator program (Gats, 2014)
which I have used in the spectral analyses of this
The AGA’s surface temperature is 15 °C, and the
concentrations of the anthropogenic GH gases as
measured in 2005: CO2 393 ppm, CH4 1.774 ppm, and
N2O 0.319 ppm) as reported by IPCC (2007b).
I have used one dimensional (1D) Polar Summer
atmosphere values, modifying the profiles where
needed. The temperature and pressure profiles of
AGA are the same as Polar Summer values, but GH
gas profiles have been adjusted to the 2005 values
using scale factors.
In Fig.1, the absorption graphs of major GH gases are
depicted up to 25 µm, because thereafter water can
totally absorb all the IR radiation (Ollila, 2012a). The
shaded green area gives a good image of the
magnitude of CO2 in the GH phenomenon. The total
contributions of GH gases are up to 120 km calculated
by the Spectral Calculator (Gats, 2014) and by the
Hitran database of Harvard-Smithsonian Center for
Astrophysics (2014): H2O 82.2%, CO2 11.0%, O3 5.2%,
and CH4 0.8 % and N2O 0.8%.
The curve of each GH gas in Fig.1 is calculated when it
is the only gas in the AGA conditions. The real
combined absorption of GH gases is not a simple
summary of the band areas of single GH gases. The
real total absorption can be calculated only when all
the GH gases are present at the same time. The total
absorption is depicted by the purple line. Therefore for
example, the total absorption curve does not follow
the green line of CO2 absorption curve, because it is
essentially caused by the total absorption of H2O and
CO2 present at the same time in the atmosphere.
Some important conclusions can be drawn from the
absorption graphs. The GH gases have different effects
on the total absorption when compared to the
absorption caused by water. Ozone pushes the total
absorption curve effectively upward, but CH4 and N2O
only minimally increase the total absorption in
comparison to water absorption. The radiation flux
transmitted into space in the clear sky conditions is
83.2 Wm-2, and it is the only potential area for
increased absorption caused by higher GH gas
concentrations. In all-sky conditions, clouds absorb
about 66% of the transmitted flux, and thus about 28.3
Wm-2 - that is, only about 7 % of the emitted LW
radiation - escapes directly into space.
Because the effects of GH gases are very nonlinear, the Development in Earth Science Volume 2, 2014
above contributions are not the actual strengths of GH
gases for the changes around the present
concentrations. The author has calculated the relative
strengths of GH gases (Ollila, 2013a) based on the
increased IR absorptions from 1990 to 2005. The most
important GH gas is water and its strength in respect
to CO2 impact (value = 1) is 15.2. The same values of
other GH gases are: CH4 0.144, N2O 0.168, and O3
0.629. Fig. 1 shows that any impact of GH gases that
could actually increase warming must do it in the
wavelength zone from 7.5 µ m to 14 µ m in the so-
called atmospheric window.
In Fig. 2 are depicted absorption graphs for various
CO2 concentrations from 10 µm to 14 µm. In this
wavelength zone 85-90% of absorption caused by
increased CO2 concentrations occurs. Even by eye, it is
easy to estimate that the absorption area increase from
379 ppm to 560 ppm is almost the same as the area
from 280 ppm to 379 ppm. The warming effect is
directly proportional to the total area caused by the
GH gases between the x-axis and the total emission
curve of the GH gases.
The Climate Sensitivity According to the
Earth’s Energy Balance
The radiative forcing (RF change) at TOA has a linear
relationship to the global mean surface temperature
change Ts if two equilibrium climate states are
Ts = RF (1)
IPCC states (2007a) that is a climate sensitivity
parameter, which is nearly invariant parameter having
a typical value about 0.5 K/(Wm-2). This value is based
on rather old calculations (Ramanathan et al., 1985)
before 1985, at which time narrow-band models were
applied and not the accurate line-by-line methods of
today. IPCC no longer keeps the climate sensitivity
parameter as a nearly invariant parameter like in AR4.
In AR5 its value varies in broad limits. The value of
the climate sensitivity parameter is 0.811 K/Wm-2 for
the CO2 forcing of 3.7 Wm.2 and the warming of 3.0 °C.
The author has used three different methods in
calculating the CS and values. The simplest analysis
of CS and is based on the total energy balance of the
Earth by equalizing the absorbed and emitted
radiation fluxes
SC(1-α) * (¶r2) = sT4 * (4¶ r2), (2)
Where SC is solar constant (1368 W/m2), α is the total
albedo of the Earth, s is Stefan-Bolzmann constant
(5.6704*10-8), and T is the temperature (K). The
temperature value of T can be solved:
T = (SC * (1 α) (4s))0.25 (3)
Where T is the temperature corresponding the emitted
longwave (LW) flux in the atmosphere. The average
albedo (Ollila, 2013b; Ollila, 2014) is (104.2 Wm-2)/(342
Wm-2) = 0.30468. Using this albedo value, the
temperature T would be -18.7 °C (=254.5 K). According
to the Planck’s equation, this temperature corresponds
to LW radiation flux 237.8 Wm-2, which is the actual
average emitted LW radiation flux of the Earth. The
most common reported global mean surface
temperature is 1C, which means that the greenhouse
effect would be 33.7 K. The surface temperature Ts can
be calculated by adding 33.7 K into T
Ts = T +33.7 (4)
The term SC(1-α)/4 is the same as the net radiative
forcing (RF) and therefore Eq. (2) can be written in the
form RF = sT4. When this equation is derived, it will
be d(RF)/dT = 4sT3 = 4(RF)/T. The ratio d(RF)/dT can
be inverted transforming it into :
dT/(d(RF)) = = T/(4RF)= T/(SC(1-α)) (5)
In the all-sky conditions the total albedo flux 104.2
Wm-2 is the sum of the cloud reflected flux of 67.8 Wm-
2, the surface reflected flux of 22.7 Wm-2 and the air
reflected flux of 13.7 Wm-2. These values as well as the
following three pairs of cloudiness and albedo values
for clear, all-sky and cloudy sky conditions are based
on energy balance analysis of global radiative fluxes
(Ollila, 2013b; Ollila, 2014; Zhang et al., 2004; Bodas-
Salcedo et al., 2008; Loeb et al., 2009): (0%,
Development in Earth Science Volume 2, 2014
53/342=0.155), (66%, 104.2/342=0.305), and (100%,
120/342=0.351). The second-order polynomial can be
fitted through these points and the result is
α = 0.15497 + 0.0028623 * CL – 0.000009 * CL2 (6)
where α is albedo and CL is cloudiness-%.
The differences between sky conditions are due to the
degrees of cloudiness in different skies. This effect is
generally called cloud forcing (CF). Normally the CF
has been calculated at TOA as the difference between
clear sky and all-sky conditions. Using the values of
Ollila (2013b), the albedo flux change 53 - 104.2 = -51.2
Wm-2. The outgoing LW radiation decrease is the
difference between OLR fluxes, which is 259 - 237.8 =
21.2 Wm-2. According to the most common definition,
the CF is the sum of these two fluxes, which in this
case is -30.0 W/m2, a cooling effect. This value is close
to the values used in other studies (Ohring and Clapp,
1980; Harrison et al., 1990; Ardanuy et al., 1991; Zhang
et al., 2004; Raschke et al., 2005; Loeb et al., 2009;
Stephens et al., 2012), which vary between -17.0 and -
28 W/m2 average being -23.4 W/m2.
Spencer and Braswell (2011) have created a more
complicated calculation method for cloud forcing by
separating the effects and feedback of the clouds. Their
final conclusion is that clouds have a negative impact
on the surface temperature. Dressler (2010) has
analysed the TOA radiation budget in response to
short-term climate variations from the years 2000 to
2010, and his results showed positive feedback of the
clouds. So the issue of cloud forcing still remains
unclear without common acceptance and
understanding but the big majority of CF studies show
the cooling effect of cloudiness increase.
