The Effect of Climate Sensitivity on the Response to
Volcanic Forcing
T.M.L. Wigley1, C.M. Ammann1, B.D. Santer2 and S.C.B. Raper3
Submitted to Journal of Climate, May 17, 2004
1
National Center for Atmospheric Research. P.O. Box 3000, Boulder, CO 80307
2
PCMDI, Lawrence Livermore National Laboratory, Livermore, CA 94550
3
Climatic Research Unit, University of East Anglia, Norwich NR4 7Tj, U.K. and Alfred Wegener
Institute for Polar and Marine research, D-27515 Bremerhaven, Germany
The Effect of Climate Sensitivity on the Response to Volcanic
Forcing
T.M.L. Wigley1, C.M. Ammann1, B.D. Santer2 and S.C.B. Raper3
1
National Center for Atmospheric Research. P.O. Box 3000, Boulder, CO 80307
2
PCMDI, Lawrence Livermore National Laboratory, Livermore, CA 94550
3
Climatic Research Unit, University of East Anglia, Norwich NR4 7Tj, U.K. and Alfred Wegener Institute for Polar
and Marine research, D-27515 Bremerhaven, Germany,
ABSTRACT
The results from 16 AOGCM simulations are used to reduce internally-generated noise and
obtain an improved estimate of the underlying response of 20 th century global-mean
temperature response to volcanic forcing. An upwelling-diffusion energy balance model (UD
EBM) with the same forcing and the same climate sensitivity as the AOGCM is then used to
emulate the AOGCM results. The UD EBM and AOGCM results are in excellent agreement,
justifying the use of the UD EBM to determine the volcanic response for different climate
sensitivities. The maximum cooling for any given eruption is shown to depend approximately on
the climate sensitivity raised to power 0.37. After the maximum cooling, for low-latitude
eruptions, the temperature relaxes back towards the initial state with an e-folding time of 29–43
months for sensitivities of 1–4oC. Comparisons of observed and modeled coolings after the
eruptions of Agung, El Chichon and Pinatubo give implied climate sensitivities that are
consistent with the IPCC range of 1.5–4.5oC equilibrium warming for 2xCO2. The cooling
associated with Pinatubo appears to require a sensitivity above the IPCC lower bound of 1.5 oC,
and none of the observed eruption responses rules out a sensitivity above 4.5 oC.
1
2
1. Introduction
An important aspect of the validation of climate models is to compare their responses to
estimates of past forcing with observed changes in climate. Such comparisons may use
observed responses to individual forcing events (such as volcanic eruptions) or, on longer time
scales, observed changes over the past 20–100+ years in response to a full suite of natural and
anthropogenic forcings.
For the latter type of analysis, comparison methods based on regression techniques may yield
probabilistic information about the climate sensitivity (T2x), well recognized as a primary
source of uncertainty in climate simulations (Mitchell et al., 2001; Allen et al., 2004).
Unfortunately, because of the noise of internally-generated variability (in both model simulations
and observations) and because of substantial uncertainties in the past forcing history (arising
primarily from anthropogenic aerosol forcing uncertainties), regression-based estimates of see
and the short time interval spanned by the event minimizes the effects of other forcings and their
uncertainties. are highly uncertain – and such estimates do little to narrow the uncertainty
bounds defined by other methods. For example, if the applied forcing underestimated the true
magnitude of sulfate aerosol-induced cooling over the 20th century, the implied value of T2x
would be too low. Spatial pattern information allows us to define joint probability density
functions for aerosol forcing and climate sensitivity, but, until aerosol forcing uncertainties can
be reduced independently, these analyses still leave large uncertainties for T2x.
