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Climate Sensitivity Parameter in the Test of the Mount Pinatubo Eruption


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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).
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Physical Science International Journal
9(4): 1-14, 2016, Article no.PSIJ.23242
ISSN: 2348-0130
SCIENCEDOMAIN international
Climate Sensitivity Parameter in the Test of the
Mount Pinatubo Eruption
Antero Ollila
Department of Civil and Environmental Engineering (Emer.), School of Engineering, Aalto University,
Otakaari 1, Box 11000, 00076 AALTO, Espoo, Finland.
Author’s contribution
The sole author designed, analyzed and interpreted and prepared the manuscript.
Article Information
DOI: 10.9734/PSIJ/2016/23242
Yichi Zhang, Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and
Natural Resources Research, Chinese Academy of Sciences, China.
Ismail Gultepe, Environment Canada, Cloud Physics and Severe Weather Res. Section, Canada.
Abbas Mohammed, Blekinge Institute of Technology, Sweden.
Anonymous, University of St. Thomas, USA.
Mahmut Dogru, Bitlis Eren University, Turkey.
S. B. Ota, Institute of Physics, Bhubaneswar, India.
Bharat Raj Singh, Technical Campus, Lucknow, India.
Complete Peer review History:
Received 21
November 2015
Accepted 14
February 2016
Published 4
March 2016
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
) and 0.5 K/(Wm
), 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
has been applied but the λ-value of 0.5 K/(Wm
) 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
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
simulations, instead of -4 Wm
, 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
Keywords: Global warming; climate sensitivity parameter; climate response time; radiative forcing
response; downward radiative fluxes; Mount Pinatubo eruption.
1.1 Objectives and Symbols
The Mount Pinatubo eruption in 1991 caused a
global cooling during the next five years as the
incoming shortwave radiation was reduced by 6
offering a unique opportunity to test and to
analyse the various phenomenon of the climate
system. Water vapour feedback has remained a
topic of debate since 1990 and the eruption can
be used to analyse this effect also. The first
objective of this paper is to test the two climate
sensitivity parameter values which have been
commonly used in the scientific studies. The
second objective is to test the climate system
time constants describing the dynamic behaviour
of the climate exposed to a relative big and
sudden change. The third objective is to estimate
and to test the downward longwave radiation
anomaly (LWDN). In the simulations a
theoretical feedback property of the climate
system has been also tested.
Table 1 includes all the symbols, abbreviations,
acronyms and definitions used repeatedly in this
1.2 The Mount Pinatubo Eruption
The main eruption of the Mount Pinatubo volcano
(15.1 °N, 120.3 °E) on the island of Luton in the
Philippines began on the 3
of June, 1991 and
concluded on the next day. Four large explosions
generated eruption columns reaching the heights
of up to 24 km in the stratosphere. The estimate
of the stratospheric mass increase was 14–20 Mt
of SO
which created 21-40 Mt of H
aerosols [1]. The eruption also injected vast
quantities of minerals and metals into the
troposphere and stratosphere in the form of ash
particles. The aerosols formed a global layer of
sulfuric acid haze over the globe and the global
temperatures dropped about 0.5°C in the years
1991 – 1993.
The sulphate aerosols caused scattering of the
visible light and therefore the incoming radiation
scattered more effectively back into space. Thus
the albedo of the Earth increased leading to a
cooling at the Earth’s surface. On the other hand
the plants utilized the climate conditions,
because they could photosynthesize more
effectively in the diffuse sunlight [2,3]. As a result
of the more intensive photosynthesis, there was
a negative anomaly of the global CO
concentration increase rate.
Table 1. List of symbols, abbreviations, and
DM One dimensional dynamic model
AT Apparent transmission
ENSO El Niño Southern Oscillation
ERBS NASA’s Earth Radiation Budget
GCM General Circulation Model
ISCCP International Satellite Cloud
Climatology Project
LW Longwave
LWDN LW radiation flux downward
LWUP LW radiation flux upward
LWSRF LW radiation emitted by the
OLR Outgoing longwave radiation
ONI Oceanic Niño Index
RF Radiative forcing change
SW Shortwave
SWATM SW radiation flux absorbed by
the atmosphere
SWIN SW radiation flux incoming at the
SWSRF SW radiation flux incoming at the
TOA Top of the atmosphere
TPW Total precipitable water
T Surface temperature
Tm 1DM-predicted surface
temperature change
Tav Average surface temperature
change by four datasets
Tmsu Surface temperature change by
UAH MSU dataset
Tav-e Tav with ENSO correction
Tmsu-e Tmsu with ENSO correction
TCS Transient climate sensitivity
λ Climate sensitivity parameter
Anomaly or change
means step n in time domain.
Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242
Because the eruption happened at one point, it
took several weeks before the global effect was
fully developed. The volcanic aerosol cloud
encircled the Earth in 21 days driven by the
easterly winds in the tropical stratosphere. It
covered about 42% of the Earth in two weeks [4].
In Fig. 1 are depicted the global temperature [5]
and the apparent transmission measured at
Mauna Loa [6] (19.3°N, 155.4°W). It can be seen
that there is delay between the temperature
response and the apparent transmission (AT)
describing the reduction of the incoming
shortwave (SW) radiation.’
Fig. 1. The global satellite temperature and
the apparent transmission measured at
Mauna Loa, Hawaii
In Fig. 2 the apparent transmissions (AT) are
depicted at the various sites on the northern
hemisphere [7]. It can be seen that the absolute
values of the AT values are different depending
mainly on the local conditions. For example, the
low values of the Japanese sites describe the air
quality of the local conditions. The large value of
the Mauna Loa is due to the fact that it is at the
altitude of 3.4 km in the middle of the Pacific. An
important feature thinking the analysis methods
of this study is that the percentage decreases are
very close to each other in the range from 10.1%
to 13.2%.
The sites in Fig. 2 cover almost 85% of the
northern hemisphere. Thomas [8] has analyzed
the global apparent transmission measurements
after the eruption. The analysis shows that the
aerosol cloud was covering the latitudes from
60S to 60N after three months and practically
uniform over the hemispheres after six months.