The specification of the CF can be criticized, because it
is based on the instant radiation flux changes after a
cloudiness change and it does not recognize the
dynamic delays of the climate system. Ollila (2014) has
concluded that the real CF is based on the SW
radiation changes only, because the Earth has yet to
reach the radiation flux balance according to the 1st
law of thermodynamics, which means that the OLR
flux must be the same as the net solar input flux. This
approach would increase the CF values by about 46 %
(Ollila, 2014b).
The equation (6) does not mean that only the total
cloudiness changes can cause albedo changes. The
changes of other reflected fluxes (by surface and air
and by different cloud types) have their effects on the
total albedo but the numerical effects are not known.
The equation (6) is well established because it is based
on the measured fluxes in the global scale.
When the changes in radiative forcing are known, the
equations (2), (3), and (4) can be used in calculating T,
ECS and values for the variations of RF and α. The
climate sensitivity parameter calculated according
equation (5) is 0.268 K/(Wm-2).
EQUATIONS (2)…(6).
The surface temperature is very sensitive for the
cloudiness and albedo changes of the Earth, as one can
see in Fig. 3.
Climate Sensitivity According to Absorption
and Longwave Radiation Changes
The author has also calculated the CS and values
applying two simulation tools available in the
network, namely Modtran (Berk et al., 2013) and the
Spectral Calculator (Gats, 2014). The results are
collected in Table 2. The all-sky conditions have been
calculated by combining the clear and cloudy sky
values (Bellouin et al., 2003; Ollila, 2013b):
(1-CL/100) * Fclear + (CL/100) * Fcloudy = Fall-sky (7)
Where F is a radiation flux of a sky in question and CL
is a cloudiness-%. Also temperatures of different skies
are combined according to this equation.
The average global atmosphere’s (AGA) surface
temperature is 15 °C, and the concentrations of the
anthropogenic GH gases measured in 2005 (AGA
2005) or in 2012 (AGA 2012) have been used. The GH
gas concentrations (2005/2012) are: CO2 (379/393 ppm),
CH4 (1.774/1.866 ppm), and N2O (0.319/0.324 ppm), as
reported by IPCC (2007c, 2013). The graphs in Fig. 1
and Fig. 2 are based on the AGA 2005 gas
concentrations and Fig. 4 graphs are based on the
AGA 2012 conditions. The parameters and choices Development in Earth Science Volume 2, 2014
applied in Modtran simulations, are depicted in Table
Tropospheric ozone
28 ppb
Stratospheric ozone scale
Water vapor scale
Ground temperature offset
1 °C (T= 288.2 K)
Holding fixed
Water vapor pressure
Subarctic summer
Clear sky
No clouds or rain
Cloudy sky
Cumulus cloud base
0.66 km, top 2.7 km
70 km
The CS and calculations are carried out to an altitude
of 70 km. In these calculations, a few iterations are
needed in both calculation tools in order to find the
surface temperature, which compensates the increased
absorption caused by a CO2 increase to 560 ppm,
bringing the OLR flux exactly to the same the OLR
flux caused by a CO2 concentration of 280 ppm.
Because both the OLR change and the temperature
change are calculated at the same time, the value can
be easily calculated. The cloudy sky values are
calculated using the Modtran simulations, which show
about 30 % lower OLR change than the clear sky
simulations. This relationship has been used in
estimating the cloudy sky values of Spectral Calculator
simulations. IPCC’s report AR5 (2013) summarizes
that according to several studies, the overall reduction
of RF values in cloudy sky conditions is in average 25
% lower than the clear sky values. The results of the
simulations carried out by Modtran and Spectral
Calculator are summarized in Table 3.
OLR, Wm-2
, K/(Wm-2)
Spectral Calculator
The change of CO2 concentration from 280 ppm to 560
ppm would increase the total absorption of shortwave
(SW) radiation by 0.40 Wm-2 according to the 1D
model simulations. This change alone would mean an
essential warming impact, but the situation is not
straightforward, because this absorption directly
decreases the SW radiation reaching the surface.
Myhre et al. (1998) have concluded that the absorption
of solar radiation in the troposphere yields a positive
RF at the tropopause and a negative RF in the
stratosphere contributing to a net cooling effect of CO2
absorption of -0.06 Wm-2 for the concentration change
from 280 ppm to 381 ppm. On these bases the author
has not included the solar radiation absorption
changes of CO2 into his calculations. The net effect of
solar radiation absorption would slightly decrease the
RF values of CO2 according to the analyses of Myhre et
al. (1998).
The clear sky OLR change 2.69 Wm-2 calculated by
Spectral Calculator at the TOA is the sum of
transmittance flux change 1.12 Wm-2 and the radiance
flux change 1.57 Wm-2. The OLR changes and the
warming values of different CO2 concentrations are
summarized in Table 4. The global warming caused
by the CO2 concentration increase from 280 ppm to 393
ppm calculated through OLR change is 0.24 °C
without water feedback.
The logarithmic fitting gives the following equation
between RF values and CO2 concentrations in Table 4:
RF = 3.12 * ln(C/280), (8)
Where RF is the radiative forcing in Wm-2, C is the CO2
concentration in ppm.
OLR, Wm-2
T, °C
CO2, 393 ppm
CO2, 560 ppm
CO2, 1370 ppm
Using Spectral Calculator simulation, a CO2
concentration of 393 ppm gives the value 0.230 and
1,370 ppm gives the value 0.269. According to
several studies (Zhang et al., 2004; Bodas-Salcedo et
al., 2008; Loeb et al., 2009), the OLR flux varies
between 233-240 Wm2 and using Eq. (3) shows that RF
Development in Earth Science Volume 2, 2014
value 233 Wm-2 gives value 0.270, and RF value 240
Wm-2 gives value 0.265. The variation of is
relatively small but is not invariant. The values
vary in totality from 0.230 to 0.319 in simulations. If
Eq. (3) is applied for OLR changes calculated by the
RF 2.16 Wm-2 of Spectral Calculator, the ECS is 0.576
°C and is 0.267. The same values using the
RF=1.834 Wm-2 of Modtran, the ECS is 0.49 °C and
is 0.267. The Modtran calculations’ results are not as
accurate and reliable as the Spectral Calculator results,
because the atmospheric conditions of Modtran cannot
be specified with the same accuracy as in Spectral
The author has also calculated the ECS value utilizing
the IR absorption in the clear atmosphere; this value is
0.46 °C. Some other researchers (Miskolczi and
Mlynczak, 2004) have calculated almost the same
value, namely 0.48 °C. The most reliable results and
best estimates are the values calculated by energy
balance equations: ECS = 0.576 °C and = 0.268
K/(Wm-2) with the uncertainty ranges of 0.460.6 °C
and 0.230.32 K/(Wm-2).
