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11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 1
Downscaling future climate change using statistical ensembles
E. Hertig, J. JacobeitInstitute of Geography University of Augsburg
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 2
Outline
1. Introduction- statistical downscaling scheme2. Statistical Downscaling- an ensemble approach
Example 1: Mean precipitation in the Mediterranean area- 10- member statistical downscaling ensembles- choice of statistical techniquesExample 2: Extreme temperature in the Mediterranean area- 5- member statistical downscaling ensembles- choice of predictors
3. Conclusions
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 3
Statistical Downscaling
geopotential heights humidity SST
time series of regional / local climate variables
time series of the large-scale predictors
calibration verification
regional / local assessmentsfor the future
statistical model
transfer functions / synoptical analysisin the observational period
...
time series of the model predictors
precipitation temperature
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 4
Statistical Downscaling – an ensemble approach
calibration (46 years) verification (5 years)
1949-1953
1954-1958
1959-1963
1964-1968
1969-1973
1974-1978
1979-1983
1984-1988
1989-1993
1994-1998
1948,1954-1998
1948-1953,1959-1998
1948-1958, 1964-1998
1948-1963, 1969-1998
1948-1968, 1974-1998
1948-1973, 1979-1998
1948-1978, 1984-1998
1948-1983, 1989-1998
1948-1988, 1994-1998
1948-1993
time
perio
d 19
48-1
998
(51
year
s)
10- member statistical downscaling ensembles
Example 1: Mean precipitation in the Mediterranean area
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 5
Example 1: Mean precipitation in the Mediterranean area
1949-1953
1954-1958
1959-1963
1964-1968
1969-1973
1974-1978
1979-1983
1984-1988
1989-1993
1994-1998
1948,1954-1998
1948-1953,1959-1998
1948-1958, 1964-1998
1948-1963, 1969-1998
1948-1968, 1974-1998
1948-1973, 1979-1998
1948-1978, 1984-1998
1948-1983, 1989-1998
1948-1988, 1994-1998
1948-1993
time
perio
d 19
48-1
998
(51
year
s)
x- member statistical downscaling ensembles
no
n-
stat
ion
arit
ies
calibration (46 years) verification (5 years)
‚best‘ model
‚failing‘ model
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 6
Choice of statistical techniques
1950 2000 2050 2100ye a r
0
20
40
60
80
100
120
140m m
January/February- precipitationTechnique: C anonical C orrela tion A nalys is
1950 2000 2050 2100ye a r
0
20
40
60
80
100
120
140m m
January/February- precipitationTechnique: Multiple R egression A nalysis
15° 10° 5° 0° 5° 10° 15° 20° 25° 30° 35°
30°
35°
40°
45°
.
verification 1949-1953
verification 1954-1958
verification 1959-1963
verification 1964-1968
verification 1969-1973
verification 1974-1978
verification 1979-1983
verification 1984-1988
verification 1989-1993
verification 1994-1998cubic trend
Canonical Correlation Analysis Multiple Regression Analysis
Hertig & Jacobeit 2008
Predictors:1000hPa-/500hPa- Geopotential,1000hPa- specific humidity
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 7
Choice of statistical techniques
1950 2000 2050 2100ye a r
0
20
40
60
80
100
120
140m m
January /February- precipitationTechnique: C anonical C orrelation A nalysis
1950 2000 2050 2100ye a r
0
20
40
60
80
100
120
140m m
January/February- prec ipitationTechnique: Multiple R egression A nalysis
15° 10° 5° 0° 5° 10° 15° 20° 25° 30° 35°
30°
35°
40°
45°
.
verification 1949-1953
verification 1954-1958
verification 1959-1963
verification 1964-1968
verification 1969-1973
verification 1974-1978
verification 1979-1983
verification 1984-1988
verification 1989-1993
verification 1994-1998cubic trend
Canonical Correlation Analysis Multiple Regression Analysis
Hertig & Jacobeit 2008
Predictors:1000hPa-/500hPa- Geopotential,1000hPa- specific humidity
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 8
Choice of statistical techniques
Downscaling with Multi-type predictors: Regression-based technique: strong dependence of the time series
on the particular calibration period used Statistical ensemble members from CCA show good agreement
amongst each other. CCA: total range of variation and trend progression far more
moderate CCA: relationships established over the whole study areas -> kind
of „signal smoothing“ Regression: selection of individual signals with different decisions in
different calibration periods Regression: consistency of different predictor variables may not be
preserved under future climate conditions
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 9
Example 2: Extreme temperature in the Mediterranean area
calibration verification (10 years)
1961-1970
1971-1980
1981-1990
first ten years (earliest 1950-1959)
last ten years (latest 1997-2006)
1950-1960, 1971-2006
1950-1970, 1981-2006
1950-1980, 1991-2006
max.1960-2006
max.1950-1996
5- member statistical downscaling ensemblesBased on station data
comparison between stations
variability within a stationmax
. tim
e pe
riod
1950
-200
6(m
ax.
