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During the whole history of the planet, astronomical factors (orbital and solar variability) have determined the energy balance of the Earth and generated natural climate oscillations affecting the life of plants, animals and human beings at all time scales. During the last decades, severe concerns have been raised about whether human activities could have been so influential as to deeply modify the natural variability of the global climate and, in particular, could have caused a significant warming since the beginning of the 20th century. To properly address the latter issue, it is required to understand the phenomenology of the natural climate fluctuations. These are well emphasized by several climate indexes such as the Atlantic Multidecadal Oscillation, the Pacific Decadal Oscillation, the El Niño–Southern Oscillation and others. This complex natural dynamic is still not reproduced by the general circulation models (GCMs) supporting the Anthropogenic Global Warming Theory (AGWT), which is mainly advocated by the Intergovernmental Panel on Climate Change (IPCC). In this “part 1” of our work we briefly introduce the general topic and statistically compare observed and GCM modeled global surface warming trends from 1860 to 2016. We find that the models have significantly overestimated the observed warming during the historical record. In addition, we compare observed and modeled temperature trends of three significant periods: from Jan/1922 to Dec/1941, from Jan/1980 to Dec/1999 and from Jan/2000 to Dec/2016. We find that only during the 1980-1999 period the observed and synthetic records show compatible warming trends within the 95% confidence level. The severe discrepancy between observations and modeled predictions found during the 1922-1941 and 2000-2016 periods further confirms, according to the criteria proposed by the AGWT advocates themselves, that the current climate models have significantly exaggerated the anthropogenic greenhouse warming effect.
The four sets of GCM simulations from the CMIP5 GCMs herein analyzed. Each set refers to a different RCP radiative forcing curve for the period 2006-2100 Figure 4 depicts the discrepancy between each of the four secular surface temperature records depicted in Figure 2A-D and the GCM mean simulation curve shown in Figure 3 with the black lines. Each record was low-pass filtered with a 12month moving average. The zero level in the graphs represents the 1980-2000 average. The four diagrams are obtained by simply subtracting the average GCM simulation from the temperature data. The figures highlight the natural variability of the climate system not reproduced by the GCMs. In fact, the observations differ significantly from the computer simulations. The fast inter-annual oscillations due to the ENSO variability are clearly not reproduced up to a maximum divergence of +0.5 o C observed at the occurrence of the 1878 super El-Niño event. Moreover, divergences up to ± 0.3 o C are also observed at the decadal scale. In particular, the GCMs fail to reproduce upward or downward trends of 30-year intervals as observed in the 1850-1880, 1880-1910, 1910-1940 and 2000-2017 periods. The period 1940-1970 is poorly reproduced at the decadal scale, while only the 1970-2000 period appears better reproduced by the GCM-mean simulation. The four diagrams also show a clear secular negative trend, which indicates that since 1860 (for the HadCRUT) and since 1880 (for the NCDC and the two GISS records), the GCM simulations have predicted a secular global surface warming that is 0.10 ± 0.05 o C/century larger than the observed one. Thus, the evaluated discrepancy in the
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1. INTRODUCTION
Weather and climate are different concepts: the former is
related to short-term and small-scale strong fluctuations of
the atmospheric conditions (e.g. temperature, humidity,
pressure, rain, etc.) of the planet or of a local region, while
the latter regards the long-term average of the same
parameters. While it is well known that weather changes
significantly day after day, the environmental situation and
the climate along the history of our planet have also changed,
but moderately: in brief, the Earth has never been in
equilibrium conditions [1]. Solar activity and orbital
variations have been the main driving factors of the natural
climatic oscillations yielding to periods far hotter or cooler
than those of present times [2]. However, the mechanisms
and the interactions explaining these changes have not yet
been completely understood, in spite of the progress of
science and technology.
