Download The impact of climate change on global river flow in HadGEM1

ATMOSPHERIC SCIENCE LETTERS
Atmos. Sci. Let. 7: 62–68 (2006)
Published online in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/asl.133
The impact of climate change on global river flow in
HadGEM1 simulations
Pete D. Falloon* and Richard A. Betts
Met Office – Hadley Centre for Climate Prediction and Research, Fitzroy Road, Exeter, Devon EX1 3PB UK
*Correspondence to:
Pete D. Falloon, Met
Office – Hadley Centre for
Climate Prediction and Research,
Fitzroy Road, Exeter, Devon EX1
3PB UK.
E-mail:
[email protected]
Received: 13 April 2006
Revised: 7 July 2006
Accepted: 10 July 2006
Abstract
Climate change could have significant impacts on the availability of freshwater and the
frequency and severity of flooding, with major implications for society. The latest version of
the Hadley Centre General Circulation Model, HadGEM1, includes the TRIP (total runoff
including pathways) global river routing scheme. We have analysed the predicted changes
in global river flow under the IPCC SRES A1B and A2 scenarios from HadGEM1-TRIP
simulations. Global total river flow was predicted to increase by 4–8% during the 2071–2100
period relative to 1961–1990, although there were large regional differences – for example,
large increases in river flow in boreal regions and western Africa, and large decreases in river
flow in southern Europe and North Africa. Significant changes in the seasonality of river
flow could also occur, such as earlier peaks in spring runoff in boreal rivers due to earlier
snow melt. In our simulations, large increases in monthly maximum flow and decreases in
monthly minimum flow were found, although the increases in monthly maximum flow were
generally larger. Thus, climate change is likely to increase the occurrence of both high and
low flows, although the increased high flows could be dominant.  Crown Copyright 2006.
Reproduced with the permission of the Controller of HMSO. Published by John Wiley &
Sons, Ltd.
Keywords:
river flow; climate change; HadGEM1; SRES; TRIP; water resources; global
1. Introduction
Predictions of future climate change suggest that large
changes in rainfall patterns could occur globally over
the next 100 years (Intergovernmental Panel for Climate Change (IPCC), 2001). This has potentially serious implications for society since it could alter the
availability of freshwater and have impacts on the frequency and severity of flooding and droughts (Arnell,
1999). River flow is a useful indicator of freshwater
availability, and can thus be used to indicate likely
impacts of climate change on water resources and
flooding. Most earlier studies of changes in river
flow at the global scale have used stand-alone river
flow models driven by climate data output from General Circulation Models (GCMs) (e.g. Arnell, 1999,
2003; Milly et al., 2005; Nijssen et al., 2001a,b).
The previous version of the Hadley Centre GCM,
HadCM3 (Gordon et al., 2000), used a very simple
river routing scheme in which surface and subsurface runoff were instantaneously advected from the
land surface through outflow points to the ocean.
This approach was unable to capture the time-lags
between runoff generation on the land surface and
outflow to the ocean, which is a significant process in long rivers and snow-melt dominated catchments.
However, embedding dynamic river routing schemes
in GCMs is advantageous since (1) river flow indicates
the state of the surface water budget over large areas,
and therefore (2) river flow is useful for validating
hydrology in large-scale models, (3) it also enables
the time lag between runoff generation and outflow
to the ocean to be reproduced, and (4) river flow is
important for a realistic thermohaline circulation in a
coupled atmosphere–ocean GCM.
The latest version of the Hadley Centre GCM,
HadGEM1 (Martin et al., 2006), includes the dynamic
river routing scheme total runoff including pathways
(TRIP) (TRIP, Oki and Sud, 1998). Simulations of climate change for the next century, under the IPCC Special Report on Emissions Scenarios (SRES – IPCC,
2000) A1B and A2, have recently been performed
with HadGEM1 (Johns et al., 2006; Stott et al., 2006)
in addition to a ‘Historic-Anthropogenic’ simulation
from 1859–2000 using time varying concentrations of
greenhouse gases ozone, and land surface/vegetation
with constant volcanic stratospheric aerosols and solar
irradiance (hereafter referred to as ‘Control-HA’) and
a longer ‘true’ control run with the 1860 land use
and no anthropogenic forcings (hereafter referred to
as ‘True Control’). We have used these simulations
to investigate predicted changes in the magnitude and
distribution of global river flow over the next century.