This is also the moment of the maximum
temperature decrease. The main role in
spreading the cloud had planetary scale waves in
high latitudes, which transported the volcanic
aerosol from the tropics to high latitudes. The
reason why the decrease of apparent
transmission value was almost the same at the
high latitudes as in the tropics is probably due to
the zenith angle. Even though the sulphate cloud
would be thinner at the high latitudes, the
sunlight has a longer pathway through the
atmosphere. This phenomenon can compensate
the effects of possible thinner cloud conditions.
Two conclusions can be drawn from these
figures. The global delay called a dead time in
process dynamics, is estimated to be 1.6 months
between the incoming SW radiation change and
the global surface temperature response. This
value is used in the dynamical analyses of this
Fig. 2. The apparent transmission values at
the various sites. The percentage values
show the maximum decreases of the
apparent transmissions after the eruption and
they are represented by light bars inside the
normal apparent values (total bar length)
Another conclusion is that after the fully
developed coverage of the sulphate cloud in the
stratosphere, the radiation effect changes can be
estimated to happen simultaneously over the
globe. Therefore it is justified to use the one
dimensional (1D) approach in developing a
dynamic model (called DM) for analysing the
temperature versus radiation flux relationships.
1.3 Literature Study
There have been numerous Pinatubo studies on
the three major fields. The first is on the aerosol
and chemical effects of the Pinatubo particles.
The second is focused on optical properties of
the aerosol particles and on the radiative forcing.
The third is on the responses to the forcing
Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242
affecting the temperature and the circulation
This paper concentrates on the dynamic
behaviour of the surface temperature changes
caused by the radiative flux changes. Therefore
the survey of the earlier studies covers only the
subjects which are relevant for this study.
Even though the Pinatubo eruption is the best
documented major eruption so far, there was an
essential radiative flux, which was not directly
measured during the eruption. This was the LW
downward radiation flux (LWDN), which is
essential, because it compensates the major
portion of the cooling effects of the reduced SW
downward radiation flux (SWIN) decrease during
the early phases of the eruption [9].
The World Climate Research Programme
(WCRP) Radiative Fluxes Working Group
initiated a new Baseline Surface Radiation
Network (BSRN) to support the research
projects. Some years later the BSRN was
incorporated into the WCRP Global Energy and
Water Cycle Experiment (GEWEX). The BSRN
network stations started to operate in 1992 and
that is why these valuable measurements were
not available during the Pinatubo eruption.
There has been a special GEWEX project to
assess the surface radiation budget datasets [10]
based on the available data at the top of the
atmosphere (TOA). By studying the GEWEX
results, the author’s conclusion is that the LWDN
fluxes could not be estimated reliably in this
project based on the other existing flux data.
Therefore a major challenge in this study is to
estimate the LWDN flux trend during the
Pinatubo eruption.
In Fig. 3 the main radiative fluxes of the Earth are
illustrated [11,12]. The climate forcing effect of a
volcano eruption can be analysed in the same
way as the cloud change forcing. Normally the
cloud forcing has been calculated as the sum of
changes in the downward SW flux change and
outgoing LW flux change between the clear and
all-sky conditions. Applying this same method,
the radiative forcing (RF) caused by the eruption,
is the sum of SWIN and LWUP and it is called
aerosol radiative forcing [13]. The change in the
flux values is calculated between the normal
conditions and during or after the eruption.
Because the outgoing LW flux is reduced during
the early phases of the eruption, it is a sign that
there is cooling happening on the surface.
The RF value calculated in this way is normally
called radiative or climate forcing (RF). Actually it
is only a measure of the real RF. There are two
fluxes which have the real forcing effect on the
Earth’s surface temperature (T) and they are
SWIN and LWDN. They are the only fluxes,
which form the radiation input on the surface. In
the change from the all-sky to the cloudy sky
conditions, the change of LWUP at the TOA is -
11 Wm
and the change of LWDN at the surface
is +14.3 Wm
[12]. These flux values show that if
the clear sky conditions do not prevail, the LWUP
change is not equal to the real warming/cooling
impact on the surface caused by the LWDN flux
change. This example also shows that the LWDN
flux change is greater than the LWUP flux
change. The major reason for this difference is
that the cloudy sky values are actually measured
in the dynamic situation and the LWUP flux is not
in the real equilibrium value.
Fig. 3. The main radiative fluxes of the Earth’s
energy balance
The small particle sizes less than 1 µm are more
effective in reflecting the SW solar radiation
SWIN than they are at reflecting the LW radiation
emitted by the surface. According to a
comprehensive study [1], the smallest particles
were sulphuric acid/water droplets and the
largest particles were ash fragments. The cooling
and warming effects of the aerosols and particles
depend on the particle sizes. The LWDN flux
increases especially during the early phases of
the eruption because there are larger aerosol
particles more in the atmosphere than in the later
phases. Therefore the warming effect of LWDN
is the most effective at the same time as the
cooling is in maximum [1,9]. The stratospheric
ash layer settled down just above the
troposphere staying there until March 1992. The
particle size measurements [1,4] showed that
Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242
there was a peak in both small and large particle
sizes after a few months after the eruption but by
1993 the high measurements values were
decaying back to pre-eruption values.
The ash cloud in the high altitudes of the
atmosphere absorbs and emits radiation. This
ash cloud had a measureable warming effect on
the northern hemisphere winter temperatures
[14,15]. The ash cloud has about the same effect
as the clouds have in the cold climate conditions,
it will prevent the cooling of the surface. In this
way it has a net warming effect.