An alternative that has been suggested is to use comparisons between the modeled and
observed effects of volcanic eruptions for model validation and estimation of the climate
3
sensitivity (Hansen et al., 1993; Lindzen and Giannitsis, 1998). There are three difficulties with
this approach, well articulated by Lindzen and Giannitsis (1998; LG98 below). First, even for the
eruption of Mt Pinatubo (June, 1991) where satellite data have allowed us to define the forcing
with reasonable accuracy, there are still differences between different estimates of the forcing
(see, e.g., Santer et al., 2001). Uncertainties in the forcings for earlier eruptions are necessarily
larger. Model-based signals therefore have considerable intrinsic uncertainty. Second, there is a
signal-to-noise ratio problem. Since the relevant response is on a monthly timescale, and since
the response to an individual eruption decays to a negligible amount after only of order 5 years,
the noise of internally-generated variability makes it difficult to define the response signal in the
observations (although some of these noise influences, such as the effects of ENSO variability,
may be removed by empirical methods; see, e.g., Wigley, 2000). Third, short timescale events
(spanning 5 years or less) are less sensitive to T2x than longer time scale processes. If the
response is relatively insensitive to T2x, then it becomes much more difficult to back out
information about T2x from any model/observed data comparison.
LG98 note that the longer time scale response to multiple consecutive eruptions is more
strongly dependent on T2x (as pointed out and quantified earlier by Wigley, 1991), so this may
provide an alternative way to obtain information on T2x from the observational record.
Unfortunately, this approach is confounded by the effects of and uncertainties in other forcings,
both natural and anthropogenic.
LG98 attempted to estimate T2x from the global-mean temperature response to volcanic
forcings using a three-box, energy-balance climate model with a limited-depth (400m) diffusive
ocean. The authors show that this very simple model emulates the results of a slightly more
4
realistic upwelling-diffusion energy-balance model (UD EBM – Hoffert et al., 1981), but the LG98
model still has admitted shortcomings. In this paper we use a more detailed UD EBM to
investigate the effect of T2x on the response to 20th century volcanic forcings. In order to
determine the credibility of the UD EBM in this context, we compare its results to those obtained
using a fully-coupled Atmosphere/Ocean General Circulation Model (AOGCM). The climate
sensitivity of the AOGCM is fixed by the model’s physics and parameterizations. The simpler
model, however, has a user-specified climate sensitivity. Thus, provided the simpler model is
able to match the results of the AOGCM when its sensitivity is set equal to that of the AOGCM,
the simpler model may be run for a range of climate sensitivities to see how various
characteristics of the response to volcanic forcing vary as the sensitivity is changed.
5
2. Analytical Results
Some important insights into the influence that climate sensitivity has on the response to
volcanic forcing can be gained by solving a simple one-box climate model. The simplest
possible model for the effect of external forcing on global-mean temperature is represented by
the equation
C dT/dt + T/S = Q(t)
where C is a heat capacity term, Q(t) is the applied external forcing, S (replacing T2x for
simplicity) is the climate sensitivity (i.e., the equilibrium temperature change for unit radiative
forcing), and T(t) is the change in global-mean temperature. T(t) must be a function of C and
S, and we can show that the relative importance of these two terms depends on the
characteristic time scale for Q(t).
To do this, suppose that Q(t) is sinusoidal, Q(t) = A sin(t). The solution is then
T(t) = [()2/(1+()2)] exp(-t/) + [S/(1+()2)][A{sin(t) – t cos(t)}]
where  is a characteristic time scale for the system,  = SC. (Note that the sine/cosine term can
be written in the form sin(t+), showing that the asymptotic response follows the forcing with a
lag, , but the expanded form is more convenient here.)
We now consider two end-member cases, for high-frequency and low-frequency forcing. For the
latter ( << 1/), the asymptotic solution is simply the equilibrium response
6
T(t) = S A sin(t)
showing no appreciable lag between forcing and response, with the response being linearly
dependent on the climate sensitivity and independent of the system’s heat capacity. For the
high-frequency case ( >> 1/) the solution is
T(t) = [A/(C)] sin(t – /2)
showing a quarter cycle lag of response behind forcing, with the response being independent of
the climate sensitivity.