The main focus of this article is on the global, rather
than local, scale impacts of climate change on river
flow, and hence the emphasis of our analysis is on the
largest rivers and the largest impacts on changes in
the overall amounts of river flow rather than percent
changes.
 Crown Copyright 2006. Reproduced with the permission of the Controller of HMSO.
Published by John Wiley & Sons, Ltd.
Impact of climate change on global river flow in HadGEM1 simulations
63
2. Methods and materials
The latest version of the Hadley Centre GCM is
HadGEM1. HadGEM1 has many improvements over
the previous version HadCM3, including improved
horizontal resolution (1.25◦ latitude by 1.875◦ longitude) and substantially improved representations of
physical processes, which include advection, boundary layer, gravity wave drag, microphysics and seaice schemes, plus major changes to convection, land
surface and cloud schemes and inclusion of aerosols
(Martin et al., 2006). This has led to a substantial
improvement in the processes represented, including
the hydrological cycle. HadGEM1 has recently been
applied to the IPCC SRES A1B and A2 scenarios
(Johns et al., 2006; Stott et al., 2006). The global
mean atmospheric surface temperature rise in these
simulations between 1961–1990 and 2070–2100 were
approximately 3.4 K and 3.8 K, respectively.
HadGEM1 includes the TRIP river routing model
(Oki and Sud, 1998). TRIP uses a simple advection
method to route total (surface and subsurface) runoff
along prescribed river channels. The river channels are
represented by two data sets giving the direction and
sequence of the flow of water at 1◦ resolution. The
model requires a universal meander coefficient and an
effective velocity to be set – the values used in these
simulations were 1.4 (dimensionless) and 0.5 ms−1 ,
respectively. The model runs on a daily time step, and
is coupled to HadGEM1 by interpolating the surface
and subsurface runoff values from the GCM grid to
the 1◦ TRIP grid – runoff at coastal outflow points is
then re-gridded to the atmosphere grid and added to the
ocean as a freshwater flux. Outputs from TRIP include
total inflow, total outflow and water storage for each
grid-box. Comparison of TRIP outputs with observed
river flow gauge data has shown good agreement, both
using an independent runoff data set (Oki, 1997; Oki
et al., 1999), and within the HadGEM1 model (Falloon
et al., 2005). In addition, the land surface scheme used
to produce runoff as input to TRIP has been shown
to reproduce observed changes in continental scale
runoff during the 20th century (Gedney et al., 2006).
Here, we describe changes in river flow over the next
century from HadGEM1-TRIP simulations under the
SRES A1B and A2 scenarios, compared to the control
simulations.
3. Results and discussion
Climate change was predicted to increase the average
global total river flow for the 2071–2100 period by
approximately 4 and 8% relative to the 1961–1990
(present day) period under the A1B and A2 scenarios, respectively (Figure 1). However, large interannual and decadal variability was noted, with an
initial decrease in global total river flow relative
to the present day until about 2060, with a sharp
increase thereafter. Considerable regional variations
Figure 1. Predicted change in average total global river flow
(%) during the 21st Century for SRES emissions scenarios for
the A1B and A2 differences from the Control-HA simulation
(average of the 1961–1990 period). Values shown are decadal
means
in predicted changes in river flow were observed
(Figure 2(a) and Figure 2(b)). For the 2071–2100
period, significant decreases in river flow were predicted for large areas of the globe, including southern Europe, the Middle East, North Africa, northern South America and Midwest USA. In contrast,
large increases in river flow were predicted for the
boreal regions of North America and Eurasia, western Africa and parts of China. Regional patterns of
changes in river flow under the two emission scenarios were similar, although changes in river flow
were generally more severe and widespread under the
A2 scenario (Figure 2(b)) relative to the A1B scenario
(Figure 2(a)). However, there were some notable differences between the two scenarios in regional patterns
of predicted changes in river flow. For example, parts
of eastern Africa, predicted to experience reductions in
river flow under the A1B scenario, were predicted to
undergo increases in river flow under the A2 scenario
and vice-versa. Under the A1B scenario, reductions in
river flow were found for most of India while increases
were mostly predicted under the A2 scenario; coastal
China was predicted to undergo decreases in river flow
under the A1B scenario but increases under the A2
scenario. It is noticeable that some already dry regions
of the globe are predicted to suffer from considerable
reductions in future river flow (e.g. southern Europe,
North Africa and midwestern USA), while some relatively wet regions could experience large increases
in river flow (e.g. equatorial western Africa). Only
small areas of the globe were predicted to undergo
little change in river flow over the next century.
