<<

. 6
( 16)



>>

conclusions about cloud-radiation feedback are to be obtained.
9 It is sometimes argued that weather and climate models are the most sophis-
ticated and soundly based models in natural science. Compare them (e.g. in
their assumptions, their scienti¬c basis, their potential accuracy, etc.) with
other computer models with which you are familiar both in natural science
and social science (e.g. models of the economy).



FURTHER READING AND REFERENCE
Solomon, S., Qin, D., Manning M., Chen, Z., Marquis, M., Averyt, K. B., Tigor, M.,
Miller, H. L. (eds.) 2007. Climate Change 2007: The Physical Science Basis. Contribution
of Working Group I to the Fourth Assessment Report of the Intergovernmental
Panel on Climate Change. Cambridge: Cambridge University Press.
Technical Summary (summarises basic information about modelling and its
applications)
Chapter 1 Historical overview of climate change science
Chapter 8 Climate models and their evaluation
Chapter 9 Understanding and attributing climate change
Chapter 10 Global climate projections
Chapter 11 Regional climate projections
McGuf¬e, K., Henderson-Sellers, A. 2005. A Climate Modeling Primer, third edition.
New York: Wiley.
Houghton, J.T. 2002. The Physics of Atmospheres, third edition. Cambridge: Cambridge
University Press.
Palmer, T., Hagedorn, R. (eds.) 2006. Predictability of Weather and Climate. Cambridge:
Cambridge University Press.
135
N OT E S F O R C H A P T E R 5




N OTE S F O R C HA P TE R 5
lapse rate (the rate of fall of temperature with
1 Richardson, L. F. 1922. Weather Prediction by Numerical
height). Such changes lead to this further feedback,
Processes. Cambridge: Cambridge University Press.
which is generally much smaller in magnitude
Reprinted by Dover, New York, 1965.
than water vapour feedback but of the opposite
2 For more details see, for instance, Houghton, The
sign, i.e. negative instead of positive. Frequently,
Physics of Atmospheres.
when overall values for water vapour feedback are
3 See Palmer, T. N. 2006, Chapter 1 in Palmer, T.,
quoted the lapse rate feedback has been included.
Hagedorn, R. (eds.) 2006 Predictability of Weather and
For more details see Houghton, The Physics of
Climate. Cambridge: Cambridge University Press.
Atmospheres.
4 For more detail see: Chapter 13 in Houghton, The
13 Lindzen, R. S. 1990. Some coolness concerning
Physics of Atmospheres; Palmer, T. N. 1993. A nonlinear
global warming. Bulletin of the American Meteorological
perspective on climate change. Weather, 48, 314“26;
Society, 71, 288“99. In this paper, Lindzen queries
Palmer and Hagedorn (eds.), Predictability of Weather
the magnitude and sign of the feedback due to
and Climate.
water vapour, especially in the upper troposphere,
5 An equation such as y = ax + b is linear; a plot of y
and suggests that it could be much less positive
against x is a straight line. Examples of non-linear
equations are y = ax 2 + b or y + xy = ax + b; plots of y than predicted by models and could even be
slightly negative. Much has been done through
against x for these equations would not be straight
observational and modelling studies to investigate
lines. In the case of the pendulum, the equations
the likely magnitude of water vapour feedback.
describing the motion are only approximately
More detail can be found in Stocker, T. F. et al.,
linear for very small angles from the vertical
Physical climate processes and feedbacks, Chapter
where the sine of the angle is approximately equal
7 in Houghton et al. (eds.), Climate Change 2001: The
to the angle; at larger angles this approximation
Science Basis. The conclusion of that chapter, whose
becomes much less accurate and the equations are
authors include Lindzen, is that ˜the balance of
non-linear.
evidence favours a positive clear-sky water vapour
6 Palmer, T. N., Chapter 1 in Palmer and Hagedorn
feedback of a magnitude comparable to that found
(eds.), Predictability of Weather and Climate.
in simulations™.
7 Named ˜Southern Oscillation™ by Sir Gilbert Walker
14 See Figure 2.8 and the de¬nition of radiative forcing
in 1928.
at the beginning of Chapter 3.
8 Folland, C. K., Owen, J., Ward, M. N., Colman, A.
15 From Figure 8.14 in Randall, D., Wood, R. A. et al.
1991. Prediction of seasonal rainfall in the Sahel
Climate Models and their Evaluation, Chapter 8 in
region using empirical and dynamical methods.
Solomon et al. (eds.) Climate Change 2007: The Physical
Journal of Forecasting, 10, 21“56.
Science Basis.
9 See for instance Shukla, J., Kinter III, J. L.,
16 Note that the variance in the total is less than
Chapter 12 in Palmer and Hagedorn (eds.),
the sum of the variances of the three parameters.
Predictability of Weather and Climate.
The total is obtained by ¬rst adding the values of the
10 Xue, Y. 1997. Biospheric feedback on regional
parameters from individual model runs.
climate in tropical north Africa. Quarterly Journal of
17 For a description of a recent model and how it
the Royal Meteorological Society, 123, 1483“515.
performs see Pope, V. et al. 2007. The Met Of¬ce
11 For a review of climate feedback processes see
Hadley Centre climate modelling capability: the
Bony, S. et al., 2006. How well do we understand and
competing requirements for improved
evaluate climate change feedback processes? Journal
resolution, complexity and dealing with uncer-
of Climate, 19, 3445“82.
tainty. Philosophical Transactions of the Royal Society A,
12 Associated with water vapour feedback is also lapse
365, 2635“2657.
rate feedback which occurs because, associated with
18 Randall et al. Chapter 8, in Solomon et al. (eds.)
changes of temperature and water vapour content
Climate Change 2007: The Physical Science Basis.
in the troposphere, are changes in the average
136 M O D E L L I N G T H E C L I M AT E



