Indices versus DataIndices versus Data Indices are information derived from Indices are information derived from

datadata Proxy for dataProxy for data More readily released than dataMore readily released than data

• Indices generally not of economic value Indices generally not of economic value – only research value and for – only research value and for applicationsapplications

• More on this laterMore on this later Are not reproducible without the dataAre not reproducible without the data

• A key component of scienceA key component of science

Many different ways to Many different ways to calculate indices for calculate indices for

extremesextremes

What types of extremes?What types of extremes? Trends in extreme events characterised by the Trends in extreme events characterised by the

size of their societal or economic impactssize of their societal or economic impacts Trends in “very rare” extreme events analysed Trends in “very rare” extreme events analysed

by the parameters of extreme value by the parameters of extreme value distributionsdistributions

Trends in observational series of phenomena Trends in observational series of phenomena

with a daily time scale and typical return with a daily time scale and typical return period < 1 yearperiod < 1 year(as indicators of extremes)(as indicators of extremes)

NO

NO

YES

Motivation for choice of Motivation for choice of “extremes”“extremes”

The detection probability of trends The detection probability of trends depends on the return period of the depends on the return period of the extreme event and the length of the extreme event and the length of the observational seriesobservational series

For extremes in daily series with For extremes in daily series with typical length ~50 yrs, the optimal typical length ~50 yrs, the optimal return period is 10-30 return period is 10-30 daysdays rather rather than 10-30 than 10-30 yearsyears

ApproachApproach Focus on counts of days crossing a Focus on counts of days crossing a

threshold; either absolute/fixed threshold; either absolute/fixed thresholds or percentile/variable thresholds or percentile/variable thresholds relative to local climatethresholds relative to local climate

Standardisation enables comparisons Standardisation enables comparisons between results obtained in different between results obtained in different countries, and even different parts of countries, and even different parts of the worldthe 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 indicesfocus on counts of days crossing a threshold; either absolute/fixed thresholds or percentile/variable thresholds relative to local climateused for both observations and models, globally as well as regionallycan 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-situNOAA

ERA-InterimECMWF

MSUUAH

Courtesy: John Christy (top), Adrian Simmons (bottom)

In-situNOAA

Example: Russian heat wave, July 2010

ETCCDI indices add relevant information

In-situNOAA

Example: Russian heat wave, July 2010

31 days withT-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

Example: Russian heat wave, July 2010

Extremes Indices

upper 10-ptile 1961-1990

the year 1996

lower 10-ptile1961-1990

Indices example

upper 10-ptile 1961-1990

the year 1996

lower 10-ptile1961-1990

“cold nights”

Indices example

upper 10-ptile 1961-1990

the year 1996

lower 10-ptile1961-1990

“warm nights”

“cold nights”

Indices example

De Bilt, the Netherlands

Indices example

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

Changes in heavy falls

Extremes table IPCC-AR4, WG1 report (IPCC, 2007)