The critical question then is, what is the appropriate time scale for volcanic forcing relative to the
characteristic time scale () for the climate system? If representative values are used for C and
S (T2x), and a realistic volcanic forcing time scale corresponding to  = 1 to 3 radians per year
is assumed, then we can show that the response to volcanic forcing should have a relatively
small, but non-negligible dependence on the climate sensitivity. This is in accord with model
simulation results obtained by Wigley (1991) and by LG98 1.
1
For a more general analytical treatment in the frequency domain accounting for ocean mixing
as an upwelling-diffusion process, see Wigley and Raper (1991). The results are qualitatively
the same as derived here.
7
3. Defining the volcanic response signal
We begin by defining the response to 20th century volcanic forcing based on simulations with a
fully coupled AOGCM, the NCAR/USDOE Parallel Climate Model (PCM; Washington et al.,
2000). We use results from simulations carried out by Ammann et al. (2003, 2004), which
employ a new forcing history developed by Ammann. In total, there are 16 simulations that
include volcanic forcing, comprising four-member ensembles for 4 experiments with: Volcanic
forcing alone (V); Volcanic plus Solar forcing (VS); Volcanic plus Solar plus Ozone forcing
(VSO); and combined Volcanic, Solar, Ozone, well-mixed Greenhouse gases and direct sulfate
Aerosol forcing (VSOGA = ‘ALL’). In addition we have 4 short unforced control-run experiments
spanning the same interval.
An estimate of the volcanic response signal can be obtained simply by averaging the 4
members of the V ensemble. This ensemble averaging reduces the noise about the volcanic
response signal by a factor of about two. To be more specific, for the control runs (which, like all
of the simulations, are drift-corrected – see below), the inter-annual standard deviation over
1890–1999, averaged over 4 ensemble members, is 0.171oC. This provides an estimate of the
AOGCM’s internally-generated variability that is superimposed on the volcanically-induced
temperature signal in any single simulation. Averaging 4 simulations should reduce this noise to
about 0.086oC.
We can obtain an independent estimate of the background variability by using the MAGICC
climate model (see below) to estimate the ‘pure’ signal (as shown in Fig. 2), subtracting this
signal from each of the 4 AOGCM volcano-only (V) runs, and then calculating the standard
deviation of the residuals. The mean of this standard deviation is 0.173 oC, consistent with the
‘raw’ control-run variability and providing a useful check on the accuracy of the MAGICC signal
8
estimate. After ensemble averaging over the 4 volcano-only runs, the residual variability about
the estimated ‘pure’ signal is reduced to 0.090oC, very close to the theoretical value of 0.173/2 =
0.086oC.
A further reduction in the noise is possible by making use of the other runs that include volcanic
forcing. For these cases, results for companion experiments to VS, VSO and ALL are available
(see Wigley et al., 2004), and these may be subtracted from the ‘with-volcanic-forcing’ cases to
give residual volcano-only results (e.g., Vresid = VS – S, where S is a solar-forcing-alone
ensemble, etc.) The gain here is less than might naively be expected because, by virtue of their
construction method, the residual volcanic cases have amplified noise, which partly offsets the
noise reduction that arises from the increase in sample size. When all 16 volcano runs (i.e., four
from V, and four each from VS – V, VSO – SO, and ALL – SOGA) are averaged the residual
variability reduces to 0.059oC. The reduction in noise compared with what would be obtained
from a single AOGCM realization is 65%.
A potentially more important consideration in defining the volcanic signal is the problem of
spatial drift in the AOGCM, which is considerable at the hemispheric-mean scale; a warming of
0.156oC/century in the SH and a cooling of 0.167oC/century in the NH. These trends are
common to all simulations (see Wigley et al., 2003). We consider only global-mean changes
here, however, for which the PCM’s drift is small (–0.011oC/century). Nevertheless, drift effects
are removed in all the data we consider.
