Transcript Indices versus Data    Indices are information derived from data Proxy for data More readily released than data • Indices generally not of economic value – only.

Indices versus Data
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Indices are information derived from
data
Proxy for data
More readily released than data
• Indices generally not of economic value –
only research value and for applications
• More on this later
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Are not reproducible without the data
• A key component of science
Many different ways to
calculate indices for extremes
What types of extremes?
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Trends in extreme events characterised
by the size of their societal or economic
NO
impacts
Trends in “very rare” extreme events
analysed by the parameters of extreme
NO
value distributions
Trends in observational series of
phenomena with a daily time scale and
typical return period < 1 year
YES
(as indicators of extremes)
Motivation for choice of
“extremes”
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The detection probability of trends
depends on the return period of the
extreme event and the length of the
observational series
For extremes in daily series with typical
length ~50 yrs, the optimal return
period is 10-30 days rather than 10-30
years
Approach
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Focus on counts of days crossing a
threshold; either absolute/fixed
thresholds or percentile/variable
thresholds relative to local climate
Standardisation enables comparisons
between results obtained in different
countries, and even different parts of
the world
Expert Team on Climate Change Detection and Indices (ETCCDI)
started in 1999
jointly sponsored by CCl, CLIVAR and JCOMM
the ETCCDI developed an internationally coordinated set of 27
climate indices
focus on counts of days crossing a threshold; either absolute/fixed
thresholds or percentile/variable thresholds relative to local climate
used for both observations and models, globally as well as regionally
can be coupled with
– simple trend analysis techniques
– standard detection and attribution methods
complements the analysis of more rare extremes using EVT
Klein Tank, Zwiers and Zhang,
2009, WCDMP-No. 72,
WMO-TD No. 1500, 56pp.
Example: Russian heat wave, July 2010
In-situ
NOAA
Example: Russian heat wave, July 2010
MSU
UAH
In-situ
NOAA
ERA-Interim
ECMWF
Courtesy: John Christy (top),
Adrian Simmons (bottom)
Example: Russian heat wave, July 2010
In-situ
NOAA
ETCCDI indices add relevant
information
Example: Russian heat wave, July 2010
31 days with
T-max > 25°C
against 9.5 days
in a normal July
Example: Russian heat wave, July 2010
16 nights with
T-min > 20°C
against 0.5
night in a
normal July
Extremes Indices
Indices example
upper 10-ptile
1961-1990
the year 1996
lower 10-ptile
1961-1990
Indices example
upper 10-ptile
1961-1990
the year 1996
lower 10-ptile
1961-1990
“cold
nights”
Indices example
“warm
nights”
upper 10-ptile
1961-1990
the year 1996
lower 10-ptile
1961-1990
“cold
nights”
Indices example
De Bilt, the Netherlands
Changes in heavy falls
1) Identify heavy falls using a
site specific threshold = 95th
percentile at wet days in the
1961-90 period
Changes in heavy falls
1) Identify heavy falls using a
site specific threshold = 95th
percentile at wet days in the
1961-90 period
2) Determine fraction of total
precipitation in each year that is
due to these days
Changes in heavy falls
1) Identify heavy falls using a
site specific threshold = 95th
percentile at wet days in the
1961-90 period
2) Determine fraction of total
precipitation in each year that is
due to these days
3) Trend analysis in series of fractions
Changes in heavy falls
Alexander et al.,2006; in IPCC-AR4
Extremes table IPCC-AR4, WG1 report (IPCC, 2007)