Transcript Geen diatitel

Climatological Extremes
13 November 2002
Albert Klein Tank
KNMI, the Netherlands
acknowledgements:
37 ECA-participants (Europe & Mediterranean)
Guide
1. Definition of extremes and the use of indices
2. Trends (1946-1999) for Europe and the world
3. ECA&D project and website (demo)
Guide
1. Definition of extremes and the use of indices
2. Trends (1946-1999) for Europe and the world
3. ECA&D project and website (demo)
What type of extremes?

Events characterised by the size of their societal or
economic impacts
NO


Events characterised by parameters of extreme value
distributions
Phenomena with a daily time scale and typical return
period < 1 year as indicators of extremes
What type of extremes?

Events characterised by the size of their societal or
economic impacts
NO

Events characterised by parameters of extreme value
distributions
NO

Phenomena with a daily time scale and typical return
period < 1 year as indicators of extremes
What type of extremes?

Events characterised by the size of their societal or
economic impacts
NO

Events characterised by parameters of extreme value
distributions
NO

Phenomena with a daily time scale and typical return
period < 1 year as indicators of extremes
YES
Approach



Use daily series of observations at meteorological
stations throughout Europe and the Mediterranean
Define descriptive indices as proposed by the joint
CCL/CLIVAR Working Group on Climate Change
Detection (Peterson et al., WMO-TD No. 1071, 2001)
Count the days crossing a threshold; either
absolute/fixed thresholds or percentile/variable
thresholds relative to local climate
Approach



Use daily series of observations at meteorological
stations throughout Europe and the Mediterranean
Define descriptive indices as proposed by the joint
CCL/CLIVAR Working Group on Climate Change
Detection (Peterson et al., WMO-TD No. 1071, 2001)
Count the days crossing a threshold; either
absolute/fixed thresholds or percentile/variable
thresholds relative to local climate
Approach



Use daily series of observations at meteorological
stations throughout Europe and the Mediterranean
Define descriptive indices as proposed by the joint
CCL/CLIVAR Working Group on Climate Change
Detection (Peterson et al., WMO-TD No. 1071, 2001)
Count the days crossing a threshold; either
absolute/fixed thresholds or percentile/variable
thresholds relative to local climate
Example of thresholds in the definition of
indices of temperature extremes
upper 10-ptile
1961-1990
the year 1996
lower 10-ptile
1961-1990
Example of thresholds in the definition of
indices of temperature extremes
upper 10-ptile
1961-1990
the year 1996
lower 10-ptile
1961-1990
“frost
days”
Example of thresholds in the definition of
indices of temperature extremes
upper 10-ptile
1961-1990
the year 1996
lower 10-ptile
1961-1990
“cold
nights”
Example of thresholds in the definition of
indices of temperature extremes
“warm
nights”
upper 10-ptile
1961-1990
the year 1996
lower 10-ptile
1961-1990
“cold
nights”
Motivation


The detection probability of trends depends on the
return period of the extreme event and the length of
the series
For extremes in daily station series with typical length
~50 years, the optimal return period is 10-30 days
rather than 10-30 years
Motivation


The detection probability of trends depends on the
return period of the extreme event and the length of
the series
For extremes in daily station series with typical length
~50 years, the optimal return period is 10-30 days
rather than 10-30 years
Example: 80% detection probability
(5% significance level)
120
(see also:
Frei & Schär,
J.Climate, 2001)
100
80
60
Ev ent return period
40
365 days
100 days
20
30 days
0
10
10 days
20
30
40
Series length
50
60
Guide
1. Definition of extremes and the use of indices
2. Trends (1946-1999) for Europe and the world
3. ECA&D project and website (demo)
Trend examples



Extreme indices for temperature related impacts /
applications
“Warm” and “cold” extreme indices describing how
temperature distributions (pdf’s) shift in time
Extreme indices of heavy precipitation
Trend examples



Extreme indices for temperature related impacts /
applications
“Warm” and “cold” extreme indices describing how
temperature distributions (pdf’s) shift in time
Extreme indices of heavy precipitation
Trend examples



Extreme indices for temperature related impacts /
applications
“Warm” and “cold” extreme indices describing how
temperature distributions (pdf’s) shift in time
Extreme indices of heavy precipitation
Heating degree days
(sum of 17°C - TG)
Growing season
length (6 days, TG 5°C)
Frich et al. (Clim.Res., 2002) in IPCC-TAR
IPCC-TAR (Ch.2, Folland and Karl)
Easterling et al. (BAMS, 2000) in IPCC-TAR
see also Groisman et al. (Clim.Change, 1999)
Linear trends in rainy season over last ~50 years
Heavy precipitation: R95%tot-index
(fraction due to very wet days)
1) Identify very wet days using a
site specific threshold = 95th
percentile of amounts at wet days
in the 1961-1990 period
2) Determine fraction
of total precipitation in
each year or season
that is due to these
days
3) Trend analysis in resulting series
Heavy precipitation: R95%tot-index
(fraction due to very wet days)
1) Identify very wet days using a
site specific threshold = 95th
percentile of amounts at wet days
in the 1961-1990 period
2) Determine fraction
of total precipitation in
each year or season
that is due to these
days
3) Trend analysis in resulting series
Heavy precipitation: R95%tot-index
(fraction due to very wet days)
1) Identify very wet days using a
site specific threshold = 95th
percentile of amounts at wet days
in the 1961-1990 period
2) Determine fraction
of total precipitation in
each year or season
that is due to these
days
3) Trend analysis in resulting series
Frich et al. (Clim.Res., 2002) in IPCC-TAR
Guide
1. Definition of extremes and the use of indices
2. Trends (1946-1999) for Europe and the world
3. ECA&D project and website (demo)
Upgraded website at: www.knmi.nl/samenw/eca
Conclusions and outlook


The standardised descriptive indices (that are based on
daily series) reveal trends in climatological extremes for
Europe that can directly be compared to the trends in
other regions of the world; the indices are adequate for
climate change detection as well as for impact
assessment
Future plans ECA&D-project: 2006 assessment report,
improved daily dataset (coverage / elements /
homogeneity / metadata / gridding / web-access),
additional participants, communication of results both
towards climate change detection and modelling
community and towards applied climatology community
Conclusions and outlook


The standardised descriptive indices (that are based on
daily series) reveal trends in climatological extremes for
Europe that can directly be compared to the trends in
other regions of the world; the indices are adequate for
climate change detection as well as for impact
assessment
Future plans ECA&D project: 2006 assessment report,
improved daily dataset (coverage / elements /
homogeneity / metadata / gridding / web-access),
additional participants, communication of results both
towards climate change detection and modelling
community and towards applied climatology community

the end...