Although on short time scales climatic changes may
appear minor compared to weather fluctuations, they have
long-term major effects in determining whether entire regions
of the Earth will get warmer or cooler, wetter or drier. Thus,
the history of mankind has been deeply influenced by
climatic and environmental occurrences [3]. Nobody disputes
that people have contributed to modify local environment,
but it would be misleading to assume that mankind has been
the main actor of a significant global climate change without
a full comprehension of the physical related issues. A lot of
INTERNATIONAL JOURNAL OF
HEAT AND TECHNOLOGY
ISSN: 0392-8764
Vol. 35, Special Issue 1, September 2017, pp. S9-S17
DOI: 10.18280/ijht.35Sp0102
Licensed under CC BY-NC 4.0
A publication of IIETA
http://www.iieta.org/Journals/IJHT
Natural climate variability, part 1: Observations versus the modeled
predictions
Nicola Scafetta1, Aberto Mirandola2*, Antonio Bianchini3, 4
1 Meteorological Observatory, Department of Earth Sciences, Environment and Georesources,
Università degli Studi di Napoli Federico II, Largo S. Marcellino, Naples 10 - 80138, Italy
2 Department of Industrial Engineering, Università degli Studi di Padova, 1 Via Venezia, Padova
35131, Italy
3 Department of Physics and Astronomy, Università degli Studi di Padova, Italy
4 INAF, Osservatorio Astronomico di Padova, Vicolo dell’Osservatorio 5, Padova I-35122, Italy
Email: alberto.mirandola@unipd.it
ABSTRACT
During the whole history of the planet, astronomical factors (orbital and solar variability) have determined the
energy balance of the Earth and generated natural climate oscillations affecting the life of plants, animals and
human beings at all time scales. During the last decades, severe concerns have been raised about whether
human activities could have been so influential as to deeply modify the natural variability of the global
climate and, in particular, could have caused a significant warming since the beginning of the 20th century.
To properly address the latter issue, it is required to understand the phenomenology of the natural climate
fluctuations. These are well emphasized by several climate indexes such as the Atlantic Multidecadal
Oscillation, the Pacific Decadal Oscillation, the El NiñoSouthern Oscillation and others. This complex
natural dynamic is still not reproduced by the general circulation models (GCMs) supporting the
Anthropogenic Global Warming Theory (AGWT), which is mainly advocated by the Intergovernmental Panel
on Climate Change (IPCC). In this “part 1” of our work we briefly introduce the general topic and statistically
compare observed and GCM modeled global surface warming trends from 1860 to 2016. We find that the
models have significantly overestimated the observed warming during the historical record. In addition, we
compare observed and modeled temperature trends of three significant periods: from Jan/1922 to Dec/1941,
from Jan/1980 to Dec/1999 and from Jan/2000 to Dec/2016. We find that only during the 1980-1999 period
the observed and synthetic records show compatible warming trends within the 95% confidence level. The
severe discrepancy between observations and modeled predictions found during the 1922-1941 and 2000-
2016 periods further confirms, according to the criteria proposed by the AGWT advocates themselves, that
the current climate models have significantly exaggerated the anthropogenic greenhouse warming effect.
Keywords: Climate Change, Post 2000 Temperature Standstill, Climate Models, Natural Climatic
Oscillations.
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parameters are involved in the climatic and environmental
history. Understanding the causes of climate changes, their
connections and their feedbacks is a great challenge.
The extreme difficulty to link and match all involved
factors would suggest cautiousness in declaring our certitudes
about future climate developments for deciding policies.
However, in the last decades some organizations and groups
of scientists and politicians, even at the highest levels, seem
to have been and still be very sure of their alarming climate
scenarios, which have been advocated mostly by the United
Nations Intergovernmental Panel on Climate Change (IPCC) [6,
7, 8]. In particular, the most widespread opinion is that the
20th century climate warming has depended almost
completely on human activities and will continue to do so in
the future: this is known as the Anthropogenic Global
Warming Theory (AGWT). The AGWT statements have
been dominating both the scientific literature and the political
decisions, including grants for research funds and economic
incentives, which are mainly oriented towards initiatives that
follow the “politically correct” majority current of thought.
However, since knowledge is continuously progressing,
scientists should also fairly acknowledge uncertainties and
consider novel scientific findings.