57 y
ears
)
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 10
Choice of predictors
15° 10° 5° 0° 5° 10° 15° 20° 25° 30° 35° 40°
30°
35°
40°
45°95th Percentile M axim um Tem perature Sum m er (2070-2099) - (1961-1990)
Predictor: 1000hPa-500hPa- thickness90 verified statistical models for 42 temperature stations
2 °C to 4 .7 °C 1 °C to 2 °C 0 .5 °C to 1 °C 0 .25 °C to 0 .5 °C 0 °C to 0 .25 °C -0 .25 °C to 0 °C -0 .5 °C to -0.25 °C -1 °C to -0.5 °C -2 °C to -1 °C -4 .7 °C to -2 °C
-2 °C to -4.7 °C -1 °C to -2 °C -0.5 °C to -1 °C -0.25 °C to -0 .5 °C 0 °C to -0.25 °C 0 .25 °C to 0 °C 0 .5 °C to 0 .25 °C 1 °C to 0 .5 °C 2 °C to 1 °C 4 .7 °C to 2 °C
Hertig et al. 2010
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 11
15° 10° 5° 0° 5° 10° 15° 20° 25° 30° 35° 40°
30°
35°
40°
45°95th Percentile M axim um Tem perature Sum m er (2070-2099) - (1961-1990)
Choice of predictors
Predictor: 500hPa-geopotential heights65 verified statistical models for 31 temperature stations
2 °C to 4 .7 °C 1 °C to 2 °C 0 .5 °C to 1 °C 0 .25 °C to 0 .5 °C 0 °C to 0 .25 °C -0 .25 °C to 0 °C -0 .5 °C to -0.25 °C -1 °C to -0.5 °C -2 °C to -1 °C -4 .7 °C to -2 °C
-2 °C to -4.7 °C -1 °C to -2 °C -0.5 °C to -1 °C -0.25 °C to -0 .5 °C 0 °C to -0.25 °C 0 .25 °C to 0 °C 0 .5 °C to 0 .25 °C 1 °C to 0 .5 °C 2 °C to 1 °C 4 .7 °C to 2 °C
Hertig et al. 2010
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 12
Choice of predictors
Hertig et al. 2010
15° 10° 5° 0° 5° 10° 15° 20° 25° 30° 35° 40°
30°
35°
40°
45°95th Percentile M axim um Tem perature Sum m er (2070-2099) - (1961-1990)
2 °C to 4 .7 °C 1 °C to 2 °C 0 .5 °C to 1 °C 0 .25 °C to 0 .5 °C 0 °C to 0 .25 °C -0 .25 °C to 0 °C -0 .5 °C to -0.25 °C -1 °C to -0.5 °C -2 °C to -1 °C -4 .7 °C to -2 °C
-2 °C to -4.7 °C -1 °C to -2 °C -0.5 °C to -1 °C -0.25 °C to -0 .5 °C 0 °C to -0.25 °C 0 .25 °C to 0 °C 0 .5 °C to 0 .25 °C 1 °C to 0 .5 °C 2 °C to 1 °C 4 .7 °C to 2 °C
Predictors: 1000hPa-500hPa- thickness, 500hPa- geopot. heights37 verified models for 26 stations
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 13
Results
Downscaling of extreme values Intra- to inter-decadal variability of predictability:
- stable connection of a predictor to extremes only in some calibration periods - varying performance in the different verification periods
Preferably long training period to sample a large number of different modes of variability
Preferably different downscaling techniques to verify results
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 14
Results (Stations and Grid-based)
Predictors: 1000hPa-500hPa- thickness, 500hPa- geopot. heightsScenario: A1BModel: ECHAM5/MPI-OMDownscaling techniques: Multiple Regression (Stations), CCA (Grid)
(2070-2099) – (1961-1990)
11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 15
Conclusions
Use of statistical downscaling ensembles
- to select suitable statistical techniques- to judge predictor performance- to detect non-stationarities- to incorporate non-stationarities through systematical
weighting, substitution, extension of predictors