Atmospheric and Climate Sciences, 2012, 2, 401-415
http://dx.doi.org/10.4236/acs.2012.24035 Published Online October 2012 (http://www.SciRP.org/journal/acs)
Causes of the Global Warming Observed since the 19th
Century
Michael J. Ring, Daniela Lindner, Emily F. Cross, Michael E. Schlesinger
Climate Research Group, Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, USA
Received May 7, 2012; revised June 10, 2012; accepted June 21, 2012
ABSTRACT
Measurements show that the Earth’s global-average near-surface temperature has increased by about 0.8˚C since the
19th century. It is critically important to determine whether this global warming is due to natural causes, as contended
by climate contrarians, or by human activities, as argued by the Intergovernmental Panel on Climate Change. This study
updates our earlier calculations which showed that the observed global warming was predominantly human-caused.
Two independent methods are used to analyze the temperature measurements: Singular Spectrum Analysis and Climate
Model Simulation. The concurrence of the results of the two methods, each using 13 additional years of temperature
measurements from 1998 through 2010, shows that it is humanity, not nature, that has increased the Earth’s global
temperature since the 19th century. Humanity is also responsible for the most recent period of warming from 1976 to
2010. Internal climate variability is primarily responsible for the early 20th century warming from 1904 to 1944 and the
subsequent cooling from 1944 to 1976. It is also found that the equilibrium climate sensitivity is on the low side of the
range given in the IPCC Fourth Assessment Report.
Keywords: Climate Change; Global Warming; Climate Forcing; Internal Variability
1. Introduction
It is well known that the global, instrumental near-sur-
face temperature records show a warming of Earth’s sur-
face since the 19th century. It is equally certain that the
concentrations of greenhouse gases have risen in Earth’s
atmosphere due to humanity’s consumption of fossil fu-
els. However, to conclude the rise of global temperature
is caused by the recent increase in greenhouse gases is
more difficult, since the greenhouse gases are not the
only factors affecting Earth’s climate. External but natu-
ral factors such as volcanoes and changes in solar irradi-
ance also alter global temperatures. Additionally, Earth’s
climate system contains a wealth of natural, internal
variability. Among these manifestations are oscillations
with a somewhat regular period such as the El Niño-
Southern Oscillation and the Atlantic Multidecadal Os-
cillation [1], as well as stochastic climate noise.
Over the course of its four Assessment Reports, the
Intergovernmental Panel on Climate Change (IPCC) has
become increasingly certain of the role that humans have
played in contributing to the observed warming. The
IPCC’s First Assessment Report in 1990 noted that the
observed global-mean near-surface temperature in the
previous 100 years “is broadly consistent with predict-
tions of climate models, but it is also of the same magni-
tude as natural climate variability. Thus the observed
increase could be largely due to this natural variability,
alternatively this variability and other human factors
could have offset a still larger human-induced green-
house warming” [2]. But in the most recent Fourth As-
sessment Report in 2007, the IPCC stated that “most of
the observed increase in global average temperatures
since the mid-20th century is very likely due to the ob-
served increase in anthropogenic greenhouse gas concen-
trations” [3]. While this conclusion is not completely
universal in the scientific community [4], the remaining
level of scientific skepticism regarding Earth’s warming,
and humanity’s contribution toward these changes, is
small.
In contrast, the level of skepticism regarding global
warming among the general public, at least in the United
States, remains much higher. In the annual Gallup envi-
ronment poll, the percentage of Americans who cited
“pollution from human activities” as the major cause of
the observed temperature increase was 50 percent in
2010 and 52 percent in 2011, while the percentage of
those citing “natural changes in the environment” as the
major cause was 46 percent in 2010 and 43 percent in
2011. But, as recently as 2008 the percentage of the pub-
lic responding “pollution from human activities” was
twenty percentage points higher than those choosing
“natural changes in the environment”. Yet the number of
C
opyright © 2012 SciRes. ACS

M. J. RING ET AL.
402
Americans who say they understand the issue “very
well” or “fairly well” has increased from 69 percent in
2001 to 80 percent in the 2011 survey [5]. While the
recognition of humanity’s role in climate change has
become stronger within the scientific community in the
past decade, the opposite has happened among the
American public. In turn, the weak public acceptance of
humanity’s role in climate change has made policy
choices in favor of emissions reductions a political im-
possibility so far.
While some of the skeptical members of the general
public may never be willing to consider scientific evi-
dence regarding the human role in climate change, we
believe there are others who are willing to consider such
evidence. However, for this to happen, the evidence pre-
sented to members of the general public must be accessi-
ble and understandable. While there have been a number
of important advances in detection and attribution of
climate change since the foundation of the IPCC, with
particular attention focused on the “optimal fingerprint-
ing” multivariate regression technique (see reviews by [6]
and [7] for further details), the methods used in those
studies have become increasingly complex and bewil-
dering to members of the general public, and even to
other scientists. We believe there is a role for simple,
effective, accessible studies of the causes of global cli-
mate change, and that the results of these studies are es-
pecially important for communication to the general pub-
lic. We therefore choose a simpler approach here, re-
turning to previous work by our Group [8]. Our goal is to
make the results of our research accessible and under-
standable to non-scientists, as well as to scientists. We
believe communication of the results of this study is im-
portant to redress the skepticism regarding anthropogenic
global warming outside the scientific community.