The resulting time series, consisting of the averaged volcanic signal plus reduced noise, is
compared with the results for a single realization in Fig. 1. A characteristic signature for the
response to an individual eruption is clearly seen for Santa Maria, Agung, El Chichon and
9
Pinatubo: a rapid cooling over the first 7–18 months, followed by an approximately exponential
relaxation back to the initial state. The relaxation time will be discussed further below.
Comparing the two panels of Fig. 1 shows how averaging over multiple realizations leads to a
much more well-defined volcano-response signature than can be seen in any individual volcanoresponse experiment. The individual response case corresponds to the real world where we
also have only a single realization, so the top panel graphically illustrates the signal-to-noise
ratio problem alluded to earlier.
10
4. Validating the UD model
We now compare the AOGCM results with results obtained using a simple upwelling-diffusion
energy balance model (UD EBM), viz. the MAGICC2 model used in various IPCC reports
(Wigley and Raper, 1992, 2001; Raper et al., 1996). As part of the IPCC Third Assessment
Report (TAR), MAGICC was calibrated by one of the present authors (Raper) against different
AOGCMs using the 1% compound CO2 increase experiments coordinated under CMIP (Covey
et al., 2003); see Raper et al. (2001) and the Appendix in Cubasch and Meehl (2001). PCM was
one of those models, so we use the TAR calibration results to define the model parameters in
MAGICC. The parameters are the climate sensitivity, the land-ocean sensitivity ratio, the
oceanic mixed-layer depth, the ocean’s effective vertical diffusivity, the rate of change of
upwelling rate as a function of temperature, and land-ocean and inter-hemispheric heat
exchange rates (note that MAGICC separates the globe into land and ocean ‘boxes’ in each
hemisphere). Applying these long time scale calibration results to the much shorter time scales
of a volcanic eruption is quite a severe test of the UD EBM.
There is, still, one unspecified parameter. The primary forcing from the AOGCM simulations is
produced as optical depth (OD) changes (defined at some specified frequency), while the UD
EBM requires input as forcing at the top of the troposphere (in W m -2). The conversion factor
between these two is uncertain. Work at the Goddard Institute for Space Studies illustrates this
uncertainty. In their early work (Lacis et al., 1992) the conversion for OD at 0.55m (for small
2
Model for the Assessment of Greenhouse-gas Induced Climate Change
11
forcings) is 30 W m-2, in Hansen et al. (1997) it is 27 W m-2, while in Hansen et al. (2002) it is 21
W m-2. Results from PCM suggest a value slightly less than the Hansen et al. (2002) value. We
chose a value of 20 W m-2 as a somewhat arbitrary estimate, and it is these results that are
shown here. Slightly better results might be obtained by ‘tuning’ this value.
Another difference between the AOGCM and UD EBM experiments is in the nature of the input
forcing. In the AOGCM, the ‘forcing’ is specified month by month as zonal-mean loading
patterns of stratospheric aerosol (Ammann et al., 2003). In the UD EBM, it is only hemisphericmean forcings that are specified, but the input is still on a monthly time scale.
Figure 2 compares the PCM results (as shown in Fig. 1b) with the MAGICC results. The very
close agreement justifies our use of MAGICC (with different climate sensitivities) to obtain
reliable estimates of how the volcanic response varies with sensitivity with some confidence3.
3
Note that we have already used the MAGICC simulation in Fig. 2 as the ‘true’ signal to
determine the residual variability about the volcanic signal in the PCM results. The similarity
between this residual variability and control-run variability provided another test of the MAGICC
results.
12
5. Effect of different climate sensitivities
Figure 3 shows MAGICC results for climate sensitivities of 1.0oC, 2.0oC and 4.0oC (expressing
the sensitivity here as the equilibrium warming for a CO2 doubling, with the forcing for CO2
doubling being 5.35 W m-2).
