–averaged
modeled ice velocity corresponding to the simulated 2002 ice sur-
face geometry, using A= 6.8× 10−15 s−1 kPa−3 implicitly includ-
ing the basal sliding (in B), and using A= 4.6× 10−15 s−1 kPa−3
and C = 10× 10−15 m a−1 Pa−3 (in (C)). (D) The contribution of
the modeled basal sliding to the velocity shown in (C).
Table 2. Average annual (September 2002 to September 2003;
accuracy ∼2 m a−1
/media/ces/Adalgeirsdottir-etal-tc-5-961-2011.pdf
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Ensemble approach for probabilistic
hydrological projections
Catchment
/media/ces/Lawrence_Deborah_CES_2010.pdf
). This can be helpful with respect to
finding a common structure in presenting as well in session reporting (for which angles
mentioned in the opening session statements of the Workshop participants can provide
checkpoints).”
Session rapporteur allocation
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/media/loftslag/Guidelines2-for-rapporteurs.pdf
Dashed lines encompass the V-shaped zone of tephra deposition. (c)
Oblique aerial view from west of the tephra plume at Grímsvötn on 2 November. Note the ashfall
from the plume. (Photo by M. J. Roberts.) (d) Weather radar image at 0400 UTC on 2 November.
The top portion shows its projection on an EW-vertical plane. The minimum detection height for
Grímsvötn is seen at 6 km, and the plume extends
/media/jar/myndsafn/2005EO260001.pdf
a systematic com-
parison of results to observed precipitation has been carried out. Un-
dercatchment of solid precipitation is dealt with by looking only at
days when precipitation is presumably liquid or by considering the
occurrence and non-occurrence of precipitation. Away from non-
resolved orography, the long term means (months, years) of observed
and simulated precipitation are often
/media/ces/Paper-Olafur-Rognvaldsson_92.pdf
and distribution network are all identified
a section at a time. In some cases, however, it might
be more valuable to focus only on a certain part of
functional model.
Fig.1. Functional Model.
The results of the risk analysis are represented
visually in a fourfold table. (Fig.2) The main idea of the
table is to provide a readily interpretable overview of
h hi hli h d i k d i i i l i
Having already been
/media/ces/ces_risk_flyer.pdf
inty in decision making linking pluriform uncertainty combining certified and tacit knowledge
Tommy Chan Mich el Laiho Patrick Driscoll Kare Lundgren Hector Guin a Barrientos
Eivind Junker Jussi Ylhäisi Athanasios Votsis
Karoliina Pilli-Sihvola Yuang Zheng Väi ö Nurmi Jiao Xi nj Wejs
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/media/loftslag/programme2---PhD-Workshop-preceding-Adaptation-Research-Conference.pdf
Hydrological Sciences Journal, 53, 100-111.
Kriauciuniené, J., Meilutyté-Barauskiené, D., Rimkus, E., Kays, J., Vincevicius, A. (2008). Climate change impact on hydrological processes in Lithuanian Nemunas river basin. Baltica, Vol. 21 (1-2), pp. 1-61. Vilnius. ISSN 3067-3064.
Lawrence, D., Haddeland, I. (2010). Uncertainty in hydrological modelling of climate change impacts in four Norwegian
/ces/publications/nr/1938
versa
• Very large range within the simulations
• Larger number of observation stations in the study area leads to
• better compatibility between different observational data sets
• smaller bias in the model simulations
CES Conference, Oslo 31.5.-2.6.2010
References
• Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P.
D. and New, M. 2008. A European daily high-resolution
/media/ces/TietavainenHanna_CES_2010.pdf
on the
Shifts of climatic
zones from
cooler or wetter
to warmer or
drier
(a) 1971-2000 (b) 2010-39 A1B
observational data set
(0.25º grid) from Haylock
et al. (2008)
(b-d) Based on CMIP3
GCM runs for A1B
& the delta-change
method
(c) 2040-69 A1B (d) 2070-99 A1B
Jylhä et al. (2010)
Uncertainties in climate change schematically
Observed
climate
Future
climate Natural
/media/loftslag/Case_B___Road_transport_operation_and_infrastructure_planning.pdf