We use our Climate Research Group’s Simple Climate
Model (SCM) [9] to determine the causes of the observed
temperature increases in four near-surface instrumental
temperature records: HADCRUT4, the record compiled
by the Hadley Centre and the University of East Anglia
[10]; GISTEMP, the record compiled by the National
Aeronautic and Space Administration’s Goddard Institute
for Space Studies [11]; the National Oceanographic and
Atmospheric Administration’s (NOAA) National Cli-
matic Data Center (NCDC) record [12]; and the Japanese
Meteorological Agency (JMA) record [13,14]. In so do-
ing we will update and extend the previous study [8]
which showed that anthropogenic forcings were the pri-
mary cause of the observed warming since pre-industri-
alization, and a co-equal cause with natural variability of
the warming in the 1976-1990 period.
We also use Singular Spectrum Analysis (SSA), a type
of Fourier spectral analysis with data-determined struc-
tures (basis functions) in time to determine both the trend
and natural variability in the observed temperatures. This
analysis does not use the SCM in any way.
Additional details regarding the SCM and SSA may be
found in Section 2. Our results are presented in Section 3.
In Section 4 we offer discussion of our findings. We
summarize in Section 5.
2. Methods
In this study we determine and compare the temperature
trend found through two separate techniques: simulations
using our SCM, and Singular Spectrum Analysis (SSA)
of the instrumental temperature records. We use our
SCM to determine the amount of temperature change
caused by each of the external radiative forcings—the
change in the net incoming radiation at the top of Earth’s
atmosphere. We use SSA to determine the amount of
temperature change caused by the internal variability.
Below we describe each method in more detail.
2.1. Summary of Simple Climate Model
We use our SCM [9] to produce simulations of the his-
torical global-mean temperature change by minimizing
the root-mean-square differences between observed and
simulated quantities. In this section, we summarize the
SCM procedure. Readers interested in more details may
read the next subsection.
Three SCM parameters are estimated based on com-
parisons to the observed data. The equilibrium climate
sensitivity, ∆T2x—the change in global-mean, equilib-
rium near-surface temperature for a radiative forcing
equivalent to a doubling of the pre-industrial CO2 con-
centration—is estimated using the observed global-mean
near-surface temperature. The aerosol radiative forcing in
reference year 2000, FA(2000), is estimated using the ob-
served interhemispheric near-surface temperature differ-
ence. The ocean thermal diffusivity, κ, is estimated using
the observed upper ocean heat uptake. For the temperature
comparisons, we consider the four different instrumental
temperature records mentioned in Section 1. The simu-
lated upper ocean heat uptake is compared to [15].
The SCM considers both natural and anthropogenic
external forcings. A summary of the global-mean mag-
nitudes of each forcing source between 1850 and present
day is found in Table 1; note that some of the radiative
forcings have different magnitudes in the Northern and
Southern Hemispheres.
We perform simulations using all the external forcings,
and simulations using only the natural, external forcings.
This allows us to assess whether the natural forcings
alone are sufficient to reproduce the observed tempera-
ture increase, or the trend found through SSA analysis
(see Section 2.3).

M. J. RING ET AL. 403
Table 1. Summary of magnitudes for individual radiative
forcing sources.
Source Change in global-mean radiative
forcing, 1850 to present
Long-lived greenhouse gases 2.62 Wm–2
Aerosols (Sum of sulfate, blac
k
carbon, and organic carbon)
–0.99 Wm–2 to –0.42 Wm–2, depend-
ing on dataset
Tropospheric ozone 0.39 Wm–2
Changes in land use –0.16 Wm–2
Changes in solar irradiance
About 0.1 Wm–2 secular increase;
11-year cycle contributes an additional
±0.1 Wm–2
Volcanoes No secular trend; occasional eruptions
of up to –2 Wm–2 global impact
Additionally, we further divide the radiative forcing
into each of the components mentioned above and com-
pute the change in temperature due to each forcing com-
ponent over the entire period of the temperature record,
as well as over shorter periods in the record. This is done
by running the model using the values of ∆T2x, FA and κ
obtained for the analogous model run using all the radia-
tive forcings, but turning off all radiative forcings except
the one we wish to consider, to obtain the temperature
change for that individual radiative forcing alone. This is
the procedure used by [8]. However, here we include
both more recent data and additional sources of radiative
forcing that [8] did not include previously.
The shorter periods we consider are: 1904-1944,
1944-1976, 1976-2010 and 1998-2008. The first three
periods are those previously examined by [8], which
correspond to periods of warming, cooling, and warming,
respectively. We also consider 1998-2008, a period dur-
ing which no warming was observed, a point that is fre-
quently mentioned by skeptics [4]. This has created in-
terest in learning why the temperature did not increase
when the radiative forcing by the long-lived greenhouse
gases continued to increase [16].
2.2. Additional Details Regarding the SCM
This subsection is intended for readers interested in de-
tails on the SCM’s optimizations and the forcings used.
The SCM calculates the changes in the temperatures of
the surface air and ocean, the latter as a function of depth.