19 For a recent review see Cane, M. A. et al. 2006. 25 Summary for policymakers, in Solomon et al. (eds.)
Progress in paleoclimate modeling. Journal of Climate Changes 2007: The Physical Science Basis. The
Climate 19, 5031“57. de¬nitions of likely, very likely, etc. are given in Note
20 Graf, H.-E. et al. 1993. Pinatubo eruption winter 1 to Chapter 4.
climate effects: model versus observations. Climate 26 Bindoff, N. Willebrand, J. et al. 2007. Observations:
Dynamics, 9, 61“73. Oceanic climate change and sea level, Chapter 5, in
21 See Policymakers™ summary. In Houghton, J. T., Solomon et al. (eds.) Climate Change 2007: The Physical
Jenkins, G. J., Ephraums, J. J. (eds.) 1990. Science Basis.
Climate Change: The IPCC Scienti¬c Assessment. 27 See Gregory, J. et al. 2002. Journal of Climate, 15,
Cambridge: Cambridge University Press. 3117“21.
22 See Summary for policymakers. In Houghton, J. T., 28 From Barnett, T. P. et al, 2005, Science 309,
Meira Filho, L. G., Callander, B. A., Harris, 284“287; modelling simulations from the Hadley
N., Kattenberg, A., Maskell, K. (eds.) 1996. Climate Centre UK.
Change 1995: The Science of Climate Change. Cambridge: 29 The regional scale is de¬ned as describing the range
of 104 to 107 km 2. The upper end of the range (107
Cambridge University Press.
km 2) is often described as a typical sub-continental
23 Detection is the process demonstrating that an
observed change is signi¬cantly different (in a scale. Circulations at larger than the sub-continental
statistical sense) than can be explained by natural scale are on the planetary scale.
variability. Attribution is the process of establish- 30 For more information see Giorgi, F.,
ing cause and effect with some de¬ned level of Hewitson, B. et al. 2001, Regional climate
con¬dence, including the assessment of competing information “ evaluation and projections. Chapter
hypotheses. For further information about 10, in Houghton et al. (eds.), Climate Change 2007: The
detection and attribution studies see Scienti¬c Basis.
Mitchell, J. F. B., Karoly, D. J. et al. 2001. Detection of 31 See www.climateprediction.net
climate change and attribution of causes, Chapter 32 The terminology of fast and slow feedbacks has
12 in Houghton et al. (eds.), Climate Change 2001: The been introduced by James Hansen “ see his Bjerknes
Scienti¬c Basis, and Hegerl, G. C., Zwiers, F. W. et al. Lecture at American Geophysical Union, 17
Understanding and attributing climate change, December 2008 at www.columbia.edu/˜jeh1/2008/
Chapter 9, in Solomon et al. (eds.) Climate Change AGUBjerknes_20081217.pdf.
2007: The Physical Science Basis. 33 Hint: recall Stefan™s blackbody radiation law that
24 Summary for policymakers. In Houghton et al. (eds.) the energy emitted is proportional to the fourth
Climate Change 2001: The Scienti¬c Basis. power of the temperature.
Climate change in the twenty-
6
¬rst century and beyond




NASA™S 2006 CloudSat (artist™s rendition) studies the role of clouds and aerosols in regulating the
Earth™s weather, climate and air quality




T HE LAST chapter explained that the most effective tool we possess for the prediction of future
climate change due to human activities is the climate model. This chapter will describe the
predictions of models for likely climate change during the twenty-¬rst century. It will also consider
other factors that might lead to climate change and assess their importance relative to the effect of
greenhouse gases.
138 C L I M AT E C H A N G E I N T H E T W E N T Y- F I R S T C E N T U RY A N D B E YO N D




Emission scenarios
A principal reason for the development of climate models is to learn about the
detail of the likely climate change this century and beyond. Because model
simulations into the future depend on assumptions regarding future anthro-
pogenic emissions of greenhouse gases, which in turn depend on assumptions
about many factors involving human behaviour, it has been thought inappro-
priate and possibly misleading to call the simulations of future climate so far
ahead ˜predictions™. They are therefore generally called ˜projections™ to empha-
sise that what is being done is to explore likely future climates which arise from
a range of assumptions regarding human activities.
A starting point for any projections of climate change into the future is a set
of descriptions of likely future global emissions of greenhouse gases. These will
depend on a variety of assumptions regarding human behaviour and activities,
including population, economic growth, energy use and the sources of energy
generation. As was mentioned in Chapter 3, such descriptions of future emis-
sions are called scenarios. A wide range of scenarios was developed by the IPCC
in a Special Report on Emission Scenarios (SRES)1 in preparation for its 2001
Report (see box below). It is these scenarios that have been used in developing
the projections of future climate presented in this chapter. In addition, because
it has been widely used in modelling studies, results are also presented using
a scenario (IS 92a) taken from a set developed by the IPCC in 1992 and widely
described as representative of ˜business-as-usual™.2 Details of these scenarios are
presented in Figure 6.1.
The storylines on which the SRES scenarios are based incorporate a wide
range of different assumptions regarding population, economic growth,
technological innovation and attitudes to social and environmental
sustainability. None of them, however, takes account of deliberate action to
combat climate change and reduce greenhouse gases. Scenarios including
such action will be presented in Chapters 10 and 11 where the possibili-
ties for stabilisation of carbon dioxide concentration in the atmosphere is
considered.
The SRES scenarios include estimates of greenhouse gas emissions resulting
from all sources including land-use change. Estimates in the different scenarios
begin from the current values for land-use change including deforestation (see
Table 3.1). Assumptions in different scenarios vary, from continued deforesta-
tion, although reducing as less forest remains available for clearance, to sub-
stantial afforestation leading to an increased carbon sink. The next stage in the
development of projections of climate change is to turn the emission pro¬les of
139
E M I S S I O N SC E N A R I O S



26
Scenarios Scenarios
A1B A1B
25 A1T A1T
24
A1FI A1FI




N2O emissions (Tg N)
CO2 emissions (GtC)




A2 A2
B1 B1
20 22
B2 B2
IS 92a IS 92a
20
15


18
10


16
5
2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100

Scenarios Scenarios
150
A1B A1B
A1T
A1T
A1FI
A1FI
CH4 emissions (Tg CH4)




A2
1000 A2

SO2 emissions (Tg S)
B1
B1 B2
B2 IS 92a
IS 92a 100

800




50
600



2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100
Year Year

Figure 6.1 Anthropogenic emissions of carbon dioxide, methane, nitrous oxide and sulphur dioxide for
the six illustrative SRES scenarios, A1B, AIT, A1FI, A2, B1 and B2. For comparison the IS 92a scenario is
also shown.


