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circulation “ the wind “ on the
ocean surface is a main driver for
Equator
Rossby wave
the ocean circulation. Also, as
we have seen, the heat input to
Tropical Pacific
the atmosphere from the ocean, Ocean
Unusually warm
especially that arising from ocean temperature
evaporation, has a big in¬‚uence
on the atmospheric circulation.
A simple model of an El Ni±o
event that shows the effect of
different kinds of wave motions Figure 5.10 Schematic to illustrate El Ni±o oscillation.
that can propagate within the
ocean is illustrated in Figure 5.10. In this model a wave in the ocean, known as a Rossby wave, propa-
gates westwards from a warm anomaly in ocean surface temperature near the equator. When it reaches
the ocean™s western boundary it is re¬‚ected as a different sort of wave, known as a Kelvin wave, which
travels eastward. This Kelvin wave cancels and reverses the sign of the original warm anomaly, so trig-
gering a cold event. The time taken for this half-cycle of the whole El Ni±o process is determined by the
speed with which the waves propagate in the ocean; it takes about two years. It is essentially driven by
ocean dynamics, the associated atmospheric changes being determined by the patterns of ocean surface
temperature (and in turn reinforcing those patterns) that result from the ocean dynamics. Expressed in
terms of this simple model, some of the characteristics of the El Ni±o process appear to be essentially
predictable.




temperature “ at higher temperatures more water can be evaporated before
the atmosphere is saturated “ evaporation from the surface and hence the heat
input to the atmosphere is particularly large in the tropics.
The most prominent examples of interactions between atmosphere and
ocean circulations are associated with the El Ni±o Southern Oscillation (ENSO)7
that was ¬rst identi¬ed in the late nineteenth century as ˜seesaw™ surface
pressure variations between South America and Indonesia (see box). It is during
El Ni±o events associated with ENSO in the east tropical Paci¬c (see Figure 5.9)
that the largest variations are found in ocean temperature.
106 M O D E L L I N G T H E C L I M AT E




Anomalies in the circulation and rainfall in all tropical regions and to a lesser
extent at mid latitudes are associated with these El Ni±o events (see Figure 1.4).
A good test of the atmospheric models described above is to run them with an
El Ni±o sequence of sea surface temperatures and see whether they are able to
simulate these climate anomalies. This has now been done with a number of
different atmospheric models; they have shown considerable skill in the simu-
lation of many of the observed anomalies, especially those in the tropics and
sub-tropics.
Because of the large heat capacity of the oceans, anomalies of ocean surface
temperature tend to persist for some months. The possibility therefore exists,
for regions where there is a strong correlation between weather and patterns of
ocean surface temperature, of making forecasts of climate (or average weather)
some weeks or months in advance. Such seasonal forecasts have been attempted
especially for regions with low rainfall; for instance, for northeast Brazil and
for the Sahel region of sub-Saharan Africa, a region where human survival is
very dependent on the marginal rainfall (see box). To make seasonal forecasts
depends on the ability to forecast changes in ocean surface temperature. To do
that requires understanding of, and the ability to model, the ocean circulation
and the way it is coupled to the atmospheric circulation. Because the largest
changes in ocean surface temperature occur in the tropics and because there
are reasons to suppose that the ocean may be more predictable in the tropics
than elsewhere, most emphasis on the prediction of ocean surface temperature
has been placed in tropical regions, in particular on the prediction of the El
Ni±o events themselves.
Later on in this chapter the coupling of atmospheric models and ocean
models is described. For the moment it will suf¬ce to say that, using coupled
models together with detailed observations of both atmosphere and ocean
in the Paci¬c region, signi¬cant skill in the prediction of El Ni±o events has
been achieved for months and up to a year in advance (Figure 5.12) (see also
Chapter 7).




The climate system
So far the forecasting of detailed weather over a few days and of average
weather for a month or so, up to perhaps a season ahead, has been described in
order to introduce the science and technology of modelling, and also because
some of the scienti¬c con¬dence in the more elaborate climate models arises
from their ability to describe and forecast the processes involved in day-to-day
weather.
107
T H E C L I M AT E S Y S T E M




Forecasting for the African Sahel region
The Sahel region of Africa forms a band about 500 km wide along the southern edge of the Sahara Desert
that gets most of its rainfall during northern hemisphere summer (particularly July to September). Rainfall
has decreased in this region since the 1960s. Particularly pronounced periods of drought occurred during
the 1970s and 1980s with devastating impact on the local economy (Figure 5.12). Variations in Sahel rain-
fall are linked to ¬‚uctuations in patterns of sea surface temperature (SST), a connection that has provided
since the 1980s a basis for generating seasonal forecasts of rainfall for the region.8 The advent of climate
models (see next sections) has brought substantial improvements in such forecasting (Figure 5.12) which
is now limited by the accuracy of model predictions of sea surface temperature “ possible for months
ahead but not, as yet, for years.9 Such forecasts can also bene¬t by including within the models variations
in the characteristics of land surface vegetation and soil that also in¬‚uence local rainfall.10


Figure 5.11 Observed
Sahel July“September
rainfall for each year
1.6
(orange) compared to
an ensemble mean of
ten simulations with a
climate model (GFDL- 1.4
CM2.0) forced with
Normalised rainfall




observed sea surface
temperatures (red). Both
1.2
model and observations
are normalised to unit
mean over 1950“2000.
1
The pink band represents
±1 standard deviation of
intra-ensemble variability.
0.8



0.6


1950 1960 1970 1980 1990 2000
Year



Climate is concerned with substantially longer periods of time, from a few
years to perhaps a decade or longer. A description of the climate over a period
involves the averages of appropriate components of the weather (for exam-
ple, temperature and rainfall) over that period together with the statistical
108 M O D E L L I N G T H E C L I M AT E




4



Sea surface temperatures (°C)
3


2

Equator
1 Ni±o“3

0


“1

Observed SST
“2
Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr
1997 1998 1999
Source: ECMWF

Figure 5.12 Plots of forecasts of El Ni±o events showing the forecast sea surface
temperature in the Ni±o-3 region from various start times throughout the large 1997“8
El Ni±o. Different lines of the same colour indicate different ensemble members. The
background indicates the location of Ni±o-3.



variations of those components. In considering the effect of human activities
such as the burning of fossil fuels, changes in climate over periods of decades
up to a century or two ahead must be predicted.
Since we live in the atmosphere the variables commonly used to describe
climate are mainly concerned with the atmosphere. But climate cannot be
described in terms of atmosphere alone. Atmospheric processes are strongly
coupled to the oceans (see above); they are also coupled to the land surface.
There is also strong coupling to those parts of the Earth covered with ice (the
cryosphere) and to the vegetation and other living systems on the land and in
the ocean (the biosphere). These ¬ve components “ atmosphere, ocean, land, ice
and biosphere “ together make up the climate system (Figure 5.13).