The ocean is subdivided vertically into 40 layers, with
the uppermost being the 67.7-meter-deep mixed layer
and the deeper layers each being 100 m thick. The ocean
is subdivided horizontally into a polar region where bot-
tom water is formed, and a nonpolar region where there
is upwelling. In the nonpolar region, heat is transported
toward the surface by upwelling and downwards by
physical processes whose effects are treated as an
equivalent diffusion. Heat is also removed from the
mixed layer in the nonpolar region by a transport to the
polar region and downwelling toward the bottom, this
heat being ultimately transported upward from the ocean
floor in the nonpolar region. The atmosphere in each
hemisphere is subdivided into the atmosphere over the
ocean and the atmosphere over land, with heat exchange
between them.
We include the changes in carbon dioxide, methane,
nitrous oxide [17] and halocarbon [18,19] concentrations
in our long-lived greenhouse gas forcing. We convert
concentrations to radiative forcing [20].
The tropospheric ozone forcing [21] was used previ-
ously by our Group [8]. These are older estimates but are
consistent with the best estimate in [22], producing a
global-mean tropospheric-ozone forcing at present of
0.39 W/m2 since pre-industrialization. The ozone forcing
partition between hemispheres is the same as that for
sulfate aerosol [23].
The SCM includes sulfate aerosol radiative forcing
based on the emissions of sulfur dioxide from 1850 to
2005 [23]. We assume that the ratio of the indirect forc-
ing to the direct forcing in a reference year (2000) is 8/3
[24]. Other choices of ratio produce similar values for the
total forcing. The indirect portion does not grow exactly
linearly with concentration. We use the same formula as
previously [25] for the indirect portion, except we use
year 2000 rather than 1990 as the reference year.
The black-carbon and organic-carbon radiative forcing
includes contributions from fossil and biofuel use [26,27]
and open vegetation burning [28]. We assume that the
strength of the direct radiative forcing of black carbon in
the year 2000 is –34/40 that of the sulfate aerosols, and
that of organic carbon is +19/40 that of sulfate aerosols.
We choose these ratios based on the values reported in
the IPCC AR4, Table 2.13 [22]. We assume that the or-
ganic-carbon indirect radiative forcing in the reference
year is 8/3 of its direct forcing, and the black-carbon in-
direct forcing is –8/3 of its direct forcing (hence the
black carbon indirect forcing is negative).
We use estimates of global radiative forcing due to
land-use changes from [29]. We interpolate between years
and extrapolate to the present day. Since the vast major-
ity of the land-use forcing in these estimates has occurred
in the Northern Hemisphere, we apply double the global-
mean value there, and zero to the Southern Hemisphere.
We include radiative forcing from volcanic eruptions
[30]. The data are weighted by a constant of 0.6. We do so
to reduce the unrealistically large negative temperature
excursions in years following volcanic eruptions in our
Group’s earlier studies [8,25]. Eliminating the volcanic
forcing entirely, however, degraded agreement between
the simulated and observed interhemispheric temperature
difference and oceanic heat content. The value of 0.6 was
found to offer the best compromise between these factors.

M. J. RING ET AL.
ACS
404
We assume the volcanic forcing post-2000 is zero.
The solar irradiance data is from [31,32].
As some of the historical radiative forcing data termi-
nate before 2010, we extend the radiative forcing data
based on the trend of the IPCC A1B emissions scenario
[33] where necessary for the long-lived greenhouse gases,
aerosols, and tropospheric ozone. However this requires
extrapolating only for the final few years. Similarly, we
extend the solar record based on a fit to the previous
three solar cycles; this requires extrapolation for only
two years. We assume the volcanic forcing post-1999 is
zero, as no eruptions of a scale that altered global climate
occurred in the 2000s.
The timeseries of global-mean radiative forcing for
each of the individual sources, as well as the total radi-
ative forcing, are shown in Figure 1. For the long-lived
greenhouse-gas forcing, tropospheric ozone forcing,
land-use change forcing, forcing due to solar irradiance,
and forcing due to volcanic eruptions, the timeseries
shown are input to the model.
(g)
Figure 1. Global-mean radiative forcing versus 1850 value for each of the following sources: (a) Long-lived greenhouse gases;
(b) All aerosols; (c) Tropospheric ozone; (d) Changes in land use; (e) Changes in solar irradiance; (f) Volcanic eruptions; (g)
Total of all sources. Aerosol forcing displayed in panel (b) is calculated based on HADCRUT4 optimization for prescribed
emissions; all other forcings are prescribed.

M. J. RING ET AL. 405
The aerosol radiative forcing shown here is the calcu-
lated timeseries of radiative forcing versus time for the
HADCRUT4 optimization. Recall that the model opti-
mizes for the strength of the aerosol forcing, so the val-
ues obtained for the forcing are not the same in each case.
The emissions records used, however, are the same in all
cases. The values of aerosol forcing found for NCDC and
JMA are more negative, while the values found for
GISTEMP are less negative. Also recall that for some of
the radiative forcings, the timeseries are different be-
tween the Northern and Southern Hemispheres.
In addition to the new sources of radiative forcing
considered, we note in particular one major difference
between the forcings used here and those used by [8].
The solar record we employ, based on [31,32], contains
much less variability with time than the records [34,35]
used by [8]. Consequently, the contributions to the tem-
perature changes caused by the changes in solar irradi-
ance that we derive are smaller than those that our Group
found previously.
