Barthelmie, R.J., Takle, G.S. & Andersen, T. (2008). The impact of climate change on wind energy resources. Proceedings of the World Renewable Energy Congress-X, 6pp (Invited Plenary presentation). 6pp.
Pryor, S.C., Barthelmie, R.J. & E.S. Riley (2007). Historical evolution of wind climates in the USA, Conference on the science of making torque from wind, Danish Technical University, August 2007
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was represented on the native grids of each individual
model. Therefore, the monthly means of the modelled radiation were first interpolated
onto a common 2.5 x 2.5 degree grid, and 30 year running means were applied to smooth
the influence of random interannual variability. Thereafter, anomalies from the baseline
period mean were calculated.
2
Fig. 2. Percentage change of incident global solar
/media/ces/CES_D2.4_solar_CMIP3.pdf
15.9.04
Forum 2
3.11.04
Forum 3
12.04.05
Interviews
Jan/Feb 05
Group Model Building -
Identify Problems & Measures
8
Simulation Models
Testing Solutions
Forum 1
15.9.04
Forum 2
3.11.04
Forum 3
12.04.05
KG
Feb/March 05
Interviews
Jan/Feb 05
Forum 4
15.06.05
Forum 3
12.04.05
r 5
07.09.05
Forum 4
15.06.05
9
x
Measures Costs Ecological
Efficiency
Accep-
tance
Needed
control
Further
Effects
1....
2
/media/loftslag/Hare_2-participation.pdf
in an overall
cold bias, compared with station measurements. To test, whether this is due to the HARMONIE
model core or the external surface scheme, biases of 2-m temperature from SURFEX are com-
pared with biases of temperature projected from the lowest two model levels to 2 mAGL. It is
found that the negative temperature biases are due to shallow inversion layers near the ground,
which are introduced
/media/vedurstofan/utgafa/skyrslur/2014/VI_2014_005.pdf
improving
management policies and practices by learning from the
outcomes of implemented management strategies. Partici-
patory integrated assessment is here a form of problem
structuring for identification of gaps, ambiguity and
multiple frames, confrontation, and integration of the
most divergent views with respect to a given problem
situation.
Additional methods and tools that AM require com/media/loftslag/Henriksen_Barlebo-2008-AWM_BBN-Journ_Env_Management.pdf
the com-
plexity of the hydrological processes through modelling, but its application is usually limited to
the short-range. Although the results demonstrated a great potential for this method, its success-
ful application in real-time will strongly depend on the quality and availability of streamflow
observations, which can be poor or simply missing during periods of variable durations, e.g
/media/vedurstofan/utgafa/skyrslur/2014/VI_2014_006.pdf
Clausen, N.-E., Pryor, S. C., Guo Larsén, X., Hyvönen, R., Venäläinen, A., Suvilampi, E., Kjellström, E., Barthelmie, R. (2009). Are we facing increasing extreme winds in the future? EWEC 2009 Marseille session DT2A, 19 March 2009.
Engen-Skaugen,T & Førland, E.J. (2010). Future change in return values and extreme precipitation at selected catchments in Norway, met.no Report 20/2010 (draft).
Engen
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Pálsson F., Rögnvaldsson
Ó., Sigurðsson O., Snorrason Á., Sveinsson Ó. G. B., Thorsteinsson Th. 2007.
Effect of climate change on hydrology and hydro-resources in Iceland. Rep.
OS-2007/011, National Energy Authority, Reykjavík.
Liang, X.-Z., Li L. and Kunke K. E. 2004 Regional climate model simulation
of U.S. precipitation during 1982–2002. Part I: Annual cycle. J. Climate, 17,
3510–3529.
Pálsson, F
/media/ces/Paper-Olafur-Rognvaldsson_91.pdf
research
VIII. Resources: extensive vs limited
IX. Institutional conditions: open vs constrained
C Scenario content - complex vs simple:
X. Temporal nature: chain vs snapshot
XI. Variables: heterogeneous vs homogenous
XII. Dynamics: peripheral vs trend
XIII. Level of deviation: alternative vs conventional
XIV. Level of integration: high vs low
Scenarios - types
EXAMPLE 1 – EXPLORATORY SCENARIOS
/media/loftslag/Kok_2-scenarios-lecture-2.pdf