The radiative forcing (RF) at TOA has a linear
relationship to the global mean surface
temperature change T, if the equilibrium state is
assumed [16]:
T = λRF, (1)
where λ is the climate sensitivity parameter,
which is a nearly invariant parameter having a
value of 0.5 K/(Wm
). IPCC uses still equation
(1) in its latest report AR5 but IPCC no longer
keeps the value of λ almost constant [17]. A
general experience and also a common practice
is to approximate the small changes around the
operating point to be linear by nature. The most
probable change of RF by the end of this century
is 6 Wm
according to RCP6 (Representative
Concentration Pathways) [17]. This change is
only 2.5% about the average value of OLR
(outgoing longwave radiation) value of 239 Wm
The author carried out a study about this issue
utilizing the MODTRAN code [18]. The
concentration of CO
varied from 357 ppm to 700
ppm and the sky conditions were clear and
cloudy, which were combined to calculate the all-
sky values. The average global atmosphere
profiles for GH gases, temperature and pressure
were applied. The results show that the
maximum nonlinearity between the OLR fluxes
was 0.01% and the maximum variation in λ
values was 2.5%, when the surface temperature
varied ±1°C. These results show that the
equation (1) is applicable for small RF and
temperature changes.
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
IPCC has calculated RCP warming values
applying GCMs but they do not inform the
possible λ values. On the other hand IPCC
reports in AR5 [17] that the transient climate
sensitivity (TCS) value is likely to lie in the range
1 to 2.5°C giving the average value 1.75°C. This
value is almost the same as calculated by
equation (1): T = 0.5 K/(Wm-2) * 3.7 Wm
1.85 K. The conclusion is that IPCC is very
inconsistent in using λ values and equation (1). If
λ is not “nearly invariant parameter”, IPCC
should have introduced something more credible
scientific evidence about the real nature of λ.
This inconsistency may be linked to the warming
values of the recent RF values. There should not
be any of IPCC’s own climate models, but in
reality there is such a model called “Radiative
Forcing by Emissions and Drivers” which has a
summary leading to the value of 2.34 Wm
according to AR5 [17]. IPCC denies that there is
any IPCC’s model but the fact is that the IPCC
organization has selected a number of research
studies, which have been used in creating their
presentation. There are private researchers who
do not make the same selections and therefore
their models are different. If equation (1) is
applied in the same way as calculating the TCS
value above, the warming value of 2.34 Wm
would be 1.17°C in 2011. IPCC does not show
this temperature increase in the AR5 [17], and
one reason might be that it is 38% greater than
the observed value of 0.85°C.
The possible water feedback is the only essential
feedback in TCS calculations. In the referred
GCM studies applied in the Pinatubo analyses,
there are no reported λ values. The lambda value
of 0.5 K/(Wm
) means that there is a positive
water feedback included into a model. The
assumption that there is a positive water
feedback in the climate models means that
relative humidity (RH) should be constant despite
the moderate warming/cooling of the
atmosphere. This property of the positive water
feedback would double the warming effects of
GH gases according to AR4 [16]. IPCC reports in
AR5 that the positive water feedback can amplify
any forcing by a typical factor between two and
three [17]. This means that understanding of
water feedback magnitude is not becoming more
accurate but it has become more inaccurate.
The issue of a constant RH can be studied by
simply looking at the RH trends since 1948,
which are depicted in Fig. 4 [20]. It is clear that
RH has varied quite a lot. Even though the early
RH measurements may be unreliable, the
measurements since 1980 have better
Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242
technology and they are very accurate and
The positive water feedback and high climate
sensitivity (CS) of climate models is a well-known
feature. Normally the equilibrium CS varies from
1.5°C to 4.5°C [21], which means that the
variation of TCS (Transient climate sensitivity) is
about half of this range. However there are
several studies, which have calculated the
climate sensitivity value to be about 1.0 1.2°C
[22-25] using the same radiative forcing value of
3.7 Wm
for CO
as IPCC uses. It means a
lower λ value of about 0.27 - 0.3 K/(Wm
). 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
). In this study these two
most common values have been applied: 0.27
) and 0.5 K/(Wm
Fig. 4. The global relative humidity trends
according to NOAA at different altitudes in
the troposhere
The forcing studies can be classified into two
categories namely forcing calculations utilising
General Circulation Models (GCM) 1) for
simulations of spatial flux and temperature
changes [8,28-31,2] other simulations resulting
the surface temperature change. In respect to
this study only the latter studies are relevant.
One of the earliest studies was that of Hansen et
al. [32]. They used the GISS global climate
model to assess the preliminary impacts of the
Pinatubo eruption. In their calculations they used
the peak value of -4 Wm
for SWIN and they
could show that the simulated T was about -0.5
°C. The most common value of SWIN has been
-6 Wm
[8,13,14,29,33]. This value is also used
in this study.
In the later study Hansen et al. [34] applied the
same peak value of -4 Wm
in the GCM
simulations by name SI94 and GRL92. Soden et
al. [35] applied a GCM and as input data they
used ERBS fluxes in calculating the RF values.
They also included the absolute atmospheric
water content as a variable. The peak value of –
4 Wm
was used for SWIN. Their major result
was the GCM simulations could calculate the
Tm values close to the measured value, if the
positive water feedback was included. The water
content was calculated using the NASA Water
Vapor Project (NVAP) values [36].
In Fig. 5 the NVAP dataset values as well the
NCEP/NCAR (National Center for Environmental
Prediction / National Center for Atmospheric
Research) values are depicted [37]. The NVAP
water content trends show great seasonal
changes of about 3 TPW mm. Soden et al. [35]
have reported that there has been ~0.75 TPW
mm peak reduction during the Pinatubo eruption.
The graphs show that the peak reduction
estimate [23] can be regarded a correct estimate.
This choice of using the peak values only can be
questioned, because the trend line of NVAP-M
values show increased rate of absolute water
content. A justified procedure would be to use
the monthly values but then the water feedback
effects would be huge. Because the seasonal
water content variations depend mainly on the
northern hemisphere seasonal changes, a better
method might be to combine zonal temperature
and water content values.
Fig. 5. The graphs of water contents
according to NVAP-M and NCEP/NCAR
In Fig. 5 it can be noticed that there are opposite
trends in these datasets during the Pinatubo
eruption. It is quite impossible to know, which of
these datasets is correct and therefore the
question of positive or negative water feedback
Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242
cannot be reliably tested utilising the Pinatubo
case and the global water content trends.
The two SWIN flux datasets available during the
eruption are ISCCP [38] and ERBS [39]. They
are depicted in Fig. 6. Both datasets are unstable
and spiky. The SWIN flux anomaly can also be
estimated using the apparent transmission (AT)
signal or optical depth measurements. In this
case the AT signal of Mauna Loa has been used.