Feedbacks in the climate system11
Chapter 2 considered the rise in global average temperature which would result
from the doubling of the concentration of atmospheric carbon dioxide assum-
ing that no other changes occurred apart from the increased temperature at
the surface and in the lower atmosphere. The rise in temperature was found
to be 1.2 °C and results from what is often called temperature feedback (see box
following this section). However, it was also established that, because of other
feedbacks (which may be positive or negative) associated with the temperature
A Saharan dust storm originating in Mali blew off the west coast of Africa on 6 June 2006. Although
partially hidden by the dust storm, the differences of the underlying landscape are still apparent as the
sands of the Sahara give way to vegetation of the south. The Sahel is particularly vulnerable to
deserti¬cation “ land degradation from climate change and/or human activity that transforms a region
to a desert.



increase, the actual rise in global average temperature was likely to be more
than doubled to about 3.0°C. This section lists the most important of these
feedbacks.


Water vapour feedback
This is the most important.12 With a warmer atmosphere more evaporation
occurs from the ocean and from wet land surfaces. On average, therefore, a
warmer atmosphere will be a wetter one; it will possess a higher water vapour
110 M O D E L L I N G T H E C L I M AT E




Figure 5.13 Schematic of the climate system.


content. Since water vapour is a powerful greenhouse gas, its potential feed-
back has been very thoroughly studied. It is found to provide on average a posi-
tive feedback of a magnitude that models estimate to approximately double the
increase in the global average temperature that would arise with ¬ xed water
vapour.13


Cloud-radiation feedback
This is more complicated as several processes are involved. Clouds interfere
with the transfer of radiation in the atmosphere in two ways (Figure 5.14).
Firstly, they re¬‚ect a certain proportion of solar radiation back to space, so
reducing the total energy available to the system. Secondly, they act as blan-
kets to thermal radiation from the Earth™s surface in a similar way to green-
house gases. By absorbing thermal radiation emitted by the Earth™s surface
below, and by themselves emitting thermal radiation, they act to reduce the
heat loss to space by the surface.
111
F E E D BAC K S I N T H E C L I M AT E S Y S T E M




The effect that dominates for any
particular cloud depends on the cloud tem-
perature (and hence on the cloud height)
Reflection of
and on its detailed optical properties (those
solar radiation
properties which determine its re¬‚ectivity Blanketing of
thermal
to solar radiation and its interaction with
radiation
thermal radiation). The latter depend on Condensation Water/ice
whether the cloud is of water or ice, on its
liquid or solid water content (how thick or
thin it is) and on the average size of the Precipitation
cloud particles. In general for low clouds
the re¬‚ectivity effect wins so they tend to
cool the Earth“atmosphere system; for high
Boundary layer
clouds, by contrast, the blanketing effect is Evaporation
dominant and they tend to warm the sys-
tem. The overall feedback effect of clouds,
therefore, can be either positive or negative Figure 5.14 Schematic of the physical processes associ-
ated with clouds.
(see box below).
Climate is very sensitive to possible changes in cloud amount or structure, as
can be seen from the results of models discussed in later chapters. To illustrate
this, Table 5.1 shows that the hypothetical effect on the climate of a small per-
centage change in cloud cover is comparable with the expected changes due to
a doubling of the carbon dioxide concentration.


Ocean-circulation feedback
The oceans play a large part in determining the existing climate of the Earth;
they are likely therefore to have an important in¬‚uence on climate change due
to human activities.
The oceans act on the climate in four important ways. Firstly, there are close
interactions between the ocean and the atmosphere; they behave as a strongly
coupled system. As we have already noted, evaporation from the oceans pro-
vides the main source of atmospheric water vapour which, through its latent
heat of condensation in clouds, provides the largest single heat source for the
atmosphere. The atmosphere in its turn acts through wind stress on the ocean
surface as the main driver of the ocean circulation.
Secondly, they possess a large heat capacity compared with the atmosphere;
in other words a large quantity of heat is needed to raise the temperature of
the oceans only slightly. In comparison, the entire heat capacity of the atmos-
phere is equivalent to less than 3 m depth of water. That means that in a world
112 M O D E L L I N G T H E C L I M AT E




Cloud radiative forcing
A concept helpful in distinguishing between the two effects of clouds mentioned in the text is that of cloud
radiative forcing (CRF). Take the radiation leaving the top of the atmosphere above a cloud; suppose it has
a value R. Now imagine the cloud to be removed, leaving everything else the same; suppose the radiation
leaving the top of the atmosphere is now R™. The difference R™ “ R is the cloud radiative forcing. It can be
separated into solar radiation and thermal radiation components that generally act in opposite senses, each
typically of magnitude between 50 and 100 W m “ 2. On average, it is found that clouds tend slightly to
cool the Earth“atmosphere system.
A map of cloud radiative forcing (Figure 5.15a) deduced from satellite observations illustrates the large
variability in CRF over the globe with both positive and negative values. It is also helpful to study separ-
ately the shortwave and longwave components of the atmosphere™s radiation budget (Figure 5.15b), the
variations of which are dominated by variations in cloud cover and type. Model simulations are able to
capture the overall pattern of these variations; the big challenge is to simulate the changing pattern with
adequate detail and accuracy (see Question 8). It is through careful comparisons with observations that
progress in the understanding of cloud feedback will be achieved.