The model seeks to optimize values of equilibrium
climate sensitivity, T2x, direct sulfate radiative forcing,
FASA, and ocean diffusivity κ. The optimized solution is
obtained by minimizing the root-mean-square error (RM-
SE) for the length of each instrumental temperature re-
cord (1850-2010 for HADCRUT4, 1880-2010 for GIS-
TEMP and NCDC, 1891-2010 for JMA) between the
simulated and observed: 1) global temperatures with re-
spect to T2x; 2) interhemispheric temperature differ-
ences with respect to FASA; and 3) upper oceanic heat
content [15] with respect to κ. The model begins by cal-
culating simulated global temperature, interhemispheric
temperature difference and ocean heat uptake records for
initial choices of T2x, FASA, and κ and determining the
RMSE for each simulation as compared to the observed.
The process continues by re-calculating the simulated
records for different guesses of T2x, FASA, and κ and
comparing the RMSE values obtained for the new guess
to those for the old guesses. The iteration is continued
until the point is found where the partial derivative of the
RMSE between simulated and observed global tempera-
ture with respect to choice of T2x, the partial derivative
of RMSE between simulated and observed interhemi-
spheric temperature difference with respect to FASA, and
the partial derivative of RMSE between simulated and
observed ocean heat anomalies with respect to κ, are all
zero. The values of T2x, FASA, and κ that meet these co-
nditions on the partial derivatives are taken as the mod-
el’s solution. For each case we performed the optimiza-
tion for several different initial guesses of T2x, FASA,
and κ to insure that the solutions we obtained are robust.
2.3. Singular Spectrum Analysis
SSA [36] is a non-parametric spectral estimation method
that is similar to Fourier analysis. Unlike Fourier analysis,
which is restricted to sinusoidal basis functions, SSA per-
mits basis functions to have any form, finding functions
which best match the data considered. While the func-
tions found are often oscillatory, there is no requirement
that they have constant amplitude or period, as they must
in Fourier analysis. Hence these functions are called
“quasi-periodic oscillations” (QPOs). The most promi-
nent of these QPOs is the Atlantic Multidecadal Oscilla-
tion (AMO), which has a period of 65 - 70 years [1]. Ad-
ditional details regarding the SSA procedure may be
found in [37], whose procedure we follow, except that
we do not first detrend the instrumental temperature re-
cords.
In addition to the QPOs, non-oscillatory principal
components (PCs), or functions that express the variabil-
ity in time of the data considered, may also be found. (If
the data are first detrended as in [37] these modes will
not appear). In our analyses [38] these non-oscillatory
modes are the two leading PCs in each of the four in-
strumental datasets, meaning they explain more of the
variability than the other modes. We shall call the com-
bination of these two leading non-oscillatory PCs the
“SSA-Trend”, which does not contain any of the internal
variability represented by the previously described QPOs.
Instead, the temperature changes represented by the SSA
trend must either be externally forced, or part of a yet
undiscovered oscillation with a period longer than half of
the record length.
The SSA temperature trend and the SCM-simulated
temperature changes are determined independently of
each other. The SSA trend contains no information on
the factors that caused it, either by nature alone—volca-
noes and variations in solar irradiance—or humanity,
while the temperature changes simulated by the SCM are
forced by natural factors alone or by anthropogenic plus
natural factors. If the SCM is unable to reproduce the
SSA trend using only the natural radiative forcing but is
able to reproduce the SSA trend using anthropogenic
plus natural radiative forcings, this is strong evidence
that the anthropogenic forcings are the cause of the rising
temperature trend found by SSA and not a yet undiscov-
ered long-period QPO.
We use the QPOs determined through SSA to under-
stand more fully the contributions of the modes of inter-
nal variability to the temperature changes. For both the
overall length of the temperature record and for the four
shorter individual time periods we consider, we deter-
mine the amount of temperature change due to each sta-
tistically significant QPO.
3. Results
We compare the SSA trend found for each of the four

M. J. RING ET AL.
406
instrumental temperature datasets over the entire period
to the global-mean temperature increase simulated by the
Simple Climate Model (SCM). We also consider the
temperature changes caused by each forcing and QPO
over five other periods. Additionally, we examine the
changes since [8] in the estimates of: 1) the climate sen-
sitivity—the change in global-mean, equilibrium near-
surface temperature for a radiative forcing equivalent to a
doubling of the pre-industrial CO2 concentration (∆T2x);
and 2) the total aerosol radiative forcing in reference year
2000 (FA(2000)).
3.1. Causes of the Warming over the Entire
Instrumental Record
The four observed temperature datasets are shown in
Figure 2. For each dataset the leading two PCs found by
SSA combine to form a trend of about 0.8˚C over the
duration of each observed record. The trend found for
HADCRUT4 increases continuously after 1880, while
the trends found for the GISTEMP, NCDC and JMA
datasets display a plateau in the mid-20th century, with
the temperature increase occurring before and after that
period.
The temperature changes simulated by the SCM when
all forcings are included, shown in Figure 2, also display
an increase of 0.8˚C over the duration of each of the four
observational datasets. The simulated data, unlike the
SSA trend, include higher-frequency variations, include-
ing the negative temperature excursions caused by vol-
canic eruptions.