The SWIN flux anomaly has been assumed to
follow exactly the trend of the AT-signal. The
time of the minimum value of the AT-signal has
been used to be also the time of the minimum
value of the SWIN flux value of -6 Wm
. This
estimate of SWIN flux is depicted in Fig. 6 and
it can be noticed that this flux is very stable and
its trend follows very well the average form of
ISCCP and ERBS fluxes. The smoothed ERBS
SWIN flux signal follows the estimated AT
transformed SWIN flux signal so well that they
could be used between each other.
Fig. 6. SW downward radiation flux anomalies
at TOA
Because there are no direct measurements of
LWDN flux, it has been estimated. As realized
before, the LWDN flux anomaly should follow the
amount of large aerosol particle amounts in the
atmosphere. Russell et al. [1] has a Fig. 6 in their
paper containing optical depth measurements of
the different particle size trends measured at
Mauna Loa during the eruption.
It has been assumed that the smaller particle
sizes from 0.382 to 0.500 µm are related to the
SWIN flux anomaly. The largest particle size is
1.020 µm and the graph of its aerosol optical
depth has been used to estimate the LWDN
flux. The peak values relationship between the
1.020 µm and 0.382/0.500 µm is 0.6. Using this
relationship the peak value of estimated LWDN
flux anomaly would be 0.6 * (-6 Wm
) = -3.6 Wm
. The LWDN is been estimated to follow the
aerosol optical depth signal of the particle size
1.020 µm at Mauna Loa and it is depicted in
Fig. 7.
Fig. 7. LW radiation flux anomalies at TOA
In Fig. 7 it can be noticed that the peak value of
estimated LWDN flux is greater than the LWUP
values measured at TOA by ISCCP and by
ERBS. One explanation is that LWUP fluxes
depend mainly on the surface temperature and
therefore there is a dynamic delay in comparison
to the LWDN flux. The full effect of this delay is
about one year. In the dynamic situations like this
Pinatubo eruption anomaly, the maximum
temperature anomaly is about from 80% to 90%
from the full effect. This difference is analyzed
more deeply in the simulation section.
In the simulations the measured surface
temperature anomaly T is a reference. There
are five dataset commonly available and four of
them are depicted in Fig. 8 [5,40-42]. There are
rather big differences in the trends. The
difference between the HadCRT4 and the UAH
MSU, which is a lower atmosphere temperature
measured by satellites, is even 0.4°C around the
beginning of the years 1992 and 1993. The UAH
MSU trend has the largest minimum value during
the eruption. Because of this situation, two
surface temperature trends have been used as
references namely Tmsu (UAH MSU dataset)
and Tav (average of all four datasets).
Hansen et al. [34] and Soden et al. [35] have
taken into account that the ENSO (El Niño
Southern Oscillation) phenomenon had the
maximum warming index in January 1992, when
the Pinatubo eruption had the strongest cooling
Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242
effects. The researchers elimated the ENSO
effect by calculating a modified surface
temperature of MSU UAH dataset. According to
the graphs of these two papers, the ENSO
corrected minimum peak of T has been from -
0.7°C to -0.75°C. They refer to the study of
Santer et al. [43]. The author reads this same
paper that the maximum mean volcanically
induced cooling T
at the surface is from-
0.35°C to -0.45°C and it is about double in the
troposphere. ENSO certainly has a warming
effect from 1991 to the end of 1992, and
therefore this result is not logical, because the
temperatures without ENSO corrections are
about the same. There is a graph [43], where the
temperature anomaly is about -0.75°C but it is for
the troposphere and not for the surface. Another
study of Thompson et al. [44] shows that the
maximum warming effect of ENSO is only
Fig. 8. Surface temperature anomalies
according to four datasets
Because the quantified effects of ENSO are so
controversial, this study has used the results of
the own analyses. The elimination of ENSO is
based on the analysis of ONI values (Oceanic
Niño Index) [45] and the global T values. The
ENSO effect creates fluctuations, which can be
identified as almost identical fluctuations of T
values after 1-12 months delay. The four most
regular El Niño / La Niña cases were selected.
The relationship from peak to peak between
these fluctuations show that T = 0.144 * ONI
on average. This temperature effect formula has
been used in modifying the measured T values
but there is no time delay applied, because the
peak values of ONI and T values match. In
Fig. 9 is depicted the ENSO effect as a
temperature anomaly and its effect on the two
global T trends. This approach gives the
maximum ENSO effect of ~0.23°C. The ENSO
during the Pinatubo eruption has a special
feature not having the negative La Niña
temperature peak at all.
Fig. 9. The ENSO signal removed from the
surface temperature measurement
The ENSO effect explains quite well why there is
a peak upward from January 1992 to July 1992,
when the surface temperature should be in
minimum because of forcing by SWIN/LWDN
anomaly. After 1993 the ENSO effect is very
small, but it caused an upward tick at the end of
1995, when the Pinatubo event was practically
over. The ENSO modified surface temperatures
Tav-e and Tmsu-e have been used as
references in this study.
The Pinatubo eruption happened in such a way
that the forcing factors in the form of SWIN and
LWDN flux anomalies changed all the time and
therefore the applied model must be dynamical.
A dynamical model is capable of simulating time
dependent variables and their impacts. In this
case a simple dynamical model DM has been
applied as described in Fig. 10.
Fig. 10. The dynamic simulation model of the
climate system
The output FLIN of the disturbance process D(t)
is the difference of SWIN and LWDN created
by the Pinatubo eruption. FLIN has been
delayed by 1.6 months called dead time in
process dynamics and it can be formulated as
Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242
) – LWDN(t-τ
) (2)
where t is time and τ
is dead time. The input
variable SWIN is a flux anomaly signal varying
according to the time as depicted in Fig. 6. Also
LWDN varies according to the time as depicted
in Fig. 7. The climate process C(t) includes two
elements: 1) the input signal FLIN is
transformed into the surface temperature change
and 2) the dynamic behaviors of the climate
system delays according two parallel first order
transfer systems are included into T effects:
T = λ * FLIN * (K
* exp(-t/Τ
+ K
* exp(-t/Τ
)) (3)
where t is time (months), exp is exponent, K
0.7, K
is 0.3, Τ
is a time constant of 2.74
months and Τ
is a time constant of 1.04
months. These values are based on the earlier
studies [12,46,47]. The values of the K
parameters are the area portions of land and
ocean of the Earth. The climate process C(t) is a
combination of two parallel processes, because
the time delays of land and ocean are different.