For maximum cooling, these results show that, for any given eruption, the cooling depends
approximately on the climate sensitivity raised to the power 0.37. (In LG98, from results in their
Fig. 4, the corresponding power is only 0.20.) The time scale for relaxation back to the preeruption state is also dependent on the climate sensitivity, with slower decay for larger
sensitivity (see below). A further difference from LG98 is that we find the time of peak cooling to
be independent of the climate sensitivity – LG98 find that this time lags behind the time of peak
forcing by 4 to 16 months, with greater lag for larger sensitivity. In our simulations, the lag varies
with eruption (as a consequence of forcing differences between the hemispheres), ranging from
1 month (Novarupta) to 8 months (Agung) behind peak forcing – i.e., 3 to 13 months after the
eruption for these particular volcanoes. For any given eruption, this lag is the same for all values
of sensitivity.
We quantified the relaxation time scale by fitting exponential decay curves to the MAGICC
results for the five largest eruptions, Santa Maria, Novarupta, Agung, El Chichon and Pinatubo.
In all cases, the decay is slower than exponential for the first 12–16 months (only a few months
for Novarupta), is well approximated by an exponential over the next 30–50 months, and then
again slower than exponential. The slow early response is a result of the initially slow removal of
aerosol from the stratosphere. The later sub-exponential decay behavior produces a long ‘tail’ in
the response, although this is obscured in most cases by a subsequent eruption. It is impossible
to identify this behavior in either the observations or the AOGCM results because, by the time
13
the sub-exponential portion is reached, the residual cooling is invariably much less than 0.1 oC
and consequently obscured by the noise of natural variability (which has a standard deviation of
about 0.17oC). For the same reason, assuming a purely exponential decay for all times provides
an excellent approximation to the ‘true’ decay curve.
The best-fit exponential decay times for sensitivities of 1.0oC, 2.0oC and 4.0oC are: Santa Maria,
27, 31 and 34 months; Novarupta, 17, 19 and 21 months; Agung, 29, 34 and 39 months; El
Chichon, 31, 37 and 43 months; and Pinatubo, 31, 37 and 42 months. Novarupta is anomalous
here, presumably because of the high-latitude Northern Hemisphere location of this volcano
compared with the much lower latitudes for the other four volcanoes (in MAGICC, this
geographical influence is captured by the hemispheric differential in the applied forcing, and by
the land-ocean and inter-hemispheric separations in the model). These results are entirely
consistent with the decay times assumed by Santer et al. (2001). They are much longer than the
decay times for forcing (approximately 12 months), a necessary consequence of the thermal
inertia of the climate system.
The decay time’s dependence on sensitivity is given approximately by
 = 30 (T2x)0.23
Using LG98 results (their Fig. 4) the corresponding result is
 = 57 (T2x)0.41
LG98 therefore have much longer decay times than those implied by MAGICC, particularly for
higher sensitivities. Such long decay times would not be consistent with PCM.
14
Given the MAGICC results for the dependence of maximum cooling on sensitivity, it is possible,
by comparing these results with observations, to estimate climate sensitivity values consistent
with the observed global-mean responses. We do this here using modeled and observed
maximum coolings for Agung, El Chichon and Pinatubo. The primary difficulty here (as noted by
LG98 and others – e.g., Bradley, 1988; Robock and Mao, 1995) is in estimating the observed
maximum coolings, since these are obscured by natural variability on the monthly to interannual time scale. We use the estimates of Wigley (2000) here, which we reproduce to two
decimals for the sake of precision (not to be confused with accuracy!): Agung, 0.30 +/- 0.1oC; El
Chichon, 0.24 +/- 0.15oC; Pinatubo. 0.61 +/- 0.1oC – where the +/- refers to estimated 2-sigma
limits. These are the most recent maximum cooling estimates for these three volcanoes that
account for the effects of ENSO, a factor that can significantly obscure the volcanic response
signal (e.g., Angell, 1988; Robock and Mao, 1995; Wigley, 2000; Santer et al., 2001). The larger
uncertainty for El Chichon arises from the difficulty in removing ENSO-related warming in this
case.