Predicted future changes in river flow were largely
driven by changes in precipitation, since the pattern
of changes in precipitation were very similar to the
pattern of changes in river flow (Figure 2(c)), and
the changes in evaporation opposed the changes in
river flow in some regions (Figure 2(d)). For example, decreases in both river flow and precipitation
 Crown Copyright 2006. Reproduced with the permission of the Controller of HMSO.
Published by John Wiley & Sons, Ltd.
Atmos. Sci. Let. 7: 62–68 (2006)
DOI: 10.1002/asl
64
P. D. Falloon and R. A. Betts
Figure 2. Predicted changes in average global river flow (%) for SRES emissions scenarios (a) A1B and (b) A2, (c) average total
precipitation (mm d−1 ) and (d) average total evaporation (mm d−1 ) for SRES emissions scenario A1B between 1961–1990 and
2071–2100. Values are differences from Control-HA simulation
Table I. Changes in annual average river flow under SRES scenarios for the 10 rivers showing the largest increases (under the A2
scenario). Values are based on averages for the 1961–1990 period (Control-HA) and 2071–2100 period (A1B and A2 scenarios)
River (Country,
longitude and latitude of
river mouth)
Congo (Zaire), Congo/Zaire, 12.5E 5.5S
Changjiang/Yangtze, China, 120.5E 31.5N
Brahmaputra, Bangladesh, 90.5E 22.5N
Irrawaddy, Myanmar, 95.5E 16.5N
Ganges, Bangladesh, 88.5E 22.5N
Lena, Russia, 124.5E 73.5N
Sao Francisco, Brazil, 37.5W 10.5S
Yukon, USA/Canada, 164.5W 62.5N
Niger, Nigeria, 6.5E 5.5N
Mekong, Vietnam, 106.5E 10.5N
Annual average outflow (m3 s−1 )
Difference in
annual average outflow
(m3 s−1 )
% Difference
in annual average
outflow
Control-HA
A1B
A2
A1B
A2
A1B
A2
29 180
47 090
28 650
14 910
13 010
14 230
13 220
7060
4241
14 790
38 100
56 040
35 330
21 460
17 000
19 320
20 430
10 960
7770
20 750
50 310
54 770
35 300
21 400
19 270
20 300
18 360
11 590
8609
18 430
8919
8950
6683
6549
3996
5089
7203
3896
3529
5966
21 140
7679
6652
6489
6264
6075
5141
4534
4369
3645
30.6
19.0
23.3
43.9
30.7
35.8
54.5
55.2
83.2
40.3
72.4
16.3
23.2
43.5
48.2
42.7
38.9
64.2
103.0
24.7
were predicted for northern South America and southern Europe while evaporation was reduced – hence
the reduction in river flow was driven mostly by the
reduction in rainfall. In high latitude rivers, increases
in river flow and rainfall were predicted along with
increases in evaporation, so the river flow changes here
were mostly driven by changes in rainfall. In tropical
Africa, increases in river flow and rainfall were predicted along with decreases in evaporation, so changes
in rainfall and evaporation both contributed to the river
flow changes.
Large changes in total annual river flow from the
largest river basins were predicted, including decreases
of over 30% and increases exceeding 70%. For smaller
rivers, increases of up to 300% and decreases up to
65% in total annual river flow were predicted. The
greatest increases in total annual river flow by volume ranged from approximately 3000 to 21 000 m3 s−1
 Crown Copyright 2006. Reproduced with the permission of the Controller of HMSO.
Published by John Wiley & Sons, Ltd.