Some researchers have paid attention to the fact that
the temperatures simulated by General Circulation
Models (GCM) have departed from the real
temperatures since 1998. There are several new
research studies, which show lower ECS values than
those of IPCC. According to these results, the best
estimates and minimum values for ECS are: (Aldrin,
2012) 2.0 °C / 1.1°C; (Bengtson & Schwartz, 2012) 2.0
°C / 1.15 °C; (Otto et al., 2013) 2.0 °C / 1.2 °C and
(Lewis, 2012) 1.6 °C / 1.2 °C. Common features of
these studies are mathematical methods like Bayes’s
theorem to analyze the impact of CO2 based on the
measured global data of radiative forcing factors,
temperatures and ocean heat content.
These studies’ minimum values of ECS are practically
same in the range 1.1-1.2 °C. Bengtson & Schwartz
(2012) draw a conclusion that this value is the same as
the no-feedback Planck sensitivity. An interesting
point is that the ECS value of this study without any
feedback mechanisms (including the Planck sensitivity
calculation which is the same as equation (3)) is in the
range 0.559…0.584 °C, and with water feedback the
ECS according to the Plank’s equation is 1.1 °C. Is this
a coincidence? There could be a very simple
explanation. All the referred studies use the radiative
forcing value of 3.7 Wm-2 for CO2 and they do not
mention, whether or not water feedback has been used
in their analyses.
The author’s conclusion is that the researchers of these
studies have applied the RF value of 3.7 Wm-2 as in the
study of Bengtson & Schwartz (2012). If this RF value
has been calculated in the atmosphere, where is
constant relative humidity, it would mean that it
includes the positive water feedback duplicating the
warming values. The author has carried further
analyses later on.
The Analysis of IPCC’s Warming
According to IPCC (2013) the water vapor/lapse rate,
albedo and cloud feedbacks are the principal
determinants of equilibrium radiative forcing and
these feedbacks are assessed to be positive. The water
provides the largest positive feedback, which doubles
the other forcing elements like GH gas effects.
According to IPCC the forced component of the global
mean surface temperature (GMST) trend responds to
the effective radiative forcing (ERF) trend rapidly and
almost linearly (medium confidence). Hence, an ERF
trend can be approximately converted to a forced-
response GMST trend. The air temperature follows the
GMST without essential time delays. It should be
noticed that ERF and RF values are same up to 2011
(IPCC, 2013).
According to IPCC, the amount of water in the
atmosphere is controlled mostly by the air
temperature and therefore water does not cause direct
radiative forcing but it is classified as a feedback
element. The temperature data show a warming of
0.85 °C, over the period 1880 to 2012, and the total
radiative forcing is 2.34 Wm-2 (IPCC, 2013). Because
water amount in the atmosphere follows the air
temperature, water feedback acts with short delay in
respect to the GH gas impacts. Therefore the GMST
increase of 0.85 °C must include the water feedback.
Otherwise the concept of water feedback does not
follow the mechanism specified by IPCC: RF trend can
be converted to GMST trend and water feedback
follows the air temperature/surface temperature
almost without time delay. Shine et al. (2009) have
analyzed the annual cycles of the surface temperature,
and the result is a mean time lag of 56 ± 11 days for
oceans and 29 ± 6 days for land. The radiative energy
budget follows the surface temperatures of land and
An example about the short time lag of the sea is the
situation of the Finnish gulf. In the beginning of May,
the surface sea water temperature is about 0 °C and in Development in Earth Science Volume 2, 2014
the end of July it is about 20 °C. This is in line with the
time lag defined by Stine and confirms IPCC’s
statement (IPCC, 2013) that ERF and GMST trends
have no time delays thinking the time scales of the
climate change.
IPCC has not introduced any other feedback
mechanisms other than water feedback in its report
AR4 and AR5 causing the observed warming up till
the year 2011. Using the warming and radiative
forcing values of AR4, the following analysis can be
carried out. The warming of 0.76 °C according to IPCC
(2007c) happens through the mechanism that a CO2
increase of 99 ppm (an addition of 35.4% since 1750)
warms the climate first by 0.38 °C. The temperature
increases another 0.38 °C because of assumed constant
relative humidity. The total water amount increases by
2.3%, from 2.6 prcm (precipitated water in
centimeters) to 2.66 prcm. This means that the strength
of water is 15.4 in comparison to CO2, which is very
close to the value of 15.2 as calculated in the AGA 2005
It is useful to compare the results of this study to those
reported by IPCC. IPCC (2013) has utilized the
logarithmic relationship the 3rd report introduced by
Myhre et al. (1998):
RF = 5.35 * ln(C/280), (9)
Where RF is the radiative forcing in Wm-2, C is the CO2
concentration in ppm. The RF values of CO2 in AR5
are still based on equation (9). Myhre et al. (1998)
informs that “only the direct forcing to a change in
WMGG (well mixed greenhouse gases) concentration
is considered here” in calculating RF values. There are
two other studies referred in AR4 (2007a). The RF
values of 560 ppm CO2 concentrations in these three
studies are: Myhre et. al. (1998) 3.71 Wm-2, Hansen et
al. (1998) 3.63 Wm-2, and Shi (1992) 3.98 Wm-2. IPCC
has regarded these three simplified expressions to be
reliable and one can see the RF values are very close to
each other. Only Shi specifies that he has used “fixed
relative humidity”, which means positive water
feedback. The other studies do not specify humidity
conditions. The author’s conclusion is that also Myhre
et al. and Hansen et al. have used the constant relative
humidity conditions in the atmosphere. Otherwise
Shi’s RF value for CO2 should be about twice as much
as in the other studies. The exact water content has not
been specified in any of these studies.
The RF value according to equation (8) for the CO2
concentration 560 ppm is 2.16 Wm-2 and it is 58.4 % of
the RF value of 3.7 Wm-2 according to equation (9). The
same RF value according to MODTRAN simulations is
exactly 50 % smaller. This is another evidence that
equation (9) has been calculated in the constant RH
conditions, because this RF value of CO2 is practically
100 % bigger than the value calculated without water
The author has carried out two analyses based on his
own calculations and the warming results as
published by IPCC. In the first analysis the warming
results have been depicted in Fig. 4 according to the
different calculation bases. The x-axis is CO2
concentration or the CO2 equivalent including all
anthropogenic radiative forcing elements in the case of
Representative Concentration Pathways (RCP) (IPCC,
2007d). The red graph is the warming calculated
according to equation (9) by transforming RF values
into temperatures by multiplying by =0.5. The actual
values in AR4 and AR5 would be 0.442 and 0.363
respectively for the years 2005 and 2011. The
temperature increases of this study based on the
absorption and OLR changes are very close to each
The most interesting curve is the one labelled
'modified Myhre et. al' (purple dashed line), which is
the original Eq. (9), in which RF has been divided by 2
to eliminate positive water feedback and thereafter
multiplied by the newly calculated value of 0.268
K/Wm-2 to get the temperature. This curve overlaps
the two other curves calculated by the author.
The latest future projections of IPCC called RCPs are
also depicted with symbols of midpoints and
whiskers. The numeric value of each RCP indicates
radiative forcing in the year 2100, and the equivalent
CO2 concentrations include the effects of GH gases.
Development in Earth Science Volume 2, 2014
The RCP warming values are lower than the warming
values caused by CO2 according to equation (9). The
author’s conclusion is that equation (9) includes very
probably water feedback i.e. the calculations for
finding the relationship have been carried out in the
constant relative humidity conditions.