Three different simulation cases have been
described and carried out: 1) SWIN and
LWUP (the proxy of the LWDN) fluxes are from
ERBS datasets, 2) SWIN and LWDN are
estimated as described above based on the AT
measurements, 3) Feedback process experiment.
The ISCCP dataset turned out to be too swaying
and unreliable and therefore it has not been used.
In cases 1) and 2) the simulations have been
carried out by λ values of 0.27 K/(Wm
) and 0.5
The dynamic processes according to eq. (2) are
first-order dynamic models, which can be
simulated in the discrete form enabling
continuously changing input variables:
where Out(n) is the output of the process in step
n, In(n) is the input of the process of step n, T is
the time constant, t is the simulation step
interval (=0.2 months), and n-1 is the previous
step value.
The results of using ERBS flux values are
depicted in Fig. 11.
It can be noticed that the simulated temperature
values vary a lot because the fluxes SWIN and
LWIN vary too much. Especially the λ value of
0.5 K/(Wm
) gives Tm peak values, which are
almost double as large as the Tm values using
the λ value of 0.27 K/(Wm
). A possible reason
for this is that the LWUP flux anomaly is not an
accurate enough estimate of the real LWDN
flux anomaly and the flux measurements are too
Fig. 11. The simulated surface temperature
according to the dynamic DM using ERBS
dataset SWIN and LWUP fluxes
Fig. 12. The simulated surface temperature
according to the dynamic model using
estimated SWIN and LWDN fluxes
In Fig. 12 the same graphs are depicted, when
the SWIN and LWDN are estimated according
to the AT and aerosol optical depth
measurements. The simulated Tm signal is
stable and the dynamic changes follow very well
the real temperature changes T. Also in
this case the λ value of 0.5 K/(Wm
gives results, which do not follow the real
changes of the surface temperature changes but
gives about 100% too great Tm during the
Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242
The question of feedback has created the two
schools of thoughts. Some researchers think that
the climate system is like the other processes of
the nature, which are built on negative
feedbacks. A positive feedback system is
dangerous, because it drives any system out of
balance sooner or later. IPCC and some other
researchers think that the climate system for
example includes the positive water feedback as
well as positive albedo and cloud feedbacks [17].
It should be noticed that the positive water
feedback is included into the climate feedback
parameter λ, when its value is 0.5 K/(Wm
) [16]
and should results in a constant RH trend in the
troposphere. The λ-value of 0.27 K/(Wm
means a constant water content of the
A theoretical feedback process is simulated
using the process model depicted in Fig. 13.
Fig. 13. A theoretical feedback process in the
case of Pinatubo eruption
The theoretical feedback process can be
constructed based on the assumption that the
SWIN flux anomaly is the only disturbance in a
very stable climate system, which tries to
eliminate this disturbance. The elimination
process is a theoretical PI-controller, which
detects a change in the surface temperature and
creates an eliminating phenomenon, which tries
to minimize the disturbance. In this case the
eliminating flux is the LWDN flux. The climate
process C(s) has as an input only the SWIN
anomaly. The PI-controller imitates the counter
effect of LWDN flux but LWDN flux values are
not needed to use in this simulation.
The mathematical form of the PI-controller
(Proportional-Integral) in time domain is
Out(t) = K
+ (1/T
)*e(t)dt (5)
Where K
is the gain of the controller, T
is the
integral time and e(t) is the error signal between
the set point and the measurement. The equation
(4) simulated in a discrete form in the time
domain is
Out(t) = K
* e(t) + (K
)Σe(t)t (6)
The PI-controller was tuned by trial and error
giving K
= 2 and T
= 500 months. The results of
the negative feedback process simulation are
depicted in Fig. 12. The output of the theoretical
feedback process follows the Tm values of DM
surprisingly closely up to the end of 1993 as well
as the measured T values.
One big difference between this study and the
three referred studies [32,35,36] is the use of
estimated LWDN instead of measured LWUP
fluxes. The basic reason is that these two fluxes
have different values. The measured LWUP
fluxes are not stable, making the results very
unstable too. This problem can be eliminated to a
certain degree by heavy smoothing or even by
removing parts of a flux signal [35].
The actual LWUP flux depends on the surface
temperature changes T which is caused by the
RF change. The RF is the sum of
SWIN+LWDN flux changes. The LWUP flux
can be calculated using the measured T
changes. The author has used two calculation
methods. The first is MODTRAN radiation code
available through Internet [18]. By applying the
average global atmosphere profile, MODTRAN
can calculate the LWUP flux change at TOA. The
main parameters selected for these calculations
were: CO
357 ppm, fixed water vapor pressure,
cloudy sky with cumulus cloud base of 0.66 km
and top of 2.7 km. The C change in the surface
temperature gives LWUP change of 3.39 Wm
for the clear sky and 3.08 Wm
for the cloudy
sky at TOA.
By combining the two sky conditions, the all-sky
value of 3.18 can be calculated [10]. Ollila [10]
has calculated the same relationship using
another commercial spectral analysis tool
Spectral Calculator for the clear sky conditions.
The cloudy sky fluxes are estimated to be 25%
less than the clear sky fluxes [16]. This
calculation method gives the LWUP change of
3.05 Wm
for the 1 °C change. The results of
MODTRAN calculation have been used, which
gives a linear relationship
LWUP = 3.18 * T. (7)
This linear relationship is applicable inside the
small temperature change of 1°C.