greenhouse gases into greenhouse gas concentrations (Figure 6.2) and then into
radiative forcing (Table 6.1). The methods by which these are done are described
in Chapter 3, where the main sources of uncertainty are also mentioned. For
the carbon dioxide concentration scenarios these uncertainties, especially
those concerning the magnitude of the climate feedback from the terrestrial
biosphere (see box on page 48“9), amount to a range of about “10% to +30% in
2100 for each pro¬le.3
For most scenarios, emissions and concentrations of the main greenhouse
gases increase during the twenty-¬ rst century. However, despite the increases
projected in fossil fuel burning “ very large increases in some cases “ emis-
sions of sulphur dioxide (Figure 6.1) and hence the concentrations of sulphate
140 C L I M AT E C H A N G E I N T H E T W E N T Y- F I R S T C E N T U RY A N D B E YO N D




The emission scenarios of the Special Report on Emission
Scenarios (SRES)
The SRES scenarios are based on a set of four different storylines within each of which a family of scenarios
has been developed “ leading to a total of 35 scenarios.4

A1 storyline
The A1 storyline and scenario family describes a future world of very rapid economic growth, a global
population that peaks in mid century and declines thereafter, and the rapid introduction of new and more
ef¬cient technologies. Major underlying themes are convergence among regions, capacity building and
increased cultural and social interactions, with a substantial reduction in regional differences in per capita
income. The A1 scenario family develops into three groups which describe alternative directions of techno-
logical change in the energy system. The three groups are distinguished by their technological emphasis:
fossil fuel intensive (A1FI), non-fossil fuel energy sources (A1T) or a balance across all sources (A1B) “ where
balance is de¬ned as not relying too heavily on one particular energy source, on the assumption that simi-
lar improvement rates apply to all energy-supply and end-use technologies.

A2 storyline
The A2 storyline and scenario family describes a very heterogeneous world. The underlying theme is self-
reliance and preservation of local identities. Fertility patterns across regions converge very slowly, which
results in a continuously increasing population. Economic development is primarily regionally oriented and
per capita economic growth and technological change more fragmented and slower than other storylines.

B1 storyline
The B1 storyline and scenario family describes a convergent world, with the same global population that
peaks in mid century and declines thereafter as in the A1 storyline, but with rapid change in economic struc-
tures towards a service and information economy, with reductions in material intensity and the introduction
of clean and resource-ef¬cient technologies. The emphasis is on global solutions to economic, social and
environmental sustainability, including improved equity, but without additional climate-related initiatives.

B2 storyline
The B2 storyline and scenario family describes a world in which the emphasis is on local solutions to eco-
nomic, social and environmental sustainability. It is a world with a continuously increasing global popula-
tion, at a rate lower than in A2, intermediate levels of economic development and less rapid and more
diverse technological change than in the B1 and A1 storylines. While the storyline is also oriented towards
environmental protection and social equity, it focuses on local and regional levels.
From the total set of 35 scenarios, an illustrative scenario was chosen for each of the six scenario groups
A1B, A1FI, A1T, A2, B1 and B2. All should be considered equally sound. It is mostly for this set of six illustra-
tive scenarios that data are presented in this chapter.
The SRES scenarios do not include additional climate initiatives, which means that no scenarios are
included that explicitly assume implementation of the United Nations Framework Convention on Climate
Change or the emissions targets of the Kyoto Protocol.
141
M O D E L P ROJE C T I O N S



4000
1300
Scenarios
Scenarios
1200
CO2 concentration (ppm)


A1B
A1B




CH4 concentration (ppb)
3500
1100 A1T
A1T
A1FI
A1FI
1000
A2
A2
3000
900 B1
B1
B2
B2
800
IS 92a
IS 92a
2500
700
600
2000
500
400
1500
300
1980 2000 2020 2040 2060 2080 2100
1980 2000 2020 2040 2060 2080 2100

500
Scenarios Figure 6.2 Atmospheric concentrations of carbon
A1B
N2O concentration (ppb)




dioxide, methane and nitrous oxide resulting from
A1T
450
A1FI the six illustrative SRES scenarios and from the
A2
IS 92a scenario. Uncertainties for each pro¬le,
B1
B2 especially those due to possible carbon feedbacks,
400
IS 92a
have been estimated as from about “10% to +30%
in 2100.
350



300
1980 2000 2020 2040 2060 2080 2100
Year


particles are expected to fall substantially because of the spread of policies to
abate the damaging consequences of air pollution and ˜acid rain™ deposition
to both humans and ecosystems.5 The in¬‚uence of sulphate particles in tend-
ing to reduce the warming due to increased greenhouse gases is therefore
now projected to be much less than for projections made in the mid 1990s
(see the IS 92a scenario for sulphur dioxide in Figure 6.1).6 In fact it is likely
that sulphate particles will be reduced to well below their 1990 levels during
the twenty-¬ rst century.7 The other anthropogenic sources of particles in the
atmosphere included in Figure 3.11 will also contribute small amounts of posi-
tive or negative radiative forcing during the twenty-¬ rst century.8 Table 6.1
includes a 2005 estimate of future total radiative forcing from all aerosol
sources.


Model projections
Results that come from the most sophisticated coupled atmosphere“ocean
models of the kind described in the last chapter provide fundamental infor-
mation on which to base climate projections. However, because they are so
142 C L I M AT E C H A N G E I N T H E T W E N T Y- F I R S T C E N T U RY A N D B E YO N D




Table 6.1 Radiative forcing (W m’2) globally averaged, for greenhouse gases and aerosols
from the year 1750 to 2005 and from SRES scenarios to 2050 and 2100

Radioactive
forcing
(W m-2)
Greenhouse gas Year A1B A1T A1FI A2 B1 B2 IS 92a

CO2 2005 1.66
2050 3.36 3.08 3.70 3.36 2.92 2.83 3.12
2100 4.94 3.85 6.61 5.88 3.52 4.19 4.94
CH4 2005 0.48
2050 0.70 0.73 0.78 0.75 0.52 0.68 0.73
2100 0.56 0.62 0.99 1.07 0.41 0.87 0.91
N 2O 2005 0.16
2050 0.25 0.23 0.33 0.32 0.27 0.23 0.29
2100 0.31 0.26 0.55 0.51 0.32 0.29 0.40
O3(trop) 2005 0.35
2050 0.59 0.72 1.01 0.78 0.39 0.63 0.67
2100 0.50 0.46 1.24 1.22 0.19 0.78 0.90
Halocarbons 2005 0.34
’1.2a
Total aerosols 2005
a
Including both direct and indirect effects.