Coefficients of determination—the square of the cor-
relation coefficient between two timeseries, R2—are pre-
sented in Table 2 for each dataset, with R2 = 1 indicating
perfect correlation, R2 = 0 indicating no correlation and
R2 < 0 indicating anti-correlation. As seen in Table 2,
there is a strong correlation between the SSA trend and
the observations for all four datasets. The agreement be-
tween the SCM simulations when all forcings are in-
cluded and the observations, and between these simula-
tions and SSA trend, is also high.
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
1850 1880 1910 1940 1970 2000
Global Temperature
Simulated
Observed
SSA Trend
Natural Forcings
Temperature Departure (
o
C)
(a) HADCRUT4
-0.6
-0.4
-0.2
1880 1910 1940 1970 2000
0
0.2
0.4
0.6
Global Temperature
Simulated
Observed
SSA Trend
Natural Forcings
e Departure (
o
C)Temperatur
(c) NCDC-0.6
-0.4
-0.2
1900 1930 1960
0
0.2
0.4
0.6
1990
Global Temperature
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
1880 1910 1940 1970 2000
Global Temperature
Simulated
Observed
SSA Trend
Natural Forcings
Temperature Departure (
o
C)
(b) GISTEMP
Simulated
Observed
SSA Trend
Natural Forcings
e Departure (
o
C)Temperatur
(d) JMA
Figure 2. Observed global-mean near-surface temperature (black), and its trend derived from SSA (blue), together with the
SCM-simulated temperature changes using all forcings (red) and natural forcings alone (green), for the HADCRUT4 (a),
GISTEMP (b), NCDC (c), and JMA (d) temperature records. Temperatures are shown as departures from the 1961–1990
mean.

M. J. RING ET AL. 407
In contrast, the SCM simulations using only the natu-
ral forcings do not replicate the observed increase in
temperature. Instead, the simulated temperature under
these conditions is roughly constant over time, with
negative excursions noted in the years immediately fol-
lowing volcanic eruptions. R2 values for both the SCM
simulations without anthropogenic forcing and observa-
tions, and for these simulations and the SSA trend, are
very low. The failure of the SCM to replicate the ob-
served warming when only natural forcings are used is
similar to the behavior of coupled general circulation
models examined in IPCC AR4 [39].
The simulation results presented in Figure 2 and Ta-
ble 2 for natural forcing alone are for the value of ∆T2x
determined under the influence of all forcings. We per-
formed SCM simulations for natural forcings alone and
tried to estimate ∆T2x, FA(2000) and κ as described above,
but no solution was found. Accordingly we performed
simulations with these quantities prescribed over a wide
range, with ∆T2x as large as 10,000˚C and as small as
0.1˚C, to compare with the observations. None of the
simulated temperature records produced thereby matches
the observed temperature increase, the SSA trend, or the
temperature increase simulated by the SCM when an-
thropogenic forcing is included. Thus the increase in
global-mean near-surface temperature is not due to ex-
ternally forced natural variability. While the difference
between the observed temperatures and both the SSA
trend and the SCM-calculated temperature changes indi-
cates the presence of internal natural variability, this is
not the cause of the global warming observed since the
19th century. This confirms [8]’s finding that human act-
ivities are the main cause of the observed warming over
the total length of the instrumental temperature records.
Figure 3 indicates the contribution to the observed
temperature change for each of the external forcings and
QPOs over the entirety of each temperature record. We
examine separately the contributions from the long-lived
greenhouse gases (LLGHGs), the aerosols, volcanic
eruptions, the sum of the statistically significant QPOs,
and the stochastic noise—that is, the portion of the tem-
perature record that cannot be explained by either the
trend found through SSA, or the statistically significant
QPOs. We also display as “other” the change in tempera-
ture that cannot be accounted for using the sources ex-
plicitly examined above. This includes the contributions
from the solar, land-use, and tropospheric-ozone forcing,
Table 2. Coefficient of Determination, R2, for Global-Mean
Temperature.
Observational Dataset
Quantities
Compareda HADCRUT4GISTEMP NCDCJMA
(a) SSA Trend and
Observed 0.77 0.86 0.84 0.85
(b) Sim (All) and
Observed 0.81 0.85 0.81 0.81
(c) Sim (All) and
SSA Trend 0.84 0.94 0.92 0.90
(d) Sim (Nat) and
Observed 0.02 0.06 0.01 –0.01
(e) Sim (Nat) and
SSA Trend 0.05 0.04 0.02 –0.03
aObs = Observations, Sim = Simulation, All = natural plus anthropogenic
radiative forcing, Nat = natural radiative forcing alone.
Figure 3. Contributions of each factor to the observed temperature increase from the beginning of each instrumental record
to 2010, for the (a) HADCRUT4, (b) GISTEMP, (c) NOAA, and (d) JMA instrumental records.

M. J. RING ET AL.
408
the other three external forcings used by the SCM. These
sources of forcing produce temperature changes that are
much smaller than the other sources.