Figure 1. [A] Northern hemisphere proxy temperature reconstruction, known as the Hockey Stick, published by the IPCC in
2001 after Mann et al. (1999). [B] Northern hemisphere proxy temperature reconstructions published after 2005 showing a
millennial oscillation [12-15]
Before attempting to predict the future, it is necessary to
test whether the past climate changes have been properly
understood and integrate the information that can be drawn
from multiple sources including historic archives and
testimonies, such as archaeological findings, artistic
creations, geological sediments, physics and astronomy. The
whole information needs to be used to produce and evaluate
proxy reconstructions of the past climate. Then, all
information must be integrated to properly interpret the
instrumental global climatic records, which are globally
available only since 1850.
For example, as already extensively explained elsewhere
[7, 8, 9], the AGWT was globally advocated by the IPCC in
2001 because it appeared to be supported by the ‘infamous’
Hockey Stick temperature reconstructions by Mann et al. [10]
and by specific computer climate models mainly based on
radiative forcings [4,11]. Those temperature reconstructions
claimed that only a very modest change in the Northern
Hemispheric climate had occurred during the pre-industrial
times from A.D. 1000 to 1900, while an abrupt warming did
occur just in the last century: see Figure 1A. Energy balance
and general circulation climate models (GCM) were used to
interpret the Hockey Stick climatic pattern as due mostly to
anthropogenic greenhouse gas emissions such as CO2 because
of coal and oil fuel consumption, which has been accelerating
since the beginning of the 20th century [11].
However, since 2005 novel Northern Hemisphere proxy
temperature reconstructions were published [12-15] revealing
the existence of a large millennial oscillation that contradicts
the Hockey Stick temperature pattern: see Figure 1B. The
new findings were consistent with alternative climatic and
solar activity records showing that a quasi-millennial
oscillation occurred throughout the entire Holocene for the
last 10,000 years [16, 17]. Thus, the existence of a large
millennial climatic oscillation would definitely question the
reliability of the climate models used to support the AGWT
by suggesting that a significant percentage, about 50%, of the
warming observed since 1850 could be natural [7, 8].
Historic research helps greatly to evaluate the credibility of
the two above alternative climate proxy reconstructions for
the last millennia. Our historical testimonies, mainly referred
to the European and Mediterranean area, are qualitative and
indirect: they mostly regard climatic effects on the life of
populations in the various regions [3]. Ancient settlements
were established in areas relatively rich of water and
characterized by mild to warm climate: these environmental
conditions favored agriculture and allowed the people to have
sufficient amount of food. When the climate became less
favorable, new regions were explored and colonized.
The ancient testimonies often mention phenomena that had
a remarkable influence on the life of a given (small or big)
community. Many accounts are referred to negative,
sometimes catastrophic events such as long periods of
drought or low temperature, causing scarcity of food, famine
and then little resistance to illness, epidemic and poverty.
Whereas warm periods have generally been more favorable.
We can check this statement looking at the main climatic
periods of the Mediterranean and European area that can be
derived from chronicles and other records:
- After the Punic wars (3rd century B.C.) and up to
the 4th century A.D. a climatic optimum, called the “Roman
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Warm Period”, allowed the Roman civilization to reach its
maximum power and strength;
- In the following “Dark Cold Ages” during the early
Middle Ages (5th - 9th century) severe (cold) climatic
conditions caused a relative backward move; massive
migrations of peoples from central and northern Europe and
Asia (Barbarian Invasions) shook Europe and brought to the
end of the Roman Western Empire, with its valuable culture
and traditions;
- The following “Medieval Warm Period” (10th - 14th
century) was much more favorable; the climate was generally
warm and there was a great development of economy and
culture, up to the Italian Renaissance; the temperature was
probably very close to that of the present time. Since the
beginning of this warm period, the Vikings colonized Iceland
and the far more inhospitable Greenland coastal areas, which
were then so warm to allow the development of traditional
Northern European farms: a fact today impossible;
- The climate worsened greatly in the subsequent
“Little Ice Age” (14th to half 19th century), during which the
temperature was generally cold and many historic and
climatic negative events contributed to upset the European
history; several devastating pandemics decimated Europe and
the Vikings had to abandon their Greenland settlements;
- At last, the present age (starting from the last
decades of the 19th century) has been characterized by a
warming. Since 1950 the exponential growth of human
population and the intense exploitation of energy sources has
caused and is still causing a significant anthropogenic impact
on the environment.