Atmos. Sci. Let. 7: 62–68 (2006)
DOI: 10.1002/asl
Impact of climate change on global river flow in HadGEM1 simulations
65
Table II. Changes in annual average river flow under SRES scenarios for the 10 rivers showing the largest decreases (under the A2
scenario). Values are based on averages for the 1961–1990 period (Control-HA) and 2071–2100 period (A1B and A2 scenarios)
River (Country,
Longitude and
Latitude of
river mouth)
Amazon, Brazil, 50.5W 0.5N
Magdalena, Colombia, 4.5W 10.5N
Orinoco, Venezuela, 61.5W 9.5N
Zambeze/Zambezi, Mozambique,
36.5W 18.5S
Parana La Plata (with Uruguay),
Argentina/Uruguay, 57.5W 34.5N
Tocantins/Araguaia, Brazil, 48.5W
1.5S
Ob, Russia, 70.5E 66.5N
Volga, Russia, 48.5W 46.5N
Columbia, USA, 123.5W 46.5N
Mississippi, USA, 90.5W 29.5N
Difference in
annual average
outflow (m3 s−1 )
Annual average
outflow (m3 s−1 )
Control-HA
A1B
A2
219 700
26 280
23 570
15 140
190 300
20 010
19 170
9863
194 800
17 800
16 650
10 370
60 660
57 680
20 340
16 390
8437
9812
16 590
A2
A1B
A2
−29 400
−6260
−4410
−5280
−24 900
−8480
−6930
−4770
−13.4
−23.8
−18.7
−34.9
−11.3
−32.3
−29.4
−31.5
56 920
−2980
−3750
−4.9
−6.2
18 890
18 040
−1450
−2300
−7.1
−11.3
15 080
7813
8156
17 580
14 200
6519
8050
15 280
−1310
−624
−1660
992.5
−2180
−1920
−1760
−1310
−8.0
−7.4
−16.9
6.0
−13.3
−22.7
−18.0
−7.9
(16–103%, Table I) while the greatest decreases in
total annual river flow by volume ranged from approximately 1000 to 30 000 m3 s−1 (5–35%, Table II). For
some large rivers, such as the Mississippi, predicted
changes in river flow under the A1B and A2 scenarios
were opposite in sign (increase and decrease, respectively). In general, predicted changes in river flow in
specific basins, whether increases or decreases, are
likely to increase in magnitude from the present day
(Figure 3). Comparison of the simulations under the
SRES A1B and A2, Control-HA and True Control,
scenarios showed that for some basins, such as the
Orinoco, a climate-driven change in river flow was
predicted to occur by the 1970s while for other basins
little change in river flow was predicted before 2000
(Figure 3).
In addition to these changes in global and long-term
basin-scale patterns of river flow, significant changes
in the seasonality of river flow could also occur
(Figure 3). For instance, an earlier peak in spring snow
melt leads to an earlier peak in spring runoff in boreal
rivers such as the Lena. Decreases and increases in the
Amazon and Chari river flow, respectively, were most
pronounced during times of peak flow; in contrast,
the reduction in flow for the Orinoco was fairly
constant across the hydrograph. The largest increases
in the maximum monthly flow values by volume
found during the year included predicted increases
of over 300% or 30 000 m3 s−1 , and the month of
maximum flow shifted by several months in some
cases, generally coming earlier in the year (Table III).
The largest decreases in the minimum monthly flow
values by volume found during the year included
predicted decreases of over 65% or 8000 m3 s−1 , with
the month of lowest flow generally occurring later in
the year (Table IV). Decreases in minimum monthly
flow values of up to 85% were found for some smaller
basins, with large shifts occurring in the timing of lowflow periods.
A1B
% Difference in
annual average
outflow
The increases in maximum monthly flow values
were generally larger than decreases in minimum
monthly flow values for the largest rivers (both in
terms of volume and percent change). It is therefore
possible that while there may be an increased occurrence of both high and low flows during the next
century, changes in high flows could be more severe.
Furthermore, our use of monthly values averaged over
30-year periods is likely to have underestimated the
impact of changes in river flow on very high and low
flows since extreme values will have been dampened
during the averaging of the daily data output by the
TRIP model.
Arnell (2003) studied the impact of climate change
on global river flow under several SRES scenarios
from a number of different GCMs, using a stand-alone
river routing model driven by precipitation from the
GCMs. Our results for the A2 emission scenarios are
in broad agreement with those of Arnell (2003) for the
2080s under the A2 emissions scenario – the different
GCMs consistently showed decreases in runoff for
northern South America, southern USA, southern
Europe, North Africa, southern Africa and Southeast
and Southwest Australia. Arnell (2003) also found
consistent increases in runoff from the different GCMs
for northern Canada and Russia, parts of China and
tropical Africa. Similar patterns of change were also
found by Milly et al. (2005), who combined runoff
data from 12 GCMs with basin-scale flow gauge data
and statistical techniques to investigate the impact of
the SRES A1B scenario on global stream flow.