The individual factor agreeing most closely with the
magnitude of the total temperature change is the contri-
bution from long-lived greenhouse gases (LLGHGs). In
three of the four cases, the LLGHG contribution exceeds
the total temperature change, indicating that the sum of
the other contributions must act to decrease the tempera-
ture change. The largest contribution in this negative
direction is the strength of the aerosol forcing; its mag-
nitude is larger for the runs based on the NOAA and
JMA datasets since the SCM finds a more negative
FA(2000) for these two runs as compared to HADCRUT4
and GISTEMP.
The contributions from the statistically significant
QPOs are smaller than the LLGHG contribution over the
entire instrumental time periods. This is expected since
even the strongest QPO has an amplitude of only 0.1˚C,
which is almost an order of magnitude smaller than the
observed 0.8˚C of temperature change [19]. Also, the
longest-period QPO, the AMO, has a period of about 70
years, whereas we are examining records that are over a
century long.
The action of the QPOs varies among the four datasets.
As noted above, the number of statistically significant
QPOs found for each observational dataset, as well as the
amplitudes and periods of those that are found, are not
identical. Additionally, we note that the start dates of the
four temperature records are different—1850 for HAD-
CRUT4, 1880 for GISTEMP and NOAA, 1891 for JMA.
These two factors account for the different behavior of
the QPO contribution among the datasets.
The volcanic eruptions make a negative contribution to
the temperature change for three of four datasets, while
they make a positive contribution for the JMA case. The
starting years for the HADCRUT4, GISTEMP, and
NOAA temperature observations are before the Krakatoa
volcano eruption in 1883. The simulated temperature
changes due to volcanoes alone for these starting years
are higher than they are for end year 2010. Thus, the
temperature changes from the starting year to 2010 due
to volcanoes are negative for these three cases. The
starting year for the JMA temperature observations is
1891, after the Krakatoa volcano eruption in 1883. The
simulated temperature change for this starting year due to
volcanoes is lower than it is for 2010. Thus, the temp-
erature change from the starting year to 2010 due to vol-
canoes is positive for the JMA dataset. Changing the start-
ing year of the other three temperature datasets to 1891
also causes the volcanic contribution to become positive.
While the LLGHGs from human activities are the
main cause of the warming over the total period of the
four records, they are not necessarily the major cause of
the observed temperature changes over shorter periods.
Accordingly, we next consider the individual contribu-
tions of each of the external forcings and the QPOs to the
observed temperature change for the four shorter time
periods mentioned previously.
3.2. Causes of the Early 20th Century Warming:
1904-1944
In Figure 4 we examine the contributions to the temp-
erature change over the 1904-1944 period. While this is a
period of observed warming, [8] found that the LLGHG
forcing was only a secondary factor during this period,
with internal variability being the most important con-
tributor to the warming (see their Figure 4). We confirm
their findings here: while between 0.53˚C to 0.71˚C of
warming is found in the instrumental records, only
0.13˚C to 0.17˚C of this warming is due to LLGHG
forcing. By contrast the sum of the statistically signifi-
cant QPOs ranges from 0.16˚C for GISTEMP to 0.34˚C
for HADCRUT4. The stochastic noise also contributes
positively over this period for each record, with values
ranging from 0.11˚C for HADCRUT4 to 0.24˚C for JMA.
An additional notable contribution is from volcanic
forcing, which produces between 0.11˚C to 0.13˚C of
warming. Since the early 20th century was a period with
few volcanic eruptions, while the late 19th century was a
volcanically active period, the 1904-1944 period is asso-
ciated with a decrease of volcanically produced aerosols
and hence a warming effect due to the smaller number of
large volcanic eruptions [40].
3.3. Causes of the Mid-20th Century Cooling:
1944-1976
We next examine 1944-1976, the period of mid-century
cooling, and display results in Figure 5. Our findings are
again consistent with those of [8] that natural causes are
the main driver during this period. While the analyses for
all four records include negative aerosol forcing over this
period, the magnitude of the positive LLGHG forcing
exceeds that of the negative aerosol forcing in each case.
The other two sources of human forcing we consider,
tropospheric ozone and land-use changes, are only minor
contributors. Hence, the total human contribution to the
temperature change is positive over this period, in con-
trast to the observed negative changes. We also note that
volcanic eruptions produce a contribution of –0.09˚C to
–0.06˚C in all four datasets.
In all four cases the contribution of the statistically
significant QPOs is negative with values ranging from
–0.31˚C to –0.20˚C. The AMO produces a negative con-
tribution to the temperature change, with its magnitude
again being largest in the HADCRUT4 analysis. The
other QPOs also contribute to the decline, as does the
stochastic noise in all four cases.

M. J. RING ET AL. 409
Figure 4. As in Figure 3, but for the 1904-1944 period.
Figure 5. As in Figure 4, but for the 1944-1976 period.

M. J. RING ET AL.
410
3.4. Causes of the Late 20th-Early 21st Century
Warming: 1976-2010
In Figure 6 we examine the contributions over the
1976-end period. [8] considered the 1976-1990 period
and found human forcing and internal variability to be
roughly co-equal factors in explaining the warming. With
the additional 20 years of data we find that the LLGHG
forcing is the dominant cause of the warming in the
1976-2010 period. The LLGHG forcing produces be-
tween 0.43˚C to 0.57˚C of warming, depending on the
dataset, while the observed temperature increase over
this period ranges from 0.66˚C to 0.79˚C. Thus, a major-
ity of the recent warming is explained by the LLGHG
forcing. The secondary contribution to the observed
warming comes from the internal variability, with the
stochastic noise being particularly notable for HAD-
CRUT4, GISTEMP and NOAA. In contrast, the stochas-
tic-noise contribution for JMA is nearly zero. The chan-
ges caused by the aerosols, volcanoes, and other external
forcings in this period are also essentially zero.