demanding on computer time only a limited number of results from such
models are available. Many studies have also therefore been carried out with
simpler models. Some of these, while possessing a full description of atmos-
pheric processes, only include a simpli¬ed description of the ocean; these
can be useful in exploring regional change. Others, sometimes called energy
balance models (see box on page 144), drastically simplify the dynamics and
physics of both atmosphere and ocean and are useful in exploring changes in
the global average response with widely different emission scenarios. Results
from simpli¬ed models need to be carefully compared with those from the
best coupled atmosphere“ocean models and the simpli¬ed models ˜tuned™
so that, for the particular parameters for which they are being employed,
agreement with the more complete models is as close as possible. The projec-
tions presented in the next sections depend on results from all these kinds
of models.
143
P ROJE C T I O N S O F G LO BA L AV E R AG E T E M P E R AT U R E




In order to assist comparison between models, experiments with many mod-
els have been run with the atmospheric concentration of carbon dioxide dou-
bled from its pre-industrial level of 280 ppm. The global average temperature
rise under steady conditions of doubled carbon dioxide concentration is known
as the climate sensitivity.9 The Intergovernmental Panel on Climate Change
(IPCC) in its 1990 Report gave a range of 1.5 to 4.5 °C for the climate sensitivity
with a ˜best estimate™ of 2.5 °C; the IPCC 1995 and 2001 Reports con¬rmed these
values. The 2007 Report stated; ˜it is likely to be in the range 2 to 4.5 °C with a
best estimate of 3 °C, and is very unlikely to be less than 1.5 °C. Cloud feedbacks
[see Chapter 5] remain the largest source of uncertainty.™10 The projections pre-
sented in this chapter follow the IPCC 2007 Assessment.11
An estimate of climate sensitivity can also be obtained from paleoclimate
information over the last million years (see Chapter 4) that connects varia-
tions of global average temperature with variations of climate forcings aris-
ing from changes in ice cover, vegetation and greenhouse gas concentrations
(Chapter 4, Figures 4.6 and 4.7). The estimate of 3 ± 0.5 ºC obtained in this
way reported by James Hansen12 agrees very well with the model estimates
mentioned above.


Projections of global average temperature
When information of the kind illustrated in Figures 6.1 and 6.2 is incorporated
into simple or more complex models, projections of climate change can be made.
As we have seen in earlier chapters, a useful proxy for climate change that has
been widely used is the change in global average temperature.
The projected increases in global average near surface temperature over
the twenty-¬rst century due to increase in greenhouse gases and aerosols as
assumed by the six marker SRES scenarios is illustrated in Figure 6.4a. It shows
increases for the different scenarios with best estimates for the year 2100 rang-
ing from about 2 to 4 °C. When uncertainties are added, the overall likely range
is from just over 1 to over 6 °C “ that wide range resulting from the large uncer-
tainty regarding future emissions and also from the uncertainty that remains
regarding the feedbacks associated with the climate response to the changing
atmospheric composition (as described in Chapter 5).
Compared with the temperature changes normally experienced from day to
day and throughout the year, changes of between 1 and 6 °C may not seem very
large. But, as was pointed out in Chapter 1, it is in fact a large amount when
considering globally averaged temperature. Compare it with the 5 or 6 °C change
in global average temperature that occurs between the middle of an ice age and
the warm period in between ice ages (Figure 4.6). The changes projected for the
144 C L I M AT E C H A N G E I N T H E T W E N T Y- F I R S T C E N T U RY A N D B E YO N D




Simple climate models
In Chapter 5 a detailed description was given of general circulation models (GCMs) of the atmosphere
and the ocean and of the way in which they are coupled together (in AOGCMs) to provide simulations
of the current climate and of climate perturbed by anthropogenic emissions of greenhouse gases. These
models provide the basis of our projections of the detail of future climate. However, because they are so
elaborate, they take a great deal of computer time so that only a few simulations can be run with these
large coupled models.
To carry out more simulations under different future emission pro¬les of greenhouse gases or of aerosols
or to explore the sensitivity of future change to different parameters (for instance, parameters describing
the feedbacks in the atmosphere which largely de¬ne the climate sensitivity), extensive use has been made
of simple climate models.13 These simpler models are ˜tuned™ so as to agree closely with the results of the
more complex AOGCMs in cases where they can be compared. The most radical simpli¬cation in the
simpler models is to remove one or more of the dimensions so that the quantities of interest are averaged
over latitude circles (in two-dimensional models) or over the whole globe (in one-dimensional models).
Such models can, of course, only simulate latitudinal or global averages “ they can provide no regional
information.
Figure 6.3 illustrates the com-
ponents of such a model in which
Infrared
Solar
the atmosphere is contained
radiation
radiation
within a ˜box™ with appropriate
radiative inputs and outputs.
Exchange of heat occurs at the
Atmosphere
land surface (another ˜box™) and
the ocean surface. Within the
Heat exchanges
ocean allowance is made for
Surface
vertical diffusion and vertical cir-
Land
Ocean
layer
culation. Such a model is appro-
priate for simulating changes
Upwelling
in global average surface tem-
perature with increasing green-
Deep ocean house gases or aerosols. When
Diffusive
mixing
exchanges of carbon dioxide
across the interfaces between
Sinking of
cold polar the atmosphere, the land and
Upwelling water
the ocean are also included, the
model can be employed to sim-
ulate the carbon cycle.
Figure 6.3 The components of a simple ˜upwelling“diffusion™ climate
model.
145
P ROJE C T I O N S O F G LO BA L AV E R AG E T E M P E R AT U R E




Uncertainty ranges
A2
6.0 at 2100
A1B
B1
Year 2000 constant
5.0
concentrations




A1F1
Twentieth century
Global surface warming (°C)