(a)




Net cloud radiative forcing (W m “2)

“ 80 “ 60 “ 40 “ 20 0 20

Figure 5.15 (a) Annual mean net cloud radiative forcing (CRF) for the period March 2000 to February 2001
as observed by the CERES instrument on the NASA Terra satellite. (b) Comparison of the observed longwave
(pink/red), shortwave (orange) and net radiation at the top of the atmosphere for the tropics (20° N“20° S) as
deviation from the mean for 1985“90; data from the ERBE instrument on the ERBS satellite and the CERES
instrument on the TRMM satellite. Note the in¬‚uence of the eruption of Pinatubo volcano.
113
F E E D BAC K S I N T H E C L I M AT E S Y S T E M




10
(b)
Longwave
Mt Pinatubo
Shortwave

Net
5
Radiation anomalies (W m “2)




0




“5




“10
1985 1987 1989 1991 1993 1995 1997 1999
Year

Figure 5.15 Continued




Table 5.1 Estimates of global average temperature changes under
different assumptions about changes in greenhouse gases and clouds

Change (in °C) from current
average global surface
temperature of 15 °C
Greenhouse gases Clouds

As now As now 0
None As now “32
None None “21
As now None 4
As now As now but +3% high 0.3
cloud
As now As now but +3% low “1.0
cloud
Doubled CO2 As now (no additional 1.2
cloud feedback)
concentration
otherwise as now
Doubled CO2 Cloud feedback included 3
concentration + best
estimate of feedbacks
114 M O D E L L I N G T H E C L I M AT E




that is warming, the oceans warm much more slowly than the atmosphere.
We experience this effect of the oceans as they tend to reduce the extremes of
atmospheric temperature. For instance, the range of temperature change both
during the day and seasonally is much less at places near the coast than at places
far inland. The oceans therefore exert a dominant control on the rate at which
atmospheric changes occur.
Thirdly, through their internal circulation the oceans redistribute heat
throughout the climate system. The total amount of heat transported from the
equator to the polar regions by the oceans is similar to that transported by
the atmosphere. However, the regional distribution of that transport is very
different (Figure 5.16). Even small changes in the regional heat transport by
the oceans could have large implications for climate change. For instance, the
amount of heat transported by the north Atlantic Ocean is over 1000 terawatts
(1 terawatt = 1 million million watts = 1012 watts). To give an idea of how large
this is, we can note that a large power station puts out about 1000 million (109)
watts and the total amount of commercial energy produced globally is about
12 terawatts. To put it further in context, considering the region of the north
Atlantic Ocean between northwest Europe and Iceland, the heat input (Figure
5.16) carried by the ocean circulation is of similar magnitude to that reaching
the ocean surface there from the incident solar radiation. Any accurate simula-
tion of likely climate change, therefore, especially of its regional variations,
must include a description of ocean structure and dynamics.



Ice-albedo feedback
An ice or snow surface is a powerful re¬‚ector of solar radiation (the albedo is a
measure of its re¬‚ectivity). As some ice melts, therefore, at the warmer surface,
solar radiation which had previously been re¬‚ected back to space by the ice or
snow is absorbed, leading to further increased warming. This is another posi-
tive feedback which on its own would increase the global average temperature
rise due to doubled carbon dioxide by about 20%.
In addition to the basic temperature feedback, four feedbacks have been
identi¬ed, all of which play a large part in the determination of climate, espe-
cially its regional distribution. It is therefore necessary to introduce them
into climate models. Because the global models allow for regional variation
and also include the important non-linear processes in their formulation,
they are able in principle to provide a full description of the effect of these
feedbacks (see Figure 5.17). They are, in fact, the only tools available with this
potential capability. It is to a description of climate prediction models that
we now turn.
115
F E E D BAC K S I N T H E C L I M AT E S Y S T E M




Figure 5.16 Estimates of trans-
port of heat by the oceans. Units 60°N 260
10
are terawatts (1012 W or 1 million
million watts). Note the linkages
1140
30° 1550
between the oceans and that
some of the heat transported by 0° 230
1440 480
the North Atlantic originates in
the Paci¬c. 490
1150 1920
30°




60
1060 1190
60°S



90°W 0° 90°E 180° 90°W




Climate feedback comparisons
Climate feedbacks affect the sensitivity of the climate in terms of the temperature change ∆Ts at the
surface that occurs for a given change ∆Q in the amount of net radiation at the top of the troposphere
(known as the radiative forcing14). ∆Q and ∆Ts are related by a feedback parameter ’ (units Wm’2K’1)
according to

∆Q = ’ ∆Ts
If nothing changes other than the temperature (see ¬g 2.8), f is just the basic temperature feedback
parameter f0 = 3.2 W m’2 K’1 (i.e the change in radiation at the top of the troposphere that leads to a 1 °C
change at the surface).
However, as we have seen other changes occur that result in feedbacks. The total feedback parameter f
allows all the feedbacks to be added together

f = f 0 + f 1 +f 2 + f 3 + ¦

where f1, f2, f3 etc. are the feedback parameters describing water vapour, cloud, ice-albedo feedbacks,
etc.
The ampli¬cation a of the temperature change ∆Ts that occurs with a total feedback parameter f com-
pared with the basic temperature feedback f0 is

a = f 0/f

Estimates of the feedback parameters for the main feedbacks from different climate models are:15

Water vapour (including lapse rate feedback “ see Note 12) “ 1.2 ± 0.5
Cloud “ 0.6 ± 0.7
Ice albedo “ 0.3 ± 0.3
Total feedback parameter (sum of f0 and the three above16) “ 1.1 ± 0.5

Note that with this total feedback parameter the ampli¬cation factor is about 2.9 and the resulting cli-
mate sensitivity to doubled carbon dioxide a little over 3 °C .
116 M O D E L L I N G T H E C L I M AT E




Models for climate prediction
For models to be successful they need to include an adequate description of
the feedbacks we have listed. The water vapour feedback and its regional dis-
tribution depend on the detailed processes of evaporation, condensation and
advection (the transfer of heat by horizontal air ¬‚ow) of water vapour, and
on the way in which convection processes (responsible for showers and thun-
derstorms) are affected by higher surface temperatures. All these processes
are already well included in weather forecasting models and water vapour
feedback has been very thoroughly studied. The most important of the others
are cloud-radiation feedback and ocean-circulation feedback. How are these
incorporated into the models?
For modelling purposes, clouds divide into two types “ layer clouds present
on scales larger than the grid size and convective clouds generally on smaller
scales than a grid box. For the introduction of layer clouds, early weather fore-
casting and climate models employed comparatively simple schemes. A typical
scheme would generate cloud at speci¬ed levels whenever the relative humidity
exceeded a critical value, chosen for broad agreement between model-generated
cloud cover and that observed from climatological records. More recent models
parameterise the processes of condensation, freezing, precipitation and cloud
formation much more completely. They also take into account detailed cloud
properties (e.g. water droplets or ice crystals and droplet number and size) that
enable their radiative properties (e.g. their re¬‚ectivity and transmissivity) to
be speci¬ed suf¬ciently well for the in¬‚uence of clouds on the atmosphere™s
overall energy budget to be properly described. The most sophisticated mod-
els also include allowance for the effect of aerosols on cloud properties (Figure
3.9) “ denoted the indirect aerosol effect in Figure 3.11. The effects of convec-
tive clouds are incorporated as part of the model™s scheme for the parameterisa-
tion of convection.
The amount and sign (positive or negative) of the average cloud-radiation
feedback in a particular climate model is dependent on many aspects of the
model™s formulation as well as on the particular scheme used for the descrip-
tion of cloud formation. Different climate models, therefore, can show average
cloud-radiation feedback which can be either positive or negative (see box); fur-
ther, the feedback can show substantial regional variation. For instance, models
differ in their treatment of low cloud such that in some models the amount
of low cloud increases with increased greenhouse gases, but in other models
it decreases. Although considerable progress has been made in recent years
in the observations of clouds and their representation in models, uncertainty
Snow-covered surfaces like the Arctic and Antarctica re¬‚ect 70% of the sunlight that hits them, but the
polar regions don™t have a large impact on the overall albedo of the Earth because the high latitudes get lit-
tle sunlight to start with. Snow covering North America and Eurasia in the springtime, as the Sun returns in
full force, has a much greater effect on the climate.