The aforementioned historical climatic inferences are quite
generic but they are definitely sufficient to question the
Hockey Stick temperature reconstructions used to validate
the climate models that, in fact, have supported the AGWT
since 2001 by the IPCC. They clearly support the reliability
of the most recent climatic reconstructions that reveal the
existence of large quasi millennial natural climatic
oscillations, as Figure 1B shows.
Furthermore, the above historical inferences are today
supported by a wide body of scientific literature. It has been
established that the Medieval Warm Period (MWP) was
globally extended [18,19]. The MWP, from the IX to the XIII
century, seems to have been equivalent to or even exceeded
the A.D. 1961-1990 mean temperature level in the extra-
tropical Northern Hemisphere [14].
In this regard, note that the Medieval temperature
reconstructions are based on proxy models and should not be
directly compared against the modern instrumental
temperature records as Mann et al. [10,13] did because recent
proxy records diverge from instrumental temperatures that
show higher readings when compared against proxies [20].
This is known as the “divergence problem” and suggests that
un-modeled non-linearity characterizes the relation between
instrumental temperatures and proxies. Consequently, the
real Medieval temperature could have been higher than what
currently estimated using linear proxy models.
A lot of bucking phenomena cause unexpected
modifications, indicating that the climate is the result of a
very complex series of phenomena. There are also
catastrophic climatic events caused by violent meteorite
impacts and volcano eruptions. An example is the big
eruption of Mount Tambora (Indonesia) that occurred in
April 1815. Its impact ravaged in the entire Northern
Hemisphere for about two years [21]. In the August of the
subsequent 1816, which was called “the year without a
summer”, the average temperature fell down of a few Celsius
degrees, keeping agriculture from supplying sufficient food
and causing poverty and famine in an Europe already upset
by the Napoleonic wars. However, since 1850 only modest
volcano eruptions occurred causing minor and short cooling
spikes.
The climate is also characterized by an important multi-
decadal variability. Since 1850 several 30-year trend-
reversals have been observed such as: a warming from 1850
to 1880; a cooling from 1880 to 1910; a warming again from
1910 to 1940; a cooling from 1940 to 1970; then the
temperature increased again from 1970 to 2000. Finally, for
almost 20 years since 1998, the global surface temperature
has been relatively stable: this period has been called the
“pause” or the “global warming hiatus” by the AGWT
advocates [22, 23]. This label was chosen to indicate that the
observed temperature stand-still period results from an
unforced internal fluctuation of the climate that the computer
climate models were claimed to occasionally reproduce
without contradicting the AGWT paradigm [22, 23] In
addition to the above evident quasi 60-year oscillation, the
climate is also characterized by other fluctuations caused
mostly by in terannual, decadal and bidecadal oscillations [7,
8].
In the following, we will discuss the trend divergences
observed during some specific periods between the climate
models predictions and the temperature records. First, we
compare the trends involving the entire historical global
surface temperature record from 1860 to 2016 to evaluate the
overall performance of the climate models in reproducing the
observed warming. Then, we focus our attention on three
specific two-decadal periods.
Two of these periods (1922-1941, 1980-1999) were
selected because they are characterized by a strong and
compatible warming rate but by very different anthropogenic
emission rate. By contrast, the 2000-2016 period is
characterized by a very strong increase of anthropogenic
emissions while the temperature has been quasi stationary.
