Continued from previous page
A number of new transient AOGCM simulations for the SRES A2 and B2 scenarios
have recently become available and a preliminary analysis was conducted by the
lead authors. This follows the procedure similar to that described in this section
in relation to Figures 10.3 to 10.6.
The results are presented in Box 10.1.
Box 10.1: Regional climate change in AOGCMs which
use SRES emission scenarios
This box summarises results on regional climate change obtained from a
set of nine AOGCM simulations undertaken using SRES preliminary marker
emission scenarios A2 and B2. The models are CGCM2, CSIRO Mk2, CSM 1.3,
ECHAM4/OPYC, GFDL_R30_c, HadCM3, MRI2, CCSR/NIES2, DOE PCM, (numbered
7, 10, 12, 15, 18, 23, 27, 31 and 30 in Chapter 9,
Table 9.1). The results are based on data
for 2071 to 2100 and 1961 to 1990 that have been directly analysed and
assessed by the lead authors. These results should be treated as preliminary
Regional changes in precipitation and temperature were calculated using
the same methodology as that of Giorgi and Francisco (2000b) (see Figures
10.1, 10.3 and 10.5).
The results were then assessed for inter-model consistency using the same
method as that used in Figures 10.4 and 10.6
for the earlier set of simulations. The results for temperature are in
Box10.1, Figure1 and for precipitation in Box10.2,
The SRES results may be compared with the earlier results summarised
in Figures 10.4 and 10.6 (which will be referred to here as the IS92a
results). However, it should be noted that these two sets of results differ
in the set of models used (both in the model versions and in the total
number of simulations), and in the scenarios contrasted in each case (for
IS92a it is GHG-only versus GHG+sulphate and for SRES it is A2 versus
B2). Also, due to differences in the number of models, thresholds for
agreement are not the same in each case (although they have been chosen
to be as nearly equivalent as possible).
Box 10.1, Figure 1: Analysis of inter-model consistency
in regional relative warming (warming relative to each model’s
global warming). Regions are classified as showing either agreement
on warming in excess of 40% above the global average (Much
greater than average warming’), agreement on warming greater
than the global average (Greater than average warming’),
agreement on warming less than the global average (Less than
average warming’), or disagreement amongst models on the magnitude
of regional relative warming (Inconsistent magnitude of warming’).
There is also a category for agreement on cooling (which never occurs).
A consistent result from at least seven of the nine models is deemed
necessary for agreement. The global annual average warming (DJF
and JJA combined) of the models used span 1.2 to 4.5°C for A2
and 0.9 to 3.4°C for B2, and therefore a regional 40% amplification
represents warming ranges of 1.7 to 6.3°C for A2 and 1.3 to
4.7°C for B2.
- Under both SRES cases, most land areas warm more rapidly than the
global average. The warming is in excess of 40% above the global average
in all high northern latitude regions and Tibet (ALA, GRL, NEU, NAS
and TIB) in DJF, and in the Mediterranean basin, central and northern
Asia and Tibet (MED, CAS, NAS, and TIB) in JJA. Only in South Asia and
southern South America (SAS and SSA) in JJA and southeast Asia (SEA)
in both seasons do the models consistently show warming less than the
- For precipitation, consistent increase is evident in both SRES scenarios
over high latitude regions (ALA, GRL, NAS and ANT) in both seasons,
northern mid-latitude regions and tropical Africa (WNA, ENA, NEU, CAS,
TIB, WAF and EAF) in DJF, and South Asia, East Asia and Tibet (SAS,
EAS and TIB) in JJA. Consistent precipitation decrease is present over
Central America (CAM) in DJF and over Australia and southern Africa
(NAU, SAU and SAF) in JJA.
- Differences between the A2 and B2 results are minor and are mainly
evident for precipitation. In the B2 scenario there are fewer regions
showing consistently large precipitation changes, and there is a slight
increase in the frequency of regions showing “inconsistent”
and “no change” results. As the climate forcing is smaller
in the B2 case and the climate response correspondingly weaker, some
differences of this nature are to be expected.
SRES versus IS92a
- In broad terms, the temperature results from SRES are similar to the
IS92a results. In each of the two SRES and IS92a cases, warming is in
excess of 40% above the global average in Alaska, northern Canada, Greenland,
northern Asia, and Tibet (ALA, GRL, NAS and TIB) in DJF and in central
Asia and Tibet (CAS and TIB) in JJA. All four cases also show warming
less than the global average in South and Southeast Asia, and southern
South America (SAS, SEA and SSA) in JJA.
- The main difference in the results is that there are substantially
more instances for the SRES cases where there is disagreement on the
magnitude of the relative regional warming. This difference is mainly
evident in tropical and Southern Hemisphere regions.
