13.5.2.1 Scaling climate model response patterns
Figure 13.7: Pattern correlations between the decadal ensemble mean
temperature (Northern Hemisphere only) from the HadCM2 experiment forced
with a 0.5%/yr increase in greenhouse gas concentrations (Gd) and: the scaled
ensemble mean pattern (solid line); the four scaled individual ensemble
member patterns – average coefficient (dashed line); and the scaled
ensemble mean pattern derived from the HadCM2 experiment forced with a 1%/yr
increase in greenhouse gas concentrations (Ga) (dotted line). The correlations
increase with time as the pattern of greenhouse gas response (the “signal”)
increasingly dominates the random effects of internal climate variability
(the “noise”). The shaded area shows the spread of correlations
between the pairs of the individual members of the Gd ensemble; these correlations
are lower than those between the realised and scaled patterns above, indicating
that the scaled pattern is not due to internal climate variability. (Source:
Mitchell et al., 1999.) |
Pattern-scaling methods allow a wider range of possible
future forcings (e.g., the full range of IS92 (Leggett et al., 1992) or SRES
emissions scenarios) and climate sensitivities (e.g., the 1.5ºC to 4.5ºC
IPCC range) to be represented in climate scenarios than if only the direct results
from GCM experiments were used. The approach involves normalising GCM response
patterns according to the global mean temperature change (although in some cases
zonal mean temperature changes have been used). These normalised patterns are
then rescaled using a scalar derived from simple climate models and representing
the particular scenario under consideration.
This pattern-scaling method was first suggested by Santer et al. (1990) and
was employed in the IPCC First Assessment Report to generate climate scenarios
for the year 2030 (Mitchell et al., 1990) using patterns from 2xCO2 GCM
experiments. It has subsequently been widely adopted in climate scenario generators
(CSGs), for example in ESCAPE (Rotmans et al., 1994), IMAGE-2 (Alcamo et al.,
1994), SCENGEN (Hulme et al., 1995a,b), SILMUSCEN (Carter et al., 1995, 1996a),
COSMIC (Schlesinger et al., 1997) and CLIMPACTS (Kenny et al., 2000). A climate
scenario generator is an integrated suite of simple models that takes emissions
or forcing scenarios as inputs and generates geographically distributed climate
scenarios combining response patterns of different greenhouse gases from GCMs
with observational climate data. CSGs allow multiple sources of uncertainty
to be easily represented in the calculated scenarios, usually by using pattern-scaling
methods.
Two fundamental assumptions of pattern-scaling are, first, that the defined
GCM response patterns adequately depict the climate “signal” under
anthropogenic forcing (see Section 13.5.2.2) and, second,
that these response patterns are representative across a wide range of possible
anthropogenic forcings. These assumptions have been explored by Mitchell et
al. (1999) who examined the effect of scaling decadal, ensemble mean temperature
and precipitation patterns in the suite of HadCM2 experiments. Although their
response patterns were defined using only 10-year means, using four-member ensemble
means improved the performance of the technique when applied to reconstructing
climate response patterns in AOGCM experiments forced with alternative scenarios
(see Figure 13.7). This confirmed earlier work by Oglesby
and Saltzman (1992), among others, who demonstrated that temperature response
patterns derived from equilibrium GCMs were fairly uniform over a wide range
of concentrations, scaling linearly with global mean temperature. The main exception
occurred in the regions of enhanced response near sea ice and snow margins.
Mitchell et al. (1999) concluded that the uncertainties introduced by scaling
ensemble decadal mean temperature patterns across different forcing scenarios
are smaller than those due to the model’s internal variability, although
this conclusion may not hold for variables with high spatial variability such
as precipitation.
Two situations where the pattern-scaling techniques may need more cautious
application are in the cases of stabilisation forcing scenarios and heterogenous
aerosol forcing. Whetton et al. (1998b) have shown that for parts of the Southern
Hemisphere a highly non-linear regional rainfall response was demonstrated in
an AOGCM forced with a stabilisation scenario, a response that could not easily
be handled using a linear pattern-scaling technique. In the case of heterogeneous
forcing, similar global mean warmings can be associated with quite different
regional patterns, depending on the magnitude and pattern of the aerosol forcing.
Pattern-scaling using single global scalars is unlikely to work in such cases.
There is some evidence, however, to suggest that separate greenhouse gas and
aerosol response patterns can be assumed to be additive (Ramaswamy and Chen,
1997) and pattern-scaling methods have subsequently been adapted by Schlesinger
et al. (1997, 2000) for the case of heterogeneously forced scenarios. This is
an area, however, where poor signal-to-noise ratios hamper the application of
the technique and caution is advised.
The above discussion demonstrates that pattern-scaling techniques provide a
low cost alternative to expensive AOGCM and RCM experiments for creating a range
of climate scenarios that embrace uncertainties relating to different emissions,
concentration and forcing scenarios and to different climate model responses.
The technique almost certainly performs best in the case of surface air temperature
and in cases where the response pattern has been constructed so as to maximise
the signal-to-noise ratio. When climate scenarios are needed that include the
effects of sulphate aerosol forcing, regionally differentiated response patterns
and scalars must be defined and signal-to-noise ratios should be quantified.
It must be remembered, however, that while these techniques are a convenient
way of handling several types of uncertainty simultaneously, they introduce
an uncertainty of their own into climate scenarios that is difficult to quantify.
Little work has been done on exploring whether patterns of change in inter-annual
or inter-daily climate variability are amenable to scaling methods.