The implied sensitivities are: Agung, 1.28(2.83)6.32oC; El Chichon, 0.30(1.54)7.73oC;and
Pinatubo, 1.79(3.03)5.21oC. The central numbers here correspond to the best-estimate cooling
values, while the other numbers correspond to the 2-sigma maximum cooling limits. The
uncertainties in estimating the observed maximum cooling, and the relative insensitivity of
volcanic responses to the value of the climate sensitivity, combine to leave large uncertainties in
the implied climate sensitivity values. Nevertheless, the results are consistent with other
empirical sensitivity estimates (e.g., Andronova and Schlesinger, 2001; Forest et al., 2002;
Harvey and Kaufmann, 2002), AOGCM-based estimates (Cubasch and Meehl, 2001; Raper et
al., 2001), and with the sensitivity range endorsed by IPCC. We note that the cooling associated
15
with Pinatubo appears to require a sensitivity above the IPCC lower bound of 1.5 oC, and that
none of the observed eruption responses rules out a sensitivity above 4.5 oC.
It is not possible to use the long time scale behavior of eruptions (times greater than a few years
after peak cooling) to obtain additional insight into the value of the climate sensitivity for a
number of reasons. First, volcanic forcing is only one of many forcing agents that have acted
over the 20th century, and the effects of these other forcings and their uncertainties are greater
than any residual long-term volcanic signal that may exist. Second, the residual cooling in the
long time scale ‘tails’ that we obtain (Fig. 3) is much less than illustrated in LG98 who have
much greater decay times. In all cases, based on our results, this residual cooling is sufficiently
small that it must, in general, be lost within the noise of natural internally-generated variability.
Third, the absolute separation between simulations with different climate sensitivities, at least
after the first few years, decreases with time, making the task of their separation from the noise
and from each other more and more difficult as time progresses.
16
6. Conclusions
We have defined the response to 20th century volcanic forcing based on simulations with the
NCAR/USDOE Parallel Climate Model (PCM). In total, there are 16 simulations that include
volcanic forcing. These multiple realizations allow us to reduce the noise due to internallygenerated variability by 65% and produce a much more well-defined volcano-response
signature than can be seen in the observational temperature record.
We then compared the AOGCM results with results obtained using a simple upwelling-diffusion
energy balance model. Model parameters for the UD EBM were chosen independently of the
volcano simulations using the results from 1% compound CO2 increase experiments. The
agreement between the AOGCM and UD EBM volcanic forcing results was excellent (see Fig.
2) justifying the use of the UD EBM to determine the volcanic response for different climate
sensitivities. The UD EBM results showed the maximum cooling for any given eruption to
depend approximately on the climate sensitivity raised to the power 0.37. We also found the
time scale for relaxation back to the pre-eruption state to depend on the climate sensitivity,
slower decay for larger sensitivity, and the time of peak cooling for any given eruption to be
independent of the climate sensitivity.
We quantified the relaxation time scale by fitting exponential decay curves to the UD EBM
results for the five largest eruptions, Santa Maria, Novarupta, Agung, El Chichon and Pinatubo.
Assuming a purely exponential decay provides an excellent approximation to the ‘true’ decay
curve. The best-fit exponential decay times for climate sensitivities of 1.0 oC, 2.0oC and 4.0oC
are: Santa Maria, 27, 31 and 34 months; Novarupta, 17, 19 and 21 months; Agung, 29, 34 and
39 months; El Chichon, 31, 37 and 43 months; and Pinatubo, 31, 37 and 42 months. Novarupta
is anomalous because of the high-latitude Northern Hemisphere location of this volcano
17
compared with the much lower latitudes for the other four volcanoes. For these volcanoes the
decay time is given approximately by  = 30 (T2x)0.23
By comparing the UD EBM results with observations, we estimated the climate sensitivity values
consistent with the observations for Agung, El Chichon and Pinatubo. The implied sensitivities
are: Agung, 1.28(2.83)6.32oC; El Chichon, 0.30(1.54)7.73oC; Pinatubo, 1.79(3.03)5.21oC (the
central numbers correspond to the best-estimate observed maximum cooling values, while the
other numbers correspond to the 2-sigma maximum cooling limits). These results are consistent
with other empirical sensitivity estimates, and with the sensitivity range endorsed by IPCC. The
observed cooling associated with Pinatubo appears to require a sensitivity above the IPCC
lower bound of 1.5oC, while none of the observed eruption responses rules out a sensitivity
above 4.5oC.