- The precipitation results from SRES are also broadly similar to the
corresponding IS92a results. There are many regions where the direction
of precipitation change (although not necessarily the magnitude of this
change) is consistent across all four cases. In DJF this is true for
increase in northern mid- to high latitude regions, Antarctica and tropical
Africa (ALA, GRL, WNA, ENA, NEU, NAS, TIB, CAS, WAF, EAF and ANT) and
decrease in Central America (CAM). In JJA it is true for increase in
high latitude regions (ALA, GRL, NAS and ANT) and for decrease in southern
and northern Australia (SAU and NAU). Little change in Southeast Asia
in DJF and little change or increase over South Asia in JJA are also
- Although there are no cases where the SRES and IS92a results indicate
precipitation changes of opposite direction, there are some notable
differences. In the Sahara and in East Asia (SAH and EAS) in JJA, the
results for both SRES scenarios show consistent increase whereas this
was not true in either of the IS92a cases. On the other hand, in central
North America and northern Australia (CNA and NAU) in DJF, and in East
Africa (EAF) in JJA, the results for both SRES scenarios show model
disagreement whereas the IS92a scenarios showed a consistent direction
of change (increase in CNA, and decrease in EAF and NAU). It is also
notable that the consistent decrease in JJA precipitation over the Mediterranean
basin (MED) seen for both IS92a cases is present for SRES only for the
A2 scenario (for which the decrease is large).
Box 10.1, Figure 2: Analysis of inter-model consistency
in regional precipitation change. Regions are classified as showing
either agreement on increase with an average change of greater than
20% (Large increase’), agreement on increase with an
average change between 5 and 20% (Small increase’), agreement
on a change between -5 and +5% or agreement with an average change
between -5 and 5% (No change’), agreement on decrease
with an average change between -5 and -20% (Small decrease’),
agreement on decrease with an average change of less than -20% (Large
decrease’), or disagreement (Inconsistent sign’).
A consistent result from at least seven of the nine models is deemed
necessary for agreement.
The above comparisons concern the quantification of two different sources
of uncertainty represented in the cascade of uncertainty described in
Chapter 13, Section 13.5.1
(Figure 13.2). These include uncertainties
in future emissions (IS92a GG and GS; SRES A2 and B2), and uncertainties
in modelling the response of the climate system to a given forcing (samples
of up to nine AOGCMs). Agreement across the different scenarios and climate
models suggests, relatively speaking, less uncertainty about the nature
of regional climate change than where there is disagreement. For example,
the agreement for northern latitude winter precipitation extends across
all emission scenarios and all models, whereas there is considerable disagreement
(greater uncertainty) for tropical areas in JJA. Note that these measures
of uncertainty are qualitative and applied on a relatively coarse spatial
scale. It should also be noted that the range of uncertainty covered by
the four emissions scenarios does not encompass the entire envelope of
uncertainty of emissions (see Chapter 9, Section
188.8.131.52, and Chapter 13, Section
13.5.1). The range of models (representing the uncertainties in modelling
the response to a given forcing) is somewhat more complete than in earlier
analyses, but also limited.
The analysis described above is for broad area-averages only and the results
described should not be assumed to apply to all areas within these regions.
More focused regional studies have examined within-region spatial patterns of
change (Joubert and Tyson, 1996; Machenhauer et al., 1996, 1998; Pittock et
al., 1995; Whetton et al., 1996b; Carril et al., 1997; Labraga and Lopez, 1997).
Such studies can reveal important features which are consistent amongst models
but are not apparent in area-average regional results. For example, Labraga
and Lopez (1997) noted a tendency for simulated rainfall to decrease in northern
Amazonia and to increase in southern parts of this region. Jones R.N. et al.
(2000) noted a predominance of rainfall increase in the central equatorial Pacific
(northern Polynesia), but in the areas to the west and south-west the direction
of rainfall change was not clearly indicated.
Figure 10.7: For the European region, simulated change in annual
precipitation, averaged by latitude and normalised to % change per °C
of global warming. Results are given for twenty-three enhanced GHG simulations
(forced by CO2 change only) produced between the years 1983 and
1998. The earlier experiments are those used in the SCENGEN climate scenario
generator (Hulme et al., 1995) and include some mixed-layer 1x and 2xCO2
equilibrium experiments; the later ones are the AOGCM experiments available
through the DDC. From Hulme et al. (2000).
To illustrate further inter-model variations in simulated
regional precipitation change, results obtained in model inter-comparison studies
for the Australian, Indian, North American and European regions are examined.
