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Science Forum Index » Geology - Meteorology Forum » Up To 69% Of Global Warming Due To Solar Variability
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| 00BNZ |
 Posted: Wed Mar 12, 2008 12:33 am |
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Guest
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Up To 69% Of Global Warming Due To Solar Variability
Nicola Scafetta, Bruce J. West
Physics Today
March 2008
http://www.fel.duke.edu/~scafetta/pdf/opinion0308.pdf
QUOTE: "In particular, since 2002 the temperature
data present a global cooling, not a
warming! This cooling seems to have
been induced by decreased solar activity
from the 2001 maximum to the 2007
minimum as depicted in two distinct
TSI reconstructions."
Nicola Scafetta is a research associate in the Duke University physics
department. Bruce West is chief scientist in the mathematical and
information science directorate, US Army Research Office, in Research
Triangle Park, North Carolina.
The causes of global warming-the
increase of approximately 0.8±0.1 °C in
the average global temperature near
Earth's surface since 1900-are not as
apparent as some recent scientific publications
and the popular media indicate.
We contend that the changes in
Earth's average surface temperature are
directly linked to two distinctly different
aspects of the Sun's dynamics: the
short-term statistical fluctuations in the
Sun's irradiance and the longer-term
solar cycles. This argument for directly
linking the Sun's dynamics to the response
of Earth's climate is based on
our research and augments the interpretation
of the causes of global warming
presented in the United Nations
2007 Intergovernmental Panel on Climate
Change (IPCC) report.1
The most debated issue in contemporary
science is the cause or causes of
global warming, with the popular
media contending that the issue has
been resolved and that the majority of
scientists concur. The "majority opinion"
is based on the analysis of global
warming done using large-scale computer
codes that incorporate all identified
physical and chemical mechanisms
into global circulation models (GCMs)
in an attempt to recreate and understand
the variability in Earth's average
temperature. The IPCC report1 concludes
that the contribution of solar
variability to global warming is negligible,
to a certainty of 95%. It is reported
that the "majority" believes the average
warming observed since the beginning
of the industrial era is due to the increase
in anthropogenic greenhouse gas
concentrations in the atmosphere.
Modeling TSI variability
Earth's atmosphere, landmasses, and
oceans absorb and redistribute the total
solar irradiance (TSI) by means of coupled
nonlinear hydrothermal, geochemical,
and radiative dynamic processes
that produce Earth's globally averaged
temperature at a given time. Versions of
those physical mechanisms are included
in the GCMs, but what is not addressed
in the simulations are the statistics
of the time series. Those series
consist of the monthly values of temperature
anomalies. The statistical variability
in Earth's average temperature is
interpreted as noise; the temperature
fluctuations are thought to contain no
useful information and are consequently
smoothed to emphasize the
presumably more important long-time
changes in the average global temperature,
typically on the order of years. According
to the central limit theorem, the
statistics of the fluctuations in such
large-dimensional networks ought to be
Gaussian.2 The fact that they are not remains
unexplained. The non-Gaussian
behavior prompted us to study temperature
fluctuations as a problem in nonequilibrium
statistical physics wherein
statistical fluctuations often provide
useful information about the transport
properties of complex phenomena. An
example would be the fluctuation-
dissipation theorem, in which the response
of a network to a perturbation is
determined by the network's unperturbed
autocorrelation function.
The variations in TSI are indicative
of the Sun's turbulent dynamics, as evidenced
by changes in the number, duration,
and intensity of solar flares and
sunspots, and by the intermittency in
the time intervals between dark spots
and bright faculae. That time variation
in TSI induces similar changes in
Earth's average temperature and produces
trends that move the global temperature
up and down for tens or even
hundreds of years. Our conclusions depart
from those of the GCM simulations.
We maintain that the variations in
Earth's temperature are not noise, but
contain substantial information about
the source of variability, in particular
the variations in TSI. Establishing this
direct connection between the complex
dynamics of the Sun and Earth requires
a new kind of linking-one associated
with the transfer of information between
complex networks, even when
the linking is extremely weak, as it is in
the Sun-Earth network.
