Continued from previous page
Sensitivity of results
A variety of sensitivity tests confirm that the detection of anthropogenic signals
is insensitive to differences between solar forcing reconstructions, the inclusion
of additional forcing through the specification of observed stratospheric ozone
concentrations, and to varying details of the analysis (including omitting the
signal-to-noise optimisation). Tett et al. (1999, 2000) also found that detection
of an anthropogenic signal continues to hold even when the standard deviation
of the control simulation is inflated by a factor of two. Uncertainty in the
signals is unavoidable when ensembles are small, as is the case in Tett et al.
(1999), and biases the estimates of the signal amplitudes towards zero. Consistent
results are obtained when this source of uncertainty is taken into account (Allen
and Stott, 2000; Stott et al., 2000a). However amplitude estimates become more
uncertain, particularly if the underlying signal is small compared with internal
climate variability. Accounting for sampling uncertainty in model-simulated
signals indicates a greater degree of greenhouse warming and compensating aerosol
cooling in the latter part of the century than shown by Tett et al. (1999).
Gillett et al. (2000b) find that discounting the temperature changes associated
with changes in the Arctic Oscillation (Thompson and Wallace, 1998; Thompson
et al., 2000), which are not simulated by the model, does not significantly
alter the Tett et al. (1999) results.
Confidence intervals and scaling factors
Confidence intervals for the signal amplitudes that are obtained from the regression
of modelled signals onto observations can be re-expressed as ranges of scaling
factors that are required to make modelled signal amplitudes consistent with
those estimated from observations (see, e.g., Allen and Tett, 1999). The results
show that the range of scaling factors includes unity (i.e., model is consistent
with observations) for both the greenhouse gas and the sulphate aerosol signal,
and that the scaling factors vary only to a reasonable (and consistent) extent
between 50-year intervals.
The scaling factors can also be used to estimate the contribution from anthropogenic
factors other than well-mixed greenhouse gases. Using the methodology of Allen
and Stott (2000) on the simulations described by Tett et al. (2000), the 5 to
95% uncertainty range for scaling the combined response changes in tropospheric
ozone and direct and indirect sulphate forcing over the last fifty years is
0.6 to 1.6. The simulated indirect effect of aerosol forcing is by far the biggest
contributor to this signal. Ignoring the possible effects of neglected forcings
and assuming that the forcing can be scaled in the same way as the response,
this translates to a -0.5 to -1.5 Wm-2 change in forcing due to the indirect
effect since pre-industrial times. This range lies well within that given in
Chapter 6 but the limits obtained are sensitive to the model used. Note that
large values of the indirect response are consistently associated with a greater
sensitivity to greenhouse gases. This would increase this model’s estimate
of future warming: a large indirect effect coupled with decreases in sulphate
emissions would further enhance future warming (Allen et al., 2000b).
Allen et al. (2000a) have determined scaling factors from other model simulations
(Figure 12.12) and found that the modelled response to
the combination of greenhouse gas and sulphate aerosol forcing is consistent
with that observed. The scaling factors ranging from 0.8 to 1.2 and the corresponding
95% confidence intervals cover the range 0.5 to 1.6. Scaling factors for 50-year
JJA trends are also easily derived from the results published in Hegerl et al.
(2000). The resulting range of factors is consistent with that of Allen et al.
(2000a), but wider because the diagnostic used in Allen et al. (2000b) enhances
the signal-to-noise ratio. If it is assumed that the combination of greenhouse
warming and sulphate cooling simulated by these AOGCMs is the only significant
external contributor to inter-decadal near-surface temperature changes over
the latter half of the 20th century, then Allen et al. (2000a) estimate that
the anthropogenic warming over the last 50 years is 0.05 to 0.11°C/decade.
Making a similar assumption, Hegerl et al. (2000) estimate 0.02 to 0.12°C/decade
with a best guess of 0.06 to 0.08°C/decade (model dependent, Figure
12.10). The smallness of the range of uncertainty compared with the observed
change indicates that natural internal variability alone is unlikely (bordering
on very unlikely) to account for the observed warming.
|
Figure 12.12: (a) Estimates of the “scaling factors”
by which we have to multiply the amplitude of several model-simulated
signals to reproduce the corresponding changes in the observed record.
