Exploratory and normative scenario developers both have acknowledged the added
value of either type of scenario process. Particularly the backcasting community has attempted to combine backcasting and
foresighting approaches. Good examples are Robinson et al. [10], Carlsson-Kanyama et al. [11], and the work of David Banister (e.g.
[12]). In this paper we describe an attempt from the community
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uncertainty that results from our incomplete understanding, and incomplete
capability to describe in climate models, the dynamics of the climate system that
determine its response to changes in external forcing.
z Natural climate variability, resulting in part from variations in solar and volcanic
activity but at least as importantly from the internal dynamics of the climate system
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data from two meteorological
stations*Vı´k in My´rdalur, close to the ice caps and about
2 km from the coastline and Hveravellir in central Iceland
(Fig. 1)*to estimate the mass balance sensitivity to
summer and annual temperature variations. Those sta-
tions were selected as they include temperature records
reaching back to 1979 and describe the different climate
conditions south and north
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be presented in a way which displays the conflicts
clearly?
My questions to ask others at the Ph.D. workshop would be: how can data best be used in the lay-out of a
Ph.D. thesis? I would propose to select some of my empirical research material and describe in which way it
relates to my Ph.D. thesis arguments. Then others could give me feedback.
2. Looking at such a wide range of political actors in my
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summarises the major robust findings and remain-
ing key uncertainties in this assessment.
A schematic framework representing anthropogenic drivers,
impacts of and responses to climate change, and their linkages, is
shown in Figure I.1. At the time of the Third Assessment Report
(TAR) in 2001, information was mainly available to describe the
linkages clockwise, i.e. to derive climatic changes
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and documented in Björnsson et al. (2018) and Crochet et al. (2007). The study, based on the
previous research of Jónas Elíasson (Elíasson, 2000; Elíasson et al., 2009) also presented
intensity-duration-frequency curves for over 40 locations in Iceland. These curves describe the
relationship between rainfall intensity, duration, and return periods, making them useful for
flood warnings
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calculate the associated plume height.
The need to numerically describe this process is because often the volcanic plume is bent by
wind action. Neglecting this aspect would cause an underestimation of the mass flow rate. As
a consequence, the way the plume model has been used has been by matching the reported
plume height for the different scenarios and identifying those erupting conditions
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of what may happen in the future after adapting the event to
modern or future conditions. Statistical models based on the instrumental record do not necessarily
include the most drastic events possible since confidence intervals usually describe how well the
models fit the data used without considering how well the data represents what may happen. There-
fore, information about historical events
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are
compared between the reference period and the late-century, a change in the direction of
winter winds in the north stands out. This is most prominent in case 2 and only seen slightly
in other cases.
According to Figure 26, the most prominent winds in Akureyri are SW-NE but in actual fact,
the winds there are closer to being SSE-NNW. The wind rose is closer to describe conditions
just south
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strike-slip records, when compared to European and North-American events of similar sizes
and source depths, on normal and oblique faults. This might point to a reason for why the
foreign models fail to describe the PGA of our reference events. Ólafsson’s and
Sigurbjörnsson’s model is also the one that fits best, within the one standard deviation region of
our model D.
Table 1 shows
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