3.5. Causes of the 1998-2008 Cooling
Finally, we examine in Figure 7 the contributions to the
temperature changes between 1998 and 2008, a period in
which the global-mean temperature cooled by between
0.11˚C to 0.22˚C, depending on the dataset. The tem-
perature changes due to LLGHGs and volcanoes are
positive, the latter because of the recovery from the
cooling caused by the eruption of the Pinatubo volcano in
1991. We note in particular that the aerosol contribution
during this period is virtually zero for all four datasets, a
finding which contradicts [16]. However, the sulfur
dataset we use [22] features an emissions decrease be-
tween 1998 and 2001 in addition to an increase from
2001 to 2005. As the emissions are not unidirectional
over this period, the impact from the increased sulfur
emissions post-2001 is lessened.
The internal variability is responsible for the tempera-
ture decrease observed during 1998-2008. The contribu-
tions vary by dataset. In HADCRUT4, GISTEMP, and
NOAA, the QPO contribution is –0.22˚C, –0.27˚C and
–0.19˚C respectively. In JMA a much weaker contribu-
tion of –0.08˚C is found. The stochastic-noise contribu-
tion (–0.31˚C) is dominant in JMA, but is smaller in
HADCRUT4 and NOAA (about –0.15˚C in each) and
nearly zero in GISTEMP.
3.6. Changes in Estimates of Climate Sensitivity
and Aerosol Forcing
While our results show that human factors are the most
important drivers of climate change over the entirety of
the instrumental records, and the post-1976 period, our
estimates of ∆T2x are lower than our previous estimates.
Table 3 shows that the cumulative changes to our analy-
sis procedure from that of [8], which analyzed an earlier
version of the HADCRU temperature record [41], have
decreased the estimate of ∆T2x from 2.5˚C to 1.6˚C.
Figure 6. As in Figure 5, but for the 1976-2010 period.

M. J. RING ET AL. 411
Figure 7. As in Figure 6, but for the 1998-2008 period.
Table 3. Index of changes to the SCM and their effects on estimation of ∆T2x.
Item ∆T2x for Cumulative
Changes (˚C)
Cumulative Change in
∆T2x (˚C)
Andronova & Schlesinger (2000) 2.5
Replace Jones et al. (1999) temperature record by HADCRUT3 2.0 –0.5
Replace Harvey et al. (1997) SO2 emission record by Smith et al. (2011) SO2 emission re-
cord but keep 80/20 division between hemispheres 2.0 –0.5
Use the actual division of emissions between hemisphere for each year 1.8 –0.7
Extend beginning of SCM comparison from 1856 to 1850 2.0 –0.5
Replace Lean et al. (1995) solar forcing by Wang et al. (2005) & Lean et al. (2005) sola
r
forcing 2.7 0.2
Add radiative forcing due to black-carbon aerosol and organic-carbon aerosol 3.2 0.7
Add radiative forcing due to land-use changes 3.1 0.6
Extend SCM termination year from 1997 to 2010 2.7 0.2
Correct code error (3 N. hemispheric coefficients in a S. hemispheric equation) & recali
b
rate
SCM using the observed annual cycle as done previously 1.8 –0.7
Weight volcanic radiative forcing by 0.6 1.9 –0.6
Include ocean heat uptake as a constraint 1.6 –0.9
Replace HADCRUT3 with HADCRUT4 1.6 –0.9

M. J. RING ET AL.
412
The climate sensitivity, ∆T2x, for each of the four
datasets is presented in Table 4, together with the corre-
sponding total aerosol radiative forcing, FA(2000) and
oceanic thermal diffusivity κ. The ∆T2x estimates for the
four datasets range from 1.45˚C to 2.01˚C. These values
are on the low side of the range given in the IPCC Fourth
Assessment Report [42]. The values for aerosol forcing
and diffusivity are consistent with other estimates of
these quantities from observed data [43].
4. Discussion
We have found that human activities, and in particular
the radiative forcing related to LLGHGs, are the domi-
nant cause of the warming observed since the beginning
of the instrumental temperature records in the 19th cen-
tury, and the most recent global warming from the
mid-1970s through 2010. This confirms the results of [8].
A number of other studies have also found that human
forcing has caused most of the late-20th century warming.
These include studies using coupled atmosphere-ocean
general circulation models [44-48], as well as studies
using simpler models such as energy-balance models [43,
49-51]. Most notably, the addition of the 1990-2010 pe-
riod in our current study increases the proportion of re-
cent warming attributable to human causes as compared
to [8].
There is a stronger diversity of views about the causes
of the early 20th century warming. Our present study
agrees with [8] that human causes are a secondary factor
during this period, with natural causes being the primary
driver over the 1904-1944 period. Other studies have also
found that natural causes are mainly responsible over this
period, with some studies finding important roles for
solar irradiance changes [46,47,52], volcanic eruptions
[40,52,53], or internal variability [44,53] over this pe-
riod.