All of these regions have been extensively studied over the years using equilibrium
2xCO2 experiments (such as those featured in IPCC, 1990), first generation
transient coupled AOGCMs (as in the SAR), and more recent AOGCMs available in
the DDC (Table 9.1). This comparison also enables
an assessment of how the regional precipitation projections have changed as
the models evolved.
In the Australian region, the pattern of simulated precipitation change in
winter (JJA) has remained broadly similar across these three groups of experiments
and consists of rainfall decrease in sub-tropical latitudes and rainfall increase
south of 35 to 40°S (Whetton et al., 1996a, 2001). However, as the latitude
of the boundary between these two zones varied between models, southernmost
parts of Australia lay in the zone where the direction of precipitation change
was inconsistent amongst models. In summer (DJF) the equilibrium 2xCO2
experiments showed a strong tendency for precipitation to increase, particularly
in the north-west of the continent. This tendency was replaced in the first
coupled AOGCMs by one of little change or precipitation decrease, which has
remained when the most recent coupled models are considered. Whetton et al.
(1996a) partly attributed the contrast in the regional precipitation response
of the two types of experiments to contrasts in their hemispheric patterns of
Lal et al. (1998b) surveyed the results for the Indian subcontinent of seventeen
climate change experiments including both equilibrium 2xCO2 and transient
AOGCM simulations with and without sulphate aerosol forcing. In the simulations
forced only by GHG increases, most models show wet season (JJA) rainfall increases
over the region of less than 5% per degree of global warming. A minority of
experiments show rainfall decreases. The experiments which included scenarios
of increasing sulphate forcing all showed reduced rainfall increases, or stronger
rainfall decreases, than their corresponding GHG-only experiments.
For the central plains of North America, IPCC (1990) noted a good deal of similarity
in the response of equilibrium 2xCO2 experiments, with precipitation
decreases prevailing in the summer and increases in the winter of less than
10%. In the second group of experiments (nine transient runs with AOGCMs) a
wider range of responses was found (in the SAR). In winter, changes in precipitation
ranged from about -12 to +20% for the time of CO2 doubling, and most
of the models (six out of nine) exhibited increases. In summer, the range of
change was narrower, within ±10%, but there was no clear majority response
towards increases or decreases. Doherty and Mearns (1999) found that the CGCM1
and HadCM2 models simulated opposite changes in precipitation in both seasons
over North America. While overall there is a tendency for more decreases to
be simulated in the summer and more increases in the winter, there does not
seem to be a reduction in the uncertainty for this region through the progression
of climate models.
Many studies have considered GCM-simulated patterns of climate change in the
European region (e.g., Barrow et al., 1996; Hulme and Brown, 1998; Osborn and
Hulme, 1998; Räisänen, 1998; Benestad et al. 1999; Osborn et al.,
1999). Hulme et al. (2000) provide an overview of simulated changes in the region
by considering the results of twenty-three climate change simulations (forced
by GHG change only) produced between the years 1983 and 1998 and including mixed-layer
1x and 2xCO2 equilibrium experiments as well as transient experiments.
Figure 10.7 shows their results for simulated change in
annual precipitation, averaged by latitude and normalised to percentage change
per degree of global warming. It may be seen that the consensus amongst current
models for drying in southern Europe and wetter conditions in northern Europe
represents a continuation of a pattern established amongst the earlier simulations.
The effect of model development has primarily been to intensify this pattern
Variations across simulations in the regional enhanced GHG results of AOGCMs,
which are particularly evident for precipitation, represent a major uncertainty
in any assessment of regional climate change. Such variation may arise due to
differences in forcing, systematic model-to-model differences in the regional
response to a given forcing or differences due to natural decadal to inter-decadal
scale variability in the models. Giorgi and Francisco (2000a,b) analysed AOGCM
simulations including different models, forcing scenarios and ensembles of simulations,
and found that the greatest source of uncertainty in regional climate change
simulation was due to inter-model differences, with intra-ensemble and inter-scenario
differences being less important (see Figures 10.3
and 10.5). However, it should be noted that Giorgi
and Francisco (2000a,b) used long (thirty year) means and large (sub-continental
scale) regions and that the uncertainty due to simulated natural variability
would be larger when shorter averaging periods, or smaller regions, are used.
The results of Hulme et al. (1999) also suggest that low-frequency natural climatic
variability is important at the sub-regional scale in Europe and can mask the
enhanced GHG signal.
Regional changes in the mean pattern of atmospheric circulation have been noted
in various studies, although typically the changes are not marked (e.g., Huth,
1997; Schubert, 1998). Indeed, the work of Conway (1998) and Wilby et al. (1998b)
suggests that the contribution of changes in synoptic circulation to regional
climate change may be relatively small compared to that of sub-synoptic processes.