regarding cloud-radiation feedback continues to be the main reason for the
wide uncertainty range in what is called climate sensitivity (see Chapter 6) or the
change in global average surface temperature due to a doubling of carbon diox-
ide concentration.
The remaining feedback that is of great importance is that due to the effects
of the ocean circulation. Compared with a global atmospheric model for weather
forecasting, the most important elaboration of a climate model is the inclusion
of the effects of the ocean. Early climate models only included the ocean very
crudely; they represented it by a simple slab some 50 or 100 m deep, the approxi-
mate depth of the ˜mixed layer™ of ocean which responds to the seasonal heating
and cooling at the Earth™s surface. In such models, adjustments had to be made
to allow for the transport of heat by ocean currents. When running the model
118 M O D E L L I N G T H E C L I M AT E




with a perturbation such as increased carbon
dioxide, it was not possible to make allowance
Radiation
for any changes in that transport which might
occur. Such models therefore possessed severe
Atmosphere: Density limitations.
Motion
For an adequate description of the in¬‚uence
Water vapour

of the ocean it is necessary to model the ocean
Heat
circulation and its coupling to the atmospheric
Exchange of: Momentum
Water
circulation. Figure 5.17 shows the ingredients
of such a model. For the atmospheric part of
Ocean: Density (incl. salinity) Sea
the model, in order to accommodate long runs
Land
ice
Motion
on available computers, the size of the grid has
to be substantially larger, typically 100“300
Figure 5.17 Component elements and parameters
km in the horizontal. Otherwise it is essen-
of a coupled atmosphere“ocean model including the
tially the same as the global model for weather
exchanges at the atmosphere“ocean interface.
forecasting described earlier. The formulation
of the dynamics and physics of the ocean part of the model is similar to that
of its atmospheric counterpart. The effects of water vapour are of course pecu-
liar to the atmosphere, but the salinity (the salt content) of the oceans has to
be included as a parameter together with its considerable effects on the water
density. Because dynamical systems, e.g. large scale eddies in the oceans, are of
smaller scale than their atmospheric counterparts, the grid size of the ocean
component is typically about half that of the atmospheric component. On the
other hand, because changes in the ocean are slower, the time step for model
integration can be greater for the ocean component.
At the ocean“atmosphere interface there are exchanges of heat, water and
momentum (exchange of momentum leads to friction) between the two ¬‚uids.
The importance of water in the atmosphere and its in¬‚uence on the atmos-
pheric circulation have already been shown. The distribution of fresh water
precipitated from the atmosphere as rain or snow also has a large in¬‚uence on
the ocean™s circulation through its effect on the distribution of salt in the ocean,
which in turn affects the ocean density. It is not surprising, therefore, to ¬nd
that the ˜climate™ described by the model is quite sensitive to the size and the
distribution of water exchanges at the interface.
Before the model can be used for prediction it has to be run for a considerable
time until it reaches a steady ˜climate™. The ˜climate™ of the model, when it is
run unperturbed by increasing greenhouse gases, should be as close as possible
to the current actual climate. If the exchanges listed above are not correctly
described, this will not be the case. Much effort has gone into model descrip-
tions of these exchanges. Until about the year 2000, many coupled models
119
VA L I DAT I O N O F T H E M O D E L




introduced arti¬cial adjustments to the ¬‚uxes at the surface of heat, water and
momentum so as to ensure that the model™s ˜climate™ was as identical as possible
to the current climate. However, since that time the ocean component of the
model has been improved especially through introducing higher resolution (100
km or less), so that models are now able to provide an adequate description of
the climate with no such adjustments.
Before leaving the oceans, there is a particular feedback that should be men-
tioned between the hydrological cycle and the deep ocean circulation (see box
below). Changes in rainfall, by altering the ocean salinity, can interact with
the ocean circulation. This could affect the climate, particularly of the North
Atlantic region; it may also have been responsible for some dramatic climate
changes in the past (see Chapter 4).
The most important feedbacks belong to the atmospheric and the ocean com-
ponents of the model. They are the largest components, and, because they are
both ¬‚uids and have to be dynamically coupled together, their incorporation
into the model is highly demanding. However, another feedback to be modelled
is the ice-albedo feedback, which arises from the variations of sea-ice and of
snow.
Sea-ice covers a large part of the polar regions in the winter. It is moved about
by both the surface wind and the ocean circulation. So that the ice-albedo feed-
back can be properly described, the growth, decay and dynamics of sea-ice have
to be included in the model. Land ice is also included, essentially as a boundary
condition “ a ¬ xed quantity “ because its coverage changes little from year to
year. However, the model needs to show whether there are likely to be changes
in ice volume, even though these are small, in order to ¬nd out their effect on
sea level (Chapter 7 considers the impacts of sea-level change).
Interactions with the land surface must also be adequately described. The
most important properties for the model are land surface wetness or, more pre-
cisely, soil moisture content (which will determine the amount of evaporation)
and albedo (re¬‚ectivity to solar radiation). The models keep track of the changes
in soil moisture through evaporation and precipitation. The albedo depends on
soil type, vegetation, snow cover and surface wetness.


