Transcript Slide 1

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Some Data Issues Related to Climate Extremes
Climate Research Division, ASTD
Xuebin Zhang
(2011-02-03)
Contents
• Definition of an extreme
• IPCC Assessment on observed changes in
Extremes
• Global data sets for IPCC TAR, AR4
• Current state of global data sets
• Conclusion and recommendations
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Extreme as defined in IPCC AR4 Glossary
An extreme weather event is an event that is rare at a
particular place and time of year. Definitions of rare vary, but
an extreme weather event would normally be as rare as or
rarer than the 10th or 90th percentile of the observed
probability density function. By definition, the characteristics
of what is called extreme weather may vary from place to
place in an absolute sense. Single extreme events cannot
be simply and directly attributed to anthropogenic climate
change, as there is always a finite chance the event in
question might have occurred naturally. When a pattern of
extreme weather persists for some time, such as a season,
it may be classed as an extreme climate event, especially if
it yields an average or total that is itself extreme (e.g.,
drought or heavy rainfall over a
season)
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What do we mean by extremes?
• Extreme events easy to recognize but difficult to
define, no unique definition for “extreme”
• Severe events create large losses
• Rare events have a low probability of
occurrence
• Extreme events have extreme values of certain
important meteorological variables
• High-impact events are severe events with longlasting impacts
• These terms have been used interchangeably
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Derivation of engineering design values:
An application
• Collect all observed extremes (say annual maximum amount of daily
precipitation)
• Fit the data into a probability distribution (a family of generalized
extreme value distribution)
• Assuming climate did not change in the past and will be the same in the
future
• Compute the design values corresponding to a pre-designed likelihood
(probability) by inversing the fitted GEV distribution.
• As climate changes, the likelihood of a particular
extreme also changes, but this has not been taken
into consideration in general.
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Evolution of IPCC Assessments
• No assessment on changes in extreme precipitation,
temperature, or tropical cyclones (Folland et al. 1992)
• No evidence globally that extreme weather events or
climate variability had increased, data and analyses
were “poor and not comprehensive” in spite of changes
in extreme weather events observed in some regions
where sufficient data were available (Nicholls et al.,
1995).
– Regional studies limited, and lack of consistency in the
definition of extremes
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Evolution of IPCC Assessments (SAR)
• IPCC SAR assessed changes in extremes
including temperature, precipitation, extratropical cyclones (Folland et al. 2001).
– Definition of common indices made it possible to
compare analyses conducted in different parts
of the world (Frish et al. 2002)
– But there were some issues related to definition
and calculation of some indices.
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Data coverage in SAR
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Evolution of IPCC Assessments (AR4)
• IPCC AR4 assessed changes in extreme temperature,
precipitation, drought, tropical and extra-tropical cyclones
etc. (Trenberth et al. 2007).
– Indices defined by the Joint CLIVAR/CCl/JCOMM Expert
Team on Climate Change Detection and Indices
– Known problem in Frich et al. (2001) fixed and some
indices modified
– Better spatial coverage due to internationally
coordinated efforts that provided standard software
indices calculation, and training workshops in less
developed world (Alexander et al. 2006).
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ETCCDI approach
• Common definitions and standard free software
– Analyses done in different parts of world can be patched
together
• Hands-on training and sometimes computers are
provided for capacity building
• Post-workshop reanalyses lead journal papers for IPCC
assessment
• Indices are shared to research community
• Problems
– Data transparency is an issue
– It’s static and update becomes a problem
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ETCCDI Regional Workshops
(complemented by APN)
Working together
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Peterson and Manton, BAMS, 2008
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Data coverage in AR4
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GHCN-D, current status
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Post AR-4 ETCCDI Regional Workshops
Africa Workshop
(complemented by GH
APN)
(WCRP/World Bank)
04/2010
Central Africa
(USA) 4/2007
Mexico (UK)
03/2009
CIIFEN
All S.A but two
Jan/2011
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Peterson and Manton, BAMS, 2008
West Indian
Ocean (France)
09/2009
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Southeast
Asia (USA)
12/2007
Indonesia, Malaysia,
Thailand, Philippines
(NL) 12/2009
Conclusions and recommendations
• There is a need to access daily data
• ETCCDI indices approach has been successful
but also at the cost of losing data transparency
• Data available to-date has poorer spatial
coverage than what was available at AR4
• Urgent action is needed to address the issue
• ETCCDI has planned more workshops to cover
data void area, but nothing is certain yet
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