The old ECS value of 3 °C (IPCC, 2007b), which is also
the mean value of CS in AR5 (IPCC, 2013), has been
depicted in Fig. 3. The curve fitting through three
points (280/0, 379/0.76, 560/3.0 values as ppm/°C)
produces an exponential curve T = - 0.6 + 0.635 *
(C/280)2.52. It is not possible to achieve such a high
value by CO2 warming and water feedback alone. The
studies of Myhre et al. (1998) and equation (8) of this
study show that the relationship between RF and CO2
concentration is very close to a logarithmic form.
According to the general laws of IR absorption, the
exponential relationship is not possible, and this fact is
illustrated in Fig. 3. This kind of exponential
relationship would be possible only, if the other
feedback effects of the climate change would be
positive and highly nonlinear. The ECS value of 3 °C is
a combination of several GCM models (IPCC, 2007a).
A recent study (von Storch et al., 2013) reveals that 23
common GCMs cannot simulate temperature even at a
2% confidence level since temperature stagnation
began in 1998.
The results of the second analysis have been depicted
in Fig. 5. The RF values of different RCP scenarios are
the same as reported by IPCC (IPCC, 2007d). The
graph named as “Linear warming 1750-2011” has been
calculated using the linear coefficient of 0.85 °C / 2.34
Wm-2, which is the value of 0.363. The RCP values
follow closely the same linear relationship as
calculated by the warming values of 2011 only the
RCP8.5 warming value is 0.7 °C higher.
One conclusion is that the RCP warming values
include the same feedback mechanisms as the
warming value of 2011 and so only water feedback can
be considered. The linear straight gives the warming
value of 1.4 °C for CS including the anthropogenic
warming 0.7 °C and the water feedback 0.7 °C. If the
ECS would be 3.0 °C, the other feedback mechanisms
would cause 1.6 °C increase. Of course the situation is
more complicated considering cross effects but this is a
rough estimate about the magnitudes of different
warming mechanisms.
The transient climate sensitivity is 1.75 °C (1.0 to 2.5
°C) according to IPCC (IPCC, 2013) and it is depicted
in Fig. 4 and Fig. 5. This value can be calculated using
equation (9) of Myhre et al. (1998) and the climate
sensitivity parameter 0.5 k(Wm-2) of the IPCC’s report
AR4 (IPCC, 2007a).
Pierrehumbert (2011) has come to a conclusion that
CO2 is not near to saturation. The total saturation has
not yet been reached, but the warming effects are
much smaller than generally believed. The reason is
that the equation of Myhre et al. includes water
feedback effect making the radiative forcing of CO2
about 100 % higher than it should be. This applies to
other GH gases as well. The evidence is based on the
almost similar results of Shi (1992) and Myhre et al.
(1998) and two analyses of this study, which are based
on the spectral analyses.
The final conclusion is that climate sensitivity and
future warming projections depend totally on the
behaviors of water in the atmosphere. If the water
content is kept constant, ECS is in the range 0.46 to
0.58 °C. If positive water feedback is applied, ECS is
about 1.1 °C, and negative water feedback can force
warming to 0 °C. The actual relative humidity (RH)
measurements since 1948 show negative trends
(NOAA, 2012) indicating strongly a negative feedback
mechanism in the climate system, Fig.6. Also studies
of tropospheric humidity have revealed descending
trends (Hoinka, 1999; Paltridge et al., 2009).
These real RH measurements show that there is no
basis for using positive water feedback in calculating
global warming.
The CS value calculations of this study can be
criticized in that they do not cover all feedback
mechanisms. On the other hand IPCC calculations can
be criticized in that there is no information about the
contributions of feedback mechanisms to the CS value Development in Earth Science Volume 2, 2014
of 3.0 °C. The role of the clouds in the climate change
according to IPCC (IPCC, 2013) is likely positive but
confidence is still low. Today the CO2 concentration
change from 280 ppm to 560 ppm has passed the point
of 40%. Regardless of this big change, feedback
mechanisms other than water feedback cannot be
quantified. Water feedback mechanism is likely
negative as proposed by Miskolczi (2010) and not
positive as assumed by IPCC.
The recent CS calculations as referred to in this study
(Aldrin, 2012; Bengtson & Schwartz, 2012; Otto et al.,
2013; Lewis, 2012) use the mathematical analyses and
the real data but they do not test the possibility of
theories like “The Sun theory”. The author has carried
out a study (Ollila, 2012b) showing that the global
temperature in the period 1871-2002 has a r2 = 0.936
correlation to the sun activity changes and a r2=0.860
correlation to the CO2 concentration changes. The
mathematical analyses alone do not provide enough
evidence to conclude, if there are several potential
mechanisms available.
One conclusion is that the original Eq. (9) of Myhre et
al. (1998) is in line with the calculations of this paper if
the RF value is reduced by 41.6 % i.e. positive water
feedback is eliminated. Confusion and different results
of climate sensitivity are based on positive water
feedback used in Eq. (9) and unrealistic high impacts
of other feedback mechanisms.
The competing theory of the anthropogenic warming
theory is the so called “Sun theory”. The majority of
clouds forcing studies show that the clouds have
played an important role in fortifying the insolation
changes of the Sun. The change in cloudiness in the
range from 60% to 70% causes a temperature change
of 1.5 °C according to energy balance analysis as
depicted in Fig. 3. The dynamic analysis (Ollila 2014)
gives the value of -0.1 °C/cloudiness-% for cloudiness
sensitivity. Applying this value, the temperature
increase of 0.76 °C could be attributed to a decrease in
the total cloudiness of 7.6%. Even though clouds
remain a subject of confusion in climatology, it is clear
that climate is very sensitive to albedo changes, and
the cloudiness changes are the biggest contributors to
albedo changes.
According to IPCC (2013) the total anthropogenic
forcing increase during the last 15 years has been
about 0.3 Wm-2. Because there has been no
temperature increase, it means that the counterforce of
the same size has been affecting in the global climate.
The global cloudiness increase of 0.54% could cause
this kind of effect (Ollila, 2014) together with
decreasing sun activity. There is a sound physical
mechanism available to explain the cooling in period
1945-1980 as well as the stagnation of the temperature
since 1998.
Aldrin, M., Holden, M., Guttorp, P., Bieltvedt Skeie, R.,
Myhre, G., and Koren Berntsen, G.T. “Bayesian
estimation on climate sensitivity based on a simple
climate model fitted to observations of hemispheric
temperature and global ocean heat content.”
Environmetrics 23 (2012): 253-271.
Ardanuy, P. E., Stowe, L.L., Gruber, A., and Weiss, M.
Shortwave, longwave, and net cloud-radiative forcing
as determined from Nimbus 7 observations.” J. Geophys.
Res. 96 (1991): 1853718549, doi:10.1029/91JD01992.
Bellouin, N., Boucher, O., Haywood, and J., Shekar Reddy,
J.M. “Global estimate of aerosol direct radiative forcing
from satellite measurement”. Nature 438 (2003): 1138-
Bengtson, L. and Schwartz, S.E. “Determination of a lower
bound on earth’s climate sensitivity.” Tellus B 65 (2012).
Accessed January, 2014. 21533,
Berk, A, Bernstein, L.S., Robertson, and D.C. “Modtran, A
moderate resolution model for lowtran 7.” Accessed
January, 2014.
Bodas-Salcedo, A., Ringer, M.A., and Jones, A. “Evaluation
of surface radiation budget in the atmospheric
component of the Hadley Centre global environmental
model (HadGEM1).” J. Climate 21 (2008): 4723-4748.