The surface temperature calculated LWUP is
depicted in Fig. 14. It can be compared to the
Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242
measured LWUP flux, which is in this case the
average of ISCCP and ERBS datasets. The flux
values are at about the same level except for the
first months of 1992. The SW+LW forcing flux is
about 1 Wm
higher than the ISCCP & ERBE
flux during the period 3/1992 – 10/1992. This
could be due to the error of LWDN flux estimate.
The LWDN flux may reduce quicker than the
optical depth measurement indicates. This is also
a probable reason for the difference between the
Tm value of DM and the measurement based
temperature anomalies during the year 1992.
This is a very good result showing that LWUP
depends on SWIN +LWDN fluxes and their
dynamic effects on the T at the Earth’s surface.
Therefore, LWUP is not really the right choice
in calculating the surface temperature changes
caused by downward radiation flux anomalies of
Fig. 14. The LW fluxes during the Pinatubo
These results can be compared to the results
calculated by Hansen et al. [34] and Soden et al.
[35] who have used complicated GCMs in their
analyses. In these models the temperature
effects are based on the eruption aerosol
amounts and properties. When comparing the
dynamic behavior, the calculated Tm of GCMs
follows very accurately the real temperature
change as does the DM. The conclusion is that
the dynamical time delays in their GCMs must
come very close to the time constants applied in
this study.
The peak values of Tm of the GCM studies are -
0.6°C [34] and -0.7°C [35] and according to their
graphs, the model-predicted values are
practically same as the observed values. The
observed values of this study vary from -0.C to
-0.6°C based on the selected temperature
measurement. One explanation could be that in
the referred GCM studies the modified UAH MSU
dataset has been used having a greater ENSO
effect correction than in this study.
In the GCM calculations the researchers [34]-[35]
have used ERBS flux values. In both cases the
maximum value of SW anomaly SWIN has
been about -4 Wm
, which differs 33% from the
value of -6 Wm
used in the majority of the other
GCM studies and also in this study. The
maximum LW anomaly LWUP used in the GCM
studies has been about -2.3 Wm
. Using
equation (1) for steady-state conditions, the
calculated peak Tm would be 0.5 * (-4 + 2.3)
= -0.85°C. This value is very close to the model-
predicted value of Soden et al. [35]. On the other
hand, if the commonly used value of -6 Wm
would have been used, the calculated peak Tm
would be 0.5 *(-6+2.3) = -1.85°C. If the average
λ-value of 1.0 K/(Wm
) commonly found in
GCMs is used, the Tm would be even larger. The
GCM simulations of Soden et al. [35] gave
results which are close to the measured T
values. The major features of these two studies
are listed in Table 2.
The model calculated Tm values are for
equilibrium conditions and the values of the real
dynamic conditions are in brackets. The dynamic
simulations of this study show that in the
dynamic change condition the real equilibrium
Tm value cannot be reached but the real
temperature change is about +0.1°C smaller.
The values in Table 2 show that the results of
Soden et al. [35] can be generated using the λ
value of = 0.5 K/(Wm
) and the flux values
applied by them.
This simple analysis shows that the model-
predicted Tm values are completely dependent
on the selected forcing fluxes, λ values and even
on the selected observed T value. It appears
that in GCM simulations [34,35] the selected
SWIN flux cannot be regarded as the justifiable
choice. Actually the greatest uncertainty is about
the right LWDN flux values, because there are
no direct measurements available. The
commonly used LWUP flux at the TOA, is not
the same flux as LWDN. LWUP is mainly
dependent on the real RF fluxes (SWIN and
LWDN) and on the surface temperature.
Therefore the LWUP flux contains the dynamic
delays of the land and ocean and the
Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242
Table 2. Comparison of the major differences between the study of Soden et al. [35] and this
Soden et al.
Min. SWIN, Wm
, min. -4.0 -6.0
Max. LWDN, Wm
, max. +2.3 +3.6
Max. radiative forcing, Wm
-1.7 -2.4
Equil. Tm according to λ = 0.5 K/(Wm
), °C -0.85 (-0.75) -1.2 (-1.1)
Equil. Tm according to λ = 0.27 K/(Wm
), °C
0.46 (
0.65 (
warming/cooling effects of the forcing radiation
fluxes. In the dynamic simulations this is a
source of error. The real measured
LWUP fluxes are very spiky – especially ISCCP
The results show that a simple one dimensional
dynamic model DM gives results that are close to
the real surface temperature changes T after
the Mount Pinatubo eruption using the climate
sensitivity parameter value of 0.27 K/(Wm
Timewise the changes follow very well the real
changes. It means that the applied time
constants for land (1.04 months) and for ocean
(2.74 months) are accurate and can be used in
any dynamic simulations. Especially the quick
and large T during the early phase of the
eruption shows that the applied DM follows very
accurately the real change rate.
The maximum temperature decrease differs
+0.05° from the lowest dataset value (UAH MSU)
and -0.04°C from the highest dataset value
(T average) being actually in the middle of the
dataset changes. This is a very good accuracy.
The climate sensitivity parameter value of 0.5
) gives the minimum peak value of -
1.02°C, which is almost double in comparison to
-0.55°C calculated by λ value of 0.27 K/(Wm
This means that the climate models are very
sensitive to the value of the climate sensitivity
parameter. The mean λ-value of 1.0 K/(Wm
commonly used in GCMs would give 200% too
high values.
In this study SWIN and LWDN fluxes have
also been estimated utilizing the apparent
transmission measurements. The simulation
using these fluxes gives the best and consistent
results. The theoretical feedback simulation gives
values which are close to the DM model values
applying also the LWDN flux values.
The correlation analysis between the model
calculated Tm and the measured Tav-e gave the
correlation r
= 0.6 and the standard error of Tm
= 0.066°C. When the standard error of Tm is
transformed into the standard error of λ,
the value is 0.036 K/(Wm
). This means that the
uncertainty of λ is in the range from
0.234 K/(Wm
) to 0.306 K/(Wm
). The main
reason for the relatively poor correlation seems
to be the inaccurate surface temperature
measurements. The correlation r
Tmsu-e and Tav-s is 0.85 and the standard error
of the estimate 0.040°C. This error is 61% of the
standard error of the DM predicted temperature.