Validation of the model
In discussing various aspects of modelling we have already indicated how some
validation of the components of climate models may be carried out.17 The suc-
cessful predictions of weather forecasting models provide validation of impor-
tant aspects of the atmospheric component, as do the simulations mentioned
earlier in the chapter of the connections between sea surface temperature
120 M O D E L L I N G T H E C L I M AT E




The ocean™s deep circulation
For climate change over periods up to a decade, only the upper layers of the ocean have any substan-
tial interaction with the atmosphere. For longer periods, however, links with the deep ocean circulation
become important. The effects of changes in the deep circulation are of particular importance.
Experiments using chemical tracers, for instance those illustrated in Figure 5.20 (see next box), have
been helpful in indicating the regions where strong coupling to the deep ocean occurs. To sink to the
deep ocean, water needs to be particularly dense, in other words both cold and salty. There are two main
regions where such dense water sinks down to the deep ocean, namely in the north Atlantic Ocean (in the
Greenland Sea between Scandinavia and Greenland and the Labrador Sea west of Greenland) and in the
region of Antarctica. Salt-laden deep water formed in this way contributes to a deep ocean circulation that
involves all the oceans (Figure 5.18) and is known as the thermohaline circulation (THC).
In Chapter 4 we mentioned the link between the THC and the melting of ice. Increases in the ice melt
can lead to the ocean surface water becoming less salty and therefore less dense. It will not sink so read-
ily, the deep water formation will be inhibited and the THC is weakened. In Chapter 6, the link between
the THC and the hydrological (water) cycle in the atmosphere is mentioned. Increased precipitation in the
North Atlantic region, for instance, can lead to a weakening of the THC.




Figure 5.18 Deep water formation and circulation “ sometimes known as the ocean
˜conveyor belt™ “ connecting the oceans together. The deep salty current (blue) largely
originates in the Nordic Seas and the Labrador Sea where northward ¬‚owing water (red)
near the surface that is unusually salty becomes cooler and even more salty through
evaporation, so increasing its density causing it to sink. Regions of upwelling in the
southern ocean feed into the warm surface current (red).
121
VA L I DAT I O N O F T H E M O D E L




anomalies and precipitation patterns in some parts of the world. Various tests
have also been carried out of the ocean component of climate models; for
instance, through comparisons between the simulation and observation of the
movement of chemical tracers (see box below).
Once a comprehensive climate model has been formulated it can be tested
in three main ways. Firstly, it can be run for a number of years of simulated
time and the climate generated by the model compared in detail to the current
climate. For the model to be seen as a valid one, the average distribution and
the seasonal variations of appropriate parameters such as surface pressure, tem-
perature and rainfall have to compare well with observation. In the same way,
the variability of the model™s climate must be similar to the observed variabil-
ity. Climate models that are currently employed for climate prediction stand up
well to such comparisons.
Recent progress in model performance has been evident in improved simula-
tions of modes of climate variability on the large scale and from intraseasonal
to interdecadal timescales. This is of particular importance because of the links
that are likely between variations in modes such as the northern and southern
annular modes (NAM and SAM) and the ENSO (El Ni±o Southern Oscillation)
and the growth of atmospheric greenhouse gases.18 Progress with the prediction
of ENSO events and associated climate anomalies was mentioned earlier in the
chapter.
Secondly, models can be compared against simulations of past climates
when the distribution of key variables was substantially different from that
at present; for example, the period around 9000 years ago when the con¬gu-
ration of the Earth™s orbit around the Sun was different (see Figure 5.19). The
perihelion (minimum Earth“Sun distance) was in July rather than in January
as it is now; also the tilt of the Earth™s axis was slightly different from its
current value (24° rather than 23.5°). Resulting from these orbital differences
(see Chapter 4), there were signi¬cant differences in the distribution of solar
radiation throughout the year. The incoming solar energy when averaged over
the northern hemisphere was about 7% greater in July and correspondingly
less in January.
When these altered parameters are incorporated into a model, a differ-
ent climate results. For instance, northern continents are warmer in sum-
mer and colder in winter. In summer a signi¬cantly expanded low-pressure
region develops over north Africa and south Asia because of the increased
land“ocean temperature contrast. The summer monsoons in these regions
are strengthened and there is increased rainfall. These simulated changes
are in qualitative agreement with palaeoclimate data; for example, these
data provide evidence for that period (around 9000 years ago) of lakes and
122 M O D E L L I N G T H E C L I M AT E




(a) (b)

500
N N
Modern




Solar radiation (W m “2)
21 December 21 June 400


Annual mean
300



S Small tilt S 200
9000 years ago
Present
N N
9000 years ago
100
Jan July Jan

21 June 21 December




Large tilt
S S
Figure 5.19 (a) Changes in the Earth™s elliptical orbit from the present con¬guration to 9000 years ago and
(b) changes in the average solar radiation during the year over the northern hemisphere.


vegetation in the southern Sahara about 1000 km north of the present limits
of vegetation.
The accuracy and the coverage of data available for these past periods are lim-
ited. However, the model simulations for 9000 years ago, described above, and
those for other periods in the past have demonstrated the value of such studies
in the validation of climate models.19
A third way in which models can be validated is to use them to predict the
effect of large perturbations on the climate such as occurs, for instance, with
volcanic eruptions, the effects of which were mentioned in Chapter 1. Several
climate models have been run in which the amount of incoming solar radia-
tion has been modi¬ed to allow for the effect of the volcanic dust from Mount
Pinatubo, which erupted in 1991 (Figure 5.20). Successful simulation of some of
the regional anomalies of climate which followed that eruption, for instance
the unusually cold winters in the Middle East and the mild winters in western
Europe, has also been achieved by the models.20
In these three ways, which cover a range of timescales, con¬dence has
been built in the ability of models to predict climate change due to human
activities.
123
VA L I DAT I O N O F T H E M O D E L




The 12 June 1991 eruption column from Mount Pinatubo taken from the east side of Clark Air Base.


Figure 5.20 The predicted and 0.4
observed changes in global land Observed land surface air temperature
and night marine air temperature
and ocean surface air tempera-
(relative to April“June 1991)
ture after the eruption of Mount 0.2
/ Model predictions
Pinatubo, in terms of three-
Anomaly from 1991 (°C)




month running averages from
0.0
April to June 1991 to March to
May 1995.

“0.2




“0.4




“0.6
1990 1991 1992 1993 1994 1995 1996 1997
Year
124 M O D E L L I N G T H E C L I M AT E




Modelling of tracers in the ocean
A test that assists in validating the ocean component of the model is to compare the distribution of a
chemical tracer as observed and as simulated by the model. In the 1950s radioactive tritium (an isotope of
hydrogen) released in the major atomic bomb tests entered the oceans and was distributed by the ocean
circulation and by mixing.
Figure 5.21 shows good agreement between the observed distribution of tritium (in tritium units) in a
section of the western North Atlantic Ocean about a decade after the major bomb tests and the distribu-
tion as simulated by a 12-level ocean model. Similar comparisons have been made more recently of the
measured uptake of one of the freons CFC-11, whose emissions into the atmosphere have increased rap-
idly since the 1950s, compared with the modelled uptake.

