and corrected data
-5 0 5 10 15
1
.
0
1
.
5
2
.
0
2
.
5
3
.
0
3
.
5
Temperature,°C
P
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e
c
i
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t
a
t
i
o
n
,
m
m
/
d
a
y
Jan
Feb Mar
Apr
May
Jun
JulAug
Sep
OctNov
Dec
Year
obs ALUKSNE
DMI 1961-1990 ALUKSNE
mod DMI 1961-1990 ALUKSNE
JanFeb
Mar
Apr
May
Jun
Jul
AugSep
Oct
Nov
Dec
Year
Jan
Feb
ar
Apr
May
Jun
Jul
Aug
Sep
Nov
Dec ear
After the
correction all 3
climate models
agree with
observed data
/media/ces/Kurpniece_Liga_CES_2010.pdf
ECHAM4/OPYC3 NorClim/HIRHAM 25x25 km
'Empirical Adjustment' to 1 x 1 km
100
150
200
250
300
350
400
450
500
550
600
650
700
750
800
850
900
1 10 100 1000
Return period (years)
P
e
a
k
d
a
i
l
y
d
i
s
c
h
a
r
g
e
(
m
3
/
s
)
1981-2010 GEV from annual max series
2021-2050 GEV from annual max series
2021 - 2050 Annual maximum series
1981 - 2010 Annual maximum series
1981-2010
200-year flood
2021
/media/ces/Lawrence_Deborah_CES_2010.pdf
-
-
by
-
.
d
-
al-
ral
l
on
on,
the
l to
in
r
so
ip-
de-
of
)
pollution sources and climate data.
Model structure uncertainty is the conceptual uncertainty
due to incomplete understanding and simplified descrip-
tions of modelled processes as compared to reality.
Parameter uncertainty, i.e. the uncertainties related to pa-
rameter values.
Model technical uncertainty is the uncertainty arising
/media/loftslag/Refsgaard_etal-2007-Uncertainty-EMS.pdf
.............................................................................. 13
Figure 2.1. The spatial extent of each group is defined by its radius, r, and the
overlap by the distance between the groups’ centres, d. .................................. 16
Figure 2.2. Two examples of joint interpretation of event distributions and focal
mechanisms. ...................................................................................................... 18
/media/vedurstofan/utgafa/skyrslur/2010/2010_003rs.pdf
-2B. Mon. Wea. Rev., 132, 2184–
2203.
Dickinson, R. E., Errico R. M., Giorgi F. and Bates G. T. 1989. A regional cli-
mate model for the western United States. Clim. Change, 15, 383–422.
Giorgi, F. 1990. On the simulation of regional climate using a limited area
model nested in a general circulation model. J. Climate, 3, 941–963.
Giorgi, F., and Mearns L. O. 1999. Introduction to special section
/media/ces/Paper-Olafur-Rognvaldsson_91.pdf
aversion should influence the first mover’s decision. The
98 Public Choice (2012) 151:91–119
Fi
g.
1
O
pt
im
al
co
n
tr
ib
u
tio
n
de
pe
nd
in
g
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n
α
,
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an
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p
Public Choice (2012) 151:91–119 99
Table 1 The predicted effect of
intrinsic preferences on first and
second movers’ contributions
1st mover 2nd mover
Disadvantageous Negative None
inequity aversion
Advantageous None Positive
inequity
/media/loftslag/Public-Choice-2012---Teyssier---Inequity-and-risk-aversion-in-sequential-public-good-games.pdf
which are significantly lower com-
pared with similar beginning and end years. Consequently, for the 2004–50 period, the average
RCM warming rates of 0.29 K per decade over the ocean, and 0.35 K per decade over the land are
somewhat larger than for the reduced IPCC ensemble mean.
Additionally, the tabulated values of SAT differences between the 1961–90 control period and
either the 2021–50
/media/ces/2010_005_.pdf
Elvehøy, H., Guðmundsson, S., Hock, R., Machguth, H., Melvold, K., Pálsson, F., Radic, V.,
Sigurðsson, O. and Þorsteinsson, Þ.
The impact of climate change on glaciers and glacial runoff in the Nordic countries .......................................... 38
Radic, V. and Hock, R.
Volume changes of the glaciers in Scandinavia and Iceland in the 21st century
/media/ces/ces-oslo2010_proceedings.pdf
(CBA can be subset
of SCBA)
• CEA: cost-effectiveness analysis – this is used if for (a part of)
the intended impacts no (shadow) price can be established (or
when that is contentious)
26.8.2011Adriaan Perrels/IL 9
Cost-benefit analysis – the basics 2
• Metrics:
• Net present value (NPV) :
• Internal Rate of Return (IRR): r = r* such that NPV = 0
• Benefit-Cost Ratio (B/C ratio):
• Macro
/media/loftslag/Perrels-CBA.pdf
r
lev
el
,
GW
h
Sweden
Reference Echam Hadam
0 14 28 42 52
0
1
2
3
4
5
6
7
104
Week
Reservoi
r
lev
el
,
GW
h
Norway
0 14 28 42 52
0
500
1;000
1;500
2;000
2;500
3;000
3;500
Week
Reservoi
r
lev
el
,
GW
h
Finland
Sintef Energy Research Quantitative system analysis 11 of 21
Introduction
Electricity system model
Simulation results
Summary and concluding remarks
Hydropower
Thermal production
Energy
/media/ces/Mo_Birger_CES_2010.pdf