4.0




A2
A1B
3.0




A1T

B2
B1
2.0



1.0



0.0



“1.0


1900 2000 2100
Year

Figure 6.4 (a) Global averages of surface warming (relative to 1980“99) for the SRES
scenarios A2, A1B and B1, shown as continuations of twentieth-century simulations.
Each curve is a multi-model average from a number (typically around 20) of AOGCMs;
shading denotes the one standard deviation range of individual model annual means.
A curve is also shown for a scenario in which greenhouse gas concentrations were held
constant at year 2000 values. The grey bars at the right indicate for year 2100 the best
estimate and likely range for the six SRES marker scenarios taking into account both the
spread of AOGCM results and uncertainties associated with representations of feedbacks
(see Chapter 5). To obtain temperature increases from pre-industrial times, add 0.6 °C.



twenty-¬rst century are from one-third to a whole ice age in terms of the degree
of climate change!
Figure 6.4b compares the observed global mean warming from 1990 to 2006
with model projections and their ranges from 1990 to 2025 as presented by the
IPCC in its ¬rst three assessment reports. Figure 6.4c illustrates the results from
21 different models (as used in constructing the average in Figure 6.4a) for the
temperature increase under SRES scenario A1B.
Beginning with the ¬rst IPCC report in 1990, the IPCC has consistently pro-
jected forecasts of global average temperature increase in the range 0.15 to
0.3 °C per decade from 1990 to 2005. This can now be compared with observed
values of about 0.2 ºC per decade and projections for all SRES scenarios of about
146 C L I M AT E C H A N G E I N T H E T W E N T Y- F I R S T C E N T U RY A N D B E YO N D



1
Trends and ranges
0.9
FAR




A2
A1B
B1
SAR
0.8
TAR
0.7
Surface wariming (°C)


Observed




Commitment
0.6


0.5


0.4
Uncertainty
SRES
ranges for
0.3
SRES
B1
A1B
0.2
A2
Commitment
0.1


0
1985 1990 1995 2000 2005 2010 2015 2020 2025
Year

Figure 6.4 (b) Model projections of global mean warming compared to observed
warming. Observed temperature anomalies (relative to 1960“90 average) are shown as
annual (black dots) and decadal average values (black line). Projected trends and their
ranges from the IPCC First (FAR) and Second (SAR) Assessment Reports in 1990 and
1995 respectively are shown as green and magenta solid lines and shaded areas and the
projected range from the Third Assessment Report (TAR) in 2001 by vertical blue bars “
all adjusted to start at the observed decadal average value in 1990. Multi-model mean
projections to 2025 from the IPCC Fourth Assessment Report (AR4) in 2007 for the
SRES scenarios B1, A1B and A2 as in Figure 6.4c are shown as blue, green and red curves
with uncertainty ranges against the right-hand axis. The orange curve shows model
projections of warming if greenhouse gas and aerosol concentrations were held constant
from year 2000.


this value (largely independent of which scenario) over the next two or three
decades (Figure 6.4b). Again, these might seem small rates of change; most
people would ¬nd it hard to detect a change in temperature of a fraction of
a degree. But remembering again that these are global averages, such rates
of change become very large. Indeed, they are much larger than any rates of
change the global climate has experienced for at least the past 10 000 years as
inferred from palaeoclimate data. As we shall see in the next chapter, the abil-
ity of both humans and ecosystems to adapt to climate change depends criti-
cally on the rate of change.
147
E Q U I VA L E N T C A R B O N D I OX I D E (CO 2 E )




The changes in global
A1B
average temperature shown 4

in Figure 6.4 from the IPCC




Temperature change (°C)
2007 Report and similar ones 3
from the 2001 Report are
substantially greater than
2
those shown in the IPCC 1995
Report. The main reason for
the difference is the much 1
smaller aerosol emissions in
the SRES scenarios compared
0
with the IS 92 scenarios. For 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090
Year
instance, the global average
temperature in 2100 relat- Figure 6.4 (c) Time series for the twenty-¬rst century from 21 models
ing to the IS 92a scenario is run by climate modelling centres around the world, of annual means
of globally averaged surface temperature change (relative to 1980“99
similar to that for the SRES
average) under SRES scenario A1B. Multi-model mean is marked with
B2 scenario even though the black dots.
carbon dioxide emissions at
that date for IS 92a are 50% greater than those for B2.
To complete this section on likely temperature changes in the twenty-¬rst
century, Figure 6.5 shows multi-model mean temperature changes for the A1B
scenario from 1990 to 2065 and 2099 at levels in the troposphere and different
depths in the ocean. It shows cooling in the stratosphere, substantial warming
in the troposphere especially in the tropics and gradual penetration of warming
into the ocean from the surface downwards.



Equivalent carbon dioxide (CO2e)
In many of the modelling studies of climate change, the situation of doubled
pre-industrial atmospheric carbon dioxide has often been introduced as a
benchmark especially to assist in comparisons between different model pro-
jections and their possible impacts. Since the pre-industrial concentration was
about 280 ppm, doubled carbon dioxide is about 560 ppm. From the curves in
Figure 6.2 this is likely to occur sometime in the second half of the twenty-
¬ rst century, depending on the scenario. But other greenhouse gases are also
increasing and contributing to the radiative forcing. To achieve an overall pic-
ture, it is convenient to convert other greenhouse gases to equivalent amounts
of carbon dioxide, in other words to amounts of carbon dioxide that would
148 C L I M AT E C H A N G E I N T H E T W E N T Y- F I R S T C E N T U RY A N D B E YO N D




(a) 2046“65 (b) 2080“99

200
200
Pressure (hPa)
400
400

600
600

800
800

1000
1000




1000 1000
Depth (m)




2000 2000

3000 3000

4000 4000

5000 5000
60 °S 30 °N 60 °N
60 °S 30 °N 30°S 0
30°S 0 60°N

(°C)
“4.5 “4 “3.5 “3 “2.5 “2 “1.5 “1 “0.5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Figure 6.5 Zonal means of change from 1990 in atmospheric (top) and oceanic
(bottom) temperatures (°C) shown as cross-sections. Values are multi-model means
for the A1B scenario for 2046“65 (a) and 2080“99 (b). Stippling denotes regions
where the multi-model ensemble mean divided by the multi-model standard deviation
exceeds 1.0 (in magnitude).