0 0

9 7
8
8 7
200 200

7
7
65 6
6
400 400
Depth (m)




Depth (m)
4
5 3
2
600 600
4
1
3 6
800 800
2
1

1000 1000
(a) (b)
20°S EQ 20° 40° 60°N 20°S EQ 20° 40° 60°N
Latitude Latitude
Figure 5.21 The tritium distribution in a section of the western North Atlantic Ocean approximately one
decade after the major atomic bomb tests, as observed in the GEOSECS programme (a) and as modelled (b).




Comparison with observations
More than 20 centres in the world located in more than ten countries are cur-
rently running climate models of the kind I have described in which the cir-
culations of the atmosphere and the ocean are fully coupled together. Some of
these models have been employed to simulate the climate of the last 150 years
allowing for variations in aspects of natural forcing (e.g. solar variations and
volcanoes) and anthropogenic forcings (i.e. increases in the concentrations of
greenhouse gases and aerosols).
125
COM PA R I S O N W I T H O B S E RVAT I O N S




(b)
(a)
1.0
1.0




Temperature anomaly (°C)
Temperature anomaly (°C)




0.5
0.5



0.0
0.0



“ 0.5
“ 0.5
Pinatubo
Pinatubo
Santa Maria Agung El Chichón
Santa Maria Agung El Chichón
“ 1.0
“ 1.0
1900 1920 1940 1960 1980 2000
1900 1920 1940 1960 1980 2000
Year Year

Figure 5.22 Global mean temperature anomalies relative to the period 1901“50 as observed (black) and
from an ensemble of 58 simulations by 14 models with individual simulations shown by very thin lines.
Simulations are shown with both anthropogenic and natural forcings (a) and with natural forcings only (b).
The multimodel ensemble means are shown in red (a) and blue (b). Vertical lines indicate the timing of major
volcanic events.


Examples of such simulations are shown in Figure 5.22, where the observed
record of global average surface air temperature is compared with model simu-
lations taking into account the combination of natural and anthropogenic forc-
ings and natural forcings on their own.
Three interesting features of Figure 5.22 can be noted. Firstly, that the
inclusion of both natural and anthropogenic forcings provides a plausible
explanation for a large part of the observed temperature changes over the
last century, and that the inclusion of anthropogenic factors is essential to
explain the rapid increase in temperature over the last 40 years. Further, it is
likely that changes in solar output and the comparative absence of volcanic
activity were the most important variations in natural forcing factors during
the ¬ rst part of the twentieth century. Secondly, the model simulations show
variability up to a tenth of a degree Celsius or more over periods of a few
years up to decades. This variability is due to internal exchanges in the model
between different parts of the climate system, and is not dissimilar to that
which appears in the observed record. Thirdly, due to the slowing effect of
the oceans on climate change, the warming observed or modelled so far is less
than would be expected if the climate system were in equilibrium under the
amount of radiative forcing due to the current increase in greenhouse gases
and aerosols.
Because of the large amount of natural variability in both the observations
and the simulations, much debate has taken place over the last two decades
126 M O D E L L I N G T H E C L I M AT E




about the strength of the evidence that global warming due to the increase in
greenhouse gases has actually been observed in the climate record. In other
words has the ˜signal™ attributed to global warming risen suf¬ciently above the
˜noise™ of natural variability? The Intergovernmental Panel on Climate Change
(IPCC) has been much involved in this debate.
The IPCC™s ¬rst Report published in 199021 made a carefully worded statement
to the effect that, although the size of the observed warming is broadly con-
sistent with the predictions of climate models, it is also of similar magnitude
to natural climate variability. An unequivocal statement that anthropogenic
climate change had been detected could not therefore be made at that time. By
1995 more evidence was available and the IPCC 1995 Report22 reached the cau-
tious conclusion as follows.

Our ability to quantify the human in¬‚uence on global climate is currently
limited because the expected signal is still emerging from the noise of
natural climate variability, and because there are uncertainties in key
factors. These include the magnitude and patterns of long term natural
variability and the time-evolving pattern of forcing by, and response to,
changes in the concentrations of greenhouse gases and aerosols, and land
surface changes. Nevertheless, the balance of evidence suggests a discern-
ible in¬‚uence on global climate.

Since 1995 a large number of studies have addressed the problems of detection
and attribution23 of climate change. Better estimates of natural variability have
been made, especially using models, and the conclusion reached that the warm-
ing over the last 100 years is very unlikely to be due to natural variability alone. In
addition to studies using globally averaged parameters, there have been detailed
statistical studies using pattern correlations based on optimum detection tech-
niques applied to both model results and observations. The conclusion reached
in the IPCC 2001 Report:24

In the light of new evidence and taking into account the remaining uncer-
tainties, most of the observed warming over the last 50 years is likely to
have been due to the increase in greenhouse gas concentrations.

was strengthened in the IPCC 2007 Report25 which summarised its conclusion
as follows:

It is very likely that anthropogenic greenhouse gas increases caused most
of the observed increase in globally averaged temperatures since the mid-
20th century. Discernible human in¬‚uences now extend to other aspects
127
COM PA R I S O N W I T H O B S E RVAT I O N S




of climate, including continental-average temperatures, atmospheric cir-
culation patterns and some types of extremes.

The 2007 Report made four further summary points:

• It is likely that greenhouse gases alone would have caused more warming
than observed because volcanic and anthropogenic aerosols have offset some
warming that would otherwise have taken place.
• The observed widespread warming of the atmosphere and ocean, together
with ice mass loss, support the conclusion that it is extremely unlikely that glo-
bal climate change of the past 50 years was caused by unforced variability
alone.
• Warming of the climate system has been detected and attributed to anthropo-
genic forcing in surface and free atmosphere temperatures, in temperatures
of the upper several hundred metres of the ocean and in contributions to sea-
level rise. The observed pattern of tropospheric warming and stratospheric
cooling can be largely attributed to the combined in¬‚uences of greenhouse gas
increases and stratospheric ozone depletion.
• It is likely that there has been signi¬cant anthropogenic warming over the
past 50 years averaged over each continent except Antarctica. The observed
patterns of warming, including greater warming over land than over the
ocean and their changes over time, are simulated by models that include
anthropogenic forcing.