Our conclusion here differs from that of Lindzen and Giannitsis (1998). These authors conclude
that the observations favor a low value for the climate sensitivity, based on the long time scale
response to eruptions. The main reason for this difference is because the long time scale
response that we obtain, using physically more comprehensive and realistic models, is
substantially less than that obtained by Lindzen and Giannitsis and so is obscured by the noise
of internally-generated variability and by the effects of other (uncertain) forcing factors. Based
on our results, it is impossible to obtain any meaningful quantitative results from the long time
scale responses to volcanic forcing.
18
Acknowledgments. Supported by NOAA Office of Climate Programs (“Climate Change Data and
Detection”) grant NA87GP0105 and U.S. Department of Energy (DOE) grant DE-FG0298ER62601. NCAR is supported by the National Science Foundation.
19
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Figure Captions
Figure 1: Single realization (run B06.77, top panel) and ensemble-mean (n=16, lower panel)
response to volcanic forcing using PCM. Eruption years for Santa Maria (1902), Agung (1963),
El Chichon (1963) and Pinatubo (1991) are shown as triangles in the upper panel.
Figure 2: Comparison of PCM volcanic response with response simulated by the MAGICC
upwelling-diffusion energy balance model.
Figure 3: Volcanic responses for climate sensitivities of 1.0oC, 2.0oC and 4.0oC equilibrium
warming for a CO2 doubling.
25
Figure 1: Single realization (run B06.77, top panel) and ensemble-mean (n=16, lower panel)
response to volcanic forcing using PCM. Eruption dates for Santa Maria (1902), Agung (1963),
El Chichon (1963) and Pinatubo (1991) are shown as triangles in the upper panel.
Top Panel
VOLCANIC SIGNAL: SINGLE REALIZATION (V-677)
0.6
TEMPERATURE CHANGE (degC)
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
0
120
240
360
480
600
720
840
MONTH (JAN. 1890=1)
26
960
1080
1200
1320
Figure 1: Single realization (run B06.77, top panel) and ensemble-mean (n=16, lower panel)
response to volcanic forcing using PCM. Eruption dates for Santa Maria (1902), Agung (1963),
El Chichon (1963) and Pinatubo (1991) are shown as triangles in the upper panel.
Bottom Panel
AMMANN VOLCANIC SIGNAL: MEAN OF 16 RUNS
0.3
TEMPERATURE CHANGE (degC)
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
-0.5
0
120
240
360
480
600
720
840
MONTH (JAN. 1890=1)
27
960
1080
1200
1320
Figure 2: Comparison of PCM volcanic response with response simulated by the MAGICC
upwelling-diffusion energy balance model.
COMPARISON OF AMMANN FORCING RESULTS : PCM vs MAGICC (vble THC)
0.3
0.2
TEMPERATURE CHANGE (degC)
0.1
0
-0.1
-0.2
-0.3
-0.4
-0.5
VARYING THC
-0.6
0
120
240
360
480
600
720
840
MONTH (JAN. 1890=1)
28
960
1080
1200
1320
Figure 3: Volcanic responses for climate sensitivities of 1.0oC, 2.0oC and 4.0oC equilibrium
warming for a CO2 doubling.
VOLCANIC RESPONSE FOR DIFFERENT SENSITIVITIES
0.1
1.0
2.0
0
TEMPERATURE CHANGE (degC)
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
4.0
-0.7
-0.8
0
120
240
360
480
600
720
MONTH (JAN. 1890 = 1)
29
840
960
1080
1200
1320