The work presented here has only investigated
the impact of two emission scenarios on patterns of
global river flow using results from one GCM and
one river routing scheme. Clearly different predictions
could arise from different combinations of emission
scenarios, GCMs and river routing schemes. The river
routing scheme currently used in HadGEM1 does not
 Crown Copyright 2006. Reproduced with the permission of the Controller of HMSO.
Published by John Wiley & Sons, Ltd.
Atmos. Sci. Let. 7: 62–68 (2006)
DOI: 10.1002/asl
66
P. D. Falloon and R. A. Betts
Figure 3. Predicted changes in 21st Century decadal mean and annual cycle of river flow (m3 s−1 ) for Amazon (a) and (b), Lena
(c) and (d), Chari/Shari (e) and (f) and Orinoco (g) and (h) river basins under Control-HA, True Control, and SRES A1B and A2
scenarios. Annual cycles are based on decadal means and for 2071–2100 (vs) 1961–1990 Control-HA values
 Crown Copyright 2006. Reproduced with the permission of the Controller of HMSO.
Published by John Wiley & Sons, Ltd.
Atmos. Sci. Let. 7: 62–68 (2006)
DOI: 10.1002/asl
Impact of climate change on global river flow in HadGEM1 simulations
67
Table III. Changes in maximum monthly river flow under SRES scenarios for the 10 rivers showing the largest increases (under
the A2 scenario). Values are based on averages for the 1961–1990 period (Control-HA) and 2071–2100 period (A1B and A2
scenarios)
River
(Country, Longitude
and Latitude of river
mouth)
Changjiang/Yangtze, China, 120.5E 31.5N
Sao Francisco, Brazil, 37.5W 10.5S
Congo (Zaire), Congo/Zaire, 12.5E 5.5S
Irrawaddy, Myanmar, 95.5E 16.5N
Ganges, Bangladesh, 88.5E 22.5N
Niger, Nigeria, 6.5E 5.5N
Brahmaputra, Bangladesh, 90.5E 22.5N
Xi/Hsi (Pearl), China, 113.5E 22.5N
Chari/Shari, Chad, 14.5E 12.5N
Kolyma, Russia, 160.5E 69.5N
Month of
maximum monthly
outflow
Maximum
monthly outflow (m3 s−1 )
Control-HA
A1B
A2
Control-HA A1B
63 440
37 700
51 990
40 120
43 880
9541
70 590
30 270
3527
12 760
83 820
67 620
52 260
58 320
58 660
19 570
87 220
43 470
9318
21 350
84 850
57 420
70 540
56 700
59 070
22 590
83 620
41 640
14 290
22 580
Oct
May
Jan
Sep
Sep
Sep
Sep
Aug
Oct
Jun
Oct
Apr
Jan
Sep
Sep
Sep
Aug
Aug
Oct
Jun
Change
%
in maximum
Change in
monthly outflow
maximum
(m3 s−1 )
monthly outflow
A2
A1B
A2
A1B
A2
Oct
Apr
Jan
Sep
Sep
Oct
Aug
Aug
Oct
Jun
20 370
29 920
270
18 200
14 780
10 030
16 630
13 200
5791
8592
21 410
19 720
18 550
16 580
15 200
13 050
13 030
11 370
10 760
9815
32.1
79.4
0.5
45.4
33.7
105.1
23.6
43.6
164.2
67.3
33.7
52.3
35.7
41.3
34.6
136.8
18.5
37.6
305.0
76.9
Table IV. Changes in minimum monthly river flow under SRES scenarios for the 10 rivers showing the largest decreases (under
the A2 scenario). Values are based on averages for the 1961–1990 period (Control-HA) and 2071–2100 period (A1B and A2
scenarios)
River
(Country, Longitude
and Latitude of river
mouth)
Parana La Plata (with Uruguay),
Argentina/Uruguay, 57.5W 34.5N
Magdalena, Colombia, 74.5W 10.5N
St.Lawrence, Canada, 70.5W 47.5N
Amazon, Brazil, 50.5W 0.5N
Orinoco, Venezuela, 61.5W 9.5N
Xi/Hsi (Pearl), China, 113.5E 22.5N
Volga, Russia, 48.5W 46.5N
Kura, Azerbaijan, 48.5E 39.5N
Mobile, USA, 88.5W 30.5N
Paraiba, Brazil, 41.5W 21.5S
Minimum
monthly outflow (m3 s−1 )
Month of
minimum monthly
outflow
Control-HA
A1B
A2
33 340
28 100
25 280
Nov
Oct
8591
11 370
89 480
3117
2814
1724
578
564
455
4108
8720
80 690
1704
2755
1716
446
420
500
2900
6533
87 280
1283
2411
1445
322
334
269
Feb
Oct
Jan
Apr
Feb
Nov
Jan
Oct
Sep
explicitly account for reservoir operation, although this
could have significant impacts on river flow, (Nilsson
et al., 2005) and a reservoir operation model for TRIP
has recently been developed (Hanasaki et al., 2006).