Development in Earth Science Volume 2, 2014
Dessler, A.E. “A Determination of Cloud Feegback from
Climate Variations over the Past Decade.” Science 330
(2010): 1523-1527, DOI: 10.1126/science.1192546.
Ellingson, R.G., Ellis, J., and Fels, S. “The intercomparison of
radiation codes used in climate models.” Journal of
Geophysical Research 96 (1991): 8929-8953.
Gats Inc. “Spectral calculations tool.” Accessed January,
Hansen, J. et al., “Global Climate Changes as Forecast by
Goddard Institute for Space Studies, Three Dimensional
Model.” J. Geophys. Res., 93 (1998): 9341-9364 .
Harrison, E.F., Minnis, P., Barkstrom, B.R., Ramanathan, V.,
Cess, R.D., and Gibson, G.G. “Seasonal Variation of
Cloud Radiation Forcing Derived from the Earth
Radiation Budget Experiment.” J. Geophys. Res. 95
(1990): 18687-18703.
Harvard-Smithsonian Center for Astrophysics. “The Hitran
database.” Accessed January, 2014.
Hoinka, K.P. “Temperature, humidity, and wind at the
global tropopause.” Mon. Wea. Rev. 27 (1999): 2248.
IPCC. “Climate response to radiative forcing.” IPCC Fourth
Assessment Report (AR4), The Physical Science Basis,
Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on
Climate Change, Cambridge University Press,
Cambridge, 2007a.
IPCC. “Expert Meeting Report. Towards new scenarios for
analysis of emissions, climate change, impacts and
response strategies.” Technical Summary, 19-12 Sep 2007,
Noordwijkerhout, The Netherlands, 2007d.
IPCC. “The Physical Science Basis.” Working Group I
Contribution to the IPCC Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge
University Press, Cambridge, 2013.
IPCC. “Water vapour and lapse rate.” IPCC Fourth
Assessment Report (AR4), The Physical Science Basis,
Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on
Climate Change, Cambridge University Press,
Cambridge, 2007c.
IPPC. “Summary for policymakers in Climate Change 2007.”
The Physical Science Basis, Contribution of Working
Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge
University Press, Cambridge, 2007b.
Kielh, J.T. and Trenbarth, K.E. “Earth’s Annual Global Mean
Energy Budget.” Bull. Amer. Meteor. Soc. 90 (1997): 311-
Lewis, N. J. “An Objective Bayesian Improved Approach for
Applying Optimal Fingerprint Techniques to Estimate
Climate Sensitivity.” J. Clim. 26 (2013): 7414-7429.
Loeb, N.G. et al. ”Toward optimal closure of the earth’s top-
of-atmosphere radiation budget.” J. Climate 22 (2009):
Miskolczi, F. “The stable stationary value of the earth’s
global average atmospheric Planck-weighted
greenhouse-gas optical thickness.” Ener. & Envir. 21
(2010): 243-262.
Miskolczi, F.M. and Mlynczak, M.G. “The greenhouse effect
and the spectral decomposition of the clear-sky terrestrial
radiation.” Idöjaras 108 (2004): 209-251.
Myhre, G., Highwood, E.J., Shine, K.P., and Stordal, F.
“New estimates of radiative forcing due to well mixed
greenhouse gases.” Geophys. Res. Lett. 25 (1998): 2715-
NOAA. “Relative humidity trends. NOAA Earth System
Research Laboratory.” Accessed January, 2014.
Ohmura, A. “Physical basis for the temperature-based melt-
index method.” J. Appl. Meteorol. 40 (1997): 753-761.
Ohring, G., and Clapp, P.F. “The Effect of Changes in Cloud
amount on the Net Radiation at the Top of the
Atmosphere.” J. Atm. Sc. 37 (1980): 447-454.
Ollila, A. “Analyses of IPCC’s warming calculation results.”
J. Chem. Biol. Phys. Sc. 4 (2013a): 2912-2930.
Ollila, A. “Changes in cosmic ray fluxes improve correlation
to global warming.” Int. J. Ph. Sc, 7(5) (2012b): 822-826.
Ollila, A. “Dynamics between clear, cloudy, and all-sky
conditions: Cloud forcing effects.” J. Chem. Biol. Phys.
Sc. 4 (2014): 557-575.
Ollila, A. “Earth’s energy balances for clear, cloudy and all-
sky conditions.” Dev. in Earth Science 1 (2013b).
Ollila, A. “The roles of greenhouse gases in global
warming.” Ener. & Envir. 23 (2012a): 781-799. Development in Earth Science Volume 2, 2014
Otto, A. et. al. “Energy budget constraints on climate
response.” Nature Geoscience, 6 (2013): 415-416.
Paltridge, G., Arking, A., and Pook, M. “Trends in middle-
and upper-level tropospheric humidity from NCEP
reanalysis data.” Theor. Appl. Climatol. 98 (2009): 351–
Pierrehumbert, R.T. “Infrared radiation and planetary
temperature.” Ph.Today 64 (2011): 33-38.
Ramanathan, V., Cicerone, R.J., Singh, H.B., and Kiehl, J.T.
“Trace gas trends and their potential roles in climate
change.” J. Geophys. Res. 90 (1985): D3 5547-5566.
Raschke, E. et al., “Cloud effects on the radiation budget
based on ISCCP data (1991 to 1995).” International
Journal of Climatology 25 (2005): 1103-1125.
Shi, G-Y. 1992. “Radiative forcing and greenhouse effect due
to the atmospheric trace gases.” Science in China (Series
B), 35 (1992): 217-229.
Shine, A.R., Huybers, P., and Fung, I.Y. “Changes in the
phase of the annual cycle of surface temperature.”
Nature, 457 (2009): 435-440.
Spencer, R.W., and W.D. Braswell. “On the diagnosis of
radiative feedback in the presence of unknown radiative
forcing.” J. Geophys. Res. 115 (2011): D16109,
Stephens, G.I. et al., “An update on Earth’s energy balance in
light of the latest global observations. Nature Geoscience
5 (2012): 691-696.
Von Storch, H., Barkhordarian, A., Hasselmann, K., and
Zorita, K.E. “Can climate models explain the recent
stagnation in global warming?” Accessed January, 2014.
Zhang, Y-C., Rossow, W.B., and Lacis, A.A. “Calculation of
radiative fluxes from the surface to top of atmosphere
based on ISCCP and other global data sets: Refinements
of the radiative model and the input data.” J. Geophys.
Res. 109 (2004): 1149-1165.
... The IPCC has used the Radiative Forcing (RF) equation of Myhre et al. [3] (henceforth MHSS98) for CO 2 in the three latest Assessment Reports TAR [4], AR4 [5] and AR5 [1] for calculating RF at the top of atmosphere (TOA). The RF equation of Ollila [6] (henceforth Ollila14) has the same form RF = k * ln(C/208) ...
... where k is 5.35 [3] or 3.12 [6] and C is the concentration of CO 2 (ppm). MHSS98 has used the term "Instantaneous Radiative Forcing" (IRF) meaning the RF value calculated by the means of Line-By-Line (LBL) spectral analysis method or by a narrow or a broadband method at the tropopause. ...
... Ollila [6] has calculated with three independent methods the value of λ: The Earth' energy balance 0.268 (K/(Wm -2 ), spectral calculations by Spectral Calculator [12] application 0.259 (K/(Wm -2 ), and by MODTRAN application [13] 0.319 (K/(Wm -2 ). The value calculated by the energy balance shows that there is no positive water feedback mechanism in the atmosphere and the author has used the λ value of 0.27 K/(Wm -2 ) thereafter. ...