The importance of evaluating the performance of the
climate models in reproducing the observed 20-years-long
trends is because the probability that the predictions could
repeatedly differ from the observations as due to some
internal climatic variability dynamic for periods longer than
about 15 years becomes vanishingly small. In fact, Meehl et
al. [22] showed that GCMs could simulate hiatus periods by
occasional deep-ocean heat uptake, by simulating, at random
times, an up-to-a-decade of steady temperature despite an
increasing anthropogenic forcing. In 2009 AGWT advocates
acknowledged that: “Near-zero and even negative trends are
common for intervals of a decade or less in the simulations,
due to the model's internal climate variability. The
simulations rule out (at the 95% level) zero trends for
intervals of 15 year or more, suggesting that an observed
absence of warming of this duration is needed to create a
discrepancy with the expected present-day warming rate
[24]. Thus, according to the AGWT advocates own criteria, a
divergence between observations and climate models
occurring at the bi-decadal scale would provide strong
convincing evidences that the GCMs used to support the
AGWT are severely flawed.
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2. DATA
The following climatic and GCM records are used for this
study. All records were downloaded from KNMI Climate
Explorer (https://climexp.knmi.nl): see Figures 2 and 3.
Three global monthly average land and sea surface
temperature records are available: HadCRUT, NCDC and
GISS. The HadCRUT record is available since 1850 and is
produced by a cooperative effort between the Hadley Centre
for Climate Prediction and Research and the University of
East Anglia's Climatic Research Unit (CRU), UK [25]. The
NCDC record is available since 1880 and is prepared by the
National Climatic Data Center [26], USA. The GISS record
is available since 1880 and is made available by the Goddard
Institute for Space Studies (GISS), at Columbia University,
New York City, USA [27]. The GISS record is available in
two formats according to a 250 km and a 1200 km spatial
interpolation, respectively.
Figure 2. The six temperature records herein analyzed with a monthly resolution
Two satellite based monthly average global lower
troposphere temperature records are available: UAH and RSS
(28, 29). The UAH record is available since December 1978.
It is provided by the National Oceanographic and
Atmospheric Administration (NOAA) TIROS-N satellite
both at the Global Hydrology and Climate Center, University
of Alabama at Huntsville, USA [28]. The RSS record is
available since January 1979 and uses remote sensing
systems data obtained by the National Oceanographic and
Atmospheric Administration (NOAA) TIROS-N satellite
[29].
We adopt the general circulation model simulations of the
Coupled Model Inter-comparison Project Phase 5 (CMIP5)
(http://cmip-pcmdi.llnl.gov/) prepared under the World
Climate Research Program (WCRP) and the Working Group
on Coupled Modelling (WGCM). These climate simulations
have been used by the IPCC Fifth Assessment Report [6].
The records herein considered are 301 GCM simulations
from 1860 to 2100 using historical radiative forcings from
1860 to 2006 and four alternative emission representative
concentration pathways (RCP): RCP26, RCP45, RCP60, and
RCP85, which are named after a possible range of radiative
forcing values in the year 2100 relative to pre-industrial
values (+2.6, +4.5, +6.0, and +8.5 W/m2, respectively). The
following run members are available: for RCP26, 65
simulations from 32 models; for RCP45, 108 simulations
from 42 models; for RCP60, 47 simulations from 25 models;
for RCP85, 81 simulations from 39 models.
3. ANALYSIS
The six temperature records herein analyzed are nearly
identical. These records highlight that since 1850 the global
surface temperature has increased by about 0.9oC. However,
the warming has not been monotonic. The 1850-1880, 1910-
1940 and 1970-2000 have mostly been warming periods. The
1880-1910 and 1940-1970 have mostly been cooling periods,
while since 2000 the temperature has mostly remained nearly
stable. The above empirical evidence yields to a quasi 60-
year oscillation modulating a warming trend [7, 8].
Figure 3 shows the four sets of GCM simulations. From
1860 to 2006 the GCMs were forced with the known
historical radiative forcings and, therefore, the four mean
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simulations indicated in the black curves appear identical.
Since 2006 the curves diverge because of the different
adopted RCP scenario for each set. These GCM simulations
show a quite monotonic warming from 1860 to 2100, which
is briefly interrupted by cooling episodes related to major
volcano eruptions such as Krakatoa (1883), Santa María
(1902), Mount Agung (1963), El Chichón (1982) and Mount
Pinatubo (1991).
