E.6 A Wider Range of Detection Techniques
Temperature
Evidence of a human influence on climate is obtained over a substantially wider
range of detection techniques. A major advance since the SAR is the increase
in the range of techniques used and the evaluation of the degree to which the
results are independent of the assumptions made in applying those techniques.
There have been studies using pattern correlations, optimal detection studies
using one or more fixed patterns and time-varying patterns, and a number of other
techniques. The increase in the number of studies, breadth of techniques, increased
rigour in the assessment of the role of anthropogenic forcing in climate, and
the robustness of results to the assumptions made using those techniques, has
increased the confidence in these aspects of detection and attribution.
Results are sensitive to the range of temporal and spatial scales that are
considered. Several decades of data are necessary to separate forced signals
from internal variability. Idealised studies have demonstrated that surface
temperature changes are detectable only on scales in the order of 5,000 km.
Such studies show that the level of agreement found between simulations and
observations in pattern correlation studies is close to what one would expect
in theory.
Most attribution studies find that, over the last 50 years, the estimated
rate and magnitude of global warming due to increasing concentrations of greenhouse
gases alone are comparable with or larger than the observed warming. Attribution
studies address the question of “whether the magnitude of the simulated response
to a particular forcing agent is consistent with observations”. The use of multi-signal
techniques has enabled studies that discriminate between the effects of different
factors on climate. The inclusion of the time dependence of signals has helped
to distinguish between natural and anthropogenic forcings. As more response
patterns are included, the problem of degeneracy (different combinations of
patterns yielding near identical fits to the observations) inevitably arises.
Nevertheless, even with all the major responses that have been included in the
analysis, a distinct greenhouse gas signal remains detectable. Furthermore,
most model estimates that take into account both greenhouse gases and sulphate
aerosols are consistent with observations over this period. The best agreement
between model simulations and observations over the last 140 years is found
when both anthropogenic and natural factors are included (see Figure
15). These results show that the forcings included are sufficient to explain
the observed changes, but do not exclude the possibility that other forcings
have also contributed. Overall, the magnitude of the temperature response to
increasing concentrations of greenhouse gases is found to be consistent with
observations on the scales considered (see Figure
16), but there remain discrepencies between modelled and observed response
to other natural and anthropogenic factors.
Figure 16: (a) Estimates of the “scaling factors”
by which the amplitude of several model-simulated signals must be multiplied
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. Signals are defined as the ensemble mean response
to external forcing expressed in large-scale (>5,000 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 with the assumption that 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 over predict 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). One is 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 internal variability, as simulated by these various models, can be
rejected 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, multiple factors are estimated 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. [Based on Figure 12.12] |
Uncertainties in other forcings that have been included do not prevent identification
of the effect of anthropogenic greenhouse gases over the last 50 years.
The sulphate forcing, while uncertain, is negative over this period. Changes
in natural forcing during most of this period are also estimated to be negative.
Detection of the influence of anthropogenic greenhouse gases therefore cannot
be eliminated either by the uncertainty in sulphate aerosol forcing or because
natural forcing has not been included in all model simulations. Studies that
distinguish the separate responses to greenhouse gas, sulphate aerosol and natural
forcing produce uncertain estimates of the amplitude of the sulphate aerosol
and natural signals, but almost all studies are nevertheless able to detect
the presence of the anthropogenic greenhouse gas signal in the recent climate
record.
The detection and attribution methods used should not be sensitive to errors
in the amplitude of the global mean response to individual forcings. In
the signal-estimation methods used in this report, the amplitude of the signal
is estimated from the observations and not the amplitude of the simulated response.
Hence the estimates are independent of those factors determining the simulated
amplitude of the response, such as the climate sensitivity of the model used.
In addition, if the signal due to a given forcing is estimated individually,
the amplitude is largely independent of the magnitude of the forcing used to
derive the response. Uncertainty in the amplitude of the solar and indirect
sulphate aerosol forcing should not affect the magnitude of the estimated signal.
Sea level
It is very likely that the 20th century warming has contributed significantly
to the observed sea level rise, through thermal expansion of sea water and widespread
loss of land ice. Within present uncertainties, observations and models
are both consistent with a lack of significant acceleration of sea level rise
during the 20th century.