give the same radiative forcing.14 Such equivalent carbon dioxide amounts are
denoted by CO2e. The information in Table 6.1 enables the conversion to be
carried out.15 For instance, the increases in greenhouse gases (including ozone)
other than carbon dioxide to date produce a radiative forcing equivalent to
about three-quarters of that from carbon dioxide to date (see Figure 3.11). This
proportion will drop substantially during the next few decades as the growth
in carbon dioxide becomes more dominant in nearly all scenarios.
Calculations of equivalent carbon dioxide (CO2e) have often been made
including only the contributions from other greenhouse gases, sometimes
only the long-lived greenhouse gases (i.e. not including ozone). However, as
Figure 3.11 and Table 6.1 show, there are substantial contributions to radia-
tive forcing from aerosols in the atmosphere that are predominantly nega-
tive. Unless otherwise stated, calculations of CO2e in this book include the
aerosol contributions.
Noting that doubled carbon dioxide produces a radiative forcing of about
3.7 W m“2, it can be seen from Table 6.1 that doubling of the CO2e amount
149
R E G I O N A L PAT T E R N S O F C L I M AT E C H A N G E




from pre-industrial times will occur for the SRES marker scenarios around
2050 or before. For scenario A1B, assuming halocarbons and the cooling effect
of aerosols in 2050 remain as in 2005 (Table 6.1), radiative forcing in 2050 is
approximately equivalent to that from doubled CO2e (i.e. 3.7 W m“2). Referring
now to Figure 6.4a, note that in 2050 for scenario A1B the temperature rise
from pre-industrial times is about 2.2 °C. This is only about 75% of the 3 °C
(the best value for climate sensitivity for the models employed to provide the
results presented in Figure 6.4) that would be expected for doubled CO2e under
steady conditions. As was shown in Chapter 5, this difference occurs because
of the slowing effect of the oceans on the temperature rise. This means that, as
the CO2e concentration continues to increase, at any given time there exists a
commitment to further signi¬cant temperature rise that has not been realised
at that time. This is illustrated by the temperature pro¬le (see Figure 6.4a and
b) for a scenario for which the concentration of all greenhouse gases and aero-
sols is kept constant at year 2000 levels. For this pro¬ le, warming continues
throughout the century beginning with about 0.1 °C per decade for the ¬rst
few decades.
What about the value of CO2e now? If the contributions to radiative forcing
for 2005 (Figure 3.11) from all greenhouse gases and aerosols are summed and
turned into CO2e,16 a value of around 375 ppm results (note that without the
aerosol and ozone contributions the value would be about 455 ppm). That is not
far from the present concentration of carbon dioxide itself, because the negative
forcing of aerosol in global average terms has approximately offset the positive
forcing of the increased contributions from gases other than carbon dioxide.
Note that this calculation is only approximate as there is substantial uncer-
tainty surrounding the magnitude of aerosol forcing (Figure 3.11). Will this
aerosol offset continue to apply in the future? All scenarios continue to include
substantial if reducing aerosol contributions during the twenty-¬rst century.17
These considerations will surface again in Chapter 10 when we are looking at
possibilities for the stabilisation of CO2e.


Regional patterns of climate change
So far we have been presenting global climate change in terms of likely increases
in global average surface temperature that provide a useful overall indicator of
the magnitude of climate change. In terms of regional implications, however,
a global average conveys rather little information. What is required is spatial
detail. It is in the regional or local changes that the effects and impacts of global
climate change will be felt.
150 C L I M AT E C H A N G E I N T H E T W E N T Y- F I R S T C E N T U RY A N D B E YO N D




2090 “ 99




0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5

Figure 6.6 Projected pattern average of surface temperature changes in °C for the
twenty-¬rst century “ the period 2090“99 compared with 1980“99 “ for the SRES
scenario A1B, from multi-model AOGCM averages.



With respect to regional change, it is important to realise that, because of
the way the atmospheric circulation operates and the interactions that govern
the behaviour of the whole climate system, climate change over the globe
will not be at all uniform. We can, for instance, expect substantial differ-
ences between the changes over large land masses and over the ocean; land
possesses a much smaller thermal capacity and so can respond more quickly.
Listed below are some of the broad features on the continental scale that
characterise the projected temperature changes; more detailed patterns are
illustrated in Figure 6.6. Reference to Chapter 4 indicates that many of these
characteristics are already being found in the observed record of the last few
decades.

• Generally greater surface warming of land areas than of the oceans typically
by about 40% compared with the global average, greater than this in north-
ern high latitudes in winter (associated with reduced sea-ice and snow cover)
and southern Europe in summer; less than 40% in south and southeast Asia
in summer and in southern South America in winter.
151
R E G I O N A L PAT T E R N S O F C L I M AT E C H A N G E




Across the world torrential rain and ¬‚ooding has increased, and sights such as these in Canada have been
more commonplace in recent years.


• Minimum warming around Antarctica and in the northern North Atlantic
which is associated with deep oceanic mixing in those areas.
• Little seasonal variation of the warming in low latitudes or over the southern
circumpolar ocean.
• A reduction in diurnal temperature range over land in most seasons and most
regions; night-time lows increase more than daytime highs.

So far we have been presenting results solely for atmospheric temperature
change. An even more important indicator of climate change is precipitation.
With warming at the Earth™s surface there is increased evaporation from the
152 C L I M AT E C H A N G E I N T H E T W E N T Y- F I R S T C E N T U RY A N D B E YO N D




oceans and also from many land areas leading on average to increased atmos-
pheric water vapour content and therefore also on average to increased precip-
itation. Since the water-holding capacity of the atmosphere increases by about
6.5% per degree Celsius,18 the increases in precipitation as surface temperature
rises can be expected to be substantial. In fact, model projections indicate
increases in precipitation broadly related to surface temperature increases of
about 3% per degree Celsius.19 Further, since the largest component of the
energy input to the atmospheric circulation comes from the release of latent
heat as water vapour condenses, the energy available to the atmosphere™s cir-
culation will increase in proportion to the atmospheric water content. A char-
acteristic therefore of anthropogenic climate change due to the increase of
greenhouse gases will be a more intense hydrological cycle. The likely effect
of this on precipitation extremes will be discussed in the next section.
In Figure 6.7 are shown projected changes in the distribution of precipita-
tion as global warming increases. Three broad characteristics of precipitation
changes are as follows.20

• In addition to overall global average precipitation increase, there are large
regional variations, areas with decreases in average precipitation, changes
in its seasonal distribution and a general increase in the spatial variability of
precipitation, contributing for instance to a reduction of rainfall in the sub-
tropics and an increase at high latitudes and parts of the tropics.
• The poleward expansion of the sub-tropical high pressure regions, com-
bined with the general tendency towards reduction in sub-tropical precip-
itation, creates robust projections of a reduction in precipitation on the
poleward edges of the sub-tropics. Most of the regional projections of reduc-
tions in precipitation in the twenty-¬ rst century are associated with areas
adjacent to these sub-tropical highs. For instance, southern Europe, Central
America, southern Africa and Australia are likely to have drier summers
with increasing risk of drought.
• A tendency for monsoonal circulations to result in increased precipitation
due to enhanced moisture convergence, despite a tendency towards weaken-
ing of the monsoonal ¬‚ows themselves. However, many aspects of tropical
climatic responses remain uncertain.

