Global/European Analysis of Extremes

- recent trends -

Albert Klein TankKNMI, the Netherlands

11 June 2002

acknowledgements:Lisa Alexander (Met Office, UK)

Janet Wijngaard, Aryan van Engelen & Günther Können (KNMI)

36 ECA-participants (Europe & Middle East)

European Study:http://www.knmi.nl /samenw/eca

What types of extremes?

Trends in extreme events characterised by the size of their societal or economic impacts

Trends in “very rare” extreme events analysed by the parameters of extreme value distributions

Trends in observational series of phenomena with a daily time scale and typical return period < 1 year(as indicators of extremes)

NO

NO

YES

Approach

Focus on counts of days crossing a threshold; either absolute/fixed thresholds or percentile/variable thresholds relative to local climate

Using derived climate indices as proposed by the joint CCL/CLIVAR Working Group on Climate Change Detection (Peterson et al., WMO-TD No. 1071, 2001)

Standardisation enables comparisons between results obtained in different parts of the world(e.g. Frich et al., Clim. Res. 2002; also in IPCC-TAR)

Motivation for choice of “extremes”

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

Series length

605040302010

Re

qu

ire

d r

ela

tive

tre

nd

(%

/de

cad

e)

120

100

80

60

40

20

0

Event return period

365 days

100 days

30 days

10 days

example: 80% detection prob. (5%-level)

(see also: Frei & Schär, J.Climate, 2001)

NN

TcTrend return

N

T

Temperature indices I

Example of “frost days” as an extreme index based on an absolute temperature threshold

Frich et al. (Clim.Res., 2002) in IPCC-TAR

Temperature indices II

Example of “warm nights” as an extreme index based on counts of events over a (seasonally varying) percentile threshold

Minimum temperature at De Bilt

Month

121110987654321

Tem

pe

ratu

re (

°C)

20

10

0

-10

-20

20

10

0

-10

-20

After: Jones et al. (Climatic Change, 1999) Yan et al. (…, 2002, IMPROVE- issue)

“warm nights”

upper 10-ptile 1961-1990

the year 1996

lower 10-ptile1961-1990

Frich et al. (Clim.Res., 2002)

Kiktev et al. (J. Climate, submitted)

HadAM3 atmosphere only GCM- simulation

“warm nights”

IPCC-TAR (Ch.2, Folland and Karl)

1946-1999

increase in mean temperature: 0.08 (-0.03 0.18) °C/decade;increase in diurnal temperature range: -0.05 (-0.07 -0.02) °C/decade

cold extremes DEcreasedays/decade

warm extremes INcreasedays/decade

TN10%

TX10%

2.0 (0.6 3.4)

0.9 (-0.9 2.7)

TN90%

TX90%

2.5 (0.9 4.1)

1.8 (0.0 3.6)

TN90%(expect)

TX90%(expect)

2.5

1.0

1976-1999

increase in mean temperature: 0.44 (0.09 0.79) °C/decade;increase in diurnal temperature range: 0.05 (-0.04 0.13) °C/decade

cold extremes DEcreasedays/decade

warm extremes INcreasedays/decade

TN10%

TX10%

3.7 (-0.6 8.0)

4.1 (-1.7 10.0)

TN90%

TX90%

11.2 (5.8 16.6)

10.8 (5.3 16.4)

TN90%(expect)

TX90%(expect)

4.6*

5.2*

Europe-average trends (86 ECA-stations)

Precipitation indices I

Easterling et al. (BAMS, 2000) in IPCC-TARsee also Groisman et al. (Clim.Change, 1999)

Linear trends in rainy season over ~50 years

Precipitation indices II

Example of “R95%tot” extreme index for the precipitation fraction due to very wet days

Precipitation fraction due to very wet days “R95%tot”

1) Identify very wet days 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

Annual amount

“R95%tot”

Frich et al. (Clim.Res., 2002) in IPCC-TAR

IPCC-TAR conclusions

Temperature extremes“lengthening of the freeze-free season in most mid- and high latitude regions”“reduction in frequency of extreme low monthly and seasonal average temperatures and smaller increase in frequency of extreme high average temperatures”

Precipitation extremes“in regions where total precipitation has increased ... even more pronounced increases in heavy precipitation events”“2 to 4% increase in frequency of heavy events in mid- and high latitudes of the NH”

Key-issues to be discussed I

Are good quality observational series available with daily resolution and adequate data coverage?

Is there a need for improved methods of homogeneity testing and gridding the daily series?

Can trend analysis of extremes in daily series go beyond phenomena that occur on average ~10 times per year?

Can trend analysis of extremes do without standardised indices if we want a global picture of changes?

YES, but possibly NO

Key-issues to be discussed II

Are the standardised simple indices adequate for:- improving our understanding of the physical mechanisms behind changes in extremes?- evaluating the capacity of climate models to reproduce occurrences of extremes?- constructing scenarios of future occurrences of extremes for impact assessment?

Do we communicate the results of extremes analysis in such a way that they can be used for climate change detection/attribution and impact analysis?

???

the end...