We showed that the stochastic properties
of the average global temperature
are linked to the statistics of TSI.2 It is the
linking of the complexity of Earth to the
complexity of the Sun that forces Earth's
temperature anomalies to adopt the TSI
statistics. Consequently, both the fluctuations
in TSI, using the solar flare time
series as a surrogate, and Earth's average
temperature time series are observed
to have inverse power-law statistical
distributions. Specifically, if t is
the time between events, where an event
is a solar flare or a fluctuation in Earth's
temperature, the distribution of time intervals
between events P(t) is an inverse
power law; that is, P(t) ? A/t?, where A
is a normalization constant. The inverse
power-law index ? turns out to be the
same for both the solar flare and temperature
anomaly time series, even
though the cross-correlation of the two
vanishes except at the lowest frequencies,
where quasi-periodic solar cycles
dominate the dynamics.
The scaling of the statistical distribution
of the TSI time series was tested by
randomly changing the order of the data
points. If the time series were internally
correlated, the resulting distribution
would have changed from the original,
but that did not happen. The invariance
of the distribution under shuffling indicates
that the statistics of the time series
is non-Poisson and renewal-meaning
that with the generation of each new
event, the process is renewed. The same
was determined to be true of the global
temperature time series.
Complexity matching
The statistics of solar flares, which we
used as a surrogate for the fluctuations
in TSI, are described by a non-Gaussian
distribution. The behavior of such limit
distributions requires a generalization
of the central limit theorem to the case
in which the second moment of the variate
diverges. Such processes were studied
by Paul Lévy before World War II,
www.physicstoday.org March 2008 Physics Today 51
and now bear his name. The solar flare
statistics were shown to be describable
by such a Lévy distribution and we assumed
that intermittent solar flares perturb
the intrinsic fluctuations in Earth's
average temperature. The end result of
this perturbation is that the statistics of
the temperature anomalies inherit the
statistical structure that was evident in
the intermittency of the solar flare data.2
The inverse power-law index ? for solar
flares was determined to be 2.14,
whereas ? for the air temperature was
2.11 globally, 2.20 for the Northern
Hemisphere, 2.09 for the Southern
Hemisphere, 2.21 over land, and 2.06
over the ocean. The near equivalence in
indices occurs because of a newly identified
phenomenon, the complexitymatching
effect,3 described below, and
suggests the presence of a subtle but
persistent solar signature in climate
fluctuations on short time scales. Note
that this climate response to complexity
is separate and distinct from the response
to solar cycles.
Thus, the Sun's influence on Earth's
temperature is subtle because it is not
just an energy transport process but
also an information transfer. According
to linear response theory in statistical
physics, a network S responds to a perturbation
P by means of a linear transfer
equation, whose kernel, the response
function, is determined by the
fluctuation-dissipation theorem given
that the perturbation is sufficiently
weak. When S and P are non-Poisson
renewal processes, the response of S is
maximal when the complexity of the
two networks, as measured by the inverse
power-law indices, is matched.3
For the Sun-Earth one-way linking, S is
the Earth and P is the Sun. The
complexity-matching effect in the
Sun-Earth network is evident in the
equality of the inverse power-law
indices.
Solar cycles
Incorporating the influence of solar cycles
into this thermodynamically closed
climate modeling strategy reveals coordinated
variability over even longer
time scales. Recent heuristic studies indicate
that the climate time response
parameter ?, analogous to the Onsager
relaxation time in statistical physics,
might be 5-10 years.4,5 By using a climate
time response ? of 7.5 years and
the phenomenological 0.1 °C amplitude
of the 11-year solar cycle (see reference
1, page 674, for details) as constraints on
a simple two-parameter model in the
tradition of the earliest climate models,
we recently showed that it is possible to
reconstruct a phenomenological solar
signature (PSS) of climate for the last
four centuries.5 In the figure, the interval
from 1950 to 2010 is displayed with
two such PSS reconstructions derived
from two alternative TSI inputs. The
figure shows excellent agreement between
the 11-year PSS cycles and the cycles
observed in the smoothed average
global temperature data; a 22-year cycle
component in the temperature also
matches the 22-year PSS cycle very well.