If the 7 months running mean is applied to Tm
and Tav-e like in the study of [35], r
= 0.76 and
the uncertainty range of λ improves from 0.245 to
The theoretical simulation of negative feedback
of the climate system gives Tm results, which
follow well both the DM results and the real T
Author has declared that no competing interests
1. Russel PB, Livingston JM, Pueschel RF,
Bauman JJ, Pollack JB, Brooks SL, et al.
Global to microscale evolution of the
Pinatubo volcanic aerosol derived from
diverse measurements and analyses.
Journal of Geophysical Research.
2. Gu L, Baldocchi DD, Wofsy SC, Munger
JW, Michalsky JJ, Urbanski SP, Boden TA.
Response of a deciduous forest to the
Mount Pinatubo eruption. Science. 2003;
3. Farquhar GD, Roderick ML. Pinatubo,
diffuse light and the carbon cycle. Science.
4. Stowe LL, Carey RM, Pellegrino PP.
Monitoring the Mount Pinatubo aerosol
layer with NOAA-11 AVHHR Data.
Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242
Geophysical Research Letters.
5. UAH MSU temperature dataset.
6. Apparent transmission dataset at Mauna
7. Wild M, Gilgen H, Roesch A, Ohmura A,
Long CN, Dutton EG, et al. From dimming
to brightening: Decadal changes in solar
radiation at Earth’s surface. Science.
8. Thomas MA. Simulation of the climate
impact of Mt. Pinatubo eruption using
ECHAM5. Dissertation at Hamburg
University; 2008.
9. Minnis P, Harrison EF, Stowe LL, Gibson
GG, Denn FM, Doelling DR, Smith WL.
Radiative climate forcing by the Mount
Pinatubo eruption. Science. 1993;
10. Raschke E, Kinne S, Stackhouse PW.
GEWEX Radiative Flux Assessment
(RFA). WCRP Report No. 19/2012; 2012.
11. Ollila A. Earth’s energy balance for clear,
cloudy and all-sky conditions. Development
in Earth Science. 2013;1.
12. Ollila A. Dynamics between clear, cloudy
and all-sky conditions: Cloud forcing
effects. Journal of Chemical, Biological and
Physical Sciences. 2013;4:557-575.
13. Stenchikov GL, Kirchner I, Robock A, Graf
HF, Antuna JC, Grainer RG, et al.
Radiative forcing from the 1991 Mount
Pinatubo volcanic eruption. Journal of
Geophysical Research. 1998;103:13837-
14. Kirchner I, Stenchikov GL, Graf H-F,
Robock A, Antuna JC. Climate model
simulation of winter warming and summer
cooling following the 1991 Mount Pinatubo
volcanic eruption. Journal of Geophysical
Research. 1999;104:19039-19055.
15. Graf HF, Kirchner A, Robock A, Schylt I.
Pinatubo eruption winter climate effects.
Model versus observations. Climate
Dynamics. 1993;9:81-93.
16. 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; 2007.
17. 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.
18. MODTRAN radiation code.
19. Ollila A. The potency of carbon dioxide
) as a greenhouse gas. Development
in Earth Science. 2014;2:20-30.
20. NOAA. Relative humidity trends. NOAA
Earth System Research Laboratory.
21. Held IM, Soden BJ. Water vapor feedback
and global warming. Annual Review of
Energy and Environment. 2000;25:441-
22. Aldrin M. Holden M, Guttorp P, Bieltvedt
Skeie R. Myhre G, Koren Berntsen GT.
Bayesian estimation on climate sensitivity
based on a simple climate model fitted to
observations of hemispheric temperature
and global ocean heat content.
Environmetrics. 2012;23:253-271.
23. Bengtson L, Schwartz SE. Determination
of a lower bound on earth’s climate
sensitivity. Tellus B; 2012.
24. Lewis NJ. An objective bayesian improved
approach for applying optimal fingerprint
techniques to estimate climate sensitivity.
Journal of Climate. 2013;26:7414-7429.
25. Otto A, Otto FEL, Boucher O, Church J,
Hegeri G, Piers M, et al. Energy budget
constraints on climate response. Nature
Geoscience. 2013;6:415-416.
26. Harde, H. Advanced two-layer climate
model for the assessment of global
warming by CO
. Open Journal of
Atmospheric and Climate Change.
27. Lindzen RS, Yong-Sang C. On the
observational determination of climate
sensitivity and its implications. Asia-Pacific
Journal of Atmospheric Sciences.
28. Ramachandran S, Ramaswamy V,
Stenchikov GL, Robock A. Radiative
impact of the Mount Pinatubo volcanic
eruption: Lower stratospheric response.
Ollila; PSIJ, 9(4): 1-14, 2016; Article no.PSIJ.23242
Journal of Geophysical Research.
29. Yang F, Schlesinger ME. On the surface
and atmospheric temperature changes
following the 1991 Pinatubo volcanic
eruption: A GCM study. Journal of
Geophysical Research. 2002;107:4073.
30. Forster F, Collins M. Quantifying the water
vapour feedback associated with post-
Pinatubo global cooling. Climate Dynamics.
31. Kelly PM, Jones PD, Pengqun J. The
spatial response of the climate system to
explosive volcanic eruptions. International
Journal of Climatology. 1996;16:537-550.
32. Hansen J, Lacis A, Ruedy R, Sato M.
Potential climate impact of Mount Pinatubo
eruption. Geophysical Research Letters.
33. Timmreck C, Graf H-F, Kirchner I. A one
and a half year interactive MAECHAM4
simulation of Mount Pinatubo aerosol.
Journal of Geophysical Research.
34. Hansen J, Sato M, Ruedy R, Lacis A,
Asamoah K, Borenstein S, et al. A
Pinatubo climate modelling investigation.
NATO ASI Series. 1996;I:233-272.
35. Soden BJ, Wetherald RT, Stenchikov GL,
Robock A. Global cooling after the eruption
of Mount Pinatubo: A test of climate
feedback by water vapor. Science.
36. Vonder Haar TH, Bytheway JL, Fortsyth
JM. Weather and climate analyses using
improved global water vapor observations.