Much natural climate variability occurs because of changes in, or oscillations
between, persistent climatic patterns or regimes. The Paci¬c“North Atlantic
Anomaly (PNA “ which is dominated by high pressure over the eastern Paci¬c
and western North America and which tends to lead to very cold winters in the
153
R E G I O N A L PAT T E R N S O F C L I M AT E C H A N G E




(a) December January February




%
“20 “10 “5 5 10 20


(b) June July August




%
“20 “10 “5 5 10 20

Figure 6.7 Projected relative changes in precipitation in per cent for the period 2090“99
relative to 1980“99, for the SRES scenario A1B, from multi-model AOGCM averages,
for (a) December to February and (b) June to August. White areas are where fewer than
66% of the models agree in the sign of the change and stippled areas are where more
than 90% of the models agree in the sign of the change.
154 C L I M AT E C H A N G E I N T H E T W E N T Y- F I R S T C E N T U RY A N D B E YO N D




eastern United States), the North Atlantic Oscillation (NAO “ which has a strong
in¬‚uence on the character of the winters in northwest Europe) and the El Ni±o
events mentioned in Chapter 5 are examples of such regimes. Important compo-
nents of climate change in response to the forcing due to the increase in green-
house gases can be expected to be in the form of changes in the intensity or the
frequency of established climate patterns illustrated by these regimes.21 There
is little consistency at the present time between models regarding projections
of many of these patterns. However, recent trends in the tropical Paci¬c for the
surface temperature to become more El Ni±o-like (see Table 4.1 on page 73“5),
with the warming in the eastern tropical Paci¬c more than that in the western
tropical Paci¬c and with a corresponding eastward shift of precipitation, are
projected to continue by many models.22 The in¬‚uence of increased greenhouse
gases on these major climate regimes, especially the El Ni±o, is an important
and urgent area of research.
A complication in the interpretation of patterns of climate change arises
because of the differing in¬‚uence of atmospheric aerosols as compared with
that of greenhouse gases. Although in the projections based on SRES sce-
narios the in¬‚uence of aerosols is less than in those based on the IS 92 sce-
narios published by the IPCC in its 1995 Report their projected radiative
forcing is still signi¬cant. When considering global average temperature
and its impact on, for instance, sea-level rise (see Chapter 7) it is appropri-
ate in the projections to use the values of globally averaged radiative forc-
ing. The negative radiative forcing from sulphate aerosol, for instance, then
becomes an offset to the positive forcing from the increase in greenhouse
gases. However, because the effects of aerosol forcing are far from uniform
over the globe (Figure 3.7), the effects of increasing aerosol cannot only be
considered as a simple offset to those of the increase in greenhouse gases.
The large variations in regional forcing due to aerosols produce substantial
regional variations in the climate response. Detailed regional information
from the best climate models is being employed to assess the climate change
under different assumptions about the increases in both greenhouse gases
and aerosols.


Changes in climate extremes
The last section looked at the likely regional patterns of climate change. Can
anything be said about likely changes in the frequency or intensity of climate
extremes in the future? It is, after all, not the changes in average climate that are
generally noticeable, but the extremes of climate “ droughts, ¬‚oods, storms and
155
C H A N G E S I N C L I M AT E E X T R E M E S




extremes of temperature in very cold or very Increase in mean




Probability of occurence
warm periods “ which provide the largest (a)
23
impact on our lives (see Chapter 1). More
hot
Previous
The most obvious change we can expect in weather
climate
More
extremes is a large increase in the number Less record hot
cold weather
of extremely warm days and heatwaves New
weather
climate
(Figure 6.8) coupled with a decrease in the
number of extremely cold days. Many conti- Cold Average Hot
nental land areas are experiencing substan-
Increase in variance
tial increases in maximum temperature and




Probability of occurence
more heatwaves. An outstanding example is (b)
Previous
climate
the heatwave in central Europe in 2003 (see
box on page 215). Model projections indicate, More More
cold
More hot
as shown in Figure 6.8c, much increased fre- record weather weather More
New
cold record hot
quency and intensity of such events as the climate
weather weather
twenty-¬rst century progresses.
Of even more impact are changes in extremes Cold Average Hot
connected with the hydrological cycle. In the
Increase in mean and variance
last section it was explained that in a warmer
Probability of occurence




(c)
world with increased greenhouse gases, aver- Previous
Much more
climate
age precipitation increases and the hydrologi- hot
weather
cal cycle becomes more intense.24 Consider More
record hot
what might occur in regions of increased rain- Less weather
change
fall. Under the more intense hydrological cycle for cold New
weather climate
the larger amounts of rainfall will come from
increased convective activity: more really heavy Cold Average Hot
showers and more intense thunderstorms. This
Figure 6.8 Schematic diagrams showing the effects
is well illustrated by Figure 6.9 which shows
on extreme temperatures when (a) the mean increases
how, on doubling the carbon dioxide concen- leading to more record hot weather, (b) the variance
tration, the number of days with large rainfall increases and (c) when both the mean and the variance
amounts (greater than 25 mm day“1) doubled. increase, leading to much more record hot weather.
Although from a climate model of some years
ago, it illustrates a robust result from all climate models that more intense pre-
cipitation events and more dry days are to be expected during the twenty-¬rst
century as global warming increases. The substantial degree of model agreement
is illustrated in Figure 6.10 which shows an analysis of results from nine different
models, for precipitation intensity. Similar information for other indices related
to extremes, for instance heatwaves, frost days, dry days, etc. is provided in the
article from which Figure 6.10 is taken.25
156 C L I M AT E C H A N G E I N T H E T W E N T Y- F I R S T C E N T U RY A N D B E YO N D



160
Over 25.6
40° S
140
40° N
120
Australian land points
Change in frequency (%)
100

80

60

40

20
6.4“12.8
0.4“ 0.8 1.6 “3.2
0

“ 20
0.2 “0.4 0.8 “1.6 3.2“ 6.4 12.8 “ 25.6
“1
Daily rainfall class (mm day )

Figure 6.9 Changes in the frequency of occurrence of different daily
rainfall amounts with doubled carbon dioxide as estimated by a CSIRO
model in Australia.