Full-text available
The anthropogenic global warming theory is based on the greenhouse (GH) effect, which is due to the longwave (LW) absorption by GH gases and clouds according to the IPCC. This LW radiation downward is the imminent cause for the GH effect increasing the surface temperature by 33°C. It has been shown that latent and sensible heating are essential parts of downward LW radiation and the total GH effect. In this study, an iteration method utilizing this basic GH effect mechanism has been applied to simulate the warming impacts of enhanced GH effect changes. The results are compatible with the Transient Climate Response (TCR) of 0.6 °C. The issue of stratospheric cooling due to increased CO2 concentration has been calculated and analyzed. The stratospheric cooling effect is real but its impact on the Effective Radiative Forcing (ERF) has been shown to be negative and not positive as generally implied. The reason is that the decreased absorption of LW radiation in the atmosphere always decreases the GH effect. This result challenges the new concept of the ERF that is the sum of Instantaneous RF (IRF) and rapid adjustments as applied in General Climate Models (GCMs). If the stratospheric adjustment has the opposite effect, then the IRF values would be also wrongly calculated in these models. Two independent validation methods were applied to test the temperature impacts of CO2 concentration increases.
... In the "Ollila model" Eq. (1) has been applied but λ has a different value. The λ value is from Ollila's three studies [59][60][61] showing that there is no positive water feedback in the atmosphere. This result is based on the λ value calculated by two methods from the Earth's energy balance and from the spectral analysis calculations and λ value is 0.27 K/(Wm -2 ). ...
... Anthropogenic warming includes only carbon dioxide because during the pause methane and nitrogen oxide forcing impact changes are insignificant (< 0.001°C). In the Ollila model the radiative forcing of CO 2 is calculated according to the earlier research study [59] dT GHG = 0.27 * 3.12 * ln (C/280) ...
... By comparing the total temperature variations and the simultaneous water vapor effects, it is easy to conclude that water vapor plays an important role because its contribution to the ENSO temperature effect is about 50%. The positive temperature effect is based on the fact that water vapor as a GH gas is about 12 times stronger than CO 2 [59]. This is a perfect example that positive water feedback is a reality in shortterm events like ENSO. ...
Full-text available
The hiatus or temperature pause during the 21 st century has been the subject of numerous research studies with very different results and proposals. In this study, two simple climate models have been applied to test the causes of global temperature changes. The climate change factors have been shortwave (SW) radiation changes, changes in cloudiness and ENSO (El Niño Southern Oscillation) events assessed as the ONI (Oceanic Niño Index) values and anthropogenic climate drivers. The results show that a simple climate model assuming no positive water feedback follows the satellite temperature changes very well, the mean absolute error (MAE) during the period from 2001 to July 2019 being 0.073°C and 0.082°C in respect to GISTEMP. The IPCC's simple climate model shows for the same period errors of 0.191°C and 0.128°C respectively. The temperature in 2017-2018 was about 0.2°C above the average value in 2002-2014. The conclusion is that the pause was over after 2014 and the SW anomaly forcing was the major reason for this temperature increase. SW anomalies have had their greatest impacts on the global temperature during very strong (super) El Niño events in 1997-98 and 2015-16, providing a new perspective for ENSO events. A positive SW anomaly continued after 2015-16 which may explain the weak La Niña 2016 Original Research Article Ollila; PSIJ, 24(2): 1-20, 2020; Article no.PSIJ.55149 2 temperature impacts, and a negative SW anomaly after 1997-98 may have contributed two strong La Niña peaks 1998-2001. No cause and effect connection could be found between the SW radiation and temperature anomalies in Nino areas.
... The transient CS (TCS) of IPCC includes water feedback, and this feature is inherently in the l value 0.5 K/(Wm À2 ) (IPCC, 2001). Ollila (2014) has calculated the l value using the energy balance of the Earth and the result was 0.268 K/(Wm À2 ), and spectral analysis gave the l value 0.259 K/(Wm À2 ). These results can be rounded to 0.27 K/(Wm À2 ), which means that the amount of water in the atmosphere is constant, without positive or negative feedback. ...
... The question is whether the positive water feedback can be confirmed by the real observations and whether the RF value of Myhre et al. (1998) is correct. Ollila (2014) has reproduced the RF value of Myhre et al. (1998) using the same spectral analysis method. The result of this study is that the RF value can be calculated using the same kind of logarithmic formula, but the coefficient k is different: ...
... The RF value for the CO 2 concentration of 560 ppm is 2.16 Wm À2 according to equation (3), which is 42 per cent smaller than 3.7 Wm À2 used by the IPCC. The same study of Ollila (2014) shows that the CS parameter l is 0.27 K/(Wm À2 ), which means that there is no water feedback. Using this l value, equation (3) gives a TCS value of 0.6°C only. ...
Full-text available
Purpose The purpose of this paper is to analyze the scientific basis of the Paris climate agreement. Design/methodology/approach The analyses are based on the IPCC’s own reports, the observed temperatures versus the IPCC model-calculated temperatures and the warming effects of greenhouse gases based on the critical studies of climate sensitivity (CS). Findings The future emission and temperature trends are calculated according to a baseline scenario by the IPCC, which is the worst-case scenario RCP8.5. The selection of RCP8.5 can be criticized because the present CO2 growth rate 2.2 ppmy⁻¹ should be 2.8 times greater, meaning a CO2 increase from 402 to 936 ppm. The emission target scenario of COP21 is 40 GtCO2 equivalent, and the results of this study confirm that the temperature increase stays below 2°C by 2100 per the IPCC calculations. The IPCC-calculated temperature for 2016 is 1.27°C, 49 per cent higher than the observed average of 0.85°C in 2000. Originality/value Two explanations have been identified for this significant difference in the IPCC’s calculations: The model is too sensitive for CO2 increase, and the positive water feedback does not exist. The CS of 0.6°C found in some critical research studies means that the temperature increase would stay below the 2°C target, even though the emissions would follow the baseline scenario. This is highly unlikely because the estimated conventional oil and gas reserves would be exhausted around the 2060s if the present consumption rate continues.
... The number of studies for calculating and analysing the contributions of GH gases is surprisingly low. The most important results are summarized in Table 2. Michell [4], Kiehl & Trenberth [5], and Ollila [6] have carried out the calculations in the clear sky conditions and Schmidt et al. [7] values are for all-sky. Kiehl & Trenberth [5] have also two percentages for cloudy sky conditions. ...
... The first calculations were carried out to find out the impacts of HITRAN 2012 and water continuum updates in the absorption calculations. The author has used in earlier studies the atmospheric one profile model called average global atmosphere (AGA) [6], [11], [13], [14], [15], [16], [17]. This model was based on the GH gas concentrations in 2005 and therefore it is called AGA05. ...
... where TPW is the absolute humidity in prcm. The warming impact of CO 2 is calculated by the equation introduced by Ollila [6]: ...
Full-text available
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.
... Ollila has analysed [19] the future warming values based on the RF values of greenhouse gases. This analysis showed that the warming values of RCP2.5, RCP4.5, and RCP6 could be calculated using the λ value of ~0.37 K/(Wm -2 ). ...
... It means a lower λ value of about 0.27 -0.3 K/(Wm -2 ). Some researchers have calculated even lower values like ~0.6°C for climate sensitivity [19,26] or 0.7°C [27]. Ollila [19] has calculated the λ value using three different methods and his results vary between 0.245 and 0.331 the most reliable value being 0.268 K/(Wm -2 ). ...
