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(see Figure 5). In this new
process, four representative concentration pathways (RCPs)
were selected from published literature to provide input to
climate models. While climate scenarios are being generated
with RCP inputs, a common set of definitions for socioeco-
nomic conditions (including both quantitative and qualitative
descriptions) called shared socioeconomic reference pathways
(SSPs) are agreed between IAV and IAM communities.
This new process of developing scenarios increases coordi-
nation across climate change research communities \u2013 most
notably, coordination between the IAV communities and the
IAM community increases by means of the common set of SSPs.
This provides a consistent basis for linking emission scenarios
with conditions that affect vulnerability. A particular challenge
involved in making the scenarios of the two communities more
consistent lies in downscaling the information provided by the
IAM community (i.e., often on a global scale with some regional
information) such that it is useful for the IAV community (that
often relies on data on smaller spatial scales).
This new scenario process \u2013 that is independent of the IPCC
but will nonetheless feed essential results into the AR5 \u2013 there-
fore involves two innovations: (1) the timing of the process
that allows each of the modeling communities to use the same
generation of models, thereby facilitating comparability; and
(2) an agreed set of SSPs that provides a common basis of
comparability across IAM and IAV communities. It facilitates
cross-discipline coordination and allows policymakers to more
clearly understand the tradeoffs and benefits of different com-
binations of mitigation and adaptation strategies, contributing
to a fuller, more comprehensive exploration of the entire solu-
tions space.
Communication of Uncertainty
As it is the aim of IPCC reports to assess the state of knowl-
edge on climate change, its impacts and mitigation strategies
in a comprehensive and objective way, clear communication
of the degree of certainty of scientific results is a key compo-
nent of its findings. Applying this to the aforementioned
scenarios and the exploration of the entire solution space
involves clarifying and making explicit (1) the underlying
value judgments of the scenarios and (2) the interaction
between ends and means in the scenarios. In other words,
how the ends (e.g., global stabilization targets) may be di-
rectly or indirectly affected by the development of different
mitigation options (means) and vice versa. Pinpointing and
clarifying options and the effects of those options to decision
makers is an early step in assuring clarity in the communica-
tion of IPCC results. Beyond this, a more comprehensive
methodology including a calibrated uncertainty language is
necessary in IPCC reports to address uncertainties about, for
example, the socioeconomic system.
Challenges in implementing calibrated uncertainty for WG
III are related largely to uncertainties in the socioeconomic
system and can be categorized as model uncertainty and
Impact, adaptation
and vulnerability (IAV)
Human settlements
and infrastructure
Sea level
and forestry
ate m
els (C
Figure 4 Schematic representation of the three modeling communities
whose cooperation is essential in the provision of consistent comparable
scenarios and the areas of overlap between those communities.
Reproduced from Moss R, Edmonds JA, Hibbard KA, et al. (2010) The
next generation of scenarios for climate change research and
assessment. Nature 463: 746\u2013756, with permission from Nature
Publishing Group.
54 Climate Change and Policy | Intergovernmental Panel on Climate Change (IPCC)
parametric uncertainty. Uncertainty in the models themselves
can be the product of different representations of socioeconomic
and technology systems. Model comparison exercises can help
identify such differences. Uncertainty in the parameters used in
models is related to different assumptions about parameter
values. This can be addressed to some degree by sensitivity
analyses andMonteCarlo simulations.WGIII\u2019s focus is therefore
directed toward the qualitative categorization of uncertainties in
the assessment of mitigation options \u2013 opportunities for quan-
titative evaluation are more limited.
In the AR5, the scope of WG II\u2019s contribution will expand
and will involve information from many different disciplines.
Different approaches to evaluating certainty may be required to
address the nature of uncertainty in varying disciplines. WG II
may also rely more on conditional findings \u2013 which character-
ize the degree of certainty into causes and effects \u2013 than the
other working groups. Finally, the degree of certainty inWG II\u2019s
findings will be very much related to the underlying assump-
tions on the relationship between impacts and adaptation,
including socioeconomic assumptions.
WG I has a long history of successfully applying calibrated
uncertainty language, strongly related to the links of quantifying
uncertainty ranges in the underlying theoretical science. Their
focus is to assure that numerical information from the physical
science basis of climate change literature is translated correctly
into text that is widely understandable and correctly interpreted.
The new AR5 guidance note provides calibrated language
for systematic evaluation and representation of these uncer-
tainties. Calibrated uncertainty language has been used in the
IPCC since the first assessment report in 1990. Despite at-
tempts to unify calibrated uncertainty language in past assess-
ment cycles, the use of confidence and likelihood scales has in
some cases been inconsistent across working groups. More-
over, the extent to which calibrated uncertainty language was
applied to each of the disciplinary sciences has varied.
It is the goal of the 5th assessment cycle to implement
calibrated uncertainty language consistently across all three
IPCC working groups, in line with the recommendations of
the IAC. In a guidance note provided to AR5 authors, cali-
brated uncertainty language for key findings is broken down
into two metrics: (1) confidence, measured qualitatively; and
(2) likelihood, measured quantitatively based on, for example,
statistical analysis. It is only possible to present likelihood
where evidence allows probabilistic quantification of uncer-
tainty. If this is not possible, confidence metrics alone are used.
In order to assess the confidence of a given finding,
authors must first evaluate evidence and agreement (see
Figure 6) and present a traceable account of this evaluation
in the text of the chapters. Evidence (presented in terms of
limited, medium, and robust) is determined based on the
type amount, quality, and consistency of evidence. Agreement
(presented in terms of low, medium, and high) is determined
based on the level of concurrence in the literature on a par-
ticular finding. With this information, authors are able to
qualify the level of confidence into one of five categories:
very low, low, medium, high, and very high. If there is insuf-
ficient evidence and agreement to evaluate confidence, sum-
mary terms are presented.
Likelihood can characterize uncertainty findings where
probabilistic information is provided via, for example, statisti-
cal or modeling analyses or other quantitative analyses. The
AR5 uncertainty guidance note provides a clear scale by which
language for describing