In particular, since 2002 the temperature
data present a global cooling, not a
warming! This cooling seems to have
been induced by decreased solar activity
from the 2001 maximum to the 2007
minimum as depicted in two distinct
TSI reconstructions.
Thus the average global temperature
record presents secular patterns of 22-
and 11-year cycles and a short timescale
fluctuation signature (with apparent
inverse power-law statistics), both
of which appear to be induced by solar
dynamics. The same patterns are poorly
reproduced by present-day GCMs and
are dismissively interpreted as internal
variability (noise) of climate. The nonequilibrium
thermodynamic models
we used suggest that the Sun is influencing
climate significantly more than
the IPCC report claims. If climate is as
sensitive to solar changes as the above
phenomenological findings suggest,
the current anthropogenic contribution
to global warming is significantly overestimated.
We estimate that the Sun
could account for as much as 69% of the
increase in Earth's average temperature,
depending on the TSI reconstruction
used.5 Furthermore, if the Sun does
cool off, as some solar forecasts predict
will happen over the next few decades,
that cooling could stabilize Earth's climate
and avoid the catastrophic consequences
predicted in the IPCC report.
The authors thank the Army Research Office
for research support and for grant W911NF-
06-1-0323.
References
1. Intergovernmental Panel on Climate
Change, Climate Change 2007: The Physical
Science Basis, Cambridge U. Press, New
York (2007). Available at http://ipccwg1.
ucar.edu/wg1/wg1-report.html.
2. N. Scafetta, B. J. West, Phys. Rev. Lett. 90,
248701 (2003).
3. P. Allegrini, M. Bologna, P. Grigolini, B. J.
West, Phys. Rev. Lett. 99, 010603 (2007); G.
Aquino, P. Grigolini, B. J. West, Europhys.
Lett. 80, 10002 (2007).
4. S. E. Schwartz, J. Geophs. Res. 112, D24S05
(2007).
5. N. Scafetta, B. J. West, J. Geophys. Res. 112,
D24S03 (2007).
www.physicstoday.org |
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| Lloyd |
| Posted: Wed Mar 12, 2008 7:25 am |
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On Mar 12, 1:33 am, "00BNZ" <00...@doooodoooooo.com.au> wrote:
OK, tell us what this means:
"temperature is subtle because it is not
just an energy transport process but
also an information transfer. According
to linear response theory in statistical
physics, a network S responds to a perturbation
P by means of a linear transfer
equation, whose kernel, the response
function, is determined by the
fluctuation-dissipation theorem given
that the perturbation is sufficiently
weak. When S and P are non-Poisson
renewal processes, the response of S is
maximal when the complexity of the
two networks, as measured by the inverse
power-law indices, is matched."
You can't, can you? |
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| Phil Hays |
| Posted: Wed Mar 12, 2008 7:52 am |
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Guest
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chemist wrote:
Quote: They claim that El Nina is causing all the cooling They claim that CO2
is rising faster than ever but the official CO2 data stops in 2004. Is
that because the CO2 hasn't risen as fast as it theoretically should ?
As for CO2, maybe you are just looking in the wrong place. Or maybe you
need to clear your browser cashe once in a while.
ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_gl.txt
--
Phil Hays |
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| V-for-Vendicar |
| Posted: Wed Mar 12, 2008 3:11 pm |
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"chemist" <tom-bolger@ntlworld.com> wrote
Quote: They claim that El Nina is causing all the cooling
They claim that CO2 is rising faster than ever
but the official CO2 data stops in 2004. Is that because
the CO2 hasn't risen as fast as it theoretically should ?
Ya, the official Co2 data stops in 2004 because it's all a conspiracy to
make you look like the MORON you are.