Con¬dence having been established in climate models in the ways we have out-
lined in the last two sections, these models can now be used to generate projec-
tions of the likely climate change in the future due to human activities. Details
of such projections will be presented in the next chapter.
Before leaving comparison with observations, I should mention observations
of the warming of the ocean that add further con¬rmation to the picture that
has been presented. In Chapter 2, the effect of an increase of greenhouse gases
was expressed in terms of radiative forcing or, in other words, a net input of
heat energy into the earth“atmosphere system. Most of this extra energy is
stored in the ocean. From large numbers of measurements of the temperature
increase in the ocean at different locations and depths down to 3 km, it has
been estimated that, over the period 1961“2003, the ocean has been absorb-
ing energy at a rate of 0.21 ± 0.04 W m’2 globally averaged over the Earth™s
surface.26 Two-thirds of this energy is stored in the upper 700 m of the ocean.
Within the limits of uncertainty, it agrees well with model estimates of ocean
heat uptake.27
128 M O D E L L I N G T H E C L I M AT E




More detail from observations and models of heat penetration into the oceans
is shown in Figure 5.2328 that demonstrates that natural forcing due to solar
variations and volcanic eruptions cannot explain the observed warming but
that the addition of human induced greenhouse gas forcings brings observa-
tions and model simulations into good agreement for all three oceans.


Is the climate chaotic?
Throughout this chapter the implicit assumption has been made that climate
change is predictable and that models can be used to provide predictions of
climate change due to human activities. Before leaving this chapter I want to
consider whether this assumption is justi¬ed.
The capability of the models themselves has been demonstrated so far as
weather forecasting is concerned. They also possess some skill in seasonal
forecasting. They can provide a good description of the current climate and its
seasonal variations. Further, they provide predictions that on the whole are
reproducible and that show substantial consistency between different models
“ although it might be argued, some of this consistency could be a property
of the models rather than of the climate. Further over several recent decades
for which comparison with observations is possible the predictions show good
agreement with observations. But is there other evidence to support the view
that climate change is predictable, particularly for the longer term?
A good place to look for further evidence is in the record of climates of the past,
presented in Chapter 4. Correlation between the Milankovitch cycles in the Earth™s
orbital parameters and the cycles of climate change over the past half million years
(see Figures 4.7 and 5.19) provides strong evidence to substantiate the Earth™s orbital
variations as the main factor responsible for the triggering of major climate changes
“ although the nature of the feedbacks which control the very different amplitudes
of response to the three orbital variations still need to be understood. Some 60 ±
10% of the variance in the record of global average temperature from palaeontologi-
cal sources over the past million years occurs close to frequencies identi¬ed in the
Milankovitch theory. The existence of this surprising amount of regularity suggests
that the climate system is not strongly chaotic so far as these large changes are con-
cerned, but responds in a largely predictable way to the Milankovitch forcing.
This Milankovitch forcing arises from changes in the distribution of solar
radiation over the Earth because of variations in the Earth™s orbit. Changes in
climate as a result of the increase of greenhouse gases are also driven by changes
in the radiative regime at the top of the atmosphere. These changes are not
dissimilar in kind (although different in distribution) from the changes that
provide the Milankovitch forcing. It can be argued therefore that the increases
in greenhouse gases will also result in a largely predictable response.
129
I S T H E C L I M AT E C H AOT I C ?




North Atlantic North Indian North Pacific
(a)


10 10 10


30 30 30


75 75 75
Depth (m)




125 125 125


200 200 200


300 300 300


500 500 500


700 700 700

-0.2 0.0 0.2 0.4 -0.2 0.0 0.2 0.4 -0.2 0.0 0.2 0.4


North Atlantic North Indian North Pacific
(b)


10 10 10


30 30 30


75 75 75
Depth (m)




125 125 125


200 200 200


300 300 300


500 500 500


700 700 700

-0.2 0.0 0.2 0.4 -0.2 0.0 0.2 0.4 -0.2 0.0 0.2 0.4
Warming (°C) Warming (°C) Warming (°C)

Figure 5.23 Warming of the top 700 m of three of the oceans resulting from natural (solar variations and
volcanic eruptions) and anthropogenic (greenhouse gases and aerosols) forcing at the surface over the period
1961“2003. Observations (red dots) of warming compared with modelled changes from both natural and
anthropogenic forcing (hatched regions in (a)) and from natural forcing only (green triangles in (b)). The
hatched regions in (b) represent the 90% con¬dence limits of natural internal variability. The ranges of model
estimates in (a) are taken from 4 runs (denoted by green dots) of the HadCM3 model at the UK Hadley Centre.
130 M O D E L L I N G T H E C L I M AT E




(a) (b) (c)




1 2 3 5 7 10
Precipitation (mm day “1)

Figure 5.24 Patterns of present-day winter precipitation over Great Britain, (a) as simulated with a 300-km
resolution global model, (b) with 50-km resolution regional model, (c) as observed with 10-km resolution.



Regional climate modelling
The simulations we have so far described in this chapter are with global circu-
lation models (GCM) that typically possess a horizontal resolution (grid size) of
200 or 300 km “ the size being limited primarily by the availability of computer
power. Weather and climate on scales large compared with the grid size are
described reasonably well. However, at scales comparable with the grid size,
described as the regional scale,29 the results from global models possess serious
limitations. The effects of forcings and circulations that exist on the regional
scale need to be properly represented. For instance, patterns of precipitation
depend critically on the major variations in orography and surface character-
istics that occur on this scale (see Figure 5.24). Patterns generated by a global
model therefore will be a poor representation of what actually occurs on the
regional scale.
To overcome these limitations regional modelling techniques have been
developed.30 That most readily applicable to climate simulation and prediction
is the Regional Climate Model (RCM). A model covering an appropriate region at
a horizontal resolution of say 25 or 50 km can be ˜nested™ in a global model. The
global model provides information about the response of the global circulation
to large-scale forcings and the evolution of boundary information for the RCM.
Within the region, physical information, for instance concerning forcings, is
entered on the scale of the regional grid and the evolution of the detailed cir-
culation is developed within the RCM. The RCM is able to account for forcings
on smaller scales than are included in the GCM (e.g. due to topography or land
131
T H E F U T U R E O F C L I M AT E M O D E L L I N G




cover inhomogeneity; see Figure 5.24) and can also simulate atmospheric circu-
lations and climate variables on these smaller scales.
A limitation of the regional modelling technique we have described is that,
although the global model provides the boundary inputs for the RCM, the RCM
provides no interaction back on to the global model. As larger computers become
available it will be possible to run global models at substantially increased reso-
lution so that this limitation becomes less serious; at the same time RCMs will
acquire an ability to deal with detail on even smaller scales. Some examples of
regional model simulations are shown in Figures 6.13 and 7.9.
Another technique is that of statistical downscaling which has been widely
employed in weather forecasting. This uses statistical methods to relate large-
scale climate variables (or ˜predictors™) to regional or local variables. The pre-
dictors from a global circulation climate model can be fed into the statistical
model to estimate the corresponding regional climate characteristics. The
advantage of this technique is that it can easily be applied. Its disadvantage
from the point of view of simulating climate change is that it is not pos-
sible to be sure how far the statistical relations apply to a climate-changed
situation.