Abstraction of water from river networks has a major
influence on flow in some basins (Alcamo et al., 2003)
and further work is needed to represent this in our
simulations. We have made no attempt to address
uncertainty in our predictions, other than by the use of
two emission scenarios, although large ensembles of
perturbed GCM simulations have been used to address
uncertainty (Murphy et al., 2004) – analysis of runoff
from these simulations indicates that uncertainties can
exceed mean predicted changes (Betts et al., 2005) and
that plant responses to elevated CO2 have a significant
role to play in future runoff changes (Betts et al.,
2006) – the latter effect has been included in these
simulations. It is important to note that the modelled
changes in river flow do not take into account the
changes in the hydrological response of the catchment
Control-HA A1B A2
Change
%
in minimum
Change in
monthly outflow
minimum
(m3 s−1 )
monthly outflow
A1B
A2
A1B
A2
Oct −5240
−8060
−15.7
−24.2
Mar Apr −4480
Nov Nov −2650
Jan
Jan −8790
May May −1410
Feb Feb
−58
Dec Dec
−9
Aug Aug −132
Oct Sep −144
Sep Sep
45
−5690
−4840
−2200
−1830
−403
−280
−255
−230
−186
−52.2
−23.3
−9.8
−45.3
−2.1
−0.5
−22.8
−25.5
9.9
−66.2
−42.5
−2.5
−58.9
−14.3
−16.2
−44.2
−40.8
−40.8
that would occur as a result of the predicted river flow
changes; thus, the feedbacks relating to changes in
vegetation type, land use and water use as a result
of high/low flows and climate change have been
neglected. These changes could have the effect of
lessening the magnitude of changes in river flow.
Further work is needed for investigating the changes
in extreme values of river flow to indicate flooding
and drought risk, and for incorporating information
on population and the demand for water to enable
assessment of water resource issues and flood risk.
4. Conclusions
We have analysed predicted changes in global river
flow under the IPCC SRES A1B and A2 scenarios from HadGEM1-TRIP simulations. Our results
are in broad agreement with previous studies (Arnell,
2003; Milly et al., 2005). Global total river flow
 Crown Copyright 2006. Reproduced with the permission of the Controller of HMSO.
Published by John Wiley & Sons, Ltd.
Atmos. Sci. Let. 7: 62–68 (2006)
DOI: 10.1002/asl
68
was predicted to increase by 4–8% during the
2071–2100 period relative to 1961–1990, although
there were large regional differences – for example,
large increases in river flow in boreal regions and
western Africa, and large decreases in river flow over
southern Europe and North Africa. Changes in river
flow are likely to increase in severity with time. Significant changes in the seasonality of river flow could
also occur, such as earlier peaks in spring runoff in
boreal rivers due to earlier snow melt. In our simulations, large increases in monthly maximum flow
and decreases in monthly minimum flow were found,
although the increases in maximum flow were generally larger. Thus, climate change is likely to increase
the occurrence of both high and low flows, although
the occurrence of high flows could be dominant. Further work is needed to (1) investigate the changes
in extreme river flow values from daily data, (2) put
these predicted changes in river flow into context, for
example, combining our results with information on
population and water demands, and (3) address the
uncertainties in our predictions.
Acknowledgements
This work was supported by the UK Department for Environment, Food and Rural Affairs (Defra) contract PECD 7/12/37
and the UK Ministry of Defence project ‘Defence and security implications of climate change’. We would like to thank
two anonymous referees, Olivier Boucher and the editor Chris
Jones for their constructive comments on an earlier version of
this article.
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 Crown Copyright 2006. Reproduced with the permission of the Controller of HMSO.
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Atmos. Sci. Let. 7: 62–68 (2006)
DOI: 10.1002/asl