The vertical bars indicate the 5 to 95% uncertainty range due to internal
variability. A range encompassing unity implies that this combination
of forcing amplitude and model-simulated response is consistent with the
corresponding observed change, while a range encompassing zero implies
that this model-simulated signal is not detectable (Allen and Stott, 2000;
Stott et al., 2000a). Signals are defined as the ensemble mean response
to external forcing expressed in large-scale (>5000 km) near-surface
temperatures over the 1946 to 1996 period relative to the 1896 to 1996
mean. The first entry (G) shows the scaling factor and 5 to 95% confidence
interval obtained if we assume the observations consist only of a response
to greenhouse gases plus internal variability. The range is significantly
less than one (consistent with results from other models), meaning that
models forced with greenhouse gases alone significantly overpredict the
observed warming signal. The next eight entries show scaling factors for
model-simulated responses to greenhouse and sulphate forcing (GS), with
two cases including indirect sulphate and tropospheric ozone forcing,
one of these also including stratospheric ozone depletion (GSI and GSIO
respectively). All but one (CGCM1) of these ranges is consistent with
unity. Hence there is little evidence that models are systematically over-
or under-predicting the amplitude of the observed response under the assumption
that model-simulated GS signals and internal variability are an adequate
representation (i.e. that natural forcing has had little net impact on
this diagnostic). Observed residual variability is consistent with this
assumption in all but one case (ECHAM3, indicated by the asterisk). We
are obliged to make this assumption to include models for which only a
simulation of the anthropogenic response is available, but uncertainty
estimates in these single-signal cases are incomplete since they do not
account for uncertainty in the naturally forced response. These ranges
indicate, however, the high level of confidence with which we can reject
internal variability as simulated by these various models as an explanation
of recent near-surface temperature change.
A more complete uncertainty analysis is provided by the next three entries,
which show corresponding scaling factors on individual greenhouse (G),
sulphate (S), solar-plus-volcanic (N), solar-only (So) and volcanic-only
(V) signals for those cases in which the relevant simulations have been
performed. In these cases, we estimate multiple factors simultaneously
to account for uncertainty in the amplitude of the naturally forced response.
The uncertainties increase but the greenhouse signal remains consistently
detectable. In one case (ECHAM3) the model appears to be overestimating
the greenhouse response (scaling range in the G signal inconsistent with
unity), but this result is sensitive to which component of the control
is used to define the detection space. It is also not known how it would
respond to the inclusion of a volcanic signal. In cases where both solar
and volcanic forcing is included (HadCM2 and HadCM3), G and S signals
remain detectable and consistent with unity independent of whether natural
signals are estimated jointly or separately (allowing for different errors
in S and V responses). (b) Estimated contributions to global mean warming
over the 20th century, based on the results shown in (a), with 5 to 95%
confidence intervals. Although the estimates vary depending on which model’s
signal and what forcing is assumed, and are less certain if more than
one signal is estimated, all show a significant contribution from anthropogenic
climate change to 20th century warming (from Allen et al., 2000a).
|
Given the uncertainties in sulphate aerosol and natural forcings and responses,
these single-pattern confidence intervals give an incomplete picture. We cannot
assume that the response to sulphate forcing (relative to the greenhouse signal)
is as simulated in these greenhouse-plus-sulphate simulations; nor can we assume
the net response to natural forcing is negligible even though observations of
surface temperature changes over the past 30 to 50 years are generally consistent
with both these assumptions. Hence we need also to consider uncertainty ranges
based on estimating several signals simultaneously (Figure
12.12, right hand panels). These are generally larger than the single-signal
estimates because we are attempting to estimate more information from the same
amount of data (Tett et al., 1999; Allen and Stott, 2000; Allen et al., 2000a).
Nevertheless, the conclusion of a substantial greenhouse contribution to the
recent observed warming trend is unchanged.
Continues on next page