Note that the GCM volcano signature is overestimated
relative to the observations. For example, the GCM Krakatoa
modeled cooling signature in 1883 and following years is
hardly visible in the temperature records depicted in Figure 2.
Also, the quasi 60-year oscillation clearly observed in the
temperature record is missing in the GCM records.
Figure 3. The four sets of GCM simulations from the CMIP5 GCMs herein analyzed. Each set refers to a different RCP radiative
forcing curve for the period 2006-2100
Figure 4 depicts the discrepancy between each of the four
secular surface temperature records depicted in Figure 2A-D
and the GCM mean simulation curve shown in Figure 3 with
the black lines. Each record was low-pass filtered with a 12-
month moving average. The zero level in the graphs
represents the 1980-2000 average. The four diagrams are
obtained by simply subtracting the average GCM simulation
from the temperature data. The figures highlight the natural
variability of the climate system not reproduced by the
GCMs. In fact, the observations differ significantly from the
computer simulations. The fast inter-annual oscillations due
to the ENSO variability are clearly not reproduced up to a
maximum divergence of +0.5 oC observed at the occurrence
of the 1878 super El-Niño event. Moreover, divergences up
to ± 0.3 oC are also observed at the decadal scale. In
particular, the GCMs fail to reproduce upward or downward
trends of 30-year intervals as observed in the 1850-1880,
1880-1910, 1910-1940 and 2000-2017 periods. The period
1940-1970 is poorly reproduced at the decadal scale, while
only the 1970-2000 period appears better reproduced by the
GCM-mean simulation. The four diagrams also show a clear
secular negative trend, which indicates that since 1860 (for
the HadCRUT) and since 1880 (for the NCDC and the two
GISS records), the GCM simulations have predicted a secular
global surface warming that is 0.10 ± 0.05 oC/century larger
than the observed one. Thus, the evaluated discrepancy in the
secular warming trend between the observational records and
the CMIP5 GCM mean prediction has a 95% confidence.
Figure 5 depicts the statistics of the variability in the
decadal linear rate between the available 301 monthly
resolved individual GCM simulations and the 6 monthly
temperature records in three relevant time intervals suggested
by the results depicted in Figure 4: 1922-1941 and 1980-1999
and 2000-2016. The diagrams 5A and 5B refer to the GCM
simulations in the intervals 1922-1941 and 1980-1999: these
use the same historical forcing functions.
The diagrams 5C refer to the GCM simulations in the
interval 2000-2016 and are separated according to the four
RCP scenarios. The diagram D depicts boxplots of the three
sets of linear decadal rate shown in 5A-C. The quartile
boundaries are determined such that 1/4 of the points have a
value equal or less than the first quartile boundary, 1/2 of the
points have a value equal or less than the second quartile
(median) value, and 3/4 of the points have a value equal or
less than the third quartile boundary. The two whiskers span
from 5% to 95% of the 301 points in each of the three sets.
All points that lie outside the range of the whiskers are
considered outliers. Each diagram also depicts the decadal
linear rate trends of the six temperature records shown in
Figure 2 in each of the three intervals. Table 1 summarizes
the various results. Note that the trends reported in Figs. 5C-
D and in Table 1 referring to the 2000-2016 period are those
S13
calculated after that the El NiñoSouthern Oscillation
signature is removed from the data as explained in Ref. [32].
Figure 5 and Table 1 reveal that in the three selected time
intervals (1922-1941, 1980-1999, 2000-2016) the GCM
simulations show a progressively increasing warming rate,
which is essentially consistent with the accelerating increase
of the radiative forcing, mostly driven by CO2 emissions.
However, the temperature records show a different
behavior. The observed mean global temperature trend for
the period 1922-1941 is significantly higher than the GCM
predictions with a statistical confidence larger than 95%, with
that observed 60 years later in the period 1980-1999. In the
period 1980-1999 the observed global temperature rate trend
is compatible with that of the GCM predictions. In the period
2000-2016, the observed mean global temperature rate trend
is lower than those observed in 1922-1941 and 1980-1999,
and is significantly lower than that of the GCM predictions
with a statistical confidence larger than 95%. The statistical
confidence was calculated on a simultaneous mean and
variance Student-t test comparison [30].
