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

elke.hertig@geo.uni-augsburg.de

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