1. 1 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 Downscaling future climate change using statistical ensembles E. Hertig, J. Jacobeit Institute of Geography University of Augsburg elke.hertig@geo.uni-augsburg.de

2. 2 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 Outline 1.Introduction- statistical downscaling scheme 2.Statistical Downscaling- an ensemble approach Example 1: Mean precipitation in the Mediterranean area - 10- member statistical downscaling ensembles - choice of statistical techniques Example 2: Extreme temperature in the Mediterranean area - 5- member statistical downscaling ensembles - choice of predictors 3. Conclusions

3. 3 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 Statistical Downscaling geopotential heightshumiditySST time series of regional / local climate variables time series of the large-scale predictors calibration verification regional / local assessments for the future statistical model transfer functions / synoptical analysis in the observational period... time series of the model predictors precipitationtemperature

4. 4 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 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 period 1948-1998 (51 years) 10- member statistical downscaling ensembles Example 1: Mean precipitation in the Mediterranean area

5. 5 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 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 period 1948-1998 (51 years) x- member statistical downscaling ensembles non- stationarities calibration (46 years) verification (5 years) ‚best‘ model ‚failing‘ model

6. 6 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 Choice of statistical techniques Canonical Correlation Analysis Multiple Regression Analysis Hertig & Jacobeit 2008 Predictors: 1000hPa-/500hPa- Geopotential, 1000hPa- specific humidity

7. 7 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 Choice of statistical techniques Canonical Correlation Analysis Multiple Regression Analysis Hertig & Jacobeit 2008 Predictors: 1000hPa-/500hPa- Geopotential, 1000hPa- specific humidity

8. 8 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 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

9. 9 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 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 ensembles Based on station data comparison between stations variability within a station max. time period 1950-2006 (max. 57 years)

10. 10 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 Choice of predictors Predictor: 1000hPa-500hPa- thickness 90 verified statistical models for 42 temperature stations Hertig et al. 2010

11. 11 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 Choice of predictors Predictor: 500hPa-geopotential heights 65 verified statistical models for 31 temperature stations Hertig et al. 2010

12. 12 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 Choice of predictors Hertig et al. 2010 Predictors: 1000hPa-500hPa- thickness, 500hPa- geopot. heights 37 verified models for 26 stations

13. 13 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 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

14. 14 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 Results (Stations and Grid-based) Predictors: 1000hPa-500hPa- thickness, 500hPa- geopot. heights Scenario: A1B Model: ECHAM5/MPI-OM Downscaling techniques: Multiple Regression (Stations), CCA (Grid) (2070-2099) – (1961-1990)

15. 15 11th INTERNATIONAL MEETING on STATISTICAL CLIMATOLOGY, EDINBURGH, JULY 12-16, 2010 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