BAMOS
Dec 2019
Table 1: Qualitative assessment of: the ability of CMIP5/CMIP6 generation global climate
models to represent specific extremes (model capability), the quality and length of the
observational record of each extreme (observations), and the level of understanding of the
physical mechanisms that lead to changes in each extreme with anthropogenic climate change
(understanding). Assessment categories correspond to confidence levels of high (H), medium
(M), and low (L). The right column identifies whether there was strong disagreement in these
assessments across the workshop participants for each phenomenon. Those elements of the
table where there was unanimous agreement are marked by an asterisk. Shading represents
timescales that were not considered relevant for the specific extreme in the Australian context.
Phenomenon
Extreme cold events
Extreme
heat/humidity
events
Drought
Extreme Rain
Tropical cyclones
Extra-tropical
cyclones and fronts
Fire weather
Fire-relevant fuels
Severe convective
storms
Marine heatwaves
East coast lows
Tropical lows
Sea-level
extremes/surges
(excluding sea-level
rise)
Timescale ~ 1 day
Model
capability
Observations
Timescale ~ 1 month
Understanding
M H M
M H H
L
L
M
L M
M
M
M M
M
M
M
L*
M
L*
L M
M
M
M M
M
L
L
L M H
Model
capability
Observations
Understanding
Strong
disagreement
(Approx % of
participants)
H H H
0%
H H H 0%
M
M M
H L
M L
L M
L M
M H H H 40%
10%
0%
0%
10%
0%
0%
0%
10%
0%
0%
Table 1:
assessment
of: assessment
the ability of of the
CMIP5/CMIP6
generation
global
climate
During
the Qualitative
workshop we
conducted an
marked. Moreover,
it should
be made
clear that the columns
capabilities
models, specific
data, and extremes
scientific (model
understanding
that assess
the capabilities
of climate
models to represent the
models
to of
represent
capability),
the quality
and length
of the
that underpin event
attribution
techniques
the Australian event
class level
refer only
to CMIP5/CMIP6 families
observational
record
of each
extreme in (observations),
and the
of understanding
of the of global models,
context. This was motivated by a table originally created by the and do not refer to results from models that dynamically or
physical
mechanisms
that (NAS,
lead 2016;
to changes
in The
each
with downscale
anthropogenic
climate
National Academies
of Sciences
Table S.1).
NAS extreme
statistically
those global
models.
change
(understanding).
Assessment
categories
correspond
to
confidence
levels
of high (H),
table (or a graphical version of it; their Figure S.4) has been used
We believe the revised table should be a useful resource for
extensively (M),
in presentations
and
publications
in recent
years whether there was strong
medium
and low (L).
The
right column
identifies
scientists and policy makers in Australia.
worldwide, but its development was in the context of North
disagreement
in
these
assessments
across
the
workshop
participants for each phenomenon.
America, and so is not entirely applicable to Australia. One In summary, the workshop was a productive process to identify
Those
elements
of
the
table
where
there
was
unanimous
agreement
marked in by
outcome of the workshop was to revise this table specifically opportunities
and are
challenges
the an area of attribution of
for the Australian
context,
based on timescales
the expert opinion
of the
extreme
events. for
As mentioned
above a more detailed
asterisk.
Shading
represents
that were
not Australian
considered
relevant
the specific
workshop
attendees.
report
will
be
produced
at
a
later
date.
extreme in the Australian context.
The table provides a qualitative assessment of the ability of
CMIP5/CMIP6 generation global climate models to represent
specific extremes (model capability), the quality and length of
the observational record of each extreme (observations), and
the level of understanding of the physical mechanisms that lead
to changes in each extreme with anthropogenic climate change
(understanding). The table broadly follows the IPCC uncertainty
language (Mastrandea et al., 2011). We revised the original table
in the following ways. First, we separated it into two timescales:
of order 1 day (from hourly through to a few days) and of order 1
month (from about a month to about a season). We modified the
list of phenomena slightly to better represent those that impact
Australia and attempted to provide a consistent assessment
across related phenomena (e.g. extreme rain and severe local
storms). We also included a measure of consensus in the final
outcome by asking participants to identify if they strongly
disagreed with the assessment (across both timescales) for each
phenomenon. A disagreement of 0% should not necessarily
be interpreted as strong and unanimous agreement for all
elements of the table for a specific extreme; those elements
of the table where there was unanimous agreement are also
Acknowledgements
Funding for the workshop was provided by CLEX. Participation
by attendees from CLEX and NESP‑ESCC Hub was supported by
those respective programs.
References
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