Climate change impacts and vulnerability in Europe 2016

Introduction
1.3 Uncertainty in observations and
projections
Many aspects of past and future climate change, its
causes and its impacts are well known and undisputed
by scientists (IPCC, 2013, 2014a, 2014b). Hence, there is
substantial robust information available to inform climate
change mitigation and adaptation policies. Nevertheless,
data on observed and projected climate change and its
impacts are always associated with some uncertainty.
This section discusses the main sources of uncertainty
that are relevant for this report, and how uncertainties
are addressed and communicated, in particular in the
'Key messages'.
Note that the term 'uncertainty' is used by scientists to
refer to partial, or imperfect, information (Sense about
Science, 2013). Thus, the direction or the approximate
magnitude of a phenomenon may be known, but the
exact magnitude may not be known. For example,
a scientific projection of the change in global mean
temperature for a given emissions scenario may
report a best estimate of 3 °C, with an uncertainty
range of 2–4.5 °C. The uncertainty interval reflects
the impossibility to forecast exactly what will happen.
However, knowing that it is virtually certain that the
Earth will continue to warm and that future warming will
probably be within a certain range still provides highly
relevant information to decision-makers concerned with
climate change mitigation and adaptation.
1.3.1 Sources of uncertainty
Uncertainties in indicators presented in this report arise
primarily from the following sources. Some of them can,
in principle, be reduced by further research, whereas
others cannot.
• Measurement errors resulting from imperfect
observational instruments (e.g. rain gauges)
and/or data processing (e.g. algorithms for estimating
surface temperature based on satellite data).
• Aggregation errors resulting from incomplete temporal
and/or spatial data coverage. Most indicators
presented in this report combine measurements from
a limited number of locations (e.g. meteorological
observation stations) and from discrete points in time
to make aggregate statements on large regions and
for whole time periods. Such aggregation introduces
uncertainties, in particular when the measurement
network is scarce and when the indicator exhibits
large variations across space and/or time.
• Natural variability resulting from unpredictable natural
processes within the climate system (internal climate
variability; e.g. atmospheric and oceanic variability),
influencing the climate system (e.g. future
volcanic eruptions) and/or within climate-sensitive
environmental and social systems (e.g. ecosystem
dynamics).
• Model limitations (of climate and climate impact
models) resulting from the limited resolution of
models (e.g. hampering the explicit resolution of
cloud physics), an incomplete understanding of
individual Earth system components (e.g. dynamic
ice sheet processes) or their interactions and
feedbacks (e.g. climate–carbon cycle feedbacks),
and/or an incomplete understanding of the
environmental or social system under consideration
(e.g. demographic development in flood risk
zones). A parameter to describe a key uncertainty
in global climate models is their climate sensitivity,
which refers to the change in the annual global
mean surface temperature following a doubling
of the atmospheric equivalent carbon dioxide
concentration, either at the time of doubling for a
stylised concentration scenario (transient climate
response) or in equilibrium (equilibrium climate
sensitivity).
• Future emissions trajectories (of greenhouse gases and
aerosols) determine the magnitude and rate of future
climate change. Future emission levels depend
on demographic, economic and technological
development, as well as on international agreements
for climate change mitigation (in particular under the
UNFCCC).
• Future development of non-climatic (socio-economic,
demographic, technological and environmental) factors
determines how a given change in climate affects
the environment and society.
• Future changes in societal preferences and political
priorities determine the importance attached to a
given climate impact (e.g. a local or regional loss of
biodiversity).
The relevance of the various sources of uncertainty
depends on the question to be answered. Their relative
importance depends, among others, on the target
system, the climate and non-climate factors the system
is sensitive to, and the time horizon of the assessment.
For example, uncertainty about future emissions levels
of long-lived greenhouse gases becomes the dominant
source of uncertainty for changes in global mean
temperature on time scales of 50 years or more, but
is of limited importance for short-term climate change
projections (see Figure 1.3) (Hawkins and Sutton,
2009, 2011; Yip et al., 2011; Booth et al., 2013; Monier
et al., 2014).
44 Climate change, impacts and vulnerability in Europe 2016 | An indicator-based report