(a) 8.0 (b)
A2
B1
Standard deviations




6.0 A1B


4.0


2.0


0.0


“2.0
1880 1920 1960 2000 2040 2080 standard deviation
Year “1.25 “1 “0.75 “0.5 “0.25 0 0.25 0.5 0.75 1 1.25


Figure 6.10 Changes in extremes based on multi-model simulations from nine global coupled climate
models, adapted from Tebaldi et al. (2002). (a) Globally averaged changes in precipitation intensity (de¬ned
as the annual total precipitation divided by the number of wet days) for low (SRES B1), middle (SRES A1B)
and high (SRES A2) scenarios. (b) Changes of spatial pattern of precipitation intensity based on simulations
between two 20-year means (2080“99 minus 1980“99) for the A1B scenario. Solid lines in (a) are the
10-year smoothed multi-model ensemble means, the envelope indicates the ensemble mean standard
deviation. Stippling in (b) denotes areas where at least ¬ve of the nine models concur in determining that the
change is statistically signi¬cant. Extreme indices are calculated only over land and are calculated following
Frich et al. 2002. Because the study focused on analysing the direction and signi¬cance of the changes and
the degree of inter-model agreement, the indices plotted are shown in units of standard deviation rather
than absolute magnitude. Each model™s time series was centred around its 1980“99 average and normalised
(rescaled) by its standard deviation computed (after detrending) over the period 1960“2099, then the
models were aggregated into an ensemble average, both at the global average and the grid-box level.
157
C H A N G E S I N C L I M AT E E X T R E M E S




Drought.


What does this mean in terms of ¬‚oods and droughts? More intense pre-
cipitation means more likelihood of ¬‚oods. Illustrated in Figure 6.11 is a
modelling study showing that if atmospheric carbon dioxide concentration
is doubled from its pre-industrial value, the probability of extreme seasonal
precipitation in winter is likely to increase substantially over large areas
of central and northern Europe. The increase in parts of central Europe is
such that the return period of extreme rainfall events would decrease by
about a factor of ¬ve (e.g. from 50 years to 10 years). Similar results have been
obtained in a study of major river basins around the world.26
Note also from Figure 6.9 that the number of days with lighter rainfall
events (less than 6 mm day’1) is expected to decrease in the globally warmed
world. This is because, with the more intense hydrological cycle, a greater
proportion of the rainfall will fall in the more intense events and, further-
more, in regions of convection the areas of downdraught become drier as the
areas of updraught become more moist and intense. In many areas with rela-
tively low rainfall, the rainfall will tend to become less. Take, for instance, the
158 C L I M AT E C H A N G E I N T H E T W E N T Y- F I R S T C E N T U RY A N D B E YO N D




Figure 6.11 The changing
2
probability of extreme seasonal
2 precipitation in Europe in winter
2
5 as estimated from an ensemble
7
of 19 runs with a climate model
5 starting from slightly different
initial conditions. The ¬gure
7
5
5 shows the ratio of probabilities
of extreme precipitation events
5 in the years 61 to 80 of 80-year
3
runs that assumed an increase of
5
5 carbon dioxide concentration of
2
3
2
1% per year (hence doubling in
2
2 about 70 years) compared with
control runs with no change in
1
carbon dioxide.


1 2 3 5 7




60
Extreme
Severe
Moderate

40
Percentage




20




0
2020 2040 2060 2080
Year

Figure 6.12 Proportion of the world™s land surface in drought (extreme, severe and moderate) each
month as projected for the twenty-¬rst century by the Hadley Centre climate model. In each case results
from three simulations with the A2 emissions scenario are shown.


situation in regions where the average summer rainfall falls substantially “
as is likely to occur, for instance, in southern Europe (Figure 6.7). The likely
result of such a drop in rainfall is not that the number of rainy days will
remain the same, with less rain falling each time; it is more likely that there
159
C H A N G E S I N C L I M AT E E X T R E M E S




The sea surface temperature in August 2005. Orange and red depict regions where the conditions are
suitable for hurricanes to form (at 28°C or higher). Hurricane winds are sustained by the heat energy of
the ocean and Hurricane Katrina in 2005 caused catastrophic damage along the Gulf coast from Florida
to Texas, and in particular in New Orleans, Louisiana




will be substantially fewer rainy days and considerably more chance of pro-
longed periods of no rainfall at all. Further, the higher temperatures will lead
to increased evaporation reducing the amount of moisture available at the
surface “ thus adding to the drought conditions. The proportional increase in
the likelihood of drought is much greater than the proportional decrease in
average rainfall.
A recent study of the incidence of drought27 has employed the Hadley Centre
climate model to simulate droughts over all continents, ¬rst during the second
160 C L I M AT E C H A N G E I N T H E T W E N T Y- F I R S T C E N T U RY A N D B E YO N D




half of the twentieth century so that con¬dence in the model could be estab-
lished by comparing simulations with observed droughts. Droughts are divided
into three categories: extreme, severe and moderate.28 Averaged over the period
1952“98, the percentages of the world™s land area at any one time under extreme,
severe and moderate drought were 1%, 5% and 20%. By the beginning of the
twenty-¬rst century these proportions had risen to 3%, 10% and 28%. Projections
for the twenty-¬rst century under the SRES A2 scenario (Figure 6.12) show the
proportions of land area under extreme drought rising to over 10% by 2050
and 30% by 2100, the increases occuring not because droughts are much more
frequent but much longer in duration. Their study indicates the areas most vul-
nerable to drought, broadly in agreement with the areas of reduced rainfall
indicated in Figure 6.7.
In the warmer world of increased greenhouse gases, therefore, different
places will experience more frequent droughts and ¬‚oods “ we noted in Chapter 1
that these are the climate extremes which cause the greatest impacts and will

<<

. 6
( 16)



>>