... Some researchers have calculated even lower values like ~0.6°C for climate sensitivity [19,26] or 0.7°C [27]. Ollila [19] has calculated the λ value using three different methods and his results vary between 0.245 and 0.331 the most reliable value being 0.268 K/(Wm -2 ). In this study these two most common values have been applied: 0.27 K/(Wm -2 ) and 0.5 K/(Wm -2 ). ...
Full-text available
The author has developed a dynamic model (DM) to simulate the surface temperature change (∆T) caused by the eruption of Mount Pinatubo. The main objectives have been 1) to test the climate sensitivity parameter (λ) values of 0.27 K/(Wm-2) and 0.5 K/(Wm-2), 2) to test the time constants of a simple first-order dynamic model, and 3) to estimate and to test the downward longwave radiation anomaly (∆LWDN). The simulations show that the calculated ∆T of DM follows very accurately the real temperature change rate. This confirms that theoretically calculated time constants of earlier studies for the ocean (2.74 months) and for the land (1.04 months) are accurate and applicable in the dynamic analyses. The DM-predicted ∆T values are close to the measured value, if the λ-value of 0.27 K/(Wm-2) has been applied but the λ-value of 0.5 K/(Wm-2) gives ∆T values, which are about 100% too large. The main uncertainty in the Mount Pinatubo analyses is the ∆LWDN flux, because there are no direct measurements available during the eruption. The author has used the measured ERBS fluxes and has also estimated ∆LWDN flux using the apparent transmission measurements. This estimate gives the best and most consistent results in the simulation. A simple analysis shows that two earlier simulations utilising General Circulation Models (GCM) by two research groups are depending on the flux value choices as well as the measured ∆T choices. If the commonly used minimum value of-6 Wm-2 would have been used for the shortwave anomaly in the GCM Original Research Article Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242 2 simulations, instead of-4 Wm-2 , the ∆T values would differ from the measured ∆T values almost 100%. The main reason for this error seems be the λ-value of 0.5 K/(Wm-2).
... Usually researchers have used some other methods than the specification of CS, which requires using spectral analysis methods. Harde [9] and Ollila [7]have used the spectral analysis method, the average global atmosphere, and the specification of CS. In both studies the CS is 0.6 °C. ...
... In both studies the CS is 0.6 °C. Ollila [7] shows that this same CS value can be calculated from the energy balance of the Earth. Ollila [7]shows also what the reasons for this big gap are: 1.85 °C versus 0.6 °C. ...
... Ollila [7] shows that this same CS value can be calculated from the energy balance of the Earth. Ollila [7]shows also what the reasons for this big gap are: 1.85 °C versus 0.6 °C. The reasons are in water feedback. ...
Full-text available
According to the IPCC’s simplest model based on the anthropogenic driving forcing factors, the temperature increase up to 2011 from 1750 is 1.15 °C, which is 35 % greater than the observed temperature 0.85 °C. In this study three other models have been analysed. The first model is a cosmic model, which is based on the galactic cosmic rays (GCR) changes and space dust amount. This model gives correlation r2=0.972. The second model is the combination of space dust changes, the calculated warming impacts of greenhouse gases and the Total Solar Irradiance (TSI) changes giving correlation r2=0.971. The third model is the combination of space dust and TSI changes giving correlation r2=0.948. All these models have negligible error in 2010. The atmospheric water has a decisive role in the real impacts of greenhouse gases. It remains uncertain, because the first global humidity measurements start from 1948. The final conclusion of this study is: the greenhouse gases cannot explain the ups and downs of the Earth’s temperature trend since 1750 and the temperature pause since 1998, but the space dust changes can do it extremely well.
... Harde [21] has used spectral analysis methods and the 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: ...
... 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 2 . 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. ...
Full-text available
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
Equilibrium climate sensitivity characterizes the Earth's long-term global temperature response to increased atmospheric CO2 concentration. It has reached almost iconic status as the single number that describes how severe climate change will be. The consensus on the 'likely' range for climate sensitivity of 1.5 °C to 4.5 °C today is the same as given by Jule Charney in 1979, but now it is based on quantitative evidence from across the climate system and throughout climate history. The quest to constrain climate sensitivity has revealed important insights into the timescales of the climate system response, natural variability and limitations in observations and climate models, but also concerns about the simple concepts underlying climate sensitivity and radiative forcing, which opens avenues to better understand and constrain the climate response to forcing. Estimates of the transient climate response are better constrained by observed warming and are more relevant for predicting warming over the next decades. Newer metrics relating global warming directly to the total emitted CO2 show that in order to keep warming to within 2 °C, future CO2 emissions have to remain strongly limited, irrespective of climate sensitivity being at the high or low end.
Full-text available
The researchers have published several studies on the radiation fluxes based on measurement data banks and radiative transfer models. The author has used available flux values and utilized different methods of obtaining the total of Earth’s energy balances for clear, cloudy and all-skies. The calculation methods include balance equations, spectral calculations and the cloudiness factor in combining energy fluxes of three sky conditions. A new idea has been introduced that the surface albedo flux is partially absorbed in cloudy conditions, as with incoming shortwave radiation. The atmospheric albedo fluxes have been calculated separately for cloud reflection and for air particles. Also the atmospheric absorption has been divided into cloud and clear air absorption fluxes.
Full-text available
The author has analyzed the dynamics of atmospheric changes between all-sky, clear and cloudy sky conditions. The basis of analyses is the calculation of flux values at the balance states. The analyses depend essentially on the time constants of basic processes, which can be analyzed separately. Two time constants are based on the former research results, and three time constants have been developed and estimated in this study. The basic processes in dynamic analyses have been the very rapid changes in cloudiness and cloud temperatures, the rapid change in upward atmospheric longwave radiation caused by solar insolation change, the slow change in temperature of the land and sea, and the transient change in the atmosphere temperature. This transient atmospheric process has an essential role in explaining why the surface temperature increases when at the same time the cloud forcing decreases. The dynamic simulations reveal that in all cases, two rapid changes in the atmosphere can bring the outgoing longwave radiation at the top of the atmosphere almost exactly (a difference of 0% to 0.3%) to the observed pseudo-balance values of clear and cloudy skies. Pseudo-balance values for clear and cloudy skies are not very time-sensitive because the values stay within ±1 W/m2 from one day to 13 days. According to the true energy balance values, the slightly nonlinear cloud forcing would be -0.56 Wm-2 per 1% increase in cloudiness and -0.1 °C per 1% increase in cloudiness over the normal cloudiness range variation from 60% to 70%. According to this study, the commonly used cloud forcing in the units of W/m2 yields effects that are about 40% too low for the long-term cloudiness changes. Cloudiness changes could alone explain the global warming.