We find that of these three factors—natural variability,
volcanic eruptions, and solar irradiance, natural variabil-
ity is the most important contributor to the temperature
increase over this period. While this time period is nota-
ble for an increasing amplitude in the AMO [1], the sto-
chastic noise is an important internal contributor as well
Table 4. Estimates of climate sensitivity, ∆T2x, total aerosol
forcing for year 2000, FA, and oceanic thermal diffusivity κ
based on each instrumental dataset.
Instrumental Dataset ∆T2x (˚C) FA (W·m–2) κ (cm2·s–1)
GISTEMP 1.45 –0.42 0.33
HADCRUT4 1.61 –0.52 0.30
NOAA 1.99 –0.99 0.31
JMA 2.01 –0.89 0.27
over this period. Volcanic eruptions are a notable second-
dary factor, approximately on par with the LLGHG con-
tribution over this period. In contrast to several earlier
studies, we find that changes in solar irradiance produce
only a very small contribution over this period—about a
few hundredths of a degree Celsius. However, the solar
irradiance record used here, based on [31,32], is much
less variable than many of the earlier records used by
[34,35], so the small solar contribution here and larger
contribution from other studies using the older records
are not inconsistent.
One of the most important reasons to pursue the sim-
ple approach that we have chosen here is to make the
results accessible to those without a scientific back-
ground. Therefore we reflect briefly on the policy impli-
cations of our results.
Most importantly, the results over the entire period of
the instrumental records demonstrate that the contribu-
tion from the LLGHG forcing is responsible for the ob-
served temperature increase. While internal variability
may be critical during shorter periods, the sum of the
QPOs’ contributions over the entirety of the temperature
record is small compared to the LLGHG forcing. As
discussed above, the contribution from variations in solar
irradiance is also small. The aerosol forcings, of course,
cannot account for the warming since they have had a
cooling tendency. Since human emissions are responsible
for the observed temperature increase, additional future
emissions will add even more radiative forcing to the
climate system. Therefore, in contrast to the claims of
climate skeptics, emissions reductions are in fact needed
to reduce future climate forcing and future warming.
Additionally, our estimates of climate sensitivity using
our SCM and the four instrumental temperature records
range from about 1.5˚C to 2.0˚C. These are on the low
end of the estimates in the IPCC’s Fourth Assessment
Report. So, while we find that most of the observed
warming is due to human emissions of LLGHGs, future
warming based on these estimations will grow more
slowly compared to that under the IPCC’s “likely” range
of climate sensitivity, from 2.0˚C to 4.5˚C. This makes it
more likely that mitigation of human emissions will be
able to hold the global temperature increase since
pre-industrial time below 2˚C, as agreed by the Confer-
ence of the Parties of the United Nations Framework
Convention on Climate Change in Cancun [54]. We find
with our SCM that our Fair Plan to reduce LLGHG
emissions from 2015 to 2065, with more aggressive
mitigation at first for industrialized countries than de-
veloping countries, will hold the global temperature in-
crease below 2˚C [55].
Although we believe, given our relatively low values
for equilibrium climate sensitivity, that the 2˚C goal is

M. J. RING ET AL. 413
attainable, we emphasize that steep emissions cuts must
begin now in order to reach this goal. It is a temptation
among members of the general public and even more
highly educated adults outside the climate sciences [56],
that CO2 concentrations can be stabilized simply by sta-
bilizing our present emissions, or that a drop in CO2
emissions would quickly cause a drop in global tempera-
ture. Climate scientists of course know that the large im-
balance between current CO2 emissions and natural re-
moval processes, and the long resident lifetime of CO2 in
the atmosphere, render the “wait-and-see” approach im-
possible and dangerous. Mitigation of human-caused
climate change requires immediate corrective action.
5. Conclusions
We have used two methods—Singular Spectrum Analy-
sis (SSA) of the instrumental temperature records and
simulations using our Simple Climate Model (SCM)—to
investigate the causes of the global temperature increase
since the instrumental records began in the 19th century,
and for selected shorter periods within the instrumental
records. The two leading modes produced by SSA com-
bine to form a trend of about 0.8˚C since pre-industri-
alization, matching the observed increase. Simulations
using our SCM also produce an increase of about 0.8˚C
when anthropogenic forcings are included, but are unable
to produce an increase when natural forcings alone are
used. Thus, human forcings are the primary cause of the
warming observed since the 19th century. Human forcing
is also the primary cause of the warming observed since
1976. However, the warming during 1904-1944 and
subsequent cooling during 1944-1976 were caused pre-
dominantly by natural internal variability in the climate
system.
In this study we have chosen simple methods in order
to make our results more accessible to other scientists
and the general public. Our findings have confirmed that
human emissions are the main cause of the global warm-
ing over the past 150 years. Since human emissions are
the cause of the global warming, reducing emissions will
reduce the amount of warming in the future. We hope
this study contributes to a public realization that emis-
sions reductions are necessary to safeguard Earth’s cli-
mate.
6. Acknowledgements
This work was funded by the United States National
Science Foundation grant ATM 08-06155. Any opinions,
findings, and conclusions or recommendations expressed
in this material are those of the authors and do not nec-
essarily reflect the views of the National Science Foun-
dation.
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