Figure 4. Diagrams showing the discrepancy between the temperature records (Fig. 2A-D) and the mean GCM simulation (Fig.
3). We simply subtracted the model average prediction from the temperature data. The “zero” represents the 1980-2000 average
value
Table 1. Statistics of the linear rate
1922-1941
1980-1999
2000-2016
oC/decade
oC/decade
oC/decade
GCMs (5%)
-0.470
0.031
0.056
GCMs (25%)
0.008
0.102
0.155
GCMs (50%)
0.046
0.151
0.209
GCMs (75%)
0.083
0.199
0.265
GCMs (95%)
0.146
0.268
0.369
Mean GCM
0.046 ± 0.058
0.156 ± 0.073
0.219 ± 0.096
MSU
0.149 ± 0.022
0.045 ± 0.018*
RSS
0.153 ± 0.021
0.033 ± 0.018*
GISS.250
0.172 ± 0.012
0.144 ± 0.014
0.103 ± 0.014*
GISS.1200
0.176 ± 0.014
0.157 ± 0.017
0.107 ± 0.016*
HadCRU4
0.139 ± 0.014
0.173 ± 0.016
0.112 ± 0.016*
NCDC
0.149 ± 0.014
0.144 ± 0.014
0.153 ± 0.015*
Mean Temp.
0.159 ± 0.022
0.153 ± 0.020
0.092 ± 0.048*
Note 1. The rows 1-5 report the percentiles of the linear rate trend of the
GCM simulations in the three-time intervals Jan/1922-Dec/1941, Jan/1980-
Dec/1999 and Jan/2000-Dec/2016. The rows 6-10 report the linear rate trend
of the six temperature records in the same three-time intervals. Data depicted
in Figure 5A-D. (*) The trend is calculated after removing the ENSO
temperature signal [32] as proposed in Scafetta et al.
4. CONCLUSION
Data analysis of the instrumental temperature records
evidences that the climate has warmed by about 0.9oC since
1850. However, in the light of a quasi-millennial natural
climatic cycle, the presence of a global warming since 1850
can still be partly explained as the natural tendency of
climate to recover from the 1600-1700 Little Ice Age. There
is no doubt that human activities have become more and
more important during the last century but climatic models
need to be properly evaluated in particular if they are
employed to predict future climate scenarios so to justify
particularly expensive policies.
As a matter of facts, we found that the current GCMs are
quite unsatisfactory. While the anthropogenic emissions and,
in particular, CO2 have been monotonically increasing, the
warming observed since 1850 has not been monotonic. This
yields to our first finding (Figure 4) that from 1860 to 2016
the CMIP5 GCMs simulations predict an excessive warming
relative to the four available long global surface temperature
records: see Figure 4.
We have also explicitly compared the observed and
simulated warming trends during three selected time intervals
(1922-1941, 1980-1999, 2000-2016) and found out that while
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the temperature of the globe warmed in an equal way during
the 1922-1941 and 1980-1999 periods, the climate
simulations predict a significantly lower warming in 1922-
1941 than in 1980-1999 (Figure 5). The simulations mirror
the well-known fact that the anthropogenic emissions
accelerated after 1950 [5,6,7] but here we showed that the
climate deviates from the simulations with a statistical
confidence larger than 95%. The fact is that there was a
strong warming during the first half of the century from
about 1910 to 1940 that cannot be explained by the
anthropogenic emission alone or by the radiative forcings
used in the models taken into account by the IPCC.
During the period Jan/2000 to Dec/2016, the
anthropogenic emissions were still accelerating and the
CMIP5 simulations predicted a warming rate even stronger
than what experienced in the 20th century. Instead, all six
available global temperature records depicted in Fig. 2
showed on average just a modest warming that again differs
significantly from the GCM simulations with statistical
confidence larger than 95%.