Full-text available
Some researchers have noticed that the warming calculations of Intergovernmental Panel on Climate Change (IPCC) are not always based on the atmospheres, which use the global average values. CO2 effect of 26% in greenhouse phenomenon is based on the modified U.S. Standard Atmosphere 1976 (USST 76 atmosphere) containing only 50% of water in comparison to the true value. The calculations prove that the warming of 0.76 °C can be achieved if the USST 76 atmospheric model is applied and constant relative humidity (RH) assumed. The analysis also reveals that IPCC’s scenario presentation contains choices, which make the warming results looking higher than they should be. All the climate sensitivity values above 1.7 °C conflict with the explanation given by IPCC for the 1750 - 2005 periods. The global warming potential (GWP) values of CH4 and N2O are applicable only for small concentration changes and in higher concentrations these greenhouse (GH) gases are even weaker than CO2. The ultimate worst case scenario is the release of methane from the methane clathrates on the ocean floor. The calculations show that the release would cause 2.1 °C temperature increase, which is only 68% of the CO2 warming effect. The spectral analysis show that in the prevailing atmospheric conditions the warming potency of methane is about 14% from the potency of CO2, and the same of N2O is about 17%. The effect of water in the same conditions is 15.2 times greater than that of CO2.
Full-text available
Scientists are still debating the reasons for "global warming". The author questions the validity of the calculations for the models published by the Intergovernmental Panel on Climate Change (IPCC) and especially the future scenarios. Through spectral calculations, the author finds that water vapour accounts for approximately 87% of the greenhouse (GH) effect and only 10% of CO2. A doubling of the present level of CO2 would increase the global temperature by only 0.51°C without water feedback. The IPCC claims that a temperature increase of 0.76°C for 2005 was caused in part by water (about 50%), because relative humidity (RH) stays constant in their model. The calculations prove that CO2 would have increased the temperature by only 0.2°C since 1750 and that the measured decrease in water since 1948 has compensated for this increase. This study has also produced results indicating a negative feedback for relative humidity. The simulations of this study propose that the IPCC's model atmospheres could be approximately 50% too dry.
Full-text available
In this study, it was found out that ion chamber measurements of cosmic ray fluxes during the last solar cycle ending in 2009 differ essentially from neutron measurements. The ion chamber measurements utilizing geomagnetic aa index as proxy for the years between 1868 and 1936 produced excellent correlation to the global temperature changes for the period of 1868 to 2009. These results indicate that solar activity changes may cause climate changes.
The radiative forcing and greenhouse effect due to the atmospheric trace gases have been calculated by using an advanced radiative-convective model developed in this paper. The relationship between radiative forcing and concentration is given for each trace gas. The results show that (i) the radiative forcing and then the greenhouse effect are significantly affected by the overlapping of atmospheric absorption bands; (ii) the increasing concentration of trace gases other than CO2, such as CH4, N2O, CFCs, etc., may potentially play an important role in the future global warming; (iii) the proposed substitutes, such as HCFC124 and HFC125, for the chlorofluorocarbons which are considered to destroy the ozone layer have still considerable greenhouse effect even though their ozone depletion potentials are much smaller than CFCs; and (iv) the feedback processes within the earth-atmosphere system have important effect on the surface temperature change due to the radiative forcing to the system.
We use a three-dimensional cimate model, the Goddard Institute for Space Studies (GISS) model II with 8o by 10o horizontal resolution, to simulate the global climate effects of time-dependent variations of atmospheric trace gases and aerosols. The experiments begin in 1958 and include measured or estimated changes in atmospheric CO2, CH4, N2O, chlorofluorocarbons (CFCs) and stratospheric aerosols for the period from 1958 to the present. Principal results are as follows: 1) Global warming to the level attained at the peak of the current interglacial and the previous interglacial occurs in all scenarios; however, there are dramatic differences in the levels of future warming, depending on trace gas growth. 2) The greenhouse warming should be clearly identifiable in the 1990s; the global warming within the next several years is predicted to reach and maintain a level at least three standard deviations above the climatology of the 1950s. 3) Regions where an unambiguous warming appears earliest are low-latitude oceans, China and interior areas in Asia, and ocean areas near Antarctica and the north pole. -from Authors
Transient and equilibrium sensitivity of Earth's climate has been calculated using global temperature, forcing and heating rate data for the period 1970-2010. We have assumed increased long-wave radiative forcing in the period due to the increase of the long-lived greenhouse gases. By assuming the change in aerosol forcing in the period to be zero, we calculate what we consider to be lower bounds to these sensitivities, as the magnitude of the negative aerosol forcing is unlikely to have diminished in this period. The radiation imbalance necessary to calculate equilibrium sensitivity is estimated from the rate of ocean heat accumulation as 0.37±0.03W m-2 (all uncertainty estimates are 1- σ). With these data, we obtain best estimates for transient climate sensitivity 0.39±0.07K (W m-2)-1 and equilibrium climate sensitivity 0.54±0.14K (W m-2)-1, equivalent to 1.5±0.3 and 2.0±0.5K (3.7W m-2)-1, respectively. The latter quantity is equal to the lower bound of the 'likely' range for this quantity given by the 2007 IPCC Assessment Report. The uncertainty attached to the lower- bound equilibrium sensitivity permits us to state, within the assumptions of this analysis, that the equilibrium sensitivity is greater than 0.31K (W m-2)-1, equivalent to 1.16K (3.7W m-2)-1, at the 95% confidence level.
Predictions of climate change are uncertain mainly because of uncertainties in the emissions of greenhouse gases and how sensitive the climate is to changes in the abundance of the atmospheric constituents. The equilibrium climate sensitivity is defined as the temperature increase because of a doubling of the CO2 concentration in the atmosphere when the climate reaches a new steady state. CO2 is only one out of the several external factors that affect the global temperature, called radiative forcing mechanisms as a collective term. In this paper, we present a model framework for estimating the climate sensitivity. The core of the model is a simple, deterministic climate model based on elementary physical laws such as energy balance. It models yearly hemispheric surface temperature and global ocean heat content as a function of historical radiative forcing. This deterministic model is combined with an empirical, stochastic model and fitted to observations on global temperature and ocean heat content, conditioned on estimates of historical radiative forcing. We use a Bayesian framework, with informative priors on a subset of the parameters and flat priors on the climate sensitivity and the remaining parameters. The model is estimated by Markov Chain Monte Carlo techniques. Copyright © 2012 John Wiley & Sons, Ltd.
A detailed reanalysis is presented of a Bayesian climate parameter study (as exemplified by Forest et al.) that estimates climate sensitivity (ECS) jointly with effective ocean diffusivity and aerosol forcing, using optimal fingerprints to compare multidecadal observations with simulations by the Massachusetts Institute of Technology 2D climate model at varying settings of the three climate parameters. Use of improved methodology primarily accounts for the 90% confidence bounds for ECS reducing from 2.1-8.9 K to 2.0-3.6 K. The revised methodology uses Bayes's theorem to derive a probability density function (PDF) for the whitened (made independent using an optimal fingerprint transformation) observations, for which a uniform prior is known to be noninformative. A dimensionally reducing change of variables onto the parameter surface is then made, deriving an objective joint PDF for the climate parameters. The PDF conversion factor from the whitened variables space to the parameter surface represents a noninformative joint parameter prior, which is far from uniform. The noninformative prior prevents more probability than data uncertainty distributions warrant being assigned to regions where data respond little to parameter changes, producing better-constrained PDFs. Incorporating 6 years of unused model simulation data and revising the experimental design to improve diagnostic power reduces the best-fit climate sensitivity. Employing the improved methodology, preferred 90% bounds of 1.2-2.2 K for ECS are then derived (mode and median 1.6 K). The mode is identical to those from Aldrin et al. and [using the same Met Office Hadley Centre Climate Research Unit temperature, version 4 (HadCRUT4), observational dataset] from Ring et al. Incorporating nonaerosol forcing and observational surface temperature uncertainties, unlike in the original study, widens the 90% range to 1.0-3.0 K.