The future of climate modelling
Very little has been said in this chapter about the biosphere. Chapter 3 referred
to comparatively simple models of the carbon cycle which include chemical and
biological processes and simple non-interactive descriptions of atmospheric proc-
esses and ocean transport. The large three-dimensional global circulation cli-
mate models described in this chapter contain a lot of dynamics and physics but
no interactive chemistry or biology. As the power of computers has increased,
it has become possible to incorporate into the physical and dynamical models
some of the biological and chemical processes that make up the carbon cycle
and the chemistry of other gases. These are enabling studies to be made of the
detailed processes and interactions that occur in the complete climate system.
Climate modelling continues to be a rapidly growing science. Although useful
attempts at simple climate models were made with early computers it is only
during the last 20 years that computers have been powerful enough for coupled
atmosphere“ocean models to be employed for climate prediction and that their
results have been suf¬ciently comprehensive and credible for them to be taken
seriously by policymakers. The climate models which have been developed are
probably the most elaborate and sophisticated of computer models developed
in any area of natural science. Further, climate models that describe the natu-
ral science of climate are now being coupled with socio-economic information
in integrated assessment models (see box in Chapter 9, page 280).
132 M O D E L L I N G T H E C L I M AT E




As the power of computers increases it becomes more possible to investigate
the sensitivity of models by running a variety of ensembles that include differ-
ent initial conditions, model parameterisations and formulations. A particularly
interesting project31 involves thousands of computer users around the world
in running state-of-the-art climate prediction models on their home, school or
work computers. By collating data from thousands of models it is generating the
world™s largest climate modelling prediction experiment.
This chapter has mainly concentrated on modelling the climate response to
anthropogenic forcing up to a century or so into the future taking into account
what have been called the fast feedbacks. However, questions are increasingly
being asked about what is likely to happen to the climate in the longer term
as a result of human activities now. Much of the response to change by three
components of the climate system, namely the oceans, ice sheets and land sur-
face (e.g. vegetation), occurs over longer time scales than a century (cf Table 7.5).
Associated with these are slow feedbacks32 that tend to be non-linear. Even if the
atmospheric composition is stabilised, warming of the deeper oceans will occur
up to at least 1000 years and major changes of the polar ice caps could occur over
many millennia. Because of the magnitude of these changes, their non-linearity
and their large impact even in the relatively short term, for instance over sea
level rise, it is vital that they are better understood. Much future research on
past climates, modelling and observations will be concerned with the character-
istics of both fast and slow responses within the climate system.




SUMM ARY

This chapter has described the basis, assumptions, methods and development
of computer numerical modelling of the atmosphere and the climate. Over the
past 30 years alongside the rapid development in the performance and speed
of computers, there has been enormous development in the sophistication,
skill and performance of atmosphere“ocean coupled general circulation mod-
els of the climate. Crucial has been the careful incorporation of the variety of
positive and negative feedback processes. Con¬dence in the ability of models
to provide useful projections of future climate is based on model simulations
that have been validated against:
• detailed observations of current and recent climate of both oceans and
atmosphere
• detailed observations of particular climatic cycles such as El Ni±o events
133
QUESTIONS




• observations of perturbations arising from particular events such as volcanic
eruptions
• palaeoclimate information from past climates under different orbital forcing.

A great deal remains to be done to narrow the uncertainty of model predic-
tions. The modelling of cloud feedback processes remains the source of the
largest uncertainty. Other priorities are to improve the modelling of ocean
processes and the ocean“atmosphere interaction. Larger and faster comput-
ers continue to be required for these and also to improve the resolution of
regional models. More thorough observations of all components of the cli-
mate system also continue to be necessary, so that more accurate validation of
the model formulations can be achieved. Very substantial national and inter-
national programmes are under way to address all these issues.




Q U E S TI O N S
QU
1 Make an estimate of the speed in operations per second of Richardson™s
˜people™ computer. Where does it fall in Figure 5.1?
2 If the spacing between the grid points in a model is 100 km and there are
20 levels in the vertical, what is the total number of grid points in a global
model? If the distance between grid points in the horizontal is halved, how
much longer will a given forecast take to run on the computer?
3 Take your local weather forecasts over a week and describe their accuracy
for 12, 24 and 48 hours ahead.
4 Estimate the average energy received from the Sun over a square region of
the ocean surface, one side of the square being a line between northern
Europe and Iceland. Compare with the average transport of energy into the
region by the North Atlantic Ocean (Figure 5.16).
(Figure 5.16).
5 Take a hypothetical situation in which a completely absorbing planetary
surface at a temperature of 280 K is covered by a non-absorbing and
non-emitting atmosphere. If a cloud which is non-absorbing in the visible
part of the spectrum but completely absorbing in the thermal infrared is
present above the surface, show that its equilibrium temperature will be 235
K (=280/20.25 K).33 Show also that if the cloud re¬‚ects 50% of solar radiation,
the rest being transmitted, the planet™s surface will receive the same amount
of energy as when the cloud is absent. Can you substantiate the statement
that the presence of low clouds tends to cool the Earth while high clouds
tend towards warming of it?
134 M O D E L L I N G T H E C L I M AT E




6 Associated with the melting of sea-ice which results in increased evapora-
tion from the water surface, additional low cloud can appear. How does this
affect the ice-albedo feedback? Does it tend to make it more or less positive?
7 Estimate from information in Chapter 3 the average net radiative forc-
ing from 1960 to 2003. Compare this with the average heating rate at the
Earth™s surface deduced from measurements of the energy absorbed by the
ocean as detailed on page 127. Comment on your results.
8 A change in radiative forcing at the top of the atmosphere of about 3 W m’2
leads to a change in surface temperature of around 1 °C providing noth-
ing else changes. Consider the plots shown in Figure 5.15b. What surface
temperature change would be expected following the Pinatubo volcano?
Compare with the information in Fig 5.21 and comment on your compari-
son. It is through the analysis of data of the kind illustrated in Figure 5.15a
and b that the magnitude and sign of cloud-radiation feedback can be
studied. Specify the requirements of a programme of measurements in terms
of accuracy (in W m’2) and coverage in both space and time if meaningful

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