Figure 5. [A, B, C] Distribution of the linear rate trends of the 301 GCM individual simulations versus the six temperature
records in the three time intervals Jan/1922 - Dec/1941, Jan/1980 - Dec/1999 and Jan/2000 - Dec/2016. [D] Percentile diagrams
of the same: the statistical intervals of the boxplots are 5%, 25%, 50%, 75% and 95%
Statistical analysis clearly shows that the observations and
the CMIP5 GCM simulations significantly differ in at least
two periods of 20 years (1922-1941) and 17 years (2000-
2016). This is sufficient to conclude that the CMIP5 models
used by the IPCC [6] to advocate the AGWT are flawed
according to the same criteria established by the AGWT
advocates themselves. In fact, a few years ago it was
acknowledged that whether such discrepancies had to occur
for more than 15 years, that would have questioned the
physical reliability of the used climate models [22, 24].
We note that among the global surface temperature
records, the one that manifests a 2000-2016 warming trend
closer to that of the CMIP5 GCMs is the NCDC: it shows a
decadal trend of 0.193 ± 0.018oC/decade (without removing
the ENSO signal) and 0.153 ± 0.015oC/decade (removing the
ENSO signal) versus a mean GCM trend of 0.219 ±
0.096oC/decade. However, the behavior of the NCDC records
is clearly inconsistent with that observed by the other records,
and the divergence is maximum versus the satellite based
UAH and RSS records. Indeed, it has been recently found
that errors in the NCDC temperature model have produced a
spurious warming trend [31].
In conclusion, the temperature records clearly manifest
several fluctuations from the inter-annual scale to the multi-
decadal one. Detailed spectral analyses have determined the
likely existence of harmonics at about 9.1, 10.5, 20 and 60-
year periods [7, 8, 9]. By contrast, the CMIP5 GCMs
simulations (Figure 3) used by the IPCC (2013) to advocate
the AGWT show a quite monotonic accelerating warming
since 1860, which is at most temporarily interrupted by
volcano eruptions and only slightly modulated by aerosol
emissions. Thus, the models are not able to reproduce the
natural variability observed in the climate system and should
not be trusted for future energy planning [33].
S15
It has been suggested that non-radiative physical processes
connected with solar activity and the “resonant” orbital
motions of the moon and the planets can cast light on the
otherwise incomprehensible temperature fluctuations [34, 35].
In fact, the magnetic activity of the sun and, probably, also
the planetary motions modulate both the solar wind and the
flux of the cosmic rays and interstellar dust on the earth with
the result of a modulation of the clouds coverage.
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S17
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It has been claimed that the early-2000s global warming slowdown or hiatus, characterized by a reduced rate of global surface warming, has been overstated, lacks sound scientific basis, or is unsupported by observations. The evidence presented here contradicts these claims.
Article
Power spectra of global surface temperature (GST) records reveal major periodicities at about 9.1, 10-11, 19-22 and 59-62 years. The Coupled Model Intercomparison Project 5 (CMIP5) general circulation models (GCMs), to be used in the IPCC (2013), are analyzed and found not able to reconstruct this variability. From 2000 to 2013.5 a GST plateau is observed while the GCMs predicted a warming rate of about 2 K/century. In contrast, the hypothesis that the climate is regulated by specific natural oscillations more accurately fits the GST records at multiple time scales. The climate sensitivity to CO2 doubling should be reduced by half, e.g. from the IPCC-2007 2.0-4.5 K range to 1.0-2.3 K with 1.5 C median. Also modern paleoclimatic temperature reconstructions yield the same conclusion. The observed natural oscillations could be driven by astronomical forcings. Herein I propose a semi empirical climate model made of six specific astronomical oscillations as constructors of the natural climate variability spanning from the decadal to the millennial scales plus a 50% attenuated radiative warming component deduced from the GCM mean simulation as a measure of the anthropogenic and volcano contributions to climatic changes. The semi empirical model reconstructs the 1850-2013 GST patterns significantly better than any CMIP5 GCM simulation. The model projects a possible 2000-2100 average warming ranging from about 0.3 C to 1.8 C that is significantly below the original CMIP5 GCM ensemble mean range (1 K to 4 K).