Geophysical Research Letters.
37. NVAP dataset. NCEP/NCAR Reanalysis.
38. ISCCP radiation fluxes.
39. ERBS radiation fluxes.
40. HadCRUT4 temperature dataset.
41. GISS/NASA temperature dataset.
42. UAH RSS temperature dataset.
43. Santer BD, Wigley TML, Doutriaux C,
Boyle JS, Hansen JE, Jones PD, et al.
Accounting for the effects of volcanoes and
ENSO in comparisons of modelled and
observed temperature trends. Journal of
Geophysical Research. 2001;106:28033-
44. Thompson DWJ, Wallace JM, Jones PD,
Kennedy JJ. Identifying signatures of
natural climate variability in time series of
global-mean surface temperature:
Methodology and insights. Journal of
Climate. 2009;22:6120-6141.
45. NOAA Oceanic Nina Index ONI.
46. Stine AR, Huybers P, Fung IY. Changes in
the phase of the annual cycle of surface
temperature. Nature. 2009;457:435-441.
47. Kauppinen J, Heinonen JT, Malmi PJ.
Major Portions in climate change: Physical
approach. International Review of Physics.
© 2016 Ollila; This is an Open Access article distributed under the terms of the Creative Commons Attribution License
(, which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.
Peer-review history:
The peer review history for this paper can be accessed here:
... dT SW = λ * RF * (K sea * exp(-t/Ƭ sea ) + K land * exp(-t/Ƭ land )) (2) where t is time (months), exp is exponent, K sea is 0.7, K land is 0.3, Ƭ sea is a time constant of 2.74 months, and Ƭ land is a time constant of 1.04 months. These values are based on the studies of [56][57][58]. The magnitude of time constants implies that the surface temperature has a settling time about one year to an RF change. ...
... The analyses depicted in Figs. 2 and 3 are started from the year 1979 by modifying temperature changes and all warming impacts to start from zero. The ENSO effect on temperature is abnormal from 1991 to 1993 because of the Mount Pinatubo eruption effects due to diffuse SW radiation[58]. The warming impacts of water are calculated based on the absorption calculations by increasing the water content of . ...
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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.
... Many estimates based on those methods have therefore been criticized; we generally have low confidence in relying on them in this assessment, but provide further references to studies and critical comments here for completeness 7,24,56,93,225,231,259,269, . Similarly, the response to volcanic eruptions provides a test for models 302 but in our view the implications for ECS are unclear since the timescale and type of forcing is very different, the feedbacks arising are different, and the response is difficult to separate from El Niño variability 185,[303][304][305][306][307][308][309][310][311] . It has also been attempted to estimate TCR from the observed temperature response to the sunspot cycle 312 . ...
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.
... Soden et al. [32] reported that there was a water positive feedback applying -0.75 mm TPW peak reduction as to the NVAP-M trend [33] during the eruption. Ollila [34] found that it was impossible to draw any conclusions based on the trend TWP values, because the two datasets had opposite trends [23], [33]. The TPW trend in Fig. 4 is after NCEP/NCAR Reanalysis dataset and there is no trend from 1991 to 1995 meaning no water feedback. ...
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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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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.
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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.
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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.
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We present an advanced two-layer climate model, especially appropriate to calculate the influence of an increasing CO2-concentration and a varying solar activity on global warming. The model describes the atmosphere and the ground as two layers acting simultaneously as absorbers and Planck radiators, and it includes additional heat transfer between these layers due to convection and evaporation. The model considers all relevant feedback processes caused by changes of water vapour, lapse-rate, surface albedo or convection and evaporation. In particular, the influence of clouds with a thermally or solar induced feedback is investigated in some detail. The short- and long-wave absorptivities of the most important greenhouse gases water vapour, carbon dioxide, methane and ozone are derived from line-by-line calculations based on the HITRAN08-databasis and are integrated in the model. Simulations including an increased solar activity over the last century give a CO2 initiated warming of 0.2 °C and a solar influence of 0.54 °C over this period, corresponding to a CO2 climate sensitivity of 0.6 °C (doubling of CO2) and a solar sensitivity of 0.5 °C (0.1 % increase of the solar constant).
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Global-mean surface temperature is affected by both natural variability and anthropogenic forcing. This study is concerned with identifying and removing from global-mean temperatures the signatures of natural climate variability over the period January 1900-March 2009. A series of simple, physically based methodologies are developed and applied to isolate the climate impacts of three known sources of natural variability: the El Nino-Southern Oscillation (ENSO), variations in the advection of marine air masses over the high-latitude continents during winter, and aerosols injected into the stratosphere by explosive volcanic eruptions. After the effects of ENSO and high-latitude temperature advection are removed from the global-mean temperature record, the signatures of volcanic eruptions and changes in instrumentation become more clearly apparent. After the volcanic eruptions are subsequently filtered from the record, the residual time series reveals a nearly monotonic global warming pattern since similar to 1950. The results also reveal coupling between the land and ocean areas on the interannual time scale that transcends the effects of ENSO and volcanic eruptions. Globally averaged land and ocean temperatures are most strongly correlated when ocean leads land by; 2-3 months. These coupled fluctuations exhibit a complicated spatial signature with largest-amplitude sea surface temperature perturbations over the Atlantic Ocean.
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.
The NASA Water Vapor Project (NVAP) dataset is a global (land and ocean) water vapor dataset created by merging multiple sources of atmospheric water vapor to form a global data base of total and layered precipitable water vapor. Under the NASA Making Earth Science Data Records for Research Environments (MEaSUREs) program, NVAP is being reprocessed and extended, increasing its 14-year coverage to include 22 years of data. The NVAP-MEaSUREs (NVAP-M) dataset is geared towards varied user needs, and biases in the original dataset caused by algorithm and input changes were removed. This is accomplished by relying on peer reviewed algorithms and producing the data in multiple “streams” to create products geared towards studies of both climate and weather. We briefly discuss the need for reprocessing and extension, steps taken to improve the product, and provide some early science results highlighting the improvements and potential scientific uses of NVAP-M.