OFFICIAL CO2 DATA
-----------------
year ppm/yr <Growth rate
1959 0.94
1960 0.50
1961 0.98
1962 0.62
1963 0.73
1964 0.25
1965 1.02
1966 1.25
1967 0.70
1968 1.06
1969 1.33
1970 0.98
1971 0.88
1972 1.72
1973 1.17
1974 0.82
1975 1.10
1976 0.90
1977 2.08
1978 1.33
1979 1.61
1980 1.84
1981 1.41
1982 0.71
1983 2.18
1984 1.39
1985 1.23
1986 1.51
1987 2.30
1988 2.14
1989 1.24
1990 1.32
1991 1.00
1992 0.49
1993 1.26
1994 1.96
1995 1.98
1996 1.19
1997 1.93
1998 3.00
1999 0.88
2000 1.73
2001 1.63
2002 2.55
2003 2.31
2004 1.56
2005 2.54
2006 1.72
2007 2.15
year mean Co2 level
1959 315.98
1960 316.91
1961 317.65
1962 318.45
1963 318.99
1964 319.61
1965 320.03
1966 321.37
1967 322.18
1968 323.05
1969 324.62
1970 325.68
1971 326.32
1972 327.46
1973 329.68
1974 330.17
1975 331.09
1976 332.06
1977 333.78
1978 335.40
1979 336.78
1980 338.70
1981 340.11
1982 341.21
1983 342.84
1984 344.40
1985 345.87
1986 347.19
1987 348.98
1988 351.45
1989 352.89
1990 354.16
1991 355.49
1992 356.27
1993 356.96
1994 358.63
1995 360.62
1996 362.37
1997 363.47
1998 366.50
1999 368.14
2000 369.41
2001 371.07
2002 373.16
2003 375.80
2004 377.55
2005 379.75
2006 381.85
2007 383.72 |
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| V-for-Vendicar |
| Posted: Wed Mar 12, 2008 5:51 pm |
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"00BNZ" <00BNZ@doooodoooooo.com.au> wrote
Quote: QUOTE: "In particular, since 2002 the temperature
data present a global cooling, not a
warming! This cooling seems to have
been induced by decreased solar activity
from the 2001 maximum to the 2007
minimum as depicted in two distinct
TSI reconstructions."
MMMMMMOOOOOOOOORRRRRRRRRROOOOOOOOOOOOONNNNNNNNNNN
From the same Opinion piece....
"The inverse power-law index ? turns out to be the same for both the solar
flare and temperature anomaly time series, even
though the cross-correlation of the two vanishes except at the lowest
frequencies, where quasi-periodic solar cycles dominate the dynamics."
Translation -> No Correlation can be seen..
MMMMMMMMMOOOOOOOOOORRRRRRRRRRRRROOOOOOOOOOONNNNNNNNNNN |
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| V-for-Vendicar |
| Posted: Thu Mar 13, 2008 7:06 am |
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"dave" <nothere@nowhere.com> wrote
Let me explain. The researches couldn't find any correlation between
earth's surface temperature and solar variability, but they could show that
a particular statistic they could compute for both solar variability and
variability of temperatures here on the earth had a distribution that was an
inverse power function and that the exponent was roughly the same in both
instances.
In other words, it's worthless Claptrap. |
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| dave |
| Posted: Thu Mar 13, 2008 8:10 am |
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Lloyd wrote:
Quote: On Mar 12, 1:33 am, "00BNZ" <00...@doooodoooooo.com.au> wrote:
Up To 69% Of Global Warming Due To Solar Variability
Nicola Scafetta, Bruce J. West
Physics Today
March 2008
http://www.fel.duke.edu/~scafetta/pdf/opinion0308.pdf
OK, tell us what this means:
"temperature is subtle because it is not
just an energy transport process but
also an information transfer. According
to linear response theory in statistical
physics, a network S responds to a perturbation
P by means of a linear transfer
equation, whose kernel, the response
function, is determined by the
fluctuation-dissipation theorem given
that the perturbation is sufficiently
weak. When S and P are non-Poisson
renewal processes, the response of S is
maximal when the complexity of the
two networks, as measured by the inverse
power-law indices, is matched."
You can't, can you?
It throbs... |
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